The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Identifying multiscale translational safety biomarkers using a network-based systems approach.

Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.

The ubiquity of the insulin superfamily across the eukaryotes detected using a bioinformatics approach.

The insulin superfamily is composed of a diverse group of proteins that share a common structural design whose most notable feature is a set of disulfide bonds. There is now sufficient experimental and bioinformatics evidence that it is represented in at least a number of well-investigated invertebrates, where they have been found to intervene mainly in complex processes such as mitosis, cell growth, castes differentiation, and fertility. In this article we automated a methodology first proposed elsewhere-that combines sequence similarity with assessing membership to the superfamily by conservation of structuraly key residues-to identify putative insulin-like peptides (ILPs) in completely sequenced genomes, and applied it as a pipeline to a group of 46 organisms both vertebrates and invertebrates. As a result, we were able to identify 1,653 putative members of the insulin superfamily, from 17 putative members in C. savigny to 58 in X. tropicalis. Moreover, we found that structural distinctions-such as peptides length-between functionally diverse members of the superfamily found in vertebrates, that is, insulins, IGFs, and relaxins, are not equally represented in invertebrates genomes, suggesting that such divergence has occurred only recently in the evolutionary history of vertebrates.

Conservation of key members in the course of the evolution of the insulin signaling pathway.

Our understanding of the evolution of the insulin signaling pathway (ISP) is still incomplete. One intriguing unanswered question is the explanation of the emergence of the glucostatic role of insulin in mammals. To find out whether this is due to the development of new sets of signaling transduction elements in these organisms, or to the establishment of new interactions between pre-existing proteins, we rebuilt putative orthologous ISPs in 17 eukaryotic organisms. Then, we computed the conservation of orthologous ISPs at different levels, from sequence similarity of orthologous proteins to co-evolution of interacting domains. We found that the emergence of glucostatic role in mammals can neither be explained by the development of new sets of signaling elements, nor by the establishment of new interactions between pre-existing proteins. The comparison of orthologous IRS molecules indicates that only in mammals have they acquired their complete functionality as efficient recruiters of effector sub-pathways.