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EEOB Publication - Bolton & Gibbs

November 3, 2025

EEOB Publication - Bolton & Gibbs

dog-eared EEOB graphic reveals word publication on following page

A multifaceted approach to identify disease response genes in the endangered massasauga rattlesnake

Peri E Bolton, Samarth Mathur, H Lisle Gibbs. 2025. Journal of Heredity, esaf088, DOI: 10.1093/jhered/esaf088

Abstract

Disease can impact the long-term viability of threatened species. Levels of genetic variation in disease response genes could influence the magnitude of these impacts, but identifying candidate genes in endangered species is difficult. We used a multifaceted approach to identify candidate genes involved in disease response and resistance of an endangered snake, the eastern massasauga rattlesnake (Sistrurus catenatus), possibly related to an emerging infectious disease, Snake Fungal Disease (SFD). We assessed whether genes differentially expressed in an SFD disease challenge experiment showed patterns of non-neutral evolution in outlier tests of nucleotide diversity and Tajima’s D. We evaluated these patterns in two populations of S. catenatus with varying effective population sizes, and a closely-related non-endangered species, Sistrurus tergeminus. In general, we find reduced diversity in functional sites in S. catenatus relative to the outbred sister species, but not between S. catenatus populations of different sizes. Further, genetic drift in the smaller populations likely limited the number of outlier genes detected. Finally, FST outlier tests identified a small set of protein coding genes that may be linked to local adaptation in disease response. In sum, our approach identified 218 candidate genes that were differentially expressed in response to disease that contain functional variation relevant to disease resistance or defense, pending further validation for SFD specifically. Our results also demonstrate how drift complicates the detection of functional variation in rare species with small population sizes, a process that is essential for assessing adaptive variation and load.