Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism.

PLoS Pathog. 2017 Mar 03;13(3):e1006256

Authors: Sychev ZE, Hu A, DiMaio TA, Gitter A, Camp ND, Noble WS, Wolf-Yadlin A, Lagunoff M

Abstract
Kaposi's Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi's Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells.

PMID: 28257516 [PubMed - as supplied by publisher]

A Systems Biology Approach to Understanding the Mechanisms of Action of an Alternative Anticancer Compound in Comparison to Cisplatin.

Proteomes. 2014 Nov 10;2(4):501-526

Authors: Wright EP, Padula MP, Higgins VJ, Aldrich-Wright JR, Coorssen JR

Abstract
Many clinically available anticancer compounds are designed to target DNA. This commonality of action often yields overlapping cellular response mechanisms and can thus detract from drug efficacy. New compounds are required to overcome resistance mechanisms that effectively neutralise compounds like cisplatin and those with similar chemical structures. Studies have shown that 56MESS is a novel compound which, unlike cisplatin, does not covalently bind to DNA, but is more toxic to many cell lines and active against cisplatin-resistant cells. Furthermore, a transcriptional study of 56MESS in yeast has implicated iron and copper metabolism as well as the general yeast stress response following challenge with 56MESS. Beyond this, the cytotoxicity of 56MESS remains largely uncharacterised. Here, yeast was used as a model system to facilitate a systems-level comparison between 56MESS and cisplatin. Preliminary experiments indicated that higher concentrations than seen in similar studies be used. Although a DNA interaction with 56MESS had been theorized, this work indicated that an effect on protein synthesis/ degradation was also implicated in the mechanism(s) of action of this novel anticancer compound. In contrast to cisplatin, the different mechanisms of action that are indicated for 56MESS suggest that this compound could overcome cisplatin resistance either as a stand-alone treatment or a synergistic component of therapeutics.

PMID: 28250393 [PubMed]

Current knowledge of metabolomic approach in infectious fish disease studies.

J Fish Dis. 2017 Mar 02;:

Authors: Low CF, Rozaini MZ, Musa N, Syarul Nataqain B

Abstract
The approaches of transcriptomic and proteomic have been widely used to study host-pathogen interactions in fish diseases, and this is comparable to the recently emerging application of metabolomic in elucidating disease-resistant mechanisms in fish that gives new insight into potential therapeutic strategies to improve fish health. Metabolomic is defined as the large-scale study of all metabolites within an organism and represents the frontline in the 'omics' approaches, providing direct information on the metabolic responses and perturbations in metabolic pathways. In this review, the current research in infectious fish diseases using metabolomic approach will be summarized. The metabolomic approach in economically important fish infected with viruses, bacteria and nematodes will also be discussed. The potential of the metabolomic approach for management of these infectious diseases as well as the challenges and the limitations of metabolomic in fish disease studies will be explored. Current review highlights the impacts of metabolomic studies in infectious fish diseases, which proposed the potential of new therapeutic strategies to enhance disease resistance in fish.

PMID: 28252175 [PubMed - as supplied by publisher]

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

BMC Bioinformatics. 2017 Feb 15;18(Suppl 2):24

Authors: Ma C, Luciani T, Terebus A, Liang J, Marai GE

Abstract
BACKGROUND: Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes.
RESULTS: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks.
CONCLUSIONS: We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

PMID: 28251874 [PubMed - in process]

Unboxing cluster heatmaps.

BMC Bioinformatics. 2017 Feb 15;18(Suppl 2):63

Authors: Engle S, Whalen S, Joshi A, Pollard KS

Abstract
BACKGROUND: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks.
RESULTS: We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results.
CONCLUSIONS: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

PMID: 28251868 [PubMed - in process]

Systems biology combining human- and animal-data miRNA and mRNA data identifies new targets in ureteropelvic junction obstruction.

BMC Syst Biol. 2017 Mar 01;11(1):31

Authors: Papadopoulos T, Casemayou A, Neau E, Breuil B, Caubet C, Calise D, Thornhill BA, Bachvarova M, Belliere J, Chevalier RL, Moulos P, Bachvarov D, Buffin-Meyer B, Decramer S, Auriol FC, Bascands JL, Schanstra JP, Klein J

Abstract
BACKGROUND: Although renal fibrosis and inflammation have shown to be involved in the pathophysiology of obstructive nephropathies, molecular mechanisms underlying evolution of these processes remain undetermined. In an attempt towards improved understanding of obstructive nephropathy and improved translatability of the results to clinical practice we have developed a systems biology approach combining omics data of both human and mouse obstructive nephropathy.
RESULTS: We have studied in parallel the urinary miRNome of infants with ureteropelvic junction obstruction and the kidney tissue miRNome and transcriptome of the corresponding neonatal partial unilateral ureteral obstruction (UUO) mouse model. Several hundreds of miRNAs and mRNAs displayed changed abundance during disease. Combination of miRNAs in both species and associated mRNAs let to the prioritization of five miRNAs and 35 mRNAs associated to disease. In vitro and in vivo validation identified consistent dysregulation of let-7a-5p and miR-29-3p and new potential targets, E3 ubiquitin-protein ligase (DTX4) and neuron navigator 1 (NAV1), potentially involved in fibrotic processes, in obstructive nephropathy in both human and mice that would not be identified otherwise.
CONCLUSIONS: Our study is the first to correlate a mouse model of neonatal partial UUO with human UPJ obstruction in a comprehensive systems biology analysis. Our data revealed let-7a and miR-29b as molecules potentially involved in the development of fibrosis in UPJ obstruction via the control of DTX4 in both man and mice that would not be identified otherwise.

PMID: 28249581 [PubMed - in process]

Reactome pathway analysis: a high-performance in-memory approach.

BMC Bioinformatics. 2017 Mar 02;18(1):142

Authors: Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, Arnau V, D'Eustachio P, Stein L, Hermjakob H

Abstract
BACKGROUND: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples.
RESULTS: Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user's sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree.
CONCLUSION: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/reactome/ ).

PMID: 28249561 [PubMed - in process]

Varying Intolerance of Gene Pathways to Mutational Classes Explain Genetic Convergence across Neuropsychiatric Disorders.

Cell Rep. 2017 Feb 28;18(9):2217-2227

Authors: Shohat S, Ben-David E, Shifman S

Abstract
Genetic susceptibility to intellectual disability (ID), autism spectrum disorder (ASD), and schizophrenia (SCZ) often arises from mutations in the same genes, suggesting that they share common mechanisms. We studied genes with de novo mutations in the three disorders and genes implicated in SCZ by genome-wide association study (GWAS). Using biological annotations and brain gene expression, we show that mutation class explains enrichment patterns more than specific disorder. Genes with loss-of-function mutations and genes with missense mutations were associated with different pathways across disorders. Conversely, gene expression patterns were specific for each disorder. ID genes were preferentially expressed in the cortex; ASD genes were expressed in the fetal cortex, cerebellum, and striatum; and genes associated with SCZ were expressed in the adolescent cortex. Our study suggests that convergence across neuropsychiatric disorders stems from common pathways that are consistently vulnerable to genetic variations but that spatiotemporal activity of genes contributes to specific phenotypes.

PMID: 28249166 [PubMed - in process]

"Omics"-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives.

Proteomes. 2016 Sep 12;4(3):

Authors: Matthews H, Hanison J, Nirmalan N

Abstract
The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the "data-heavy" outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences-genomics, transcriptomics, proteomics and metabolomics-have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

PMID: 28248238 [PubMed - in process]

Distinct Antigen Delivery Systems Induce Dendritic Cells' Divergent Transcriptional Response: New Insights from a Comparative and Reproducible Computational Analysis.

Int J Mol Sci. 2017 Feb 25;18(3):

Authors: Costa V, Righelli D, Russo F, De Berardinis P, Angelini C, D'Apice L

Abstract
Vaccination is the most successful and cost-effective method to prevent infectious diseases. However, many vaccine antigens have poor in vivo immunogenic potential and need adjuvants to enhance immune response. The application of systems biology to immunity and vaccinology has yielded crucial insights about how vaccines and adjuvants work. We have previously characterized two safe and powerful delivery systems derived from non-pathogenic prokaryotic organisms: E2 and fd filamentous bacteriophage systems. They elicit an in vivo immune response inducing CD8+ T-cell responses, even in absence of adjuvants or stimuli for dendritic cells' maturation. Nonetheless, a systematic and comparative analysis of the complex gene expression network underlying such activation is missing. Therefore, we compared the transcriptomes of ex vivo isolated bone marrow-derived dendritic cells exposed to these antigen delivery systems. Significant differences emerged, especially for genes involved in innate immunity, co-stimulation, and cytokine production. Results indicate that E2 drives polarization toward the Th2 phenotype, mainly mediated by Irf4, Ccl17, and Ccr4 over-expression. Conversely, fd-scαDEC-205 triggers Th1 T cells' polarization through the induction of Il12b, Il12rb, Il6, and other molecules involved in its signal transduction. The data analysis was performed using RNASeqGUI, hence, addressing the increasing need of transparency and reproducibility of computational analysis.

PMID: 28245601 [PubMed - in process]

Data-driven identification of potential Zika virus vectors.

Elife. 2017 Feb 28;6:

Authors: Evans MV, Dallas TA, Han BA, Murdock CC, Drake JM

Abstract
Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States.

PMID: 28244371 [PubMed - as supplied by publisher]

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case.

Biomed Res Int. 2017;2017:2483264

Authors: Hu G, Di Paola L, Liang Z, Giuliani A

Abstract
The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

PMID: 28243596 [PubMed - in process]

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network.

Front Aging Neurosci. 2017;9:23

Authors: Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J

Abstract
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

PMID: 28243200 [PubMed - in process]

Biomedical applications of cell- and tissue-specific metabolic network models.

J Biomed Inform. 2017 Feb 24;:

Authors: Fouladiha H, Marashi SA

Abstract
The essential goal of biomedical research is to understand the underlying mechanism of disease development. Unfortunately, achieving this goal requires expensive and time-consuming efforts in medical biotechnology. This review focuses on how context-specific genome-scale metabolic network models may facilitate reaching this goal. Such models provide an in silico framework for computational simulation of cellular metabolism, predicting the outcome of experiments. Therefore, by using these models at the initial stages of experimental design, time and cost in biomedical researches may be reduced. Furthermore, with the availability of such models, not only important pathways involved in cell dysfunction may be better understood, but also drug targets predicted based on these models can be seen as novel targets for in vivo validation. The main point of this review is that metabolic modeling can predict drug targets and biomarkers without the need for kinetics data. We provide a comprehensive review of human metabolic models and their applications, in addition to the methods used for analyzing models. We discuss how these models have been used in analyzing metabolic capabilities of different cells and tissues, in identifying disease-related metabolic pathways and biomarkers, and in understanding the human-microbe interaction.

PMID: 28242343 [PubMed - as supplied by publisher]

Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments.

Alzheimers Dement. 2017 Feb 24;:

Authors: Mukherjee S, Russell JC, Carr DT, Burgess JD, Younkin MA, Serie D, Boehme KL, Kauwe JS, Naj AC, Fardo DW, Dickson DW, Montine TJ, Ertekin-Taner N, Kaeberlein MR, Crane PK

Abstract
INTRODUCTION: We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.
METHODS: We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.
RESULTS: We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex.
DISCUSSION: Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex.

PMID: 28242297 [PubMed - as supplied by publisher]

Predicting network modules of cell cycle regulators using relative protein abundance statistics.

BMC Syst Biol. 2017 Feb 28;11(1):30

Authors: Oguz C, Watson LT, Baumann WT, Tyson JJ

Abstract
BACKGROUND: Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network.
RESULTS: Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50.
CONCLUSIONS: By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.

PMID: 28241833 [PubMed - in process]

Drug voyager: a computational platform for exploring unintended drug action.

BMC Bioinformatics. 2017 Feb 28;18(1):131

Authors: Oh M, Ahn J, Lee T, Jang G, Park C, Yoon Y

Abstract
BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking.
RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature.
CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

PMID: 28241745 [PubMed - in process]

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype.

Int J Mol Sci. 2017 Feb 22;18(2):

Authors: Te Pas MF, Madsen O, Calus MP, Smits MA

Abstract
With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.

PMID: 28241430 [PubMed - in process]

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future.

J Mass Spectrom. 2016 Aug;51(8):535-548

Authors: Barnes S, Benton HP, Casazza K, Cooper SJ, Cui X, Du X, Engler J, Kabarowski JH, Li S, Pathmasiri W, Prasain JK, Renfrow MB, Tiwari HK

Abstract
Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

PMID: 28239968 [PubMed - in process]

Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers.

Front Neuroinform. 2017;11:13

Authors: Chen W, De Schutter E

Abstract
Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation.

PMID: 28239346 [PubMed - in process]