RNA-sequencing analysis of gene expression evolution in mole rats

Understanding the genetic basis of phenotypic adaptation poses a significant challenge in evolutionary genomics. Mutations in regulatory sequences that influence gene expression, such as tissue-specific enhancers, are widely recognised as key drivers of phenotypic evolution. However, because cis-regulatory elements evolve rapidly, linking changes in their activity to corresponding changes in gene expression across species is challenging. This study aims to identify and connect accelerated evolution (shifts) in gene expression and cis-regulatory elements, and assess their potential impact on phenotypic adaptations. Using African mole-rats as a model, we profiled gene expression in heart and liver tissues across two mole-rat species and two rodent outgroups, and applied a phylogenetic comparative approach to identify genes that have undergone expression shifts within the mole-rat clade and specific genera. The identified genes are associated with known adaptative functions, including metabolic rewiring such as increased glycolysis in the heart. We then integrated evolution of gene expression and the activity of promoters and enhancers into a unified phylogenetic framework by: (1) characterising the regulatory landscape around transcription start sites of genes with shifted expression across species, and (2) directly integrating regulatory and gene expression shifts. With both approaches, we identified a coordinated shift in regulatory activity and expression levels. Genes showing expression shifts enriched for shifted cis-regulatory elements, and the number of shifted promoters and enhancers correlated with the magnitude of gene expression changes. Our findings represent one of the first systematic demonstrations of parallel evolution acting on gene regulation and expression. Together, these results provide new insights into the molecular mechanisms underlying phenotypic evolution in mole-rats, and highlight the value of combining regulatory and expression-level data in comparative genomics.