1
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Pannala VR, Wallqvist A. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int J Mol Sci 2023; 24:17425. [PMID: 38139254 PMCID: PMC10743995 DOI: 10.3390/ijms242417425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023] Open
Abstract
To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.
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Affiliation(s)
- Venkat R. Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
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2
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Goel H, Printz RL, Shiota C, Estes SK, Pannala V, AbdulHameed MDM, Shiota M, Wallqvist A. Assessing Kidney Injury Induced by Mercuric Chloride in Guinea Pigs with In Vivo and In Vitro Experiments. Int J Mol Sci 2023; 24:7434. [PMID: 37108594 PMCID: PMC10138559 DOI: 10.3390/ijms24087434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Acute kidney injury, which is associated with high levels of morbidity and mortality, affects a significant number of individuals, and can be triggered by multiple factors, such as medications, exposure to toxic chemicals or other substances, disease, and trauma. Because the kidney is a critical organ, understanding and identifying early cellular or gene-level changes can provide a foundation for designing medical interventions. In our earlier work, we identified gene modules anchored to histopathology phenotypes associated with toxicant-induced liver and kidney injuries. Here, using in vivo and in vitro experiments, we assessed and validated these kidney injury-associated modules by analyzing gene expression data from the kidneys of male Hartley guinea pigs exposed to mercuric chloride. Using plasma creatinine levels and cell-viability assays as measures of the extent of renal dysfunction under in vivo and in vitro conditions, we performed an initial range-finding study to identify the appropriate doses and exposure times associated with mild and severe kidney injuries. We then monitored changes in kidney gene expression at the selected doses and time points post-toxicant exposure to characterize the mechanisms of kidney injury. Our injury module-based analysis revealed a dose-dependent activation of several phenotypic cellular processes associated with dilatation, necrosis, and fibrogenesis that were common across the experimental platforms and indicative of processes that initiate kidney damage. Furthermore, a comparison of activated injury modules between guinea pigs and rats indicated a strong correlation between the modules, highlighting their potential for cross-species translational studies.
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Affiliation(s)
- Himanshu Goel
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Richard L. Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Chiyo Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shanea K. Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Venkat Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
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3
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Tawa GJ, Braisted J, Gerhold D, Grewal G, Mazcko C, Breen M, Sittampalam G, LeBlanc AK. Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species. PLoS Comput Biol 2021; 17:e1009450. [PMID: 34570764 PMCID: PMC8523068 DOI: 10.1371/journal.pcbi.1009450] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 10/18/2021] [Accepted: 09/14/2021] [Indexed: 12/25/2022] Open
Abstract
Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.
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Affiliation(s)
- Gregory J. Tawa
- National Institutes of Health, National Center for Advancing Translational Sciences, Division of Preclinical Innovation, Therapeutic Development Branch, Rockville, Maryland, United States of America
| | - John Braisted
- National Institutes of Health, National Center for Advancing Translational Sciences, Division of Preclinical Innovation, Therapeutic Development Branch, Rockville, Maryland, United States of America
| | - David Gerhold
- National Institutes of Health, National Center for Advancing Translational Sciences, Division of Preclinical Innovation, Therapeutic Development Branch, Rockville, Maryland, United States of America
| | - Gurmit Grewal
- National Institutes of Health, National Center for Advancing Translational Sciences, Division of Preclinical Innovation, Therapeutic Development Branch, Rockville, Maryland, United States of America
| | - Christina Mazcko
- National Institutes of Health, National Cancer Institute, Center for Cancer Research, Comparative Oncology Program, Bethesda, Maryland, United States of America
| | - Matthew Breen
- Department of Molecular Biomedical Sciences, North Carolina State University, College of Veterinary Medicine, Raleigh, North Carolina, United States of America
| | - Gurusingham Sittampalam
- National Institutes of Health, National Center for Advancing Translational Sciences, Division of Preclinical Innovation, Therapeutic Development Branch, Rockville, Maryland, United States of America
| | - Amy K. LeBlanc
- National Institutes of Health, National Cancer Institute, Center for Cancer Research, Comparative Oncology Program, Bethesda, Maryland, United States of America
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4
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Schyman P, Xu Z, Desai V, Wallqvist A. TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries. Front Pharmacol 2021; 12:601511. [PMID: 33633572 PMCID: PMC7900624 DOI: 10.3389/fphar.2021.601511] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/08/2021] [Indexed: 11/30/2022] Open
Abstract
Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.
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Affiliation(s)
- Patric Schyman
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States.,The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | - Zhen Xu
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States.,The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | - Valmik Desai
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States.,The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | - Anders Wallqvist
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
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5
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Identification of early liver toxicity gene biomarkers using comparative supervised machine learning. Sci Rep 2020; 10:19128. [PMID: 33154507 PMCID: PMC7645727 DOI: 10.1038/s41598-020-76129-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 10/12/2020] [Indexed: 02/08/2023] Open
Abstract
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.
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6
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Sutherland JJ, Stevens JL, Johnson K, Elango N, Webster YW, Mills BJ, Robertson DH. A Novel Open Access Web Portal for Integrating Mechanistic and Toxicogenomic Study Results. Toxicol Sci 2020; 170:296-309. [PMID: 31020328 PMCID: PMC6657575 DOI: 10.1093/toxsci/kfz101] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.
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Affiliation(s)
- Jeffrey J Sutherland
- Indiana Biosciences Research Institute, 1345 W. 16th St. Suite 300, Indianapolis, IN 46202
| | - James L Stevens
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285.,Paradox Found LLC, 212 Wooded Lake Drive, Apex, NC 27523
| | - Kamin Johnson
- Corteva AgriscienceTM, Agriculture Division of DowDuPontTM, 9330 Zionsville Rd, Indianapolis, Indiana, 46268
| | - Navin Elango
- Corteva AgriscienceTM, Agriculture Division of DowDuPontTM, 9330 Zionsville Rd, Indianapolis, Indiana, 46268
| | - Yue W Webster
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285
| | - Bradley J Mills
- Indiana Biosciences Research Institute, 1345 W. 16th St. Suite 300, Indianapolis, IN 46202
| | - Daniel H Robertson
- Indiana Biosciences Research Institute, 1345 W. 16th St. Suite 300, Indianapolis, IN 46202
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7
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Schyman P, Printz RL, Estes SK, O'Brien TP, Shiota M, Wallqvist A. Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity. Front Genet 2019; 10:1233. [PMID: 31850077 PMCID: PMC6901980 DOI: 10.3389/fgene.2019.01233] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 11/06/2019] [Indexed: 12/18/2022] Open
Abstract
Consumers are exposed to thousands of chemicals with potentially adverse health effects. However, these chemicals will never be tested for toxicity because of the immense resources needed for animal-based (in vivo) toxicological studies. Today, there are no viable in vitro alternatives to these types of animal studies. To develop an in vitro approach, we investigated whether we could predict in vivo organ injuries in rats with the use of RNA-seq data acquired from tissues early in the development of toxicant-induced injury, by comparing gene expression data from RNA isolated from these rat tissues with those obtained from in vitro exposure of primary liver and kidney cells. We collected RNA-seq data from the liver and kidney tissues of Sprague-Dawley rats 8 or 24 h after exposing them to vehicle (control), low (25 mg/kg), or high (100 mg/kg) doses of thioacetamide, a known liver toxicant that promotes fibrosis; we used these doses and exposure times to cause only mild toxicant-induced injury. For the in vitro study, we treated two cell types from Sprague-Dawley rats, primary hepatocytes (vehicle; low, 0.025 mM; or high, 0.125 mM dose), and renal tube epithelial cells (vehicle; low, 0.125 mM; or high, 0.500 mM) dose) with the thioacetamide metabolite, thioacetamide-S-oxide, selecting in vitro doses and exposure times to recreate the early-stage toxicant-induced injury model that we achieved in vivo. RNA-seq data were collected 9 or 24 h after application of vehicle or thioacetamide-S-oxide. We found that our modular approach for the analysis of gene expression data derived from in vivo RNA-seq strongly correlated (R2 > 0.6) with the in vitro results at two different dose levels of thioacetamide/thioacetamide-S-oxide after 24 h of exposure. The top-ranked liver injury modules in vitro correctly identified the ensuing development of liver fibrosis.
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Affiliation(s)
- Patric Schyman
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States.,The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc. (HJF), Bethesda, MD, United States
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Tracy P O'Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Anders Wallqvist
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
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8
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Vo AH, Van Vleet TR, Gupta RR, Liguori MJ, Rao MS. An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation. Chem Res Toxicol 2019; 33:20-37. [DOI: 10.1021/acs.chemrestox.9b00227] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Andy H. Vo
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Terry R. Van Vleet
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Rishi R. Gupta
- Information Research, Research and Development, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Michael J. Liguori
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
| | - Mohan S. Rao
- Department of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064, United States
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9
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Wang H, Liu R, Schyman P, Wallqvist A. Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses. Front Pharmacol 2019; 10:42. [PMID: 30804783 PMCID: PMC6370634 DOI: 10.3389/fphar.2019.00042] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 01/14/2019] [Indexed: 12/17/2022] Open
Abstract
Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype per se for non-chemically induced injuries revealed the robust performance of the DNN models across these additional external testing datasets. Thus, the DNN models learned features specific to the injury phenotype contained in the gene expression data.
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Affiliation(s)
- Hao Wang
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States.,Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Frederick, MD, United States
| | - Ruifeng Liu
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States.,Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Frederick, MD, United States
| | - Patric Schyman
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States.,Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Frederick, MD, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Frederick, MD, United States
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10
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Taškova K, Fontaine JF, Mrowka R, Andrade-Navarro MA. Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets. PLoS One 2019; 14:e0210467. [PMID: 30640953 PMCID: PMC6331104 DOI: 10.1371/journal.pone.0210467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/24/2018] [Indexed: 11/30/2022] Open
Abstract
The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.
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Affiliation(s)
| | | | - Ralf Mrowka
- Experimentelle Nephrologie, Universitätsklinikum Jena, KIM III, Jena, Germany
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11
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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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12
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Schyman P, Printz RL, Estes SK, Boyd KL, Shiota M, Wallqvist A. Identification of the Toxicity Pathways Associated With Thioacetamide-Induced Injuries in Rat Liver and Kidney. Front Pharmacol 2018; 9:1272. [PMID: 30459623 PMCID: PMC6232954 DOI: 10.3389/fphar.2018.01272] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 10/18/2018] [Indexed: 12/25/2022] Open
Abstract
Ingestion or exposure to chemicals poses a serious health risk. Early detection of cellular changes induced by such events is vital to identify appropriate countermeasures to prevent organ damage. We hypothesize that chemically induced organ injuries are uniquely associated with a set (module) of genes exhibiting significant changes in expression. We have previously identified gene modules specifically associated with organ injuries by analyzing gene expression levels in liver and kidney tissue from rats exposed to diverse chemical insults. Here, we assess and validate our injury-associated gene modules by analyzing gene expression data in liver, kidney, and heart tissues obtained from Sprague-Dawley rats exposed to thioacetamide, a known liver toxicant that promotes fibrosis. The rats were injected intraperitoneally with a low (25 mg/kg) or high (100 mg/kg) dose of thioacetamide for 8 or 24 h, and definite organ injury was diagnosed by histopathology. Injury-associated gene modules indicated organ injury specificity, with the liver being most affected by thioacetamide. The most activated liver gene modules were those associated with inflammatory cell infiltration and fibrosis. Previous studies on thioacetamide toxicity and our histological analyses supported these results, signifying the potential of gene expression data to identify organ injuries.
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Affiliation(s)
- Patric Schyman
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, United States
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Kelli L Boyd
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Anders Wallqvist
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, United States
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13
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Barel G, Herwig R. Network and Pathway Analysis of Toxicogenomics Data. Front Genet 2018; 9:484. [PMID: 30405693 PMCID: PMC6204403 DOI: 10.3389/fgene.2018.00484] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/28/2018] [Indexed: 12/20/2022] Open
Abstract
Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.
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Affiliation(s)
| | - Ralf Herwig
- Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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14
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Alexander-Dann B, Pruteanu LL, Oerton E, Sharma N, Berindan-Neagoe I, Módos D, Bender A. Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Mol Omics 2018; 14:218-236. [PMID: 29917034 PMCID: PMC6080592 DOI: 10.1039/c8mo00042e] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/08/2018] [Indexed: 12/12/2022]
Abstract
The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.
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Affiliation(s)
- Benjamin Alexander-Dann
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Lavinia Lorena Pruteanu
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
- Babeş-Bolyai University
, Institute for Doctoral Studies
,
1 Kogălniceanu Street
, Cluj-Napoca 400084
, Romania
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, MedFuture Research Centre for Advanced Medicine
,
23 Marinescu Street/4-6 Pasteur Street
, Cluj-Napoca 400337
, Romania
| | - Erin Oerton
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Nitin Sharma
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Ioana Berindan-Neagoe
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, MedFuture Research Centre for Advanced Medicine
,
23 Marinescu Street/4-6 Pasteur Street
, Cluj-Napoca 400337
, Romania
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, Research Center for Functional Genomics
, Biomedicine and Translational Medicine
,
23 Marinescu Street
, Cluj-Napoca 400337
, Romania
- The Oncology Institute “Prof. Dr Ion Chiricuţă”
, Department of Functional Genomics and Experimental Pathology
,
34-36 Republicii Street
, Cluj-Napoca 400015
, Romania
| | - Dezső Módos
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Andreas Bender
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
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15
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McDyre BC, AbdulHameed MDM, Permenter MG, Dennis WE, Baer CE, Koontz JM, Boyle MH, Wallqvist A, Lewis JA, Ippolito DL. Comparative Proteomic Analysis of Liver Steatosis and Fibrosis after Oral Hepatotoxicant Administration in Sprague-Dawley Rats. Toxicol Pathol 2018; 46:202-223. [PMID: 29378501 DOI: 10.1177/0192623317747549] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The past decade has seen an increase in the development and clinical use of biomarkers associated with histological features of liver disease. Here, we conduct a comparative histological and global proteomics analysis to identify coregulated modules of proteins in the progression of hepatic steatosis or fibrosis. We orally administered the reference chemicals bromobenzene (BB) or 4,4'-methylenedianiline (4,4'-MDA) to male Sprague-Dawley rats for either 1 single administration or 5 consecutive daily doses. Livers were preserved for histopathology and global proteomics assessment. Analysis of liver sections confirmed a dose- and time-dependent increase in frequency and severity of histopathological features indicative of lipid accumulation after BB or fibrosis after 4,4'-MDA. BB administration resulted in a dose-dependent increase in the frequency and severity of inflammation and vacuolation. 4,4'-MDA administration resulted in a dose-dependent increase in the frequency and severity of periportal collagen accumulation and inflammation. Pathway analysis identified a time-dependent enrichment of biological processes associated with steatogenic or fibrogenic initiating events, cellular functions, and toxicological states. Differentially expressed protein modules were consistent with the observed histology, placing physiologically linked protein networks into context of the disease process. This study demonstrates the potential for protein modules to provide mechanistic links between initiating events and histopathological outcomes.
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Affiliation(s)
- B Claire McDyre
- 1 Oak Ridge Institute for Science and Education (ORISE), Frederick, Maryland, USA
| | - Mohamed Diwan M AbdulHameed
- 2 Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | | | - William E Dennis
- 4 U.S. Army Center for Environmental Health Research (USACEHR), Fort Detrick, Maryland, USA
| | | | - Jason M Koontz
- 4 U.S. Army Center for Environmental Health Research (USACEHR), Fort Detrick, Maryland, USA
| | | | - Anders Wallqvist
- 2 Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - John A Lewis
- 4 U.S. Army Center for Environmental Health Research (USACEHR), Fort Detrick, Maryland, USA
| | - Danielle L Ippolito
- 4 U.S. Army Center for Environmental Health Research (USACEHR), Fort Detrick, Maryland, USA
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16
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Kindrat I, Dreval K, Shpyleva S, Tryndyak V, de Conti A, Mudalige TK, Chen T, Erstenyuk AM, Beland FA, Pogribny IP. Effect of methapyrilene hydrochloride on hepatic intracellular iron metabolism in vivo and in vitro. Toxicol Lett 2017; 281:65-73. [DOI: 10.1016/j.toxlet.2017.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 08/22/2017] [Accepted: 09/17/2017] [Indexed: 12/20/2022]
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17
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Sutherland JJ, Webster YW, Willy JA, Searfoss GH, Goldstein KM, Irizarry AR, Hall DG, Stevens JL. Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity. THE PHARMACOGENOMICS JOURNAL 2017; 18:377-390. [DOI: 10.1038/tpj.2017.17] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 02/19/2017] [Accepted: 02/28/2017] [Indexed: 12/11/2022]
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18
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Abstract
BACKGROUND Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes. METHODS In this study, we describe a pipeline for network comparison and its application to the analysis of gene expression datasets from chemical perturbation experiments, with the goal of elucidating the modes of actions of the profiled perturbations. We apply our pipeline to the analysis of the DrugMatrix and the TG-GATEs, two of the largest toxicogenomics resources available, containing gene expression measurements for model organisms exposed to hundreds of chemical compounds with varying carcinogenicity and genotoxicity. RESULTS Starting from chemical-specific transcriptional networks inferred from these data, we show that the proposed comparative analysis of their associated networks identifies groups of chemicals with similar functions and similar carcinogenicity/genotoxicity profiles. We also show that the in-silico annotation by pathway enrichment analysis of the gene modules with a significant gain or loss of connectivity for specific groups of compounds can reveal molecular pathways significantly associated with the chemical perturbations and their likely modes of action. CONCLUSIONS The proposed pipeline for transcriptional network inference and comparison is highly reproducible and allows grouping chemicals with similar functions and carcinogenicity/genotoxicity profiles. In the context of drug discovery or drug repositioning, the methods presented here could help assign new functions to novel or existing drugs, based on the similarity of their associated network with those built for other known compounds. Additionally, the method has broad applicability beyond the uses here described and could be used as an alternative or as a complement to standard approaches of differential gene expression analysis.
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19
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Liu R, Yu X, Wallqvist A. Using Chemical-Induced Gene Expression in Cultured Human Cells to Predict Chemical Toxicity. Chem Res Toxicol 2016; 29:1883-1893. [PMID: 27768846 DOI: 10.1021/acs.chemrestox.6b00287] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Chemical toxicity is conventionally evaluated in animal models. However, animal models are resource intensive; moreover, they face ethical and scientific challenges because the outcomes obtained by animal testing may not correlate with human responses. To develop an alternative method for assessing chemical toxicity, we investigated the feasibility of using chemical-induced genome-wide expression changes in cultured human cells to predict the potential of a chemical to cause specific organ injuries in humans. We first created signatures of chemical-induced gene expression in a vertebral-cancer of the prostate cell line for ∼15,000 chemicals tested in the US National Institutes of Health Library of Integrated Network-Based Cellular Signatures program. We then used the signatures to create naı̈ve Bayesian prediction models for chemical-induced human liver cholestasis, interstitial nephritis, and long QT syndrome. Detailed cross-validation analyses indicated that the models were robust with respect to false positives and false negatives in the samples we used to train the models and could predict the likelihood that chemicals would cause specific organ injuries. In addition, we performed a literature search for drugs and dietary supplements, not formally categorized as causing organ injuries in humans but predicted by our models to be most likely to do so. We found a high percentage of these compounds associated with case reports of relevant organ injuries, lending support to the idea that in vitro cell-based experiments can be used to predict the toxic potential of chemicals. We believe that this approach, combined with a robust technique to model human exposure to chemicals, may serve as a promising alternative to animal-based chemical toxicity assessment.
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Affiliation(s)
- Ruifeng Liu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command , Fort Detrick, Maryland 21702, United States
| | - Xueping Yu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command , Fort Detrick, Maryland 21702, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command , Fort Detrick, Maryland 21702, United States
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20
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AbdulHameed MDM, Ippolito DL, Stallings JD, Wallqvist A. Mining kidney toxicogenomic data by using gene co-expression modules. BMC Genomics 2016; 17:790. [PMID: 27724849 PMCID: PMC5057266 DOI: 10.1186/s12864-016-3143-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 09/29/2016] [Indexed: 12/15/2022] Open
Abstract
Background Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease. Results We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury. Conclusions Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA
| | - Danielle L Ippolito
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Jonathan D Stallings
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA.
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21
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AbdulHameed MDM, Ippolito DL, Wallqvist A. Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chem Res Toxicol 2016; 29:1729-1740. [DOI: 10.1021/acs.chemrestox.6b00227] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, Maryland 21702, United States
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort
Detrick, Maryland 21702, United States
| | - Anders Wallqvist
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, Maryland 21702, United States
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22
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Te JA, AbdulHameed MDM, Wallqvist A. Systems toxicology of chemically induced liver and kidney injuries: histopathology-associated gene co-expression modules. J Appl Toxicol 2016; 36:1137-49. [PMID: 26725466 PMCID: PMC5064727 DOI: 10.1002/jat.3278] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 11/18/2015] [Accepted: 11/23/2015] [Indexed: 11/11/2022]
Abstract
Organ injuries caused by environmental chemical exposures or use of pharmaceutical drugs pose a serious health risk that may be difficult to assess because of a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific histopathology outcomes via biomarkers will provide a foundation for designing precise and robust diagnostic tests. We identified co-expressed genes (modules) specific to injury endpoints using the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) - a toxicogenomics database containing organ-specific gene expression data matched to dose- and time-dependent chemical exposures and adverse histopathology assessments in Sprague-Dawley rats. We proposed a protocol for selecting gene modules associated with chemical-induced injuries that classify 11 liver and eight kidney histopathology endpoints based on dose-dependent activation of the identified modules. We showed that the activation of the modules for a particular chemical exposure condition, i.e., chemical-time-dose combination, correlated with the severity of histopathological damage in a dose-dependent manner. Furthermore, the modules could distinguish different types of injuries caused by chemical exposures as well as determine whether the injury module activation was specific to the tissue of origin (liver and kidney). The generated modules provide a link between toxic chemical exposures, different molecular initiating events among underlying molecular pathways and resultant organ damage. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Jerez A Te
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
| | - Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
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Gonzalez GH, Tahsin T, Goodale BC, Greene AC, Greene CS. Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery. Brief Bioinform 2015; 17:33-42. [PMID: 26420781 PMCID: PMC4719073 DOI: 10.1093/bib/bbv087] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Indexed: 02/06/2023] Open
Abstract
Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine.
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Ippolito DL, AbdulHameed MDM, Tawa GJ, Baer CE, Permenter MG, McDyre BC, Dennis WE, Boyle MH, Hobbs CA, Streicker MA, Snowden BS, Lewis JA, Wallqvist A, Stallings JD. Gene Expression Patterns Associated With Histopathology in Toxic Liver Fibrosis. Toxicol Sci 2015; 149:67-88. [DOI: 10.1093/toxsci/kfv214] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
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25
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Chandrashekar DS, Dey P, Acharya KK. GREAM: A Web Server to Short-List Potentially Important Genomic Repeat Elements Based on Over-/Under-Representation in Specific Chromosomal Locations, Such as the Gene Neighborhoods, within or across 17 Mammalian Species. PLoS One 2015. [PMID: 26208093 PMCID: PMC4514817 DOI: 10.1371/journal.pone.0133647] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Background Genome-wide repeat sequences, such as LINEs, SINEs and LTRs share a considerable part of the mammalian nuclear genomes. These repeat elements seem to be important for multiple functions including the regulation of transcription initiation, alternative splicing and DNA methylation. But it is not possible to study all repeats and, hence, it would help to short-list before exploring their potential functional significance via experimental studies and/or detailed in silico analyses. Result We developed the ‘Genomic Repeat Element Analyzer for Mammals’ (GREAM) for analysis, screening and selection of potentially important mammalian genomic repeats. This web-server offers many novel utilities. For example, this is the only tool that can reveal a categorized list of specific types of transposons, retro-transposons and other genome-wide repetitive elements that are statistically over-/under-represented in regions around a set of genes, such as those expressed differentially in a disease condition. The output displays the position and frequency of identified elements within the specified regions. In addition, GREAM offers two other types of analyses of genomic repeat sequences: a) enrichment within chromosomal region(s) of interest, and b) comparative distribution across the neighborhood of orthologous genes. GREAM successfully short-listed a repeat element (MER20) known to contain functional motifs. In other case studies, we could use GREAM to short-list repetitive elements in the azoospermia factor a (AZFa) region of the human Y chromosome and those around the genes associated with rat liver injury. GREAM could also identify five over-represented repeats around some of the human and mouse transcription factor coding genes that had conserved expression patterns across the two species. Conclusion GREAM has been developed to provide an impetus to research on the role of repetitive sequences in mammalian genomes by offering easy selection of more interesting repeats in various contexts/regions. GREAM is freely available at http://resource.ibab.ac.in/GREAM/.
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Affiliation(s)
- Darshan Shimoga Chandrashekar
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bengaluru (Bangalore), 560100, Karnataka state, India
- Manipal University, Manipal, 576104, Karnataka state, India
| | - Poulami Dey
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bengaluru (Bangalore), 560100, Karnataka state, India
- Manipal University, Manipal, 576104, Karnataka state, India
| | - Kshitish K. Acharya
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bengaluru (Bangalore), 560100, Karnataka state, India
- Shodhaka Life Sciences Pvt. Ltd., IBAB, Biotech Park, Bengaluru (Bangalore), 560100, Karnataka state, India
- * E-mail:
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26
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Madejczyk MS, Baer CE, Dennis WE, Minarchick VC, Leonard SS, Jackson DA, Stallings JD, Lewis JA. Temporal changes in rat liver gene expression after acute cadmium and chromium exposure. PLoS One 2015; 10:e0127327. [PMID: 25993096 PMCID: PMC4437902 DOI: 10.1371/journal.pone.0127327] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 04/13/2015] [Indexed: 11/18/2022] Open
Abstract
U.S. Service Members and civilians are at risk of exposure to a variety of environmental health hazards throughout their normal duty activities and in industrial occupations. Metals are widely used in large quantities in a number of industrial processes and are a common environmental toxicant, which increases the possibility of being exposed at toxic levels. While metal toxicity has been widely studied, the exact mechanisms of toxicity remain unclear. In order to further elucidate these mechanisms and identify candidate biomarkers, rats were exposed via a single intraperitoneal injection to three concentrations of CdCl2 and Na2Cr2O7, with livers harvested at 1, 3, or 7 days after exposure. Cd and Cr accumulated in the liver at 1 day post exposure. Cd levels remained elevated over the length of the experiment, while Cr levels declined. Metal exposures induced ROS, including hydroxyl radical (•OH), resulting in DNA strand breaks and lipid peroxidation. Interestingly, ROS and cellular damage appeared to increase with time post-exposure in both metals, despite declines in Cr levels. Differentially expressed genes were identified via microarray analysis. Both metals perturbed gene expression in pathways related to oxidative stress, metabolism, DNA damage, cell cycle, and inflammatory response. This work provides insight into the temporal effects and mechanistic pathways involved in acute metal intoxication, leading to the identification of candidate biomarkers.
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Affiliation(s)
- Michael S. Madejczyk
- ORISE Postdoctoral Fellow at the US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | | | - William E. Dennis
- US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Valerie C. Minarchick
- National Institute for Occupational Safety and Health, Morgantown, WV, United States of America
| | - Stephen S. Leonard
- National Institute for Occupational Safety and Health, Morgantown, WV, United States of America
| | - David A. Jackson
- US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Jonathan D. Stallings
- US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - John A. Lewis
- US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
- * E-mail:
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27
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Effects of valproic acid and dexamethasone administration on early bio-markers and gene expression profile in acute kidney ischemia-reperfusion injury in the rat. PLoS One 2015; 10:e0126622. [PMID: 25970334 PMCID: PMC4430309 DOI: 10.1371/journal.pone.0126622] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 04/05/2015] [Indexed: 12/11/2022] Open
Abstract
Renal ischemia-reperfusion (IR) causes acute kidney injury (AKI) with high mortality and morbidity. The objective of this investigation was to ameliorate kidney IR injury and identify novel biomarkers for kidney injury and repair. Under general anesthesia, left renal ischemia was induced in Wister rats by occluding renal artery for 45 minutes, followed by reperfusion and right nephrectomy. Thirty minutes prior to ischemia, rats (n = 8/group) received Valproic Acid (150 mg/kg; VPA), Dexamethasone (3 mg/kg; Dex) or Vehicle (saline) intraperitoneally. Animals were sacrificed at 3, 24 or 120 h post-IR. Plasma creatinine (mg/dL) at 24 h was reduced (P<0.05) in VPA (2.7±1.8) and Dex (2.3±1.2) compared to Vehicle (3.8±0.5) group. At 3 h, urine albumin (mg/mL) was higher in Vehicle (1.47±0.10), VPA (0.84±0.62) and Dex (1.04±0.73) compared to naïve (uninjured/untreated control) (0.14±0.26) group. At 24 h post-IR urine lipocalin-2 (μg/mL) was higher (P<0.05) in VPA, Dex and Vehicle groups (9.61–11.36) compared to naïve group (0.67±0.29); also, kidney injury molecule-1 (KIM-1; ng/mL) was higher (P<0.05) in VPA, Dex and Vehicle groups (13.7–18.7) compared to naïve group (1.7±1.9). Histopathology demonstrated reduced (P<0.05) ischemic injury in the renal cortex in VPA (Grade 1.6±1.5) compared to Vehicle (Grade 2.9±1.1). Inflammatory cytokines IL1β and IL6 were downregulated and anti-apoptotic molecule BCL2 was upregulated in VPA group. Furthermore, kidney DNA microarray demonstrated reduced injury, stress, and apoptosis related gene expression in the VPA administered rats. VPA appears to ameliorate kidney IR injury via reduced inflammatory cytokine, apoptosis/stress related gene expression, and improved regeneration. KIM-1, lipocalin-2 and albumin appear to be promising early urine biomarkers for the diagnosis of AKI.
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28
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AbdulHameed MDM, Tawa GJ, Kumar K, Ippolito DL, Lewis JA, Stallings JD, Wallqvist A. Systems level analysis and identification of pathways and networks associated with liver fibrosis. PLoS One 2014; 9:e112193. [PMID: 25380136 PMCID: PMC4224449 DOI: 10.1371/journal.pone.0112193] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 10/13/2014] [Indexed: 01/18/2023] Open
Abstract
Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.
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Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Gregory J. Tawa
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Kamal Kumar
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - John A. Lewis
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Jonathan D. Stallings
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail:
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