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Kuijpers TJM, Wolters JEJ, Kleinjans JCS, Jennen DGJ. DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks. BMC Bioinformatics 2019; 20:417. [PMID: 31409281 PMCID: PMC6693283 DOI: 10.1186/s12859-019-2995-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 07/16/2019] [Indexed: 01/11/2023] Open
Abstract
Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.
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Affiliation(s)
- T J M Kuijpers
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.
| | - J E J Wolters
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.,Present Address: School for Mental Health and Neuroscience (MHeNS), University Eye clinic Maastricht, Maastricht University Medical Centre + (MUMC+), P.O. Box 5800, Maastricht, 6229 HX, The Netherlands
| | - J C S Kleinjans
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands
| | - D G J Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands
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Souza T, Trairatphisan P, Piñero J, Furlong LI, Saez-Rodriguez J, Kleinjans J, Jennen D. Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury. Front Genet 2018; 9:527. [PMID: 30515189 PMCID: PMC6255978 DOI: 10.3389/fgene.2018.00527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 10/19/2018] [Indexed: 12/03/2022] Open
Abstract
In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases – termed “dark genes” – were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C – whose links to hepatotoxicity have been confirmed – were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.
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Affiliation(s)
- Terezinha Souza
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Panuwat Trairatphisan
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Janet Piñero
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain
| | - Laura I Furlong
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain
| | - Julio Saez-Rodriguez
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany.,European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Cambridge, United Kingdom
| | - Jos Kleinjans
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
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Piñero J, Furlong LI, Sanz F. In silico models in drug development: where we are. Curr Opin Pharmacol 2018; 42:111-121. [PMID: 30205360 DOI: 10.1016/j.coph.2018.08.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/30/2018] [Accepted: 08/13/2018] [Indexed: 02/07/2023]
Abstract
The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.
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Affiliation(s)
- Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.
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Liu Z, Delavan B, Roberts R, Tong W. Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems. Front Genet 2018; 9:74. [PMID: 29616076 PMCID: PMC5870427 DOI: 10.3389/fgene.2018.00074] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 02/19/2018] [Indexed: 01/03/2023] Open
Abstract
Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.
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Affiliation(s)
- Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Brian Delavan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,Department of Biosciences, University of Arkansas at Little Rock, Little Rock, AR, United States
| | - Ruth Roberts
- ApconiX, Alderley Edge, United Kingdom.,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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Wolters JEJ, van Breda SGJ, Grossmann J, Fortes C, Caiment F, Kleinjans JCS. Integrated 'omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes. Toxicol Lett 2018; 289:1-13. [PMID: 29501571 DOI: 10.1016/j.toxlet.2018.02.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/02/2018] [Accepted: 02/23/2018] [Indexed: 12/11/2022]
Abstract
We performed a multiple 'omics study by integrating data on epigenomic, transcriptomic, and proteomic perturbations associated with mitochondrial dysfunction in primary human hepatocytes caused by the liver toxicant valproic acid (VPA), to deeper understand downstream events following epigenetic alterations in the mitochondrial genome. Furthermore, we investigated persistence of cross-omics changes after terminating drug treatment. Upon transient methylation changes of mitochondrial genes during VPA-treatment, increasing complexities of gene-interaction networks across time were demonstrated, which normalized during washout. Furthermore, co-expression between genes and their corresponding proteins increased across time. Additionally, in relation to persistently decreased ATP production, we observed decreased expression of mitochondrial complex I and III-V genes. Persistent transcripts and proteins were related to citric acid cycle and β-oxidation. In particular, we identified a potential novel mitochondrial-nuclear signaling axis, MT-CO2-FN1-MYC-CPT1. In summary, this cross-omics study revealed dynamic responses of the mitochondrial epigenome to an impulse toxicant challenge resulting in persistent mitochondrial dysfunctioning. Moreover, this approach allowed for discriminating between the toxic effect of VPA and adaptation.
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Affiliation(s)
- Jarno E J Wolters
- Department of Toxicogenomics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
| | - Simone G J van Breda
- Department of Toxicogenomics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
| | - Jonas Grossmann
- Functional Genomics Center Zurich, Functional Genomics Center Zurich, University Zurich/ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
| | - Claudia Fortes
- Functional Genomics Center Zurich, Functional Genomics Center Zurich, University Zurich/ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
| | - Florian Caiment
- Department of Toxicogenomics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
| | - Jos C S Kleinjans
- Department of Toxicogenomics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
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Souza TM, Kleinjans JCS, Jennen DGJ. Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks. Front Genet 2017; 8:142. [PMID: 29085386 PMCID: PMC5649202 DOI: 10.3389/fgene.2017.00142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 09/21/2017] [Indexed: 11/19/2022] Open
Abstract
Perturbation of biological networks is often observed during exposure to xenobiotics, and the identification of disturbed processes, their dynamic traits, and dose–response relationships are some of the current challenges for elucidating the mechanisms determining adverse outcomes. In this scenario, reverse engineering of gene regulatory networks (GRNs) from expression data may provide a system-level snapshot embedded within accurate molecular events. Here, we investigate the composition of GRNs inferred from groups of chemicals with two distinct outcomes, namely carcinogenicity [azathioprine (AZA) and cyclophosphamide (CYC)] and drug-induced liver injury (DILI; diclofenac, nitrofurantoin, and propylthiouracil), and a non-carcinogenic/non-DILI group (aspirin, diazepam, and omeprazole). For this, we analyzed publicly available exposed in vitro human data, taking into account dose and time dependencies. Dose–Time Network Identification (DTNI) was applied to gene sets from exposed primary human hepatocytes using four stress pathways, namely endoplasmic reticulum (ER), NF-κB, NRF2, and TP53. Inferred GRNs suggested case specificity, varying in interactions, starting nodes, and target genes across groups. DILI and carcinogenic compounds were shown to directly affect all pathway-based GRNs, while non-DILI/non-carcinogenic chemicals only affected NF-κB. NF-κB-based GRNs clearly illustrated group-specific disturbances, with the cancer-related casein kinase CSNK2A1 being a target gene only in the carcinogenic group, and opposite regulation of NF-κB subunits being observed in DILI and non-DILI/non-carcinogenic groups. Target genes in NRF2-based GRNs shared by DILI and carcinogenic compounds suggested markers of hepatotoxicity. Finally, we indicate several of these group-specific interactions as potentially novel. In summary, our reversed-engineered GRNs are capable of revealing dose dependent, chemical-specific mechanisms of action in stress-related biological networks.
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Affiliation(s)
- Terezinha M Souza
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Jos C S Kleinjans
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Danyel G J Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
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