Genetics-Based Approach to Identify Novel Genes Regulated by the Aryl Hydrocarbon Receptor in Mouse Liver.

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor in the Per-Arnt-Sim superfamily of environmental sensors that is linked to several metabolic diseases, including nonalcoholic fatty liver disease. Much remains unknown regarding the impact of genetic variation in AHR-driven disease, as past studies have focused on a small number of inbred strains. Recently, the presence of a wide range of interindividual variability amongst humans was reported in response to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the prototypical ligand of the AHR. In this study, a panel of 14 diverse mouse strains was exposed to TCDD for 10 days to characterize the AHR-mediated response across genetic backgrounds. Responses to TCDD are heavily dependent on genetic background. Although mice carry 1 of 4 Ahr alleles known to impact the affinity to AHR-ligands, we observed significant intra-allelic variability suggesting the presence of novel genetic modifiers of AHR signaling. A regression-based approach was used to scan for genes regulated by the AHR and/or associated with TCDD-induced phenotypes. The approach identified 7 genes, 2 of which are novel, that are likely regulated by the AHR based on association with hepatic TCDD burden (p ≤ .05). Finally, we identified 1 gene, Dio1, which was associated with change in percent body fat across the diverse set of strains (p ≤ .05). Overall, the results in this study exemplify the power of genetics-based approaches in identifying novel genes that are putatively regulated by the AHR.

An Automated Method To Predict Mouse Gene and Protein Sequences Using Variant Data.

With recent advances in sequencing technologies, the scientific community has begun to probe the potential genetic bases behind complex phenotypes in humans and model organisms. In many cases, the genomes of genetically distinct strains of model organisms, such as the mouse (Mus musculus), have not been fully sequenced. Here, we report on a tool designed to use single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data to predict gene, mRNA, and protein sequences for up to 36 genetically distinct mouse strains. By automated querying of freely accessible databases through a graphical interface, the software requires no data and little computational experience. As a proof of concept, we predicted the gene and amino acid sequence of the aryl hydrocarbon receptor (Ahr) for all inbred mouse strains of which variant data were currently available through Mouse Genome Project. Predicted sequences were compared with fully sequenced genomes to show that the tool is effective in predicting gene and protein sequences.