Bioinformatics on regulatory networks for phenotypic evolution across species

Abstract

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. It could lead to enormous problems using mouse models: (i) disease mechanisms could be different and (ii) a mode of actions could be different in in humans and mice. The first challenge occurs in every single laboratory using mice, hindering clear understanding of human disease mechanisms. The second challenge brings about huge financial loss in drug developments, delaying patient treatments in various diseases. In the first part of my research, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN). We further tested the usefulness of GRNs and phenotypic similarity between human and mouse orthologous genes to infer drug approvals in clinical trials. We curated approval information about 5,000 drugs with over 10,000 drug targets, and observed that drugs failing in clinical trials significantly have drug targets with rewired GRNs and phenotypic difference between humans and mice. In conclusion, we have established (i) the comparative systems of GRNs to understand molecular mechanisms in phenotypic differences of orthologous genes between humans and mice and (ii) the analysis system to help infer drug approvals with GRN and phenotype information of target genes. While further validation of our approach in additional genes and other layers in central dogma is required, we have demonstrated the potential utility of GRNs in orthologous genes in understanding of phenotypic evolution and its application in drug approvals.