Molecular EcologyGenomicsPeer-reviewed journal article
Genome structure predicts modular transcriptome responses to genetic and environmental conditions
Stephanie Mark, Joerg G Weiss, Eesha SharmaiD, Ting LiuiD, Wei WangiD, Julie M. ClaycombiD, Asher D. CutteriD
Abstract
Understanding the plasticity, robustness, and modularity of transcriptome expression to genetic and environmental conditions is crucial to deciphering how organisms adapt in nature. To test how genome architecture influences transcriptome profiles, we quantified expression responses for distinct temperature-adapted genotypes of the nematode Caenorhabditis briggsae when exposed to chronic temperature stresses throughout development. We found that 56% of the 8795 differentially-expressed genes show genotype-specific changes in expression in response to temperature (genotype-by-environment interactions, GxE). Most genotype-specific responses occur under heat stress, indicating that cold versus heat stress responses involve distinct genomic architectures. The 22 co-expression modules that we identified differ in their enrichment of genes with genetic versus environmental versus interaction effects, as well as their genomic spatial distributions, functional attributes, and rates of molecular evolution at the sequence level. Genes in modules enriched for simple effects of either genotype or temperature alone tend to evolve especially rapidly, consistent with disproportionate influence of adaptation or weaker constraint on these subsets of loci. Chromosome scale heterogeneity in nucleotide polymorphism, however, rather than the scale of individual genes, predominate as the source of genetic differences among expression profiles, and natural selection regimes are largely decoupled between coding sequences and non-coding flanking sequences that contain cis-regulatory elements. These results illustrate how the form of transcriptome modularity and genome structure contribute to predictable profiles of evolutionary change.
- organism: C. elegans
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 11
- FWCI
- 1.20
- Field percentile
- top 22%
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