NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

PubMed Central

Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

2016-01-01

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

Strength of Temporal White Matter Pathways Predicts Semantic Learning.

PubMed

Ripollés, Pablo; Biel, Davina; Peñaloza, Claudia; Kaufmann, Jörn; Marco-Pallarés, Josep; Noesselt, Toemme; Rodríguez-Fornells, Antoni

2017-11-15

Learning the associations between words and meanings is a fundamental human ability. Although the language network is cortically well defined, the role of the white matter pathways supporting novel word-to-meaning mappings remains unclear. Here, by using contextual and cross-situational word learning, we tested whether learning the meaning of a new word is related to the integrity of the language-related white matter pathways in 40 adults (18 women). The arcuate, uncinate, inferior-fronto-occipital and inferior-longitudinal fasciculi were virtually dissected using manual and automatic deterministic fiber tracking. Critically, the automatic method allowed assessing the white matter microstructure along the tract. Results demonstrate that the microstructural properties of the left inferior-longitudinal fasciculus predict contextual learning, whereas the left uncinate was associated with cross-situational learning. In addition, we identified regions of special importance within these pathways: the posterior middle temporal gyrus, thought to serve as a lexical interface and specifically related to contextual learning; the anterior temporal lobe, known to be an amodal hub for semantic processing and related to cross-situational learning; and the white matter near the hippocampus, a structure fundamental for the initial stages of new-word learning and, remarkably, related to both types of word learning. No significant associations were found for the inferior-fronto-occipital fasciculus or the arcuate. While previous results suggest that learning new phonological word forms is mediated by the arcuate fasciculus, these findings show that the temporal pathways are the crucial neural substrate supporting one of the most striking human abilities: our capacity to identify correct associations between words and meanings under referential indeterminacy. SIGNIFICANCE STATEMENT The language-processing network is cortically (i.e., gray matter) well defined. However, the role of the

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

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

PubMed

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

2018-06-07

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

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

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

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

Meta-analysis of pathway enrichment: combining independent and dependent omics data sets.

PubMed

Kaever, Alexander; Landesfeind, Manuel; Feussner, Kirstin; Morgenstern, Burkhard; Feussner, Ivo; Meinicke, Peter

2014-01-01

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

[Formulation of combined predictive indicators using logistic regression model in predicting sepsis and prognosis].

PubMed

Duan, Liwei; Zhang, Sheng; Lin, Zhaofen

2017-02-01

To explore the method and performance of using multiple indices to diagnose sepsis and to predict the prognosis of severe ill patients. Critically ill patients at first admission to intensive care unit (ICU) of Changzheng Hospital, Second Military Medical University, from January 2014 to September 2015 were enrolled if the following conditions were satisfied: (1) patients were 18-75 years old; (2) the length of ICU stay was more than 24 hours; (3) All records of the patients were available. Data of the patients was collected by searching the electronic medical record system. Logistic regression model was formulated to create the new combined predictive indicator and the receiver operating characteristic (ROC) curve for the new predictive indicator was built. The area under the ROC curve (AUC) for both the new indicator and original ones were compared. The optimal cut-off point was obtained where the Youden index reached the maximum value. Diagnostic parameters such as sensitivity, specificity and predictive accuracy were also calculated for comparison. Finally, individual values were substituted into the equation to test the performance in predicting clinical outcomes. A total of 362 patients (218 males and 144 females) were enrolled in our study and 66 patients died. The average age was (48.3±19.3) years old. (1) For the predictive model only containing categorical covariants [including procalcitonin (PCT), lipopolysaccharide (LPS), infection, white blood cells count (WBC) and fever], increased PCT, increased WBC and fever were demonstrated to be independent risk factors for sepsis in the logistic equation. The AUC for the new combined predictive indicator was higher than that of any other indictor, including PCT, LPS, infection, WBC and fever (0.930 vs. 0.661, 0.503, 0.570, 0.837, 0.800). The optimal cut-off value for the new combined predictive indicator was 0.518. Using the new indicator to diagnose sepsis, the sensitivity, specificity and diagnostic accuracy

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

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

PubMed

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

2017-01-01

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

Pathway Distiller - multisource biological pathway consolidation.

PubMed

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

2012-01-01

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

A hadoop-based method to predict potential effective drug combination.

PubMed

Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

A Hadoop-Based Method to Predict Potential Effective Drug Combination

PubMed Central

Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. PMID:25147789

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

PubMed

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

2015-12-01

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

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

PubMed Central

2011-01-01

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

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

PubMed

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

2018-05-01

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

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

PubMed

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

2018-02-05

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

Motor pathway convergence predicts syllable repertoire size in oscine birds

PubMed Central

Moore, Jordan M.; Székely, Tamás; Büki, József; DeVoogd, Timothy J.

2011-01-01

Behavioral specializations are frequently associated with expansions of the brain regions controlling them. This principle of proper mass spans sensory, motor, and cognitive abilities and has been observed in a wide variety of vertebrate species. Yet, it is unknown if this concept extrapolates to entire neural pathways or how selection on a behavioral capacity might otherwise shape circuit structure. We investigate these questions by comparing the songs and neuroanatomy of 49 species from 17 families of songbirds, which vary immensely in the number of unique song components they produce and possess a conserved neural network dedicated to this behavior. We find that syllable repertoire size is strongly related to the degree of song motor pathway convergence. Repertoire size is more accurately predicted by the number of neurons in higher motor areas relative to that in their downstream targets than by the overall number of neurons in the song motor pathway. Additionally, the convergence values along serial premotor and primary motor projections account for distinct portions of the behavioral variation. These findings suggest that selection on song has independently shaped different components of this hierarchical pathway, and they elucidate how changes in pathway structure could have underlain elaborations of this learned motor behavior. PMID:21918109

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

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

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

PubMed Central

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

2011-01-01

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

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs

Predictive Validity of National Basketball Association Draft Combine on Future Performance.

PubMed

Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

2018-02-01

Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

Combining Physicochemical and Evolutionary Information for Protein Contact Prediction

PubMed Central

Schneider, Michael; Brock, Oliver

2014-01-01

We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information—evolutionary and physicochemical—we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/. PMID:25338092

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.

Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

PubMed

Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

2015-02-01

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

Combination of Entner-Doudoroff Pathway with MEP Increases Isoprene Production in Engineered Escherichia coli

PubMed Central

Liu, Huaiwei; Sun, Yuanzhang; Ramos, Kristine Rose M.; Nisola, Grace M.; Valdehuesa, Kris Niño G.; Lee, Won–Keun; Park, Si Jae; Chung, Wook-Jin

2013-01-01

Embden-Meyerhof pathway (EMP) in tandem with 2-C-methyl-D-erythritol 4-phosphate pathway (MEP) is commonly used for isoprenoid biosynthesis in E. coli. However, this combination has limitations as EMP generates an imbalanced distribution of pyruvate and glyceraldehyde-3-phosphate (G3P). Herein, four glycolytic pathways—EMP, Entner-Doudoroff Pathway (EDP), Pentose Phosphate Pathway (PPP) and Dahms pathway were tested as MEP feeding modules for isoprene production. Results revealed the highest isoprene production from EDP containing modules, wherein pyruvate and G3P were generated simultaneously; isoprene titer and yield were more than three and six times higher than those of the EMP module, respectively. Additionally, the PPP module that generates G3P prior to pyruvate was significantly more effective than the Dahms pathway, in which pyruvate production precedes G3P. In terms of precursor generation and energy/reducing-equivalent supply, EDP+PPP was found to be the ideal feeding module for MEP. These findings may launch a new direction for the optimization of MEP-dependent isoprenoid biosynthesis pathways. PMID:24376679

The low-dose combination preparation Vertigoheel activates cyclic nucleotide pathways and stimulates vasorelaxation.

PubMed

Heinle, H; Tober, C; Zhang, D; Jäggi, R; Kuebler, W M

2010-01-01

Vertigo of various and often unknown aetiologies has been associated with and attributed to impaired microvascular perfusion in the inner ear or the vertebrobasilar system. Vertigoheel is a low-dose combination preparation of proven value in the symptomatic treatment of vertigo. In the present study we tested the hypothesis that Vertigoheel's anti-vertiginous properties may in part be due to a vasodilatory effect exerted via stimulation of the adenylate and/or guanylate cyclase pathways. Thus, the influence of Vertigoheel or its single constituents on synthesis and degradation of cyclic nucleotides was measured. Furthermore, vessel myography was used to observe the effect of Vertigoheel on the vasoreactivity of rat carotid arteries. Vertigoheel and one of its constituents, Anamirta cocculus, stimulated adenylate cyclase activity, while another constituent, Conium maculatum, inhibited phosphodiesterase 5, suggesting that the individual constituents of Vertigoheel contribute differentially to a synergistic stimulation of cyclic nucleotide signalling pathways. In rat carotid artery rings, Vertigoheel counteracted phenylephrine-induced tonic vasoconstriction. The present data demonstrate a vasorelaxant effect of Vertigoheel that goes along with a synergistic stimulation of cyclic nucleotide pathways and may provide a mechanistic basis for the documented anti-vertiginous effects of this combination preparation.

Combination of degradation pathways for naphthalene utilization in Rhodococcus sp. strain TFB

PubMed Central

Tomás-Gallardo, Laura; Gómez-Álvarez, Helena; Santero, Eduardo; Floriano, Belén

2014-01-01

Rhodococcus sp. strain TFB is a metabolic versatile bacterium able to grow on naphthalene as the only carbon and energy source. Applying proteomic, genetic and biochemical approaches, we propose in this paper that, at least, three coordinated but independently regulated set of genes are combined to degrade naphthalene in TFB. First, proteins involved in tetralin degradation are also induced by naphthalene and may carry out its conversion to salicylaldehyde. This is the only part of the naphthalene degradation pathway showing glucose catabolite repression. Second, a salicylaldehyde dehydrogenase activity that converts salicylaldehyde to salicylate is detected in naphthalene-grown cells but not in tetralin-or salicylate-grown cells. Finally, we describe the chromosomally located nag genes, encoding the gentisate pathway for salicylate conversion into fumarate and pyruvate, which are only induced by salicylate and not by naphthalene. This work shows how biodegradation pathways in Rhodococcus sp. strain TFB could be assembled using elements from different pathways mainly because of the laxity of the regulatory systems and the broad specificity of the catabolic enzymes. PMID:24325207

Lycopene overproduction in Saccharomyces cerevisiae through combining pathway engineering with host engineering.

PubMed

Chen, Yan; Xiao, Wenhai; Wang, Ying; Liu, Hong; Li, Xia; Yuan, Yingjin

2016-06-21

Microbial production of lycopene, a commercially and medically important compound, has received increasing concern in recent years. Saccharomyces cerevisiae is regarded as a safer host for lycopene production than Escherichia coli. However, to date, the lycopene yield (mg/g DCW) in S. cerevisiae was lower than that in E. coli and did not facilitate downstream extraction process, which might be attributed to the incompatibility between host cell and heterologous pathway. Therefore, to achieve lycopene overproduction in S. cerevisiae, both host cell and heterologous pathway should be delicately engineered. In this study, lycopene biosynthesis pathway was constructed by integration of CrtE, CrtB and CrtI in S. cerevisiae CEN.PK2. When YPL062W, a distant genetic locus, was deleted, little acetate was accumulated and approximately 100 % increase in cytosolic acetyl-CoA pool was achieved relative to that in parental strain. Through screening CrtE, CrtB and CrtI from diverse species, an optimal carotenogenic enzyme combination was obtained, and CrtI from Blakeslea trispora (BtCrtI) was found to have excellent performance on lycopene production as well as lycopene proportion in carotenoid. Then, the expression level of BtCrtI was fine-tuned and the effect of cell mating types was also evaluated. Finally, potential distant genetic targets (YJL064W, ROX1, and DOS2) were deleted and a stress-responsive transcription factor INO2 was also up-regulated. Through the above modifications between host cell and carotenogenic pathway, lycopene yield was increased by approximately 22-fold (from 2.43 to 54.63 mg/g DCW). Eventually, in fed-batch fermentation, lycopene production reached 55.56 mg/g DCW, which is the highest reported yield in yeasts. Saccharomyces cerevisiae was engineered to produce lycopene in this study. Through combining host engineering (distant genetic loci and cell mating types) with pathway engineering (enzyme screening and gene fine-tuning), lycopene yield was

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

EPA Science Inventory

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

Prediction of a service demand using combined forecasting approach

NASA Astrophysics Data System (ADS)

Zhou, Ling

2017-08-01

Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast short-term logistic demand for a LTL carrier. Combined approach depends on several forecasting methods simultaneously, instead of a single method. It can offset the weakness of a forecasting method with the strength of another, which could improve the precision performance of prediction. Main issues of combined forecast modeling are how to select methods for combination, and how to find out weight coefficients among methods. The principles of method selection include that each method should apply to the problem of forecasting itself, also methods should differ in categorical feature as much as possible. Based on these principles, exponential smoothing, ARIMA and Neural Network are chosen to form the combined approach. Besides, least square technique is employed to settle the optimal weight coefficients among forecasting methods. Simulation results show the advantage of combined approach over the three single methods. The work done in the paper helps manager to select prediction method in practice.

Combination of serum angiopoietin-2 and uterine artery Doppler for prediction of preeclampsia.

PubMed

Puttapitakpong, Ploynin; Phupong, Vorapong

2016-02-01

The aim of this study was to determine the predictive value of the combination of serum angiopoietin-2 (Ang-2) levels and uterine artery Doppler for the detection of preeclampsia in women at 16-18 weeks of gestation and to identify other pregnancy complications that could be predicted with these combined tests. Maternal serum Ang-2 levels were measured, and uterine artery Doppler was performed in 400 pregnant women. The main outcome was preeclampsia. The predictive values of this combination were calculated. Twenty-five women (6.3%) developed preeclampsia. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of uterine artery Doppler combined with serum Ang-2 levels for the prediction of preeclampsia were 24.0%, 94.4%, 22.2% and 94.9%, respectively. For the prediction of early-onset preeclampsia, the sensitivity, specificity, PPV and NPV were 57.1%, 94.1%, 14.8% and 99.2%, respectively. Patients with abnormal uterine artery Doppler and abnormal serum Ang-2 levels (above 19.5 ng ml(-1)) were at higher risk for preterm delivery (relative risk=2.7, 95% confidence interval 1.2-5.8). Our findings revealed that the combination of uterine artery Doppler and serum Ang-2 levels at 16-18 weeks of gestation can be used to predict early-onset preeclampsia but not overall preeclampsia. Thus, this combination may be a useful early second trimester screening test for the prediction of early-onset preeclampsia.

Reduce Manual Curation by Combining Gene Predictions from Multiple Annotation Engines, a Case Study of Start Codon Prediction

PubMed Central

Ederveen, Thomas H. A.; Overmars, Lex; van Hijum, Sacha A. F. T.

2013-01-01

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity. PMID:23675487

Targeting the PI3K/Akt/mTOR pathway: effective combinations and clinical considerations

PubMed Central

LoPiccolo, Jaclyn; Blumenthal, Gideon M.; Bernstein, Wendy B.; Dennis, Phillip A.

2008-01-01

The PI3K/Akt/mTOR pathway is a prototypic survival pathway that is constitutively activated in many types of cancer. Mechanisms for pathway activation include loss of tumor suppressor PTEN function, amplification or mutation of PI3K, amplification or mutation of Akt, activation of growth factor receptors, and exposure to carcinogens. Once activated, signaling through Akt can be propagated to a diverse array of substrates, including mTOR, a key regulator of protein translation. This pathway is an attractive therapeutic target in cancer because it serves as a convergence point for many growth stimuli, and through its downstream substrates, controls cellular processes that contribute to the initiation and maintenance of cancer. Moreover, activation of the Akt/mTOR pathway confers resistance to many types of cancer therapy, and is a poor prognostic factor for many types of cancers. This review will provide an update on the clinical progress of various agents that target the pathway, such as the Akt inhibitors perifosine and PX-866 and mTOR inhibitors (rapamycin, CCI-779, RAD-001) and discuss strategies to combine these pathway inhibitors with conventional chemotherapy, radiotherapy, as well as newer targeted agents. We will also discuss how the complex regulation of the PI3K/Akt/mTOR pathway poses practical issues concerning the design of clinical trials, potential toxicities and criteria for patient selection. PMID:18166498

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.

PubMed

Hess, Becky M; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C; Wiley, H Steven; Ahring, Birgitte K; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

PubMed Central

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. Steven; Ahring, Birgitte K.; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions. PMID:23840410

A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

NASA Astrophysics Data System (ADS)

Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

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

PubMed Central

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

2007-01-01

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

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

PubMed

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

2017-03-01

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

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

PubMed Central

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.

PubMed

Gu, Xiang; Liu, Cong-Jian; Wei, Jian-Jie

2017-11-13

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

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

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng

2013-06-19

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. Wemore » found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.« less

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

PubMed

Schrag, Tobias A; Westhues, Matthias; Schipprack, Wolfgang; Seifert, Felix; Thiemann, Alexander; Scholten, Stefan; Melchinger, Albrecht E

2018-04-01

The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates. Copyright © 2018 by the Genetics Society of America.

Technical note: Combining quantile forecasts and predictive distributions of streamflows

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

A Synthetic Lethal Screen Identifies DNA Repair Pathways that Sensitize Cancer Cells to Combined ATR Inhibition and Cisplatin Treatments

PubMed Central

Mohni, Kareem N.; Thompson, Petria S.; Luzwick, Jessica W.; Glick, Gloria G.; Pendleton, Christopher S.; Lehmann, Brian D.; Pietenpol, Jennifer A.; Cortez, David

2015-01-01

The DNA damage response kinase ATR may be a useful cancer therapeutic target. ATR inhibition synergizes with loss of ERCC1, ATM, XRCC1 and DNA damaging chemotherapy agents. Clinical trials have begun using ATR inhibitors in combination with cisplatin. Here we report the first synthetic lethality screen with a combination treatment of an ATR inhibitor (ATRi) and cisplatin. Combination treatment with ATRi/cisplatin is synthetically lethal with loss of the TLS polymerase ζ and 53BP1. Other DNA repair pathways including homologous recombination and mismatch repair do not exhibit synthetic lethal interactions with ATRi/cisplatin, even though loss of some of these repair pathways sensitizes cells to cisplatin as a single-agent. We also report that ATRi strongly synergizes with PARP inhibition, even in homologous recombination-proficient backgrounds. Lastly, ATR inhibitors were able to resensitize cisplatin-resistant cell lines to cisplatin. These data provide a comprehensive analysis of DNA repair pathways that exhibit synthetic lethality with ATR inhibitors when combined with cisplatin chemotherapy, and will help guide patient selection strategies as ATR inhibitors progress into the cancer clinic. PMID:25965342

Combined blockade of vascular endothelial growth factor and programmed death 1 pathways in advanced kidney cancer.

PubMed

Einstein, David J; McDermott, David F

2017-06-01

Targeted and immune-based therapies have improved outcomes in advanced kidney cancer, yet novel strategies are needed to extend the duration of these benefits and expand them to more patients. Combined inhibition of vascular endothelial growth factor (VEGF) and the programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) pathways with therapeutic agents already in clinical use may offer such a strategy. Here, we describe the development and clinical evaluation of VEGF inhibitors and, separately, PD-1/PD-L1 inhibitors. We present preclinical evidence of interaction between these pathways and the rationale for combined blockade. Beyond well-known effects on pathologic angiogenesis, VEGF blockade also may decrease immune tolerance and enhance PD-1/PD-L1 blockade. We conclude with the results of several early trials of combined VEGF and PD-1/PD-L1 blockade, which demonstrate encouraging antitumor activity, and we pose questions for future study.

Combining medical informatics and bioinformatics toward tools for personalized medicine.

PubMed

Sarachan, B D; Simmons, M K; Subramanian, P; Temkin, J M

2003-01-01

Key bioinformatics and medical informatics research areas need to be identified to advance knowledge and understanding of disease risk factors and molecular disease pathology in the 21 st century toward new diagnoses, prognoses, and treatments. Three high-impact informatics areas are identified: predictive medicine (to identify significant correlations within clinical data using statistical and artificial intelligence methods), along with pathway informatics and cellular simulations (that combine biological knowledge with advanced informatics to elucidate molecular disease pathology). Initial predictive models have been developed for a pilot study in Huntington's disease. An initial bioinformatics platform has been developed for the reconstruction and analysis of pathways, and work has begun on pathway simulation. A bioinformatics research program has been established at GE Global Research Center as an important technology toward next generation medical diagnostics. We anticipate that 21 st century medical research will be a combination of informatics tools with traditional biology wet lab research, and that this will translate to increased use of informatics techniques in the clinic.

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

PubMed

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

2015-01-01

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

A Method for Finding Metabolic Pathways Using Atomic Group Tracking.

PubMed

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

2017-01-01

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

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

PubMed Central

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

2017-01-01

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

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-01-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

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

PubMed

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

2017-08-22

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

Managing care pathways combining SNOMED CT, archetypes and an electronic guideline system.

PubMed

Bernstein, Knut; Andersen, Ulrich

2008-01-01

Today electronic clinical guideline systems exist, but they are not well integrated with electronic health records. This paper thus proposes that the patient's "position" in the pathway during the patient journey should be made visible to all involved healthcare parties and the patient. This requires that the generic knowledge, which is represented in the guidelines, is combined with the patient specific information - and then made accessible for all relevant parties. In addition to the decision support provided by the guideline system documentation support can be provided by templates based on archetypes. This paper provides a proposal for how the guideline system and the EHR can be integrated by the use of archetypes and SNOMED CT. SNOMED CT provides the common reference terminology and the semantic links between the systems. The proposal also includes the use of a National Patient Index for storing data about the patient's position in the pathway and for sharing this information by all involved parties.

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

DTIC Science & Technology

2016-08-27

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

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

PubMed

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

2013-02-14

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

Transmission of Predictable Sensory Signals to the Cerebellum via Climbing Fiber Pathways Is Gated during Exploratory Behavior

PubMed Central

Lawrenson, Charlotte L.; Watson, Thomas C.

2016-01-01

Pathways arising from the periphery that target the inferior olive [spino-olivocerebellar pathways (SOCPs)] are a vital source of information to the cerebellum and are modulated (gated) during active movements. This limits their ability to forward signals to climbing fibers in the cerebellar cortex. We tested the hypothesis that the temporal pattern of gating is related to the predictability of a sensory signal. Low-intensity electrical stimulation of the ipsilateral hindlimb in awake rats evoked field potentials in the C1 zone in the copula pyramidis of the cerebellar cortex. Responses had an onset latency of 12.5 ± 0.3 ms and were either short or long duration (8.7 ± 0.1 vs 31.2 ± 0.3 ms, respectively). Both types of response were shown to be mainly climbing fiber in origin and therefore evoked by transmission in hindlimb SOCPs. Changes in response size (area of field, millivolts per millisecond) were used to monitor differences in transmission during rest and three phases of rearing: phase 1, rearing up; phase 2, upright; and phase 3, rearing down. Responses evoked during phase 2 were similar in size to rest but were smaller during phases 1 and 3, i.e., transmission was reduced during active movement when self-generated (predictable) sensory signals from the hindlimbs are likely to occur. To test whether the pattern of gating was related to the predictability of the sensory signal, some animals received the hindlimb stimulation only during phase 2. Over ∼10 d, the responses became progressively smaller in size, consistent with gating-out transmission of predictable sensory signals relayed via SOCPs. SIGNIFICANCE STATEMENT A major route for peripheral information to gain access to the cerebellum is via ascending climbing fiber pathways. During active movements, gating of transmission in these pathways controls when climbing fiber signals can modify cerebellar activity. We investigated this phenomenon in rats during their exploratory behavior of rearing

Transmission of Predictable Sensory Signals to the Cerebellum via Climbing Fiber Pathways Is Gated during Exploratory Behavior.

PubMed

Lawrenson, Charlotte L; Watson, Thomas C; Apps, Richard

2016-07-27

Pathways arising from the periphery that target the inferior olive [spino-olivocerebellar pathways (SOCPs)] are a vital source of information to the cerebellum and are modulated (gated) during active movements. This limits their ability to forward signals to climbing fibers in the cerebellar cortex. We tested the hypothesis that the temporal pattern of gating is related to the predictability of a sensory signal. Low-intensity electrical stimulation of the ipsilateral hindlimb in awake rats evoked field potentials in the C1 zone in the copula pyramidis of the cerebellar cortex. Responses had an onset latency of 12.5 ± 0.3 ms and were either short or long duration (8.7 ± 0.1 vs 31.2 ± 0.3 ms, respectively). Both types of response were shown to be mainly climbing fiber in origin and therefore evoked by transmission in hindlimb SOCPs. Changes in response size (area of field, millivolts per millisecond) were used to monitor differences in transmission during rest and three phases of rearing: phase 1, rearing up; phase 2, upright; and phase 3, rearing down. Responses evoked during phase 2 were similar in size to rest but were smaller during phases 1 and 3, i.e., transmission was reduced during active movement when self-generated (predictable) sensory signals from the hindlimbs are likely to occur. To test whether the pattern of gating was related to the predictability of the sensory signal, some animals received the hindlimb stimulation only during phase 2. Over ∼10 d, the responses became progressively smaller in size, consistent with gating-out transmission of predictable sensory signals relayed via SOCPs. A major route for peripheral information to gain access to the cerebellum is via ascending climbing fiber pathways. During active movements, gating of transmission in these pathways controls when climbing fiber signals can modify cerebellar activity. We investigated this phenomenon in rats during their exploratory behavior of rearing. During rearing up and down

Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity

PubMed Central

Bal-Price, Anna; Lein, Pamela J.; Keil, Kimberly P.; Sethi, Sunjay; Shafer, Timothy; Barenys, Marta; Fritsche, Ellen; Sachana, Magdalini; Meek, M.E. (Bette)

2016-01-01

The Adverse Outcome Pathway (AOP) concept has recently been proposed to support a paradigm shift in regulatory toxicology testing and risk assessment. This concept is similar to the Mode of Action (MOA), in that it describes a sequence of measurable key events triggered by a molecular initiating event in which a stressor interacts with a biological target. The resulting cascade of key events includes molecular, cellular, structural and functional changes in biological systems, resulting in a measurable adverse outcome. Thereby, an AOP ideally provides information relevant to chemical structure-activity relationships as a basis for predicting effects of structurally similar compounds. AOPs could potentially also form the basis for qualitative and quantitative predictive modeling of the human adverse outcome resulting from molecular initiating or other key events for which higher-throughput testing methods are available or can be developed. A variety of cellular and molecular processes are known to be critical for normal function of the central (CNS) and peripheral nervous systems (PNS). Because of the biological and functional complexity of the CNS and PNS, it has been challenging to establish causative links and quantitative relationships between key events that comprise the pathways leading from chemical exposure to an adverse outcome in the nervous system. Following introduction of the principles of MOA and AOPs, examples of potential or putative adverse outcome pathways specific for developmental or adult neurotoxicity are summarized and aspects of their assessment considered. Their possible application in developing mechanistically informed Integrated Approaches to Testing and Assessment (IATA) is also discussed. PMID:27212452

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study.

PubMed

Kattou, Panayiotis; Lian, Guoping; Glavin, Stephen; Sorrell, Ian; Chen, Tao

2017-10-01

The development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented. The follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared. The integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked. The follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.

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

PubMed Central

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

2018-01-01

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

PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm.

PubMed

Xu, Qian; Xiong, Yi; Dai, Hao; Kumari, Kotni Meena; Xu, Qin; Ou, Hong-Yu; Wei, Dong-Qing

2017-03-21

Combinatorial therapy is a promising strategy for combating complex diseases by improving the efficacy and reducing the side effects. To facilitate the identification of drug combinations in pharmacology, we proposed a new computational model, termed PDC-SGB, to predict effective drug combinations by integrating biological, chemical and pharmacological information based on a stochastic gradient boosting algorithm. To begin with, a set of 352 golden positive samples were collected from the public drug combination database. Then, a set of 732 dimensional feature vector involving biological, chemical and pharmaceutical information was constructed for each drug combination to describe its properties. To avoid overfitting, the maximum relevance & minimum redundancy (mRMR) method was performed to extract useful ones by removing redundant subsets. Based on the selected features, the three different type of classification algorithms were employed to build the drug combination prediction models. Our results demonstrated that the model based on the stochastic gradient boosting algorithm yield out the best performance. Furthermore, it is indicated that the feature patterns of therapy had powerful ability to discriminate effective drug combinations from non-effective ones. By analyzing various features, it is shown that the enriched features occurred frequently in golden positive samples can help predict novel drug combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

PubMed Central

Piegorsch, Walter W.; Lussier, Yves A.

2015-01-01

Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome). Availability and implementation: http://www.lussierlab.net/publications/N-of-1

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

PubMed

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

2017-01-01

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

Pathway collages: personalized multi-pathway diagrams.

PubMed

Paley, Suzanne; O'Maille, Paul E; Weaver, Daniel; Karp, Peter D

2016-12-13

Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions? We define software for constructing personalized multi-pathway diagrams called pathway-collages using a combination of manual and automatic layouts. The user specifies a set of pathways of interest for the collage from a Pathway/Genome Database. Layouts for the individual pathways are generated by the Pathway Tools software, and are sent to a Javascript Pathway Collage application implemented using Cytoscape.js. That application allows the user to re-position pathways; define connections between pathways; change visual style parameters; and paint metabolomics, gene expression, and reaction flux data onto the collage to obtain a desired multi-pathway diagram. We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Escherichia coli metabolic model. Pathway collages enable facile construction of personalized multi-pathway diagrams.

Validation of RetroPath, a computer-aided design tool for metabolic pathway engineering.

PubMed

Fehér, Tamás; Planson, Anne-Gaëlle; Carbonell, Pablo; Fernández-Castané, Alfred; Grigoras, Ioana; Dariy, Ekaterina; Perret, Alain; Faulon, Jean-Loup

2014-11-01

Metabolic engineering has succeeded in biosynthesis of numerous commodity or high value compounds. However, the choice of pathways and enzymes used for production was many times made ad hoc, or required expert knowledge of the specific biochemical reactions. In order to rationalize the process of engineering producer strains, we developed the computer-aided design (CAD) tool RetroPath that explores and enumerates metabolic pathways connecting the endogenous metabolites of a chassis cell to the target compound. To experimentally validate our tool, we constructed 12 top-ranked enzyme combinations producing the flavonoid pinocembrin, four of which displayed significant yields. Namely, our tool queried the enzymes found in metabolic databases based on their annotated and predicted activities. Next, it ranked pathways based on the predicted efficiency of the available enzymes, the toxicity of the intermediate metabolites and the calculated maximum product flux. To implement the top-ranking pathway, our procedure narrowed down a list of nine million possible enzyme combinations to 12, a number easily assembled and tested. One round of metabolic network optimization based on RetroPath output further increased pinocembrin titers 17-fold. In total, 12 out of the 13 enzymes tested in this work displayed a relative performance that was in accordance with its predicted score. These results validate the ranking function of our CAD tool, and open the way to its utilization in the biosynthesis of novel compounds. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A combined-slip predictive control of vehicle stability with experimental verification

NASA Astrophysics Data System (ADS)

Jalali, Milad; Hashemi, Ehsan; Khajepour, Amir; Chen, Shih-ken; Litkouhi, Bakhtiar

2018-02-01

In this paper, a model predictive vehicle stability controller is designed based on a combined-slip LuGre tyre model. Variations in the lateral tyre forces due to changes in tyre slip ratios are considered in the prediction model of the controller. It is observed that the proposed combined-slip controller takes advantage of the more accurate tyre model and can adjust tyre slip ratios based on lateral forces of the front axle. This results in an interesting closed-loop response that challenges the notion of braking only the wheels on one side of the vehicle in differential braking. The performance of the proposed controller is evaluated in software simulations and is compared to a similar pure-slip controller. Furthermore, experimental tests are conducted on a rear-wheel drive electric Chevrolet Equinox equipped with differential brakes to evaluate the closed-loop response of the model predictive control controller.

Probing the coagulation pathway with aptamers identifies combinations that synergistically inhibit blood clot formation

PubMed Central

Bompiani, Kristin M; Lohrmann, Jens L; Pitoc, George A; Frederiksen, James W; Mackensen, George B; Sullenger, Bruce A

2014-01-01

SUMMARY Coordinated enzymatic reactions regulate blood clot generation. To explore the contributions of various coagulation enzymes in this process, we utilized a panel of aptamers against factors VIIa, IXa, Xa, and prothrombin. Each aptamer dose-dependently inhibited clot formation, yet none was able to completely impede this process in highly procoagulant settings. However several combinations of two aptamers synergistically impaired clot formation. One extremely potent aptamer combination was able to maintain human blood fluidity even during extracorporeal circulation, a highly procoagulant setting encountered during cardiopulmonary bypass surgery. Moreover, this aptamer cocktail could be rapidly reversed with antidotes to restore normal hemostasis, indicating that even highly potent aptamer combinations can be rapidly controlled. These studies highlight the potential utility of using sets of aptamers to probe the functions of proteins in molecular pathways for research and therapeutic ends. PMID:25065530

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

PubMed Central

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

2015-01-01

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

Combined smoking cues enhance reactivity and predict immediate subsequent smoking.

PubMed

Conklin, Cynthia A; McClernon, F Joseph; Vella, Elizabeth J; Joyce, Christopher J; Salkeld, Ronald P; Parzynski, Craig S; Bennett, Lee

2018-01-23

Cue reactivity (CR) research has reliably demonstrated robust cue-induced responding among smokers exposed to common proximal smoking cues (e.g., cigarettes, lighter). More recent work demonstrates that distal stimuli, most notably the actual environments in which smoking previously occurred, can also gain associative control over craving. In the real world proximal cues always occur within an environment; thus, a more informative test of how cues affect smokers might be to present these two cue types simultaneously. Using a combined-cue counterbalanced cue reactivity paradigm, the present study tested the impact of proximal (smoking and neutral) + personal environment (smoking and nonsmoking places) pictorial cues, on smokers' subjective and behavioral cue reactivity; as well as the extent to which cue-induced craving predicts immediate subsequent smoking in a within-subjects design. As anticipated, the dual smoking cue combination (ProxS+EnvS) led to the greatest cue-induced craving relative to the other three cue combinations (ProxS+EnvN, ProxN+EnvS, ProxN+-EnvN), p's < .004. Dual smoking cues also led to significantly shorter post-trial latencies to smoke, p's < .01. Overall cue reactivity difference score (post-trial craving minus baseline craving) was predictive of subsequent immediate smoking indexed by: Post-trial latency to smoke (B= -2.69, SE= 9.02; t(143) = -2.98, p = .003); total puff volume (B= 2.99, SE= 1.13; t(143)= 2.65, p = .009); and total number of puffs (B= .053, SE= .027; t(143)= 1.95, p = .05). The implications of these findings for better understanding the impact of cues on smoking behavior and cessation are discussed. This novel cue reactivity study examined smokers' reactivity to combined proximal and distal smoking cues. Exposure to a combination of two smoking cues (proximal and environment) led to the greatest increases in cue-induced craving and smoking behavior compared to all other cue combinations. Further, the overall magnitude of

NetCTLpan: pan-specific MHC class I pathway epitope predictions

PubMed Central

Larsen, Mette Voldby; Lundegaard, Claus; Nielsen, Morten

2010-01-01

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/. Electronic supplementary material The online version of this article (doi:10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users. PMID

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

PubMed

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

2018-01-01

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

Research of trace metals as markers of entry pathways in combined sewers.

PubMed

Gounou, C; Varrault, G; Amedzro, K; Gasperi, J; Moilleron, R; Garnaud, S; Chebbo, G

2011-01-01

Combined sewers receive high toxic trace metal loads emitted by various sources, such as traffic, industry, urban heating and building materials. During heavy rain events, Combined Sewer Overflows (CSO) can occur and, if so, are discharged directly into the aquatic system and therefore could have an acute impact on receiving waters. In this study, the concentrations of 18 metals have been measured in 89 samples drawn from the three pollutant Entry Pathways in Combined Sewers (EPCS): i) roof runoff, ii) street runoff, and iii) industrial and domestic effluents and also drawn from sewer deposits (SD). The aim of this research is to identify metallic markers for each EPCS; the data matrix was submitted to principal component analysis in order to determine metallic markers for the three EPCS and SD. This study highlights the fact that metallic content variability across samples from different EPCS and SD exceeds the spatio-temporal variability of samples from the same EPCS. In the catchment studied here, the most valuable EPCS and SD markers are lead, sodium, boron, antimony and zinc; these markers could be used in future studies to identify the contributions of each EPCS to CSO metallic loads.

Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data.

PubMed

Mounce, S R; Shepherd, W; Sailor, G; Shucksmith, J; Saul, A J

2014-01-01

Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

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

DOE PAGES

Zarkevich, Nickolai A.; Johnson, Duane D.

2016-01-15

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

Prediction of pelvic pathology in subfertile women with combined Chlamydia antibody and CA-125 tests.

PubMed

Penninx, Josien; Brandes, Monique; de Bruin, Jan Peter; Schneeberger, Peter M; Hamilton, Carl J C M

2009-12-01

Chlamydia antibody test (CAT) has been proposed to predict tubal disease. A correlation between CA-125 and the extent of endometriosis has been found by others. In this study we explored whether a combination of the two tests adds to the predictive value of the individual tests for predicting tubal disease or endometriosis. We also used the combination of tests as a new index test to screen for severe pelvic pathology. This retrospective study compares the findings of 240 laparoscopies with the serological test results. Findings were classified according to the existing ASRM scoring systems for adnexal adhesions, distal tubal occlusion and endometriosis. Severe pelvic pathology was defined as the presence of ASRM classes III and IV tubal disease or ASRM classes III and IV endometriosis. The predictive value was calculated for both tests separately and for the combined test. The combined test was positive if at least one test result was abnormal (CAT positive and/or CA-125 > or =35 IU/ml). 67/240 women had tubal disease, 81/240 had some degree of endometriosis. The odds ratios (ORs) of the CAT and the combined test to diagnose severe tubal disease were 6.6 (2.6-17.0) and 7.3 (2.9-19.3), respectively. The ORs of the CA-125 and the combined test to diagnose severe endometriosis were 15.6 (6.2-40.2) and 3.0 (1.2-8.0), respectively. Severe pelvic pathology was present in 65/240 women (27%). The ORs for severe pelvic pathology of the CAT, CA-125 and the combined test were 2.5 (1.4-5.3), 4.9 (1.9-9.6) and 6.6 (3.3-13.4), respectively. If the combined test was normal 15 out 131 women (11%) were shown to have severe pelvic pathology. The combined test adds hardly anything to the predictive value of CAT alone to diagnose severe tubal disease. The combined test is better than the CAT to predict severe pelvic pathology, but is not significantly better than the CA-125. If both the CAT and CA-125 are normal one could consider not performing a laparoscopy.

Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression

PubMed Central

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

2016-01-01

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

Computer-assisted engineering of the synthetic pathway for biodegradation of a toxic persistent pollutant.

PubMed

Kurumbang, Nagendra Prasad; Dvorak, Pavel; Bendl, Jaroslav; Brezovsky, Jan; Prokop, Zbynek; Damborsky, Jiri

2014-03-21

Anthropogenic halogenated compounds were unknown to nature until the industrial revolution, and microorganisms have not had sufficient time to evolve enzymes for their degradation. The lack of efficient enzymes and natural pathways can be addressed through a combination of protein and metabolic engineering. We have assembled a synthetic route for conversion of the highly toxic and recalcitrant 1,2,3-trichloropropane to glycerol in Escherichia coli, and used it for a systematic study of pathway bottlenecks. Optimal ratios of enzymes for the maximal production of glycerol, and minimal toxicity of metabolites were predicted using a mathematical model. The strains containing the expected optimal ratios of enzymes were constructed and characterized for their viability and degradation efficiency. Excellent agreement between predicted and experimental data was observed. The validated model was used to quantitatively describe the kinetic limitations of currently available enzyme variants and predict improvements required for further pathway optimization. This highlights the potential of forward engineering of microorganisms for the degradation of toxic anthropogenic compounds.

Metformin and pioglitazone combination therapy ameliorate polycystic ovary syndrome through AMPK/PI3K/JNK pathway

PubMed Central

Wu, Yuanyuan; Li, Pengfen; Zhang, Dan; Sun, Yingpu

2018-01-01

Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disorder, which results in health problems such as menstrual disorders, hyperandrogenism and persistent anovulation. Hyperandrogenism and insulin resistance are the basic characteristics of PCOS. To investigate the combined effect of metformin and pioglitazone on POCS and the potential mechanisms, a rat model of PCOS was established by intramuscular injection of estradiol valerate (EV). The effect of metformin and pioglitazone monotherapy or combination therapy in control rats and PCOS rats was evaluated, involving the testosterone level, follicular development and insulin resistance. The potential mechanism for the therapeutic effect of metformin and pioglitazone on POCS was explored through using three inhibitors of the 5′adenosine monophosphate-activated protein kinase (AMPK)/phosphoinositide-3 kinase (PI3K)/c-Jun N-terminal kinase (JNK) pathway (Compound C, Wortmannin and SP600125). The results showed that EV-induced PCOS rats demonstrated hyperandrogenemia, hyperinsulinemia and follicular dysplasia. Metformin or pioglitazone monotherapy significantly suppressed the high level of testosterone, reduced the raised percentage of cystic follicles and primary follicles, promoted the number of early antral follicles, and markedly decreased the high concentration of fasting insulin and homeostatic model assessment for insulin resistance index in PCOS rats. In addition, metformin and pioglitazone combination therapy demonstrated greater efficacy than its individual components. Furthermore, individual or joint treatment with metformin and pioglitazone affected the phosphorylation level of JNK in PCOS rats. Compound C and Wortmannin eliminated the effect of metformin and pioglitazone combination therapy on improving the follicular growth in PCOS rats, whereas SP600125 treatment enhanced this combination therapy effect. These data suggested that metformin and pioglitazone combination therapy

Normal mode-guided transition pathway generation in proteins

PubMed Central

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

2017-01-01

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

Genetic variants of cell cycle pathway genes predict disease-free survival of hepatocellular carcinoma.

PubMed

Liu, Shun; Yang, Tian-Bo; Nan, Yue-Li; Li, An-Hua; Pan, Dong-Xiang; Xu, Yang; Li, Shu; Li, Ting; Zeng, Xiao-Yun; Qiu, Xiao-Qiang

2017-07-01

Disruption of the cell cycle pathway has previously been related to development of human cancers. However, associations between genetic variants of cell cycle pathway genes and prognosis of hepatocellular carcinoma (HCC) remain largely unknown. In this study, we evaluated the associations between 24 potential functional single nucleotide polymorphisms (SNPs) of 16 main cell cycle pathway genes and disease-free survival (DFS) of 271 HCC patients who had undergone radical surgery resection. We identified two SNPs, i.e., SMAD3 rs11556090 A>G and RBL2 rs3929G>C, that were independently predictive of DFS in an additive genetic model with false-positive report probability (FPRP) <0.2. The SMAD3 rs11556090G allele was associated with a poorer DFS, compared with the A allele [hazard ratio (HR) = 1.46, 95% confidential interval (95% CI) = 1.13-1.89, P = 0.004]; while the RBL2 rs3929 C allele was associated with a superior DFS, compared with the G allele (HR = 0.74, 95% CI = 0.57-0.96, P = 0.023). Additionally, patients with an increasing number of unfavorable genotypes (NUGs) of these loci had a significant shorter DFS (P trend  = 0.0001). Further analysis using receiver operating characteristic (ROC) curves showed that the model including the NUGs and known prognostic clinical variables demonstrated a significant improvement in predicting the 1-year DFS (P = 0.011). Moreover, the RBL2 rs3929 C allele was significantly associated with increased mRNA expression levels of RBL2 in liver tissue (P = 1.8 × 10 -7 ) and the whole blood (P = 3.9 × 10 -14 ). Our data demonstrated an independent or a joint effect of SMAD3 rs11556090 and RBL2 rs3929 in the cell cycle pathway on DFS of HCC, which need to be validated by large cohort and biological studies. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

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

Energetic-environmental-economic assessment of the biogas system with three utilization pathways: Combined heat and power, biomethane and fuel cell.

PubMed

Wu, Bin; Zhang, Xiangping; Shang, Dawei; Bao, Di; Zhang, Suojiang; Zheng, Tao

2016-08-01

A typical biogas system with three utilization pathways, i.e., biogas upgrading, biogas combined heat and power (CHP), biogas solid oxide fuel cells (SOFCs) were designed. It was assessed from the viewpoint of energy, environment and economy by using energy efficiency, green degree and net present value index respectively. The assessment considered the trade-off relationships among these indexes, which is more comprehensive than previous systematic evaluation work only included single or two of the pathway(s) by using one or two of the index(es). Assessment results indicated that biogas upgrading pathway has the highest systematic energy efficiency (46.5%) and shortest payback period (8.9year) with the green degree production is the lowest (9.29gd/day). While for biogas SOFC pathway, although the green degree production is the highest (21.77gd/day), the payback period is longer (14.5year) and the energy efficiency is 13.6% lower than the biogas upgrading pathway. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exosomes/tricalcium phosphate combination scaffolds can enhance bone regeneration by activating the PI3K/Akt signaling pathway.

PubMed

Zhang, Jieyuan; Liu, Xiaolin; Li, Haiyan; Chen, Chunyuan; Hu, Bin; Niu, Xin; Li, Qing; Zhao, Bizeng; Xie, Zongping; Wang, Yang

2016-09-20

Recently, accumulating evidence has shown that exosomes, the naturally secreted nanocarriers of cells, can exert therapeutic effects in various disease models in the absence of parent cells. However, application of exosomes in bone defect repair and regeneration has been rarely reported, and little is known regarding their underlying mechanisms. Exosomes derived from human-induced pluripotent stem cell-derived mesenchymal stem cells (hiPS-MSC-Exos) were combined with tricalcium phosphate (β-TCP) to repair critical-sized calvarial bone defects, and the efficacy was assessed by histological examination. We evaluated the in vitro effects of hiPSC-MSC-Exos on the proliferation, migration, and osteogenic differentiation of human bone marrow-derived mesenchymal stem cells (hBMSCs) by cell-counting, scratch assays, and qRT-PCR, respectively. Gene expression profiling and bioinformatics analyses were also used to identify the underlying mechanisms in the repair. We found that the exosome/β-TCP combination scaffolds could enhance osteogenesis as compared to pure β-TCP scaffolds. In vitro assays showed that the exosomes could release from β-TCP and could be internalized by hBMSCs. In addition, the internalization of exosomes into hBMSCs could profoundly enhance the proliferation, migration, and osteogenic differentiation of hBMSCs. Furthermore, gene expression profiling and bioinformatics analyses demonstrated that exosome/β-TCP combination scaffolds significantly altered the expression of a network of genes involved in the PI3K/Akt signaling pathway. Functional studies further confirmed that the PI3K/Akt signaling pathway was the critical mediator during the exosome-induced osteogenic responses of hBMSCs. We propose that the exosomes can enhance the osteoinductivity of β-TCP through activating the PI3K/Akt signaling pathway of hBMSCs, which means that the exosome/β-TCP combination scaffolds possess better osteogenesis activity than pure β-TCP scaffolds. These

The National Football League (NFL) combine: does normalized data better predict performance in the NFL draft?

PubMed

Robbins, Daniel W

2010-11-01

The objective of this study was to investigate the predictive ability of National Football League (NFL) combine physical test data to predict draft order over the years 2005-2009. The NFL combine provides a setting in which NFL personnel can evaluate top draft prospects. The predictive ability of combine data in its raw form and when normalized in both a ratio and allometric manner was examined for 17 positions. Data from 8 combine physical performance tests were correlated with draft order to determine the direction and strength of relationship between the various combine measures and draft order. Players invited to the combine and subsequently drafted in the same year (n = 1,155) were included in the study. The primary finding was that performance in the combine physical test battery, whether normalized or not, has little association with draft success. In terms of predicting draft order from outcomes of the 8 tests making up the combine battery, normalized data provided no advantage over raw data. Of the 8 performance measures investigated, straight sprint time and jumping ability seem to hold the most weight with NFL personnel responsible for draft decisions. The NFL should consider revising the combine test battery to reflect the physical characteristics it deems important. It may be that NFL teams are more interested in attributes other than the purely physical traits reflected in the combine test battery. Players with aspirations of entering the NFL may be well advised to develop mental and technical skills in addition to developing the physical characteristics necessary to optimize performance.

Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer

PubMed Central

Makondi, Precious Takondwa; Lee, Chia-Hwa; Huang, Chien-Yu; Chu, Chi-Ming; Chang, Yu-Jia

2018-01-01

Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways

A novel pathway for the biosynthesis of heme in Archaea: genome-based bioinformatic predictions and experimental evidence.

PubMed

Storbeck, Sonja; Rolfes, Sarah; Raux-Deery, Evelyne; Warren, Martin J; Jahn, Dieter; Layer, Gunhild

2010-12-13

Heme is an essential prosthetic group for many proteins involved in fundamental biological processes in all three domains of life. In Eukaryota and Bacteria heme is formed via a conserved and well-studied biosynthetic pathway. Surprisingly, in Archaea heme biosynthesis proceeds via an alternative route which is poorly understood. In order to formulate a working hypothesis for this novel pathway, we searched 59 completely sequenced archaeal genomes for the presence of gene clusters consisting of established heme biosynthetic genes and colocalized conserved candidate genes. Within the majority of archaeal genomes it was possible to identify such heme biosynthesis gene clusters. From this analysis we have been able to identify several novel heme biosynthesis genes that are restricted to archaea. Intriguingly, several of the encoded proteins display similarity to enzymes involved in heme d(1) biosynthesis. To initiate an experimental verification of our proposals two Methanosarcina barkeri proteins predicted to catalyze the initial steps of archaeal heme biosynthesis were recombinantly produced, purified, and their predicted enzymatic functions verified.

Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter

NASA Astrophysics Data System (ADS)

Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling

2017-08-01

The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.

Metformin combined with quercetin synergistically repressed prostate cancer cells via inhibition of VEGF/PI3K/Akt signaling pathway.

PubMed

Sun, Shuben; Gong, Fanger; Liu, Ping; Miao, Qilong

2018-04-17

The aim of present study was to examine whether metformin in association with quercetin has any synergistically anti-tumor effects on prostate cancer. Our findings showed that metformin in combination with quercetin synergistically inhibited the growth, migration and invasion of both PC-3 and LNCaP cells. Co-treatment of these two agents induced more apoptosis than single agent treatment. The co-treatment-induced apoptosis was caspase-dependent and accompanied by the down-regulation of Bcl-2 family members. Our data also indicated that co-treatment of metformin and quercetin strongly inhibited the VEGF/Akt/PI3K pathway. Moreover, these two agents acted synergistically to repress the growth of human prostate cancer cell xenograft in vivo in nude mice. In conclusion, our findings indicate that the combination therapy of metformin and quercetin exerted synergistic antitumor effects in prostate cancers via inhibition of VEGF/Akt/PI3K pathway. Thus, combination treatment of metformin and quercetin would be a promising therapeutic strategy for prostate cancer patients. Copyright © 2017. Published by Elsevier B.V.

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

PubMed

Collins, Chris D; Finnegan, Eilis

2010-02-01

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

Prediction of high-energy radiation belt electron fluxes using a combined VERB-NARMAX model

NASA Astrophysics Data System (ADS)

Pakhotin, I. P.; Balikhin, M. A.; Shprits, Y.; Subbotin, D.; Boynton, R.

2013-12-01

This study is concerned with the modelling and forecasting of energetic electron fluxes that endanger satellites in space. By combining data-driven predictions from the NARMAX methodology with the physics-based VERB code, it becomes possible to predict electron fluxes with a high level of accuracy and across a radial distance from inside the local acceleration region to out beyond geosynchronous orbit. The model coupling also makes is possible to avoid accounting for seed electron variations at the outer boundary. Conversely, combining a convection code with the VERB and NARMAX models has the potential to provide even greater accuracy in forecasting that is not limited to geostationary orbit but makes predictions across the entire outer radiation belt region.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

[Enhanced growth inhibition by combined two pathway inhibitors on K-ras mutated non-small cell lung cancer cells].

PubMed

Yang, Zhenli; Li, Zhanwen; Feng, Hailiang; Bian, Xiaocui; Liu, Yanyan; Liu, Yuqin

2014-09-01

To evaluate the effect of combined targeting of MEK and PI3K signaling pathways on K-ras mutated non-small cell lung cancer cell line A549 cells and the relevant mechanisms. A549 cells were treated with different concentrations of two inhibitors. Growth inhibition was determined by MTT assay. According to the results of MTT test, the cells were divided into four groups: the control group, PI3K inhibitor group (GDC-0941,0.5 and 5.0 µmol/L), combination group I (0.5 µmol/L AZD6244+0.5 µmol/L GDC-0941) and combination group II (5.0 µmol/L AZD6244+5.0 µmol/L GDC-0941). The cell cycle and apoptosis were analyzed by flow cytometry. The expression of proteins related to apoptosis was tested with Western blot. Both GDC-0941 and AZD6244 inhibited the cell proliferation. The combination group II led to a stronger growth inhibition. The combination group I showed an antagonistic effect and combination group II showed an additive or synergistic effect. Compared with the control group, the combination group I led to reduced apoptotic rate [(20.70 ± 0.99)% vs. (18.65 ± 0.92 )%, P > 0.05]; Combination group II exhibited enhanced apoptotic rate [(37.85 ± 3.18)% vs. (52.27 ± 4.36)%, P < 0.01]. In addition, in the combination group II, more A549 cells were arrested in G0/G1 phase and decreased S phase (P < 0.01), due to the reduced expressions of CyclinD1 and Cyclin B1, the increased cleaved PARP and the diminished ratio of Bcl-2/Bax. For single K-ras mutated NSCLC cell line A549 cells, combination of RAS/MEK/ERK and PI3K/AKT/mTOR inhibition showed synergistic effects depending on the drug doses. Double pathways targeted therapy may be beneficial for these patients.

An Ancient Pathway Combining Carbon Dioxide Fixation with the Generation and Utilization of a Sodium Ion Gradient for ATP Synthesis

PubMed Central

Poehlein, Anja; Schmidt, Silke; Kaster, Anne-Kristin; Goenrich, Meike; Vollmers, John; Thürmer, Andrea; Bertsch, Johannes; Schuchmann, Kai; Voigt, Birgit; Hecker, Michael; Daniel, Rolf; Thauer, Rudolf K.; Gottschalk, Gerhard; Müller, Volker

2012-01-01

Synthesis of acetate from carbon dioxide and molecular hydrogen is considered to be the first carbon assimilation pathway on earth. It combines carbon dioxide fixation into acetyl-CoA with the production of ATP via an energized cell membrane. How the pathway is coupled with the net synthesis of ATP has been an enigma. The anaerobic, acetogenic bacterium Acetobacterium woodii uses an ancient version of this pathway without cytochromes and quinones. It generates a sodium ion potential across the cell membrane by the sodium-motive ferredoxin:NAD oxidoreductase (Rnf). The genome sequence of A. woodii solves the enigma: it uncovers Rnf as the only ion-motive enzyme coupled to the pathway and unravels a metabolism designed to produce reduced ferredoxin and overcome energetic barriers by virtue of electron-bifurcating, soluble enzymes. PMID:22479398

Understanding Lymphatic Drainage Pathways of the Ovaries to Predict Sites for Sentinel Nodes in Ovarian Cancer

PubMed Central

Kleppe, Marjolein; Kraima, Anne C.; Kruitwagen, Roy F.P.M.; Van Gorp, Toon; Smit, Noeska N.; van Munsteren, Jacoba C.; DeRuiter, Marco C.

2015-01-01

Objective In ovarian cancer, detection of sentinel nodes is an upcoming procedure. Perioperative determination of the patient’s sentinel node(s) might prevent a radical lymphadenectomy and associated morbidity. It is essential to understand the lymphatic drainage pathways of the ovaries, which are surprisingly up till now poorly investigated, to predict the anatomical regions where sentinel nodes can be found. We aimed to describe the lymphatic drainage pathways of the human ovaries including their compartmental fascia borders. Methods A series of 3 human female fetuses and tissues samples from 1 human cadaveric specimen were studied. Immunohistochemical analysis was performed on paraffin-embedded transverse sections (8 or 10 μm) using antibodies against Lyve-1, S100, and α-smooth muscle actin to identify the lymphatic endothelium, Schwann, and smooth muscle cells, respectively. Three-dimensional reconstructions were created. Results Two major and 1 minor lymphatic drainage pathways from the ovaries were detected. One pathway drained via the proper ligament of the ovaries (ovarian ligament) toward the lymph nodes in the obturator fossa and the internal iliac artery. Another pathway drained the ovaries via the suspensory ligament (infundibulopelvic ligament) toward the para-aortic and paracaval lymph nodes. A third minor pathway drained the ovaries via the round ligament to the inguinal lymph nodes. Lymph vessels draining the fallopian tube all followed the lymphatic drainage pathways of the ovaries. Conclusions The lymphatic drainage pathways of the ovaries invariably run via the suspensory ligament (infundibulopelvic ligament) and the proper ligament of the ovaries (ovarian ligament), as well as through the round ligament of the uterus. Because ovarian cancer might spread lymphogenously via these routes, the sentinel node can be detected in the para-aortic and paracaval regions, obturator fossa and surrounding internal iliac arteries, and inguinal regions

Metabolic pathways for the whole community.

PubMed

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

2014-07-22

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

Subcortical pathways serving cortical language sites: initial experience with diffusion tensor imaging fiber tracking combined with intraoperative language mapping.

PubMed

Henry, Roland G; Berman, Jeffrey I; Nagarajan, Srikantan S; Mukherjee, Pratik; Berger, Mitchel S

2004-02-01

The combination of mapping functional cortical neurons by intraoperative cortical stimulation and axonal architecture by diffusion tensor MRI fiber tracking can be used to delineate the pathways between functional regions. In this study the authors investigated the feasibility of combining these techniques to yield connectivity associated with motor speech and naming. Diffusion tensor MRI fiber tracking provides maps of axonal bundles and was combined with intraoperative mapping of eloquent cortex for a patient undergoing brain tumor surgery. Tracks from eight stimulated sites in the inferior frontal cortex including mouth motor, speech arrest, and anomia were generated from the diffusion tensor MRI data. The regions connected by the fiber tracking were compared to foci from previous functional imaging reports on language tasks. Connections were found between speech arrest, mouth motor, and anomia sites and the SMA proper and cerebral peduncle. The speech arrest and a mouth motor site were also seen to connect to the putamen via the external capsule. This is the first demonstration of delineation of subcortical pathways using diffusion tensor MRI fiber tracking with intraoperative cortical stimulation. The combined techniques may provide improved preservation of eloquent regions during neurological surgery, and may provide access to direct connectivity information between functional regions of the brain.

Subcortical pathways serving cortical language sites: initial experience with diffusion tensor imaging fiber tracking combined with intraoperative language mapping

PubMed Central

Henry, Roland G.; Berman, Jeffrey I.; Nagarajan, Srikantan S.; Mukherjee, Pratik; Berger, Mitchel S.

2014-01-01

The combination of mapping functional cortical neurons by intraoperative cortical stimulation and axonal architecture by diffusion tensor MRI fiber tracking can be used to delineate the pathways between functional regions. In this study the authors investigated the feasibility of combining these techniques to yield connectivity associated with motor speech and naming. Diffusion tensor MRI fiber tracking provides maps of axonal bundles and was combined with intraoperative mapping of eloquent cortex for a patient undergoing brain tumor surgery. Tracks from eight stimulated sites in the inferior frontal cortex including mouth motor, speech arrest, and anomia were generated from the diffusion tensor MRI data. The regions connected by the fiber tracking were compared to foci from previous functional imaging reports on language tasks. Connections were found between speech arrest, mouth motor, and anomia sites and the SMA proper and cerebral peduncle. The speech arrest and a mouth motor site were also seen to connect to the putamen via the external capsule. This is the first demonstration of delineation of subcortical pathways using diffusion tensor MRI fiber tracking with intraoperative cortical stimulation. The combined techniques may provide improved preservation of eloquent regions during neurological surgery, and may provide access to direct connectivity information between functional regions of the brain. PMID:14980564

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

PubMed

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

2012-12-15

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

Grade-Dependent Metabolic Reprogramming in Kidney Cancer Revealed by Combined Proteomics and Metabolomics Analysis [Combined Proteomics and Metabolomics Analysis Reveals Grade-Dependent Metabolism Pathways in Kidney Cancer

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

Wettersten, Hiromi I.; Hakimi, A. Ari; Morin, Dexter

Kidney cancer [or renal cell carcinoma (RCC)] is known as “the internist's tumor” because it has protean systemic manifestations, suggesting that it utilizes complex, nonphysiologic metabolic pathways. Given the increasing incidence of this cancer and its lack of effective therapeutic targets, we undertook an extensive analysis of human RCC tissue employing combined grade-dependent proteomics and metabolomics analysis to determine how metabolic reprogramming occurring in this disease allows it to escape available therapeutic approaches. After validation experiments in RCC cell lines that were wild-type or mutant for the Von Hippel–Lindau tumor suppressor, in characterizing higher-grade tumors, we found that the Warburgmore » effect is relatively more prominent at the expense of the tricarboxylic acid cycle and oxidative metabolism in general. Further, we found that the glutamine metabolism pathway acts to inhibit reactive oxygen species, as evidenced by an upregulated glutathione pathway, whereas the β-oxidation pathway is inhibited, leading to increased fatty acylcarnitines. In support of findings from previous urine metabolomics analyses, we also documented tryptophan catabolism associated with immune suppression, which was highly represented in RCC compared with other metabolic pathways. Altogether, our results offer a rationale to evaluate novel antimetabolic treatment strategies being developed in other disease settings as therapeutic strategies in RCC« less

Grade-Dependent Metabolic Reprogramming in Kidney Cancer Revealed by Combined Proteomics and Metabolomics Analysis [Combined Proteomics and Metabolomics Analysis Reveals Grade-Dependent Metabolism Pathways in Kidney Cancer

DOE PAGES

Wettersten, Hiromi I.; Hakimi, A. Ari; Morin, Dexter; ...

2015-05-07

Kidney cancer [or renal cell carcinoma (RCC)] is known as “the internist's tumor” because it has protean systemic manifestations, suggesting that it utilizes complex, nonphysiologic metabolic pathways. Given the increasing incidence of this cancer and its lack of effective therapeutic targets, we undertook an extensive analysis of human RCC tissue employing combined grade-dependent proteomics and metabolomics analysis to determine how metabolic reprogramming occurring in this disease allows it to escape available therapeutic approaches. After validation experiments in RCC cell lines that were wild-type or mutant for the Von Hippel–Lindau tumor suppressor, in characterizing higher-grade tumors, we found that the Warburgmore » effect is relatively more prominent at the expense of the tricarboxylic acid cycle and oxidative metabolism in general. Further, we found that the glutamine metabolism pathway acts to inhibit reactive oxygen species, as evidenced by an upregulated glutathione pathway, whereas the β-oxidation pathway is inhibited, leading to increased fatty acylcarnitines. In support of findings from previous urine metabolomics analyses, we also documented tryptophan catabolism associated with immune suppression, which was highly represented in RCC compared with other metabolic pathways. Altogether, our results offer a rationale to evaluate novel antimetabolic treatment strategies being developed in other disease settings as therapeutic strategies in RCC« less

Metabolic pathway reconstruction of eugenol to vanillin bioconversion in Aspergillus niger

PubMed Central

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

2010-01-01

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

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

PubMed

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

2016-03-09

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

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species.

PubMed

Perumal, Deepak; Lim, Chu Sing; Chow, Vincent T K; Sakharkar, Kishore R; Sakharkar, Meena K

2008-09-10

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.

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

EPA Science Inventory

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

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

The utility of combining RSA indices in depression prediction.

PubMed

Yaroslavsky, Ilya; Rottenberg, Jonathan; Kovacs, Maria

2013-05-01

Depression is associated with protracted despondent mood, blunted emotional reactivity, and dysregulated parasympathetic nervous system (PNS) activity. PNS activity is commonly indexed via cardiac output, using indictors of its level (resting respiratory sinus arrhythmia [RSA]) or fluctuations (RSA reactivity). RSA reactivity can reflect increased or decreased PNS cardiac output (RSA augmentation and RSA withdrawal, respectively). Because a single index of a dynamic physiological system may be inadequate to characterize interindividual differences, we investigated whether the interaction of RSA reactivity and resting RSA is a better predictor of depression. Adult probands with childhood-onset depressive disorder histories (n = 113) and controls with no history of major mental disorders (n = 93) completed a psychophysiology protocol involving assessment of RSA at multiple rest periods and while watching a sad film. When examined independently, resting RSA and RSA reactivity were unrelated to depression, but their interaction predicted latent depression levels and proband status. In the context of high resting RSA, RSA withdrawal from the sad film predicted the lowest levels of depressive symptoms (irrespective of depression histories) and the greatest likelihood of having had no history of major mental disorder (irrespective of current distress). Our findings highlight the utility of combining indices of physiological responses in studying depression; combinations of RSA indices should be given future consideration as reflecting depression endophenotypes. © 2013 American Psychological Association

Predicting Bobsled Pushing Ability from Various Combine Testing Events.

PubMed

Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

2018-03-12

The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

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

PubMed Central

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

2010-01-01

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

A Novel Pathway for the Biosynthesis of Heme in Archaea: Genome-Based Bioinformatic Predictions and Experimental Evidence

PubMed Central

Storbeck, Sonja; Rolfes, Sarah; Raux-Deery, Evelyne; Warren, Martin J.; Jahn, Dieter; Layer, Gunhild

2010-01-01

Heme is an essential prosthetic group for many proteins involved in fundamental biological processes in all three domains of life. In Eukaryota and Bacteria heme is formed via a conserved and well-studied biosynthetic pathway. Surprisingly, in Archaea heme biosynthesis proceeds via an alternative route which is poorly understood. In order to formulate a working hypothesis for this novel pathway, we searched 59 completely sequenced archaeal genomes for the presence of gene clusters consisting of established heme biosynthetic genes and colocalized conserved candidate genes. Within the majority of archaeal genomes it was possible to identify such heme biosynthesis gene clusters. From this analysis we have been able to identify several novel heme biosynthesis genes that are restricted to archaea. Intriguingly, several of the encoded proteins display similarity to enzymes involved in heme d 1 biosynthesis. To initiate an experimental verification of our proposals two Methanosarcina barkeri proteins predicted to catalyze the initial steps of archaeal heme biosynthesis were recombinantly produced, purified, and their predicted enzymatic functions verified. PMID:21197080

Efficient key pathway mining: combining networks and OMICS data.

PubMed

Alcaraz, Nicolas; Friedrich, Tobias; Kötzing, Timo; Krohmer, Anton; Müller, Joachim; Pauling, Josch; Baumbach, Jan

2012-07-01

Systems biology has emerged over the last decade. Driven by the advances in sophisticated measurement technology the research community generated huge molecular biology data sets. These comprise rather static data on the interplay of biological entities, for instance protein-protein interaction network data, as well as quite dynamic data collected for studying the behavior of individual cells or tissues in accordance with changing environmental conditions, such as DNA microarrays or RNA sequencing. Here we bring the two different data types together in order to gain higher level knowledge. We introduce a significantly improved version of the KeyPathwayMiner software framework. Given a biological network modelled as a graph and a set of expression studies, KeyPathwayMiner efficiently finds and visualizes connected sub-networks where most components are expressed in most cases. It finds all maximal connected sub-networks where all nodes but k exceptions are expressed in all experimental studies but at most l exceptions. We demonstrate the power of the new approach by comparing it to similar approaches with gene expression data previously used to study Huntington's disease. In addition, we demonstrate KeyPathwayMiner's flexibility and applicability to non-array data by analyzing genome-scale DNA methylation profiles from colorectal tumor cancer patients. KeyPathwayMiner release 2 is available as a Cytoscape plugin and online at http://keypathwayminer.mpi-inf.mpg.de.

Kinetics of protein–ligand unbinding: Predicting pathways, rates, and rate-limiting steps

PubMed Central

Tiwary, Pratyush; Limongelli, Vittorio; Salvalaglio, Matteo; Parrinello, Michele

2015-01-01

The ability to predict the mechanisms and the associated rate constants of protein–ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin–benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate koff is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate kon. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein–ligand systems and a valid support for drug design. PMID:25605901

Combining Modeling and Gaming for Predictive Analytics

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

Riensche, Roderick M.; Whitney, Paul D.

2012-08-22

Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describemore » our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.« less

Combination of Mitochondrial and Plasma Membrane Citrate Transporter Inhibitors Inhibits De Novo Lipogenesis Pathway and Triggers Apoptosis in Hepatocellular Carcinoma Cells

PubMed Central

Phokrai, Phornpun; Suwankulanan, Somrudee; Phakdeeto, Narinthorn; Phunsomboon, Pattamaphorn; Pekthong, Dumrongsak; Richert, Lysiane; Pongcharoen, Sutatip

2018-01-01

Increased expression levels of both mitochondrial citrate transporter (CTP) and plasma membrane citrate transporter (PMCT) proteins have been found in various cancers. The transported citrates by these two transporter proteins provide acetyl-CoA precursors for the de novo lipogenesis (DNL) pathway to support a high rate of cancer cell viability and development. Inhibition of the DNL pathway promotes cancer cell apoptosis without apparent cytotoxic to normal cells, leading to the representation of selective and powerful targets for cancer therapy. The present study demonstrates that treatments with CTP inhibitor (CTPi), PMCT inhibitor (PMCTi), and the combination of CTPi and PMCTi resulted in decreased cell viability in two hepatocellular carcinoma cell lines (HepG2 and HuH-7). Treatment with citrate transporter inhibitors caused a greater cytotoxic effect in HepG2 cells than in HuH-7 cells. A lower concentration of combined CTPi and PMCTi promotes cytotoxic effect compared with either of a single compound. An increased cell apoptosis and an induced cell cycle arrest in both cell lines were reported after administration of the combined inhibitors. A combination treatment exhibits an enhanced apoptosis through decreased intracellular citrate levels, which consequently cause inhibition of fatty acid production in HepG2 cells. Apoptosis induction through the mitochondrial-dependent pathway was found as a consequence of suppressed carnitine palmitoyl transferase-1 (CPT-1) activity and enhanced ROS generation by combined CTPi and PMCTi treatment. We showed that accumulation of malonyl-CoA did not correlate with decreasing CPT-1 activity. The present study showed that elevated ROS levels served as an inhibition on Bcl-2 activity that is at least in part responsible for apoptosis. Moreover, inhibition of the citrate transporter is selectively cytotoxic to HepG2 cells but not in primary human hepatocytes, supporting citrate-mediating fatty acid synthesis as a promising

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.

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

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

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

1981-08-01

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

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

PubMed

Carlson, Ross; Srienc, Friedrich

2004-04-20

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

Predictive accuracy of combined genetic and environmental risk scores.

PubMed

Dudbridge, Frank; Pashayan, Nora; Yang, Jian

2018-02-01

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

Predictive accuracy of combined genetic and environmental risk scores

PubMed Central

Pashayan, Nora; Yang, Jian

2017-01-01

ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

Combined loss of three DNA damage response pathways renders C. elegans intolerant to light.

PubMed

van Bostelen, Ivo; Tijsterman, Marcel

2017-06-01

Infliction of DNA damage initiates a complex cellular reaction - the DNA damage response - that involves both signaling and DNA repair networks with many redundancies and parallel pathways. Here, we reveal the three strategies that the simple multicellular eukaryote, C. elegans, uses to deal with DNA damage induced by light. Separately inactivating repair or replicative bypass of photo-lesions results in cellular hypersensitivity towards UV-light, but impeding repair of replication associated DNA breaks does not. Yet, we observe an unprecedented synergistic relationship when these pathways are inactivated in combination. C. elegans mutants that lack nucleotide excision repair (NER), translesion synthesis (TLS) and alternative end joining (altEJ) grow undisturbed in the dark, but become sterile when grown in light. Even exposure to very low levels of normal daylight impedes animal growth. We show that NER and TLS operate to suppress the formation of lethal DNA breaks that require polymerase theta-mediated end joining (TMEJ) for their repair. Our data testifies to the enormous genotoxicity of light and to the demand of multiple layers of protection against an environmental threat that is so common. Copyright © 2017 Elsevier B.V. All rights reserved.

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

NASA Astrophysics Data System (ADS)

Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

2018-02-01

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-03-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed Central

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-01-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

PubMed

Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Metabolic network prediction through pairwise rational kernels.

PubMed

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

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

Computationally predicted Adverse Outcome Pathway networks for liver-related diseases using publicly available data sources: Case studies and lessons learned

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework summarizes key information about mechanistic events leading to an adverse health or ecological outcome. In recent years computationally predicted AOPs (cpAOP) making use of publicly available data have been proposed as a means of accele...

EGFR Gene Amplification and KRAS Mutation Predict Response to Combination Targeted Therapy in Metastatic Colorectal Cancer.

PubMed

Khan, Sajid A; Zeng, Zhaoshi; Shia, Jinru; Paty, Philip B

2017-07-01

Genetic variability in KRAS and EGFR predicts response to cetuximab in irinotecan refractory colorectal cancer. Whether these markers or others remain predictive in combination biologic therapies including bevacizumab is unknown. We identified predictive biomarkers from patients with irinotecan refractory metastatic colorectal cancer treated with cetuximab plus bevacizumab. Patients who received cetuximab plus bevacizumab for irinotecan refractory colorectal cancer in either of two Phase II trials conducted were identified. Tumor tissue was available for 33 patients. Genomic DNA was extracted and used for mutational analysis of KRAS, BRAF, and p53 genes. Fluorescence in situ hybridization was performed to assess EGFR copy number. The status of single genes and various combinations were tested for association with response. Seven of 33 patients responded to treatment. KRAS mutations were found in 14/33 cases, and 0 responded to treatment (p = 0.01). EGFR gene amplification was seen in 3/33 of tumors and in every case was associated with response to treatment (p < 0.001). TP53 and BRAF mutations were found in 18/33 and 0/33 tumors, respectively, and there were no associations with response to either gene. EGFR gene amplification and KRAS mutations are predictive markers for patients receiving combination biologic therapy of cetuximab plus bevacizumab for metastatic colorectal cancer. One marker or the other is present in the tumor of half of all patients allowing treatment response to be predicted with a high degree of certainty. The role for molecular markers in combination biologic therapy seems promising.

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

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

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

Taking the Next Step: Combining Incrementally Valid Indicators to Improve Recidivism Prediction

ERIC Educational Resources Information Center

Walters, Glenn D.

2011-01-01

The possibility of combining indicators to improve recidivism prediction was evaluated in a sample of released federal prisoners randomly divided into a derivation subsample (n = 550) and a cross-validation subsample (n = 551). Five incrementally valid indicators were selected from five domains: demographic (age), historical (prior convictions),…

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

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

2014-01-01

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

Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years.

PubMed

Schlaeger, Regina; Schindler, Christian; Grize, Leticia; Dellas, Sophie; Radue, Ernst W; Kappos, Ludwig; Fuhr, Peter

2014-09-01

The development of predictors of multiple sclerosis (MS) disability is difficult due to the complex interplay of pathophysiological and adaptive processes. The purpose of this study was to investigate whether combined evoked potential (EP)-measures allow prediction of MS disability after 20 years. We examined 28 patients with clinically definite MS according to Poser's criteria with Expanded Disability Status Scale (EDSS) scores, combined visual and motor EPs at entry (T0), 6 (T1), 12 (T2) and 24 (T3) months, and a cranial magnetic resonance imaging (MRI) scan at T0 and T2. EDSS testing was repeated at year 14 (T4) and year 20 (T5). Spearman rank correlation was used. We performed a multivariable regression analysis to examine predictive relationships of the sum of z-transformed EP latencies (s-EPT0) and other baseline variables with EDSST5. We found that s-EPT0 correlated with EDSST5 (rho=0.72, p<0.0001) and ΔEDSST5-T0 (rho=0.50, p=0.006). Backward selection resulted in the prediction model: E (EDSST5)=3.91-2.22×therapy+0.079×age+0.057×s-EPT0 (Model 1, R (2)=0.58) with therapy as binary variable (1=any disease-modifying therapy between T3 and T5, 0=no therapy). Neither EDSST0 nor T2-lesion or gadolinium (Gd)-enhancing lesion quantities at T0 improved prediction of EDSST5. The area under the receiver operating characteristic (ROC) curve was 0.89 for model 1. These results further support a role for combined EP-measures as predictors of long-term disability in MS. © The Author(s) 2014.

A network pharmacology approach to determine active ingredients and rationality of herb combinations of Modified-Simiaowan for treatment of gout.

PubMed

Zhao, Fangli; Guochun, Li; Yang, Yanhua; Shi, Le; Xu, Li; Yin, Lian

2015-06-20

Modified Simiaowan (MSW) is a traditional Chinese medicine (TCM) formula and is widely used as a clinically medication formula for its efficiency in treating gouty diseases.To predict the active ingredients in MSW and uncover the rationality of herb combinations of MSW. Three drug-target networks including the "candidate ingredient-target network" (cI-cT) that links the candidate ingredients and targets, the "core ingredient-target-pathway network" connecting core potential ingredients and targets through related pathways, and the "rationality of herb combinations of MSW network", which was derived from the cI-cT network, were developed to dissect the active ingredients in MSW and relationship between ingredients in herb combinations and their targets for gouty diseases. On the other hand, herbal ingredients comparisons were also conducted based on six physicochemical properties to investigate whether the herbs in MSW are similar in chemicals. Moreover, HUVEC viability and expression levels of ICAM-1 induced by monosodium urate (MSU) crystals were assessed to determine the activities of potential ingredients in MSW. Predicted by the core ingredient-target-pathway network, we collected 30 core ingredients in MSW and 25 inflammatory cytokines and uric acid synthetase or transporters, which are effective for gouty treatment through some related pathways. Experimental results also confirmed that those core ingredients could significantly increase HUVEC viability and attenuate the expression of ICAM-1, which supported the effectiveness of MSW in treating gouty diseases. Moreover, heat-clearing and dampness-eliminating herbs in MSW have similar physicochemical properties, which stimulate all the inflammatory and uric acid-lowing targets respectively, while the core drug and basic prescription in MSW stimulate the major and almost all the core targets, respectively. Our work successfully predicts the active ingredients in MSW and explains the cooperation between these

Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers.

PubMed

Kaluarachchi, Manuja R; Boulangé, Claire L; Garcia-Perez, Isabel; Lindon, John C; Minet, Emmanuel F

2016-10-01

Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

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.

Clinical, Electrocardiographic, and Echocardiographic Parameter Combination Predicts the Onset of Atrial Fibrillation.

PubMed

Soeki, Takeshi; Matsuura, Tomomi; Tobiume, Takeshi; Bando, Sachiko; Matsumoto, Kazuhisa; Nagano, Hiromi; Uematsu, Etsuko; Kusunose, Kenya; Ise, Takayuki; Yamaguchi, Koji; Yagi, Shusuke; Fukuda, Daiju; Yamada, Hirotsugu; Wakatsuki, Tetsuzo; Shimabukuro, Michio; Sata, Masataka

2018-05-30

The ability to identify risk markers for new-onset atrial fibrillation (AF) is critical to the development of preventive strategies, but it remains unknown whether a combination of clinical, electrocardiographic, and echocardiographic parameters predicts the onset of AF. In the present study, we evaluated the predictive value of a combined score that includes these parameters.Methods and Results:We retrospectively studied 1,040 patients without AF who underwent both echocardiography and 24-h Holter electrocardiography between May 2005 and December 2010. During a median follow-up period of 68.4 months (IQR, 49.9-93.3 months), we investigated the incidence of new-onset AF. Of the 1,040 patients, 103 (9.9%) developed AF. Patients who developed AF were older than patients who did not. Total heart beats, premature atrial contraction (PAC) count, maximum RR interval, and frequency of sinus pause quantified on 24-h electrocardiography were associated with new-onset AF. LA diameter (LAD) on echocardiography was also associated with the development of AF. On multivariate Cox analysis, age ≥58 years, PAC count ≥80 beats/day, maximum RR interval ≥1.64 s, and LAD ≥4.5 cm were independently associated with the development of AF. The incidence rate of new-onset AF significantly increased as the combined score (i.e., the sum of the risk score determined using hazard ratios) increased. A combined score that includes age, PAC count, maximum RR interval, and LAD could help characterize the risk of new-onset AF.

Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.

PubMed

Meisner, Allison; Kerr, Kathleen F; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2016-02-01

Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict AKI were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias, and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles), or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable, and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis, and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.

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

PubMed Central

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

2006-01-01

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

Brain injury prediction: assessing the combined probability of concussion using linear and rotational head acceleration.

PubMed

Rowson, Steven; Duma, Stefan M

2013-05-01

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

Berberine, a natural product, combined with cisplatin enhanced apoptosis through a mitochondria/caspase-mediated pathway in HeLa cells.

PubMed

Youn, Myung-Ja; So, Hong-Seob; Cho, Hea-Joong; Kim, Hyung-Jin; Kim, Yunha; Lee, Jeong-Han; Sohn, Jung Sook; Kim, Yong Kyu; Chung, Sang-Young; Park, Raekil

2008-05-01

Berberine, a main component of Coptidis Rhizoma, has been extensively studied and is known to exhibit multiple pharmacologic activities. In this study, we investigated whether the combination of berberine and cisplatin exhibited significant cytotoxicity in HeLa cells. Apoptosis was evaluated based on DNA fragmentation and cytofluorometrically with the annexin-V/propidium iodide labeling method. Combined treatment with berberine and cisplatin acted in concert to induce loss of mitochondrial membrane potential (Delta Psi m), release of cytochrome-c from mitochondria, and decreased expression of antiapoptotic Bcl-2, Bcl-x/L, resulting in activation of caspases and apoptosis. Further study showed that cell death induced by the combined treatment was associated with increased reactive oxygen species generation and lipid peroxidation. Moreover, we discovered that the combined treatment-induced apoptosis was mediated by the activation of the caspase cascade. These results indicated that the potential of cytotoxicity mediated through the mitochondria-caspase pathway is primarily involved in the combined treatment-induced apoptosis.

Predictive Value of National Football League Scouting Combine on Future Performance of Running Backs and Wide Receivers.

PubMed

Teramoto, Masaru; Cross, Chad L; Willick, Stuart E

2016-05-01

The National Football League (NFL) Scouting Combine is held each year before the NFL Draft to measure athletic abilities and football skills of college football players. Although the NFL Scouting Combine can provide the NFL teams with an opportunity to evaluate college players for the upcoming NFL Draft, its value for predicting future success of players has been questioned. This study examined whether the NFL Combine measures can predict future performance of running backs (RBs) and wide receivers (WRs) in the NFL. We analyzed the 2000-09 Combine data of RBs (N = 276) and WRs (N = 447) and their on-field performance for the first 3 years after the draft and over their entire careers in the NFL, using correlation and regression analyses, along with a principal component analysis (PCA). The results of the analyses showed that, after accounting for the number of games played, draft position, height (HT), and weight (WT), the time on 10-yard dash was the most important predictor of rushing yards per attempt of the first 3 years (p = 0.002) and of the careers (p < 0.001) in RBs. For WRs, vertical jump was found to be significantly associated with receiving yards per reception of the first 3 years (p = 0.001) and of the careers (p = 0.004) in the NFL, after adjusting for the covariates above. Furthermore, HT was most important in predicting future performance of WRs. The analyses also revealed that the 8 athletic drills in the Combine seemed to have construct validity. It seems that the NFL Scouting Combine has some value for predicting future performance of RBs and WRs in the NFL.

Predictive discomfort in single- and combined-axis whole-body vibration considering different seated postures.

PubMed

DeShaw, Jonathan; Rahmatalla, Salam

2014-08-01

The aim of this study was to develop a predictive discomfort model in single-axis, 3-D, and 6-D combined-axis whole-body vibrations of seated occupants considering different postures. Non-neutral postures in seated whole-body vibration play a significant role in the resulting level of perceived discomfort and potential long-term injury. The current international standards address contact points but not postures. The proposed model computes discomfort on the basis of static deviation of human joints from their neutral positions and how fast humans rotate their joints under vibration. Four seated postures were investigated. For practical implications, the coefficients of the predictive discomfort model were changed into the Borg scale with psychophysical data from 12 volunteers in different vibration conditions (single-axis random fore-aft, lateral, and vertical and two magnitudes of 3-D). The model was tested under two magnitudes of 6-D vibration. Significant correlations (R = .93) were found between the predictive discomfort model and the reported discomfort with different postures and vibrations. The ISO 2631-1 correlated very well with discomfort (R2 = .89) but was not able to predict the effect of posture. Human discomfort in seated whole-body vibration with different non-neutral postures can be closely predicted by a combination of static posture and the angular velocities of the joint. The predictive discomfort model can assist ergonomists and human factors researchers design safer environments for seated operators under vibration. The model can be integrated with advanced computer biomechanical models to investigate the complex interaction between posture and vibration.

Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined with Insar and Leveling

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

2018-04-01

The principle of exponent Knothe model was introduced in detail and the variation process of mining subsidence with time was analysed based on the formulas of subsidence, subsidence velocity and subsidence acceleration in the paper. Five scenes of radar images and six levelling measurements were collected to extract ground deformation characteristics in one coal mining area in this study. Then the unknown parameters of exponent Knothe model were estimated by combined levelling data with deformation information along the line of sight obtained by InSAR technique. By compared the fitting and prediction results obtained by InSAR and levelling with that obtained only by levelling, it was shown that the accuracy of fitting and prediction combined with InSAR and levelling was obviously better than the other that. Therefore, the InSAR measurements can significantly improve the fitting and prediction accuracy of exponent Knothe model.

Using a combined computational-experimental approach to predict antibody-specific B cell epitopes.

PubMed

Sela-Culang, Inbal; Benhnia, Mohammed Rafii-El-Idrissi; Matho, Michael H; Kaever, Thomas; Maybeno, Matt; Schlossman, Andrew; Nimrod, Guy; Li, Sheng; Xiang, Yan; Zajonc, Dirk; Crotty, Shane; Ofran, Yanay; Peters, Bjoern

2014-04-08

Antibody epitope mapping is crucial for understanding B cell-mediated immunity and required for characterizing therapeutic antibodies. In contrast to T cell epitope mapping, no computational tools are in widespread use for prediction of B cell epitopes. Here, we show that, utilizing the sequence of an antibody, it is possible to identify discontinuous epitopes on its cognate antigen. The predictions are based on residue-pairing preferences and other interface characteristics. We combined these antibody-specific predictions with results of cross-blocking experiments that identify groups of antibodies with overlapping epitopes to improve the predictions. We validate the high performance of this approach by mapping the epitopes of a set of antibodies against the previously uncharacterized D8 antigen, using complementary techniques to reduce method-specific biases (X-ray crystallography, peptide ELISA, deuterium exchange, and site-directed mutagenesis). These results suggest that antibody-specific computational predictions and simple cross-blocking experiments allow for accurate prediction of residues in conformational B cell epitopes. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2014-12-01

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

Combined heat transfer and kinetic models to predict cooking loss during heat treatment of beef meat.

PubMed

Kondjoyan, Alain; Oillic, Samuel; Portanguen, Stéphane; Gros, Jean-Bernard

2013-10-01

A heat transfer model was used to simulate the temperature in 3 dimensions inside the meat. This model was combined with a first-order kinetic models to predict cooking losses. Identification of the parameters of the kinetic models and first validations were performed in a water bath. Afterwards, the performance of the combined model was determined in a fan-assisted oven under different air/steam conditions. Accurate knowledge of the heat transfer coefficient values and consideration of the retraction of the meat pieces are needed for the prediction of meat temperature. This is important since the temperature at the center of the product is often used to determine the cooking time. The combined model was also able to predict cooking losses from meat pieces of different sizes and subjected to different air/steam conditions. It was found that under the studied conditions, most of the water loss comes from the juice expelled by protein denaturation and contraction and not from evaporation. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2015-01-01

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

Predictive value of orthopedic evaluation and injury history at the NFL combine.

PubMed

Brophy, Robert H; Chehab, Eric L; Barnes, Ronnie P; Lyman, Stephen; Rodeo, Scott A; Warren, Russell F

2008-08-01

The National Football League (NFL) holds an annual combine to evaluate college football athletes likely to be drafted for physical skills, to review their medical history, and to perform a physical examination. The athletes receive an orthopedic grade on their ability to participate in the NFL. The purpose of this study was to test the hypothesis that this orthopedic rating at the combine predicts the percent of athletes who play in the NFL and the length of their careers. A database for all athletes reviewed at the combine by the medical staff of one team from 1987 to 2000 was created and linked to a data set containing the number of seasons and the games played in the NFL for each athlete. Players were grouped by orthopedic grade: high, low, and orthopedic failure. The percent of players who played in the NFL and the mean length of their careers was calculated and compared for these groups. The orthopedic grade assigned at the NFL combine correlated with the probability of playing in the league. Whereas 58% of athletes with a high grade and 55% of athletes with a low grade played at least one game, only 36% of athletes given a failing grade did so (P < 0.001). Players with a high grade had a mean career of 41.5 games compared with 34.2 games for players with a low grade and 19.0 games for orthopedic failures. This is the first study to report on the predictive value of a grading system for college athletes before participation in professional sports. Other professional sports may benefit from using a similar grading system for the evaluation of potential players.

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

PubMed

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

2005-05-01

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

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

PubMed Central

Tian, Weidong; Zhang, Lan V; Taşan, Murat; Gibbons, Francis D; King, Oliver D; Park, Julie; Wunderlich, Zeba; Cherry, J Michael; Roth, Frederick P

2008-01-01

Background: Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships. Results: We have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships. Conclusion: Funckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions. PMID:18613951

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount

Disrupting BCR-ABL in combination with secondary leukemia-specific pathways in CML cells leads to enhanced apoptosis and decreased proliferation.

PubMed

Woessner, David W; Lim, Carol S

2013-01-07

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by expression of the fusion gene BCR-ABL following a chromosomal translocation in the hematopoietic stem cell. Therapeutic management of CML uses tyrosine kinase inhibitors (TKIs), which block ABL-signaling and effectively kill peripheral cells with BCR-ABL. However, TKIs are not curative, and chronic use is required in order to treat CML. The primary failure for TKIs is through the development of a resistant population due to mutations in the TKI binding regions. This led us to develop the mutant coiled-coil, CC(mut2), an alternative method for BCR-ABL signaling inhibition by targeting the N-terminal oligomerization domain of BCR, necessary for ABL activation. In this article, we explore additional pathways that are important for leukemic stem cell survival in K562 cells. Using a candidate-based approach, we test the combination of CC(mut2) and inhibitors of unique secondary pathways in leukemic cells. Transformative potential was reduced following silencing of the leukemic stem cell factor Alox5 by RNA interference. Furthermore, blockade of the oncogenic protein MUC-1 by the novel peptide GO-201 yielded reductions in proliferation and increased cell death. Finally, we found that inhibiting macroautophagy using chloroquine in addition to blocking BCR-ABL signaling with the CC(mut2) was most effective in limiting cell survival and proliferation. This study has elucidated possible combination therapies for CML using novel blockade of BCR-ABL and secondary leukemia-specific pathways.

Computational identification of signalling pathways in Plasmodium falciparum.

PubMed

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

2011-06-01

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

Pathway Pathology

PubMed Central

Rosner, Andrea; Miyoshi, Keiko; Landesman-Bollag, Esther; Xu, Xin; Seldin, David C.; Moser, Amy R.; MacLeod, Carol L.; Shyamala, G.; Gillgrass, Amy E.; Cardiff, Robert D.

2002-01-01

To study phenotype-genotype correlations, ErbB/Ras pathway tumors (transgenic for ErbB2, c-Neu, mutants of c-Neu, polyomavirus middle T antigene (PyV-mT), Ras, and bi-transgenic for ErbB2/Neu with ErbB3 and with progesterone receptor) from four different institutions were histopathologically compared with Wnt pathway tumors [transgenes Wnt1, Wnt10b, dominant-negative glycogen synthase kinase 3-β, β-Catenin, and spontaneous mutants of adenomatous polyposis coli gene (Apc)]. ErbB/Ras pathway tumors tend to form solid nodules consisting of poorly differentiated cells with abundant cytoplasm. ErbB/Ras pathway tumors also have scanty stroma and lack myoepithelial or squamous differentiation. In contrast, Wnt pathway tumors exhibit myoepithelial, acinar, or glandular differentiation, and, frequently, combinations of these. Squamous metaplasia is frequent and may include transdifferentiation to epidermal and pilar structures. Most Wnt pathway tumors form caricatures of elongated, branched ductules, and have well-developed stroma, inflammatory infiltrates, and pushing margins. Tumors transgenic for interacting genes such as protein kinase CK2α (casein kinase IIα), and the fibroblast growth factors (Fgf) Int2/Fgf3 or keratinocyte growth factor (Kgf/Fgf7) also have the Wnt pathway phenotype. Because the tumors from the ErbB/Ras and the Wnt pathway are so distinct and can be readily identified using routine hematoxylin and eosin sections, we suggest that pathway pathology is applicable in both basic and clinical cancer research. PMID:12213737

The combination of HDAC and aminopeptidase inhibitors is highly synergistic in myeloma and leads to disruption of the NFκB signalling pathway

PubMed Central

Smith, Emma M.; Zhang, Lei; Walker, Brian A.; Davenport, Emma L.; Aronson, Lauren I.; Krige, David; Hooftman, Leon; Drummond, Alan H.; Morgan, Gareth J.; Davies, Faith E.

2015-01-01

There is a growing body of evidence supporting the use of epigenetic therapies in the treatment of multiple myeloma. We show the novel HDAC inhibitor CHR-3996 induces apoptosis in myeloma cells at concentrations in the nanomolar range and with apoptosis mediated by p53 and caspase pathways. In addition, HDAC inhibitors are highly synergistic, both in vitro and in vivo, with the aminopeptidase inhibitor tosedostat (CHR-2797). We demonstrate that the basis for this synergy is a consequence of changes in the levels of NFκB regulators BIRC3/cIAP2, A20, CYLD, and IκB, which were markedly affected by the combination. When co-administered the HDAC and aminopeptidase inhibitors caused rapid nuclear translocation of NFκB family members p65 and p52, following activation of both canonical and non-canonical NFκB signalling pathways. The subsequent up-regulation of inhibitors of NFκB activation (most significantly BIRC3/cIAP2) turned off the cytoprotective effects of the NFκB signalling response in a negative feedback loop. These results provide a rationale for combining HDAC and aminopeptidase inhibitors clinically for the treatment of myeloma patients and support the disruption of the NFκB signalling pathway as a therapeutic strategy. PMID:26015393

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

PubMed

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

1995-01-01

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

Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models

USGS Publications Warehouse

McKenna, James E.; Carlson, Douglas M.; Payne-Wynne, Molly L.

2013-01-01

Aim: Rare aquatic species are a substantial component of biodiversity, and their conservation is a major objective of many management plans. However, they are difficult to assess, and their optimal habitats are often poorly known. Methods to effectively predict the likely locations of suitable rare aquatic species habitats are needed. We combine two modelling approaches to predict occurrence and general abundance of several rare fish species. Location: Allegheny watershed of western New York State (USA) Methods: Our method used two empirical neural network modelling approaches (species specific and assemblage based) to predict stream-by-stream occurrence and general abundance of rare darters, based on broad-scale habitat conditions. Species-specific models were developed for longhead darter (Percina macrocephala), spotted darter (Etheostoma maculatum) and variegate darter (Etheostoma variatum) in the Allegheny drainage. An additional model predicted the type of rare darter-containing assemblage expected in each stream reach. Predictions from both models were then combined inclusively and exclusively and compared with additional independent data. Results Example rare darter predictions demonstrate the method's effectiveness. Models performed well (R2 ≥ 0.79), identified where suitable darter habitat was most likely to occur, and predictions matched well to those of collection sites. Additional independent data showed that the most conservative (exclusive) model slightly underestimated the distributions of these rare darters or predictions were displaced by one stream reach, suggesting that new darter habitat types were detected in the later collections. Main conclusions Broad-scale habitat variables can be used to effectively identify rare species' habitats. Combining species-specific and assemblage-based models enhances our ability to make use of the sparse data on rare species and to identify habitat units most likely and least likely to support those species

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms.

PubMed

Bianco, Luca; Riccadonna, Samantha; Lavezzo, Enrico; Falda, Marco; Formentin, Elide; Cavalieri, Duccio; Toppo, Stefano; Fontana, Paolo

2017-02-01

Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact:paolo.fontana@fmach.it

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

PubMed Central

Bouhaddou, Mehdi; Koch, Rick J.; DiStefano, Matthew S.; Tan, Annie L.; Mertz, Alex E.

2018-01-01

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy. PMID:29579036

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

PubMed Central

Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

PubMed

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

PubMed Central

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods. PMID:26880879

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

PubMed

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

PubMed

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

USGS Publications Warehouse

Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

2009-01-01

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

Identification of differential pathways in papillary thyroid carcinoma utilizing pathway co-expression analysis.

PubMed

Qiu, Wei-Hai; Chen, Gui-Yan; Cui, Lu; Zhang, Ting-Ming; Wei, Feng; Yang, Yong

2016-01-01

To identify differential pathways between papillary thyroid carcinoma (PTC) patients and normal controls utilizing a novel method which combined pathway with co-expression network. The proposed method included three steps. In the first step, we conducted pretreatments for background pathways and gained representative pathways in PTC. Subsequently, a co-expression network for representative pathways was constructed using empirical Bayes (EB) approach to assign a weight value for each pathway. Finally, random model was extracted to set the thresholds of identifying differential pathways. We obtained 1267 representative pathways and their weight values based on the co-expressed pathway network, and then by meeting the criterion (Weight > 0.0296), 87 differential pathways in total across PTC patients and normal controls were identified. The top three ranked differential pathways were CREB phosphorylation, attachment of GPI anchor to urokinase plasminogen activator receptor (uPAR) and loss of function of SMAD2/3 in cancer. In conclusion, we successfully identified differential pathways (such as CREB phosphorylation, attachment of GPI anchor to uPAR and post-translational modification: synthesis of GPI-anchored proteins) for PTC using the proposed pathway co-expression method, and these pathways might be potential biomarkers for target therapy and detection of PTC.

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

PubMed

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

2017-01-01

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

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

PubMed

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Kosek, Wiesław

2008-02-01

This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

The combination of work organizational climate and individual work commitment predicts return to work in women but not in men.

PubMed

Holmgren, Kristina; Ekbladh, Elin; Hensing, Gunnel; Dellve, Lotta

2013-02-01

To analyze if the combination of organizational climate and work commitment can predict return to work (RTW). This prospective Swedish study was based on 2285 participants, 19 to 64 years old, consecutively selected from the employed population, newly sick-listed for more than 14 days. Data were collected in 2008 through postal questionnaire and from register data. Among women, the combination of good organizational climate and fair work commitment predicted an early RTW with an adjusted relative risk of 2.05 (1.32 to 3.18). Among men, none of the adjusted variables or combinations of variables was found significantly to predict RTW. This study demonstrated the importance of integrative effects of organizational climate and individual work commitment on RTW among women. These factors did not predict RTW in men. More research is needed to understand the RTW process among men.

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

PubMed

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

2017-01-01

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

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

PubMed

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

2010-04-23

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

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

NASA Astrophysics Data System (ADS)

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

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

Kinetic network study of the diversity and temperature dependence of Trp-Cage folding pathways: combining transition path theory with stochastic simulations.

PubMed

Zheng, Weihua; Gallicchio, Emilio; Deng, Nanjie; Andrec, Michael; Levy, Ronald M

2011-02-17

We present a new approach to study a multitude of folding pathways and different folding mechanisms for the 20-residue mini-protein Trp-Cage using the combined power of replica exchange molecular dynamics (REMD) simulations for conformational sampling, transition path theory (TPT) for constructing folding pathways, and stochastic simulations for sampling the pathways in a high dimensional structure space. REMD simulations of Trp-Cage with 16 replicas at temperatures between 270 and 566 K are carried out with an all-atom force field (OPLSAA) and an implicit solvent model (AGBNP). The conformations sampled from all temperatures are collected. They form a discretized state space that can be used to model the folding process. The equilibrium population for each state at a target temperature can be calculated using the weighted-histogram-analysis method (WHAM). By connecting states with similar structures and creating edges satisfying detailed balance conditions, we construct a kinetic network that preserves the equilibrium population distribution of the state space. After defining the folded and unfolded macrostates, committor probabilities (P(fold)) are calculated by solving a set of linear equations for each node in the network and pathways are extracted together with their fluxes using the TPT algorithm. By clustering the pathways into folding "tubes", a more physically meaningful picture of the diversity of folding routes emerges. Stochastic simulations are carried out on the network, and a procedure is developed to project sampled trajectories onto the folding tubes. The fluxes through the folding tubes calculated from the stochastic trajectories are in good agreement with the corresponding values obtained from the TPT analysis. The temperature dependence of the ensemble of Trp-Cage folding pathways is investigated. Above the folding temperature, a large number of diverse folding pathways with comparable fluxes flood the energy landscape. At low temperature

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms

PubMed Central

Bianco, Luca; Riccadonna, Samantha; Lavezzo, Enrico; Falda, Marco; Formentin, Elide; Cavalieri, Duccio; Toppo, Stefano

2017-01-01

Abstract Summary: Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Availability and Implementation: Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact: paolo.fontana@fmach.it PMID:28158604

Production of bulk chemicals via novel metabolic pathways in microorganisms.

PubMed

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

2013-11-01

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

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.

PubMed

Leong, Ivone U S; Stuckey, Alexander; Lai, Daniel; Skinner, Jonathan R; Love, Donald R

2015-05-13

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

PubMed

Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

PubMed Central

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

Refining the quantitative pathway of the Pathways to Mathematics model.

PubMed

Sowinski, Carla; LeFevre, Jo-Anne; Skwarchuk, Sheri-Lynn; Kamawar, Deepthi; Bisanz, Jeffrey; Smith-Chant, Brenda

2015-03-01

In the current study, we adopted the Pathways to Mathematics model of LeFevre et al. (2010). In this model, there are three cognitive domains--labeled as the quantitative, linguistic, and working memory pathways--that make unique contributions to children's mathematical development. We attempted to refine the quantitative pathway by combining children's (N=141 in Grades 2 and 3) subitizing, counting, and symbolic magnitude comparison skills using principal components analysis. The quantitative pathway was examined in relation to dependent numerical measures (backward counting, arithmetic fluency, calculation, and number system knowledge) and a dependent reading measure, while simultaneously accounting for linguistic and working memory skills. Analyses controlled for processing speed, parental education, and gender. We hypothesized that the quantitative, linguistic, and working memory pathways would account for unique variance in the numerical outcomes; this was the case for backward counting and arithmetic fluency. However, only the quantitative and linguistic pathways (not working memory) accounted for unique variance in calculation and number system knowledge. Not surprisingly, only the linguistic pathway accounted for unique variance in the reading measure. These findings suggest that the relative contributions of quantitative, linguistic, and working memory skills vary depending on the specific cognitive task. Copyright © 2014 Elsevier Inc. All rights reserved.

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

PubMed

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

2013-10-31

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

Mapping the pathways of resistance to targeted therapies

PubMed Central

Wood, Kris C.

2015-01-01

Resistance substantially limits the depth and duration of clinical responses to targeted anticancer therapies. Through the use of complementary experimental approaches, investigators have revealed that cancer cells can achieve resistance through adaptation or selection driven by specific genetic, epigenetic, or microenvironmental alterations. Ultimately, these diverse alterations often lead to the activation of signaling pathways that, when co-opted, enable cancer cells to survive drug treatments. Recently developed methods enable the direct and scalable identification of the signaling pathways capable of driving resistance in specific contexts. Using these methods, novel pathways of resistance to clinically approved drugs have been identified and validated. By combining systematic resistance pathway mapping methods with studies revealing biomarkers of specific resistance pathways and pharmacological approaches to block these pathways, it may be possible to rationally construct drug combinations that yield more penetrant and lasting responses in patients. PMID:26392071

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease.

PubMed

Zhang, Mingming; Mu, Hongbo; Shang, Zhenwei; Kang, Kai; Lv, Hongchao; Duan, Lian; Li, Jin; Chen, Xinren; Teng, Yanbo; Jiang, Yongshuai; Zhang, Ruijie

2017-01-06

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD. Copyright © 2016. Published by Elsevier Ltd.

Epoxide pathways improve model predictions of isoprene markers and reveal key role of acidity in aerosol formation.

PubMed

Pye, Havala O T; Pinder, Robert W; Piletic, Ivan R; Xie, Ying; Capps, Shannon L; Lin, Ying-Hsuan; Surratt, Jason D; Zhang, Zhenfa; Gold, Avram; Luecken, Deborah J; Hutzell, William T; Jaoui, Mohammed; Offenberg, John H; Kleindienst, Tadeusz E; Lewandowski, Michael; Edney, Edward O

2013-10-01

Isoprene significantly contributes to organic aerosol in the southeastern United States where biogenic hydrocarbons mix with anthropogenic emissions. In this work, the Community Multiscale Air Quality model is updated to predict isoprene aerosol from epoxides produced under both high- and low-NOx conditions. The new aqueous aerosol pathways allow for explicit predictions of two key isoprene-derived species, 2-methyltetrols and 2-methylglyceric acid, that are more consistent with observations than estimates based on semivolatile partitioning. The new mechanism represents a significant source of organic carbon in the lower 2 km of the atmosphere and captures the abundance of 2-methyltetrols relative to organosulfates during the simulation period. For the parametrization considered here, a 25% reduction in SOx emissions effectively reduces isoprene aerosol, while a similar reduction in NOx leads to small increases in isoprene aerosol.

Pathways of fluid transport and reabsorption across the peritoneal membrane.

PubMed

Asghar, R B; Davies, S J

2008-05-01

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

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2012-01-01

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

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

Quantifying the Pathway and Predicting Spontaneous Emulsification during Material Exchange in a Two Phase Liquid System.

PubMed

Spooner, Stephen; Rahnama, Alireza; Warnett, Jason M; Williams, Mark A; Li, Zushu; Sridhar, Seetharaman

2017-10-30

Kinetic restriction of a thermodynamically favourable equilibrium is a common theme in materials processing. The interfacial instability in systems where rate of material exchange is far greater than the mass transfer through respective bulk phases is of specific interest when tracking the transient interfacial area, a parameter integral to short processing times for productivity streamlining in all manufacturing where interfacial reaction occurs. This is even more pertinent in high-temperature systems for energy and cost savings. Here the quantified physical pathway of interfacial area change due to material exchange in liquid metal-molten oxide systems is presented. In addition the predicted growth regime and emulsification behaviour in relation to interfacial tension as modelled using phase-field methodology is shown. The observed in-situ emulsification behaviour links quantitatively the geometry of perturbations as a validation method for the development of simulating the phenomena. Thus a method is presented to both predict and engineer the formation of micro emulsions to a desired specification.

Identification of altered pathways in breast cancer based on individualized pathway aberrance score.

PubMed

Shi, Sheng-Hong; Zhang, Wei; Jiang, Jing; Sun, Long

2017-08-01

The objective of the present study was to identify altered pathways in breast cancer based on the individualized pathway aberrance score (iPAS) method combined with the normal reference (nRef). There were 4 steps to identify altered pathways using the iPAS method: Data preprocessing conducted by the robust multi-array average (RMA) algorithm; gene-level statistics based on average Z ; pathway-level statistics according to iPAS; and a significance test dependent on 1 sample Wilcoxon test. The altered pathways were validated by calculating the changed percentage of each pathway in tumor samples and comparing them with pathways from differentially expressed genes (DEGs). A total of 688 altered pathways with P<0.01 were identified, including kinesin (KIF)- and polo-like kinase (PLK)-mediated events. When the percentage of change reached 50%, 310 pathways were involved in the total 688 altered pathways, which may validate the present results. In addition, there were 324 DEGs and 155 common genes between DEGs and pathway genes. DEGs and common genes were enriched in the same 9 significant terms, which also were members of altered pathways. The iPAS method was suitable for identifying altered pathways in breast cancer. Altered pathways (such as KIF and PLK mediated events) were important for understanding breast cancer mechanisms and for the future application of customized therapeutic decisions.

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

PubMed

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

2017-11-24

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

Combining Spot Sign and Intracerebral Hemorrhage Score to Estimate Functional Outcome: Analysis From the PREDICT Cohort.

PubMed

Schneider, Hauke; Huynh, Thien J; Demchuk, Andrew M; Dowlatshahi, Dar; Rodriguez-Luna, David; Silva, Yolanda; Aviv, Richard; Dzialowski, Imanuel

2018-06-01

The intracerebral hemorrhage (ICH) score is the most commonly used grading scale for stratifying functional outcome in patients with acute ICH. We sought to determine whether a combination of the ICH score and the computed tomographic angiography spot sign may improve outcome prediction in the cohort of a prospective multicenter hemorrhage trial. Prospectively collected data from 241 patients from the observational PREDICT study (Prediction of Hematoma Growth and Outcome in Patients With Intracerebral Hemorrhage Using the CT-Angiography Spot Sign) were analyzed. Functional outcome at 3 months was dichotomized using the modified Rankin Scale (0-3 versus 4-6). Performance of (1) the ICH score and (2) the spot sign ICH score-a scoring scale combining ICH score and spot sign number-was tested. Multivariable analysis demonstrated that ICH score (odds ratio, 3.2; 95% confidence interval, 2.2-4.8) and spot sign number (n=1: odds ratio, 2.7; 95% confidence interval, 1.1-7.4; n>1: odds ratio, 3.8; 95% confidence interval, 1.2-17.1) were independently predictive of functional outcome at 3 months with similar odds ratios. Prediction of functional outcome was not significantly different using the spot sign ICH score compared with the ICH score alone (spot sign ICH score area under curve versus ICH score area under curve: P =0.14). In the PREDICT cohort, a prognostic score adding the computed tomographic angiography-based spot sign to the established ICH score did not improve functional outcome prediction compared with the ICH score. © 2018 American Heart Association, Inc.

IL-10 combined with procalcitonin improves early prediction of complications of febrile neutropenia in hematological patients.

PubMed

Vänskä, Matti; Koivula, Irma; Jantunen, Esa; Hämäläinen, Sari; Purhonen, Anna-Kaisa; Pulkki, Kari; Juutilainen, Auni

2012-12-01

Early diagnosis of complicated course in febrile neutropenia is cumbersome due to the non-specificity of clinical and laboratory signs of severe infection. This prospective study included 100 adult hematological patients with febrile neutropenia after intensive chemotherapy at the onset of fever (d0) and for 3 days (d1-d3) thereafter. The study aim was to find early predictors for complicated course of febrile neutropenia, defined as bacteremia or septic shock. Interleukin 6 (IL-6), interleukin 10 (IL-10), procalcitonin (PCT) and C-reactive protein (CRP) all predicted complicated course of febrile neutropenia on d0, but only PCT was predictive throughout the study period. For IL-10 on d0-1 with cut-off 37 ng/L, sensitivity was 0.71, specificity 0.82, positive predictive value 0.52 and negative predictive value 0.92. For PCT on d0-1 with cut-off 0.13 μg/L, the respective measures were 0.95, 0.53, 0.36, and 0.98. For the combination of IL-10 and PCT on d0-1 with the same cut-offs, specificity improved to 0.85 and positive predictive value to 0.56. In conclusion, the present study confirms the high negative predictive value of PCT and provides new evidence for IL-10 as an early predictor for complicated course of febrile neutropenia in hematological patients. Combining IL-10 with PCT improves the early prediction for complicated course of febrile neutropenia. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2015-01-01

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

An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data.

PubMed

Jin, Guangxu; Zhao, Hong; Zhou, Xiaobo; Wong, Stephen T C

2011-07-01

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data. We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens. We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism. The software implemented in Python 2.7 programming language is available from request. stwong@tmhs.org.

The predictive value of quantitative fibronectin testing in combination with cervical length measurement in symptomatic women.

PubMed

Bruijn, Merel M C; Kamphuis, Esme I; Hoesli, Irene M; Martinez de Tejada, Begoña; Loccufier, Anne R; Kühnert, Maritta; Helmer, Hanns; Franz, Marie; Porath, Martina M; Oudijk, Martijn A; Jacquemyn, Yves; Schulzke, Sven M; Vetter, Grit; Hoste, Griet; Vis, Jolande Y; Kok, Marjolein; Mol, Ben W J; van Baaren, Gert-Jan

2016-12-01

The combination of the qualitative fetal fibronectin test and cervical length measurement has a high negative predictive value for preterm birth within 7 days; however, positive prediction is poor. A new bedside quantitative fetal fibronectin test showed potential additional value over the conventional qualitative test, but there is limited evidence on the combination with cervical length measurement. The purpose of this study was to compare quantitative fetal fibronectin and qualitative fetal fibronectin testing in the prediction of spontaneous preterm birth within 7 days in symptomatic women who undergo cervical length measurement. We performed a European multicenter cohort study in 10 perinatal centers in 5 countries. Women between 24 and 34 weeks of gestation with signs of active labor and intact membranes underwent quantitative fibronectin testing and cervical length measurement. We assessed the risk of preterm birth within 7 days in predefined strata based on fibronectin concentration and cervical length. Of 455 women who were included in the study, 48 women (11%) delivered within 7 days. A combination of cervical length and qualitative fibronectin resulted in the identification of 246 women who were at low risk: 164 women with a cervix between 15 and 30 mm and a negative fibronectin test (<50 ng/mL; preterm birth rate, 2%) and 82 women with a cervix at >30 mm (preterm birth rate, 2%). Use of quantitative fibronectin alone resulted in a predicted risk of preterm birth within 7 days that ranged from 2% in the group with the lowest fibronectin level (<10 ng/mL) to 38% in the group with the highest fibronectin level (>500 ng/mL), with similar accuracy as that of the combination of cervical length and qualitative fibronectin. Combining cervical length and quantitative fibronectin resulted in the identification of an additional 19 women at low risk (preterm birth rate, 5%), using a threshold of 10 ng/mL in women with a cervix at <15 mm, and 6 women at high risk

Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

PubMed

Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

2015-12-01

It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). Copyright © 2015 Elsevier Inc. All rights reserved.

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

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

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

2013-10-01

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

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

PubMed

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

2017-11-02

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

Robust prediction of protein subcellular localization combining PCA and WSVMs.

PubMed

Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

2011-08-01

Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

PATHWAYS - ELECTRON TUNNELING PATHWAYS IN PROTEINS

NASA Technical Reports Server (NTRS)

Beratan, D. N.

1994-01-01

The key to understanding the mechanisms of many important biological processes such as photosynthesis and respiration is a better understanding of the electron transfer processes which take place between metal atoms (and other groups) fixed within large protein molecules. Research is currently focused on the rate of electron transfer and the factors that influence it, such as protein composition and the distance between metal atoms. Current models explain the swift transfer of electrons over considerable distances by postulating bridge-mediated tunneling, or physical tunneling pathways, made up of interacting bonds in the medium around and between donor and acceptor sites. The program PATHWAYS is designed to predict the route along which electrons travel in the transfer processes. The basic strategy of PATHWAYS is to begin by recording each possible path element on a connectivity list, including in each entry which two atoms are connected and what contribution the connection would make to the overall rate if it were included in a pathway. The list begins with the bonded molecular structure (including the backbone sequence and side chain connectivity), and then adds probable hydrogen bond links and through-space contacts. Once this list is completed, the program runs a tree search from the donor to the acceptor site to find the dominant pathways. The speed and efficiency of the computer search offers an improvement over manual techniques. PATHWAYS is written in FORTRAN 77 for execution on DEC VAX series computers running VMS. The program inputs data from four data sets and one structure file. The software was written to input BIOGRAF (old format) structure files based on x-ray crystal structures and outputs ASCII files listing the best pathways and BIOGRAF vector files containing the paths. Relatively minor changes could be made in the input format statements for compatibility with other graphics software. The executable and source code are included with the

Towards direct synthesis of alane: A predicted defect-mediated pathway confirmed experimentally

DOE PAGES

Wang, Lin -Lin; Herwadkar, Aditi; Reich, Jason M.; ...

2016-08-18

Here, alane (AlH 3) is a unique energetic material that has not found a broad practical use for over 70 years because it is difficult to synthesize directly from its elements. Using density functional theory, we examine the defect-mediated formation of alane monomers on Al(111) in a two-step process: (1) dissociative adsorption of H 2 and (2) alane formation, which are both endothermic on a clean surface. Only with Ti dopant to facilitate H 2 dissociation and vacancies to provide Al adatoms, both processes become exothermic. In agreement, in situ scanning tunneling microscopy showed that during H 2 exposure, alanemore » monomers and clusters form primarily in the vicinity of Al vacancies and Ti atoms. Moreover, ball milling of the Al samples with Ti (providing necessary defects) showed a 10 % conversion of Al into AlH 3 or closely related species at 344 bar H 2, indicating that the predicted pathway may lead to the direct synthesis of alane from elements at pressures much lower than the 104 bar expected from bulk thermodynamics.« less

Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS.

PubMed

Hu, Meng; Müller, Erik; Schymanski, Emma L; Ruttkies, Christoph; Schulze, Tobias; Brack, Werner; Krauss, Martin

2018-03-01

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.

A Fatigue Life Prediction Model of Welded Joints under Combined Cyclic Loading

NASA Astrophysics Data System (ADS)

Goes, Keurrie C.; Camarao, Arnaldo F.; Pereira, Marcos Venicius S.; Ferreira Batalha, Gilmar

2011-01-01

A practical and robust methodology is developed to evaluate the fatigue life in seam welded joints when subjected to combined cyclic loading. The fatigue analysis was conducted in virtual environment. The FE stress results from each loading were imported to fatigue code FE-Fatigue and combined to perform the fatigue life prediction using the S x N (stress x life) method. The measurement or modelling of the residual stresses resulting from the welded process is not part of this work. However, the thermal and metallurgical effects, such as distortions and residual stresses, were considered indirectly through fatigue curves corrections in the samples investigated. A tube-plate specimen was submitted to combined cyclic loading (bending and torsion) with constant amplitude. The virtual durability analysis result was calibrated based on these laboratory tests and design codes such as BS7608 and Eurocode 3. The feasibility and application of the proposed numerical-experimental methodology and contributions for the technical development are discussed. Major challenges associated with this modelling and improvement proposals are finally presented.

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

PubMed

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

2017-11-01

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C 0 ) and chemical properties. The most predictive variables in the resulting model were C 0 , molecular weight, log K ow , and food type (R 2 =0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C 0 based on the functional role of chemicals in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R 2 =0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

PubMed Central

Latendresse, Mario; Paley, Suzanne M.; Krummenacker, Markus; Ong, Quang D.; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M.; Caspi, Ron

2016-01-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. PMID:26454094

Combining lymphovascular invasion with reactive stromal grade predicts prostate cancer mortality.

PubMed

Saeter, Thorstein; Vlatkovic, Ljiljana; Waaler, Gudmund; Servoll, Einar; Nesland, Jahn M; Axcrona, Karol; Axcrona, Ulrika

2016-09-01

Previous studies suggest that lymphovascular invasion (LVI) has a weak and variable effect on prognosis. It is uncertain whether LVI, determined by diagnostic prostate biopsy, predicts prostate cancer death. Data from experimental studies have indicated that carcinoma-associated fibroblasts in the reactive stroma could promote LVI and progression to metastasis. Thus, combining LVI with reactive stromal grade may identify prostate cancer patients at high risk of an unfavorable outcome. The purpose of the present study was to examine if LVI, determined by diagnostic biopsy, alone and in combination with reactive stromal grade could predict prostate cancer death. This population-based study included 283 patients with prostate cancer diagnosed by needle biopsy in Aust-Agder County (Norway) from 1991 to 1999. Clinical data were obtained by medical charts review. Two uropathologists evaluated LVI and reactive stromal grade. The endpoint was prostate cancer death. Patients with LVI had marginally higher risk of prostate cancer death compared to patients without LVI (hazard ratio: 1.8, P-value = 0.04). LVI had a stronger effect on prostate cancer death risk when a high reactive stromal grade was present (hazard ratio: 16.0, P-value <0.001). Therefore, patients with concomitant LVI and high reactive stromal grade were at particularly high risk for prostate cancer death. Evaluating LVI together with reactive stromal grade on diagnostic biopsies could be used to identify patients at high risk of death from prostate cancer. Prostate 76:1088-1094, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

NASA Astrophysics Data System (ADS)

Greitzer, Frank L.; Frincke, Deborah A.

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, to support a move from an insider threat detection stance to one that enables prediction of potential insider presence. Twodistinctiveaspects of the approach are the objectiveof predicting or anticipating potential risksandthe useoforganizational datain additiontocyber datato support the analysis. The chapter describes the challenges of this endeavor and reports on progressin definingausablesetof predictiveindicators,developingaframeworkfor integratingthe analysisoforganizationalandcyber securitydatatoyield predictions about possible insider exploits, and developing the knowledge base and reasoning capabilityof the system.We also outline the typesof errors that oneexpectsina predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.

Kinetic Network Study of the Diversity and Temperature Dependence of Trp-Cage Folding Pathways: Combining Transition Path Theory with Stochastic Simulations

PubMed Central

Zheng, Weihua; Gallicchio, Emilio; Deng, Nanjie; Andrec, Michael; Levy, Ronald M.

2011-01-01

We present a new approach to study a multitude of folding pathways and different folding mechanisms for the 20-residue mini-protein Trp-Cage using the combined power of replica exchange molecular dynamics (REMD) simulations for conformational sampling, Transition Path Theory (TPT) for constructing folding pathways and stochastic simulations for sampling the pathways in a high dimensional structure space. REMD simulations of Trp-Cage with 16 replicas at temperatures between 270K and 566K are carried out with an all-atom force field (OPLSAA) and an implicit solvent model (AGBNP). The conformations sampled from all temperatures are collected. They form a discretized state space that can be used to model the folding process. The equilibrium population for each state at a target temperature can be calculated using the Weighted-Histogram-Analysis Method (WHAM). By connecting states with similar structures and creating edges satisfying detailed balance conditions, we construct a kinetic network that preserves the equilibrium population distribution of the state space. After defining the folded and unfolded macrostates, committor probabilities (Pfold) are calculated by solving a set of linear equations for each node in the network and pathways are extracted together with their fluxes using the TPT algorithm. By clustering the pathways into folding “tubes”, a more physically meaningful picture of the diversity of folding routes emerges. Stochastic simulations are carried out on the network and a procedure is developed to project sampled trajectories onto the folding tubes. The fluxes through the folding tubes calculated from the stochastic trajectories are in good agreement with the corresponding values obtained from the TPT analysis. The temperature dependence of the ensemble of Trp-Cage folding pathways is investigated. Above the folding temperature, a large number of diverse folding pathways with comparable fluxes flood the energy landscape. At low temperature

Depressive symptoms predict head and neck cancer survival: Examining plausible behavioral and biological pathways.

PubMed

Zimmaro, Lauren A; Sephton, Sandra E; Siwik, Chelsea J; Phillips, Kala M; Rebholz, Whitney N; Kraemer, Helena C; Giese-Davis, Janine; Wilson, Liz; Bumpous, Jeffrey M; Cash, Elizabeth D

2018-03-01

Head and neck cancers are associated with high rates of depression, which may increase the risk for poorer immediate and long-term outcomes. Here it was hypothesized that greater depressive symptoms would predict earlier mortality, and behavioral (treatment interruption) and biological (treatment response) mediators were examined. Patients (n = 134) reported depressive symptomatology at treatment planning. Clinical data were reviewed at the 2-year follow-up. Greater depressive symptoms were associated with significantly shorter survival (hazard ratio, 0.868; 95% confidence interval [CI], 0.819-0.921; P < .001), higher rates of chemoradiation interruption (odds ratio, 0.865; 95% CI, 0.774-0.966; P = .010), and poorer treatment response (odds ratio, 0.879; 95% CI, 0.803-0.963; P = .005). The poorer treatment response partially explained the depression-survival relation. Other known prognostic indicators did not challenge these results. Depressive symptoms at the time of treatment planning predict overall 2-year mortality. Effects are partly influenced by the treatment response. Depression screening and intervention may be beneficial. Future studies should examine parallel biological pathways linking depression to cancer survival, including endocrine disruption and inflammation. Cancer 2018;124:1053-60. © 2018 American Cancer Society. © 2018 American Cancer Society.

Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures

PubMed Central

Foroushani, Amir B.K.; Brinkman, Fiona S.L.

2013-01-01

Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways. Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results. Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/. PMID:24432194

Consecutive epigenetically-active agent combinations act in ID1-RUNX3-TET2 and HOXA pathways for Flt3ITD+ve AML.

PubMed

Sayar, Hamid; Liu, Yan; Gao, Rui; Zaid, Mohammad Abu; Cripe, Larry D; Weisenbach, Jill; Sargent, Katie J; Nassiri, Mehdi; Li, Lang; Konig, Heiko; Suvannasankha, Attaya; Pan, Feng; Shanmugam, Rajasubramaniam; Goswami, Chirayu; Kapur, Reuben; Xu, Mingjiang; Boswell, H Scott

2018-01-19

Co-occurrence of Flt3ITD and TET2 mutations provoke an animal model of AML by epigenetic repression of Wnt pathway antagonists, including RUNX3, and by hyperexpression of ID1, encoding Wnt agonist. These affect HOXA over-expression and treatment resistance. A comparable epigenetic phenotype was identified among adult AML patients needing novel intervention. We chose combinations of targeted agents acting on distinct effectors, at the levels of both signal transduction and chromatin remodeling, in relapsed/refractory AML's, including Flt3ITD+ve, described with a signature of repressed tumor suppressor genes, involving Wnt antagonist RUNX3 , occurring along with ID1 and HOXA over-expressions. We tracked patient response to combination of Flt3/Raf inhibitor, Sorafenib, and Vorinostat, pan-histone deacetylase inhibitor, without or with added Bortezomib, in consecutive phase I trials. A striking association of rapid objective remissions (near-complete, complete responses) was noted to accompany induced early pharmacodynamic changes within patient blasts in situ, involving these effectors, significantly linking RUNX3 /Wnt antagonist de-repression (80%) and ID1 downregulation (85%), to a response, also preceded by profound HOXA9 repression. Response occurred in context of concurrent TET2 mutation/hypomorphy and Flt3ITD+ve mutation (83% of complete responses). Addition of Bortezomib to the combination was vital to attainment of complete response in Flt3ITD+ve cases exhibiting such Wnt pathway dysregulation.

Stress transgenerationally programs metabolic pathways linked to altered mental health.

PubMed

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

2016-12-01

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

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

PubMed

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

2006-02-01

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

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.

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

PubMed

Bruno, C; Patin, F; Bocca, C; Nadal-Desbarats, L; Bonnier, F; Reynier, P; Emond, P; Vourc'h, P; Joseph-Delafont, K; Corcia, P; Andres, C R; Blasco, H

2018-01-30

Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering. We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained. Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed

Combination of honokiol and magnolol inhibits hepatic steatosis through AMPK-SREBP-1 c pathway.

PubMed

Lee, Ju-Hee; Jung, Ji Yun; Jang, Eun Jeong; Jegal, Kyung Hwan; Moon, Soo Young; Ku, Sae Kwang; Kang, Seung Ho; Cho, Il Je; Park, Sook Jahr; Lee, Jong Rok; Zhao, Rong Jie; Kim, Sang Chan; Kim, Young Woo

2015-04-01

Honokiol and magnolol, as pharmacological biphenolic compounds of Magnolia officinalis, have been reported to have antioxidant and anti-inflammatory properties. Sterol regulatory element binding protein-1 c (SREBP-1 c) plays an important role in the development and processing of steatosis in the liver. In the present study, we investigated the effects of a combination of honokiol and magnolol on SREBP-1 c-dependent lipogenesis in hepatocytes as well as in mice with fatty liver due to consumption of high-fat diet (HFD). Liver X receptor α (LXRα) agonists induced activation of SREBP-1 c and expression of lipogenic genes, which were blocked by co-treatment of honokiol and magnolol (HM). Moreover, a combination of HM potently increased mRNA of fatty acid oxidation genes. HM induced AMP-activated protein kinase (AMPK), an inhibitory kinase of the LXRα-SREBP-1 c pathway. The role of AMPK activation induced by HM was confirmed using an inhibitor of AMPK, Compound C, which reversed the ability of HM to both inhibit SREBP-1 c induction as well as induce genes for fatty acid oxidation. In mice, HM administration for four weeks ameliorated HFD-induced hepatic steatosis and liver dysfunction, as indicated by plasma parameters and Oil Red O staining. Taken together, our results demonstrated that a combination of HM has beneficial effects on inhibition of fatty liver and SREBP-1 c-mediated hepatic lipogenesis, and these events may be mediated by AMPK activation. © 2014 by the Society for Experimental Biology and Medicine.

Combination of honokiol and magnolol inhibits hepatic steatosis through AMPK-SREBP-1 c pathway

PubMed Central

Lee, Ju-Hee; Jung, Ji Yun; Jang, Eun Jeong; Jegal, Kyung Hwan; Moon, Soo Young; Ku, Sae Kwang; Kang, Seung Ho; Cho, Il Je; Park, Sook Jahr; Lee, Jong Rok; Zhao, Rong Jie; Kim, Sang Chan

2015-01-01

Honokiol and magnolol, as pharmacological biphenolic compounds of Magnolia officinalis, have been reported to have antioxidant and anti-inflammatory properties. Sterol regulatory element binding protein-1 c (SREBP-1 c) plays an important role in the development and processing of steatosis in the liver. In the present study, we investigated the effects of a combination of honokiol and magnolol on SREBP-1 c-dependent lipogenesis in hepatocytes as well as in mice with fatty liver due to consumption of high-fat diet (HFD). Liver X receptor α (LXRα) agonists induced activation of SREBP-1 c and expression of lipogenic genes, which were blocked by co-treatment of honokiol and magnolol (HM). Moreover, a combination of HM potently increased mRNA of fatty acid oxidation genes. HM induced AMP-activated protein kinase (AMPK), an inhibitory kinase of the LXRα-SREBP-1 c pathway. The role of AMPK activation induced by HM was confirmed using an inhibitor of AMPK, Compound C, which reversed the ability of HM to both inhibit SREBP-1 c induction as well as induce genes for fatty acid oxidation. In mice, HM administration for four weeks ameliorated HFD-induced hepatic steatosis and liver dysfunction, as indicated by plasma parameters and Oil Red O staining. Taken together, our results demonstrated that a combination of HM has beneficial effects on inhibition of fatty liver and SREBP-1 c-mediated hepatic lipogenesis, and these events may be mediated by AMPK activation. PMID:25125496

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

An Efficient Computational Model to Predict Protonation at the Amide Nitrogen and Reactivity along the C–N Rotational Pathway

PubMed Central

Szostak, Roman; Aubé, Jeffrey

2015-01-01

N-protonation of amides is critical in numerous biological processes, including amide bonds proteolysis and protein folding, as well as in organic synthesis as a method to activate amide bonds towards unconventional reactivity. A computational model enabling prediction of protonation at the amide bond nitrogen atom along the C–N rotational pathway is reported. Notably, this study provides a blueprint for the rational design and application of amides with a controlled degree of rotation in synthetic chemistry and biology. PMID:25766378

Pathway Design, Engineering, and Optimization.

PubMed

Garcia-Ruiz, Eva; HamediRad, Mohammad; Zhao, Huimin

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

Cerebellar tDCS Modulates Neural Circuits during Semantic Prediction: A Combined tDCS-fMRI Study.

PubMed

D'Mello, Anila M; Turkeltaub, Peter E; Stoodley, Catherine J

2017-02-08

It has been proposed that the cerebellum acquires internal models of mental processes that enable prediction, allowing for the optimization of behavior. In language, semantic prediction speeds speech production and comprehension. Right cerebellar lobules VI and VII (including Crus I/II) are engaged during a variety of language processes and are functionally connected with cerebral cortical language networks. Further, right posterolateral cerebellar neuromodulation modifies behavior during predictive language processing. These data are consistent with a role for the cerebellum in semantic processing and semantic prediction. We combined transcranial direct current stimulation (tDCS) and fMRI to assess the behavioral and neural consequences of cerebellar tDCS during a sentence completion task. Task-based and resting-state fMRI data were acquired in healthy human adults ( n = 32; μ = 23.1 years) both before and after 20 min of 1.5 mA anodal ( n = 18) or sham ( n = 14) tDCS applied to the right posterolateral cerebellum. In the sentence completion task, the first four words of the sentence modulated the predictability of the final target word. In some sentences, the preceding context strongly predicted the target word, whereas other sentences were nonpredictive. Completion of predictive sentences increased activation in right Crus I/II of the cerebellum. Relative to sham tDCS, anodal tDCS increased activation in right Crus I/II during semantic prediction and enhanced resting-state functional connectivity between hubs of the reading/language networks. These results are consistent with a role for the right posterolateral cerebellum beyond motor aspects of language, and suggest that cerebellar internal models of linguistic stimuli support semantic prediction. SIGNIFICANCE STATEMENT Cerebellar involvement in language tasks and language networks is now well established, yet the specific cerebellar contribution to language processing remains unclear. It is thought that the

A novel 2-step approach combining the NAFLD fibrosis score and liver stiffness measurement for predicting advanced fibrosis.

PubMed

Chan, Wah-Kheong; Nik Mustapha, Nik Raihan; Mahadeva, Sanjiv

2015-10-01

The non-alcoholic fatty liver disease (NAFLD) fibrosis score (NFS) is indeterminate in a proportion of NAFLD patients. Combining the NFS with liver stiffness measurement (LSM) may improve prediction of advanced fibrosis. We aim to evaluate the NFS and LSM in predicting advanced fibrosis in NAFLD patients. The NFS was calculated and LSM obtained for consecutive adult NAFLD patients scheduled for liver biopsy. The accuracy of predicting advanced fibrosis using either modality and in combination were assessed. An algorithm combining the NFS and LSM was developed from a training cohort and subsequently tested in a validation cohort. There were 101 and 46 patients in the training and validation cohort, respectively. In the training cohort, the percentages of misclassifications using the NFS alone, LSM alone, LSM alone (with grey zone), both tests for all patients and a 2-step approach using LSM only for patients with indeterminate and high NFS were 5.0, 28.7, 2.0, 2.0 and 4.0 %, respectively. The percentages of patients requiring liver biopsy were 30.7, 0, 36.6, 36.6 and 18.8 %, respectively. In the validation cohort, the percentages of misclassifications were 8.7, 28.3, 2.2, 2.2 and 8.7 %, respectively. The percentages of patients requiring liver biopsy were 28.3, 0, 41.3, 43.5 and 19.6 %, respectively. The novel 2-step approach further reduced the number of patients requiring a liver biopsy whilst maintaining the accuracy to predict advanced fibrosis. The combination of NFS and LSM for all patients provided no apparent advantage over using either of the tests alone.

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

PubMed

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

2018-03-01

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

Inherent vulnerabilities in monoaminergic pathways predict the emergence of depressive impairments in an animal model of chronic epilepsy.

PubMed

Medel-Matus, Jesús-Servando; Shin, Don; Sankar, Raman; Mazarati, Andrey

2017-08-01

The objective was to determine whether the depression comorbid with epilepsy could be predicted based on inherent premorbid patterns of monoaminergic transmission. In male Wistar rats, despair-like and anhedonia-like behaviors were examined using forced swimming and taste preference tests, respectively. Serotonergic raphe nucleus (RN)-prefrontal cortex (PFC) and dopaminergic ventral tegmental area (VTA)-nucleus accumbens (NAcc) pathways were interrogated by fast scan cyclic voltammetry (FSCV). The assays were performed before and 2 months after pilocarpine status epilepticus. In a subset of naive rats, FSCV, coupled with the intensity-dependent stimulation paradigm, detected specific deviations in each pathway (six rats for RN-PFC and seven rats for VTA-NAcc, with overlap in two, of 19 total subjects) in the absence of behavioral impairments. During epilepsy, animals with preexisting deviations in RN-PFC invariably developed despair, and rats with deviations in VTA-NAcc developed anhedonia. Serotonergic and dopaminergic pathways, respectively, showed signs of explicit deterioration. We suggest that epilepsy triggers decompensations in the already vulnerable depression-relevant neuronal circuits, which culminate in depression. The established connection between the identified specific signatures in monoamine transmission in naive rats and specific symptoms of epilepsy-associated depression may help in understanding causes of comorbidity and in developing its early biomarkers. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

PubMed

Karp, Peter D; Latendresse, Mario; Paley, Suzanne M; Krummenacker, Markus; Ong, Quang D; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M; Caspi, Ron

2016-09-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

PubMed

Bouktif, Salah; Hanna, Eileen Marie; Zaki, Nazar; Abu Khousa, Eman

2014-01-01

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

Design and analysis of synthetic carbon fixation pathways

PubMed Central

Bar-Even, Arren; Noor, Elad; Lewis, Nathan E.; Milo, Ron

2010-01-01

Carbon fixation is the process by which CO2 is incorporated into organic compounds. In modern agriculture in which water, light, and nutrients can be abundant, carbon fixation could become a significant growth-limiting factor. Hence, increasing the fixation rate is of major importance in the road toward sustainability in food and energy production. There have been recent attempts to improve the rate and specificity of Rubisco, the carboxylating enzyme operating in the Calvin–Benson cycle; however, they have achieved only limited success. Nature employs several alternative carbon fixation pathways, which prompted us to ask whether more efficient novel synthetic cycles could be devised. Using the entire repertoire of approximately 5,000 metabolic enzymes known to occur in nature, we computationally identified alternative carbon fixation pathways that combine existing metabolic building blocks from various organisms. We compared the natural and synthetic pathways based on physicochemical criteria that include kinetics, energetics, and topology. Our study suggests that some of the proposed synthetic pathways could have significant quantitative advantages over their natural counterparts, such as the overall kinetic rate. One such cycle, which is predicted to be two to three times faster than the Calvin–Benson cycle, employs the most effective carboxylating enzyme, phosphoenolpyruvate carboxylase, using the core of the naturally evolved C4 cycle. Although implementing such alternative cycles presents daunting challenges related to expression levels, activity, stability, localization, and regulation, we believe our findings suggest exciting avenues of exploration in the grand challenge of enhancing food and renewable fuel production via metabolic engineering and synthetic biology. PMID:20410460

Performance of third-trimester combined screening model for prediction of adverse perinatal outcome.

PubMed

Miranda, J; Triunfo, S; Rodriguez-Lopez, M; Sairanen, M; Kouru, H; Parra-Saavedra, M; Crovetto, F; Figueras, F; Crispi, F; Gratacós, E

2017-09-01

To explore the potential value of third-trimester combined screening for the prediction of adverse perinatal outcome (APO) in the general population and among small-for-gestational-age (SGA) fetuses. This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester evaluation (32 + 0 to 36 + 6 weeks' gestation). Maternal baseline characteristics, mean arterial blood pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently had an APO (n = 148) and in a control group without perinatal complications (n = 902). APO was defined as the occurrence of stillbirth, umbilical artery cord blood pH < 7.15, 5-min Apgar score < 7 or emergency operative delivery for fetal distress. Logistic regression models were developed for the prediction of APO in the general population and among SGA cases (defined as customized birth weight < 10 th centile). The prevalence of APO was 9.3% in the general population and 27.4% among SGA cases. In the general population, a combined screening model including a-priori risk (maternal characteristics), estimated fetal weight (EFW) centile, umbilical artery pulsatility index (UA-PI), estriol and PlGF achieved a detection rate for APO of 26% (area under receiver-operating characteristics curve (AUC), 0.59 (95% CI, 0.54-0.65)), at a 10% false-positive rate (FPR). Among SGA cases, a model including a-priori risk, EFW centile, UA-PI, cerebroplacental ratio, estriol and PlGF predicted 62% of APO (AUC, 0.86 (95% CI, 0.80-0.92)) at a FPR of 10%. The use of fetal ultrasound and maternal biochemical markers at 32-36 weeks provides a poor prediction of APO in the general population. Although it remains limited, the performance of the screening model is improved when applied to fetuses with suboptimal fetal growth. Copyright © 2016 ISUOG. Published by John Wiley & Sons

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

PubMed

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

2018-05-01

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

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

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

Young, M; Craft, D

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

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

PubMed Central

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

2012-01-01

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

Prediction and Biochemical Demonstration of a Catabolic Pathway for the Osmoprotectant Proline Betaine

PubMed Central

Kumar, Ritesh; Zhao, Suwen; Vetting, Matthew W.; Wood, B. McKay; Sakai, Ayano; Cho, Kyuil; Solbiati, José; Almo, Steven C.; Sweedler, Jonathan V.; Jacobson, Matthew P.; Gerlt, John A.; Cronan, John E.

2014-01-01

ABSTRACT Through the use of genetic, enzymatic, metabolomic, and structural analyses, we have discovered the catabolic pathway for proline betaine, an osmoprotectant, in Paracoccus denitrificans and Rhodobacter sphaeroides. Genetic and enzymatic analyses showed that several of the key enzymes of the hydroxyproline betaine degradation pathway also function in proline betaine degradation. Metabolomic analyses detected each of the metabolic intermediates of the pathway. The proline betaine catabolic pathway was repressed by osmotic stress and cold stress, and a regulatory transcription factor was identified. We also report crystal structure complexes of the P. denitrificans HpbD hydroxyproline betaine epimerase/proline betaine racemase with l-proline betaine and cis-hydroxyproline betaine. PMID:24520058

Combination therapy in combating cancer

PubMed Central

Mokhtari, Reza Bayat; Homayouni, Tina S.; Baluch, Narges; Morgatskaya, Evgeniya; Kumar, Sushil; Das, Bikul; Yeger, Herman

2017-01-01

Combination therapy, a treatment modality that combines two or more therapeutic agents, is a cornerstone of cancer therapy. The amalgamation of anti-cancer drugs enhances efficacy compared to the mono-therapy approach because it targets key pathways in a characteristically synergistic or an additive manner. This approach potentially reduces drug resistance, while simultaneously providing therapeutic anti-cancer benefits, such as reducing tumour growth and metastatic potential, arresting mitotically active cells, reducing cancer stem cell populations, and inducing apoptosis. The 5-year survival rates for most metastatic cancers are still quite low, and the process of developing a new anti-cancer drug is costly and extremely time-consuming. Therefore, new strategies that target the survival pathways that provide efficient and effective results at an affordable cost are being considered. One such approach incorporates repurposing therapeutic agents initially used for the treatment of different diseases other than cancer. This approach is effective primarily when the FDA-approved agent targets similar pathways found in cancer. Because one of the drugs used in combination therapy is already FDA-approved, overall costs of combination therapy research are reduced. This increases cost efficiency of therapy, thereby benefiting the “medically underserved”. In addition, an approach that combines repurposed pharmaceutical agents with other therapeutics has shown promising results in mitigating tumour burden. In this systematic review, we discuss important pathways commonly targeted in cancer therapy. Furthermore, we also review important repurposed or primary anti-cancer agents that have gained popularity in clinical trials and research since 2012. PMID:28410237

Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

PubMed Central

Cohen, Justin M; Singh, Inder; O'Brien, Megan E

2008-01-01

Background An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT) and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding. Methods Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries. Results Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p < 0.0001). Additionally, predicted ACT procurement, extrapolated from forecasted disbursements, was correlated strongly with actual ACT procurement supported by two grants from the Global Fund's first (r = 0.945, p < 0.0001) and fourth (r = 0.938, p < 0.0001) funding rounds. Conclusion This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability. PMID:18831742

Two pathways through adversity: Predicting well-being and housing outcomes among homeless service users.

PubMed

Walter, Zoe C; Jetten, Jolanda; Dingle, Genevieve A; Parsell, Cameron; Johnstone, Melissa

2016-06-01

People who experience homelessness face many challenges and disadvantages that negatively impact health and well-being and form barriers to achieving stable housing. Further, people who are homeless often have limited social connections and support. Building on previous research that has shown the beneficial effect of group identification on health and well-being, the current study explores the relationship between two social identity processes - multiple group memberships and service identification - and well-being and positive housing outcomes. Measures were collected from 76 participants while they were residing in a homeless accommodation service (T1) and again 2-4 weeks after leaving the service (or 3 months after T1 if participants had not left the service). Mediation analyses revealed that multiple group memberships and service identification at T1 independently predicted well-being at T2 indirectly, via social support. Further, both social identity processes also indirectly predicted housing outcomes via social support. The implications of these findings are twofold. First, while belonging to multiple social groups may provide a pathway to gaining social support and well-being, group belonging may not necessarily be beneficial to achieve stable housing. Second, fostering identification with homeless services may be particularly important as a source of support that contributes to well-being. © 2015 The British Psychological Society.

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

PubMed

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

2017-04-26

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

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions.

PubMed

Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben

2017-08-22

A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.

A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades.

PubMed

Zhu, Shun-Peng; Yue, Peng; Yu, Zheng-Yong; Wang, Qingyuan

2017-06-26

Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting from high frequency vibrations and low cycle fatigue (LCF) from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner's rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors.

Detection of phytohormones in temperate forest fungi predicts consistent abscisic acid production and a common pathway for cytokinin biosynthesis.

PubMed

Morrison, Erin N; Knowles, Sarah; Hayward, Allison; Thorn, R Greg; Saville, Barry J; Emery, R J N

2015-01-01

The phytohormones, abscisic acid and cytokinin, once were thought to be present uniquely in plants, but increasing evidence suggests that these hormones are present in a wide variety of organisms. Few studies have examined fungi for the presence of these "plant" hormones or addressed whether their levels differ based on the nutrition mode of the fungus. This study examined 20 temperate forest fungi of differing nutritional modes (ectomycorrhizal, wood-rotting, saprotrophic). Abscisic acid and cytokinin were present in all fungi sampled; this indicated that the sampled fungi have the capacity to synthesize these two classes of phytohormones. Of the 27 cytokinins analyzed by HPLC-ESI MS/MS, seven were present in all fungi sampled. This suggested the existence of a common cytokinin metabolic pathway in fungi that does not vary among different nutritional modes. Predictions regarding the source of isopentenyl, cis-zeatin and methylthiol CK production stemming from the tRNA degradation pathway among fungi are discussed. © 2015 by The Mycological Society of America.

The Pursuit of Pathways: Combining Rigorous Academics with Career Training

ERIC Educational Resources Information Center

Schwartz, Robert B.

2014-01-01

In February 2011 author Robert Schwartz, along with with two colleagues--economist Ronald Ferguson and journalist William Symonds--released a report, "Pathways to Prosperity: Meeting the Challenge of Preparing Young Americans for the 21st Century," which was published by Harvard University's Graduate School of Education. When they…

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

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

PubMed Central

Puthiamadathil, Jeevan M; Weinberg, Benjamin A

2017-01-01

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

A chain reaction approach to modelling gene pathways.

PubMed

Cheng, Gary C; Chen, Dung-Tsa; Chen, James J; Soong, Seng-Jaw; Lamartiniere, Coral; Barnes, Stephen

2012-08-01

BACKGROUND: Of great interest in cancer prevention is how nutrient components affect gene pathways associated with the physiological events of puberty. Nutrient-gene interactions may cause changes in breast or prostate cells and, therefore, may result in cancer risk later in life. Analysis of gene pathways can lead to insights about nutrient-gene interactions and the development of more effective prevention approaches to reduce cancer risk. To date, researchers have relied heavily upon experimental assays (such as microarray analysis, etc.) to identify genes and their associated pathways that are affected by nutrient and diets. However, the vast number of genes and combinations of gene pathways, coupled with the expense of the experimental analyses, has delayed the progress of gene-pathway research. The development of an analytical approach based on available test data could greatly benefit the evaluation of gene pathways, and thus advance the study of nutrient-gene interactions in cancer prevention. In the present study, we have proposed a chain reaction model to simulate gene pathways, in which the gene expression changes through the pathway are represented by the species undergoing a set of chemical reactions. We have also developed a numerical tool to solve for the species changes due to the chain reactions over time. Through this approach we can examine the impact of nutrient-containing diets on the gene pathway; moreover, transformation of genes over time with a nutrient treatment can be observed numerically, which is very difficult to achieve experimentally. We apply this approach to microarray analysis data from an experiment which involved the effects of three polyphenols (nutrient treatments), epigallo-catechin-3-O-gallate (EGCG), genistein, and resveratrol, in a study of nutrient-gene interaction in the estrogen synthesis pathway during puberty. RESULTS: In this preliminary study, the estrogen synthesis pathway was simulated by a chain reaction model. By

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

PubMed

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

2013-01-01

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

A Dual Pathway of Student Motivation: Combining an Implicit and Explicit Measure of Student Motivation

ERIC Educational Resources Information Center

Hornstra, Lisette; Kamsteeg, Antoinette; Pot, Sara; Verheij, Lydia

2018-01-01

Abundant research in social psychology shows human behaviour is guided by beliefs through two pathways, a deliberate and automatic pathway. Research on student motivation has thus far focused mostly on the deliberate pathway and consequently almost exclusively relied on explicit measures (i.e. self-reports of motivation) to assess student…

Heritable temperament pathways to early callous-unemotional behaviour.

PubMed

Waller, Rebecca; Trentacosta, Christopher J; Shaw, Daniel S; Neiderhiser, Jenae M; Ganiban, Jody M; Reiss, David; Leve, Leslie D; Hyde, Luke W

2016-12-01

Early callous-unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous-unemotional behaviours is qualified by interactions with positive parenting. To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous-unemotional behaviours and whether parenting moderates these associations. Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous-unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Biological mother fearlessness predicted child callous-unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous-unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous-unemotional behaviour pathway. Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous-unemotional behaviours. Positive parenting can buffer these risky pathways. © The Royal College of Psychiatrists 2016.

Heritable temperament pathways to early callous–unemotional behaviour

PubMed Central

Waller, Rebecca; Trentacosta, Christopher J.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Ganiban, Jody M.; Reiss, David; Leve, Leslie D.; Hyde, Luke W.

2016-01-01

Background Early callous–unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous–unemotional behaviours is qualified by interactions with positive parenting. Aims To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous–unemotional behaviours and whether parenting moderates these associations. Method Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous–unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Results Biological mother fearlessness predicted child callous–unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous–unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous–unemotional behaviour pathway. Conclusions Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous–unemotional behaviours. Positive parenting can buffer these risky pathways. PMID:27765772

SPATIAL PREDICTION USING COMBINED SOURCES OF DATA

EPA Science Inventory

For improved environmental decision-making, it is important to develop new models for spatial prediction that accurately characterize important spatial and temporal patterns of air pollution. As the U .S. Environmental Protection Agency begins to use spatial prediction in the reg...

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

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

Greitzer, Frank L.; Frincke, Deborah A.

2010-09-01

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data tomore » yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.« less

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

PubMed

Durante, Marco; Formenti, Silvia C

2018-01-01

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

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

PubMed Central

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

2015-01-01

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

Handling imbalance data in churn prediction using combined SMOTE and RUS with bagging method

NASA Astrophysics Data System (ADS)

Pura Hartati, Eka; Adiwijaya; Arif Bijaksana, Moch

2018-03-01

Customer churn has become a significant problem and also a challenge for Telecommunication company such as PT. Telkom Indonesia. It is necessary to evaluate whether the big problems of churn customer and the company’s managements will make appropriate strategies to minimize the churn and retaining the customer. Churn Customer data which categorized churn Atas Permintaan Sendiri (APS) in this Company is an imbalance data, and this issue is one of the challenging tasks in machine learning. This study will investigate how is handling class imbalance in churn prediction using combined Synthetic Minority Over-Sampling (SMOTE) and Random Under-Sampling (RUS) with Bagging method for a better churn prediction performance’s result. The dataset that used is Broadband Internet data which is collected from Telkom Regional 6 Kalimantan. The research firstly using data preprocessing to balance the imbalanced dataset and also to select features by sampling technique SMOTE and RUS, and then building churn prediction model using Bagging methods and C4.5.

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.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

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

2010-01-01

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

Effect on the partial least-squares prediction of yarn properties combining raman and infrared measurements and applying wavelength selection.

PubMed

de Groot, P J; Swierenga, H; Postma, G J; Melssen, W J; Buydens, L M C

2003-06-01

The combination of Raman and infrared spectroscopy on the one hand and wavelength selection on the other hand is used to improve the partial least-squares (PLS) prediction of seven selected yarn properties. These properties are important for on-line quality control during production. From 71 yarn samples, the Raman and infrared spectra are measured and reference methods are used to determine the selected properties. Making separate PLS models for all yarn properties using the Raman and infrared spectra, prior to wavelength selection, reveals that Raman spectroscopy outperforms infrared spectroscopy. If wavelength selection is applied, the PLS prediction error decreases and the correlation coefficient increases for all properties. However, a substantial wavelength selection effect is present for the infrared spectra compared to the Raman spectra. For the infrared spectra, wavelength selection results in PLS prediction errors comparable with the prediction performance of the Raman spectra prior to wavelength selection. Concatenating the Raman and infrared spectra does not enhance the PLS prediction performance, not even after wavelength selection. It is concluded that an infrared spectrometer, combined with a wavelength selection procedure, can be used if no (suitable) Raman instrument is available.

High gain signal averaged electrocardiogram combined with 24 hour monitoring in patients early after myocardial infarction for bedside prediction of arrhythmic events.

PubMed Central

Cripps, T; Bennett, E D; Camm, A J; Ward, D E

1988-01-01

The value of the high gain, signal averaged electrocardiogram combined with 24 hour electrocardiographic monitoring in the prediction of arrhythmic events was assessed in 159 patients in the first week after myocardial infarction. Eleven patients (7%) suffered arrhythmic events during a mean (SD) of 12 (6) months of follow up (range 2-22, median 13 months). The combination of high gain, signal averaged electrocardiography and 24 hour electrocardiographic monitoring was more accurate than either technique alone or than clinical information collected during admission in predicting these events. The combination identified a high risk group of 13 (8%) patients, with an arrhythmic event rate of 62% and a low risk group with an event rate of 2%. The combination of high gain, signal averaged electrocardiography and 24 hour electrocardiographic monitoring in the first week after myocardial infarction provides a rapid, cheap, and non-invasive bedside method for the prediction of arrhythmias. PMID:3179133

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

2015-06-22

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions.

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

PubMed Central

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

2005-01-01

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

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

DOE PAGES

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; ...

2017-04-01

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

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

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

Safety and feasibility of targeted agent combinations in solid tumours.

PubMed

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

2013-03-01

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

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

PubMed

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

2018-04-25

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

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

PubMed

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

2017-05-09

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

Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

PubMed

Peterson, Lenna X; Kim, Hyungrae; Esquivel-Rodriguez, Juan; Roy, Amitava; Han, Xusi; Shin, Woong-Hee; Zhang, Jian; Terashi, Genki; Lee, Matt; Kihara, Daisuke

2017-03-01

We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Nonparametric predictive inference for combining diagnostic tests with parametric copula

NASA Astrophysics Data System (ADS)

Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

2017-09-01

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension.

PubMed

Bonhage, Corinna E; Mueller, Jutta L; Friederici, Angela D; Fiebach, Christian J

2015-07-01

It is widely agreed upon that linguistic predictions are an integral part of language comprehension. Yet, experimental proof of their existence remains challenging. Here, we introduce a new predictive eye gaze reading task combining eye tracking and functional magnetic resonance imaging (fMRI) that allows us to infer the existence and timing of linguistic predictions via anticipatory eye-movements. Participants read different types of word sequences (i.e., regular sentences, meaningless jabberwocky sentences, non-word lists) up to the pre-final word. The final target word was displayed with a temporal delay and its screen position was dependent on the syntactic word category (nouns vs verbs). During the delay, anticipatory eye-movements into the correct target word area were indicative of linguistic predictions. For fMRI analysis, the predictive sentence conditions were contrasted to the non-word condition, with the anticipatory eye-movements specifying differences in timing across conditions. A conjunction analysis of both sentence conditions revealed the neural substrate of word category prediction, namely a distributed network of cortical and subcortical brain regions including language systems, basal ganglia, thalamus, and hippocampus. Direct contrasts between the regular sentence condition and the jabberwocky condition indicate that prediction of word category in meaningless jabberwocky sentences relies on classical left-hemispheric language systems involving Brodman's area 44/45 in the left inferior frontal gyrus, left superior temporal areas, and the dorsal caudate nucleus. Regular sentences, in contrast, allowed for the prediction of specific words. Word-specific predictions were specifically associated with more widely distributed temporal and parietal cortical systems, most prominently in the right hemisphere. Our results support the presence of linguistic predictions during sentence processing and demonstrate the validity of the predictive eye gaze

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

PubMed

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

2017-05-01

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

Combination of Cyclopamine and Tamoxifen Promotes Survival and Migration of MCF-7 Breast Cancer Cells – Interaction of Hedgehog-Gli and Estrogen Receptor Signaling Pathways

PubMed Central

Uzarevic, Zvonimir; Ozretic, Petar; Musani, Vesna; Rafaj, Maja; Cindric, Mario; Levanat, Sonja

2014-01-01

Hedgehog-Gli (Hh-Gli) signaling pathway is one of the new molecular targets found upregulated in breast tumors. Estrogen receptor alpha (ERα) signaling has a key role in the development of hormone-dependent breast cancer. We aimed to investigate the effects of inhibiting both pathways simultaneously on breast cancer cell survival and the potential interactions between these two signaling pathways. ER-positive MCF-7 cells show decreased viability after treatment with cyclopamine, a Hh-Gli pathway inhibitor, as well as after tamoxifen (an ERα inhibitor) treatment. Simultaneous treatment with cyclopamine and tamoxifen on the other hand, causes short-term survival of cells, and increased migration. We found upregulated Hh-Gli signaling under these conditions and protein profiling revealed increased expression of proteins involved in cell proliferation and migration. Therefore, even though Hh-Gli signaling seems to be a good potential target for breast cancer therapy, caution must be advised, especially when combining therapies. In addition, we also show a potential direct interaction between the Shh protein and ERα in MCF-7 cells. Our data suggest that the Shh protein is able to activate ERα independently of the canonical Hh-Gli signaling pathway. Therefore, this may present an additional boost for ER-positive cells that express Shh, even in the absence of estrogen. PMID:25503972

A Global Genomic and Genetic Strategy to Identify, Validate and Use Gene Signatures of Xenobiotic-Responsive Transcription Factors in Prediction of Pathway Activation in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors. Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening as well as their involvement in disease states. ...

A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

PubMed Central

Yue, Peng; Yu, Zheng-Yong; Wang, Qingyuan

2017-01-01

Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting from high frequency vibrations and low cycle fatigue (LCF) from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner’s rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors. PMID:28773064

Molecular Pathways: Extracting Medical Knowledge from High Throughput Genomic Data

PubMed Central

Goldstein, Theodore; Paull, Evan O.; Ellis, Matthew J.; Stuart, Joshua M.

2013-01-01

High-throughput genomic data that measures RNA expression, DNA copy number, mutation status and protein levels provide us with insights into the molecular pathway structure of cancer. Genomic lesions (amplifications, deletions, mutations) and epigenetic modifications disrupt biochemical cellular pathways. While the number of possible lesions is vast, different genomic alterations may result in concordant expression and pathway activities, producing common tumor subtypes that share similar phenotypic outcomes. How can these data be translated into medical knowledge that provides prognostic and predictive information? First generation mRNA expression signatures such as Genomic Health's Oncotype DX already provide prognostic information, but do not provide therapeutic guidance beyond the current standard of care – which is often inadequate in high-risk patients. Rather than building molecular signatures based on gene expression levels, evidence is growing that signatures based on higher-level quantities such as from genetic pathways may provide important prognostic and diagnostic cues. We provide examples of how activities for molecular entities can be predicted from pathway analysis and how the composite of all such activities, referred to here as the “activitome,” help connect genomic events to clinical factors in order to predict the drivers of poor outcome. PMID:23430023

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

PubMed

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; Kim, Taehyong; Banf, Michael; Chae, Lee; Dreher, Kate; Chavali, Arvind K; Nilo-Poyanco, Ricardo; Bernard, Thomas; Kahn, Daniel; Rhee, Seung Y

2017-04-01

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. © 2017 American Society of Plant Biologists. All Rights Reserved.

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.

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

Associative Encoding and Retrieval Are Predicted by Functional Connectivity in Distinct Hippocampal Area CA1 Pathways

PubMed Central

Duncan, Katherine; Tompary, Alexa

2014-01-01

Determining how the hippocampus supports the unique demands of memory encoding and retrieval is fundamental for understanding the biological basis of episodic memory. One possibility proposed by theoretical models is that the distinct computational demands of encoding and retrieval are accommodated by shifts in the functional interaction between the hippocampal CA1 subregion and its input structures. However, empirical tests of this hypothesis are lacking. To test this in humans, we used high-resolution fMRI to measure functional connectivity between hippocampal area CA1 and regions of the medial temporal lobe and midbrain during extended blocks of associative encoding and retrieval tasks. We found evidence for a double dissociation between the pathways supporting successful encoding and retrieval. Specifically, during the associative encoding task, but not the retrieval task, functional connectivity only between area CA1 and the ventral tegmental area predicted associative long-term memory. In contrast, connectivity between area CA1 and DG/CA3 was greater, on average, during the retrieval task compared with the encoding task, and, importantly, the strength of this connectivity significantly correlated with retrieval success. Together, these findings serve as an important first step toward understanding how the demands of fundamental memory processes may be met by changes in the relative strength of connectivity within hippocampal pathways. PMID:25143600

Shenqi detoxification granule combined with P311 inhibits epithelial-mesenchymal transition in renal fibrosis via TGF-β1-Smad-ILK pathway.

PubMed

Cai, Pingping; Liu, Xiang; Xu, Yuan; Qi, Fanghua; Si, Guomin

2017-01-01

Shenqi detoxification granule (SDG), a traditional Chinese herbal formula, has been shown to have nephroprotective and anti-fibrotic activities in patients with chronic kidney disease (CKD). However, its mechanisms in renal fibrosis and the progression of CKD remain largely unknown. P311, a highly conserved 8-kDa intracellular protein, plays a key role in renal fibrosis by regulating epithelial-mesenchymal transition (EMT). Previously, we found P311 might be involved in the pathogenesis of renal fibrosis by inhibiting EMT via the TGF-β1-Smad-ILK pathway. We also found SDG combined with P311 could ameliorate renal fibrosis by regulating the expression of EMT markers. Here we further examined the effect and mechanism of SDG combined with P311 on TGF-β1-mediated EMT in a rat model of unilateral ureteral occlusion (UUO) renal fibrosis. After establishment of the UUO model successfully, the rats were gavaged with SDG daily and/or injected with recombinant adenovirus p311 (also called Ad-P311) through the tail vein each week for 4 weeks. Serum creatinine (Cr), blood urea nitrogen (BUN) and albumin (ALB) levels were tested to observe renal function, and hematoxylin eosin (HE) and Masson staining were performed to observe kidney histopathology. Furthermore, the expression of EMT markers (E-cadherin and α-smooth muscle actin (α-SMA)) and EMT-related molecules TGF-β1, pSmad2/3, Smad7 and ILK were observed using immunohistochemical staining and Western blot analysis. Treatment with SDG and P311 improved renal function and histopathological abnormalities, as well as reversing the changes of EMT markers and EMT-related molecules, which indicated SDG combined with P311 could attenuate renal fibrosis in UUO rats, and the underlying mechanism might involve TGF-β1-mediated EMT and the TGF-β1-Smad-ILK signaling pathway. Therefore, SDG might be a novel alternative therapy for treating renal fibrosis and delaying the progression of CKD. Furthermore, SDG combined with P311 might

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Predicting stress urinary incontinence during pregnancy: combination of pelvic floor ultrasound parameters and clinical factors.

PubMed

Chen, Ling; Luo, Dan; Yu, Xiajuan; Jin, Mei; Cai, Wenzhi

2018-05-12

The aim of this study was to develop and validate a predictive tool that combining pelvic floor ultrasound parameters and clinical factors for stress urinary incontinence during pregnancy. A total of 535 women in first or second trimester were included for an interview and transperineal ultrasound assessment from two hospitals. Imaging data sets were analyzed offline to assess for bladder neck vertical position, urethra angles (α, β, and γ angles), hiatal area and bladder neck funneling. All significant continuous variables at univariable analysis were analyzed by receiver-operating characteristics. Three multivariable logistic models were built on clinical factor, and combined with ultrasound parameters. The final predictive model with best performance and fewest variables was selected to establish a nomogram. Internal and external validation of the nomogram were performed by both discrimination represented by C-index and calibration measured by Hosmer-Lemeshow test. A decision curve analysis was conducted to determine the clinical utility of the nomogram. After excluding 14 women with invalid data, 521 women were analyzed. β angle, γ angle and hiatal area had limited predictive value for stress urinary incontinence during pregnancy, with area under curves of 0.558-0.648. The final predictive model included body mass index gain since pregnancy, constipation, previous delivery mode, β angle at rest, and bladder neck funneling. The nomogram based on the final model showed good discrimination with a C-index of 0.789 and satisfactory calibration (P=0.828), both of which were supported by external validation. Decision curve analysis showed that the nomogram was clinical useful. The nomogram incorporating both the pelvic floor ultrasound parameters and clinical factors has been validated to show good discrimination and calibration, and could be an important tool for stress urinary incontinence risk prediction at an early stage of pregnancy. This article is

SAR202 Genomes from the Dark Ocean Predict Pathways for the Oxidation of Recalcitrant Dissolved Organic Matter.

PubMed

Landry, Zachary; Swan, Brandon K; Herndl, Gerhard J; Stepanauskas, Ramunas; Giovannoni, Stephen J

2017-04-18

Deep-ocean regions beyond the reach of sunlight contain an estimated 615 Pg of dissolved organic matter (DOM), much of which persists for thousands of years. It is thought that bacteria oxidize DOM until it is too dilute or refractory to support microbial activity. We analyzed five single-amplified genomes (SAGs) from the abundant SAR202 clade of dark-ocean bacterioplankton and found they encode multiple families of paralogous enzymes involved in carbon catabolism, including several families of oxidative enzymes that we hypothesize participate in the degradation of cyclic alkanes. The five partial genomes encoded 152 flavin mononucleotide/F420-dependent monooxygenases (FMNOs), many of which are predicted to be type II Baeyer-Villiger monooxygenases (BVMOs) that catalyze oxygen insertion into semilabile alicyclic alkanes. The large number of oxidative enzymes, as well as other families of enzymes that appear to play complementary roles in catabolic pathways, suggests that SAR202 might catalyze final steps in the biological oxidation of relatively recalcitrant organic compounds to refractory compounds that persist. IMPORTANCE Carbon in the ocean is massively sequestered in a complex mixture of biologically refractory molecules that accumulate as the chemical end member of biological oxidation and diagenetic change. However, few details are known about the biochemical machinery of carbon sequestration in the deep ocean. Reconstruction of the metabolism of a deep-ocean microbial clade, SAR202, led to postulation of new biochemical pathways that may be the penultimate stages of DOM oxidation to refractory forms that persist. These pathways are tied to a proliferation of oxidative enzymes. This research illuminates dark-ocean biochemistry that is broadly consequential for reconstructing the global carbon cycle. Copyright © 2017 Landry et al.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

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

PubMed

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

2018-05-08

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

Validation of the Combined Comorbidity Index of Charlson and Elixhauser to Predict 30-Day Mortality Across ICD-9 and ICD-10.

PubMed

Simard, Marc; Sirois, Caroline; Candas, Bernard

2018-05-01

To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848-0.856)] performed better than original Charlson [0.841 (95% CI, 0.835-0.844)] or Elixhauser [0.841 (95% CI, 0.837-0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems. The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Neural Elements for Predictive Coding.

PubMed

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

Neural Elements for Predictive Coding

PubMed Central

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by

Predictive inference for best linear combination of biomarkers subject to limits of detection.

PubMed

Coolen-Maturi, Tahani

2017-08-15

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks.

PubMed

Mei, Suyu

2018-05-04

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L 2 -regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

Human and Server Docking Prediction for CAPRI Round 30–35 Using LZerD with Combined Scoring Functions

PubMed Central

Peterson, Lenna X.; Kim, Hyungrae; Esquivel-Rodriguez, Juan; Roy, Amitava; Han, Xusi; Shin, Woong-Hee; Zhang, Jian; Terashi, Genki; Lee, Matt; Kihara, Daisuke

2016-01-01

We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues’ spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, i.e. whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. PMID:27654025

Pathway-based identification of biomarkers for targeted therapeutics: personalized oncology with PI3K pathway inhibitors.

PubMed

Andersen, Jannik N; Sathyanarayanan, Sriram; Di Bacco, Alessandra; Chi, An; Zhang, Theresa; Chen, Albert H; Dolinski, Brian; Kraus, Manfred; Roberts, Brian; Arthur, William; Klinghoffer, Rich A; Gargano, Diana; Li, Lixia; Feldman, Igor; Lynch, Bethany; Rush, John; Hendrickson, Ronald C; Blume-Jensen, Peter; Paweletz, Cloud P

2010-08-04

Although we have made great progress in understanding the complex genetic alterations that underlie human cancer, it has proven difficult to identify which molecularly targeted therapeutics will benefit which patients. Drug-specific modulation of oncogenic signaling pathways in specific patient subpopulations can predict responsiveness to targeted therapy. Here, we report a pathway-based phosphoprofiling approach to identify and quantify clinically relevant, drug-specific biomarkers for phosphatidylinositol 3-kinase (PI3K) pathway inhibitors that target AKT, phosphoinositide-dependent kinase 1 (PDK1), and PI3K-mammalian target of rapamycin (mTOR). We quantified 375 nonredundant PI3K pathway-relevant phosphopeptides, all containing AKT, PDK1, or mitogen-activated protein kinase substrate recognition motifs. Of these phosphopeptides, 71 were drug-regulated, 11 of them by all three inhibitors. Drug-modulated phosphoproteins were enriched for involvement in cytoskeletal reorganization (filamin, stathmin, dynamin, PAK4, and PTPN14), vesicle transport (LARP1, VPS13D, and SLC20A1), and protein translation (S6RP and PRAS40). We then generated phosphospecific antibodies against selected, drug-regulated phosphorylation sites that would be suitable as biomarker tools for PI3K pathway inhibitors. As proof of concept, we show clinical translation feasibility for an antibody against phospho-PRAS40(Thr246). Evaluation of binding of this antibody in human cancer cell lines, a PTEN (phosphatase and tensin homolog deleted from chromosome 10)-deficient mouse prostate tumor model, and triple-negative breast tumor tissues showed that phospho-PRAS40(Thr246) positively correlates with PI3K pathway activation and predicts AKT inhibitor sensitivity. In contrast to phosphorylation of AKT(Thr308), the phospho-PRAS40(Thr246) epitope is highly stable in tissue samples and thus is ideal for immunohistochemistry. In summary, our study illustrates a rational approach for discovery of drug

Simultaneous Identification of Multiple Driver Pathways in Cancer

PubMed Central

Leiserson, Mark D. M.; Blokh, Dima

2013-01-01

Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software. PMID:23717195

In silico prediction of pharmaceutical degradation pathways: a benchmarking study.

PubMed

Kleinman, Mark H; Baertschi, Steven W; Alsante, Karen M; Reid, Darren L; Mowery, Mark D; Shimanovich, Roman; Foti, Chris; Smith, William K; Reynolds, Dan W; Nefliu, Marcela; Ott, Martin A

2014-11-03

Zeneth is a new software application capable of predicting degradation products derived from small molecule active pharmaceutical ingredients. This study was aimed at understanding the current status of Zeneth's predictive capabilities and assessing gaps in predictivity. Using data from 27 small molecule drug substances from five pharmaceutical companies, the evolution of Zeneth predictions through knowledge base development since 2009 was evaluated. The experimentally observed degradation products from forced degradation, accelerated, and long-term stability studies were compared to Zeneth predictions. Steady progress in predictive performance was observed as the knowledge bases grew and were refined. Over the course of the development covered within this evaluation, the ability of Zeneth to predict experimentally observed degradants increased from 31% to 54%. In particular, gaps in predictivity were noted in the areas of epimerizations, N-dealkylation of N-alkylheteroaromatic compounds, photochemical decarboxylations, and electrocyclic reactions. The results of this study show that knowledge base development efforts have increased the ability of Zeneth to predict relevant degradation products and aid pharmaceutical research. This study has also provided valuable information to help guide further improvements to Zeneth and its knowledge base.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

EPA Science Inventory

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose–response, and time-course p...

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

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

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

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

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

PubMed

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

2017-05-25

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

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

PubMed

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

PubMed

Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

2016-10-01

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Use of the adverse outcome pathway framework to represent cross-species consequences of specific pathway perturbations

EPA Science Inventory

The adverse outcome pathway (AOP) framework has been developed as a means for assembling scientifically defensible descriptions of how particular molecular perturbations, termed molecular initiating events (MIEs), can evoke a set of predictable responses at different levels of bi...

Prediction of Continental-Scale Net Ecosystem Carbon Exchange by Combining MODIS and AmeriFlux Data

NASA Astrophysics Data System (ADS)

Xiao, J.; Zhuang, Q.

2007-12-01

There is growing interest in scaling up net ecosystem exchange (NEE) measured at eddy covariance flux towers to regional scales. Here we used remote sensing data from the MODIS instrument on board NASA's Terra satellite to extrapolate NEE measured at AmeriFlux sites to the continental scale. We combined MODIS data and NEE measurements from a number of AmeriFlux sites with a variety of vegetation types (e.g., forests, grasslands, shrublands, savannas, and croplands) to develop a predictive NEE model using a regression tree approach. The model was trained using 2000-2003 NEE measurements, and the performance of the model was evaluated using independent data over the period 2004-2006. We found that the model predicted NEE with reasonable accuracy at the continental scale. The R-squared values are 0.50 for all vegetation types combined and 0.72 for deciduous forests. We then applied the model to the conterminous U.S. and predicted NEE for each 500m by 500m cell over the period 2001-2006. Based on the wall-to-wall NEE estimates, we examined the spatial and temporal distributions of annual NEE and interannual variability of annual NEE across the conterminous U.S. over the study period (2001-2006). Our scaling-up approach implicitly considered the effects of climate variability, land use/land cover change, disturbances, extreme climate events, and management practices, and thus our annual NEE estimates represents the net carbon fluxes between the terrestrial biosphere and the atmosphere in the conterminous U.S.

Theoretical prediction of the mechanistic pathways and kinetics of methylcyclohexane initiated by OH radicals

NASA Astrophysics Data System (ADS)

Begum, Saheen Shehnaz; Deka, Ramesh Chandra; Gour, Nand Kishor

2018-06-01

In this manuscript, we have systematically depicted the theoretical prediction of H-absorption from methylcyclohexane initiated by OH radical. For this we have performed dual-level of quantum chemical calculations on the gas-phase reactions between methylcyclohexane (MCH) and OH radical. Geometry optimisation and vibrational frequency calculations have been performed at BHandHLYP/6-311G(d,p) level of theory along with energetic calculations at coupled cluster CCSD(T) method using the same basis set. All the stationary points of titled reaction have been located on the potential energy surface. It has also been found that the H-abstraction takes place from -CH site of MCH, which is the minimum energy pathway than others. The rate constant was calculated using canonical transition state theory for MCH with OH radical and is found to be 3.27 × 10-12 cm3 molecule-1 s-1, which is in sound agreement with reported experimental data. The atmospheric lifetime of MCH and branching ratios of the reaction channels are also reported in the manuscript.

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

PubMed

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

2018-04-27

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

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

PubMed Central

Aguilera, Teodoro; Lozano, Jesús; Paredes, José A.; Álvarez, Fernando J.; Suárez, José I.

2012-01-01

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. PMID:22969387

Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance

ERIC Educational Resources Information Center

Pardo, Abelardo; Han, Feifei; Ellis, Robert A.

2017-01-01

Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

Measures of Hindu Pathways: Development and Preliminary Evidence of Reliability and Validity.

ERIC Educational Resources Information Center

Tarakeshwar, Nalini; Pargament, Kenneth I.; Mahoney, Annette

2003-01-01

Examines religious practices of Hindus in the United States and develops measures of their religious pathways. Four religious pathways were identified: devotion, ethical action, knowledge, and physical restraint/yoga. Results indicate that the measures of the religious pathways possessed adequate psychometric properties and were predictive of…

Advancing adverse outcome pathways for integrated toxicology and regulatory applications

EPA Science Inventory

Recent regulatory efforts in many countries have focused on a toxicological pathway-based vision for human health assessments relying on in vitro systems and predictive models to generate the toxicological data needed to evaluate chemical hazard. A pathway-based vision is equally...

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

PubMed

de Ávila, Maurício Boff; de Azevedo, Walter Filgueira

2018-04-20

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

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

PubMed

Ozyurt, A Sinem; Selby, Thomas L

2008-07-01

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

Combination of herbal extracts and platelet-rich plasma induced dermal papilla cell proliferation: involvement of ERK and Akt pathways.

PubMed

Rastegar, Hosein; Ahmadi Ashtiani, Hamidreza; Aghaei, Mahmoud; Ehsani, Amirohushang; Barikbin, Behrooz

2013-06-01

Recently, platelet-rich plasma (PRP) has attracted attention in various medical fields, including plastic surgery, treatment for problematic wounds, and dermatology. Specifically, PRP has been tested during hair transplantation to reduce swelling and pain and to increase hair density. We examined the effects of PRP and herbal extracts combination in order to identify potential stimulants of hair growth. PRP was prepared using the double-spin method and applied to dermal papilla cells (DPCs). MTT viability test and BrdU cell proliferation assay were used to study the effect of herbal extracts and PRP on proliferation of DPCs. To understand the mechanisms of herbal extracts and PRP involved in the regulation of hair growth, we evaluated signaling pathways and measured the expressions of ERK and Akt, by Western blot. Combination of herbal extracts and PRP was found to induce significant proliferation of human DPCs at concentrations ranging from 1.5% to 4.5%. The present study shows that herbal extracts and PRP affect the expressions of extracellular signal-regulated kinase (ERK) and Akt in DPCs. In this study, we have shown that combination of herbal extracts and PRP plays an active role in promoting the proliferation of human dermal papilla (DP) cells via the regulation of ERK and Akt proteins, and this may be applicable to the future development of herbal extracts and PRP combination therapeutics to enhance hair growth. © 2013 Wiley Periodicals, Inc.

Novel Parameter Predicting Grade 2 Rectal Bleeding After Iodine-125 Prostate Brachytherapy Combined With External Beam Radiation Therapy

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

Shiraishi, Yutaka, E-mail: shiraishi@rad.med.keio.ac.jp; Hanada, Takashi; Ohashi, Toshio

2013-09-01

Purpose: To propose a novel parameter predicting rectal bleeding on the basis of generalized equivalent uniform doses (gEUD) after {sup 125}I prostate brachytherapy combined with external beam radiation therapy and to assess the predictive value of this parameter. Methods and Materials: To account for differences among radiation treatment modalities and fractionation schedules, rectal dose–volume histograms (DVHs) of 369 patients with localized prostate cancer undergoing combined therapy retrieved from corresponding treatment planning systems were converted to equivalent dose-based DVHs. The gEUDs for the rectum were calculated from these converted DVHs. The total gEUD (gEUD{sub sum}) was determined by a summation ofmore » the brachytherapy and external-beam radiation therapy components. Results: Thirty-eight patients (10.3%) developed grade 2+ rectal bleeding. The grade 2+ rectal bleeding rate increased as the gEUD{sub sum} increased: 2.0% (2 of 102 patients) for <70 Gy, 10.3% (15 of 145 patients) for 70-80 Gy, 15.8% (12 of 76 patients) for 80-90 Gy, and 19.6% (9 of 46 patients) for >90 Gy (P=.002). Multivariate analysis identified age (P=.024) and gEUD{sub sum} (P=.000) as risk factors for grade 2+ rectal bleeding. Conclusions: Our results demonstrate gEUD to be a potential predictive factor for grade 2+ late rectal bleeding after combined therapy for prostate cancer.« less

Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome.

PubMed

Chang, Alice Y; Lalia, Antigoni Z; Jenkins, Gregory D; Dutta, Tumpa; Carter, Rickey E; Singh, Ravinder J; Nair, K Sreekumaran

2017-06-01

Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS). Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses. This multiethnic, obese sample was matched by age (PCOS, 37±6; MetS, 40±6years) and body mass index (BMI) (PCOS, 34.6±5.1; MetS, 33.7±5.2kg/m 2 ). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P=.02), essential amino acids (P=.03), the essential amino acid lysine (P=.02), and the lysine metabolite α-aminoadipic acid (P=.02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between

Combining a Nontargeted and Targeted Metabolomics Approach to Identify Metabolic Pathways Significantly Altered in Polycystic Ovary Syndrome

PubMed Central

Chang, Alice Y.; Lalia, Antigoni Z.; Jenkins, Gregory D.; Dutta, Tumpa; Carter, Rickey E.; Singh, Ravinder J.; Sreekumaran Nair, K.

2017-01-01

Objective Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS). Methods Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses. Results This multiethnic, obese sample was matched by age (PCOS, 37 ± 6; MetS, 40 ± 6 years) and body mass index (BMI) (PCOS, 34.6 ± 5.1; MetS, 33.7 ± 5.2 kg/m2). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P = .02), essential amino acids (P = .03), the essential amino acid lysine (P = .02), and the lysine metabolite α-aminoadipic acid (P = .02) in models adjusted for surrogate variables representing technical variation in

Pharmacodynamically optimized erythropoietin treatment combined with phlebotomy reduction predicted to eliminate blood transfusions in selected preterm infants.

PubMed

Rosebraugh, Matthew R; Widness, John A; Nalbant, Demet; Cress, Gretchen; Veng-Pedersen, Peter

2014-02-01

Preterm very-low-birth-weight (VLBW) infants weighing <1.5 kg at birth develop anemia, often requiring multiple red blood cell transfusions (RBCTx). Because laboratory blood loss is a primary cause of anemia leading to RBCTx in VLBW infants, our purpose was to simulate the extent to which RBCTx can be reduced or eliminated by reducing laboratory blood loss in combination with pharmacodynamically optimized erythropoietin (Epo) treatment. Twenty-six VLBW ventilated infants receiving RBCTx were studied during the first month of life. RBCTx simulations were based on previously published RBCTx criteria and data-driven Epo pharmacodynamic optimization of literature-derived RBC life span and blood volume data corrected for phlebotomy loss. Simulated pharmacodynamic optimization of Epo administration and reduction in phlebotomy by ≥ 55% predicted a complete elimination of RBCTx in 1.0-1.5 kg infants. In infants <1.0 kg with 100% reduction in simulated phlebotomy and optimized Epo administration, a 45% reduction in RBCTx was predicted. The mean blood volume drawn from all infants was 63 ml/kg: 33% required for analysis and 67% discarded. When reduced laboratory blood loss and optimized Epo treatment are combined, marked reductions in RBCTx in ventilated VLBW infants were predicted, particularly among those with birth weights >1.0 kg.

Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates.

PubMed

Hu, Hang-Wei; Chen, Deli; He, Ji-Zheng

2015-09-01

The continuous increase of the greenhouse gas nitrous oxide (N2O) in the atmosphere due to increasing anthropogenic nitrogen input in agriculture has become a global concern. In recent years, identification of the microbial assemblages responsible for soil N2O production has substantially advanced with the development of molecular technologies and the discoveries of novel functional guilds and new types of metabolism. However, few practical tools are available to effectively reduce in situ soil N2O flux. Combating the negative impacts of increasing N2O fluxes poses considerable challenges and will be ineffective without successfully incorporating microbially regulated N2O processes into ecosystem modeling and mitigation strategies. Here, we synthesize the latest knowledge of (i) the key microbial pathways regulating N2O production and consumption processes in terrestrial ecosystems and the critical environmental factors influencing their occurrence, and (ii) the relative contributions of major biological pathways to soil N2O emissions by analyzing available natural isotopic signatures of N2O and by using stable isotope enrichment and inhibition techniques. We argue that it is urgently necessary to incorporate microbial traits into biogeochemical ecosystem modeling in order to increase the estimation reliability of N2O emissions. We further propose a molecular methodology oriented framework from gene to ecosystem scales for more robust prediction and mitigation of future N2O emissions. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

NASA Astrophysics Data System (ADS)

Shi, Tiezhu; Wang, Junjie; Chen, Yiyun; Wu, Guofeng

2016-10-01

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350-2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg-1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg-1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg-1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

PubMed Central

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

2017-01-01

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE PAGES

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.; ...

2018-01-17

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

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

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

PubMed Central

Hoyt, David W.; Nicora, Carrie D.; Kinmonth-Schultz, Hannah A.; Ward, Joy K.

2018-01-01

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. PMID:29342073

Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

PubMed

Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

2015-06-01

Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

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

PubMed

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

2014-03-01

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

Combined Experimental and Computational Approach to Predict the Glass-Water Reaction

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

Pierce, Eric M.; Bacon, Diana H.

2011-10-01

The use of mineral and glass dissolution rates measured in laboratory experiments to predict the weathering of primary minerals and volcanic and nuclear waste glasses in field studies requires the construction of rate models that accurately describe the weathering process over geologic timescales. Additionally, the need to model the long-term behavior of nuclear waste glass for the purpose of estimating radionuclide release rates requires that rate models be validated with long-term experiments. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test B, and pressurized unsaturated flow (PUF)], thereby reducingmore » the duration required to evaluate long-term performance. Currently, the PUF test is the only method that mimics the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitors the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior. A one-dimensional reactive chemical transport simulation of glass dissolution and secondary phase formation during a 1.5-year-long PUF experiment was conducted with the Subsurface Transport Over Reactive Multiphases (STORM) code. Results show that parameterization of the computer model by combining direct bench scale laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over the length of the experiment. Over the 1.5-year-long test duration, the rate decreased from 0.2 to 0.01 g/(m2 day) based on B release for low-activity waste glass LAWA44. The observed decrease is approximately two orders of magnitude higher than the decrease observed under static conditions with the SON68 glass (estimated to be a decrease by four orders of magnitude) and suggests that the gel-layer properties are less protective under these dynamic

Combined Experimental and Computational Approach to Predict the Glass-Water Reaction

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

Pierce, Eric M; Bacon, Diana

2011-01-01

The use of mineral and glass dissolution rates measured in laboratory experiments to predict the weathering of primary minerals and volcanic and nuclear waste glasses in field studies requires the construction of rate models that accurately describe the weathering process over geologic time-scales. Additionally, the need to model the long-term behavior of nuclear waste glass for the purpose of estimating radionuclide release rates requires that rate models are validated with long-term experiments. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test-B, and pressurized unsaturated flow (PUF)], thereby reducing themore » duration required to evaluate long-term performance. Currently, the PUF test is the only method that mimics the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitors the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior. A one-dimensional reactive chemical transport simulation of glass dissolution and secondary phase formation during a 1.5-year long PUF experiment was conducted with the subsurface transport over reactive multi-phases code. Results show that parameterization of the computer model by combining direct bench-scale laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over the length of the experiment. Over the 1.5-year long test duration, the rate decreased from 0.2 to 0.01 g/(m2 d) base on B release. The observed decrease is approximately two orders of magnitude higher than the decrease observed under static conditions with the SON68 glass (estimated to be a decrease by 4 orders of magnitude) and suggest the gel-layer properties are less protective under these dynamic conditions.« less

Mortality prediction to hospitalized patients with influenza pneumonia: PO2 /FiO2 combined lymphocyte count is the answer.

PubMed

Shi, Shu Jing; Li, Hui; Liu, Meng; Liu, Ying Mei; Zhou, Fei; Liu, Bo; Qu, Jiu Xin; Cao, Bin

2017-05-01

Community-acquired pneumonia (CAP) severity scores perform well in predicting mortality of CAP patients, but their applicability in influenza pneumonia is powerless. The aim of our research was to test the efficiency of PO 2 /FiO 2 and CAP severity scores in predicting mortality and intensive care unit (ICU) admission with influenza pneumonia patients. We reviewed all patients with positive influenza virus RNA detection in Beijing Chao-Yang Hospital during the 2009-2014 influenza seasons. Outpatients, inpatients with no pneumonia and incomplete data were excluded. We used receiver operating characteristic curves (ROCs) to verify the accuracy of severity scores or indices as mortality predictors in the study patients. Among 170 hospitalized patients with influenza pneumonia, 30 (17.6%) died. Among those who were classified as low-risk (predicted mortality 0.1%-2.1%) by pneumonia severity index (PSI) or confusion, urea, respiratory rate, blood pressure, age ≥65 year (CURB-65), the actual mortality ranged from 5.9 to 22.1%. Multivariate logistic regression indicated that hypoxia (PO 2 /FiO 2  ≤ 250) and lymphopenia (peripheral blood lymphocyte count <0.8 × 10 9 /L) were independent risk factors for mortality, with OR value of 22.483 (95% confidence interval 4.927-102.598) and 5.853 (95% confidence interval 1.887-18.152), respectively. PO 2 /FiO 2 combined lymphocyte count performed well for mortality prediction with area under the curve (AUC) of 0.945, which was significantly better than current CAP severity scores of PSI, CURB-65 and confusion, respiratory rate, blood pressure, age ≥65 years for mortality prediction (P < 0.001). The scores or indices for ICU admission prediction to hospitalized patients with influenza pneumonia confirmed a similar pattern and PO 2 /FiO 2 combined lymphocyte count was also the best predictor for predicting ICU admission. In conclusion, we found that PO 2 /FiO 2 combined lymphocyte count is simple and reliable predictor

Affiliation buffers stress: cumulative genetic risk in oxytocin-vasopressin genes combines with early caregiving to predict PTSD in war-exposed young children.

PubMed

Feldman, R; Vengrober, A; Ebstein, R P

2014-03-11

Research indicates that risk for post-traumatic stress disorder (PTSD) is shaped by the interaction between genetic vulnerability and early caregiving experiences; yet, caregiving has typically been assessed by adult retrospective accounts. Here, we employed a prospective longitudinal design with real-time observations of early caregiving combined with assessment of genetic liability along the axis of vasopressin-oxytocin (OT) gene pathways to test G × E contributions to PTSD. Participants were 232 young Israeli children (1.5-5 years) and their parents, including 148 living in zones of continuous war and 84 controls. A cumulative genetic risk factor was computed for each family member by summing five risk alleles across three genes (OXTR, CD38 and AVPR1a) previously associated with psychopathology, sociality and caregiving. Child PTSD was diagnosed and mother-child interactions were observed in multiple contexts. In middle childhood (7-8 years), child psychopathology was re-evaluated. War exposure increased propensity to develop Axis-I disorder by threefold: 60% of exposed children displayed a psychiatric disorder by middle childhood and 62% of those showed several comorbid disorders. On the other hand, maternal sensitive support reduced risk for psychopathology. G × E effect was found for child genetic risk: in the context of war exposure, greater genetic risk on the vasopressin-OT pathway increased propensity for psychopathology. Among exposed children, chronicity of PTSD from early to middle childhood was related to higher child, maternal and paternal genetic risk, low maternal support and greater initial avoidance symptoms. Child avoidance was predicted by low maternal support and reduced mother-child reciprocity. These findings underscore the saliency of both genetic and behavioral facets of the human affiliation system in shaping vulnerability to PTSD as well as providing an underlying mechanism of post-traumatic resilience.

Affiliation buffers stress: cumulative genetic risk in oxytocin–vasopressin genes combines with early caregiving to predict PTSD in war-exposed young children

PubMed Central

Feldman, R; Vengrober, A; Ebstein, R P

2014-01-01

Research indicates that risk for post-traumatic stress disorder (PTSD) is shaped by the interaction between genetic vulnerability and early caregiving experiences; yet, caregiving has typically been assessed by adult retrospective accounts. Here, we employed a prospective longitudinal design with real-time observations of early caregiving combined with assessment of genetic liability along the axis of vasopressin–oxytocin (OT) gene pathways to test G × E contributions to PTSD. Participants were 232 young Israeli children (1.5–5 years) and their parents, including 148 living in zones of continuous war and 84 controls. A cumulative genetic risk factor was computed for each family member by summing five risk alleles across three genes (OXTR, CD38 and AVPR1a) previously associated with psychopathology, sociality and caregiving. Child PTSD was diagnosed and mother–child interactions were observed in multiple contexts. In middle childhood (7–8 years), child psychopathology was re-evaluated. War exposure increased propensity to develop Axis-I disorder by threefold: 60% of exposed children displayed a psychiatric disorder by middle childhood and 62% of those showed several comorbid disorders. On the other hand, maternal sensitive support reduced risk for psychopathology. G × E effect was found for child genetic risk: in the context of war exposure, greater genetic risk on the vasopressin–OT pathway increased propensity for psychopathology. Among exposed children, chronicity of PTSD from early to middle childhood was related to higher child, maternal and paternal genetic risk, low maternal support and greater initial avoidance symptoms. Child avoidance was predicted by low maternal support and reduced mother–child reciprocity. These findings underscore the saliency of both genetic and behavioral facets of the human affiliation system in shaping vulnerability to PTSD as well as providing an underlying mechanism of post-traumatic resilience. PMID:24618689

Pathways for virus assembly around nucleic acids

PubMed Central

Perlmutter, Jason D; Perkett, Matthew R

2014-01-01

Understanding the pathways by which viral capsid proteins assemble around their genomes could identify key intermediates as potential drug targets. In this work we use computer simulations to characterize assembly over a wide range of capsid protein-protein interaction strengths and solution ionic strengths. We find that assembly pathways can be categorized into two classes, in which intermediates are either predominantly ordered or disordered. Our results suggest that estimating the protein-protein and the protein-genome binding affinities may be sufficient to predict which pathway occurs. Furthermore, the calculated phase diagrams suggest that knowledge of the dominant assembly pathway and its relationship to control parameters could identify optimal strategies to thwart or redirect assembly to block infection. Finally, analysis of simulation trajectories suggests that the two classes of assembly pathways can be distinguished in single molecule fluorescence correlation spectroscopy or bulk time resolved small angle x-ray scattering experiments. PMID:25036288

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

PubMed

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

2017-01-01

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

Framework for computationally-predicted AOPs

EPA Science Inventory

Framework for computationally-predicted AOPs Given that there are a vast number of existing and new chemicals in the commercial pipeline, emphasis is placed on developing high throughput screening (HTS) methods for hazard prediction. Adverse Outcome Pathways (AOPs) represent a...

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example.

PubMed

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C

2017-01-01

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

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

PubMed Central

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

2009-01-01

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

Integrating publicly-available data to generate computationally-predicted adverse outcome pathways for hepatic steatosis

EPA Science Inventory

The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity ...

Combined In-Plane and Through-the-Thickness Analysis for Failure Prediction of Bolted Composite Joints

NASA Technical Reports Server (NTRS)

Kradinov, V.; Madenci, E.; Ambur, D. R.

2004-01-01

Although two-dimensional methods provide accurate predictions of contact stresses and bolt load distribution in bolted composite joints with multiple bolts, they fail to capture the effect of thickness on the strength prediction. Typically, the plies close to the interface of laminates are expected to be the most highly loaded, due to bolt deformation, and they are usually the first to fail. This study presents an analysis method to account for the variation of stresses in the thickness direction by augmenting a two-dimensional analysis with a one-dimensional through the thickness analysis. The two-dimensional in-plane solution method based on the combined complex potential and variational formulation satisfies the equilibrium equations exactly, and satisfies the boundary conditions and constraints by minimizing the total potential. Under general loading conditions, this method addresses multiple bolt configurations without requiring symmetry conditions while accounting for the contact phenomenon and the interaction among the bolts explicitly. The through-the-thickness analysis is based on the model utilizing a beam on an elastic foundation. The bolt, represented as a short beam while accounting for bending and shear deformations, rests on springs, where the spring coefficients represent the resistance of the composite laminate to bolt deformation. The combined in-plane and through-the-thickness analysis produces the bolt/hole displacement in the thickness direction, as well as the stress state in each ply. The initial ply failure predicted by applying the average stress criterion is followed by a simple progressive failure. Application of the model is demonstrated by considering single- and double-lap joints of metal plates bolted to composite laminates.

AOP: An R Package For Sufficient Causal Analysis in Pathway ...

EPA Pesticide Factsheets

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the “aop” package which uses backdoor analysis of causal net-works to identify these minimal sets of key events that are suf-ficient for making causal predictions. Availability and Implementation: The source and binary are available online through the Bioconductor project (http://www.bioconductor.org/) as an R package titled “aop”. The R/Bioconductor package runs within the R statistical envi-ronment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs us-ing the R/Bioconductor “graph” package. The “aop” package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions.Contact: burgoon.lyle@epa.gov This paper describes an R/Bioconductor package that was developed to facilitate the identification of key events within an AOP that are the minimal set of sufficient key events that need to be tested/monit

Prediction of first-trimester preeclampsia: Relevance of the oxidative stress marker MDA in a combination model with PP-13, PAPP-A and beta-HCG.

PubMed

Asiltas, Burak; Surmen-Gur, Esma; Uncu, Gurkan

2018-02-27

Early diagnosis of preeclampsia (PE) is very important and various parameters, individually or in combined models, are reported useful for prediction of PE. The objective of this study is to investigate the predictive value of pregnancy-associated plasma protein-A (PAPP-A), placental protein-13 (PP-13), human Chorionic Gonadotropin (B-HCG), and oxidative stress marker malondialdehyde (MDA), individually and in combination. Maternal sera of 38 cases with PE and 122 controls were collected for first trimester screening and tested for PAPP-A and B-HCG by chemiluminescence, for PP-13 by using ELISA, and for MDA by high-performance liquid chromatography. Combined models of parameters were constituted as "MDA + PP-13", "PP-13 + PAPP-A + B-HCG" and "MDA + PP-13 + PAPP-A + B-HCG". The diagnostic performances of serum markers of preeclampsia were examined by nonparametric receiver-operator characteristics (ROC) analysis. PP-13 levels were significantly lower (p < 0.001) and MDA levels were significantly higher (p < 0.001) in PE. The area under the ROC curve (AUC) for MDA and PP-13 were greater than those for PAPP-A and B-HCG (p < 0.001). The AUCs of the combined models were significantly larger than those of individual parameters. The combined model "MDA + PP-13 + PAPP-A + B-HCG" exhibited the best predictive outcome with an AUC of 0.91 [95% CI 0.86-0.95], 97% [95% CI 86.2-99.9] sensitivity and 75% [95% CI 66.5-82.6] specificity, and was significantly different from that of "PAPP-A + PP-13 + B-HCG" model, but similar to that of "MDA + PP-13" model. Combined models consisting of various parameters of different origin, may provide better predictive outcomes, and oxidative markers should be considered in combination with other placental biomarkers in prediction of PE. Copyright © 2018 Elsevier B.V. All rights reserved.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Junction detection and pathway selection

NASA Astrophysics Data System (ADS)

Peck, Alex N.; Lim, Willie Y.; Breul, Harry T.

1992-02-01

The ability to detect junctions and make choices among the possible pathways is important for autonomous navigation. In our script-based navigation approach where a journey is specified as a script of high-level instructions, actions are frequently referenced to junctions, e.g., `turn left at the intersection.' In order for the robot to carry out these kind of instructions, it must be able (1) to detect an intersection (i.e., an intersection of pathways), (2) know that there are several possible pathways it can take, and (3) pick the pathway consistent with the high level instruction. In this paper we describe our implementation of the ability to detect junctions in an indoor environment, such as corners, T-junctions and intersections, using sonar. Our approach uses a combination of partial scan of the local environment and recognition of sonar signatures of certain features of the junctions. In the case where the environment is known, we use additional sensor information (such as compass bearings) to help recognize the specific junction. In general, once a junction is detected and its type known, the number of possible pathways can be deduced and the correct pathway selected. Then the appropriate behavior for negotiating the junction is activated.

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

PubMed

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

2009-01-01

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

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Enhancing microbial production of biofuels by expanding microbial metabolic pathways.

PubMed

Yu, Ping; Chen, Xingge; Li, Peng

2017-09-01

Fatty acid, isoprenoid, and alcohol pathways have been successfully engineered to produce biofuels. By introducing three genes, atfA, adhE, and pdc, into Escherichia coli to expand fatty acid pathway, up to 1.28 g/L of fatty acid ethyl esters can be achieved. The isoprenoid pathway can be expanded to produce bisabolene with a high titer of 900 mg/L in Saccharomyces cerevisiae. Short- and long-chain alcohols can also be effectively biosynthesized by extending the carbon chain of ketoacids with an engineered "+1" alcohol pathway. Thus, it can be concluded that expanding microbial metabolic pathways has enormous potential for enhancing microbial production of biofuels for future industrial applications. However, some major challenges for microbial production of biofuels should be overcome to compete with traditional fossil fuels: lowering production costs, reducing the time required to construct genetic elements and to increase their predictability and reliability, and creating reusable parts with useful and predictable behavior. To address these challenges, several aspects should be further considered in future: mining and transformation of genetic elements related to metabolic pathways, assembling biofuel elements and coordinating their functions, enhancing the tolerance of host cells to biofuels, and creating modular subpathways that can be easily interconnected. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

Creation of a Genome-Wide Metabolic Pathway Database for Populus trichocarpa Using a New Approach for Reconstruction and Curation of Metabolic Pathways for Plants1[W][OA

PubMed Central

Zhang, Peifen; Dreher, Kate; Karthikeyan, A.; Chi, Anjo; Pujar, Anuradha; Caspi, Ron; Karp, Peter; Kirkup, Vanessa; Latendresse, Mario; Lee, Cynthia; Mueller, Lukas A.; Muller, Robert; Rhee, Seung Yon

2010-01-01

Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We developed a general approach for reconstructing metabolic pathway complements of plant genomes. Two new reference databases were created and added to the core of the infrastructure: a comprehensive, all-plant reference pathway database, PlantCyc, and a reference enzyme sequence database, RESD, for annotating metabolic functions of protein sequences. PlantCyc (version 3.0) includes 714 metabolic pathways and 2,619 reactions from over 300 species. RESD (version 1.0) contains 14,187 literature-supported enzyme sequences from across all kingdoms. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. PoplarCyc (version 1.0) contains 321 pathways with 1,807 assigned enzymes. Comparing PoplarCyc (version 1.0) with AraCyc (version 6.0, Arabidopsis [Arabidopsis thaliana]) showed comparable numbers of pathways distributed across all domains of metabolism in both databases, except for a higher number of AraCyc pathways in secondary metabolism and a 1.5-fold increase in carbohydrate metabolic enzymes in PoplarCyc. Here, we introduce these new resources and demonstrate the feasibility of using them to identify candidate enzymes for specific pathways and to analyze metabolite profiling data through concrete examples. These resources can be searched by text or BLAST, browsed, and downloaded from our project Web site (http://plantcyc.org). PMID:20522724

Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case

PubMed Central

Li, Guowei; Thabane, Lehana; Delate, Thomas; Witt, Daniel M.; Levine, Mitchell A. H.; Cheng, Ji; Holbrook, Anne

2016-01-01

Objectives To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both) in patients with atrial fibrillation (AF) with and without warfarin therapy. Methods Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR) modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling. Results We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632), there were 136 strokes (2.94%), 280 major bleedings (6.04%) and 1194 deaths (25.78%) occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances. Conclusions In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making PMID:27513986

Combining Satellite Measurements and Numerical Flood Prediction Models to Save Lives and Property from Flooding

NASA Astrophysics Data System (ADS)

Saleh, F.; Garambois, P. A.; Biancamaria, S.

2017-12-01

Floods are considered the major natural threats to human societies across all continents. Consequences of floods in highly populated areas are more dramatic with losses of human lives and substantial property damage. This risk is projected to increase with the effects of climate change, particularly sea-level rise, increasing storm frequencies and intensities and increasing population and economic assets in such urban watersheds. Despite the advances in computational resources and modeling techniques, significant gaps exist in predicting complex processes and accurately representing the initial state of the system. Improving flood prediction models and data assimilation chains through satellite has become an absolute priority to produce accurate flood forecasts with sufficient lead times. The overarching goal of this work is to assess the benefits of the Surface Water Ocean Topography SWOT satellite data from a flood prediction perspective. The near real time methodology is based on combining satellite data from a simulator that mimics the future SWOT data, numerical models, high resolution elevation data and real-time local measurement in the New York/New Jersey area.

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

PubMed

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

2004-05-05

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

Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors.

PubMed

Balabanov, Stefan; Wilhelm, Thomas; Venz, Simone; Keller, Gunhild; Scharf, Christian; Pospisil, Heike; Braig, Melanie; Barett, Christine; Bokemeyer, Carsten; Walther, Reinhard; Brümmendorf, Tim H; Schuppert, Andreas

2013-01-01

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.

Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors

PubMed Central

Balabanov, Stefan; Wilhelm, Thomas; Venz, Simone; Keller, Gunhild; Scharf, Christian; Pospisil, Heike; Braig, Melanie; Barett, Christine; Bokemeyer, Carsten; Walther, Reinhard

2013-01-01

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs. PMID:23326482

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

DTIC Science & Technology

2007-09-01

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

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

PubMed

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

2017-01-09

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

PubMed Central

Mueller, Wolf-Dieter; Hornberger, Helga

2014-01-01

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

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis.

PubMed

Chen, X Y; Chen, Y H; Zhang, L J; Wang, Y; Tong, Z C

2017-02-16

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

PubMed Central

Chen, X.Y.; Chen, Y.H.; Zhang, L.J.; Wang, Y.; Tong, Z.C.

2017-01-01

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor. PMID:28225867

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

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

PubMed Central

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

2015-01-01

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

A novel pathway combining calreticulin exposure and ATP secretion in immunogenic cancer cell death

PubMed Central

Garg, Abhishek D; Krysko, Dmitri V; Verfaillie, Tom; Kaczmarek, Agnieszka; Ferreira, Gabriela B; Marysael, Thierry; Rubio, Noemi; Firczuk, Malgorzata; Mathieu, Chantal; Roebroek, Anton J M; Annaert, Wim; Golab, Jakub; de Witte, Peter; Vandenabeele, Peter; Agostinis, Patrizia

2012-01-01

Surface-exposed calreticulin (ecto-CRT) and secreted ATP are crucial damage-associated molecular patterns (DAMPs) for immunogenic apoptosis. Inducers of immunogenic apoptosis rely on an endoplasmic reticulum (ER)-based (reactive oxygen species (ROS)-regulated) pathway for ecto-CRT induction, but the ATP secretion pathway is unknown. We found that after photodynamic therapy (PDT), which generates ROS-mediated ER stress, dying cancer cells undergo immunogenic apoptosis characterized by phenotypic maturation (CD80high, CD83high, CD86high, MHC-IIhigh) and functional stimulation (NOhigh, IL-10absent, IL-1βhigh) of dendritic cells as well as induction of a protective antitumour immune response. Intriguingly, early after PDT the cancer cells displayed ecto-CRT and secreted ATP before exhibiting biochemical signatures of apoptosis, through overlapping PERK-orchestrated pathways that require a functional secretory pathway and phosphoinositide 3-kinase (PI3K)-mediated plasma membrane/extracellular trafficking. Interestingly, eIF2α phosphorylation and caspase-8 signalling are dispensable for this ecto-CRT exposure. We also identified LRP1/CD91 as the surface docking site for ecto-CRT and found that depletion of PERK, PI3K p110α and LRP1 but not caspase-8 reduced the immunogenicity of the cancer cells. These results unravel a novel PERK-dependent subroutine for the early and simultaneous emission of two critical DAMPs following ROS-mediated ER stress. PMID:22252128

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

PubMed Central

Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-01-01

Background Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Methods Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. Results A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. Conclusions When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further

Accelerating Adverse Outcome Pathway (AOP) development via computationally predicted AOP networks

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

NASA Astrophysics Data System (ADS)

Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing

2017-02-01

UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.

Prediction of quality attributes of chicken breast fillets by using Vis/NIR spectroscopy combined with factor analysis method

USDA-ARS?s Scientific Manuscript database

Visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm combined with factor analysis method was tested to predict quality attributes of chicken breast fillets. Quality attributes, including color (L*, a*, b*), pH, and drip loss were analyzed using factor analysis ...

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

PubMed

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J; Grau, Raúl; Barat, José M

2016-10-19

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

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

PubMed

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

2015-08-18

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

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

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

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

DOE PAGES

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei; ...

2017-12-22

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

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

PubMed

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

2010-09-08

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

Wolf, Matthew B

2002-12-01

To show that a three-pathway pore model can describe extensive transport data in cat and rat skeletal muscle microvascular beds and in frog mesenteric microvessels. A three-pathway pore model was used to predict transport data measured in various microcirculatory preparations. The pathways consist of 4- and 24-nm radii pore systems with a 2.5:1 ratio of hydraulic conductivities and a water-only pathway of variable conductivity. The pore sizes and relative hydraulic conductivities of the small- and large-pore systems were derived from a model fit to reflection coefficient (sigma) data in the cat hindlimb. The fraction (alpha(w)) of total hydraulic conductivity (L(p)) or hydraulic capacity (L(p)S) contributed by the water-only pathway was uniquely determined for each preparation by a fit of the three-pathway model (parameters fixed as above) to sigma data measured in that preparation. These parameter values were unchanged when the model was used to predict diffusion capacity (permeability-surface area product, P(d)S) data in the cat or rat preparations or diffusional permeability (P(d)) data in frog microvessels. The values for L(p) or L(p)S used to predict diffusional data in each preparation were taken from the literature. Predictions of P(d) ratios for solute pairs were also compared with experimental data. The three-pathway model closely predicted the trend of P(d)S or P(d) experimental data in all three preparations; in general, predicted P(d) ratios for paired solutes were quite similar to experimental data. For these comparisons, the only parameter varied between these preparations was alpha(w). It varied considerably, from 7 to 16 to 41% of total in frog, rat, and cat preparations. Individual P(d)S or P(d) experimental data were closely predicted in the cat but somewhat overestimated in the frog and rat. This result could be due the use of L(p) or L(p)S values in the model that were affected by methodological problems. Calculated hydraulic conductivities of