Variation in juvenile Chinook salmon (Oncorhynchus tshawytscha) transcription profiles among and within eight population crosses from British Columbia, Canada

Abstract

Phenotypic differences among populations within a species have been reported for a variety of traits, ranging from life history to physiology to gene transcription. Population‐level phenotypic variation has been attributed to genetic differences resulting from genetic drift and/or local adaptation as well as environmental differences resulting from plasticity. We studied population‐ and family‐level variation in gene transcription for 22 fitness‐related genes, comprising immune, growth, metabolic, and stress processes in Chinook salmon (Oncorhynchus tshawytscha). We created hybrid Chinook salmon families from eight populations and treated them with an immune stimulus, a handling stress challenge, and held some as a no‐treatment control group. Population effects, sire effects, and narrow‐sense heritability (h2) were calculated for each candidate gene within each treatment group. We expected population to have a significant effect on gene transcription for many of our genes; however, we found a population effect for transcription at only one immune gene at rest. The limited number of significant population effects on gene transcription, combined with significant additive genetic variance within each population does not support the expectation of past strong selection pressures acting on heritable transcription profiles among populations. Instead, our results indicate that Chinook salmon likely adapt to their local environment through transcriptional plasticity rather than fixed differences. The expectation for fixed population‐level differences in gene transcription at fitness‐related genes, reflecting accepted models of local adaptation is high; however, comparisons among multiple populations using half‐sibling breeding designs are rare. Our work fills an important gap in our growing understanding of the process of among and within‐population divergence.

The effects of host genetic architecture on the gut microbiome composition of Chinook salmon (Oncorhynchus tshawytscha)

“…Using the same study system and hybrid crosses, the CHILL hybrid cross was shown to vary from the other crosses in related studies. For example, in a study designed to detect gene expression differences among and within the hybrid crosses, CHILL exhibited a marked difference in gene transcription profile relative to the other hybrid cross stocks (including YIAL) consistent with the observe divergence pattern in this study (Toews, Wellband, Dixon & Heath, 2019). Furthermore, over the entire production period, CHILL was found to exhibit the lowest survival relative to the other crosses (Semeniuk et al, 2019).…”

Distinct early life stage gene expression effects of hybridization among European and North American farmed and wild Atlantic salmon populations

“…Although maternal effects are known to be strongly influential during the embryonic stage of fish, paternal effects have also been observed to influence F 1 hybrid gene expression patterns (e.g., brook charr [Bougas et al, 2013]; zebrafish [Jiang et al, 2013]). Moreover, gene expression differences at the population level can also be due to environmental effects (Amaral et al, 2008; Toews et al, 2019), but our common garden experimental approach was designed to minimize such effects on transcriptional variation. However, the transcript expression differences we found in our controlled laboratory study, may not have been the same had the study been conducted in the wild.…”

“…Moreover, gene expression differences at the population F I G U R E 5 Hierarchical clustering analyses of mean expression values of all samples using redox homeostasis relevant genes from RP DEPs list that were differentially expressed in different cross types (see Appendix A for details, i.e., microarray fold-changes; functional annotation). For the individual expression patterns among cross types see Figure S3 level can also be due to environmental effects (Amaral et al, 2008;Toews et al, 2019), but our common garden experimental approach was designed to minimize such effects on transcriptional variation. However, the transcript expression differences we found in our controlled laboratory study, may not have been the same had the study been conducted in the wild.…”

Regulation of Host Gene Expression by Gastrointestinal Tract Microbiota in Chinook Salmon (Oncorhynchus tshawytscha)

“…The final mean mass of the fish was 23.3 g (+-7.2 SE) across all families and treatments (no treatment effect on fish body weight was detected). Three fish were dip netted from each tank and humanely euthanized immediately in an overdose solution of clove oil (Toews et al, 2019). Of the 72 tanks, four tanks (control) had 100 % mortality and those replicates were excluded from the study, bringing the total number of samples to 204 fish (72 probiotic treated fish, 72 antibiotic treated fish, 60 control fish) and 68 water samples (one per tank)).…”

“…qPCR Primer/probe optimization and cDNA synthesis Primer and probe optimization : Fifty transcripts (genes) that were significantly DE between antibiotic and probiotic treatments versus the control treatment in the DESeq2 analysis were selected for printing on Ope-nArray Taqman qPCR chips (Supplementary Table S2). Four endogenous control genes (β-2-microglobulin, β-Actin, ribosomal protein L13, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH)) were selected from previous studies (Geffroy et al, 2021;Limbu et al, 2018;Toews et al, 2019) to normalise the transcription profiles of the candidate transcripts Primers for the candidate transcripts were designed using Geneious Software v7.1.5 (http://www.geneious.com) and optimized on DNA from Chinook salmon fry. After PCR optimization, the primers were tested on a subset of our cDNA samples with SyBr® Green Dye I (Thermo Fisher Scientific) following the manufacturer's protocol on the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific).…”