Dataset Information

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

ABSTRACT: The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver/HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.

SUBMITTER: Sutherland JJ

PROVIDER: S-EPMC4814051 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

Sutherland Jeffrey J JJ Jolly Robert A RA Goldstein Keith M KM Stevens James L JL

PLoS computational biology 20160330 3

The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo st ...[more]

PMID: 27028627

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Project description:The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived with would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), primary human hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA on a quantitative metric when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal low concordance for all methods. We investigate differences in the baseline state of cultured cells in the context of drug-induced perturbations in rat liver and highlight the striking similarity between toxicant-exposed cells in vivo and untreated cells in vitro. Gene expression studies in model systems are widely used for understanding the mechanism of drugs and other perturbations in biological systems. Other researchers have examined the reproducibility of microarray studies between laboratories, or comparing microarrays and/or RNA sequencing. However, no large scale studies have compared results from protocols which differ in minor details, or results generated in vivo vs. in vitro culture system thought to serve as useful models. The rat liver is by far the most extensively studied model evaluating effects of drugs and other perturbations, and existing data allowed us to assess the level of concordance between rat liver and rat primary hepatocytes cultured in collagen-coated plates (i.e. âflatâ culture) for hundreds of drugs. We found that the mouse liver serves as a better model of the rat liver than do rat primary hepatocytes, even after allowing for differences due to pharmacokinetics. The low concordance observed between rat liver and rat hepatocytes suggests that validating the utility of âomics data generated on emerging cell culture approaches (e.g. âorgan-on-a-chipâ, 3D-printed tissues) using rat cells and comparison to the rat liver may be necessary in order to gain confidence these approaches substantially improve on traditional culture models of human cells. To identify transcriptional changes in culture, rat primary hepatocytes (RPH) were isolated from three male Sprague Dawley rats. During the isolation and prior to perfusion, a lobe of liver was tied off to serve as the liver in situ reference sample. Cells were isolated and samples from the cell pellet (time zero) and cells cultured for 4, 24 and 48 hours. Three biological replicates were generated for each group (one from each rat). Each biological replicate was analyzed via 3 technical replicates, for a total of 9 array hybridizations per group.

Project description:The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived with would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), primary human hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA on a quantitative metric when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal low concordance for all methods. We investigate differences in the baseline state of cultured cells in the context of drug-induced perturbations in rat liver and highlight the striking similarity between toxicant-exposed cells in vivo and untreated cells in vitro. Gene expression studies in model systems are widely used for understanding the mechanism of drugs and other perturbations in biological systems. Other researchers have examined the reproducibility of microarray studies between laboratories, or comparing microarrays and/or RNA sequencing. However, no large scale studies have compared results from protocols which differ in minor details, or results generated in vivo vs. in vitro culture system thought to serve as useful models. The rat liver is by far the most extensively studied model evaluating effects of drugs and other perturbations, and existing data allowed us to assess the level of concordance between rat liver and rat primary hepatocytes cultured in collagen-coated plates (i.e. “flat” culture) for hundreds of drugs. We found that the mouse liver serves as a better model of the rat liver than do rat primary hepatocytes, even after allowing for differences due to pharmacokinetics. The low concordance observed between rat liver and rat hepatocytes suggests that validating the utility of ‘omics data generated on emerging cell culture approaches (e.g. “organ-on-a-chip”, 3D-printed tissues) using rat cells and comparison to the rat liver may be necessary in order to gain confidence these approaches substantially improve on traditional culture models of human cells.

Project description:Drug induced liver injury (DILI), a major cause of pre- and post-approval failure, is challenging to predict pre-clinically due to varied underlying direct and indirect mechanisms. Nevirapine, a non-nucleoside reverse transcriptase inhibitor (NNRTI) and Ritonavir, a protease inhibitor, are antiviral drugs that cause clinical DILI with different phenotypes via different mechanisms. Assessing DILI in vitro in hepatocyte cultures typically requires drug exposures significantly higher than clinical plasma Cmax concentrations, making clinical interpretations of mechanistic pathway changes challenging. We previously described a system that uses liver-derived hemodynamic blood flow and transport parameters to restore primary human hepatocyte biology, and drug responses at concentrations relevant to in vivo or clinical exposure levels. Using this system, primary hepatocytes from 5 human donors were exposed to concentrations approximating clinical therapeutic and supra-therapeutic levels of Nevirapine (11.3 and 175.0 ?M) and Ritonavir (3.5 and 62.4 ?M) for 48 h. Whole genome transcriptomics was performed by RNAseq along with functional assays for metabolic activity and function. We observed effects at both doses, but a greater number of genes were differentially expressed with higher probability at the toxic concentrations. At the toxic doses, both drugs showed direct cholestatic potential with Nevirapine increasing bile synthesis and Ritonavir inhibiting bile acid transport. Clear differences in antigen presentation were noted, with marked activation of MHC Class I by Nevirapine and suppression by Ritonavir. This suggests CD8+ T cell involvement for Nevirapine and possibly NK Killer cells for Ritonavir. Both compounds induced several drug metabolizing genes (including CYP2B6, CYP3A4 and UGT1A1), mediated by CAR activation in Nevirapine and PXR in Ritonavir. Unlike Ritonavir, Nevirapine did not increase fatty acid synthesis or activate the respiratory electron chain with simultaneous mitochondrial uncoupling supporting clinical reports of a lower propensity for steatosis. This in vitro study offers insights into the disparate direct and immune-mediated toxicity mechanisms underlying Nevirapine and Ritonavir toxicity in the clinic.

Project description:Drug-induced liver injury is a major reason for drug candidate attrition from development, denied commercialization, market withdrawal, and restricted prescribing of pharmaceuticals. The metabolic bioactivation of drugs to chemically reactive metabolites (CRMs) contribute to liver-associated adverse drug reactions in humans that often goes undetected in conventional animal toxicology studies. A challenge for pharmaceutical drug discovery has been reliably selecting drug candidates with a low liability of forming CRM and reduced drug-induced liver injury potential, at projected therapeutic doses, without falsely restricting the development of safe drugs. We have developed an in vivo rat liver transcriptional signature biomarker reflecting the cellular response to drug bioactivation. Measurement of transcriptional activation of integrated nuclear factor erythroid 2-related factor 2 (NRF2)/Kelch-like ECH-associated protein 1 (KEAP1) electrophilic stress, and nuclear factor erythroid 2-related factor 1 (NRF1) proteasomal endoplasmic reticulum (ER) stress responses, is described for discerning estimated clinical doses of drugs with potential for bioactivation-mediated hepatotoxicity. The approach was established using well benchmarked CRM forming test agents from our company. This was subsequently tested using curated lists of commercial drugs and internal compounds, anchored in the clinical experience with human hepatotoxicity, while agnostic to mechanism. Based on results with 116 compounds in short-term rat studies, with consideration of the maximum recommended daily clinical dose, this CRM mechanism-based approach yielded 32% sensitivity and 92% specificity for discriminating safe from hepatotoxic drugs. The approach adds new information for guiding early candidate selection and informs structure activity relationships (SAR) thus enabling lead optimization and mechanistic problem solving. Additional refinement of the model is ongoing. Case examples are provided describing the strengths and limitations of the approach.

Dataset Information

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

Publications

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

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