Rapid environmental changes impact the global distribution and abundance of species, highlighting the urgency to understand and predict how populations will respond. The analysis of differentially expressed genes has elucidated areas of the genome involved in adaptive divergence to past and present environmental change. Such studies however have been hampered by large numbers of differentially expressed genes and limited knowledge of how these genes work in conjunction with each other. Recent methods (broadly termed “pathway analyses”) have emerged that aim to group genes that behave in a coordinated fashion to a factor of interest. These methods aid in functional annotation and uncovering biological pathways, thereby collapsing complex datasets into more manageable units, providing more nuanced understandings of both the organism‐level effects of modified gene expression, and the targets of adaptive divergence. Here, we reanalyze a dataset that investigated temperature‐induced changes in gene expression in marine‐adapted and freshwater‐adapted threespine stickleback (Gasterosteus aculeatus), using Weighted Gene Co‐expression Network Analysis (WGCNA) with PANTHER Gene Ontology (GO)‐Slim overrepresentation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Six modules exhibited a conserved response and six a divergent response between marine and freshwater stickleback when acclimated to 7°C or 22°C. One divergent module showed freshwater‐specific response to temperature, and the remaining divergent modules showed differences in height of reaction norms. PPARAa, a transcription factor that regulates fatty acid metabolism and has been implicated in adaptive divergence, was located in a module that had higher expression at 7°C and in freshwater stickleback. This updated methodology revealed patterns that were not found in the original publication. Although such methods hold promise toward predicting population response to environmental stressors, many limitations remain, particularly with regard to module expression representation, database resources, and cross‐database integration.

A challenge of modern ecological genomics is reducing uncertainty surrounding the biological inferences from gene expression. For example, approximately 40% of proteins in eukaryotic model organisms do not contain characterized domains (Gollery et al., 2006). Even proteins of "known function" are typically only characterized in the sense that they have a domain function, but provide no information on their biological role within the cell (e.g., activation, pathways or targets). Yet, as molecular ecologists, a common objective is to elucidate how organisms respond to environ- Differential gene expression (DE) has typically been the foundation of methods used in experiments designed to elucidate how genomic information interacts with the environment. Genes and proteins do not act alone. To discover biologically relevant information, it is often necessary to elucidate the context of the genetic and transcriptome expression in a given environment. However, responses may implicate many genes of small effect, whereby detecting subtle changes compared to the "noise" of other transcripts is difficult.Temporal differences in gene expression and tissue-specific expression add a level of complexity for accurate sampling and

Billions of salmonids are produced annually by artificial reproduction for harvest and conservation. Morphologically, behaviourally and physiologically these fish differ from wild-born fish, including in ways consistent with domestication. Unlike most studied domesticates, which diverged from wild ancestors millennia ago, salmonids offer a tractable model for early-stage domestication. Here, we review a fundamental mechanism for domestication-driven differences in early-stage domestication, differentially expressed genes (DEGs), in salmonids. We found 34 publications examining DEGs under domestication driven by environment and genotype, covering six species, over a range of life-history stages and tissues. Three trends emerged. First, domesticated genotypes have increased expression of growth hormone and related metabolic genes, with differences magnified under artificial environments with increased food. Regulatory consequences of these DEGs potentially drive overall DEG patterns. Second, immune genes are often DEGs under domestication and not simply owing to release from growth-immune trade-offs under increased food. Third, domesticated genotypes exhibit reduced gene expression plasticity, with plasticity further reduced in low-complexity environments typical of production systems. Recommendations for experimental design improvements, coupled with tissue-specific expression and emerging analytical approaches for DEGs present tractable avenues to understand the evolution of domestication in salmonids and other species.

No abstract

No abstract