Parallelogram approach using rat-human In vitro and rat in vivo toxicogenomics predicts acetaminophen-induced hepatotoxicity in humans

The frequent use of rodent hepatic in vitro systems in pharmacological and toxicological investigations challenges extrapolation of in vitro results to the situation in vivo and interspecies extrapolation from rodents to humans. The toxicogenomics approach may aid in evaluating relevance of these model systems for human risk assessment by direct comparison of toxicant-induced gene expression profiles and infers mechanisms between several systems. In the present study, acetaminophen (APAP) was used as a model compound to compare gene expression responses between rat and human using in vitro cellular models, hepatocytes, and between rat in vitro and in vivo. Comparison at the level of modulated biochemical pathways and biological processes rather than at that of individual genes appears preferable as it increases the overlap between various systems. Pathway analysis by T-profiler revealed similar biochemical pathways and biological processes repressed in rat and human hepatocytes in vitro, as well as in rat liver in vitro and in vivo. Repressed pathways comprised energy-consuming biochemical pathways, mitochondrial function, and oxidoreductase activity. The present study is the first that used a toxicogenomics-based parallelogram approach, extrapolating in vitro to in vivo and interspecies, to reveal relevant mechanisms indicative of APAP-induced liver toxicity in humans in vivo. © The Author 2008. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved

Utility of extrapolating human S1500+ genes to the whole transcriptome: tunicamycin case study

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the "measured only" and "whole transcriptome" (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the "measured only" genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original "measured only" dataset. Furthermore, the inclusion of the extrapolated genes raised "tunicamycin" from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.Toxicolog