Advanced statistical analysis of RNA-Seq differential gene expression profiles of animal gene-environment interactions

RNA-sequencing (RNA-seq) analysis enables to study gene-environment interactions and their impact on the transcriptome. The variety of techniques and software packages for statistical analysis available tailors RNA-seq processing for a specific experiment. Three individual studies, each dissecting a different environmental factor including exercise and a specific immunological challenge, resulted in significant findings. In the first study, mice with the indoleamine 2,3-dioxygenase gene knocked out suffered from the same inflammation as wild type mice when injected with Bacillus Calmette-Guerin, but interestingly did not exhibit any of the depressive symptomatology which characteristically accompanies the infection. RNA-seq data was extracted from these mice, and statistical analysis revealed which genes may be responsible for this phenomenon, which could potentially provide researchers with the tools to create genetic treatments of depression occurring as a byproduct of inflammatory disorders. The second study of gene factors in Myostatin knock-out mice that were either allowed to or restricted from exercise revealed novel gene links for muscle development. Knowledge of genes involved in muscle growth post-development can allow for treatment of muscle atrophy in adults. A final study aimed to use a dependence-like preference for exercise as a murine model for substance addiction. Mice were selected for their drive towards exercise and then either allowed or deprived of the reward. These results showcase the power of advanced statistical methods of RNA-seq analysis.