Transcriptomic analysis of cave, surface, and hybrid samples of the isopod Asellus aquaticus and identification of chromosomal location of candidate genes for cave phenotype evolution

对洞穴、地表和杂交等足类动物 Asellus aquaticus 的样本进行转录组分析,并鉴定洞穴表型进化候选基因的染色体位置

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Abstract

BACKGROUND: Transcriptomic methods can be used to elucidate genes and pathways responsible for phenotypic differences between populations. Asellus aquaticus is a freshwater isopod crustacean with surface- and cave-dwelling ecomorphs that differ greatly in multiple phenotypes including pigmentation and eye size. Multiple genetic resources have been generated for this species, but the genes and pathways responsible for cave-specific characteristics have not yet been identified. Our goal was to generate transcriptomic resources in tandem with taking advantage of the species' ability to interbreed and generate hybrid individuals. RESULTS: We generated transcriptomes of the Rakov Škocjan surface population and the Rak Channel of Planina Cave population that combined Illumina short-read assemblies and PacBio Iso-seq long-read sequences. We investigated differential expression at two different embryonic time points as well as allele-specific expression of F(1) hybrids between cave and surface individuals. RNAseq of F(2) hybrids, as well as genotyping of a backcross, allowed for positional information of multiple candidate genes from the differential expression and allele-specific analyses. CONCLUSIONS: As expected, genes involved in phototransduction and ommochrome synthesis were under-expressed in the cave samples as compared to the surface samples. Allele-specific expression analysis of F(1) hybrids identified genes with cave-biased (cave allele has higher mRNA levels than the surface allele) and surface-biased expression (surface allele has higher mRNA levels than the cave allele). RNAseq of F(2) hybrids allowed for multiple genes to be placed to previously mapped genomic regions responsible for eye and pigmentation phenotypes. In the future, these transcriptomic resources will guide prioritization of candidates for functional analysis.

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