Final Report Summary - PRIMATE_REG_EVOL (A comparative genomic study of the contribution of epigenetic mechanisms to regulatory evolution in primates.)
A long-standing hypothesis is that changes in gene regulation play an important role in adaptive evolution, notably in primates. Yet, in spite of the evidence accumulated in the past decade that regulatory changes contribute to many species-specific adaptations, we still know remarkably little about the mechanisms of regulatory evolution. In this study we focused on DNA methylation, an epigenetic mechanism whose contribution to the evolution of gene expression remains unclear.
To interrogate the methylation status of the vast majority of cytosines in the genome, we performed whole-genome bisulfite conversion followed by high-throughput sequencing across 4 tissues (heart, kidney, liver and lung) in 3 primate species (human, chimpanzee and macaque). Because the 4 tissues are from the same individuals, we were able to monitor methylation differences between individuals, tissues and species. In parallel, we collected gene expression profiles using RNA-seq from the same tissue samples, allowing us to perform a high resolution scan for genes and pathways whose regulation evolved under natural selection.
We analyzed and integrated these datasets to characterize better the genome features where methylation changes are associated with expression changes. Notably we developed a statistical model to quantify the proportion of variation in gene expression levels across tissues and species that can be explained by changes in methylation. At the conclusion of this work, these datasets allowed to determine the mechanisms that explain, at least in part, regulatory differences between the species.
To interrogate the methylation status of the vast majority of cytosines in the genome, we performed whole-genome bisulfite conversion followed by high-throughput sequencing across 4 tissues (heart, kidney, liver and lung) in 3 primate species (human, chimpanzee and macaque). Because the 4 tissues are from the same individuals, we were able to monitor methylation differences between individuals, tissues and species. In parallel, we collected gene expression profiles using RNA-seq from the same tissue samples, allowing us to perform a high resolution scan for genes and pathways whose regulation evolved under natural selection.
We analyzed and integrated these datasets to characterize better the genome features where methylation changes are associated with expression changes. Notably we developed a statistical model to quantify the proportion of variation in gene expression levels across tissues and species that can be explained by changes in methylation. At the conclusion of this work, these datasets allowed to determine the mechanisms that explain, at least in part, regulatory differences between the species.