The strength and form of natural selection on transcript abundance in the wild.

Freed Ahmad,Riho Gross,Paul V Debes,Siim Kahar,Ilkka Nousiainen,Anti Vasemägi,Mikhail Ozerov,Lilian Pukk

doi:10.1111/mec.15743

Abstract

Gene transcription variation is known to contribute to disease susceptibility and adaptation, but we currently know very little about how contemporary natural selection shapes transcript abundance. Here, we propose a novel analytical framework to quantify the strength and form of ongoing natural selection at the transcriptome level in a wild vertebrate. We estimated selection on transcript abundance in a cohort of a wild salmonid fish (Salmo trutta) affected by an extracellular myxozoan parasite (Tetracapsuloides bryosalmonae) through mark–recapture field sampling and the integration of RNA‐sequencing with classical regression‐based selection analysis. We show, based on fin transcriptomes of the host, that infection by the parasite and subsequent host survival is linked to upregulation of mitotic cell cycle process. We also detect a widespread signal of disruptive selection on transcripts linked to host immune defence, host–pathogen interactions, cellular repair and maintenance. Our results provide insights into how selection can be measured at the transcriptome level to dissect the molecular mechanisms of contemporary evolution driven by climate change and emerging anthropogenic threats. We anticipate that the approach described here will enable critical information on the molecular processes and targets of natural selection to be obtained in real time.

Highlights

Understanding how natural selection acts on traits and eventually on organisms represents a fundamental challenge in biology (Mayr, 1982)
Using a classical regression-based approach (Lande & Arnold, 1983), ecologists have generated thousands of phenotypic selection estimates over the past 35 years; these estimates help to understand the contemporary selection processes in nature and enable comparisons of the strength and mode of selection across traits and species (Kingsolver et al, 2001; Kingsolver & Pfennig, 2007; Siepielski et al, 2017). Despite this wealth of phenotypic selection estimates and a large number of studies that indirectly infer the roles of different evolutionary forces in shaping gene expression patterns (Fraser et al, 2010; Gilad et al, 2006), we know very little about how natural selection affects transcript abundance in the wild (Miller et al, 2011)
We present an integrative approach investigating how contemporary natural selection shapes transcriptomic variation by combining analyses of selection differentials and gradients (Lande & Arnold, 1983) with the high-throughput screening of molecular phenotypes at the gene transcription level. Such use of the socalled molecular phenotypes has been highly successful in medical science for discovering the mechanisms underlying complex human diseases (e.g.,Chaussabel et al, 2008; Cobb et al, 2005), but we currently know very little about how within-generation natural selection in the wild translates to changes at the RNA and protein levels (Husak, 2016)

Summary

| INTRODUCTION

Understanding how natural selection acts on traits and eventually on organisms represents a fundamental challenge in biology (Mayr, 1982). Using a classical regression-based approach (Lande & Arnold, 1983), ecologists have generated thousands of phenotypic selection estimates over the past 35 years; these estimates help to understand the contemporary selection processes in nature and enable comparisons of the strength and mode of selection across traits and species (Kingsolver et al, 2001; Kingsolver & Pfennig, 2007; Siepielski et al, 2017) Despite this wealth of phenotypic selection estimates and a large number of studies that indirectly infer the roles of different evolutionary forces in shaping gene expression patterns (Fraser et al, 2010; Gilad et al, 2006), we know very little about how natural selection affects transcript abundance in the wild (Miller et al, 2011). To further elucidate the transcriptional signatures linked with the observed mortality, we measured the T. bryosalmonae load in kidney tissue among survivors to identify transcripts and protein–protein interaction (PPI) networks associated with both survival and parasite load (PL)

| MATERIALS AND METHODS

Findings

| DISCUSSION