Limit of validity of Ostwald's rule of stages in a statistical mechanical model of crystallization.

PubMed

Hedges, Lester O; Whitelam, Stephen

2011-10-28

We have only rules of thumb with which to predict how a material will crystallize, chief among which is Ostwald's rule of stages. It states that the first phase to appear upon transformation of a parent phase is the one closest to it in free energy. Although sometimes upheld, the rule is without theoretical foundation and is not universally obeyed, highlighting the need for microscopic understanding of crystallization controls. Here we study in detail the crystallization pathways of a prototypical model of patchy particles. The range of crystallization pathways it exhibits is richer than can be predicted by Ostwald's rule, but a combination of simulation and analytic theory reveals clearly how these pathways are selected by microscopic parameters. Our results suggest strategies for controlling self-assembly pathways in simulation and experiment.

Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis

DOE PAGES

Wang, Jack P.; Matthews, Megan L.; Williams, Cranos M.; ...

2018-04-20

A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux,more » metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.« less

Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jack P.; Matthews, Megan L.; Williams, Cranos M.

A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux,more » metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.« less

Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis.

PubMed

Wang, Jack P; Matthews, Megan L; Williams, Cranos M; Shi, Rui; Yang, Chenmin; Tunlaya-Anukit, Sermsawat; Chen, Hsi-Chuan; Li, Quanzi; Liu, Jie; Lin, Chien-Yuan; Naik, Punith; Sun, Ying-Hsuan; Loziuk, Philip L; Yeh, Ting-Feng; Kim, Hoon; Gjersing, Erica; Shollenberger, Todd; Shuford, Christopher M; Song, Jina; Miller, Zachary; Huang, Yung-Yun; Edmunds, Charles W; Liu, Baoguang; Sun, Yi; Lin, Ying-Chung Jimmy; Li, Wei; Chen, Hao; Peszlen, Ilona; Ducoste, Joel J; Ralph, John; Chang, Hou-Min; Muddiman, David C; Davis, Mark F; Smith, Chris; Isik, Fikret; Sederoff, Ronald; Chiang, Vincent L

2018-04-20

A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.

Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

DTIC Science & Technology

2016-08-27

acted to inhibit both TAK1 and MEK. Experimental data for these prediction tests are shown in Figure 4, and comparison between predictions and valida...would decrease did not contain this interaction. The fact that phospho-cJun did decrease in the experimental test of this prediction (Figure 4...pathways primarily through TAK1. Does IL-1 signal through MEKK1 in HepG2 cells? Given the potential importance of MEKK1, we experimentally tested whether IL

Nuclear phospho-Akt increase predicts synergy of PI3K inhibition and doxorubicin in breast and ovarian cancer.

PubMed

Wallin, Jeffrey J; Guan, Jane; Prior, Wei Wei; Edgar, Kyle A; Kassees, Robert; Sampath, Deepak; Belvin, Marcia; Friedman, Lori S

2010-09-08

The phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway is frequently disrupted in cancer and implicated in multiple aspects of tumor growth and survival. In addition, increased activity of this pathway in cancer is associated with resistance to chemotherapeutic agents. Therefore, it has been hypothesized that PI3K inhibitors could help to overcome resistance to chemotherapies. We used preclinical cancer models to determine the effects of combining the DNA-damaging drug doxorubicin with GDC-0941, a class I PI3K inhibitor that is currently being tested in early-stage clinical trials. We found that PI3K inhibition significantly increased apoptosis and enhanced the antitumor effects of doxorubicin in a defined set of breast and ovarian cancer models. Doxorubicin treatment caused an increase in the amount of nuclear phospho-Akt(Ser473) in cancer cells that rely on the PI3K pathway for survival. This increased phospho-Akt(Ser473) response to doxorubicin correlates with the strength of GDC-0941's effect to augment doxorubicin action. These studies predict that clinical use of combination therapies with GDC-0941 in addition to DNA-damaging agents will be effective in tumors that rely on the PI3K pathway for survival.

The kinetics and location of intra-host HIV evolution to evade cellular immunity are predictable

NASA Astrophysics Data System (ADS)

Barton, John; Goonetilleke, Nilu; Butler, Thomas; Walker, Bruce; McMichael, Andrew; Chakraborty, Arup

Human immunodeficiency virus (HIV) evolves within infected persons to escape targeting and clearance by the host immune system, thereby preventing effective immune control of infection. Knowledge of the timing and pathways of escape that result in loss of control of the virus could aid in the design of effective strategies to overcome the challenge of viral diversification and immune escape. We combined methods from statistical physics and evolutionary dynamics to predict the course of in vivo viral sequence evolution in response to T cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Our predictions agree well with both the location of documented escape mutations and the clinically observed time to escape. We also find that that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways, and the duration over which control is maintained by specific immune responses prior to escape, could be exploited for the rational design of immunotherapeutic strategies that may enable long-term control of HIV infection.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking.

PubMed

Huang, Yiran; Zhong, Cheng; Lin, Hai Xiang; Wang, Jianyi

2017-01-01

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

PubMed Central

Zhong, Cheng; Lin, Hai Xiang; Wang, Jianyi

2017-01-01

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways. PMID:28068354

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

PubMed Central

Doktorova, T. Y.; Yildirimman, Reha; Ceelen, Liesbeth; Vilardell, Mireia; Vanhaecke, Tamara; Vinken, Mathieu; Ates, Gamze; Heymans, Anja; Gmuender, Hans; Bort, Roque; Corvi, Raffaella; Phrakonkham, Pascal; Li, Ruoya; Mouchet, Nicolas; Chesne, Christophe; van Delft, Joost; Kleinjans, Jos; Castell, Jose; Herwig, Ralf; Rogiers, Vera

2014-01-01

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances. PMID:26417288

High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization.

PubMed

Biryol, Derya; Nicolas, Chantel I; Wambaugh, John; Phillips, Katherine; Isaacs, Kristin

2017-11-01

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C 0 ) and chemical properties. The most predictive variables in the resulting model were C 0 , molecular weight, log K ow , and food type (R 2 =0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C 0 based on the functional role of chemicals in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R 2 =0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority-setting. Published by Elsevier Ltd.

Combined inhibition of heat shock proteins 90 and 70 leads to simultaneous degradation of the oncogenic signaling proteins involved in muscle invasive bladder cancer

PubMed Central

Cavanaugh, Alice; Juengst, Brendon; Sheridan, Kathleen; Danella, John F.; Williams, Heinric

2015-01-01

Heat shock protein 90 (HSP90) plays a critical role in the survival of cancer cells including muscle invasive bladder cancer (MIBC). The addiction of tumor cells to HSP90 has promoted the development of numerous HSP90 inhibitors and their use in clinical trials. This study evaluated the role of inhibiting HSP90 using STA9090 (STA) alone or in combination with the HSP70 inhibitor VER155008 (VER) in several human MIBC cell lines. While both STA and VER inhibited MIBC cell growth and migration and promoted apoptosis, combination therapy was more effective. Therefore, the signaling pathways involved in MIBC were systematically interrogated following STA and/or VER treatments. STA and not VER reduced the expression of proteins in the p53/Rb, PI3K and SWI/SWF pathways. Interestingly, STA was not as effective as VER or combination therapy in degrading proteins involved in the histone modification pathway such as KDM6A (demethylase) and EP300 (acetyltransferase) as predicted by The Cancer Genome Atlas (TCGA) data. This data suggests that dual HSP90 and HSP70 inhibition can simultaneously disrupt the key signaling pathways in MIBC. PMID:26556859

Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Different Roles of Direct and Indirect Frontoparietal Pathways for Individual Working Memory Capacity.

PubMed

Ekman, Matthias; Fiebach, Christian J; Melzer, Corina; Tittgemeyer, Marc; Derrfuss, Jan

2016-03-09

The ability to temporarily store and manipulate information in working memory is a hallmark of human intelligence and differs considerably across individuals, but the structural brain correlates underlying these differences in working memory capacity (WMC) are only poorly understood. In two separate studies, diffusion MRI data and WMC scores were collected for 70 and 109 healthy individuals. Using a combination of probabilistic tractography and network analysis of the white matter tracts, we examined whether structural brain network properties were predictive of individual WMC. Converging evidence from both studies showed that lateral prefrontal cortex and posterior parietal cortex of high-capacity individuals are more densely connected compared with low-capacity individuals. Importantly, our network approach was further able to dissociate putative functional roles associated with two different pathways connecting frontal and parietal regions: a corticocortical pathway and a subcortical pathway. In Study 1, where participants were required to maintain and update working memory items, the connectivity of the direct and indirect pathway was predictive of WMC. In contrast, in Study 2, where participants were required to maintain working memory items without updating, only the connectivity of the direct pathway was predictive of individual WMC. Our results suggest an important dissociation in the circuitry connecting frontal and parietal regions, where direct frontoparietal connections might support storage and maintenance, whereas subcortically mediated connections support the flexible updating of working memory content. Copyright © 2016 the authors 0270-6474/16/362894-10$15.00/0.

Differentiating Human Multipotent Mesenchymal Stromal Cells Regulate microRNAs: Prediction of microRNA Regulation by PDGF During Osteogenesis

PubMed Central

Goff, Loyal A.; Boucher, Shayne; Ricupero, Christopher L.; Fenstermacher, Sara; Swerdel, Mavis; Chase, Lucas; Adams, Christopher; Chesnut, Jonathan; Lakshmipathy, Uma; Hart, Ronald P.

2009-01-01

Objective Human multipotent mesenchymal stromal cells (MSC) have the potential to differentiate into multiple cell types, although little is known about factors that control their fate. Differentiation-specific microRNAs may play a key role in stem cell self renewal and differentiation. We propose that specific intracellular signalling pathways modulate gene expression during differentiation by regulating microRNA expression. Methods Illumina mRNA and NCode microRNA expression analyses were performed on MSC and their differentiated progeny. A combination of bioinformatic prediction and pathway inhibition was used to identify microRNAs associated with PDGF signalling. Results The pattern of microRNA expression in MSC is distinct from that in pluripotent stem cells such as human embryonic stem cells. Specific populations of microRNAs are regulated in MSC during differentiation targeted towards specific cell types. Complementary mRNA expression analysis increases the pool of markers characteristic of MSC or differentiated progeny. To identify microRNA expression patterns affected by signalling pathways, we examined the PDGF pathway found to be regulated during osteogenesis by microarray studies. A set of microRNAs bioinformatically predicted to respond to PDGF signalling was experimentally confirmed by direct PDGF inhibition. Conclusion Our results demonstrate that a subset of microRNAs regulated during osteogenic differentiation of MSCs is responsive to perturbation of the PDGF pathway. This approach not only identifies characteristic classes of differentiation-specific mRNAs and microRNAs, but begins to link regulated molecules with specific cellular pathways. PMID:18657893

Pre-silencing of genes involved in the electron transport chain (ETC) pathway is associated with responsiveness to abatacept in rheumatoid arthritis.

PubMed

Derambure, C; Dzangue-Tchoupou, G; Berard, C; Vergne, N; Hiron, M; D'Agostino, M A; Musette, P; Vittecoq, O; Lequerré, T

2017-05-25

In the current context of personalized medicine, one of the major challenges in the management of rheumatoid arthritis (RA) is to identify biomarkers that predict drug responsiveness. From the European APPRAISE trial, our main objective was to identify a gene expression profile associated with responsiveness to abatacept (ABA) + methotrexate (MTX) and to understand the involvement of this signature in the pathophysiology of RA. Whole human genome microarrays (4 × 44 K) were performed from a first subset of 36 patients with RA. Data validation by quantitative reverse-transcription (qRT)-PCR was performed from a second independent subset of 32 patients with RA. Gene Ontology and WikiPathways database allowed us to highlight the specific biological mechanisms involved in predicting response to ABA/MTX. From the first subset of 36 patients with RA, a combination including 87 transcripts allowed almost perfect separation between responders and non-responders to ABA/MTX. Next, the second subset of patients 32 with RA allowed validation by qRT-PCR of a minimal signature with only four genes. This latter signature categorized 81% of patients with RA with 75% sensitivity, 85% specificity and 85% negative predictive value. This combination showed a significant enrichment of genes involved in electron transport chain (ETC) pathways. Seven transcripts from ETC pathways (NDUFA6, NDUFA4, UQCRQ, ATP5J, COX7A2, COX7B, COX6A1) were significantly downregulated in responders versus non-responders to ABA/MTX. Moreover, dysregulation of these genes was independent of inflammation and was specific to ABA response. Pre-silencing of ETC genes is associated with future response to ABA/MTX and might be a crucial key to susceptibility to ABA response.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2004-05-05

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states.

PubMed

Carlson, Ross; Srienc, Friedrich

2004-04-20

We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures. Copyright 2004 Wiley Periodicals, Inc.

Empirical testing AOP network-based hazard prediction combined effect of aromatase inhibition & androgen receptor agonism

EPA Science Inventory

Adverse outcome pathways (AOPs) describe linkages between a specific molecular perturbation resulting from interaction of a chemical with a biomolecule in an organism and one possible adverse outcome of regulatory significance. While individual AOPs have utility, it is recognized...

Knowledge-driven genomic interactions: an application in ovarian cancer.

PubMed

Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D

2014-01-01

Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.

Identification of type 2 diabetes-associated combination of SNPs using support vector machine.

PubMed

Ban, Hyo-Jeong; Heo, Jee Yeon; Oh, Kyung-Soo; Park, Keun-Joon

2010-04-23

Type 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways. We analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method. Support Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.

Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression

PubMed Central

Li, Feng; Li, Xiang; Feng, Li; Shi, Xinrui; Wang, Lihua; Li, Xia

2016-01-01

Glioma is a malignant nervous system tumor with a high fatality rate and poor prognosis. MicroRNAs (miRNAs) are important post-transcriptional modulators of glioma initiation and progression. Tumor progression often results from dysfunctional co-operation between pathways regulated by miRNAs. We therefore constructed a glioma progression-related miRNA-pathway crosstalk network that not only revealed some key miRNA-pathway patterns, but also helped characterize the functional roles of miRNAs during glioma progression. Our data indicate that crosstalk between cell cycle and p53 pathways is associated with grade II to grade III progression, while cell communications-related pathways involving regulation of actin cytoskeleton and adherens junctions are associated with grade IV glioblastoma progression. Furthermore, miRNAs and their crosstalk pathways may be useful for stratifying glioma and glioblastoma patients into groups with short or long survival times. Our data indicate that a combination of miRNA and pathway crosstalk information can be used for survival prediction. PMID:27013589

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway.

PubMed

Collins, Chris D; Finnegan, Eilis

2010-02-01

The soil-air-plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil-air-plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log K(OA) > 9 and log K(AW) < -3. For those pollutants with log K(OA) < 9 and log K(AW) > -3 there was a higher deposition of pollutant via the soil-air-plant pathway than for those chemicals with log K(OA) > 9 and log K(AW) < -3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil-root-shoot pathway. The incorporation of the soil-air-plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log K(OA). One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg(-1).

The PD-1 pathway as a therapeutic target to overcome immune escape mechanisms in cancer.

PubMed

Henick, Brian S; Herbst, Roy S; Goldberg, Sarah B

2014-12-01

Immunotherapy is emerging as a powerful approach in cancer treatment. Preclinical data predicted the antineoplastic effects seen in clinical trials of programmed death-1 (PD-1) pathway inhibitors, as well as their observed toxicities. The results of early clinical trials are extraordinarily promising in several cancer types and have shaped the direction of ongoing and future studies. This review describes the biological rationale for targeting the PD-1 pathway with monoclonal antibodies for the treatment of cancer as a context for examining the results of early clinical trials. It also surveys the landscape of ongoing clinical trials and discusses their anticipated strengths and limitations. PD-1 pathway inhibition represents a new frontier in cancer immunotherapy, which shows clear evidence of activity in various tumor types including NSCLC and melanoma. Ongoing and upcoming trials will examine optimal combinations of these agents, which should further define their role across tumor types. Current limitations include the absence of a reliable companion diagnostic to predict likely responders, as well as lack of data in early-stage cancer when treatment has the potential to increase cure rates.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling.

PubMed

Wen, Shi; Zhan, Bohan; Feng, Jianghua; Hu, Weize; Lin, Xianchao; Bai, Jianxi; Huang, Heguang

2017-11-02

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy. By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1 H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight. An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation. The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

The Influence of MgH2 on the Assessment of Electrochemical Data to Predict the Degradation Rate of Mg and Mg Alloys

PubMed Central

Mueller, Wolf-Dieter; Hornberger, Helga

2014-01-01

Mg and Mg alloys are becoming more and more of interest for several applications. In the case of biomaterial applications, a special interest exists due to the fact that a predictable degradation should be given. Various investigations were made to characterize and predict the corrosion behavior in vitro and in vivo. Mostly, the simple oxidation of Mg to Mg2+ ions connected with adequate hydrogen development is assumed, and the negative difference effect (NDE) is attributed to various mechanisms and electrochemical results. The aim of this paper is to compare the different views on the corrosion pathway of Mg or Mg alloys and to present a neglected pathway based on thermodynamic data as a guideline for possible reactions combined with experimental observations of a delay of visible hydrogen evolution during cyclic voltammetry. Various reaction pathways are considered and discussed to explain these results, like the stability of the Mg+ intermediate state, the stability of MgH2 and the role of hydrogen overpotential. Finally, the impact of MgH2 formation is shown as an appropriate base for the prediction of the degradation behavior and calculation of the corrosion rate of Mg and Mg alloys. PMID:24972140

Genomancy: predicting tumour response to cancer therapy based on the oracle of genetics.

PubMed

Williams, P D; Lee, J K; Theodorescu, D

2009-01-01

Cells are complex systems that regulate a multitude of biologic pathways involving a diverse array of molecules. Cancer can develop when these pathways become deregulated as a result of mutations in the genes coding for these proteins or of epigenetic changes that affect gene expression, or both1,2. The diversity and interconnectedness of these pathways and their molecular components implies that a variety of mutations may lead to tumorigenic cellular deregulation3-6. This variety, combined with the requirement to overcome multiple anticancer defence mechanisms7, contributes to the heterogeneous nature of cancer. Consequently, tumours with similar histology may vary in their underlying molecular circuitry8-10, with resultant differences in biologic behaviour, manifested in proliferation rate, invasiveness, metastatic potential, and unfortunately, response to cytotoxic therapy. Thus, cancer can be thought of as a family of related tumour subtypes, highlighting the need for individualized prediction both of disease progression and of treatment response, based on the molecular characteristics of the tumour.

MAP3K8/TPL-2/COT is a potential predictive marker for MEK inhibitor treatment in high-grade serous ovarian carcinomas.

PubMed

Gruosso, Tina; Garnier, Camille; Abelanet, Sophie; Kieffer, Yann; Lemesre, Vincent; Bellanger, Dorine; Bieche, Ivan; Marangoni, Elisabetta; Sastre-Garau, Xavier; Mieulet, Virginie; Mechta-Grigoriou, Fatima

2015-10-12

Ovarian cancer is a silent disease with a poor prognosis that urgently requires new therapeutic strategies. In low-grade ovarian tumours, mutations in the MAP3K BRAF gene constitutively activate the downstream kinase MEK. Here we demonstrate that an additional MAP3K, MAP3K8 (TPL-2/COT), accumulates in high-grade serous ovarian carcinomas (HGSCs) and is a potential prognostic marker for these tumours. By combining analyses on HGSC patient cohorts, ovarian cancer cells and patient-derived xenografts, we demonstrate that MAP3K8 controls cancer cell proliferation and migration by regulating key players in G1/S transition and adhesion dynamics. In addition, we show that the MEK pathway is the main pathway involved in mediating MAP3K8 function, and that MAP3K8 exhibits a reliable predictive value for the effectiveness of MEK inhibitor treatment. Our data highlight key roles for MAP3K8 in HGSC and indicate that MEK inhibitors could be a useful treatment strategy, in combination with conventional chemotherapy, for this disease.

Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer

PubMed Central

Börnigen, Daniela; Tyekucheva, Svitlana; Wang, Xiaodong; Rider, Jennifer R.; Lee, Gwo-Shu; Mucci, Lorelei A.; Sweeney, Christopher; Huttenhower, Curtis

2016-01-01

Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies. PMID:27078000

Asthma pharmacogenetics and the development of genetic profiles for personalized medicine

PubMed Central

Ortega, Victor E; Meyers, Deborah A; Bleecker, Eugene R

2015-01-01

Human genetics research will be critical to the development of genetic profiles for personalized or precision medicine in asthma. Genetic profiles will consist of gene variants that predict individual disease susceptibility and risk for progression, predict which pharmacologic therapies will result in a maximal therapeutic benefit, and predict whether a therapy will result in an adverse response and should be avoided in a given individual. Pharmacogenetic studies of the glucocorticoid, leukotriene, and β2-adrenergic receptor pathways have focused on candidate genes within these pathways and, in addition to a small number of genome-wide association studies, have identified genetic loci associated with therapeutic responsiveness. This review summarizes these pharmacogenetic discoveries and the future of genetic profiles for personalized medicine in asthma. The benefit of a personalized, tailored approach to health care delivery is needed in the development of expensive biologic drugs directed at a specific biologic pathway. Prior pharmacogenetic discoveries, in combination with additional variants identified in future studies, will form the basis for future genetic profiles for personalized tailored approaches to maximize therapeutic benefit for an individual asthmatic while minimizing the risk for adverse events. PMID:25691813

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy.

PubMed

Kim, Eunjung; Kim, Jae-Young; Smith, Matthew A; Haura, Eric B; Anderson, Alexander R A

2018-03-01

During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.

Mechanism and microstructures in Ga2O3 pseudomartensitic solid phase transition.

PubMed

Zhu, Sheng-Cai; Guan, Shu-Hui; Liu, Zhi-Pan

2016-07-21

Solid-to-solid phase transition, although widely exploited in making new materials, challenges persistently our current theory for predicting its complex kinetics and rich microstructures in transition. The Ga2O3α-β phase transformation represents such a common but complex reaction with marked change in cation coordination and crystal density, which was known to yield either amorphous or crystalline products under different synthetic conditions. Here we, via recently developed stochastic surface walking (SSW) method, resolve for the first time the atomistic mechanism of Ga2O3α-β phase transformation, the pathway of which turns out to be the first reaction pathway ever determined for a new type of diffusionless solid phase transition, namely, pseudomartensitic phase transition. We demonstrate that the sensitivity of product crystallinity is caused by its multi-step, multi-type reaction pathway, which bypasses seven intermediate phases and involves all types of elementary solid phase transition steps, i.e. the shearing of O layers (martensitic type), the local diffusion of Ga atoms (reconstructive type) and the significant lattice dilation (dilation type). While the migration of Ga atoms across the close-packed O layers is the rate-determining step and yields "amorphous-like" high energy intermediates, the shearing of O layers contributes to the formation of coherent biphase junctions and the presence of a crystallographic orientation relation, (001)α//(201[combining macron])β + [120]α//[13[combining macron]2]β. Our experiment using high-resolution transmission electron microscopy further confirms the theoretical predictions on the atomic structure of biphase junction and the formation of (201[combining macron])β twin, and also discovers the late occurrence of lattice expansion in the nascent β phase that grows out from the parent α phase. By distinguishing pseudomartensitic transition from other types of mechanisms, we propose general rules to predict the product crystallinity of solid phase transition. The new knowledge on the kinetics of pseudomartensitic transition complements the theory of diffusionless solid phase transition.

Using metagenomic and metatranscriptomic observations to test a thermodynamic-based model of community metabolic expression over time and space

NASA Astrophysics Data System (ADS)

Vallino, J. J.; Huber, J. A.

2016-02-01

Marine biogeochemistry is orchestrated by a complex and dynamic community of microorganisms that attempt to maximize their own fecundity through a combination of competition and cooperation. At a systems level, the community can be described as a distributed metabolic network, where different species contribute their own unique set of metabolic capabilities. Our current project attempts to understand the governing principles that describe amplification or attenuation of metabolic pathways within the network through a combination of modeling and metagenomic, metatranscriptomic and biogeochemical observations. We will describe and present results from our thermodynamic-based model that determines optimal pathway expression from available resources based on the principle of maximum entropy production (MEP); that is, based on the hypothesis that non-equilibrium systems organize to maximize energy dissipation. The MEP model currently predicts metabolic pathway expression over time, and one spatial dimension. Model predictions will be compared to biogeochemical observations and gene presence and expression from samples collected over time and space from a costal meromictic basin (Siders Pond) located in Falmouth MA, US. Siders Pond permanent stratification, caused by occasional seawater intrusion, results in steep chemoclines and redox gradients, which supports both aerobic and anaerobic phototrophs as well as sulfur, Fe and Mn redox cycles. The diversity of metabolic capability and expression we have observed over depth makes it an ideal system to test our thermodynamic-based model.

Normal mode-guided transition pathway generation in proteins

PubMed Central

Lee, Byung Ho; Seo, Sangjae; Kim, Min Hyeok; Kim, Youngjin; Jo, Soojin; Choi, Moon-ki; Lee, Hoomin; Choi, Jae Boong

2017-01-01

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this. PMID:29020017

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

LINKING 'OMIC AND GENETIC DATA TO PHYSIOLOGICALLY-BASED PHARMACOKINETIC AND PHARMACODYNAMIC MODELING TO ENHANCE ECOLOGICAL AND HUMAN HEALTH RISK ASSESSMENT

EPA Science Inventory

A great deal of academic, private sector, and government research has been initiated to apply advanced molecular biological methods to the discovery of toxicity pathways in wildlife and humans. One aim is the prediction of health outcomes based on the combination of refined chemi...

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

PubMed

de Ávila, Maurício Boff; de Azevedo, Walter Filgueira

2018-04-20

In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure. © 2018 John Wiley & Sons A/S.

Promoter library-based module combination (PLMC) technology for optimization of threonine biosynthesis in Corynebacterium glutamicum.

PubMed

Wei, Liang; Xu, Ning; Wang, Yiran; Zhou, Wei; Han, Guoqiang; Ma, Yanhe; Liu, Jun

2018-05-01

Due to the lack of efficient control elements and tools, the fine-tuning of gene expression in the multi-gene metabolic pathways is still a great challenge for engineering microbial cell factories, especially for the important industrial microorganism Corynebacterium glutamicum. In this study, the promoter library-based module combination (PLMC) technology was developed to efficiently optimize the expression of genes in C. glutamicum. A random promoter library was designed to contain the putative - 10 (NNTANANT) and - 35 (NNGNCN) consensus motifs, and refined through a three-step screening procedure to achieve numerous genetic control elements with different strength levels, including fluorescence-activated cell sorting (FACS) screening, agar plate screening, and 96-well plate screening. Multiple conventional strategies were employed for further precise characterizations of the promoter library, such as real-time quantitative PCR, sodium dodecyl sulfate polyacrylamide gel electrophoresis, FACS analysis, and the lacZ reporter system. These results suggested that the established promoter elements effectively regulated gene expression and showed varying strengths over a wide range. Subsequently, a multi-module combination technology was created based on the efficient promoter elements for combination and optimization of modules in the multi-gene pathways. Using this technology, the threonine biosynthesis pathway was reconstructed and optimized by predictable tuning expression of five modules in C. glutamicum. The threonine titer of the optimized strain was significantly improved to 12.8 g/L, an approximate 6.1-fold higher than that of the control strain. Overall, the PLMC technology presented in this study provides a rapid and effective method for combination and optimization of multi-gene pathways in C. glutamicum.

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies.

PubMed

Cannistraci, Carlo V; Ogorevc, Jernej; Zorc, Minja; Ravasi, Timothy; Dovc, Peter; Kunej, Tanja

2013-02-14

Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes.

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

PubMed Central

Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

2012-01-01

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

Urine peptidome analysis predicts risk of end-stage renal disease and reveals proteolytic pathways involved in autosomal dominant polycystic kidney disease progression.

PubMed

Pejchinovski, Martin; Siwy, Justyna; Metzger, Jochen; Dakna, Mohammed; Mischak, Harald; Klein, Julie; Jankowski, Vera; Bae, Kyongtae T; Chapman, Arlene B; Kistler, Andreas D

2017-03-01

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by slowly progressive bilateral renal cyst growth ultimately resulting in loss of kidney function and end-stage renal disease (ESRD). Disease progression rate and age at ESRD are highly variable. Therapeutic interventions therefore require early risk stratification of patients and monitoring of disease progression in response to treatment. We used a urine peptidomic approach based on capillary electrophoresis-mass-spectrometry (CE-MS) to identify potential biomarkers reflecting the risk for early progression to ESRD in the Consortium of Radiologic Imaging in Polycystic Kidney Disease (CRISP) cohort. A biomarker-based classifier consisting of 20 urinary peptides allowed the prediction of ESRD within 10-13 years of follow-up in patients 24-46 years of age at baseline. The performance of the biomarker score approached that of height-adjusted total kidney volume (htTKV) and the combination of the biomarker panel with htTKV improved prediction over either one alone. In young patients (<24 years at baseline), the same biomarker model predicted a 30 mL/min/1.73 m 2 glomerular filtration rate decline over 8 years. Sequence analysis of the altered urinary peptides and the prediction of the involved proteases by in silico analysis revealed alterations in distinct proteolytic pathways, in particular matrix metalloproteinases and cathepsins. We developed a urinary test that accurately predicts relevant clinical outcomes in ADPKD patients and suggests altered proteolytic pathways involved in disease progression. © The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Prediction of functional profiles of gut microbiota from 16S rRNA metagenomic data provides a more robust evaluation of gut dysbiosis occurring in Japanese type 2 diabetic patients.

PubMed

Inoue, Ryo; Ohue-Kitano, Ryuji; Tsukahara, Takamitsu; Tanaka, Masashi; Masuda, Shinya; Inoue, Takayuki; Yamakage, Hajime; Kusakabe, Toru; Hasegawa, Koji; Shimatsu, Akira; Satoh-Asahara, Noriko

2017-11-01

We assessed whether gut microbial functional profiles predicted from 16S rRNA metagenomics differed in Japanese type 2 diabetic patients. A total of 22 Japanese subjects were recruited from our outpatient clinic in an observational study. Fecal samples were obtained from 12 control and 10 type 2 diabetic subjects. 16S rRNA metagenomic data were generated and functional profiles predicted using "Phylogenetic Investigation of Communities by Reconstruction of Unobserved States" software. We measured the parameters of glucose metabolism, gut bacterial taxonomy and functional profile, and examined the associations in a cross-sectional manner. Eleven of 288 "Kyoto Encyclopedia of Genes and Genomes" pathways were significantly enriched in diabetic patients compared with control subjects ( p <0.05, q<0.1). The relative abundance of almost all pathways, including the Insulin signaling pathway and Glycolysis/Gluconeogenesis , showed strong, positive correlations with hemoglobin A 1c (HbA 1c ) and fasting plasma glucose (FPG) levels. Bacterial taxonomic analysis showed that genus Blautia significantly differed between groups and had negative correlations with HbA 1c and FPG levels. Our findings suggest a novel pathophysiological relationship between gut microbial communities and diabetes, further highlighting the significance and utility of combining prediction of functional profiles with ordinal bacterial taxonomic analysis (UMIN Clinical Trails Registry number: UMIN000026592).

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Synergistic effects of concurrent blockade of PI3K and MEK pathways in pancreatic cancer preclinical models.

PubMed

Zhong, Hua; Sanchez, Cesar; Spitzer, Dirk; Spitrzer, Dirk; Plambeck-Suess, Stacy; Gibbs, Jesse; Hawkins, Williams G; Denardo, David; Gao, Feng; Pufahl, Robert A; Lockhart, Albert C; Xu, Mai; Linehan, David; Weber, Jason; Wang-Gillam, Andrea

2013-01-01

Patients with pancreatic cancer have dismal prognoses, and novel therapies are urgently needed. Mutations of the KRAS oncogene occur frequently in pancreatic cancer and represent an attractive target. Direct targeting of the predominant KRAS pathways have been challenging and research into therapeutic strategies have been now refocused on pathways downstream of KRAS, phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK [MEK]). We hypothesized that concurrent inhibition of the PI3K and MEK pathways would result in synergistic antitumor activity, as it would circumvent the compensatory feedback loop between the two pathways. We investigated the combined effect of the PI3K inhibitor, GDC0941, and the MEK inhibitor, AZD6244, on cell viability, apoptosis and cell signaling in a panel of pancreatic cancer cell lines. An in vivo analysis was conducted on pancreatic cancer xenografts. While BxPC-3 (KRAS wild type) and MIA PaCa-2 (KRAS mutated) cell lines were sensitive to GDC0941 and AZD6244 as single agents, synergistic inhibition of tumor cell growth and induction of apoptosis were observed in both cell lines when the two drugs were combined. Interestingly, phosphorylation of the cap-dependent translational components, 4E-binding protein (p-4E-BP1) and S6 was found to be closely associated with sensitivity to GDC0941 and AZD6244. In BxPC-3 cell xenografts, survival differences were observed between the control and the AZD6244, GDC0941, and combination groups. Our study provides the rationale for concurrent targeting of the PI3K and MEK pathways, regardless of KRAS status, and suggests that phosphorylation of 4E-BP1and S6 can serve as a predictive biomarker for response to treatment.

Titanium α-ω phase transformation pathway and a predicted metastable structure

DOE PAGES

Zarkevich, Nickolai A.; Johnson, Duane D.

2016-01-15

A titanium is a highly utilized metal for structural lightweighting and its phases, transformation pathways (transition states), and structures have scientific and industrial importance. Using a proper solid-state nudged elastic band method employing two climbing images combined with density functional theory DFT + U methods for accurate energetics, we detail the pressure-induced α (ductile) to ω (brittle) transformation at the coexistence pressure. We also find two transition states along the minimal-enthalpy path and discover a metastable body-centered orthorhombic structure, with stable phonons, a lower density than the end-point phases, and decreasing stability with increasing pressure.

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Using Answer Set Programming to Integrate RNA Expression with Signalling Pathway Information to Infer How Mutations Affect Ageing

PubMed Central

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M.

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects. PMID:23251396

Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing.

PubMed

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

PubMed

Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

2018-05-08

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

Metabolic pathway reconstruction of eugenol to vanillin bioconversion in Aspergillus niger

PubMed Central

Srivastava, Suchita; Luqman, Suaib; Khan, Feroz; Chanotiya, Chandan S; Darokar, Mahendra P

2010-01-01

Identification of missing genes or proteins participating in the metabolic pathways as enzymes are of great interest. One such class of pathway is involved in the eugenol to vanillin bioconversion. Our goal is to develop an integral approach for identifying the topology of a reference or known pathway in other organism. We successfully identify the missing enzymes and then reconstruct the vanillin biosynthetic pathway in Aspergillus niger. The procedure combines enzyme sequence similarity searched through BLAST homology search and orthologs detection through COG & KEGG databases. Conservation of protein domains and motifs was searched through CDD, PFAM & PROSITE databases. Predictions regarding how proteins act in pathway were validated experimentally and also compared with reported data. The bioconversion of vanillin was screened on UV-TLC plates and later confirmed through GC and GC-MS techniques. We applied a procedure for identifying missing enzymes on the basis of conserved functional motifs and later reconstruct the metabolic pathway in target organism. Using the vanillin biosynthetic pathway of Pseudomonas fluorescens as a case study, we indicate how this approach can be used to reconstruct the reference pathway in A. niger and later results were experimentally validated through chromatography and spectroscopy techniques. PMID:20978605

Skill Acquisition and Use across the Life Course: Current Trends, Future Prospects

ERIC Educational Resources Information Center

Martin, Bill

2007-01-01

People's life pathways are no longer as predictable as they were in the second half of the 20th century. It is no longer as simple as moving from school to work, probably via tertiary education, to living independently, then getting married and starting a family. Changes in how people combine education with life-course transitions will influence…

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl; Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht; Institute for Risk Assessment Sciences

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol andmore » saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.« less

Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer’s disease

PubMed Central

Li, Xinzhong; Long, Jintao; He, Taigang; Belshaw, Robert; Scott, James

2015-01-01

Previous studies have evaluated gene expression in Alzheimer’s disease (AD) brains to identify mechanistic processes, but have been limited by the size of the datasets studied. Here we have implemented a novel meta-analysis approach to identify differentially expressed genes (DEGs) in published datasets comprising 450 late onset AD (LOAD) brains and 212 controls. We found 3124 DEGs, many of which were highly correlated with Braak stage and cerebral atrophy. Pathway Analysis revealed the most perturbed pathways to be (a) nitric oxide and reactive oxygen species in macrophages (NOROS), (b) NFkB and (c) mitochondrial dysfunction. NOROS was also up-regulated, and mitochondrial dysfunction down-regulated, in healthy ageing subjects. Upstream regulator analysis predicted the TLR4 ligands, STAT3 and NFKBIA, for activated pathways and RICTOR for mitochondrial genes. Protein-protein interaction network analysis emphasised the role of NFKB; identified a key interaction of CLU with complement; and linked TYROBP, TREM2 and DOK3 to modulation of LPS signalling through TLR4 and to phosphatidylinositol metabolism. We suggest that NEUROD6, ZCCHC17, PPEF1 and MANBAL are potentially implicated in LOAD, with predicted links to calcium signalling and protein mannosylation. Our study demonstrates a highly injurious combination of TLR4-mediated NFKB signalling, NOROS inflammatory pathway activation, and mitochondrial dysfunction in LOAD. PMID:26202100

Radiation-Induced Chromosomal Aberrations and Immunotherapy: Micronuclei, Cytosolic DNA, and Interferon-Production Pathway.

PubMed

Durante, Marco; Formenti, Silvia C

2018-01-01

Radiation-induced chromosomal aberrations represent an early marker of late effects, including cell killing and transformation. The measurement of cytogenetic damage in tissues, generally in blood lymphocytes, from patients treated with radiotherapy has been studied for many years to predict individual sensitivity and late morbidity. Acentric fragments are lost during mitosis and create micronuclei (MN), which are well correlated to cell killing. Immunotherapy is rapidly becoming a most promising new strategy for metastatic tumors, and combination with radiotherapy is explored in several pre-clinical studies and clinical trials. Recent evidence has shown that the presence of cytosolic DNA activates immune response via the cyclic GMP-AMP synthase/stimulator of interferon genes pathway, which induces type I interferon transcription. Cytosolic DNA can be found after exposure to ionizing radiation either as MN or as small fragments leaking through nuclear envelope ruptures. The study of the dependence of cytosolic DNA and MN on dose and radiation quality can guide the optimal combination of radiotherapy and immunotherapy. The role of densely ionizing charged particles is under active investigation to define their impact on the activation of the interferon pathway.

Integrated analysis of germline and somatic variants in ovarian cancer.

PubMed

Kanchi, Krishna L; Johnson, Kimberly J; Lu, Charles; McLellan, Michael D; Leiserson, Mark D M; Wendl, Michael C; Zhang, Qunyuan; Koboldt, Daniel C; Xie, Mingchao; Kandoth, Cyriac; McMichael, Joshua F; Wyczalkowski, Matthew A; Larson, David E; Schmidt, Heather K; Miller, Christopher A; Fulton, Robert S; Spellman, Paul T; Mardis, Elaine R; Druley, Todd E; Graubert, Timothy A; Goodfellow, Paul J; Raphael, Benjamin J; Wilson, Richard K; Ding, Li

2014-01-01

We report the first large-scale exome-wide analysis of the combined germline-somatic landscape in ovarian cancer. Here we analyse germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2 and PALB2. In addition, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility (NF1, MAP3K4, CDKN2B and MLL3). Evidence for loss of heterozygosity was found in 100 and 76% of cases with germline BRCA1 and BRCA2 truncations, respectively. Germline-somatic interaction analysis combined with extensive bioinformatics annotation identifies 222 candidate functional germline truncation and missense variants, including two pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK and MLL pathways.

Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations.

PubMed

Reem, Nathan T; Chen, Han-Yi; Hur, Manhoi; Zhao, Xuefeng; Wurtele, Eve Syrkin; Li, Xu; Li, Ling; Zabotina, Olga

2018-03-01

This research provides new insights into plant response to cell wall perturbations through correlation of transcriptome and metabolome datasets obtained from transgenic plants expressing cell wall-modifying enzymes. Plants respond to changes in their cell walls in order to protect themselves from pathogens and other stresses. Cell wall modifications in Arabidopsis thaliana have profound effects on gene expression and defense response, but the cell signaling mechanisms underlying these responses are not well understood. Three transgenic Arabidopsis lines, two with reduced cell wall acetylation (AnAXE and AnRAE) and one with reduced feruloylation (AnFAE), were used in this study to investigate the plant responses to cell wall modifications. RNA-Seq in combination with untargeted metabolome was employed to assess differential gene expression and metabolite abundance. RNA-Seq results were correlated with metabolite abundances to determine the pathways involved in response to cell wall modifications introduced in each line. The resulting pathway enrichments revealed the deacetylation events in AnAXE and AnRAE plants induced similar responses, notably, upregulation of aromatic amino acid biosynthesis and changes in regulation of primary metabolic pathways that supply substrates to specialized metabolism, particularly those related to defense responses. In contrast, genes and metabolites of lipid biosynthetic pathways and peroxidases involved in lignin polymerization were downregulated in AnFAE plants. These results elucidate how primary metabolism responds to extracellular stimuli. Combining the transcriptomics and metabolomics datasets increased the power of pathway prediction, and demonstrated the complexity of pathways involved in cell wall-mediated signaling.

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

PubMed

Chen, Vicky; Paisley, John; Lu, Xinghua

2017-03-14

Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying patterns of pathway perturbations would provide insights into common disease mechanisms shared among tumors, which is important for guiding treatment and predicting outcome. However, identifying perturbed pathways is challenging, because different tumors can have the same perturbed pathways that are perturbed by different SGAs. Here, we designed novel semantic representations that capture the functional similarity of distinct SGAs perturbing a common pathway in different tumors. Combining this representation with topic modeling would allow us to identify patterns in altered signaling pathways. We represented each gene with a vector of words describing its function, and we represented the SGAs of a tumor as a text document by pooling the words representing individual SGAs. We applied the nested hierarchical Dirichlet process (nHDP) model to a collection of tumors of 5 cancer types from TCGA. We identified topics (consisting of co-occurring words) representing the common functional themes of different SGAs. Tumors were clustered based on their topic associations, such that each cluster consists of tumors sharing common functional themes. The resulting clusters contained mixtures of cancer types, which indicates that different cancer types can share disease mechanisms. Survival analysis based on the clusters revealed significant differences in survival among the tumors of the same cancer type that were assigned to different clusters. The results indicate that applying topic modeling to semantic representations of tumors identifies patterns in the combinations of altered functional pathways in cancer.

Antiangiogenic Therapy for Glioblastoma: Current Status and Future Prospects

PubMed Central

Batchelor, Tracy T.; Reardon, David A.; de Groot, John F.; Wick, Wolfgang; Weller, Michael

2014-01-01

Glioblastoma is characterized by high expression levels of pro-angiogenic cytokines and microvascular proliferation, highlighting the potential value of treatments targeting angiogenesis. Antiangiogenic treatment likely achieves a beneficial impact through multiple mechanisms of action. Ultimately, however, alternative pro-angiogenic signal transduction pathways are activated leading to the development of resistance, even in tumors that initially respond. The identification of biomarkers or imaging parameters to predict response and to herald resistance is of high priority. Despite promising phase 2 clinical trial results and patient benefit in terms of clinical improvement and longer progression-free survival, an overall survival benefit has not been demonstrated in 4 randomized phase 3 trials of bevacizumab or cilengitide in newly diagnosed glioblastoma or cediranib or enzastaurin recurrent glioblastoma. However, future studies are warranted: predictive markers may allow appropriate patient enrichment, combination with chemotherapy may ultimately prove successful in improving overall survival, and novel agents targeting multiple pro-angiogenic pathways may prove effective. PMID:25398844

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors.

PubMed

Loboda, Andrey; Nebozhyn, Michael; Klinghoffer, Rich; Frazier, Jason; Chastain, Michael; Arthur, William; Roberts, Brian; Zhang, Theresa; Chenard, Melissa; Haines, Brian; Andersen, Jannik; Nagashima, Kumiko; Paweletz, Cloud; Lynch, Bethany; Feldman, Igor; Dai, Hongyue; Huang, Pearl; Watters, James

2010-06-30

Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

A New Approach to Predict Microbial Community Assembly and Function Using a Stochastic, Genome-Enabled Modeling Framework

NASA Astrophysics Data System (ADS)

King, E.; Brodie, E.; Anantharaman, K.; Karaoz, U.; Bouskill, N.; Banfield, J. F.; Steefel, C. I.; Molins, S.

2016-12-01

Characterizing and predicting the microbial and chemical compositions of subsurface aquatic systems necessitates an understanding of the metabolism and physiology of organisms that are often uncultured or studied under conditions not relevant for one's environment of interest. Cultivation-independent approaches are therefore important and have greatly enhanced our ability to characterize functional microbial diversity. The capability to reconstruct genomes representing thousands of populations from microbial communities using metagenomic techniques provides a foundation for development of predictive models for community structure and function. Here, we discuss a genome-informed stochastic trait-based model incorporated into a reactive transport framework to represent the activities of coupled guilds of hypothetical microorganisms. Metabolic pathways for each microbe within a functional guild are parameterized from metagenomic data with a unique combination of traits governing organism fitness under dynamic environmental conditions. We simulate the thermodynamics of coupled electron donor and acceptor reactions to predict the energy available for cellular maintenance, respiration, biomass development, and enzyme production. While `omics analyses can now characterize the metabolic potential of microbial communities, it is functionally redundant as well as computationally prohibitive to explicitly include the thousands of recovered organisms into biogeochemical models. However, one can derive potential metabolic pathways from genomes along with trait-linkages to build probability distributions of traits. These distributions are used to assemble groups of microbes that couple one or more of these pathways. From the initial ensemble of microbes, only a subset will persist based on the interaction of their physiological and metabolic traits with environmental conditions, competing organisms, etc. Here, we analyze the predicted niches of these hypothetical microbes and assess the ability of a stochastically assembled community of organisms to predict subsurface biogeochemical dynamics.

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view.

Whole-Genome Analysis of the SHORT-ROOT Developmental Pathway in Arabidopsis

PubMed Central

Busch, Wolfgang; Cui, Hongchang; Wang, Jean Y; Blilou, Ikram; Hassan, Hala; Nakajima, Keiji; Matsumoto, Noritaka; Lohmann, Jan U; Scheres, Ben

2006-01-01

Stem cell function during organogenesis is a key issue in developmental biology. The transcription factor SHORT-ROOT (SHR) is a critical component in a developmental pathway regulating both the specification of the root stem cell niche and the differentiation potential of a subset of stem cells in the Arabidopsis root. To obtain a comprehensive view of the SHR pathway, we used a statistical method called meta-analysis to combine the results of several microarray experiments measuring the changes in global expression profiles after modulating SHR activity. Meta-analysis was first used to identify the direct targets of SHR by combining results from an inducible form of SHR driven by its endogenous promoter, ectopic expression, followed by cell sorting and comparisons of mutant to wild-type roots. Eight putative direct targets of SHR were identified, all with expression patterns encompassing subsets of the native SHR expression domain. Further evidence for direct regulation by SHR came from binding of SHR in vivo to the promoter regions of four of the eight putative targets. A new role for SHR in the vascular cylinder was predicted from the expression pattern of several direct targets and confirmed with independent markers. The meta-analysis approach was then used to perform a global survey of the SHR indirect targets. Our analysis suggests that the SHR pathway regulates root development not only through a large transcription regulatory network but also through hormonal pathways and signaling pathways using receptor-like kinases. Taken together, our results not only identify the first nodes in the SHR pathway and a new function for SHR in the development of the vascular tissue but also reveal the global architecture of this developmental pathway. PMID:16640459

18-Month Predictors of Later Outcomes in Younger Siblings of Children With Autism Spectrum Disorder: A Baby Siblings Research Consortium Study

PubMed Central

Chawarska, Katarzyna; Shic, Frederick; Macari, Suzanne; Campbell, Daniel J.; Brian, Jessica; Landa, Rebecca; Hutman, Ted; Nelson, Charles A.; Ozonoff, Sally; Tager-Flusberg, Helen; Young, Gregory S.; Zwaigenbaum, Lonnie; Cohen, Ira L.; Charman, Tony; Messinger, Daniel S.; Klin, Ami; Johnson, Scott; Bryson, Susan

2014-01-01

Objective Younger siblings of children with autism spectrum disorder (ASD) are at high risk (HR) for developing ASD as well as features of the broader autism phenotype. While this complicates early diagnostic considerations in this cohort, it also provides an opportunity to examine patterns of behavior associated specifically with ASD compared to other developmental outcomes. Method We applied Classification and Regression Trees (CART) analysis to individual items of the Autism Diagnostic Observation Schedule (ADOS) in 719 HR siblings to identify behavioral features at 18 months predictive of diagnostic outcomes (ASD, atypical development, and typical development) at 36 months. Results Three distinct combinations of features at 18 months were predictive of ASD outcome: 1) poor eye contact combined with lack of communicative gestures and giving; 2) poor eye contact combined with a lack of imaginative play; and 3) lack of giving and presence of repetitive behaviors, but with intact eye contact. These 18-month behavioral profiles predicted ASD versus non-ASD status at 36 months with 82.7% accuracy in an initial test sample and 77.3% accuracy in a validation sample. Clinical features at age 3 among children with ASD varied as a function of their 18-month symptom profiles. Children with ASD who were misclassified at 18 months were higher functioning, and their autism symptoms increased between 18 and 36 months. Conclusion These findings suggest the presence of different developmental pathways to ASD in HR siblings. Understanding such pathways will provide clearer targets for neural and genetic research and identification of developmentally specific treatments for ASD. PMID:25457930

18-month predictors of later outcomes in younger siblings of children with autism spectrum disorder: a baby siblings research consortium study.

PubMed

Chawarska, Katarzyna; Shic, Frederick; Macari, Suzanne; Campbell, Daniel J; Brian, Jessica; Landa, Rebecca; Hutman, Ted; Nelson, Charles A; Ozonoff, Sally; Tager-Flusberg, Helen; Young, Gregory S; Zwaigenbaum, Lonnie; Cohen, Ira L; Charman, Tony; Messinger, Daniel S; Klin, Ami; Johnson, Scott; Bryson, Susan

2014-12-01

Younger siblings of children with autism spectrum disorder (ASD) are at high risk (HR) for developing ASD as well as features of the broader autism phenotype. Although this complicates early diagnostic considerations in this cohort, it also provides an opportunity to examine patterns of behavior associated specifically with ASD compared to other developmental outcomes. We applied Classification and Regression Trees (CART) analysis to individual items of the Autism Diagnostic Observation Schedule (ADOS) in 719 HR siblings to identify behavioral features at 18 months that were predictive of diagnostic outcomes (ASD, atypical development, and typical development) at 36 months. Three distinct combinations of features at 18 months were predictive of ASD outcome: poor eye contact combined with lack of communicative gestures and giving; poor eye contact combined with a lack of imaginative play; and lack of giving and presence of repetitive behaviors, but with intact eye contact. These 18-month behavioral profiles predicted ASD versus non-ASD status at 36 months with 82.7% accuracy in an initial test sample and 77.3% accuracy in a validation sample. Clinical features at age 3 years among children with ASD varied as a function of their 18-month symptom profiles. Children with ASD who were misclassified at 18 months were higher functioning, and their autism symptoms increased between 18 and 36 months. These findings suggest the presence of different developmental pathways to ASD in HR siblings. Understanding such pathways will provide clearer targets for neural and genetic research and identification of developmentally specific treatments for ASD. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine.

PubMed

Liu, Zhongyang; Guo, Feifei; Wang, Yong; Li, Chun; Zhang, Xinlei; Li, Honglei; Diao, Lihong; Gu, Jiangyong; Wang, Wei; Li, Dong; He, Fuchu

2016-02-16

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM's diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients' target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ's cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the "multi-component, multi-target and multi-pathway" combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM's molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

Discovering Anti-platelet Drug Combinations with an Integrated Model of Activator-Inhibitor Relationships, Activator-Activator Synergies and Inhibitor-Inhibitor Synergies

PubMed Central

Lombardi, Federica; Golla, Kalyan; Fitzpatrick, Darren J.; Casey, Fergal P.; Moran, Niamh; Shields, Denis C.

2015-01-01

Identifying effective therapeutic drug combinations that modulate complex signaling pathways in platelets is central to the advancement of effective anti-thrombotic therapies. However, there is no systems model of the platelet that predicts responses to different inhibitor combinations. We developed an approach which goes beyond current inhibitor-inhibitor combination screening to efficiently consider other signaling aspects that may give insights into the behaviour of the platelet as a system. We investigated combinations of platelet inhibitors and activators. We evaluated three distinct strands of information, namely: activator-inhibitor combination screens (testing a panel of inhibitors against a panel of activators); inhibitor-inhibitor synergy screens; and activator-activator synergy screens. We demonstrated how these analyses may be efficiently performed, both experimentally and computationally, to identify particular combinations of most interest. Robust tests of activator-activator synergy and of inhibitor-inhibitor synergy required combinations to show significant excesses over the double doses of each component. Modeling identified multiple effects of an inhibitor of the P2Y12 ADP receptor, and complementarity between inhibitor-inhibitor synergy effects and activator-inhibitor combination effects. This approach accelerates the mapping of combination effects of compounds to develop combinations that may be therapeutically beneficial. We integrated the three information sources into a unified model that predicted the benefits of a triple drug combination targeting ADP, thromboxane and thrombin signaling. PMID:25875950

A systems approach to model the relationship between aflatoxin gene cluster expression, environmental factors, growth and toxin production by Aspergillus flavus

PubMed Central

Abdel-Hadi, Ahmed; Schmidt-Heydt, Markus; Parra, Roberto; Geisen, Rolf; Magan, Naresh

2012-01-01

A microarray analysis was used to examine the effect of combinations of water activity (aw, 0.995–0.90) and temperature (20–42°C) on the activation of aflatoxin biosynthetic genes (30 genes) in Aspergillus flavus grown on a conducive YES (20 g yeast extract, 150 g sucrose, 1 g MgSO4·7H2O) medium. The relative expression of 10 key genes (aflF, aflD, aflE, aflM, aflO, aflP, aflQ, aflX, aflR and aflS) in the biosynthetic pathway was examined in relation to different environmental factors and phenotypic aflatoxin B1 (AFB1) production. These data, plus data on relative growth rates and AFB1 production under different aw × temperature conditions were used to develop a mixed-growth-associated product formation model. The gene expression data were normalized and then used as a linear combination of the data for all 10 genes and combined with the physical model. This was used to relate gene expression to aw and temperature conditions to predict AFB1 production. The relationship between the observed AFB1 production provided a good linear regression fit to the predicted production based in the model. The model was then validated by examining datasets outside the model fitting conditions used (37°C, 40°C and different aw levels). The relationship between structural genes (aflD, aflM) in the biosynthetic pathway and the regulatory genes (aflS, aflJ) was examined in relation to aw and temperature by developing ternary diagrams of relative expression. These findings are important in developing a more integrated systems approach by combining gene expression, ecophysiological influences and growth data to predict mycotoxin production. This could help in developing a more targeted approach to develop prevention strategies to control such carcinogenic natural metabolites that are prevalent in many staple food products. The model could also be used to predict the impact of climate change on toxin production. PMID:21880616

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors

PubMed Central

2010-01-01

Background Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. Methods We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. Results The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. Conclusions These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors. PMID:20591134

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

PubMed

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xiaohong; Molinelli, Evan J; Babur, Özgün; Bemis, Debra L; Onur Sumer, Selcuk; Solit, David B; Pratilas, Christine A; Sander, Chris

2015-08-18

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.

Emerging combination therapies for metastatic colorectal cancer – impact of trifluridine/tipiracil

PubMed Central

Puthiamadathil, Jeevan M; Weinberg, Benjamin A

2017-01-01

Patients with metastatic colorectal cancer (mCRC) are surviving longer now than ever before, but mortality rates are still high and more effective therapies are clearly needed. For patients with disease that is refractory to fluoropyrimidines, oxaliplatin, irinotecan, and biologic agents targeting the vascular endothelial growth factor and epidermal growth factor receptor pathways, novel treatment options trifluridine/tipiracil (TAS-102) and regorafenib can be effective disease stabilizers. However, objective clinical responses are rare and toxicities are manageable but common. In order to tackle poor clinical responses to TAS-102, there is an ongoing effort to effectively combine this drug with other agents, particularly those targeting angiogenesis. Certain subpopulations appear to benefit more than others from TAS-102; those that benefit often have underlying genetic defects in DNA repair pathways and/or develop neutropenia. In this review, we focus on the role of TAS-102 in the treatment of mCRC, including its use in combination with other agents, potential predictive biomarkers of response to TAS-102, and possible future directions. PMID:29056855

Anaerobic biosynthesis of the lower ligand of vitamin B12

PubMed Central

Hazra, Amrita B.; Han, Andrew W.; Mehta, Angad P.; Mok, Kenny C.; Osadchiy, Vadim; Begley, Tadhg P.; Taga, Michiko E.

2015-01-01

Vitamin B12 (cobalamin) is required by humans and other organisms for diverse metabolic processes, although only a subset of prokaryotes is capable of synthesizing B12 and other cobamide cofactors. The complete aerobic and anaerobic pathways for the de novo biosynthesis of B12 are known, with the exception of the steps leading to the anaerobic biosynthesis of the lower ligand, 5,6-dimethylbenzimidazole (DMB). Here, we report the identification and characterization of the complete pathway for anaerobic DMB biosynthesis. This pathway, identified in the obligate anaerobic bacterium Eubacterium limosum, is composed of five previously uncharacterized genes, bzaABCDE, that together direct DMB production when expressed in anaerobically cultured Escherichia coli. Expression of different combinations of the bza genes revealed that 5-hydroxybenzimidazole, 5-methoxybenzimidazole, and 5-methoxy-6-methylbenzimidazole, all of which are lower ligands of cobamides produced by other organisms, are intermediates in the pathway. The bza gene content of several bacterial and archaeal genomes is consistent with experimentally determined structures of the benzimidazoles produced by these organisms, indicating that these genes can be used to predict cobamide structure. The identification of the bza genes thus represents the last remaining unknown component of the biosynthetic pathway for not only B12 itself, but also for three other cobamide lower ligands whose biosynthesis was previously unknown. Given the importance of cobamides in environmental, industrial, and human-associated microbial metabolism, the ability to predict cobamide structure may lead to an improved ability to understand and manipulate microbial metabolism. PMID:26246619

Circulating Tumor Cells: What Is in It for the Patient? A Vision towards the Future

PubMed Central

van de Stolpe, Anja; den Toonder, Jaap M. J.

2014-01-01

Knowledge on cellular signal transduction pathways as drivers of cancer growth and metastasis has fuelled development of “targeted therapy” which “targets” aberrant oncogenic signal transduction pathways. These drugs require nearly invariably companion diagnostic tests to identify the tumor-driving pathway and the cause of the abnormal pathway activity in a tumor sample, both for therapy response prediction as well as for monitoring of therapy response and emerging secondary drug resistance. Obtaining sufficient tumor material for this analysis in the metastatic setting is a challenge, and circulating tumor cells (CTCs) may provide an attractive alternative to biopsy on the premise that they can be captured from blood and the companion diagnostic test results are correctly interpreted. We discuss novel companion diagnostic directions, including the challenges, to identify the tumor driving pathway in CTCs, which in combination with a digital pathology platform and algorithms to quantitatively interpret complex CTC diagnostic results may enable optimized therapy response prediction and monitoring. In contrast to CTC-based companion diagnostics, CTC enumeration is envisioned to be largely replaced by cell free tumor DNA measurements in blood for therapy response and recurrence monitoring. The recent emergence of novel in vitro human model systems in the form of cancer-on-a-chip may enable elucidation of some of the so far elusive characteristics of CTCs, and is expected to contribute to more efficient CTC capture and CTC-based diagnostics. PMID:24879438

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

PubMed

Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

2016-05-01

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

DARTAB: a program to combine airborne radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of predicted health impacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Begovich, C.L.; Eckerman, K.F.; Schlatter, E.C.

1981-08-01

The DARTAB computer code combines radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of the predicted impact of radioactive airborne effluents. DARTAB is independent of the environmental transport code used to generate the environmental exposure data and the codes used to produce the dosimetric and health effects data. Therefore human dose and risk calculations need not be added to every environmental transport code. Options are included in DARTAB to permit the user to request tabulations by various topics (e.g., cancer site, exposure pathway, etc.) to facilitate characterization of the human health impacts of the effluents.more » The DARTAB code was written at ORNL for the US Environmental Protection Agency, Office of Radiation Programs.« less

Chemoprevention with Cyclooxygenase and Epidermal Growth Factor Receptor Inhibitors in Familial Adenomatous Polyposis Patients: mRNA Signatures of Duodenal Neoplasia.

PubMed

Delker, Don A; Wood, Austin C; Snow, Angela K; Samadder, N Jewel; Samowitz, Wade S; Affolter, Kajsa E; Boucher, Kenneth M; Pappas, Lisa M; Stijleman, Inge J; Kanth, Priyanka; Byrne, Kathryn R; Burt, Randall W; Bernard, Philip S; Neklason, Deborah W

2018-01-01

To identify gene expression biomarkers and pathways targeted by sulindac and erlotinib given in a chemoprevention trial with a significant decrease in duodenal polyp burden at 6 months ( P < 0.001) in familial adenomatous polyposis (FAP) patients, we biopsied normal and polyp duodenal tissues from patients on drug versus placebo and analyzed the RNA expression. RNA sequencing was performed on biopsies from the duodenum of FAP patients obtained at baseline and 6-month endpoint endoscopy. Ten FAP patients on placebo and 10 on sulindac and erlotinib were selected for analysis. Purity of biopsied polyp tissue was calculated from RNA expression data. RNAs differentially expressed between endpoint polyp and paired baseline normal were determined for each group and mapped to biological pathways. Key genes in candidate pathways were further validated by quantitative RT-PCR. RNA expression analyses of endpoint polyp compared with paired baseline normal for patients on placebo and drug show that pathways activated in polyp growth and proliferation are blocked by this drug combination. Directly comparing polyp gene expression between patients on drug and placebo also identified innate immune response genes (IL12 and IFNγ) preferentially expressed in patients on drug. Gene expression analyses from tissue obtained at endpoint of the trial demonstrated inhibition of the cancer pathways COX2/PGE2, EGFR, and WNT. These findings provide molecular evidence that the drug combination of sulindac and erlotinib reached the intended tissue and was on target for the predicted pathways. Furthermore, activation of innate immune pathways from patients on drug may have contributed to polyp regression. Cancer Prev Res; 11(1); 4-15. ©2017 AACR See related editorial by Shureiqi, p. 1 . ©2017 American Association for Cancer Research.

Imaging and Quantitation of a Succession of Transient Intermediates Reveal the Reversible Self-Assembly Pathway of a Simple Icosahedral Virus Capsid.

PubMed

Medrano, María; Fuertes, Miguel Ángel; Valbuena, Alejandro; Carrillo, Pablo J P; Rodríguez-Huete, Alicia; Mateu, Mauricio G

2016-11-30

Understanding the fundamental principles underlying supramolecular self-assembly may facilitate many developments, from novel antivirals to self-organized nanodevices. Icosahedral virus particles constitute paradigms to study self-assembly using a combination of theory and experiment. Unfortunately, assembly pathways of the structurally simplest virus capsids, those more accessible to detailed theoretical studies, have been difficult to study experimentally. We have enabled the in vitro self-assembly under close to physiological conditions of one of the simplest virus particles known, the minute virus of mice (MVM) capsid, and experimentally analyzed its pathways of assembly and disassembly. A combination of electron microscopy and high-resolution atomic force microscopy was used to structurally characterize and quantify a succession of transient assembly and disassembly intermediates. The results provided an experiment-based model for the reversible self-assembly pathway of a most simple (T = 1) icosahedral protein shell. During assembly, trimeric capsid building blocks are sequentially added to the growing capsid, with pentamers of building blocks and incomplete capsids missing one building block as conspicuous intermediates. This study provided experimental verification of many features of self-assembly of a simple T = 1 capsid predicted by molecular dynamics simulations. It also demonstrated atomic force microscopy imaging and automated analysis, in combination with electron microscopy, as a powerful single-particle approach to characterize at high resolution and quantify transient intermediates during supramolecular self-assembly/disassembly reactions. Finally, the efficient in vitro self-assembly achieved for the oncotropic, cell nucleus-targeted MVM capsid may facilitate its development as a drug-encapsidating nanoparticle for anticancer targeted drug delivery.

Comprehensive investigations of kinetics of alkaline hydrolysis of TNT (2,4,6-trinitrotoluene), DNT (2,4-dinitrotoluene), and DNAN (2,4-dinitroanisole).

PubMed

Sviatenko, Liudmyla; Kinney, Chad; Gorb, Leonid; Hill, Frances C; Bednar, Anthony J; Okovytyy, Sergiy; Leszczynski, Jerzy

2014-09-02

Combined experimental and computational techniques were used to analyze multistep chemical reactions in the alkaline hydrolysis of three nitroaromatic compounds: 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), and 2,4-dinitroanisole (DNAN). The study reveals common features and differences in the kinetic behavior of these compounds. The analysis of the predicted pathways includes modeling of the reactions, along with simulation of UV-vis spectra, experimental monitoring of reactions using LC/MS techniques, development of the kinetic model by designing and solving the system of differential equations, and obtaining computationally predicted kinetics for decay and accumulation of reactants and products. Obtained results suggest that DNT and DNAN are more resistant to alkaline hydrolysis than TNT. The direct substitution of a nitro group by a hydroxide represents the most favorable pathway for all considered compounds. The formation of Meisenheimer complexes leads to the kinetic first-step intermediates in the hydrolysis of TNT. Janovsky complexes can also be formed during hydrolysis of TNT and DNT but in small quantities. Methyl group abstraction is one of the suggested pathways of DNAN transformation during alkaline hydrolysis.

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling.

PubMed

Kuijper, Isoude A; Yang, Huan; Van De Water, Bob; Beltman, Joost B

2017-01-01

Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI. Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver. Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitro testing might offer novel screening methods for the harmful side-effects of drugs.

Prevention of antipsychotic-induced hyperglycaemia by vitamin D: a data mining prediction followed by experimental exploration of the molecular mechanism.

PubMed

Nagashima, Takuya; Shirakawa, Hisashi; Nakagawa, Takayuki; Kaneko, Shuji

2016-05-20

Atypical antipsychotics are associated with an increased risk of hyperglycaemia, thus limiting their clinical use. This study focused on finding the molecular mechanism underlying antipsychotic-induced hyperglycaemia. First, we searched for drug combinations in the FDA Adverse Event Reporting System (FAERS) database wherein a coexisting drug reduced the hyperglycaemia risk of atypical antipsychotics, and found that a combination with vitamin D analogues significantly decreased the occurrence of quetiapine-induced adverse events relating diabetes mellitus in FAERS. Experimental validation using mice revealed that quetiapine acutely caused insulin resistance, which was mitigated by dietary supplementation with cholecalciferol. Further database analysis of the relevant signalling pathway and gene expression predicted quetiapine-induced downregulation of Pik3r1, a critical gene acting downstream of insulin receptor. Focusing on the phosphatidylinositol 3-kinase (PI3K) signalling pathway, we found that the reduced expression of Pik3r1 mRNA was reversed by cholecalciferol supplementation in skeletal muscle, and that insulin-stimulated glucose uptake into C2C12 myotube was inhibited in the presence of quetiapine, which was reversed by concomitant calcitriol in a PI3K-dependent manner. Taken together, these results suggest that vitamin D coadministration prevents antipsychotic-induced hyperglycaemia and insulin resistance by upregulation of PI3K function.

Combining RNA interference and kinase inhibitors against cell signalling components involved in cancer

PubMed Central

O'Grady, Michael; Raha, Debasish; Hanson, Bonnie J; Bunting, Michaeline; Hanson, George T

2005-01-01

Background The transcription factor activator protein-1 (AP-1) has been implicated in a large variety of biological processes including oncogenic transformation. The tyrosine kinases of the epidermal growth factor receptor (EGFR) constitute the beginning of one signal transduction cascade leading to AP-1 activation and are known to control cell proliferation and differentiation. Drug discovery efforts targeting this receptor and other pathway components have centred on monoclonal antibodies and small molecule inhibitors. Resistance to such inhibitors has already been observed, guiding the prediction of their use in combination therapies with other targeted agents such as RNA interference (RNAi). This study examines the use of RNAi and kinase inhibitors for qualification of components involved in the EGFR/AP-1 pathway of ME180 cells, and their inhibitory effects when evaluated individually or in tandem against multiple components of this important disease-related pathway. Methods AP-1 activation was assessed using an ME180 cell line stably transfected with a beta-lactamase reporter gene under the control of AP-1 response element following epidermal growth factor (EGF) stimulation. Immunocytochemistry allowed for further quantification of small molecule inhibition on a cellular protein level. RNAi and RT-qPCR experiments were performed to assess the amount of knockdown on an mRNA level, and immunocytochemistry was used to reveal cellular protein levels for the targeted pathway components. Results Increased potency of kinase inhibitors was shown by combining RNAi directed towards EGFR and small molecule inhibitors acting at proximal or distal points in the pathway. After cellular stimulation with EGF and analysis at the level of AP-1 activation using a β-lactamase reporter gene, a 10–12 fold shift or 2.5–3 fold shift toward greater potency in the IC50 was observed for EGFR and MEK-1 inhibitors, respectively, in the presence of RNAi targeting EGFR. Conclusion EGFR pathway components were qualified as targets for inhibition of AP-1 activation using RNAi and small molecule inhibitors. The combination of these two targeted agents was shown to increase the efficacy of EGFR and MEK-1 kinase inhibitors, leading to possible implications for overcoming or preventing drug resistance, lowering effective drug doses, and providing new strategies for interrogating cellular signalling pathways. PMID:16202132

Computational active site analysis of molecular pathways to improve functional classification of enzymes.

PubMed

Ozyurt, A Sinem; Selby, Thomas L

2008-07-01

This study describes a method to computationally assess the function of homologous enzymes through small molecule binding interaction energy. Three experimentally determined X-ray structures and four enzyme models from ornithine cyclo-deaminase, alanine dehydrogenase, and mu-crystallin were used in combination with nine small molecules to derive a function score (FS) for each enzyme-model combination. While energy values varied for a single molecule-enzyme combination due to differences in the active sites, we observe that the binding energies for the entire pathway were proportional for each set of small molecules investigated. This proportionality of energies for a reaction pathway appears to be dependent on the amino acids in the active site and their direct interactions with the small molecules, which allows a function score (FS) to be calculated to assess the specificity of each enzyme. Potential of mean force (PMF) calculations were used to obtain the energies, and the resulting FS values demonstrate that a measurement of function may be obtained using differences between these PMF values. Additionally, limitations of this method are discussed based on: (a) larger substrates with significant conformational flexibility; (b) low homology enzymes; and (c) open active sites. This method should be useful in accurately predicting specificity for single enzymes that have multiple steps in their reactions and in high throughput computational methods to accurately annotate uncharacterized proteins based on active site interaction analysis. 2008 Wiley-Liss, Inc.

Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

PubMed Central

Stanberry, Larissa; Mias, George I.; Haynes, Winston; Higdon, Roger; Snyder, Michael; Kolker, Eugene

2013-01-01

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. PMID:24958148

Bacterial fermentation platform for producing artificial aromatic amines

PubMed Central

Masuo, Shunsuke; Zhou, Shengmin; Kaneko, Tatsuo; Takaya, Naoki

2016-01-01

Aromatic amines containing an aminobenzene or an aniline moiety comprise versatile natural and artificial compounds including bioactive molecules and resources for advanced materials. However, a bio-production platform has not been implemented. Here we constructed a bacterial platform for para-substituted aminobenzene relatives of aromatic amines via enzymes in an alternate shikimate pathway predicted in a Pseudomonad bacterium. Optimization of the metabolic pathway in Escherichia coli cells converted biomass glucose to 4-aminophenylalanine with high efficiency (4.4 g L−1 in fed-batch cultivation). We designed and produced artificial pathways that mimicked the fungal Ehrlich pathway in E. coli and converted 4-aminophenylalanine into 4-aminophenylethanol and 4-aminophenylacetate at 90% molar yields. Combining these conversion systems or fungal phenylalanine decarboxylases, the 4-aminophenylalanine-producing platform fermented glucose to 4-aminophenylethanol, 4-aminophenylacetate, and 4-phenylethylamine. This original bacterial platform for producing artificial aromatic amines highlights their potential as heteroatoms containing bio-based materials that can replace those derived from petroleum. PMID:27167511

[Current Possibilities for Predicting Responses to EGFR Blockade in Metastatic Colorectal Cancer].

PubMed

Němeček, R; Svoboda, M; Slabý, O

2016-01-01

The combination of modern systemic chemotherapy and anti-EGFR monoclonal antibodies improves overall survival and quality of life for patients with metastatic colorecal cancer. By contrast, the addition of anti-EGFR therapy to the treatment regime of resistant patients may lead to worse progression-free survival and overall survival. Therefore, identifying sensitive and resistant patients prior to targeted therapy of metastatic colorecal cancer is a key point during the initial decision making process. Previous research shows that primary resistance to EGFR blockade is in most cases caused by constitutive activation of signaling pathways downstream of EGFR. Of all relevant factors (mutation of KRAS, NRAS, BRAF, and PIK3CA oncogenes, inactivation of tumor suppressors PTEN and TP53, amplification of EGFR and HER2, and expression of epiregulin and amphiregulin, mikroRNA miR-31-3p, and miR-31-5p), only evaluation of KRAS and NRAS mutations has entered routine clinical practice. The role of the other markers still needs to be validated. The ongoing benefit of anti-EGFR therapy could be indicated by specific clinical parameters measured after the initiation of targeted therapy, including early tumor shrinkage, the deepness of the response, or hypomagnesemia. The accuracy of predictive dia-gnostic tools could be also increased by examining a combination of predictive markers using next generation sequencing methods. However, unjustified investigation of many molecular markers should be resisted as this may complicate interpretation of the results, particularly in terms of their specific clinical relevance. The aim of this review is to describe current possibilities with respect to predicting responses to EGFR blockade in the context of the EGFR pathway, and the utilization of such results in routine clinical practice.

Accuracy of algorithms to predict accessory pathway location in children with Wolff-Parkinson-White syndrome.

PubMed

Wren, Christopher; Vogel, Melanie; Lord, Stephen; Abrams, Dominic; Bourke, John; Rees, Philip; Rosenthal, Eric

2012-02-01

The aim of this study was to examine the accuracy in predicting pathway location in children with Wolff-Parkinson-White syndrome for each of seven published algorithms. ECGs from 100 consecutive children with Wolff-Parkinson-White syndrome undergoing electrophysiological study were analysed by six investigators using seven published algorithms, six of which had been developed in adult patients. Accuracy and concordance of predictions were adjusted for the number of pathway locations. Accessory pathways were left-sided in 49, septal in 20 and right-sided in 31 children. Overall accuracy of prediction was 30-49% for the exact location and 61-68% including adjacent locations. Concordance between investigators varied between 41% and 86%. No algorithm was better at predicting septal pathways (accuracy 5-35%, improving to 40-78% including adjacent locations), but one was significantly worse. Predictive accuracy was 24-53% for the exact location of right-sided pathways (50-71% including adjacent locations) and 32-55% for the exact location of left-sided pathways (58-73% including adjacent locations). All algorithms were less accurate in our hands than in other authors' own assessment. None performed well in identifying midseptal or right anteroseptal accessory pathway locations.

Prediction of novel synthetic pathways for the production of desired chemicals.

PubMed

Cho, Ayoun; Yun, Hongseok; Park, Jin Hwan; Lee, Sang Yup; Park, Sunwon

2010-03-28

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response

PubMed Central

van der Lee, Robin; ter Horst, Rob; Szklarczyk, Radek; Netea, Mihai G.; Andeweg, Arno C.; van Kuppeveld, Frank J. M.; Huynen, Martijn A.

2015-01-01

The RIG-I-like receptor (RLR) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons (IFNα/β) that initiate an innate antiviral response. Through systematic assessment of a wide variety of genomics data, we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members. We demonstrate that RLR pathway genes, among others, tend to evolve rapidly, interact with viral proteins, contain a limited set of protein domains, are regulated by specific transcription factors, and form a tightly connected interaction network. Using a Bayesian approach to integrate these signatures, we propose likely novel RLR regulators. RNAi knockdown experiments revealed a high prediction accuracy, identifying 94 genes among 187 candidates tested (~50%) that affected viral RNA-induced production of IFNβ. The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense (e.g. MAP3K11, CDK11B, PSMA3, TRIM14, HSPA9B, CDC37, NUP98, G3BP1), and include uncharacterized factors (DDX17, C6orf58, C16orf57, PKN2, SNW1). Our validated RLR pathway list (http://rlr.cmbi.umcn.nl/), obtained using a combination of integrative genomics and experiments, is a new resource for innate antiviral immunity research. PMID:26485378

A network pharmacology approach to discover active compounds and action mechanisms of San-Cao Granule for treatment of liver fibrosis

PubMed Central

Wei, Shizhang; Niu, Ming; Wang, Jian; Wang, Jiabo; Su, Haibin; Luo, Shengqiang; Zhang, Xiaomei; Guo, Yanlei; Liu, Liping; Liu, Fengqun; Zhao, Qingguo; Chen, Hongge; Xiao, Xiaohe; Zhao, Pan; Zhao, Yanling

2016-01-01

Ethnopharmacological relevance San-Cao Granule (SCG) has been used in patients with liver fibrosis for many years and has shown good effect. However, its mechanism of therapeutic action is not clear because of its complex chemical system. The purpose of our study is to establish a comprehensive and systemic method that can predict the mechanism of action of SCG in antihepatic fibrosis. Materials and methods In this study, a “compound–target–disease” network was constructed by combining the SCG-specific and liver fibrosis–specific target proteins with protein–protein interactions, and network pharmacology was used to screen out the underlying targets and mechanisms of SCG for treatment of liver fibrosis. Then, some key molecules of the enriched pathway were chosen to verify the effects of SCG on liver fibrosis induced by thioacetamide (TAA). Results This systematic approach had successfully revealed that 16 targets related to 11 SCG compounds were closely associated with liver fibrosis therapy. The pathway-enrichment analysis of them showed that the TGF-β1/Smad signaling pathway is relatively important. Animal experiments also proved that SCG could significantly ameliorate liver fibrosis by inhibiting the TGF-β1/Smad pathway. Conclusion SCG could alleviate liver fibrosis through the molecular mechanisms predicted by network pharmacology. Furthermore, network pharmacology could provide deep insight into the pharmacological mechanisms of Chinese herbal formulas. PMID:26929602

A network pharmacology approach to discover active compounds and action mechanisms of San-Cao Granule for treatment of liver fibrosis.

PubMed

Wei, Shizhang; Niu, Ming; Wang, Jian; Wang, Jiabo; Su, Haibin; Luo, Shengqiang; Zhang, Xiaomei; Guo, Yanlei; Liu, Liping; Liu, Fengqun; Zhao, Qingguo; Chen, Hongge; Xiao, Xiaohe; Zhao, Pan; Zhao, Yanling

2016-01-01

San-Cao Granule (SCG) has been used in patients with liver fibrosis for many years and has shown good effect. However, its mechanism of therapeutic action is not clear because of its complex chemical system. The purpose of our study is to establish a comprehensive and systemic method that can predict the mechanism of action of SCG in antihepatic fibrosis. In this study, a "compound-target-disease" network was constructed by combining the SCG-specific and liver fibrosis-specific target proteins with protein-protein interactions, and network pharmacology was used to screen out the underlying targets and mechanisms of SCG for treatment of liver fibrosis. Then, some key molecules of the enriched pathway were chosen to verify the effects of SCG on liver fibrosis induced by thioacetamide (TAA). This systematic approach had successfully revealed that 16 targets related to 11 SCG compounds were closely associated with liver fibrosis therapy. The pathway-enrichment analysis of them showed that the TGF-β1/Smad signaling pathway is relatively important. Animal experiments also proved that SCG could significantly ameliorate liver fibrosis by inhibiting the TGF-β1/Smad pathway. SCG could alleviate liver fibrosis through the molecular mechanisms predicted by network pharmacology. Furthermore, network pharmacology could provide deep insight into the pharmacological mechanisms of Chinese herbal formulas.

Prediction of enzymatic pathways by integrative pathway mapping

PubMed Central

Wichelecki, Daniel J; San Francisco, Brian; Zhao, Suwen; Rodionov, Dmitry A; Vetting, Matthew W; Al-Obaidi, Nawar F; Lin, Henry; O'Meara, Matthew J; Scott, David A; Morris, John H; Russel, Daniel; Almo, Steven C; Osterman, Andrei L

2018-01-01

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology. PMID:29377793

Phase Transition between Black and Blue Phosphorenes: A Quantum Monte Carlo Study

NASA Astrophysics Data System (ADS)

Li, Lesheng; Yao, Yi; Reeves, Kyle; Kanai, Yosuke

Phase transition of the more common black phosphorene to blue phosphorene is of great interest because they are predicted to exhibit unique electronic and optical properties. However, these two phases are predicted to be separated by a rather large energy barrier. In this work, we study the transition pathway between black and blue phosphorenes by using the variable cell nudge elastic band method combined with density functional theory calculation. We show how diffusion quantum Monte Carlo method can be used for determining the energetics of the phase transition and demonstrate the use of two approaches for removing finite-size errors. Finally, we predict how applied stress can be used to control the energetic balance between these two different phases of phosphorene.

p53 suppresses type II endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans

PubMed Central

Wild, Peter J; Ikenberg, Kristian; Fuchs, Thomas J; Rechsteiner, Markus; Georgiev, Strahil; Fankhauser, Niklaus; Noske, Aurelia; Roessle, Matthias; Caduff, Rosmarie; Dellas, Athanassios; Fink, Daniel; Moch, Holger; Krek, Wilhelm; Frew, Ian J

2012-01-01

Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours. PMID:22678923

How Sensitive Are Transdermal Transport Predictions by Microscopic Stratum Corneum Models to Geometric and Transport Parameter Input?

PubMed

Wen, Jessica; Koo, Soh Myoung; Lape, Nancy

2018-02-01

While predictive models of transdermal transport have the potential to reduce human and animal testing, microscopic stratum corneum (SC) model output is highly dependent on idealized SC geometry, transport pathway (transcellular vs. intercellular), and penetrant transport parameters (e.g., compound diffusivity in lipids). Most microscopic models are limited to a simple rectangular brick-and-mortar SC geometry and do not account for variability across delivery sites, hydration levels, and populations. In addition, these models rely on transport parameters obtained from pure theory, parameter fitting to match in vivo experiments, and time-intensive diffusion experiments for each compound. In this work, we develop a microscopic finite element model that allows us to probe model sensitivity to variations in geometry, transport pathway, and hydration level. Given the dearth of experimentally-validated transport data and the wide range in theoretically-predicted transport parameters, we examine the model's response to a variety of transport parameters reported in the literature. Results show that model predictions are strongly dependent on all aforementioned variations, resulting in order-of-magnitude differences in lag times and permeabilities for distinct structure, hydration, and parameter combinations. This work demonstrates that universally predictive models cannot fully succeed without employing experimentally verified transport parameters and individualized SC structures. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Endoscopic Features of Mucous Cap Polyps: A Way to Predict Serrated Polyps.

PubMed

Moy, Brian T; Forouhar, Faripour; Kuo, Chia-Ling; Devers, Thomas J

2018-04-27

The aims of the study were to identify whether a mucous-cap predicts the presence of serrated polyps, and to determine whether additional endoscopic findings predict the presence of a sessile serrated adenomas/polyp (SSA/P). We analyzed 147 mucous-capped polyps with corresponding histology, during 2011-2014. Eight endoscopic features (presence of borders, elevation, rim of debris, location in the colon, size ≥10 mm, varicose vessels, nodularity, and alteration in mucosal folds) of mucous-capped polyps were examined to see if they can predict SSA/Ps. A total of 86% (n=126) of mucous-capped polyps were from the right sided serrated pathway (right-sided hyperplastic [n=83], SSA/Ps [n=43], traditional serrated adenoma [n=1]), 10% (n=15) were left-sided hyperplastic polyps, and 3% (n=5) were from the adenoma-carcinoma sequence. The presence of a mucous cap combined with varicose vessels was the only significant predictor for SSA/Ps. The other seven characteristics were not found to be statistically significant for SSA/Ps, although location in the colon and the presence of nodularity trended towards significance. Our study suggests that mucous-capped polyps have high predictability for being a part of the serrated pathway. Gastroenterologists should be alert for a mucous-capped polyp with varicose veins, as these lesions have a higher risk of SSA/P.

Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

NASA Astrophysics Data System (ADS)

Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

2016-08-01

Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

Survey of Genes Involved in Biosynthesis, Transport, and Signaling of Phytohormones with Focus on Solanum lycopersicum

PubMed Central

Simm, Stefan; Scharf, Klaus-Dieter; Jegadeesan, Sridharan; Chiusano, Maria Luisa; Firon, Nurit; Schleiff, Enrico

2016-01-01

Phytohormones control the development and growth of plants, as well as their response to biotic and abiotic stress. The seven most well-studied phytohormone classes defined today are as follows: auxins, ethylene, cytokinin, abscisic acid, jasmonic acid, gibberellins, and brassinosteroids. The basic principle of hormone regulation is conserved in all plants, but recent results suggest adaptations of synthesis, transport, or signaling pathways to the architecture and growth environment of different plant species. Thus, we aimed to define the extent to which information from the model plant Arabidopsis thaliana is transferable to other plants such as Solanum lycopersicum. We extracted the co-orthologues of genes coding for major pathway enzymes in A. thaliana from the translated genomes of 12 species from the clade Viridiplantae. Based on predicted domain architecture and localization of the identified proteins from all 13 species, we inspected the conservation of phytohormone pathways. The comparison was complemented by expression analysis of (co-) orthologous genes in S. lycopersicum. Altogether, this information allowed the assignment of putative functional equivalents between A. thaliana and S. lycopersicum but also pointed to some variations between the pathways in eudicots, monocots, mosses, and green algae. These results provide first insights into the conservation of the various phytohormone pathways between the model system A. thaliana and crop plants such as tomato. We conclude that orthologue prediction in combination with analysis of functional domain architecture and intracellular localization and expression studies are sufficient tools to transfer information from model plants to other plant species. Our results support the notion that hormone synthesis, transport, and response for most part of the pathways are conserved, and species-specific variations can be found. PMID:27695302

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Lauffenburger, Douglas A.; Alexopoulos, Leonidas G.

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms. PMID:23226239

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

PubMed

Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

PubMed Central

Newaz, Khalique; Sriram, K.; Bera, Debajyoti

2015-01-01

Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these immunological pathways occupy influential positions in the PFNs (protein functional networks) that are related to prion disease. Importantly, this functional network involvement is prevalent in all the five different mouse strain-prion strain combinations that we studied. We also conclude that the dysregulation of the core elements of the bow-tie structure, which belongs to PI3K-Akt signaling pathway, leads to dysregulation of the downstream components corresponding to other biological pathways. PMID:26646948

Phosphoinositide 3-kinase (PI3K) pathway alterations are associated with histologic subtypes and are predictive of sensitivity to PI3K inhibitors in lung cancer preclinical models.

PubMed

Spoerke, Jill M; O'Brien, Carol; Huw, Ling; Koeppen, Hartmut; Fridlyand, Jane; Brachmann, Rainer K; Haverty, Peter M; Pandita, Ajay; Mohan, Sankar; Sampath, Deepak; Friedman, Lori S; Ross, Leanne; Hampton, Garret M; Amler, Lukas C; Shames, David S; Lackner, Mark R

2012-12-15

Class 1 phosphatidylinositol 3-kinase (PI3K) plays a major role in cell proliferation and survival in a wide variety of human cancers. Here, we investigated biomarker strategies for PI3K pathway inhibitors in non-small-cell lung cancer (NSCLC). Molecular profiling for candidate PI3K predictive biomarkers was conducted on a collection of NSCLC tumor samples. Assays included comparative genomic hybridization, reverse-transcription polymerase chain reaction gene expression, mutation detection for PIK3CA and other oncogenes, PTEN immunohistochemistry, and FISH for PIK3CA copy number. In addition, a panel of NSCLC cell lines characterized for alterations in the PI3K pathway was screened with PI3K and dual PI3K/mTOR inhibitors to assess the preclinical predictive value of candidate biomarkers. PIK3CA amplification was detected in 37% of squamous tumors and 5% of adenocarcinomas, whereas PIK3CA mutations were found in 9% of squamous and 0% of adenocarcinomas. Total loss of PTEN immunostaining was found in 21% of squamous tumors and 4% of adenocarcinomas. Cell lines harboring pathway alterations (receptor tyrosine kinase activation, PI3K mutation or amplification, and PTEN loss) were exquisitely sensitive to the PI3K inhibitor GDC-0941. A dual PI3K/mTOR inhibitor had broader activity across the cell line panel and in tumor xenografts. The combination of GDC-0941 with paclitaxel, erlotinib, or a mitogen-activated protein-extracellular signal-regulated kinase inhibitor had greater effects on cell viability than PI3K inhibition alone. Candidate biomarkers for PI3K inhibitors have predictive value in preclinical models and show histology-specific alterations in primary tumors, suggesting that distinct biomarker strategies may be required in squamous compared with nonsquamous NSCLC patient populations. ©2012 AACR.

MutSα's Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning

NASA Astrophysics Data System (ADS)

Melvin, Ryan L.; Thompson, William G.; Godwin, Ryan C.; Gmeiner, William H.; Salsbury, Freddie R.

2017-03-01

MutSalpha is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents - carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSalpha has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutS↵to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin - primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA - and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue - known to stack with a mismatched or unmatched bases in MMR - stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutS↵complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage.

Cumulative Effects of In Utero Administration of Mixtures of Reproductive Toxicants that Disrupt Common Target Tissues via Diverse Mechanisms of Toxicity

PubMed Central

Rider, Cynthia V.; Furr, Johnathan R.; Wilson, Vickie S.; Gray, L. Earl

2010-01-01

Although risk assessments are typically conducted on a chemical-by-chemical basis, the 1996 Food Quality Protection Act required the US Environmental Protection Agency to consider cumulative risk of chemicals that act via a common mechanism of toxicity. To this end, we are conducting studies with mixtures of chemicals to elucidate mechanisms of joint action at the systemic level with the end goal of providing a framework for assessing the cumulative effects of reproductive toxicants. Previous mixture studies conducted with antiandrogenic chemicals are reviewed briefly and two new studies are described in detail. In all binary mixture studies, rats were dosed during pregnancy with chemicals, singly or in pairs at dosage levels equivalent to approximately one half of the ED50 for hypospadias or epididymal agenesis. The binary mixtures included: androgen receptor (AR) antagonists (vinclozolin plus procymidone), phthalate esters (DBP plus BBP and DEHP plus DBP), a phthalate ester plus an AR antagonist (DBP plus procymidone), a mixed mechanism androgen signaling disruptor (linuron) plus BBP, and two chemicals which disrupt epididymal differentiation through entirely different toxicity pathways: DBP (AR pathway) plus 2,3,7,8 TCDD (AhR pathway). We also conducted multi-component mixture studies combining several “antiandrogens” together. In the first study, seven chemicals (four pesticides and three phthalates) that elicit antiandrogenic effects at two different sites in the androgen signaling pathway (i.e. AR antagonist or inhibition of androgen synthesis) were combined. In the second study, three additional phthalates were added to make a ten chemical mixture. In both the binary mixture studies and the multi-component mixture studies, chemicals that targeted male reproductive tract development displayed cumulative effects that exceeded predictions based upon a response addition model and most often were in accordance with predictions based upon dose addition models. In summary, our results indicate that compounds that act by disparate mechanisms of toxicity to disrupt the dynamic interactions among the interconnected signaling pathways in differentiating tissues produce cumulative dose-additive effects, regardless of the mechanism or mode of action of the individual mixture component. PMID:20487044

Synergy as design principle for metabolic engineering of 1-propanol production in Escherichia coli.

PubMed

Shen, Claire R; Liao, James C

2013-05-01

Synthesis of a desired product can often be achieved via more than one metabolic pathway. Whether naturally evolved or synthetically engineered, these pathways often exhibit specific properties that are suitable for production under distinct conditions and host organisms. Synergy between pathways arises when the underlying pathway characteristics, such as reducing equivalent demand, ATP requirement, intermediate utilization, and cofactor preferences, are complementary to each other. Utilization of such pathways in combination leads to an increased metabolite productivity and/or yield compared to using each pathway alone. This work illustrates the principle of synergy between two different pathways for 1-propanol production in Escherichia coli. A model-guided design based on maximum theoretical yield calculations identified synergy of the native threonine pathway and the heterologous citramalate pathway in terms of production yield across all flux ratios between the two pathways. Characterization of the individual pathways by host gene deletions demonstrates their distinct metabolic characteristics: the necessity of TCA cycle for threonine pathway and the independence of TCA cycle for the citramalate pathway. The two pathways are also complementary in driving force demands. Production experiments verified the synergistic effects predicted by the yield model, in which the platform with dual pathway for 2-ketobutyrate synthesis achieved higher yield (0.15g/g of glucose) and productivity (0.12g/L/h) of 1-propanol than individual ones alone: the threonine pathway (0.09g/g; 0.04g/L/h) or the citramalate pathway (0.11g/g; 0.04g/L/h). Thus, incorporation of synergy into the design principle of metabolic engineering may improve the production yield and rate of the desired compound. Copyright © 2013 Elsevier Inc. All rights reserved.

Integration of parallel 13 C-labeling experiments and in silico pathway analysis for enhanced production of ascomycin.

PubMed

Qi, Haishan; Lv, Mengmeng; Song, Kejing; Wen, Jianping

2017-05-01

Herein, the hyper-producing strain for ascomycin was engineered based on 13 C-labeling experiments and elementary flux modes analysis (EFMA). First, the metabolism of non-model organism Streptomyces hygroscopicus var. ascomyceticus SA68 was investigated and an updated network model was reconstructed using 13 C- metabolic flux analysis. Based on the precise model, EFMA was further employed to predict genetic targets for higher ascomycin production. Chorismatase (FkbO) and pyruvate carboxylase (Pyc) were predicted as the promising overexpression and deletion targets, respectively. The corresponding mutant TD-FkbO and TD-ΔPyc exhibited the consistency effects between model prediction and experimental results. Finally, the combined genetic manipulations were performed, achieving a high-yield ascomycin engineering strain TD-ΔPyc-FkbO with production up to 610 mg/L, 84.8% improvement compared with the parent strain SA68. These results manifested that the integration of 13 C-labeling experiments and in silico pathway analysis could serve as a promising concept to enhance ascomycin production, as well as other valuable products. Biotechnol. Bioeng. 2017;114: 1036-1044. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance.

PubMed

Atkins, Katherine E; Lafferty, Erin I; Deeny, Sarah R; Davies, Nicholas G; Robotham, Julie V; Jit, Mark

2018-06-01

Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine-pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Targeting Epidermal Growth Factor Receptor-Related Signaling Pathways in Pancreatic Cancer.

PubMed

Philip, Philip A; Lutz, Manfred P

2015-10-01

Pancreatic cancer is aggressive, chemoresistant, and characterized by complex and poorly understood molecular biology. The epidermal growth factor receptor (EGFR) pathway is frequently activated in pancreatic cancer; therefore, it is a rational target for new treatments. However, the EGFR tyrosine kinase inhibitor erlotinib is currently the only targeted therapy to demonstrate a very modest survival benefit when added to gemcitabine in the treatment of patients with advanced pancreatic cancer. There is no molecular biomarker to predict the outcome of erlotinib treatment, although rash may be predictive of improved survival; EGFR expression does not predict the biologic activity of anti-EGFR drugs in pancreatic cancer, and no EGFR mutations are identified as enabling the selection of patients likely to benefit from treatment. Here, we review clinical studies of EGFR-targeted therapies in combination with conventional cytotoxic regimens or multitargeted strategies in advanced pancreatic cancer, as well as research directed at molecules downstream of EGFR as alternatives or adjuncts to receptor targeting. Limitations of preclinical models, patient selection, and trial design, as well as the complex mechanisms underlying resistance to EGFR-targeted agents, are discussed. Future clinical trials must incorporate translational research end points to aid patient selection and circumvent resistance to EGFR inhibitors.

Discovery of new enzymes and metabolic pathways by using structure and genome context.

PubMed

Zhao, Suwen; Kumar, Ritesh; Sakai, Ayano; Vetting, Matthew W; Wood, B McKay; Brown, Shoshana; Bonanno, Jeffery B; Hillerich, Brandan S; Seidel, Ronald D; Babbitt, Patricia C; Almo, Steven C; Sweedler, Jonathan V; Gerlt, John A; Cronan, John E; Jacobson, Matthew P

2013-10-31

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with 'metabolite docking' to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by 'genome neighbourhoods' (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by 'predicting' the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

Preclinical screening of histone deacetylase inhibitors combined with ABT-737, rhTRAIL/MD5-1 or 5-azacytidine using syngeneic Vk*MYC multiple myeloma.

PubMed

Matthews, G M; Lefebure, M; Doyle, M A; Shortt, J; Ellul, J; Chesi, M; Banks, K M; Vidacs, E; Faulkner, D; Atadja, P; Bergsagel, P L; Johnstone, R W

2013-09-12

Multiple myeloma (MM) is an incurable malignancy with an unmet need for innovative treatment options. Histone deacetylase inhibitors (HDACi) are a new class of anticancer agent that have demonstrated activity in hematological malignancies. Here, we investigated the efficacy and safety of HDACi (vorinostat, panobinostat, romidepsin) and novel combination therapies using in vitro human MM cell lines and in vivo preclinical screening utilizing syngeneic transplanted Vk*MYC MM. HDACi were combined with ABT-737, which targets the intrinsic apoptosis pathway, recombinant human tumour necrosis factor-related apoptosis-inducing ligand (rhTRAIL/MD5-1), that activates the extrinsic apoptosis pathway or the DNA methyl transferase inhibitor 5-azacytidine. We demonstrate that in vitro cell line-based studies provide some insight into drug activity and combination therapies that synergistically kill MM cells; however, they do not always predict in vivo preclinical efficacy or toxicity. Importantly, utilizing transplanted Vk*MYC MM, we report that panobinostat and 5-azacytidine synergize to prolong the survival of tumor-bearing mice. In contrast, combined HDACi/rhTRAIL-based strategies, while efficacious, demonstrated on-target dose-limiting toxicities that precluded prolonged treatment. Taken together, our studies provide evidence that the transplanted Vk*MYC model of MM is a useful screening tool for anti-MM drugs and should aid in the prioritization of novel drug testing in the clinic.

Preclinical screening of histone deacetylase inhibitors combined with ABT-737, rhTRAIL/MD5-1 or 5-azacytidine using syngeneic Vk*MYC multiple myeloma

PubMed Central

Matthews, G M; Lefebure, M; Doyle, M A; Shortt, J; Ellul, J; Chesi, M; Banks, K-M; Vidacs, E; Faulkner, D; Atadja, P; Bergsagel, P L; Johnstone, R W

2013-01-01

Multiple myeloma (MM) is an incurable malignancy with an unmet need for innovative treatment options. Histone deacetylase inhibitors (HDACi) are a new class of anticancer agent that have demonstrated activity in hematological malignancies. Here, we investigated the efficacy and safety of HDACi (vorinostat, panobinostat, romidepsin) and novel combination therapies using in vitro human MM cell lines and in vivo preclinical screening utilizing syngeneic transplanted Vk*MYC MM. HDACi were combined with ABT-737, which targets the intrinsic apoptosis pathway, recombinant human tumour necrosis factor-related apoptosis-inducing ligand (rhTRAIL/MD5-1), that activates the extrinsic apoptosis pathway or the DNA methyl transferase inhibitor 5-azacytidine. We demonstrate that in vitro cell line-based studies provide some insight into drug activity and combination therapies that synergistically kill MM cells; however, they do not always predict in vivo preclinical efficacy or toxicity. Importantly, utilizing transplanted Vk*MYC MM, we report that panobinostat and 5-azacytidine synergize to prolong the survival of tumor-bearing mice. In contrast, combined HDACi/rhTRAIL-based strategies, while efficacious, demonstrated on-target dose-limiting toxicities that precluded prolonged treatment. Taken together, our studies provide evidence that the transplanted Vk*MYC model of MM is a useful screening tool for anti-MM drugs and should aid in the prioritization of novel drug testing in the clinic. PMID:24030150

Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets.

PubMed

Miller, Martin L; Molinelli, Evan J; Nair, Jayasree S; Sheikh, Tahir; Samy, Rita; Jing, Xiaohong; He, Qin; Korkut, Anil; Crago, Aimee M; Singer, Samuel; Schwartz, Gary K; Sander, Chris

2013-09-24

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.

Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets

PubMed Central

Miller, Martin L.; Molinelli, Evan J.; Nair, Jayasree S.; Sheikh, Tahir; Samy, Rita; Jing, Xiaohong; He, Qin; Korkut, Anil; Crago, Aimee M.; Singer, Samuel; Schwartz, Gary K.; Sander, Chris

2014-01-01

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depend on activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies. PMID:24065146

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

PubMed

Liseron-Monfils, Christophe; Lewis, Tim; Ashlock, Daniel; McNicholas, Paul D; Fauteux, François; Strömvik, Martina; Raizada, Manish N

2013-03-15

The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis-acting elements, but none have been adapted for maize. A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter motifs in their promoters, perhaps uncovering a broader co-regulated gene network. Promzea was also tested against tissue-specific microarray data from maize. An online tool customized for promoter motif discovery in plants has been generated called Promzea. Promzea was validated in silico by its ability to retrieve benchmark motifs and experimentally defined motifs and was tested using tissue-specific microarray data. Promzea predicted broader networks of gene regulation associated with the historic anthocyanin and phlobaphene biosynthetic pathways. Promzea is a new bioinformatics tool for understanding transcriptional gene regulation in maize and has been expanded to include rice and Arabidopsis.

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

PubMed Central

Shirdel, Elize A.; Xie, Wing; Mak, Tak W.; Jurisica, Igor

2011-01-01

Background MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP). Results mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Conclusions Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. PMID:21364759

Salinity effect on the metabolic pathway and microbial function in phenanthrene degradation by a halophilic consortium.

PubMed

Wang, Chongyang; Huang, Yong; Zhang, Zuotao; Wang, Hui

2018-04-25

With the close relationship between saline environments and industry, polycyclic aromatic hydrocarbons (PAHs) accumulate in saline/hypersaline environments. Therefore, PAHs degradation by halotolerant/halophilic bacteria has received increasing attention. In this study, the metabolic pathway of phenanthrene degradation by halophilic consortium CY-1 was first studied which showed a single upstream pathway initiated by dioxygenation at the C1 and C2 positions, and at several downstream pathways, including the catechol pathway, gentisic acid pathway and protocatechuic acid pathway. The effects of salinity on the community structure and expression of catabolic genes were further studied by a combination of high-throughput sequencing, catabolic gene clone library and real-time PCR. Pure cultures were also isolated from consortium CY-1 to investigate the contribution made by different microbes in the PAH-degrading process. Marinobacter is the dominant genus that contributed to the upstream degradation of phenanthrene especially in high salt content. Genus Halomonas made a great contribution in transforming intermediates in the subsequent degradation of catechol by using catechol 1,2-dioxygenase (C12O). Other microbes were predicted to be mediating bacteria that were able to utilize intermediates via different downstream pathways. Salinity was investigated to have negative effects on both microbial diversity and activity of consortium CY-1 and consortium CY-1 was found with a high degree of functional redundancy in saline environments.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. PMID:23248599

Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes.

PubMed

Mohd Fauzi, Fazlin; John, Cini Mathew; Karunanidhi, Arunkumar; Mussa, Hamse Y; Ramasamy, Rajesh; Adam, Aishah; Bender, Andreas

2017-02-02

Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer

PubMed Central

Swanton, Charles; Szallasi, Zoltan; Brenton, James D; Downward, Julian

2008-01-01

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts. PMID:18986507

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks

DOE PAGES

Zhao, Suwen; Sakai, Ayano; Zhang, Xinshuai; ...

2014-06-30

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins inmore » 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.« less

Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells

PubMed Central

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xiaohong; Molinelli, Evan J; Babur, Özgün; Bemis, Debra L; Onur Sumer, Selcuk; Solit, David B; Pratilas, Christine A; Sander, Chris

2015-01-01

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs. DOI: http://dx.doi.org/10.7554/eLife.04640.001 PMID:26284497

Identification of ageing-associated naturally occurring peptides in human urine

PubMed Central

Nkuipou-Kenfack, Esther; Bhat, Akshay; Klein, Julie; Jankowski, Vera; Mullen, William; Vlahou, Antonia; Dakna, Mohammed; Koeck, Thomas; Schanstra, Joost P.; Zürbig, Petra; Rudolph, Karl L.; Schumacher, Björn; Pich, Andreas; Mischak, Harald

2015-01-01

To assess normal and pathological peptidomic changes that may lead to an improved understanding of molecular mechanisms underlying ageing, urinary peptidomes of 1227 healthy and 10333 diseased individuals between 20 and 86 years of age were investigated. The diseases thereby comprised diabetes mellitus, renal and cardiovascular diseases. Using age as a continuous variable, 116 peptides were identified that significantly (p < 0.05; |ρ|≥0.2) correlated with age in the healthy cohort. The same approach was applied to the diseased cohort. Upon comparison of the peptide patterns of the two cohorts 112 common age-correlated peptides were identified. These 112 peptides predominantly originated from collagen, uromodulin and fibrinogen. While most fibrillar and basement membrane collagen fragments showed a decreased age-related excretion, uromodulin, beta-2-microglobulin and fibrinogen fragments showed an increase. Peptide-based in silico protease analysis was performed and 32 proteases, including matrix metalloproteinases and cathepsins, were predicted to be involved in ageing. Identified peptides, predicted proteases and patient information were combined in a systems biology pathway analysis to identify molecular pathways associated with normal and/or pathological ageing. While perturbations in collagen homeostasis, trafficking of toll-like receptors and endosomal pathways were commonly identified, degradation of insulin-like growth factor-binding proteins was uniquely identified in pathological ageing. PMID:26431327

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

PubMed

Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

2018-02-05

Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

Stress transgenerationally programs metabolic pathways linked to altered mental health.

PubMed

Kiss, Douglas; Ambeskovic, Mirela; Montina, Tony; Metz, Gerlinde A S

2016-12-01

Stress is among the primary causes of mental health disorders, which are the most common reason for disability worldwide. The ubiquity of these disorders, and the costs associated with them, lends a sense of urgency to the efforts to improve prediction and prevention. Down-stream metabolic changes are highly feasible and accessible indicators of pathophysiological processes underlying mental health disorders. Here, we show that remote and cumulative ancestral stress programs central metabolic pathways linked to mental health disorders. The studies used a rat model consisting of a multigenerational stress lineage (the great-great-grandmother and each subsequent generation experienced stress during pregnancy) and a transgenerational stress lineage (only the great-great-grandmother was stressed during pregnancy). Urine samples were collected from adult male F4 offspring and analyzed using 1 H NMR spectroscopy. The results of variable importance analysis based on random variable combination were used for unsupervised multivariate principal component analysis and hierarchical clustering analysis, as well as metabolite set enrichment analysis (MSEA) and pathway analysis. We identified distinct metabolic profiles associated with the multigenerational and transgenerational stress phenotype, with consistent upregulation of hippurate and downregulation of tyrosine, threonine, and histamine. MSEA and pathway analysis showed that these metabolites are involved in catecholamine biosynthesis, immune responses, and microbial host interactions. The identification of metabolic signatures linked to ancestral programming assists in the discovery of gene targets for future studies of epigenetic regulation in pathogenic processes. Ultimately, this research can lead to biomarker discovery for better prediction and prevention of mental health disorders.

Tracking Training-Related Plasticity by Combining fMRI and DTI: The Right Hemisphere Ventral Stream Mediates Musical Syntax Processing.

PubMed

Oechslin, Mathias S; Gschwind, Markus; James, Clara E

2018-04-01

As a functional homolog for left-hemispheric syntax processing in language, neuroimaging studies evidenced involvement of right prefrontal regions in musical syntax processing, of which underlying white matter connectivity remains unexplored so far. In the current experiment, we investigated the underlying pathway architecture in subjects with 3 levels of musical expertise. Employing diffusion tensor imaging tractography, departing from seeds from our previous functional magnetic resonance imaging study on music syntax processing in the same participants, we identified a pathway in the right ventral stream that connects the middle temporal lobe with the inferior frontal cortex via the extreme capsule, and corresponds to the left hemisphere ventral stream, classically attributed to syntax processing in language comprehension. Additional morphometric consistency analyses allowed dissociating tract core from more dispersed fiber portions. Musical expertise related to higher tract consistency of the right ventral stream pathway. Specifically, tract consistency in this pathway predicted the sensitivity for musical syntax violations. We conclude that enduring musical practice sculpts ventral stream architecture. Our results suggest that training-related pathway plasticity facilitates the right hemisphere ventral stream information transfer, supporting an improved sound-to-meaning mapping in music.

Identification of the dominant runoff pathways from data-based mechanistic modelling of nested catchments in temperate UK

NASA Astrophysics Data System (ADS)

Ockenden, M. C.; Chappell, N. A.

2011-05-01

SummaryUnderstanding hydrological flow pathways is important for modelling stream response, in order to address a range of environmental problems such as flood prediction, prediction of chemical loads and identification of contaminant pathways for subsequent remediation. This paper describes the use of parametrically efficient, low order models to identify the dominant modes of stream response for catchments within the Upper Eden, UK. A first order linear model adequately identified the dominant mode in all but one of the sub-catchments. A consistent pattern of time constants and pure time delays between catchments was observed over different periods of data. In the nested catchments, time constants increased as the catchment size increased from 1.1 km 2 at Gais Gill (2-7 h) to 69.4 km 2 at Kirkby Stephen (5-10 h) to 223.4 km 2 at Great Musgrave (7-16 h) to 616.4 km 2 at Temple Sowerby (11-22 h), but Blind Beck (a small catchment 8.8 km 2, time constants 11-21 h) had time constants most similar to Temple Sowerby. This was attributed to a combination of the storage role of permeable rock strata, where present, and the effect of scale on sub-surface and channel routing. A first order model could not be identified for the 1.0 km 2 Low Hall catchment, which comprises permeable sandstone overlain by Quaternary sediments. A second-order model of Low Hall stream showed a higher proportion of water taking a slower pathway (76% via a slow pathway; time constant 252 h) than a model with the same structure for the 8.8 km 2 Blind Beck (46% via slow pathway; time constant 60 h), where only 38% of the basin was underlain by the same permeable sandstone. This highlights the need to quantify the role of deep pathways through permeable rock, where present, in addition to the effect of catchment size on response times.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

PubMed

Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

2015-12-01

The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p < 0.001). Arruda was significantly better than d'Avila in predicting adjacent sites (p = 0.035) and the percent of the contralateral site prediction was higher with d'Avila than Arruda (p = 0.013). All algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

Uniform tissue lesion formation induced by high-intensity focused ultrasound along a spiral pathway.

PubMed

Qian, Kui; Li, Chenghai; Ni, Zhengyang; Tu, Juan; Guo, Xiasheng; Zhang, Dong

2017-05-01

Both theoretical and experimental studies were performed here to investigate the lesion formation induced by high-intensity focused ultrasound (HIFU) operating in continuous scanning mode along a spiral pathway. The Khokhlov-Zabolotskaya-Kuznetsov equation and bio-heat equation were combined in the current model to predict HIFU-induced temperature distribution and lesion formation. The shape of lesion and treatment efficiency were assessed for a given scanning speed at two different grid spacing (3mm and 4mm) in the gel phantom studies and further researched in ex vivo studies. The results show that uniform lesions can be generated with continuous HIFU scanning along a spiral pathway. The complete coverage of the entire treated volume can be achieved as long as the spacing grid of the spiral pathway is small enough for heat to diffuse and deposit, and the treatment efficiency can be optimized by selecting an appropriate scanning speed. This study can provide guidance for further optimization of the treatment efficiency and safety of HIFU therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

PubMed Central

Benson, Helen E; Sharman, Joanna L; Mpamhanga, Chido P; Parton, Andrew; Southan, Christopher; Harmar, Anthony J; Ghazal, Peter

2017-01-01

Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses. PMID:28910500

Mesoscale models for stacking faults, deformation twins and martensitic transformations: Linking atomistics to continuum

NASA Astrophysics Data System (ADS)

Kibey, Sandeep A.

We present a hierarchical approach that spans multiple length scales to describe defect formation---in particular, formation of stacking faults (SFs) and deformation twins---in fcc crystals. We link the energy pathways (calculated here via ab initio density functional theory, DFT) associated with formation of stacking faults and twins to corresponding heterogeneous defect nucleation models (described through mesoscale dislocation mechanics). Through the generalized Peieirls-Nabarro model, we first correlate the width of intrinsic SFs in fcc alloy systems to their nucleation pathways called generalized stacking fault energies (GSFE). We then establish a qualitative dependence of twinning tendency in fee metals and alloys---specifically, in pure Cu and dilute Cu-xAl (x= 5.0 and 8.3 at.%)---on their twin-energy pathways called the generalized planar fault energies (GPFE). We also link the twinning behavior of Cu-Al alloys to their electronic structure by determining the effect of solute Al on the valence charge density redistribution at the SF through ab initio DFT. Further, while several efforts have been undertaken to incorporate twinning for predicting stress-strain response of fcc materials, a fundamental law for critical twinning stress has not yet emerged. We resolve this long-standing issue by linking quantitatively the twin-energy pathways (GPFE) obtained via ab initio DFT to heterogeneous, dislocation-based twin nucleation models. We establish an analytical expression that quantitatively predicts the critical twinning stress in fcc metals in agreement with experiments without requiring any empiricism at any length scale. Our theory connects twinning stress to twin-energy pathways and predicts a monotonic relation between stress and unstable twin stacking fault energy revealing the physics of twinning. We further demonstrate that the theory holds for fcc alloys as well. Our theory inherently accounts for directional nature of twinning which available qualitative models do not necessarily account for. Finally, we extend the present work to martensitic transformations and determine the energy pathway for B2→B19 transformation in NiTi. Based on our ab initio DFT calculations, we propose a combined distortion-shuffle pathway for B2→B19 transformation in NiTi. Our results indicate that in NiTi, a barrier of 0.48 mRyd/atom (relative to B2 phase) must be overcome to transform the parent B2 into orthorhombic B19 phase.

Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'.

PubMed

Saito, Kazuki; Hirai, Masami Y; Yonekura-Sakakibara, Keiko

2008-01-01

Following the sequencing of whole genomes of model plants, high-throughput decoding of gene function is a major challenge in modern plant biology. In view of remarkable technical advances in transcriptomics and metabolomics, integrated analysis of these 'omics' by data-mining informatics is an excellent tool for prediction and identification of gene function, particularly for genes involved in complicated metabolic pathways. The availability of Arabidopsis public transcriptome datasets containing data of >1000 microarrays reinforces the potential for prediction of gene function by transcriptome coexpression analysis. Here, we review the strategy of combining transcriptome and metabolome as a powerful technology for studying the functional genomics of model plants and also crop and medicinal plants.

Analysis of aromatic catabolic pathways in Pseudomonas putida KT 2440 using a combined proteomic approach: 2-DE/MS and cleavable isotope-coded affinity tag analysis.

PubMed

Kim, Young Hwan; Cho, Kun; Yun, Sung-Ho; Kim, Jin Young; Kwon, Kyung-Hoon; Yoo, Jong Shin; Kim, Seung Il

2006-02-01

Proteomic analysis of Pseudomonas putida KT2440 cultured in monocyclic aromatic compounds was performed using 2-DE/MS and cleavable isotope-coded affinity tag (ICAT) to determine whether proteins involved in aromatic compound degradation pathways were altered as predicted by genomic analysis (Jiménez et al., Environ Microbiol. 2002, 4, 824-841). Eighty unique proteins were identified by 2-DE/MS or MS/MS analysis from P. putida KT2440 cultured in the presence of six different organic compounds. Benzoate dioxygenase (BenA, BenD) and catechol 1,2-dioxygenase (CatA) were induced by benzoate. Protocatechuate 3,4-dixoygenase (PcaGH) was induced by p-hydroxybenzoate and vanilline. beta-Ketoadipyl CoA thiolase (PcaF) and 3-oxoadipate enol-lactone hydrolase (PcaD) were induced by benzoate, p-hydroxybenzoate and vanilline, suggesting that benzoate, p-hydroxybenzoate and vanilline were degraded by different dioxygenases and then converged in the same beta-ketoadipate degradation pathway. An additional 110 proteins, including 19 proteins from 2-DE analysis, were identified by cleavable ICAT analysis for benzoate-induced proteomes, which complemented the 2-DE results. Phenylethylamine exposure induced beta-ketoacyl CoA thiolase (PhaD) and ring-opening enzyme (PhaL), both enzymes of the phenylacetate (pha) biodegradation pathway. Phenylalanine induced 4-hydroxyphenyl-pyruvate dioxygenase (Hpd) and homogentisate 1,2-dioxygenase (HmgA), key enzymes in the homogentisate degradation pathway. Alkyl hydroperoxide reductase (AphC) was induced under all aromatic compounds conditions. These results suggest that proteome analysis complements and supports predictive information obtained by genomic sequence analysis.

Identification of several circulating microRNAs from a genome-wide circulating microRNA expression profile as potential biomarkers for impaired glucose metabolism in polycystic ovarian syndrome.

PubMed

Jiang, Linlin; Huang, Jia; Chen, Yaxiao; Yang, Yabo; Li, Ruiqi; Li, Yu; Chen, Xiaoli; Yang, Dongzi

2016-07-01

This study aimed to detect serum microRNAs (miRNAs) differentially expressed between polycystic ovary syndrome (PCOS) patients with impaired glucose metabolism (IGM), PCOS patients with normal glucose tolerance (NGT), and healthy controls. A TaqMan miRNA array explored serum miRNA profiles as a pilot study, then selected miRNAs were analyzed in a validation cohort consisting of 65 PCOS women with IGM, 65 PCOS women with NGT, and 45 healthy women The relative expression of miR-122, miR-193b, and miR-194 was up-regulated in PCOS patients compared with controls, whereas that of miR-199b-5p was down-regulated. Furthermore, miR-122, miR-193b, and miR-194 were increased in the PCOS-IGM group compared with the PCOS-NGT group. Multiple linear regression analyses revealed that miR-193b and body mass index contributed independently to explain 43.7 % (P < 0.0001) of homeostasis model assessment-insulin resistance after adjustment for age. Investigation of diagnostic values confirmed the optimal combination of BMI and miR-193b to explore the possibility of IGM in PCOS women with area under the curve of 0.752 (95 % CI 0.667-0.837, P < 0.001). Bioinformatics analysis indicated that the predicted target functions of these miRNAs mainly involved glycometabolism and ovarian follicle development pathways, including the insulin signaling pathway, the neurotrophin signaling pathway, the PI3K-AKT signaling pathway, and regulation of actin cytoskeleton. This study expands our knowledge of the serum miRNA expression profiles of PCOS patients with IGM and the predicted target signal pathways involved in disease pathophysiology.

Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects

PubMed Central

Shao, Hongwei; Peng, Tao; Ji, Zhiwei; Su, Jing; Zhou, Xiaobo

2013-01-01

Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials. PMID:24339888

TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.

PubMed

Aben, Nanne; Vis, Daniel J; Michaut, Magali; Wessels, Lodewyk F A

2016-09-01

Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem). m.michaut@nki.nl or l.wessels@nki.nl Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metabolomic Profiling of the Synergistic Effects of Melittin in Combination with Cisplatin on Ovarian Cancer Cells

PubMed Central

Alonezi, Sanad; Tusiimire, Jonans; Wallace, Jennifer; Dufton, Mark J.; Parkinson, John A.; Young, Louise C.; Clements, Carol J.; Park, Jin-Kyu; Jeon, Jong-Woon; Ferro, Valerie A.; Watson, David G.

2017-01-01

Melittin, the main peptide present in bee venom, has been proposed as having potential for anticancer therapy; the addition of melittin to cisplatin, a first line treatment for ovarian cancer, may increase the therapeutic response in cancer treatment via synergy, resulting in improved tolerability, reduced relapse, and decreased drug resistance. Thus, this study was designed to compare the metabolomic effects of melittin in combination with cisplatin in cisplatin-sensitive (A2780) and resistant (A2780CR) ovarian cancer cells. Liquid chromatography (LC) coupled with mass spectrometry (MS) was applied to identify metabolic changes in A2780 (combination treatment 5 μg/mL melittin + 2 μg/mL cisplatin) and A2780CR (combination treatment 2 μg/mL melittin + 10 μg/mL cisplatin) cells. Principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) multivariate data analysis models were produced using SIMCA-P software. All models displayed good separation between experimental groups and high-quality goodness of fit (R2) and goodness of prediction (Q2), respectively. The combination treatment induced significant changes in both cell lines involving reduction in the levels of metabolites in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, purine and pyrimidine metabolism, and the arginine/proline pathway. The combination of melittin with cisplatin that targets these pathways had a synergistic effect. The melittin-cisplatin combination had a stronger effect on the A2780 cell line in comparison with the A2780CR cell line. The metabolic effects of melittin and cisplatin in combination were very different from those of each agent alone. PMID:28420117

Effect of chemical mutagens and carcinogens on gene expression profiles in human TK6 cells.

PubMed

Godderis, Lode; Thomas, Reuben; Hubbard, Alan E; Tabish, Ali M; Hoet, Peter; Zhang, Luoping; Smith, Martyn T; Veulemans, Hendrik; McHale, Cliona M

2012-01-01

Characterization of toxicogenomic signatures of carcinogen exposure holds significant promise for mechanistic and predictive toxicology. In vitro transcriptomic studies allow the comparison of the response to chemicals with diverse mode of actions under controlled experimental conditions. We conducted an in vitro study in TK6 cells to characterize gene expression signatures of exposure to 15 genotoxic carcinogens frequently used in European industries. We also examined the dose-responsive changes in gene expression, and perturbation of biochemical pathways in response to these carcinogens. TK6 cells were exposed at 3 dose levels for 24 h with and without S9 human metabolic mix. Since S9 had an impact on gene expression (885 genes), we analyzed the gene expression data from cells cultures incubated with S9 and without S9 independently. The ribosome pathway was affected by all chemical-dose combinations. However in general, no similar gene expression was observed among carcinogens. Further, pathways, i.e. cell cycle, DNA repair mechanisms, RNA degradation, that were common within sets of chemical-dose combination were suggested by clustergram. Linear trends in dose-response of gene expression were observed for Trichloroethylene, Benz[a]anthracene, Epichlorohydrin, Benzene, and Hydroquinone. The significantly altered genes were involved in the regulation of (anti-) apoptosis, maintenance of cell survival, tumor necrosis factor-related pathways and immune response, in agreement with several other studies. Similarly in S9+ cultures, Benz[a]pyrene, Styrene and Trichloroethylene each modified over 1000 genes at high concentrations. Our findings expand our understanding of the transcriptomic response to genotoxic carcinogens, revealing the alteration of diverse sets of genes and pathways involved in cellular homeostasis and cell cycle control.

Effect of Chemical Mutagens and Carcinogens on Gene Expression Profiles in Human TK6 Cells

PubMed Central

Godderis, Lode; Thomas, Reuben; Hubbard, Alan E.; Tabish, Ali M.; Hoet, Peter; Zhang, Luoping; Smith, Martyn T.; Veulemans, Hendrik; McHale, Cliona M.

2012-01-01

Characterization of toxicogenomic signatures of carcinogen exposure holds significant promise for mechanistic and predictive toxicology. In vitro transcriptomic studies allow the comparison of the response to chemicals with diverse mode of actions under controlled experimental conditions. We conducted an in vitro study in TK6 cells to characterize gene expression signatures of exposure to 15 genotoxic carcinogens frequently used in European industries. We also examined the dose-responsive changes in gene expression, and perturbation of biochemical pathways in response to these carcinogens. TK6 cells were exposed at 3 dose levels for 24 h with and without S9 human metabolic mix. Since S9 had an impact on gene expression (885 genes), we analyzed the gene expression data from cells cultures incubated with S9 and without S9 independently. The ribosome pathway was affected by all chemical-dose combinations. However in general, no similar gene expression was observed among carcinogens. Further, pathways, i.e. cell cycle, DNA repair mechanisms, RNA degradation, that were common within sets of chemical-dose combination were suggested by clustergram. Linear trends in dose–response of gene expression were observed for Trichloroethylene, Benz[a]anthracene, Epichlorohydrin, Benzene, and Hydroquinone. The significantly altered genes were involved in the regulation of (anti-) apoptosis, maintenance of cell survival, tumor necrosis factor-related pathways and immune response, in agreement with several other studies. Similarly in S9+ cultures, Benz[a]pyrene, Styrene and Trichloroethylene each modified over 1000 genes at high concentrations. Our findings expand our understanding of the transcriptomic response to genotoxic carcinogens, revealing the alteration of diverse sets of genes and pathways involved in cellular homeostasis and cell cycle control. PMID:22723965

PI3K inhibition to overcome endocrine resistance in breast cancer.

PubMed

Keegan, Niamh M; Gleeson, Jack P; Hennessy, Bryan T; Morris, Patrick G

2018-01-01

Activation of the phosphatidylinositol-3 kinase (PI3K) pathway is a critical step in oncogenesis and plays a role in the development of treatment resistance for both estrogen receptor (ER) positive and human epidermal growth factor receptor 2 (HER2) positive breast cancers. Hence, there have been efforts to therapeutically inhibit this pathway. Areas covered: Several inhibitors of PI3K are now progressing through clinical trials with varying degrees of efficacy and toxicity to date. Numerous unresolved questions remain concerning the optimal isoform selectivity of PI3K inhibitors and use of predictive biomarkers. This review examines the most important PI3K inhibitors in ER positive breast cancer to date, with a particular focus on their role in overcoming endocrine therapy resistance and the possible use of PIK3CA mutations as a predictive biomarker. Expert opinion: We discuss some of the emerging challenges and questions encountered during the development of PI3K inhibitors from preclinical to phase III studies, including other novel biomarkers and future combinations to overcome endocrine resistance.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Supercritical water oxidation of quinazoline: Reaction kinetics and modeling.

PubMed

Gong, Yanmeng; Guo, Yang; Wang, Shuzhong; Song, Wenhan; Xu, Donghai

2017-03-01

This paper presents a first quantitative kinetic model for supercritical water oxidation (SCWO) of quinazoline that describes the formation and interconversion of intermediates and final products at 673-873 K. The set of 11 reaction pathways for phenol, pyrimidine, naphthalene, NH 3 , etc, involved in the simplified reaction network proved sufficient for fitting the experimental results satisfactorily. We validated the model prediction ability on CO 2 yields at initial quinazoline loading not used in the parameter estimation. Reaction rate analysis and sensitivity analysis indicate that nearly all reactions reach their thermodynamic equilibrium within 300 s. The pyrimidine yielding from quinazoline is the dominant ring-opening pathway and provides a significant contribution to CO 2 formation. Low sensitivity of NH 3 decomposition rate to concentration confirms its refractory nature in SCWO. Nitrogen content in liquid products decreases whereas that in gaseous phase increases as reaction time prolonged. The nitrogen predicted by the model in gaseous phase combined with the experimental nitrogen in liquid products gives an accurate nitrogen balance of conversion process. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

Responses to the Selective Bruton's Tyrosine Kinase (BTK) Inhibitor Tirabrutinib (ONO/GS-4059) in Diffuse Large B-cell Lymphoma Cell Lines.

PubMed

Kozaki, Ryohei; Vogler, Meike; Walter, Harriet S; Jayne, Sandrine; Dinsdale, David; Siebert, Reiner; Dyer, Martin J S; Yoshizawa, Toshio

2018-04-23

Bruton's tyrosine kinase (BTK) is a key regulator of the B-cell receptor signaling pathway, and aberrant B-cell receptor (BCR) signaling has been implicated in the survival of malignant B-cells. However, responses of the diffuse large B-cell lymphoma (DLBCL) to inhibitors of BTK (BTKi) are infrequent, highlighting the need to identify mechanisms of resistance to BTKi as well as predictive biomarkers. We investigated the response to the selective BTKi, tirabrutinib, in a panel of 64 hematopoietic cell lines. Notably, only six cell lines were found to be sensitive. Although activated B-cell type DLBCL cells were most sensitive amongst all cell types studied, sensitivity to BTKi did not correlate with the presence of activating mutations in the BCR pathway. To improve efficacy of tirabrutinib, we investigated combination strategies with 43 drugs inhibiting 34 targets in six DLBCL cell lines. Based on the results, an activated B-cell-like (ABC)-DLBCL cell line, TMD8, was the most sensitive cell line to those combinations, as well as tirabrutinib monotherapy. Furthermore, tirabrutinib in combination with idelalisib, palbociclib, or trametinib was more effective in TMD8 with acquired resistance to tirabrutinib than in the parental cells. These targeted agents might be usefully combined with tirabrutinib in the treatment of ABC-DLBCL.

Responses to the Selective Bruton’s Tyrosine Kinase (BTK) Inhibitor Tirabrutinib (ONO/GS-4059) in Diffuse Large B-cell Lymphoma Cell Lines

PubMed Central

Vogler, Meike; Jayne, Sandrine; Dinsdale, David; Siebert, Reiner

2018-01-01

Bruton’s tyrosine kinase (BTK) is a key regulator of the B-cell receptor signaling pathway, and aberrant B-cell receptor (BCR) signaling has been implicated in the survival of malignant B-cells. However, responses of the diffuse large B-cell lymphoma (DLBCL) to inhibitors of BTK (BTKi) are infrequent, highlighting the need to identify mechanisms of resistance to BTKi as well as predictive biomarkers. We investigated the response to the selective BTKi, tirabrutinib, in a panel of 64 hematopoietic cell lines. Notably, only six cell lines were found to be sensitive. Although activated B-cell type DLBCL cells were most sensitive amongst all cell types studied, sensitivity to BTKi did not correlate with the presence of activating mutations in the BCR pathway. To improve efficacy of tirabrutinib, we investigated combination strategies with 43 drugs inhibiting 34 targets in six DLBCL cell lines. Based on the results, an activated B-cell-like (ABC)-DLBCL cell line, TMD8, was the most sensitive cell line to those combinations, as well as tirabrutinib monotherapy. Furthermore, tirabrutinib in combination with idelalisib, palbociclib, or trametinib was more effective in TMD8 with acquired resistance to tirabrutinib than in the parental cells. These targeted agents might be usefully combined with tirabrutinib in the treatment of ABC-DLBCL. PMID:29690649

A framework for the use of single-chemical transcriptomics data in predicting the hazards associated with complex mixtures of polycyclic aromatic hydrocarbons.

PubMed

Labib, Sarah; Williams, Andrew; Kuo, Byron; Yauk, Carole L; White, Paul A; Halappanavar, Sabina

2017-07-01

The assumption of additivity applied in the risk assessment of environmental mixtures containing carcinogenic polycyclic aromatic hydrocarbons (PAHs) was investigated using transcriptomics. MutaTMMouse were gavaged for 28 days with three doses of eight individual PAHs, two defined mixtures of PAHs, or coal tar, an environmentally ubiquitous complex mixture of PAHs. Microarrays were used to identify differentially expressed genes (DEGs) in lung tissue collected 3 days post-exposure. Cancer-related pathways perturbed by the individual or mixtures of PAHs were identified, and dose-response modeling of the DEGs was conducted to calculate gene/pathway benchmark doses (BMDs). Individual PAH-induced pathway perturbations (the median gene expression changes for all genes in a pathway relative to controls) and pathway BMDs were applied to models of additivity [i.e., concentration addition (CA), generalized concentration addition (GCA), and independent action (IA)] to generate predicted pathway-specific dose-response curves for each PAH mixture. The predicted and observed pathway dose-response curves were compared to assess the sensitivity of different additivity models. Transcriptomics-based additivity calculation showed that IA accurately predicted the pathway perturbations induced by all mixtures of PAHs. CA did not support the additivity assumption for the defined mixtures; however, GCA improved the CA predictions. Moreover, pathway BMDs derived for coal tar were comparable to BMDs derived from previously published coal tar-induced mouse lung tumor incidence data. These results suggest that in the absence of tumor incidence data, individual chemical-induced transcriptomics changes associated with cancer can be used to investigate the assumption of additivity and to predict the carcinogenic potential of a mixture.

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

PubMed Central

Kawakami, Takao; Nagasaka, Keiko; Takami, Sachiko; Wada, Kazuya; Tu, Hsiao-Kun; Otsuji, Makiko; Kyono, Yutaka; Dobashi, Tae; Komatsu, Yasuhiko; Kihara, Makoto; Akimoto, Shingo; Peers, Ian S.; South, Marie C.; Higenbottam, Tim; Fukuoka, Masahiro; Nakata, Koichiro; Ohe, Yuichiro; Kudoh, Shoji; Clausen, Ib Groth; Nishimura, Toshihide; Marko-Varga, György; Kato, Harubumi

2011-01-01

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control. PMID:21799770

Deconstructing Visual Scenes in Cortex: Gradients of Object and Spatial Layout Information

PubMed Central

Kravitz, Dwight J.; Baker, Chris I.

2013-01-01

Real-world visual scenes are complex cluttered, and heterogeneous stimuli engaging scene- and object-selective cortical regions including parahippocampal place area (PPA), retrosplenial complex (RSC), and lateral occipital complex (LOC). To understand the unique contribution of each region to distributed scene representations, we generated predictions based on a neuroanatomical framework adapted from monkey and tested them using minimal scenes in which we independently manipulated both spatial layout (open, closed, and gradient) and object content (furniture, e.g., bed, dresser). Commensurate with its strong connectivity with posterior parietal cortex, RSC evidenced strong spatial layout information but no object information, and its response was not even modulated by object presence. In contrast, LOC, which lies within the ventral visual pathway, contained strong object information but no background information. Finally, PPA, which is connected with both the dorsal and the ventral visual pathway, showed information about both objects and spatial backgrounds and was sensitive to the presence or absence of either. These results suggest that 1) LOC, PPA, and RSC have distinct representations, emphasizing different aspects of scenes, 2) the specific representations in each region are predictable from their patterns of connectivity, and 3) PPA combines both spatial layout and object information as predicted by connectivity. PMID:22473894

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

PubMed Central

Kolch, Walter; Kholodenko, Boris N.; Ambrosi, Cristina De; Barla, Annalisa; Biganzoli, Elia M.; Nencioni, Alessio; Patrone, Franco; Ballestrero, Alberto; Zoppoli, Gabriele; Verri, Alessandro; Parodi, Silvio

2015-01-01

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis. We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions. Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model. Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal. PMID:25671297

Predicting hepatocellular carcinoma through cross-talk genes identified by risk pathways

PubMed Central

Shao, Zhuo; Huo, Diwei; Zhang, Denan; Xie, Hongbo; Yang, Jingbo; Liu, Qiuqi; Chen, Xiujie

2018-01-01

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC. PMID:29765536

Convergence of isoprene and polyketide biosynthetic machinery: isoprenyl-S-carrier proteins in the pksX pathway of Bacillus subtilis.

PubMed

Calderone, Christopher T; Kowtoniuk, Walter E; Kelleher, Neil L; Walsh, Christopher T; Dorrestein, Pieter C

2006-06-13

The pksX gene cluster from Bacillus subtilis is predicted to encode the biosynthesis of an as yet uncharacterized hybrid nonribosomal peptide/polyketide secondary metabolite. We used a combination of biochemical and mass spectrometric techniques to assign functional roles to the proteins AcpK, PksC, PksL, PksF, PksG, PksH, and PksI, and we conclude that they act to incorporate an acetate-derived beta-methyl branch on an acetoacetyl-S-carrier protein and ultimately generate a Delta(2)-isoprenyl-S-carrier protein. This work highlights the power of mass spectrometry to elucidate the functions of orphan biosynthetic enzymes, and it details a mechanism by which single-carbon beta-branches can be inserted into polyketide-like structures. This pathway represents a noncanonical route to the construction of prenyl units and serves as a prototype for the intersection of isoprenoid and polyketide biosynthetic manifolds in other natural product biosynthetic pathways.

A redox proteomics approach to investigate the mode of action of nanomaterials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riebeling, Christian; Wiemann, Martin; Schnekenburger, Jürgen

2016-05-15

Numbers of engineered nanomaterials (ENMs) are steadily increasing. Therefore, alternative testing approaches with reduced costs and high predictivity suitable for high throughput screening and prioritization are urgently needed to ensure a fast and effective development of safe products. In parallel, extensive research efforts are targeted to understanding modes of action of ENMs, which may also support the development of new predictive assays. Oxidative stress is a widely accepted paradigm associated with different adverse outcomes of ENMs. It has frequently been identified in in vitro and in vivo studies and different assays have been developed for this purpose. Fluorescent dye basedmore » read-outs are most frequently used for cell testing in vitro but may be limited due to possible interference of the ENMs. Recently, other assays have been put forward such as acellular determination of ROS production potential using methods like electron spin resonance, antioxidant quantification or the use of specific sensors. In addition, Omics based approaches have gained increasing attention. In particular, redox proteomics can combine the assessment of oxidative stress with the advantage of getting more detailed mechanistic information. Here we propose a comprehensive testing strategy for assessing the oxidative stress potential of ENMs, which combines acellular methods and fast in vitro screening approaches, as well as a more involved detailed redox proteomics approach. This allows for screening and prioritization in a first tier and, if required, also for unraveling mechanistic details down to compromised signaling pathways. - Highlights: • Oxidative stress is a general paradigm for nanomaterial hazard mechanism of action. • Reactive oxygen species generation can be predicted using acellular assays. • Cellular assays based on fluorescence suffer from interference by nanomaterials. • Protein carbonylation is an irreversible and predictive mark of oxidative stress. • Proteomics of carbonylation indicates affected pathways and mechanism of action.« less

From Position-Specific Labeling to Environmental Fluxomics: Elucidating Biogeochemical Cycles from the Metabolic Perspective (BG Division Outstanding ECS Award Lecture)

NASA Astrophysics Data System (ADS)

Dippold, Michaela; Apostel, Carolin; Dijkstra, Paul; Kuzyakov, Yakov

2017-04-01

Understanding soil and sedimentary organic matter (SOM) dynamics is one of the most important challenges in biogeoscience. To disentangle the fluxes and transformations of C in soils a detailed knowledge on the biochemical pathways and its controlling factors is required. Biogeochemists' view on the C transformation of microorganisms in soil has rarely exceed a strongly simplified concept assuming that C gets either oxidized to CO2 via the microbial catabolism or incorporated into biomass via the microbial anabolism. Biochemists, however, thoroughly identified in the past decades the individual reactions of glycolysis, pentose-phosphate pathway and citric acid cycle underlying the microbial catabolism. At various points within that metabolic network the anabolic fluxes feeding biomass formation branch off. Recent studies on metabolic flux tracing by position-specific isotope labeling allowed tracing these C transformations in soils in situ, an approach which is qunatitatively complemented by metabolic flux modeling. This approach has reached new impact by the cutting-edge combination of position-specific 13C labeling with compound-specific isotope analysis of microbial biomarkers and metabolites which allows 1) tracing specific anabolic pathways in diverse microbial communities in soils and 2) identification of specific pathways of individual functional microbial groups. Thus, the combination of position-specific labeling, compound-specific isotope incorporation in biomarkers and quantitative metabolic flux modelling provide the toolbox for quantitative soil fluxomics. Our studies combining position-specific labeled glucose with amino sugar 13C analysis showed that up to 55% of glucose, incorporated into the glucose derivative glucosamine, first passed glycolysis before allocated back via gluconeogenesis. Similarly, glutamate-derived C is allocated via anaplerotic pathways towards fatty acid synthesis and in parallel to its oxidation in citric acid cycle. Thus, oxidizing catabolic pathways and anabolic pathways, i.e. building-up new cellular compounds, occurred in soils simultaneously, a combination unlikely to occur in pure cultures, where constant growth conditions under high C supply allow a straight unidirectional regulation of C metabolism. However, unstable environmental conditions, C scarcity and interactions between a still unknown diversity of microorganisms in soils are likely to induce the observed metabolic diversity. Coupling these results with the position-specific fingerprint of microbial biomarkers revealed that microbial groups show deviating adaptation strategies and that they react on environmental changes by activation or deactivation of specific metabolic pathways such as anaplerotic fluxes. To understand how microorganisms catalyze the biogeochemical fluxes in soil a profound understanding of their metabolic adaptation strategies such as recycling or switching between pathways is crucial. Metabolic flux models adapted to soil microbial communities and their regulatory strategies will not only deepen our understanding on the microorganims' reactions to environmental changes but also create the prerequisits for a quantitative prediction of biogeochemical fluxes based on the underlying microbial processes.

Probing interfacial energetics and charge transfer kinetics in semiconductor nanocomposites: New insights into heterostructured TiO 2/BiVO 4 photoanodes

DOE PAGES

Hess, Lucas H.; Cooper, Jason K.; Loiudice, Anna; ...

2017-02-28

Heterostructured nanocomposites offer promise for creating systems exhibiting functional properties that exceed those of the isolated components. For solar energy conversion, such combinations of semiconducting nanomaterials can be used to direct charge transfer along pathways that reduce recombination and promote efficient charge extraction. However, interfacial energetics and associated kinetic pathways often differ significantly from predictions derived from the characteristics of pure component materials, particularly at the nanoscale. Here, the emergent properties of TiO 2/BiVO 4 nanocomposite photoanodes are explored using a combination of X-ray and optical spectroscopies, together with photoelectrochemical (PEC) characterization. Application of these methods to both the puremore » components and the fully assembled nanocomposites reveals unpredicted interfacial energetic alignment, which promotes ultrafast injection of electrons from BiVO 4 into TiO 2. Physical charge separation yields extremely long-lived photoexcited states and correspondingly enhanced photoelectrochemical functionality. This work highlights the importance of probing emergent interfacial energetic alignment and kinetic processes for understanding mechanisms of solar energy conversion in complex nanocomposites.« less

Possible pathways used to predict different stages of lung adenocarcinoma.

PubMed

Chen, Xiaodong; Duan, Qiongyu; Xuan, Ying; Sun, Yunan; Wu, Rong

2017-04-01

We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed with KOBAS database and Fisher exact test. Euclidean distance algorithm was used to calculate total deviation score. The diagnostic model was constructed with SVM algorithm.Eighteen specific genes were obtained by getting intersection of 4 group differentially expressed genes. Ten significantly enriched pathways were obtained. In the distribution map of 10 pathways score in different groups, degrees that sample groups deviated from the normal level were as follows: stage I < stage II < stage III < stage IV. The pathway score of 4 stages exhibited linear change in some pathways, and the score of 1 or 2 stages were significantly different from the rest stages in some pathways. There was significant difference between dead and alive for these pathways except thyroid hormone signaling pathway.Those 10 pathways are associated with the development of lung adenocarcinoma and may be able to predict different stages of it. Furthermore, these pathways except thyroid hormone signaling pathway may be able to predict the prognosis.

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

PubMed

Al Sharif, Merilin; Tsakovska, Ivanka; Pajeva, Ilza; Alov, Petko; Fioravanzo, Elena; Bassan, Arianna; Kovarich, Simona; Yang, Chihae; Mostrag-Szlichtyng, Aleksandra; Vitcheva, Vessela; Worth, Andrew P; Richarz, Andrea-N; Cronin, Mark T D

2017-12-01

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC 50 ). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC 50 of PPARγ full agonists had the following statistical parameters: q 2 cv =0.610, N opt =7, SEP cv =0.505, r 2 pr =0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Prognostic and Pathogenetic Value of Combining Clinical and Biochemical Indices in Patients With Acute Lung Injury

PubMed Central

Koyama, Tatsuki; Billheimer, D. Dean; Wu, William; Bernard, Gordon R.; Thompson, B. Taylor; Brower, Roy G.; Standiford, Theodore J.; Martin, Thomas R.; Matthay, Michael A.

2010-01-01

Background: No single clinical or biologic marker reliably predicts clinical outcomes in acute lung injury (ALI)/ARDS. We hypothesized that a combination of biologic and clinical markers would be superior to either biomarkers or clinical factors alone in predicting ALI/ARDS mortality and would provide insight into the pathogenesis of clinical ALI/ARDS. Methods: Eight biologic markers that reflect endothelial and epithelial injury, inflammation, and coagulation (von Willebrand factor antigen, surfactant protein D [SP-D]), tumor necrosis factor receptor-1, interleukin [IL]-6, IL-8, intercellular adhesion molecule-1, protein C, plasminogen activator inhibitor-1) were measured in baseline plasma from 549 patients in the ARDSNet trial of low vs high positive end-expiratory pressure. Mortality was modeled with multivariable logistic regression. Predictors were selected using backward elimination. Comparisons between candidate models were based on the receiver operating characteristics (ROC) and tests of integrated discrimination improvement. Results: Clinical predictors (Acute Physiology And Chronic Health Evaluation III [APACHE III], organ failures, age, underlying cause, alveolar-arterial oxygen gradient, plateau pressure) predicted mortality with an area under the ROC curve (AUC) of 0.82; a combination of eight biomarkers and the clinical predictors had an AUC of 0.85. The best performing biomarkers were the neutrophil chemotactic factor, IL-8, and SP-D, a product of alveolar type 2 cells, supporting the concept that acute inflammation and alveolar epithelial injury are important pathogenetic pathways in human ALI/ARDS. Conclusions: A combination of biomarkers and clinical predictors is superior to clinical predictors or biomarkers alone for predicting mortality in ALI/ARDS and may be useful for stratifying patients in clinical trials. From a pathogenesis perspective, the degree of acute inflammation and alveolar epithelial injury are highly associated with the outcome of human ALI/ARDS. PMID:19858233

[Immune checkpoints inhibitors: Recent data from ASCO's meeting 2017 and perspectives].

PubMed

Kfoury, Maria; Disdero, Valentine; Vicier, Cécilé; Le Saux, Olivia; Gougis, Paul; Sajous, Christophe; Vignot, Stéphane

2018-06-19

Immune checkpoint inhibitors anti-PD-1, anti-PD-L1 and anti-CTLA-4 have been in development in several indications and have changed the face of cancer patients' management. Cancer immunotherapy was central in ASCO's meeting 2017. The identification of patients who could benefit most from immune checkpoint inhibitors is essential. The predictive value of PD-L1 status remains insufficient to select patients who could respond to immunotherapy. An extended search for new biomarkers predictive of response (INF-γ, mutational load) is ongoing, in order to better select responders. Immune checkpoint inhibitors have mainly been developed as monotherapy. However, the low response rate, between 10 and 30%, and the occurrence of resistance, contributes to the increment of new therapeutic strategies. This review summarizes the results of combination trials of two immune checkpoint inhibitors, combination of immunotherapy with conventional chemotherapy, radiotherapy or targeted therapies active on the oncogenic addiction pathway. Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

Value of local electrogram characteristics predicting successful catheter ablation of left-versus right-sided accessory atrioventricular pathways by radiofrequency current.

PubMed

Lin, J L; Schie, J T; Tseng, C D; Chen, W J; Cheng, T F; Tsou, S S; Chen, J J; Tseng, Y Z; Lien, W P

1995-01-01

Despite similar guidance by local electrogram criteria, catheter ablation of right-sided accessory atrioventricular (AV) pathways by radiofrequency current has been less effective than that of left-sided ones. In order to elucidate the possible diversities in local electrosignal criteria, we systematically analyzed the morphological and timing characteristics of 215 bipolar local electrograms from catheter ablation sites of 65 left-sided accessory AV pathways and of 356 from those of 37 right-sided ones in 92 consecutive patients with Wolff-Parkinson-White syndrome or AV reentrant tachycardia incorporating concealed accessory AV pathways. After stepwise multivariate analysis, we selected the presence of a possible accessory pathway potential, local ventricular activation preceding QRS complex for 20 ms or more during ventricular insertion mapping, and the local retrograde ventriculoatrial (VA) continuity, local retrograde VA interval < or = 50 ms, electrogram stability (left-sided targets only), retrograde accessory pathway potential (right-sided targets only) during atrial insertion mapping, as independent local electrogram predictors for successful ablation of left- and right-sided accessory AV pathways. Combination of all local electrogram predictors could have moderate chance of success (80 and 51%) for the ventricular and atrial insertion ablation of left-sided accessory AV pathways, but only low probability of success (40% in ventricular insertion ablation) or very low sensitivity (12.5% in atrial insertion ablation) for right-sided ones. In conclusion, with the present approach, successful catheter ablation of right-sided accessory AV pathways, compared to left-sided ones, still necessitate a breakthrough in the precision mapping and the efficiency of energy delivery.

Dynamic interactions between visual working memory and saccade target selection

PubMed Central

Schneegans, Sebastian; Spencer, John P.; Schöner, Gregor; Hwang, Seongmin; Hollingworth, Andrew

2014-01-01

Recent psychophysical experiments have shown that working memory for visual surface features interacts with saccadic motor planning, even in tasks where the saccade target is unambiguously specified by spatial cues. Specifically, a match between a memorized color and the color of either the designated target or a distractor stimulus influences saccade target selection, saccade amplitudes, and latencies in a systematic fashion. To elucidate these effects, we present a dynamic neural field model in combination with new experimental data. The model captures the neural processes underlying visual perception, working memory, and saccade planning relevant to the psychophysical experiment. It consists of a low-level visual sensory representation that interacts with two separate pathways: a spatial pathway implementing spatial attention and saccade generation, and a surface feature pathway implementing color working memory and feature attention. Due to bidirectional coupling between visual working memory and feature attention in the model, the working memory content can indirectly exert an effect on perceptual processing in the low-level sensory representation. This in turn biases saccadic movement planning in the spatial pathway, allowing the model to quantitatively reproduce the observed interaction effects. The continuous coupling between representations in the model also implies that modulation should be bidirectional, and model simulations provide specific predictions for complementary effects of saccade target selection on visual working memory. These predictions were empirically confirmed in a new experiment: Memory for a sample color was biased toward the color of a task-irrelevant saccade target object, demonstrating the bidirectional coupling between visual working memory and perceptual processing. PMID:25228628

Interaction of TLR-IFN and HLA polymorphisms on susceptibility of chronic HBV infection in Southwest Han Chinese.

PubMed

He, Dengming; Tao, Shiqi; Guo, Shimin; Li, Maoshi; Wu, Junqiu; Huang, Hongfei; Guo, Xinwu; Yan, Guohua; Zhu, Peng; Wang, Yuming

2015-08-01

The toll-like receptor-interferon (TLR-IFN) signalling pathway plays a crucial role in HBV infection. Human leucocyte antigen (HLA) polymorphisms are associated with chronic HBV infection by genome wide association study (GWAS). We aimed to explore interaction between TLR-IFN and HLA gene polymorphisms in susceptibility of chronic HBV infection. In the Chinese Southwest Han population, 1191 chronic HBV infection patients and 273 HBV clearance were selected. A total of 39 single nucleotide polymorphism loci in 23 genes of the TLR-IFN pathway and four HLA polymorphism loci associated with chronic HBV infection identified by GWAS were selected for genotyping. SNPStats, QVALUE, and multifactor dimensionality reduction were used for statistical analysis. A significant association was seen in several of the TLR-IFN pathway genes, TLR9 rs352140 (OR = 0.70, P = 0.0088), IL1B rs16944 (OR = 0.67, P = 0.016), IL12B rs3212227 (OR = 1.38, P = 0.021), IFNGR1 rs3799488 (OR = 1.48, P = 0.0048), IFNGR2 rs1059293 (OR = 0.27, P = 0.011), MX1 rs467960 (OR = 0.68, P = 0.022), as well as four loci in HLA, rs3077 (OR = 0.55, P < 0.0001), rs2856718 (OR = 0.60, P = 4e-04), rs9277535 (OR = 0.54, P < 0.0001) and rs7453920 (OR = 0.43, P < 0.0001). A synergistic relationship was seen between rs9277535 and rs16944 (0.13%), rs1143623 and rs6613 (0.10%). The combination of rs9277535 in HLA and rs16944 in IL1B was the best model to predict chronic HBV infection (testing accuracy = 0.6040, P = 0.0010, cross-validation consistency = 10/10). TLR-IFN pathway gene polymorphisms are associated with chronic HBV infection. Interactions with polymorphisms in these genes may be one mechanism by which HLA polymorphisms influence susceptibility to chronic HBV infection, as specific single nucleotide polymorphism combinations are highly predictive of chronic HBV infection. © 2014 The Authors. Liver International Published by John Wiley & Sons Ltd.

Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves' disease and orbitopathy.

PubMed

Zhang, Lei; Masetti, Giulia; Colucci, Giuseppe; Salvi, Mario; Covelli, Danila; Eckstein, Anja; Kaiser, Ulrike; Draman, Mohd Shazli; Muller, Ilaria; Ludgate, Marian; Lucini, Luigi; Biscarini, Filippo

2018-05-30

Graves' Disease (GD) is an autoimmune condition in which thyroid-stimulating antibodies (TRAB) mimic thyroid-stimulating hormone function causing hyperthyroidism. 5% of GD patients develop inflammatory Graves' orbitopathy (GO) characterized by proptosis and attendant sight problems. A major challenge is to identify which GD patients are most likely to develop GO and has relied on TRAB measurement. We screened sera/plasma from 14 GD, 19 GO and 13 healthy controls using high-throughput proteomics and miRNA sequencing (Illumina's HiSeq2000 and Agilent-6550 Funnel quadrupole-time-of-flight mass spectrometry) to identify potential biomarkers for diagnosis or prognosis evaluation. Euclidean distances and differential expression (DE) based on miRNA and protein quantification were analysed by multidimensional scaling (MDS) and multinomial regression respectively. We detected 3025 miRNAs and 1886 proteins and MDS revealed good separation of the 3 groups. Biomarkers were identified by combined DE and Lasso-penalized predictive models; accuracy of predictions was 0.86 (±0:18), and 5 miRNA and 20 proteins were found including Zonulin, Alpha-2 macroglobulin, Beta-2 glycoprotein 1 and Fibronectin. Functional analysis identified relevant metabolic pathways, including hippo signaling, bacterial invasion of epithelial cells and mRNA surveillance. Proteomic and miRNA analyses, combined with robust bioinformatics, identified circulating biomarkers applicable to diagnose GD, predict GO disease status and optimize patient management.

Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

PubMed Central

Berthet, Pierre; Hellgren-Kotaleski, Jeanette; Lansner, Anders

2012-01-01

Several studies have shown a strong involvement of the basal ganglia (BG) in action selection and dopamine dependent learning. The dopaminergic signal to striatum, the input stage of the BG, has been commonly described as coding a reward prediction error (RPE), i.e., the difference between the predicted and actual reward. The RPE has been hypothesized to be critical in the modulation of the synaptic plasticity in cortico-striatal synapses in the direct and indirect pathway. We developed an abstract computational model of the BG, with a dual pathway structure functionally corresponding to the direct and indirect pathways, and compared its behavior to biological data as well as other reinforcement learning models. The computations in our model are inspired by Bayesian inference, and the synaptic plasticity changes depend on a three factor Hebbian–Bayesian learning rule based on co-activation of pre- and post-synaptic units and on the value of the RPE. The model builds on a modified Actor-Critic architecture and implements the direct (Go) and the indirect (NoGo) pathway, as well as the reward prediction (RP) system, acting in a complementary fashion. We investigated the performance of the model system when different configurations of the Go, NoGo, and RP system were utilized, e.g., using only the Go, NoGo, or RP system, or combinations of those. Learning performance was investigated in several types of learning paradigms, such as learning-relearning, successive learning, stochastic learning, reversal learning and a two-choice task. The RPE and the activity of the model during learning were similar to monkey electrophysiological and behavioral data. Our results, however, show that there is not a unique best way to configure this BG model to handle well all the learning paradigms tested. We thus suggest that an agent might dynamically configure its action selection mode, possibly depending on task characteristics and also on how much time is available. PMID:23060764

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

PTEN status is a crucial determinant of the functional outcome of combined MEK and mTOR inhibition in cancer.

PubMed

Milella, Michele; Falcone, Italia; Conciatori, Fabiana; Matteoni, Silvia; Sacconi, Andrea; De Luca, Teresa; Bazzichetto, Chiara; Corbo, Vincenzo; Simbolo, Michele; Sperduti, Isabella; Benfante, Antonina; Del Curatolo, Anais; Cesta Incani, Ursula; Malusa, Federico; Eramo, Adriana; Sette, Giovanni; Scarpa, Aldo; Konopleva, Marina; Andreeff, Michael; McCubrey, James Andrew; Blandino, Giovanni; Todaro, Matilde; Stassi, Giorgio; De Maria, Ruggero; Cognetti, Francesco; Del Bufalo, Donatella; Ciuffreda, Ludovica

2017-02-21

Combined MAPK/PI3K pathway inhibition represents an attractive, albeit toxic, therapeutic strategy in oncology. Since PTEN lies at the intersection of these two pathways, we investigated whether PTEN status determines the functional response to combined pathway inhibition. PTEN (gene, mRNA, and protein) status was extensively characterized in a panel of cancer cell lines and combined MEK/mTOR inhibition displayed highly synergistic pharmacologic interactions almost exclusively in PTEN-loss models. Genetic manipulation of PTEN status confirmed a mechanistic role for PTEN in determining the functional outcome of combined pathway blockade. Proteomic analysis showed greater phosphoproteomic profile modification(s) in response to combined MEK/mTOR inhibition in PTEN-loss contexts and identified JAK1/STAT3 activation as a potential mediator of synergistic interactions. Overall, our results show that PTEN-loss is a crucial determinant of synergistic interactions between MAPK and PI3K pathway inhibitors, potentially exploitable for the selection of cancer patients at the highest chance of benefit from combined therapeutic strategies.

PTEN status is a crucial determinant of the functional outcome of combined MEK and mTOR inhibition in cancer

PubMed Central

Milella, Michele; Falcone, Italia; Conciatori, Fabiana; Matteoni, Silvia; Sacconi, Andrea; De Luca, Teresa; Bazzichetto, Chiara; Corbo, Vincenzo; Simbolo, Michele; Sperduti, Isabella; Benfante, Antonina; Del Curatolo, Anais; Cesta Incani, Ursula; Malusa, Federico; Eramo, Adriana; Sette, Giovanni; Scarpa, Aldo; Konopleva, Marina; Andreeff, Michael; McCubrey, James Andrew; Blandino, Giovanni; Todaro, Matilde; Stassi, Giorgio; De Maria, Ruggero; Cognetti, Francesco; Del Bufalo, Donatella; Ciuffreda, Ludovica

2017-01-01

Combined MAPK/PI3K pathway inhibition represents an attractive, albeit toxic, therapeutic strategy in oncology. Since PTEN lies at the intersection of these two pathways, we investigated whether PTEN status determines the functional response to combined pathway inhibition. PTEN (gene, mRNA, and protein) status was extensively characterized in a panel of cancer cell lines and combined MEK/mTOR inhibition displayed highly synergistic pharmacologic interactions almost exclusively in PTEN-loss models. Genetic manipulation of PTEN status confirmed a mechanistic role for PTEN in determining the functional outcome of combined pathway blockade. Proteomic analysis showed greater phosphoproteomic profile modification(s) in response to combined MEK/mTOR inhibition in PTEN-loss contexts and identified JAK1/STAT3 activation as a potential mediator of synergistic interactions. Overall, our results show that PTEN-loss is a crucial determinant of synergistic interactions between MAPK and PI3K pathway inhibitors, potentially exploitable for the selection of cancer patients at the highest chance of benefit from combined therapeutic strategies. PMID:28220839

NutrimiRAging: Micromanaging Nutrient Sensing Pathways through Nutrition to Promote Healthy Aging.

PubMed

Micó, Víctor; Berninches, Laura; Tapia, Javier; Daimiel, Lidia

2017-04-26

Current sociodemographic predictions point to a demographic shift in developed and developing countries that will result in an unprecedented increase of the elderly population. This will be accompanied by an increase in age-related conditions that will strongly impair human health and quality of life. For this reason, aging is a major concern worldwide. Healthy aging depends on a combination of individual genetic factors and external environmental factors. Diet has been proved to be a powerful tool to modulate aging and caloric restriction has emerged as a valuable intervention in this regard. However, many questions about how a controlled caloric restriction intervention affects aging-related processes are still unanswered. Nutrient sensing pathways become deregulated with age and lose effectiveness with age. These pathways are a link between diet and aging. Thus, fully understanding this link is a mandatory step before bringing caloric restriction into practice. MicroRNAs have emerged as important regulators of cellular functions and can be modified by diet. Some microRNAs target genes encoding proteins and enzymes belonging to the nutrient sensing pathways and, therefore, may play key roles in the modulation of the aging process. In this review, we aimed to show the relationship between diet, nutrient sensing pathways and microRNAs in the context of aging.

Pathways of fluid transport and reabsorption across the peritoneal membrane.

PubMed

Asghar, R B; Davies, S J

2008-05-01

The three-pore model of peritoneal fluid transport predicts that once the osmotic gradient has dissipated, fluid reabsorption will be due to a combination of small-pore reabsorption driven by the intravascular oncotic pressure, and an underlying disappearance of fluid from the cavity by lymphatic drainage. Our study measured fluid transport by these pathways in the presence and absence of an osmotic gradient. Paired hypertonic and standard glucose-dwell studies were performed using radio-iodinated serum albumin as an intraperitoneal volume marker and changes in intraperitoneal sodium mass to determine small-pore versus transcellular fluid transport. Disappearance of iodinated albumin was considered to indicate lymphatic drainage. Variability in transcellular ultrafiltration was largely explained by the rate of small-solute transport across the membrane. In the absence of an osmotic gradient, fluid reabsorption occurred via the small-pore pathway, the rate being proportional to the small-solute transport characteristics of the membrane. In most cases, fluid removal from the peritoneal cavity by this pathway was faster than by lymphatic drainage. Our study shows that the three-pore model describes the pathways of peritoneal fluid transport well. In the presence of high solute transport, poor transcellular ultrafiltration was due to loss of the osmotic gradient and an enhanced small-pore reabsorption rate after this gradient dissipated.

PI3K/Akt/mTOR Intracellular Pathway and Breast Cancer: Factors, Mechanism and Regulation.

PubMed

Sharma, Var Ruchi; Gupta, Girish Kumar; Sharma, A K; Batra, Navneet; Sharma, Daljit K; Joshi, Amit; Sharma, Anil K

2017-01-01

The most recurrent and considered second most frequent cause of cancer-related deaths worldwide in women is the breast cancer. The key to diagnosis is early prediction and a curable stage but still treatment remains a great clinical challenge. Origin of the Problem: A number of studies have been carried out for the treatment of breast cancer which includes the targeted therapies and increased survival rates in women. Essential PI3K/mTOR signaling pathway activation has been observed in most breast cancers. The cell growth and tumor development in such cases involve phosphoinositide 3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) complex intracellular pathway. Through preclinical and clinical trials, it has been observed that there are a number of other inhibitors of PI3K/Akt/mTOR pathway, which either alone or in combination with cytotoxic agents can be used for endocrine therapies. Structure and regulation/deregulation of mTOR provides a greater insight into the action mechanism. Also, through this review, one could easily scan first and second generation inhibitors for PI3K/Akt/mTOR pathway besides targeted therapies for breast cancer and the precise role of mTOR. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Phosphoproteomic biomarkers predicting histologic nonalcoholic steatohepatitis and fibrosis.

PubMed

Younossi, Zobair M; Baranova, Ancha; Stepanova, Maria; Page, Sandra; Calvert, Valerie S; Afendy, Arian; Goodman, Zachary; Chandhoke, Vikas; Liotta, Lance; Petricoin, Emanuel

2010-06-04

The progression of nonalcoholic fatty liver disease (NAFLD) has been linked to deregulated exchange of the endocrine signaling between adipose and liver tissue. Proteomic assays for the phosphorylation events that characterize the activated or deactivated state of the kinase-driven signaling cascades in visceral adipose tissue (VAT) could shed light on the pathogenesis of nonalcoholic steatohepatitis (NASH) and related fibrosis. Reverse-phase protein microarrays (RPMA) were used to develop biomarkers for NASH and fibrosis using VAT collected from 167 NAFLD patients (training cohort, N = 117; testing cohort, N = 50). Three types of models were developed for NASH and advanced fibrosis: clinical models, proteomics models, and combination models. NASH was predicted by a model that included measurements of two components of the insulin signaling pathway: AKT kinase and insulin receptor substrate 1 (IRS1). The models for fibrosis were less reliable when predictions were based on phosphoproteomic, clinical, or the combination data. The best performing model relied on levels of the phosphorylation of GSK3 as well as on two subunits of cyclic AMP regulated protein kinase A (PKA). Phosphoproteomics technology could potentially be used to provide pathogenic information about NASH and NASH-related fibrosis. This information can lead to a clinically relevant diagnostic/prognostic biomarker for NASH.

Hysteresis and parent-metabolite analyses unravel characteristic pesticide transport mechanisms in a mixed land use catchment.

PubMed

Tang, Ting; Stamm, Christian; van Griensven, Ann; Seuntjens, Piet; Bronders, Jan

2017-11-01

To properly estimate and manage pesticide occurrence in urban rivers, it is essential, but often highly challenging, to identify the key pesticide transport pathways in association to the main sources. This study examined the concentration-discharge hysteresis behaviour (hysteresis analysis) for three pesticides and the parent-metabolite concentration dynamics for two metabolites at sites with different levels of urban influence in a mixed land use catchment (25 km 2 ) within the Swiss Greifensee area, aiming to identify the dominant pesticide transport pathways. Combining an adapted hysteresis classification framework with prior knowledge of the field conditions and pesticide usage, we demonstrated the possibility of using hysteresis analysis to qualitatively infer the dominant pesticide transport pathway in mixed land-use catchments. The analysis showed that hysteresis types, and therefore the dominant transport pathway, vary among pesticides, sites and rainfall events. Hysteresis loops mostly correspond to dominant transport by flow components with intermediate response time, although pesticide sources indicate that fast transport pathways are responsible in most cases (e.g. urban runoff and combined sewer overflows). The discrepancy suggests the fast transport pathways can be slowed down due to catchment storages, such as topographic depressions in agricultural areas, a wastewater treatment plant (WWTP) and other artificial storage units (e.g. retention basins) in urban areas. Moreover, the WWTP was identified as an important factor modifying the parent-metabolite concentration dynamics during rainfall events. To properly predict and manage pesticide occurrence in catchments of mixed land uses, the hydrological delaying effect and chemical processes within the artificial structures need to be accounted for, in addition to the catchment hydrology and the diversity of pesticide sources. This study demonstrates that in catchments with diverse pesticide sources and complex transport mechanisms, the adapted hysteresis analysis can help to improve our understanding on pesticide transport behaviours and provide a basis for effective management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

A combined PHREEQC-2/parallel fracture model for the simulation of laminar/non-laminar flow and contaminant transport with reactions

NASA Astrophysics Data System (ADS)

Masciopinto, Costantino; Volpe, Angela; Palmiotta, Domenico; Cherubini, Claudia

2010-09-01

A combination of a parallel fracture model with the PHREEQC-2 geochemical model was developed to simulate sequential flow and chemical transport with reactions in fractured media where both laminar and turbulent flows occur. The integration of non-laminar flow resistances in one model produced relevant effects on water flow velocities, thus improving model prediction capabilities on contaminant transport. The proposed conceptual model consists of 3D rock-blocks, separated by horizontal bedding plane fractures with variable apertures. Particle tracking solved the transport equations for conservative compounds and provided input for PHREEQC-2. For each cluster of contaminant pathways, PHREEQC-2 determined the concentration for mass-transfer, sorption/desorption, ion exchange, mineral dissolution/precipitation and biodegradation, under kinetically controlled reactive processes of equilibrated chemical species. Field tests have been performed for the code verification. As an example, the combined model has been applied to a contaminated fractured aquifer of southern Italy in order to simulate the phenol transport. The code correctly fitted the field available data and also predicted a possible rapid depletion of phenols as a result of an increased biodegradation rate induced by a simulated artificial injection of nitrates, upgradient to the sources.

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine

NASA Astrophysics Data System (ADS)

Liu, Zhongyang; Guo, Feifei; Wang, Yong; Li, Chun; Zhang, Xinlei; Li, Honglei; Diao, Lihong; Gu, Jiangyong; Wang, Wei; Li, Dong; He, Fuchu

2016-02-01

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM’s diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients’ target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ’s cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM’s molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine

PubMed Central

Liu, Zhongyang; Guo, Feifei; Wang, Yong; Li, Chun; Zhang, Xinlei; Li, Honglei; Diao, Lihong; Gu, Jiangyong; Wang, Wei; Li, Dong; He, Fuchu

2016-01-01

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM’s diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients’ target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ’s cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM’s molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm. PMID:26879404

Delineation of Steroid-Degrading Microorganisms through Comparative Genomic Analysis

PubMed Central

Bergstrand, Lee H.; Cardenas, Erick; Holert, Johannes; Van Hamme, Jonathan D.

2016-01-01

ABSTRACT Steroids are ubiquitous in natural environments and are a significant growth substrate for microorganisms. Microbial steroid metabolism is also important for some pathogens and for biotechnical applications. This study delineated the distribution of aerobic steroid catabolism pathways among over 8,000 microorganisms whose genomes are available in the NCBI RefSeq database. Combined analysis of bacterial, archaeal, and fungal genomes with both hidden Markov models and reciprocal BLAST identified 265 putative steroid degraders within only Actinobacteria and Proteobacteria, which mainly originated from soil, eukaryotic host, and aquatic environments. These bacteria include members of 17 genera not previously known to contain steroid degraders. A pathway for cholesterol degradation was conserved in many actinobacterial genera, particularly in members of the Corynebacterineae, and a pathway for cholate degradation was conserved in members of the genus Rhodococcus. A pathway for testosterone and, sometimes, cholate degradation had a patchy distribution among Proteobacteria. The steroid degradation genes tended to occur within large gene clusters. Growth experiments confirmed bioinformatic predictions of steroid metabolism capacity in nine bacterial strains. The results indicate there was a single ancestral 9,10-seco-steroid degradation pathway. Gene duplication, likely in a progenitor of Rhodococcus, later gave rise to a cholate degradation pathway. Proteobacteria and additional Actinobacteria subsequently obtained a cholate degradation pathway via horizontal gene transfer, in some cases facilitated by plasmids. Catabolism of steroids appears to be an important component of the ecological niches of broad groups of Actinobacteria and individual species of Proteobacteria. PMID:26956583

Proteome response of fish under multiple stress exposure: Effects of pesticide mixtures and temperature increase.

PubMed

Gandar, Allison; Laffaille, Pascal; Marty-Gasset, Nathalie; Viala, Didier; Molette, Caroline; Jean, Séverine

2017-03-01

Aquatic systems can be subjected to multiple stressors, including pollutant cocktails and elevated temperature. Evaluating the combined effects of these stressors on organisms is a great challenge in environmental sciences. To the best of our knowledge, this is the first study to assess the molecular stress response of an aquatic fish species subjected to individual and combined pesticide mixtures and increased temperatures. For that, goldfish (Carassius auratus) were acclimated to two different temperatures (22 and 32°C) for 15 days. They were then exposed for 96h to a cocktail of herbicides and fungicides (S-metolachlor, isoproturon, linuron, atrazine-desethyl, aclonifen, pendimethalin and tebuconazole) at two environmentally relevant concentrations (total concentrations of 8.4μgL -1 and 42μgL -1 ) at these two temperatures (22 and 32°C). The molecular response in liver was assessed by 2D-proteomics. Identified proteins were integrated using pathway enrichment analysis software to determine the biological functions involved in the individual or combined stress responses and to predict the potential deleterious outcomes. The pesticide mixtures elicited pathways involved in cellular stress response, carbohydrate, protein and lipid metabolisms, methionine cycle, cellular functions, cell structure and death control, with concentration- and temperature-dependent profiles of response. We found that combined temperature increase and pesticide exposure affected the cellular stress response: the effects of oxidative stress were more marked and there was a deregulation of the cell cycle via apoptosis inhibition. Moreover a decrease in the formation of glucose by liver and in ketogenic activity was observed in this multi-stress condition. The decrease in both pathways could reflect a shift from a metabolic compensation strategy to a conservation state. Taken together, our results showed (1) that environmental cocktails of herbicides and fungicides induced important changes in pathways involved in metabolism, cell structure and cell cycle, with possible deleterious outcomes at higher biological scales and (2) that increasing temperature could affect the response of fish to pesticide exposure. Copyright © 2017 Elsevier B.V. All rights reserved.

Transcriptome analysis reveals candidate genes involved in luciferin metabolism in Luciola aquatilis (Coleoptera: Lampyridae)

PubMed Central

Vongsangnak, Wanwipa; Chumnanpuen, Pramote

2016-01-01

Bioluminescence, which living organisms such as fireflies emit light, has been studied extensively for over half a century. This intriguing reaction, having its origins in nature where glowing insects can signal things such as attraction or defense, is now widely used in biotechnology with applications of bioluminescence and chemiluminescence. Luciferase, a key enzyme in this reaction, has been well characterized; however, the enzymes involved in the biosynthetic pathway of its substrate, luciferin, remains unsolved at present. To elucidate the luciferin metabolism, we performed a de novo transcriptome analysis using larvae of the firefly species, Luciola aquatilis. Here, a comparative analysis is performed with the model coleopteran insect Tribolium casteneum to elucidate the metabolic pathways in L. aquatilis. Based on a template luciferin biosynthetic pathway, combined with a range of protein and pathway databases, and various prediction tools for functional annotation, the candidate genes, enzymes, and biochemical reactions involved in luciferin metabolism are proposed for L. aquatilis. The candidate gene expression is validated in the adult L. aquatilis using reverse transcription PCR (RT-PCR). This study provides useful information on the bio-production of luciferin in the firefly and will benefit to future applications of the valuable firefly bioluminescence system. PMID:27761329

The mechanism of ureido-pyrimidinone:2,7-diamido-naphthyridine complexation and the presence of kinetically controlled pathways in multicomponent hydrogen-bonded systems.

PubMed

de Greef, Tom F A; Ligthart, G B W L; Lutz, Martin; Spek, Anthony L; Meijer, E W; Sijbesma, Rint P

2008-04-23

The kinetics of association of ureido-pyrimidinone (U) dimers, present either in the 4[1H]-keto form or in the pyrimidin-4-ol form, with 2,7-diamido-1,8-naphthyridine (N) into a complementary heterodimer have been investigated. The formation of heterodimers with 2,7-diamido-1,8-naphthyridine from pyrimidin-4-ol dimers is much faster than from 4[1H]-pyrimidinone dimers. Using a combination of simple measurements and simulations, evidence for a bimolecular tautomerization step is presented. Finally, the acquired kinetic knowledge of the different pathways leading from ureido-pyrimidinone homodimers to ureido-pyrimidinone:diamido-naphthyridine (U:N) heterodimers allows the prediction and observation of kinetically determined ureido-pyrimidinone heterodimers which slowly convert back to the corresponding homodimers.

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

PubMed

Tenzer, S; Peters, B; Bulik, S; Schoor, O; Lemmel, C; Schatz, M M; Kloetzel, P-M; Rammensee, H-G; Schild, H; Holzhütter, H-G

2005-05-01

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

Production of bulk chemicals via novel metabolic pathways in microorganisms.

PubMed

Shin, Jae Ho; Kim, Hyun Uk; Kim, Dong In; Lee, Sang Yup

2013-11-01

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources. Copyright © 2012 Elsevier Inc. All rights reserved.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

PubMed Central

Caspi, Ron; Altman, Tomer; Dale, Joseph M.; Dreher, Kate; Fulcher, Carol A.; Gilham, Fred; Kaipa, Pallavi; Karthikeyan, Athikkattuvalasu S.; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Mueller, Lukas A.; Paley, Suzanne; Popescu, Liviu; Pujar, Anuradha; Shearer, Alexander G.; Zhang, Peifen; Karp, Peter D.

2010-01-01

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism. PMID:19850718

Combination therapeutics of Nilotinib and radiation in acute lymphoblastic leukemia as an effective method against drug-resistance.

PubMed

Kaveh, Kamran; Takahashi, Yutaka; Farrar, Michael A; Storme, Guy; Guido, Marcucci; Piepenburg, Jamie; Penning, Jackson; Foo, Jasmine; Leder, Kevin Z; Hui, Susanta K

2017-07-01

Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) is characterized by a very poor prognosis and a high likelihood of acquired chemo-resistance. Although tyrosine kinase inhibitor (TKI) therapy has improved clinical outcome, most ALL patients relapse following treatment with TKI due to the development of resistance. We developed an in vitro model of Nilotinib-resistant Ph+ leukemia cells to investigate whether low dose radiation (LDR) in combination with TKI therapy overcome chemo-resistance. Additionally, we developed a mathematical model, parameterized by cell viability experiments under Nilotinib treatment and LDR, to explain the cellular response to combination therapy. The addition of LDR significantly reduced drug resistance both in vitro and in computational model. Decreased expression level of phosphorylated AKT suggests that the combination treatment plays an important role in overcoming resistance through the AKT pathway. Model-predicted cellular responses to the combined therapy provide good agreement with experimental results. Augmentation of LDR and Nilotinib therapy seems to be beneficial to control Ph+ leukemia resistance and the quantitative model can determine optimal dosing schedule to enhance the effectiveness of the combination therapy.

Outcome prediction of third ventriculostomy: a proposed hydrocephalus grading system.

PubMed

Kehler, U; Regelsberger, J; Gliemroth, J; Westphal, M

2006-08-01

An important factor in making a recommendation for different treatment modalities in hydrocephalus patients (VP shunt versus endoscopic third ventriculostomy) is the definition of the underlying pathology which determines the prognosis/outcome of the surgical procedure. Third ventriculostomies (3rd VS) are successful mainly in obstructive hydrocephalus but also in some subtypes of communicating hydrocephalus. A simple, easily applicable grading system that is designed to predict the outcome of 3rd VS is proposed. The hydrocephalus is graded on the basis of the extent of downward bulging of the floor of the third ventricle, which reflects the pressure gradient between the 3rd ventricle and the basal cisterns, presence of directly visualised CSF pathway obstruction in MRI, and the progression of the clinical symptoms resulting in five different grades. In this proposed grading system, grade 1 hydrocephalus subtype shows no downward bulged floor of the 3rd ventricle, no obstruction of the CSF pathway, and no progressive symptoms of hydrocephalus. There is no indication for 3rd VS. Grades 2 to 4 show different combinations of the described parameters. Grade 5 subtype shows a markedly downward bulged floor of the 3rd ventricle and direct detection of the CSF pathway obstruction (i.e., aqueductal stenosis) with progressive clinical deterioration. Retrospective application of this grading scheme to a series of 72 3rd VS has demonstrated a high correlation with the outcome: The success rate in grade 3 reached 40%, in grade 4: 58%, and in grade 5: 95%. This standardised grading system predicts the outcome of 3rd VS and helps in decision making for 3rd VS versus VP shunting.

Tales from the Paleoclimate Underground: Lessons Learned from Reconstructing Extreme Events

NASA Astrophysics Data System (ADS)

Frappier, A. E.

2017-12-01

Tracing patterns of paleoclimate extremes over the past two millennia is becoming ever more important in the effort to understand and predict costly weather hazards and their varied societal impacts. I present three paleoclimate vignettes from the past ten years of different paleotempestology projects I have worked on closely, illustrating our collective challenges and productive pathways in reconstructing rainfall extremes: temporal, spatial, and combining information from disparate proxies. Finally, I aim to share new results from modeling multiple extremes and hazards in Yucatan, a climate change hotspot.

Backbone N xH compounds at high pressures

DOE PAGES

Goncharov, Alexander F.; Holtgrewe, Nicholas; Qian, Guangrui; ...

2015-06-05

Optical and synchrotron x-ray diffraction diamond anvil cell experiments have been combined with first principles theoretical structure predictions to investigate mixtures of N 2 and H 2 up to 55 GPa. Our experiments show the formation of structurally complex van der Waals compounds above 10 GPa. However, we found that these N xH (0.52, H 2, and NH 3 above approximately 40 GPa. Lastly, our results suggest new pathways for synthesis of environmentally benign high energy-density materials. These materials could also exist as alternative planetary ices.

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

PubMed

Fakhry, Carl Tony; Kulkarni, Prajna; Chen, Ping; Kulkarni, Rahul; Zarringhalam, Kourosh

2017-08-22

Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class. In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy. We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae. The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

PubMed

Thomas, Reuben; Thomas, Russell S; Auerbach, Scott S; Portier, Christopher J

2013-01-01

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

PDT-based combinations in overcoming chemoresistance from stromal and heterotypic cellular communication (Conference Presentation)

NASA Astrophysics Data System (ADS)

Rizvi, Imran; Bulin, Anne-Laure; Anbil, Sriram R.; Briars, Emma A.; Vecchio, Daniela; Celli, Jonathan P.; Broekgaarden, Mans; Hasan, Tayyaba

2017-02-01

Targeting the molecular and cellular cues that influence treatment resistance in tumors is critical to effectively treating unresponsive populations of stubborn disease. The informed design of mechanism-based combinations is emerging as increasingly important to targeting resistance and improving the efficacy of conventional treatments, while minimizing toxicity. Photodynamic therapy (PDT) has been shown to synergize with conventional agents and to overcome the evasion pathways that cause resistance. Increasing evidence shows that PDT-based combinations cooperate mechanistically with, and improve the therapeutic index of, traditional chemotherapies. These and other findings emphasize the importance of including PDT as part of comprehensive treatment plans for cancer, particularly in complex disease sites. Identifying effective combinations requires a multi-faceted approach that includes the development of bioengineered cancer models and corresponding image analysis tools. The molecular and phenotypic basis of verteporfin-mediated PDT-based enhancement of chemotherapeutic efficacy and predictability in complex 3D models for ovarian cancer will be presented.

Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

PubMed

Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

2013-01-02

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

Stability and ionic mobility in argyrodite-related lithium-ion solid electrolytes.

PubMed

Chen, Hao Min; Maohua, Chen; Adams, Stefan

2015-07-07

In the search for fast lithium-ion conducting solids for the development of safe rechargeable all-solid-state batteries with high energy density, thiophosphates and related compounds have been demonstrated to be particularly promising both because of their record ionic conductivities and their typically low charge transfer resistances. In this work we explore a wide range of known and predicted thiophosphates with a particular focus on the cubic argyrodite phase with a robust three-dimensional network of ion migration pathways. Structural and hydrolysis stability are calculated employing density functional method in combination with a generally applicable method of predicting the relevant critical reaction. The activation energy for ion migration in these argyrodites is then calculated using the empirical bond valence pathway method developed in our group, while bandgaps of selected argyrodites are calculated as a basis for assessing the electrochemical window. Findings for the lithium compounds are also compared to those of previously known copper argyrodites and hypothetical sodium argyrodites. Therefrom, guidelines for experimental work are derived to yield phases with the optimum balance between chemical stability and ionic conductivity in the search for practical lithium and sodium solid electrolyte materials.

Genetic and physiological bases for phenological responses to current and predicted climates

PubMed Central

Wilczek, A. M.; Burghardt, L. T.; Cobb, A. R.; Cooper, M. D.; Welch, S. M.; Schmitt, J.

2010-01-01

We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology. PMID:20819808

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Analysis of EAWAG-BBD pathway prediction system for the identification of malathion degrading microbes

PubMed Central

Sivakumar, Subramaniam; Anitha, Palanivel; Ramesh, Balsubramanian; Suresh, Gopal

2017-01-01

Insecticides are the toxic substances that are used to kill insects. The use of insecticides is believed to be one of the major factors behind the increase in agricultural productivity in the 20th century. The organophosphates are now the largest and most versatile class of insecticide used and Malathion is the predominant type utilized. The accumulation of Malathion in environment is the biggest threat to the environment because of its toxicity. Malathion is lethal to beneficial insects, snails, micro crustaceans, fish, birds, amphibians, and soil microorganisms. Chronic exposure of non-diabetic farmers to organophosphorus Malathion pesticides may induce insulin resistance, which might ultimately results in diabetes mellitus. Given the potential carcinogenic risk from the pesticides there is serious need to develop remediation processes to eliminate or minimize contamination in the environment. Biodegradation could be a reliable and cost effective technique for pesticide abatement. Since today as there were no metabolic pathway predicted for the degradation of organophosphates pesticide Malathion in KEGG database or in any of the other pathway databases. Thus in the present study, an attempt has been made to predict the microbial biodegradation pathway of Malathion using bioinformatics tools. The present study predicted the degradation pathway for Malathion. The present study also identifies, Streptomyces sp. and E.coli are capable of degrading Malathion through pathway prediction system. PMID:28584447

Analysis of EAWAG-BBD pathway prediction system for the identification of malathion degrading microbes.

PubMed

Sivakumar, Subramaniam; Anitha, Palanivel; Ramesh, Balsubramanian; Suresh, Gopal

2017-01-01

Insecticides are the toxic substances that are used to kill insects. The use of insecticides is believed to be one of the major factors behind the increase in agricultural productivity in the 20th century. The organophosphates are now the largest and most versatile class of insecticide used and Malathion is the predominant type utilized. The accumulation of Malathion in environment is the biggest threat to the environment because of its toxicity. Malathion is lethal to beneficial insects, snails, micro crustaceans, fish, birds, amphibians, and soil microorganisms. Chronic exposure of non-diabetic farmers to organophosphorus Malathion pesticides may induce insulin resistance, which might ultimately results in diabetes mellitus. Given the potential carcinogenic risk from the pesticides there is serious need to develop remediation processes to eliminate or minimize contamination in the environment. Biodegradation could be a reliable and cost effective technique for pesticide abatement. Since today as there were no metabolic pathway predicted for the degradation of organophosphates pesticide Malathion in KEGG database or in any of the other pathway databases. Thus in the present study, an attempt has been made to predict the microbial biodegradation pathway of Malathion using bioinformatics tools. The present study predicted the degradation pathway for Malathion. The present study also identifies, Streptomyces sp. and E.coli are capable of degrading Malathion through pathway prediction system.

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction

PubMed Central

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

PubMed

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

PubMed Central

Gentili, Rodolphe J.; Papaxanthis, Charalambos; Ebadzadeh, Mehdi; Eskiizmirliler, Selim; Ouanezar, Sofiane; Darlot, Christian

2009-01-01

Background Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model). Methodology/Principal Findings This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements. Conclusions/Significance This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field. PMID:19384420

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hay, J.; Schwender, J.

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganicmore » nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.« less

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

An integrative systems biology approach to understanding pulmonary diseases.

PubMed

Auffray, Charles; Adcock, Ian M; Chung, Kian Fan; Djukanovic, Ratko; Pison, Christophe; Sterk, Peter J

2010-06-01

Chronic inflammatory pulmonary diseases such as COPD and asthma are highly prevalent and associated with a major health burden worldwide. Despite a wealth of biologic and clinical information on normal and pathologic airway structure and function, the primary causes and mechanisms of disease remain to a large extent unknown, preventing the development of more efficient diagnosis and treatment. We propose to overcome these limitations through an integrative systems biology research strategy designed to identify the functional and regulatory pathways that play central roles in respiratory pathophysiology, starting with severe asthma. This approach relies on global genome, transcriptome, proteome, and metabolome data sets collected in cross-sectional patient cohorts with high-throughput measurement platforms and integrated with biologic and clinical data to inform predictive multiscale models ranging from the molecular to the organ levels. Working hypotheses formulated on the mechanisms and pathways involved in various disease states are tested through perturbation experiments using model simulation combined with targeted and global technologies in cellular and animal models. The responses observed are compared with those predicted by the initial models, which are refined to account better for the results. Novel perturbation experiments are designed and tested both computationally and experimentally to arbitrate between competing hypotheses. The process is iterated until the derived knowledge allows a better classification and subphenotyping of severe asthma using complex biomarkers, which will facilitate the development of novel diagnostic and therapeutic interventions targeting multiple components of the molecular and cellular pathways involved. This can be tested and validated in prospective clinical trials.

Combining differential expression, chromosomal and pathway analyses for the molecular characterization of renal cell carcinoma

PubMed Central

Furge, Kyle A; Dykema, Karl; Petillo, David; Westphal, Michael; Zhang, Zhongfa; Kort, Eric J; Teh, Bin Tean

2007-01-01

Using high-throughput gene-expression profiling technology, we can now gain a better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup of the cancer cells. This abnormal genetic makeup can have profound effects on cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeutic agents and radiation. These approaches will lead to better molecular subclassification of tumours, the basis of personalized medicine. We have, to date, done whole-genome microarray gene-expression profiling on several hundreds of kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used to identify both cytogenetic abnormalities and molecular pathways that are deregulated in renal cell carcinoma (RCC). For example, we have identified the deregulation of the VHL-hypoxia pathway in clear-cell RCC, as previously known, and the c-Myc pathway in aggressive papillary RCC. Besides the more common clear-cell, papillary and chromophobe RCCs, we are currently characterizing the molecular signatures of rarer forms of renal neoplasia such as carcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosome Xp11 translocations associated with papillary RCC, renal medullary carcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response. PMID:18542781

Mechanisms of CCl4-induced liver fibrosis with combined transcriptomic and proteomic analysis.

PubMed

Dong, Shu; Chen, Qi-Long; Song, Ya-Nan; Sun, Yang; Wei, Bin; Li, Xiao-Yan; Hu, Yi-Yang; Liu, Ping; Su, Shi-Bing

2016-01-01

The classic toxicity of carbon tetrachloride (CCl4) is to induce liver lesion and liver fibrosis. Liver fibrosis is a consequence of chronic liver lesion, which can progress into liver cirrhosis even hepatocarcinoma. However, the toxicological mechanisms of CCl4-induced liver fibrosis remain not fully understood. We combined transcriptomic and proteomic analysis and biological network technology, predicted toxicological targets and regulatory networks of CCl4 in liver fibrosis. Wistar rats were treated with CCl4 for 9 weeks. Histopathological changes, hydroxyproline (Hyp) contents, serum ALT and AST in the CCl4-treated group were significantly higher than that of CCl4-untreated group. CCl4-treated and -untreated liver tissues were examined by microarray and iTRAQ. The results showed that 3535 genes (fold change ≥ 1.5, P < 0.05) and 1412 proteins (fold change ≥ 1.2, P < 0.05) were differentially expressed. Moreover, the integrative analysis of transcriptomics and proteomics data showed 523 overlapped proteins, enriched in 182 GO terms including oxidation reduction, response to oxidative stress, inflammatory response, extracellular matrix organization, etc. Furthermore, KEGG pathway analysis showed that 36 pathways including retinol metabolism, PPAR signaling pathway, glycolysis/gluconeogenesis, arachidonic acid metabolism, metabolism of xenobiotics by cytochrome P450 and drug metabolism. Network of protein-protein interaction (PPI) and key function with their related targets were performed and the degree of network was calculated with Cytoscape. The expression of key targets such as CYP4A3, ALDH2 and ALDH7A1 decreased after CCl4 treatment. Therefore, the toxicological mechanisms of CCl4-induced liver fibrosis may be related with multi biological process, pathway and targets which may provide potential protection reaction mechanism for CCl4 detoxication in the liver.

Pathway-specific polygenic risk scores as predictors of β-amyloid deposition and cognitive function in a sample at increased risk for Alzheimer’s disease

PubMed Central

Darst, Burcu F.; Koscik, Rebecca L.; Racine, Annie M.; Oh, Jennifer M.; Krause, Rachel A.; Carlsson, Cynthia M.; Zetterberg, Henrik; Blennow, Kaj; Christian, Bradley T.; Bendlin, Barbara B.; Okonkwo, Ozioma C.; Hogan, Kirk J.; Hermann, Bruce P.; Sager, Mark A.; Asthana, Sanjay; Johnson, Sterling C.; Engelman, Corinne D.

2016-01-01

Polygenic risk scores (PRSs) have been used to combine the effects of variants with small effects identified by genome-wide association studies. We explore the potential for using pathway-specific PRSs as predictors of early changes in Alzheimer’s disease (AD)-related biomarkers and cognitive function. Participants were from the Wisconsin Registry for Alzheimer’s Prevention, a longitudinal study of adults who were cognitively asymptomatic at enrollment and enriched for a parental history of AD. Using genes associated with AD in the International Genomics of Alzheimer’s Project’s meta-analysis, we identified clusters of genes that grouped into pathways involved in β-amyloid (Aβ) deposition and neurodegeneration: Aβ clearance, cholesterol metabolism, and immune response. Weighted pathway-specific and overall PRSs were developed and compared to APOE alone. Mixed models were used to assess whether each PRS was associated with cognition in 1,200 individuals, cerebral Aβ deposition measured using amyloid ligand (Pittsburgh compound B) positron emission imaging (PET) in 168 individuals, and cerebrospinal fluid (CSF) Aβ deposition, neurodegeneration, and tau pathology in 111 individuals, with replication performed in an independent sample. We found that PRSs including APOE appeared to be driven by the inclusion of APOE, suggesting that the pathway-specific PRSs used here were not more predictive than an overall PRS or APOE alone. However, pathway-specific PRSs could prove to be useful as more knowledge is gained on the genetic variants involved in specific biological pathways of AD. PMID:27662287

Competing Pathways and Multiple Folding Nuclei in a Large Multidomain Protein, Luciferase.

PubMed

Scholl, Zackary N; Yang, Weitao; Marszalek, Piotr E

2017-05-09

Proteins obtain their final functional configuration through incremental folding with many intermediate steps in the folding pathway. If known, these intermediate steps could be valuable new targets for designing therapeutics and the sequence of events could elucidate the mechanism of refolding. However, determining these intermediate steps is hardly an easy feat, and has been elusive for most proteins, especially large, multidomain proteins. Here, we effectively map part of the folding pathway for the model large multidomain protein, Luciferase, by combining single-molecule force-spectroscopy experiments and coarse-grained simulation. Single-molecule refolding experiments reveal the initial nucleation of folding while simulations corroborate these stable core structures of Luciferase, and indicate the relative propensities for each to propagate to the final folded native state. Both experimental refolding and Monte Carlo simulations of Markov state models generated from simulation reveal that Luciferase most often folds along a pathway originating from the nucleation of the N-terminal domain, and that this pathway is the least likely to form nonnative structures. We then engineer truncated variants of Luciferase whose sequences corresponded to the putative structure from simulation and we use atomic force spectroscopy to determine their unfolding and stability. These experimental results corroborate the structures predicted from the folding simulation and strongly suggest that they are intermediates along the folding pathway. Taken together, our results suggest that initial Luciferase refolding occurs along a vectorial pathway and also suggest a mechanism that chaperones may exploit to prevent misfolding. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Deregulated hedgehog pathway signaling is inhibited by the smoothened antagonist LDE225 (Sonidegib) in chronic phase chronic myeloid leukaemia

PubMed Central

Irvine, David A.; Zhang, Bin; Kinstrie, Ross; Tarafdar, Anuradha; Morrison, Heather; Campbell, Victoria L.; Moka, Hothri A.; Ho, Yinwei; Nixon, Colin; Manley, Paul W.; Wheadon, Helen; Goodlad, John R.; Holyoake, Tessa L.; Bhatia, Ravi; Copland, Mhairi

2016-01-01

Targeting the Hedgehog (Hh) pathway represents a potential leukaemia stem cell (LSC)-directed therapy which may compliment tyrosine kinase inhibitors (TKIs) to eradicate LSC in chronic phase (CP) chronic myeloid leukaemia (CML). We set out to elucidate the role of Hh signaling in CP-CML and determine if inhibition of Hh signaling, through inhibition of smoothened (SMO), was an effective strategy to target CP-CML LSC. Assessment of Hh pathway gene and protein expression demonstrated that the Hh pathway is activated in CD34+ CP-CML stem/progenitor cells. LDE225 (Sonidegib), a small molecule, clinically investigated SMO inhibitor, used alone and in combination with nilotinib, inhibited the Hh pathway in CD34+ CP-CML cells, reducing the number and self-renewal capacity of CML LSC in vitro. The combination had no effect on normal haemopoietic stem cells. When combined, LDE225 + nilotinib reduced CD34+ CP-CML cell engraftment in NSG mice and, upon administration to EGFP+ /SCLtTA/TRE-BCR-ABL mice, the combination enhanced survival with reduced leukaemia development in secondary transplant recipients. In conclusion, the Hh pathway is deregulated in CML stem and progenitor cells. We identify Hh pathway inhibition, in combination with nilotinib, as a potentially effective therapeutic strategy to improve responses in CP-CML by targeting both stem and progenitor cells. PMID:27157927

A three-pathway pore model describes extensive transport data from Mammalian microvascular beds and frog microvessels.

PubMed

Wolf, Matthew B

2002-12-01

To show that a three-pathway pore model can describe extensive transport data in cat and rat skeletal muscle microvascular beds and in frog mesenteric microvessels. A three-pathway pore model was used to predict transport data measured in various microcirculatory preparations. The pathways consist of 4- and 24-nm radii pore systems with a 2.5:1 ratio of hydraulic conductivities and a water-only pathway of variable conductivity. The pore sizes and relative hydraulic conductivities of the small- and large-pore systems were derived from a model fit to reflection coefficient (sigma) data in the cat hindlimb. The fraction (alpha(w)) of total hydraulic conductivity (L(p)) or hydraulic capacity (L(p)S) contributed by the water-only pathway was uniquely determined for each preparation by a fit of the three-pathway model (parameters fixed as above) to sigma data measured in that preparation. These parameter values were unchanged when the model was used to predict diffusion capacity (permeability-surface area product, P(d)S) data in the cat or rat preparations or diffusional permeability (P(d)) data in frog microvessels. The values for L(p) or L(p)S used to predict diffusional data in each preparation were taken from the literature. Predictions of P(d) ratios for solute pairs were also compared with experimental data. The three-pathway model closely predicted the trend of P(d)S or P(d) experimental data in all three preparations; in general, predicted P(d) ratios for paired solutes were quite similar to experimental data. For these comparisons, the only parameter varied between these preparations was alpha(w). It varied considerably, from 7 to 16 to 41% of total in frog, rat, and cat preparations. Individual P(d)S or P(d) experimental data were closely predicted in the cat but somewhat overestimated in the frog and rat. This result could be due the use of L(p) or L(p)S values in the model that were affected by methodological problems. Calculated hydraulic conductivities of the water-only pathway in the three preparations were quite similar. : These results support the hypothesis of a common structure of the transmembrane pathways in these three, very different, microcirculatory preparations. What varies considerably between them is the total number of solute-conducting pathways, but not their dimensions, nor the hydraulic conductivities of their water-only pathways. Because of the wide variation of alpha(w) among these preparations, the ratio of P(d) to L(p) for any solute is not constant, but the deviation from constancy may not be detectable because of errors in the experimental data.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

PubMed

Chen, Lei; Zhang, Yu-Hang; Wang, ShaoPeng; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong

2017-01-01

Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

Safety and feasibility of targeted agent combinations in solid tumours.

PubMed

Park, Sook Ryun; Davis, Myrtle; Doroshow, James H; Kummar, Shivaani

2013-03-01

The plethora of novel molecular-targeted agents (MTAs) has provided an opportunity to selectively target pathways involved in carcinogenesis and tumour progression. Combination strategies of MTAs are being used to inhibit multiple aberrant pathways in the hope of optimizing antitumour efficacy and to prevent development of resistance. While the selection of specific agents in a given combination has been based on biological considerations (including the role of the putative targets in cancer) and the interactions of the agents used in combination, there has been little exploration of the possible enhanced toxicity of combinations resulting from alterations in multiple signalling pathways in normal cell biology. Owing to the complex networks and crosstalk that govern normal and tumour cell proliferation, inhibiting multiple pathways with MTA combinations can result in unpredictable disturbances in normal physiology. This Review focuses on the main toxicities and the lack of tolerability of some common MTA combinations, particularly where evidence of enhanced toxicity compared to either agent alone is documented or there is development of unexpected toxicity. Toxicities caused by MTA combinations highlight the need to introduce new preclinical testing paradigms early in the drug development process for the assessment of chronic toxicities resulting from such combinations.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property

PubMed Central

Cai, Yu-Dong; Chou, Kuo-Chen

2011-01-01

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area. PMID:21980418

Emergent biological properties of arrestin pathway-selective biased agonism.

PubMed

Appleton, Kathryn M; Luttrell, Louis M

2013-06-01

Our growing appreciation of the pluridimensionality of G protein-coupled receptor (GPCR) signaling, combined with the phenomenon of orthosteric ligand "bias", has created the possibility of drugs that selectively modulate different aspects of GPCR function for therapeutic benefit. When viewed from the short-term perspective, e.g. changes in receptor conformation, effector coupling or second messenger generation, biased ligands appear to activate a subset of the response profile produced by a conventional agonist. Yet when examined in vivo, the limited data available suggest that biased ligand effects can diverge from their conventional counterparts in ways that cannot be predicted from their in vitro efficacy profile. What is currently missing, at least with respect to G protein and arrestin pathway-selective ligands, is a rational framework for relating the in vitro efficacy of a "biased" agonist to its in vivo actions that will enable drug screening programs to identify ligands with the desired biological effects.

Computational identification of signalling pathways in Plasmodium falciparum.

PubMed

Oyelade, Jelili; Ewejobi, Itunu; Brors, Benedikt; Eils, Roland; Adebiyi, Ezekiel

2011-06-01

Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins. Copyright © 2010 Elsevier B.V. All rights reserved.

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed). PMID:16283923

Predicting the points of interaction of small molecules in the NF-κB pathway

PubMed Central

2011-01-01

Background The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway. Results Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis. Conclusions The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis. PMID:21342508

Pathway level alterations rather than mutations in single genes predict response to HER2-targeted therapies in the neo-ALTTO trial.

PubMed

Shi, W; Jiang, T; Nuciforo, P; Hatzis, C; Holmes, E; Harbeck, N; Sotiriou, C; Peña, L; Loi, S; Rosa, D D; Chia, S; Wardley, A; Ueno, T; Rossari, J; Eidtmann, H; Armour, A; Piccart-Gebhart, M; Rimm, D L; Baselga, J; Pusztai, L

2017-01-01

We performed whole-exome sequencing of pretreatment biopsies and examined whether genome-wide metrics of overall mutational load, clonal heterogeneity or alterations at variant, gene, and pathway levels are associated with treatment response and survival. Two hundred and three biopsies from the NeoALTTO trial were analyzed. Mutations were called with MuTect, and Strelka, using pooled normal DNA. Associations between DNA alterations and outcome were evaluated by logistic and Cox-proportional hazards regression. There were no recurrent single gene mutations significantly associated with pathologic complete response (pCR), except PIK3CA [odds ratio (OR) = 0.42, P = 0.0185]. Mutations in 33 of 714 pathways were significantly associated with response, but different genes were affected in different individuals. PIK3CA was present in 23 of these pathways defining a ‘trastuzumab resistance-network’ of 459 genes. Cases with mutations in this network had low pCR rates to trastuzumab (2/50, 4%) compared with cases with no mutations (9/16, 56%), OR = 0.035; P < 0.001. Mutations in the ‘Regulation of RhoA activity’ pathway were associated with higher pCR rate to lapatinib (OR = 14.8, adjusted P = 0.001), lapatinib + trastuzumab (OR = 3.0, adjusted P = 0.09), and all arms combined (OR = 3.77, adjusted P = 0.02). Patients (n = 124) with mutations in the trastuzumab resistance network but intact RhoA pathway had 2% (1/41) pCR rate with trastuzumab alone (OR = 0.026, P = 0.001) but adding lapatinib increased pCR rate to 45% (17/38, OR = 1.68, P = 0.3). Patients (n = 46) who had no mutations in either gene set had 6% pCR rate (1/15) with lapatinib, but had the highest pCR rate, 52% (8/15) with trastuzumab alone. Mutations in the RhoA pathway are associated with pCR to lapatinib and mutations in a PIK3CA-related network are associated with resistance to trastuzumab. The combined mutation status of these two pathways could define patients with very low response rate to trastuzumab alone that can be augmented by adding lapatinib or substituting trastuzumab with lapatinib.

Replication and extension of the dual pathway model of disordered eating: The role of fear of negative evaluation, suggestibility, rumination, and self-compassion.

PubMed

Maraldo, Toni M; Zhou, Wanni; Dowling, Jessica; Vander Wal, Jillon S

2016-12-01

The dual pathway model, a theoretical model of eating disorder development, suggests that thin ideal internalization leads to body dissatisfaction which leads to disordered eating via the dual pathways of negative affect and dietary restraint. While the dual pathway model has been a valuable guide for eating disorder prevention, greater knowledge of characteristics that predict thin ideal internalization is needed. The present study replicated and extended the dual pathway model by considering the addition of fear of negative evaluation, suggestibility, rumination, and self-compassion in a sample of community women and female university students. Results showed that fear of negative evaluation and suggestibility predicted thin ideal internalization whereas rumination and self-compassion (inversely) predicted body dissatisfaction. Negative affect was predicted by fear of negative evaluation, rumination, and self-compassion (inversely). The extended model fit the data well in both samples. Analogue and longitudinal study of these constructs is warranted in future research. Copyright © 2016 Elsevier Ltd. All rights reserved.

New natural products identified by combined genomics-metabolomics profiling of marine Streptomyces sp. MP131-18.

PubMed

Paulus, Constanze; Rebets, Yuriy; Tokovenko, Bogdan; Nadmid, Suvd; Terekhova, Larisa P; Myronovskyi, Maksym; Zotchev, Sergey B; Rückert, Christian; Braig, Simone; Zahler, Stefan; Kalinowski, Jörn; Luzhetskyy, Andriy

2017-02-10

Marine actinobacteria are drawing more and more attention as a promising source of new natural products. Here we report isolation, genome sequencing and metabolic profiling of new strain Streptomyces sp. MP131-18 isolated from marine sediment sample collected in the Trondheim Fjord, Norway. The 16S rRNA and multilocus phylogenetic analysis showed that MP131-18 belongs to the genus Streptomyces. The genome of MP131-18 isolate was sequenced, and 36 gene clusters involved in the biosynthesis of 18 different types of secondary metabolites were predicted using antiSMASH analysis. The combined genomics-metabolics profiling of the strain led to the identification of several new biologically active compounds. As a result, the family of bisindole pyrroles spiroindimicins was extended with two new members, spiroindimicins E and F. Furthermore, prediction of the biosynthetic pathway for unusual α-pyrone lagunapyrone isolated from MP131-18 resulted in foresight and identification of two new compounds of this family - lagunapyrones D and E. The diversity of identified and predicted compounds from Streptomyces sp. MP131-18 demonstrates that marine-derived actinomycetes are not only a promising source of new natural products, but also represent a valuable pool of genes for combinatorial biosynthesis of secondary metabolites.

New natural products identified by combined genomics-metabolomics profiling of marine Streptomyces sp. MP131-18

PubMed Central

Paulus, Constanze; Rebets, Yuriy; Tokovenko, Bogdan; Nadmid, Suvd; Terekhova, Larisa P.; Myronovskyi, Maksym; Zotchev, Sergey B.; Rückert, Christian; Braig, Simone; Zahler, Stefan; Kalinowski, Jörn; Luzhetskyy, Andriy

2017-01-01

Marine actinobacteria are drawing more and more attention as a promising source of new natural products. Here we report isolation, genome sequencing and metabolic profiling of new strain Streptomyces sp. MP131-18 isolated from marine sediment sample collected in the Trondheim Fjord, Norway. The 16S rRNA and multilocus phylogenetic analysis showed that MP131-18 belongs to the genus Streptomyces. The genome of MP131-18 isolate was sequenced, and 36 gene clusters involved in the biosynthesis of 18 different types of secondary metabolites were predicted using antiSMASH analysis. The combined genomics-metabolics profiling of the strain led to the identification of several new biologically active compounds. As a result, the family of bisindole pyrroles spiroindimicins was extended with two new members, spiroindimicins E and F. Furthermore, prediction of the biosynthetic pathway for unusual α-pyrone lagunapyrone isolated from MP131-18 resulted in foresight and identification of two new compounds of this family – lagunapyrones D and E. The diversity of identified and predicted compounds from Streptomyces sp. MP131-18 demonstrates that marine-derived actinomycetes are not only a promising source of new natural products, but also represent a valuable pool of genes for combinatorial biosynthesis of secondary metabolites. PMID:28186197

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma.

PubMed

Soliman, Bangly; Salem, Ahmed; Ghazy, Mohamed; Abu-Shahba, Nourhan; El Hefnawi, Mahmoud

2018-05-01

Let-7a, miR-34a, and miR-199 a/b have gained a great attention as master regulators for cellular processes. In particular, these three micro-RNAs act as potential onco-suppressors for hepatocellular carcinoma. Bioinformatics can reveal the functionality of these micro-RNAs through target prediction and functional annotation analysis. In the current study, in silico analysis using innovative servers (miRror Suite, DAVID, miRGator V3.0, GeneTrail) has demonstrated the combinatorial and the individual target genes of these micro-RNAs and further explored their roles in hepatocellular carcinoma progression. There were 87 common target messenger RNAs (p ≤ 0.05) that were predicted to be regulated by the three micro-RNAs using miRror 2.0 target prediction tool. In addition, the functional enrichment analysis of these targets that was performed by DAVID functional annotation and REACTOME tools revealed two major immune-related pathways, eight hepatocellular carcinoma hallmarks-linked pathways, and two pathways that mediate interconnected processes between immune system and hepatocellular carcinoma hallmarks. Moreover, protein-protein interaction network for the predicted common targets was obtained by using STRING database. The individual analysis of target genes and pathways for the three micro-RNAs of interest using miRGator V3.0 and GeneTrail servers revealed some novel predicted target oncogenes such as SOX4, which we validated experimentally, in addition to some regulated pathways of immune system and hepatocarcinogenesis such as insulin signaling pathway and adipocytokine signaling pathway. In general, our results demonstrate that let-7a, miR-34a, and miR-199 a/b have novel interactions in different immune system pathways and major hepatocellular carcinoma hallmarks. Thus, our findings shed more light on the roles of these miRNAs as cancer silencers.

Combined effects of pharmaceuticals, personal care products, biocides and organic contaminants on the growth of Skeletonema pseudocostatum.

PubMed

Petersen, Karina; Heiaas, Harald Hasle; Tollefsen, Knut Erik

2014-05-01

Organisms in the environment are exposed to a number of pollutants from different compound groups. In addition to the classic pollutants like the polychlorinated biphenyls, polyaromatic hydrocarbons (PAHs), alkylphenols, biocides, etc. other compound groups of concern are constantly emerging. Pharmaceuticals and personal care products (PPCPs) can be expected to co-occur with other organic contaminants like biocides, PAHs and alkylphenols in areas affected by wastewater, industrial effluents and intensive recreational activity. In this study, representatives from these four different compound groups were tested individually and in mixtures in a growth inhibition assay with the marine algae Skeletonema pseudocostatum (formerly Skeletonema costatum) to determine whether the combined effects could be predicted by models for additive effects; the concentration addition (CA) and independent action (IA) prediction model. The eleven tested compounds reduced the growth of S. pseudocostatum in the microplate test in a concentration-dependent manner. The order of toxicity of these chemicals were irgarol>fluoxetine>diuron>benzo(a)pyrene>thioguanine>triclosan>propranolol>benzophenone 3>cetrimonium bromide>4-tert-octylphenol>endosulfan. Several binary mixtures and a mixture of eight compounds from the four different compound groups were tested. All tested mixtures were additive as model deviation ratios, the deviation between experimental and predicted effect concentrations, were within a factor of 2 from one or both prediction models (e.g. CA and IA). Interestingly, a concentration dependent shift from IA to CA, potentially due to activation of similar toxicity pathways at higher concentrations, was observed for the mixture of eight compounds. The combined effects of the multi-compound mixture were clearly additive and it should therefore be expected that PPCPs, biocides, PAHs and alkylphenols will collectively contribute to the risk in areas contaminated by such complex mixtures. Copyright © 2014 Elsevier B.V. All rights reserved.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

PubMed

Jeong, Hyeri; Kim, Jongwoon; Kim, Youngjun

2017-09-30

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

The possible reduction pathways of 2,4,6-trinitrotoluene (TNT) by sulfide under simulated anaerobic conditions.

PubMed

Qiao, Hua; Feng, Hua-jun; Liu, Shao-ying; Wang, Chao-jun; Zhang, Yuan; Gao, Yan-ni; Li, Wen-bing; Yao, Jun; Wang, Mei-zhen; Shen, Dong-sheng

2011-01-01

To predict the final fate of 2,4,6-trinitrotoluene (TNT) and its intermediates in an anaerobic fermentative solution containing reduced sulfur species and to provide a basis for the adoption of remediation methods, we investigated the pathways of TNT (TNT(0) = 50 mg/L) reduction by Na(2)S at 30 ± 1 °C in an acetic acid-sodium bicarbonate buffer. Liquid chromatography/mass spectrometry (LC/MS) was used to identify TNT metabolites at different reaction times. The law of growth and decline of TNT and its metabolites was determined with time. The LC/MS result, combined with the physicochemical characteristics of related products and information from the literature, indicated possible TNT conversion pathways. Sulfide can initiate both nitroreduction and denitration of TNT simultaneously. Nitroreduction led to the accumulation of primary intermediates 4-hydroxylaminodinitrotoluene and 4-aminodinitrotoluene, whereas denitration resulted in the production of unidentified substances with molecular weight less than that of TNT. Also, polyreaction between the above intermediates formed many unidentified substances. Humification was concluded to be the best choice for remediation of TNT-contaminated soil and water due to the formation of intermediates with stable, intact aromatic systems. However, the denitration pathway of TNT offered the possibility of mineralization.

Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot

PubMed Central

Brown, Kerry A.; Parks, Katherine E.; Bethell, Colin A.; Johnson, Steig E.; Mulligan, Mark

2015-01-01

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar’s plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future. PMID:25856241

Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot.

PubMed

Brown, Kerry A; Parks, Katherine E; Bethell, Colin A; Johnson, Steig E; Mulligan, Mark

2015-01-01

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar's plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future.

20171015 - Predicting Exposure Pathways with Machine Learning (ISES)

EPA Science Inventory

Prioritizing the risk posed to human health from the thousands of chemicals in the environment requires tools that can estimate exposure rates from limited information. High throughput models exist to make predictions of exposure via specific, important pathways such as residenti...

Convergent and divergent pathways decoding hierarchical additive mechanisms in treating cerebral ischemia-reperfusion injury.

PubMed

Zhang, Ying-Ying; Li, Hai-Xia; Chen, Yin-Ying; Fang, Hong; Yu, Ya-Nan; Liu, Jun; Jing, Zhi-Wei; Wang, Zhong; Wang, Yong-Yan

2014-03-01

Cerebral ischemia is considered to be a highly complex disease resulting from the complicated interplay of multiple pathways. Disappointedly, most of the previous studies were limited to a single gene or a single pathway. The extent to which all involved pathways are translated into fusing mechanisms of a combination therapy is of fundamental importance. We report an integrative strategy to reveal the additive mechanism that a combination (BJ) of compound baicalin (BA) and jasminoidin (JA) fights against cerebral ischemia based on variation of pathways and functional communities. We identified six pathways of BJ group that shared diverse additive index from 0.09 to 1, which assembled broad cross talks from seven pathways of BA and 16 pathways of JA both at horizontal and vertical levels. Besides a total of 60 overlapping functions as a robust integration background among the three groups based on significantly differential subnetworks, additive mechanism with strong confidence by networks altered functions. These results provide strong evidence that the additive mechanism is more complex than previously appreciated, and an integrative analysis of pathways may suggest an important paradigm for revealing pharmacological mechanisms underlying drug combinations. © 2013 John Wiley & Sons Ltd.

Induction of allograft tolerance through costimulatory blockade: first selection of drugs in vitro.

PubMed

Vierboom, Michel P M; Ossevoort, Miriam; Sick, Ella A; Haanstra, Krista; Jonker, Margreet

2003-01-01

The development of an in vitro assay predicting the chances of graft survival after treatment with immunoregulatory agents is a major topic in transplantation. Antibodies (Abs) interfering in the costimulatory pathway are promising candidates for the induction of tolerance. To evaluate these antibodies for clinical use studies non-human primates are the only feasible option due to species specificity of the antibodies. Peripheral blood mononuclear cells, isolated from a large panel of rhesus monkeys, were used in a unidirectional mixed lymphocyte reaction to evaluate the ability of antibodies blocking the costimulatory pathway, to affect both primary and secondary proliferative and cytolytic allospecific immune responses in vitro. These blocking antibodies were also used in protocols prolonging allograft survival in a life-supporting kidney allotransplant model in rhesus macaques. The ultimate aim is to establish a correlation between parameters obtained in vitro and the success of transplantation in vivo. The combination of anti-CD80 and anti-CD86 resulted in a complete abrogation of the primary alloresponse as measured in a proliferation assay. Adding anti-CD40 significantly reduced this inhibitory effect although the in vivo effects of this antibody have been shown to be beneficial. The secondary response was most prominently inhibited by the combination of anti-CD80/86. Paradoxically, anti-CD40 alone markedly inhibited the secondary proliferative response, but did not add to the inhibitory effect of the combination of anti-CD80/86. The cytolytic response was inhibited maximally only when CsA was added to the combination of anti-CD80/86. Treatment with monoclonal antibodies alone without immunosuppressive drugs was sufficient to maintain graft survival during the time of treatment in most animals. However, rejection was initiated as soon as the treatment ceased and no tolerance, resulting in long-term graft and patient survival, was established. The complete inhibition of primary alloresponses and the partial inhibition of secondary proliferative alloresponses correlate with prolonged graft survival during treatment, but have no predictive value for the success of tolerance induction for kidney allografts in rhesus monkeys.

A Hypothesis for Using Pathway Genetic Load Analysis for Understanding Complex Outcomes in Bilirubin Encephalopathy

PubMed Central

Riordan, Sean M.; Bittel, Douglas C.; Le Pichon, Jean-Baptiste; Gazzin, Silvia; Tiribelli, Claudio; Watchko, Jon F.; Wennberg, Richard P.; Shapiro, Steven M.

2016-01-01

Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus) is still poorly understood. Neonatal jaundice affects 60–80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a “load” is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load (PGL) that affects the response to a specific insult has been previously reported as a PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system's response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP) causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin induced neurological disorders (BIND) such as kernicterus and could be the key to understanding ranges of outcome severity in complex diseases. We anticipate that this method could be useful for improving the care of jaundiced newborns through its use as an at-risk screen. Importantly, this method would also be useful in uncovering basic knowledge about this and other polygenetic diseases whose genetic source is difficult to discern through traditional means such as a genome-wide association study. PMID:27587993

Mechanism of Chemoprevention against Colon Cancer Cells Using Combined Gelam Honey and Ginger Extract via mTOR and Wnt/β-catenin Pathways.

PubMed

Wee, Lee Heng; Morad, Noor Azian; Aan, Goon Jo; Makpol, Suzana; Wan Ngah, Wan Zurinah; Mohd Yusof, Yasmin Anum

2015-01-01

The PI3K-Akt-mTOR, Wnt/β-catenin and apoptosis signaling pathways have been shown to be involved in genesis of colorectal cancer (CRC). The aim of this study was to elucidate whether combination of Gelam honey and ginger might have chemopreventive properties in HT29 colon cancer cells by modulating the mTOR, Wnt/β-catenin and apoptosis signaling pathways. Treatment with Gelam honey and ginger reduced the viability of the HT29 cells dose dependently with IC50 values of 88 mg/ml and 2.15 mg/ml respectively, their while the combined treatment of 2 mg/ml of ginger with 31 mg/ml of Gelam honey inhibited growth of most HT29 cells. Gelam honey, ginger and combination induced apoptosis in a dose dependent manner with the combined treatment exhibiting the highest apoptosis rate. The combined treatment downregulated the gene expressions of Akt, mTOR, Raptor, Rictor, β-catenin, Gsk3β, Tcf4 and cyclin D1 while cytochrome C and caspase 3 genes were shown to be upregulated. In conclusion, the combination of Gelam honey and ginger may serve as a potential therapy in the treatment of colorectal cancer through inhibiton of mTOR, Wnt/β catenin signaling pathways and induction of apoptosis pathway.

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Detecting uber-operons in prokaryotic genomes.

PubMed

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga.edu/uber, the first of its kind.

Detecting uber-operons in prokaryotic genomes

PubMed Central

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: , the first of its kind. PMID:16682449

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

PubMed Central

Teeguarden, Justin. G.; Tan, Yu-Mei; Edwards, Stephen W.; Leonard, Jeremy A.; Anderson, Kim A.; Corley, Richard A.; Harding, Anna K; Kile, Molly L.; Simonich, Staci M; Stone, David; Tanguay, Robert L.; Waters, Katrina M.; Harper, Stacey L.; Williams, David E.

2016-01-01

Synopsis Driven by major scientific advances in analytical methods, biomonitoring, computational tools, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the Aggregate Exposure Pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the Adverse Outcome Pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more efficient integration of exposure assessment and hazard identification. Together, the two pathways form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making. PMID:26759916

Transcriptomic profiling as a screening tool to detect trenbolone treatment in beef cattle.

PubMed

Pegolo, S; Cannizzo, F T; Biolatti, B; Castagnaro, M; Bargelloni, L

2014-06-01

The effects of steroid hormone implants containing trenbolone alone (Finaplix-H), combined with 17β-oestradiol (17β-E; Revalor-H), or with 17β-E and dexamethasone (Revalor-H plus dexamethasone per os) on the bovine muscle transcriptome were examined by DNA-microarray. Overall, large sets of genes were shown to be modulated by the different growth promoters (GPs) and the regulated pathways and biological processes were mostly shared among the treatment groups. Using the Prediction Analysis of Microarray program, GP-treated animals were accurately identified by a small number of predictive genes. A meta-analysis approach was also carried out for the Revalor group to potentially increase the robustness of class prediction analysis. After data pre-processing, a high level of accuracy (90%) was obtained in the classification of samples, using 105 predictive gene markers. Transcriptomics could thus help in the identification of indirect biomarkers for anabolic treatment in beef cattle to be applied for the screening of muscle samples collected after slaughtering. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

PubMed Central

Ngendahimana, David K.; Fagerholm, Cara L.; Sun, Jiayang; Bruckman, Laura S.

2017-01-01

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. PMID:28498875

Multivariate Models for Prediction of Human Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2016-01-01

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.

PubMed

Teeguarden, Justin G; Tan, Yu-Mei; Edwards, Stephen W; Leonard, Jeremy A; Anderson, Kim A; Corley, Richard A; Kile, Molly L; Simonich, Staci M; Stone, David; Tanguay, Robert L; Waters, Katrina M; Harper, Stacey L; Williams, David E

2016-05-03

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the "systems approaches" used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.

Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109

Synergistic Inhibition of Her2/neu and p53-MDM2 Pathways. Addendum

DTIC Science & Technology

2007-09-01

Therefore, combination of drugs targeting HER2/neu and MDM2 pathways will allow for a two-pronged attack on breast cancer. The overall objective of our...proposal is to determine if small molecule drugs designed to inhibit HER2/neu can be applied in combination with drugs designed to inhibit p53-MDM2...able to inhibit either the HER2/neu pathway or the p53-MDM2 pathway. Subsequently, designed small molecule drugs able to strongly induce apoptosis

Identifying Drug-Target Interactions with Decision Templates.

PubMed

Yan, Xiao-Ying; Zhang, Shao-Wu

2018-01-01

During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Comparative proteomics of cerebrospinal fluid reveals a predictive model for differential diagnosis of pneumococcal, meningococcal, and enteroviral meningitis, and novel putative therapeutic targets

PubMed Central

2015-01-01

Background Meningitis is the inflammation of the meninges in response to infection or chemical agents. While aseptic meningitis, most frequently caused by enteroviruses, is usually benign with a self-limiting course, bacterial meningitis remains associated with high morbidity and mortality rates, despite advances in antimicrobial therapy and intensive care. Fast and accurate differential diagnosis is crucial for assertive choice of the appropriate therapeutic approach for each form of meningitis. Methods We used 2D-PAGE and mass spectrometry to identify the cerebrospinal fluid proteome specifically related to the host response to pneumococcal, meningococcal, and enteroviral meningitis. The disease-specific proteome signatures were inspected by pathway analysis. Results Unique cerebrospinal fluid proteome signatures were found to the three aetiological forms of meningitis investigated, and a qualitative predictive model with four protein markers was developed for the differential diagnosis of these diseases. Nevertheless, pathway analysis of the disease-specific proteomes unveiled that Kallikrein-kinin system may play a crucial role in the pathophysiological mechanisms leading to brain damage in bacterial meningitis. Proteins taking part in this cellular process are proposed as putative targets to novel adjunctive therapies. Conclusions Comparative proteomics of cerebrospinal fluid disclosed candidate biomarkers, which were combined in a qualitative and sequential predictive model with potential to improve the differential diagnosis of pneumococcal, meningococcal and enteroviral meningitis. Moreover, we present the first evidence of the possible implication of Kallikrein-kinin system in the pathophysiology of bacterial meningitis. PMID:26040285

Comparative proteomics of cerebrospinal fluid reveals a predictive model for differential diagnosis of pneumococcal, meningococcal, and enteroviral meningitis, and novel putative therapeutic targets.

PubMed

Cordeiro, Ana Paula; Silva Pereira, Rosiane Aparecida; Chapeaurouge, Alex; Coimbra, Clarice Semião; Perales, Jonas; Oliveira, Guilherme; Sanchez Candiani, Talitah Michel; Coimbra, Roney Santos

2015-01-01

Meningitis is the inflammation of the meninges in response to infection or chemical agents. While aseptic meningitis, most frequently caused by enteroviruses, is usually benign with a self-limiting course, bacterial meningitis remains associated with high morbidity and mortality rates, despite advances in antimicrobial therapy and intensive care. Fast and accurate differential diagnosis is crucial for assertive choice of the appropriate therapeutic approach for each form of meningitis. We used 2D-PAGE and mass spectrometry to identify the cerebrospinal fluid proteome specifically related to the host response to pneumococcal, meningococcal, and enteroviral meningitis. The disease-specific proteome signatures were inspected by pathway analysis. Unique cerebrospinal fluid proteome signatures were found to the three aetiological forms of meningitis investigated, and a qualitative predictive model with four protein markers was developed for the differential diagnosis of these diseases. Nevertheless, pathway analysis of the disease-specific proteomes unveiled that Kallikrein-kinin system may play a crucial role in the pathophysiological mechanisms leading to brain damage in bacterial meningitis. Proteins taking part in this cellular process are proposed as putative targets to novel adjunctive therapies. Comparative proteomics of cerebrospinal fluid disclosed candidate biomarkers, which were combined in a qualitative and sequential predictive model with potential to improve the differential diagnosis of pneumococcal, meningococcal and enteroviral meningitis. Moreover, we present the first evidence of the possible implication of Kallikrein-kinin system in the pathophysiology of bacterial meningitis.

Identification of Viscum album L. miRNAs and prediction of their medicinal values

PubMed Central

Adolf, Jacob; Melzig, Matthias F.

2017-01-01

MicroRNAs (miRNAs) are a class of approximately 22 nucleotides single-stranded non-coding RNA molecules that play crucial roles in gene expression. It has been reported that the plant miRNAs might enter mammalian bloodstream and have a functional role in human metabolism, indicating that miRNAs might be one of the hidden bioactive ingredients in medicinal plants. Viscum album L. (Loranthaceae, European mistletoe) has been widely used for the treatment of cancer and cardiovascular diseases, but its functional compounds have not been well characterized. We considered that miRNAs might be involved in the pharmacological activities of V. album. High-throughput Illumina sequencing was performed to identify the novel and conserved miRNAs of V. album. The putative human targets were predicted. In total, 699 conserved miRNAs and 1373 novel miRNAs have been identified from V. album. Based on the combined use of TargetScan, miRanda, PITA, and RNAhybrid methods, the intersection of 30697 potential human genes have been predicted as putative targets of 29 novel miRNAs, while 14559 putative targets were highly enriched in 33 KEGG pathways. Interestingly, these highly enriched KEGG pathways were associated with some human diseases, especially cancer, cardiovascular diseases and neurological disorders, which might explain the clinical use as well as folk medicine use of mistletoe. However, further experimental validation is necessary to confirm these human targets of mistletoe miRNAs. Additionally, target genes involved in bioactive components synthesis in V. album were predicted as well. A total of 68 miRNAs were predicted to be involved in terpenoid biosynthesis, while two miRNAs including val-miR152 and miR9738 were predicted to target viscotoxins and lectins, respectively, which increased the knowledge regarding miRNA-based regulation of terpenoid biosynthesis, lectin and viscotoxin expressions in V. album. PMID:29112983

Systems modeling accurately predicts responses to genotoxic agents and their synergism with BCL-2 inhibitors in triple negative breast cancer cells.

PubMed

Lucantoni, Federico; Lindner, Andreas U; O'Donovan, Norma; Düssmann, Heiko; Prehn, Jochen H M

2018-01-19

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer which accounts for 15-20% of this disease and is currently treated with genotoxic chemotherapy. The BCL2 (B-cell lymphoma 2) family of proteins controls the process of mitochondrial outer membrane permeabilization (MOMP), which is required for the activation of the mitochondrial apoptosis pathway in response to genotoxic agents. We previously developed a deterministic systems model of BCL2 protein interactions, DR_MOMP that calculates the sensitivity of cells to undergo mitochondrial apoptosis. Here we determined whether DR_MOMP predicts responses of TNBC cells to genotoxic agents and the re-sensitization of resistant cells by BCL2 inhibitors. Using absolute protein levels of BAX, BAK, BCL2, BCL(X)L and MCL1 as input for DR_MOMP, we found a strong correlation between model predictions and responses of a panel of TNBC cells to 24 and 48 h cisplatin (R 2  = 0.96 and 0.95, respectively) and paclitaxel treatments (R 2  = 0.94 and 0.95, respectively). This outperformed single protein correlations (best performer BCL(X)L with R 2 of 0.69 and 0.50 for cisplatin and paclitaxel treatments, respectively) and BCL2 proteins ratio (R 2 of 0.50 for cisplatin and 0.49 for paclitaxel). Next we performed synergy studies using the BCL2 selective antagonist Venetoclax /ABT199, the BCL(X)L selective antagonist WEHI-539, or the MCL1 selective antagonist A-1210477 in combination with cisplatin. In silico predictions by DR_MOMP revealed substantial differences in treatment responses of BCL(X)L, BCL2 or MCL1 inhibitors combinations with cisplatin that were successfully validated in cell lines. Our findings provide evidence that DR_MOMP predicts responses of TNBC cells to genotoxic therapy, and can aid in the choice of the optimal BCL2 protein antagonist for combination treatments of resistant cells.

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

PubMed

Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong

2018-05-01

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Matrine combined with cisplatin synergistically inhibited urothelial bladder cancer cells via down-regulating VEGF/PI3K/Akt signaling pathway.

PubMed

Liao, Xiao-Zhong; Tao, Lan-Ting; Liu, Jia-Hui; Gu, Yue-Yu; Xie, Jun; Chen, Yuling; Lin, Mei-Gui; Liu, Tao-Li; Wang, Dong-Mei; Guo, Hai-Yan; Mo, Sui-Lin

2017-01-01

Cisplatin is one of the first-line drugs for urothelial bladder cancer (UBC) treatment. However, its considerable side effects and the emergence of drug resistance are becoming major limitations for its application. This study aimed to investigate whether matrine and cisplatin could present a synergistic anti-tumor effect on UBC cells. Cell viability assay was used to assess the suppressive effect of matrine and cisplatin on the proliferation of the UBC cells. Wound healing assay and transwell assay were applied respectively to determine the migration and invasion ability of the cells. The distribution of cell cycles, the generation of reactive oxygen species (ROS) and the apoptosis rate were detected by flow cytometry (FCM). The expressions of the relative proteins in apoptotic signal pathways and the epithelial-mesenchymal transition (EMT) related genes were surveyed by western blotting. The binding modes of the drugs within the proteins were detected by CDOCKER module in DS 2.5. Both matrine and cisplatin could inhibit the growth of the UBC cells in a time- and dose-dependent manner. When matrine combined with cisplatin at the ratio of 2000:1, they presented a synergistic inhibitory effect on the UBC cells. The combinative treatment could impair cell migration and invasion ability, arrest cell cycle in the G1 and S phases, increase the level of ROS, and induce apoptosis in EJ and T24 cells in a synergistic way. In all the treated groups, the expressions of E-cadherin, β-catenin, Bax, and Cleaved Caspase-3 were up-regulated, while the expressions of Fibronectin, Vimentin, Bcl-2, Caspase-3, p-Akt, p-PI3K, VEGFR2, and VEGF proteins were down-regulated, and among them, the combination of matrine and cisplatin showed the most significant difference. Molecular docking algorithms predicted that matrine and cisplatin could be docked into the same active sites and interact with different residues within the tested proteins. Our results suggested that the combination of matrine and cisplatin could synergistically inhibit the UBC cells' proliferation through down-regulating VEGF/PI3K/Akt signaling pathway, indicating that matrine may serve as a new option in the combinative therapy in the treatment of UBC.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions.

PubMed

Wuchty, S; Rajagopala, S V; Blazie, S M; Parrish, J R; Khuri, S; Finley, R L; Uetz, P

2017-01-01

The functions of roughly a third of all proteins in Streptococcus pneumoniae , a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae . We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae , the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Genomics and expression profiles of the Hedgehog and Notch signaling pathways in sea urchin development.

PubMed

Walton, Katherine D; Croce, Jenifer C; Glenn, Thomas D; Wu, Shu-Yu; McClay, David R

2006-12-01

The Hedgehog (Hh) and Notch signal transduction pathways control a variety of developmental processes including cell fate choice, differentiation, proliferation, patterning and boundary formation. Because many components of these pathways are conserved, it was predicted and confirmed that pathway components are largely intact in the sea urchin genome. Spatial and temporal location of these pathways in the embryo, and their function in development offer added insight into their mechanistic contributions. Accordingly, all major components of both pathways were identified and annotated in the sea urchin Strongylocentrotus purpuratus genome and the embryonic expression of key components was explored. Relationships of the pathway components, and modifiers predicted from the annotation of S. purpuratus, were compared against cnidarians, arthropods, urochordates, and vertebrates. These analyses support the prediction that the pathways are highly conserved through metazoan evolution. Further, the location of these two pathways appears to be conserved among deuterostomes, and in the case of Notch at least, display similar capacities in endomesoderm gene regulatory networks. RNA expression profiles by quantitative PCR and RNA in situ hybridization reveal that Hedgehog is produced by the endoderm beginning just prior to invagination, and signals to the secondary mesenchyme-derived tissues at least until the pluteus larva stage. RNA in situ hybridization of Notch pathway members confirms that Notch functions sequentially in the vegetal-most secondary mesenchyme cells and later in the endoderm. Functional analyses in future studies will embed these pathways into the growing knowledge of gene regulatory networks that govern early specification and morphogenesis.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Integrated Decision Strategies for Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Kleinstreuer, Nicole; Paris, Michael; Lehmann, David M.; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Lowit, Anna; Allen, David; Casey, Warren

2016-01-01

One of the top priorities of ICCVAM is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by OECD. Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico, and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens assay. Data for six physicochemical properties that may affect skin penetration were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89–96% for the test set and 96–99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. PMID:26851134

Genomic characterization of a new endophytic Streptomyces kebangsaanensis identifies biosynthetic pathway gene clusters for novel phenazine antibiotic production

PubMed Central

Remali, Juwairiah; Sarmin, Nurul ‘Izzah Mohd; Ng, Chyan Leong; Tiong, John J.L.; Aizat, Wan M.; Keong, Loke Kok

2017-01-01

Background Streptomyces are well known for their capability to produce many bioactive secondary metabolites with medical and industrial importance. Here we report a novel bioactive phenazine compound, 6-((2-hydroxy-4-methoxyphenoxy) carbonyl) phenazine-1-carboxylic acid (HCPCA) extracted from Streptomyces kebangsaanensis, an endophyte isolated from the ethnomedicinal Portulaca oleracea. Methods The HCPCA chemical structure was determined using nuclear magnetic resonance spectroscopy. We conducted whole genome sequencing for the identification of the gene cluster(s) believed to be responsible for phenazine biosynthesis in order to map its corresponding pathway, in addition to bioinformatics analysis to assess the potential of S. kebangsaanensis in producing other useful secondary metabolites. Results The S. kebangsaanensis genome comprises an 8,328,719 bp linear chromosome with high GC content (71.35%) consisting of 12 rRNA operons, 81 tRNA, and 7,558 protein coding genes. We identified 24 gene clusters involved in polyketide, nonribosomal peptide, terpene, bacteriocin, and siderophore biosynthesis, as well as a gene cluster predicted to be responsible for phenazine biosynthesis. Discussion The HCPCA phenazine structure was hypothesized to derive from the combination of two biosynthetic pathways, phenazine-1,6-dicarboxylic acid and 4-methoxybenzene-1,2-diol, originated from the shikimic acid pathway. The identification of a biosynthesis pathway gene cluster for phenazine antibiotics might facilitate future genetic engineering design of new synthetic phenazine antibiotics. Additionally, these findings confirm the potential of S. kebangsaanensis for producing various antibiotics and secondary metabolites. PMID:29201559

ABI3, a component of the WAVE2 complex, is potentially regulated by PI3K/AKT pathway

PubMed Central

Moraes, Lais; Zanchin, Nilson I.T.; Cerutti, Janete M.

2017-01-01

We previously reported that ABI3 expression is lost in follicular thyroid carcinomas and its restoration significantly inhibited cell growth, invasiveness, migration, and reduced tumor growth in vivo. The mechanistic basis by which ABI3 exerts its tumor suppressive effects is not fully understood. In this study, we show that ABI3 is a phosphoprotein. Using proteomic array analysis, we showed that ABI3 modulated distinct cancer-related pathways in thyroid cancer cells. The KEA analysis found that PI3K substrates were enriched and forced expression of ABI3 markedly decreased the phosphorylation of AKT and the downstream-targeted protein pGSK3β. We next used immunoprecipitation combined with mass spectrometry to identify ABI3-interacting proteins that may be involved in modulating/integrating signaling pathways. We identified 37 ABI3 partners, including several components of the canonical WAVE regulatory complex (WRC) such as WAVE2/CYF1P1/NAP1, suggesting that ABI3 function might be regulated through WRC. Both, pharmacological inhibition of the PI3K/AKT pathway and mutation at residue S342 of ABI3, which is predicted to be phosphorylated by AKT, provided evidences that the non-phosphorylated form of ABI3 is preferentially present in the WRC protein complex. Collectively, our findings suggest that ABI3 might be a downstream mediator of the PI3K/AKT pathway that might disrupt WRC via ABI3 phosphorylation. PMID:28978070

ABI3, a component of the WAVE2 complex, is potentially regulated by PI3K/AKT pathway.

PubMed

Moraes, Lais; Zanchin, Nilson I T; Cerutti, Janete M

2017-09-15

We previously reported that ABI3 expression is lost in follicular thyroid carcinomas and its restoration significantly inhibited cell growth, invasiveness, migration, and reduced tumor growth in vivo . The mechanistic basis by which ABI3 exerts its tumor suppressive effects is not fully understood. In this study, we show that ABI3 is a phosphoprotein. Using proteomic array analysis, we showed that ABI3 modulated distinct cancer-related pathways in thyroid cancer cells. The KEA analysis found that PI3K substrates were enriched and forced expression of ABI3 markedly decreased the phosphorylation of AKT and the downstream-targeted protein pGSK3β. We next used immunoprecipitation combined with mass spectrometry to identify ABI3-interacting proteins that may be involved in modulating/integrating signaling pathways. We identified 37 ABI3 partners, including several components of the canonical WAVE regulatory complex (WRC) such as WAVE2/CYF1P1/NAP1, suggesting that ABI3 function might be regulated through WRC. Both, pharmacological inhibition of the PI3K/AKT pathway and mutation at residue S342 of ABI3, which is predicted to be phosphorylated by AKT, provided evidences that the non-phosphorylated form of ABI3 is preferentially present in the WRC protein complex. Collectively, our findings suggest that ABI3 might be a downstream mediator of the PI3K/AKT pathway that might disrupt WRC via ABI3 phosphorylation.

Population effect model identifies gene expression predictors of survival outcomes in lung adenocarcinoma for both Caucasian and Asian patients

PubMed Central

Cai, Guoshuai; Xiao, Feifei; Cheng, Chao; Li, Yafang; Amos, Christopher I.; Whitfield, Michael L.

2017-01-01

Background We analyzed and integrated transcriptome data from two large studies of lung adenocarcinomas on distinct populations. Our goal was to investigate the variable gene expression alterations between paired tumor-normal tissues and prospectively identify those alterations that can reliably predict lung disease related outcomes across populations. Methods We developed a mixed model that combined the paired tumor-normal RNA-seq from two populations. Alterations in gene expression common to both populations were detected and validated in two independent DNA microarray datasets. A 10-gene prognosis signature was developed through a l1 penalized regression approach and its prognostic value was evaluated in a third independent microarray cohort. Results Deregulation of apoptosis pathways and increased expression of cell cycle pathways were identified in tumors of both Caucasian and Asian lung adenocarcinoma patients. We demonstrate that a 10-gene biomarker panel can predict prognosis of lung adenocarcinoma in both Caucasians and Asians. Compared to low risk groups, high risk groups showed significantly shorter overall survival time (Caucasian patients data: HR = 3.63, p-value = 0.007; Asian patients data: HR = 3.25, p-value = 0.001). Conclusions This study uses a statistical framework to detect DEGs between paired tumor and normal tissues that considers variances among patients and ethnicities, which will aid in understanding the common genes and signalling pathways with the largest effect sizes in ethnically diverse cohorts. We propose multifunctional markers for distinguishing tumor from normal tissue and prognosis for both populations studied. PMID:28426704

Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT.

PubMed

Zabelina, Darya L; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity--an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately.

Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT

PubMed Central

Zabelina, Darya L.; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity–an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately. PMID:26783754

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants

PubMed Central

Hill, Camilla Beate; Czauderna, Tobias; Klapperstück, Matthias; Roessner, Ute; Schreiber, Falk

2015-01-01

Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions. PMID:26557642

Childhood adversity and social functioning in psychosis: Exploring clinical and cognitive mediators.

PubMed

Palmier-Claus, Jasper; Berry, Katherine; Darrell-Berry, Hannah; Emsley, Richard; Parker, Sophie; Drake, Richard; Bucci, Sandra

2016-04-30

Childhood adversity may increase risk of impaired social functioning across the continuum of psychosis. However, the pathways by which adversity dictates functional outcome remain underexplored. This study investigated the association between childhood adversity and social functioning, and the clinical and cognitive mediators of this relationship. Fifty-four clinical (20 chronic, 20 first episode, 14 at ultra-high risk) and 120 non-clinical participants completed standardised questionnaires, semi-structured interviews and tests of theory of mind ability. The authors used multiple group structural equation modelling to fit mediation models allowing for differential relationships between the clinical and non-clinical samples. When examining each pathway separately, depression, paranoia and anxious attachment mediated the effect of childhood adversity on social functioning. In a combined model, depression was the only significant mediating variable with greater adversity predicting lower mood across groups. Childhood adversity did not significantly predict theory of mind ability in any of the models. This is the first study to indicate that childhood adversity acts on social functioning by increasing levels of depression, suggesting a common mechanism across the spectrum of psychosis. Clinical interventions should target low mood in order to improve social functioning at all stages of psychotic disorder. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ReactPRED: a tool to predict and analyze biochemical reactions.

PubMed

Sivakumar, Tadi Venkata; Giri, Varun; Park, Jin Hwan; Kim, Tae Yong; Bhaduri, Anirban

2016-11-15

Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: anirban.b@samsung.com or ty76.kim@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

PubMed Central

Foerster, Hartmut; Bombarely, Aureliano; Battey, James N D; Sierro, Nicolas; Ivanov, Nikolai V; Mueller, Lukas A

2018-01-01

Abstract SolCyc is the entry portal to pathway/genome databases (PGDBs) for major species of the Solanaceae family hosted at the Sol Genomics Network. Currently, SolCyc comprises six organism-specific PGDBs for tomato, potato, pepper, petunia, tobacco and one Rubiaceae, coffee. The metabolic networks of those PGDBs have been computationally predicted by the pathologic component of the pathway tools software using the manually curated multi-domain database MetaCyc (http://www.metacyc.org/) as reference. SolCyc has been recently extended by taxon-specific databases, i.e. the family-specific SolanaCyc database, containing only curated data pertinent to species of the nightshade family, and NicotianaCyc, a genus-specific database that stores all relevant metabolic data of the Nicotiana genus. Through manual curation of the published literature, new metabolic pathways have been created in those databases, which are complemented by the continuously updated, relevant species-specific pathways from MetaCyc. At present, SolanaCyc comprises 199 pathways and 29 superpathways and NicotianaCyc accounts for 72 pathways and 13 superpathways. Curator-maintained, taxon-specific databases such as SolanaCyc and NicotianaCyc are characterized by an enrichment of data specific to these taxa and free of falsely predicted pathways. Both databases have been used to update recently created Nicotiana-specific databases for Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis by propagating verifiable data into those PGDBs. In addition, in-depth curation of the pathways in N.tabacum has been carried out which resulted in the elimination of 156 pathways from the 569 pathways predicted by pathway tools. Together, in-depth curation of the predicted pathway network and the supplementation with curated data from taxon-specific databases has substantially improved the curation status of the species–specific N.tabacum PGDB. The implementation of this strategy will significantly advance the curation status of all organism-specific databases in SolCyc resulting in the improvement on database accuracy, data analysis and visualization of biochemical networks in those species. Database URL https://solgenomics.net/tools/solcyc/ PMID:29762652

Convergent evolution at the pathway level: predictable regulatory changes during flower color transitions.

PubMed

Larter, Maximilian; Dunbar-Wallis, Amy; Berardi, Andrea E; Smith, Stacey D

2018-06-07

The predictability of evolution, or whether lineages repeatedly follow the same evolutionary trajectories during phenotypic convergence remains an open question of evolutionary biology. In this study, we investigate evolutionary convergence at the biochemical pathway level and test the predictability of evolution using floral anthocyanin pigmentation, a trait with a well-understood genetic and regulatory basis. We reconstructed the evolution of floral anthocyanin content across 28 species of the Andean clade Iochrominae (Solanaceae) and investigated how shifts in pigmentation are related to changes in expression of 7 key anthocyanin pathway genes. We used phylogenetic multivariate analysis of gene expression to test for phenotypic and developmental convergence at a macroevolutionary scale. Our results show that the four independent losses of the ancestral pigment delphinidin involved convergent losses of expression of the three late pathway genes (F3'5'h, Dfr and Ans). Transitions between pigment types affecting floral hue (e.g. blue to red) involve changes to the expression of branching genes F3'h and F3'5'h, while the expression levels of early steps of the pathway are strongly conserved in all species. These patterns support the idea that the macroevolution of floral pigmentation follows predictable evolutionary trajectories to reach convergent phenotype space, repeatedly involving regulatory changes. This is likely driven by constraints at the pathway level, such as pleiotropy and regulatory structure.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Novel Biomarkers to Improve the Prediction of Cardiovascular Event Risk in Type 2 Diabetes Mellitus.

PubMed

van der Leeuw, Joep; Beulens, Joline W J; van Dieren, Susan; Schalkwijk, Casper G; Glatz, Jan F C; Hofker, Marten H; Verschuren, W M Monique; Boer, Jolanda M A; van der Graaf, Yolanda; Visseren, Frank L J; Peelen, Linda M; van der Schouw, Yvonne T

2016-05-31

We evaluated the ability of 23 novel biomarkers representing several pathophysiological pathways to improve the prediction of cardiovascular event (CVE) risk in patients with type 2 diabetes mellitus beyond traditional risk factors. We used data from 1002 patients with type 2 diabetes mellitus from the Second Manifestations of ARTertial disease (SMART) study and 288 patients from the European Prospective Investigation into Cancer and Nutrition-NL (EPIC-NL). The associations of 23 biomarkers (adiponectin, C-reactive protein, epidermal-type fatty acid binding protein, heart-type fatty acid binding protein, basic fibroblast growth factor, soluble FMS-like tyrosine kinase-1, soluble intercellular adhesion molecule-1 and -3, matrix metalloproteinase [MMP]-1, MMP-3, MMP-9, N-terminal prohormone of B-type natriuretic peptide, osteopontin, osteonectin, osteocalcin, placental growth factor, serum amyloid A, E-selectin, P-selectin, tissue inhibitor of MMP-1, thrombomodulin, soluble vascular cell adhesion molecule-1, and vascular endothelial growth factor) with CVE risk were evaluated by using Cox proportional hazards analysis adjusting for traditional risk factors. The incremental predictive performance was assessed with use of the c-statistic and net reclassification index (NRI; continuous and based on 10-year risk strata 0-10%, 10-20%, 20-30%, >30%). A multimarker model was constructed comprising those biomarkers that improved predictive performance in both cohorts. N-terminal prohormone of B-type natriuretic peptide, osteopontin, and MMP-3 were the only biomarkers significantly associated with an increased risk of CVE and improved predictive performance in both cohorts. In SMART, the combination of these biomarkers increased the c-statistic with 0.03 (95% CI 0.01-0.05), and the continuous NRI was 0.37 (95% CI 0.21-0.52). In EPIC-NL, the multimarker model increased the c-statistic with 0.03 (95% CI 0.00-0.03), and the continuous NRI was 0.44 (95% CI 0.23-0.66). Based on risk strata, the NRI was 0.12 (95% CI 0.03-0.21) in SMART and 0.07 (95% CI -0.04-0.17) in EPIC-NL. Of the 23 evaluated biomarkers from different pathophysiological pathways, N-terminal prohormone of B-type natriuretic peptide, osteopontin, MMP-3, and their combination improved CVE risk prediction in 2 separate cohorts of patients with type 2 diabetes mellitus beyond traditional risk factors. However, the number of patients reclassified to a different risk stratum was limited. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses

PubMed Central

Serbus, Laura R.; Rodriguez, Brian Garcia; Sharmin, Zinat; Momtaz, A. J. M. Zehadee; Christensen, Steen

2017-01-01

The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale. PMID:28455417

Bacillus anthracis secretome time course under host-simulated conditions and identification of immunogenic proteins.

PubMed

Walz, Alexander; Mujer, Cesar V; Connolly, Joseph P; Alefantis, Tim; Chafin, Ryan; Dake, Clarissa; Whittington, Jessica; Kumar, Srikanta P; Khan, Akbar S; DelVecchio, Vito G

2007-07-27

The secretion time course of Bacillus anthracis strain RA3R (pXO1+/pXO2-) during early, mid, and late log phase were investigated under conditions that simulate those encountered in the host. All of the identified proteins were analyzed by different software algorithms to characterize their predicted mode of secretion and cellular localization. In addition, immunogenic proteins were identified using sera from humans with cutaneous anthrax. A total of 275 extracellular proteins were identified by a combination of LC MS/MS and MALDI-TOF MS. All of the identified proteins were analyzed by SignalP, SecretomeP, PSORT, LipoP, TMHMM, and PROSITE to characterize their predicted mode of secretion, cellular localization, and protein domains. Fifty-three proteins were predicted by SignalP to harbor the cleavable N-terminal signal peptides and were therefore secreted via the classical Sec pathway. Twenty-three proteins were predicted by SecretomeP for secretion by the alternative Sec pathway characterized by the lack of typical export signal. In contrast to SignalP and SecretomeP predictions, PSORT predicted 171 extracellular proteins, 7 cell wall-associated proteins, and 6 cytoplasmic proteins. Moreover, 51 proteins were predicted by LipoP to contain putative Sec signal peptides (38 have SpI sites), lipoprotein signal peptides (13 have SpII sites), and N-terminal membrane helices (9 have transmembrane helices). The TMHMM algorithm predicted 25 membrane-associated proteins with one to ten transmembrane helices. Immunogenic proteins were also identified using sera from patients who have recovered from anthrax. The charge variants (83 and 63 kDa) of protective antigen (PA) were the most immunodominant secreted antigens, followed by charge variants of enolase and transketolase. This is the first description of the time course of protein secretion for the pathogen Bacillus anthracis. Time course studies of protein secretion and accumulation may be relevant in elucidation of the progression of pathogenicity, identification of therapeutics and diagnostic markers, and vaccine development. This study also adds to the continuously growing list of identified Bacillus anthracis secretome proteins.

Bacillus anthracis secretome time course under host-simulated conditions and identification of immunogenic proteins

PubMed Central

Walz, Alexander; Mujer, Cesar V; Connolly, Joseph P; Alefantis, Tim; Chafin, Ryan; Dake, Clarissa; Whittington, Jessica; Kumar, Srikanta P; Khan, Akbar S; DelVecchio, Vito G

2007-01-01

Background The secretion time course of Bacillus anthracis strain RA3R (pXO1+/pXO2-) during early, mid, and late log phase were investigated under conditions that simulate those encountered in the host. All of the identified proteins were analyzed by different software algorithms to characterize their predicted mode of secretion and cellular localization. In addition, immunogenic proteins were identified using sera from humans with cutaneous anthrax. Results A total of 275 extracellular proteins were identified by a combination of LC MS/MS and MALDI-TOF MS. All of the identified proteins were analyzed by SignalP, SecretomeP, PSORT, LipoP, TMHMM, and PROSITE to characterize their predicted mode of secretion, cellular localization, and protein domains. Fifty-three proteins were predicted by SignalP to harbor the cleavable N-terminal signal peptides and were therefore secreted via the classical Sec pathway. Twenty-three proteins were predicted by SecretomeP for secretion by the alternative Sec pathway characterized by the lack of typical export signal. In contrast to SignalP and SecretomeP predictions, PSORT predicted 171 extracellular proteins, 7 cell wall-associated proteins, and 6 cytoplasmic proteins. Moreover, 51 proteins were predicted by LipoP to contain putative Sec signal peptides (38 have SpI sites), lipoprotein signal peptides (13 have SpII sites), and N-terminal membrane helices (9 have transmembrane helices). The TMHMM algorithm predicted 25 membrane-associated proteins with one to ten transmembrane helices. Immunogenic proteins were also identified using sera from patients who have recovered from anthrax. The charge variants (83 and 63 kDa) of protective antigen (PA) were the most immunodominant secreted antigens, followed by charge variants of enolase and transketolase. Conclusion This is the first description of the time course of protein secretion for the pathogen Bacillus anthracis. Time course studies of protein secretion and accumulation may be relevant in elucidation of the progression of pathogenicity, identification of therapeutics and diagnostic markers, and vaccine development. This study also adds to the continuously growing list of identified Bacillus anthracis secretome proteins. PMID:17662140

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

A Global Genomic and Genetic Strategy to Predict Pathway Activation of Xenobiotic Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors(TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Identification and characterization of three Vibrio alginolyticus non-coding RNAs involved in adhesion, chemotaxis, and motility processes.

PubMed

Huang, Lixing; Hu, Jiao; Su, Yongquan; Qin, Yingxue; Kong, Wendi; Ma, Ying; Xu, Xiaojin; Lin, Mao; Yan, Qingpi

2015-01-01

The capability of Vibrio alginolyticus to adhere to fish mucus is a key virulence factor of the bacteria. Our previous research showed that stress conditions, such as Cu(2+), Pb(2+), Hg(2+), and low pH, can reduce this adhesion ability. Non-coding (nc) RNAs play a crucial role in regulating bacterial gene expression, affecting the bacteria's pathogenicity. To investigate the mechanism(s) underlying the decline in adhesion ability caused by stressors, we combined high-throughput sequencing with computational techniques to detect stressed ncRNA dynamics. These approaches yielded three commonly altered ncRNAs that are predicted to regulate the bacterial chemotaxis pathway, which plays a key role in the adhesion process of bacteria. We hypothesized they play a key role in the adhesion process of V. alginolyticus. In this study, we validated the effects of these three ncRNAs on their predicted target genes and their role in the V. alginolyticus adhesion process with RNA interference (i), quantitative real-time polymerase chain reaction (qPCR), northern blot, capillary assay, and in vitro adhesion assays. The expression of these ncRNAs and their predicted target genes were confirmed by qPCR and northern blot, which reinforced the reliability of the sequencing data and the target prediction. Overexpression of these ncRNAs was capable of reducing the chemotactic and adhesion ability of V. alginolyticus, and the expression levels of their target genes were also significantly reduced. Our results indicated that these three ncRNAs: (1) are able to regulate the bacterial chemotaxis pathway, and (2) play a key role in the adhesion process of V. alginolyticus.

Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response.

PubMed

Martin-Lorenzo, Marta; Martinez, Paula J; Baldan-Martin, Montserrat; Ruiz-Hurtado, Gema; Prado, Jose Carlos; Segura, Julian; de la Cuesta, Fernando; Barderas, Maria G; Vivanco, Fernando; Ruilope, Luis Miguel; Alvarez-Llamas, Gloria

2017-11-01

Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered ( P <0.0001). Metabolic concentrations were quantified and ranged from ng/mL malate to μg/mL citrate. Citrate and oxaloacetate increased in RH versus pseudoresistant. Together with α-ketoglutarate and malate, they were able to discriminate between responders and nonresponders, being the 4 metabolites increased in nonresponders. Combined as a prediction panel, they showed receiver operating characteristiccurve with area under the curve of 0.96. We show that citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone. © 2017 American Heart Association, Inc.

Mechanistic Insights into Molecular Targeting and Combined Modality Therapy for Aggressive, Localized Prostate Cancer

PubMed Central

Dal Pra, Alan; Locke, Jennifer A.; Borst, Gerben; Supiot, Stephane; Bristow, Robert G.

2016-01-01

Radiation therapy (RT) is one of the mainstay treatments for prostate cancer (PCa). The potentially curative approaches can provide satisfactory results for many patients with non-metastatic PCa; however, a considerable number of individuals may present disease recurrence and die from the disease. Exploiting the rich molecular biology of PCa will provide insights into how the most resistant tumor cells can be eradicated to improve treatment outcomes. Important for this biology-driven individualized treatment is a robust selection procedure. The development of predictive biomarkers for RT efficacy is therefore of utmost importance for a clinically exploitable strategy to achieve tumor-specific radiosensitization. This review highlights the current status and possible opportunities in the modulation of four key processes to enhance radiation response in PCa by targeting the: (1) androgen signaling pathway; (2) hypoxic tumor cells and regions; (3) DNA damage response (DDR) pathway; and (4) abnormal extra-/intracell signaling pathways. In addition, we discuss how and which patients should be selected for biomarker-based clinical trials exploiting and validating these targeted treatment strategies with precision RT to improve cure rates in non-indolent, localized PCa. PMID:26909338

MicroRNA-1908 functions as a glioblastoma oncogene by suppressing PTEN tumor suppressor pathway.

PubMed

Xia, Xuewei; Li, Yong; Wang, Wenbo; Tang, Fang; Tan, Jie; Sun, Liyuan; Li, Qinghua; Sun, Li; Tang, Bo; He, Songqing

2015-08-12

We aimed to investigate whether miRNA-1908 is an oncogene in human glioblastoma and find the possible mechanism of miR-1908. We investigated the growth potentials of miRNA-1908-overexpressing SW-1783 cells in vitro and in vivo. In order to identify the target molecule of miRNA-1908, a luciferase reporter assay was performed, and the corresponding downstream signaling pathway was examined using immunohistochemistry of human glioblastoma tissues. We also investigated the miRNA-1908 expression in 34 patients according to the postoperative risk of recurrence. The overexpression of miRNA-1908 significantly promoted anchorage-independent growth in vitro and significantly increased the tumor forming potential in vivo. MiRNA-1908 significantly suppressed the luciferase activity of mRNA combined with the PTEN 3'-UTR. Furthermore, the expression levels of miRNA-1908 were significantly increased in the patients with a high risk of recurrence compared to that observed in the low-risk patients, and this higher expression correlated with a poor survival. miRNA-1908 functions as an oncogene in glioblastoma by repressing the PTEN pathway. MiR-1908 is a potential new molecular marker for predicting the risk of recurrence and prognosis of glioblastoma.

Microscale frictional strains determine chondrocyte fate in loaded cartilage.

PubMed

Bonnevie, Edward D; Delco, Michelle L; Bartell, Lena R; Jasty, Naveen; Cohen, Itai; Fortier, Lisa A; Bonassar, Lawrence J

2018-06-06

Mounting evidence suggests that altered lubricant levels within synovial fluid have acute biological consequences on chondrocyte homeostasis. While these responses have been connected to increased friction, the mechanisms behind this response remain unknown. Here, we combine a frictional bioreactor with confocal elastography and image-based cellular assays to establish the link between cartilage friction, microscale shear strain, and acute, adverse cellular responses. Our incorporation of cell-scale strain measurements reveals that elevated friction generates high shear strains localized near the tissue surface, and that these elevated strains are closely associated with mitochondrial dysfunction, apoptosis, and cell death. Collectively, our data establish two pathways by which chondrocytes negatively respond to friction: an immediate necrotic response and a longer term pathway involving mitochondrial dysfunction and apoptosis. Specifically, in the surface region, where shear strains can exceed 0.07, cells are predisposed to acute death; however, below this surface region, cells exhibit a pathway consistent with apoptosis in a manner predicted by local shear strains. These data reveal a mechanism through which cellular damage in cartilage arises from compromised lubrication and show that in addition to boundary lubricants, there are opportunities upstream of apoptosis to preserve chondrocyte health in arthritis therapy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change.

PubMed

Leibold, Christian; Monsalve-Mercado, Mauro M

2016-08-01

Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.

Systems Pharmacology Dissection of the Anti-Inflammatory Mechanism for the Medicinal Herb Folium Eriobotryae

PubMed Central

Zhang, Jingxiao; Li, Yan; Chen, Su-Shing; Zhang, Lilei; Wang, Jinghui; Yang, Yinfeng; Zhang, Shuwei; Pan, Yanqiu; Wang, Yonghua; Yang, Ling

2015-01-01

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated. Here, a novel systems pharmacology approach based on a large number of chemical, biological and pharmacological data was developed and exemplified by a probe herb Folium Eriobotryae, a widely used clinical anti-inflammatory botanic drug. The results show that 11 ingredients of this herb with favorable pharmacokinetic properties are predicted as active compounds for anti-inflammatory treatment. In addition, via systematic network analyses, their targets are identified to be 43 inflammation-associated proteins including especially COX2, ALOX5, PPARG, TNF and RELA that are mainly involved in the mitogen-activated protein kinase (MAPK) signaling pathway, the rheumatoid arthritis pathway and NF-κB signaling pathway. All these demonstrate that the integrated systems pharmacology method provides not only an effective tool to illustrate the anti-inflammatory mechanisms of herbs, but also a new systems-based approach for drug discovery from, but not limited to, herbs, especially when combined with further experimental validations. PMID:25636035

Combination Treatment with Apricoxib and IL-27 Enhances Inhibition of Epithelial-Mesenchymal Transition in Human Lung Cancer Cells through a STAT1 Dominant Pathway

PubMed Central

Lee, Mi-Heon; Kachroo, Puja; Pagano, Paul C; Yanagawa, Jane; Wang, Gerald; Walser, Tonya C; Krysan, Kostyantyn; Sharma, Sherven; John, Maie St.; Dubinett, Steven M; Lee, Jay M

2015-01-01

Background The cyclooxygenase 2 (COX-2) pathway has been implicated in the molecular pathogenesis of many malignancies, including lung cancer. Apricoxib, a selective COX-2 inhibitor, has been described to inhibit epithelial-mesenchymal transition (EMT) in human malignancies. The mechanism by which apricoxib may alter the tumor microenvironment by affecting EMT through other important signaling pathways is poorly defined. IL-27 has been shown to have anti-tumor activity and our recent study showed that IL-27 inhibited EMT through a STAT1 dominant pathway. Objective The purpose of this study is to investigate the role of apricoxib combined with IL-27 in inhibiting lung carcinogenesis by modulation of EMT through STAT signaling. Methods and Results Western blot analysis revealed that IL-27 stimulation of human non-small cell lung cancer (NSCLC) cell lines results in STAT1 and STAT3 activation, decreased Snail protein and mesenchymal markers (N-cadherin and vimentin) and a concomitant increase in expression of epithelial markers (E-cadherin, β-and γ-catenins), and inhibition of cell migration. The combination of apricoxib and IL-27 resulted in augmentation of STAT1 activation. However, IL-27 mediated STAT3 activation was decreased by the addition of apricoxib. STAT1 siRNA was used to determine the involvement of STAT1 pathway in the enhanced inhibition of EMT and cell migration by the combined IL-27 and apricoxib treatment. Pretreatment of cells with STAT1 siRNA inhibited the effect of combined IL-27 and apricoxib in the activation of STAT1 and STAT3. In addition, the augmented expression of epithelial markers, decreased expression mesenchymal markers, and inhibited cell migration by the combination treatment were also inhibited by STAT1 siRNA, suggesting that the STAT1 pathway is important in the enhanced effect from the combination treatment. Conclusion Combined apricoxib and IL-27 has an enhanced effect in inhibition of epithelial-mesenchymal transition and cell migration in human lung cancer cells through a STAT1 dominant pathway. PMID:26523208

Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

PubMed Central

2012-01-01

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance. PMID:22339582

Metabolomic Profiling of the Malaria Box Reveals Antimalarial Target Pathways

PubMed Central

Allman, Erik L.; Painter, Heather J.; Samra, Jasmeet; Carrasquilla, Manuela

2016-01-01

The threat of widespread drug resistance to frontline antimalarials has renewed the urgency for identifying inexpensive chemotherapeutic compounds that are effective against Plasmodium falciparum, the parasite species responsible for the greatest number of malaria-related deaths worldwide. To aid in the fight against malaria, a recent extensive screening campaign has generated thousands of lead compounds with low micromolar activity against blood stage parasites. A subset of these leads has been compiled by the Medicines for Malaria Venture (MMV) into a collection of structurally diverse compounds known as the MMV Malaria Box. Currently, little is known regarding the activity of these Malaria Box compounds on parasite metabolism during intraerythrocytic development, and a majority of the targets for these drugs have yet to be defined. Here we interrogated the in vitro metabolic effects of 189 drugs (including 169 of the drug-like compounds from the Malaria Box) using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). The resulting metabolic fingerprints provide information on the parasite biochemical pathways affected by pharmacologic intervention and offer a critical blueprint for selecting and advancing lead compounds as next-generation antimalarial drugs. Our results reveal several major classes of metabolic disruption, which allow us to predict the mode of action (MoA) for many of the Malaria Box compounds. We anticipate that future combination therapies will be greatly informed by these results, allowing for the selection of appropriate drug combinations that simultaneously target multiple metabolic pathways, with the aim of eliminating malaria and forestalling the expansion of drug-resistant parasites in the field. PMID:27572391

Bayesian network analyses of resistance pathways against efavirenz and nevirapine

PubMed Central

Deforche, Koen; Camacho, Ricardo J.; Grossman, Zehave; Soares, Marcelo A.; Laethem, Kristel Van; Katzenstein, David A.; Harrigan, P. Richard; Kantor, Rami; Shafer, Robert; Vandamme, Anne-Mieke

2016-01-01

Objective To clarify the role of novel mutations selected by treatment with efavirenz or nevirapine, and investigate the influence of HIV-1 subtype on nonnucleoside reverse transcriptase inhibitor (nNRTI) resistance pathways. Design By finding direct dependencies between treatment-selected mutations, the involvement of these mutations as minor or major resistance mutations against efavirenz, nevirapine, or coadministrated nucleoside analogue reverse transcriptase inhibitors (NRTIs) is hypothesized. In addition, direct dependencies were investigated between treatment-selected mutations and polymorphisms, some of which are linked with subtype, and between NRTI and nNRTI resistance pathways. Methods Sequences from a large collaborative database of various subtypes were jointly analyzed to detect mutations selected by treatment. Using Bayesian network learning, direct dependencies were investigated between treatment-selected mutations, NRTI and nNRTI treatment history, and known NRTI resistance mutations. Results Several novel minor resistance mutations were found: 28K and 196R (for resistance against efavirenz), 101H and 138Q (nevirapine), and 31L (lamivudine). Robust interactions between NRTI mutations (65R, 74V, 75I/M, and 184V) and nNRTI resistance mutations (100I, 181C, 190E and 230L) may affect resistance development to particular treatment combinations. For example, an interaction between 65R and 181C predicts that the nevirapine and tenofovir and lamivudine/emtricitabine combination should be more prone to failure than efavirenz and tenofovir and lamivudine/emtricitabine. Conclusion Bayesian networks were helpful in untangling the selection of mutations by NRTI versus nNRTI treatment, and in discovering interactions between resistance mutations within and between these two classes of inhibitors. PMID:18832874

In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms

NASA Astrophysics Data System (ADS)

Hao, Ling; Greer, Tyler; Page, David; Shi, Yatao; Vezina, Chad M.; Macoska, Jill A.; Marker, Paul C.; Bjorling, Dale E.; Bushman, Wade; Ricke, William A.; Li, Lingjun

2016-08-01

Lower urinary tract symptoms (LUTS) are a range of irritative or obstructive symptoms that commonly afflict aging population. The diagnosis is mostly based on patient-reported symptoms, and current medication often fails to completely eliminate these symptoms. There is a pressing need for objective non-invasive approaches to measure symptoms and understand disease mechanisms. We developed an in-depth workflow combining urine metabolomics analysis and machine learning bioinformatics to characterize metabolic alterations and support objective diagnosis of LUTS. Machine learning feature selection and statistical tests were combined to identify candidate biomarkers, which were statistically validated with leave-one-patient-out cross-validation and absolutely quantified by selected reaction monitoring assay. Receiver operating characteristic analysis showed highly-accurate prediction power of candidate biomarkers to stratify patients into disease or non-diseased categories. The key metabolites and pathways may be possibly correlated with smooth muscle tone changes, increased collagen content, and inflammation, which have been identified as potential contributors to urinary dysfunction in humans and rodents. Periurethral tissue staining revealed a significant increase in collagen content and tissue stiffness in men with LUTS. Together, our study provides the first characterization and validation of LUTS urinary metabolites and pathways to support the future development of a urine-based diagnostic test for LUTS.

A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

PubMed Central

Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

2013-01-01

The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

Cid1, a Fission Yeast Protein Required for S-M Checkpoint Control when DNA Polymerase δ or ɛ Is Inactivated

PubMed Central

Wang, Shao-Win; Toda, Takashi; MacCallum, Robert; Harris, Adrian L.; Norbury, Chris

2000-01-01

The S-M checkpoint is an intracellular signaling pathway that ensures that mitosis is not initiated in cells undergoing DNA replication. We identified cid1, a novel fission yeast gene, through its ability when overexpressed to confer specific resistance to a combination of hydroxyurea, which inhibits DNA replication, and caffeine, which overrides the S-M checkpoint. Cid1 overexpression also partially suppressed the hydroxyurea sensitivity characteristic of DNA polymerase δ mutants and mutants defective in the “checkpoint Rad” pathway. Cid1 is a member of a family of putative nucleotidyltransferases including budding yeast Trf4 and Trf5, and mutation of amino acid residues predicted to be essential for this activity resulted in loss of Cid1 function in vivo. Two additional Cid1-like proteins play similar but nonredundant checkpoint-signaling roles in fission yeast. Cells lacking Cid1 were found to be viable but specifically sensitive to the combination of hydroxyurea and caffeine and to be S-M checkpoint defective in the absence of Cds1. Genetic data suggest that Cid1 acts in association with Crb2/Rhp9 and through the checkpoint-signaling kinase Chk1 to inhibit unscheduled mitosis specifically when DNA polymerase δ or ɛ is inhibited. PMID:10757807

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

NASA Astrophysics Data System (ADS)

Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

2017-03-01

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

PubMed Central

Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

2017-01-01

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent. PMID:28272488

A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study.

PubMed

Garcia-Aloy, Mar; Llorach, Rafael; Urpi-Sarda, Mireia; Jáuregui, Olga; Corella, Dolores; Ruiz-Canela, Miguel; Salas-Salvadó, Jordi; Fitó, Montserrat; Ros, Emilio; Estruch, Ramon; Andres-Lacueva, Cristina

2015-02-01

The aim of the current study was to apply an untargeted metabolomics strategy to characterize a model of cocoa intake biomarkers in a free-living population. An untargeted HPLC-q-ToF-MS based metabolomics approach was applied to human urine from 32 consumers of cocoa or derived products (CC) and 32 matched control subjects with no consumption of cocoa products (NC). The multivariate statistical analysis (OSC-PLS-DA) showed clear differences between CC and NC groups. The discriminant biomarkers identified were mainly related to the metabolic pathways of theobromine and polyphenols, as well as to cocoa processing. Consumption of cocoa products was also associated with reduced urinary excretions of methylglutarylcarnitine, which could be related to effects of cocoa exposure on insulin resistance. To improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. ROC curves were performed to evaluate the model and individual metabolites. The AUC values (95% CI) for the model were 95.7% (89.8-100%) and 92.6% (81.9-100%) in training and validation sets, respectively, whereas the AUCs for individual metabolites were <90%. The metabolic signature of cocoa consumption in free-living subjects reveals that combining different metabolites as biomarker models improves prediction of dietary exposure to cocoa. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

DOE PAGES

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.; ...

2016-01-13

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Molecular Pathways: Extracting Medical Knowledge from High Throughput Genomic Data

PubMed Central

Goldstein, Theodore; Paull, Evan O.; Ellis, Matthew J.; Stuart, Joshua M.

2013-01-01

High-throughput genomic data that measures RNA expression, DNA copy number, mutation status and protein levels provide us with insights into the molecular pathway structure of cancer. Genomic lesions (amplifications, deletions, mutations) and epigenetic modifications disrupt biochemical cellular pathways. While the number of possible lesions is vast, different genomic alterations may result in concordant expression and pathway activities, producing common tumor subtypes that share similar phenotypic outcomes. How can these data be translated into medical knowledge that provides prognostic and predictive information? First generation mRNA expression signatures such as Genomic Health's Oncotype DX already provide prognostic information, but do not provide therapeutic guidance beyond the current standard of care – which is often inadequate in high-risk patients. Rather than building molecular signatures based on gene expression levels, evidence is growing that signatures based on higher-level quantities such as from genetic pathways may provide important prognostic and diagnostic cues. We provide examples of how activities for molecular entities can be predicted from pathway analysis and how the composite of all such activities, referred to here as the “activitome,” help connect genomic events to clinical factors in order to predict the drivers of poor outcome. PMID:23430023

Mapping the pathways of resistance to targeted therapies

PubMed Central

Wood, Kris C.

2015-01-01

Resistance substantially limits the depth and duration of clinical responses to targeted anticancer therapies. Through the use of complementary experimental approaches, investigators have revealed that cancer cells can achieve resistance through adaptation or selection driven by specific genetic, epigenetic, or microenvironmental alterations. Ultimately, these diverse alterations often lead to the activation of signaling pathways that, when co-opted, enable cancer cells to survive drug treatments. Recently developed methods enable the direct and scalable identification of the signaling pathways capable of driving resistance in specific contexts. Using these methods, novel pathways of resistance to clinically approved drugs have been identified and validated. By combining systematic resistance pathway mapping methods with studies revealing biomarkers of specific resistance pathways and pharmacological approaches to block these pathways, it may be possible to rationally construct drug combinations that yield more penetrant and lasting responses in patients. PMID:26392071

Evaluation of non-animal methods for assessing skin sensitisation hazard: A Bayesian Value-of-Information analysis.

PubMed

Leontaridou, Maria; Gabbert, Silke; Van Ierland, Ekko C; Worth, Andrew P; Landsiedel, Robert

2016-07-01

This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP). 2016 FRAME.

Synergy in free radical generation is blunted by high-fat diet induced alterations in skeletal muscle mitochondrial metabolism.

PubMed

Li, Yanjun; Periwal, Vipul

2013-03-05

Due to their role in cellular energetics and metabolism, skeletal muscle mitochondria appear to play a key role in the development of insulin resistance and type II diabetes. High-fat diet can induce higher levels of reactive oxygen species (ROS), evidenced by hydrogen peroxide (H2O2) emission from mitochondria, which may be causal for insulin resistance in skeletal muscle. The underlying mechanisms are unclear. Recent published data on single substrate (pyruvate, succinate, fat) metabolism in both normal diet (CON) and high-fat diet (HFD) states of skeletal muscle allowed us to develop an integrated mathematical model of skeletal muscle mitochondrial metabolism. Model simulations suggested that long-term HFD may affect specific metabolic reaction/pathways by altering enzyme activities. Our model allows us to predict oxygen consumption and ROS generation for any combination of substrates. In particular, we predict a synergy between (iso-membrane potential) combinations of pyruvate and fat in ROS production compared to the sum of ROS production with each substrate singly in both CON and HFD states. This synergy is blunted in the HFD state. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma

PubMed Central

Liu, Zhicheng; Nahon, Pierre; Li, Zaifang; Yin, Peiyuan; Li, Yanli; Amathieu, Roland; Ganne-Carrié, Nathalie; Ziol, Marianne; Sellier, Nicolas; Seror, Olivier; Le Moyec, Laurence; Savarin, Philippe; Xu, Guowang

2018-01-01

Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum samples obtained before and after RFA therapy. Significant variations were observed in metabolites in the glycerolipid, tricarboxylic acid (TCA) cycle, fatty acid, and amino acid pathways between recurrent and non-recurrent patients. Observed differences in metabolites associated with recurrence did not coincide before and after treatment except for fatty acids. Based on the comparison of serum metabolomes between recurrent and non-recurrent patients, key discriminatory metabolites were defined by a random forest (RF) test. Two combinations of these metabolites before and after RFA treatment showed outstanding performance in predicting HCV-related HCC recurrence, they were further confirmed by an external validation set. Our study showed that the determined combination of metabolites may be potential biomarkers for the prediction of HCC recurrence before and after RFA treatment. PMID:29464069

Accelerating cancer therapy development: the importance of combination strategies and collaboration. Summary of an Institute of Medicine workshop.

PubMed

LoRusso, Patricia M; Canetta, Renzo; Wagner, John A; Balogh, Erin P; Nass, Sharyl J; Boerner, Scott A; Hohneker, John

2012-11-15

Cancer cells contain multiple genetic changes in cell signaling pathways that drive abnormal cell survival, proliferation, invasion, and metastasis. Unfortunately, patients treated with single agents inhibiting only one of these pathways--even if showing an initial response--often develop resistance with subsequent relapse or progression of their cancer, typically via the activation of an alternative uninhibited pathway. Combination therapies offer the potential for inhibiting multiple targets and pathways simultaneously to more effectively kill cancer cells and prevent or delay the emergence of drug resistance. However, there are many unique challenges to developing combination therapies, including devising and applying appropriate preclinical tests and clinical trial designs, prioritizing which combination therapies to test, avoiding overlapping toxicity of multiple agents, and overcoming legal, cultural, and regulatory barriers that impede collaboration among multiple companies, organizations, and/or institutions. More effective strategies to efficiently develop combination cancer therapies are urgently needed. Thus, the Institute of Medicine's National Cancer Policy Forum recently convened a workshop with the goal of identifying barriers that may be impeding the development of combination investigational cancer therapies, as well as potential solutions to overcome those barriers, improve collaboration, and ultimately accelerate the development of promising combinations of investigational cancer therapies. ©2012 AACR.

Heterotrophs are key contributors to nitrous oxide production in activated sludge under low C-to-N ratios during nitrification-Batch experiments and modeling.

PubMed

Domingo-Félez, Carlos; Pellicer-Nàcher, Carles; Petersen, Morten S; Jensen, Marlene M; Plósz, Benedek G; Smets, Barth F

2017-01-01

Nitrous oxide (N 2 O), a by-product of biological nitrogen removal during wastewater treatment, is produced by ammonia-oxidizing bacteria (AOB) and heterotrophic denitrifying bacteria (HB). Mathematical models are used to predict N 2 O emissions, often including AOB as the main N 2 O producer. Several model structures have been proposed without consensus calibration procedures. Here, we present a new experimental design that was used to calibrate AOB-driven N 2 O dynamics of a mixed culture. Even though AOB activity was favoured with respect to HB, oxygen uptake rates indicated HB activity. Hence, rigorous experimental design for calibration of autotrophic N 2 O production from mixed cultures is essential. The proposed N 2 O production pathways were examined using five alternative process models confronted with experimental data inferred. Individually, the autotrophic and heterotrophic denitrification pathway could describe the observed data. In the best-fit model, which combined two denitrification pathways, the heterotrophic was stronger than the autotrophic contribution to N 2 O production. Importantly, the individual contribution of autotrophic and heterotrophic to the total N 2 O pool could not be unambiguously elucidated solely based on bulk N 2 O measurements. Data on NO would increase the practical identifiability of N 2 O production pathways. Biotechnol. Bioeng. 2017;114: 132-140. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis

PubMed Central

Kim, Jongwoon

2017-01-01

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer. PMID:28973975

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

PubMed Central

Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

2016-01-01

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

Developmental Pathways Linking Externalizing Symptoms, Internalizing Symptoms, and Academic Competence to Adolescent Substance Use

ERIC Educational Resources Information Center

Englund, Michelle M.; Siebenbruner, Jessica

2012-01-01

This study extends previous research investigating the developmental pathways predicting adolescent alcohol and marijuana use by examining the cascading effects of externalizing and internalizing symptoms and academic competence in the prediction of use and level of use of these substances in adolescence. Participants (N = 191) were drawn from a…

Computationally predicted Adverse Outcome Pathway networks for liver-related diseases using publicly available data sources: Case studies and lessons learned

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework summarizes key information about mechanistic events leading to an adverse health or ecological outcome. In recent years computationally predicted AOPs (cpAOP) making use of publicly available data have been proposed as a means of accele...

Measures of Hindu Pathways: Development and Preliminary Evidence of Reliability and Validity.

ERIC Educational Resources Information Center

Tarakeshwar, Nalini; Pargament, Kenneth I.; Mahoney, Annette

2003-01-01

Examines religious practices of Hindus in the United States and develops measures of their religious pathways. Four religious pathways were identified: devotion, ethical action, knowledge, and physical restraint/yoga. Results indicate that the measures of the religious pathways possessed adequate psychometric properties and were predictive of…

Robustness of atomistic Gō models in predicting native-like folding intermediates

NASA Astrophysics Data System (ADS)

Estácio, S. G.; Fernandes, C. S.; Krobath, H.; Faísca, P. F. N.; Shakhnovich, E. I.

2012-08-01

Gō models are exceedingly popular tools in computer simulations of protein folding. These models are native-centric, i.e., they are directly constructed from the protein's native structure. Therefore, it is important to understand up to which extent the atomistic details of the native structure dictate the folding behavior exhibited by Gō models. Here we address this challenge by performing exhaustive discrete molecular dynamics simulations of a Gō potential combined with a full atomistic protein representation. In particular, we investigate the robustness of this particular type of Gō models in predicting the existence of intermediate states in protein folding. We focus on the N47G mutational form of the Spc-SH3 folding domain (x-ray structure) and compare its folding pathway with that of alternative native structures produced in silico. Our methodological strategy comprises equilibrium folding simulations, structural clustering, and principal component analysis.

Aerobic activated sludge transformation of vincristine and identification of the transformation products.

PubMed

Kosjek, Tina; Negreira, Noelia; Heath, Ester; López de Alda, Miren; Barceló, Damià

2018-01-01

This study aims to identify (bio)transformation products of vincristine, a plant alkaloid chemotherapy drug. A batch biotransformation experiment was set-up using activated sludge at two concentration levels with and without the addition of a carbon source. Sample analysis was performed on an ultra-high performance liquid chromatograph coupled to a high-resolution hybrid quadrupole-Orbitrap tandem mass spectrometer. To identify molecular ions of vincristine transformation products and to propose molecular and chemical structures, we performed data-dependent acquisition experiments combining full-scan mass spectrometry data with product ion spectra. In addition, the use of non-commercial detection and prediction algorithms such as MZmine 2 and EAWAG-BBD Pathway Prediction System, was proven to be proficient for screening for transformation products in complex wastewater matrix total ion chromatograms. In this study eleven vincristine transformation products were detected, nine of which were tentatively identified. Copyright © 2017 Elsevier B.V. All rights reserved.

Beyond the conventional understanding of water-rock reactivity

NASA Astrophysics Data System (ADS)

Fischer, Cornelius; Luttge, Andreas

2017-01-01

A common assumption is that water-rock reaction rates should converge to a mean value. There is, however, an emerging consensus on the genuine nature of reaction rate variations under identical chemical conditions. Thus, the further use of mean reaction rates for the prediction of material fluxes is environmentally and economically risky, manifest for example in the management of nuclear waste or the evolution of reservoir rocks. Surface-sensitive methods and resulting information about heterogeneous surface reactivity illustrate the inherent rate variability. Consequently, a statistical analysis was developed in order to quantify the heterogeneity of surface rates. We show how key components of the rate combine to give an overall rate and how the identification of those individual rate contributors provide mechanistic insight into complex heterogeneous reactions. This generates a paradigm change by proposing a new pathway to reaction model parameterization and for the prediction of reaction rates.

The GSK3 Signaling Axis Regulates Adaptive Glutamine Metabolism in Lung Squamous Cell Carcinoma.

PubMed

Momcilovic, Milica; Bailey, Sean T; Lee, Jason T; Fishbein, Michael C; Braas, Daniel; Go, James; Graeber, Thomas G; Parlati, Francesco; Demo, Susan; Li, Rui; Walser, Tonya C; Gricowski, Michael; Shuman, Robert; Ibarra, Julio; Fridman, Deborah; Phelps, Michael E; Badran, Karam; St John, Maie; Bernthal, Nicholas M; Federman, Noah; Yanagawa, Jane; Dubinett, Steven M; Sadeghi, Saman; Christofk, Heather R; Shackelford, David B

2018-05-14

Altered metabolism is a hallmark of cancer growth, forming the conceptual basis for development of metabolic therapies as cancer treatments. We performed in vivo metabolic profiling and molecular analysis of lung squamous cell carcinoma (SCC) to identify metabolic nodes for therapeutic targeting. Lung SCCs adapt to chronic mTOR inhibition and suppression of glycolysis through the GSK3α/β signaling pathway, which upregulates glutaminolysis. Phospho-GSK3α/β protein levels are predictive of response to single-therapy mTOR inhibition while combinatorial treatment with the glutaminase inhibitor CB-839 effectively overcomes therapy resistance. In addition, we identified a conserved metabolic signature in a broad spectrum of hypermetabolic human tumors that may be predictive of patient outcome and response to combined metabolic therapies targeting mTOR and glutaminase. Copyright © 2018 Elsevier Inc. All rights reserved.

Hysteresis in the Cell Response to Time-Dependent Substrate Stiffness

PubMed Central

Besser, Achim; Schwarz, Ulrich S.

2010-01-01

Abstract Mechanical cues like the rigidity of the substrate are main determinants for the decision-making of adherent cells. Here we use a mechano-chemical model to predict the cellular response to varying substrate stiffnesses. The model equations combine the mechanics of contractile actin filament bundles with a model for the Rho-signaling pathway triggered by forces at cell-matrix contacts. A bifurcation analysis of cellular contractility as a function of substrate stiffness reveals a bistable response, thus defining a lower threshold of stiffness, below which cells are not able to build up contractile forces, and an upper threshold of stiffness, above which cells are always in a strongly contracted state. Using the full dynamical model, we predict that rate-dependent hysteresis will occur in the cellular traction forces when cells are exposed to substrates of time-dependent stiffness. PMID:20655823

Synergistic antitumor effect between gefitinib and fractionated irradiation in anaplastic oligodendrogliomas cannot be predicted by the Egfr signaling activity.

PubMed

Pinel, Sophie; Mriouah, Jihane; Vandamme, Marc; Chateau, Alicia; Plénat, François; Guérin, Eric; Taillandier, Luc; Bernier-Chastagner, Valérie; Merlin, Jean-Louis; Chastagner, Pascal

2013-01-01

In high-grade gliomas, the identification of patients that could benefit from EGFR inhibitors remains a challenge, hindering the use of these agents. Using xenografts models, we evaluated the antitumor effect of the combined treatment "gefitinib + radiotherapy" and aimed to identify the profile of responsive tumors. Expression of phosphorylated proteins involved in the EGFR-dependent signaling pathways was analyzed in 10 glioma models. We focused on three models of anaplastic oligodendrogliomas (TCG2, TCG3 and TCG4) harboring high levels of phospho-EGFR, phospho-AKT and phospho-MEK1. They were treated with gefitinib (GEF 75 mg/kg/day x 5 days/week, for 2 weeks) and/or fractionated radiotherapy (RT: 5x2Gy/week for 2 weeks). Our results showed that GEF and/or RT induced significant tumor growth delays. However, only the TCG3 xenografts were highly responsive to the combination GEF+RT, with ∼50% of tumor cure. Phosphoproteins analysis five days after treatment onset demonstrated in TCG3 xenografts, but not in TCG2 model, that the EGFR-dependent pathways were inhibited after GEF treatment. Moreover, TCG3-bearing mice receiving GEF monotherapy exhibited a transient beneficial therapeutic response, rapidly followed by tumor regrowth, along with a major vascular remodeling. Taken together, our data evoked an "EGFR-addictive" behavior for TCG3 tumors. This study confirms that combination of gefitinib with fractionated irradiation could be a potent therapeutic strategy for anaplastic oligodendrogliomas harboring EGFR abnormalities but this treatment seems mainly beneficial for "EGFR-addictive" tumors. Unfortunately, neither the usual molecular markers (EGFR amplification, PTEN loss) nor the basal overexpression of phosphoproteins were useful to distinguish this responsive tumor. Evaluating the impact of TKIs on the EGFR-dependent pathways during the treatment might be more relevant, and requires further validation.

Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin on vasoconstriction.

PubMed

Yin, Anyue; Yamada, Akihiro; Stam, Wiro B; van Hasselt, Johan G C; van der Graaf, Piet H

2018-06-02

Development of combination therapies has received significant interest in recent years. Previously a two-receptor one-transducer (2R-1T) model was proposed to characterize drug interactions with two receptors that lead to the same phenotypic response through a common transducer pathway. We applied, for the first time, the 2R-1T model to characterize the interaction of noradrenaline and arginine-vasopressin on vasoconstriction, and performed inter-species scaling to humans using this mechanism-based model. Contractile data was obtained from in vitro rat small mesenteric arteries after exposure to single or combined challenges of noradrenaline and arginine-vasopressin with or without pre-treatment with the irreversible α-adrenoceptor antagonist, phenoxybenzamine. Data was analysed using the 2R-1T model to characterize the observed exposure-response relationships and drug-drug interaction. The model was then scaled to humans by accounting for differences in receptor density. With receptor affinities set to literature values, the 2R-1T model satisfactorily characterized the interaction between noradrenaline and arginine-vasopressin in rat small mesenteric arteries (relative standard error ≤ 20%), as well as the effect of phenoxybenzamine. Furthermore, after scaling the model to human vascular tissue, the model also adequately predicted the interaction between both agents on human renal arteries. The 2R-1T model can be of relevance to quantitatively characterize the interaction between two drugs that interact via different receptors and a common transducer pathway. Its mechanistic properties are valuable for scaling the model across species. This approach is therefore of significant value to rationally optimize novel combination treatments. This article is protected by copyright. All rights reserved.

PPARα siRNA–Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy

PubMed Central

Dai, Xudong; Souza, Angus T. De; Dai, Hongyue; Lewis, David L; Lee, Chang-kyu; Spencer, Andy G; Herweijer, Hans; Hagstrom, Jim E; Linsley, Peter S; Bassett, Douglas E; Ulrich, Roger G; He, Yudong D

2007-01-01

Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor α (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARα-induced liver hypertrophy is supported by their ability to predict non-PPARα–induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events. PMID:17335344

Dispersal limitation drives successional pathways in Central Siberian forests under current and intensified fire regimes.

PubMed

Tautenhahn, Susanne; Lichstein, Jeremy W; Jung, Martin; Kattge, Jens; Bohlman, Stephanie A; Heilmeier, Hermann; Prokushkin, Anatoly; Kahl, Anja; Wirth, Christian

2016-06-01

Fire is a primary driver of boreal forest dynamics. Intensifying fire regimes due to climate change may cause a shift in boreal forest composition toward reduced dominance of conifers and greater abundance of deciduous hardwoods, with potential biogeochemical and biophysical feedbacks to regional and global climate. This shift has already been observed in some North American boreal forests and has been attributed to changes in site conditions. However, it is unknown if the mechanisms controlling fire-induced changes in deciduous hardwood cover are similar among different boreal forests, which differ in the ecological traits of the dominant tree species. To better understand the consequences of intensifying fire regimes in boreal forests, we studied postfire regeneration in five burns in the Central Siberian dark taiga, a vast but poorly studied boreal region. We combined field measurements, dendrochronological analysis, and seed-source maps derived from high-resolution satellite images to quantify the importance of site conditions (e.g., organic layer depth) vs. seed availability in shaping postfire regeneration. We show that dispersal limitation of evergreen conifers was the main factor determining postfire regeneration composition and density. Site conditions had significant but weaker effects. We used information on postfire regeneration to develop a classification scheme for successional pathways, representing the dominance of deciduous hardwoods vs. evergreen conifers at different successional stages. We estimated the spatial distribution of different successional pathways under alternative fire regime scenarios. Under intensified fire regimes, dispersal limitation of evergreen conifers is predicted to become more severe, primarily due to reduced abundance of surviving seed sources within burned areas. Increased dispersal limitation of evergreen conifers, in turn, is predicted to increase the prevalence of successional pathways dominated by deciduous hardwoods. The likely fire-induced shift toward greater deciduous hardwood cover may affect climate-vegetation feedbacks via surface albedo, Bowen ratio, and carbon cycling. © 2015 John Wiley & Sons Ltd.

Immunity to community: what can immune pathways tell us about disease patterns in corals?

NASA Astrophysics Data System (ADS)

Mydlarz, L. D.; Fuess, L.; Pinzon, J. C.; Weil, E.

2016-02-01

Predicting species composition and abundances is one of the most fundamental questions in ecology. This question is even more pressing in marine ecology and coral reefs since communities are changing at a rapid pace due to climate-related changes. Increases in disease prevalence and severity are just some of the consequences of these environmental changes. Particularly in coral reef ecosystems, diseases are increasing and driving region-wide population collapses. It has become clear, however, that not all reefs or coral species are affected by disease equally. In fact, the Caribbean is a concentrated area for diseases. The patterns in which disease manifests itself on an individual reef are also proving interesting, as not all coral species are affected by disease equally. Some species are host to different diseases, but seem to successfully fight them reducing mortality. Other species are disproportionately infected on any given reef and experience high mortality due to disease. We are interested in the role immunity can play in directing these patterns and are evaluating coral immunity using several novel approaches. We exposed 4 species of corals with different disease susceptibilities to immune stimulators and quantified of coral immunity using a combination of full transcriptome sequencing and protein activity assays for gene to phenotype analysis. We also mapped gene expression changes onto immune pathways (i.e. melanin-cascade, antimicrobial peptide synthesis, complement cascade, lectin-opsonization) to evaluate expression of immune pathways between species. In our preliminary data we found many immune genes in the disease susceptible Orbicella faveolata underwent changes in gene expression opposite of the predictions and may disply `dysfunctional' patterns of expression. We will present expression data for 4 species of coral and assess how these transcriptional and protein immune responses are related to disease susceptibility in nature, thus scaling up from immune pathway to natural patterns of disease.

Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction.

PubMed

Lamontagne, Maxime; Timens, Wim; Hao, Ke; Bossé, Yohan; Laviolette, Michel; Steiling, Katrina; Campbell, Joshua D; Couture, Christian; Conti, Massimo; Sherwood, Karen; Hogg, James C; Brandsma, Corry-Anke; van den Berge, Maarten; Sandford, Andrew; Lam, Stephen; Lenburg, Marc E; Spira, Avrum; Paré, Peter D; Nickle, David; Sin, Don D; Postma, Dirkje S

2014-11-01

COPD is a complex chronic disease with poorly understood pathogenesis. Integrative genomic approaches have the potential to elucidate the biological networks underlying COPD and lung function. We recently combined genome-wide genotyping and gene expression in 1111 human lung specimens to map expression quantitative trait loci (eQTL). To determine causal associations between COPD and lung function-associated single nucleotide polymorphisms (SNPs) and lung tissue gene expression changes in our lung eQTL dataset. We evaluated causality between SNPs and gene expression for three COPD phenotypes: FEV(1)% predicted, FEV(1)/FVC and COPD as a categorical variable. Different models were assessed in the three cohorts independently and in a meta-analysis. SNPs associated with a COPD phenotype and gene expression were subjected to causal pathway modelling and manual curation. In silico analyses evaluated functional enrichment of biological pathways among newly identified causal genes. Biologically relevant causal genes were validated in two separate gene expression datasets of lung tissues and bronchial airway brushings. High reliability causal relations were found in SNP-mRNA-phenotype triplets for FEV(1)% predicted (n=169) and FEV(1)/FVC (n=80). Several genes of potential biological relevance for COPD were revealed. eQTL-SNPs upregulating cystatin C (CST3) and CD22 were associated with worse lung function. Signalling pathways enriched with causal genes included xenobiotic metabolism, apoptosis, protease-antiprotease and oxidant-antioxidant balance. By using integrative genomics and analysing the relationships of COPD phenotypes with SNPs and gene expression in lung tissue, we identified CST3 and CD22 as potential causal genes for airflow obstruction. This study also augmented the understanding of previously described COPD pathways. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Cetuximab Prevents Methotrexate-Induced Cytotoxicity in Vitro through Epidermal Growth Factor Dependent Regulation of Renal Drug Transporters

PubMed Central

2017-01-01

The combination of methotrexate with epidermal growth factor receptor (EGFR) recombinant antibody, cetuximab, is currently being investigated in treatment of head and neck carcinoma. As methotrexate is cleared by renal excretion, we studied the effect of cetuximab on renal methotrexate handling. We used human conditionally immortalized proximal tubule epithelial cells overexpressing either organic anion transporter 1 or 3 (ciPTEC-OAT1/ciPTEC-OAT3) to examine OAT1 and OAT3, and the efflux pumps breast cancer resistance protein (BCRP), multidrug resistance protein 4 (MRP4), and P-glycoprotein (P-gp) in methotrexate handling upon EGF or cetuximab treatment. Protein kinase microarrays and knowledge-based pathway analysis were used to predict EGFR-mediated transporter regulation. Cytotoxic effects of methotrexate were evaluated using the dimethylthiazol bromide (MTT) viability assay. Methotrexate inhibited OAT-mediated fluorescein uptake and decreased efflux of Hoechst33342 and glutathione-methylfluorescein (GS-MF), which suggested involvement of OAT1/3, BCRP, and MRP4 in transepithelial transport, respectively. Cetuximab reversed the EGF-increased expression of OAT1 and BCRP as well as their membrane expressions and transport activities, while MRP4 and P-gp were increased. Pathway analysis predicted cetuximab-induced modulation of PKC and PI3K pathways downstream EGFR/ERBB2/PLCg. Pharmacological inhibition of ERK decreased expression of OAT1 and BCRP, while P-gp and MRP4 were increased. AKT inhibition reduced all transporters. Exposure to methotrexate for 24 h led to a decreased viability, an effect that was reversed by cetuximab. In conclusion, cetuximab downregulates OAT1 and BCRP while upregulating P-gp and MRP4 through an EGFR-mediated regulation of PI3K-AKT and MAPKK-ERK pathways. Consequently, cetuximab attenuates methotrexate-induced cytotoxicity, which opens possibilities for further research into nephroprotective comedication therapies. PMID:28493713

Cetuximab Prevents Methotrexate-Induced Cytotoxicity in Vitro through Epidermal Growth Factor Dependent Regulation of Renal Drug Transporters.

PubMed

Caetano-Pinto, Pedro; Jamalpoor, Amer; Ham, Janneke; Goumenou, Anastasia; Mommersteeg, Monique; Pijnenburg, Dirk; Ruijtenbeek, Rob; Sanchez-Romero, Natalia; van Zelst, Bertrand; Heil, Sandra G; Jansen, Jitske; Wilmer, Martijn J; van Herpen, Carla M L; Masereeuw, Rosalinde

2017-06-05

The combination of methotrexate with epidermal growth factor receptor (EGFR) recombinant antibody, cetuximab, is currently being investigated in treatment of head and neck carcinoma. As methotrexate is cleared by renal excretion, we studied the effect of cetuximab on renal methotrexate handling. We used human conditionally immortalized proximal tubule epithelial cells overexpressing either organic anion transporter 1 or 3 (ciPTEC-OAT1/ciPTEC-OAT3) to examine OAT1 and OAT3, and the efflux pumps breast cancer resistance protein (BCRP), multidrug resistance protein 4 (MRP4), and P-glycoprotein (P-gp) in methotrexate handling upon EGF or cetuximab treatment. Protein kinase microarrays and knowledge-based pathway analysis were used to predict EGFR-mediated transporter regulation. Cytotoxic effects of methotrexate were evaluated using the dimethylthiazol bromide (MTT) viability assay. Methotrexate inhibited OAT-mediated fluorescein uptake and decreased efflux of Hoechst33342 and glutathione-methylfluorescein (GS-MF), which suggested involvement of OAT1/3, BCRP, and MRP4 in transepithelial transport, respectively. Cetuximab reversed the EGF-increased expression of OAT1 and BCRP as well as their membrane expressions and transport activities, while MRP4 and P-gp were increased. Pathway analysis predicted cetuximab-induced modulation of PKC and PI3K pathways downstream EGFR/ERBB2/PLCg. Pharmacological inhibition of ERK decreased expression of OAT1 and BCRP, while P-gp and MRP4 were increased. AKT inhibition reduced all transporters. Exposure to methotrexate for 24 h led to a decreased viability, an effect that was reversed by cetuximab. In conclusion, cetuximab downregulates OAT1 and BCRP while upregulating P-gp and MRP4 through an EGFR-mediated regulation of PI3K-AKT and MAPKK-ERK pathways. Consequently, cetuximab attenuates methotrexate-induced cytotoxicity, which opens possibilities for further research into nephroprotective comedication therapies.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions.

PubMed

Zhang, Aidi; He, Libo; Wang, Yaping

2017-03-02

Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare. In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation. Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.

Prediction of Multiple Infections After Severe Burn Trauma: a Prospective Cohort Study

PubMed Central

Yan, Shuangchun; Tsurumi, Amy; Que, Yok-Ai; Ryan, Colleen M.; Bandyopadhaya, Arunava; Morgan, Alexander A.; Flaherty, Patrick J.; Tompkins, Ronald G.; Rahme, Laurence G.

2014-01-01

Objective To develop predictive models for early triage of burn patients based on hyper-susceptibility to repeated infections. Background Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking. Methods Secondary analysis of 459 burn patients (≥16 years old) with ≥20% total body surface area burns recruited from six US burn centers. We compared blood transcriptomes with a 180-h cut-off on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hyper-susceptible patients (multiple [≥2] infection episodes [MIE]). We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation. Results Three predictive models were developed covariates of: (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status (AUROCGenomic = 0.946 [95% CI, 0.906–0.986]); AUROCClinical = 0.864 [CI, 0.794–0.933]; AUROCGenomic/AUROCClinical P = 0.044). Combined model has an increased AUROCCombined of 0.967 (CI, 0.940–0.993) compared to the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hyper-susceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation and chromatin remodeling. Conclusions Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hyper-susceptibility to infection may lead to novel potential therapeutic or prophylactic targets. PMID:24950278

XLS (c9orf142) is a new component of mammalian DNA double-stranded break repair.

PubMed

Craxton, A; Somers, J; Munnur, D; Jukes-Jones, R; Cain, K; Malewicz, M

2015-06-01

Repair of double-stranded DNA breaks (DSBs) in mammalian cells primarily occurs by the non-homologous end-joining (NHEJ) pathway, which requires seven core proteins (Ku70/Ku86, DNA-PKcs (DNA-dependent protein kinase catalytic subunit), Artemis, XRCC4-like factor (XLF), XRCC4 and DNA ligase IV). Here we show using combined affinity purification and mass spectrometry that DNA-PKcs co-purifies with all known core NHEJ factors. Furthermore, we have identified a novel evolutionary conserved protein associated with DNA-PKcs-c9orf142. Computer-based modelling of c9orf142 predicted a structure very similar to XRCC4, hence we have named c9orf142-XLS (XRCC4-like small protein). Depletion of c9orf142/XLS in cells impaired DSB repair consistent with a defect in NHEJ. Furthermore, c9orf142/XLS interacted with other core NHEJ factors. These results demonstrate the existence of a new component of the NHEJ DNA repair pathway in mammalian cells.

Climate and human intervention effects on future fire activity and consequences for air pollution across the 21st century

NASA Astrophysics Data System (ADS)

Val Martin, M.; Pierce, J. R.; Heald, C. L.; Li, F.; Lawrence, D. M.; Wiedinmyer, C.; Tilmes, S.; Vitt, F.

2016-12-01

Emissions of aerosols and gases from fires have been shown to adversely affect air quality across the world. Fire activity is strongly related to climate and anthropogenic activities. Current fire projections for the 21st century seem very uncertain, ranging from increasing to declining depending on the climate, land cover change and population growth scenarios used. Here we present an analysis of the changes in future wildfire activity and consequences on air quality, with focus on PM2.5 and surface O3 over regions vulnerable to fire. We use the global Community Earth System Model (CESM) with a process-based fire model to simulate emissions from agriculture, peatland, deforestation and landscape fires for present-day and throughout the current century. We consider two future Representative Concentration Pathways climate scenarios combined with population density changes predicted from Shared Socio-economic Pathways to project climate and demographic effects on fire activity and further consequences for future air quality.

Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses.

PubMed

Serbus, Laura R; Rodriguez, Brian Garcia; Sharmin, Zinat; Momtaz, A J M Zehadee; Christensen, Steen

2017-06-07

The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale. Copyright © 2017 Serbus et al.

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study.

PubMed

Kattou, Panayiotis; Lian, Guoping; Glavin, Stephen; Sorrell, Ian; Chen, Tao

2017-10-01

The development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented. The follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared. The integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked. The follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.

VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.

PubMed

Hu, Zhenjun; Ng, David M; Yamada, Takuji; Chen, Chunnuan; Kawashima, Shuichi; Mellor, Joe; Linghu, Bolan; Kanehisa, Minoru; Stuart, Joshua M; DeLisi, Charles

2007-07-01

With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Opposite correlations between cation disordering and amorphization resistance in spinels versus pyrochlores

PubMed Central

Uberuaga, Blas Pedro; Tang, Ming; Jiang, Chao; Valdez, James A.; Smith, Roger; Wang, Yongqiang; Sickafus, Kurt E.

2015-01-01

Understanding and predicting radiation damage evolution in complex materials is crucial for developing next-generation nuclear energy sources. Here, using a combination of ion beam irradiation, transmission electron microscopy and X-ray diffraction, we show that, contrary to the behaviour observed in pyrochlores, the amorphization resistance of spinel compounds correlates directly with the energy to disorder the structure. Using a combination of atomistic simulation techniques, we ascribe this behaviour to structural defects on the cation sublattice that are present in spinel but not in pyrochlore. Specifically, because of these structural defects, there are kinetic pathways for the relaxation of disorder in spinel that are absent in pyrochlore. This leads to a direct correlation between amorphization resistance and disordering energetics in spinel, the opposite of that observed in pyrochlores. These results provide new insight into the origins of amorphization resistance in complex oxides beyond fluorite derivatives. PMID:26510750

Opposite correlations between cation disordering and amorphization resistance in spinels versus pyrochlores

DOE PAGES

Uberuaga, Blas Pedro; Tang, Ming; Jiang, Chao; ...

2015-10-29

Understanding and predicting radiation damage evolution in complex materials is crucial for developing next-generation nuclear energy sources. Here, using a combination of ion beam irradiation, transmission electron microscopy and X-ray diffraction, we show that, contrary to the behaviour observed in pyrochlores, the amorphization resistance of spinel compounds correlates directly with the energy to disorder the structure. Using a combination of atomistic simulation techniques, we ascribe this behaviour to structural defects on the cation sublattice that are present in spinel but not in pyrochlore. Specifically, because of these structural defects, there are kinetic pathways for the relaxation of disorder in spinelmore » that are absent in pyrochlore. This leads to a direct correlation between amorphization resistance and disordering energetics in spinel, the opposite of that observed in pyrochlores. Furthermore, these results provide new insight into the origins of amorphization resistance in complex oxides beyond fluorite derivatives.« less

Opposite correlations between cation disordering and amorphization resistance in spinels versus pyrochlores.

PubMed

Uberuaga, Blas Pedro; Tang, Ming; Jiang, Chao; Valdez, James A; Smith, Roger; Wang, Yongqiang; Sickafus, Kurt E

2015-10-29

Understanding and predicting radiation damage evolution in complex materials is crucial for developing next-generation nuclear energy sources. Here, using a combination of ion beam irradiation, transmission electron microscopy and X-ray diffraction, we show that, contrary to the behaviour observed in pyrochlores, the amorphization resistance of spinel compounds correlates directly with the energy to disorder the structure. Using a combination of atomistic simulation techniques, we ascribe this behaviour to structural defects on the cation sublattice that are present in spinel but not in pyrochlore. Specifically, because of these structural defects, there are kinetic pathways for the relaxation of disorder in spinel that are absent in pyrochlore. This leads to a direct correlation between amorphization resistance and disordering energetics in spinel, the opposite of that observed in pyrochlores. These results provide new insight into the origins of amorphization resistance in complex oxides beyond fluorite derivatives.

Development and Validation of a Computational Model for Androgen Receptor Activity

PubMed Central

2016-01-01

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity. PMID:27933809

Pharmaceuticals in water, fish and osprey nestlings in Delaware River and Bay

USGS Publications Warehouse

Bean, Thomas G.; Rattner, Barnett A.; Lazarus, Rebecca S.; Day, Daniel D.; Burket, S. Rebekah; Brooks, Bryan W.; Haddad, Samuel P.; Bowerman, William W.

2018-01-01

Exposure of wildlife to Active Pharmaceutical Ingredients (APIs) is likely to occur but studies of risk are limited. One exposure pathway that has received attention is trophic transfer of APIs in a water-fish-osprey food chain. Samples of water, fish plasma and osprey plasma were collected from Delaware River and Bay, and analyzed for 21 APIs. Only 2 of 21 analytes exceeded method detection limits in osprey plasma (acetaminophen and diclofenac) with plasma levels typically 2–3 orders of magnitude below human therapeutic concentrations (HTC). We built upon a screening level model used to predict osprey exposure to APIs in Chesapeake Bay and evaluated whether exposure levels could have been predicted in Delaware Bay had we just measured concentrations in water or fish. Use of surface water and BCFs did not predict API concentrations in fish well, likely due to fish movement patterns, and partitioning and bioaccumulation uncertainties associated with these ionizable chemicals. Input of highest measured API concentration in fish plasma combined with pharmacokinetic data accurately predicted that diclofenac and acetaminophen would be the APIs most likely detected in osprey plasma. For the majority of APIs modeled, levels were not predicted to exceed 1 ng/mL or method detection limits in osprey plasma. Based on the target analytes examined, there is little evidence that APIs represent a significant risk to ospreys nesting in Delaware Bay. If an API is present in fish orders of magnitude below HTC, sampling of fish-eating birds is unlikely to be necessary. However, several human pharmaceuticals accumulated in fish plasma within a recommended safety factor for HTC. It is now important to expand the scope of diet-based API exposure modeling to include alternative exposure pathways (e.g., uptake from landfills, dumps and wastewater treatment plants) and geographic locations (developing countries) where API contamination of the environment may represent greater risk.

The Dual Role of Media Internalization in Adolescent Sexual Behavior.

PubMed

Rousseau, Ann; Beyens, Ine; Eggermont, Steven; Vandenbosch, Laura

2017-08-01

Sexualizing media content is prevalent in various media types. Sexualizing media messages and portrayals emphasize unattainable body and appearance ideals as the primary components of sexual desirability. The internalization of these ideals is positively related to self-objectification and sexual body consciousness. In turn, self-objectification and sexual body consciousness affect adolescents' sexual behavior, albeit in opposing directions. While objectifying self-perceptions are linked to higher levels of sexual behavior, body consciousness during physical intimacy is linked to lower levels of sexual behavior. Based on this knowledge, the present three-wave panel study of 824 Belgian, predominant heterosexual adolescents (M age  = 15.33; SD = 1.45) proposes a dual-pathway model that investigates two different pathways through which the internalization of media ideals may impact adolescents' sexual behavior. An inhibitory pathway links media internalization to lower levels of sexual behavior through sexual body consciousness, and a supportive pathway links media internalization to higher levels of sexual behavior through self-objectification. Structural equation analyses supported the proposed dual-pathway, showing that the impact of media internalization on adolescents' sexual behavior proceeds through an inhibitory pathway and a supportive pathway. Regarding the supportive pathway, media internalization (W1) positively predicted sexual behavior (W3), through valuing appearance over competence (W2). Regarding the inhibitory pathway, media internalization (W1) positively predicted body surveillance, which, in turn, positively predicted sexual body consciousness (all W2). Sexual body consciousness (W2) is negatively related to sexual behavior (W3). From a sexual developmental perspective, these findings emphasize the importance of guiding adolescents in interpreting and processing sexualizing media messages.

Prediction of recovery of motor function after stroke.

PubMed

Stinear, Cathy

2010-12-01

Stroke is a leading cause of disability. The ability to live independently after stroke depends largely on the reduction of motor impairment and the recovery of motor function. Accurate prediction of motor recovery assists rehabilitation planning and supports realistic goal setting by clinicians and patients. Initial impairment is negatively related to degree of recovery, but inter-individual variability makes accurate prediction difficult. Neuroimaging and neurophysiological assessments can be used to measure the extent of stroke damage to the motor system and predict subsequent recovery of function, but these techniques are not yet used routinely. The use of motor impairment scores and neuroimaging has been refined by two recent studies in which these investigations were used at multiple time points early after stroke. Voluntary finger extension and shoulder abduction within 5 days of stroke predicted subsequent recovery of upper-limb function. Diffusion-weighted imaging within 7 days detected the effects of stroke on caudal motor pathways and was predictive of lasting motor impairment. Thus, investigations done soon after stroke had good prognostic value. The potential prognostic value of cortical activation and neural plasticity has been explored for the first time by two recent studies. Functional MRI detected a pattern of cortical activation at the acute stage that was related to subsequent reduction in motor impairment. Transcranial magnetic stimulation enabled measurement of neural plasticity in the primary motor cortex, which was related to subsequent disability. These studies open interesting new lines of enquiry. WHERE NEXT?: The accuracy of prediction might be increased by taking into account the motor system's capacity for functional reorganisation in response to therapy, in addition to the extent of stroke-related damage. Improved prognostic accuracy could also be gained by combining simple tests of motor impairment with neuroimaging, genotyping, and neurophysiological assessment of neural plasticity. The development of algorithms to guide the sequential combinations of these assessments could also further increase accuracy, in addition to improving rehabilitation planning and outcomes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Utility of miR‑133a‑3p as a diagnostic indicator for hepatocellular carcinoma: An investigation combined with GEO, TCGA, meta‑analysis and bioinformatics.

PubMed

Liang, Hai-Wei; Yang, Xia; Wen, Dong-Yue; Gao, Li; Zhang, Xiang-Yu; Ye, Zhi-Hua; Luo, Jie; Li, Zu-Yun; He, Yun; Pang, Yu-Yan; Chen, Gang

2018-01-01

Increasing evidence has demonstrated that microRNA (miR)‑133a‑3p is an important regulator of hepatocellular carcinoma (HCC). In the present study, the diagnostic role of miR‑133a‑3p in HCC, and the potential functional pathways, were both explored based on publicly available data. Eligible microarray datasets were collected from NCBI Gene Expression Omnibus (GEO) database and ArrayExpress database. The data related to HCC and matched adjacent normal tissues were also downloaded from The Cancer Genome Atlas (TCGA). Published studies reporting the association between miR‑133a‑3p expression and HCC were reviewed from multiple databases. By combining the data derived from three sources (GEO, TCGA and published studies), the authors analyzed the comprehensive relationship between miR‑133a‑3p expression and clinicopathological features of HCC. Eventually, putative targets of miR‑133a‑3p in HCC were selected for further bioinformatics prediction. A total of eight published microarray datasets were gathered, and the pooled results demonstrated that the expression of miR‑133a‑3p in the tumor group was lower than that in normal groups [standardized mean difference (SMD)=‑0.54; 95% confidence interval (CI), ‑0.74 to ‑0.35; P<0.001]. Consistently, the level of miR‑133a‑1 in HCC was reduced markedly compared to normal tissues (P<0.001) based on TCGA data, and the AUC value of low miR‑133a‑1 expression for HCC diagnosis was 0.670 (P<0.001). Furthermore, the combined SMD of all datasets (GEO, TCGA and literature) suggested that significant difference was observed between the HCC group and the normal control group, and lower miR‑133a‑3p expression in HCC group was noted (SMD=‑0.69; 95% CI, ‑1.10 to ‑0.29; P=0.001). In addition, the authors discovered five key genes of the calcium signaling pathway (NOS1, ADRA1A, ADRA1B, ADRA1D and TBXA2R) that may probably be targeted by miR‑133a‑3p in HCC. The study reveals that miR‑133a‑3p may function as a tumor suppressor in HCC. The prospective novel pathways and key genes of miR‑133a‑3p could offer potential biomarkers for HCC; however, the predictions require further confirmation.

Experimental Determination and Prediction of the Fitness Effects of Random Point Mutations in the Biosynthetic Enzyme HisA

PubMed Central

Lundin, Erik; Tang, Po-Cheng; Guy, Lionel; Näsvall, Joakim; Andersson, Dan I

2018-01-01

Abstract The distribution of fitness effects of mutations is a factor of fundamental importance in evolutionary biology. We determined the distribution of fitness effects of 510 mutants that each carried between 1 and 10 mutations (synonymous and nonsynonymous) in the hisA gene, encoding an essential enzyme in the l-histidine biosynthesis pathway of Salmonella enterica. For the full set of mutants, the distribution was bimodal with many apparently neutral mutations and many lethal mutations. For a subset of 81 single, nonsynonymous mutants most mutations appeared neutral at high expression levels, whereas at low expression levels only a few mutations were neutral. Furthermore, we examined how the magnitude of the observed fitness effects was correlated to several measures of biophysical properties and phylogenetic conservation.We conclude that for HisA: (i) The effect of mutations can be masked by high expression levels, such that mutations that are deleterious to the function of the protein can still be neutral with regard to organism fitness if the protein is expressed at a sufficiently high level; (ii) the shape of the fitness distribution is dependent on the extent to which the protein is rate-limiting for growth; (iii) negative epistatic interactions, on an average, amplified the combined effect of nonsynonymous mutations; and (iv) no single sequence-based predictor could confidently predict the fitness effects of mutations in HisA, but a combination of multiple predictors could predict the effect with a SD of 0.04 resulting in 80% of the mutations predicted within 12% of their observed selection coefficients. PMID:29294020

Brain mediators of predictive cue effects on perceived pain

PubMed Central

Atlas, Lauren Y.; Bolger, Niall; Lindquist, Martin A.; Wager, Tor D.

2010-01-01

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for low or high painful thermal stimulation, and we assessed how cues influenced human subjects’ pain reports and BOLD fMRI responses to matched levels of noxious heat. We used multi-level mediation analysis to identify brain regions that 1) are modulated by predictive cues, 2) predict trial-to-trial variations in pain reports, and 3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pre-task expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing. PMID:20881115

A parieto-medial temporal pathway for the strategic control over working memory biases in human visual attention.

PubMed

Soto, David; Greene, Ciara M; Kiyonaga, Anastasia; Rosenthal, Clive R; Egner, Tobias

2012-12-05

The contents of working memory (WM) can both aid and disrupt the goal-directed allocation of visual attention. WM benefits attention when its contents overlap with goal-relevant stimulus features, but WM leads attention astray when its contents match features of currently irrelevant stimuli. Recent behavioral data have documented that WM biases of attention may be subject to strategic cognitive control processes whereby subjects are able to either enhance or inhibit the influence of WM contents on attention. However, the neural mechanisms supporting cognitive control over WM biases on attention are presently unknown. Here, we characterize these mechanisms by combining human functional magnetic resonance imaging with a task that independently manipulates the relationship between WM cues and attention targets during visual search (with WM contents matching either search targets or distracters), as well as the predictability of this relationship (100 vs 50% predictability) to assess participants' ability to strategically enhance or inhibit WM biases on attention when WM contents reliably matched targets or distracter stimuli, respectively. We show that cues signaling predictable (> unpredictable) WM-attention relations reliably enhanced search performance, and that this strategic modulation of the interplay between WM contents and visual attention was mediated by a neuroanatomical network involving the posterior parietal cortex, the posterior cingulate, and medial temporal lobe structures, with responses in the hippocampus proper correlating with behavioral measures of strategic control of WM biases. Thus, we delineate a novel parieto-medial temporal pathway implementing cognitive control over WM biases to optimize goal-directed selection.

A Global Genomic and Genetic Strategy to Identify, Validate and Use Gene Signatures of Xenobiotic-Responsive Transcription Factors in Prediction of Pathway Activation in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors. Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening as well as their involvement in disease states. ...

Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures

EPA Science Inventory

Toxicity pathways have been defined as normal cellular pathways that, when sufficiently perturbed as a consequence of chemical exposure, lead to an adverse outcome. If an exposure alters one or more normal biological pathways to an extent that leads to an adverse toxicity outcome...

Measured glomerular filtration rate does not improve prediction of mortality by cystatin C and creatinine.

PubMed

Sundin, Per-Ola; Sjöström, Per; Jones, Ian; Olsson, Lovisa A; Udumyan, Ruzan; Grubb, Anders; Lindström, Veronica; Montgomery, Scott

2017-04-01

Cystatin C may add explanatory power for associations with mortality in combination with other filtration markers, possibly indicating pathways other than glomerular filtration rate (GFR). However, this has not been firmly established since interpretation of associations independent of measured GFR (mGFR) is limited by potential multicollinearity between markers of GFR. The primary aim of this study was to assess associations between cystatin C and mortality, independent of mGFR. A secondary aim was to evaluate the utility of combining cystatin C and creatinine to predict mortality risk. Cox regression was used to assess the associations of cystatin C and creatinine with mortality in 1157 individuals referred for assessment of plasma clearance of iohexol. Since cystatin C and creatinine are inversely related to mGFR, cystatin C - 1 and creatinine - 1 were used. After adjustment for mGFR, lower cystatin C - 1 (higher cystatin C concentration) and higher creatinine - 1 (lower creatinine concentration) were independently associated with increased mortality. When nested models were compared, avoiding the potential influence of multicollinearity, the independence of the associations was supported. Among models combining the markers of GFR, adjusted for demographic factors and comorbidity, cystatin C - 1 and creatinine - 1 combined explained the largest proportion of variance in associations with mortality risk ( R 2  = 0.61). Addition of mGFR did not improve the model. Our results suggest that both creatinine and cystatin C have independent associations with mortality not explained entirely by mGFR and that mGFR does not offer a more precise mortality risk assessment than these endogenous filtration markers combined. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Neural Elements for Predictive Coding.

PubMed

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron's function by its individual 'connectome,' combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

Neural Elements for Predictive Coding

PubMed Central

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons). PMID:27917138

Human Health Risk Assessment Calculator. In: SMARTe20ll, EPA/600/C-10/007

EPA Science Inventory

This calculator is aimed at supporting a human health risk assessment. Risk scenarios can be built by combining various health effects, exposure pathways, exposure parameters, and analytes. Scenario risk are calculated for each exposure pathway and analyte combination. The out...

Logic programming to predict cell fate patterns and retrodict genotypes in organogenesis.

PubMed

Hall, Benjamin A; Jackson, Ethan; Hajnal, Alex; Fisher, Jasmin

2014-09-06

Caenorhabditis elegans vulval development is a paradigm system for understanding cell differentiation in the process of organogenesis. Through temporal and spatial controls, the fate pattern of six cells is determined by the competition of the LET-23 and the Notch signalling pathways. Modelling cell fate determination in vulval development using state-based models, coupled with formal analysis techniques, has been established as a powerful approach in predicting the outcome of combinations of mutations. However, computing the outcomes of complex and highly concurrent models can become prohibitive. Here, we show how logic programs derived from state machines describing the differentiation of C. elegans vulval precursor cells can increase the speed of prediction by four orders of magnitude relative to previous approaches. Moreover, this increase in speed allows us to infer, or 'retrodict', compatible genomes from cell fate patterns. We exploit this technique to predict highly variable cell fate patterns resulting from dig-1 reduced-function mutations and let-23 mosaics. In addition to the new insights offered, we propose our technique as a platform for aiding the design and analysis of experimental data. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Mutant JAK3 phosphoproteomic profiling predicts synergism between JAK3 inhibitors and MEK/BCL2 inhibitors for the treatment of T-cell acute lymphoblastic leukemia

PubMed Central

Degryse, S; de Bock, C E; Demeyer, S; Govaerts, I; Bornschein, S; Verbeke, D; Jacobs, K; Binos, S; Skerrett-Byrne, D A; Murray, H C; Verrills, N M; Van Vlierberghe, P; Cools, J; Dun, M D

2018-01-01

Mutations in the interleukin-7 receptor (IL7R) or the Janus kinase 3 (JAK3) kinase occur frequently in T-cell acute lymphoblastic leukemia (T-ALL) and both are able to drive cellular transformation and the development of T-ALL in mouse models. However, the signal transduction pathways downstream of JAK3 mutations remain poorly characterized. Here we describe the phosphoproteome downstream of the JAK3(L857Q)/(M511I) activating mutations in transformed Ba/F3 lymphocyte cells. Signaling pathways regulated by JAK3 mutants were assessed following acute inhibition of JAK1/JAK3 using the JAK kinase inhibitors ruxolitinib or tofacitinib. Comprehensive network interrogation using the phosphoproteomic signatures identified significant changes in pathways regulating cell cycle, translation initiation, mitogen-activated protein kinase and phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/AKT signaling, RNA metabolism, as well as epigenetic and apoptotic processes. Key regulatory proteins within pathways that showed altered phosphorylation following JAK inhibition were targeted using selumetinib and trametinib (MEK), buparlisib (PI3K) and ABT-199 (BCL2), and found to be synergistic in combination with JAK kinase inhibitors in primary T-ALL samples harboring JAK3 mutations. These data provide the first detailed molecular characterization of the downstream signaling pathways regulated by JAK3 mutations and provide further understanding into the oncogenic processes regulated by constitutive kinase activation aiding in the development of improved combinatorial treatment regimens. PMID:28852199

Design and analysis of synthetic carbon fixation pathways

PubMed Central

Bar-Even, Arren; Noor, Elad; Lewis, Nathan E.; Milo, Ron

2010-01-01

Carbon fixation is the process by which CO2 is incorporated into organic compounds. In modern agriculture in which water, light, and nutrients can be abundant, carbon fixation could become a significant growth-limiting factor. Hence, increasing the fixation rate is of major importance in the road toward sustainability in food and energy production. There have been recent attempts to improve the rate and specificity of Rubisco, the carboxylating enzyme operating in the Calvin–Benson cycle; however, they have achieved only limited success. Nature employs several alternative carbon fixation pathways, which prompted us to ask whether more efficient novel synthetic cycles could be devised. Using the entire repertoire of approximately 5,000 metabolic enzymes known to occur in nature, we computationally identified alternative carbon fixation pathways that combine existing metabolic building blocks from various organisms. We compared the natural and synthetic pathways based on physicochemical criteria that include kinetics, energetics, and topology. Our study suggests that some of the proposed synthetic pathways could have significant quantitative advantages over their natural counterparts, such as the overall kinetic rate. One such cycle, which is predicted to be two to three times faster than the Calvin–Benson cycle, employs the most effective carboxylating enzyme, phosphoenolpyruvate carboxylase, using the core of the naturally evolved C4 cycle. Although implementing such alternative cycles presents daunting challenges related to expression levels, activity, stability, localization, and regulation, we believe our findings suggest exciting avenues of exploration in the grand challenge of enhancing food and renewable fuel production via metabolic engineering and synthetic biology. PMID:20410460

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

PubMed

Lawless, Nathan; Reinhardt, Timothy A; Bryan, Kenneth; Baker, Mike; Pesch, Bruce; Zimmerman, Duane; Zuelke, Kurt; Sonstegard, Tad; O'Farrelly, Cliona; Lippolis, John D; Lynn, David J

2014-01-27

Bovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per year. Because disease susceptibility is a multifactorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach using next-generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time points (0, 12, 24, 36 and 48 hr) in milk and blood FACS-isolated CD14(+) monocytes from animals infected in vivo with Streptococcus uberis. More than 3700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Upregulated genes were significantly enriched for inflammatory pathways, whereas downregulated genes were enriched for nonglycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes upregulated in blood-isolated monocytes (BIMs) showed a significant association with interferon and chemokine signaling. Furthermore, 26 miRNAs were DE in MIMs and three were DE in BIMs. Pathway analysis revealed that predicted targets of downregulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E-8), particularly TLR signaling, whereas upregulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways. Copyright © 2014 Lawless et al.

Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

PubMed

Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

2015-01-01

Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis.

PubMed

Kraus, William E; Muoio, Deborah M; Stevens, Robert; Craig, Damian; Bain, James R; Grass, Elizabeth; Haynes, Carol; Kwee, Lydia; Qin, Xuejun; Slentz, Dorothy H; Krupp, Deidre; Muehlbauer, Michael; Hauser, Elizabeth R; Gregory, Simon G; Newgard, Christopher B; Shah, Svati H

2015-11-01

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6-2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.

Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis

PubMed Central

Kraus, William E.; Muoio, Deborah M.; Stevens, Robert; Craig, Damian; Bain, James R.; Grass, Elizabeth; Haynes, Carol; Kwee, Lydia; Qin, Xuejun; Slentz, Dorothy H.; Krupp, Deidre; Muehlbauer, Michael; Hauser, Elizabeth R.; Gregory, Simon G.; Newgard, Christopher B.; Shah, Svati H.

2015-01-01

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6–2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk. PMID:26540294

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

PubMed Central

Rajagopala, S. V.; Blazie, S. M.; Parrish, J. R.; Khuri, S.; Finley, R. L.

2017-01-01

ABSTRACT The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. PMID:28744484

Single Nucleotide Polymorphisms of Stemness Genes Predicted to Regulate RNA Splicing, microRNA and Oncogenic Signaling are Associated with Prostate Cancer Survival.

PubMed

Freedman, Jennifer A; Wang, Yanru; Li, Xuechan; Liu, Hongliang; Moorman, Patricia G; George, Daniel J; Lee, Norman H; Hyslop, Terry; Wei, Qingyi; Patierno, Steven R

2018-05-03

Prostate cancer is a clinically and molecularly heterogeneous disease, with variation in outcomes only partially predicted by grade and stage. Additional tools to distinguish indolent from aggressive disease are needed. Phenotypic characteristics of stemness correlate with poor cancer prognosis. Given this correlation, we identified single nucleotide polymorphisms (SNPs) of stemness-related genes and examined their associations with prostate cancer survival. SNPs within stemness-related genes were analyzed for association with overall survival of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Significant SNPs predicted to be functional were selected for linkage disequilibrium analysis and combined and stratified analyses. Identified SNPs were evaluated for association with gene expression. SNPs of CD44 (rs9666607), ABCC1 (rs35605 and rs212091) and GDF15 (rs1058587) were associated with prostate cancer survival and predicted to be functional. A role for rs9666607 of CD44 and rs35605 of ABCC1 in RNA splicing regulation, rs212091 of ABCC1 in miRNA binding site activity and rs1058587 of GDF15 in causing an amino acid change was predicted. These SNPs represent potential novel prognostic markers for overall survival of prostate cancer and support a contribution of the stemness pathway to prostate cancer patient outcome.

Growth differentiation factor-15 predicts mortality and morbidity after cardiac resynchronization therapy.

PubMed

Foley, Paul W X; Stegemann, Berthold; Ng, Kelvin; Ramachandran, Sud; Proudler, Anthony; Frenneaux, Michael P; Ng, Leong L; Leyva, Francisco

2009-11-01

The aim of this study was to determine whether growth differentiation factor-15 (GDF-15) predicts mortality and morbidity after cardiac resynchronization therapy (CRT). Growth differentiation factor-15, a transforming growth factor-beta-related cytokine which is up-regulated in cardiomyocytes via multiple stress pathways, predicts mortality in patients with heart failure treated pharmacologically. Growth differentiation factor-15 was measured before and 360 days (median) after implantation in 158 patients with heart failure [age 68 +/- 11 years (mean +/- SD), left ventricular ejection fraction (LVEF) 23.1 +/- 9.8%, New York Class Association (NYHA) class III (n = 117) or IV (n = 41), and QRS 153.9 +/- 28.2 ms] undergoing CRT and followed up for a maximum of 5.4 years for events. In a stepwise Cox proportional hazards model with bootstrapping, adopting log GDF-15, log NT pro-BNP, LVEF, and NYHA class as independent variables, only log GDF-15 [hazard ratio (HR), 3.76; P = 0.0049] and log NT pro-BNP (HR, 2.12; P = 0.0171) remained in the final model. In the latter, the bias-corrected slope was 0.85, the optimism (O) was -0.06, and the c-statistic was 0.74, indicating excellent internal validity. In univariate analyses, log GDF-15 [HR, 5.31; 95% confidence interval (CI), 2.31-11.9; likelihood ratio (LR) chi(2) = 14.6; P < 0.0001], NT pro-BNP (HR, 2.79; 95% CI, 1.55-5.26; LR chi(2) = 10.4; P = 0.0004), and the combination of both biomarkers (HR, 7.03; 95% CI, 2.91-17.5; LR chi(2) = 19.1; P < 0.0001) emerged as significant predictors. The biomarker combination was associated with the highest LR chi(2) for all endpoints. Pre-implant GDF-15 is a strong predictor of mortality and morbidity after CRT, independent of NT pro-BNP. The predictive value of these analytes is enhanced by combined measurement.

Identification and integrated analysis of differentially expressed lncRNAs and circRNAs reveal the potential ceRNA networks during PDLSC osteogenic differentiation.

PubMed

Gu, Xiuge; Li, Mengying; Jin, Ye; Liu, Dongxu; Wei, Fulan

2017-12-02

Researchers have been exploring the molecular mechanisms underlying the control of periodontal ligament stem cell (PDLSC) osteogenic differentiation. Recently, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) were shown to function as competitive endogenous RNAs (ceRNAs) to regulate the effect of microRNAs (miRNAs) on their target genes during cell differentiation. However, comprehensive identification and integrated analysis of lncRNAs and circRNAs acting as ceRNAs during PDLSC osteogenic differentiation have not been performed. PDLSCs were derived from healthy human periodontal ligament and cultured separately with osteogenic induction and normal media for 7 days. Cultured PDLSCs were positive for STRO-1 and CD146 and negative for CD31 and CD45. Osteo-induced PDLSCs showed increased ALP (alkaline phosphatase) activity and up-regulated expression levels of the osteogenesis-related markers ALP, Runt-related transcription factor 2 and osteocalcin. Then, a total of 960 lncRNAs and 1456 circRNAs were found to be differentially expressed by RNA sequencing. The expression profiles of eight lncRNAs and eight circRNAs were measured with quantitative real-time polymerase chain reaction and were shown to agree with the RNA-seq results. Furthermore, the potential functions of lncRNAs and circRNAs as ceRNAs were predicted based on miRanda and were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. In total, 147 lncRNAs and 1382 circRNAs were predicted to combine with 148 common miRNAs and compete for miRNA binding sites with 744 messenger RNAs. These mRNAs were predicted to significantly participate in osteoblast differentiation, the MAPK pathway, the Wnt pathway and the signaling pathways regulating pluripotency of stem cells. Among them, lncRNAs coded as TCONS_00212979 and TCONS_00212984, as well as circRNA BANP and circRNA ITCH, might interact with miRNA34a and miRNA146a to regulate PDLSC osteogenic differentiation via the MAPK pathway. This study comprehensively identified lncRNAs/circRNAs and first integrated their potential ceRNA function during PDLSC osteogenic differentiation. These findings suggest that specific lncRNAs and circRNAs might function as ceRNAs to promote PDLSC osteogenic differentiation and periodontal regeneration.

Chemoprevention of Head and Neck Cancer by Simultaneous Blocking of Epidermal Growth Factor Receptor and Cyclooxygenase-2 Signaling Pathways: Preclinical and Clinical Studies

PubMed Central

Shin, Dong M.; Zhang, Hongzheng; Saba, Nabil; Chen, Amy; Nannapaneni, Sreenivas; Amin, A.R.M. Ruhul; Müller, Susan; Lewis, Melinda; Sica, Gabriel; Kono, Scott; Brandes, Johann C.; Grist, William; Moreno-Williams, Rachel; Beitler, Jonathan J.; Thomas, Sufi M.; Chen, Zhengjia; Shin, Hyung Ju C.; Grandis, Jennifer R.; Khuri, Fadlo R.; Chen, Zhuo Georgia

2013-01-01

Purpose We investigated the efficacy and underlying molecular mechanism of a novel chemopreventive strategy combining epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with cyclooxygenase-2 inhibitor (COX-2I). Experimental Design We examined the inhibition of tumor cell growth by combined EGFR-TKI (erlotinib) and COX-2I (celecoxib) treatment using head and neck cancer (HNC) cell lines and a preventive xenograft model. We studied the antiangiogenic activity of these agents and examined the affected signaling pathways by immunoblotting analysis in tumor cell lysates and immunohistochemistry (IHC) and enzyme immunoassay (EIA) analyses on the mouse xenograft tissues and blood, respectively. Biomarkers in these signaling pathways were studied by IHC, EIA, and an antibody array analysis in samples collected from participants in a phase I chemoprevention trial of erlotinib and celecoxib. Results The combined treatment inhibited HNC cell growth significantly more potently than either single agent alone in cell line and xenograft models, and resulted in greater inhibition of cell cycle progression at G1 phase than either single drug. The combined treatment modulated the EGFR and mTOR signaling pathways. A phase I chemoprevention trial of combined erlotinib and celecoxib revealed an overall pathologic response rate of 71% at time of data analysis. Analysis of tissue samples from participants consistently showed downregulation of EGFR, pERK and pS6 levels after treatment, which correlated with clinical response. Conclusion Treatment with erlotinib combined with celecoxib offers an effective chemopreventive approach through inhibition of EGFR and mTOR pathways, which may serve as potential biomarkers to monitor the intervention of this combination in the clinic. PMID:23422093

Fine-grained temporal coding of visually-similar categories in the ventral visual pathway and prefrontal cortex

PubMed Central

Xu, Yang; D'Lauro, Christopher; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2013-01-01

Humans are remarkably proficient at categorizing visually-similar objects. To better understand the cortical basis of this categorization process, we used magnetoencephalography (MEG) to record neural activity while participants learned–with feedback–to discriminate two highly-similar, novel visual categories. We hypothesized that although prefrontal regions would mediate early category learning, this role would diminish with increasing category familiarity and that regions within the ventral visual pathway would come to play a more prominent role in encoding category-relevant information as learning progressed. Early in learning we observed some degree of categorical discriminability and predictability in both prefrontal cortex and the ventral visual pathway. Predictability improved significantly above chance in the ventral visual pathway over the course of learning with the left inferior temporal and fusiform gyri showing the greatest improvement in predictability between 150 and 250 ms (M200) during category learning. In contrast, there was no comparable increase in discriminability in prefrontal cortex with the only significant post-learning effect being a decrease in predictability in the inferior frontal gyrus between 250 and 350 ms (M300). Thus, the ventral visual pathway appears to encode learned visual categories over the long term. At the same time these results add to our understanding of the cortical origins of previously reported signature temporal components associated with perceptual learning. PMID:24146656

A mathematical model for regulating monomer composition of the microbially synthesized polyhydroxyalkanoate copolymers.

PubMed

Xu, Jun; Guo, Baohua; Zhang, Zengmin; Wu, Qiong; Zhou, Quan; Chen, Jinchun; Chen, Guoqiang; Li, Guodong

2005-06-30

A mathematical model is proposed for predicting the copolymer composition of the microbially synthesized polyhydroxyalkanoate (PHA) copolymers. Based on the biochemical reactions involved in the precursor formation and polymerization pathways, the model correlates the copolymer composition with the cultivation conditions, the enzyme levels and selectivity, and the metabolic pathways. It suggests the following points: (1) in the case of a sole carbon source, the copolymer composition depends mainly on the topology of the metabolic pathways and the selectivity of both the enzymes involved in the precursor formation and the polymerization route; (2) the copolymer composition can be varied in a wide range via alteration of the flux ratio of different types of monomers channeled from two or more independent and simultaneous pathways; (3) the enzymes which should be over-expressed or inhibited to obtain the desired copolymer composition can be predicted. For example, inhibition of the beta-oxidation pathway will increase the content of the monomer units with longer chain length. To test the model, various experiments were envisaged by varying cultivation time, concentration and chain length of the sole carbon source, and molar ratio of the cosubstrates. The predictions from the model agree well with the experimental results. Therefore, the proposed model will be useful in predicting the PHA copolymer composition under different biochemical reaction conditions. In other words, it can provide a guide for the synthesis of desired PHA copolymers.

Influence of mantle viscosity structure and mineral grain size on fluid migration pathways in the mantle wedge.

NASA Astrophysics Data System (ADS)

Cerpa, N. G.; Wada, I.; Wilson, C. R.; Spiegelman, M. W.

2016-12-01

We develop a 2D numerical porous flow model that incorporates both grain size distribution and matrix compaction to explore the fluid migration (FM) pathways in the mantle wedge. Melt generation for arc volcanism is thought to be triggered by slab-derived fluids that migrate into the hot overlying mantle and reduce its melting temperature. While the narrow location of the arcs relative to the top of the slab ( 100±30 km) is a robust observation, the release of fluids is predicted to occur over a wide range of depth. Reconciling such observations and predictions remains a challenge for the geodynamic community. Fluid transport by porous flow depends on the permeability of the medium which in turn depends on fluid fraction and mineral grain size. The grain size distribution in the mantle wedge predicted by laboratory derived laws was found to be a possible mechanism to focusing of fluids beneath the arcs [Wada and Behn, 2015]. The viscous resistance of the matrix to the volumetric strain generates compaction pressure that affects fluid flow and can also focus fluids towards the arc [Wilson et al, 2014]. We thus have developed a 2D one-way coupled Darcy's-Stokes flow model (solid flow independent of fluid flow) for the mantle wedge that combines both effects. For the solid flow calculation, we use a kinematic-dynamic approach where the system is driven by the prescribed slab velocity. The solid rheology accounts for both dislocation and diffusion creep and we calculate the grain size distribution following Wada and Behn [2015]. In our fluid flow model, the permeability of the medium is grain size dependent and the matrix bulk viscosity depends on solid shear viscosity and fluid fraction. The fluid influx from the slab is imposed as a boundary condition at the base of the mantle wedge. We solve the discretized governing equations using the software package TerraFERMA. Applying a range of model parameter values, including slab age, slab dip, subduction rate, and fluid influx, we quantify the combined effects of grain size and compaction on fluid flow paths.

In vivo and in silico determination of essential genes of Campylobacter jejuni.

PubMed

Metris, Aline; Reuter, Mark; Gaskin, Duncan J H; Baranyi, Jozsef; van Vliet, Arnoud H M

2011-11-01

In the United Kingdom, the thermophilic Campylobacter species C. jejuni and C. coli are the most frequent causes of food-borne gastroenteritis in humans. While campylobacteriosis is usually a relatively mild infection, it has a significant public health and economic impact, and possible complications include reactive arthritis and the autoimmune diseases Guillain-Barré syndrome. The rapid developments in "omics" technologies have resulted in the availability of diverse datasets allowing predictions of metabolism and physiology of pathogenic micro-organisms. When combined, these datasets may allow for the identification of potential weaknesses that can be used for development of new antimicrobials to reduce or eliminate C. jejuni and C. coli from the food chain. A metabolic model of C. jejuni was constructed using the annotation of the NCTC 11168 genome sequence, a published model of the related bacterium Helicobacter pylori, and extensive literature mining. Using this model, we have used in silico Flux Balance Analysis (FBA) to determine key metabolic routes that are essential for generating energy and biomass, thus creating a list of genes potentially essential for growth under laboratory conditions. To complement this in silico approach, candidate essential genes have been determined using a whole genome transposon mutagenesis method. FBA and transposon mutagenesis (both this study and a published study) predict a similar number of essential genes (around 200). The analysis of the intersection between the three approaches highlights the shikimate pathway where genes are predicted to be essential by one or more method, and tend to be network hubs, based on a previously published Campylobacter protein-protein interaction network, and could therefore be targets for novel antimicrobial therapy. We have constructed the first curated metabolic model for the food-borne pathogen Campylobacter jejuni and have presented the resulting metabolic insights. We have shown that the combination of in silico and in vivo approaches could point to non-redundant, indispensable genes associated with the well characterised shikimate pathway, and also genes of unknown function specific to C. jejuni, which are all potential novel Campylobacter intervention targets.

MicroRNA-124-3p expression and its prospective functional pathways in hepatocellular carcinoma: A quantitative polymerase chain reaction, gene expression omnibus and bioinformatics study.

PubMed

He, Rong-Quan; Yang, Xia; Liang, Liang; Chen, Gang; Ma, Jie

2018-04-01

The present study aimed to explore the potential clinical significance of microRNA (miR)-124-3p expression in the hepatocarcinogenesis and development of hepatocellular carcinoma (HCC), as well as the potential target genes of functional HCC pathways. Reverse transcription-quantitative polymerase chain reaction was performed to evaluate the expression of miR-124-3p in 101 HCC and adjacent non-cancerous tissue samples. Additionally, the association between miR-124-3p expression and clinical parameters was also analyzed. Differentially expressed genes identified following miR-124-3p transfection, the prospective target genes predicted in silico and the key genes of HCC obtained from Natural Language Processing (NLP) were integrated to obtain potential target genes of miR-124-3p in HCC. Relevant signaling pathways were assessed with protein-protein interaction (PPI) networks, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Protein Annotation Through Evolutionary Relationships (PANTHER) pathway enrichment analysis. miR-124-3p expression was significantly reduced in HCC tissues compared with expression in adjacent non-cancerous liver tissues. In HCC, miR-124-3p was demonstrated to be associated with clinical stage. The mean survival time of the low miR-124-3p expression group was reduced compared with that of the high expression group. A total of 132 genes overlapped from differentially expressed genes, miR-124-3p predicted target genes and NLP identified genes. PPI network construction revealed a total of 109 nodes and 386 edges, and 20 key genes were identified. The major enriched terms of three GO categories included regulation of cell proliferation, positive regulation of cellular biosynthetic processes, cell leading edge, cytosol and cell projection, protein kinase activity, transcription activator activity and enzyme binding. KEGG analysis revealed pancreatic cancer, prostate cancer and non-small cell lung cancer as the top three terms. Angiogenesis, the endothelial growth factor receptor signaling pathway and the fibroblast growth factor signaling pathway were identified as the most significant terms in the PANTHER pathway analysis. The present study confirmed that miR-124-3p acts as a tumor suppressor in HCC. miR-124-3p may target multiple genes, exerting its effect spatiotemporally, or in combination with a diverse range of processes in HCC. Functional characterization of miR-124-3p targets will offer novel insight into the molecular changes that occur in HCC progression.

A Synthetic Lethal Screen Identifies DNA Repair Pathways that Sensitize Cancer Cells to Combined ATR Inhibition and Cisplatin Treatments

PubMed Central

Mohni, Kareem N.; Thompson, Petria S.; Luzwick, Jessica W.; Glick, Gloria G.; Pendleton, Christopher S.; Lehmann, Brian D.; Pietenpol, Jennifer A.; Cortez, David

2015-01-01

The DNA damage response kinase ATR may be a useful cancer therapeutic target. ATR inhibition synergizes with loss of ERCC1, ATM, XRCC1 and DNA damaging chemotherapy agents. Clinical trials have begun using ATR inhibitors in combination with cisplatin. Here we report the first synthetic lethality screen with a combination treatment of an ATR inhibitor (ATRi) and cisplatin. Combination treatment with ATRi/cisplatin is synthetically lethal with loss of the TLS polymerase ζ and 53BP1. Other DNA repair pathways including homologous recombination and mismatch repair do not exhibit synthetic lethal interactions with ATRi/cisplatin, even though loss of some of these repair pathways sensitizes cells to cisplatin as a single-agent. We also report that ATRi strongly synergizes with PARP inhibition, even in homologous recombination-proficient backgrounds. Lastly, ATR inhibitors were able to resensitize cisplatin-resistant cell lines to cisplatin. These data provide a comprehensive analysis of DNA repair pathways that exhibit synthetic lethality with ATR inhibitors when combined with cisplatin chemotherapy, and will help guide patient selection strategies as ATR inhibitors progress into the cancer clinic. PMID:25965342

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

PubMed Central

Huang, Ruili; Xia, Menghang; Sakamuru, Srilatha; Zhao, Jinghua; Shahane, Sampada A.; Attene-Ramos, Matias; Zhao, Tongan; Austin, Christopher P.; Simeonov, Anton

2016-01-01

Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing. PMID:26811972

Longitudinal examination of peer and partner influences on gender-specific pathways from child abuse to adult crime.

PubMed

Lee, Jungeun Olivia; Herrenkohl, Todd I; Jung, Hyunzee; Skinner, Martie L; Klika, J Bart

2015-09-01

Research provides increasing evidence of the association of child abuse with adult antisocial behavior. However, less is known about the developmental pathways that underlie this association. Building on the life course model of antisocial behavior, the present study examined possible developmental pathways linking various forms of child abuse (physical, emotional, sexual) to adult antisocial behavior. These pathways include child and adolescent antisocial behavior, as well as adulthood measures of partner risk taking, warmth, and antisocial peer influences. Data are from the Lehigh Longitudinal Study, a prospective longitudinal study examining long-term developmental outcomes subsequent to child maltreatment. Participant families in the Lehigh Longitudinal Study were followed from preschool age into adulthood. Analyses of gender differences addressed the consistency of path coefficients across genders. Results for 297 adult participants followed from early childhood showed that, for both genders, physical and emotional child abuse predicted adult crime indirectly through child and adolescent antisocial behavior, as well as adult partner and antisocial peer influences. However, for females, having an antisocial partner predicted an affiliation with antisocial peers, and that in turn predicted adult crime. For males, having an antisocial partner was associated with less partner warmth, which in turn predicted an affiliation with antisocial peers, itself a proximal predictor of adult crime. Sexual abuse also predicted adolescent antisocial behavior, but only for males, supporting what some have called "a delayed-onset pathway" for females, whereby the exposure to early risks produce much later developmental outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.

2011-01-01

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212

Experimental Approaches to Systematic Discovery and Development of Reproductive Adverse Outcome Pathways in Fish

EPA Science Inventory

Adverse outcome pathways (AOPs) are conceptual frameworks that portray causal and predictive linkages between key events at multiple scales of biological organization that connect molecular initiating events and early cellular perturbations (e.g., initiation of toxicity pathways)...

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Mosaic and Concerted Evolution in the Visual System of Birds

PubMed Central

Gutiérrez-Ibáñez, Cristián; Iwaniuk, Andrew N.; Moore, Bret A.; Fernández-Juricic, Esteban; Corfield, Jeremy R.; Krilow, Justin M.; Kolominsky, Jeffrey; Wylie, Douglas R.

2014-01-01

Two main models have been proposed to explain how the relative size of neural structures varies through evolution. In the mosaic evolution model, individual brain structures vary in size independently of each other, whereas in the concerted evolution model developmental constraints result in different parts of the brain varying in size in a coordinated manner. Several studies have shown variation of the relative size of individual nuclei in the vertebrate brain, but it is currently not known if nuclei belonging to the same functional pathway vary independently of each other or in a concerted manner. The visual system of birds offers an ideal opportunity to specifically test which of the two models apply to an entire sensory pathway. Here, we examine the relative size of 9 different visual nuclei across 98 species of birds. This includes data on interspecific variation in the cytoarchitecture and relative size of the isthmal nuclei, which has not been previously reported. We also use a combination of statistical analyses, phylogenetically corrected principal component analysis and evolutionary rates of change on the absolute and relative size of the nine nuclei, to test if visual nuclei evolved in a concerted or mosaic manner. Our results strongly indicate a combination of mosaic and concerted evolution (in the relative size of nine nuclei) within the avian visual system. Specifically, the relative size of the isthmal nuclei and parts of the tectofugal pathway covary across species in a concerted fashion, whereas the relative volume of the other visual nuclei measured vary independently of one another, such as that predicted by the mosaic model. Our results suggest the covariation of different neural structures depends not only on the functional connectivity of each nucleus, but also on the diversity of afferents and efferents of each nucleus. PMID:24621573

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties.

PubMed

Batke, Monika; Gütlein, Martin; Partosch, Falko; Gundert-Remy, Ursula; Helma, Christoph; Kramer, Stefan; Maunz, Andreas; Seeland, Madeleine; Bitsch, Annette

2016-01-01

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

CMPF: class-switching minimized pathfinding in metabolic networks.

PubMed

Lim, Kevin; Wong, Limsoon

2012-01-01

The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell. We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings. We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.

Virtual Institute of Microbial Stress and Survival: Deduction of Stress Response Pathways in Metal and Radionuclide Reducing Microorganisms

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2004-04-17

The projects application goals are to: (1) To understand bacterial stress-response to the unique stressors in metal/radionuclide contamination sites; (2) To turn this understanding into a quantitative, data-driven model for exploring policies for natural and biostimulatory bioremediation; (3) To implement proposed policies in the field and compare results to model predictions; and (4) Close the experimental/computation cycle by using discrepancies between models and predictions to drive new measurements and construction of new models. The projects science goals are to: (1) Compare physiological and molecular response of three target microorganisms to environmental perturbation; (2) Deduce the underlying regulatory pathways that controlmore » these responses through analysis of phenotype, functional genomic, and molecular interaction data; (3) Use differences in the cellular responses among the target organisms to understand niche specific adaptations of the stress and metal reduction pathways; (4) From this analysis derive an understanding of the mechanisms of pathway evolution in the environment; and (5) Ultimately, derive dynamical models for the control of these pathways to predict how natural stimulation can optimize growth and metal reduction efficiency at field sites.« less

Integrating publicly-available data to generate computationally-predicted adverse outcome pathways for hepatic steatosis

EPA Science Inventory

The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity ...

Advancing adverse outcome pathways for integrated toxicology and regulatory applications

EPA Science Inventory

Recent regulatory efforts in many countries have focused on a toxicological pathway-based vision for human health assessments relying on in vitro systems and predictive models to generate the toxicological data needed to evaluate chemical hazard. A pathway-based vision is equally...

Symmetries of a generic utricular projection: neural connectivity and the distribution of utricular information.

PubMed

Chartrand, Thomas; McCollum, Gin; Hanes, Douglas A; Boyle, Richard D

2016-02-01

Sensory contribution to perception and action depends on both sensory receptors and the organization of pathways (or projections) reaching the central nervous system. Unlike the semicircular canals that are divided into three discrete sensitivity directions, the utricle has a relatively complicated anatomical structure, including sensitivity directions over essentially 360° of a curved, two-dimensional disk. The utricle is not flat, and we do not assume it to be. Directional sensitivity of individual utricular afferents decreases in a cosine-like fashion from peak excitation for movement in one direction to a null or near null response for a movement in an orthogonal direction. Directional sensitivity varies slowly between neighboring cells except within the striolar region that separates the medial from the lateral zone, where the directional selectivity abruptly reverses along the reversal line. Utricular primary afferent pathways reach the vestibular nuclei and cerebellum and, in many cases, converge on target cells with semicircular canal primary afferents and afference from other sources. Mathematically, some canal pathways are known to be characterized by symmetry groups related to physical space. These groups structure rotational information and movement. They divide the target neural center into distinct populations according to the innervation patterns they receive. Like canal pathways, utricular pathways combine symmetries from the utricle with those from target neural centers. This study presents a generic set of transformations drawn from the known structure of the utricle and therefore likely to be found in utricular pathways, but not exhaustive of utricular pathway symmetries. This generic set of transformations forms a 32-element group that is a semi-direct product of two simple abelian groups. Subgroups of the group include order-four elements corresponding to discrete rotations. Evaluation of subgroups allows us to functionally identify the spatial implications of otolith and canal symmetries regarding action and perception. Our results are discussed in relation to observed utricular pathways, including those convergent with canal pathways. Oculomotor and other sensorimotor systems are organized according to canal planes. However, the utricle is evolutionarily prior to the canals and may provide a more fundamental spatial framework for canal pathways as well as for movement. The fullest purely otolithic pathway is likely that which reaches the lumbar spine via Deiters' cells in the lateral vestibular nucleus. It will be of great interest to see whether symmetries predicted from the utricle are identified within this pathway.

Neighbouring group processes in the deamination of protonated phenylalanine derivatives.

PubMed

Lioe, Hadi; O'Hair, Richard A J

2005-10-21

The gas-phase fragmentation of protonated phenylalanine and a series of its derivatives (tyrosine, 4-methylphenylalanine, 4-aminophenylalanine, 4-methoxyphenylalanine, 4-tert-butylphenylalanine, 4-fluorophenylalanine, 4-chlorophenylalanine, 4-bromophenylalanine, 4-iodophenylalanine, 4-cyanophenylalanine, 4-nitrophenylalanine, 3-fluorophenylalanine, and 3,4-dichlorophenylalanine) were examined using a combination of low energy CID in a quadrupole ion trap mass spectrometer as well as DFT calculations and RRKM modelling. In particular, the relationship between the electron-donating ability of the substituent and the competitive losses of H2O + CO and NH3 were explored through the application of the Hammett equation. It was found that electron-donating substituents promote the loss of NH3, while electron-withdrawing substituents suppress the loss of NH3 and favour the H2O + CO loss fragmentation channel instead. These observations are consistent with a neighbouring group pathway operating for the loss of NH3. Molecular orbital calculation (at the B3LYP/6-31+G(d,p) level of theory) were also performed for a range of derivatives to compare the relative transition state energy barriers for three competing mechanisms: (i) the combined loss of H2O + CO, which is triggered by an initial intramolecular proton transfer from the ammonium group to hydroxyl OH, followed by the combined loss of H2O and CO to form an immonium ion; (ii) loss of NH3 via an aryl assisted neighbouring group pathway to yield a phenonium ion; (iii) loss of NH3 via a 1,2-hydride migration process, which results in the formation of a benzyl cation. The relative energy barriers for H2O + CO loss remain nearly constant, while that for both NH3 pathways increase as the substituent moves from electron-donating to electron-withdrawing. The relative transition state energy for loss of NH3 via the aryl assisted neighbouring group pathway is always lower than that of the 1,2-hydride migration process. RRKM modelling of the DFT predicted barrier heights suggest that the rate constants for H2O + CO loss are insensitive to the substituent on the ring, while the NH3 loss channels are greatly affected by the substituent. These theoretical results are consistent with the experimental observation of the relative yields of the competing fragmentation channels. Finally, comparisons with published gas phase and condensed phase studies on related systems are made.

Novel and ultra-rare damaging variants in neuropeptide signaling are associated with disordered eating behaviors

PubMed Central

Bahl, Ethan; Hannah, Claire; Hofammann, Dabney; Acevedo, Summer; Cui, Huxing; McAdams, Carrie J.

2017-01-01

Objective Eating disorders develop through a combination of genetic vulnerability and environmental stress, however the genetic basis of this risk is unknown. Methods To understand the genetic basis of this risk, we performed whole exome sequencing on 93 unrelated individuals with eating disorders (38 restricted-eating and 55 binge-eating) to identify novel damaging variants. Candidate genes with an excessive burden of predicted damaging variants were then prioritized based upon an unbiased, data-driven bioinformatic analysis. One top candidate pathway was empirically tested for therapeutic potential in a mouse model of binge-like eating. Results An excessive burden of novel damaging variants was identified in 186 genes in the restricted-eating group and 245 genes in the binge-eating group. This list is significantly enriched (OR = 4.6, p<0.0001) for genes involved in neuropeptide/neurotrophic pathways implicated in appetite regulation, including neurotensin-, glucagon-like peptide 1- and BDNF-signaling. Administration of the glucagon-like peptide 1 receptor agonist exendin-4 significantly reduced food intake in a mouse model of ‘binge-like’ eating. Conclusions These findings implicate ultra-rare and novel damaging variants in neuropeptide/neurotropic factor signaling pathways in the development of eating disorder behaviors and identify glucagon-like peptide 1-receptor agonists as a potential treatment for binge eating. PMID:28846695

Defining a Computational Framework for the Assessment of ...

EPA Pesticide Factsheets

The Adverse Outcome Pathway (AOP) framework describes the effects of environmental stressors across multiple scales of biological organization and function. This includes an evaluation of the potential for each key event to occur across a broad range of species in order to determine the taxonomic applicability of each AOP. Computational tools are needed to facilitate this process. Recently, we developed a tool that uses sequence homology to evaluate the applicability of molecular initiating events across species (Lalone et al., Toxicol. Sci., 2016). To extend our ability to make computational predictions at higher levels of biological organization, we have created the AOPdb. This database links molecular targets identified associated with key events in the AOPwiki to publically available data (e.g. gene-protein, pathway, species orthology, ontology, chemical, disease) including ToxCast assay information. The AOPdb combines different data types in order to characterize the impacts of chemicals to human health and the environment and serves as a decision support tool for case study development in the area of taxonomic applicability. As a proof of concept, the AOPdb allows identification of relevant molecular targets, biological pathways, and chemical and disease associations across species for four AOPs from the AOP-Wiki (https://aopwiki.org): Estrogen receptor antagonism leading to reproductive dysfunction (Aop:30); Aromatase inhibition leading to reproductive d

Estimation of rate constants of PCB dechlorination reactions using an anaerobic dehalogenation model.

PubMed

Karakas, Filiz; Imamoglu, Ipek

2017-02-15

This study aims to estimate anaerobic dechlorination rate constants (k m ) of reactions of individual PCB congeners using data from four laboratory microcosms set up using sediment from Baltimore Harbor. Pathway k m values are estimated by modifying a previously developed model as Anaerobic Dehalogenation Model (ADM) which can be applied to any halogenated hydrophobic organic (HOC). Improvements such as handling multiple dechlorination activities (DAs) and co-elution of congeners, incorporating constraints, using new goodness of fit evaluation led to an increase in accuracy, speed and flexibility of ADM. DAs published in the literature in terms of chlorine substitutions as well as specific microorganisms and their combinations are used for identification of pathways. The best fit explaining the congener pattern changes was found for pathways of Phylotype DEH10, which has the ability to remove doubly flanked chlorines in meta and para positions, para flanked chlorines in meta position. The range of estimated k m values is between 0.0001-0.133d -1 , the median of which is found to be comparable to the few available published biologically confirmed rate constants. Compound specific modelling studies such as that performed by ADM can enable monitoring and prediction of concentration changes as well as toxicity during bioremediation. Copyright © 2016 Elsevier B.V. All rights reserved.

Prevention and diagnosis of invasive fungal disease in high-risk patients within an integrative care pathway.

PubMed

Barnes, Rosemary A; Stocking, Kate; Bowden, Sarah; Poynton, Matthew H; White, P Lewis

2013-09-01

The aim of this study was to assess the clinical utility of enhanced diagnostics on the management of invasive fungal disease in high risk patients within an integrated care pathway and to audit compliance and efficacy of antifungal prophylaxis. A cohort of 549 high risk haematology and stem-cell transplant recipients was followed over a 5 year period. The routine standard of care involved the use of antimould prophylaxis and a neutropenic care pathway utilizing twice weekly antigen and PCR testing. Prophylaxis with itraconazole was poorly tolerated and therapeutic levels could not be maintained. Antigen testing and PCR showed good clinical utility in the management of invasive aspergilosis with high sensitivity (98%) and negative predictive value (99.6%) when both tests were used together, allowing a diagnosis IA to be excluded and obviating the need for empirical antifungal agents. When used serially, multiple positive PCR and antigen test results enabled accurate diagnosis of IA with a specificity of 95% and a positive likelihood ratio of 11. Biomarkers preceded clinical signs in 85% of proven and probable invasive disease. The combination of both tests showed optimum clinical utility for the diagnosis and management of IA in this high risk group. Copyright © 2013 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Resource utilization in lung cancer diagnostic procedures: Current use and budget consequences.

PubMed

Brinkhof, Sander; Groen, Harry J M; Siesling, Sabine S; IJzerman, Maarten J

2017-01-01

The main objective of this study is to determine the current use of lung cancer diagnostic procedures in two large hospitals in the Netherlands, to explore deviations in guideline adherence between the hospitals, and to estimate the budget impact of the diagnostic work-up as well as the over- and underutilization. A state transition model for the diagnostic pathway for lung cancer patients was developed using existing clinical practice guidelines (CPG) combined with a systematic literature. In addition to the CPGs depicting current practice, diagnostic utilization was gathered in two large hospitals representing an academic tertiary care hospital and a large regional teaching hospital for patients, who were selected from the Netherlands cancer registry. The total population consisted of 376 patients with lung cancer. Not in all cases the guideline was followed, for instance in the usage of MR brain with stage III lung cancer patients (n = 70). The state-transition model predicts an average budget impact for the diagnostic pathway per patient estimated of € 2496 in the academic tertiary care hospital and € 2191 in the large regional teaching hospital. The adherence to the CPG's differed between hospitals, which questions the adherence to CPG's in general. Adherence to CPG's could lead to less costs in the diagnostic pathway for lung cancer patients.

Distributions of experimental protein structures on coarse-grained free energy landscapes

PubMed Central

Liu, Jie; Jernigan, Robert L.

2015-01-01

Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca2+ adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes. PMID:26723638

Pyrolysis reaction networks for lignin model compounds: unraveling thermal deconstruction of β-O-4 and α-O-4 compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, Yong S.; Singh, Rahul; Zhang, Jing

2016-01-01

Although lignin is one of the main components of biomass, its pyrolysis chemistry is not well understood due to complex heterogeneity. To gain insights into this chemistry, the pyrolysis of seven lignin model compounds (five ..beta..-O-4 and two ..alpha..-O-4 linked molecules) was investigated in a micropyrolyzer connected to GC-MS/FID. According to quantitative product mole balance for the reaction networks, concerted retro-ene fragmentation and homolytic dissociation were strongly suggested as the initial reaction step for ..beta..-O-4 compounds and ..alpha..-O-4 compounds, respectively. The difference in reaction pathway between compounds with different linkages was believed to result from thermodynamics of the radical initiation.more » The rate constants for the different reaction pathways were predicted from ab initio density functional theory calculations and pre-exponential literature values. The computational findings were consistent with the experiment results, further supporting the different pyrolysis mechanisms for the ..beta..-ether linked and ..alpha..-ether linked compounds. A combination of the two pathways from the dimeric model compounds was able to describe qualitatively the pyrolysis of a trimeric lignin model compound containing both ..beta..-O-4 and ..alpha..-O-4 linkages.« less

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

PubMed Central

Tyner, Jeffrey W.; Yang, Wayne F.; Bankhead, Armand; Fan, Guang; Fletcher, Luke B.; Bryant, Jade; Glover, Jason M.; Chang, Bill H.; Spurgeon, Stephen E.; Fleming, William H.; Kovacsovics, Tibor; Gotlib, Jason R.; Oh, Stephen T.; Deininger, Michael W.; Zwaan, C. Michel; Den Boer, Monique L.; van den Heuvel-Eibrink, Marry M.; O’Hare, Thomas; Druker, Brian J.; Loriaux, Marc M.

2012-01-01

Kinases are dysregulated in most cancer but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of gene targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options. PMID:23087056

A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium.

PubMed

Kazakiewicz, Denis; Karr, Jonathan R; Langner, Karol M; Plewczynski, Dariusz

2015-12-01

Bacteria are increasingly resistant to existing antibiotics, which target a narrow range of pathways. New methods are needed to identify targets, including repositioning targets among distantly related species. We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species. We applied this approach to Mycoplasma genitalium, a common cause of urethritis. First, we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate. Second, we searched the literature for inhibitors of homologs of the most fragile enzymes. Next, we used sequence alignment to assess that the binding site is shared by M. genitalium, but not by humans. Lastly, we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs. Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds. Further work is needed to experimentally validate piperidinylthymines. In summary, combined systems and structural modeling is a powerful tool for drug repositioning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effective drug combination for Caenorhabditis elegans nematodes discovered by output-driven feedback system control technique

PubMed Central

Ding, Xianting; Njus, Zach; Kong, Taejoon; Su, Wenqiong; Ho, Chih-Ming; Pandey, Santosh

2017-01-01

Infections from parasitic nematodes (or roundworms) contribute to a significant disease burden and productivity losses for humans and livestock. The limited number of anthelmintics (or antinematode drugs) available today to treat these infections are rapidly losing their efficacy as multidrug resistance in parasites becomes a global health challenge. We propose an engineering approach to discover an anthelmintic drug combination that is more potent at killing wild-type Caenorhabditis elegans worms than four individual drugs. In the experiment, freely swimming single worms are enclosed in microfluidic drug environments to assess the centroid velocity and track curvature of worm movements. After analyzing the behavioral data in every iteration, the feedback system control (FSC) scheme is used to predict new drug combinations to test. Through a differential evolutionary search, the winning drug combination is reached that produces minimal centroid velocity and high track curvature, while requiring each drug in less than their EC50 concentrations. The FSC approach is model-less and does not need any information on the drug pharmacology, signaling pathways, or animal biology. Toward combating multidrug resistance, the method presented here is applicable to the discovery of new potent combinations of available anthelmintics on C. elegans, parasitic nematodes, and other small model organisms. PMID:28983514

Visual Cortical Entrainment to Motion and Categorical Speech Features during Silent Lipreading

PubMed Central

O’Sullivan, Aisling E.; Crosse, Michael J.; Di Liberto, Giovanni M.; Lalor, Edmund C.

2017-01-01

Speech is a multisensory percept, comprising an auditory and visual component. While the content and processing pathways of audio speech have been well characterized, the visual component is less well understood. In this work, we expand current methodologies using system identification to introduce a framework that facilitates the study of visual speech in its natural, continuous form. Specifically, we use models based on the unheard acoustic envelope (E), the motion signal (M) and categorical visual speech features (V) to predict EEG activity during silent lipreading. Our results show that each of these models performs similarly at predicting EEG in visual regions and that respective combinations of the individual models (EV, MV, EM and EMV) provide an improved prediction of the neural activity over their constituent models. In comparing these different combinations, we find that the model incorporating all three types of features (EMV) outperforms the individual models, as well as both the EV and MV models, while it performs similarly to the EM model. Importantly, EM does not outperform EV and MV, which, considering the higher dimensionality of the V model, suggests that more data is needed to clarify this finding. Nevertheless, the performance of EMV, and comparisons of the subject performances for the three individual models, provides further evidence to suggest that visual regions are involved in both low-level processing of stimulus dynamics and categorical speech perception. This framework may prove useful for investigating modality-specific processing of visual speech under naturalistic conditions. PMID:28123363

Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer

PubMed Central

Makondi, Precious Takondwa; Lee, Chia-Hwa; Huang, Chien-Yu; Chu, Chi-Ming; Chang, Yu-Jia

2018-01-01

Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways can be potential targets and predictors of therapeutic resistance and prognosis in bevacizumab-treated patients with mCRC. PMID:29342159

Embryotoxic and pharmacologic potency ranking of six azoles in the rat whole embryo culture by morphological and transcriptomic analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimopoulou, Myrto, E-mail: myrto.dimopoulou@wur.nl

Differential gene expression analysis in the rat whole embryo culture (WEC) assay provides mechanistic insight into the embryotoxicity of test compounds. In our study, we hypothesized that comparative analysis of the transcriptomes of rat embryos exposed to six azoles (flusilazole, triadimefon, ketoconazole, miconazole, difenoconazole and prothioconazole) could lead to a better mechanism-based understanding of their embryotoxicity and pharmacological action. For evaluating embryotoxicity, we applied the total morphological scoring system (TMS) in embryos exposed for 48 h. The compounds tested showed embryotoxicity in a dose-response fashion. Functional analysis of differential gene expression after 4 h exposure at the ID{sub 10} (effectivemore » dose for 10% decreased TMS), revealed the sterol biosynthesis pathway and embryonic development genes, dominated by genes in the retinoic acid (RA) pathway, albeit in a differential way. Flusilazole, ketoconazole and triadimefon were the most potent compounds affecting the RA pathway, while in terms of regulation of sterol function, difenoconazole and ketoconazole showed the most pronounced effects. Dose-dependent analysis of the effects of flusilazole revealed that the RA pathway related genes were already differentially expressed at low dose levels while the sterol pathway showed strong regulation at higher embryotoxic doses, suggesting that this pathway is less predictive for the observed embryotoxicity. A similar analysis at the 24-hour time point indicated an additional time-dependent difference in the aforementioned pathways regulated by flusilazole. In summary, the rat WEC assay in combination with transcriptomics could add a mechanistic insight into the embryotoxic potency ranking and pharmacological mode of action of the tested compounds. - Highlights: • Embryonic exposure to azoles revealed concentration-dependent malformations. • Transcriptomics could enhance the mechanistic knowledge of embryotoxicants. • Retinoic acid gene set identifies early embryotoxic responses to azoles. • Toxic versus pharmacologic potency determines functional efficacy.« less

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

Combustion modeling and kinetic rate calculations for a stoichiometric cyclohexane flame. 1. Major reaction pathways.

PubMed

Zhang, Hongzhi R; Huynh, Lam K; Kungwan, Nawee; Yang, Zhiwei; Zhang, Shaowen

2007-05-17

The Utah Surrogate Mechanism was extended in order to model a stoichiometric premixed cyclohexane flame (P = 30 Torr). Generic rates were assigned to reaction classes of hydrogen abstraction, beta scission, and isomerization, and the resulting mechanism was found to be adequate in describing the combustion chemistry of cyclohexane. Satisfactory results were obtained in comparison with the experimental data of oxygen, major products and important intermediates, which include major soot precursors of C2-C5 unsaturated species. Measured concentrations of immediate products of fuel decomposition were also successfully reproduced. For example, the maximum concentrations of benzene and 1,3-butadiene, two major fuel decomposition products via competing pathways, were predicted within 10% of the measured values. Ring-opening reactions compete with those of cascading dehydrogenation for the decomposition of the conjugate cyclohexyl radical. The major ring-opening pathways produce 1-buten-4-yl radical, molecular ethylene, and 1,3-butadiene. The butadiene species is formed via beta scission after a 1-4 internal hydrogen migration of 1-hexen-6-yl radical. Cascading dehydrogenation also makes an important contribution to the fuel decomposition and provides the exclusive formation pathway of benzene. Benzene formation routes via combination of C2-C4 hydrocarbon fragments were found to be insignificant under current flame conditions, inferred by the later concentration peak of fulvene, in comparison with benzene, because the analogous species series for benzene formation via dehydrogenation was found to be precursors with regard to parent species of fulvene.

Synthetic and systems biology for microbial production of commodity chemicals.

PubMed

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J; Keasling, Jay D; Martín, Héctor García

2016-01-01

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Mercury deposition and re-emission pathways in boreal forest soils investigated with Hg isotope signatures.

PubMed

Jiskra, Martin; Wiederhold, Jan G; Skyllberg, Ulf; Kronberg, Rose-Marie; Hajdas, Irka; Kretzschmar, Ruben

2015-06-16

Soils comprise the largest terrestrial mercury (Hg) pool in exchange with the atmosphere. To predict how anthropogenic emissions affect global Hg cycling and eventually human Hg exposure, it is crucial to understand Hg deposition and re-emission of legacy Hg from soils. However, assessing Hg deposition and re-emission pathways remains difficult because of an insufficient understanding of the governing processes. We measured Hg stable isotope signatures of radiocarbon-dated boreal forest soils and modeled atmospheric Hg deposition and re-emission pathways and fluxes using a combined source and process tracing approach. Our results suggest that Hg in the soils was dominantly derived from deposition of litter (∼90% on average). The remaining fraction was attributed to precipitation-derived Hg, which showed increasing contributions in older, deeper soil horizons (up to 27%) indicative of an accumulation over decades. We provide evidence for significant Hg re-emission from organic soil horizons most likely caused by nonphotochemical abiotic reduction by natural organic matter, a process previously not observed unambiguously in nature. Our data suggest that Histosols (peat soils), which exhibit at least seasonally water-saturated conditions, have re-emitted up to one-third of previously deposited Hg back to the atmosphere. Re-emission of legacy Hg following reduction by natural organic matter may therefore be an important pathway to be considered in global models, further supporting the need for a process-based assessment of land/atmosphere Hg exchange.

Synthetic and systems biology for microbial production of commodity chemicals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks.

PubMed

Mei, Suyu

2018-05-04

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L 2 -regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

Synthetic and systems biology for microbial production of commodity chemicals

DOE PAGES

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.; ...

2016-04-07

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

microRNA 31 functions as an endometrial cancer oncogene by suppressing Hippo tumor suppressor pathway

PubMed Central

2014-01-01

Background We aimed to investigate whether MIR31 is an oncogene in human endometrial cancer and identify the target molecules associated with the malignant phenotype. Methods We investigated the growth potentials of MIR31-overexpressing HEC-50B cells in vitro and in vivo. In order to identify the target molecule of MIR31, a luciferase reporter assay was performed, and the corresponding downstream signaling pathway was examined using immunohistochemistry of human endometrial cancer tissues. We also investigated the MIR31 expression in 34 patients according to the postoperative risk of recurrence. Results The overexpression of MIR31 significantly promoted anchorage-independent growth in vitro and significantly increased the tumor forming potential in vivo. MIR31 significantly suppressed the luciferase activity of mRNA combined with the LATS2 3’-UTR and consequently promoted the translocation of YAP1, a key molecule in the Hippo pathway, into the nucleus. Meanwhile, the nuclear localization of YAP1 increased the transcription of CCND1. Furthermore, the expression levels of MIR31 were significantly increased (10.7-fold) in the patients (n = 27) with a high risk of recurrence compared to that observed in the low-risk patients (n = 7), and this higher expression correlated with a poor survival. Conclusions MIR31 functions as an oncogene in endometrial cancer by repressing the Hippo pathway. MIR31 is a potential new molecular marker for predicting the risk of recurrence and prognosis of endometrial cancer. PMID:24779718

A computational study of the Diels-Alder reactions between 2,3-dibromo-1,3-butadiene and maleic anhydride

NASA Astrophysics Data System (ADS)

Rivero, Uxía; Meuwly, Markus; Willitsch, Stefan

2017-09-01

The neutral and cationic Diels-Alder-type reactions between 2,3-dibromo-1,3-butadiene and maleic anhydride have been computationally explored as the first step of a combined experimental and theoretical study. Density functional theory calculations show that the neutral reaction is concerted while the cationic reaction can be either concerted or stepwise. Further isomerizations of the Diels-Alder products have been studied in order to predict possible fragmentation pathways in gas-phase experiments. Rice-Ramsperger-Kassel-Marcus (RRKM) calculations suggest that under single-collision experimental conditions the neutral product may reform the reactants and the cationic product will most likely eliminate CO2.

Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kleinstreuer, N.C., E-mail: kleinstreuer.nicole@epa.gov; Smith, A.M.; West, P.R.

2011-11-15

Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast Trade-Mark-Sign chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoAmore » biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox Registered-Sign model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: Black-Right-Pointing-Pointer We tested 11 environmental compounds in a hESC metabolomics platform. Black-Right-Pointing-Pointer Significant changes in secreted small molecule metabolites were observed. Black-Right-Pointing-Pointer Perturbed mass features map to pathways critical for normal development and pregnancy. Black-Right-Pointing-Pointer Arginine, proline, nicotinate, nicotinamide and glutathione pathways were affected.« less

Reciprocal feedback regulation of PI3K and androgen receptor signaling in PTEN-deficient prostate cancer

PubMed Central

Carver, Brett S; Chapinski, Caren; Wongvipat, John; Hieronymus, Haley; Chen, Yu; Chandarlapaty, Sarat; Arora, Vivek K; Le, Carl; Koutcher, Jason; Scher, Howard; Scardino, Peter T; Rosen, Neal; Sawyers, Charles L

2011-01-01

Summary Prostate cancer is characterized by its dependence on androgen receptor and frequent activation of PI3K signaling. We find that AR transcriptional output is decreased in human and murine tumors with PTEN deletion and that PI3K pathway inhibition activates AR signaling by relieving feedback inhibition of HER kinases. Similarly, AR inhibition activates AKT signaling by reducing levels of the AKT phosphatase PHLPP. Thus, these two oncogenic pathways cross-regulate each other by reciprocal feedback. Inhibition of one activates the other, thereby maintaining tumor cell survival. However, combined pharmacologic inhibition of PI3K and AR signaling caused near complete prostate cancer regressions in a Pten-deficient murine prostate cancer model and in human prostate cancer xenografts, indicating that both pathways coordinately support survival. Significance The two most frequently activated signaling pathways in prostate cancer are driven by AR and PI3K. Inhibitors of the PI3K pathway are in early clinical trials and AR inhibitors confer clinical responses in most patients. However, these inhibitors rarely induce tumor regression in preclinical models. Here we show that these pathways regulate each other by reciprocal negative feedback, such that inhibition of one activates the other. Therefore, tumor cells can adapt and survive when either single pathway is inhibited pharmacologically. Our demonstration of profound tumor regressions with combined pathway inhibition in preclinical prostate tumor models provides rationale for combination therapy in patients. PMID:21575859

Genetic and Pharmacological Screens Converge in Identifying FLIP, BCL2, and IAP Proteins as Key Regulators of Sensitivity to the TRAIL-Inducing Anticancer Agent ONC201/TIC10.

PubMed

Allen, Joshua E; Prabhu, Varun V; Talekar, Mala; van den Heuvel, A Pieter J; Lim, Bora; Dicker, David T; Fritz, Jennifer L; Beck, Adam; El-Deiry, Wafik S

2015-04-15

ONC201/TIC10 is a small-molecule inducer of the TRAIL gene under current investigation as a novel anticancer agent. In this study, we identify critical molecular determinants of ONC201 sensitivity offering potential utility as pharmacodynamic or predictive response markers. By screening a library of kinase siRNAs in combination with a subcytotoxic dose of ONC201, we identified several kinases that ablated tumor cell sensitivity, including the MAPK pathway-inducer KSR1. Unexpectedly, KSR1 silencing did not affect MAPK signaling in the presence or absence of ONC201, but instead reduced expression of the antiapoptotic proteins FLIP, Mcl-1, Bcl-2, cIAP1, cIAP2, and survivin. In parallel to this work, we also conducted a synergy screen in which ONC201 was combined with approved small-molecule anticancer drugs. In multiple cancer cell populations, ONC201 synergized with diverse drug classes, including the multikinase inhibitor sorafenib. Notably, combining ONC201 and sorafenib led to synergistic induction of TRAIL and its receptor DR5 along with a potent induction of cell death. In a mouse xenograft model of hepatocellular carcinoma, we demonstrated that ONC201 and sorafenib cooperatively and safely triggered tumor regressions. Overall, our results established a set of determinants for ONC201 sensitivity that may predict therapeutic response, particularly in settings of sorafenib cotreatment to enhance anticancer responses. ©2015 American Association for Cancer Research.

Genetic and pharmacological screens converge in identifying FLIP, BCL2 and IAP proteins as key regulators of sensitivity to the TRAIL-inducing anti-cancer agent ONC201/TIC10

PubMed Central

Allen, Joshua E.; Prabhu, Varun V.; Talekar, Mala; van den Heuvel, AP; Lim, Bora; Dicker, David T.; Fritz, Jennifer L.; Beck, Adam; El-Deiry, Wafik S.

2015-01-01

ONC201/TIC10 is a small molecule inducer of the TRAIL gene under current investigation as a novel anticancer agent. In this study, we identify critical molecular determinants of ONC201 sensitivity offering potential utility as pharmacodynamic or predictive response markers. By screening a library of kinase siRNAs in combination with a subcytotoxic dose of ONC201, we identified several kinases that ablated tumor cell sensitivity, including the MAPK pathway inducer KSR1. Unexpectedly, KSR1 silencing did not affect MAPK signaling in the presence or absence of ONC201, but instead reduced expression of the anti-apoptotic proteins FLIP, Mcl-1, Bcl-2, cIAP1, cIAP2, and survivin. In parallel to this work, we also conducted a synergy screen in which ONC201 was combined with approved small molecule anticancer drugs. In multiple cancer cell populations, ONC201 synergized with diverse drug classes including the multi-kinase inhibitor sorafenib. Notably, combining ONC201 and sorafenib led to synergistic induction of TRAIL and its receptor DR5 along with a potent induction of cell death. In a mouse xenograft model of hepatocellular carcinoma, we demonstrated that ONC201 and sorafenib cooperatively and safely triggered tumor regressions. Overall, our results established a set of determinants for ONC201 sensitivity that may predict therapeutic response, particularly in settings of sorafenib co-treatment to enhance anticancer responses. PMID:25681273

Plant MetGenMAP: an integrative analysis system for plant systems biology

USDA-ARS?s Scientific Manuscript database

We have developed a web-based system, Plant MetGenMAP, which can identify significantly altered biochemical pathways and highly affected biological processes, predict functional roles of pathway genes, and potential pathway-related regulatory motifs from transcript and metabolite profile datasets. P...

Use of the adverse outcome pathway framework to represent cross-species consequences of specific pathway perturbations

EPA Science Inventory

The adverse outcome pathway (AOP) framework has been developed as a means for assembling scientifically defensible descriptions of how particular molecular perturbations, termed molecular initiating events (MIEs), can evoke a set of predictable responses at different levels of bi...

Arctigenin in combination with quercetin synergistically enhances the antiproliferative effect in prostate cancer cells.

PubMed

Wang, Piwen; Phan, Tien; Gordon, David; Chung, Seyung; Henning, Susanne M; Vadgama, Jaydutt V

2015-02-01

We investigated whether a combination of two promising chemopreventive agents arctigenin (Arc) and quercetin (Q) increases the anticarcinogenic potency at lower concentrations than necessary when used individually in prostate cancer. Androgen-dependent LAPC-4 and LNCaP prostate cancer cells were treated with low doses of Arc and Q alone or in combination for 48 h. The antiproliferative activity of Arc was 10- to 20-fold stronger than Q in both cell lines. Their combination synergistically enhanced the antiproliferative effect, with a stronger effect in androgen receptor (AR) wild-type LAPC-4 cells than in AR mutated LNCaP cells. Arc demonstrated a strong ability to inhibit AR protein expression in LAPC-4 cells. The combination treatment significantly inhibited both AR and PI3K/Akt pathways compared to control. A protein array analysis revealed that the mixture targets multiple pathways particularly in LAPC-4 cells including Stat3 pathway. The mixture significantly inhibited the expression of several oncogenic microRNAs including miR-21, miR-19b, and miR-148a compared to control. The mixture also enhanced the inhibition of cell migration in both cell lines compared to individual compounds tested. The combination of Arc and Q that target similar pathways, at low physiological doses, provides a novel regimen with enhanced chemoprevention in prostate cancer. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Arctigenin in combination with quercetin synergistically enhances the anti-proliferative effect in prostate cancer cells

PubMed Central

Wang, Piwen; Phan, Tien; Gordon, David; Chung, Seyung; Henning, Susanne M.; Vadgama, Jaydutt V.

2014-01-01

Scope We investigated whether a combination of two promising chemopreventive agents arctigenin and quercetin increases the anti-carcinogenic potency at lower concentrations than necessary when used individually in prostate cancer. Methods and results Androgen-dependent LAPC-4 and LNCaP prostate cancer cells were treated with low doses of arctigenin and quercetin alone or in combination for 48h. The anti-proliferative activity of arctigenin was 10-20 fold stronger than quercetin in both cell lines. Their combination synergistically enhanced the anti-proliferative effect, with a stronger effect in androgen receptor (AR) wild-type LAPC-4 cells than in AR mutated LNCaP cells. Arctigenin demonstrated a strong ability to inhibit AR protein expression in LAPC-4 cells. The combination treatment significantly inhibited both AR and PI3K/Akt pathways compared to control. A protein array analysis revealed that the mixture targets multiple pathways particularly in LAPC-4 cells including Stat3 pathway. The mixture significantly inhibited the expression of several oncogenic microRNAs including miR-21, miR-19b, and miR-148a compared to control. The mixture also enhanced the inhibition of cell migration in both cell lines compared to individual compounds tested. Conclusion The combination of arctigenin and quercetin, that target similar pathways, at low physiological doses, provides a novel regimen with enhanced chemoprevention in prostate cancer. PMID:25380086

Combination of Entner-Doudoroff Pathway with MEP Increases Isoprene Production in Engineered Escherichia coli

PubMed Central

Liu, Huaiwei; Sun, Yuanzhang; Ramos, Kristine Rose M.; Nisola, Grace M.; Valdehuesa, Kris Niño G.; Lee, Won–Keun; Park, Si Jae; Chung, Wook-Jin

2013-01-01

Embden-Meyerhof pathway (EMP) in tandem with 2-C-methyl-D-erythritol 4-phosphate pathway (MEP) is commonly used for isoprenoid biosynthesis in E. coli. However, this combination has limitations as EMP generates an imbalanced distribution of pyruvate and glyceraldehyde-3-phosphate (G3P). Herein, four glycolytic pathways—EMP, Entner-Doudoroff Pathway (EDP), Pentose Phosphate Pathway (PPP) and Dahms pathway were tested as MEP feeding modules for isoprene production. Results revealed the highest isoprene production from EDP containing modules, wherein pyruvate and G3P were generated simultaneously; isoprene titer and yield were more than three and six times higher than those of the EMP module, respectively. Additionally, the PPP module that generates G3P prior to pyruvate was significantly more effective than the Dahms pathway, in which pyruvate production precedes G3P. In terms of precursor generation and energy/reducing-equivalent supply, EDP+PPP was found to be the ideal feeding module for MEP. These findings may launch a new direction for the optimization of MEP-dependent isoprenoid biosynthesis pathways. PMID:24376679

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

PubMed Central

Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

2015-01-01

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

Beyond Bevacizumab: An Outlook to New Anti-Angiogenics for the Treatment of Ovarian Cancer.

PubMed

Mahner, Sven; Woelber, Linn; Mueller, Volkmar; Witzel, Isabell; Prieske, Katharina; Grimm, Donata; Keller-V Amsberg, Gunhild; Trillsch, Fabian

2015-01-01

In addition to the monoclonal vascular endothelial growth factor (VEGF) antibody bevacizumab, several alternative anti-angiogenic treatment strategies for ovarian cancer patients have been evaluated in clinical trials. Apart from targeting extracellular receptors by the antibody aflibercept or the peptibody trebananib, the multikinase inhibitors pazopanib, nintedanib, cediranib, sunitinib, and sorafenib were developed to interfere with VEGF receptors and multiple additional intracellular pathways. Nintedanib and pazopanib significantly improved progression-free survival in two positive phase III trials for first-line therapy. A reliable effect on overall survival could, however, not be observed for any anti-angiogenic first-line therapies so far. In terms of recurrent disease, two positive phase III trials revealed that trebananib and cediranib are effective anti-angiogenic agents for this indication. Patient selection and biomarker guided prediction of response seems to be a central aspect for future studies. Combining anti-angiogenics with other targeted therapies to possibly spare chemotherapy in certain constellations represents another very interesting future perspective for clinical trials. This short review gives an overview of current clinical trials for anti-angiogenic treatment strategies beyond bevacizumab. In this context, possible future perspectives combining anti-angiogenics with other targeted therapies and the need for specific biomarkers predicting response are elucidated.

Effect-Based Tools for Monitoring and Predicting the Ecotoxicological Effects of Chemicals in the Aquatic Environment

PubMed Central

Connon, Richard E.; Geist, Juergen; Werner, Inge

2012-01-01

Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water quality standards; currently applied effect-based tools are presented based on whether exposure occurs in a controlled laboratory environment or in the field. With increasing ecological relevance the reproducibility, specificity and thus suitability for standardisation of methods tends to diminish. We discuss the use of biomarkers in ecotoxicology including ecotoxicogenomics-based endpoints, which are becoming increasingly important for the detection of sublethal effects. Carefully selected sets of biomarkers allow an assessment of exposure to and effects of toxic chemicals, as well as the health status of organisms and, when combined with chemical analysis, identification of toxicant(s). The promising concept of “adverse outcome pathways (AOP)” links mechanistic responses on the cellular level with whole organism, population, community and potentially ecosystem effects and services. For most toxic mechanisms, however, practical application of AOPs will require more information and the identification of key links between responses, as well as key indicators, at different levels of biological organization, ecosystem functioning and ecosystem services. PMID:23112741

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Prediction of target genes for miR-140-5p in pulmonary arterial hypertension using bioinformatics methods.

PubMed

Li, Fangwei; Shi, Wenhua; Wan, Yixin; Wang, Qingting; Feng, Wei; Yan, Xin; Wang, Jian; Chai, Limin; Zhang, Qianqian; Li, Manxiang

2017-12-01

The expression of microRNA (miR)-140-5p is known to be reduced in both pulmonary arterial hypertension (PAH) patients and monocrotaline-induced PAH models in rat. Identification of target genes for miR-140-5p with bioinformatics analysis may reveal new pathways and connections in PAH. This study aimed to explore downstream target genes and relevant signaling pathways regulated by miR-140-5p to provide theoretical evidences for further researches on role of miR-140-5p in PAH. Multiple downstream target genes and upstream transcription factors (TFs) of miR-140-5p were predicted in the analysis. Gene ontology (GO) enrichment analysis indicated that downstream target genes of miR-140-5p were enriched in many biological processes, such as biological regulation, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathways. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis found that downstream target genes were mainly located in Notch, TGF-beta, PI3K/Akt, and Hippo signaling pathway. According to TF-miRNA-mRNA network, the important downstream target genes of miR-140-5p were PPI, TGF-betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, LAMC1, TLR4, and CREB. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR-140-5p in various tissues or cells; most of these verified targets were in accordance with our present prediction. Other predicted targets still need further verification in vivo and in vitro .

Combination of PI3K/Akt/mTOR inhibitors and PDT in endothelial and tumor cells

NASA Astrophysics Data System (ADS)

Fateye, Babasola; Chen, Bin

2011-02-01

The PI3/Akt/mTOR kinase signaling pathway is a major signaling pathway in eukaryotic cells, and dysregulation of this signaling pathway has been implicated in tumorigenesis and malignancy in several cancers including prostate cancer. We assessed the effects of combination PI3K pathway inhibition on the efficacy of PDT in human prostate tumor cell line (PC3) and SV40-transformed mouse endothelial cell line (SVEC-40). Combination of PDT and BEZ 235 (BEZ), a pan-PI3/ mTOR kinase inhibitor additively enhanced efficacy of sub-lethal PDT in both cell lines. The combination of the pan-PI3/ mTOR kinase inhibitor LY294002 (LY) with PDT also enhanced efficacy of PDT in PC3 in an additive manner but synergistically in SVEC. In order to determine the mechanism of enhancement of efficacy, we assessed apoptosis and autophagy following PDT. PDT-mediated apoptosis was enhanced in endothelial cells, by both BEZ and LY rapidly after treatment. Compared to SVEC, PC3 cells are apoptosis-deficient and apoptosis was not significantly enhanced by either LY or BEZ. However, lethal PDT of PC3 cells induced a delayed autophagic response which may be enhanced by combination, depending on PI3K inhibitor and dose.

Critical role of reactive oxygen species (ROS) for synergistic enhancement of apoptosis by vemurafenib and the potassium channel inhibitor TRAM-34 in melanoma cells.

PubMed

Bauer, Daniel; Werth, Felix; Nguyen, Ha An; Kiecker, Felix; Eberle, Jürgen

2017-02-02

Inhibition of MAP kinase pathways by selective BRAF inhibitors, such as vemurafenib and dabrafenib, have evolved as key therapies of BRAF-mutated melanoma. However, tumor relapse and therapy resistance have remained as major problems, which may be addressed by combination with other pathway inhibitors. Here we identified the potassium channel inhibitor TRAM-34 as highly effective in combination with vemurafenib. Thus apoptosis was significantly enhanced and cell viability was decreased. The combination vemurafenib/TRAM-34 was also effective in vemurafenib-resistant cells, suggesting that acquired resistance may be overcome. Vemurafenib decreased ERK phosphorylation, suppressed antiapoptotic Mcl-1 and enhanced proapoptotic Puma and Bim. The combination resulted in enhancement of proapoptotic pathways as caspase-3 and loss of mitochondrial membrane potential. Indicating a special mechanism of vemurafenib-induced apoptosis, we found strong enhancement of intracellular ROS levels already at 1 h of treatment. The critical role of ROS was demonstrated by the antioxidant vitamin E (α-tocopherol), which decreased intracellular ROS as well as apoptosis. Also caspase activation and loss of mitochondrial membrane potential were suppressed, proving ROS as an upstream effect. Thus ROS represents an initial and independent apoptosis pathway in melanoma cells that is of particular importance for vemurafenib and its combination with TRAM-34.

Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data.

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is becoming a widely used tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse ecological and human health outcomes. However, the conventional process...

USE OF PHARMACOKINETIC MODELING TO DESIGN STUDIES FOR PATHWAY-SPECIFIC EXPOSURE MODEL EVALUATION

EPA Science Inventory

Validating an exposure pathway model is difficult because the biomarker, which is often used to evaluate the model prediction, is an integrated measure for exposures from all the exposure routes/pathways. The purpose of this paper is to demonstrate a method to use pharmacokeneti...

An integrated groundwater and surface water approach to quantifying the contribution of hydrological pathways to streamflow

NASA Astrophysics Data System (ADS)

O'Brien, R. J.; Deakin, J.; Misstear, B.; Gill, L.; Flynn, R. M.

2012-12-01

An appreciation of the quantity of streamflow derived from the main hydrological groundwater and surface water pathways transporting diffuse pollutants is critical when addressing a wide range of water resource management issues. The Pathways Project, funded by the Irish EPA, is developing a Catchment Management Tool (CMT) as an aid to water resource decision makers. The pollutants investigated by the CMT include phosphorus, nitrogen, sediments, pesticides and pathogens. An important first step in this process is to provide reliable estimates of the slower responding groundwater pathways in conjunction with the quicker overland and interflow pathways. Four watersheds are being investigated, with continuous rainfall, discharge, temperature and conductivity data being collected at gauging points within each of the watersheds. These datasets are being used to populate the semi-distributed, lumped flow model, NAM and also the distributed, finite difference model, MODFLOW. One of the main challenges is to achieve credible separations of the hydrograph into the main pathways in relatively small catchments (sometimes less than 5km2) with short response times. To assist the numerical modelling, physical separation techniques have been used to constrain the separations within probable limits. Physical techniques include: Master Recession Analysis; a modified Lyne and Hollick one-parameter digital separation; an approach developed in Ireland involving the application of recharge coefficients to hydrologically effective rainfall estimates; and finally using the NAM and MODFLOW models themselves as means of investigating separations. The contribution from each of the pathways, combined with an understanding of the attenuation of the contaminants along those pathways, will inform the CMT. This understanding will lay the foundation for linking the parameters of the NAM model to watershed descriptors such as slope, drainage density, watershed area, soil type, etc., in order to predict the response of a watershed to rainfall. This is an important deliverable of this research and will be fundamental for initial investigations in ungauged watersheds. This approach to quantifying hydrological pathways will therefore have wider applicability across Ireland and in hydrological settings elsewhere internationally. The research is being carried out for the Environmental Protection Agency by a consortium involving Queen's University Belfast, University College Dublin and Trinity College Dublin. Pathway separations in a karst watershed. Observed discharge (Black) with separated pathways: quick diffuse flow (Blue); slow diffuse flow (Green); interflow (Light Blue) and overland flow (Red).

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.

PubMed

Hess, Becky M; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C; Wiley, H Steven; Ahring, Birgitte K; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

PubMed Central

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. Steven; Ahring, Birgitte K.; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions. PMID:23840410

Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng

2013-06-19

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. Wemore » found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.« less

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

A chain reaction approach to modelling gene pathways.

PubMed

Cheng, Gary C; Chen, Dung-Tsa; Chen, James J; Soong, Seng-Jaw; Lamartiniere, Coral; Barnes, Stephen

2012-08-01

BACKGROUND: Of great interest in cancer prevention is how nutrient components affect gene pathways associated with the physiological events of puberty. Nutrient-gene interactions may cause changes in breast or prostate cells and, therefore, may result in cancer risk later in life. Analysis of gene pathways can lead to insights about nutrient-gene interactions and the development of more effective prevention approaches to reduce cancer risk. To date, researchers have relied heavily upon experimental assays (such as microarray analysis, etc.) to identify genes and their associated pathways that are affected by nutrient and diets. However, the vast number of genes and combinations of gene pathways, coupled with the expense of the experimental analyses, has delayed the progress of gene-pathway research. The development of an analytical approach based on available test data could greatly benefit the evaluation of gene pathways, and thus advance the study of nutrient-gene interactions in cancer prevention. In the present study, we have proposed a chain reaction model to simulate gene pathways, in which the gene expression changes through the pathway are represented by the species undergoing a set of chemical reactions. We have also developed a numerical tool to solve for the species changes due to the chain reactions over time. Through this approach we can examine the impact of nutrient-containing diets on the gene pathway; moreover, transformation of genes over time with a nutrient treatment can be observed numerically, which is very difficult to achieve experimentally. We apply this approach to microarray analysis data from an experiment which involved the effects of three polyphenols (nutrient treatments), epigallo-catechin-3-O-gallate (EGCG), genistein, and resveratrol, in a study of nutrient-gene interaction in the estrogen synthesis pathway during puberty. RESULTS: In this preliminary study, the estrogen synthesis pathway was simulated by a chain reaction model. By applying it to microarray data, the chain reaction model computed a set of reaction rates to examine the effects of three polyphenols (EGCG, genistein, and resveratrol) on gene expression in this pathway during puberty. We first performed statistical analysis to test the time factor on the estrogen synthesis pathway. Global tests were used to evaluate an overall gene expression change during puberty for each experimental group. Then, a chain reaction model was employed to simulate the estrogen synthesis pathway. Specifically, the model computed the reaction rates in a set of ordinary differential equations to describe interactions between genes in the pathway (A reaction rate K of A to B represents gene A will induce gene B per unit at a rate of K; we give details in the "method" section). Since disparate changes of gene expression may cause numerical error problems in solving these differential equations, we used an implicit scheme to address this issue. We first applied the chain reaction model to obtain the reaction rates for the control group. A sensitivity study was conducted to evaluate how well the model fits to the control group data at Day 50. Results showed a small bias and mean square error. These observations indicated the model is robust to low random noises and has a good fit for the control group. Then the chain reaction model derived from the control group data was used to predict gene expression at Day 50 for the three polyphenol groups. If these nutrients affect the estrogen synthesis pathways during puberty, we expect discrepancy between observed and expected expressions. Results indicated some genes had large differences in the EGCG (e.g., Hsd3b and Sts) and the resveratrol (e.g., Hsd3b and Hrmt12) groups. CONCLUSIONS: In the present study, we have presented (I) experimental studies of the effect of nutrient diets on the gene expression changes in a selected estrogen synthesis pathway. This experiment is valuable because it allows us to examine how the nutrient-containing diets regulate gene expression in the estrogen synthesis pathway during puberty; (II) global tests to assess an overall association of this particular pathway with time factor by utilizing generalized linear models to analyze microarray data; and (III) a chain reaction model to simulate the pathway. This is a novel application because we are able to translate the gene pathway into the chemical reactions in which each reaction channel describes gene-gene relationship in the pathway. In the chain reaction model, the implicit scheme is employed to efficiently solve the differential equations. Data analysis results show the proposed model is capable of predicting gene expression changes and demonstrating the effect of nutrient-containing diets on gene expression changes in the pathway. One of the objectives of this study is to explore and develop a numerical approach for simulating the gene expression change so that it can be applied and calibrated when the data of more time slices are available, and thus can be used to interpolate the expression change at a desired time point without conducting expensive experiments for a large amount of time points. Hence, we are not claiming this is either essential or the most efficient way for simulating this problem, rather a mathematical/numerical approach that can model the expression change of a large set of genes of a complex pathway. In addition, we understand the limitation of this experiment and realize that it is still far from being a complete model of predicting nutrient-gene interactions. The reason is that in the present model, the reaction rates were estimated based on available data at two time points; hence, the gene expression change is dependent upon the reaction rates and a linear function of the gene expressions. More data sets containing gene expression at various time slices are needed in order to improve the present model so that a non-linear variation of gene expression changes at different time can be predicted.

The Neurodynamics of Affect in the Laboratory Predicts Persistence of Real-World Emotional Responses.

PubMed

Heller, Aaron S; Fox, Andrew S; Wing, Erik K; McQuisition, Kaitlyn M; Vack, Nathan J; Davidson, Richard J

2015-07-22

Failure to sustain positive affect over time is a hallmark of depression and other psychopathologies, but the mechanisms supporting the ability to sustain positive emotional responses are poorly understood. Here, we investigated the neural correlates associated with the persistence of positive affect in the real world by conducting two experiments in humans: an fMRI task of reward responses and an experience-sampling task measuring emotional responses to a reward obtained in the field. The magnitude of DLPFC engagement to rewards administered in the laboratory predicted reactivity of real-world positive emotion following a reward administered in the field. Sustained ventral striatum engagement in the laboratory positively predicted the duration of real-world positive emotional responses. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Significance statement: How real-world emotion, experienced over seconds, minutes, and hours, is instantiated in the brain over the course of milliseconds and seconds is unknown. We combined a novel, real-world experience-sampling task with fMRI to examine how individual differences in real-world emotion, experienced over minutes and hours, is subserved by affective neurodynamics of brain activity over the course of seconds. When winning money in the real world, individuals sustaining positive emotion the longest were those with the most prolonged ventral striatal activity. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Copyright © 2015 the authors 0270-6474/15/3510503-07$15.00/0.

Epithelial ovarian cancer: the molecular genetics of epithelial ovarian cancer.

PubMed

Krzystyniak, J; Ceppi, L; Dizon, D S; Birrer, M J

2016-04-01

Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide, despite gains in diagnostics and treatments made over the last three decades. Existing markers of ovarian cancer possess very limited clinical relevance highlighting the emerging need for identification of novel prognostic biomarkers as well as better predictive factors that might allow the stratification of patients who could benefit from a more targeted approach. A summary of molecular genetics of EOC. Large-scale high-throughput genomic technologies appear to be powerful tools for investigations into the genetic abnormalities in ovarian tumors, including studies on dysregulated genes and aberrantly activated signaling pathways. Such technologies can complement well-established clinical histopathology analysis and tumor grading and will hope to result in better, more tailored treatments in the future. Genomic signatures obtained by gene expression profiling of EOC may be able to predict survival outcomes and other important clinical outcomes, such as the success of surgical treatment. Finally, genomic analyses may allow for the identification of novel predictive biomarkers for purposes of treatment planning. These data combined suggest a pathway to progress in the treatment of advanced ovarian cancer and the promise of fulfilling the objective of providing personalized medicine to women with ovarian cancer. The understanding of basic molecular events in the tumorigenesis and chemoresistance of EOC together with discovery of potential biomarkers may be greatly enhanced through large-scale genomic studies. In order to maximize the impact of these technologies, however, extensive validation studies are required. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Endocrine disrupting chemicals in fish: developing exposure indicators and predictive models of effects based on mechanism of action.

PubMed

Ankley, Gerald T; Bencic, David C; Breen, Michael S; Collette, Timothy W; Conolly, Rory B; Denslow, Nancy D; Edwards, Stephen W; Ekman, Drew R; Garcia-Reyero, Natalia; Jensen, Kathleen M; Lazorchak, James M; Martinović, Dalma; Miller, David H; Perkins, Edward J; Orlando, Edward F; Villeneuve, Daniel L; Wang, Rong-Lin; Watanabe, Karen H

2009-05-05

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.

Use of integrated analogue and numerical modelling to predict tridimensional fracture intensity in fault-related-folds.

NASA Astrophysics Data System (ADS)

Pizzati, Mattia; Cavozzi, Cristian; Magistroni, Corrado; Storti, Fabrizio

2016-04-01

Fracture density pattern predictions with low uncertainty is a fundamental issue for constraining fluid flow pathways in thrust-related anticlines in the frontal parts of thrust-and-fold belts and accretionary prisms, which can also provide plays for hydrocarbon exploration and development. Among the drivers that concur to determine the distribution of fractures in fold-and-thrust-belts, the complex kinematic pathways of folded structures play a key role. In areas with scarce and not reliable underground information, analogue modelling can provide effective support for developing and validating reliable hypotheses on structural architectures and their evolution. In this contribution, we propose a working method that combines analogue and numerical modelling. We deformed a sand-silicone multilayer to eventually produce a non-cylindrical thrust-related anticline at the wedge toe, which was our test geological structure at the reservoir scale. We cut 60 serial cross-sections through the central part of the deformed model to analyze faults and folds geometry using dedicated software (3D Move). The cross-sections were also used to reconstruct the 3D geometry of reference surfaces that compose the mechanical stratigraphy thanks to the use of the software GoCad. From the 3D model of the experimental anticline, by using 3D Move it was possible to calculate the cumulative stress and strain underwent by the deformed reference layers at the end of the deformation and also in incremental steps of fold growth. Based on these model outputs it was also possible to predict the orientation of three main fractures sets (joints and conjugate shear fractures) and their occurrence and density on model surfaces. The next step was the upscaling of the fracture network to the entire digital model volume, to create DFNs.

Systemic Inflammatory Response Syndrome After Major Abdominal Surgery Predicted by Early Upregulation of TLR4 and TLR5.

PubMed

Lahiri, Rajiv; Derwa, Yannick; Bashir, Zora; Giles, Edward; Torrance, Hew D T; Owen, Helen C; O'Dwyer, Michael J; O'Brien, Alastair; Stagg, Andrew J; Bhattacharya, Satyajit; Foster, Graham R; Alazawi, William

2016-05-01

To study innate immune pathways in patients undergoing hepatopancreaticobiliary surgery to understand mechanisms leading to enhanced inflammatory responses and identifying biomarkers of adverse clinical consequences. Patients undergoing major abdominal surgery are at risk of life-threatening systemic inflammatory response syndrome (SIRS) and sepsis. Early identification of at-risk patients would allow tailored postoperative care and improve survival. Two separate cohorts of patients undergoing major hepatopancreaticobiliary surgery were studied (combined n = 69). Bloods were taken preoperatively, on day 1 and day 2 postoperatively. Peripheral blood mononuclear cells and serum were separated and immune phenotype and function assessed ex vivo. Early innate immune dysfunction was evident in 12 patients who subsequently developed SIRS (postoperative day 6) compared with 27 who did not, when no clinical evidence of SIRS was apparent (preoperatively or days 1 and 2). Serum interleukin (IL)-6 concentration and monocyte Toll-like receptor (TLR)/NF-κB/IL-6 functional pathways were significantly upregulated and overactive in patients who developed SIRS (P < 0.0001). Interferon α-mediated STAT1 phosphorylation was higher preoperatively in patients who developed SIRS. Increased TLR4 and TLR5 gene expression in whole blood was demonstrated in a separate validation cohort of 30 patients undergoing similar surgery. Expression of TLR4/5 on monocytes, particularly intermediate CD14CD16 monocytes, on day 1 or 2 predicted SIRS with accuracy 0.89 to 1.0 (areas under receiver operator curves). These data demonstrate the mechanism for IL-6 overproduction in patients who develop postoperative SIRS and identify markers that predict patients at risk of SIRS 5 days before the onset of clinical signs.

Glucose Metabolism in Legionella pneumophila: Dependence on the Entner-Doudoroff Pathway and Connection with Intracellular Bacterial Growth† ▿

PubMed Central

Harada, Eiji; Iida, Ken-Ichiro; Shiota, Susumu; Nakayama, Hiroaki; Yoshida, Shin-Ichi

2010-01-01

Glucose metabolism in Legionella pneumophila was studied by focusing on the Entner-Doudoroff (ED) pathway with a combined genetic and biochemical approach. The bacterium utilized exogenous glucose for synthesis of acid-insoluble cell components but manifested no discernible increase in the growth rate. Assays with permeabilized cell preparations revealed the activities of three enzymes involved in the pathway, i.e., glucokinase, phosphogluconate dehydratase, and 2-dehydro-3-deoxy-phosphogluconate aldolase, presumed to be encoded by the glk, edd, and eda genes, respectively. Gene-disrupted mutants for the three genes and the ywtG gene encoding a putative sugar transporter were devoid of the ability to metabolize exogenous glucose, indicating that the pathway is almost exclusively responsible for glucose metabolism and that the ywtG gene product is the glucose transporter. It was also established that these four genes formed part of an operon in which the gene order was edd-glk-eda-ywtG, as predicted by genomic information. Intriguingly, while the mutants exhibited no appreciable change in growth characteristics in vitro, they were defective in multiplication within eukaryotic cells, strongly indicating that the ED pathway must be functional for the intracellular growth of the bacterium to occur. Curiously, while the deficient glucose metabolism of the ywtG mutant was successfully complemented by the ywtG+ gene supplied in trans via plasmid, its defect in intracellular growth was not. However, the latter defect was also manifested in wild-type cells when a plasmid carrying the mutant ywtG gene was introduced. This phenomenon, resembling so-called dominant negativity, awaits further investigation. PMID:20363943

Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantation.

PubMed

Hulme, Charlotte H; Wilson, Emma L; Fuller, Heidi R; Roberts, Sally; Richardson, James B; Gallacher, Pete; Peffers, Mandy J; Shirran, Sally L; Botting, Catherine H; Wright, Karina T

2018-05-02

Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders. However, we were able to identify only a few differentially abundant proteins at baseline. In the present study, we built upon these previous findings by assessing higher-abundance proteins within this SF, providing a more global proteomic analysis on the basis of which more of the biology underlying ACI success or failure can be understood. Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic analysis was used to assess SF from ACI responders (mean Lysholm improvement of 33; n = 14) and non-responders (mean Lysholm decrease of 14; n = 13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins in iTRAQ and combined iTRAQ and LF datasets were investigated using pathway and network analyses. iTRAQ proteomic analysis confirmed our previous finding that there is a marked proteomic shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥ 2.0-fold change and p < 0.05 between stages I and II in responders and non-responders, respectively). Further, it highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, which were not found in the LF study, 16 of which were altered at baseline. The differential expression of two proteins (complement C1s subcomponent and matrix metalloproteinase 3) was confirmed biochemically. Combination of the iTRAQ and LF proteomic datasets generated in-depth SF proteome information that was used to generate interactome networks representing ACI success or failure. Functional pathways that are dysregulated in ACI non-responders were identified, including acute-phase response signalling. Several candidate biomarkers for baseline prediction of ACI outcome were identified. A holistic overview of the SF proteome in responders and non-responders to ACI  has been profiled, providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.

A systems approach to identifying correlated gene targets for the loss of colour pigmentation in plants

PubMed Central

2011-01-01

Background The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and in silico methods to identify correlated gene targets for the loss of colour pigmentation in plants from a whole cell perspective based on the full metabolic network of Arabidopsis. This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell. Results Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation correspond with existing experimental observations. Two examples are: i) two MCSs which require the simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational results of the same genes being co-regulated for eliminating floral pigmentation in Aquilegia and; ii) the impact of another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent. Conclusions From the various MCSs identified for eliminating colour pigmentation, several correlate to existing experimental observations, indicating that different MCSs are suitable for different plants, different cells, and different conditions and could also be related to regulatory genes. Being able to correlate the predictions with experimental results gives credence to the use of these mathematical and in silico analyses methods in the design of experiments. The methods could be used to prioritize target enzymes for different objectives to achieve desired outcomes, especially for less understood pathways. PMID:21849042

Exploratory Application of Neuropharmacometabolomics in Severe Childhood Traumatic Brain Injury.

PubMed

Hagos, Fanuel T; Empey, Philip E; Wang, Pengcheng; Ma, Xiaochao; Poloyac, Samuel M; Bayır, Hülya; Kochanek, Patrick M; Bell, Michael J; Clark, Robert S B

2018-05-07

To employ metabolomics-based pathway and network analyses to evaluate the cerebrospinal fluid metabolome after severe traumatic brain injury in children and the capacity of combination therapy with probenecid and N-acetylcysteine to impact glutathione-related and other pathways and networks, relative to placebo treatment. Analysis of cerebrospinal fluid obtained from children enrolled in an Institutional Review Board-approved, randomized, placebo-controlled trial of a combination of probenecid and N-acetylcysteine after severe traumatic brain injury (Trial Registration NCT01322009). Thirty-six-bed PICU in a university-affiliated children's hospital. Twelve children 2-18 years old after severe traumatic brain injury and five age-matched control subjects. Probenecid (25 mg/kg) and N-acetylcysteine (140 mg/kg) or placebo administered via naso/orogastric tube. The cerebrospinal fluid metabolome was analyzed in samples from traumatic brain injury patients 24 hours after the first dose of drugs or placebo and control subjects. Feature detection, retention time, alignment, annotation, and principal component analysis and statistical analysis were conducted using XCMS-online. The software "mummichog" was used for pathway and network analyses. A two-component principal component analysis revealed clustering of each of the groups, with distinct metabolomics signatures. Several novel pathways with plausible mechanistic involvement in traumatic brain injury were identified. A combination of metabolomics and pathway/network analyses showed that seven glutathione-centered pathways and two networks were enriched in the cerebrospinal fluid of traumatic brain injury patients treated with probenecid and N-acetylcysteine versus placebo-treated patients. Several additional pathways/networks consisting of components that are known substrates of probenecid-inhibitable transporters were also identified, providing additional mechanistic validation. This proof-of-concept neuropharmacometabolomics assessment reveals alterations in known and previously unidentified metabolic pathways and supports therapeutic target engagement of the combination of probenecid and N-acetylcysteine treatment after severe traumatic brain injury in children.

A Dual Pathway of Student Motivation: Combining an Implicit and Explicit Measure of Student Motivation

ERIC Educational Resources Information Center

Hornstra, Lisette; Kamsteeg, Antoinette; Pot, Sara; Verheij, Lydia

2018-01-01

Abundant research in social psychology shows human behaviour is guided by beliefs through two pathways, a deliberate and automatic pathway. Research on student motivation has thus far focused mostly on the deliberate pathway and consequently almost exclusively relied on explicit measures (i.e. self-reports of motivation) to assess student…

Creation of a Genome-Wide Metabolic Pathway Database for Populus trichocarpa Using a New Approach for Reconstruction and Curation of Metabolic Pathways for Plants1[W][OA

PubMed Central

Zhang, Peifen; Dreher, Kate; Karthikeyan, A.; Chi, Anjo; Pujar, Anuradha; Caspi, Ron; Karp, Peter; Kirkup, Vanessa; Latendresse, Mario; Lee, Cynthia; Mueller, Lukas A.; Muller, Robert; Rhee, Seung Yon

2010-01-01

Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We developed a general approach for reconstructing metabolic pathway complements of plant genomes. Two new reference databases were created and added to the core of the infrastructure: a comprehensive, all-plant reference pathway database, PlantCyc, and a reference enzyme sequence database, RESD, for annotating metabolic functions of protein sequences. PlantCyc (version 3.0) includes 714 metabolic pathways and 2,619 reactions from over 300 species. RESD (version 1.0) contains 14,187 literature-supported enzyme sequences from across all kingdoms. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. PoplarCyc (version 1.0) contains 321 pathways with 1,807 assigned enzymes. Comparing PoplarCyc (version 1.0) with AraCyc (version 6.0, Arabidopsis [Arabidopsis thaliana]) showed comparable numbers of pathways distributed across all domains of metabolism in both databases, except for a higher number of AraCyc pathways in secondary metabolism and a 1.5-fold increase in carbohydrate metabolic enzymes in PoplarCyc. Here, we introduce these new resources and demonstrate the feasibility of using them to identify candidate enzymes for specific pathways and to analyze metabolite profiling data through concrete examples. These resources can be searched by text or BLAST, browsed, and downloaded from our project Web site (http://plantcyc.org). PMID:20522724

EFICAz2: enzyme function inference by a combined approach enhanced by machine learning.

PubMed

Arakaki, Adrian K; Huang, Ying; Skolnick, Jeffrey

2009-04-13

We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz2, exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz2 and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz2 generates considerably more unique assignments than KEGG. Performance benchmarks and the comparison with KEGG demonstrate that EFICAz2 is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz2 web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.html.

Sonic Hedgehog Signaling in Thyroid Cancer

PubMed Central

Xu, Xiulong; Lu, Yurong; Li, Yi; Prinz, Richard A.

2017-01-01

Thyroid cancer is the most common malignancy of the endocrine system. The initiation of thyroid cancer is often triggered by a genetic mutation in the phosphortidylinositol-3 kinase (PI3K) or mitogen-activated protein kinase (MAPK) pathway, such as RAS and BRAF, or by the rearrangement of growth factor receptor tyrosine kinase genes such as RET/PTC. The sonic hedgehog (Shh) pathway is evolutionarily conserved and plays an important role in the embryonic development of normal tissues and organs. Gene mutations in the Shh pathway are involved in basal cell carcinomas (BCC). Activation of the Shh pathway due to overexpression of the genes encoding the components of this pathway stimulates the growth and spread of a wide range of cancer types. The Shh pathway also plays an important role in cancer stem cell (CSC) self-renewal. GDC-0449 and LDE-225, two inhibitors of this pathway, have been approved for treating BCC and are being tested as a single agent or in combination with other drugs for treating various other cancers. Here, we review the recent findings on activation of the Shh pathway in thyroid cancer and its role in maintaining thyroid CSC self-renewal. We also summarize the recent developments on crosstalk of the Shh pathway with the MAPK and PI3K oncogenic pathways, and its implications for combination therapy. PMID:29163356

Contribution of new technologies to characterization and prediction of adverse effects.

PubMed

Rouquié, David; Heneweer, Marjoke; Botham, Jane; Ketelslegers, Hans; Markell, Lauren; Pfister, Thomas; Steiling, Winfried; Strauss, Volker; Hennes, Christa

2015-02-01

Identification of the potential hazards of chemicals has traditionally relied on studies in laboratory animals where changes in clinical pathology and histopathology compared to untreated controls defined an adverse effect. In the past decades, increased consistency in the definition of adversity with chemically-induced effects in laboratory animals, as well as in the assessment of human relevance has been reached. More recently, a paradigm shift in toxicity testing has been proposed, mainly driven by concerns over animal welfare but also thanks to the development of new methods. Currently, in vitro approaches, toxicogenomic technologies and computational tools, are available to provide mechanistic insight in toxicological Mode of Action (MOA) of the adverse effects observed in laboratory animals. The vision described as Tox21c (Toxicity Testing in the 21st century) aims at predicting in vivo toxicity using a bottom-up-approach, starting with understanding of MOA based on in vitro data to ultimately predict adverse effects in humans. At present, a practical application of the Tox21c vision is still far away. While moving towards toxicity prediction based on in vitro data, a stepwise reduction of in vivo testing is foreseen by combining in vitro with in vivo tests. Furthermore, newly developed methods will also be increasingly applied, in conjunction with established methods in order to gain trust in these new methods. This confidence is based on a critical scientific prerequisite: the establishment of a causal link between data obtained with new technologies and adverse effects manifested in repeated-dose in vivo toxicity studies. It is proposed to apply the principles described in the WHO/IPCS framework of MOA to obtain this link. Finally, an international database of known MOAs obtained in laboratory animals using data-rich chemicals will facilitate regulatory acceptance and could further help in the validation of the toxicity pathway and adverse outcome pathway concepts.

Affiliation buffers stress: cumulative genetic risk in oxytocin-vasopressin genes combines with early caregiving to predict PTSD in war-exposed young children.

PubMed

Feldman, R; Vengrober, A; Ebstein, R P

2014-03-11

Research indicates that risk for post-traumatic stress disorder (PTSD) is shaped by the interaction between genetic vulnerability and early caregiving experiences; yet, caregiving has typically been assessed by adult retrospective accounts. Here, we employed a prospective longitudinal design with real-time observations of early caregiving combined with assessment of genetic liability along the axis of vasopressin-oxytocin (OT) gene pathways to test G × E contributions to PTSD. Participants were 232 young Israeli children (1.5-5 years) and their parents, including 148 living in zones of continuous war and 84 controls. A cumulative genetic risk factor was computed for each family member by summing five risk alleles across three genes (OXTR, CD38 and AVPR1a) previously associated with psychopathology, sociality and caregiving. Child PTSD was diagnosed and mother-child interactions were observed in multiple contexts. In middle childhood (7-8 years), child psychopathology was re-evaluated. War exposure increased propensity to develop Axis-I disorder by threefold: 60% of exposed children displayed a psychiatric disorder by middle childhood and 62% of those showed several comorbid disorders. On the other hand, maternal sensitive support reduced risk for psychopathology. G × E effect was found for child genetic risk: in the context of war exposure, greater genetic risk on the vasopressin-OT pathway increased propensity for psychopathology. Among exposed children, chronicity of PTSD from early to middle childhood was related to higher child, maternal and paternal genetic risk, low maternal support and greater initial avoidance symptoms. Child avoidance was predicted by low maternal support and reduced mother-child reciprocity. These findings underscore the saliency of both genetic and behavioral facets of the human affiliation system in shaping vulnerability to PTSD as well as providing an underlying mechanism of post-traumatic resilience.

Affiliation buffers stress: cumulative genetic risk in oxytocin–vasopressin genes combines with early caregiving to predict PTSD in war-exposed young children

PubMed Central

Feldman, R; Vengrober, A; Ebstein, R P

2014-01-01

Research indicates that risk for post-traumatic stress disorder (PTSD) is shaped by the interaction between genetic vulnerability and early caregiving experiences; yet, caregiving has typically been assessed by adult retrospective accounts. Here, we employed a prospective longitudinal design with real-time observations of early caregiving combined with assessment of genetic liability along the axis of vasopressin–oxytocin (OT) gene pathways to test G × E contributions to PTSD. Participants were 232 young Israeli children (1.5–5 years) and their parents, including 148 living in zones of continuous war and 84 controls. A cumulative genetic risk factor was computed for each family member by summing five risk alleles across three genes (OXTR, CD38 and AVPR1a) previously associated with psychopathology, sociality and caregiving. Child PTSD was diagnosed and mother–child interactions were observed in multiple contexts. In middle childhood (7–8 years), child psychopathology was re-evaluated. War exposure increased propensity to develop Axis-I disorder by threefold: 60% of exposed children displayed a psychiatric disorder by middle childhood and 62% of those showed several comorbid disorders. On the other hand, maternal sensitive support reduced risk for psychopathology. G × E effect was found for child genetic risk: in the context of war exposure, greater genetic risk on the vasopressin–OT pathway increased propensity for psychopathology. Among exposed children, chronicity of PTSD from early to middle childhood was related to higher child, maternal and paternal genetic risk, low maternal support and greater initial avoidance symptoms. Child avoidance was predicted by low maternal support and reduced mother–child reciprocity. These findings underscore the saliency of both genetic and behavioral facets of the human affiliation system in shaping vulnerability to PTSD as well as providing an underlying mechanism of post-traumatic resilience. PMID:24618689

Evidence against the facilitation of the vergence command during saccade-vergence interactions.

PubMed

Hendel, Tal; Gur, Moshe

2012-11-01

Combined saccade-vergence movements result when gaze shifts are made to targets that differ both in direction and in depth from the momentary fixation point. Currently, there are two rivaling schemes to explain these eye movements. According to the first, such eye movements are due to a combination of a conjugate saccadic command and a symmetric vergence command; the two commands are not taken to be independent but instead are suggested to interact in a nonlinear manner, which leads to an intra-saccadic facilitation of the vergence command. According to the second scheme, the saccade generator is disconjugate, thus encoding vergence information in the saccadic commands themselves, and the remaining vergence requirement is provided by an asymmetric mechanism. Here, we test the scheme that suggests an intra-saccadic facilitation of the vergence command. We analyze this scheme and show that it has two fundamental properties. The first is that the vergence command is always symmetric, even during the intra-saccadic facilitation. The second is that the facilitated (and symmetric) vergence command sums linearly with the conjugate saccadic command at the final common pathway. Taking these properties together, this scheme predicts that the total magnitude of the saccadic component of combined saccade-vergence movements can be decomposed into a conjugate part and a symmetric part. When we tested this prediction in combined saccade-vergence movements of humans, we found that it was not confirmed. Thus, our results are incompatible with the facilitation of the vergence command hypothesis. Although these results do not directly verify the rivaling hypothesis, which suggests a disconjugate saccade generator, they do provide it with indirect support.

Modeling central metabolism and energy biosynthesis across microbial life

DOE PAGES

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal; ...

2016-08-08

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life.

PubMed

Edirisinghe, Janaka N; Weisenhorn, Pamela; Conrad, Neal; Xia, Fangfang; Overbeek, Ross; Stevens, Rick L; Henry, Christopher S

2016-08-08

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Completing the Link between Exposure Science and ...

EPA Pesticide Factsheets

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making. The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports G

Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa[W

PubMed Central

Wang, Jack P.; Naik, Punith P.; Chen, Hsi-Chuan; Shi, Rui; Lin, Chien-Yuan; Liu, Jie; Shuford, Christopher M.; Li, Quanzi; Sun, Ying-Hsuan; Tunlaya-Anukit, Sermsawat; Williams, Cranos M.; Muddiman, David C.; Ducoste, Joel J.; Sederoff, Ronald R.; Chiang, Vincent L.

2014-01-01

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants. PMID:24619611

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teeguarden, Justin G.; Tan, Yu-Mei; Edwards, Stephen W.

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) conceptmore » in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.« less

Longitudinal Examination of Peer and Partner Influences on Gender-specific Pathways From Child Abuse to Adult Crime

PubMed Central

Lee, Jungeun Olivia; Herrenkohl, Todd I.; Jung, Hyunzee; Skinner, Martie L.; Klika, J. Bart

2015-01-01

Research provides increasing evidence of the association of child abuse with adult antisocial behavior. However, less is known about the developmental pathways that underlie this association. Building on the life course model of antisocial behavior, the present study examined possible developmental pathways linking various forms of child abuse (physical, emotional, sexual) to adult antisocial behavior. These pathways include child and adolescent antisocial behavior, as well as adulthood measures of partner risk taking, warmth, and antisocial peer influences. Data are from the Lehigh Longitudinal Study, a prospective longitudinal study examining long-term developmental outcomes subsequent to child maltreatment. Participant families in the Lehigh Longitudinal Study were followed from preschool age into adulthood. Analyses of gender differences addressed the consistency of path coefficients across genders. Results for 297 adult participants followed from early childhood showed that, for both genders, physical and emotional child abuse predicted adult crime indirectly through child and adolescent antisocial behavior, as well as adult partner and antisocial peer influences. However, for females, having an antisocial partner predicted an affiliation with antisocial peers, and that in turn predicted adult crime. For males, having an antisocial partner was associated with less partner warmth, which in turn predicted an affiliation with antisocial peers, itself a proximal predictor of adult crime. Sexual abuse also predicted adolescent antisocial behavior, but only for males, supporting what some have called “a delayed-onset pathway” for females, whereby the exposure to early risks produce much later developmental outcomes. PMID:26271556

Fisetin, a phytochemical, potentiates sorafenib-induced apoptosis and abrogates tumor growth in athymic nude mice implanted with BRAF-mutated melanoma cells

PubMed Central

Pal, Harish Chandra; Baxter, Ronald D.; Hunt, Katherine M.; Agarwal, Jyoti; Elmets, Craig A.; Athar, Mohammad; Afaq, Farrukh

2015-01-01

Melanoma is the most deadly form of cutaneous malignancy, and its incidence rates are rising worldwide. In melanoma, constitutive activation of the BRAF/MEK/ERK (MAPK) and PI3K/AKT/mTOR (PI3K) signaling pathways plays a pivotal role in cell proliferation, survival and tumorigenesis. A combination of compounds that lead to an optimal blockade of these critical signaling pathways may provide an effective strategy for prevention and treatment of melanoma. The phytochemical fisetin is known to possess anti-proliferative and pro-apoptotic activities. We found that fisetin treatment inhibited PI3K signaling pathway in melanoma cells. Therefore, we investigated the effect of fisetin and sorafenib (an RAF inhibitor) alone and in combination on cell proliferation, apoptosis and tumor growth. Combination treatment (fisetin + sorafenib) more effectively reduced the growth of BRAF-mutated human melanoma cells at lower doses when compared to individual agents. In addition, combination treatment resulted in enhanced (i) apoptosis, (ii) cleavage of caspase-3 and PARP, (iii) expression of Bax and Bak, (iv) inhibition of Bcl2 and Mcl-1, and (v) inhibition of expression of PI3K, phosphorylation of MEK1/2, ERK1/2, AKT and mTOR. In athymic nude mice subcutaneously implanted with melanoma cells (A375 and SK-MEL-28), we found that combination therapy resulted in greater reduction of tumor growth when compared to individual agents. Furthermore, combination therapy was more effective than monotherapy in: (i) inhibition of proliferation and angiogenesis, (ii) induction of apoptosis, and (iii) inhibition of the MAPK and PI3K pathways in xenograft tumors. These data suggest that simultaneous inhibition of both these signaling pathways using combination of fisetin and sorafenib may serve as a therapeutic option for the management of melanoma. PMID:26299806

Fisetin, a phytochemical, potentiates sorafenib-induced apoptosis and abrogates tumor growth in athymic nude mice implanted with BRAF-mutated melanoma cells.

PubMed

Pal, Harish Chandra; Baxter, Ronald D; Hunt, Katherine M; Agarwal, Jyoti; Elmets, Craig A; Athar, Mohammad; Afaq, Farrukh

2015-09-29

Melanoma is the most deadly form of cutaneous malignancy, and its incidence rates are rising worldwide. In melanoma, constitutive activation of the BRAF/MEK/ERK (MAPK) and PI3K/AKT/mTOR (PI3K) signaling pathways plays a pivotal role in cell proliferation, survival and tumorigenesis. A combination of compounds that lead to an optimal blockade of these critical signaling pathways may provide an effective strategy for prevention and treatment of melanoma. The phytochemical fisetin is known to possess anti-proliferative and pro-apoptotic activities. We found that fisetin treatment inhibited PI3K signaling pathway in melanoma cells. Therefore, we investigated the effect of fisetin and sorafenib (an RAF inhibitor) alone and in combination on cell proliferation, apoptosis and tumor growth. Combination treatment (fisetin + sorafenib) more effectively reduced the growth of BRAF-mutated human melanoma cells at lower doses when compared to individual agents. In addition, combination treatment resulted in enhanced (i) apoptosis, (ii) cleavage of caspase-3 and PARP, (iii) expression of Bax and Bak, (iv) inhibition of Bcl2 and Mcl-1, and (v) inhibition of expression of PI3K, phosphorylation of MEK1/2, ERK1/2, AKT and mTOR. In athymic nude mice subcutaneously implanted with melanoma cells (A375 and SK-MEL-28), we found that combination therapy resulted in greater reduction of tumor growth when compared to individual agents. Furthermore, combination therapy was more effective than monotherapy in: (i) inhibition of proliferation and angiogenesis, (ii) induction of apoptosis, and (iii) inhibition of the MAPK and PI3K pathways in xenograft tumors. These data suggest that simultaneous inhibition of both these signaling pathways using combination of fisetin and sorafenib may serve as a therapeutic option for the management of melanoma.

XLS (c9orf142) is a new component of mammalian DNA double-stranded break repair

PubMed Central

Craxton, A; Somers, J; Munnur, D; Jukes-Jones, R; Cain, K; Malewicz, M

2015-01-01

Repair of double-stranded DNA breaks (DSBs) in mammalian cells primarily occurs by the non-homologous end-joining (NHEJ) pathway, which requires seven core proteins (Ku70/Ku86, DNA-PKcs (DNA-dependent protein kinase catalytic subunit), Artemis, XRCC4-like factor (XLF), XRCC4 and DNA ligase IV). Here we show using combined affinity purification and mass spectrometry that DNA-PKcs co-purifies with all known core NHEJ factors. Furthermore, we have identified a novel evolutionary conserved protein associated with DNA-PKcs—c9orf142. Computer-based modelling of c9orf142 predicted a structure very similar to XRCC4, hence we have named c9orf142—XLS (XRCC4-like small protein). Depletion of c9orf142/XLS in cells impaired DSB repair consistent with a defect in NHEJ. Furthermore, c9orf142/XLS interacted with other core NHEJ factors. These results demonstrate the existence of a new component of the NHEJ DNA repair pathway in mammalian cells. PMID:25941166

Genome mining of astaxanthin biosynthetic genes from Sphingomonas sp. ATCC 55669 for heterologous overproduction in Escherichia coli

PubMed Central

Ma, Tian; Zhou, Yuanjie; Li, Xiaowei; Zhu, Fayin; Cheng, Yongbo; Liu, Yi; Deng, Zixin

2015-01-01

Abstract As a highly valued keto‐carotenoid, astaxanthin is widely used in nutritional supplements and pharmaceuticals. Therefore, the demand for biosynthetic astaxanthin and improved efficiency of astaxanthin biosynthesis has driven the investigation of metabolic engineering of native astaxanthin producers and heterologous hosts. However, microbial resources for astaxanthin are limited. In this study, we found that the α‐Proteobacterium Sphingomonas sp. ATCC 55669 could produce astaxanthin naturally. We used whole‐genome sequencing to identify the astaxanthin biosynthetic pathway using a combined PacBio‐Illumina approach. The putative astaxanthin biosynthetic pathway in Sphingomonas sp. ATCC 55669 was predicted. For further confirmation, a high‐efficiency targeted engineering carotenoid synthesis platform was constructed in E. coli for identifying the functional roles of candidate genes. All genes involved in astaxanthin biosynthesis showed discrete distributions on the chromosome. Moreover, the overexpression of exogenous E. coli idi in Sphingomonas sp. ATCC 55669 increased astaxanthin production by 5.4‐fold. This study described a new astaxanthin producer and provided more biosynthesis components for bioengineering of astaxanthin in the future. PMID:26580858

Controlling specific locomotor behaviors through multidimensional monoaminergic modulation of spinal circuitries

PubMed Central

Musienko, Pavel; van den Brand, Rubia; Märzendorfer, Olivia; Roy, Roland R.; Gerasimenko, Yury; Edgerton, V. Reggie; Courtine, Grégoire

2012-01-01

Descending monoaminergic inputs markedly influence spinal locomotor circuits, but the functional relationships between specific receptors and the control of walking behavior remain poorly understood. To identify these interactions, we manipulated serotonergic, dopaminergic, and noradrenergic neural pathways pharmacologically during locomotion enabled by electrical spinal cord stimulation in adult spinal rats in vivo. Using advanced neurobiomechanical recordings and multidimensional statistical procedures, we reveal that each monoaminergic receptor modulates a broad but distinct spectrum of kinematic, kinetic and EMG characteristics, which we expressed into receptor–specific functional maps. We then exploited this catalogue of monoaminergic tuning functions to devise optimal pharmacological combinations to encourage locomotion in paralyzed rats. We found that, in most cases, receptor-specific modulatory influences summed near algebraically when stimulating multiple pathways concurrently. Capitalizing on these predictive interactions, we elaborated a multidimensional monoaminergic intervention that restored coordinated hindlimb locomotion with normal levels of weight bearing and partial equilibrium maintenance in spinal rats. These findings provide new perspectives on the functions of and interactions between spinal monoaminergic receptor systems in producing stepping, and define a framework to tailor pharmacotherapies for improving neurological functions after CNS disorders. PMID:21697376

Biomolecularmodeling and simulation: a field coming of age

PubMed Central

Schlick, Tamar; Collepardo-Guevara, Rosana; Halvorsen, Leif Arthur; Jung, Segun; Xiao, Xia

2013-01-01

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right. PMID:21226976

Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting.

PubMed

Helfrich, Randolph F; Mander, Bryce A; Jagust, William J; Knight, Robert T; Walker, Matthew P

2018-01-03

The coupled interaction between slow-wave oscillations and sleep spindles during non-rapid-eye-movement (NREM) sleep has been proposed to support memory consolidation. However, little evidence in humans supports this theory. Moreover, whether such dynamic coupling is impaired as a consequence of brain aging in later life, contributing to cognitive and memory decline, is unknown. Combining electroencephalography (EEG), structural MRI, and sleep-dependent memory assessment, we addressed these questions in cognitively normal young and older adults. Directional cross-frequency coupling analyses demonstrated that the slow wave governs a precise temporal coordination of sleep spindles, the quality of which predicts overnight memory retention. Moreover, selective atrophy within the medial frontal cortex in older adults predicted a temporal dispersion of this slow wave-spindle coupling, impairing overnight memory consolidation and leading to forgetting. Prefrontal-dependent deficits in the spatiotemporal coordination of NREM sleep oscillations therefore represent one pathway explaining age-related memory decline. Copyright © 2017 Elsevier Inc. All rights reserved.

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

NASA Astrophysics Data System (ADS)

Moftakhari, Hamed; AghaKouchak, Amir; Sanders, Brett F.; Matthew, Richard A.; Mazdiyasni, Omid

2017-12-01

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998-2063) and mid-future (2018-2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950-2015) if adaptation measures are not implemented.

Characterization of Protein Tyrosine Phosphatase 1B Inhibition by Chlorogenic Acid and Cichoric Acid.

PubMed

Lipchock, James M; Hendrickson, Heidi P; Douglas, Bonnie B; Bird, Kelly E; Ginther, Patrick S; Rivalta, Ivan; Ten, Nicholas S; Batista, Victor S; Loria, J Patrick

2017-01-10

Protein tyrosine phosphatase 1B (PTP1B) is a known regulator of the insulin and leptin signaling pathways and is an active target for the design of inhibitors for the treatment of type II diabetes and obesity. Recently, cichoric acid (CHA) and chlorogenic acid (CGA) were predicted by docking methods to be allosteric inhibitors that bind distal to the active site. However, using a combination of steady-state inhibition kinetics, solution nuclear magnetic resonance experiments, and molecular dynamics simulations, we show that CHA is a competitive inhibitor that binds in the active site of PTP1B. CGA, while a noncompetitive inhibitor, binds in the second aryl phosphate binding site, rather than the predicted benzfuran binding pocket. The molecular dynamics simulations of the apo enzyme and cysteine-phosphoryl intermediate states with and without bound CGA suggest CGA binding inhibits PTP1B by altering hydrogen bonding patterns at the active site. This study provides a mechanistic understanding of the allosteric inhibition of PTP1B.

Prediction of thermodynamically reversible hydrogen storage reactions utilizing Ca-M(M = Li, Na, K)-B-H systems: a first-principles study.

PubMed

Guo, Yajuan; Ren, Ying; Wu, Haishun; Jia, Jianfeng

2013-12-01

Calcium borohydride is a potential candidate for onboard hydrogen storage because it has a high gravimetric capacity (11.5 wt.%) and a high volumetric hydrogen content (∼130 kg m(-3)). Unfortunately, calcium borohydride suffers from the drawback of having very strongly bound hydrogen. In this study, Ca(BH₄)₂ was predicted to form a destabilized system when it was mixed with LiBH₄, NaBH₄, or KBH₄. The release of hydrogen from Ca(BH₄)₂ was predicted to proceed via two competing reaction pathways (leading to CaB₆ and CaH₂ or CaB₁₂H₁₂ and CaH₂) that were found to have almost equal free energies. Using a set of recently developed theoretical methods derived from first principles, we predicted five new hydrogen storage reactions that are among the most attractive of those presently known. These combine high gravimetric densities (>6.0 wt.% H₂) with have low enthalpies [approximately 35 kJ/(mol(-1) H₂)] and are thermodynamically reversible at low pressure within the target window for onboard storage that is actively being considered for hydrogen storage applications. Thus, the first-principles theoretical design of new materials for energy storage in future research appears to be possible.

Bacterial community composition and predicted functional ecology of sponges, sediment and seawater from the thousand islands reef complex, West Java, Indonesia.

PubMed

de Voogd, Nicole J; Cleary, Daniel F R; Polónia, Ana R M; Gomes, Newton C M

2015-04-01

In the present study, we assessed the composition of Bacteria in four biotopes namely sediment, seawater and two sponge species (Stylissa massa and Xestospongia testudinaria) at four different reef sites in a coral reef ecosystem in West Java, Indonesia. In addition to this, we used a predictive metagenomic approach to estimate to what extent nitrogen metabolic pathways differed among bacterial communities from different biotopes. We observed marked differences in bacterial composition of the most abundant bacterial phyla, classes and orders among sponge species, water and sediment. Proteobacteria were by far the most abundant phylum in terms of both sequences and Operational Taxonomic Units (OTUs). Predicted counts for genes associated with the nitrogen metabolism suggested that several genes involved in the nitrogen cycle were enriched in sponge samples, including nosZ, nifD, nirK, norB and nrfA genes. Our data show that a combined barcoded pyrosequencing and predictive metagenomic approach can provide novel insights into the potential ecological functions of the microbial communities. Not only is this approach useful for our understanding of the vast microbial diversity found in sponges but also to understand the potential response of microbial communities to environmental change. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

PubMed

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Knowledge-Based Identification of Soluble Biomarkers: Hepatic Fibrosis in NAFLD as an Example

PubMed Central

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases. PMID:23405244

Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

PubMed

Hu, Peizhen; Chung, Leland W K; Berel, Dror; Frierson, Henry F; Yang, Hua; Liu, Chunyan; Wang, Ruoxiang; Li, Qinlong; Rogatko, Andre; Zhau, Haiyen E

2013-01-01

We reported (PLoS One 6 (12):e28670, 2011) that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC) tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE) tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1) expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

Multiple successional pathways in human-modified tropical landscapes: new insights from forest succession, forest fragmentation and landscape ecology research.

PubMed

Arroyo-Rodríguez, Víctor; Melo, Felipe P L; Martínez-Ramos, Miguel; Bongers, Frans; Chazdon, Robin L; Meave, Jorge A; Norden, Natalia; Santos, Bráulio A; Leal, Inara R; Tabarelli, Marcelo

2017-02-01

Old-growth tropical forests are being extensively deforested and fragmented worldwide. Yet forest recovery through succession has led to an expansion of secondary forests in human-modified tropical landscapes (HMTLs). Secondary forests thus emerge as a potential repository for tropical biodiversity, and also as a source of essential ecosystem functions and services in HMTLs. Such critical roles are controversial, however, as they depend on successional, landscape and socio-economic dynamics, which can vary widely within and across landscapes and regions. Understanding the main drivers of successional pathways of disturbed tropical forests is critically needed for improving management, conservation, and restoration strategies. Here, we combine emerging knowledge from tropical forest succession, forest fragmentation and landscape ecology research to identify the main driving forces shaping successional pathways at different spatial scales. We also explore causal connections between land-use dynamics and the level of predictability of successional pathways, and examine potential implications of such connections to determine the importance of secondary forests for biodiversity conservation in HMTLs. We show that secondary succession (SS) in tropical landscapes is a multifactorial phenomenon affected by a myriad of forces operating at multiple spatio-temporal scales. SS is relatively fast and more predictable in recently modified landscapes and where well-preserved biodiversity-rich native forests are still present in the landscape. Yet the increasing variation in landscape spatial configuration and matrix heterogeneity in landscapes with intermediate levels of disturbance increases the uncertainty of successional pathways. In landscapes that have suffered extensive and intensive human disturbances, however, succession can be slow or arrested, with impoverished assemblages and reduced potential to deliver ecosystem functions and services. We conclude that: (i) succession must be examined using more comprehensive explanatory models, providing information about the forces affecting not only the presence but also the persistence of species and ecological groups, particularly of those taxa expected to be extirpated from HMTLs; (ii) SS research should integrate new aspects from forest fragmentation and landscape ecology research to address accurately the potential of secondary forests to serve as biodiversity repositories; and (iii) secondary forest stands, as a dynamic component of HMTLs, must be incorporated as key elements of conservation planning; i.e. secondary forest stands must be actively managed (e.g. using assisted forest restoration) according to conservation goals at broad spatial scales. © 2015 Cambridge Philosophical Society.

Clinging to the Past: The Air Force’s War on Dual-Career Families

DTIC Science & Technology

2014-06-01

combines existing research on stress and work-family conflict with new primary research on current USAF dual-career families in the form of a case...incompatibilities between the Air Force family schema (conceptions of, and practices relating to, USAF families). The study combines existing research on stress ... Stress Pathways ..................................................... 74 Figure 5, Civilian Dual-Career Stress Pathways

Novel therapeutic strategy targeting the Hedgehog signalling and mTOR pathways in biliary tract cancer

PubMed Central

Zuo, M; Rashid, A; Churi, C; Vauthey, J-N; Chang, P; Li, Y; Hung, M-C; Li, D; Javle, M

2015-01-01

Background: Activation of the PI3K/mTOR and Hedgehog (Hh) signalling pathways occurs frequently in biliary tract cancer (BTC). Crosstalk between these pathways occurs in other gastrointestinal cancers. The respective signalling inhibitors rapamycin and vismodegib may inhibit BTC synergistically and suppress cancer stem cells (CSCs). Methods: Gene expression profiling for p70S6k and Gli1 was performed with BTC cell lines. Tumour and pathway inhibitory effects of rapamycin and vismodegib were investigated in BTC preclinical models and CSCs. Results: Rapamycin and vismodegib synergistically reduced BTC cell viability and proliferation. This drug combination arrested BTC Mz-ChA-1 cells in the G1 phase but had no significant effect on the cell cycle of BTC Sk-ChA-1 cells. Combined treatment inhibited the proliferation of CSCs and ALDH-positive cells. Nanog and Oct-4 expression in CSCs was decreased by the combination treatment. Western blotting results showed the p-p70S6K, p-Gli1, p-mTOR, and p-AKT protein expression were inhibited by the combination treatment in BTC cells. In an Mz-ChA-1 xenograft model, combination treatment resulted in 80% inhibition of tumour growth and prolonged tumour doubling time. In 4 of 10 human BTC specimens, tumour p-p70S6K and Gli1 protein expression levels were decreased with the combination treatment. Conclusions: Targeted inhibition of the PI3K/mTOR and Hhpathways indicates a new avenue for BTC treatment with combination therapy. PMID:25742482

The synergistic antitumor activity of arsenic trioxide and vitamin K2 in HL-60 cells involves increased ROS generation and regulation of the ROS-dependent MAPK signaling pathway.

PubMed

Qu, Hui; Tong, Danan; Zhang, Yanqing; Kang, Kai; Zhang, Yuling; Chen, Lan; Ren, Lihong

2013-10-01

The aim of this study was to investigate the synergistic anticancer effects of arsenic trioxide (ATO) and vitamin K2 (VK2) in HL-60 cells, and elucidate the potential mechanisms. HL-60 cells were exposed to ATO and VK2, either alone or in combination. Cell proliferation and apoptosis were assessed. The combination index (CI) method was used to evaluate whether the action of the drug combination was synergistic, additive or antagonistic. Reactive oxygen species (ROS) and the mitogen-activated protein kinase (MAPK) signaling pathway were also studied, to provide insight into potential mechanisms. The results showed that combining ATO with VK2 significantly inhibited HL-60 cell growth more than either agent alone, indicating a synergistic effect with CI < 1. Annexin V staining demonstrated that the inhibition of cell growth by the drug combination was mediated through an increase in apoptosis; this was supported by examination of caspase-3 and caspase-9 with Western blot assays. Furthermore, induction of ROS, and phosphorylation and activation of the JNK and p38 (but not ERK1/2) pathways, was observed in cells administered the drug combination. Prior treatment with the antioxidant, N-acetylcysteine, partly blocked the apoptosis and expression of caspase-3 induced by the drug combination; apoptosis and expression of caspase-3 were also reversed by inhibitors of JNK or p38. These results suggest that ATO and VK2 act synergistically to increase HL-60 cell apoptosis, through ROS generation and regulation of the MAPK signaling pathway.

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

PubMed

Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

2018-06-27

Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction.

PubMed

Cang, Zixuan; Wei, Guo-Wei

2018-02-01

Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40Å  away from the binding site, which has a significant ramification to drug and protein design. Copyright © 2017 John Wiley & Sons, Ltd.