R(NA)‐tistic expression: The art of matching unknown mRNA and proteins to environmental response in ecological genomics

Abstract

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

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

“…If significant overall effects are found, one can conduct post hoc contrasts between specific levels of the factor to determine where the differences lie reaction norms are: (a) functional enrichment tests to infer which molecular and physiological processes vary [e.g. using AmiGO (Carbon et al, 2009) or PANTHER (Mi et al, 2019)], (b) expression Quantitative Trait Loci (eQTLs) to pinpoint the genomic basis of gene expression variation (Huang et al, 2020;Lafuente & Beldade, 2019;Majewski & Pastinen, 2011), (c) allele-specific expression (Khansefid et al, 2018) and patterns of alternative splicing (Engström et al, 2013;Verta & Jacobs, 2022) from RNA-seq data to reveal more detailed, gene-specific phenomena relevant for particular species' responses to the environment and (d) weighted gene co-expression network analysis (WGCNA; Langfelder & Horvath, 2008) to infer plastic and evolved differences in gene regulatory networks (Casasa et al, 2020(Casasa et al, , 2021Huang et al, 2020;Rose et al, 2016) and functions of unknown genes (Orsini et al, 2018;Stanford & Rogers, 2018). Note that weighted gene co-expression network analysis decomposes genome-wide expression patterns into co-expression modules that can be similarly integrated into a linear modelling framework for partitioning of phenotypic variance (Aubin-Horth & Renn, 2009).…”

Editorial 2019

“…Microbial communities are clearly an emerging area of interest, with papers examining the genetic basis of host-fungal symbiont interactions, particularly relating to the evolution of host specialization (Gladieux, 2018), and the transcriptomics of biofilms (Veach & Griffiths, 2018). Other Perspectives discussed the predictability of evolutionary genetic changes (Linnen, 2018), the maintenance of diversity because of selection driven by sexual conflict (Meisel, 2018), the value of improved annotation of gene functions by analysing expression responses to environmental conditions (Stanford & Rogers, 2018), gene expression differences in brain tissue from females, territorial and sneaker male peacock blennies (Böhne, 2018) and the potential importance of crop-wild hybrids in weedy populations in maintaining important crop allelic diversity (Ellstrand, 2018).…”

The power and limitations of gene expression pathway analyses toward predicting population response to environmental stressors

“…For example, Filteau, Pavey, St-Cyr, and Bernatchez (2013) used WGCNA and GO terms in lake whitefish (Coregonus clupeaformis) to show that bone morphogenetic protein and calcium signaling may be conserved mechanisms that rapidly evolve in response to trophic behavior, while Healy, Bryant, and Schulte (2017) coupled DE with GO terms and KEGG pathway analysis to illustrate that different mitochondrial genotypes may have limited influence in killifish (Fundulus heteroclitus) response to cold acclimation. Additionally, WGCNA has been shown to be powerful in capturing coordinated, low-level changes across hundreds of genes in response to a stressor where DE analysis failed to detect K E Y W O R D S adaptation, gene expression, gene networks, pathway analysis, phenotypic plasticity, population persistence, stickleback, temperature tolerance enough genes to establish biological inference (Orsini et al, 2018;Stanford & Rogers, 2018). However, functional annotation of the genes in these modules is still limited by current knowledge (e.g., Orsini et al, 2018;Rose, Seneca, & Palumbi, 2016).…”

“…adaptation, gene expression, gene networks, pathway analysis, phenotypic plasticity, population persistence, stickleback, temperature tolerance enough genes to establish biological inference (Orsini et al, 2018;Stanford & Rogers, 2018). However, functional annotation of the genes in these modules is still limited by current knowledge (e.g., Orsini et al, 2018;Rose, Seneca, & Palumbi, 2016).…”