Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

[1]  Jaques Reifman,et al.  Inferring high-confidence human protein-protein interactions , 2012, BMC Bioinformatics.

[2]  M. Petković,et al.  Estradiol enhances effects of fructose rich diet on cardiac fatty acid transporter CD36 and triglycerides accumulation. , 2012, European journal of pharmacology.

[3]  Darrell R Abernethy,et al.  Systems pharmacology to predict drug toxicity: integration across levels of biological organization. , 2013, Annual review of pharmacology and toxicology.

[4]  Timothy W Gant,et al.  Novel genomic methods for drug discovery and mechanism-based toxicological assessment. , 2009, Current opinion in drug discovery & development.

[5]  Xi Yang,et al.  Current and emerging biomarkers of hepatotoxicity , 2012 .

[6]  H. Yamada,et al.  The Japanese toxicogenomics project: application of toxicogenomics. , 2010, Molecular nutrition & food research.

[7]  T. Nagamine,et al.  Attenuation of N‐nitrosodiethylamine‐induced liver fibrosis by high‐molecular‐weight fucoidan derived from Cladosiphon okamuranus , 2010, Journal of gastroenterology and hepatology.

[8]  K. Rabe,et al.  Phosphodiesterase-4 inhibitor therapy for lung diseases. , 2013, American journal of respiratory and critical care medicine.

[9]  A. Hevener,et al.  The role of estrogens in control of energy balance and glucose homeostasis. , 2013, Endocrine reviews.

[10]  M. Bulsara,et al.  Assessing liver fibrosis with serum marker models. , 2007, The Clinical biochemist. Reviews.

[11]  J. Ozer,et al.  The current state of serum biomarkers of hepatotoxicity. , 2008, Toxicology.

[12]  L. Crofford,et al.  Incidence and US costs of corticosteroid-associated adverse events: a systematic literature review. , 2011, Clinical therapeutics.

[13]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2013 , 2012, Nucleic Acids Res..

[14]  D. Jump,et al.  Fatty acid regulation of hepatic gene transcription. , 2005, The Journal of nutrition.

[15]  A. Burdick,et al.  The toxicology of ligands for peroxisome proliferator-activated receptors (PPAR). , 2006, Toxicological sciences : an official journal of the Society of Toxicology.

[16]  Shu-Dong Zhang,et al.  Application of connectivity mapping in predictive toxicology based on gene-expression similarity. , 2010, Toxicology.

[17]  J. Larson,et al.  The toxicity of repeated exposures to rolipram, a type IV phosphodiesterase inhibitor, in rats. , 1996, Pharmacology & toxicology.

[18]  C. O'brien,et al.  Chemotherapy-induced hepatotoxicity. , 2013, Clinics in liver disease.

[19]  F. Buttgereit,et al.  The molecular basis for the effectiveness, toxicity, and resistance to glucocorticoids: focus on the treatment of rheumatoid arthritis , 2005, Scandinavian journal of rheumatology.

[20]  M. Fielden,et al.  Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. , 2005, Journal of biotechnology.

[21]  R. Gentleman,et al.  Independent filtering increases detection power for high-throughput experiments , 2010, Proceedings of the National Academy of Sciences.

[22]  Søren Brunak,et al.  Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation. , 2008, Toxicological sciences : an official journal of the Society of Toxicology.

[23]  L. Dubertret,et al.  [Atorvastatin-induced drug reaction with eosinophilia and systemic symptoms (DRESS)]. , 2009, Annales de dermatologie et de venereologie.

[24]  M. Fielden,et al.  The liver pharmacological and xenobiotic gene response repertoire , 2008, Molecular systems biology.

[25]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[26]  V. Meador,et al.  Selection and interpretation of clinical pathology indicators of hepatic injury in preclinical studies. , 2005, Veterinary clinical pathology.

[27]  O. Taboureau,et al.  The impact of network biology in pharmacology and toxicology , 2012, SAR and QSAR in environmental research.

[28]  P. Roderick,et al.  Systematic review of the diagnostic performance of serum markers of liver fibrosis in alcoholic liver disease , 2012, Comparative hepatology.

[29]  M. Teixeira,et al.  Phosphodiesterase (PDE)4 inhibitors: anti-inflammatory drugs of the future? , 1997, Trends in pharmacological sciences.

[30]  P. Jares,et al.  Ghrelin attenuates hepatocellular injury and liver fibrogenesis in rodents and influences fibrosis progression in humans , 2010, Hepatology.

[31]  H. McLeod,et al.  Genomics: applications in mechanism elucidation. , 2009, Advanced drug delivery reviews.

[32]  M. Viguier,et al.  [Atorvastatin-induced drug reaction with eosinophilia and systemic symptoms (DRESS)]. , 2009, Annales de dermatologie et de venereologie.

[33]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[34]  Yong Zhang,et al.  SPD—a web-based secreted protein database , 2004, Nucleic Acids Res..

[35]  J. Waring,et al.  Use of toxicogenomics to understand mechanisms of drug-induced hepatotoxicity during drug discovery and development. , 2009, Toxicology letters.

[36]  Jaques Reifman,et al.  Systems biology approaches for discovering biomarkers for traumatic brain injury. , 2013, Journal of neurotrauma.

[37]  Zhen Li,et al.  A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model , 2008, BMC Bioinformatics.

[38]  R. Snyder,et al.  Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. , 2006, Pharmacogenomics.

[39]  Gert R. G. Lanckriet,et al.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. , 2005, Genome research.

[40]  Sven Bergmann,et al.  Iterative signature algorithm for the analysis of large-scale gene expression data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[42]  Audrey Kauffmann,et al.  Bioinformatics Applications Note Arrayqualitymetrics—a Bioconductor Package for Quality Assessment of Microarray Data , 2022 .

[43]  Alessandro Rinaldo,et al.  Characterization of multilocus linkage disequilibrium , 2005, Genetic epidemiology.

[44]  Sven Bergmann,et al.  Modular analysis of gene expression data with R , 2010, Bioinform..

[45]  P. Silver,et al.  The conserved npl4 protein complex mediates proteasome-dependent membrane-bound transcription factor activation. , 2001, Molecular biology of the cell.

[46]  Enrico Rossi,et al.  Validation of the FibroTest biochemical markers score in assessing liver fibrosis in hepatitis C patients. , 2003, Clinical chemistry.

[47]  D. Koller,et al.  A module map showing conditional activity of expression modules in cancer , 2004, Nature Genetics.

[48]  Katarzyna H. Kaminska,et al.  Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding , 2013, Molecular systems biology.

[49]  Melinda R. Dwinell,et al.  The Rat Genome Database 2013—data, tools and users , 2013, Briefings Bioinform..

[50]  Yaniv Ziv,et al.  Revealing modular organization in the yeast transcriptional network , 2002, Nature Genetics.

[51]  D. Pessayre,et al.  Acute hepatitis induced by HMG-CoA reductase inhibitor, lovastatin , 1994, Digestive Diseases and Sciences.

[52]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[53]  Huiru Tang,et al.  Systems responses of rats to aflatoxin B1 exposure revealed with metabonomic changes in multiple biological matrices. , 2011, Journal of proteome research.

[54]  D. Brenner,et al.  Erratum: Liver fibrosis (Journal of Clinical Investigation (2005) 115 (209-218) DOI:10.1172/JCI200524282) , 2005 .