Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

PubMed Central

2014-01-01

Background Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA), in which activated synovial fibroblasts (SFBs) play a major role. Since the detailed mechanisms underlying this activation are still unclear, simultaneous investigation of multi-stimuli activation of SFBs offers the possibility to elucidate the regulatory effects of multiple mediators and to gain new insights into disease pathogenesis. Methods A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1 β, TNF- α, TGF- β, and PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133 Plus 2.0) were batch-corrected applying ‘ComBat’, analyzed for differentially expressed genes over time with ‘Limma’, and used for the inference of a robust GRN with NetGenerator V2.0, a heuristic ordinary differential equation-based method with soft integration of prior knowledge. Results Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting the genes belonging to the most significant gene ontology (GO) term, i.e., ‘cartilage development’, a dynamic, robust, moderately complex multi-stimuli GRN was generated with 24 genes and 57 edges in total, 31 of which were gene-to-gene edges. Prior literature-based knowledge derived from Pathway Studio or manual searches was reflected in the final network by 25/57 confirmed edges (44%). The model contained known network motifs crucial for dynamic cellular behavior, e.g., cross-talk among pathways, positive feed-back loops, and positive feed-forward motifs (including suppression of the transcriptional repressor OSR2 by all 4 stimuli. Conclusion A multi-stimuli GRN highly concordant with literature data was successfully generated by network inference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since 10 predicted edges were independently validated by literature findings post network inference. The selected GO term ‘cartilage development’ contained a number of differentiation markers, growth factors, and transcription factors with potential relevance for RA. Finally, the model provided new insight into the response of RA-SFBs to multiple stimuli implicated in the pathogenesis of RA, in particular to the ‘novel’ potent growth factor PDGF-D. PMID:24989895

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.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

PubMed Central

Mourão, Márcio A.; Srividhya, Jeyaraman; McSharry, Patrick E.; Crampin, Edmund J.; Schnell, Santiago

2011-01-01

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1). PMID:22096591

A graphical user interface for a method to infer kinetics and network architecture (MIKANA).

PubMed

Mourão, Márcio A; Srividhya, Jeyaraman; McSharry, Patrick E; Crampin, Edmund J; Schnell, Santiago

2011-01-01

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

PubMed

Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J

2013-12-08

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

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.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Transcript abundance on its own cannot be used to infer fluxes in central metabolism

DOE PAGES

Schwender, Jorg; Konig, Christina; Klapperstuck, Matthias; ...

2014-11-28

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism,more » some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. Lastly, this limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.« less

Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response.

PubMed

Nickerson, M L; Witte, N; Im, K M; Turan, S; Owens, C; Misner, K; Tsang, S X; Cai, Z; Wu, S; Dean, M; Costello, J C; Theodorescu, D

2017-01-05

The utility of tumor-derived cell lines is dependent on their ability to recapitulate underlying genomic aberrations and primary tumor biology. Here, we sequenced the exomes of 25 bladder cancer (BCa) cell lines and compared mutations, copy number alterations (CNAs), gene expression and drug response to BCa patient profiles in The Cancer Genome Atlas (TCGA). We observed a mutation pattern associated with altered CpGs and APOBEC-family cytosine deaminases similar to mutation signatures derived from somatic alterations in muscle-invasive (MI) primary tumors, highlighting a major mechanism(s) contributing to cancer-associated alterations in the BCa cell line exomes. Non-silent sequence alterations were confirmed in 76 cancer-associated genes, including mutations that likely activate oncogenes TERT and PIK3CA, and alter chromatin-associated proteins (MLL3, ARID1A, CHD6 and KDM6A) and established BCa genes (TP53, RB1, CDKN2A and TSC1). We identified alterations in signaling pathways and proteins with related functions, including the PI3K/mTOR pathway, altered in 60% of lines; BRCA DNA repair, 44%; and SYNE1-SYNE2, 60%. Homozygous deletions of chromosome 9p21 are known to target the cell cycle regulators CDKN2A and CDKN2B. This loci was commonly lost in BCa cell lines and we show the deletions extended to the polyamine enzyme methylthioadenosine (MTA) phosphorylase (MTAP) in 36% of lines, transcription factor DMRTA1 (27%) and antiviral interferon epsilon (IFNE, 19%). Overall, the BCa cell line genomic aberrations were concordant with those found in BCa patient tumors. We used gene expression and copy number data to infer pathway activities for cell lines, then used the inferred pathway activities to build a predictive model of cisplatin response. When applied to platinum-treated patients gathered from TCGA, the model predicted treatment-specific response. Together, these data and analysis represent a valuable community resource to model basic tumor biology and to study the pharmacogenomics of BCa.

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

PubMed

Zhang, Tianyu; Xu, Jielin; Deng, Siyuan; Zhou, Fengqi; Li, Jin; Zhang, Liwei; Li, Lang; Wang, Qi-En; Li, Fuhai

2018-01-01

Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.

Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor.

PubMed

Li, Xueling; Zhu, Min; Brasier, Allan R; Kudlicki, Andrzej S

2015-04-01

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

PubMed

Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

2016-06-01

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways *

PubMed Central

Erdem, Cemal; Nagle, Alison M.; Casa, Angelo J.; Litzenburger, Beate C.; Wang, Yu-fen; Taylor, D. Lansing; Lee, Adrian V.; Lezon, Timothy R.

2016-01-01

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. PMID:27364358

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

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...

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

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.

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways.

PubMed

Erdem, Cemal; Nagle, Alison M; Casa, Angelo J; Litzenburger, Beate C; Wang, Yu-Fen; Taylor, D Lansing; Lee, Adrian V; Lezon, Timothy R

2016-09-01

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

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

Investigating dysregulated pathways in Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network.

PubMed

Zhou, Wei; Zhang, Yan; Li, Yue-Hua; Wang, Shuang; Zhang, Jing-Jing; Zhang, Cui-Xia; Zhang, Zhi-Sheng

2017-02-01

This work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN). The inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein-protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model. A total of 20,545 genes, 449,833 interactions and 1189 pathways were obtained in the gene expression data, PPI data and pathway data, respectively. The PIN was consisted of 8388 interactions and 1189 nodes, and Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins was identified as the seed pathway. Finally, 15 dysregulated pathways in MSP (AUC=0.999) were obtained for SA infected samples, such as Respiratory electron transport and DNA Replication. We have identified 15 dysregulated pathways for SA infected macrophages based on PIN. The findings might provide potential biomarkers for early detection and therapy of SA infection, and give insights to reveal the molecular mechanism underlying SA infections. However, how these dysregulated pathways worked together still needs to be studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tox21 Enricher: Web-based Chemical/Biological Functional Annotation Analysis Tool Based on Tox21 Toxicity Screening Platform.

PubMed

Hur, Junguk; Danes, Larson; Hsieh, Jui-Hua; McGregor, Brett; Krout, Dakota; Auerbach, Scott

2018-05-01

The US Toxicology Testing in the 21st Century (Tox21) program was established to develop more efficient and human-relevant toxicity assessment methods. The Tox21 program screens >10,000 chemicals using quantitative high-throughput screening (qHTS) of assays that measure effects on toxicity pathways. To date, more than 70 assays have yielded >12 million concentration-response curves. The patterns of activity across assays can be used to define similarity between chemicals. Assuming chemicals with similar activity profiles have similar toxicological properties, we may infer toxicological properties based on its neighbourhood. One approach to inference is chemical/biological annotation enrichment analysis. Here, we present Tox21 Enricher, a web-based chemical annotation enrichment tool for the Tox21 toxicity screening platform. Tox21 Enricher identifies over-represented chemical/biological annotations among lists of chemicals (neighbourhoods), facilitating the identification of the toxicological properties and mechanisms in the chemical set. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pathway-based personalized analysis of cancer

PubMed Central

Drier, Yotam; Sheffer, Michal; Domany, Eytan

2013-01-01

We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers. PMID:23547110

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host–Pathogen Interaction Networks

PubMed Central

Wu, Chia-Chou; Chen, Bor-Sen

2016-01-01

Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host–pathogen dynamic interaction networks. The consideration of host–pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host–pathogen molecular interaction networks, and consequent inferences of the host–pathogen relationship could be translated into biomedical applications. PMID:26881892

A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host-Pathogen Interaction Networks.

PubMed

Wu, Chia-Chou; Chen, Bor-Sen

2016-01-01

Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host-pathogen dynamic interaction networks. The consideration of host-pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host-pathogen molecular interaction networks, and consequent inferences of the host-pathogen relationship could be translated into biomedical applications.

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.

Analysis of corkscrew signaling in the Drosophila epidermal growth factor receptor pathway during myogenesis.

PubMed

Johnson Hamlet, M R; Perkins, L A

2001-11-01

The Drosophila nonreceptor protein tyrosine phosphatase, Corkscrew (Csw), functions positively in multiple receptor tyrosine kinase (RTK) pathways, including signaling by the epidermal growth factor receptor (EGFR). Detailed phenotypic analyses of csw mutations have revealed that Csw activity is required in many of the same developmental processes that require EGFR function. However, it is still unclear where in the signaling hierarchy Csw functions relative to other proteins whose activities are also required downstream of the receptor. To address this issue, genetic interaction experiments were performed to place csw gene activity relative to the EGFR, spitz (spi), rhomboid (rho), daughter of sevenless (DOS), kinase-suppressor of ras (ksr), ras1, D-raf, pointed (pnt), and moleskin. We followed the EGFR-dependent formation of VA2 muscle precursor cells as a sensitive assay for these genetic interaction studies. First, we established that Csw has a positive function during mesoderm development. Second, we found that tissue-specific expression of a gain-of-function csw construct rescues loss-of-function mutations in other positive signaling genes upstream of rolled (rl)/MAPK in the EGFR pathway. Third, we were able to infer levels of EGFR signaling in various mutant backgrounds during myogenesis. This work extends previous studies of Csw during Torso and Sevenless RTK signaling to include an in-depth analysis of the role of Csw in the EGFR signaling pathway.

Analysis of corkscrew signaling in the Drosophila epidermal growth factor receptor pathway during myogenesis.

PubMed Central

Johnson Hamlet, M R; Perkins, L A

2001-01-01

The Drosophila nonreceptor protein tyrosine phosphatase, Corkscrew (Csw), functions positively in multiple receptor tyrosine kinase (RTK) pathways, including signaling by the epidermal growth factor receptor (EGFR). Detailed phenotypic analyses of csw mutations have revealed that Csw activity is required in many of the same developmental processes that require EGFR function. However, it is still unclear where in the signaling hierarchy Csw functions relative to other proteins whose activities are also required downstream of the receptor. To address this issue, genetic interaction experiments were performed to place csw gene activity relative to the EGFR, spitz (spi), rhomboid (rho), daughter of sevenless (DOS), kinase-suppressor of ras (ksr), ras1, D-raf, pointed (pnt), and moleskin. We followed the EGFR-dependent formation of VA2 muscle precursor cells as a sensitive assay for these genetic interaction studies. First, we established that Csw has a positive function during mesoderm development. Second, we found that tissue-specific expression of a gain-of-function csw construct rescues loss-of-function mutations in other positive signaling genes upstream of rolled (rl)/MAPK in the EGFR pathway. Third, we were able to infer levels of EGFR signaling in various mutant backgrounds during myogenesis. This work extends previous studies of Csw during Torso and Sevenless RTK signaling to include an in-depth analysis of the role of Csw in the EGFR signaling pathway. PMID:11729154

IDO chronic immune activation and tryptophan metabolic pathway: A potential pathophysiological link between depression and obesity.

PubMed

Chaves Filho, Adriano José Maia; Lima, Camila Nayane Carvalho; Vasconcelos, Silvânia Maria Mendes; de Lucena, David Freitas; Maes, Michael; Macedo, Danielle

2018-01-03

Obesity and depression are among the most pressing health problems in the contemporary world. Obesity and depression share a bidirectional relationship, whereby each condition increases the risk of the other. By inference, shared pathways may underpin the comorbidity between obesity and depression. Activation of cell-mediated immunity (CMI) is a key factor in the pathophysiology of depression. CMI cytokines, including IFN-γ, TNFα and IL-1β, induce the catabolism of tryptophan (TRY) by stimulating indoleamine 2,3-dioxygenase (IDO) resulting in the synthesis of kynurenine (KYN) and other tryptophan catabolites (TRYCATs). In the CNS, TRYCATs have been related to oxidative damage, inflammation, mitochondrial dysfunction, cytotoxicity, excitotoxicity, neurotoxicity and lowered neuroplasticity. The pathophysiology of obesity is also associated with a state of aberrant inflammation that activates aryl hydrocarbon receptor (AHR), a pathway involved in the detection of intracellular or environmental changes as well as with increases in the production of TRYCATs, being KYN an agonists of AHR. Both AHR and TRYCATS are involved in obesity and related metabolic disorders. These changes in the TRYCAT pathway may contribute to the onset of neuropsychiatric symptoms in obesity. This paper reviews the role of immune activation, IDO stimulation and increased TRYCAT production in the pathophysiology of depression and obesity. Here we suggest that increased synthesis of detrimental TRYCATs is implicated in comorbid obesity and depression and is a new drug target to treat both diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Induction of apoptosis through ER stress and TP53 in MCF-7 cells by the nanoparticle [Gd@C82(OH)22]n: A systems biology study.

PubMed

Wang, Lin; Meng, Jie; Cao, Weipeng; Li, Qizhai; Qiu, Yuqing; Sun, Baoyun; Li, Lei M

2014-06-01

The nanoparticle gadolinium endohedral metallofullerenol [Gd@C82(OH)22]n is a new candidate for cancer treatment with low toxicity. However, its anti-cancer mechanisms remain mostly unknown. In this study, we took a systems biology view of the gene expression profiles of human breast cancer cells (MCF-7) and human umbilical vein endothelial cells (ECV304) treated with and without [Gd@C82(OH)22]n, respectively, measured by the Agilent Gene Chip G4112F. To properly analyze these data, we modified a suit of statistical methods we developed. For the first time we applied the sub-sub normalization to Agilent two-color microarrays. Instead of a simple linear regression, we proposed to use a one-knot SPLINE model in the sub-sub normalization to account for nonlinear spatial effects. The parameters estimated by least trimmed squares- and S-estimators show similar normalization results. We made several kinds of inferences by integrating the expression profiles with the bioinformatic knowledge in KEGG pathways, Gene Ontology, JASPAR, and TRANSFAC. In the transcriptional inference, we proposed the BASE2.0 method to infer a transcription factor's up-regulation and down-regulation activities separately. Overall, [Gd@C82(OH)22]n induces more differentiation in MCF-7 cells than in ECV304 cells, particularly in the reduction of protein processing such as protein glucosylation, folding, targeting, exporting, and transporting. Among the KEGG pathways, the ErbB signaling pathway is up-regulated, whereas protein processing in endoplasmic reticulum (ER) is down-regulated. CHOP, a key pro-apoptotic gene downstream of the ER stress pathway, increases to nine folds in MCF-7 cells after treatment. These findings indicate that ER stress may be one important factor that induces apoptosis in MCF-7 cells after [Gd@C82(OH)22]n treatment. The expression profiles of genes associated with ER stress and apoptosis are statistically consistent with other profiles reported in the literature, such as those of HEK293T and MCF-7 cells induced by the miR-23a∼27a∼24-2 cluster. Furthermore, one of the inferred regulatory mechanisms comprises the apoptosis network centered around TP53, whose effective regulation of apoptosis is somehow reestablished after [Gd@C82(OH)22]n treatment. These results elucidate the application and development of [Gd@C82(OH)22]n and other fullerene derivates. Copyright © 2014 Elsevier Inc. All rights reserved.

Using genetic data to strengthen causal inference in observational research.

PubMed

Pingault, Jean-Baptiste; O'Reilly, Paul F; Schoeler, Tabea; Ploubidis, George B; Rijsdijk, Frühling; Dudbridge, Frank

2018-06-05

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach.

PubMed

Peng, Huiming; Peng, Tao; Wen, Jianguo; Engler, David A; Matsunami, Risë K; Su, Jing; Zhang, Le; Chang, Chung-Che Jeff; Zhou, Xiaobo

2014-07-01

p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mouse chronic social stress increases blood and brain kynurenine pathway activity and fear behaviour: Both effects are reversed by inhibition of indoleamine 2,3-dioxygenase.

PubMed

Fuertig, René; Azzinnari, Damiano; Bergamini, Giorgio; Cathomas, Flurin; Sigrist, Hannes; Seifritz, Erich; Vavassori, Stefano; Luippold, Andreas; Hengerer, Bastian; Ceci, Angelo; Pryce, Christopher R

2016-05-01

Psychosocial stress is a major risk factor for mood and anxiety disorders, in which excessive reactivity to aversive events/stimuli is a major psychopathology. In terms of pathophysiology, immune-inflammation is an important candidate, including high blood and brain levels of metabolites belonging to the kynurenine pathway. Animal models are needed to study causality between psychosocial stress, immune-inflammation and hyper-reactivity to aversive stimuli. The present mouse study investigated effects of psychosocial stress as chronic social defeat (CSD) versus control-handling (CON) on: Pavlovian tone-shock fear conditioning, activation of the kynurenine pathway, and efficacy of a specific inhibitor (IDOInh) of the tryptophan-kynurenine catabolising enzyme indoleamine 2,3-dioxygenase (IDO1), in reversing CSD effects on the kynurenine pathway and fear. CSD led to excessive fear learning and memory, whilst repeated oral escitalopram (antidepressant and anxiolytic) reversed excessive fear memory, indicating predictive validity of the model. CSD led to higher blood levels of TNF-α, IFN-γ, kynurenine (KYN), 3-hydroxykynurenine (3-HK) and kynurenic acid, and higher KYN and 3-HK in amygdala and hippocampus. CSD was without effect on IDO1 gene or protein expression in spleen, ileum and liver, whilst increasing liver TDO2 gene expression. Nonetheless, oral IDOInh reduced blood and brain levels of KYN and 3-HK in CSD mice to CON levels, and we therefore infer that CSD increases IDO1 activity by increasing its post-translational activation. Furthermore, repeated oral IDOInh reversed excessive fear memory in CSD mice to CON levels. IDOInh reversal of CSD-induced hyper-activity in the kynurenine pathway and fear system contributes significantly to the evidence for a causal pathway between psychosocial stress, immune-inflammation and the excessive fearfulness that is a major psychopathology in stress-related neuropsychiatric disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration

PubMed Central

Lobo, Daniel; Levin, Michael

2015-01-01

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form. PMID:26042810

Analyzing the molecular mechanism of lipoprotein localization in Brucella

PubMed Central

Goolab, Shivani; Roth, Robyn L.; van Heerden, Henriette; Crampton, Michael C.

2015-01-01

Bacterial lipoproteins possess diverse structure and functionality, ranging from bacterial physiology to pathogenic processes. As such many lipoproteins, originating from Brucella are exploited as potential vaccines to countermeasure brucellosis infection in the host. These membrane proteins are translocated from the cytoplasm to the cell membrane where they are anchored peripherally by a multifaceted targeting mechanism. Although much research has focused on the identification and classification of Brucella lipoproteins and their potential use as vaccine candidates for the treatment of Brucellosis, the underlying route for the translocation of these lipoproteins to the outer surface of the Brucella (and other pathogens) outer membrane (OM) remains mostly unknown. This is partly due to the complexity of the organism and evasive tactics used to escape the host immune system, the variation in biological structure and activity of lipoproteins, combined with the complex nature of the translocation machinery. The biosynthetic pathway of Brucella lipoproteins involves a distinct secretion system aiding translocation from the cytoplasm, where they are modified by lipidation, sorted by the lipoprotein localization machinery pathway and thereafter equipped for export to the OM. Surface localized lipoproteins in Brucella may employ a lipoprotein flippase or the β-barrel assembly complex for translocation. This review provides an overview of the characterized Brucella OM proteins that form part of the OM, including a handful of other characterized bacterial lipoproteins and their mechanisms of translocation. Lipoprotein localization pathways in gram negative bacteria will be used as a model to identify gaps in Brucella lipoprotein localization and infer a potential pathway. Of particular interest are the dual topology lipoproteins identified in Escherichia coli and Haemophilus influenza. The localization and topology of these lipoproteins from other gram negative bacteria are well characterized and may be useful to infer a solution to better understand the translocation process in Brucella. PMID:26579096

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

PubMed Central

Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

2017-01-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. PMID:27994170

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

PubMed Central

Shin, Junha; Lee, Insuk

2015-01-01

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes. PMID:26394049

Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model through the Application of Quantitative Systems Pharmacology.

PubMed

Pei, Fen; Li, Hongchun; Henderson, Mark J; Titus, Steven A; Jadhav, Ajit; Simeonov, Anton; Cobanoglu, Murat Can; Mousavi, Seyed H; Shun, Tongying; McDermott, Lee; Iyer, Prema; Fioravanti, Michael; Carlisle, Diane; Friedlander, Robert M; Bahar, Ivet; Taylor, D Lansing; Lezon, Timothy R; Stern, Andrew M; Schurdak, Mark E

2017-12-19

Quantitative Systems Pharmacology (QSP) is a drug discovery approach that integrates computational and experimental methods in an iterative way to gain a comprehensive, unbiased understanding of disease processes to inform effective therapeutic strategies. We report the implementation of QSP to Huntington's Disease, with the application of a chemogenomics platform to identify strategies to protect neuronal cells from mutant huntingtin induced death. Using the STHdh Q111 cell model, we investigated the protective effects of small molecule probes having diverse canonical modes-of-action to infer pathways of neuronal cell protection connected to drug mechanism. Several mechanistically diverse protective probes were identified, most of which showed less than 50% efficacy. Specific combinations of these probes were synergistic in enhancing efficacy. Computational analysis of these probes revealed a convergence of pathways indicating activation of PKA. Analysis of phospho-PKA levels showed lower cytoplasmic levels in STHdh Q111 cells compared to wild type STHdh Q7 cells, and these levels were increased by several of the protective compounds. Pharmacological inhibition of PKA activity reduced protection supporting the hypothesis that protection may be working, in part, through activation of the PKA network. The systems-level studies described here can be broadly applied to any discovery strategy involving small molecule modulation of disease phenotype.

MoCha: Molecular Characterization of Unknown Pathways.

PubMed

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome

PubMed Central

Diener, Christian; Muñoz-Gonzalez, Felipe; Encarnación, Sergio; Resendis-Antonio, Osbaldo

2016-01-01

During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway. PMID:27335086

Machine learning assembly landscapes from particle tracking data.

PubMed

Long, Andrew W; Zhang, Jie; Granick, Steve; Ferguson, Andrew L

2015-11-07

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data. We apply this technique to the nonequilibrium self-assembly of metallodielectric Janus colloids in an oscillating electric field, and quantify the impact of field strength, oscillation frequency, and salt concentration on the dominant assembly pathways and terminal aggregates. This combined computational and experimental framework furnishes new understanding of self-assembling systems, and quantitatively informs rational engineering of experimental conditions to drive assembly along desired aggregation pathways.

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network.

PubMed

Huang, Yezhou; Li, Shao

2010-01-18

Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity. With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor alpha (TNF-alpha) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs. A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-alpha on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-beta, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-alpha respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity. The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis.

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network

PubMed Central

2010-01-01

Background Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity. Methods With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs. Results A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-α on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-β, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-α respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity. Conclusion The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis. PMID:20122205

Intestinal Serotonin Transporter Inhibition by Toll-Like Receptor 2 Activation. A Feedback Modulation.

PubMed

Latorre, Eva; Layunta, Elena; Grasa, Laura; Castro, Marta; Pardo, Julián; Gomollón, Fernando; Alcalde, Ana I; Mesonero, José E

2016-01-01

TLR2 is a microbiota recognition receptor that has been described to contribute to intestinal homeostasis and to ameliorate inflammatory intestinal injury. In this context, serotonin (5-HT) has shown to be an essential intestinal physiological neuromodulator that is also involved in intestinal inflammatory diseases. Since the interaction between TLR2 activation and the intestinal serotoninergic system remains non-investigated, our main aim was to analyze the effect of TLR2 on intestinal serotonin transporter (SERT) activity and expression and the intracellular pathways involved. Caco-2/TC7 cells were used to analyze SERT and TLR2 molecular expression and SERT activity by measuring 5-HT uptake. The results showed that apical TLR2 activation inhibits SERT activity in Caco-2/TC7 cells mainly by reducing SERT protein level either in the plasma membrane, after short-term TLR2 activation or in both the plasma membrane and cell lysate, after long-term activation. cAMP/PKA pathway appears to mediate short-term inhibitory effect of TLR2 on SERT; however, p38 MAPK pathway has been shown to be involved in both short- and long-term TLR2 effect. Reciprocally, 5-HT long-term treatment yielded TLR2 down regulation in Caco-2/TC7 cells. Finally, results from in vivo showed an augmented intestinal SERT expression in mice Tlr2-/-, thus confirming our inhibitory effect of TLR2 on intestinal SERT in vitro. The present work infers that TLR2 may act in intestinal pathophysiology, not only by its inherent innate immune role, but also by regulating the intestinal serotoninergic system.

Intestinal Serotonin Transporter Inhibition by Toll-Like Receptor 2 Activation. A Feedback Modulation

PubMed Central

Layunta, Elena; Grasa, Laura; Castro, Marta; Pardo, Julián; Gomollón, Fernando; Mesonero, José E.

2016-01-01

TLR2 is a microbiota recognition receptor that has been described to contribute to intestinal homeostasis and to ameliorate inflammatory intestinal injury. In this context, serotonin (5-HT) has shown to be an essential intestinal physiological neuromodulator that is also involved in intestinal inflammatory diseases. Since the interaction between TLR2 activation and the intestinal serotoninergic system remains non-investigated, our main aim was to analyze the effect of TLR2 on intestinal serotonin transporter (SERT) activity and expression and the intracellular pathways involved. Caco-2/TC7 cells were used to analyze SERT and TLR2 molecular expression and SERT activity by measuring 5-HT uptake. The results showed that apical TLR2 activation inhibits SERT activity in Caco-2/TC7 cells mainly by reducing SERT protein level either in the plasma membrane, after short-term TLR2 activation or in both the plasma membrane and cell lysate, after long-term activation. cAMP/PKA pathway appears to mediate short-term inhibitory effect of TLR2 on SERT; however, p38 MAPK pathway has been shown to be involved in both short- and long-term TLR2 effect. Reciprocally, 5-HT long-term treatment yielded TLR2 down regulation in Caco-2/TC7 cells. Finally, results from in vivo showed an augmented intestinal SERT expression in mice Tlr2-/-, thus confirming our inhibitory effect of TLR2 on intestinal SERT in vitro. The present work infers that TLR2 may act in intestinal pathophysiology, not only by its inherent innate immune role, but also by regulating the intestinal serotoninergic system. PMID:28033388

Tracing the HIV-1 subtype B mobility in Europe: a phylogeographic approach

PubMed Central

Paraskevis, Dimitrios; Pybus, Oliver; Magiorkinis, Gkikas; Hatzakis, Angelos; Wensing, Annemarie MJ; van de Vijver, David A; Albert, Jan; Angarano, Guiseppe; Åsjö, Birgitta; Balotta, Claudia; Boeri, Enzo; Camacho, Ricardo; Chaix, Marie-Laure; Coughlan, Suzie; Costagliola, Dominique; De Luca, Andrea; de Mendoza, Carmen; Derdelinckx, Inge; Grossman, Zehava; Hamouda, Osama; Hoepelman, IM; Horban, Andrzej; Korn, Klaus; Kücherer, Claudia; Leitner, Thomas; Loveday, Clive; MacRae, Eilidh; Maljkovic-Berry, I; Meyer, Laurence; Nielsen, Claus; Op de Coul, Eline LM; Ormaasen, Vidar; Perrin, Luc; Puchhammer-Stöckl, Elisabeth; Ruiz, Lidia; Salminen, Mika O; Schmit, Jean-Claude; Schuurman, Rob; Soriano, Vincent; Stanczak, J; Stanojevic, Maja; Struck, Daniel; Van Laethem, Kristel; Violin, M; Yerly, Sabine; Zazzi, Maurizio; Boucher, Charles A; Vandamme, Anne-Mieke

2009-01-01

Background The prevalence and the origin of HIV-1 subtype B, the most prevalent circulating clade among the long-term residents in Europe, have been studied extensively. However the spatial diffusion of the epidemic from the perspective of the virus has not previously been traced. Results In the current study we inferred the migration history of HIV-1 subtype B by way of a phylogeography of viral sequences sampled from 16 European countries and Israel. Migration events were inferred from viral phylogenies by character reconstruction using parsimony. With regard to the spatial dispersal of the HIV subtype B sequences across viral phylogenies, in most of the countries in Europe the epidemic was introduced by multiple sources and subsequently spread within local networks. Poland provides an exception where most of the infections were the result of a single point introduction. According to the significant migratory pathways, we show that there are considerable differences across Europe. Specifically, Greece, Portugal, Serbia and Spain, provide sources shedding HIV-1; Austria, Belgium and Luxembourg, on the other hand, are migratory targets, while for Denmark, Germany, Italy, Israel, Norway, the Netherlands, Sweden, Switzerland and the UK we inferred significant bidirectional migration. For Poland no significant migratory pathways were inferred. Conclusion Subtype B phylogeographies provide a new insight about the geographical distribution of viral lineages, as well as the significant pathways of virus dispersal across Europe, suggesting that intervention strategies should also address tourists, travellers and migrants. PMID:19457244

Tracing the HIV-1 subtype B mobility in Europe: a phylogeographic approach

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

Leitner, Thomas; Paraskevis, D; Pybus, O

2008-01-01

The prevalence and the origin of HIV-1 subtype B, the most prevalent circulating clade among the long-term residents in Europe, have been studied extensively. However the spatial diffusion of the epidemic from the perspective of the virus has not previously been traced. In the current study we inferred the migration history of HIV-1 subtype B by way of a phylogeography of viral sequences sampled from 16 European countries and Israel. Migration events were inferred from viral phylogenies by character reconstruction using parsimony. With regard to the spatial dispersal of the HIV subtype B sequences across viral phylogenies, in most ofmore » the countries in Europe the epidemic was introduced by multiple sources and subsequently spread within local networks. Poland provides an exception where most of the infections were the result of a single point introduction. According to the significant migratory pathways, we show that there are considerable differences across Europe. Specifically, Greece, Portugal, Serbia and Spain, provide sources shedding HIV-1; Austria, Belgium and Luxembourg, on the other hand, are migratory targets, while for Denmark, Germany, Italy, Israel, Norway, the Netherlands, Sweden, Switzerland and the UK we inferred significant bidirectional migration. For Poland no significant migratory pathways were inferred. Subtype B phylogeographies provide a new insight about the geographical distribution of viral lineages, as well as the significant pathways of virus dispersal across Europe, suggesting that intervention strategies should also address tourists, travellers and migrants.« less

Metabolite fingerprinting, pathway analyses, and bioactivity correlations for plant species belonging to the Cornaceae, Fabaceae, and Rosaceae families.

PubMed

Son, Su Young; Kim, Na Kyung; Lee, Sunmin; Singh, Digar; Kim, Ga Ryun; Lee, Jong Seok; Yang, Hee-Sun; Yeo, Joohong; Lee, Sarah; Lee, Choong Hwan

2016-09-01

A multi-parallel approach gauging the mass spectrometry-based metabolite fingerprinting coupled with bioactivity and pathway evaluations could serve as an efficacious tool for inferring plant taxonomic orders. Thirty-four species from three plant families, namely Cornaceae (7), Fabaceae (9), and Rosaceae (18) were subjected to metabolite profiling using gas chromatography-time-of-flight-mass spectrometry (GC-TOF-MS) and ultrahigh performance liquid chromatography-linear trap quadrupole-ion trap-mass spectrometry (UHPLC-LTQ-IT-MS/MS), followed by multivariate analyses to determine the metabolites characteristic of these families. The partial least squares discriminant analysis (PLS-DA) revealed the distinct clustering pattern of metabolites for each family. The pathway analysis further highlighted the relatively higher proportions of flavonols and ellagitannins in the Cornaceae family than in the other two families. Higher levels of phenolic acids and flavan-3-ols were observed among species from the Rosaceae family, while amino acids, flavones, and isoflavones were more abundant among the Fabaceae family members. The antioxidant activities of plant extracts were measured using ABTS, DPPH, and FRAP assays, and indicated that extracts from the Rosaceae family had the highest activity, followed by those from Cornaceae and Fabaceae. The correlation map analysis positively links the proportional concentration of metabolites with their relative antioxidant activities, particularly in Cornaceae and Rosaceae. This work highlights the pre-eminence of the multi-parallel approach involving metabolite profiling and bioactivity evaluations coupled with metabolic pathways as an efficient methodology for the evaluation of plant phylogenies.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

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

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment.more » Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.« less

How adverse outcome pathways can aid the development and ...

EPA Pesticide Factsheets

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. The present manuscript reports on expert opinion and case studies that came out of a European Commission, Joint Research Centre-sponsored work

Large-Scale 13C Flux Profiling Reveals Conservation of the Entner-Doudoroff Pathway as a Glycolytic Strategy among Marine Bacteria That Use Glucose

PubMed Central

Klingner, Arne; Bartsch, Annekathrin; Dogs, Marco; Wagner-Döbler, Irene; Jahn, Dieter; Simon, Meinhard; Brinkhoff, Thorsten; Becker, Judith

2015-01-01

Marine bacteria form one of the largest living surfaces on Earth, and their metabolic activity is of fundamental importance for global nutrient cycling. Here, we explored the largely unknown intracellular pathways in 25 microbes representing different classes of marine bacteria that use glucose: Alphaproteobacteria, Gammaproteobacteria, and Flavobacteriia of the Bacteriodetes phylum. We used 13C isotope experiments to infer metabolic fluxes through their carbon core pathways. Notably, 90% of all strains studied use the Entner-Doudoroff (ED) pathway for glucose catabolism, whereas only 10% rely on the Embden-Meyerhof-Parnas (EMP) pathway. This result differed dramatically from the terrestrial model strains studied, which preferentially used the EMP pathway yielding high levels of ATP. Strains using the ED pathway exhibited a more robust resistance against the oxidative stress typically found in this environment. An important feature contributing to the preferential use of the ED pathway in the oceans could therefore be enhanced supply of NADPH through this pathway. The marine bacteria studied did not specifically rely on a distinct anaplerotic route, but the carboxylation of phosphoenolpyruvate (PEP) or pyruvate for fueling of the tricarboxylic acid (TCA) cycle was evenly distributed. The marine isolates studied belong to clades that dominate the uptake of glucose, a major carbon source for bacteria in seawater. Therefore, the ED pathway may play a significant role in the cycling of mono- and polysaccharides by bacterial communities in marine ecosystems. PMID:25616803

What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633

Gramene 2013: comparative plant genomics resources.

PubMed

Monaco, Marcela K; Stein, Joshua; Naithani, Sushma; Wei, Sharon; Dharmawardhana, Palitha; Kumari, Sunita; Amarasinghe, Vindhya; Youens-Clark, Ken; Thomason, James; Preece, Justin; Pasternak, Shiran; Olson, Andrew; Jiao, Yinping; Lu, Zhenyuan; Bolser, Dan; Kerhornou, Arnaud; Staines, Dan; Walts, Brandon; Wu, Guanming; D'Eustachio, Peter; Haw, Robin; Croft, David; Kersey, Paul J; Stein, Lincoln; Jaiswal, Pankaj; Ware, Doreen

2014-01-01

Gramene (http://www.gramene.org) is a curated online resource for comparative functional genomics in crops and model plant species, currently hosting 27 fully and 10 partially sequenced reference genomes in its build number 38. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. Whole-genome alignments complemented by phylogenetic gene family trees help infer syntenic and orthologous relationships. Genetic variation data, sequences and genome mappings available for 10 species, including Arabidopsis, rice and maize, help infer putative variant effects on genes and transcripts. The pathways section also hosts 10 species-specific metabolic pathways databases developed in-house or by our collaborators using Pathway Tools software, which facilitates searches for pathway, reaction and metabolite annotations, and allows analyses of user-defined expression datasets. Recently, we released a Plant Reactome portal featuring 133 curated rice pathways. This portal will be expanded for Arabidopsis, maize and other plant species. We continue to provide genetic and QTL maps and marker datasets developed by crop researchers. The project provides a unique community platform to support scientific research in plant genomics including studies in evolution, genetics, plant breeding, molecular biology, biochemistry and systems biology.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

PubMed

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M E Bette; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M; Whelan, Maurice

2017-02-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology.

Differential association for N-acetyltransferase 2 genotype and phenotype with bladder cancer risk in Chinese population.

PubMed

Quan, Lei; Chattopadhyay, Koushik; Nelson, Heather H; Chan, Kenneth K; Xiang, Yong-Bing; Zhang, Wei; Wang, Renwei; Gao, Yu-Tang; Yuan, Jian-Min

2016-06-28

N-acetyltransferase 2 (NAT2) is involved in both carcinogen detoxification through hepatic N-acetylation and carcinogen activation through local O-acetylation. NAT2 slow acetylation status is significantly associated with increased bladder cancer risk among European populations, but its association in Asian populations is inconclusive. NAT2 acetylation status was determined by both single nucleotide polymorphisms (SNPs) and caffeine metabolic ratio (CMR), in a population-based study of 494 bladder cancer patients and 507 control subjects in Shanghai, China. The CMR, a functional measure of hepatic N-acetylation, was significantly reduced in a dose-dependent manner among both cases and controls possessing the SNP-inferred NAT2 slow acetylation status (all P-values<5.0×10-10). The CMR-determined slow N-acetylation status (CMR<0.34) was significantly associated with a 50% increased risk of bladder cancer (odds ratio = 1.50, 95% confidence interval = 1.10-2.06) whereas the SNP-inferred slow acetylation statuses were significantly associated with an approximately 50% decreased risk of bladder cancer. The genotype-disease association was strengthened after the adjustment for CMR and was primarily observed among never smokers. The apparent differential associations for phenotypic and genetic measures of acetylation statuses with bladder cancer risk may reflect dual functions of NAT2 in bladder carcinogenesis because the former only measures the capacity of carcinogen detoxification pathway while the latter represents both carcinogen activation and detoxification pathways. Future studies are warranted to ascertain the specific role of N- and O-acetylation in bladder carcinogenesis, particularly in populations exposed to different types of bladder carcinogens.

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways

PubMed Central

Sacco, Francesca; Boldt, Karsten; Calderone, Alberto; Panni, Simona; Paoluzi, Serena; Castagnoli, Luisa; Ueffing, Marius; Cesareni, Gianni

2014-01-01

Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively. PMID:24847354

Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

PubMed Central

Li, Yue; Liang, Minggao; Zhang, Zhaolei

2014-01-01

Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML prognostic marker. Together, we provided a novel framework that successfully integrated the TCGA and ENCODE data in revealing AML-specific regulatory program at global level. PMID:25340776

Inferring introduction history and spread of Falcaria vulgaris Bernh. (Apiaceae) in the United States based on herbarium records

Treesearch

Sarbottam Piya; Madhav P. Nepal; Achal Neupane; Gary E. Larson; Jack L. Butler

2012-01-01

Herbarium records were studied to infer the introduction history and spread of the exotic Eurasian sickleweed (Falcaria vulgaris Bernh.) in the United States. The spread of the plant was reconstructed using the location of early collections as the possible sites of primary introduction, and the location of subsequent collections as potential pathways along which this...

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

PubMed

Yang, Jihong; Li, Zheng; Fan, Xiaohui; Cheng, Yiyu

2014-09-22

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

Evolution of enzymes in a series is driven by dissimilar functional demands.

PubMed

Salvador, Armindo; Savageau, Michael A

2006-02-14

That distinct enzyme activities in an unbranched metabolic pathway are evolutionarily tuned to a single functional requirement is a pervasive assumption. Here we test this assumption by examining the activities of two consecutively acting enzymes in human erythrocytes with an approach to quantitative evolutionary design that avoids the above-mentioned assumption. We previously found that avoidance of NADPH depletion during the pulses of oxidative load to which erythrocytes are normally exposed is the main functional requirement mediating selection for high glucose-6-phosphate dehydrogenase activity. In the present study, we find that, in contrast, the maintenance of oxidized glutathione at low concentrations is the main functional requirement mediating selection for high glutathione reductase activity. The results in this case show that, contrary to the assumption of a single functional requirement, natural selection for the normal activities of the distinct enzymes in the pathway is mediated by different requirements. On the other hand, the results agree with the more general principles that underlie our approach. Namely, that (i) the values of biochemical parameters evolve so as to fulfill the various performance requirements that are relevant to achieve high fitness, and (ii) these performance requirements can be inferred from quantitative systems theory considerations, informed by knowledge of specific aspects of the biochemistry, physiology, genetics, and ecology of the organism.

DNA-Catalyzed DNA Cleavage by a Radical Pathway with Well-Defined Products.

PubMed

Lee, Yujeong; Klauser, Paul C; Brandsen, Benjamin M; Zhou, Cong; Li, Xinyi; Silverman, Scott K

2017-01-11

We describe an unprecedented DNA-catalyzed DNA cleavage process in which a radical-based reaction pathway cleanly results in excision of most atoms of a specific guanosine nucleoside. Two new deoxyribozymes (DNA enzymes) were identified by in vitro selection from N 40 or N 100 random pools initially seeking amide bond hydrolysis, although they both cleave simple single-stranded DNA oligonucleotides. Each deoxyribozyme generates both superoxide (O 2 -• or HOO • ) and hydrogen peroxide (H 2 O 2 ) and leads to the same set of products (3'-phosphoglycolate, 5'-phosphate, and base propenal) as formed by the natural product bleomycin, with product assignments by mass spectrometry and colorimetric assay. We infer the same mechanistic pathway, involving formation of the C4' radical of the guanosine nucleoside that is subsequently excised. Consistent with a radical pathway, glutathione fully suppresses catalysis. Conversely, adding either superoxide or H 2 O 2 from the outset strongly enhances catalysis. The mechanism of generation and involvement of superoxide and H 2 O 2 by the deoxyribozymes is not yet defined. The deoxyribozymes do not require redox-active metal ions and function with a combination of Zn 2+ and Mg 2+ , although including Mn 2+ increases the activity, and Mn 2+ alone also supports catalysis. In contrast to all of these observations, unrelated DNA-catalyzed radical DNA cleavage reactions require redox-active metals and lead to mixtures of products. This study reports an intriguing example of a well-defined, DNA-catalyzed, radical reaction process that cleaves single-stranded DNA and requires only redox-inactive metal ions.

Dynamic change of SGK expression and its role in neuron apoptosis after traumatic brain injury.

PubMed

Wu, Xinmin; Mao, Hui; Liu, Jiao; Xu, Jian; Cao, Jianhua; Gu, Xingxing; Cui, Gang

2013-01-01

Activation of specific signaling pathways in response to mechanical trauma causes delayed neuronal apoptosis; GSK-3β/β-catenin signaling plays a critical role in the apoptosis of neurons in CNS diseases, SGK was discovered as a regulator of GSK-3β/β-catenin pathway, The goal of this study was to determine if the mechanism of cell death or survival mediated by the SGK/GSK-3β/β-catenin pathway is involved in a rat model of TBI. Here, an acute traumatic brain injury model was applied to investigate the expression change and possible roles of SGK, Expression of SGK, and total-GSK-3β, phospho-GSK3β on ser-9, beta-catenin, and caspase-3 were examined by immunohistochemistry and Western blot analysis. Double immunofluorescent staining was used to observe the SGK localizations. Si-RNA was performed to identify whether SGK regulates neuron apoptosis via GSK-3β/β-catenin pathway, ultimately inhibit caspase-3 activation. Temporally, SGK expression showed an increase pattern after TBI and reached a peak at day 3. Spatially, SGK was widely expressed in the neuron, rarely in astrocytes and oligodendrocytes; in addition, the expression patterns of active caspase-3 and phospho-GSK3β were parallel with that of SGK, at the same time, the expression of β-catenin shows similarity with SGK. In vitro, to further investigate the function of SGK, a neuronal cell line PC12 was employed to establish an apoptosis model. We analyzed the association of SGK with apoptosis on PC12 cells by western blot, immunofluorescent labeling and siRNA. the results implied that SGK plays an important role in neuron apoptosis via the regulation of GSK3β/β-catenin signaling pathway; ultimately inhibit caspase-3 activation. Taken together, we inferred traumatic brain injury induced an upregulation of SGK in the central nervous system, which show a protective role in neuron apoptosis.

Contribution of different taste cells and signaling pathways to the discrimination of "bitter" taste stimuli by an insect.

PubMed

Glendinning, John I; Davis, Adrienne; Ramaswamy, Sudha

2002-08-15

Animals can discriminate among many different types of foods. This discrimination process involves multiple sensory systems, but the sense of taste is known to play a central role. We asked how the taste system contributes to the discrimination of different "bitter" taste stimuli in Manduca sexta caterpillars. This insect has approximately eight bilateral pairs of taste cells that respond selectively to bitter taste stimuli. Each bilateral pair of bitter-sensitive taste cells has a different molecular receptive range (MRR); some of these taste cells also contain two signaling pathways with distinctive MRRs and temporal patterns of spiking. To test for discrimination, we habituated the caterpillar's taste-mediated aversive response to one bitter taste stimulus (salicin) and then asked whether this habituation phenomenon generalized to four other bitter taste stimuli (caffeine, aristolochic acid, Grindelia extract, and Canna extract). We inferred that the two compounds were discriminable if the habituation phenomenon failed to generalize (e.g., from salicin to aristolochic acid). We found that M. sexta could discriminate between salicin and those bitter taste stimuli that activate (1) different populations of bitter-sensitive taste cells (Grindelia extract and Canna extract) or (2) different signaling pathways within the same bitter-sensitive taste cell (aristolochic acid). M. sexta could not discriminate between salicin and a bitter taste stimulus that activates the same signaling pathway within the same bitter-sensitive taste cell (caffeine). We propose that the heterogeneous population of bitter-sensitive taste cells and signaling pathways within this insect facilitates the discrimination of bitter taste stimuli.

Differentiable cortical networks for inferences concerning people’s intentions versus physical causality

PubMed Central

Mason, Robert A.; Just, Marcel Adam

2010-01-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist’s intention or a physical consequence of a character’s action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists’ intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. PMID:21229617

Inferring Molecular Processes Heterogeneity from Transcriptional Data.

PubMed

Gogolewski, Krzysztof; Wronowska, Weronika; Lech, Agnieszka; Lesyng, Bogdan; Gambin, Anna

2017-01-01

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.

Inferring Molecular Processes Heterogeneity from Transcriptional Data

PubMed Central

Wronowska, Weronika; Lesyng, Bogdan; Gambin, Anna

2017-01-01

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs. PMID:29362714

Active Inference, homeostatic regulation and adaptive behavioural control

PubMed Central

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-01-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. PMID:26365173

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

The Features of Fecal and Ileal Mucosa-Associated Microbiota in Dairy Calves during Early Infection with Mycobacterium avium Subspecies paratuberculosis.

PubMed

Derakhshani, Hooman; De Buck, Jeroen; Mortier, Rienske; Barkema, Herman W; Krause, Denis O; Khafipour, Ehsan

2016-01-01

Current diagnostic tests for Johne's disease (JD), a chronic granulomatous inflammation of the gastrointestinal tract of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), lack the sensitivity to identify infected animals at early (asymptomatic) stages of the disease. The objective was to determine the pattern of MAP-associated dysbiosis of intestinal microbiota as a potential biomarker for early detection of infected cattle. To that end, genomic DNA was extracted from ileal mucosa and fecal samples collected from 28 MAP-positive and five control calves. High-throughput Illumina sequencing of the V4 hypervariable region of the 16S rRNA gene was used for community profiling of ileal mucosa-associated (MAM) or fecal microbiota. The PERMANOVA analysis of unweighted UniFrac distances revealed distinct clustering of ileal MAM (P = 0.049) and fecal microbiota (P = 0.068) in MAP-infected vs. control cattle. Microbiota profile of MAP-infected animals was further investigated by linear discriminant analysis effective size (LEfSe); several bacterial taxa within the phylum Proteobacteria were overrepresented in ileal MAM of control calves. Moreover, based on reconstructed metagenomes (PICRUSt) of ileal MAM, functional pathways associated with MAP infection were inferred. Enrichment of lysine and histidine metabolism pathways, and underrepresentation of glutathione metabolism and leucine and isoleucine degradation pathways in MAP-infected calves suggested potential contributions of ileal MAM in development of intestinal inflammation. Finally, simultaneous overrepresentation of families Planococcaceae and Paraprevotellaceae, as well as underrepresentation of genera Faecalibacterium and Akkermansia in the fecal microbiota of infected cattle, served as potential biomarker for identifying infected cattle during subclinical stages of JD. Collectively, based on compositional and functional shifts in intestinal microbiota of infected cattle, we inferred that this dynamic network of microorganisms had an active role in intestinal homeostasis.

Cavallin et al 2016

EPA Pesticide Factsheets

Adult fathead minnows were exposed to dilutions of a historically estrogenic wastewater treatment plant effluent in a 21-d reproduction study. This dataset is comprised of a variety of endpoints representing key events along adverse outcome pathways linking estrogen receptor activation and other molecular initiating events to reproductive impairment. This study demonstrates the value of using an integrative approach that encompasses analytical chemistry, in vitro bioassays, and in vivo apical and pathway-based approaches with endpoints spanning from molecular- (e.g., gene expression) to organismal- (e.g., reproduction) levels of biological organization to help infer causal relationships between chemistry and potential effects on reproduction.This dataset is associated with the following publication:Cavallin , J., K. Jensen , M. Kahl , D. Villeneuve , K. Lee, A. Schroeder , J. Mayasich, E. Eid, K. Nelson, R. Milsk, B. Blackwell, J. Berninger , C. LaLone, C. Blanksma, T. Jicha , C. Elonen , R. Johnson , and G. Ankley. Pathway-based approaches for assessment of real-time exposure to an estrogenic wastewater treatment plant effluent on fathead minnow reproduction. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 35(3): 702-716, (2016).

A global analysis of protein expression profiles in Sinorhizobium meliloti: discovery of new genes for nodule occupancy and stress adaptation.

PubMed

Djordjevic, Michael A; Chen, Han Cai; Natera, Siria; Van Noorden, Giel; Menzel, Christian; Taylor, Scott; Renard, Clotilde; Geiger, Otto; Weiller, Georg F

2003-06-01

A proteomic examination of Sinorhizobium meliloti strain 1021 was undertaken using a combination of 2-D gel electrophoresis, peptide mass fingerprinting, and bioinformatics. Our goal was to identify (i) putative symbiosis- or nutrient-stress-specific proteins, (ii) the biochemical pathways active under different conditions, (iii) potential new genes, and (iv) the extent of posttranslational modifications of S. meliloti proteins. In total, we identified the protein products of 810 genes (13.1% of the genome's coding capacity). The 810 genes generated 1,180 gene products, with chromosomal genes accounting for 78% of the gene products identified (18.8% of the chromosome's coding capacity). The activity of 53 metabolic pathways was inferred from bioinformatic analysis of proteins with assigned Enzyme Commission numbers. Of the remaining proteins that did not encode enzymes, ABC-type transporters composed 12.7% and regulatory proteins 3.4% of the total. Proteins with up to seven transmembrane domains were identified in membrane preparations. A total of 27 putative nodule-specific proteins and 35 nutrient-stress-specific proteins were identified and used as a basis to define genes and describe processes occurring in S. meliloti cells in nodules and under stress. Several nodule proteins from the plant host were present in the nodule bacteria preparations. We also identified seven potentially novel proteins not predicted from the DNA sequence. Post-translational modifications such as N-terminal processing could be inferred from the data. The posttranslational addition of UMP to the key regulator of nitrogen metabolism, PII, was demonstrated. This work demonstrates the utility of combining mass spectrometry with protein arraying or separation techniques to identify candidate genes involved in important biological processes and niche occupations that may be intransigent to other methods of gene expression profiling.

Differentiable cortical networks for inferences concerning people's intentions versus physical causality.

PubMed

Mason, Robert A; Just, Marcel Adam

2011-02-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist's intention or a physical consequence of a character's action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists' intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. Copyright © 2010 Wiley-Liss, Inc.

Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review.

PubMed

Haber, Noah; Smith, Emily R; Moscoe, Ellen; Andrews, Kathryn; Audy, Robin; Bell, Winnie; Brennan, Alana T; Breskin, Alexander; Kane, Jeremy C; Karra, Mahesh; McClure, Elizabeth S; Suarez, Elizabeth A

2018-01-01

The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies' strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies' causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. We find a large disparity between the strength of language as presented to the research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer.

Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review

PubMed Central

Smith, Emily R.; Moscoe, Ellen; Audy, Robin; Bell, Winnie; Brennan, Alana T.; Breskin, Alexander; Kane, Jeremy C.; Suarez, Elizabeth A.

2018-01-01

Background The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. Methods We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies’ strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. Results We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies’ causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. Conclusions We find a large disparity between the strength of language as presented to the research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer. PMID:29847549

Fair Inference on Outcomes

PubMed Central

Nabi, Razieh; Shpitser, Ilya

2017-01-01

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are “sensitive,” in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

Mental Models of Invisible Logical Networks

NASA Technical Reports Server (NTRS)

Sanderson, P.

1984-01-01

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

Active Inference, homeostatic regulation and adaptive behavioural control.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Ancestral sequence reconstruction in primate mitochondrial DNA: compositional bias and effect on functional inference.

PubMed

Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D

2004-10-01

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and flexibility. To determine whether biased reconstructions using optimization methods might affect inferences of functional properties, ancestral primate mitochondrial tRNA sequences were inferred and helix-forming propensities for conserved pairs were evaluated in silico. For ambiguously reconstructed nucleotides at sites with high base composition variability, ancestral tRNA sequences from Bayesian analyses were more compatible with canonical base pairing than were those inferred by other methods. Thus, nucleotide bias in reconstructed sequences apparently can lead to serious bias and inaccuracies in functional predictions.

TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.

PubMed

Cordero, Pablo; Stuart, Joshua M

2017-01-01

The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolvingco-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Active inference and robot control: a case study

PubMed Central

Nizard, Ange; Friston, Karl; Pezzulo, Giovanni

2016-01-01

Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours. PMID:27683002

Forward Inferences: From Activation to Long-Term Memory.

ERIC Educational Resources Information Center

Klin, Celia M.; Murray, John D.; Levine, William H.; Guzman, Alexandria E.

1999-01-01

Investigates the extent to which forward inferences are activated and encoded during reading, as well as their prevalence and their time course. Finds that inferences were encoded and retained in working memory in both high- and low-predictability conditions, and that high-predictability forward inferences were encoded into long-term memory.…

Mitochondrial DNA and trade data support multiple origins of Helicoverpa armigera (Lepidoptera, Noctuidae) in Brazil

PubMed Central

Tay, Wee Tek; Walsh, Thomas K.; Downes, Sharon; Anderson, Craig; Jermiin, Lars S.; Wong, Thomas K. F.; Piper, Melissa C.; Chang, Ester Silva; Macedo, Isabella Barony; Czepak, Cecilia; Behere, Gajanan T.; Silvie, Pierre; Soria, Miguel F.; Frayssinet, Marie; Gordon, Karl H. J.

2017-01-01

The Old World bollworm Helicoverpa armigera is now established in Brazil but efforts to identify incursion origin(s) and pathway(s) have met with limited success due to the patchiness of available data. Using international agricultural/horticultural commodity trade data and mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) and cytochrome b (Cyt b) gene markers, we inferred the origins and incursion pathways into Brazil. We detected 20 mtDNA haplotypes from six Brazilian states, eight of which were new to our 97 global COI-Cyt b haplotype database. Direct sequence matches indicated five Brazilian haplotypes had Asian, African, and European origins. We identified 45 parsimoniously informative sites and multiple substitutions per site within the concatenated (945 bp) nucleotide dataset, implying that probabilistic phylogenetic analysis methods are needed. High diversity and signatures of uniquely shared haplotypes with diverse localities combined with the trade data suggested multiple incursions and introduction origins in Brazil. Increasing agricultural/horticultural trade activities between the Old and New Worlds represents a significant biosecurity risk factor. Identifying pest origins will enable resistance profiling that reflects countries of origin to be included when developing a resistance management strategy, while identifying incursion pathways will improve biosecurity protocols and risk analysis at biosecurity hotspots including national ports. PMID:28350004

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

PubMed Central

2014-01-01

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility. PMID:25276860

Mitochondrial DNA and trade data support multiple origins of Helicoverpa armigera (Lepidoptera, Noctuidae) in Brazil.

PubMed

Tay, Wee Tek; Walsh, Thomas K; Downes, Sharon; Anderson, Craig; Jermiin, Lars S; Wong, Thomas K F; Piper, Melissa C; Chang, Ester Silva; Macedo, Isabella Barony; Czepak, Cecilia; Behere, Gajanan T; Silvie, Pierre; Soria, Miguel F; Frayssinet, Marie; Gordon, Karl H J

2017-03-28

The Old World bollworm Helicoverpa armigera is now established in Brazil but efforts to identify incursion origin(s) and pathway(s) have met with limited success due to the patchiness of available data. Using international agricultural/horticultural commodity trade data and mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) and cytochrome b (Cyt b) gene markers, we inferred the origins and incursion pathways into Brazil. We detected 20 mtDNA haplotypes from six Brazilian states, eight of which were new to our 97 global COI-Cyt b haplotype database. Direct sequence matches indicated five Brazilian haplotypes had Asian, African, and European origins. We identified 45 parsimoniously informative sites and multiple substitutions per site within the concatenated (945 bp) nucleotide dataset, implying that probabilistic phylogenetic analysis methods are needed. High diversity and signatures of uniquely shared haplotypes with diverse localities combined with the trade data suggested multiple incursions and introduction origins in Brazil. Increasing agricultural/horticultural trade activities between the Old and New Worlds represents a significant biosecurity risk factor. Identifying pest origins will enable resistance profiling that reflects countries of origin to be included when developing a resistance management strategy, while identifying incursion pathways will improve biosecurity protocols and risk analysis at biosecurity hotspots including national ports.

Mitochondrial DNA and trade data support multiple origins of Helicoverpa armigera (Lepidoptera, Noctuidae) in Brazil

NASA Astrophysics Data System (ADS)

Tay, Wee Tek; Walsh, Thomas K.; Downes, Sharon; Anderson, Craig; Jermiin, Lars S.; Wong, Thomas K. F.; Piper, Melissa C.; Chang, Ester Silva; Macedo, Isabella Barony; Czepak, Cecilia; Behere, Gajanan T.; Silvie, Pierre; Soria, Miguel F.; Frayssinet, Marie; Gordon, Karl H. J.

2017-03-01

The Old World bollworm Helicoverpa armigera is now established in Brazil but efforts to identify incursion origin(s) and pathway(s) have met with limited success due to the patchiness of available data. Using international agricultural/horticultural commodity trade data and mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) and cytochrome b (Cyt b) gene markers, we inferred the origins and incursion pathways into Brazil. We detected 20 mtDNA haplotypes from six Brazilian states, eight of which were new to our 97 global COI-Cyt b haplotype database. Direct sequence matches indicated five Brazilian haplotypes had Asian, African, and European origins. We identified 45 parsimoniously informative sites and multiple substitutions per site within the concatenated (945 bp) nucleotide dataset, implying that probabilistic phylogenetic analysis methods are needed. High diversity and signatures of uniquely shared haplotypes with diverse localities combined with the trade data suggested multiple incursions and introduction origins in Brazil. Increasing agricultural/horticultural trade activities between the Old and New Worlds represents a significant biosecurity risk factor. Identifying pest origins will enable resistance profiling that reflects countries of origin to be included when developing a resistance management strategy, while identifying incursion pathways will improve biosecurity protocols and risk analysis at biosecurity hotspots including national ports.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

The Cysteine Dioxygenase Homologue from Pseudomonas aeruginosa Is a 3-Mercaptopropionate Dioxygenase*

PubMed Central

Tchesnokov, Egor P.; Fellner, Matthias; Siakkou, Eleni; Kleffmann, Torsten; Martin, Lois W.; Aloi, Sekotilani; Lamont, Iain L.; Wilbanks, Sigurd M.; Jameson, Guy N. L.

2015-01-01

Thiol dioxygenation is the initial oxidation step that commits a thiol to important catabolic or biosynthetic pathways. The reaction is catalyzed by a family of specific non-heme mononuclear iron proteins each of which is reported to react efficiently with only one substrate. This family of enzymes includes cysteine dioxygenase, cysteamine dioxygenase, mercaptosuccinate dioxygenase, and 3-mercaptopropionate dioxygenase. Using sequence alignment to infer cysteine dioxygenase activity, a cysteine dioxygenase homologue from Pseudomonas aeruginosa (p3MDO) has been identified. Mass spectrometry of P. aeruginosa under standard growth conditions showed that p3MDO is expressed in low levels, suggesting that this metabolic pathway is available to the organism. Purified recombinant p3MDO is able to oxidize both cysteine and 3-mercaptopropionic acid in vitro, with a marked preference for 3-mercaptopropionic acid. We therefore describe this enzyme as a 3-mercaptopropionate dioxygenase. Mössbauer spectroscopy suggests that substrate binding to the ferrous iron is through the thiol but indicates that each substrate could adopt different coordination geometries. Crystallographic comparison with mammalian cysteine dioxygenase shows that the overall active site geometry is conserved but suggests that the different substrate specificity can be related to replacement of an arginine by a glutamine in the active site. PMID:26272617

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

Schwender, Jorg; Konig, Christina; Klapperstuck, Matthias

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism,more » some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. Lastly, this limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.« less

Neural correlates of species-typical illogical cognitive bias in human inference.

PubMed

Ogawa, Akitoshi; Yamazaki, Yumiko; Ueno, Kenichi; Cheng, Kang; Iriki, Atsushi

2010-09-01

The ability to think logically is a hallmark of human intelligence, yet our innate inferential abilities are marked by implicit biases that often lead to illogical inference. For example, given AB ("if A then B"), people frequently but fallaciously infer the inverse, BA. This mode of inference, called symmetry, is logically invalid because, although it may be true, it is not necessarily true. Given pairs of conditional relations, such as AB and BC, humans reflexively perform two additional modes of inference: transitivity, whereby one (validly) infers AC; and equivalence, whereby one (invalidly) infers CA. In sharp contrast, nonhuman animals can handle transitivity but can rarely be made to acquire symmetry or equivalence. In the present study, human subjects performed logical and illogical inferences about the relations between abstract, visually presented figures while their brain activation was monitored with fMRI. The prefrontal, medial frontal, and intraparietal cortices were activated during all modes of inference. Additional activation in the precuneus and posterior parietal cortex was observed during transitivity and equivalence, which may reflect the need to retrieve the intermediate stimulus (B) from memory. Surprisingly, the patterns of brain activation in illogical and logical inference were very similar. We conclude that the observed inference-related fronto-parietal network is adapted for processing categorical, but not logical, structures of association among stimuli. Humans might prefer categorization over the memorization of logical structures in order to minimize the cognitive working memory load when processing large volumes of information.

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

Discovering causal signaling pathways through gene-expression patterns

PubMed Central

Parikh, Jignesh R.; Klinger, Bertram; Xia, Yu; Marto, Jarrod A.; Blüthgen, Nils

2010-01-01

High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/. PMID:20494976

Learning from instructional explanations: effects of prompts based on the active-constructive-interactive framework.

PubMed

Roelle, Julian; Müller, Claudia; Roelle, Detlev; Berthold, Kirsten

2015-01-01

Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active < constructive learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active < constructive learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive < interactive learning hypothesis, the learners who received adapted remedial explanations and revision prompts as add-ons to reduced explanations and inference prompts acquired more conceptual knowledge.

Learning from Instructional Explanations: Effects of Prompts Based on the Active-Constructive-Interactive Framework

PubMed Central

Roelle, Julian; Müller, Claudia; Roelle, Detlev; Berthold, Kirsten

2015-01-01

Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active < constructive learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active < constructive learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive < interactive learning hypothesis, the learners who received adapted remedial explanations and revision prompts as add-ons to reduced explanations and inference prompts acquired more conceptual knowledge. PMID:25853629

The merged basins of signal transduction pathways in spatiotemporal cell biology.

PubMed

Hou, Yingchun; Hou, Yang; He, Siyu; Ma, Caixia; Sun, Mengyao; He, Huimin; Gao, Ning

2014-03-01

Numerous evidences have indicated that a signal system is composed by signal pathways, each pathway is composed by sub-pathways, and the sub-pathway is composed by the original signal terminals initiated with a protein/gene. We infer the terminal signals merged signal transduction system as "signal basin". In this article, we discussed the composition and regulation of signal basins, and the relationship between the signal basin control and triple W of spatiotemporal cell biology. Finally, we evaluated the importance of the systemic regulation to gene expression by signal basins under triple W. We hope our discussion will be the beginning to cause the attention for this area from the scientists of life science. © 2013 Wiley Periodicals, Inc.

Computational methods for analysis and inference of kinase/inhibitor relationships

PubMed Central

Ferrè, Fabrizio; Palmeri, Antonio; Helmer-Citterich, Manuela

2014-01-01

The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies. PMID:25071826

Reverse engineering biological networks :applications in immune responses to bio-toxins.

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

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Unfolded Protein Response and PERK Kinase as a New Therapeutic Target in the Pathogenesis of Alzheimer's Disease.

PubMed

Rozpedek, Wioletta; Markiewicz, Lukasz; Diehl, J Alan; Pytel, Dariusz; Majsterek, Ireneusz

2015-01-01

Recent evidence suggests that the development of Alzheimer's disease (AD) and related cognitive loss is due to mutations in the Amyloid Precursor Protein (APP) gene on chromosome 21 and increased activation of eukaryotic translation initiation factor-2α (eIF2α) phosphorylation. The high level of misfolded and unfolded proteins loading in Endoplasmic Reticulum (ER) lumen triggers ER stress and as a result Unfolded Protein Response (UPR) pathways are activated. Stress-dependent activation of the protein kinase RNA-like endoplasmic reticulum kinase (PERK) leads to the significant elevation of phospho-eIF2α. That attenuates general translation and, on the other hand, promotes the preferential synthesis of Activating Transcription Factor 4 (ATF4) and secretase β (BACE1) - a pivotal enzyme responsible for the initiation of the amyloidogenic pathway resulting in the generation of the amyloid β (Aβ) variant with high ability to form toxic senile plaques in AD brains. Moreover, excessive, long-term stress conditions may contribute to inducing neuronal death by apoptosis as a result of the overactivated expression of pro-apoptotic proteins via ATF4. These findings allow to infer that dysregulated translation, increased expression of BACE1 and ATF4, as a result of eIF2α phosphorylation, may be a major contributor to structural and functional neuronal loss resulting in memory impairment. Thus, blocking PERK-dependent eIF2α phosphorylation through specific, small-molecule PERK branch inhibitors seems to be a potential treatment strategy for AD individuals. That may contribute to the restoration of global translation rates and reduction of expression of ATF4 and BACE1. Hence, the treatment strategy can block accelerated β -amyloidogenesis by reduction in APP cleaving via the BACE1-dependent amyloidogenic pathway.

Systematic network assessment of the carcinogenic activities of cadmium

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

Chen, Peizhan; Duan, Xiaohua; Li, Mian

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscapemore » software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.« less

Novel Approaches for Delineating and Studying "Hotspots" and "Hot Moments" in Fluvial Environments

NASA Astrophysics Data System (ADS)

Williams, K. H.; Bücker, M.; Flores Orozco, A.; Hobson, C.; Robbins, M.

2014-12-01

Experiments at the Department of Energy's Rifle, CO (USA) field site have long focused on stimulated biogeochemical pathways arising from organic carbon injection. While reductive pathways and their relation to uranium immobilization have been a focus since 2002, ongoing studies are exploring oxidative pathways and their role in mediating fluxes of C, N, S, and aqueous metals. Insights gained from 'stimulation' experiments are providing insight into analogous natural biogeochemical pathways that mediate elemental cycling in the absence of exogenous carbon. Such reactions are instead mediated by endogenous pools of natural organic matter (NOM) deposited during aggradation of aquifer sediments associated with fluvial processes within the Colorado River floodplain. Discrete lenses of fine-grained, organic-rich sediments enriched in reduced species, such as Fe(II) and iron sulfides have been identified along the active margin of the floodplain. Referred to as "naturally reduced zones" (NRZs), these localities constitute a distinct facies type within an otherwise gravel-dominated, largely NOM-deficient matrix. NRZs represent 'hotspots' of seasonally intense C, N, S, and U cycling during excursions in groundwater elevation. Air bubble imbibition within the capillary fringe is inferred to contribute to seasonally oxic groundwater, with its puntuated, 'hot moment' like impact on redox-mediated reactions exhibiting close correspondence to those induced through the intentional introduction of oxidants. Reactions induce sharp gradients in nitrate and sulfate resulting from elevated rates of nitrification and oxidation of reduced sulfur as dissolved oxygen becomes non-limiting. Given their outsized role in constraining the location and timing of critcal element cycling pathways, delineating the distribution of NRZs across scales of relevance to natural field systems is of great importance. Novel mapping approaches borrowed from the field of exploration geophysics provide one means for identifying such 'hotspots' across a variety of environments where their formation is favored. Drilling activities and deployment of monitoring approaches to study cycling pathways of interest and as a function of hydrologic perturbation may then be performed in a targeted and scientificlally-informed manner.

Interlocked positive and negative feedback network motifs regulate β-catenin activity in the adherens junction pathway

PubMed Central

Klinke, David J.; Horvath, Nicholas; Cuppett, Vanessa; Wu, Yueting; Deng, Wentao; Kanj, Rania

2015-01-01

The integrity of epithelial tissue architecture is maintained through adherens junctions that are created through extracellular homotypic protein–protein interactions between cadherin molecules. Cadherins also provide an intracellular scaffold for the formation of a multiprotein complex that contains signaling proteins, including β-catenin. Environmental factors and controlled tissue reorganization disrupt adherens junctions by cleaving the extracellular binding domain and initiating a series of transcriptional events that aim to restore tissue homeostasis. However, it remains unclear how alterations in cell adhesion coordinate transcriptional events, including those mediated by β-catenin in this pathway. Here were used quantitative single-cell and population-level in vitro assays to quantify the endogenous pathway dynamics after the proteolytic disruption of the adherens junctions. Using prior knowledge of isolated elements of the overall network, we interpreted these data using in silico model-based inference to identify the topology of the regulatory network. Collectively the data suggest that the regulatory network contains interlocked network motifs consisting of a positive feedback loop, which is used to restore the integrity of adherens junctions, and a negative feedback loop, which is used to limit β-catenin–induced gene expression. PMID:26224311

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Inference Generation during Text Comprehension by Adults with Right Hemisphere Brain Damage: Activation Failure Versus Multiple Activation.

ERIC Educational Resources Information Center

Tompkins, Connie A.; Fassbinder, Wiltrud; Blake, Margaret Lehman; Baumgaertner, Annette; Jayaram, Nandini

2004-01-01

ourse comprehensionEvidence conflicts as to whether adults with right hemisphere brain damage (RHD) generate inferences during text comprehension. M. Beeman (1993) reported that adults with RHD fail to activate the lexical-semantic bases of routine bridging inferences, which are necessary for comprehension. But other evidence indicates that adults…

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

The NCBI BioSystems database.

PubMed

Geer, Lewis Y; Marchler-Bauer, Aron; Geer, Renata C; Han, Lianyi; He, Jane; He, Siqian; Liu, Chunlei; Shi, Wenyao; Bryant, Stephen H

2010-01-01

The NCBI BioSystems database, found at http://www.ncbi.nlm.nih.gov/biosystems/, centralizes and cross-links existing biological systems databases, increasing their utility and target audience by integrating their pathways and systems into NCBI resources. This integration allows users of NCBI's Entrez databases to quickly categorize proteins, genes and small molecules by metabolic pathway, disease state or other BioSystem type, without requiring time-consuming inference of biological relationships from the literature or multiple experimental datasets.

Glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) metabolism computational network analysis between chimpanzee and human left cerebrum.

PubMed

Sun, Lingjun; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Lin, Hong

2011-12-01

We identified significantly higher expression of the genes glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) from human left cerebrums versus chimpanzees. Yet the distinct low- and high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism networks between chimpanzee and human left cerebrum remain to be elucidated. Here, we constructed low- and high-expression activated and inhibited upstream and downstream AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network between chimpanzee and human left cerebrum in GEO data set by gene regulatory network inference method based on linear programming and decomposition procedure, under covering AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 pathway and matching metabolism enrichment analysis by CapitalBio MAS 3.0 integration of public databases, including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, etc. Our results show that the AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network has more activated and less inhibited molecules in chimpanzee, but less activated and more inhibited in the human left cerebrum. We inferred stronger carbohydrate, glutathione and proteoglycan metabolism, ATPase activity, but weaker base excision repair, arachidonic acid and drug metabolism as a result of inducing cell growth in low-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of chimpanzee left cerebrum; whereas stronger lipid metabolism, amino acid catabolism, DNA repair but weaker inflammatory response, cell proliferation, glutathione and carbohydrate metabolism as a result of inducing cell differentiation in high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of human left cerebrum. Our inferences are consistent with recent reports and computational activation and inhibition gene number patterns, respectively.

ShinyKGode: an interactive application for ODE parameter inference using gradient matching.

PubMed

Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk

2018-07-01

Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. Supplementary data are available at Bioinformatics online.

Chlorine partitioning in the lowermost Arctic vortex during the cold winter 2015/2016

NASA Astrophysics Data System (ADS)

Marsing, Andreas; Jurkat, Tina; Voigt, Christiane; Kaufmann, Stefan; Schlage, Romy; Engel, Andreas; Hoor, Peter; Krause, Jens

2017-04-01

Reactive chlorine compounds in the polar winter stratosphere are central to the formation of the Arctic ozone hole. To study the distribution and partitioning of active chlorine and reservoir species in the lower stratosphere, we performed in-situ measurements of HCl and ClONO2 with the mass spectrometer AIMS during the POLSTRACC aircraft campaign in the Arctic winter 2015/2016 between 320 K and 410 K. In addition to chlorine reservoir gases, in-situ measurements of chemically stable tracers provide means to identify vortex air masses and to infer total inorganic chlorine (Cly). The distribution of chlorine and the degree of activation during the winter, as well as the reformation of the reservoir species at the end of the polar winter vary with altitude and potential temperature. Using trajectory calculations, we demonstrate transport pathways that distribute high amounts of previously activated chlorine into the lowermost stratosphere. Here, active chlorine may have a large oxidation capacity with respect to climate relevant trace gases.

Characterizing Protein Interactions Employing a Genome-Wide siRNA Cellular Phenotyping Screen

PubMed Central

Suratanee, Apichat; Schaefer, Martin H.; Betts, Matthew J.; Soons, Zita; Mannsperger, Heiko; Harder, Nathalie; Oswald, Marcus; Gipp, Markus; Ramminger, Ellen; Marcus, Guillermo; Männer, Reinhard; Rohr, Karl; Wanker, Erich; Russell, Robert B.; Andrade-Navarro, Miguel A.; Eils, Roland; König, Rainer

2014-01-01

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest. PMID:25255318

Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders.

PubMed

Andrews, Tallulah; Meader, Stephen; Vulto-van Silfhout, Anneke; Taylor, Avigail; Steinberg, Julia; Hehir-Kwa, Jayne; Pfundt, Rolph; de Leeuw, Nicole; de Vries, Bert B A; Webber, Caleb

2015-03-01

Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

Evolution of amino acid metabolism inferred through cladistic analysis.

PubMed

Cunchillos, Chomin; Lecointre, Guillaume

2003-11-28

Because free amino acids were most probably available in primitive abiotic environments, their metabolism is likely to have provided some of the very first metabolic pathways of life. What were the first enzymatic reactions to emerge? A cladistic analysis of metabolic pathways of the 16 aliphatic amino acids and 2 portions of the Krebs cycle was performed using four criteria of homology. The analysis is not based on sequence comparisons but, rather, on coding similarities in enzyme properties. The properties used are shared specific enzymatic activity, shared enzymatic function without substrate specificity, shared coenzymes, and shared functional family. The tree shows that the earliest pathways to emerge are not portions of the Krebs cycle but metabolisms of aspartate, asparagine, glutamate, and glutamine. The views of Horowitz (Horowitz, N. H. (1945) Proc. Natl. Acad. Sci. U. S. A. 31, 153-157) and Cordón (Cordón, F. (1990) Tratado Evolucionista de Biologia, Aguilar, Madrid, Spain), according to which the upstream reactions in the catabolic pathways and the downstream reactions in the anabolic pathways are the earliest in evolution, are globally corroborated; however, with some exceptions. These are due to later opportunistic connections of pathways (actually already suggested by these authors). Earliest enzymatic functions are mostly catabolic; they were deaminations, transaminations, and decarboxylations. From the consensus tree we extracted four time spans for amino acid metabolism development. For some amino acids catabolism and biosynthesis occurred at the same time (Asp, Glu, Lys, Leu, Ala, Val, Ile, Pro, Arg). For others ultimate reactions that use amino acids as a substrate or as a product are distinct in time, with catabolism preceding anabolism for Asn, Gln, and Cys and anabolism preceding catabolism for Ser, Met, and Thr. Cladistic analysis of the structure of biochemical pathways makes hypotheses in biochemical evolution explicit and parsimonious.

Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling.

PubMed

Kittas, Aristotelis; Delobelle, Aurélien; Schmitt, Sabrina; Breuhahn, Kai; Guziolowski, Carito; Grabe, Niels

2016-01-01

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: https://bitbucket.org/akittas/biosubg. © 2015 FEBS.

Active inference, communication and hermeneutics☆

PubMed Central

Friston, Karl J.; Frith, Christopher D.

2015-01-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

Active inference, communication and hermeneutics.

PubMed

Friston, Karl J; Frith, Christopher D

2015-07-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Features of Fecal and Ileal Mucosa-Associated Microbiota in Dairy Calves during Early Infection with Mycobacterium avium Subspecies paratuberculosis

PubMed Central

Derakhshani, Hooman; De Buck, Jeroen; Mortier, Rienske; Barkema, Herman W.; Krause, Denis O.; Khafipour, Ehsan

2016-01-01

Current diagnostic tests for Johne's disease (JD), a chronic granulomatous inflammation of the gastrointestinal tract of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), lack the sensitivity to identify infected animals at early (asymptomatic) stages of the disease. The objective was to determine the pattern of MAP-associated dysbiosis of intestinal microbiota as a potential biomarker for early detection of infected cattle. To that end, genomic DNA was extracted from ileal mucosa and fecal samples collected from 28 MAP-positive and five control calves. High-throughput Illumina sequencing of the V4 hypervariable region of the 16S rRNA gene was used for community profiling of ileal mucosa-associated (MAM) or fecal microbiota. The PERMANOVA analysis of unweighted UniFrac distances revealed distinct clustering of ileal MAM (P = 0.049) and fecal microbiota (P = 0.068) in MAP-infected vs. control cattle. Microbiota profile of MAP-infected animals was further investigated by linear discriminant analysis effective size (LEfSe); several bacterial taxa within the phylum Proteobacteria were overrepresented in ileal MAM of control calves. Moreover, based on reconstructed metagenomes (PICRUSt) of ileal MAM, functional pathways associated with MAP infection were inferred. Enrichment of lysine and histidine metabolism pathways, and underrepresentation of glutathione metabolism and leucine and isoleucine degradation pathways in MAP-infected calves suggested potential contributions of ileal MAM in development of intestinal inflammation. Finally, simultaneous overrepresentation of families Planococcaceae and Paraprevotellaceae, as well as underrepresentation of genera Faecalibacterium and Akkermansia in the fecal microbiota of infected cattle, served as potential biomarker for identifying infected cattle during subclinical stages of JD. Collectively, based on compositional and functional shifts in intestinal microbiota of infected cattle, we inferred that this dynamic network of microorganisms had an active role in intestinal homeostasis. PMID:27065983

Cerebellarlike corrective model inference engine for manipulation tasks.

PubMed

Luque, Niceto Rafael; Garrido, Jesús Alberto; Carrillo, Richard Rafael; Coenen, Olivier J-M D; Ros, Eduardo

2011-10-01

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

Luminance noise as a novel approach for measuring contrast sensitivity within the magnocellular and parvocellular pathways.

PubMed

Hall, Cierra M; McAnany, J Jason

2017-07-01

This study evaluated the extent to which different types of luminance noise can be used to target selectively the inferred magnocellular (MC) and parvocellular (PC) visual pathways. Letter contrast sensitivity (CS) was measured for three visually normal subjects for letters of different size (0.8°-5.3°) under established paradigms intended to target the MC pathway (steady-pedestal paradigm) and PC pathway (pulsed-pedestal paradigm). Results obtained under these paradigms were compared to those obtained in asynchronous static noise (a field of unchanging luminance noise) and asynchronous dynamic noise (a field of randomly changing luminance noise). CS was measured for letters that were high- and low-pass filtered using a range of filter cutoffs to quantify the object frequency information (cycles per letter) mediating letter identification, which was used as an index of the pathway mediating CS. A follow-up experiment was performed to determine the range of letter duration over which MC and PC pathway CS can be targeted. Analysis of variance indicated that the object frequencies measured under the static noise and steady-pedestal paradigms did not differ significantly (p ≥ 0.065), but differed considerably from those measured under the dynamic noise (both p < 0.001) and pulsed-pedestal (both p < 0.001) paradigms. The object frequencies mediating letter identification increased as duration increased under the steady-pedestal paradigm, but were independent of target duration (50-800 ms) under the pulsed-pedestal paradigm, in static noise, and in dynamic noise. These data suggest that the spatiotemporal characteristics of noise can be manipulated to target the inferred MC (static noise) and PC (dynamic noise) pathways. The results also suggest that CS within these pathways can be measured at long stimulus durations, which has potential importance in the design of future clinical CS tests.

Luminance noise as a novel approach for measuring contrast sensitivity within the magnocellular and parvocellular pathways

PubMed Central

Hall, Cierra M.; McAnany, J. Jason

2017-01-01

This study evaluated the extent to which different types of luminance noise can be used to target selectively the inferred magnocellular (MC) and parvocellular (PC) visual pathways. Letter contrast sensitivity (CS) was measured for three visually normal subjects for letters of different size (0.8°–5.3°) under established paradigms intended to target the MC pathway (steady-pedestal paradigm) and PC pathway (pulsed-pedestal paradigm). Results obtained under these paradigms were compared to those obtained in asynchronous static noise (a field of unchanging luminance noise) and asynchronous dynamic noise (a field of randomly changing luminance noise). CS was measured for letters that were high- and low-pass filtered using a range of filter cutoffs to quantify the object frequency information (cycles per letter) mediating letter identification, which was used as an index of the pathway mediating CS. A follow-up experiment was performed to determine the range of letter duration over which MC and PC pathway CS can be targeted. Analysis of variance indicated that the object frequencies measured under the static noise and steady-pedestal paradigms did not differ significantly (p ≥ 0.065), but differed considerably from those measured under the dynamic noise (both p < 0.001) and pulsed-pedestal (both p < 0.001) paradigms. The object frequencies mediating letter identification increased as duration increased under the steady-pedestal paradigm, but were independent of target duration (50–800 ms) under the pulsed-pedestal paradigm, in static noise, and in dynamic noise. These data suggest that the spatiotemporal characteristics of noise can be manipulated to target the inferred MC (static noise) and PC (dynamic noise) pathways. The results also suggest that CS within these pathways can be measured at long stimulus durations, which has potential importance in the design of future clinical CS tests. PMID:28672370

An integrative method for testing form–function linkages and reconstructed evolutionary pathways of masticatory specialization

PubMed Central

Tseng, Z. Jack; Flynn, John J.

2015-01-01

Morphology serves as a ubiquitous proxy in macroevolutionary studies to identify potential adaptive processes and patterns. Inferences of functional significance of phenotypes or their evolution are overwhelmingly based on data from living taxa. Yet, correspondence between form and function has been tested in only a few model species, and those linkages are highly complex. The lack of explicit methodologies to integrate form and function analyses within a deep-time and phylogenetic context weakens inferences of adaptive morphological evolution, by invoking but not testing form–function linkages. Here, we provide a novel approach to test mechanical properties at reconstructed ancestral nodes/taxa and the strength and direction of evolutionary pathways in feeding biomechanics, in a case study of carnivorous mammals. Using biomechanical profile comparisons that provide functional signals for the separation of feeding morphologies, we demonstrate, using experimental optimization criteria on estimation of strength and direction of functional changes on a phylogeny, that convergence in mechanical properties and degree of evolutionary optimization can be decoupled. This integrative approach is broadly applicable to other clades, by using quantitative data and model-based tests to evaluate interpretations of function from morphology and functional explanations for observed macroevolutionary pathways. PMID:25994295

Activation of Background Knowledge for Inference Making: Effects on Reading Comprehension

ERIC Educational Resources Information Center

Elbro, Carsten; Buch-Iversen, Ida

2013-01-01

Failure to "activate" relevant, existing background knowledge may be a cause of poor reading comprehension. This failure may cause particular problems with inferences that depend heavily on prior knowledge. Conversely, teaching how to use background knowledge in the context of gap-filling inferences could improve reading comprehension in…

The NCBI BioSystems database

PubMed Central

Geer, Lewis Y.; Marchler-Bauer, Aron; Geer, Renata C.; Han, Lianyi; He, Jane; He, Siqian; Liu, Chunlei; Shi, Wenyao; Bryant, Stephen H.

2010-01-01

The NCBI BioSystems database, found at http://www.ncbi.nlm.nih.gov/biosystems/, centralizes and cross-links existing biological systems databases, increasing their utility and target audience by integrating their pathways and systems into NCBI resources. This integration allows users of NCBI’s Entrez databases to quickly categorize proteins, genes and small molecules by metabolic pathway, disease state or other BioSystem type, without requiring time-consuming inference of biological relationships from the literature or multiple experimental datasets. PMID:19854944

Developing confidence in adverse outcome pathway-based ...

EPA Pesticide Factsheets

An adverse outcome pathway (AOP) description linking inhibition of aromatase (cytochrome P450 [cyp] 19) to reproductive dysfunction was reviewed for scientific and technical quality and endorsed by the OECD. An intended application of the AOP framework is to support the use of mechanistic or pathway-based data to infer or predict chemical hazards and apical adverse outcomes. As part of this work, ToxCast high throughput screening data were used to identify a chemicals’ ability to inhibit aromatase activity in vitro. Twenty-four hour in vivo exposures, focused on effects on production and circulating concentrations of 17β-estradiol (E2), key events in the AOP, were conducted to verify in vivo activity. Based on these results, imazalil was selected as a case study chemical to test an AOP-based hazard prediction. A computational model of the fish hypothalamic-pituitary-gonadal-liver axis and a statistically-based model of oocyte growth dynamics were used to predict impacts of different concentrations of imazalil on multiple key events along the AOP, assuming continuous exposure for 21 d. Results of the model simulations were used to select test concentrations and design a fathead minnow reproduction study in which fish were exposed to 20, 60, or 200 µg imazalil/L for durations of 2.5, 10, or 21d. Within 60 h of exposure, female fathead minnows showed significant reductions in ex vivo production of E2, circulating E2 concentrations, and significant increases in

Developing confidence in adverse outcome pathway-based ...

EPA Pesticide Factsheets

An adverse outcome pathway (AOP) description linking inhibition of aromatase (cytochrome P450 [cyp] 19) to reproductive dysfunction was reviewed for scientific and technical quality and endorsed by the OECD (https://aopwiki.org/wiki/index.php/Aop:25). An intended application of the AOP framework is to support the use of mechanistic or pathway-based data to infer or predict chemical hazards and apical adverse outcomes. As part of this work, ToxCast high throughput screening data were used to identify a chemicals’ ability to inhibit aromatase activity in vitro. Twenty-four hour in vivo exposures, focused on effects on production and circulating concentrations of 17â-estradiol (E2), key events in the AOP, were conducted to verify in vivo activity. Based on these results, imazalil was selected as a case study chemical to test an AOP-based hazard prediction. A computational model of the fish hypothalamic-pituitary-gonadal-liver axis and a statistically-based model of oocyte growth dynamics were used to predict impacts of different concentrations of imazalil on multiple key events along the AOP, assuming continuous exposure for 21 d. Results of the model simulations were used to select test concentrations and design a fathead minnow reproduction study in which fish were exposed to 20, 60, or 200 µg imazalil/L for durations of 2.5, 10, or 21d. Within 60 h of exposure, female fathead minnows showed significant reductions in ex vivo production of E2, circulating E2 c

Effects of size and surface of zinc oxide and aluminum-doped zinc oxide nanoparticles on cell viability inferred by proteomic analyses.

PubMed

Pan, Chih-Hong; Liu, Wen-Te; Bien, Mauo-Ying; Lin, I-Chan; Hsiao, Ta-Chih; Ma, Chih-Ming; Lai, Ching-Huang; Chen, Mei-Chieh; Chuang, Kai-Jen; Chuang, Hsiao-Chi

2014-01-01

Although the health effects of zinc oxide nanoparticles (ZnONPs) on the respiratory system have been reported, the fate, potential toxicity, and mechanisms in biological cells of these particles, as related to particle size and surface characteristics, have not been well elucidated. To determine the physicochemical properties of ZnONPs that govern cytotoxicity, we investigated the effects of size, electronic properties, zinc concentration, and pH on cell viability using human alveolar-basal epithelial A549 cells as a model. We observed that a 2-hour or longer exposure to ZnONPs induced changes in cell viability. The alteration in cell viability was associated with the zeta potentials and pH values of the ZnONPs. Proteomic profiling of A549 exposed to ZnONPs for 2 and 4 hours was used to determine the biological mechanisms of ZnONP toxicity. p53-pathway activation was the core mechanism regulating cell viability in response to particle size. Activation of the Wnt and TGFβ signaling pathways was also important in the cellular response to ZnONPs of different sizes. The cadherin and Wnt signaling pathways were important cellular mechanisms triggered by surface differences. These results suggested that the size and surface characteristics of ZnONPs might play an important role in their observed cytotoxicity. This approach facilitates the design of more comprehensive systems for the evaluation of nanoparticles.

Identification of core pathways based on attractor and crosstalk in ischemic stroke.

PubMed

Diao, Xiufang; Liu, Aijuan

2018-02-01

Ischemic stroke is a leading cause of mortality and disability around the world. It is an important task to identify dysregulated pathways which infer molecular and functional insights existing in high-throughput experimental data. Gene expression profile of E-GEOD-16561 was collected. Pathways were obtained from the database of Kyoto Encyclopedia of Genes and Genomes and Retrieval of Interacting Genes was used to download protein-protein interaction sets. Attractor and crosstalk approaches were applied to screen dysregulated pathways. A total of 20 differentially expressed genes were identified in ischemic stroke. Thirty-nine significant differential pathways were identified according to P<0.01 and 28 pathways were identified with RP<0.01 and 17 pathways were identified with impact factor >250. On the basis of the three criteria, 11 significant dysfunctional pathways were identified. Among them, Epstein-Barr virus infection was the most significant differential pathway. In conclusion, with the method based on attractor and crosstalk, significantly dysfunctional pathways were identified. These pathways are expected to provide molecular mechanism of ischemic stroke and represents a novel potential therapeutic target for ischemic stroke treatment.

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

PubMed

Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

2013-01-01

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

Cellular compartmentalization of secondary metabolism

PubMed Central

Kistler, H. Corby; Broz, Karen

2015-01-01

Fungal secondary metabolism is often considered apart from the essential housekeeping functions of the cell. However, there are clear links between fundamental cellular metabolism and the biochemical pathways leading to secondary metabolite synthesis. Besides utilizing key biochemical precursors shared with the most essential processes of the cell (e.g., amino acids, acetyl CoA, NADPH), enzymes for secondary metabolite synthesis are compartmentalized at conserved subcellular sites that position pathway enzymes to use these common biochemical precursors. Co-compartmentalization of secondary metabolism pathway enzymes also may function to channel precursors, promote pathway efficiency and sequester pathway intermediates and products from the rest of the cell. In this review we discuss the compartmentalization of three well-studied fungal secondary metabolite biosynthetic pathways for penicillin G, aflatoxin and deoxynivalenol, and summarize evidence used to infer subcellular localization. We also discuss how these metabolites potentially are trafficked within the cell and may be exported. PMID:25709603

Inferring molecular interactions pathways from eQTL data

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

Rashid, Imran; McDermott, Jason E.; Samudrala, Ram

Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring graphs of molecular interactions. We show several simple methodsmore » can find complete pathways the explain the mechanism of differential expression in eQTL data.« less

Sphingolipid metabolism potential in fecal microbiome and bronchiolitis in infants: a case-control study.

PubMed

Hasegawa, Kohei; Stewart, Christopher J; Mansbach, Jonathan M; Linnemann, Rachel W; Ajami, Nadim J; Petrosino, Joseph F; Camargo, Carlos A

2017-07-26

Emerging evidence demonstrated that the structure of fecal microbiome is associated with the likelihood of bronchiolitis in infants. However, no study has examined functional profiles of fecal microbiome in infants with bronchiolitis. In this context, we conducted a case-control study. As a part of multicenter prospective study, we collected stool samples from 40 infants hospitalized with bronchiolitis (cases). We concurrently enrolled 115 age-matched healthy controls. First, by applying 16S rRNA gene sequencing to these 155 fecal samples, we identified the taxonomic profiles of fecal microbiome. Next, based on the taxonomy data, we inferred the functional capabilities of fecal microbiome and tested for differences in the functional capabilities between cases and controls. Overall, the median age was 3 months and 45% were female. Among 274 metabolic pathways surveyed, there were significant differences between bronchiolitis cases and healthy controls for 37 pathways, including lipid metabolic pathways (false discovery rate [FDR] <0.05). Particularly, the fecal microbiome of bronchiolitis cases had consistently higher abundances of gene function related to the sphingolipid metabolic pathways compared to that of controls (FDR <0.05). These pathways were more abundant in infants with Bacteroides-dominant microbiome profile compared to the others (FDR <0.001). On the basis of the predicted metagenome in this case-control study, we found significant differences in the functional potential of fecal microbiome between infants with bronchiolitis and healthy controls. Although causal inferences remain premature, our data suggest a potential link between the bacteria-derived metabolites, modulations of host immune response, and development of bronchiolitis.

Cot/Tpl-2 protein kinase as a target for the treatment of inflammatory disease.

PubMed

George, D; Salmeron, A

2009-01-01

Cot/Tpl-2/MAP3K8 is a serine/threonine protein kinase that is essential for lipopolysaccharide (LPS)-induced activation of the MEK/ERK pathway in macrophages as demonstrated in Cot/Tpl-2-deficient mice. Cot/Tpl-2 kinase activation plays an integral role in the production of pro-inflammatory cytokines such as TNF and IL-1beta in this immune cell type. Elevated levels of these cytokines have been clinically implicated as mediators of a number of autoimmune diseases, in particular, the pain and joint destruction of rheumatoid arthritis. By inference, pharmaceutical agents that inhibit Cot/Tpl-2 kinase have the potential to be novel and effective therapies for the treatment of these diseases. This review will describe the physiological regulation and importance of Cot/Tpl-2 in inflammation as well as the landscape of small molecules that have been reported as Cot/Tpl-2 inhibitors.

Electrical activity of the cingulate cortex. II. Cholinergic modulation.

PubMed

Borst, J G; Leung, L W; MacFabe, D F

1987-03-24

The role of the cholinergic innervation in the modulation of cingulate electrical activity was studied by means of pharmacological manipulations and brain lesions. In the normal rat, an irregular slow activity (ISA) accompanied with EEG-spikes was recorded in the cingulate cortex during immobility as compared to walking. Atropine sulfate, but not atropine methyl nitrate, increased ISA and the frequency of cingulate EEG-spikes. Pilocarpine suppressed ISA and EEG-spikes during immobility, and induced a slow (4-7 Hz) theta rhythm. Unilateral or bilateral lesions of the substantia innominata and ventral globus pallidus area using kainic acid did not significantly change the cingulate EEG or its relation to behavior. Large electrolytic lesions of the medial septal nuclei and vertical limbs of the diagonal band generally decreased or abolished all theta activity in the cingulate cortex and the hippocampus. However, in 5 rats the cingulate theta rhythm increased while the hippocampal theta disappeared after a medial septal lesion. The large, postlesion cingulate theta, accompanied by sharp EEG-spikes during its negative phase, is an unequivocal demonstration of the existence of a theta rhythm in the cingulate cortex, independent of the hippocampal rhythm. Cholinergic afferents from the medial septum and diagonal band nuclei are inferred to be responsible for the behavioral suppression of cingulate EEG-spikes and ISA, and partially for the generation of a local cingulate theta rhythm. However, an atropine-resistant pathway and a theta-suppressing pathway, possibly coming from the medial septum or the hippocampus, may also be important in cingulate theta generation.

Modulation of the nuclear factor-kappa B (NF-κB) signalling pathway by glutamine in peritoneal macrophages of a murine model of protein malnutrition.

PubMed

da Silva Lima, Fabiana; Rogero, Marcelo Macedo; Ramos, Mayara Caldas; Borelli, Primavera; Fock, Ricardo Ambrósio

2013-06-01

Protein malnutrition affects resistance to infection by impairing the inflammatory response, modifying the function of effector cells, such as macrophages. Recent studies have revealed that glutamine-a non-essential amino acid, which could become conditionally essential in some situations like trauma, infection, post-surgery and sepsis-is able to modulate the synthesis of cytokines. The aim of this study was to evaluate the effect of glutamine on the expression of proteins involved in the nuclear factor-kappa B (NF-κB) signalling pathway of peritoneal macrophages from malnourished mice. Two-month-old male Balb/c mice were submitted to protein-energy malnutrition (n = 10) with a low-protein diet containing 2 % protein, whereas control mice (n = 10) were fed a 12 % protein-containing diet. The haemogram and analysis of plasma glutamine and corticosterone were evaluated. Peritoneal macrophages were pre-treated in vitro with glutamine (0, 0.6, 2 and 10 mmol/L) for 24 h and then stimulated with 1.25 μg LPS for 30 min, and the synthesis of TNF-α and IL-1α and the expression of proteins related to the NF-κB pathway were evaluated. Malnourished animals had anaemia, leucopoenia, lower plasma glutamine and increased corticosterone levels. TNF-α production of macrophages stimulated with LPS was significantly lower in cells from malnourished animals when cultivated in supraphysiological (2 and 10 mmol/L) concentrations of glutamine. Further, glutamine has a dose-dependent effect on the activation of macrophages, in both groups, when stimulated with LPS, inducing a decrease in TNF-α and IL-1α production and negatively modulating the NF-κB signalling pathway. These data lead us to infer that the protein malnutrition state interferes with the activation of macrophages and that higher glutamine concentrations, in vitro, have the capacity to act negatively in the NF-κB signalling pathway.

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information. PMID:25162401

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

PubMed

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Role of adverse outcome pathways in developing computational models for regulatory toxicology

EPA Science Inventory

Regulatory toxicology for both human health and the environment increasingly is moving from a sole reliance on direct observation of apical toxicity outcomes in whole organism toxicity tests, to predictive approaches in which unacceptable outcomes and risk are inferred from mecha...

The active transport of histidine and its role in ATP production in Trypanosoma cruzi.

PubMed

Barisón, M J; Damasceno, F S; Mantilla, B S; Silber, A M

2016-08-01

Trypanosoma cruzi, the aetiological agent of Chagas's disease, metabolizes glucose, and after its exhaustion, degrades amino acids as energy source. Here, we investigate histidine uptake and its participation in energy metabolism. No putative genes for the histidine biosynthetic pathway have been identified in genome databases of T. cruzi, suggesting that its uptake from extracellular medium is a requirement for the viability of the parasite. From this assumption, we characterized the uptake of histidine in T. cruzi, showing that this amino acid is incorporated through a single and saturable active system. We also show that histidine can be completely oxidised to CO2. This finding, together with the fact that genes encoding the putative enzymes for the histidine - glutamate degradation pathway were annotated, led us to infer its participation in the energy metabolism of the parasite. Here, we show that His is capable of restoring cell viability after long-term starvation. We confirm that as an energy source, His provides electrons to the electron transport chain, maintaining mitochondrial inner membrane potential and O2 consumption in a very efficient manner. Additionally, ATP biosynthesis from oxidative phosphorylation was found when His was the only oxidisable metabolite present, showing that this amino acid is involved in bioenergetics and parasite persistence within its invertebrate host.

Rapid variability of Antarctic Bottom Water transport into the Pacific Ocean inferred from GRACE

NASA Astrophysics Data System (ADS)

Mazloff, Matthew R.; Boening, Carmen

2016-04-01

Air-ice-ocean interactions in the Antarctic lead to formation of the densest waters on Earth. These waters convect and spread to fill the global abyssal oceans. The heat and carbon storage capacity of these water masses, combined with their abyssal residence times that often exceed centuries, makes this circulation pathway the most efficient sequestering mechanism on Earth. Yet monitoring this pathway has proven challenging due to the nature of the formation processes and the depth of the circulation. The Gravity Recovery and Climate Experiment (GRACE) gravity mission is providing a time series of ocean mass redistribution and offers a transformative view of the abyssal circulation. Here we use the GRACE measurements to infer, for the first time, a 2003-2014 time series of Antarctic Bottom Water export into the South Pacific. We find this export highly variable, with a standard deviation of 1.87 sverdrup (Sv) and a decorrelation timescale of less than 1 month. A significant trend is undetectable.

Invasion of Ancestral Mammals into Dim-light Environments Inferred from Adaptive Evolution of the Phototransduction Genes.

PubMed

Wu, Yonghua; Wang, Haifeng; Hadly, Elizabeth A

2017-04-20

Nocturnality is a key evolutionary innovation of mammals that enables mammals to occupy relatively empty nocturnal niches. Invasion of ancestral mammals into nocturnality has long been inferred from the phylogenetic relationships of crown Mammalia, which is primarily nocturnal, and crown Reptilia, which is primarily diurnal, although molecular evidence for this is lacking. Here we used phylogenetic analyses of the vision genes involved in the phototransduction pathway to predict the diel activity patterns of ancestral mammals and reptiles. Our results demonstrated that the common ancestor of the extant Mammalia was dominated by positive selection for dim-light vision, supporting the predominate nocturnality of the ancestral mammals. Further analyses showed that the nocturnality of the ancestral mammals was probably derived from the predominate diurnality of the ancestral amniotes, which featured strong positive selection for bright-light vision. Like the ancestral amniotes, the common ancestor of the extant reptiles and various taxa in Squamata, one of the main competitors of the temporal niches of the ancestral mammals, were found to be predominate diurnality as well. Despite this relatively apparent temporal niche partitioning between ancestral mammals and the relevant reptiles, our results suggested partial overlap of their temporal niches during crepuscular periods.

Fluid source inferred from strontium isotopes in pore fluid and carbonate recovered during Expedition 337 off Shimokita, Japan

NASA Astrophysics Data System (ADS)

Hong, W.; Moen, N.; Haley, B. A.

2013-12-01

IODP Expedition 337 was designed to understand the relationship between a deep-buried (2000 meters below seafloor) hydrocarbon reservoir off the Shimokita peninsula (Japan), and the microbial community that this carbon reservoir sustains at such depth. Understanding sources and pathways of flow of fluids that carry hydrocarbons, nutrients, and other reduced components is of particular interest to fulfilling the expedition objectives, since this migrating fluid supports microbial activity not only of the deep-seated communities but also to the shallow-dwelling organisms. To this aim, the concentration and isotopic signature of Sr can be valuable due to that it is relatively free from biogenic influence and pristine in terms of drill fluid contamination. From the pore water Sr profile, concentration gradually increases from 1500 to 2400 mbsf. The depth where highest Sr concentration is observed corresponds to the depths where couple layers of carbonate were observed. Such profile suggests an upward-migrating fluid carries Sr from those deep-seated carbonate layers (>2400 mbsf) to shallower sediments. To confirm this inference, pore water, in-situ formation fluid, and carbonate samples were analyzed for Sr isotopes to investigate the fluid source.

Role of Utility and Inference in the Evolution of Functional Information

PubMed Central

Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are constructed within each communication system to represent reality and they evolve towards higher adaptability on a long time scale. PMID:20160960

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Iron Absorption in Drosophila melanogaster

PubMed Central

Mandilaras, Konstantinos; Pathmanathan, Tharse; Missirlis, Fanis

2013-01-01

The way in which Drosophila melanogaster acquires iron from the diet remains poorly understood despite iron absorption being of vital significance for larval growth. To describe the process of organismal iron absorption, consideration needs to be given to cellular iron import, storage, export and how intestinal epithelial cells sense and respond to iron availability. Here we review studies on the Divalent Metal Transporter-1 homolog Malvolio (iron import), the recent discovery that Multicopper Oxidase-1 has ferroxidase activity (iron export) and the role of ferritin in the process of iron acquisition (iron storage). We also describe what is known about iron regulation in insect cells. We then draw upon knowledge from mammalian iron homeostasis to identify candidate genes in flies. Questions arise from the lack of conservation in Drosophila for key mammalian players, such as ferroportin, hepcidin and all the components of the hemochromatosis-related pathway. Drosophila and other insects also lack erythropoiesis. Thus, systemic iron regulation is likely to be conveyed by different signaling pathways and tissue requirements. The significance of regulating intestinal iron uptake is inferred from reports linking Drosophila developmental, immune, heat-shock and behavioral responses to iron sequestration. PMID:23686013

Genomic analyses identify molecular subtypes of pancreatic cancer.

PubMed

Bailey, Peter; Chang, David K; Nones, Katia; Johns, Amber L; Patch, Ann-Marie; Gingras, Marie-Claude; Miller, David K; Christ, Angelika N; Bruxner, Tim J C; Quinn, Michael C; Nourse, Craig; Murtaugh, L Charles; Harliwong, Ivon; Idrisoglu, Senel; Manning, Suzanne; Nourbakhsh, Ehsan; Wani, Shivangi; Fink, Lynn; Holmes, Oliver; Chin, Venessa; Anderson, Matthew J; Kazakoff, Stephen; Leonard, Conrad; Newell, Felicity; Waddell, Nick; Wood, Scott; Xu, Qinying; Wilson, Peter J; Cloonan, Nicole; Kassahn, Karin S; Taylor, Darrin; Quek, Kelly; Robertson, Alan; Pantano, Lorena; Mincarelli, Laura; Sanchez, Luis N; Evers, Lisa; Wu, Jianmin; Pinese, Mark; Cowley, Mark J; Jones, Marc D; Colvin, Emily K; Nagrial, Adnan M; Humphrey, Emily S; Chantrill, Lorraine A; Mawson, Amanda; Humphris, Jeremy; Chou, Angela; Pajic, Marina; Scarlett, Christopher J; Pinho, Andreia V; Giry-Laterriere, Marc; Rooman, Ilse; Samra, Jaswinder S; Kench, James G; Lovell, Jessica A; Merrett, Neil D; Toon, Christopher W; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Moran-Jones, Kim; Jamieson, Nigel B; Graham, Janet S; Duthie, Fraser; Oien, Karin; Hair, Jane; Grützmann, Robert; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Corbo, Vincenzo; Bassi, Claudio; Rusev, Borislav; Capelli, Paola; Salvia, Roberto; Tortora, Giampaolo; Mukhopadhyay, Debabrata; Petersen, Gloria M; Munzy, Donna M; Fisher, William E; Karim, Saadia A; Eshleman, James R; Hruban, Ralph H; Pilarsky, Christian; Morton, Jennifer P; Sansom, Owen J; Scarpa, Aldo; Musgrove, Elizabeth A; Bailey, Ulla-Maja Hagbo; Hofmann, Oliver; Sutherland, Robert L; Wheeler, David A; Gill, Anthony J; Gibbs, Richard A; Pearson, John V; Waddell, Nicola; Biankin, Andrew V; Grimmond, Sean M

2016-03-03

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

The anatomy of choice: dopamine and decision-making

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2014-01-01

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making. PMID:25267823

The anatomy of choice: dopamine and decision-making.

PubMed

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2014-11-05

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Neural basis for inferring false beliefs and social emotions in others among individuals with schizophrenia and those at ultra-high risk for psychosis.

PubMed

Takano, Yosuke; Aoki, Yuta; Yahata, Noriaki; Kawakubo, Yuki; Inoue, Hideyuki; Iwashiro, Norichika; Natsubori, Tatsunobu; Koike, Shinsuke; Gonoi, Wataru; Sasaki, Hiroki; Takao, Hidemasa; Kasai, Kiyoto; Yamasue, Hidenori

2017-01-30

Inferring beliefs and social emotions of others has different neural substrates and possibly different roles in the pathophysiology of different clinical phases of schizophrenia. The current study investigated the neural basis for inferring others' beliefs and social emotions, as individual concepts, in 17 subjects at ultra-high risk for psychosis (UHR), 16 patients with schizophrenia and 20 healthy controls. Brain activity significantly differed from normal in both the left superior temporal sulcus (STS) and the inferior frontal gyrus (IFG) in the schizophrenia group while inferring others' beliefs, whereas those of UHR group were in the middle of those in the schizophrenia and healthy-control groups. Brain activity during inferring others' social emotions significantly differed in both the left STS and right IFG among individuals at UHR; however, there was no significant difference in the schizophrenia group. In contrast, brain activity differed in the left IFG of those in both the schizophrenia and UHR groups while inferring social emotion. Regarding the difference in direction of the abnormality, both the UHR and schizophrenia groups were characterized by hyper-STS and hypo-IFG activations when inferring others' beliefs and emotions. These findings might reflect different aspects of the same pathophysiological process at different clinical phases of psychosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparative transcriptomic analysis of the evolution and development of flower size in Saltugilia (Polemoniaceae).

PubMed

Landis, Jacob B; Soltis, Douglas E; Soltis, Pamela S

2017-06-23

Flower size varies dramatically across angiosperms, representing innovations over the course of >130 million years of evolution and contributing substantially to relationships with pollinators. However, the genetic underpinning of flower size is not well understood. Saltugilia (Polemoniaceae) provides an excellent non-model system for extending the genetic study of flower size to interspecific differences that coincide with variation in pollinators. Using targeted gene capture methods, we infer phylogenetic relationships among all members of Saltugilia to provide a framework for investigating the genetic control of flower size differences via RNA-Seq de novo assembly. Nuclear concatenation and species tree inference methods provide congruent topologies. The inferred evolutionary trajectory of flower size is from small flowers to larger flowers. We identified 4 to 10,368 transcripts that are differentially expressed during flower development, with many unigenes associated with cell wall modification and components of the auxin and gibberellin pathways. Saltugilia is an excellent model for investigating covarying floral and pollinator evolution. Four candidate genes from model systems (BIG BROTHER, BIG PETAL, GASA, and LONGIFOLIA) show differential expression during development of flowers in Saltugilia, and four other genes (FLOWERING-PROMOTING FACTOR 1, PECTINESTERASE, POLYGALACTURONASE, and SUCROSE SYNTHASE) fit into hypothesized organ size pathways. Together, these gene sets provide a strong foundation for future functional studies to determine their roles in specifying interspecific differences in flower size.

Comparison of Channel Catfish and Blue Catfish Gut Microbiota Assemblages Shows Minimal Effects of Host Genetics on Microbial Structure and Inferred Function.

PubMed

Bledsoe, Jacob W; Waldbieser, Geoffrey C; Swanson, Kelly S; Peterson, Brian C; Small, Brian C

2018-01-01

The microbiota of teleost fish has gained a great deal of research attention within the past decade, with experiments suggesting that both host-genetics and environment are strong ecological forces shaping the bacterial assemblages of fish microbiomes. Despite representing great commercial and scientific importance, the catfish within the family Ictaluridae , specifically the blue and channel catfish, have received very little research attention directed toward their gut-associated microbiota using 16S rRNA gene sequencing. Within this study we utilize multiple genetically distinct strains of blue and channel catfish, verified via microsatellite genotyping, to further quantify the role of host-genetics in shaping the bacterial communities in the fish gut, while maintaining environmental and husbandry parameters constant. Comparisons of the gut microbiota among the two catfish species showed no differences in bacterial species richness (observed and Chao1) or overall composition (weighted and unweighted UniFrac) and UniFrac distances showed no correlation with host genetic distances (Rst) according to Mantel tests. The microbiota of environmental samples (diet and water) were found to be significantly more diverse than that of the catfish gut associated samples, suggesting that factors within the host were further regulating the bacterial communities, despite the lack of a clear connection between microbiota composition and host genotype. The catfish gut communities were dominated by the phyla Fusobacteria, Proteobacteria, and Firmicutes; however, differential abundance analysis between the two catfish species using analysis of composition of microbiomes detected two differential genera, Cetobacterium and Clostridium XI . The metagenomic pathway features inferred from our dataset suggests the catfish gut bacterial communities possess pathways beneficial to their host such as those involved in nutrient metabolism and antimicrobial biosynthesis, while also containing pathways involved in virulence factors of pathogens. Testing of the inferred KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways by DESeq2 revealed minor difference in microbiota function, with only two metagenomic pathways detected as differentially abundant between the two catfish species. As the first study to characterize the gut microbiota of blue catfish, our study results have direct implications on future ictalurid catfish research. Additionally, our insight into the intrinsic factors driving microbiota structure has basic implications for the future study of fish gut microbiota.

A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

PubMed Central

Han, Junwei; Li, Chunquan; Yang, Haixiu; Xu, Yanjun; Zhang, Chunlong; Ma, Jiquan; Shi, Xinrui; Liu, Wei; Shang, Desi; Yao, Qianlan; Zhang, Yunpeng; Su, Fei; Feng, Li; Li, Xia

2015-01-01

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI). PMID:25551156

The transcriptional response of microbial communities in thawing Alaskan permafrost soils.

PubMed

Coolen, Marco J L; Orsi, William D

2015-01-01

Thawing of permafrost soils is expected to stimulate microbial decomposition and respiration of sequestered carbon. This could, in turn, increase atmospheric concentrations of greenhouse gasses, such as carbon dioxide and methane, and create a positive feedback to climate warming. Recent metagenomic studies suggest that permafrost has a large metabolic potential for carbon processing, including pathways for fermentation and methanogenesis. Here, we performed a pilot study using ultrahigh throughput Illumina HiSeq sequencing of reverse transcribed messenger RNA to obtain a detailed overview of active metabolic pathways and responsible organisms in up to 70 cm deep permafrost soils at a moist acidic tundra location in Arctic Alaska. The transcriptional response of the permafrost microbial community was compared before and after 11 days of thaw. In general, the transcriptional profile under frozen conditions suggests a dominance of stress responses, survival strategies, and maintenance processes, whereas upon thaw a rapid enzymatic response to decomposing soil organic matter (SOM) was observed. Bacteroidetes, Firmicutes, ascomycete fungi, and methanogens were responsible for largest transcriptional response upon thaw. Transcripts indicative of heterotrophic methanogenic pathways utilizing acetate, methanol, and methylamine were found predominantly in the permafrost table after thaw. Furthermore, transcripts involved in acetogenesis were expressed exclusively after thaw suggesting that acetogenic bacteria are a potential source of acetate for acetoclastic methanogenesis in freshly thawed permafrost. Metatranscriptomics is shown here to be a useful approach for inferring the activity of permafrost microbes that has potential to improve our understanding of permafrost SOM bioavailability and biogeochemical mechanisms contributing to greenhouse gas emissions as a result of permafrost thaw.

The transcriptional response of microbial communities in thawing Alaskan permafrost soils

PubMed Central

Coolen, Marco J. L.; Orsi, William D.

2015-01-01

Thawing of permafrost soils is expected to stimulate microbial decomposition and respiration of sequestered carbon. This could, in turn, increase atmospheric concentrations of greenhouse gasses, such as carbon dioxide and methane, and create a positive feedback to climate warming. Recent metagenomic studies suggest that permafrost has a large metabolic potential for carbon processing, including pathways for fermentation and methanogenesis. Here, we performed a pilot study using ultrahigh throughput Illumina HiSeq sequencing of reverse transcribed messenger RNA to obtain a detailed overview of active metabolic pathways and responsible organisms in up to 70 cm deep permafrost soils at a moist acidic tundra location in Arctic Alaska. The transcriptional response of the permafrost microbial community was compared before and after 11 days of thaw. In general, the transcriptional profile under frozen conditions suggests a dominance of stress responses, survival strategies, and maintenance processes, whereas upon thaw a rapid enzymatic response to decomposing soil organic matter (SOM) was observed. Bacteroidetes, Firmicutes, ascomycete fungi, and methanogens were responsible for largest transcriptional response upon thaw. Transcripts indicative of heterotrophic methanogenic pathways utilizing acetate, methanol, and methylamine were found predominantly in the permafrost table after thaw. Furthermore, transcripts involved in acetogenesis were expressed exclusively after thaw suggesting that acetogenic bacteria are a potential source of acetate for acetoclastic methanogenesis in freshly thawed permafrost. Metatranscriptomics is shown here to be a useful approach for inferring the activity of permafrost microbes that has potential to improve our understanding of permafrost SOM bioavailability and biogeochemical mechanisms contributing to greenhouse gas emissions as a result of permafrost thaw. PMID:25852660

Imaging White Matter in Human Brainstem

PubMed Central

Ford, Anastasia A.; Colon-Perez, Luis; Triplett, William T.; Gullett, Joseph M.; Mareci, Thomas H.; FitzGerald, David B.

2013-01-01

The human brainstem is critical for the control of many life-sustaining functions, such as consciousness, respiration, sleep, and transfer of sensory and motor information between the brain and the spinal cord. Most of our knowledge about structure and organization of white and gray matter within the brainstem is derived from ex vivo dissection and histology studies. However, these methods cannot be applied to study structural architecture in live human participants. Tractography from diffusion-weighted magnetic resonance imaging (MRI) may provide valuable insights about white matter organization within the brainstem in vivo. However, this method presents technical challenges in vivo due to susceptibility artifacts, functionally dense anatomy, as well as pulsatile and respiratory motion. To investigate the limits of MR tractography, we present results from high angular resolution diffusion imaging of an intact excised human brainstem performed at 11.1 T using isotropic resolution of 0.333, 1, and 2 mm, with the latter reflecting resolution currently used clinically. At the highest resolution, the dense fiber architecture of the brainstem is evident, but the definition of structures degrades as resolution decreases. In particular, the inferred corticopontine/corticospinal tracts (CPT/CST), superior (SCP) and middle cerebellar peduncle (MCP), and medial lemniscus (ML) pathways are clearly discernable and follow known anatomical trajectories at the highest spatial resolution. At lower resolutions, the CST/CPT, SCP, and MCP pathways are artificially enlarged due to inclusion of collinear and crossing fibers not inherent to these three pathways. The inferred ML pathways appear smaller at lower resolutions, indicating insufficient spatial information to successfully resolve smaller fiber pathways. Our results suggest that white matter tractography maps derived from the excised brainstem can be used to guide the study of the brainstem architecture using diffusion MRI in vivo. PMID:23898254

Imaging white matter in human brainstem.

PubMed

Ford, Anastasia A; Colon-Perez, Luis; Triplett, William T; Gullett, Joseph M; Mareci, Thomas H; Fitzgerald, David B

2013-01-01

The human brainstem is critical for the control of many life-sustaining functions, such as consciousness, respiration, sleep, and transfer of sensory and motor information between the brain and the spinal cord. Most of our knowledge about structure and organization of white and gray matter within the brainstem is derived from ex vivo dissection and histology studies. However, these methods cannot be applied to study structural architecture in live human participants. Tractography from diffusion-weighted magnetic resonance imaging (MRI) may provide valuable insights about white matter organization within the brainstem in vivo. However, this method presents technical challenges in vivo due to susceptibility artifacts, functionally dense anatomy, as well as pulsatile and respiratory motion. To investigate the limits of MR tractography, we present results from high angular resolution diffusion imaging of an intact excised human brainstem performed at 11.1 T using isotropic resolution of 0.333, 1, and 2 mm, with the latter reflecting resolution currently used clinically. At the highest resolution, the dense fiber architecture of the brainstem is evident, but the definition of structures degrades as resolution decreases. In particular, the inferred corticopontine/corticospinal tracts (CPT/CST), superior (SCP) and middle cerebellar peduncle (MCP), and medial lemniscus (ML) pathways are clearly discernable and follow known anatomical trajectories at the highest spatial resolution. At lower resolutions, the CST/CPT, SCP, and MCP pathways are artificially enlarged due to inclusion of collinear and crossing fibers not inherent to these three pathways. The inferred ML pathways appear smaller at lower resolutions, indicating insufficient spatial information to successfully resolve smaller fiber pathways. Our results suggest that white matter tractography maps derived from the excised brainstem can be used to guide the study of the brainstem architecture using diffusion MRI in vivo.

Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam

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

Chai, Juanjuan; Kora, Guruprasad; Ahn, Tae-Hyuk

2014-10-09

To supply some background, phylogenetic studies have provided detailed knowledge on the evolutionary mechanisms of genes and species in Bacteria and Archaea. However, the evolution of cellular functions, represented by metabolic pathways and biological processes, has not been systematically characterized. Many clades in the prokaryotic tree of life have now been covered by sequenced genomes in GenBank. This enables a large-scale functional phylogenomics study of many computationally inferred cellular functions across all sequenced prokaryotes. Our results show a total of 14,727 GenBank prokaryotic genomes were re-annotated using a new protein family database, UniFam, to obtain consistent functional annotations for accuratemore » comparison. The functional profile of a genome was represented by the biological process Gene Ontology (GO) terms in its annotation. The GO term enrichment analysis differentiated the functional profiles between selected archaeal taxa. 706 prokaryotic metabolic pathways were inferred from these genomes using Pathway Tools and MetaCyc. The consistency between the distribution of metabolic pathways in the genomes and the phylogenetic tree of the genomes was measured using parsimony scores and retention indices. The ancestral functional profiles at the internal nodes of the phylogenetic tree were reconstructed to track the gains and losses of metabolic pathways in evolutionary history. In conclusion, our functional phylogenomics analysis shows divergent functional profiles of taxa and clades. Such function-phylogeny correlation stems from a set of clade-specific cellular functions with low parsimony scores. On the other hand, many cellular functions are sparsely dispersed across many clades with high parsimony scores. These different types of cellular functions have distinct evolutionary patterns reconstructed from the prokaryotic tree.« less

Complex Ancestries of Isoprenoid Synthesis in Dinoflagellates.

PubMed

Bentlage, Bastian; Rogers, Travis S; Bachvaroff, Tsvetan R; Delwiche, Charles F

2016-01-01

Isoprenoid metabolism occupies a central position in the anabolic metabolism of all living cells. In plastid-bearing organisms, two pathways may be present for de novo isoprenoid synthesis, the cytosolic mevalonate pathway (MVA) and nuclear-encoded, plastid-targeted nonmevalonate pathway (DOXP). Using transcriptomic data we find that dinoflagellates apparently make exclusive use of the DOXP pathway. Using phylogenetic analyses of all DOXP genes we inferred the evolutionary origins of DOXP genes in dinoflagellates. Plastid replacements led to a DOXP pathway of multiple evolutionary origins. Dinoflagellates commonly referred to as dinotoms due to their relatively recent acquisition of a diatom plastid, express two completely redundant DOXP pathways. Dinoflagellates with a tertiary plastid of haptophyte origin, by contrast, express a hybrid pathway of dual evolutionary origin. Here, changes in the targeting motif of signal/transit peptide likely allow for targeting the new plastid by the proteins of core isoprenoid metabolism proteins. Parasitic dinoflagellates of the Amoebophyra species complex appear to have lost the DOXP pathway, suggesting that they may rely on their host for sterol synthesis. © 2015 The Author(s) Journal of Eukaryotic Microbiology © 2015 International Society of Protistologists.

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

The mechanism for enhanced oxidation degradation of dioxin-like PCBs (PCB-77) in the atmosphere by the solvation effect.

PubMed

Xin, Mei-Ling; Yang, Jia-Wen; Li, Yu

2017-07-11

The reaction pathways of PCB-77 in the atmosphere with ·OH, O 2 , NO x , and 1 O 2 were inferred based on density functional theory calculations with the 6-31G* basis set. The structures the reactants, transition states, intermediates, and products were optimized. The energy barriers and reaction heats were obtained to determine the energetically favorable reaction pathways. To study the solvation effect, the energy barriers and reaction rates for PCB-77 with different polar and nonpolar solvents (cyclohexane, benzene, carbon tetrachloride, chloroform, acetone, dichloromethane, ethanol, methanol, acetonitrile, dimethylsulfoxide, and water) were calculated. The results showed that ·OH preferentially added to the C5 atom of PCB-77, which has no Cl atom substituent, to generate the intermediate IM5. This intermediate subsequently reacted with O 2 via pathway A to generate IM5a, with an energy barrier of 7.27 kcal/mol and total reaction rate of 8.45 × 10 -8  cm 3 /molecule s. Pathway B involved direct dehydrogenation of IM5 to produce the OH-PCBs intermediate IM5b, with an energy barrier of 28.49 kcal/mol and total reaction rate of 1.15 × 10 -5  cm 3 /molecule s. The most likely degradation pathway of PCB-77 in the atmosphere is pathway A to produce IM5a. The solvation effect results showed that cyclohexane, carbon tetrachloride, and benzene could reduce the reaction energy barrier of pathway A. Among these solvents, the solvation effect of benzene was the largest, and could reduce the total reaction energy barrier by 25%. Cyclohexane, carbon tetrachloride, benzene, dichloromethane, acetone, and ethanol could increase the total reaction rate of pathway A. The increase in the reaction rate of pathway A with benzene was 8%. The effect of solvents on oxidative degradation of PCB-77 in the atmosphere is important. Graphical abstract The reaction pathways of PCB-77 in the atmosphere with •OH, O2, NOx, and 1O2 were inferred based on density functional theory calculations with the 6-31G* basis set. Different polar and nonpolar solvents: cyclohexane, benzene, carbon tetrachloride, chloroform, acetone, dichloromethane, ethanol, methanol, acetonitrile, dimethylsulfoxide, and water were selected to study the solvation effect on the favorable reaction pathways. The investigated results showed what kind of pathway was most likely to occur and the solvent effect on the reaction pathway.

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.

VIRTUAL LIVER: AN IN SILICO FRAMEWORK FOR ANALYZING CHEMICAL-INDUCED HEPATOTOXICITY

EPA Science Inventory

The US EPA Virtual Liver (v-LiverTM) is an in silico framework for the dose-dependent perturbation of normal hepatic functions by chemicals using in vitro data. The framework consists of a computable knowledge-base (KB) to infer putative pathways in hepatotoxicity and a cellular...

How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology

EPA Science Inventory

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumu...

Global population genomics and comparisons of selective signatures from two invasions of melon fly, Zeugodacus cucurbitae (Diptera: Tephritidae)

USDA-ARS?s Scientific Manuscript database

Population genetics is a powerful tool for invasion biology and pest management, from tracing invasion pathways to informing management decisions with inference of population demographics. Genomics greatly increases the resolution of population-scale analyses, yet outside of model species with exten...

Gas Phase Probe Molecules for Assessing In vitro Metabolism to Infer an In vivo Response

EPA Science Inventory

Efficient and accurate in vitro high-throughput screening (HTS) methods use cellular and molecular based adverse outcome pathways (AOPs) as central elements for exposure assessment and chemical prioritization. However, not all AOPs are based on human or animal systems biology, bu...

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

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

Lu, Jinghua; Marnell, Lorraine L.; Marjon, Kristopher D.

Pentraxins are a family of ancient innate immune mediators conserved throughout evolution. The classical pentraxins include serum amyloid P component (SAP) and C-reactive protein, which are two of the acute-phase proteins synthesized in response to infection. Both recognize microbial pathogens and activate the classical complement pathway through C1q. More recently, members of the pentraxin family were found to interact with cell-surface Fc{gamma} receptors (Fc{gamma}R) and activate leukocyte-mediated phagocytosis. Here we describe the structural mechanism for pentraxin's binding to Fc{gamma}R and its functional activation of Fc{gamma}R-mediated phagocytosis and cytokine secretion. The complex structure between human SAP and Fc{gamma}RIIa reveals a diagonallymore » bound receptor on each SAP pentamer with both D1 and D2 domains of the receptor contacting the ridge helices from two SAP subunits. The 1:1 stoichiometry between SAP and Fc{gamma}RIIa infers the requirement for multivalent pathogen binding for receptor aggregation. Mutational and binding studies show that pentraxins are diverse in their binding specificity for Fc{gamma}R isoforms but conserved in their recognition structure. The shared binding site for SAP and IgG results in competition for Fc{gamma}R binding and the inhibition of immune-complex-mediated phagocytosis by soluble pentraxins. These results establish antibody-like functions for pentraxins in the Fc{gamma}R pathway, suggest an evolutionary overlap between the innate and adaptive immune systems, and have new therapeutic implications for autoimmune diseases.« less

Experimental and Metabolic Modeling Evidence for a Folate-Cleaving Side-Activity of Ketopantoate Hydroxymethyltransferase (PanB)

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

Thiaville, Jennifer J.; Frelin, Océane; García-Salinas, Carolina

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanBmore » has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and—unexpectedly—to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.« less

A formal model of interpersonal inference

PubMed Central

Moutoussis, Michael; Trujillo-Barreto, Nelson J.; El-Deredy, Wael; Dolan, Raymond J.; Friston, Karl J.

2014-01-01

Introduction: We propose that active Bayesian inference—a general framework for decision-making—can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: (1) Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to “mentalizing” in the psychological literature, is based upon the outcomes of interpersonal exchanges. (2) We show how some well-known social-psychological phenomena (e.g., self-serving biases) can be explained in terms of active interpersonal inference. (3) Mentalizing naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one's own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted. PMID:24723872

Modeling the evolution of drug resistance in malaria.

PubMed

Hecht, David; Fogel, Gary B

2012-12-01

Plasmodium falciparum, the causal agent of malaria, continues to evolve resistance to frontline therapeutics such as chloroquine and sulfadoxine-pyrimethamine. Here we study the amino acid replacements in dihydrofolate reductase (DHFR) that confer resistance to pyrimethamine while still binding the natural DHFR substrate, 7,8-dihydrofolate, and cofactor, NADPH. The chain of amino acid replacements that has led to resistance can be inferred in a computer, leading to a broader understanding of the coevolution between the drug and target. This in silico approach suggests that only a small set of specific active site replacements in the proper order could have led to the resistant strains in the wild today. A similar approach can be used on any target of interest to anticipate likely pathways of future resistance for more effective drug development.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.

PubMed

Lezon, Timothy R; Banavar, Jayanth R; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V

2006-12-12

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

The hippocampus and memory for orderly stimulus relations

PubMed Central

Dusek, Jeffery A.; Eichenbaum, Howard

1997-01-01

Human declarative memory involves a systematic organization of information that supports generalizations and inferences from acquired knowledge. This kind of memory depends on the hippocampal region in humans, but the extent to which animals also have declarative memory, and whether inferential expression of memory depends on the hippocampus in animals, remains a major challenge in cognitive neuroscience. To examine these issues, we used a test of transitive inference pioneered by Piaget to assess capacities for systematic organization of knowledge and logical inference in children. In our adaptation of the test, rats were trained on a set of four overlapping odor discrimination problems that could be encoded either separately or as a single representation of orderly relations among the odor stimuli. Normal rats learned the problems and demonstrated the relational memory organization through appropriate transitive inferences about items not presented together during training. By contrast, after disconnection of the hippocampus from either its cortical or subcortical pathway, rats succeeded in acquiring the separate discrimination problems but did not demonstrate transitive inference, indicating that they had failed to develop or could not inferentially express the orderly organization of the stimulus elements. These findings strongly support the view that the hippocampus mediates a general declarative memory capacity in animals, as it does in humans. PMID:9192700

Inference of scale-free networks from gene expression time series.

PubMed

Daisuke, Tominaga; Horton, Paul

2006-04-01

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

Inference of cancer-specific gene regulatory networks using soft computing rules.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Diversity of bile salts in fish and amphibians: evolution of a complex biochemical pathway.

PubMed

Hagey, Lee R; Møller, Peter R; Hofmann, Alan F; Krasowski, Matthew D

2010-01-01

Bile salts are the major end metabolites of cholesterol and are also important in lipid and protein digestion, as well as shaping of the gut microflora. Previous studies had demonstrated variation of bile salt structures across vertebrate species. We greatly extend prior surveys of bile salt variation in fish and amphibians, particularly in analysis of the biliary bile salts of Agnatha and Chondrichthyes. While there is significant structural variation of bile salts across all fish orders, bile salt profiles are generally stable within orders of fish and do not correlate with differences in diet. This large data set allowed us to infer evolutionary changes in the bile salt synthetic pathway. The hypothesized ancestral bile salt synthetic pathway, likely exemplified in extant hagfish, is simpler and much shorter than the pathway of most teleost fish and terrestrial vertebrates. Thus, the bile salt synthetic pathway has become longer and more complex throughout vertebrate evolution. Analysis of the evolution of bile salt synthetic pathways provides a rich model system for the molecular evolution of a complex biochemical pathway in vertebrates.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

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

Cannon, William; Zucker, Jeremy; Baxter, Douglas

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrationsmore » and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.« less

Active sensing of target location encoded by cortical microstimulation.

PubMed

Venkatraman, Subramaniam; Carmena, Jose M

2011-06-01

Cortical microstimulation has been proposed as a method to deliver sensory percepts to circumvent damaged sensory receptors or pathways. However, much of perception involves the active movement of sensory organs and the integration of information across sensory and motor modalities. The efficacy of cortical microstimulation in such an active sensing paradigm has not been demonstrated. We report a novel behavioral paradigm which delivers microstimulation in real-time based on a rat's movements and show that rats can perform sensorimotor integration with electrically delivered stimuli. Using a real-time whisker tracking system, we delivered microstimulation in barrel cortex of actively whisking rats when their whisker crossed a particular spatial location which defined the target. Rats learned to integrate microstimulation cues with their knowledge of whisker position to infer target location along the rostro-caudal axis in less than 200 ms. In a separate experiment, we found that rats trained to respond to cortical microstimulation responded similarly to whisker deflections while ignoring auditory distracters, suggesting that barrel cortex stimulation may be perceptually similar to somatosensory stimuli. This ability to deliver sensory percepts using cortical microstimulation in an active sensing system might have significant implications for the development of sensorimotor neuroprostheses.

Chemical-agnostic hazard prediction: statistical inference of in ...

EPA Pesticide Factsheets

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, a significant correlation must exist between the exposure, the extent of pathway alteration, and the degree of adverse outcome. Biological pathways are regulated at multiple levels, including transcriptional, post-transcriptional, post-translational, and targeted degradation, each of which can affect the levels and extents of modification of proteins involved in the pathways. Significant alterations of toxicity pathways resulting from changes in regulation at any of these levels therefore are likely to be detectable as alterations in the proteome. We hypothesize that significant correlations between exposures, adverse outcomes, and changes in the proteome have the potential to identify putative toxicity pathways, facilitating selection of candidate targets for high throughput screening, even in the absence of a priori knowledge of either the specific pathways involved or the specific agents inducing the pathway alterations. We explored this hypothesis in vitro in BEAS-2B human airway epithelial cells exposed to different concentrations of Ni2+, Cd2+, and Cr6+, alone and in defined mixtures. Levels and phosphorylation status of a variety of signaling pathway proteins and cytokines were

Inhibition of lipid A-mediated type I interferon induction by bactericidal/permeability-increasing protein (BPI).

PubMed

Azuma, Masahiro; Matsuo, Aya; Fujimoto, Yukari; Fukase, Koichi; Hazeki, Kaoru; Hazeki, Osamu; Matsumoto, Misako; Seya, Tsukasa

2007-03-09

Lipopolysaccharide (LPS), a major constituent of the outer membrane of gram-negative bacteria, consists of polysaccharides and a lipid structure named lipid A. Lipid A is a typical microbial pattern molecule that serves as a ligand for Toll-like receptor 4 (TLR4). TLR4 signals the presence of lipid A to recruit adaptor molecules and induces cytokines and type I interferon (IFN) by activating transcription factor, NF-kappaB or IRF-3. Here we showed that chemically synthesized TLR4-agonistic lipid A analogues but not antagonistic lipid A activate IFN-beta promoter in TLR4-expressing HEK293 cells. The amplitude of IFN-beta promoter activation was in parallel with that of NF-kappaB. LPS-binding protein (LBP) was required for efficient IFN-beta induction in this system, and this LBP activity was antagonized by bactericidal/permeability-increasing protein (BPI). Thus, we first show that BPI blocks the TLR4 responses by exogenous administration of BPI to lipid A-sensitive cells. Although the functional mechanism whereby extra-cellular BPI modulates the intra-cellular signal pathways selected by the TLR adaptors, MyD88 and TICAM-1 (TRIF), remains unknown, we infer that the lipid A portion of LPS participates in LBP-amplified IFN-beta induction and that BPI binding to LPS leads to inhibition of the activation of NF-kappaB and IFN-beta by LPS or agonistic lipid A via TLR4 in an extrinsic mode. BPI may serve as a therapeutic potential against endotoxin shock by acting as a regulator for the MyD88- and TICAM-1 pathways in the LPS-TLR4 signaling.

Finding a common path: predicting gene function using inferred evolutionary trees.

PubMed

Reynolds, Kimberly A

2014-07-14

Reporting in Cell, Li and colleagues (2014) describe an innovative method to functionally classify genes using evolutionary information. This approach demonstrates broad utility for eukaryotic gene annotation and suggests an intriguing new decomposition of pathways and complexes into evolutionarily conserved modules. Copyright © 2014 Elsevier Inc. All rights reserved.

Population genetic analysis infers mMigration pathways of Phytophthora ramorum in US nurseries

Treesearch

Erica M. Goss; Meg Larsen; Gary A. Chastagner; Donald R. Givens; Niklaus J. Grünwald; Barbara Jane Howlett

2009-01-01

Recently introduced, exotic plant pathogens may exhibit low genetic diversity and be limited to clonal reproduction. However, rapidly mutating molecular markers such as microsatellites can reveal genetic variation within these populations and be used to model putative migration patterns. Phytophthora ramorum is the exotic pathogen, discovered in...

Discrete kinetic models from funneled energy landscape simulations.

PubMed

Schafer, Nicholas P; Hoffman, Ryan M B; Burger, Anat; Craig, Patricio O; Komives, Elizabeth A; Wolynes, Peter G

2012-01-01

A general method for facilitating the interpretation of computer simulations of protein folding with minimally frustrated energy landscapes is detailed and applied to a designed ankyrin repeat protein (4ANK). In the method, groups of residues are assigned to foldons and these foldons are used to map the conformational space of the protein onto a set of discrete macrobasins. The free energies of the individual macrobasins are then calculated, informing practical kinetic analysis. Two simple assumptions about the universality of the rate for downhill transitions between macrobasins and the natural local connectivity between macrobasins lead to a scheme for predicting overall folding and unfolding rates, generating chevron plots under varying thermodynamic conditions, and inferring dominant kinetic folding pathways. To illustrate the approach, free energies of macrobasins were calculated from biased simulations of a non-additive structure-based model using two structurally motivated foldon definitions at the full and half ankyrin repeat resolutions. The calculated chevrons have features consistent with those measured in stopped flow chemical denaturation experiments. The dominant inferred folding pathway has an "inside-out", nucleation-propagation like character.

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

PubMed

Şenbabaoğlu, Yasin; Sümer, Selçuk Onur; Sánchez-Vega, Francisco; Bemis, Debra; Ciriello, Giovanni; Schultz, Nikolaus; Sander, Chris

2016-02-01

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

Multi-layered mutation in hedgehog-related genes in Gorlin syndrome may affect the phenotype.

PubMed

Onodera, Shoko; Saito, Akiko; Hasegawa, Daigo; Morita, Nana; Watanabe, Katsuhito; Nomura, Takeshi; Shibahara, Takahiko; Ohba, Shinsuke; Yamaguchi, Akira; Azuma, Toshifumi

2017-01-01

Gorlin syndrome is a genetic disorder of autosomal dominant inheritance that predisposes the affected individual to a variety of disorders that are attributed largely to heterozygous germline patched1 (PTCH1) mutations. PTCH1 is a hedgehog (Hh) receptor as well as a repressor, mutation of which leads to constitutive activation of Hh pathway. Hh pathway encompasses a wide variety of cellular signaling cascades, which involve several molecules; however, no associated genotype-phenotype correlations have been reported. Recently, mutations in Suppressor of fused homolog (SUFU) or PTCH2 were reported in patients with Gorlin syndrome. These facts suggest that multi-layered mutations in Hh pathway may contribute to the development of Gorlin syndrome. We demonstrated multiple mutations of Hh-related genes in addition to PTCH1, which possibly act in an additive or multiplicative manner and lead to Gorlin syndrome. High-throughput sequencing was performed to analyze exome sequences in four unrelated Gorlin syndrome patient genomes. Mutations in PTCH1 gene were detected in all four patients. Specific nucleotide variations or frameshift variations of PTCH1 were identified along with the inferred amino acid changes in all patients. We further filtered 84 different genes which are closely related to Hh signaling. Fifty three of these had enough coverage of over ×30. The sequencing results were filtered and compared to reduce the number of sequence variants identified in each of the affected individuals. We discovered three genes, PTCH2, BOC, and WNT9b, with mutations with a predicted functional impact assessed by MutationTaster2 or PolyPhen-2 (Polymorphism Phenotyping v2) analysis. It is noticeable that PTCH2 and BOC are Hh receptor molecules. No significant mutations were observed in SUFU. Multi-layered mutations in Hh pathway may change the activation level of the Hh signals, which may explain the wide phenotypic variability of Gorlin syndrome.

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

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

NASA Astrophysics Data System (ADS)

Karacan, C. Özgen; Olea, Ricardo A.

2014-06-01

Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile. For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs. Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal-Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths. Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas source within their zone of influence, paths may develop and allow methane movement towards sealed or active gobs under pressure differentials. Knowledge gained from this research will better prepare mine operations for potential methane inflows, thus improving mine safety.

Invasion of Ancestral Mammals into Dim-light Environments Inferred from Adaptive Evolution of the Phototransduction Genes

PubMed Central

Wu, Yonghua; Wang, Haifeng; Hadly, Elizabeth A.

2017-01-01

Nocturnality is a key evolutionary innovation of mammals that enables mammals to occupy relatively empty nocturnal niches. Invasion of ancestral mammals into nocturnality has long been inferred from the phylogenetic relationships of crown Mammalia, which is primarily nocturnal, and crown Reptilia, which is primarily diurnal, although molecular evidence for this is lacking. Here we used phylogenetic analyses of the vision genes involved in the phototransduction pathway to predict the diel activity patterns of ancestral mammals and reptiles. Our results demonstrated that the common ancestor of the extant Mammalia was dominated by positive selection for dim-light vision, supporting the predominate nocturnality of the ancestral mammals. Further analyses showed that the nocturnality of the ancestral mammals was probably derived from the predominate diurnality of the ancestral amniotes, which featured strong positive selection for bright-light vision. Like the ancestral amniotes, the common ancestor of the extant reptiles and various taxa in Squamata, one of the main competitors of the temporal niches of the ancestral mammals, were found to be predominate diurnality as well. Despite this relatively apparent temporal niche partitioning between ancestral mammals and the relevant reptiles, our results suggested partial overlap of their temporal niches during crepuscular periods. PMID:28425474

High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

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

Pan, Jian-Bo; Ji, Nan; Pan, Wen

2014-01-01

Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPsmore » that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing. - Highlights: • A novel computational framework was developed for mechanistic study of SADRs. • Off-targets of drugs were identified in large scale and in a high-throughput manner. • SADRs like cardiac disorders were systematically explored in molecular networks. • A number of ADR-associated proteins were identified.« less

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

CHOP Contributes to, But Is Not the Only Mediator of, IAPP Induced β-Cell Apoptosis.

PubMed

Gurlo, T; Rivera, J F; Butler, A E; Cory, M; Hoang, J; Costes, S; Butler, Peter C

2016-04-01

The islet in type 2 diabetes is characterized by β-cell loss, increased β-cell apoptosis, and islet amyloid derived from islet amyloid polypeptide (IAPP). When protein misfolding protective mechanisms are overcome, human IAPP (h-IAPP) forms membrane permeant toxic oligomers that induce β-cell dysfunction and apoptosis. In humans with type 2 diabetes (T2D) and mice transgenic for h-IAPP, endoplasmic reticulum (ER) stress has been inferred from nuclear translocation of CCAAT/enhancer-binding protein homologous protein (CHOP), an established mediator of ER stress. To establish whether h-IAPP toxicity is mediated by ER stress, we evaluated diabetes onset and β-cell mass in h-IAPP transgenic (h-TG) mice with and without deletion of CHOP in comparison with wild-type controls. Diabetes was delayed in h-TG CHOP(-/-) mice, with relatively preserved β-cell mass and decreased β-cell apoptosis. Deletion of CHOP attenuates dysfunction of the autophagy/lysosomal pathway in β-cells of h-TG mice, uncovering a role for CHOP in mediating h-IAPP-induced dysfunction of autophagy. As deletion of CHOP delayed but did not prevent h-IAPP-induced β-cell loss and diabetes, we examined CHOP-independent stress pathways. JNK, a target of the IRE-1pTRAF2 complex, and the Bcl-2 family proapoptotic mediator BIM, a target of ATF4, were comparably activated by h-IAPP expression in the presence and absence of CHOP. Therefore, although these studies affirm that CHOP is a mediator of h-IAPP-induced ER stress, it is not the only one. Therefore, suppression of CHOP alone is unlikely to be a durable therapeutic strategy to protect against h-IAPP toxicity because multiple stress pathways are activated.

Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs

PubMed Central

Wessely, Frank; Bartl, Martin; Guthke, Reinhard; Li, Pu; Schuster, Stefan; Kaleta, Christoph

2011-01-01

While previous studies have shed light on the link between the structure of metabolism and its transcriptional regulation, the extent to which transcriptional regulation controls metabolism has not yet been fully explored. In this work, we address this problem by integrating a large number of experimental data sets with a model of the metabolism of Escherichia coli. Using a combination of computational tools including the concept of elementary flux patterns, methods from network inference and dynamic optimization, we find that transcriptional regulation of pathways reflects the protein investment into these pathways. While pathways that are associated to a high protein cost are controlled by fine-tuned transcriptional programs, pathways that only require a small protein cost are transcriptionally controlled in a few key reactions. As a reason for the occurrence of these different regulatory strategies, we identify an evolutionary trade-off between the conflicting requirements to reduce protein investment and the requirement to be able to respond rapidly to changes in environmental conditions. PMID:21772263

Active inference and learning.

PubMed

Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

2016-09-01

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Bayesian model of signal rewiring reveals mechanisms of gene dysregulation in acquired drug resistance in breast cancer

PubMed Central

Azad, A. K. M.; Keith, Jonathan M.

2017-01-01

Small molecule inhibitors, such as lapatinib, are effective against breast cancer in clinical trials, but tumor cells ultimately acquire resistance to the drug. Maintaining sensitization to drug action is essential for durable growth inhibition. Recently, adaptive reprogramming of signaling circuitry has been identified as a major cause of acquired resistance. We developed a computational framework using a Bayesian statistical approach to model signal rewiring in acquired resistance. We used the p1-model to infer potential aberrant gene-pairs with differential posterior probabilities of appearing in resistant-vs-parental networks. Results were obtained using matched gene expression profiles under resistant and parental conditions. Using two lapatinib-treated ErbB2-positive breast cancer cell-lines: SKBR3 and BT474, our method identified similar dysregulated signaling pathways including EGFR-related pathways as well as other receptor-related pathways, many of which were reported previously as compensatory pathways of EGFR-inhibition via signaling cross-talk. A manual literature survey provided strong evidence that aberrant signaling activities in dysregulated pathways are closely related to acquired resistance in EGFR tyrosine kinase inhibitors. Our approach predicted literature-supported dysregulated pathways complementary to both node-centric (SPIA, DAVID, and GATHER) and edge-centric (ESEA and PAGI) methods. Moreover, by proposing a novel pattern of aberrant signaling called V-structures, we observed that genes were dysregulated in resistant-vs-sensitive conditions when they were involved in the switch of dependencies from targeted to bypass signaling events. A literature survey of some important V-structures suggested they play a role in breast cancer metastasis and/or acquired resistance to EGFR-TKIs, where the mRNA changes of TGFBR2, LEF1 and TP53 in resistant-vs-sensitive conditions were related to the dependency switch from targeted to bypass signaling links. Our results suggest many signaling pathway structures are compromised in acquired resistance, and V-structures of aberrant signaling within/among those pathways may provide further insights into the bypass mechanism of targeted inhibition. PMID:28288164

Mode of action for reproductive and hepatic toxicity inferred from a genomic study of triazole antifungals.

PubMed

Goetz, Amber K; Dix, David J

2009-08-01

The mode of action for the reproductive toxicity of some triazole antifungals has been characterized as an increase in serum testosterone and hepatic response, and reduced insemination and fertility indices. In order to refine our mechanistic understanding of these potential modes of action, gene expression profiling was conducted on liver and testis from male Wistar Han IGS rats exposed to myclobutanil (500, 2000 ppm), propiconazole (500, 2500 ppm), or triadimefon (500, 1800 ppm) from gestation day six to postnatal day 92. Gene expression profiles indicated that all three triazoles significantly perturbed the fatty acid, steroid, and xenobiotic metabolism pathways in the male rat liver. In addition, triadimefon modulated expression of genes in the liver from the sterol biosynthesis pathway. Although expression of individual genes were affected, there were no common pathways modulated by all three triazoles in the testis. The pathways identified in the liver included numerous genes involved in phase I-III metabolism (Aldh1a1, Cyp1a1, Cyp2b2, Cyp3a1, Cyp3a2, Slco1a4, Udpgtr2), fatty acid metabolism (Cyp4a10, Pcx, Ppap2b), and steroid metabolism (Ugt1a1, Ugt2a1) for which expression was altered by the triazoles. These differentially expressed genes form part of a network involving lipid, sterol, and steroid homeostatic pathways regulated by the constitutive androstane (CAR), pregnane X (PXR), peroxisome proliferator-activated alpha, and other nuclear receptors in liver. These relatively high dose and long-term exposures to triazole antifungals appeared to perturb fatty acid and steroid metabolism in the male rat liver predominantly through the CAR and PXR signaling pathways. These toxicogenomic effects describe a plausible series of key events contributing to the disruption in steroid homeostasis and reproductive toxicity of select triazole antifungals.

Causal Inferences from Mining ToxCast Data and the Biomedical Literature for Molecular Pathways and Cellular Processes in Cleft Palate (SOT)

EPA Science Inventory

Sixty-five chemicals in the ToxCast high-throughput screening (HTS) dataset have been linked to cleft palate based on data from ToxRefDB (rat or rabbit prenatal developmental toxicity studies) or from literature reports. These compounds are structurally diverse and thus likely to...

Regulating the effects of GPR21, a novel target for type 2 diabetes

NASA Astrophysics Data System (ADS)

Leonard, Siobhán; Kinsella, Gemma K.; Benetti, Elisa; Findlay, John B. C.

2016-05-01

Type 2 diabetes is a chronic metabolic disorder primarily caused by insulin resistance to which obesity is a major contributor. Expression levels of an orphan G protein-coupled receptor (GPCR), GPR21, demonstrated a trend towards a significant increase in the epididymal fat pads of wild type high fat high sugar (HFHS)-fed mice. To gain further insight into the potential role this novel target may play in the development of obesity-associated type 2 diabetes, the signalling capabilities of the receptor were investigated. Overexpression studies in HEK293T cells revealed GPR21 to be a constitutively active receptor, which couples to Gαq type G proteins leading to the activation of mitogen activated protein kinases (MAPKs). Overexpression of GPR21 in vitro also markedly attenuated insulin signalling. Interestingly, the effect of GPR21 on the MAPKs and insulin signalling was reduced in the presence of serum, inferring the possibility of a native inhibitory ligand. Homology modelling and ligand docking studies led to the identification of a novel compound that inhibited GPR21 activity. Its effects offer potential as an anti-diabetic pharmacological strategy as it was found to counteract the influence of GPR21 on the insulin signalling pathway.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes.

PubMed

Schwartenbeck, Philipp; FitzGerald, Thomas H B; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-10-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. © The Author 2014. Published by Oxford University Press.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-01-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a “limited offer” game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. PMID:25056572

Task constraints distinguish perspective inferences from perspective use during discourse interpretation in a false belief task.

PubMed

Ferguson, Heather J; Apperly, Ian; Ahmad, Jumana; Bindemann, Markus; Cane, James

2015-06-01

Interpreting other peoples' actions relies on an understanding of their current mental states (e.g. beliefs, desires and intentions). In this paper, we distinguish between listeners' ability to infer others' perspectives and their explicit use of this knowledge to predict subsequent actions. In a visual-world study, two groups of participants (passive observers vs. active participants) watched short videos, depicting transfer events, where one character ('Jane') either held a true or false belief about an object's location. We tracked participants' eye-movements around the final visual scene, time-locked to related auditory descriptions (e.g. "Jane will look for the chocolates in the container on the left".). Results showed that active participants had already inferred the character's belief in the 1s preview period prior to auditory onset, before it was possible to use this information to predict an outcome. Moreover, they used this inference to correctly anticipate reference to the object's initial location on false belief trials at the earliest possible point (i.e. from "Jane" onwards). In contrast, passive observers only showed evidence of a belief inference from the onset of "Jane", and did not show reliable use of this inference to predict Jane's behaviour on false belief trials until much later, when the location ("left/right") was auditorily available. These results show that active engagement in a task activates earlier inferences about others' perspectives, and drives immediate use of this information to anticipate others' actions, compared to passive observers, who are susceptible to influences from egocentric or reality biases. Finally, we review evidence that using other peoples' perspectives to predict their behaviour is more cognitively effortful than simply using one's own. Copyright © 2015 Elsevier B.V. All rights reserved.

Independent saturation of three TrpRS subsites generates a partially assembled state similar to those observed in molecular simulations

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

Laowanapiban, Poramaet; Kapustina, Maryna; Vonrhein, Clemens

2009-03-05

Two new crystal structures of Bacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) afford evidence that a closed interdomain hinge angle requires a covalent bond between AMP and an occupant of either pyrophosphate or tryptophan subsite. They also are within experimental error of a cluster of structures observed in a nonequilibrium molecular dynamics simulation showing partial active-site assembly. Further, the highest energy structure in a minimum action pathway computed by using elastic network models for Open and Pretransition state (PreTS) conformations for the fully liganded TrpRS monomer is intermediate between that simulated structure and a partially disassembled structure from a nonequilibrium molecular dynamicsmore » trajectory for the unliganded PreTS. These mutual consistencies provide unexpected validation of inferences drawn from molecular simulations.« less

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

PubMed Central

Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

2006-01-01

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. PMID:17138668

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Functional characterization of somatic mutations in cancer using network-based inference of protein activity | Office of Cancer Genomics

Cancer.gov

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible.

The Impact of Experiential Avoidance on the Inference of Characters' Emotions: Evidence for an Emotional Processing Bias.

PubMed

Pickett, Scott M; Kurby, Christopher A

2010-12-01

Experiential avoidance is a functional class of maladaptive strategies that contribute to the development and maintenance of psychopathology. Although previous research has demonstrated group differences in the interpretation of aversive stimuli, there is limited work on the influence of experiential avoidance during the online processing of emotion. An experimental design investigated the influence of self-reported experiential avoidance during emotion processing by assessing emotion inferences during the comprehension of narratives that imply different emotions. Results suggest that experiential avoidance is partially characterized by an emotional information processing bias. Specifically, individuals reporting higher experiential avoidance scores exhibited a bias towards activating negative emotion inferences, whereas individuals reporting lower experiential avoidance scores exhibited a bias towards activating positive emotion inferences. Minimal emotional inference was observed for the non-bias affective valence. Findings are discussed in terms of the implications of experiential avoidance as a cognitive vulnerability for psychopathology.

Impact of age on exercise-induced ATP supply during supramaximal plantar flexion in humans

PubMed Central

Trinity, Joel D.; Hart, Corey R.; Kim, Seong-Eun; Groot, H. Jonathan; Fur, Yann Le; Sorensen, Jacob R.; Jeong, Eun-Kee; Richardson, Russell S.

2015-01-01

Currently, the physiological factors responsible for exercise intolerance and bioenergetic alterations with age are poorly understood due, at least in art, to the confounding effect of reduced physical activity in the elderly. Thus, in 40 healthy young (22 ± 2 yr) and old (74 ± 8 yr) activity-matched subjects, we assessed the impact of age on: 1) the relative contribution of the three major pathways of ATP synthesis (oxidative ATP synthesis, glycolysis, and the creatine kinase reaction) and 2) the ATP cost of contraction during high-intensity exercise. Specifically, during supramaximal plantar flexion (120% of maximal aerobic power), to stress the functional limits of the skeletal muscle energy systems, we used 31P-labeled magnetic resonance spectroscopy to assess metabolism. Although glycolytic activation was delayed in the old, ATP synthesis from the main energy pathways was not significantly different between groups. Similarly, the inferred peak rate of mitochondrial ATP synthesis was not significantly different between the young (25 ± 8 mM/min) and old (24 ± 6 mM/min). In contrast, the ATP cost of contraction was significantly elevated in the old compared with the young (5.1 ± 2.0 and 3.7 ± 1.7 mM·min−1·W−1, respectively; P < 0.05). Overall, these findings suggest that, when young and old subjects are activity matched, there is no evidence of age-related mitochondrial and glycolytic dysfunction. However, this study does confirm an abnormal elevation in exercise-induced skeletal muscle metabolic demand in the old that may contribute to the decline in exercise capacity with advancing age. PMID:26041112

Optimal inference with suboptimal models: Addiction and active Bayesian inference

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases.

PubMed

Jamialahmadi, Oveis; Motamedian, Ehsan; Hashemi-Najafabadi, Sameereh

2016-10-18

Development of an interface tool between the Biochemical, Genetic and Genomic (BiGG) and KEGG databases is necessary for simultaneous access to the features of both databases. For this purpose, we present the BiKEGG toolbox, an open source COBRA toolbox extension providing a set of functions to infer the reaction correspondences between the KEGG reaction identifiers and those in the BiGG knowledgebase using a combination of manual verification and computational methods. Inferred reaction correspondences using this approach are supported by evidence from the literature, which provides a higher number of reconciled reactions between these two databases compared to the MetaNetX and MetRxn databases. This set of equivalent reactions is then used to automatically superimpose the predicted fluxes using COBRA methods on classical KEGG pathway maps or to create a customized metabolic map based on the KEGG global metabolic pathway, and to find the corresponding reactions in BiGG based on the genome annotation of an organism in the KEGG database. Customized metabolic maps can be created for a set of pathways of interest, for the whole KEGG global map or exclusively for all pathways for which there exists at least one flux carrying reaction. This flexibility in visualization enables BiKEGG to indicate reaction directionality as well as to visualize the reaction fluxes for different static or dynamic conditions in an animated manner. BiKEGG allows the user to export (1) the output visualized metabolic maps to various standard image formats or save them as a video or animated GIF file, and (2) the equivalent reactions for an organism as an Excel spreadsheet.

Transcriptome Profiles of the Protoscoleces of Echinococcus granulosus Reveal that Excretory-Secretory Products Are Essential to Metabolic Adaptation

PubMed Central

Pan, Wei; Shen, Yujuan; Han, Xiuming; Wang, Ying; Liu, Hua; Jiang, Yanyan; Zhang, Yumei; Wang, Yanjuan; Xu, Yuxin; Cao, Jianping

2014-01-01

Background Cystic hydatid disease (CHD) is caused by the larval stages of the cestode and affects humans and domestic animals worldwide. Protoscoleces (PSCs) are one component of the larval stages that can interact with both definitive and intermediate hosts. Previous genomic and transcriptomic data have provided an overall snapshot of the genomics of the growth and development of this parasite. However, our understanding of how PSCs subvert the immune response of hosts and maintains metabolic adaptation remains unclear. In this study, we used Roche 454 sequencing technology and in silico secretome analysis to explore the transcriptome profiles of the PSCs from E. granulosus and elucidate the potential functions of the excretory-secretory proteins (ESPs) released by the parasite. Methodology/Principal Findings A large number of nonredundant sequences as unigenes were generated (26,514), of which 22,910 (86.4%) were mapped to the newly published E. granulosus genome and 17,705 (66.8%) were distributed within the coding sequence (CDS) regions. Of the 2,280 ESPs predicted from the transcriptome, 138 ESPs were inferred to be involved in the metabolism of carbohydrates, while 124 ESPs were inferred to be involved in the metabolism of protein. Eleven ESPs were identified as intracellular enzymes that regulate glycolysis/gluconeogenesis (GL/GN) pathways, while a further 44 antigenic proteins, 25 molecular chaperones and four proteases were highly represented. Many proteins were also found to be significantly enriched in development-related signaling pathways, such as the TGF-β receptor pathways and insulin pathways. Conclusions/Significance This study provides valuable information on the metabolic adaptation of parasites to their hosts that can be used to aid the development of novel intervention targets for hydatid treatment and control. PMID:25500817

Stability-activity tradeoffs constrain the adaptive evolution of RubisCO.

PubMed

Studer, Romain A; Christin, Pascal-Antoine; Williams, Mark A; Orengo, Christine A

2014-02-11

A well-known case of evolutionary adaptation is that of ribulose-1,5-bisphosphate carboxylase (RubisCO), the enzyme responsible for fixation of CO2 during photosynthesis. Although the majority of plants use the ancestral C3 photosynthetic pathway, many flowering plants have evolved a derived pathway named C4 photosynthesis. The latter concentrates CO2, and C4 RubisCOs consequently have lower specificity for, and faster turnover of, CO2. The C4 forms result from convergent evolution in multiple clades, with substitutions at a small number of sites under positive selection. To understand the physical constraints on these evolutionary changes, we reconstructed in silico ancestral sequences and 3D structures of RubisCO from a large group of related C3 and C4 species. We were able to precisely track their past evolutionary trajectories, identify mutations on each branch of the phylogeny, and evaluate their stability effect. We show that RubisCO evolution has been constrained by stability-activity tradeoffs similar in character to those previously identified in laboratory-based experiments. The C4 properties require a subset of several ancestral destabilizing mutations, which from their location in the structure are inferred to mainly be involved in enhancing conformational flexibility of the open-closed transition in the catalytic cycle. These mutations are near, but not in, the active site or at intersubunit interfaces. The C3 to C4 transition is preceded by a sustained period in which stability of the enzyme is increased, creating the capacity to accept the functionally necessary destabilizing mutations, and is immediately followed by compensatory mutations that restore global stability.

Probing function and structure of trehalose-6-phosphate phosphatases from pathogenic organisms suggests distinct molecular groupings.

PubMed

Cross, Megan; Lepage, Romain; Rajan, Siji; Biberacher, Sonja; Young, Neil D; Kim, Bo-Na; Coster, Mark J; Gasser, Robin B; Kim, Jeong-Sun; Hofmann, Andreas

2017-03-01

The trehalose biosynthetic pathway is of great interest for the development of novel therapeutics because trehalose is an essential disaccharide in many pathogens but is neither required nor synthesized in mammalian hosts. As such, trehalose-6-phosphate phosphatase (TPP), a key enzyme in trehalose biosynthesis, is likely an attractive target for novel chemotherapeutics. Based on a survey of genomes from a panel of parasitic nematodes and bacterial organisms and by way of a structure-based amino acid sequence alignment, we derive the topological structure of monoenzyme TPPs and classify them into 3 groups. Comparison of the functional roles of amino acid residues located in the active site for TPPs belonging to different groups reveal nuanced variations. Because current literature on this enzyme family shows a tendency to infer functional roles for individual amino acid residues, we investigated the roles of the strictly conserved aspartate tetrad in TPPs of the nematode Brugia malayi by using a conservative mutation approach. In contrast to aspartate-213, the residue inferred to carry out the nucleophilic attack on the substrate, we found that aspartate-215 and aspartate-428 of Bm TPP are involved in the chemistry steps of enzymatic hydrolysis of the substrate. Therefore, we suggest that homology-based inference of functionally important amino acids by sequence comparison for monoenzyme TPPs should only be carried out for each of the 3 groups.-Cross, M., Lepage, R., Rajan, S., Biberacher, S., Young, N. D., Kim, B.-N., Coster, M. J., Gasser, R. B., Kim, J.-S., Hofmann, A. Probing function and structure of trehalose-6-phosphate phosphatases from pathogenic organisms suggests distinct molecular groupings. © FASEB.

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

NASA Technical Reports Server (NTRS)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Quantitative trait loci and metabolic pathways

PubMed Central

McMullen, M. D.; Byrne, P. F.; Snook, M. E.; Wiseman, B. R.; Lee, E. A.; Widstrom, N. W.; Coe, E. H.

1998-01-01

The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for “channeling” of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits. PMID:9482823

Hemispheric Inference Priming during Comprehension of Conversations and Narratives

ERIC Educational Resources Information Center

Powers, Chivon; Bencic, Rachel; Horton, William S.; Beeman, Mark

2012-01-01

In this study we examined asymmetric semantic activation patterns as people listened to conversations and narratives that promoted causal inferences. Based on the hypothesis that understanding the unique features of conversational input may benefit from or require a modified pattern of conceptual activation during conversation, we compared…

Adult Attachment Affects Neural Response to Preference-Inferring in Ambiguous Scenarios: Evidence From an fMRI Study

PubMed Central

Zhang, Xing; Ran, Guangming; Xu, Wenjian; Ma, Yuanxiao; Chen, Xu

2018-01-01

Humans are highly social animals, and the ability to cater to the preferences of other individuals is encouraged by society. Preference-inferring is an important aspect of the theory of mind (TOM). Many previous studies have shown that attachment style is closely related to TOM ability. However, little is known about the effects of adult attachment style on preferences inferring under different levels of certainty. Here, we investigated how adult attachment style affects neural activity underlying preferences inferred under different levels of certainty by using functional magnetic resonance imaging (fMRI). The fMRI results demonstrated that adult attachment influenced the activation of anterior insula (AI) and inferior parietal lobule (IPL) in response to ambiguous preference-inferring. More specifically, in the ambiguous preference condition, the avoidant attached groups exhibited a significantly enhanced activation than secure and anxious attached groups in left IPL; the anxious attached groups exhibited a significantly reduced activation secure attached group in left IPL. In addition, the anxious attached groups exhibited a significantly reduced activation than secure and avoidant attached groups in left AI. These results were also further confirmed by the subsequent PPI analysis. The results from current study suggest that, under ambiguous situations, the avoidant attached individuals show lower sensitivity to the preference of other individuals and need to invest more cognitive resources for preference-reasoning; while compared with avoidant attached group, the anxious attached individuals express high tolerance for uncertainty and a higher ToM proficiency. Results from the current study imply that differences in preference-inferring under ambiguous conditions associated with different levels of individual attachment may explain the differences in interpersonal interaction. PMID:29559932

Denitrification potential of the eastern oyster microbiome using a 16S rRNA gene based metabolic inference approach

PubMed Central

Bowman, Jeff S.; Piehler, Michael

2017-01-01

The eastern oyster (Crassostrea virginica) is a foundation species providing significant ecosystem services. However, the roles of oyster microbiomes have not been integrated into any of the services, particularly nitrogen removal through denitrification. We investigated the composition and denitrification potential of oyster microbiomes with an approach that combined 16S rRNA gene analysis, metabolic inference, qPCR of the nitrous oxide reductase gene (nosZ), and N2 flux measurements. Microbiomes of the oyster digestive gland, the oyster shell, and sediments adjacent to the oyster reef were examined based on next generation sequencing (NGS) of 16S rRNA gene amplicons. Denitrification potentials of the microbiomes were determined by metabolic inferences using a customized denitrification gene and genome database with the paprica (PAthway PRediction by phylogenetIC plAcement) bioinformatics pipeline. Denitrification genes examined included nitrite reductase (nirS and nirK) and nitrous oxide reductase (nosZ), which was further subdivided by genotype into clade I (nosZI) or clade II (nosZII). Continuous flow through experiments measuring N2 fluxes were conducted with the oysters, shells, and sediments to compare denitrification activities. Paprica properly classified the composition of microbiomes, showing similar classification results from Silva, Greengenes and RDP databases. Microbiomes of the oyster digestive glands and shells were quite different from each other and from the sediments. The relative abundance of denitrifying bacteria inferred by paprica was higher in oysters and shells than in sediments suggesting that oysters act as hotspots for denitrification in the marine environment. Similarly, the inferred nosZI gene abundances were also higher in the oyster and shell microbiomes than in the sediment microbiome. Gene abundances for nosZI were verified with qPCR of nosZI genes, which showed a significant positive correlation (F1,7 = 14.7, p = 6.0x10-3, R2 = 0.68). N2 flux rates were significantly higher in the oyster (364.4 ± 23.5 μmol N-N2 m-2 h-1) and oyster shell (355.3 ± 6.4 μmol N-N2 m-2 h-1) compared to the sediment (270.5 ± 20.1 μmol N-N2 m-2 h-1). Thus, bacteria carrying nosZI genes were found to be an important denitrifier, facilitating nitrogen removal in oyster reefs. In addition, this is the first study to validate the use of 16S gene based metabolic inference as a method for determining microbiome function, such as denitrification, by comparing inference results with qPCR gene quantification and rate measurements. PMID:28934286

Denitrification potential of the eastern oyster microbiome using a 16S rRNA gene based metabolic inference approach.

PubMed

Arfken, Ann; Song, Bongkeun; Bowman, Jeff S; Piehler, Michael

2017-01-01

The eastern oyster (Crassostrea virginica) is a foundation species providing significant ecosystem services. However, the roles of oyster microbiomes have not been integrated into any of the services, particularly nitrogen removal through denitrification. We investigated the composition and denitrification potential of oyster microbiomes with an approach that combined 16S rRNA gene analysis, metabolic inference, qPCR of the nitrous oxide reductase gene (nosZ), and N2 flux measurements. Microbiomes of the oyster digestive gland, the oyster shell, and sediments adjacent to the oyster reef were examined based on next generation sequencing (NGS) of 16S rRNA gene amplicons. Denitrification potentials of the microbiomes were determined by metabolic inferences using a customized denitrification gene and genome database with the paprica (PAthway PRediction by phylogenetIC plAcement) bioinformatics pipeline. Denitrification genes examined included nitrite reductase (nirS and nirK) and nitrous oxide reductase (nosZ), which was further subdivided by genotype into clade I (nosZI) or clade II (nosZII). Continuous flow through experiments measuring N2 fluxes were conducted with the oysters, shells, and sediments to compare denitrification activities. Paprica properly classified the composition of microbiomes, showing similar classification results from Silva, Greengenes and RDP databases. Microbiomes of the oyster digestive glands and shells were quite different from each other and from the sediments. The relative abundance of denitrifying bacteria inferred by paprica was higher in oysters and shells than in sediments suggesting that oysters act as hotspots for denitrification in the marine environment. Similarly, the inferred nosZI gene abundances were also higher in the oyster and shell microbiomes than in the sediment microbiome. Gene abundances for nosZI were verified with qPCR of nosZI genes, which showed a significant positive correlation (F1,7 = 14.7, p = 6.0x10-3, R2 = 0.68). N2 flux rates were significantly higher in the oyster (364.4 ± 23.5 μmol N-N2 m-2 h-1) and oyster shell (355.3 ± 6.4 μmol N-N2 m-2 h-1) compared to the sediment (270.5 ± 20.1 μmol N-N2 m-2 h-1). Thus, bacteria carrying nosZI genes were found to be an important denitrifier, facilitating nitrogen removal in oyster reefs. In addition, this is the first study to validate the use of 16S gene based metabolic inference as a method for determining microbiome function, such as denitrification, by comparing inference results with qPCR gene quantification and rate measurements.

Online and offline inferential and textual processing of poor comprehenders: Evidence from a probing method.

PubMed

Yeari, Menahem; Elentok, Shiri; Schiff, Rachel

2017-03-01

Numerous studies have demonstrated that poor inferential processing underlies the specific deficit of poor comprehenders. However, it is still not clear why poor comprehenders have difficulties in generating inferences while reading and whether this impairment is general or specific to one or more types of inferences. The current study employed an online probing method to examine the spontaneous immediate activation of two inference types-forward-predictive inferences and backward-explanatory inferences-during reading. In addition, we examined the ability of poor comprehenders to retain, suppress, and reactivate text information (relevant for inferencing) in working memory. The participants, 10- to 12-year-old good and poor comprehenders, read short narratives and name inference or text word probes following a predictive, intervening, or bridging sentence. Comparing the size of probe-naming facilitations revealed that poor comprehenders generate predictive inferences, albeit more slowly than good comprehenders, and generate explanatory inferences to a lesser extent than good comprehenders. Moreover, we found that this inferior inferential processing is presumably a result of poor retention and reactivation of inference-evoking text information during reading. Finally, poorer reading comprehension was associated with higher activation of information when it was less relevant following the intervening sentences. Taken together, the current findings demonstrate the manner in which poor regulation of relevant and less relevant information during reading underlies the specific comprehension difficulties experienced by poor comprehenders. Copyright © 2016 Elsevier Inc. All rights reserved.

The influence of environmental factors on the generalisability of public health research evidence: physical activity as a worked example

PubMed Central

2011-01-01

Background It is rare that decisions about investing in public health interventions in a city, town or other location can be informed by research generated in that specific place. It is therefore necessary to base decisions on evidence generated elsewhere and to make inferences about the extent to which this evidence is generalisable to the place of interest. In this paper we discuss the issues involved in making such inferences, using physical activity as an example. We discuss the ways in which elements of the structural, physical, social and/or cultural environment (environmental factors [EFs]) can shape physical activity (PA) and also how EFs may influence the effectiveness of interventions that aim to promote PA. We then highlight the ways in which EFs may impact on the generalisability of different types of evidence. Discussion We present a framework for thinking about the influence of EFs when assessing the generalisability of evidence from the location in which the evidence was generated (place A) to the location to which the evidence is to be applied (place B). The framework relates to similarities and differences between place A and place B with respect to: a) the distributions of EFs; b) the causal pathways through which EFs or interventions are thought to exert their effect on PA and c) the ways in which EFs interact with each other. We suggest, using examples, how this scheme can be used by public health professionals who are designing, executing, reporting and synthesising research on PA; or designing/implementing interventions. Summary Our analysis and scheme, although developed for physical activity, may potentially be adapted and applied to other evidence and interventions which are likely to be sensitive to influence by elements of the structural, physical, social and/or cultural environment such as the epidemiology of obesity and healthy weight promotion. PMID:22087556

ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

PubMed Central

2014-01-01

Background Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures. Results In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process. Conclusions This study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally. PMID:24739361

Metabolic engineering of Bacillus subtilis fueled by systems biology: Recent advances and future directions.

PubMed

Liu, Yanfeng; Li, Jianghua; Du, Guocheng; Chen, Jian; Liu, Long

By combining advanced omics technology and computational modeling, systems biologists have identified and inferred thousands of regulatory events and system-wide interactions of the bacterium Bacillus subtilis, which is commonly used both in the laboratory and in industry. This dissection of the multiple layers of regulatory networks and their interactions has provided invaluable information for unraveling regulatory mechanisms and guiding metabolic engineering. In this review, we discuss recent advances in the systems biology and metabolic engineering of B. subtilis and highlight current gaps in our understanding of global metabolism and global pathway engineering in this organism. We also propose future perspectives in the systems biology of B. subtilis and suggest ways that this approach can be used to guide metabolic engineering. Specifically, although hundreds of regulatory events have been identified or inferred via systems biology approaches, systematic investigation of the functionality of these events in vivo has lagged, thereby preventing the elucidation of regulatory mechanisms and further rational pathway engineering. In metabolic engineering, ignoring the engineering of multilayer regulation hinders metabolic flux redistribution. Post-translational engineering, allosteric engineering, and dynamic pathway analyses and control will also contribute to the modulation and control of the metabolism of engineered B. subtilis, ultimately producing the desired cellular traits. We hope this review will aid metabolic engineers in making full use of available systems biology datasets and approaches for the design and perfection of microbial cell factories through global metabolism optimization. Copyright © 2016 Elsevier Inc. All rights reserved.

Inferring individual-level processes from population-level patterns in cultural evolution.

PubMed

Kandler, Anne; Wilder, Bryan; Fortunato, Laura

2017-09-01

Our species is characterized by a great degree of cultural variation, both within and between populations. Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We develop a simulation model capturing demographic and cultural dynamics relevant to human cultural evolution, focusing on the interface between population-level patterns and individual-level processes. The model tracks the distribution of variants of cultural traits across individuals in a population over time, conditioned on different pathways for the transmission of information between individuals. From these data, we obtain theoretical expectations for a range of statistics commonly used to capture population-level characteristics (e.g. the degree of cultural diversity). Consistent with previous theoretical work, our results show that the patterns observed at the level of groups are rooted in the interplay between the transmission pathways and the age structure of the population. We also explore whether, and under what conditions, the different pathways can be distinguished based on their group-level signatures, in an effort to establish theoretical limits to inference. Our results show that the temporal dynamic of cultural change over time retains a stronger signature than the cultural composition of the population at a specific point in time. Overall, the results suggest a shift in focus from identifying the one individual-level process that likely produced the observed data to excluding those that likely did not. We conclude by discussing the implications for empirical studies of human cultural evolution.

Geophysical framework of the southwestern Nevada volcanic field and hydrogeologic implications

USGS Publications Warehouse

Grauch, V.J.; Sawyer, David A.; Fridrich, Chris J.; Hudson, Mark R.

1999-01-01

Gravity and magnetic data, when integrated with other geophysical, geological, and rock-property data, provide a regional framework to view the subsurface geology in the southwestern Nevada volcanic field. The region has been loosely divided into six domains based on structural style and overall geophysical character. For each domain, the subsurface tectonic and magmatic features that have been inferred or interpreted from previous geophysical work has been reviewed. Where possible, abrupt changes in geophysical fields as evidence for potential structural lithologic control on ground-water flow has been noted. Inferred lithology is used to suggest associated hydrogeologic units in the subsurface. The resulting framework provides a basis for investigators to develop hypotheses from regional ground-water pathways where no drill-hole information exists.

Why the Simple View of Reading Is Not Simplistic: Unpacking Component Skills of Reading Using a Direct and Indirect Effect Model of Reading (DIER)

ERIC Educational Resources Information Center

Kim, Young-Suk Grace

2017-01-01

Pathways of relations of language, cognitive, and literacy skills (i.e., working memory, vocabulary, grammatical knowledge, inference, comprehension monitoring, word reading, and listening comprehension) to reading comprehension were examined by comparing four variations of direct and indirect effects model of reading. Results from 350…

Cognitive pathways and historical research.

PubMed

Sutherland, J A

1997-01-01

The nursing literature is replete with articles detailing the logical reasoning processes required by the individual scientist to implement the rigors of research and theory development. Much less attention has been focused on creative and critical thinking as modes for deriving explanations, inferences, and conclusions essential to science as a product. Historical research, as a particular kind of qualitative research, is dependent on and compatible with such mental strategies as logical, creative, and critical thinking. These strategies depict an intellectual framework for the scientist examining archival data and offer a structure for such inquiry. A model for analyzing historical data delineating the cognitive pathways of logical reasoning, creative processing, and critical thinking is proposed.

Active Inference, Epistemic Value, and Vicarious Trial and Error

ERIC Educational Resources Information Center

Pezzulo, Giovanni; Cartoni, Emilio; Rigoli, Francesco; io-Lopez, Léo; Friston, Karl

2016-01-01

Balancing habitual and deliberate forms of choice entails a comparison of their respective merits--the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments…

The anatomy of choice: active inference and agency.

PubMed

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

PubMed

Brubaker, Douglas; Difeo, Analisa; Chen, Yanwen; Pearl, Taylor; Zhai, Kaide; Bebek, Gurkan; Chance, Mark; Barnholtz-Sloan, Jill

2014-01-01

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

The Pathway Coexpression Network: Revealing pathway relationships

PubMed Central

Tanzi, Rudolph E.

2018-01-01

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/. PMID:29554099

Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry

PubMed Central

Ungar, Eugene D.; Schoenbaum, Iris; Henkin, Zalmen; Dolev, Amit; Yehuda, Yehuda; Brosh, Arieh

2011-01-01

The advent of the Global Positioning System (GPS) has transformed our ability to track livestock on rangelands. However, GPS data use would be greatly enhanced if we could also infer the activity timeline of an animal. We tested how well animal activity could be inferred from data provided by Lotek GPS collars, alone or in conjunction with IceRobotics IceTag pedometers. The collars provide motion and head position data, as well as location. The pedometers count steps, measure activity levels, and differentiate between standing and lying positions. We gathered synchronized data at 5-min resolution, from GPS collars, pedometers, and human observers, for free-grazing cattle (n = 9) at the Hatal Research Station in northern Israel. Equations for inferring activity during 5-min intervals (n = 1,475), classified as Graze, Rest (or Lie and Stand separately), and Travel were derived by discriminant and partition (classification tree) analysis of data from each device separately and from both together. When activity was classified as Graze, Rest and Travel, the lowest overall misclassification rate (10%) was obtained when data from both devices together were subjected to partition analysis; separate misclassification rates were 8, 12, and 3% for Graze, Rest and Travel, respectively. When Rest was subdivided into Lie and Stand, the lowest overall misclassification rate (10%) was again obtained when data from both devices together were subjected to partition analysis; misclassification rates were 6, 1, 26, and 17% for Graze, Lie, Stand, and Travel, respectively. The primary problem was confusion between Rest (or Stand) and Graze. Overall, the combination of Lotek GPS collars with IceRobotics IceTag pedometers was found superior to either device alone in inferring animal activity. PMID:22346582

Theorising and testing environmental pathways to behaviour change: natural experimental study of the perception and use of new infrastructure to promote walking and cycling in local communities

PubMed Central

Panter, Jenna; Ogilvie, David

2015-01-01

Objective Some studies have assessed the effectiveness of environmental interventions to promote physical activity, but few have examined how such interventions work. We investigated the environmental mechanisms linking an infrastructural intervention with behaviour change. Design Natural experimental study. Setting Three UK municipalities (Southampton, Cardiff and Kenilworth). Participants Adults living within 5 km of new walking and cycling infrastructure. Intervention Construction or improvement of walking and cycling routes. Exposure to the intervention was defined in terms of residential proximity. Outcome measures Questionnaires at baseline and 2-year follow-up assessed perceptions of the supportiveness of the environment, use of the new infrastructure, and walking and cycling behaviours. Analysis proceeded via factor analysis of perceptions of the physical environment (step 1) and regression analysis to identify plausible pathways involving physical and social environmental mediators and refine the intervention theory (step 2) to a final path analysis to test the model (step 3). Results Participants who lived near and used the new routes reported improvements in their perceptions of provision and safety. However, path analysis (step 3, n=967) showed that the effects of the intervention on changes in time spent walking and cycling were largely (90%) explained by a simple causal pathway involving use of the new routes, and other pathways involving changes in environmental cognitions explained only a small proportion of the effect. Conclusions Physical improvement of the environment itself was the key to the effectiveness of the intervention, and seeking to change people's perceptions may be of limited value. Studies of how interventions lead to population behaviour change should complement those concerned with estimating their effects in supporting valid causal inference. PMID:26338837

Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease

PubMed Central

Ravichandran, Srikanth; Michelucci, Alessandro; del Sol, Antonio

2018-01-01

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases. PMID:29551980

Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease.

PubMed

Ravichandran, Srikanth; Michelucci, Alessandro; Del Sol, Antonio

2018-01-01

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.

Carbon isotope discrepancy between precambrian stromatolites and their modern analogs: Inferences from hypersaline microbial mats of the sinai coast

NASA Astrophysics Data System (ADS)

Schidlowski, Manfred

1985-12-01

The isotopic composition of organic carbon from extant stromatolite-type microbial ecosystems is commonly slanted toward heavy δ13 C values as compared to respective compositions of average organic matter (including that from Precambrian stromatolites). This seems the more enigmatic as the bulk of primary producers from benthic microbial communities are known to fix carbon via the C3 pathway normally entailing the sizable fractionations of the RuBP carboxylase reaction. There is reason to believe that the small fractionations displayed by aquatic microorganisms result from the limitations of a diffusion-controlled assimilatory pathway in which the isotope effect of the enzymatic reaction is largely suppressed. Apart from the diffusion-control exercised by the aqueous environment, transport of CO2 to the photosynthetically active sites will be further impeded by the protective slime (polysaccharide) coatings commonly covering microbial mats in which gas diffusivities are extremely low. Ineffective discrimination against13C becomes, however, most pronounced in hypersaline environments where substantially reduced CO2 solubilities tend to push carbon into the role of a limiting nutrient (brine habitats constitute preferential sanctuaries of mat-forming microbenthos since the emergence of Metazoan grazers ˜ 0.7 Ga ago). As the same microbial communities had been free to colonize normal marine environments during the Precambrian, the CO2 concentration effect was irrelevant to the carbon-fixing pathway of these ancient forms. Therefore, it might not surprise that organic matter from Precambrian stromatolites displays the large fractionations commonly associated with C3 photosynthesis. Increased mixing ratios of CO2 in the Precambrian atmosphere may have additionally contributed to the elimination of the diffusion barrier in the carbon-fixing pathways of ancient mat-forming microbiota.

Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

ERIC Educational Resources Information Center

Rubin, Donald B.

2004-01-01

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both…

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

PubMed Central

Kagohara, Luciane T; Stein-O’Brien, Genevieve L; Kelley, Dylan; Flam, Emily; Wick, Heather C; Danilova, Ludmila V; Easwaran, Hariharan; Favorov, Alexander V; Qian, Jiang; Gaykalova, Daria A; Fertig, Elana J

2018-01-01

Abstract Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies. PMID:28968850

P-glycoprotein (ABCB1) inhibited network of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum by systems-theoretical analysis.

PubMed

Lin, Hong; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Qi, Lianxiu

2012-10-01

We constructed the significant low-expression P-glycoprotein (ABCB1) inhibited transport and signal network in chimpanzee compared with high-expression (fold change ≥2) the human left cerebrum in GEO data set, by using integration of gene regulatory activated and inhibited network inference method with gene ontology (GO) analysis. Our result showed that ABCB1 transport and signal upstream network RAB2A inhibited ABCB1, and downstream ABCB1-inhibited SMAD1_2, NCK2, SLC25A46, GDF10, RASGRP1, EGFR, LRPPRC, RASSF2, RASA4, CA2, CBLB, UBR5, SLC25A16, ITGB3BP, DDIT4, PDPN, RAB2A in chimpanzee left cerebrum. We obtained that the different biological processes of ABCB1 inhibited transport and signal network repressed carbon dioxide transport, ER to Golgi vesicle-mediated transport, folic acid transport, mitochondrion transport along microtubule, water transport, BMP signaling pathway, Ras protein signal transduction, transforming growth factor beta receptor signaling pathway in chimpanzee compared with the inhibited network of the human left cerebrum, as a result of inducing inhibition of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum. Our hypothesis was verified by the same and different biological processes of ABCB1 inhibited transport and signal network of chimpanzee compared with the corresponding activated network of chimpanzee and the human left cerebrum, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Did she jump because she was the big sister or because the trampoline was safe? Causal inference and the development of social attribution.

PubMed

Seiver, Elizabeth; Gopnik, Alison; Goodman, Noah D

2013-01-01

Children rely on both evidence and prior knowledge to make physical causal inferences; this study explores whether they make attributions about others' behavior in the same manner. A total of one hundred and fifty-nine 4- and 6-year-olds saw 2 dolls interacting with 2 activities, and explained the dolls' actions. In the person condition, each doll acted consistently across activities, but differently from each other. In the situation condition, the two dolls acted differently for each activity, but both performed the same actions. Both age groups provided more "person" explanations (citing features of the doll) in the person condition than in the situation condition. In addition, 6-year-olds showed an overall bias toward "person" explanations. As in physical causal inference, social causal inference combines covariational evidence and prior knowledge. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Unconscious relational inference recruits the hippocampus.

PubMed

Reber, Thomas P; Luechinger, Roger; Boesiger, Peter; Henke, Katharina

2012-05-02

Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped ("winter-red", "red-computer") or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word ("winter-computer" related through "red") or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations.

Inconsistencies in spontaneous and intentional trait inferences.

PubMed

Ma, Ning; Vandekerckhove, Marie; Baetens, Kris; Van Overwalle, Frank; Seurinck, Ruth; Fias, Wim

2012-11-01

This study explores the fMRI correlates of observers making trait inferences about other people under conflicting social cues. Participants were presented with several behavioral descriptions involving an agent that implied a particular trait. The last behavior was either consistent or inconsistent with the previously implied trait. This was done under instructions that elicited either spontaneous trait inferences ('read carefully') or intentional trait inferences ('infer a trait'). The results revealed that when the behavioral descriptions violated earlier trait implications, regardless of instruction, the medial prefrontal cortex (mPFC) was more strongly recruited as well as the domain-general conflict network including the posterior medial frontal cortex (pmFC) and the right prefrontal cortex (rPFC). These latter two areas were more strongly activated under intentional than spontaneous instructions. These findings suggest that when trait-relevant behavioral information is inconsistent, not only is activity increased in the mentalizing network responsible for trait processing, but control is also passed to a higher level conflict monitoring network in order to detect and resolve the contradiction.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

PubMed

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Trait inferences in goal-directed behavior: ERP timing and localization under spontaneous and intentional processing

PubMed Central

Van den Eede, Sofie; Baetens, Kris; Vandekerckhove, Marie

2009-01-01

This study measured event-related potentials (ERPs) during multiple goal and trait inferences, under spontaneous or intentional instructions. Participants read sentences describing several goal-implying behaviors of a target person from which also a strong trait could be inferred or not. The last word of each sentence determined the consistency with the inference induced during preceding sentences. In comparison with behaviors that implied only a goal, stronger waveforms beginning at ∼150 ms were obtained when the behaviors additionally implied a trait. These ERPs showed considerable parallels between spontaneous and intentional inferences. This suggests that traits embedded in a stream of goal-directed behaviors were detected more rapidly and automatically than mere goals, irrespective of the participants’ spontaneous or intentional instructions. In line with this, source localization (LORETA) of the ERPs show predominantly activation in the temporo-parietal junction (TPJ) during 150–200 ms, suggesting that goals were detected at that time interval. During 200–300 ms, activation was stronger at the medial prefrontal cortex (mPFC) for multiple goals and traits as opposed to goals only, suggesting that traits were inferred during this time window. A cued recall measure taken after the presentation of the stimulus material support the occurrence of goal and trait inferences and shows significant correlations with the neural components, indicating that these components are valid neural indices of spontaneous and intentional social inferences. The early detection of multiple goal and trait inferences is explained in terms of their greater social relevance, leading to privileged attention allocation and processing in the brain. PMID:19270041

Consistency of biological networks inferred from microarray and sequencing data.

PubMed

Vinciotti, Veronica; Wit, Ernst C; Jansen, Rick; de Geus, Eco J C N; Penninx, Brenda W J H; Boomsma, Dorret I; 't Hoen, Peter A C

2016-06-24

Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges. Careful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data.

Cognitive Inference Device for Activity Supervision in the Elderly

PubMed Central

2014-01-01

Human activity, life span, and quality of life are enhanced by innovations in science and technology. Aging individual needs to take advantage of these developments to lead a self-regulated life. However, maintaining a self-regulated life at old age involves a high degree of risk, and the elderly often fail at this goal. Thus, the objective of our study is to investigate the feasibility of implementing a cognitive inference device (CI-device) for effective activity supervision in the elderly. To frame the CI-device, we propose a device design framework along with an inference algorithm and implement the designs through an artificial neural model with different configurations, mapping the CI-device's functions to minimise the device's prediction error. An analysis and discussion are then provided to validate the feasibility of CI-device implementation for activity supervision in the elderly. PMID:25405211

The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture.

PubMed

Caetano-Anollés, Gustavo; Kim, Hee Shin; Mittenthal, Jay E

2007-05-29

Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world.

Functional imaging and the cerebellum: recent developments and challenges. Editorial.

PubMed

Habas, Christophe

2012-06-01

Recent neuroimaging developments allow a better in vivo characterization of the structural and functional connectivity of the human cerebellum. Ultrahigh fields, which considerably increase spatial resolution, enable to visualize deep cerebellar nuclei and cerebello-cortical sublayers. Tractography reconstructs afferent and efferent pathway of the cerebellum. Resting-state functional connectivity individualizes the prewired, parallel close-looped sensorimotor, cognitive, and affective networks passing through the cerebellum. These results are un agreement with activation maps obtained during stimulation functional neuroimaging or inferred from neurological deficits due to cerebellar lesions. Therefore, neuroimaging supports the hypothesis that cerebellum constitutes a general modulator involved in optimizing mental performance and computing internal models. However, the great challenges will remain to unravel: (1) the functional role of red and bulbar olivary nuclei, (2) the information processing in the cerebellar microcircuitry, and (3) the abstract computation performed by the cerebellum and shared by sensorimotor, cognitive, and affective domains.

Constructing networks with correlation maximization methods.

PubMed

Mellor, Joseph C; Wu, Jie; Delisi, Charles

2004-01-01

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.

A Coronavirus E Protein Is Present in Two Distinct Pools with Different Effects on Assembly and the Secretory Pathway

PubMed Central

Westerbeck, Jason W.

2015-01-01

ABSTRACT Coronaviruses (CoVs) assemble by budding into the lumen of the early Golgi complex prior to exocytosis. The small CoV envelope (E) protein plays roles in assembly, virion release, and pathogenesis. CoV E has a single hydrophobic domain (HD), is targeted to Golgi complex membranes, and has cation channel activity in vitro. However, the precise functions of the CoV E protein during infection are still enigmatic. Structural data for the severe acute respiratory syndrome (SARS)-CoV E protein suggest that it assembles into a homopentamer. Specific residues in the HD regulate the ion-conducting pore formed by SARS-CoV E in artificial bilayers and the pathogenicity of the virus during infection. The E protein from the avian infectious bronchitis virus (IBV) has dramatic effects on the secretory system which require residues in the HD. Here, we use the known structural data from SARS-CoV E to infer the residues important for ion channel activity and the oligomerization of IBV E. We present biochemical data for the formation of two distinct oligomeric pools of IBV E in transfected and infected cells and the residues required for their formation. A high-order oligomer of IBV E is required for the production of virus-like particles (VLPs), implicating this form of the protein in virion assembly. Additionally, disruption of the secretory pathway by IBV E correlates with a form that is likely monomeric, suggesting that the effects on the secretory pathway are independent of E ion channel activity. IMPORTANCE CoVs are important human pathogens with significant zoonotic potential, as demonstrated by the emergence of SARS-CoV and Middle East respiratory syndrome (MERS)-CoV. Progress has been made toward identifying potential vaccine candidates in mouse models of CoV infection, including the use of attenuated viruses that lack the CoV E protein or express E-protein mutants. However, no approved vaccines or antiviral therapeutics exist. We previously reported that the hydrophobic domain of the IBV E protein, a putative viroporin, causes disruption of the mammalian secretory pathway when exogenously expressed in cells. Understanding the mechanism of this disruption could lead to the identification of novel antiviral therapeutics. Here, we present biochemical evidence for two distinct oligomeric forms of IBV E, one essential for assembly and the other with a role in disruption of the secretory pathway. Discovery of two forms of CoV E protein will provide additional targets for antiviral therapeutics. PMID:26136577

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses.

PubMed

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-07-08

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

PubMed Central

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-01-01

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses. PMID:27387525

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

NASA Astrophysics Data System (ADS)

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-07-01

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy

PubMed Central

Liu, Ning; Cui, Xu; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. PMID:25798327

Sand sources and transport pathways for the San Francisco Bay coastal system, based on X-ray diffraction mineralogy

USGS Publications Warehouse

Hein, James R.; Mizell, Kira; Barnard, Patrick L.; Barnard, P.L.; Jaffee, B.E.; Schoellhamer, D.H.

2013-01-01

The mineralogical compositions of 119 samples collected from throughout the San Francisco Bay coastal system, including bayfloor and seafloor, area beaches, cliff outcrops, and major drainages, were determined using X-ray diffraction (XRD). Comparison of the mineral concentrations and application of statistical cluster analysis of XRD spectra allowed for the determination of provenances and transport pathways. The use of XRD mineral identifications provides semi-quantitative compositions needed for comparisons of beach and offshore sands with potential cliff and river sources, but the innovative cluster analysis of XRD diffraction spectra provides a unique visualization of how groups of samples within the San Francisco Bay coastal system are related so that sand-sized sediment transport pathways can be inferred. The main vector for sediment transport as defined by the XRD analysis is from San Francisco Bay to the outer coast, where the sand then accumulates on the ebb tidal delta and also moves alongshore. This mineralogical link defines a critical pathway because large volumes of sediment have been removed from the Bay over the last century via channel dredging, aggregate mining, and borrow pit mining, with comparable volumes of erosion from the ebb tidal delta over the same period, in addition to high rates of shoreline retreat along the adjacent, open-coast beaches. Therefore, while previously only a temporal relationship was established, the transport pathway defined by mineralogical and geochemical tracers support the link between anthropogenic activities in the Bay and widespread erosion outside the Bay. The XRD results also establish the regional and local importance of sediment derived from cliff erosion, as well as both proximal and distal fluvial sources. This research is an important contribution to a broader provenance study aimed at identifying the driving forces for widespread geomorphic change in a heavily urbanized coastal-estuarine system.

Removal of N-6-methyladenine by the nucleotide excision repair pathway triggers the repair of mismatches in yeast gap-repair intermediates.

PubMed

Guo, Xiaoge; Jinks-Robertson, Sue

2013-12-01

Gap-repair assays have been an important tool for studying the genetic control of homologous recombination in yeast. Sequence analysis of recombination products derived when a gapped plasmid is diverged relative to the chromosomal repair template additionally has been used to infer structures of strand-exchange intermediates. In the absence of the canonical mismatch repair pathway, mismatches present in these intermediates are expected to persist and segregate at the next round of DNA replication. In a mismatch repair defective (mlh1Δ) background, however, we have observed that recombination-generated mismatches are often corrected to generate gene conversion or restoration events. In the analyses reported here, the source of the aberrant mismatch removal during gap repair was examined. We find that most mismatch removal is linked to the methylation status of the plasmid used in the gap-repair assay. Whereas more than half of Dam-methylated plasmids had patches of gene conversion and/or restoration interspersed with unrepaired mismatches, mismatch removal was observed in less than 10% of products obtained when un-methylated plasmids were used in transformation experiments. The methylation-linked removal of mismatches in recombination intermediates was due specifically to the nucleotide excision repair pathway, with such mismatch removal being partially counteracted by glycosylases of the base excision repair pathway. These data demonstrate that nucleotide excision repair activity is not limited to bulky, helix-distorting DNA lesions, but also targets removal of very modest perturbations in DNA structure. In addition to its effects on mismatch removal, methylation reduced the overall gap-repair efficiency, but this reduction was not affected by the status of excision repair pathways. Finally, gel purification of DNA prior to transformation reduced gap-repair efficiency four-fold in a nucleotide excision repair-defective background, indicating that the collateral introduction of UV damage can potentially compromise genetic interpretations. Copyright © 2013 Elsevier B.V. All rights reserved.

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

PubMed Central

Abubucker, Sahar; Segata, Nicola; Goll, Johannes; Schubert, Alyxandria M.; Izard, Jacques; Cantarel, Brandi L.; Rodriguez-Mueller, Beltran; Zucker, Jeremy; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T.; Methé, Barbara; Schloss, Patrick D.; Gevers, Dirk; Mitreva, Makedonka; Huttenhower, Curtis

2012-01-01

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies. PMID:22719234

Removal of N-6-methyladenine by the nucleotide excision repair pathway triggers the repair of mismatches in yeast gap-repair intermediates

PubMed Central

Guo, Xiaoge; Jinks-Robertson, Sue

2013-01-01

Gap-repair assays have been an important tool for studying the genetic control of homologous recombination in yeast. Sequence analysis of recombination products derived when a gapped plasmid is diverged relative to the chromosomal repair template additionally has been used to infer structures of strand-exchange intermediates. In the absence of the canonical mismatch repair pathway, mismatches present in these intermediates are expected to persist and segregate at the next round of DNA replication. In a mismatch repair defective (mlh1Δ) background, however, we have observed that recombination-generated mismatches are often corrected to generate gene conversion or restoration events. In the analyses reported here, the source of the aberrant mismatch removal during gap repair was examined. We find that most mismatch removal is linked to the methylation status of the plasmid used in the gap-repair assay. Whereas more than half of Dam-methylated plasmids had patches of gene conversion and/or restoration interspersed with unrepaired mismatches, mismatch removal was observed in less than 10% of products obtained when un-methylated plasmids were used in transformation experiments. The methylation-linked removal of mismatches in recombination intermediates was due specifically to the nucleotide excision repair pathway, with such mismatch removal being partially counteracted by glycosylases of the base excision repair pathway. These data demonstrate that nucleotide excision repair activity is not limited to bulky, helix-distorting DNA lesions, but also targets removal of very modest perturbations in DNA structure. In addition to its effects on mismatch removal, methylation reduced the overall gap-repair efficiency, but this reduction was not affected by the status of excision repair pathways. Finally, gel purification of DNA prior to transformation reduced gap-repair efficiency four-fold in a nucleotide excision repair-defective background, indicating that the cillateral introduction of UV damage can potentially compromise genetic interpretations. PMID:24120148

The anatomy of choice: active inference and agency

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control. PMID:24093015

Social representations and contextual adjustments as two distinct components of the Theory of Mind brain network: Evidence from the REMICS task.

PubMed

Lavoie, Marie-Audrey; Vistoli, Damien; Sutliff, Stephanie; Jackson, Philip L; Achim, Amélie M

2016-08-01

Theory of mind (ToM) refers to the ability to infer the mental states of others. Behavioral measures of ToM usually present information about both a character and the context in which this character is placed, and these different pieces of information can be used to infer the character's mental states. A set of brain regions designated as the ToM brain network is recognized to support (ToM) inferences. Different brain regions within that network could however support different ToM processes. This functional magnetic resonance imaging (fMRI) study aimed to distinguish the brain regions supporting two aspects inherent to many ToM tasks, i.e., the ability to infer or represent mental states and the ability to use the context to adjust these inferences. Nineteen healthy subjects were scanned during the REMICS task, a novel task designed to orthogonally manipulate mental state inferences (as opposed to physical inferences) and contextual adjustments of inferences (as opposed to inferences that do not require contextual adjustments). We observed that mental state inferences and contextual adjustments, which are important aspects of most behavioral ToM tasks, rely on distinct brain regions or subregions within the classical brain network activated in previous ToM research. Notably, an interesting dissociation emerged within the medial prefrontal cortex (mPFC) and temporo-parietal junctions (TPJ) such that the inferior part of these brain regions responded to mental state inferences while the superior part of these brain regions responded to the requirement for contextual adjustments. This study provides evidence that the overall set of brain regions activated during ToM tasks supports different processes, and highlights that cognitive processes related to contextual adjustments have an important role in ToM and should be further studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells

PubMed Central

He, Feng; Chen, Hairong; Probst-Kepper, Michael; Geffers, Robert; Eifes, Serge; del Sol, Antonio; Schughart, Klaus; Zeng, An-Ping; Balling, Rudi

2012-01-01

Human FOXP3+CD25+CD4+ regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4+ T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function. PMID:23169000

Loneliness and life satisfaction of boys with developmental coordination disorder: the impact of leisure participation and perceived freedom in leisure.

PubMed

Poulsen, Anne A; Ziviani, Jenny M; Johnson, Helen; Cuskelly, Monica

2008-04-01

A theoretical model linking motor ability with perceived freedom in leisure, participation in team sports, loneliness, and global life satisfaction was tested using linear confirmatory path analysis. Participants were 173 boys aged 10-13 years who filled in self-report questionnaires about perceived freedom in leisure, loneliness, and global life satisfaction. Parents of boys completed 7-day diaries and 12-month retrospective recall questionnaires about their son's leisure-time activity participation. Results of path analyses confirmed that the fit of the hypothetical model was consistent with predictions. The inferred direct pathways of influence between both total loneliness and global life satisfaction on motor ability were in the expected directions (i.e., inverse and positive relationships, respectively). Perceived Freedom in Leisure (PFL) and participation in team sports were two intermediate variables indirectly influencing these relationships. Although PFL was identified as a motivational process influencing participation levels in team sports it was noted that other psychological and environmental factors must also be considered when evaluating child-activity-environment fit for boys with developmental coordination disorder.

CYP2D6 activity and the risk of recurrence of Plasmodium vivax malaria in the Brazilian Amazon: a prospective cohort study.

PubMed

Brasil, Larissa W; Rodrigues-Soares, Fernanda; Santoro, Ana B; Almeida, Anne C G; Kühn, Andrea; Ramasawmy, Rajendranath; Lacerda, Marcus V G; Monteiro, Wuelton M; Suarez-Kurtz, Guilherme

2018-02-01

CYP2D6 pathway mediates the activation of primaquine into active metabolite(s) in hepatocytes. CYP2D6 is highly polymorphic, encoding CYP2D6 isoforms with normal, reduced, null or increased activity. It is hypothesized that Plasmodium vivax malaria patients with defective CYP2D6 function would be at increased risk for primaquine failure to prevent recurrence. The aim of this study was to investigate the association of CYP2D6 polymorphisms and inferred CYP2D6 phenotypes with malaria recurrence in patients from the Western Brazilian Amazon, following chloroquine/primaquine combined therapy. The prospective cohort consisted of P. vivax malaria patients who were followed for 6 months after completion of the chloroquine/primaquine therapy. Recurrence was defined as one or more malaria episodes, 28-180 days after the initial episode. Genotyping for nine CYP2D6 SNPs and copy number variation was performed using TaqMan assays in a Fast 7500 Real-Time System. CYP2D6 star alleles (haplotypes), diplotypes and CYP2D6 phenotypes were inferred, and the activity score system was used to define the functionality of the CYP2D6 diplotypes. CYP2D6 activity scores (AS) were dichotomized at ≤ 1 (gPM, gIM and gNM-S phenotypes) and ≥ 1.5 (gNM-F and gUM phenotypes). Genotyping was successfully performed in 190 patients (44 with recurrence and 146 without recurrences). Recurrence incidence was higher in individuals presenting reduced activity CYP2D6 phenotypes (adjusted relative risk = 1.89, 95% CI 1.01-3.70; p = 0.049). Attributable risk and population attributable fraction were 11.5 and 9.9%, respectively. The time elapsed from the first P. vivax malaria episode until the recurrence did not differ between patients with AS of ≤ 1 versus ≥ 1.5 (p = 0.917). The results suggest that CYP2D6 polymorphisms are associated with increased risk of recurrence of vivax malaria, following chloroquine-primaquine combined therapy. This association is interpreted as the result of reduced conversion of primaquine into its active metabolites in patients with reduced CYP2D6 enzymatic activity.

The role of the hippocampus in transitive inference

PubMed Central

Zalesak, Martin; Heckers, Stephan

2009-01-01

Transitive inference (TI) is the ability to infer the relationship between items (e.g., A>C) after having learned a set of premise pairs (e.g., A>B and B>C). Previous studies in humans have identified a distributed neural network, including cortex, hippocampus, and thalamus, during TI judgments. We studied two aspects of TI using fMRI of subjects who had acquired the 6-item sequence (A>B>C>D>E>F) of visual stimuli. First, the identification of novel pairs not containing end items (i.e., B>D, C>E, B>E) was associated with greater left hippocampal activation when compared to the identification of novel pairs containing end items A and F. This demonstrates that the identification of stimulus pairs requiring the flexible representation of a sequence is associated with hippocampal activation. Second, for the three novel pairs devoid of end items we found greater right hippocampal activation for pairs B>D and C>E compared with pair B>E. This indicates that TI decisions on pairs derived from more adjacent items in the sequence are associated with greater hippocampal activation. Hippocampal activation thus scales with the degree of relational processing necessary for TI judgments. Both findings confirm a role of the hippocampus in transitive inference in humans. PMID:19216061

Theory of mind broad and narrow: reasoning about social exchange engages ToM areas, precautionary reasoning does not.

PubMed

Ermer, Elsa; Guerin, Scott A; Cosmides, Leda; Tooby, John; Miller, Michael B

2006-01-01

Baron-Cohen (1995) proposed that the theory of mind (ToM) inference system evolved to promote strategic social interaction. Social exchange--a form of co-operation for mutual benefit--involves strategic social interaction and requires ToM inferences about the contents of other individuals' mental states, especially their desires, goals, and intentions. There are behavioral and neuropsychological dissociations between reasoning about social exchange and reasoning about equivalent problems tapping other, more general content domains. It has therefore been proposed that social exchange behavior is regulated by social contract algorithms: a domain-specific inference system that is functionally specialized for reasoning about social exchange. We report an fMRI study using the Wason selection task that provides further support for this hypothesis. Precautionary rules share so many properties with social exchange rules--they are conditional, deontic, and involve subjective utilities--that most reasoning theories claim they are processed by the same neurocomputational machinery. Nevertheless, neuroimaging shows that reasoning about social exchange activates brain areas not activated by reasoning about precautionary rules, and vice versa. As predicted, neural correlates of ToM (anterior and posterior temporal cortex) were activated when subjects interpreted social exchange rules, but not precautionary rules (where ToM inferences are unnecessary). We argue that the interaction between ToM and social contract algorithms can be reciprocal: social contract algorithms requires ToM inferences, but their functional logic also allows ToM inferences to be made. By considering interactions between ToM in the narrower sense (belief-desire reasoning) and all the social inference systems that create the logic of human social interaction--ones that enable as well as use inferences about the content of mental states--a broader conception of ToM may emerge: a computational model embodying a Theory of Human Nature (ToHN).

Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways

PubMed Central

Jo, Kyuri; Jung, Inuk; Moon, Ji Hwan; Kim, Sun

2016-01-01

Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension. Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock. TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway. Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: sunkim.bioinfo@snu.ac.kr PMID:27307609

The 1.25 Å resolution structure of phosphoribosyl-ATP pyrophosphohydrolase from Mycobacterium tuberculosis

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

Javid-Majd, Farah; Yang, Dong; Ioerger, Thomas R.

2008-06-01

The crystal structure of M. tuberculosis phosphoribosyl-ATP pyrophosphohydrolase, the second enzyme in the histidine-biosynthetic pathway, is presented. The structural and inferred functional relationships between M. tuberculosis phosphoribosyl-ATP pyrophosphohydrolase and other members of the nucleoside-triphosphate pyrophosphatase-fold family are described. Phosphoribosyl-ATP pyrophosphohydrolase is the second enzyme in the histidine-biosynthetic pathway, irreversibly hydrolyzing phosphoribosyl-ATP to phosphoribosyl-AMP and pyrophosphate. It is encoded by the hisE gene, which is present as a separate gene in many bacteria and archaea but is fused to hisI in other bacteria, fungi and plants. Because of its essentiality for growth in vitro, HisE is a potential drug target formore » tuberculosis. The crystal structures of two native (uncomplexed) forms of HisE from Mycobacterium tuberculosis have been determined to resolutions of 1.25 and 1.79 Å. The structure of the apoenzyme reveals that the protein is composed of five α-helices with connecting loops and is a member of the α-helical nucleoside-triphosphate pyrophosphatase superfamily. The biological unit of the protein is a homodimer, with an active site on each subunit composed of residues exclusively from that subunit. A comparison with the Campylobacter jejuni dUTPase active site allowed the identification of putative metal- and substrate-binding sites in HisE, including four conserved glutamate and glutamine residues in the sequence that are consistent with a motif for pyrophosphohydrolase activity. However, significant differences between family members are observed in the loop region between α-helices H1 and H3. The crystal structure of M. tuberculosis HisE provides insights into possible mechanisms of substrate binding and the diversity of the nucleoside-triphosphate pyrophosphatase superfamily.« less

Active inference and cognitive-emotional interactions in the brain.

PubMed

Pezzulo, Giovanni; Barca, Laura; Friston, Karl J

2015-01-01

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Separable Processes Before, During, and After the N400 Elicited by Previously Inferred and New Information: Evidence from Time-Frequency Decompositions

PubMed Central

Steele, Vaughn R.; Bernat, Edward M.; van den Broek, Paul; Collins, Paul F.; Patrick, Christopher J.; Marsolek, Chad J.

2012-01-01

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1–6 Hz (theta) and 0–2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. PMID:23165117

Neural system prediction and identification challenge.

PubMed

Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

2013-01-01

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Neural system prediction and identification challenge

PubMed Central

Vlachos, Ioannis; Zaytsev, Yury V.; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

2013-01-01

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered. PMID:24399966

Transcriptome identification of putative genes involved in protein catabolism and innate immune response in human body louse (Pediculicidae: Pediculus humanus).

PubMed

Pedra, Joao H F; Brandt, Amanda; Li, Hong-Mei; Westerman, Rick; Romero-Severson, Jeanne; Pollack, Richard J; Murdock, Larry L; Pittendrigh, Barry R

2003-11-01

Genomics information relating to human body lice is surprisingly scarce, and this has constrained studies of their physiology, immunology and vector biology. To identify novel body louse genes, we used engorged adult lice to generate a cDNA library. Initially, 1152 clones were screened for inserts, edited for removal of vector sequences and base pairs of poor quality, and viewed for splicing variations, gene families and polymorphism. Computational methods identified 506 inferred open reading frames including the first predicted louse defensin. The inferred defensin aligns well with other insect defensins and has highly conserved cysteine residues, as are known for other defensin sequences. Two cysteine and five serine proteinases were categorized according to their inferred catalytic sites. We also discovered seven putative ubiquitin-pathway genes and four iron metabolizing deduced enzymes. Finally, glutathione-S-transferases and cytochrome P450 genes were among the detoxification enzymes found. Results from this first systematic effort to discover human body louse genes should promote further studies in Phthiraptera and lice.

TLR and IMD signaling pathways from Caligus rogercresseyi (Crustacea: Copepoda): in silico gene expression and SNPs discovery.

PubMed

Valenzuela-Muñoz, V; Gallardo-Escárate, C

2014-02-01

The Toll and IMD signaling pathways represent one of the first lines of innate immune defense in invertebrates like Drosophila. However, for crustaceans like Caligus rogercresseyi, there is very little genomic information and, consequently, understanding of immune mechanisms. Massive sequencing data obtained for three developmental stages of C. rogercresseyi were used to evaluate in silico the expression patterns and presence of SNPs variants in genes involved in the Toll and IMD pathways. Through RNA-seq analysis, which used 20 contigs corresponding to relevant genes of the Toll and IMD pathways, an overexpression of genes linked to the Toll pathway, such as toll3 and Dorsal, were observed in the copepod stage. For the chalimus and adult stages, overexpression of genes in both pathways, such as Akirin and Tollip and IAP and Toll9, respectively, were observed. On the other hand, PCA statistical analysis inferred that in the chalimus and adult stages, the immune response mechanism was more developed, as evidenced by a relation between these two stages and the genes of both pathways. Moreover, 136 SNPs were identified for 20 contigs in genes of the Toll and IMD pathways. This study provides transcriptomic information about the immune response mechanisms of Caligus, thus providing a foundation for the development of new control strategies through blocking the innate immune response. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions.

PubMed

Gilson, Matthieu; Deco, Gustavo; Friston, Karl J; Hagmann, Patric; Mantini, Dante; Betti, Viviana; Romani, Gian Luca; Corbetta, Maurizio

2017-10-09

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain. Copyright © 2017. Published by Elsevier Inc.

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

PubMed Central

Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

PubMed Central

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation. PMID:28191459

Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence.

PubMed

Guan, Kaiyu; Berry, Joseph A; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B

2016-02-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change. © 2015 John Wiley & Sons Ltd.

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

NASA Technical Reports Server (NTRS)

Guan, Kaiyu; Berry, Joseph A.; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B.

2015-01-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Methane and benzene in drinking-water wells overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas

USGS Publications Warehouse

McMahon, Peter B.; Barlow, Jeannie R.; Engle, Mark A.; Belitz, Kenneth; Ging, Patricia B.; Hunt, Andrew G.; Jurgens, Bryant; Kharaka, Yousif K.; Tollett, Roland W.; Kresse, Timothy M.

2017-01-01

Water wells (n = 116) overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas were sampled for chemical, isotopic, and groundwater-age tracers to investigate the occurrence and sources of selected hydrocarbons in groundwater. Methane isotopes and hydrocarbon gas compositions indicate most of the methane in the wells was biogenic and produced by the CO2 reduction pathway, not from thermogenic shale gas. Two samples contained methane from the fermentation pathway that could be associated with hydrocarbon degradation based on their co-occurrence with hydrocarbons such as ethylbenzene and butane. Benzene was detected at low concentrations (<0.15 μg/L), but relatively high frequencies (2.4–13.3% of samples), in the study areas. Eight of nine samples containing benzene had groundwater ages >2500 years, indicating the benzene was from subsurface sources such as natural hydrocarbon migration or leaking hydrocarbon wells. One sample contained benzene that could be from a surface release associated with hydrocarbon production activities based on its age (10 ± 2.4 years) and proximity to hydrocarbon wells. Groundwater travel times inferred from the age-data indicate decades or longer may be needed to fully assess the effects of potential subsurface and surface releases of hydrocarbons on the wells.

Methane and Benzene in Drinking-Water Wells Overlying the Eagle Ford, Fayetteville, and Haynesville Shale Hydrocarbon Production Areas.

PubMed

McMahon, Peter B; Barlow, Jeannie R B; Engle, Mark A; Belitz, Kenneth; Ging, Patricia B; Hunt, Andrew G; Jurgens, Bryant C; Kharaka, Yousif K; Tollett, Roland W; Kresse, Timothy M

2017-06-20

Water wells (n = 116) overlying the Eagle Ford, Fayetteville, and Haynesville Shale hydrocarbon production areas were sampled for chemical, isotopic, and groundwater-age tracers to investigate the occurrence and sources of selected hydrocarbons in groundwater. Methane isotopes and hydrocarbon gas compositions indicate most of the methane in the wells was biogenic and produced by the CO 2 reduction pathway, not from thermogenic shale gas. Two samples contained methane from the fermentation pathway that could be associated with hydrocarbon degradation based on their co-occurrence with hydrocarbons such as ethylbenzene and butane. Benzene was detected at low concentrations (<0.15 μg/L), but relatively high frequencies (2.4-13.3% of samples), in the study areas. Eight of nine samples containing benzene had groundwater ages >2500 years, indicating the benzene was from subsurface sources such as natural hydrocarbon migration or leaking hydrocarbon wells. One sample contained benzene that could be from a surface release associated with hydrocarbon production activities based on its age (10 ± 2.4 years) and proximity to hydrocarbon wells. Groundwater travel times inferred from the age-data indicate decades or longer may be needed to fully assess the effects of potential subsurface and surface releases of hydrocarbons on the wells.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

PubMed

Williams, Tim D; Turan, Nil; Diab, Amer M; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L; Hrydziuszko, Olga; Lyons, Brett P; Stentiford, Grant D; Herbert, John M; Abraham, Joseph K; Katsiadaki, Ioanna; Leaver, Michael J; Taggart, John B; George, Stephen G; Viant, Mark R; Chipman, Kevin J; Falciani, Francesco

2011-08-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Differential metabolism of L-phenylalanine in the formation of aromatic volatiles in melon (Cucumis melo L.) fruit.

PubMed

Gonda, Itay; Davidovich-Rikanati, Rachel; Bar, Einat; Lev, Shery; Jhirad, Pliaa; Meshulam, Yuval; Wissotsky, Guy; Portnoy, Vitaly; Burger, Joseph; Schaffer, Arthur A; Tadmor, Yaakov; Giovannoni, James J; Fei, Zhangjun; Fait, Aaron; Katzir, Nurit; Lewinsohn, Efraim

2018-04-01

Studies on the active pathways and the genes involved in the biosynthesis of L-phenylalanine-derived volatiles in fleshy fruits are sparse. Melon fruit rinds converted stable-isotope labeled L-phe into more than 20 volatiles. Phenylpropanes, phenylpropenes and benzenoids are apparently produced via the well-known phenylpropanoid pathway involving phenylalanine ammonia lyase (PAL) and being (E)-cinnamic acid a key intermediate. Phenethyl derivatives seemed to be derived from L-phe via a separate biosynthetic route not involving (E)-cinnamic acid and PAL. To explore for a biosynthetic route to (E)-cinnamaldehyde in melon rinds, soluble protein cell-free extracts were assayed with (E)-cinnamic acid, CoA, ATP, NADPH and MgSO 4 , producing (E)-cinnamaldehyde in vitro. In this context, we characterized CmCNL, a gene encoding for (E)-cinnamic acid:coenzyme A ligase, inferred to be involved in the biosynthesis of (E)-cinnamaldehyde. Additionally we describe CmBAMT, a SABATH gene family member encoding a benzoic acid:S-adenosyl-L-methionine carboxyl methyltransferase having a role in the accumulation of methyl benzoate. Our approach leads to a more comprehensive understanding of L-phe metabolism into aromatic volatiles in melon fruit. Copyright © 2018 Elsevier Ltd. All rights reserved.

Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach

PubMed Central

Williams, Tim D.; Turan, Nil; Diab, Amer M.; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L.; Hrydziuszko, Olga; Lyons, Brett P.; Stentiford, Grant D.; Herbert, John M.; Abraham, Joseph K.; Katsiadaki, Ioanna; Leaver, Michael J.; Taggart, John B.; George, Stephen G.; Viant, Mark R.; Chipman, Kevin J.; Falciani, Francesco

2011-01-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations. PMID:21901081

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search.

PubMed

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Santos-García, Gustavo; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing , which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

Transformation of diclofenac in hybrid biofilm-activated sludge processes.

PubMed

Jewell, Kevin S; Falås, Per; Wick, Arne; Joss, Adriano; Ternes, Thomas A

2016-11-15

The biotransformation of diclofenac during wastewater treatment was investigated. Attached growth biomass from a carrier-filled compartment of a hybrid-MBBR at the wastewater treatment plant (WWTP) in Bad Ragaz, Switzerland was used to test the biotransformation. Laboratory-scale incubation experiments were performed with diclofenac and carriers and high-resolution LC-QTof-MS was implemented to monitor the biotransformation. Up to 20 diclofenac transformation products (TPs) were detected. Tentative structures were proposed for 16 of the TPs after characterization by MS 2 fragmentation and/or inferring the structure from the transformation pathway and the molecular formula given by the high resolution ionic mass. The remaining four TPs were unambiguously identified via analytical reference standards. The postulated reactions forming the TPs were: hydroxylation, decarboxylation, oxidation, amide formation, ring-opening and reductive dechlorination. Incubation experiments of individual TPs, those which were available as reference standards, provided a deeper look into the transformation pathways. It was found that the transformation consists of four main pathways but no pathway accounted for a clear majority of the transformation. A 10-day monitoring campaign of the full-scale plant confirmed an 88% removal of diclofenac (from approximately 1.6 μg/L in WWTP influent) and the formation of TPs as found in the laboratory was observed. One of the TPs, N-(2,6-dichlorophenyl)-2-indolinone detected at concentrations of around 0.25 μg/L in WWTP effluent, accounting for 16% of the influent diclofenac concentration. The biotransformation of carriers was compared to a second WWTP not utilising carriers. It was found that in contact with activated sludge, similar hydroxylation and decarboxylation reactions occurred but at much slower rates, whereas some reactions, e.g. reductive dechlorination, were not detected at all. Finally, incubation experiments were performed with attached growth biomass from a third WWTP with a similar process configuration to Bad Ragaz WWTP. A similarly effective removal of diclofenac was found with a similar presence of TPs. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Fluid-driven Earthquake Cycle, Omori's Law, and Fluid-driven Aftershocks

NASA Astrophysics Data System (ADS)

Miller, S. A.

2015-12-01

Few models exist that predict the Omori's Law of aftershock rate decay, with rate-state friction the only physically-based model. ETAS is a probabilistic model of cascading failures, and is sometimes used to infer rate-state frictional properties. However, the (perhaps dominant) role of fluids in the earthquake process is being increasingly realised, so a fluid-based physical model for Omori's Law should be available. In this talk, I present an hypothesis for a fluid-driven earthquake cycle where dehydration and decarbonization at depth provides continuous sources of buoyant high pressure fluids that must eventually make their way back to the surface. The natural pathway for fluid escape is along plate boundaries, where in the ductile regime high pressure fluids likely play an integral role in episodic tremor and slow slip earthquakes. At shallower levels, high pressure fluids pool at the base of seismogenic zones, with the reservoir expanding in scale through the earthquake cycle. Late in the cycle, these fluids can invade and degrade the strength of the brittle crust and contribute to earthquake nucleation. The mainshock opens permeable networks that provide escape pathways for high pressure fluids and generate aftershocks along these flow paths, while creating new pathways by the aftershocks themselves. Thermally activated precipitation then seals up these pathways, returning the system to a low-permeability environment and effective seal during the subsequent tectonic stress buildup. I find that the multiplicative effect of an exponential dependence of permeability on the effective normal stress coupled with an Arrhenius-type, thermally activated exponential reduction in permeability results in Omori's Law. I simulate this scenario using a very simple model that combines non-linear diffusion and a step-wise increase in permeability when a Mohr Coulomb failure condition is met, and allow permeability to decrease as an exponential function in time. I show very strong spatial correlations of the simulated evolved permeability and fluid pressure field with aftershock hypocenters from this 1992 Landers and 1994 Northridge aftershock sequences, and reproduce the observed aftershock decay rates. Controls on the decay rates (p-value) will also be discussed.

When giving is good: Ventromedial prefrontal cortex activation for others’ intentions

PubMed Central

Cooper, Jeffrey C.; Kreps, Tamar A.; Wiebe, Taylor; Pirkl, Tristana; Knutson, Brian

2010-01-01

Summary In social decision-making, people care both about others’ outcomes and their intentions to help or harm. How the brain integrates representations of others’ intentions with their outcomes, however, is unknown. In this study, participants inferred others’ decisions in an economic game during functional magnetic resonance imaging. When the game was described in terms of donations, ventromedial prefrontal cortex (VMPFC) activation increased for inferring generous play and decreased for inferring selfish play. When the game was described in terms of individual savings, however, VMPFC activation did not distinguish between strategies. Distinct medial prefrontal regions also encoded consistency with situational norms. A separate network, including right temporoparietal junction and parahippocampal gyrus, was more activated for inferential errors in the donation than in the savings condition. These results for the first time demonstrate that neural responses to others’ generosity or selfishness depend not only on their actions but also on their perceived intentions. PMID:20696386

The concept of crosstalk-directed embryological target mining and its application to essential hypertension treatment failures.

PubMed

Sag, Alan Alper; Sal, Oguzhan; Kilic, Yagmur; Onal, Emine Meltem; Kanbay, Mehmet

2017-05-01

This review aims to introduce the novel concept of embryological target mining applied to interorgan crosstalk network genesis, and applies embryological target mining to multidrug-resistant essential hypertension (a prototype, complex, undertreated, multiorgan systemic syndrome) to uncover new treatment targets and critique why existing strategies fail. Briefly, interorgan crosstalk pathways represent the next frontier for target mining in molecular medicine. This is because stereotyped stepwise organogenesis presents a unique opportunity to infer interorgan crosstalk pathways that may be crucial to discovering novel treatment targets. Insights gained from this review will be applied to patient management in a clinician-directed fashion. ©2017 Wiley Periodicals, Inc.

Inferring individual-level processes from population-level patterns in cultural evolution

PubMed Central

Wilder, Bryan

2017-01-01

Our species is characterized by a great degree of cultural variation, both within and between populations. Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We develop a simulation model capturing demographic and cultural dynamics relevant to human cultural evolution, focusing on the interface between population-level patterns and individual-level processes. The model tracks the distribution of variants of cultural traits across individuals in a population over time, conditioned on different pathways for the transmission of information between individuals. From these data, we obtain theoretical expectations for a range of statistics commonly used to capture population-level characteristics (e.g. the degree of cultural diversity). Consistent with previous theoretical work, our results show that the patterns observed at the level of groups are rooted in the interplay between the transmission pathways and the age structure of the population. We also explore whether, and under what conditions, the different pathways can be distinguished based on their group-level signatures, in an effort to establish theoretical limits to inference. Our results show that the temporal dynamic of cultural change over time retains a stronger signature than the cultural composition of the population at a specific point in time. Overall, the results suggest a shift in focus from identifying the one individual-level process that likely produced the observed data to excluding those that likely did not. We conclude by discussing the implications for empirical studies of human cultural evolution. PMID:28989786

Modeling the evolution of protein domain architectures using maximum parsimony.

PubMed

Fong, Jessica H; Geer, Lewis Y; Panchenko, Anna R; Bryant, Stephen H

2007-02-09

Domains are basic evolutionary units of proteins and most proteins have more than one domain. Advances in domain modeling and collection are making it possible to annotate a large fraction of known protein sequences by a linear ordering of their domains, yielding their architecture. Protein domain architectures link evolutionarily related proteins and underscore their shared functions. Here, we attempt to better understand this association by identifying the evolutionary pathways by which extant architectures may have evolved. We propose a model of evolution in which architectures arise through rearrangements of inferred precursor architectures and acquisition of new domains. These pathways are ranked using a parsimony principle, whereby scenarios requiring the fewest number of independent recombination events, namely fission and fusion operations, are assumed to be more likely. Using a data set of domain architectures present in 159 proteomes that represent all three major branches of the tree of life allows us to estimate the history of over 85% of all architectures in the sequence database. We find that the distribution of rearrangement classes is robust with respect to alternative parsimony rules for inferring the presence of precursor architectures in ancestral species. Analyzing the most parsimonious pathways, we find 87% of architectures to gain complexity over time through simple changes, among which fusion events account for 5.6 times as many architectures as fission. Our results may be used to compute domain architecture similarities, for example, based on the number of historical recombination events separating them. Domain architecture "neighbors" identified in this way may lead to new insights about the evolution of protein function.

Modeling the Evolution of Protein Domain Architectures Using Maximum Parsimony

PubMed Central

Fong, Jessica H.; Geer, Lewis Y.; Panchenko, Anna R.; Bryant, Stephen H.

2007-01-01

Domains are basic evolutionary units of proteins and most proteins have more than one domain. Advances in domain modeling and collection are making it possible to annotate a large fraction of known protein sequences by a linear ordering of their domains, yielding their architecture. Protein domain architectures link evolutionarily related proteins and underscore their shared functions. Here, we attempt to better understand this association by identifying the evolutionary pathways by which extant architectures may have evolved. We propose a model of evolution in which architectures arise through rearrangements of inferred precursor architectures and acquisition of new domains. These pathways are ranked using a parsimony principle, whereby scenarios requiring the fewest number of independent recombination events, namely fission and fusion operations, are assumed to be more likely. Using a data set of domain architectures present in 159 proteomes that represent all three major branches of the tree of life allows us to estimate the history of over 85% of all architectures in the sequence database. We find that the distribution of rearrangement classes is robust with respect to alternative parsimony rules for inferring the presence of precursor architectures in ancestral species. Analyzing the most parsimonious pathways, we find 87% of architectures to gain complexity over time through simple changes, among which fusion events account for 5.6 times as many architectures as fission. Our results may be used to compute domain architecture similarities, for example, based on the number of historical recombination events separating them. Domain architecture “neighbors” identified in this way may lead to new insights about the evolution of protein function. PMID:17166515

Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy

PubMed Central

Godard, Patrice; van Eyll, Jonathan

2015-01-01

MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods. PMID:25800743

The role of fMRI in cognitive neuroscience: where do we stand?

PubMed

Poldrack, Russell A

2008-04-01

Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.

Hierarchical Active Inference: A Theory of Motivated Control.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl J

2018-04-01

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Impaired self-agency inferences in schizophrenia: The role of cognitive capacity and causal reasoning style.

PubMed

Prikken, M; van der Weiden, A; Kahn, R S; Aarts, H; van Haren, N E M

2018-01-01

The sense of self-agency, i.e., experiencing oneself as the cause of one's own actions, is impaired in patients with schizophrenia. Normally, inferences of self-agency are enhanced when actual outcomes match with pre-activated outcome information, where this pre-activation can result from explicitly set goals (i.e., goal-based route) or implicitly primed outcome information (i.e., prime-based route). Previous research suggests that patients show specific impairments in the prime-based route, implicating that they do not rely on matches between implicitly available outcome information and actual action-outcomes when inferring self-agency. The question remains: Why? Here, we examine whether neurocognitive functioning and self-serving bias (SSB) may explain abnormalities in patients' agency inferences. Thirty-six patients and 36 healthy controls performed a commonly used agency inference task to measure goal- and prime-based self-agency inferences. Neurocognitive functioning was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS) and the SSB was assessed with the Internal Personal and Situational Attributions Questionnaire. Results showed a substantial smaller effect of primed outcome information on agency experiences in patients compared with healthy controls. Whereas patients and controls differed on BACS and marginally on SSB scores, these differences were not related to patients' impairments in prime-based agency inferences. Patients showed impairments in prime-based agency inferences, thereby replicating previous studies. This finding could not be explained by cognitive dysfunction or SSB. Results are discussed in the context of the recent surge to understand and examine deficits in agency experiences in schizophrenia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Toward a Unified Sub-symbolic Computational Theory of Cognition

PubMed Central

Butz, Martin V.

2016-01-01

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development toward the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification, and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper. PMID:27445895

From medical student to clinician-scientist: where is the pathway in Australia?

PubMed

Eley, D S; Benham, H

2016-12-01

Clinician-scientists are a valuable resource and are crucial to ensuring that high-quality health and medical research is undertaken and translated to patients. Although the literature notes the global decline in clinician-scientists and infers the worldwide similarity in the challenges to reverse this decline, Australia continues to lag behind in establishing an infrastructure to address the dilemma. © 2016 Royal Australasian College of Physicians.

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

Inflammatory and apoptotic signalling pathways and concussion severity: a genetic association study.

PubMed

Mc Fie, Sarah; Abrahams, Shameemah; Patricios, Jon; Suter, Jason; Posthumus, Michael; September, Alison V

2018-03-06

The objective was to investigate the relationship between IL-1B rs16944, IL-6 rs1800795, and CASP8 rs3834129 genetic polymorphisms and concussion severity. Rugby players from high school, senior amateur, and professional teams completed a concussion severity questionnaire and donated a DNA sample. Participants (n = 163) were split into symptom severity groups around the median number and duration of symptoms. The frequency of participants with high symptom counts (more than five symptoms) increased across the IL-1B (C/C: 35%; C/T: 51%; T/T: 56%; P = 0.047) and the IL-6 (C/C: 31%; C/G: 44%; G/G: 58%; P = 0.027) genotypes. The C-C inferred interleukin allele construct frequency, created from combining the IL-1B and IL-6 genotype data, was lower in participants reporting a high symptom count (18%), compared to those with a low symptom count (fewer than six symptoms, 36%, P = 0.002). Similarly, the C-C inferred interleukin allele construct frequency was lower in those reporting prolonged symptom duration (more than one week, 16%), as opposed to short symptom duration (less than one week, 34%, P = 0.015). This study provides evidence of novel inflammatory pathway genetic associations with concussion severity, which supports the hypothesis implicating neuroinflammation in the development of concussion symptoms.

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study

PubMed Central

Feygin, Sidney; Dembo, Aluma; Aguilera, Adrian; Recht, Benjamin

2017-01-01

Background Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel and ubiquitous opportunity to track individuals’ behavior and may be useful for inferring and automatically monitoring mental well-being. Objective The aim of this study was to assess the extent to which activity and sleep tracking with a smartphone can be used for monitoring individuals’ mental well-being. Methods A cohort of 106 individuals was recruited to install an app on their smartphone that would track their well-being with daily surveys and track their behavior with activity inferences from their phone’s accelerometer data. Of the participants recruited, 53 had sufficient data to infer activity and sleep measures. For this subset of individuals, we related measures of activity and sleep to the individuals’ well-being and used these measures to predict their well-being. Results We found that smartphone-measured approximations for daily physical activity were positively correlated with both mood (P=.004) and perceived energy level (P<.001). Sleep duration was positively correlated with mood (P=.02) but not energy. Our measure for sleep disturbance was not found to be significantly related to either mood or energy, which could imply too much noise in the measurement. Models predicting the well-being measures from the activity and sleep measures were found to be significantly better than naive baselines (P<.01), despite modest overall improvements. Conclusions Measures of activity and sleep inferred from smartphone activity were strongly related to and somewhat predictive of participants’ well-being. Whereas the improvement over naive models was modest, it reaffirms the importance of considering physical activity and sleep for predicting mood and for making automatic mood monitoring a reality. PMID:28982643

Chemical probes and engineered constructs reveal a detailed unfolding mechanism for a solvent-free multi-domain protein

PubMed Central

Eschweiler, Joseph D.; Martini, Rachel M.; Ruotolo, Brandon T.

2017-01-01

Despite the growing application of gas-phase measurements in structural biology and drug discovery, the factors that govern protein stabilities and structures in a solvent-free environment are still poorly understood. Here, we examine the solvent-free unfolding pathway for a group of homologous serum albumins. Utilizing a combination of chemical probes and non-covalent reconstructions, we draw new specific conclusions regarding the unfolding of albumins in the gas-phase, as well as more-general inferences regarding the sensitivity of collision induced unfolding to changes in protein primary and tertiary structure. Our findings suggest that the general unfolding pathway of low charge state albumin ions is largely unaffected by changes in primary structure; however, the stabilities of intermediates along these pathways vary widely as sequences diverge. Additionally, we find that human albumin follows a domain associated unfolding pathway, and are able to assign each unfolded form observed in our gas-phase dataset to the disruption of specific domains within the protein. The totality of our data informs the first detailed mechanism for multi-domain protein unfolding in the gas phase, and highlights key similarities and differences from the known the solution-phase pathway. PMID:27959526

Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment

PubMed Central

2012-01-01

Background The inflammatory bowel diseases (IBD) Crohn's disease and ulcerative colitis result from alterations in intestinal microbes and the immune system. However, the precise dysfunctions of microbial metabolism in the gastrointestinal microbiome during IBD remain unclear. We analyzed the microbiota of intestinal biopsies and stool samples from 231 IBD and healthy subjects by 16S gene pyrosequencing and followed up a subset using shotgun metagenomics. Gene and pathway composition were assessed, based on 16S data from phylogenetically-related reference genomes, and associated using sparse multivariate linear modeling with medications, environmental factors, and IBD status. Results Firmicutes and Enterobacteriaceae abundances were associated with disease status as expected, but also with treatment and subject characteristics. Microbial function, though, was more consistently perturbed than composition, with 12% of analyzed pathways changed compared with 2% of genera. We identified major shifts in oxidative stress pathways, as well as decreased carbohydrate metabolism and amino acid biosynthesis in favor of nutrient transport and uptake. The microbiome of ileal Crohn's disease was notable for increases in virulence and secretion pathways. Conclusions This inferred functional metagenomic information provides the first insights into community-wide microbial processes and pathways that underpin IBD pathogenesis. PMID:23013615

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

NASA Astrophysics Data System (ADS)

Yang, Yunlai; Arouri, Khaled

2016-03-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

PubMed Central

Yang, Yunlai; Arouri, Khaled

2016-01-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations. PMID:26965479

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways.

PubMed

Yang, Yunlai; Arouri, Khaled

2016-03-11

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

Reconstructing Dynamic Promoter Activity Profiles from Reporter Gene Data.

PubMed

Kannan, Soumya; Sams, Thomas; Maury, Jérôme; Workman, Christopher T

2018-03-16

Accurate characterization of promoter activity is important when designing expression systems for systems biology and metabolic engineering applications. Promoters that respond to changes in the environment enable the dynamic control of gene expression without the necessity of inducer compounds, for example. However, the dynamic nature of these processes poses challenges for estimating promoter activity. Most experimental approaches utilize reporter gene expression to estimate promoter activity. Typically the reporter gene encodes a fluorescent protein that is used to infer a constant promoter activity despite the fact that the observed output may be dynamic and is a number of steps away from the transcription process. In fact, some promoters that are often thought of as constitutive can show changes in activity when growth conditions change. For these reasons, we have developed a system of ordinary differential equations for estimating dynamic promoter activity for promoters that change their activity in response to the environment that is robust to noise and changes in growth rate. Our approach, inference of dynamic promoter activity (PromAct), improves on existing methods by more accurately inferring known promoter activity profiles. This method is also capable of estimating the correct scale of promoter activity and can be applied to quantitative data sets to estimate quantitative rates.

Metaproteomics of cellulose methanisation under thermophilic conditions reveals a surprisingly high proteolytic activity

PubMed Central

Lü, Fan; Bize, Ariane; Guillot, Alain; Monnet, Véronique; Madigou, Céline; Chapleur, Olivier; Mazéas, Laurent; He, Pinjing; Bouchez, Théodore

2014-01-01

Cellulose is the most abundant biopolymer on Earth. Optimising energy recovery from this renewable but recalcitrant material is a key issue. The metaproteome expressed by thermophilic communities during cellulose anaerobic digestion was investigated in microcosms. By multiplying the analytical replicates (65 protein fractions analysed by MS/MS) and relying solely on public protein databases, more than 500 non-redundant protein functions were identified. The taxonomic community structure as inferred from the metaproteomic data set was in good overall agreement with 16S rRNA gene tag pyrosequencing and fluorescent in situ hybridisation analyses. Numerous functions related to cellulose and hemicellulose hydrolysis and fermentation catalysed by bacteria related to Caldicellulosiruptor spp. and Clostridium thermocellum were retrieved, indicating their key role in the cellulose-degradation process and also suggesting their complementary action. Despite the abundance of acetate as a major fermentation product, key methanogenesis enzymes from the acetoclastic pathway were not detected. In contrast, enzymes from the hydrogenotrophic pathway affiliated to Methanothermobacter were almost exclusively identified for methanogenesis, suggesting a syntrophic acetate oxidation process coupled to hydrogenotrophic methanogenesis. Isotopic analyses confirmed the high dominance of the hydrogenotrophic methanogenesis. Very surprising was the identification of an abundant proteolytic activity from Coprothermobacter proteolyticus strains, probably acting as scavenger and/or predator performing proteolysis and fermentation. Metaproteomics thus appeared as an efficient tool to unravel and characterise metabolic networks as well as ecological interactions during methanisation bioprocesses. More generally, metaproteomics provides direct functional insights at a limited cost, and its attractiveness should increase in the future as sequence databases are growing exponentially. PMID:23949661

Metaproteomics of cellulose methanisation under thermophilic conditions reveals a surprisingly high proteolytic activity.

PubMed

Lü, Fan; Bize, Ariane; Guillot, Alain; Monnet, Véronique; Madigou, Céline; Chapleur, Olivier; Mazéas, Laurent; He, Pinjing; Bouchez, Théodore

2014-01-01

Cellulose is the most abundant biopolymer on Earth. Optimising energy recovery from this renewable but recalcitrant material is a key issue. The metaproteome expressed by thermophilic communities during cellulose anaerobic digestion was investigated in microcosms. By multiplying the analytical replicates (65 protein fractions analysed by MS/MS) and relying solely on public protein databases, more than 500 non-redundant protein functions were identified. The taxonomic community structure as inferred from the metaproteomic data set was in good overall agreement with 16S rRNA gene tag pyrosequencing and fluorescent in situ hybridisation analyses. Numerous functions related to cellulose and hemicellulose hydrolysis and fermentation catalysed by bacteria related to Caldicellulosiruptor spp. and Clostridium thermocellum were retrieved, indicating their key role in the cellulose-degradation process and also suggesting their complementary action. Despite the abundance of acetate as a major fermentation product, key methanogenesis enzymes from the acetoclastic pathway were not detected. In contrast, enzymes from the hydrogenotrophic pathway affiliated to Methanothermobacter were almost exclusively identified for methanogenesis, suggesting a syntrophic acetate oxidation process coupled to hydrogenotrophic methanogenesis. Isotopic analyses confirmed the high dominance of the hydrogenotrophic methanogenesis. Very surprising was the identification of an abundant proteolytic activity from Coprothermobacter proteolyticus strains, probably acting as scavenger and/or predator performing proteolysis and fermentation. Metaproteomics thus appeared as an efficient tool to unravel and characterise metabolic networks as well as ecological interactions during methanisation bioprocesses. More generally, metaproteomics provides direct functional insights at a limited cost, and its attractiveness should increase in the future as sequence databases are growing exponentially.

CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network

PubMed Central

Thakar, Manjusha; Howard, Jason D.; Kagohara, Luciane T.; Krigsfeld, Gabriel; Ranaweera, Ruchira S.; Hughes, Robert M.; Perez, Jimena; Jones, Siân; Favorov, Alexander V.; Carey, Jacob; Stein-O'Brien, Genevieve; Gaykalova, Daria A.; Ochs, Michael F.; Chung, Christine H.

2016-01-01

Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance. PMID:27650546

A Bayesian method for inferring transmission chains in a partially observed epidemic.

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

Marzouk, Youssef M.; Ray, Jaideep

2008-10-01

We present a Bayesian approach for estimating transmission chains and rates in the Abakaliki smallpox epidemic of 1967. The epidemic affected 30 individuals in a community of 74; only the dates of appearance of symptoms were recorded. Our model assumes stochastic transmission of the infections over a social network. Distinct binomial random graphs model intra- and inter-compound social connections, while disease transmission over each link is treated as a Poisson process. Link probabilities and rate parameters are objects of inference. Dates of infection and recovery comprise the remaining unknowns. Distributions for smallpox incubation and recovery periods are obtained from historicalmore » data. Using Markov chain Monte Carlo, we explore the joint posterior distribution of the scalar parameters and provide an expected connectivity pattern for the social graph and infection pathway.« less

Separable processes before, during, and after the N400 elicited by previously inferred and new information: evidence from time-frequency decompositions.

PubMed

Steele, Vaughn R; Bernat, Edward M; van den Broek, Paul; Collins, Paul F; Patrick, Christopher J; Marsolek, Chad J

2013-01-25

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1-6 Hz (theta) and 0-2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. Copyright © 2012 Elsevier B.V. All rights reserved.

Evolution of the biosynthesis of the branched-chain amino acids

NASA Technical Reports Server (NTRS)

Keefe, Anthony D.; Lazcano, Antonio; Miller, Stanley L.

1995-01-01

The origins of the biosynthetic pathways for the branched-chain amino acids cannot be understood in terms of the backwards development of the present acetolactate pathway because it contains unstable intermediates. We propose that the first biosynthesis of the branched-chain amino acids was by the reductive carboxylation of short branched chain fatty acids giving keto acids which were then transaminated. Similar reaction sequences mediated by nonspecific enzymes would produce serine and threomine from the abundant prebiotic compounds glycolic and lactic acids. The aromatic amino acids may also have first been synthesized in this way, e.g. tryptophan from indole acetic acid. The next step would have been the biosynthesis of leucine from alpha-ketoisovalerc acid. The acetolactate pathway developed subsequently. The first version of the Krebs cycle, which was used for amino acid biosynthesis, would have been assembled by making use fo the reductive carboxylation and leucine biosynthesis enzymes, and completed with the development of a single new enzyme, succinate dehydrogenase. This evolutionary scheme suggests that there may be limitations to inferring the origins of metabolism by a simple back extrapolation of current pathways.

Inferring the temperature dependence of population parameters: the effects of experimental design and inference algorithm

PubMed Central

Palamara, Gian Marco; Childs, Dylan Z; Clements, Christopher F; Petchey, Owen L; Plebani, Marco; Smith, Matthew J

2014-01-01

Understanding and quantifying the temperature dependence of population parameters, such as intrinsic growth rate and carrying capacity, is critical for predicting the ecological responses to environmental change. Many studies provide empirical estimates of such temperature dependencies, but a thorough investigation of the methods used to infer them has not been performed yet. We created artificial population time series using a stochastic logistic model parameterized with the Arrhenius equation, so that activation energy drives the temperature dependence of population parameters. We simulated different experimental designs and used different inference methods, varying the likelihood functions and other aspects of the parameter estimation methods. Finally, we applied the best performing inference methods to real data for the species Paramecium caudatum. The relative error of the estimates of activation energy varied between 5% and 30%. The fraction of habitat sampled played the most important role in determining the relative error; sampling at least 1% of the habitat kept it below 50%. We found that methods that simultaneously use all time series data (direct methods) and methods that estimate population parameters separately for each temperature (indirect methods) are complementary. Indirect methods provide a clearer insight into the shape of the functional form describing the temperature dependence of population parameters; direct methods enable a more accurate estimation of the parameters of such functional forms. Using both methods, we found that growth rate and carrying capacity of Paramecium caudatum scale with temperature according to different activation energies. Our study shows how careful choice of experimental design and inference methods can increase the accuracy of the inferred relationships between temperature and population parameters. The comparison of estimation methods provided here can increase the accuracy of model predictions, with important implications in understanding and predicting the effects of temperature on the dynamics of populations. PMID:25558365

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

PubMed

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

PubMed Central

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109

Anatomically informed mesoscale electrical impedance spectroscopy in southern pine and the electric field distribution for pin-type electric moisture metres

Treesearch

Samuel L. Zelinka; Alex C. Wiedenhoeft; Samuel V. Glass; Flavio Ruffinatto

2015-01-01

Electrical impedance spectra of wood taken at macroscopic scales below the fibre saturation point have led to inferences that the mechanism of charge conduction involves a percolation phenomenon. The pathways responsible for charge conduction would necessarily be influenced by wood structure at a variety of sub-macroscopic scales – at a mesoscale – but these questions...

Active interoceptive inference and the emotional brain

PubMed Central

Friston, Karl J.

2016-01-01

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080966

Explorations of electric current system in solar active regions. I - Empirical inferences of the current flows

NASA Technical Reports Server (NTRS)

Ding, Y. J.; Hong, Q. F.; Hagyard, M. J.; Deloach, A. C.; Liu, X. P.

1987-01-01

Techniques to identify sources of electric current systems and their channels of flow in solar active regions are explored. Measured photospheric vector magnetic fields together with high-resolution white-light and H-alpha filtergrams provide the data base to derive the current systems in the photosphere and chromosphere. As an example, the techniques are then applied to infer current systems in AR 2372 in early April 1980.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference.

PubMed

Limanowski, Jakub; Friston, Karl

2018-01-01

One of the central claims of the Self-model Theory of Subjectivity is that the experience of being someone - even in a minimal form - arises through a transparent phenomenal self-model, which itself can in principle be reduced to brain processes. Here, we consider whether it is possible to distinguish between phenomenally transparent and opaque states in terms of active inference. We propose a relationship of phenomenal opacity to expected uncertainty or precision; i.e., the capacity for introspective attention and implicit mental action. Thus we associate introspective attention with the deployment of 'precision' that may render the perceptual evidence (for action) opaque, while treating transparency as a necessary aspect of beliefs about action, i.e., 'what I am' doing. We conclude by proposing how we may have to nuance our conception of minimal phenomenal selfhood and agency in light of this active inference conception of transparency-opacity.

Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

PubMed

Bang, Sunlee; Kim, Minho; Song, Sa-Kwang; Park, Soo-Jun

2008-01-01

As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home. Particularly, in order to robustly infer ADLs, we present component ADL, which is decided with conjunction of human motion together, not just only contacted object identification. It is an important component in inferring ADL. In special, component ADL decision firstly refines misclassified initial activities, which improves the accuracy of recognizing ADL. Preliminary experiments results for proposed system provides overall recognition rate of over 97% over 8 component ADLs, which can be effectively applicable to recognize the final ADLs.

Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks.

PubMed

Souza, Terezinha M; Kleinjans, Jos C S; Jennen, Danyel G J

2017-01-01

Perturbation of biological networks is often observed during exposure to xenobiotics, and the identification of disturbed processes, their dynamic traits, and dose-response relationships are some of the current challenges for elucidating the mechanisms determining adverse outcomes. In this scenario, reverse engineering of gene regulatory networks (GRNs) from expression data may provide a system-level snapshot embedded within accurate molecular events. Here, we investigate the composition of GRNs inferred from groups of chemicals with two distinct outcomes, namely carcinogenicity [azathioprine (AZA) and cyclophosphamide (CYC)] and drug-induced liver injury (DILI; diclofenac, nitrofurantoin, and propylthiouracil), and a non-carcinogenic/non-DILI group (aspirin, diazepam, and omeprazole). For this, we analyzed publicly available exposed in vitro human data, taking into account dose and time dependencies. Dose-Time Network Identification (DTNI) was applied to gene sets from exposed primary human hepatocytes using four stress pathways, namely endoplasmic reticulum (ER), NF-κB, NRF2, and TP53. Inferred GRNs suggested case specificity, varying in interactions, starting nodes, and target genes across groups. DILI and carcinogenic compounds were shown to directly affect all pathway-based GRNs, while non-DILI/non-carcinogenic chemicals only affected NF-κB. NF-κB-based GRNs clearly illustrated group-specific disturbances, with the cancer-related casein kinase CSNK2A1 being a target gene only in the carcinogenic group, and opposite regulation of NF-κB subunits being observed in DILI and non-DILI/non-carcinogenic groups. Target genes in NRF2-based GRNs shared by DILI and carcinogenic compounds suggested markers of hepatotoxicity. Finally, we indicate several of these group-specific interactions as potentially novel. In summary, our reversed-engineered GRNs are capable of revealing dose dependent, chemical-specific mechanisms of action in stress-related biological networks.

False belief and counterfactual reasoning in a social environment.

PubMed

Van Hoeck, Nicole; Begtas, Elizabet; Steen, Johan; Kestemont, Jenny; Vandekerckhove, Marie; Van Overwalle, Frank

2014-04-15

Behavioral studies indicate that theory of mind and counterfactual reasoning are strongly related cognitive processes. In a neuroimaging study, we explored the common and distinct regions underlying these inference processes. We directly compared false belief reasoning (inferring an agent's false belief about an object's location or content) and counterfactual reasoning (inferring what the object's location or content would be if an agent had acted differently), both in contrast with a baseline condition of conditional reasoning (inferring what the true location or content of an object is). Results indicate that these three types of reasoning about social scenarios are supported by activations in the mentalizing network (left temporo-parietal junction and precuneus) and the executive control network (bilateral prefrontal cortex [PFC] and right inferior parietal lobule). In addition, representing a false belief or counterfactual state (both not directly observable in the external world) recruits additional activity in the executive control network (left dorsolateral PFC and parietal lobe). The results further suggest that counterfactual reasoning is a more complex cognitive process than false belief reasoning, showing stronger activation of the dorsomedial, left dorsolateral PFC, cerebellum and left temporal cortex. Copyright © 2013 Elsevier Inc. All rights reserved.

Learning language from within: Children use semantic generalizations to infer word meanings.

PubMed

Srinivasan, Mahesh; Al-Mughairy, Sara; Foushee, Ruthe; Barner, David

2017-02-01

One reason that word learning presents a challenge for children is because pairings between word forms and meanings are arbitrary conventions that children must learn via observation - e.g., the fact that "shovel" labels shovels. The present studies explore cases in which children might bypass observational learning and spontaneously infer new word meanings: By exploiting the fact that many words are flexible and systematically encode multiple, related meanings. For example, words like shovel and hammer are nouns for instruments, and verbs for activities involving those instruments. The present studies explored whether 3- to 5-year-old children possess semantic generalizations about lexical flexibility, and can use these generalizations to infer new word meanings: Upon learning that dax labels an activity involving an instrument, do children spontaneously infer that dax can also label the instrument itself? Across four studies, we show that at least by age four, children spontaneously generalize instrument-activity flexibility to new words. Together, our findings point to a powerful way in which children may build their vocabulary, by leveraging the fact that words are linked to multiple meanings in systematic ways. Copyright © 2016 Elsevier B.V. All rights reserved.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

PubMed

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Fluctuations in Species-Level Protein Expression Occur during Element and Nutrient Cycling in the Subsurface

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

Wilkins, Michael J.; Wrighton, Kelly C.; Nicora, Carrie D.

2013-03-05

While microbial activities in environmental systems play a key role in the utilization and cycling of essential elements and compounds, microbial activity and growth frequently fluctuates in response to environmental stimuli and perturbations. To investigate these fluctuations within a saturated aquifer system, we monitored a carbon-stimulated in situ Geobacter population while iron reduction was occurring, using 16S rRNA abundances and high-resolution tandem mass spectrometry proteome measurements. Following carbon amendment, 16S rRNA analysis of temporally separated samples revealed the rapid enrichment of Geobacter-like environmental strains with strong similarity to G. bemidjiensis. Tandem mass spectrometry proteomics measurements suggest high carbon flux throughmore » Geobacter respiratory pathways, and the synthesis of anapleurotic four carbon compounds from acetyl-CoA via pyruvate ferredoxin oxidoreductase activity. Across a 40-day period where Fe(III) reduction was occurring, fluctuations in protein expression reflected changes in anabolic versus catabolic reactions, with increased levels of biosynthesis occurring soon after acetate arrival in the aquifer. In addition, localized shifts in nutrient limitation were inferred based on expression of nitrogenase enzymes and phosphate uptake proteins. These temporal data offer the first example of differing microbial protein expression associated with changing geochemical conditions in a subsurface environment.« less

Interoceptive inference: From computational neuroscience to clinic.

PubMed

Owens, Andrew P; Allen, Micah; Ondobaka, Sasha; Friston, Karl J

2018-04-22

The central and autonomic nervous systems can be defined by their anatomical, functional and neurochemical characteristics, but neither functions in isolation. For example, fundamental components of autonomically mediated homeostatic processes are afferent interoceptive signals reporting the internal state of the body and efferent signals acting on interoceptive feedback assimilated by the brain. Recent predictive coding (interoceptive inference) models formulate interoception in terms of embodied predictive processes that support emotion and selfhood. We propose interoception may serve as a way to investigate holistic nervous system function and dysfunction in disorders of brain, body and behaviour. We appeal to predictive coding and (active) interoceptive inference, to describe the homeostatic functions of the central and autonomic nervous systems. We do so by (i) reviewing the active inference formulation of interoceptive and autonomic function, (ii) survey clinical applications of this formulation and (iii) describe how it offers an integrative approach to human physiology; particularly, interactions between the central and peripheral nervous systems in health and disease. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Pride displays communicate self-interest and support for meritocracy.

PubMed

Horberg, E J; Kraus, Michael W; Keltner, Dacher

2013-07-01

The present studies examined how observers infer moral attributes and beliefs from nonverbal pride displays. Pride is a self-focused positive emotion triggered by appraisals of the self's success, status, and competence. We hypothesized that when a target emits nonverbal cues of pride, he or she will be viewed by observers as higher in self-interest and therefore more likely to endorse ideologies that would benefit the self-specifically, merit-based resource distributions (meritocracy) as opposed to equality-based resource distributions (egalitarianism). Across studies, experimentally manipulated pride displays (Studies 1 and 3) and naturally occurring expressions of pride (Study 4) led observers to infer heightened support for meritocracy as opposed to egalitarianism. Analyses also revealed that people intuitively associate higher self-interest with enhanced support for meritocracy as opposed to egalitarianism (Study 2), and this association mediates the pathway from pride displays to inferences of heightened support for meritocracy and reduced support for egalitarianism (Studies 3 and 4). Across studies, we compare pride to expressions of joy or no emotion and demonstrate these effects using thin slices as well as static images. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Ensemble stacking mitigates biases in inference of synaptic connectivity.

PubMed

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Active learning of cortical connectivity from two-photon imaging data

PubMed Central

Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. PMID:29718955

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

PubMed Central

2011-01-01

Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. Results We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals. Conclusions Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia. PMID:21575198

Using adaptive processes and adverse outcome pathways to develop meaningful, robust, and actionable environmental monitoring programs.

PubMed

Arciszewski, Tim J; Munkittrick, Kelly R; Scrimgeour, Garry J; Dubé, Monique G; Wrona, Fred J; Hazewinkel, Rod R

2017-09-01

The primary goals of environmental monitoring are to indicate whether unexpected changes related to development are occurring in the physical, chemical, and biological attributes of ecosystems and to inform meaningful management intervention. Although achieving these objectives is conceptually simple, varying scientific and social challenges often result in their breakdown. Conceptualizing, designing, and operating programs that better delineate monitoring, management, and risk assessment processes supported by hypothesis-driven approaches, strong inference, and adverse outcome pathways can overcome many of the challenges. Generally, a robust monitoring program is characterized by hypothesis-driven questions associated with potential adverse outcomes and feedback loops informed by data. Specifically, key and basic features are predictions of future observations (triggers) and mechanisms to respond to success or failure of those predictions (tiers). The adaptive processes accelerate or decelerate the effort to highlight and overcome ignorance while preventing the potentially unnecessary escalation of unguided monitoring and management. The deployment of the mutually reinforcing components can allow for more meaningful and actionable monitoring programs that better associate activities with consequences. Integr Environ Assess Manag 2017;13:877-891. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC). © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Transcriptomic response and perturbation of toxicity pathways in zebrafish larvae after exposure to graphene quantum dots (GQDs).

PubMed

Deng, Shun; Jia, Pan-Pan; Zhang, Jing-Hui; Junaid, Muhammad; Niu, Aping; Ma, Yan-Bo; Fu, Ailing; Pei, De-Sheng

2018-05-29

Graphene quantum dots (GQDs) are widely used for biomedical applications. Previously, the low-level toxicity of GQDs in vivo and in vitro has been elucidated, but the underlying molecular mechanisms remained largely unknown. Here, we employed the Illumina high-throughput RNA-sequencing to explore the whole-transcriptome profiling of zebrafish larvae after exposure to GQDs. Comparative transcriptome analysis identified 2116 differentially expressed genes between GQDs exposed groups and control. Functional classification demonstrated that a large proportion of genes involved in acute inflammatory responses and detoxifying process were significantly up-regulated by GQDs. The inferred gene regulatory network suggested that activator protein 1 (AP-1) was the early-response transcription factor in the linkage of a cascade of downstream (pro-) inflammatory signals with the apoptosis signals. Moreover, hierarchical signaling threshold determined the high sensitivity of complement system in zebrafish when exposed to the sublethal dose of GQDs. Further, 35 candidate genes from various signaling pathways were further validated by qPCR after exposure to 25, 50, and 100 μg/mL of GQDs. Taken together, our study provided a valuable insight into the molecular mechanisms of potential bleeding risks and detoxifying processes in response to GQDs exposure, thereby establishing a mechanistic basis for the biosafety evaluation of GQDs. Copyright © 2018 Elsevier B.V. All rights reserved.

Theorising and testing environmental pathways to behaviour change: natural experimental study of the perception and use of new infrastructure to promote walking and cycling in local communities.

PubMed

Panter, Jenna; Ogilvie, David

2015-09-03

Some studies have assessed the effectiveness of environmental interventions to promote physical activity, but few have examined how such interventions work. We investigated the environmental mechanisms linking an infrastructural intervention with behaviour change. Natural experimental study. Three UK municipalities (Southampton, Cardiff and Kenilworth). Adults living within 5 km of new walking and cycling infrastructure. Construction or improvement of walking and cycling routes. Exposure to the intervention was defined in terms of residential proximity. Questionnaires at baseline and 2-year follow-up assessed perceptions of the supportiveness of the environment, use of the new infrastructure, and walking and cycling behaviours. Analysis proceeded via factor analysis of perceptions of the physical environment (step 1) and regression analysis to identify plausible pathways involving physical and social environmental mediators and refine the intervention theory (step 2) to a final path analysis to test the model (step 3). Participants who lived near and used the new routes reported improvements in their perceptions of provision and safety. However, path analysis (step 3, n=967) showed that the effects of the intervention on changes in time spent walking and cycling were largely (90%) explained by a simple causal pathway involving use of the new routes, and other pathways involving changes in environmental cognitions explained only a small proportion of the effect. Physical improvement of the environment itself was the key to the effectiveness of the intervention, and seeking to change people's perceptions may be of limited value. Studies of how interventions lead to population behaviour change should complement those concerned with estimating their effects in supporting valid causal inference. 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.

Phosphate starvation promoted the accumulation of phenolic acids by inducing the key enzyme genes in Salvia miltiorrhiza hairy roots.

PubMed

Liu, Lin; Yang, DongFeng; Liang, TongYao; Zhang, HaiHua; He, ZhiGui; Liang, ZongSuo

2016-09-01

Phosphate starvation increased the production of phenolic acids by inducing the key enzyme genes in a positive feedback pathway in Saliva miltiorrhiza hairy roots. SPX may be involved in this process. Salvia miltiorrhiza is a wildly popular traditional Chinese medicine used for the treatment of coronary heart diseases and inflammation. Phosphate is an essential plant macronutrient that is often deficient, thereby limiting crop yield. In this study, we investigated the effects of phosphate concentration on the biomass and accumulation of phenolic acid in S. miltiorrhiza. Results show that 0.124 mM phosphate was favorable for plant growth. Moreover, 0.0124 mM phosphate was beneficial for the accumulation of phenolic acids, wherein the contents of danshensu, caffeic acid, rosmarinic acid, and salvianolic acid B were, respectively, 2.33-, 1.02-, 1.68-, and 2.17-fold higher than that of the control. By contrast, 12.4 mM phosphate inhibited the accumulation of phenolic acids. The key enzyme genes in the phenolic acid biosynthesis pathway were investigated to elucidate the mechanism of phosphate starvation-induced increase of phenolic acids. The results suggest that phosphate starvation induced the gene expression from the downstream pathway to the upstream pathway, i.e., a feedback phenomenon. In addition, phosphate starvation response gene SPX (SYG1, Pho81, and XPR1) was promoted by phosphate deficiency (0.0124 mM). We inferred that SPX responded to phosphate starvation, which then affected the expression of later responsive key enzyme genes in phenolic acid biosynthesis, resulting in the accumulation of phenolic acids. Our findings provide a resource-saving and environmental protection strategy to increase the yield of active substance in herbal preparations. The relationship between SPX and key enzyme genes and the role they play in phenolic acid biosynthesis during phosphate deficiency need further studies.

Molecular Signatures of Microbial Metabolism in an Actively Growing, Silicified, Microbial Structure from Yellowstone National Park

NASA Astrophysics Data System (ADS)

Ferreira, M.; Creveling, J.; Hilburn, I.; Karlsson, E.; Pepe-Ranney, C.; Spear, J.; Dawson, S.; Geobio2008, I.

2008-12-01

Silicified structures that exhibit a putative biologic component in their formation permeate the rock record as stromatolites. We have studied a silicified microbial structure from a hot spring in Yellowstone National Park using phenotypic, phylogenetic, and metagenomic analyses to determine microbial carbon metabolic pathways and the phylogenetic affiliations of microbes present in this unique structure. In this multi-faceted approach, dominant physiologies, specifically with regards to anaerobic and aerobic metabolisms, were inferred from 16S rRNA gene sequences and 454 sequencing data from bulk DNA samples of the structure. Carbon utilization as indicated by ECO Biolog plates showed abundant heterotrophy and heterotrophic diversity throughout the microbial structure. Microbes within the structure are able to utilize all tested sources of carbohydrates, lipids/fatty acids, and protein/amino acids as carbon sources. ECO plate testing of the hot spring water yielded considerable less carbohydrate consumption (only 4 out of 13 tested carbohydrates) and similar lipids/fatty acids and protein/amino acids consumption (2 out of 3 and 5 out of 5 tested sources respectively). Full length 16S rRNA gene sequences and metagenomic 454 pyrosequencing of community DNA showed limited diversity among primary producers. From the 16S data, the majority of the autotrophs are inferred to utilize the Calvin cycle for CO2 fixation, followed by 3-hydroxypropionate/4- hydroxybutyrate CO2 fixation. However, an analysis of the metagenomic data compared to the KEGG database does not show genes directly involved with Calvin cycle carbon fixation. Further BLAST searches of our data failed to find significant matches within our 6514 metagenomic sequences to known RuBisCo sequences taken from the NCBI database. This is likely due to a far under-sampled dataset of metagenomic sequences, and the low number (958) that had matches to the KEGG pathways database. Anaerobic versus aerobic physiology also can be estimated from the 16S clone libraries. Phylogenetic analysis of recovered 16S sequences suggests that 15% of the 16S sequences can be attributed to anaerobic microbes while 42% likely come from aerobes. The remaining 43% of 16S rRNA gene sequences belong to metabolically unassigned phyla both known and novel. This preliminary study demonstrates that the small spatially stratified silicified microbial structure present on the margins of a hot spring contains a rich and complex microbial community with different trophic levels and enzymatic pathways.

Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

PubMed Central

Klein, Allon M; Mazutis, Linas; Akartuna, Ilke; Tallapragada, Naren; Veres, Adrian; Li, Victor; Peshkin, Leonid; Weitz, David A; Kirschner, Marc W

2015-01-01

Summary It has long been the dream of biologists to map gene expression at the single cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility of these high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. PMID:26000487

Exploiting single-cell variability to infer the dynamics of immune responses

NASA Astrophysics Data System (ADS)

Höfer, Thomas

Cell division, differentiation, migration and death determine the dynamics of immune responses. These processes are regulated by a multitude of biochemical signals which, at present, cannot faithfully be reconstituted outside the living organism. However, quantitative measurements in living organisms have been limited. In recent years experimental techniques for the ``fate mapping'' of single immune cells have been developed that allow performing parallel single-cell experiments in an experimental animal. The resulting data are more informative about underlying biological processes than traditional measurements. I will show how the theory of stochastic dynamical systems can be used to infer the topology and dynamics of cell differentiation pathways from such data. The focus will be on joint theoretical and experimental work addressing: (i) the development of immune cells during hematopoiesis, and (ii) T cell responses to diverse pathogens. I will discuss questions on the nature of cellular variability that are posed by these new findings.

Potential for microbial H2 and metal transformations associated with novel bacteria and archaea in deep terrestrial subsurface sediments.

PubMed

Hernsdorf, Alex W; Amano, Yuki; Miyakawa, Kazuya; Ise, Kotaro; Suzuki, Yohey; Anantharaman, Karthik; Probst, Alexander; Burstein, David; Thomas, Brian C; Banfield, Jillian F

2017-08-01

Geological sequestration in deep underground repositories is the prevailing proposed route for radioactive waste disposal. After the disposal of radioactive waste in the subsurface, H 2 may be produced by corrosion of steel and, ultimately, radionuclides will be exposed to the surrounding environment. To evaluate the potential for microbial activities to impact disposal systems, we explored the microbial community structure and metabolic functions of a sediment-hosted ecosystem at the Horonobe Underground Research Laboratory, Hokkaido, Japan. Overall, we found that the ecosystem hosted organisms from diverse lineages, including many from the phyla that lack isolated representatives. The majority of organisms can metabolize H 2 , often via oxidative [NiFe] hydrogenases or electron-bifurcating [FeFe] hydrogenases that enable ferredoxin-based pathways, including the ion motive Rnf complex. Many organisms implicated in H 2 metabolism are also predicted to catalyze carbon, nitrogen, iron and sulfur transformations. Notably, iron-based metabolism is predicted in a novel lineage of Actinobacteria and in a putative methane-oxidizing ANME-2d archaeon. We infer an ecological model that links microorganisms to sediment-derived resources and predict potential impacts of microbial activity on H 2 consumption and retardation of radionuclide migration.

Genome-wide methylation analysis identified sexually dimorphic methylated regions in hybrid tilapia

PubMed Central

Wan, Zi Yi; Xia, Jun Hong; Lin, Grace; Wang, Le; Lin, Valerie C. L.; Yue, Gen Hua

2016-01-01

Sexual dimorphism is an interesting biological phenomenon. Previous studies showed that DNA methylation might play a role in sexual dimorphism. However, the overall picture of the genome-wide methylation landscape in sexually dimorphic species remains unclear. We analyzed the DNA methylation landscape and transcriptome in hybrid tilapia (Oreochromis spp.) using whole genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq). We found 4,757 sexually dimorphic differentially methylated regions (DMRs), with significant clusters of DMRs located on chromosomal regions associated with sex determination. CpG methylation in promoter regions was negatively correlated with the gene expression level. MAPK/ERK pathway was upregulated in male tilapia. We also inferred active cis-regulatory regions (ACRs) in skeletal muscle tissues from WGBS datasets, revealing sexually dimorphic cis-regulatory regions. These results suggest that DNA methylation contribute to sex-specific phenotypes and serve as resources for further investigation to analyze the functions of these regions and their contributions towards sexual dimorphisms. PMID:27782217

"What" and "where" in word reading: ventral coding of written words revealed by parietal atrophy.

PubMed

Vinckier, Fabien; Naccache, Lionel; Papeix, Caroline; Forget, Joaquim; Hahn-Barma, Valerie; Dehaene, Stanislas; Cohen, Laurent

2006-12-01

The visual system of literate adults develops a remarkable perceptual expertise for printed words. To delineate the aspects of this competence intrinsic to the occipitotemporal "what" pathway, we studied a patient with bilateral lesions of the occipitoparietal "where" pathway. Depending on critical geometric features of the display (rotation angle, letter spacing, mirror reversal, etc.), she switched from a good performance, when her intact ventral pathway was sufficient to encode words, to severely impaired reading, when her parietal lesions prevented the use of alternative reading strategies as a result of spatial and attentional impairments. In particular, reading was disrupted (a) by rotating word by more than 50 degrees , providing an approximation of the invariance range for words encoding in the ventral pathway; (b) by separating letters with double spaces, revealing the limits of letter grouping into perceptual wholes; (c) by mirror-reversing words, showing that words escape the default mirror-invariant representation of visual objects in the ventral pathway. Moreover, because of her parietal lesions, she was unable to discriminate mirror images of common objects, although she was excellent with reversible pseudowords, confirming that the breaking of mirror symmetry was intrinsic to the occipitotemporal cortex. Thus, charting the display conditions associated with preserved or impaired performance allowed us to infer properties of word coding in the normal ventral pathway and to delineate the roles of the parietal lobes in single-word recognition.

Inferring Invasion History of Red Swamp Crayfish (Procambarus clarkii) in China from Mitochondrial Control Region and Nuclear Intron Sequences

PubMed Central

Li, Yanhe; Guo, Xianwu; Chen, Liping; Bai, Xiaohui; Wei, Xinlan; Zhou, Xiaoyun; Huang, Songqian; Wang, Weimin

2015-01-01

Identifying the dispersal pathways of an invasive species is useful for adopting the appropriate strategies to prevent and control its spread. However, these processes are exceedingly complex. So, it is necessary to apply new technology and collect representative samples for analysis. This study used Approximate Bayesian Computation (ABC) in combination with traditional genetic tools to examine extensive sample data and historical records to infer the invasion history of the red swamp crayfish, Procambarus clarkii, in China. The sequences of the mitochondrial control region and the proPOx intron in the nuclear genome of samples from 37 sites (35 in China and one each in Japan and the USA) were analyzed. The results of combined scenarios testing and historical records revealed a much more complex invasion history in China than previously believed. P. clarkii was most likely originally introduced into China from Japan from an unsampled source, and the species then expanded its range primarily into the middle and lower reaches and, to a lesser extent, into the upper reaches of the Changjiang River in China. No transfer was observed from the upper reaches to the middle and lower reaches of the Changjiang River. Human-mediated jump dispersal was an important dispersal pathway for P. clarkii. The results provide a better understanding of the evolutionary scenarios involved in the rapid invasion of P. clarkii in China. PMID:26132567

Gang membership and substance use: guilt as a gendered causal pathway

PubMed Central

Coffman, Donna L.; Melde, Chris; Esbensen, Finn-Aage

2014-01-01

Objectives We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. Methods We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth (N=1,113). Results The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Conclusions Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors. PMID:26190954

Plant evolution at the interface of paleontology and developmental biology: An organism-centered paradigm.

PubMed

Rothwell, Gar W; Wyatt, Sarah E; Tomescu, Alexandru M F

2014-06-01

Paleontology yields essential evidence for inferring not only the pattern of evolution, but also the genetic basis of evolution within an ontogenetic framework. Plant fossils provide evidence for the pattern of plant evolution in the form of transformational series of structure through time. Developmentally diagnostic structural features that serve as "fingerprints" of regulatory genetic pathways also are preserved by plant fossils, and here we provide examples of how those fingerprints can be used to infer the mechanisms by which plant form and development have evolved. When coupled with an understanding of variations and systematic distributions of specific regulatory genetic pathways, this approach provides an avenue for testing evolutionary hypotheses at the organismal level that is analogous to employing bioinformatics to explore genetics at the genomic level. The positions where specific genes, gene families, and developmental regulatory mechanisms first appear in phylogenies are correlated with the positions where fossils with the corresponding structures occur on the tree, thereby yielding testable hypotheses that extend our understanding of the role of developmental changes in the evolution of the body plans of vascular plant sporophytes. As a result, we now have new and powerful methodologies for characterizing major evolutionary changes in morphology, anatomy, and physiology that have resulted from combinations of genetic regulatory changes and that have produced the synapomorphies by which we recognize major clades of plants. © 2014 Botanical Society of America, Inc.

Gang membership and substance use: guilt as a gendered causal pathway.

PubMed

Coffman, Donna L; Melde, Chris; Esbensen, Finn-Aage

2015-03-01

We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth ( N =1,113). The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors.

Suppression of the alternative lengthening of telomere pathway by the chromatin remodelling factor ATRX

PubMed Central

Clynes, David; Jelinska, Clare; Xella, Barbara; Ayyub, Helena; Scott, Caroline; Mitson, Matthew; Taylor, Stephen; Higgs, Douglas R.; Gibbons, Richard J.

2015-01-01

Fifteen per cent of cancers maintain telomere length independently of telomerase by the homologous recombination (HR)-associated alternative lengthening of telomeres (ALT) pathway. A unifying feature of these tumours are mutations in ATRX. Here we show that expression of ectopic ATRX triggers a suppression of the pathway and telomere shortening. Importantly ATRX-mediated ALT suppression is dependent on the histone chaperone DAXX. Re-expression of ATRX is associated with a reduction in replication fork stalling, a known trigger for HR and loss of MRN from telomeres. A G-quadruplex stabilizer partially reverses the effect of ATRX, inferring ATRX may normally facilitate replication through these sequences that, if they persist, promote ALT. We propose that defective telomere chromatinization through loss of ATRX promotes the persistence of aberrant DNA secondary structures, which in turn present a barrier to DNA replication, leading to replication fork stalling, collapse, HR and subsequent recombination-mediated telomere synthesis in ALT cancers. PMID:26143912

Evolutionary Proteomics Uncovers Ancient Associations of Cilia with Signaling Pathways.

PubMed

Sigg, Monika Abedin; Menchen, Tabea; Lee, Chanjae; Johnson, Jeffery; Jungnickel, Melissa K; Choksi, Semil P; Garcia, Galo; Busengdal, Henriette; Dougherty, Gerard W; Pennekamp, Petra; Werner, Claudius; Rentzsch, Fabian; Florman, Harvey M; Krogan, Nevan; Wallingford, John B; Omran, Heymut; Reiter, Jeremy F

2017-12-18

Cilia are organelles specialized for movement and signaling. To infer when during evolution signaling pathways became associated with cilia, we characterized the proteomes of cilia from sea urchins, sea anemones, and choanoflagellates. We identified 437 high-confidence ciliary candidate proteins conserved in mammals and discovered that Hedgehog and G-protein-coupled receptor pathways were linked to cilia before the origin of bilateria and transient receptor potential (TRP) channels before the origin of animals. We demonstrated that candidates not previously implicated in ciliary biology localized to cilia and further investigated ENKUR, a TRP channel-interacting protein identified in the cilia of all three organisms. ENKUR localizes to motile cilia and is required for patterning the left-right axis in vertebrates. Moreover, mutation of ENKUR causes situs inversus in humans. Thus, proteomic profiling of cilia from diverse eukaryotes defines a conserved ciliary proteome, reveals ancient connections to signaling, and uncovers a ciliary protein that underlies development and human disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Mapping remodeling of thalamocortical projections in the living reeler mouse brain by diffusion tractography

PubMed Central

Harsan, Laura-Adela; Dávid, Csaba; Reisert, Marco; Schnell, Susanne; Hennig, Jürgen; von Elverfeldt, Dominik; Staiger, Jochen F.

2013-01-01

A major challenge in neuroscience is to accurately decipher in vivo the entire brain circuitry (connectome) at a microscopic level. Currently, the only methodology providing a global noninvasive window into structural brain connectivity is diffusion tractography. The extent to which the reconstructed pathways reflect realistic neuronal networks depends, however, on data acquisition and postprocessing factors. Through a unique combination of approaches, we designed and evaluated herein a framework for reliable fiber tracking and mapping of the living mouse brain connectome. One important wiring scheme, connecting gray matter regions and passing fiber-crossing areas, was closely examined: the lemniscal thalamocortical (TC) pathway. We quantitatively validated the TC projections inferred from in vivo tractography with correlative histological axonal tracing in the same wild-type and reeler mutant mice. We demonstrated noninvasively that changes in patterning of the cortical sheet, such as highly disorganized cortical lamination in reeler, led to spectacular compensatory remodeling of the TC pathway. PMID:23610438

Characterization of galactomannan derivatives in roasted coffee beverages.

PubMed

Nunes, Fernando M; Reis, Ana; Domingues, M Rosário M; Coimbra, Manuel A

2006-05-03

In this work, the galactomannans from roasted coffee infusions were purified by 50% ethanol precipitation, anion exchange chromatography, and phenylboronic acid-immobilized Sepharose chromatography. Specific enzymatic hydrolysis of the beta-(1-->4)-D-mannan backbone allowed us to conclude that the galactomannans of roasted coffee infusions are high molecular weight supports of low molecular weight brown compounds. Also, the molecular weight of the brown compounds linked to the galactomannan increases with the increase of the coffee degree of roast. The reaction pathways of galactomannans during the coffee roasting process were inferred from the detection of specific chemical markers by gas chromatography-electron impact mass spectrometry and/or electrospray ionization tandem mass spectrometry. Maillard reaction, caramelization, isomerization, oxidation, and decarboxylation pathways were identified by detection of Amadori compounds, 1,6-beta-anhydromannose, fructose, glucose, mannonic acid, 2-ketogluconic acid, and arabinonic acid in the reducing end of the obtained oligosaccharides. The implication of the several competitive reaction pathways is discussed and related to the structural changes of the galactomannans present in the roasted coffee infusions.

Protein-driven inference of miRNA–disease associations

PubMed Central

Mørk, Søren; Pletscher-Frankild, Sune; Palleja Caro, Albert; Gorodkin, Jan; Jensen, Lars Juhl

2014-01-01

Motivation: MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA–disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer. Results: Here we present miRPD in which miRNA–Protein–Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA–protein associations with protein–disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA–disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis. Availability and implementation: All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org. Contact: lars.juhl.jensen@cpr.ku.dk or gorodkin@rth.dk PMID:24273243

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study.

PubMed

DeMasi, Orianna; Feygin, Sidney; Dembo, Aluma; Aguilera, Adrian; Recht, Benjamin

2017-10-05

Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel and ubiquitous opportunity to track individuals' behavior and may be useful for inferring and automatically monitoring mental well-being. The aim of this study was to assess the extent to which activity and sleep tracking with a smartphone can be used for monitoring individuals' mental well-being. A cohort of 106 individuals was recruited to install an app on their smartphone that would track their well-being with daily surveys and track their behavior with activity inferences from their phone's accelerometer data. Of the participants recruited, 53 had sufficient data to infer activity and sleep measures. For this subset of individuals, we related measures of activity and sleep to the individuals' well-being and used these measures to predict their well-being. We found that smartphone-measured approximations for daily physical activity were positively correlated with both mood (P=.004) and perceived energy level (P<.001). Sleep duration was positively correlated with mood (P=.02) but not energy. Our measure for sleep disturbance was not found to be significantly related to either mood or energy, which could imply too much noise in the measurement. Models predicting the well-being measures from the activity and sleep measures were found to be significantly better than naive baselines (P<.01), despite modest overall improvements. Measures of activity and sleep inferred from smartphone activity were strongly related to and somewhat predictive of participants' well-being. Whereas the improvement over naive models was modest, it reaffirms the importance of considering physical activity and sleep for predicting mood and for making automatic mood monitoring a reality. ©Orianna DeMasi, Sidney Feygin, Aluma Dembo, Adrian Aguilera, Benjamin Recht. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 05.10.2017.

Artemisinin dimer anti-cancer activity correlates with heme-catalyzed ROS generation and ER stress induction

PubMed Central

Stockwin, Luke H.; Han, Bingnan; Yu, Sherry X.; Hollingshead, Melinda G.; ElSohly, Mahmoud A.; Gul, Waseem; Slade, Desmond; Galal, Ahmed M.; Newton, Dianne L.

2009-01-01

Analogs of the malaria therapeutic, artemisinin, possess in vitro and in vivo anti-cancer activity. In this study, two dimeric artemisinins (NSC724910 and 735847) were studied to determine their mechanism of action. Dimers were >1000 fold more active than monomer and treatment was associated with increased reactive oxygen species (ROS) and apoptosis induction. Dimer activity was inhibited by the anti-oxidant L-NAC, the iron chelator desferroxamine, and exogenous hemin. Similarly, induction of heme oxygenase (HMOX) with CoPPIX inhibited activity while inhibition of HMOX with SnPPIX enhanced it. These results emphasize the importance of iron, heme and ROS in activity. Microarray analysis of dimer treated cells identified DNA damage; iron/heme and cysteine/methionine metabolism, antioxidant response, and endoplasmic reticulum (ER) stress as affected pathways. Detection of an ER-stress response was relevant because in malaria, artemisinin inhibits pfATP6, the plasmodium orthologue of mammalian ER-resident SERCA Ca2+-ATPases. A comparative study of NSC735847 with thapsigargin, a specific SERCA inhibitor and ER-stress inducer showed similar behavior in terms of transcriptomic changes, induction of endogenous SERCA and ER calcium mobilization. However, thapsigargin had little effect on ROS production, modulated different ER-stress proteins and had greater potency against purified SERCA1. Furthermore, an inactive derivative of NSC735847 that lacked the endoperoxide had identical inhibitory activity against purified SERCA1, suggesting that direct inhibition of SERCA has little inference on overall cytotoxicity. In summary, these data implicate indirect ER-stress induction as a central mechanism of artemisinin dimer activity. PMID:19533749

Descending pathways controlling visually guided updating of reaching in cats.

PubMed

Pettersson, L-G; Perfiliev, S

2002-10-01

This study uses a previously described paradigm (Pettersson et al., 1997) to investigate the ability of cats to change the direction of ongoing reaching when the target is shifted sideways; the effect on the switching latency of spinal cord lesions was investigated. Large ventral lesions transecting the ventral funicle and the ventral half of the lateral funicle gave a 20-30 ms latency prolongation of switching in the medial (right) direction, but less prolongation of switching directed laterally (left), and in one cat the latencies of switching directed laterally were unchanged. It may be inferred that the command for switching in the lateral direction can be mediated by the dorsally located cortico- and rubrospinal tracts whereas the command for short-latency switching in the medial direction is mediated by ventral pathways. A restricted ventral lesion transecting the tectospinal pathway did not change the switching latency. Comparison of different ventral lesions revealed prolongation of the latency if the lesion included a region extending dorsally along the ventral horn and from there ventrally as a vertical strip, so it may be postulated that the command for fast switching, directed medially, is mediated by a reticulospinal pathway within this location. A hypothesis is forwarded suggesting that the visual control is exerted via ponto-cerebellar pathways.

Discovery of cancer common and specific driver gene sets

PubMed Central

2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

Inferring network structure from cascades.

PubMed

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

Inferring network structure from cascades

NASA Astrophysics Data System (ADS)

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

Everyday conversation requires cognitive inference: neural bases of comprehending implicated meanings in conversations.

PubMed

Jang, Gijeong; Yoon, Shin-ae; Lee, Sung-Eun; Park, Haeil; Kim, Joohan; Ko, Jeong Hoon; Park, Hae-Jeong

2013-11-01

In ordinary conversations, literal meanings of an utterance are often quite different from implicated meanings and the inference about implicated meanings is essentially required for successful comprehension of the speaker's utterances. Inference of finding implicated meanings is based on the listener's assumption that the conversational partner says only relevant matters according to the maxim of relevance in Grice's theory of conversational implicature. To investigate the neural correlates of comprehending implicated meanings under the maxim of relevance, a total of 23 participants underwent an fMRI task with a series of conversational pairs, each consisting of a question and an answer. The experimental paradigm was composed of three conditions: explicit answers, moderately implicit answers, and highly implicit answers. Participants were asked to decide whether the answer to the Yes/No question meant 'Yes' or 'No'. Longer reaction time was required for the highly implicit answers than for the moderately implicit answers without affecting the accuracy. The fMRI results show that the left anterior temporal lobe, left angular gyrus, and left posterior middle temporal gyrus had stronger activation in both moderately and highly implicit conditions than in the explicit condition. Comprehension of highly implicit answers had increased activations in additional regions including the left inferior frontal gyrus, left medial prefrontal cortex, left posterior cingulate cortex and right anterior temporal lobe. The activation results indicate involvement of these regions in the inference process to build coherence between literally irrelevant but pragmatically associated utterances under the maxim of relevance. Especially, the left anterior temporal lobe showed high sensitivity to the level of implicitness and showed increased activation for highly versus moderately implicit conditions, which imply its central role in inference such as semantic integration. The right hemisphere activation, uniquely found in the anterior temporal lobe for highly implicit utterances, suggests its competence for integrating distant concepts in implied utterances under the relevance principle. Copyright © 2013 Elsevier Inc. All rights reserved.

Several immune escape patterns in non-Hodgkin's lymphomas

PubMed Central

Laurent, Camille; Charmpi, Konstantina; Gravelle, Pauline; Tosolini, Marie; Franchet, Camille; Ysebaert, Loïc; Brousset, Pierre; Bidaut, Alexandre; Ycart, Bernard; Fournié, Jean-Jacques

2015-01-01

Follicular Lymphomas (FL) and diffuse large B cell lymphomas (DLBCL) must evolve some immune escape strategy to develop from lymphoid organs, but their immune evasion pathways remain poorly characterized. We investigated this issue by transcriptome data mining and immunohistochemistry (IHC) of FL and DLBCL lymphoma biopsies. A set of genes involved in cancer immune-evasion pathways (Immune Escape Gene Set, IEGS) was defined and the distribution of the expression levels of these genes was compared in FL, DLBCL and normal B cell transcriptomes downloaded from the GEO database. The whole IEGS was significantly upregulated in all the lymphoma samples but not in B cells or other control tissues, as shown by the overexpression of the PD-1, PD-L1, PD-L2 and LAG3 genes. Tissue microarray immunostainings for PD-1, PD-L1, PD-L2 and LAG3 proteins on additional biopsies from 27 FL and 27 DLBCL patients confirmed the expression of these proteins. The immune infiltrates were more abundant in FL than DLBCL samples, and the microenvironment of FL comprised higher rates of PD-1+ lymphocytes. Further, DLBCL tumor cells comprised a higher proportion of PD-1+, PD-L1+, PD-L2+ and LAG3+ lymphoma cells than the FL tumor cells, confirming that DLBCL mount immune escape strategies distinct from FL. In addition, some cases of DLBCL had tumor cells co-expressing both PD-1, PD-L1 and PD-L2. Among the DLBCLs, the activated B cell (ABC) subtype comprised more PD-L1+ and PD-L2+ lymphoma cells than the GC subtype. Thus, we infer that FL and DLBCL evolved several pathways of immune escape. PMID:26405585

Constraining the SIF - GPP relationship via estimation of NPQ

NASA Astrophysics Data System (ADS)

Silva, C. E.; Yang, X.; Tang, J.; Lee, J. E.; Cushman, K.; Toh Yuan Kun, L.; Kellner, J. R.

2016-12-01

Airborne and satellite measurements of solar-induced fluorescence (SIF) have the potential to improve estimates of gross primary production (GPP). Plants dissipate absorbed photosynthetically active radiation (APAR) among three de-excitation pathways: SIF, photochemical quenching (PQ), which results in electron transport and the production of ATP and NADPH consumed during carbon fixation (i.e., GPP), and heat dissipation via conversion of xanthophyll pigments (non-photochemical quenching: NPQ). As a result, the relationship between SIF and GPP is a function of NPQ and may vary temporally and spatially with environmental conditions (e.g., light and water availability) and plant traits (e.g., leaf N content). Accurate estimates of any one of the de-excitation pathways require measurement of the other two. Here we combine half-hourly measurements of canopy APAR and SIF with eddy covariance estimates of GPP at Harvard Forest to close the canopy radiation budget and infer canopy NPQ throughout the 2013 growing season. We use molecular-level photosynthesis equations to compute PQ (umol photons m-2s-1) from GPP (umol CO2 m-2s-1) and invert an integrated canopy radiative transfer and leaf-level photosynthesis/fluorescence model (SCOPE) to quantify hemispherically and spectrally-integrated SIF emission (umol photons m-2s-1) from single band (760 nm) top-of-canopy SIF measurements. We estimate half-hourly NPQ as the residual required to close the radiation budget (NPQ = APAR - SIF - PQ). Our future work will test estimated NPQ against simultaneously acquired measurements of the photochemical reflectance index (PRI), a spectral index sensitive to xanthopyll pigments. By constraining two of the three de-excitation pathways, simultaneous SIF and PRI measurements are likely to improve GPP estimates, which are crucial to the study of climate - carbon cycle interactions.

Double stranded viral RNA induces inflammation and insulin resistance in skeletal muscle from pregnant women in vitro.

PubMed

Lappas, Martha

2015-05-01

Maternal peripheral insulin resistance and increased inflammation are two features of pregnancies complicated by pre-existing maternal obesity and gestational diabetes mellitus (GDM). There is now increasing evidence that activation of Toll-like receptor (TLR) signalling pathways by viral products may play a role in the pathophysiology of diabetes. Thus, the aim of this study was to assess the effect of the TLR3 ligand and viral dsRNA analogue polyinosinic polycytidilic acid (poly(I:C)) on inflammation and the insulin signalling pathway in skeletal muscle from pregnant women. Human skeletal muscle tissue explants were performed to determine the effect of poly(I:C) on the expression and secretion of markers of inflammation, and the insulin signalling pathway and glucose uptake. Poly(I:C) significantly increased the expression of a number of inflammatory markers in skeletal muscle from pregnant women. Specifically, there was an increase in the expression and/or secretion of the pro-inflammatory cytokines TNF-α, and IL-6 and the pro-inflammatory chemokines IL-8 and MCP-1. These effect of poly(I:C) appear to mediated via a number of signalling molecules including the pro-inflammatory transcription factor NF-κB, and the serine threonine kinases GSK3 and AMPKα. Additionally, poly(I:C) decreased insulin stimulated GLUT-4 expression and glucose uptake in skeletal muscle from pregnant women. The in vitro data presented in this study suggests that viral infection may contribute to the pathophysiology of pregnancies complicated by pre-existing maternal obesity and/or GDM. It should be noted that the in vitro studies cannot be directly used to infer the same outcomes in the intact subject. Copyright © 2015 Elsevier Inc. All rights reserved.

Causal Loop Analysis of coastal geomorphological systems

NASA Astrophysics Data System (ADS)

Payo, Andres; Hall, Jim W.; French, Jon; Sutherland, James; van Maanen, Barend; Nicholls, Robert J.; Reeve, Dominic E.

2016-03-01

As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion-accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a model, the modeller can readily assess if critical feedback loops are included.

Wnt signalling pathway parameters for mammalian cells.

PubMed

Tan, Chin Wee; Gardiner, Bruce S; Hirokawa, Yumiko; Layton, Meredith J; Smith, David W; Burgess, Antony W

2012-01-01

Wnt/β-catenin signalling regulates cell fate, survival, proliferation and differentiation at many stages of mammalian development and pathology. Mutations of two key proteins in the pathway, APC and β-catenin, have been implicated in a range of cancers, including colorectal cancer. Activation of Wnt signalling has been associated with the stabilization and nuclear accumulation of β-catenin and consequential up-regulation of β-catenin/TCF gene transcription. In 2003, Lee et al. constructed a computational model of Wnt signalling supported by experimental data from analysis of time-dependent concentration of Wnt signalling proteins in Xenopus egg extracts. Subsequent studies have used the Xenopus quantitative data to infer Wnt pathway dynamics in other systems. As a basis for understanding Wnt signalling in mammalian cells, a confocal live cell imaging measurement technique is developed to measure the cell and nuclear volumes of MDCK, HEK293T cells and 3 human colorectal cancer cell lines and the concentrations of Wnt signalling proteins β-catenin, Axin, APC, GSK3β and E-cadherin. These parameters provide the basis for formulating Wnt signalling models for kidney/intestinal epithelial mammalian cells. There are significant differences in concentrations of key proteins between Xenopus extracts and mammalian whole cell lysates. Higher concentrations of Axin and lower concentrations of APC are present in mammalian cells. Axin concentrations are greater than APC in kidney epithelial cells, whereas in intestinal epithelial cells the APC concentration is higher than Axin. Computational simulations based on Lee's model, with this new data, suggest a need for a recalibration of the model.A quantitative understanding of Wnt signalling in mammalian cells, in particular human colorectal cancers requires a detailed understanding of the concentrations of key protein complexes over time. Simulations of Wnt signalling in mammalian cells can be initiated with the parameters measured in this report.

Fluid pathways from mantle wedge up to forearc seafloor in the coseismic slip area of the 2011 Tohoku earthquake

NASA Astrophysics Data System (ADS)

Park, J. O.; Tsuru, T.; Fujie, G.; Kagoshima, T.; Sano, Y.

2017-12-01

A lot of fluids at subduction zones are exchanged between the solid Earth and ocean, affecting the earthquake and tsunami generation. New multi-channel seismic reflection and sub-bottom profiling data reveal normal and reverse faults as the fluid pathways in the coseismic slip area of the 2011 Tohoku earthquake (M9.0). Based on seismic reflection characteristics and helium isotope anomalies, we recognize variations in fluid pathways (i.e., faults) from the mantle wedge up to forearc seafloor in the Japan Trench margin. Some fluids are migrated from the mantle wedge along plate interface and then normal or reverse faults cutting through the overriding plate. Others from the mantle wedge are migrated directly up to seafloor along normal faults, without passing through the plate interface. Locations of the normal faults are roughly consistent with aftershocks of the 2011 Tohoku earthquake, which show focal mechanism of normal faulting. It is noticeable that landward-dipping normal faults developing down into Unit C (Cretaceous basement) from seafloor are dominant in the middle slope region where basal erosion is inferred to be most active. A high-amplitude, reverse-polarity reflection of the normal faults within Unit C suggests that the fluids are locally trapped along the faults in high pore pressures. The 2011 Tohoku mainshock and subsequent aftershocks could lead the pre-existing normal faults to be reactive and more porous so that the trapped fluids are easily transported up to seafloor through the faults. Elevated fluid pressures can decrease the effective normal stress for the fault plane, allowing easier slip of the landward-dipping normal fault and also enhancing its tsunamigenic potential.

An interactive activation and competition model of person knowledge, suggested by proactive interference by traits spontaneously inferred from behaviours.

PubMed

Wang, Yuanbo E; Higgins, Nancy C; Uleman, James S; Michaux, Aaron; Vipond, Douglas

2016-03-01

People unconsciously and unintentionally make inferences about others' personality traits based on their behaviours. In this study, a classic memory phenomenon--proactive interference (PI)--is for the first time used to detect spontaneous trait inferences. PI should occur when lists of behaviour descriptions, all implying the same trait, are to be remembered. Switching to a new trait should produce 'release' from proactive interference (or RPI). Results from two experiments supported these predictions. PI and RPI effects are consistent with an interactive activation and competition model of person perception (e.g., McNeill & Burton, 2002, J. Exp. Psychol., 55A, 1141), which predicts categorical organization of social behaviours based on personality traits. Advantages of this model are discussed. © 2015 The British Psychological Society.

A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD).

PubMed

Sokolova, Elena; Oerlemans, Anoek M; Rommelse, Nanda N; Groot, Perry; Hartman, Catharina A; Glennon, Jeffrey C; Claassen, Tom; Heskes, Tom; Buitelaar, Jan K

2017-06-01

Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are often comorbid. The purpose of this study is to explore the relationships between ASD and ADHD symptoms by applying causal modeling. We used a large phenotypic data set of 417 children with ASD and/or ADHD, 562 affected and unaffected siblings, and 414 controls, to infer a structural equation model using a causal discovery algorithm. Three distinct pathways between ASD and ADHD were identified: (1) from impulsivity to difficulties with understanding social information, (2) from hyperactivity to stereotypic, repetitive behavior, (3) a pairwise pathway between inattention, difficulties with understanding social information, and verbal IQ. These findings may inform future studies on understanding the pathophysiological mechanisms behind the overlap between ASD and ADHD.

Biologically Inspired Model for Inference of 3D Shape from Texture

PubMed Central

Gomez, Olman; Neumann, Heiko

2016-01-01

A biologically inspired model architecture for inferring 3D shape from texture is proposed. The model is hierarchically organized into modules roughly corresponding to visual cortical areas in the ventral stream. Initial orientation selective filtering decomposes the input into low-level orientation and spatial frequency representations. Grouping of spatially anisotropic orientation responses builds sketch-like representations of surface shape. Gradients in orientation fields and subsequent integration infers local surface geometry and globally consistent 3D depth. From the distributions in orientation responses summed in frequency, an estimate of the tilt and slant of the local surface can be obtained. The model suggests how 3D shape can be inferred from texture patterns and their image appearance in a hierarchically organized processing cascade along the cortical ventral stream. The proposed model integrates oriented texture gradient information that is encoded in distributed maps of orientation-frequency representations. The texture energy gradient information is defined by changes in the grouped summed normalized orientation-frequency response activity extracted from the textured object image. This activity is integrated by directed fields to generate a 3D shape representation of a complex object with depth ordering proportional to the fields output, with higher activity denoting larger distance in relative depth away from the viewer. PMID:27649387

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

PubMed

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into distinctive associations between pathway activities in case and control samples.

Pim-2 activates API-5 to inhibit the apoptosis of hepatocellular carcinoma cells through NF-kappaB pathway.

PubMed

Ren, Ke; Zhang, Wei; Shi, Yujun; Gong, Jianping

2010-06-01

Pim-2 is proved to be relevant to the tumorigenesis of hepatocellular carcinoma (HCC), but the mechanism is unclear. We studied the relationship among Pim-2, NF-kappaB and API-5. In our experiment, expression level of the three factors and phosphorylation level of API-5, as well as NF-kappaB activity, were detected in HCC tissues and the nontumorous controls. Then Pim-2 gene was transfected into nontumorous liver cells L02, and Pim-2 SiRNA was transfected into hepatoblastoma cell line HepG2. Parthenolide was added as NF-kappaB inhibitor. The same detections as above were repeated in the cells, along with the apoptosis analysis. We found the levels of Pim-2, NF-kappaB and API-5, as well as NF-kappaB activity, were significantly higher in HCC tissues. Pim-2 level was increased in L02 cells after the transfection of Pim-2 gene, but decreased in HepG2 cells after the transfection of Pim-2 SiRNA. The levels of NF-kappaB and API-5, as well as NF-kappaB activity and API-5 phosphorylation level, were in accordance with Pim-2 level, but could be reversed by Parthenolide. Cell apoptosis rates were negatively correlated with API-5 phosphorylation level. Therefore, we infer that Pim-2 could activate API-5 to inhibit the apoptosis of liver cells, and NF-kappaB is the key regulator.

Continuous release of bone morphogenetic protein-2 through nano-graphene oxide-based delivery influences the activation of the NF-κB signal transduction pathway

PubMed Central

Zhong, Cheng; Feng, Jun; Lin, Xiangjin; Bao, Qi

2017-01-01

Graphene oxide (GO) has been used as a delivery vehicle for small molecule drugs and nucleotides. To further investigate GO as a smart biomaterial for the controlled release of cargo molecules, we hypothesized that GO may be an appropriate delivery vehicle because it releases bone morphogenetic protein 2 (BMP2). GO characterization indicated that the size distribution of the GO flakes ranged from 81.1 nm to 45,749.7 nm, with an approximate thickness of 2 nm. After BMP2 adsorption onto GO, Fourier-transformed infrared spectroscopy (FTIR) and thermal gravimetric analysis were performed. Compared to GO, BMP2-GO did not induce significant changes in the characteristics of the materials. GO continuously released BMP2 for at least 40 days. Bone marrow stem cells (BMSCs) and chondrocytes were treated with BMP2-GO in interleukin-1 media and assessed in terms of cell viability, flow cytometric characterization, and expression of particular mRNA. Compared to GO, BMP2-GO did not induce any significant changes in biocompatibility. We treated osteoarthritic rats with BMP2 and BMP2-GO, which showed significant differences in Osteoarthritis Research Society International (OARSI) scores (P<0.05). Quantitative assessment revealed significant differences compared to that using BMP2 and BMP2-GO (P<0.05). These findings indicate that GO may be potentially used to control the release of carrier materials. The combination of BMP2 and GO slowed the progression of NF-κB-activated degenerative changes in osteoarthritis. Therefore, we infer that our BMP2-GO strategy could alleviate the NF-κB pathway by inducing continuous BMP2 release. PMID:28243085

New Insights into the Role of RNase L in Innate Immunity

PubMed Central

Chakrabarti, Arindam; Jha, Babal Kant

2011-01-01

The interferon (IFN)-inducible 2′-5′-oligoadenylate synthetase (OAS)/RNase L pathway blocks infections by some types of viruses through cleavage of viral and cellular single-stranded RNA. Viruses induce type I IFNs that initiate signaling to the OAS genes. OAS proteins are pathogen recognition receptors for the viral pathogen-associated molecular pattern, double-stranded RNA. Double-stranded RNA activates OAS to produce px5′A(2′p5′A)n; x = 1–3; n > 2 (2-5A) from ATP. Upon binding 2-5A, RNase L is converted from an inactive monomer to a potently active dimeric endoribonuclease for single-stranded RNA. RNase L contains, from N- to C-terminus, a series of 9 ankyrin repeats, a linker, several protein kinase-like motifs, and a ribonuclease domain homologous to Ire1 (involved in the unfolded protein response). In the past few years, it has become increasingly apparent that RNase L and OAS contribute to innate immunity in many ways. For example, small RNA cleavage products produced by RNase L during viral infections can signal to the retinoic acid-inducible-I like receptors to amplify and perpetuate signaling to the IFN-β gene. In addition, RNase L is now implicated in protecting the central nervous system against viral-induced demyelination. A role in tumor suppression was inferred by mapping of the RNase L gene to the hereditary prostate cancer 1 (HPC1) gene, which in turn led to discovery of the xenotropic murine leukemia-related virus. A broader role in innate immunity is suggested by involvement of RNase L in cytokine induction and endosomal pathways that suppress bacterial infections. These newly described findings about RNase L could eventually provide the basis for developing broad-spectrum antimicrobial drugs. PMID:21190483

S-aryl-L-cysteine sulphoxides and related organosulphur compounds alter oral biofilm development and AI-2-based cell-cell communication.

PubMed

Kasper, S H; Samarian, D; Jadhav, A P; Rickard, A H; Musah, R A; Cady, N C

2014-11-01

To design and synthesize a library of structurally related, small molecules related to homologues of compounds produced by the plant Petiveria alliacea and determine their ability to interfere with AI-2 cell-cell communication and biofilm formation by oral bacteria. Many human diseases are associated with persistent bacterial biofilms. Oral biofilms (dental plaque) are problematic as they are often associated with tooth decay, periodontal disease and systemic disorders such as heart disease and diabetes. Using a microplate-based approach, a bio-inspired small molecule library was screened for anti-biofilm activity against the oral species Streptococcus mutans UA159, Streptococcus sanguis 10556 and Actinomyces oris MG1. To complement the static screen, a flow-based BioFlux microfluidic system screen was also performed under conditions representative of the human oral cavity. Several compounds were found to display biofilm inhibitory activity in all three of the oral bacteria tested. These compounds were also shown to inhibit bioluminescence by Vibrio harveyi and were thus inferred to be quorum sensing (QS) inhibitors. Due to the structural similarity of these compounds to each other, and to key molecules in AI-2 biosynthetic pathways, we propose that these molecules potentially reduce biofilm formation via antagonism of QS or QS-related pathways. This study highlights the potential for a non-antimicrobial-based strategy, focused on AI-2 cell-cell signalling, to control the development of dental plaque. Considering that many bacterial species use AI-2 cell-cell signalling, as well as the increased concern of the use of antimicrobials in healthcare products, such an anti-biofilm approach could also be used to control biofilms in environments beyond the human oral cavity. © 2014 The Society for Applied Microbiology.

Electrophysiological time course and brain areas of spontaneous and intentional trait inferences

PubMed Central

Van Duynslaeger, Marijke; Verstraeten, Edwin

2007-01-01

This study measured event-related potentials during spontaneous and intentional trait inferences. Participants read sentences describing the behavior of a target person from which a strong moral trait could be inferred. The last word of each sentence determined the consistency with the trait induced during an introductory paragraph. In comparison with behaviors that were consistent with the implied trait, a P300 waveform was obtained when the behaviors were evaluative inconsistent with that trait. This dependency on behavioral consistency indicates that trait inferences were made previously while reading the preceding behaviors, irrespective of the participants’ spontaneous or intentional goals. Overall, the P300 shows considerable parallels between spontaneous and intentional inferences, indicating that the type and timing of the inconsistency process is very similar. In contrast, source localization (LORETA) of the event-related potentials suggest that spontaneous inferences show greater activation in the temporo-parietal junction compared to intentional inferences following an inconsistency. Memory measures taken after the presentation of the stimulus material involved sentence completion and trait-cued recall, and supported the occurrence of trait inferences associated with the actor. They also showed significant correlations with the neural components (i.e. P300 and its current density at the temporo-parietal junction) predominantly following spontaneous instructions, indicating that these components are valid neural indices of spontaneous inferences. PMID:18985139

In vitro C3 Deposition on Cryptococcus Capsule Occurs Via Multiple Complement Activation Pathways

PubMed Central

Mershon-Shier, Kileen L.; Vasuthasawat, Alex; Takahashi, Kazue; Morrison, Sherie L.; Beenhouwer, David O.

2011-01-01

Complement can be activated via three pathways: classical, alternative, and lectin. Cryptococcus gattii and C. neoformans are closely related fungal pathogens possessing a polysaccharide capsule composed mainly of glucuronoxylomannan (GXM), which serves as a site for complement activation and deposition of complement components. We determined C3 deposition on Cryptococcus spp. by flow cytometry and confocal microscopy after incubation with serum from C57BL/6J mice as well as mice deficient in complement components C4, C3, factor B, and mannose binding lectin (MBL). C. gattii and C. neoformans activate complement in EGTA-treated serum indicating that they can activate the alternative pathway. However, complement activation was seen with factor B−/− serum suggesting activation could also take place in the absence of a functional alternative pathway. Furthermore, we uncovered a role for C4 in the alternative pathway activation by Cryptococcus spp. We also identified an unexpected and complex role for MBL in complement activation by Cryptococcus spp. No complement activation occurred in the absence of MBL-A and -C proteins although activation took place when the lectin binding activity of MBL was disrupted by calcium chelation. In addition, alternative pathway activation by C. neoformans required both MBL-A and -C, while either MBL-A or -C was sufficient for alternative pathway activation by C. gattii. Thus, complement activation by Cryptococcus spp. can take place through multiple pathways and complement activation via the alternative pathway requires the presence of C4 and MBL proteins. PMID:21723612

Murine Polyomavirus Cell Surface Receptors Activate Distinct Signaling Pathways Required for Infection.

PubMed

O'Hara, Samantha D; Garcea, Robert L

2016-11-01

Virus binding to the cell surface triggers an array of host responses, including activation of specific signaling pathways that facilitate steps in virus entry. Using mouse polyomavirus (MuPyV), we identified host signaling pathways activated upon virus binding to mouse embryonic fibroblasts (MEFs). Pathways activated by MuPyV included the phosphatidylinositol 3-kinase (PI3K), FAK/SRC, and mitogen-activated protein kinase (MAPK) pathways. Gangliosides and α4-integrin are required receptors for MuPyV infection. MuPyV binding to both gangliosides and the α4-integrin receptors was required for activation of the PI3K pathway; however, either receptor interaction alone was sufficient for activation of the MAPK pathway. Using small-molecule inhibitors, we confirmed that the PI3K and FAK/SRC pathways were required for MuPyV infection, while the MAPK pathway was dispensable. Mechanistically, the PI3K pathway was required for MuPyV endocytosis, while the FAK/SRC pathway enabled trafficking of MuPyV along microtubules. Thus, MuPyV interactions with specific cell surface receptors facilitate activation of signaling pathways required for virus entry and trafficking. Understanding how different viruses manipulate cell signaling pathways through interactions with host receptors could lead to the identification of new therapeutic targets for viral infection. Virus binding to cell surface receptors initiates outside-in signaling that leads to virus endocytosis and subsequent virus trafficking. How different viruses manipulate cell signaling through interactions with host receptors remains unclear, and elucidation of the specific receptors and signaling pathways required for virus infection may lead to new therapeutic targets. In this study, we determined that gangliosides and α4-integrin mediate mouse polyomavirus (MuPyV) activation of host signaling pathways. Of these pathways, the PI3K and FAK/SRC pathways were required for MuPyV infection. Both the PI3K and FAK/SRC pathways have been implicated in human diseases, such as heart disease and cancer, and inhibitors directed against these pathways are currently being investigated as therapies. It is possible that these pathways play a role in human PyV infections and could be targeted to inhibit PyV infection in immunosuppressed patients. Copyright © 2016 O’Hara and Garcea.

Identification of active miRNA and transcription factor regulatory pathways in human obesity-related inflammation.

PubMed

Zhang, Xi-Mei; Guo, Lin; Chi, Mei-Hua; Sun, Hong-Mei; Chen, Xiao-Wen

2015-03-07

Obesity-induced chronic inflammation plays a fundamental role in the pathogenesis of metabolic syndrome (MS). Recently, a growing body of evidence supports that miRNAs are largely dysregulated in obesity and that specific miRNAs regulate obesity-associated inflammation. We applied an approach aiming to identify active miRNA-TF-gene regulatory pathways in obesity. Firstly, we detected differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) from mRNA and miRNA expression profiles, respectively. Secondly, by mapping the DEGs and DEmiRs to the curated miRNA-TF-gene regulatory network as active seed nodes and connect them with their immediate neighbors, we obtained the potential active miRNA-TF-gene regulatory subnetwork in obesity. Thirdly, using a Breadth-First-Search (BFS) algorithm, we identified potential active miRNA-TF-gene regulatory pathways in obesity. Finally, through the hypergeometric test, we identified the active miRNA-TF-gene regulatory pathways that were significantly related to obesity. The potential active pathways with FDR < 0.0005 were considered to be the active miRNA-TF regulatory pathways in obesity. The union of the active pathways is visualized and identical nodes of the active pathways were merged. We identified 23 active miRNA-TF-gene regulatory pathways that were significantly related to obesity-related inflammation.

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout

PubMed Central

2013-01-01

Background Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non‐trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused. Results Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps. Conclusions This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result. PMID:23953132

Ethics and end of life care: the Liverpool Care Pathway and the Neuberger Review.

PubMed

Wrigley, Anthony

2015-08-01

The Liverpool Care Pathway for the Dying has recently been the topic of substantial media interest and also been subject to the independent Neuberger Review. This review has identified clear failings in some areas of care and recommended the Liverpool Care Pathway be phased out. I argue that while the evidence gathered of poor incidences of practice by the Review is of genuine concern for end of life care, the inferences drawn from this evidence are inconsistent with the causes for the concern. Seeking to end an approach that is widely seen as best practice and which can genuinely deliver high quality care because of negative impressions that have been formed from failing to implement it properly is not a good basis for radically overhauling our approach to end of life care. I conclude that improvements in training, communication and ethical decision-making, without the added demand to end the Liverpool Care Pathway, would have resulted in a genuine advance in end of life care. 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.

Fluid circulation and structural system of Cerritos Colorados geothermal field in La Primavera volcanic caldera (Mexico) inferred from geophysical surveys

NASA Astrophysics Data System (ADS)

Bolós, X.; Cifuentes-Nava, G.; Macias, J. L.; Sosa-Ceballos, G.; García-Tenorio, F.; Albor, M., III; Juarez, M.; Gamez, V.

2017-12-01

Hydrothermal activity in volcanic calderas is the consequence of energy transfer between deep magmatic chambers and subsurface layers saturated in water. This hydrothermal system is generated by convection of the groundwater supplied by meteoric water recharged and the ascent of hot volcanic gasses exsolved from deep magma reservoirs. Calderas are heterogeneous geological structures that due to their formation and evolution produced a complex stratigraphy. All of these heterogeneities can be affected by deformation and also by the presence of fractures and faults which constitute the main pathways whereby hydrothermal fluids can move easily through the surface as spring discharges and fumarolic activity. Geophysical methods have been used in the last decades to investigate the relationship between structural geology and hydrothermal systems in different volcanic areas around the world. In this work, we have focused on the role of subsurface structures to understand and localize the pathways of fluids related to the hydrothermal system of the Cerritos Colorados geothermal field. We focused in the central area of the caldera (P12 well and Cerritos Colorados graben), where active hydrothermal activity is evidenced by fumaroles, thermal anomalies, CO2 diffuse emission, and sulfur precipitation. We have applied a self-potential method (SP) that combined with temperature measurements that allowed to identify the main infiltration and ascending fluid zones in the area, and their specific surface temperature coinciding with fumarolic activity. From this data we an applied Electrical Resistivity Tomography (ERT) survey in two selected places. One ERT profile (1.2 km in length) was located in the P12 well area. A 3D resistivity model used with the equatorial method was carried out on the Cerritos Colorados graben area. Combining the results of the SP, TºC, and ERT data with a detailed structural map we identified the main degassing zones (i.e. fumaroles) that correspond to higher permeability zones located along normal and strike-slip faults. In conclusion, a strong structural control of the surface manifestation of these hydrothermal systems is deduced from our new data. Then, our results emphasize the importance of old structural boundaries that are controlled by intra-caldera tectonic structures.

‘Seeing the Dark’: Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference

PubMed Central

Limanowski, Jakub; Friston, Karl

2018-01-01

One of the central claims of the Self-model Theory of Subjectivity is that the experience of being someone – even in a minimal form – arises through a transparent phenomenal self-model, which itself can in principle be reduced to brain processes. Here, we consider whether it is possible to distinguish between phenomenally transparent and opaque states in terms of active inference. We propose a relationship of phenomenal opacity to expected uncertainty or precision; i.e., the capacity for introspective attention and implicit mental action. Thus we associate introspective attention with the deployment of ‘precision’ that may render the perceptual evidence (for action) opaque, while treating transparency as a necessary aspect of beliefs about action, i.e., ‘what I am’ doing. We conclude by proposing how we may have to nuance our conception of minimal phenomenal selfhood and agency in light of this active inference conception of transparency-opacity. PMID:29780343

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

Where the wild things are: informal experience and ecological reasoning.

PubMed

Coley, John D

2012-01-01

Category-based induction requires selective use of different relations to guide inferences; this article examines the development of inferences based on ecological relations among living things. Three hundred and forty-six 6-, 8-, and 10-year-old children from rural, suburban, and urban communities projected novel diseases or insides from one species to an ecologically or taxonomically related species; they were also surveyed about hobbies and activities. Frequency of ecological inferences increased with age and with reports of informal exploration of nature, and decreased with population density. By age 10, children preferred taxonomic inferences for insides and ecological inferences for disease, but this pattern emerged earlier among rural children. These results underscore the importance of context by demonstrating effects of both domain-relevant experience and environment on biological reasoning. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.

PubMed

Deng, Wenping; Zhang, Kui; Busov, Victor; Wei, Hairong

2017-01-01

Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental validation.

Principle-Based Inferences in Young Children's Categorization: Revisiting the Impact of Function on the Naming of Artifacts.

ERIC Educational Resources Information Center

Nelson, Deborah G. Kemler

1995-01-01

Three studies investigated the influence of principle-based inferences and unprincipled similarity relations on new category learning by three- to six-year-old children. Results indicated that categorization into newly learned categories may activate self-initiated, principle-based reasoning in young children, suggesting that spontaneous…

Methodological Limitations of the Application of Expert Systems Methodology in Reading.

ERIC Educational Resources Information Center

Willson, Victor L.

Methodological deficiencies inherent in expert-novice reading research make it impossible to draw inferences about curriculum change. First, comparisons of intact groups are often used as a basis for making causal inferences about how observed characteristics affect behaviors. While comparing different groups is not by itself a useless activity,…

Brain Activity Associated with Logical Inferences in Geometry: Focusing on Students with Different Levels of Ability

ERIC Educational Resources Information Center

Waisman, Ilana; Leikin, Mark; Leikin, Roza

2016-01-01

Mathematical processing associated with solving short geometry problems requiring logical inference was examined among students who differ in their levels of general giftedness (G) and excellence in mathematics (EM) using ERP research methodology. Sixty-seven male adolescents formed four major research groups designed according to various…

Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

PubMed

Zhang, Lei; Zeng, Zhi; Ji, Qiang

2011-09-01

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

The common ground of genomics and systems biology

PubMed Central

2014-01-01

The rise of systems biology is intertwined with that of genomics, yet their primordial relationship to one another is ill-defined. We discuss how the growth of genomics provided a critical boost to the popularity of systems biology. We describe the parts of genomics that share common areas of interest with systems biology today in the areas of gene expression, network inference, chromatin state analysis, pathway analysis, personalized medicine, and upcoming areas of synergy as genomics continues to expand its scope across all biomedical fields. PMID:25033072

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Accounting for cyanide and its degradation products at three Nevada gold mines; constraints from stable C- and N-isotopes

USGS Publications Warehouse

Johnson, C.A.; Grimes, D.J.; Rye, R.O.

1998-01-01

An understanding of the fate of cyanide (CN-) in mine process waters is important for addressing environmental concerns and for taking steps to minimize reagent costs. The utility of stable isotope methods in identifying cyanide loss pathways has been investigated in case studies at three Nevada gold mines. Freshly prepared barren solutions at the mines have cyanide d15N and d13C values averaging -4 ? and -36 ?, respectively, reflecting the nitrogen and carbon sources used by commercial manufacturers, air and natural gas methane. Pregnant solutions returning from ore heaps display small isotopic shifts to lower d15N and d13C values. The shifts are similar to those observed in laboratory experiments where cyanide was progressively precipitated as a cyanometallic compound, and are opposite in sign and much smaller in magnitude than the shifts observed in experiments where HCN was offgassed. Offgassing is inferred to be a minor cyanide loss mechanism in the heap leach operations at the three mines, and precipitation as cyanometallic compounds, and possibly coprecipitation with ferric oxides, is inferred to be an important loss mechanism. Isotopic analysis of dissolved inorganic carbon (DIC) shows that uptake of high d13C air CO2 has been important in many barren and pregnant solutions. However, DIC in reclaim pond waters at all three mines has low d13C values of -28 to -34 ? indicating cyanide breakdown either by hydrolysis or by other chemical pathways that break the C-N bond. Isotope mass balance calculations indicate that about 40 % of the DIC load in the ponds, at a minimum, was derived from cyanide breakdown. This level of cyanide hydrolysis accounts for 14-100 % of the dissolved inorganic nitrogen species present in the ponds. Overall, isotope data provide quantitative evidence that only minor amounts of cyanide are lost via offgassing and that significant amounts are destroyed via hydrolysis and related pathways. The data also highlight the possibility that significant cyanide may be either retained in the ore heaps or destroyed via other chemical pathways.

Mechanical trapping of particles in granular media

NASA Astrophysics Data System (ADS)

Kerimov, Abdulla; Mavko, Gary; Mukerji, Tapan; Al Ibrahim, Mustafa A.

2018-02-01

Mechanical trapping of fine particles in the pores of granular materials is an essential mechanism in a wide variety of natural and industrial filtration processes. The progress of invading particles is primarily limited by the network of pore throats and connected pathways encountered by the particles during their motion through the porous medium. Trapping of invading particles is limited to a depth defined by the size, shape, and distribution of the invading particles with respect to the size, shape, and distribution of the host porous matrix. Therefore, the trapping process, in principle, can be used to obtain information about geometrical properties, such as pore throat and particle size, of the underlying host matrix. A numerical framework is developed to simulate the mechanical trapping of fine particles in porous granular media with prescribed host particle size, shape, and distribution. The trapping of invading particles is systematically modeled in host packings with different host particle distributions: monodisperse, bidisperse, and polydisperse distributions of host particle sizes. Our simulation results show quantitatively and qualitatively to what extent trapping behavior is different in the generated monodisperse, bidisperse, and polydisperse packings of spherical particles. Depending on host particle size and distribution, the information about extreme estimates of minimal pore throat sizes of the connected pathways in the underlying host matrix can be inferred from trapping features, such as the fraction of trapped particles as a function of invading particle size. The presence of connected pathways with minimum and maximum of minimal pore throat diameters can be directly obtained from trapping features. This limited information about the extreme estimates of pore throat sizes of the connected pathways in the host granular media inferred from our numerical simulations is consistent with simple geometrical estimates of extreme value of pore and throat sizes of the densest structural arrangements of spherical particles and geometrical Delaunay tessellation analysis of the pore space of host granular media. Our results suggest simple relations between the host particle size and trapping features. These relationships can be potentially used to describe both the dynamics of the mechanical trapping process and the geometrical properties of the host granular media.

Mechanical trapping of particles in granular media.

PubMed

Kerimov, Abdulla; Mavko, Gary; Mukerji, Tapan; Al Ibrahim, Mustafa A

2018-02-01

Mechanical trapping of fine particles in the pores of granular materials is an essential mechanism in a wide variety of natural and industrial filtration processes. The progress of invading particles is primarily limited by the network of pore throats and connected pathways encountered by the particles during their motion through the porous medium. Trapping of invading particles is limited to a depth defined by the size, shape, and distribution of the invading particles with respect to the size, shape, and distribution of the host porous matrix. Therefore, the trapping process, in principle, can be used to obtain information about geometrical properties, such as pore throat and particle size, of the underlying host matrix. A numerical framework is developed to simulate the mechanical trapping of fine particles in porous granular media with prescribed host particle size, shape, and distribution. The trapping of invading particles is systematically modeled in host packings with different host particle distributions: monodisperse, bidisperse, and polydisperse distributions of host particle sizes. Our simulation results show quantitatively and qualitatively to what extent trapping behavior is different in the generated monodisperse, bidisperse, and polydisperse packings of spherical particles. Depending on host particle size and distribution, the information about extreme estimates of minimal pore throat sizes of the connected pathways in the underlying host matrix can be inferred from trapping features, such as the fraction of trapped particles as a function of invading particle size. The presence of connected pathways with minimum and maximum of minimal pore throat diameters can be directly obtained from trapping features. This limited information about the extreme estimates of pore throat sizes of the connected pathways in the host granular media inferred from our numerical simulations is consistent with simple geometrical estimates of extreme value of pore and throat sizes of the densest structural arrangements of spherical particles and geometrical Delaunay tessellation analysis of the pore space of host granular media. Our results suggest simple relations between the host particle size and trapping features. These relationships can be potentially used to describe both the dynamics of the mechanical trapping process and the geometrical properties of the host granular media.

Computational State Space Models for Activity and Intention Recognition. A Feasibility Study

PubMed Central

Krüger, Frank; Nyolt, Martin; Yordanova, Kristina; Hein, Albert; Kirste, Thomas

2014-01-01

Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance. PMID:25372138

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

PubMed Central

Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

2014-01-01

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152

When knowledge activated from memory intrudes on probabilistic inferences from description - the case of stereotypes.

PubMed

Dorrough, Angela R; Glöckner, Andreas; Betsch, Tilmann; Wille, Anika

2017-10-01

To make decisions in probabilistic inference tasks, individuals integrate relevant information partly in an automatic manner. Thereby, potentially irrelevant stimuli that are additionally presented can intrude on the decision process (e.g., Söllner, Bröder, Glöckner, & Betsch, 2014). We investigate whether such an intrusion effect can also be caused by potentially irrelevant or even misleading knowledge activated from memory. In four studies that combine a standard information board paradigm from decision research with a standard manipulation from social psychology, we investigate the case of stereotypes and demonstrate that stereotype knowledge can yield intrusion biases in probabilistic inferences from description. The magnitude of these biases increases with stereotype accessibility and decreases with a clarification of the rational solution. Copyright © 2017 Elsevier B.V. All rights reserved.

Activating gender stereotypes during online spoken language processing: evidence from Visual World Eye Tracking.

PubMed

Pyykkönen, Pirita; Hyönä, Jukka; van Gompel, Roger P G

2010-01-01

This study used the visual world eye-tracking method to investigate activation of general world knowledge related to gender-stereotypical role names in online spoken language comprehension in Finnish. The results showed that listeners activated gender stereotypes elaboratively in story contexts where this information was not needed to build coherence. Furthermore, listeners made additional inferences based on gender stereotypes to revise an already established coherence relation. Both results are consistent with mental models theory (e.g., Garnham, 2001). They are harder to explain by the minimalist account (McKoon & Ratcliff, 1992) which suggests that people limit inferences to those needed to establish coherence in discourse.

Is the canonical RAF-MEK-ERK signaling pathway a therapeutic target in SCLC?

PubMed Central

Cristea, Sandra; Sage, Julien

2017-01-01

The activity of the RAF-MEK-ERK signaling pathway is critical for the proliferation of normal and cancerous cells. Oncogenic mutations driving the development of lung adenocarcinoma often activate this signaling pathway. In contrast, pathway activity levels and their biological roles are not well established in small cell lung cancer (SCLC), a fast-growing neuroendocrine lung cancer subtype. Here we discuss the function of the RAF-MEK-ERK kinase pathway and the mechanisms leading to its activation in SCLC cells. In particular, we argue that activation of this pathway may be beneficial to the survival, proliferation and spread of SCLC cells in response to multiple stimuli. We also consider evidence that high levels of RAF-MEK-ERK pathway activity may be detrimental to SCLC tumors, including in part by interfering with their neuroendocrine fate. Based on these observations, we examine when small molecules targeting kinases in the RAF-MEK-ERK pathway may be useful therapeutically in SCLC patients, including in combination with other therapeutic agents. PMID:27133774

Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone.

PubMed

Yang, Wan; Zhang, Wenyi; Kargbo, David; Yang, Ruifu; Chen, Yong; Chen, Zeliang; Kamara, Abdul; Kargbo, Brima; Kandula, Sasikiran; Karspeck, Alicia; Liu, Chao; Shaman, Jeffrey

2015-11-06

Understanding the growth and spatial expansion of (re)emerging infectious disease outbreaks, such as Ebola and avian influenza, is critical for the effective planning of control measures; however, such efforts are often compromised by data insufficiencies and observational errors. Here, we develop a spatial-temporal inference methodology using a modified network model in conjunction with the ensemble adjustment Kalman filter, a Bayesian inference method equipped to handle observational errors. The combined method is capable of revealing the spatial-temporal progression of infectious disease, while requiring only limited, readily compiled data. We use this method to reconstruct the transmission network of the 2014-2015 Ebola epidemic in Sierra Leone and identify source and sink regions. Our inference suggests that, in Sierra Leone, transmission within the network introduced Ebola to neighbouring districts and initiated self-sustaining local epidemics; two of the more populous and connected districts, Kenema and Port Loko, facilitated two independent transmission pathways. Epidemic intensity differed by district, was highly correlated with population size (r = 0.76, p = 0.0015) and a critical window of opportunity for containing local Ebola epidemics at the source (ca one month) existed. This novel methodology can be used to help identify and contain the spatial expansion of future (re)emerging infectious disease outbreaks. © 2015 The Author(s).

Impact of Prematurity and Perinatal Antibiotics on the Developing Intestinal Microbiota: A Functional Inference Study.

PubMed

Arboleya, Silvia; Sánchez, Borja; Solís, Gonzalo; Fernández, Nuria; Suárez, Marta; Hernández-Barranco, Ana M; Milani, Christian; Margolles, Abelardo; de Los Reyes-Gavilán, Clara G; Ventura, Marco; Gueimonde, Miguel

2016-04-29

The microbial colonization of the neonatal gut provides a critical stimulus for normal maturation and development. This process of early microbiota establishment, known to be affected by several factors, constitutes an important determinant for later health. We studied the establishment of the microbiota in preterm and full-term infants and the impact of perinatal antibiotics upon this process in premature babies. To this end, 16S rRNA gene sequence-based microbiota assessment was performed at phylum level and functional inference analyses were conducted. Moreover, the levels of the main intestinal microbial metabolites, the short-chain fatty acids (SCFA) acetate, propionate and butyrate, were measured by Gas-Chromatography Flame ionization/Mass spectrometry detection. Prematurity affects microbiota composition at phylum level, leading to increases of Proteobacteria and reduction of other intestinal microorganisms. Perinatal antibiotic use further affected the microbiota of the preterm infant. These changes involved a concomitant alteration in the levels of intestinal SCFA. Moreover, functional inference analyses allowed for identifying metabolic pathways potentially affected by prematurity and perinatal antibiotics use. A deficiency or delay in the establishment of normal microbiota function seems to be present in preterm infants. Perinatal antibiotic use, such as intrapartum prophylaxis, affected the early life microbiota establishment in preterm newborns, which may have consequences for later health.

Calcium at fertilization and in early development

PubMed Central

Whitaker, Michael

2012-01-01

Fertilization calcium waves are introduced and the evidence from which we can infer general mechanisms of these waves is presented. The two main classes of hypothesis put forward to explain the generation of the fertilization calcium wave are set out and it is concluded that initiation of the fertilization calcium wave can be most generally explained in inverterbrates by a mechanism in which an activating substance enters the egg from the sperm on sperm-egg fusion, activating the egg by stimulating phospholipase C activation through a src family kinase pathway and in mammals by the diffusion of a sperm-specific phospholipase C from sperm to egg on sperm-egg fusion. The fertilization calcium wave is then set into the context of cell cycle control and the mechanism of repetitive calcium spiking in mammalian eggs is investigated. Evidence that calcium signals control cell division in early embryos is reviewed, and it is concluded that calcium signals are essential at all three stages of cell division in early embryos. Evidence that phosphoinositide signalling pathways control the resumption of meiosis during oocyte maturation is considered. It is concluded on balance that the evidence points to a need for phosphoinositide/calcium signalling during resumption of meiosis. Changes to the calcium signalling machinery occur during meiosis to enable the production of a calcium wave in the mature oocyte when it is fertilized; evidence that the shape and structure of the endoplasmic reticulum alters dynamically during maturation and after fertilization is reviewed and the link between ER dynamics and the cytoskeleton is discussed. There is evidence that calcium signalling plays a key part in the development of patterning in early embryos. Morphogenesis in ascidian, frog and zebrafish embryos is briefly described to provide the developmental context in which calcium signals act. Intracellular calcium waves that may play a role in axis formation in ascidian are discussed. Evidence that the Wingless/calcium signalling pathway is a strong ventralizing signal in Xenopus, mediated by phoshoinositide signalling is adumbrated. The central role that calcium channels play in morphogenetic movements during gastrulation and in ectodermal and mesodermal gene expression during late gastrulation is demonstrated. Experiments in zebrafish provide a strong indication that calcium signals are essential for pattern formation and organogenesis. PMID:16371595

Characterization of the 2′,3′ cyclic phosphodiesterase activities of Clostridium thermocellum polynucleotide kinase-phosphatase and bacteriophage λ phosphatase

PubMed Central

Keppetipola, Niroshika; Shuman, Stewart

2007-01-01

Clostridium thermocellum polynucleotide kinase-phosphatase (CthPnkp) catalyzes 5′ and 3′ end-healing reactions that prepare broken RNA termini for sealing by RNA ligase. The central phosphatase domain of CthPnkp belongs to the dinuclear metallophosphoesterase superfamily exemplified by bacteriophage λ phosphatase (λ-Pase). CthPnkp is a Ni2+/Mn2+-dependent phosphodiesterase-monoesterase, active on nucleotide and non-nucleotide substrates, that can be transformed toward narrower metal and substrate specificities via mutations of the active site. Here we characterize the Mn2+-dependent 2′,3′ cyclic nucleotide phosphodiesterase activity of CthPnkp, the reaction most relevant to RNA repair pathways. We find that CthPnkp prefers a 2′,3′ cyclic phosphate to a 3′,5′ cyclic phosphate. A single H189D mutation imposes strict specificity for a 2′,3′ cyclic phosphate, which is cleaved to form a single 2′-NMP product. Analysis of the cyclic phosphodiesterase activities of mutated CthPnkp enzymes illuminates the active site and the structural features that affect substrate affinity and kcat. We also characterize a previously unrecognized phosphodiesterase activity of λ-Pase, which catalyzes hydrolysis of bis-p-nitrophenyl phosphate. λ-Pase also has cyclic phosphodiesterase activity with nucleoside 2′,3′ cyclic phosphates, which it hydrolyzes to yield a mixture of 2′-NMP and 3′-NMP products. We discuss our results in light of available structural and functional data for other phosphodiesterase members of the binuclear metallophosphoesterase family and draw inferences about how differences in active site composition influence catalytic repertoire. PMID:17986465

Juvenile magma recognition and eruptive dynamics inferred from the analysis of ash time series: The 2015 reawakening of Cotopaxi volcano

USGS Publications Warehouse

Gaunt, H. Elizabeth; Bernard, Benjamin; Hidalgo, Silvana; Proano, Antonio; Wright, Heather M.; Mothes, Patricia; Criollo, Evelyn; Kueppers, Ulrich

2016-01-01

Forecasting future activity and performing hazard assessments during the reactivation of volcanoes remain great challenges for the volcanological community. On August 14, 2015 Cotopaxi volcano erupted for the first time in 73 years after approximately four months of precursory activity, which included an increase in seismicity, gas emissions, and minor ground deformation. Here we discuss the use of near real-time petrological monitoring of ash samples as a complementary aid to geophysical monitoring, in order to infer eruption dynamics and evaluate possible future eruptive activity at Cotopaxi. Twenty ash samples were collected between August 14 and November 23, 2015 from a monitoring site on the west flank of the volcano. These samples contain a range of grain types that we classified as: hydrothermal/altered, lithic, juvenile, and free crystals. The relative proportions of theses grains evolved as the eruption progressed, with increasing amounts of juvenile material and a decrease in hydrothermally altered material. In samples from the initial explosion, juvenile grains are glassy, microlite-poor and contain hydrothermal minerals (opal and alunite). The rising magma came in contact with the hydrothermal system under confinement, causing hydro-magmatic explosions that cleared the upper part of the plumbing system. Subsequently, the magmatic column produced a thermal aureole in the conduit and dried out the hydrothermal system, allowing for dry eruptions. Magma ascent rates were low enough to allow for efficient outgassing and microlite growth. Constant supply of magma from below caused quasi-continuous disruption of the uppermost magma volume through a combination of shear-deformation and gas expansion. The combination of increasing crystallinity of juvenile grains, and high measured SO2 flux indicate decreasing integrated magma ascent rates and clearing of the hydrothermal system along transport pathways in a system open to gas loss. The near real-time monitoring of ash samples combined with traditional geophysical monitoring techniques during the reawakening of Cotopaxi allowed us to gain a much clearer understanding of events than when using traditional geophysical monitoring alone.

Interaction of the mitochondria-targeted antioxidant MitoQ with phospholipid bilayers and ubiquinone oxidoreductases.

PubMed

James, Andrew M; Sharpley, Mark S; Manas, Abdul-Rahman B; Frerman, Frank E; Hirst, Judy; Smith, Robin A J; Murphy, Michael P

2007-05-18

MitoQ(10) is a ubiquinone that accumulates within mitochondria driven by a conjugated lipophilic triphenylphosphonium cation (TPP(+)). Once there, MitoQ(10) is reduced to its active ubiquinol form, which has been used to prevent mitochondrial oxidative damage and to infer the involvement of reactive oxygen species in signaling pathways. Here we show MitoQ(10) is effectively reduced by complex II, but is a poor substrate for complex I, complex III, and electron-transferring flavoprotein (ETF):quinone oxidoreductase (ETF-QOR). This differential reactivity could be explained if the bulky TPP(+) moiety sterically hindered access of the ubiquinone group to enzyme active sites with a long, narrow access channel. Using a combination of molecular modeling and an uncharged analog of MitoQ(10) with similar sterics (tritylQ(10)), we infer that the interaction of MitoQ(10) with complex I and ETF-QOR, but not complex III, is inhibited by its bulky TPP(+) moiety. To explain its lack of reactivity with complex III we show that the TPP(+) moiety of MitoQ(10) is ineffective at quenching pyrene fluorophors deeply buried within phospholipid bilayers and thus is positioned near the membrane surface. This superficial position of the TPP(+) moiety, as well as the low solubility of MitoQ(10) in non-polar organic solvents, suggests that the concentration of the entire MitoQ(10) molecule in the membrane core is very limited. As overlaying MitoQ(10) onto the structure of complex III indicates that MitoQ(10) cannot react with complex III without its TPP(+) moiety entering the low dielectric of the membrane core, we conclude that the TPP(+) moiety does anchor the tethered ubiquinol group out of reach of the active site(s) of complex III, thus explaining its slow oxidation. In contrast the ubiquinone moiety of MitoQ(10) is able to quench fluorophors deep within the membrane core, indicating a high concentration of the ubiquinone moiety within the membrane and explaining its good anti-oxidant efficacy. These findings will facilitate the rational design of future mitochondria-targeted molecules.

Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy

NASA Astrophysics Data System (ADS)

Amaral, Selene da Rocha; Baccalá, Luiz A.; Barbosa, Leonardo S.; Caticha, Nestor

2017-06-01

Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

Mathematical inference and control of molecular networks from perturbation experiments

NASA Astrophysics Data System (ADS)

Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five melanoma cell lines, who exhibit different motility/invasion behavior under the same perturbation experiment of gene Wnt5a. The results of the simulations validate both the steady state levels and the experimental data of the perturbation experiments of all five cell lines. The goal of this study is to answer important questions that link the response of the network to perturbations, as measured by the experiments, to its structure, i.e., connectivity. Answers to these questions shed novel insights on the structure of networks and how they react to perturbations.

A statistical framework for biomedical literature mining.

PubMed

Chung, Dongjun; Lawson, Andrew; Zheng, W Jim

2017-09-30

In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not only promote understanding of pathway regulation mechanisms but also allow identification of novel targets for therapeutics. Recently, biomedical literature has been considered as a valuable resource to investigate pathway-modulating genes. While the majority of currently available approaches are based on the co-occurrence of genes within an abstract, it has been reported that these approaches show only sub-optimal performances because 70% of abstracts contain information only for a single gene. To overcome such limitation, we propose a novel statistical framework based on the concept of ontology fingerprint that uses gene ontology to extract information from large biomedical literature data. The proposed framework simultaneously identifies pathway-modulating genes and facilitates interpreting functions of these new genes. We also propose a computationally efficient posterior inference procedure based on Metropolis-Hastings within Gibbs sampler for parameter updates and the poor man's reversible jump Markov chain Monte Carlo approach for model selection. We evaluate the proposed statistical framework with simulation studies, experimental validation, and an application to studies of pathway-modulating genes in yeast. The R implementation of the proposed model is currently available at https://dongjunchung.github.io/bayesGO/. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

ERIC Educational Resources Information Center

Koparan, Timur; Yilmaz, Gül Kaleli

2015-01-01

The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

Diagnostic causal reasoning with verbal information.

PubMed

Meder, Björn; Mayrhofer, Ralf

2017-08-01

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people's inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., "X occasionally causes A"). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes' prior probabilities and the effects' likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework. Copyright © 2017 Elsevier Inc. All rights reserved.

Knockdown of human serine/threonine kinase 33 suppresses human small cell lung carcinoma by blocking RPS6/BAD signaling transduction.

PubMed

Sun, E L; Liu, C X; Ma, Z X; Mou, X Y; Mu, X A; Ni, Y H; Li, X L; Zhang, D; Ju, Y R

2017-01-01

Small cell lung cancer (SCLC) is characterized by rapid growth rate and a tendency to metastasize to distinct sites of patients' bodies. The human serine/threonine kinase 33 (STK33) gene has shown its potency as a therapeutic target for prevention of lung carcinomas including non-small cell lung cancer (NSCLC), but its function in the oncogenesis and development of SCLC remains unrevealed. In the current study, it was hypothesized that STK33 played a key role in the proliferation, survival, and invasion of SCLC cells. The expression of STK33 in human SCLC cell lines NCI-H466 and DMS153 was inhibited by specific shRNA. The cell proliferation, cell apoptosis, and cell invasion of the cells were assessed with a series of in vitro assays. To explore the mechanism through which STK33 gene exerted its function in the carcinogenesis of SCLC cells, the effect of STK33 knockdown on the activity of S6K1/RPS6/BAD signaling was detected. Then the results were further confirmed with STK33 inhibitor ML281 and in vivo assays. The results demonstrated that inhibition of STK33 in SCLC cells suppressed the cell proliferation and invasion while induced cell apoptosis. Associated with the change in the phenotypic features, knockdown of STK33 also decreased the phosphorylation of RPS6 and BAD while increased the expression of cleaved caspase 9, indicating that apoptosis induced by STK33 suppression was mediated via mitochondrial pathway. Similar to the results of STK33 knockdown, incubating NCI-H466 cells with STK33 inhibitor also reduced the cell viability by suppressing RPS6/BAD pathways. Additionally, STK33 knockdown also inhibited tumor growth and RPS6/BAD activity in mice models. Findings outlined in our study were different from that in NSCLC to some extent: knockdown of STK33 in SCLC cells induced the apoptosis through mitochondrial pathway but independent of S6K1 function, inferring that the function of STK33 might be cancer type specific.

A Priori Method of Using Photon Activation Analysis to Determine Unknown Trace Element Concentrations in NIST Standards

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

Green, Jaromy; Sun Zaijing; Wells, Doug

2009-03-10

Photon activation analysis detected elements in two NIST standards that did not have reported concentration values. A method is currently being developed to infer these concentrations by using scaling parameters and the appropriate known quantities within the NIST standard itself. Scaling parameters include: threshold, peak and endpoint energies; photo-nuclear cross sections for specific isotopes; Bremstrahlung spectrum; target thickness; and photon flux. Photo-nuclear cross sections and energies from the unknown elements must also be known. With these quantities, the same integral was performed for both the known and unknown elements resulting in an inference of the concentration of the un-reported elementmore » based on the reported value. Since Rb and Mn were elements that were reported in the standards, and because they had well-identified peaks, they were used as the standards of inference to determine concentrations of the unreported elements of As, I, Nb, Y, and Zr. This method was tested by choosing other known elements within the standards and inferring a value based on the stated procedure. The reported value of Mn in the first NIST standard was 403{+-}15 ppm and the reported value of Ca in the second NIST standard was 87000 ppm (no reported uncertainty). The inferred concentrations were 370{+-}23 ppm and 80200{+-}8700 ppm respectively.« less

Task-dependent activation of distinct fast and slow(er) motor pathways during motor imagery.

PubMed

Keller, Martin; Taube, Wolfgang; Lauber, Benedikt

2018-02-22

Motor imagery and actual movements share overlapping activation of brain areas but little is known about task-specific activation of distinct motor pathways during mental simulation of movements. For real contractions, it was demonstrated that the slow(er) motor pathways are activated differently in ballistic compared to tonic contractions but it is unknown if this also holds true for imagined contractions. The aim of the present study was to assess the activity of fast and slow(er) motor pathways during mentally simulated movements of ballistic and tonic contractions. H-reflexes were conditioned with transcranial magnetic stimulation at different interstimulus intervals to assess the excitability of fast and slow(er) motor pathways during a) the execution of tonic and ballistic contractions, b) motor imagery of these contraction types, and c) at rest. In contrast to the fast motor pathways, the slow(er) pathways displayed a task-specific activation: for imagined ballistic as well as real ballistic contractions, the activation was reduced compared to rest whereas enhanced activation was found for imagined tonic and real tonic contractions. This study provides evidence that the excitability of fast and slow(er) motor pathways during motor imagery resembles the activation pattern observed during real contractions. The findings indicate that motor imagery results in task- and pathway-specific subliminal activation of distinct subsets of neurons in the primary motor cortex. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Analyzing gene perturbation screens with nested effects models in R and bioconductor.

PubMed

Fröhlich, Holger; Beissbarth, Tim; Tresch, Achim; Kostka, Dennis; Jacob, Juby; Spang, Rainer; Markowetz, F

2008-11-01

Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.

Extraction of intracellular protein from Glaciozyma antarctica for proteomics analysis

NASA Astrophysics Data System (ADS)

Faizura, S. Nor; Farahayu, K.; Faizal, A. B. Mohd; Asmahani, A. A. S.; Amir, R.; Nazalan, N.; Diba, A. B. Farah; Muhammad, M. Nor; Munir, A. M. Abdul

2013-11-01

Two preparation methods of crude extracts of psychrophilic yeast Glaciozyma antarctica were compared in order to obtain a good recovery of intracellular proteins. Extraction with mechanical procedures using sonication was found to be more effective for obtaining good yield compare to alkaline treatment method. The procedure is simple, rapid, and produce better yield. A total of 52 proteins were identified by combining both extraction methods. Most of the proteins identified in this study involves in the metabolic process including glycolysis pathway, pentose phosphate pathway, pyruyate decarboxylation and also urea cyle. Several chaperons were identified including probable cpr1-cyclophilin (peptidylprolyl isomerase), macrolide-binding protein fkbp12 and heat shock proteins which were postulate to accelerate proper protein folding. Characteristic of the fundamental cellular processes inferred from the expressed-proteome highlight the evolutionary and functional complexity existing in this domain of life.

RSPOs facilitated HSC activation and promoted hepatic fibrogenesis

PubMed Central

Yin, Xinguang; Yi, Huixing; Wang, Linlin; Wu, Wanxin; Wu, Xiaojun; Yu, Linghua

2016-01-01

Roof plate-specific spondin (RSPO) proteins are potent Wnt pathway agonists and involve in a broad range of developmental and physiological processes. This study investigated the activities and mechanisms of RSPOs in liver fibrogenesis, especially in hepatic stellate cell (HSC) activation. HSC activation was assessed by fibrosis biomarker (α-smooth muscle actin and Collagen-I), phenotypic change (accumulation of lipid droplets), and increased proliferation. Similarly, Wnt pathway activity was evaluated by the expression of nuclear β-catenin and T cell-specific transcription factors (TCF) activity. We found RSPOs were overexpressed in human fibrotic liver tissue and the expressions were correlated with liver fibrosis stages. In vitro studies showed RSPOs level increased during HSC activation, and stimuli with RSPOs enhanced Wnt pathway activity and promoted HSC activation subsequently. Furthermore, in vivo experiments demonstrated that the knockdown of RSPOs suppressed both Wnt pathway activity and HSC activation. Interestingly, the inhibitor of the Wnt signaling pathway Dickkopf1 impairs RSPOs effects on HSCs. Taken together, our results revealed that RSPOs facilitated HSC activation and promote liver fibrogenesis by enhancing the Wnt pathway. PMID:27572318

RSPOs facilitated HSC activation and promoted hepatic fibrogenesis.

PubMed

Yin, Xinguang; Yi, Huixing; Wang, Linlin; Wu, Wanxin; Wu, Xiaojun; Yu, Linghua

2016-09-27

Roof plate-specific spondin (RSPO) proteins are potent Wnt pathway agonists and involve in a broad range of developmental and physiological processes. This study investigated the activities and mechanisms of RSPOs in liver fibrogenesis, especially in hepatic stellate cell (HSC) activation. HSC activation was assessed by fibrosis biomarker (α-smooth muscle actin and Collagen-I), phenotypic change (accumulation of lipid droplets), and increased proliferation. Similarly, Wnt pathway activity was evaluated by the expression of nuclear β-catenin and T cell-specific transcription factors (TCF) activity. We found RSPOs were overexpressed in human fibrotic liver tissue and the expressions were correlated with liver fibrosis stages. In vitro studies showed RSPOs level increased during HSC activation, and stimuli with RSPOs enhanced Wnt pathway activity and promoted HSC activation subsequently. Furthermore, in vivo experiments demonstrated that the knockdown of RSPOs suppressed both Wnt pathway activity and HSC activation. Interestingly, the inhibitor of the Wnt signaling pathway Dickkopf1 impairs RSPOs effects on HSCs. Taken together, our results revealed that RSPOs facilitated HSC activation and promote liver fibrogenesis by enhancing the Wnt pathway.

Progranulin promotes peripheral nerve regeneration and reinnervation: role of notch signaling.

PubMed

Altmann, Christine; Vasic, Verica; Hardt, Stefanie; Heidler, Juliana; Häussler, Annett; Wittig, Ilka; Schmidt, Mirko H H; Tegeder, Irmgard

2016-10-22

Peripheral nerve injury is a frequent cause of lasting motor deficits and chronic pain. Although peripheral nerves are capable of regrowth they often fail to re-innervate target tissues. Using newly generated transgenic mice with inducible neuronal progranulin overexpression we show that progranulin accelerates axonal regrowth, restoration of neuromuscular synapses and recovery of sensory and motor functions after injury of the sciatic nerve. Oppositely, progranulin deficient mice have long-lasting deficits in motor function tests after nerve injury due to enhanced losses of motor neurons and stronger microglia activation in the ventral horn of the spinal cord. Deep proteome and gene ontology (GO) enrichment analysis revealed that the proteins upregulated in progranulin overexpressing mice were involved in 'regulation of transcription' and 'response to insulin' (GO terms). Transcription factor prediction pointed to activation of Notch signaling and indeed, co-immunoprecipitation studies revealed that progranulin bound to the extracellular domain of Notch receptors, and this was functionally associated with higher expression of Notch target genes in the dorsal root ganglia of transgenic mice with neuronal progranulin overexpression. Functionally, these transgenic mice recovered normal gait and running, which was not achieved by controls and was stronger impaired in progranulin deficient mice. We infer that progranulin activates Notch signaling pathways, enhancing thereby the regenerative capacity of partially injured neurons, which leads to improved motor function recovery.

Simultaneous Detection of Metalloprotease Activities in Complex Biological Samples Using the PrAMA (Proteolytic Activity Matrix Assay) Method.

PubMed

Conrad, Catharina; Miller, Miles A; Bartsch, Jörg W; Schlomann, Uwe; Lauffenburger, Douglas A

2017-01-01

Proteolytic Activity Matrix Analysis (PrAMA) is a method for simultaneously determining the activities of specific Matrix Metalloproteinases (MMPs) and A Disintegrin and Metalloproteinases (ADAMs) in complex biological samples. In mixtures of unknown proteases, PrAMA infers selective metalloproteinase activities by using a panel of moderately specific FRET-based polypeptide protease substrates in parallel, typically monitored by a plate-reader in a 96-well format. Fluorescence measurements are then quantitatively compared to a standard table of catalytic efficiencies measured from purified mixtures of individual metalloproteinases and FRET substrates. Computational inference of specific activities is performed with an easily used Matlab program, which is provided herein. Thus, we describe PrAMA as a combined experimental and mathematical approach to determine real-time metalloproteinase activities, which has previously been applied to live-cell cultures, cellular lysates, cell culture supernatants, and body fluids from patients.

The Effects of Magnetic Activity on Lithium-Inferred Ages of Stars

NASA Astrophysics Data System (ADS)

Juarez, Aaron J.; Cargile, Phillip A.; James, David J.; Stassun, Keivan G.

2014-08-01

In this project, we investigate the effects of magnetic activity on the Lithium Depletion Boundary (LDB) to recalibrate the measured ages for star clusters, using the open cluster Blanco 1 as a pilot study. We apply the LDB technique on low-mass Pre-Main-Sequence (PMS) stars to derive an accurate age for Blanco 1, and we consider the effect of magnetic activity on this inferred age. Although observations have shown that magnetic activity directly affects stellar radius and temperature, most PMS models do not include the effects of magnetic activity on stellar properties. Since the lithium abundance of a star depends on its radius and temperature, we expect that LDB ages are affected by magnetic activity. After empirically accounting for the effects of magnetic activity, we find the age of Blanco 1 to be ~100 Myr, which is ~30 Myr younger than the standard LDB age of ~130 Myr.

Inferring Toxicological Responses of HepG2 Cells from ...

EPA Pesticide Factsheets

Understanding the dynamic perturbation of cell states by chemicals can aid in for predicting their adverse effects. High-content imaging (HCI) was used to measure the state of HepG2 cells over three time points (1, 24, and 72 h) in response to 976 ToxCast chemicals for 10 different concentrations (0.39-200µM). Cell state was characterized by p53 activation (p53), c-Jun activation (SK), phospho-Histone H2A.x (OS), phospho-Histone H3 (MA), alpha tubulin (Mt), mitochondrial membrane potential (MMP), mitochondrial mass (MM), cell cycle arrest (CCA), nuclear size (NS) and cell number (CN). Dynamic cell state perturbations due to each chemical concentration were utilized to infer coarse-grained dependencies between cellular functions as Boolean networks (BNs). BNs were inferred from data in two steps. First, the data for each state variable were discretized into changed/active (> 1 standard deviation), and unchanged/inactive values. Second, the discretized data were used to learn Boolean relationships between variables. In our case, a BN is a wiring diagram between nodes that represent 10 previously described observable phenotypes. Functional relationships between nodes were represented as Boolean functions. We found that inferred BN show that HepG2 cell response is chemical and concentration specific. We observed presence of both point and cycle BN attractors. In addition, there are instances where Boolean functions were not found. We believe that this may be either

Interplanetary Coronal Mass Ejection effects on thermospheric density as inferred from International Space Station orbital data

NASA Astrophysics Data System (ADS)

Mendaza, T.; Blanco-Ávalos, J. J.; Martín-Torres, J.

2017-11-01

The solar activity induces long term and short term periodical variations in the dynamics and composition of Earth's atmosphere. The Sun also shows non periodical (i.e., impulsive) activity that reaches the planets orbiting around it. In particular, Interplanetary Coronal Mass Ejections (ICMEs) reach Earth and interact with its magnetosphere and upper neutral atmosphere. Nevertheless, the interaction with the upper atmosphere is not well characterized because of the absence of regular and dedicated in situ measurements at high altitudes; thus, current descriptions of the thermosphere are based on semi empirical models. In this paper, we present the total neutral mass densities of the thermosphere retrieved from the orbital data of the International Space Station (ISS) using the General Perturbation Method, and we applied these densities to routinely compiled trajectories of the ISS in low Earth orbit (LEO). These data are explicitly independent of any atmospheric model. Our density values are consistent with atmospheric models, which demonstrates that our method is reliable for the inference of thermospheric density. We have inferred the thermospheric total neutral density response to impulsive solar activity forcing from 2001 to the end of 2006 and determined how solar events affect this response. Our results reveal that the ISS orbital parameters can be used to infer the thermospheric density and analyze solar effects on the thermosphere.

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

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

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significancemore » analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.« less

Inferring glacial flow pathways with DNA-labelled microparticle tracers at the Wolverine Glacier in Alaska

NASA Astrophysics Data System (ADS)

McNew, Coy; Dahlke, Helen; O'Neel, Shad; McLaughlin, Seanna

2017-04-01

Though recent advances have been made in the understanding of glacial hydrologic pathways, accurate predictions and descriptions of glacial hydrologic processes remain a challenge. The most common method to investigate subglacial pathways tends to be dye tracing. Due to the limited number of unique dye tracers, the photodegradability of some, and the typically long breakthrough times associated with such pathways, dye tracing experiments tend to be restricted to only a few injections, and therefore the contribution of only a few pathways can be investigated at a time. Five uniquely DNA-labelled microparticle tracers were injected in five different locations throughout the Wolverine Glacier ablation zone, one of two "benchmark glaciers" in Alaska and the subject of long term study by the United States Geological Survey. Stream water was sampled several hundred meters downstream at regular intervals and later analyzed for the presence of each tracer. Since each tracer was tagged with a unique sequence of DNA, the contribution of each to the total outflow can be quantified independently. Preliminary results indicate relatively short transit times, suggesting that the ablation zone is characterized by a high-volume (low pressure) subglacial hydrologic network (i.e. conduits). Here we present the results of the study, the challenges faced, and a discussion on the potential of the DNA-labelled microtracer technology.

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

PubMed Central

Park, Christopher Y.; Krishnan, Arjun; Zhu, Qian; Wong, Aaron K.; Lee, Young-Suk; Troyanskaya, Olga G.

2015-01-01

Motivation: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses. Results: We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry). Availability and implementation: An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org. Contact: ogt@cs.princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25431329

Tyrosine kinase Btk regulates E-selectin-mediated integrin activation and neutrophil recruitment by controlling phospholipase C (PLC) gamma2 and PI3Kgamma pathways.

PubMed

Mueller, Helena; Stadtmann, Anika; Van Aken, Hugo; Hirsch, Emilio; Wang, Demin; Ley, Klaus; Zarbock, Alexander

2010-04-15

Selectins mediate leukocyte rolling, trigger beta(2)-integrin activation, and promote leukocyte recruitment into inflamed tissue. E-selectin binding to P-selectin glycoprotein ligand 1 (PSGL-1) leads to activation of an immunoreceptor tyrosine-based activation motif (ITAM)-dependent pathway, which in turn activates the spleen tyrosine kinase (Syk). However, the signaling pathway linking Syk to integrin activation after E-selectin engagement is unknown. To identify the pathway, we used different gene-deficient mice in autoperfused flow chamber, intravital microscopy, peritonitis, and biochemical studies. We report here that the signaling pathway downstream of Syk divides into a phospholipase C (PLC) gamma2- and phosphoinositide 3-kinase (PI3K) gamma-dependent pathway. The Tec family kinase Bruton tyrosine kinase (Btk) is required for activating both pathways, generating inositol-3,4,5-trisphosphate (IP(3)), and inducing E-selectin-mediated slow rolling. Inhibition of this signal-transduction pathway diminished Galpha(i)-independent leukocyte adhesion to and transmigration through endothelial cells in inflamed postcapillary venules of the cremaster. Galpha(i)-independent neutrophil recruitment into the inflamed peritoneal cavity was reduced in Btk(-/-) and Plcg2(-/-) mice. Our data demonstrate the functional importance of this newly identified signaling pathway mediated by E-selectin engagement.

Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations

PubMed Central

Friston, Karl J.; Kleinschmidt, Andreas

2010-01-01

Perceptual decisions can be made when sensory input affords an inference about what generated that input. Here, we report findings from two independent perceptual experiments conducted during functional magnetic resonance imaging (fMRI) with a sparse event-related design. The first experiment, in the visual modality, involved forced-choice discrimination of coherence in random dot kinematograms that contained either subliminal or periliminal motion coherence. The second experiment, in the auditory domain, involved free response detection of (non-semantic) near-threshold acoustic stimuli. We analysed fluctuations in ongoing neural activity, as indexed by fMRI, and found that neuronal activity in sensory areas (extrastriate visual and early auditory cortex) biases perceptual decisions towards correct inference and not towards a specific percept. Hits (detection of near-threshold stimuli) were preceded by significantly higher activity than both misses of identical stimuli or false alarms, in which percepts arise in the absence of appropriate sensory input. In accord with predictive coding models and the free-energy principle, this observation suggests that cortical activity in sensory brain areas reflects the precision of prediction errors and not just the sensory evidence or prediction errors per se. PMID:20369004

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic. PMID:26694379

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.

Out of Borneo: biogeography, phylogeny and divergence date estimates of Artocarpus (Moraceae)

PubMed Central

Gardner, Elliot M.; Harris, Robert; Chaveerach, Arunrat; Pereira, Joan T.

2017-01-01

Abstract Background and Aims The breadfruit genus (Artocarpus, Moraceae) includes valuable underutilized fruit tree crops with a centre of diversity in Southeast Asia. It belongs to the monophyletic tribe Artocarpeae, whose only other members include two small neotropical genera. This study aimed to reconstruct the phylogeny, estimate divergence dates and infer ancestral ranges of Artocarpeae, especially Artocarpus, to better understand spatial and temporal evolutionary relationships and dispersal patterns in a geologically complex region. Methods To investigate the phylogeny and biogeography of Artocarpeae, this study used Bayesian and maximum likelihood approaches to analyze DNA sequences from six plastid and two nuclear regions from 75% of Artocarpus species, both neotropical Artocarpeae genera, and members of all other Moraceae tribes. Six fossil-based calibrations within the Moraceae family were used to infer divergence times. Ancestral areas and estimated dispersal events were also inferred. Key Results Artocarpeae, Artocarpus and four monophyletic Artocarpus subgenera were well supported. A late Cretaceous origin of the Artocarpeae tribe in the Americas is inferred, followed by Eocene radiation of Artocarpus in Asia, with the greatest diversification occurring during the Miocene. Borneo is reconstructed as the ancestral range of Artocarpus, with dozens of independent in situ diversification events inferred there, as well as dispersal events to other regions of Southeast Asia. Dispersal pathways of Artocarpus and its ancestors are proposed. Conclusions Borneo was central in the diversification of the genus Artocarpus and probably served as the centre from which species dispersed and diversified in several directions. The greatest amount of diversification is inferred to have occurred during the Miocene, when sea levels fluctuated and land connections frequently existed between Borneo, mainland Asia, Sumatra and Java. Many species found in these areas have extant overlapping ranges, suggesting that sympatric speciation may have occurred. By contrast, Artocarpus diversity east of Borneo (where many of the islands have no historical connections to the landmasses of the Sunda and Sahul shelves) is unique and probably the product of over water long-distance dispersal events and subsequent diversification in allopatry. This work represents the most comprehensive Artocarpus phylogeny and biogeography study to date and supports Borneo as an evolutionary biodiversity hotspot. PMID:28073771

Combinatorial influence of environmental parameters on transcription factor activity.

PubMed

Knijnenburg, T A; Wessels, L F A; Reinders, M J T

2008-07-01

Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. The Matlab code is available from the authors upon request. Supplementary data are available at Bioinformatics online.

Integrating discrete stochastic models and single-cell experiments to infer predictive models of MAPK-induced transcription dynamics

NASA Astrophysics Data System (ADS)

Munsky, Brian

2015-03-01

MAPK signal-activated transcription plays central roles in myriad biological processes including stress adaptation responses and cell fate decisions. Recent single-cell and single-molecule experiments have advanced our ability to quantify the spatial, temporal, and stochastic fluctuations for such signals and their downstream effects on transcription regulation. This talk explores how integrating such experiments with discrete stochastic computational analyses can yield quantitative and predictive understanding of transcription regulation in both space and time. We use single-molecule mRNA fluorescence in situ hybridization (smFISH) experiments to reveal locations and numbers of multiple endogenous mRNA species in 100,000's of individual cells, at different times and under different genetic and environmental perturbations. We use finite state projection methods to precisely and efficiently compute the full joint probability distributions of these mRNA, which capture measured spatial, temporal and correlative fluctuations. By combining these experimental and computational tools with uncertainty quantification, we systematically compare models of varying complexity and select those which give optimally precise and accurate predictions in new situations. We use these tools to explore two MAPK-activated gene regulation pathways. In yeast adaptation to osmotic shock, we analyze Hog1 kinase activation of transcription for three different genes STL1 (osmotic stress), CTT1 (oxidative stress) and HSP12 (heat shock). In human osteosarcoma cells under serum induction, we analyze ERK activation of c-Fos transcription.

Metabolic Respiration Induces AMPK- and Ire1p-Dependent Activation of the p38-Type HOG MAPK Pathway

PubMed Central

Adhikari, Hema; Cullen, Paul J.

2014-01-01

Evolutionarily conserved mitogen activated protein kinase (MAPK) pathways regulate the response to stress as well as cell differentiation. In Saccharomyces cerevisiae, growth in non-preferred carbon sources (like galactose) induces differentiation to the filamentous cell type through an extracellular-signal regulated kinase (ERK)-type MAPK pathway. The filamentous growth MAPK pathway shares components with a p38-type High Osmolarity Glycerol response (HOG) pathway, which regulates the response to changes in osmolarity. To determine the extent of functional overlap between the MAPK pathways, comparative RNA sequencing was performed, which uncovered an unexpected role for the HOG pathway in regulating the response to growth in galactose. The HOG pathway was induced during growth in galactose, which required the nutrient regulatory AMP-dependent protein kinase (AMPK) Snf1p, an intact respiratory chain, and a functional tricarboxylic acid (TCA) cycle. The unfolded protein response (UPR) kinase Ire1p was also required for HOG pathway activation in this context. Thus, the filamentous growth and HOG pathways are both active during growth in galactose. The two pathways redundantly promoted growth in galactose, but paradoxically, they also inhibited each other's activities. Such cross-modulation was critical to optimize the differentiation response. The human fungal pathogen Candida albicans showed a similar regulatory circuit. Thus, an evolutionarily conserved regulatory axis links metabolic respiration and AMPK to Ire1p, which regulates a differentiation response involving the modulated activity of ERK and p38 MAPK pathways. PMID:25356552

Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders.

PubMed

Liu, Bin; Govindan, Ramesh; Uzzi, Brian

2016-01-01

Emotions are increasingly inferred linguistically from online data with a goal of predicting off-line behavior. Yet, it is unknown whether emotions inferred linguistically from online communications correlate with actual changes in off-line activity. We analyzed all 886,000 trading decisions and 1,234,822 instant messages of 30 professional day traders over a continuous 2 year period. Linguistically inferring the traders' emotional states from instant messages, we find that emotions expressed in online communications reflect the same distributions of emotions found in controlled experiments done on traders. Further, we find that expressed online emotions predict the profitability of actual trading behavior. Relative to their baselines, traders who expressed little emotion or traders that expressed high levels of emotion made relatively unprofitable trades. Conversely, traders expressing moderate levels of emotional activation made relatively profitable trades.

Dopamine, reward learning, and active inference

PubMed Central

FitzGerald, Thomas H. B.; Dolan, Raymond J.; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings. PMID:26581305

Dopamine, reward learning, and active inference.

PubMed

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders

PubMed Central

Liu, Bin; Govindan, Ramesh; Uzzi, Brian

2016-01-01

Emotions are increasingly inferred linguistically from online data with a goal of predicting off-line behavior. Yet, it is unknown whether emotions inferred linguistically from online communications correlate with actual changes in off-line activity. We analyzed all 886,000 trading decisions and 1,234,822 instant messages of 30 professional day traders over a continuous 2 year period. Linguistically inferring the traders’ emotional states from instant messages, we find that emotions expressed in online communications reflect the same distributions of emotions found in controlled experiments done on traders. Further, we find that expressed online emotions predict the profitability of actual trading behavior. Relative to their baselines, traders who expressed little emotion or traders that expressed high levels of emotion made relatively unprofitable trades. Conversely, traders expressing moderate levels of emotional activation made relatively profitable trades. PMID:26765539

Adolescent physical activity and health: a systematic review.

PubMed

Hallal, Pedro C; Victora, Cesar G; Azevedo, Mario R; Wells, Jonathan C K

2006-01-01

Physical activity in adolescence may contribute to the development of healthy adult lifestyles, helping reduce chronic disease incidence. However, definition of the optimal amount of physical activity in adolescence requires addressing a number of scientific challenges. This article reviews the evidence on short- and long-term health effects of adolescent physical activity. Systematic reviews of the literature were undertaken using a reference period between 2000 and 2004, based primarily on the MEDLINE/PubMed database. Relevant studies were identified by examination of titles, abstracts and full papers, according to inclusion criteria defined a priori. A conceptual framework is proposed to outline how adolescent physical activity may contribute to adult health, including the following pathways: (i) pathway A--tracking of physical activity from adolescence to adulthood; (ii) pathway B--direct influence of adolescent physical activity on adult morbidity; (iii) pathway C--role of physical activity in treating adolescent morbidity; and (iv) pathway D - short-term benefits of physical activity in adolescence on health. The literature reviews showed consistent evidence supporting pathway 'A', although the magnitude of the association appears to be moderate. Thus, there is an indirect effect on all health benefits resulting from adult physical activity. Regarding pathway 'B', adolescent physical activity seems to provide long-term benefits on bone health, breast cancer and sedentary behaviours. In terms of pathway 'C', water physical activities in adolescence are effective in the treatment of asthma, and exercise is recommended in the treatment of cystic fibrosis. Self-esteem is also positively affected by adolescent physical activity. Regarding pathway 'D', adolescent physical activity provides short-term benefits; the strongest evidence refers to bone and mental health. Appreciation of different mechanisms through which adolescent physical activity may influence adult health is essential for drawing recommendations; however, the amount of exercise needed for achieving different benefits may vary. Physical activity promotion must start in early life; although the 'how much' remains unknown and needs further research, the lifelong benefits of adolescent physical activity on adult health are unequivocal.

Great apes and children infer causal relations from patterns of variation and covariation.

PubMed

Völter, Christoph J; Sentís, Inés; Call, Josep

2016-10-01

We investigated whether nonhuman great apes (N=23), 2.5-year-old (N=20), and 3-year-old children (N=40) infer causal relations from patterns of variation and covariation by adapting the blicket detector paradigm for apes. We presented chimpanzees (Pan troglodytes), bonobos (Pan paniscus), orangutans (Pongo abelii), gorillas (Gorilla gorilla), and children (Homo sapiens) with a novel reward dispenser, the blicket detector. The detector was activated by inserting specific (yet randomly determined) objects, the so-called blickets. Once activated a reward was released, accompanied by lights and a short tone. Participants were shown different patterns of variation and covariation between two different objects and the activation of the detector. When subsequently choosing between one of the two objects to activate the detector on their own all species, except gorillas (who failed the training), took these patterns of correlation into account. In particular, apes and 2.5-year-old children ignored objects whose effect on the detector completely depended on the presence of another object. Follow-up experiments explored whether the apes and children were also able to re-evaluate evidence retrospectively. Only children (3-year-olds in particular) were able to make such retrospective inferences about causal structures from observing the effects of the experimenter's actions. Apes succeeded here only when they observed the effects of their own interventions. Together, this study provides evidence that apes, like young children, accurately infer causal structures from patterns of (co)variation and that they use this information to inform their own interventions. Copyright © 2016 Elsevier B.V. All rights reserved.

Designing microarray and RNA-Seq experiments for greater systems biology discovery in modern plant genomics.

PubMed

Yang, Chuanping; Wei, Hairong

2015-02-01

Microarray and RNA-seq experiments have become an important part of modern genomics and systems biology. Obtaining meaningful biological data from these experiments is an arduous task that demands close attention to many details. Negligence at any step can lead to gene expression data containing inadequate or composite information that is recalcitrant for pattern extraction. Therefore, it is imperative to carefully consider experimental design before launching a time-consuming and costly experiment. Contemporarily, most genomics experiments have two objectives: (1) to generate two or more groups of comparable data for identifying differentially expressed genes, gene families, biological processes, or metabolic pathways under experimental conditions; (2) to build local gene regulatory networks and identify hierarchically important regulators governing biological processes and pathways of interest. Since the first objective aims to identify the active molecular identities and the second provides a basis for understanding the underlying molecular mechanisms through inferring causality relationships mediated by treatment, an optimal experiment is to produce biologically relevant and extractable data to meet both objectives without substantially increasing the cost. This review discusses the major issues that researchers commonly face when embarking on microarray or RNA-seq experiments and summarizes important aspects of experimental design, which aim to help researchers deliberate how to generate gene expression profiles with low background noise but with more interaction to facilitate novel biological discoveries in modern plant genomics. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

The nutrient transceptor/PKA pathway functions independently of TOR and responds to leucine and Gcn2 in a TOR-independent manner.

PubMed

Conrad, Michaela; Kankipati, Harish Nag; Kimpe, Marlies; Van Zeebroeck, Griet; Zhang, Zhiqiang; Thevelein, Johan M

2017-08-01

Two nutrient-controlled signalling pathways, the PKA and TOR pathway, play a major role in nutrient regulation of growth as well as growth-correlated properties in yeast. The relationship between the two pathways is not well understood. We have used Gap1 and Pho84 transceptor-mediated activation of trehalase and phosphorylation of fragmented Sch9 as a read-out for rapid nutrient activation of PKA or TORC1, respectively. We have identified conditions in which L-citrulline-induced activation of Sch9 phosphorylation is compromised, but not activation of trehalase: addition of the TORC1 inhibitor, rapamycin and low levels of L-citrulline. The same disconnection was observed for phosphate activation in phosphate-starved cells. The leu2 auxotrophic mutation reduces amino acid activation of trehalase, which is counteracted by deletion of GCN2. Both effects were also independent of TORC1. Our results show that rapid activation of the TOR pathway by amino acids is not involved in rapid activation of the PKA pathway and that effects of Gcn2 inactivation as well as leu2 auxotrophy all act independently of the TOR pathway. Hence, rapid nutrient signalling to PKA and TOR in cells arrested by nutrient starvation acts through parallel pathways. © FEMS 2017.

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

PubMed

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

KSHV LANA inhibits TGF-β signaling through epigenetic silencing of the TGF-β type II receptor

PubMed Central

Di Bartolo, Daniel L.; Cannon, Mark; Liu, Yi-Fang; Renne, Rolf; Chadburn, Amy; Boshoff, Chris

2008-01-01

Signaling through the transforming growth factor–β (TGF-β) pathway results in growth inhibition and induction of apoptosis in various cell types. We show that this pathway is blocked in Kaposi sarcoma herpesvirus (KSHV)–infected primary effusion lymphoma through down-regulation of the TGF-β type II receptor (TβRII) by epigenetic mechanisms. Our data also suggest that KSHV infection may result in lower expression of TβRII in Kaposi sarcoma and multicentric Castleman disease. KSHV-encoded LANA associates with the promoter of TβRII and leads to its methylation and to the deacetylation of proximal histones. Reestablishment of signaling through this pathway reduces viability of these cells, inferring that KSHV-mediated blockage of TGF-β signaling plays a role in the establishment and progression of KSHV-associated neoplasia. These data suggest a mechanism whereby KSHV evades both the antiproliferative effects of TGF-β signaling by silencing TβRII gene expression and immune recognition by suppressing TGF-β–responsive immune cells through the elevated secretion of TGF-β1. PMID:18199825

Possible formation pathways for the low-density Neptune-mass planet HAT-P-26b

NASA Astrophysics Data System (ADS)

Ali-Dib, Mohamad; Lakhlani, Gunjan

2018-01-01

We investigate possible pathways for the formation of the low-density Neptune-mass planet HAT-P-26b. We use two different formation models based on pebble and planetesimal accretion, and includes gas accretion, disc migration and simple photoevaporation. The models track the atmospheric oxygen abundance, in addition to the orbital period, and mass of the forming planets, which we compare to HAT-P-26b. We find that pebble accretion can explain this planet more naturally than planetesimal accretion that fails completely unless we artificially enhance the disc metallicity significantly. Pebble accretion models can reproduce HAT-P-26b with either a high initial core mass and low amount of envelope enrichment through core erosion or pebbles dissolution, or the opposite, with both scenarios being possible. Assuming a low envelope enrichment factor as expected from convection theory and comparable to the values we can infer from the D/H measurements in Uranus and Neptune, our most probable formation pathway for HAT-P-26b is through pebble accretion starting around 10 au early in the disc's lifetime.

Transdermal transport pathway creation: Electroporation pulse order.

PubMed

Becker, Sid; Zorec, Barbara; Miklavčič, Damijan; Pavšelj, Nataša

2014-11-01

In this study we consider the physics underlying electroporation which is administered to skin in order to radically increase transdermal drug delivery. The method involves the application of intense electric fields to alter the structure of the impermeable outer layer, the stratum corneum. A generally held view in the field of skin electroporation is that the skin's drop in resistance (to transport) is proportional to the total power of the pulses (which may be inferred by the number of pulses administered). Contrary to this belief, experiments conducted in this study show that the application of high voltage pulses prior to the application of low voltage pulses result in lower transport than when low voltage pulses alone are applied (when less total pulse power is administered). In order to reconcile these unexpected experimental results, a computational model is used to conduct an analysis which shows that the high density distribution of very small aqueous pathways through the stratum corneum associated with high voltage pulses is detrimental to the evolution of larger pathways that are associated with low voltage pulses. Copyright © 2014 Elsevier Inc. All rights reserved.

Folding of polyglutamine chains

NASA Astrophysics Data System (ADS)

Chopra, Manan; Reddy, Allam S.; Abbott, N. L.; de Pablo, J. J.

2008-10-01

Long polyglutamine chains have been associated with a number of neurodegenerative diseases. These include Huntington's disease, where expanded polyglutamine (PolyQ) sequences longer than 36 residues are correlated with the onset of symptoms. In this paper we study the folding pathway of a 54-residue PolyQ chain into a β-helical structure. Transition path sampling Monte Carlo simulations are used to generate unbiased reactive pathways between unfolded configurations and the folded β-helical structure of the polyglutamine chain. The folding process is examined in both explicit water and an implicit solvent. Both models reveal that the formation of a few critical contacts is necessary and sufficient for the molecule to fold. Once the primary contacts are formed, the fate of the protein is sealed and it is largely committed to fold. We find that, consistent with emerging hypotheses about PolyQ aggregation, a stable β-helical structure could serve as the nucleus for subsequent polymerization of amyloid fibrils. Our results indicate that PolyQ sequences shorter than 36 residues cannot form that nucleus, and it is also shown that specific mutations inferred from an analysis of the simulated folding pathway exacerbate its stability.

Theory of optimal information transmission in E. coli chemotaxis pathway

NASA Astrophysics Data System (ADS)

Micali, Gabriele; Endres, Robert G.

Bacteria live in complex microenvironments where they need to make critical decisions fast and reliably. These decisions are inherently affected by noise at all levels of the signaling pathway, and cells are often modeled as an input-output device that transmits extracellular stimuli (input) to internal proteins (channel), which determine the final behavior (output). Increasing the amount of transmitted information between input and output allows cells to better infer extracellular stimuli and respond accordingly. However, in contrast to electronic devices, the separation into input, channel, and output is not always clear in biological systems. Output might feed back into the input, and the channel, made by proteins, normally interacts with the input. Furthermore, a biological channel is affected by mutations and can change under evolutionary pressure. Here, we present a novel approach to maximize information transmission: given cell-external and internal noise, we analytically identify both input distributions and input-output relations that optimally transmit information. Using E. coli chemotaxis as an example, we conclude that its pathway is compatible with an optimal information transmission device despite the ultrasensitive rotary motors.

Inferencing Processes After Right Hemisphere Brain Damage: Effects of Contextual Bias

PubMed Central

Blake, Margaret Lehman

2009-01-01

Purpose Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method Fourteen adults with no brain damage (NBD) and 14 with RHD read stories constructed with either high predictability or low predictability of a specific outcome. Reading time for a sentence that disconfirmed the target outcome was measured and compared with a control story context. Results Adults with RHD evidenced activation of predictive inferences only for highly predictive conditions, whereas NBD adults generated inferences in both high- and low-predictability stories. Adults with RHD were more likely than those with NBD to require additional time to integrate inferences in high-predictability conditions. The latter finding was related to working memory for the RHD group. Results are interpreted in light of previous findings obtained using the same stimuli. Conclusions RHD does not abolish the ability to use context. Evidence of predictive inferencing is influenced by task and strength of inference activation. Treatment considerations and cautions regarding interpreting results from one methodology are discussed. PMID:19252126

Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors.

PubMed

Peterson, Christine; Vannucci, Marina; Karakas, Cemal; Choi, William; Ma, Lihua; Maletić-Savatić, Mirjana

2013-10-01

Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.

Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors

PubMed Central

PETERSON, CHRISTINE; VANNUCCI, MARINA; KARAKAS, CEMAL; CHOI, WILLIAM; MA, LIHUA; MALETIĆ-SAVATIĆ, MIRJANA

2014-01-01

Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation. PMID:24533172

Inferring a dual-stream model of mentalizing from associative white matter fibres disconnection.

PubMed

Herbet, Guillaume; Lafargue, Gilles; Bonnetblanc, François; Moritz-Gasser, Sylvie; Menjot de Champfleur, Nicolas; Duffau, Hugues

2014-03-01

In the field of cognitive neuroscience, it is increasingly accepted that mentalizing is subserved by a complex frontotemporoparietal cortical network. Some researchers consider that this network can be divided into two distinct but interacting subsystems (the mirror system and the mentalizing system per se), which respectively process low-level, perceptive-based aspects and high-level, inference-based aspects of this sociocognitive function. However, evidence for this type of functional dissociation in a given neuropsychological population is currently lacking and the structural connectivities of the two mentalizing subnetworks have not been established. Here, we studied mentalizing in a large sample of patients (n = 93; 46 females; age range: 18-65 years) who had been resected for diffuse low-grade glioma-a rare tumour that migrates preferentially along associative white matter pathways. This neurological disorder constitutes an ideal pathophysiological model in which to study the functional anatomy of associative pathways. We mapped the location of each patient's resection cavity and residual lesion infiltration onto the Montreal Neurological Institute template brain and then performed multilevel lesion analyses (including conventional voxel-based lesion-symptom mapping and subtraction lesion analyses). Importantly, we estimated each associative pathway's degree of disconnection (i.e. the degree of lesion infiltration) and built specific hypotheses concerning the connective anatomy of the mentalizing subnetworks. As expected, we found that impairments in mentalizing were mainly related to the disruption of right frontoparietal connectivity. More specifically, low-level and high-level mentalizing accuracy were correlated with the degree of disconnection in the arcuate fasciculus and the cingulum, respectively. To the best of our knowledge, our findings constitute the first experimental data on the structural connectivity of the mentalizing network and suggest the existence of a dual-stream hodological system. Our results may lead to a better understanding of disorders that affect social cognition, especially in neuropathological conditions characterized by atypical/aberrant structural connectivity, such as autism spectrum disorders.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

PubMed Central

Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C.

2014-01-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology. PMID:24921649

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

PubMed

Chen, Guocai; Cairelli, Michael J; Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C

2014-06-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

PTPRS Regulates Colorectal Cancer RAS Pathway Activity by Inactivating Erk and Preventing Its Nuclear Translocation.

PubMed

Davis, Thomas B; Yang, Mingli; Schell, Michael J; Wang, Heiman; Ma, Le; Pledger, W Jack; Yeatman, Timothy J

2018-06-18

Colorectal cancer (CRC) growth and progression is frequently driven by RAS pathway activation through upstream growth factor receptor activation or through mutational activation of KRAS or BRAF. Here we describe an additional mechanism by which the RAS pathway may be modulated in CRC. PTPRS, a receptor-type protein tyrosine phosphatase, appears to regulate RAS pathway activation through ERK. PTPRS modulates ERK phosphorylation and subsequent translocation to the nucleus. Native mutations in PTPRS, present in ~10% of CRC, may reduce its phosphatase activity while increasing ERK activation and downstream transcriptional signaling.

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

PubMed

Klein, Allon M; Mazutis, Linas; Akartuna, Ilke; Tallapragada, Naren; Veres, Adrian; Li, Victor; Peshkin, Leonid; Weitz, David A; Kirschner, Marc W

2015-05-21

It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT. Copyright © 2015 Elsevier Inc. All rights reserved.

Perspective: Maximum caliber is a general variational principle for dynamical systems

NASA Astrophysics Data System (ADS)

Dixit, Purushottam D.; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A.

2018-01-01

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics—such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production—are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Perspective: Maximum caliber is a general variational principle for dynamical systems.

PubMed

Dixit, Purushottam D; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A

2018-01-07

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics-such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production-are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Reverse engineering of gene regulatory networks.

PubMed

Cho, K H; Choo, S M; Jung, S H; Kim, J R; Choi, H S; Kim, J

2007-05-01

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

Chemotext: A Publicly Available Web Server for Mining Drug-Target-Disease Relationships in PubMed.

PubMed

Capuzzi, Stephen J; Thornton, Thomas E; Liu, Kammy; Baker, Nancy; Lam, Wai In; O'Banion, Colin P; Muratov, Eugene N; Pozefsky, Diane; Tropsha, Alexander

2018-02-26

Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known DTD relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu .

Concurrent activation of striatal direct and indirect pathways during action initiation.

PubMed

Cui, Guohong; Jun, Sang Beom; Jin, Xin; Pham, Michael D; Vogel, Steven S; Lovinger, David M; Costa, Rui M

2013-02-14

The basal ganglia are subcortical nuclei that control voluntary actions, and they are affected by a number of debilitating neurological disorders. The prevailing model of basal ganglia function proposes that two orthogonal projection circuits originating from distinct populations of spiny projection neurons (SPNs) in the striatum--the so-called direct and indirect pathways--have opposing effects on movement: activity of direct-pathway SPNs is thought to facilitate movement, whereas activity of indirect-pathway SPNs is presumed to inhibit movement. This model has been difficult to test owing to the lack of methods to selectively measure the activity of direct- and indirect-pathway SPNs in freely moving animals. Here we develop a novel in vivo method to specifically measure direct- and indirect-pathway SPN activity, using Cre-dependent viral expression of the genetically encoded calcium indicator (GECI) GCaMP3 in the dorsal striatum of D1-Cre (direct-pathway-specific) and A2A-Cre (indirect-pathway-specific) mice. Using fibre optics and time-correlated single-photon counting (TCSPC) in mice performing an operant task, we observed transient increases in neural activity in both direct- and indirect-pathway SPNs when animals initiated actions, but not when they were inactive. Concurrent activation of SPNs from both pathways in one hemisphere preceded the initiation of contraversive movements and predicted the occurrence of specific movements within 500 ms. These observations challenge the classical view of basal ganglia function and may have implications for understanding the origin of motor symptoms in basal ganglia disorders.

Drosophila insulin and target of rapamycin (TOR) pathways regulate GSK3 beta activity to control Myc stability and determine Myc expression in vivo.

PubMed

Parisi, Federica; Riccardo, Sara; Daniel, Margaret; Saqcena, Mahesh; Kundu, Nandini; Pession, Annalisa; Grifoni, Daniela; Stocker, Hugo; Tabak, Esteban; Bellosta, Paola

2011-09-27

Genetic studies in Drosophila melanogaster reveal an important role for Myc in controlling growth. Similar studies have also shown how components of the insulin and target of rapamycin (TOR) pathways are key regulators of growth. Despite a few suggestions that Myc transcriptional activity lies downstream of these pathways, a molecular mechanism linking these signaling pathways to Myc has not been clearly described. Using biochemical and genetic approaches we tried to identify novel mechanisms that control Myc activity upon activation of insulin and TOR signaling pathways. Our biochemical studies show that insulin induces Myc protein accumulation in Drosophila S2 cells, which correlates with a decrease in the activity of glycogen synthase kinase 3-beta (GSK3β ) a kinase that is responsible for Myc protein degradation. Induction of Myc by insulin is inhibited by the presence of the TOR inhibitor rapamycin, suggesting that insulin-induced Myc protein accumulation depends on the activation of TOR complex 1. Treatment with amino acids that directly activate the TOR pathway results in Myc protein accumulation, which also depends on the ability of S6K kinase to inhibit GSK3β activity. Myc upregulation by insulin and TOR pathways is a mechanism conserved in cells from the wing imaginal disc, where expression of Dp110 and Rheb also induces Myc protein accumulation, while inhibition of insulin and TOR pathways result in the opposite effect. Our functional analysis, aimed at quantifying the relative contribution of Myc to ommatidial growth downstream of insulin and TOR pathways, revealed that Myc activity is necessary to sustain the proliferation of cells from the ommatidia upon Dp110 expression, while its contribution downstream of TOR is significant to control the size of the ommatidia. Our study presents novel evidence that Myc activity acts downstream of insulin and TOR pathways to control growth in Drosophila. At the biochemical level we found that both these pathways converge at GSK3β to control Myc protein stability, while our genetic analysis shows that insulin and TOR pathways have different requirements for Myc activity during development of the eye, suggesting that Myc might be differentially induced by these pathways during growth or proliferation of cells that make up the ommatidia.

The permeability evolution of tuffisites and outgassing from dense rhyolitic magma

NASA Astrophysics Data System (ADS)

Heap, M. J.; Tuffen, H.; Wadsworth, F. B.; Reuschlé, T.; Castro, J. M.; Schipper, C. I.

2017-12-01

Recent observations of rhyolitic lava effusion from eruptions in Chile indicate that simultaneous pyroclastic venting facilitates outgassing. Venting from conduit-plugging lava domes is pulsatory and occurs through shallow fracture networks that deliver pyroclastic debris and exsolved gases to the surface. However, these fractures become blocked as the particulate fracture infill sinters viscously, thus drastically reducing permeability. Tuffisites, fossilized debris-filled fractures of this venting process, are abundant in pyroclastic material ejected during hybrid explosive-effusive activity. Dense tuffisite-hosting obsidian bombs ejected from Volcán Chaitén (Chile) in 2008 afford an opportunity to better understand the permeability evolution of tuffisites within low-permeability conduit plugs, wherein gas mobility is reliant upon fracture pathways. We use laboratory measurements of the permeability and porosity of tuffisites that preserve different degrees of sintering, combined with a grainsize-based sintering model and constraints on pressure-time paths from H2O diffusion, to place first-order constraints on tuffisite permeability evolution. Inferred timescales of sintering-driven tuffisite compaction and permeability loss, spanning minutes to hours, coincide with observed vent pulsations during hybrid rhyolitic activity and, more broadly, timescales of pressurization accompanying silicic lava dome extrusion. We therefore conclude that sintering exerts a first-order control on fracture-assisted outgassing from low-permeability, conduit-plugging silicic magma.

Potential for microbial H2 and metal transformations associated with novel bacteria and archaea in deep terrestrial subsurface sediments

PubMed Central

Hernsdorf, Alex W; Amano, Yuki; Miyakawa, Kazuya; Ise, Kotaro; Suzuki, Yohey; Anantharaman, Karthik; Probst, Alexander; Burstein, David; Thomas, Brian C; Banfield, Jillian F

2017-01-01

Geological sequestration in deep underground repositories is the prevailing proposed route for radioactive waste disposal. After the disposal of radioactive waste in the subsurface, H2 may be produced by corrosion of steel and, ultimately, radionuclides will be exposed to the surrounding environment. To evaluate the potential for microbial activities to impact disposal systems, we explored the microbial community structure and metabolic functions of a sediment-hosted ecosystem at the Horonobe Underground Research Laboratory, Hokkaido, Japan. Overall, we found that the ecosystem hosted organisms from diverse lineages, including many from the phyla that lack isolated representatives. The majority of organisms can metabolize H2, often via oxidative [NiFe] hydrogenases or electron-bifurcating [FeFe] hydrogenases that enable ferredoxin-based pathways, including the ion motive Rnf complex. Many organisms implicated in H2 metabolism are also predicted to catalyze carbon, nitrogen, iron and sulfur transformations. Notably, iron-based metabolism is predicted in a novel lineage of Actinobacteria and in a putative methane-oxidizing ANME-2d archaeon. We infer an ecological model that links microorganisms to sediment-derived resources and predict potential impacts of microbial activity on H2 consumption and retardation of radionuclide migration. PMID:28350393

Genomic pathway analysis reveals that EZH2 and HDAC4 represent mutually exclusive epigenetic pathways across human cancers

PubMed Central

2013-01-01

Background Alterations in epigenetic marks, including methylation or acetylation, are common in human cancers. For many epigenetic pathways, however, direct measures of activity are unknown, making their role in various cancers difficult to assess. Gene expression signatures facilitate the examination of patterns of epigenetic pathway activation across and within human cancer types allowing better understanding of the relationships between these pathways. Methods We used Bayesian regression to generate gene expression signatures from normal epithelial cells before and after epigenetic pathway activation. Signatures were applied to datasets from TCGA, GEO, CaArray, ArrayExpress, and the cancer cell line encyclopedia. For TCGA data, signature results were correlated with copy number variation and DNA methylation changes. GSEA was used to identify biologic pathways related to the signatures. Results We developed and validated signatures reflecting downstream effects of enhancer of zeste homolog 2(EZH2), histone deacetylase(HDAC) 1, HDAC4, sirtuin 1(SIRT1), and DNA methyltransferase 2(DNMT2). By applying these signatures to data from cancer cell lines and tumors in large public repositories, we identify those cancers that have the highest and lowest activation of each of these pathways. Highest EZH2 activation is seen in neuroblastoma, hepatocellular carcinoma, small cell lung cancer, and melanoma, while highest HDAC activity is seen in pharyngeal cancer, kidney cancer, and pancreatic cancer. Across all datasets studied, activation of both EZH2 and HDAC4 is significantly underrepresented. Using breast cancer and glioblastoma as examples to examine intrinsic subtypes of particular cancers, EZH2 activation was highest in luminal breast cancers and proneural glioblastomas, while HDAC4 activation was highest in basal breast cancer and mesenchymal glioblastoma. EZH2 and HDAC4 activation are associated with particular chromosome abnormalities: EZH2 activation with aberrations in genes from the TGF and phosphatidylinositol pathways and HDAC4 activation with aberrations in inflammatory and chemokine related genes. Conclusion Gene expression patterns can reveal the activation level of epigenetic pathways. Epigenetic pathways define biologically relevant subsets of human cancers. EZH2 activation and HDAC4 activation correlate with growth factor signaling and inflammation, respectively, and represent two distinct states for cancer cells. This understanding may allow us to identify targetable drivers in these cancer subsets. PMID:24079712

Copper-transporting P-type ATPases use a unique ion-release pathway

PubMed Central

Andersson, Magnus; Mattle, Daniel; Sitsel, Oleg; Nielsen, Anna Marie; White, Stephen H.; Nissen, Poul; Gourdon, Pontus

2014-01-01

Heavy metals in cells are typically regulated by PIB-type ATPases such as the copper transporting Cu+-ATPases. The first crystal structure of a Cu+-ATPase (LpCopA) was trapped in a transition state of dephosphorylation (E2.Pi) and inferred to be occluded. The structure revealed a PIB-specific topology and suggested a copper transport pathway across the membrane. Here we show by molecular dynamics (MD) simulations that extracellular water solvates the transmembrane (TM) domain, indicative of a pathway for Cu+ release. Furthermore, a new LpCopA crystal structure determined at 2.8 Å resolution, trapped in the E2P state (which is associated with extracellular exchange in PII-type ATPases), delineates the same conduit as also further supported by site-directed mutagenesis. The E2P and E2.Pi states therefore appear equivalent and open to the extracellular side, in contrast to PII-type ATPases where the E2.Pi state is occluded. This indicates that Cu+-ATPases couple dephosphorylation differently to the conformational changes associated with ion extrusion. The ion pathway may explain why Menkes’ and Wilson’s disease mutations at the extracellular side impair protein function, and points to an accessible site for novel inhibitors targeting Cu+-ATPases of pathogens. PMID:24317491

Integrating publicly-available data to generate computationally ...

EPA Pesticide Factsheets

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 is limited. Methods are needed to assimilate large amounts of available molecular data and quickly generate putative AOPs for further testing and use in hazard assessment. A graph-based workflow was used to facilitate the integration of multiple data types to generate computationally-predicted (cp) AOPs. Edges between graph entities were identified through direct experimental or literature information or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20,000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways measured by differential gene expression and high-throughput screening targets. Sub-networks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (hepatic steatosis) were extracted using the network topology. Comparison of the cpAOP subnetworks to published mechanistic descriptions for both CCl4 toxicity and hepatic steatosis demonstrate that computational approaches can be used to replicate manually curated AOPs and identify pathway targets that lack genomic mar

Cardiac extrinsic apoptotic pathway is silent in young but activated in elder mice overexpressing bovine GH: interplay with the intrinsic pathway.

PubMed

Bogazzi, Fausto; Russo, Dania; Raggi, Francesco; Bohlooly-Y, Mohammad; Tornell, Jan; Sardella, Chiara; Lombardi, Martina; Urbani, Claudio; Manetti, Luca; Brogioni, Sandra; Martino, Enio

2011-08-01

Apoptosis may occur through the mitochondrial (intrinsic) pathway and activation of death receptors (extrinsic pathway). Young acromegalic mice have reduced cardiac apoptosis whereas elder animals have increased cardiac apoptosis. Multiple intrinsic apoptotic pathways have been shown to be modulated by GH and other stimuli in the heart of acromegalic mice. However, the role of the extrinsic apoptotic pathways in acromegalic hearts is currently unknown. In young (3-month-old) acromegalic mice, expression of proteins of the extrinsic apoptotic pathway did not differ from that of wild-type animals, suggesting that this mechanism did not participate in the lower cardiac apoptosis levels observed at this age. On the contrary, the extrinsic pathway was active in elder (9-month-old) animals (as shown by increased expression of TRAIL, FADD, TRADD and increased activation of death inducing signaling complex) leading to increased levels of active caspase 8. It is worth noting that changes of some pro-apoptotic proteins were induced by GH, which seemed to have, in this context, pro-apoptotic effects. The extrinsic pathway influenced the intrinsic pathway by modulating t-Bid, the cellular levels of which were reduced in young and increased in elder animals. However, in young animals this effect was due to reduced levels of Bid regulated by the extrinsic pathway, whereas in elder animals the increased levels of t-Bid were due to the increased levels of active caspase 8. In conclusion, the extrinsic pathway participates in the cardiac pro-apoptotic phenotype of elder acromegalic animals either directly, enhancing caspase 8 levels or indirectly, increasing t-Bid levels and conveying death signals to the intrinsic pathway.

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity.

PubMed

Aggio, Raphael B M; Ruggiero, Katya; Villas-Bôas, Silas Granato

2010-12-01

Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity. These datasets are available in http://www.4shared.com/file/hTWyndYU/extra.html and http://www.4shared.com/file/VbQIIDeu/intra.html. PAPi package is available in: http://www.4shared.com/file/s0uIYWIg/PAPi_10.html s.villas-boas@auckland.ac.nz Supplementary data are available at Bioinformatics online.

Hedgehog signal transduction: key players, oncogenic drivers, and cancer therapy

PubMed Central

Pak, Ekaterina; Segal, Rosalind A.

2016-01-01

Summary The Hedgehog (Hh) signaling pathway governs complex developmental processes, including proliferation and patterning within diverse tissues. These activities rely on a tightly-regulated transduction system that converts graded Hh input signals into specific levels of pathway activity. Uncontrolled activation of Hh signaling drives tumor initiation and maintenance. However, recent entry of pathway-specific inhibitors into the clinic reveals mixed patient responses and thus prompts further exploration of pathway activation and inhibition. In this review, we share emerging insights on regulated and oncogenic Hh signaling, supplemented with updates on the development and use of Hh pathway-targeted therapies. PMID:27554855

Evaluating the Impact of a Multistrategy Inference Intervention for Middle-Grade Struggling Readers

PubMed Central

Elleman, Amy

2017-01-01

Purpose We examined the effectiveness of a multistrategy inference intervention designed to increase inference making and reading comprehension for middle-grade struggling readers. Method A total of 66 middle-grade struggling readers were randomized to treatment (n = 33) and comparison (n = 33) conditions. Students in the treatment group received explicit instruction in 4 inference strategies (i.e., clarification using text clues; activating and using prior knowledge; understanding character perspectives and author's purpose; answering inferential questions). In addition, narrative and informational texts were carefully chosen and sequenced to build requisite background knowledge to form inferences. Intervention was delivered in small groups of 3 students for 10 days of instruction. Results One-way analysis of covariance models on outcome measures with the respective pretest scores as a covariate revealed significant gains on a proximal measure of Egyptian-content knowledge (g = 1.37) and on a standardized measure of reading comprehension—i.e., Wechsler Individual Achievement Test–Third Edition Reading Comprehension (g = 0.46). Conclusion The moderate effect on a standardized measure of reading comprehension provides preliminary evidence for the effectiveness of this multistrategy inference intervention in improving reading comprehension of middle-grade struggling readers. PMID:27776201

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Context Inference for Mobile Applications in the UPCASE Project

NASA Astrophysics Data System (ADS)

Santos, André C.; Tarrataca, Luís; Cardoso, João M. P.; Ferreira, Diogo R.; Diniz, Pedro C.; Chainho, Paulo

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.