Gene expression variation in geographically diverse populations of two North American songbird species

Abstract

The range distributions of many bird species cover extensive geographic distances, exposing each local population to unique ecological challenges. Understanding the molecular basis of how species adapt to diverse habitats across their geographic range is crucial for identifying populations at risk and implementing effective conservation strategies. In this study, we employed two passerine species, the black-capped chickadee (Poecile atricapillus) and the American goldfinch (Spinus tristis), which are widely distributed across North America. This study focused on examining changes in gene expression within their distinct populations inhabiting diverse habitats across various geographical locations. A comparative transcriptomic study was conducted on wild-caught birds from two geographically separate locations, Boston, Massachusetts, and Kent, Ohio, characterized by considerable annual variability in winter severity. We tested the hypothesis that populations of both species in Kent and Boston would show differential gene expression patterns in their brains in response to unique local environmental conditions. Analyzing the differentially expressed genes (DEGs) in black-capped chickadees revealed associations with neural processes such as the generation and maintenance of neurons, activity-dependent plasticity, and cognitive ability. Many of these genes were linked to brain variation in chickadee populations related to spatial cognition associated with food caching. We also compared changes in gene expression levels with coding sequence variability to explore the underlying basis of differential gene expression patterns. We tested the hypothesis that expression differences are driven by underlying genetic variation. A population genetic analysis on transcriptome data from both species revealed no highly divergent genetic variants (single nucleotide polymorphisms or SNPs) in the coding regions of genes identified as differentially expressed. However, some of the DEGs themselves were transcription factors or regulatory molecules, as were some of the genes with highly divergent SNPs. These findings suggest that the genetic architecture underlying the differential gene expression patterns is mostly regulatory rather than protein-coding changes.