Comparative transcriptomics allows for rapid development of population-level nuclear markers in Hesperoperla pacifica (Plecoptera:Perlidae)

Abstract

Lack of nonmicrosatellite nuclear markers that are informative at the intraspecific level has limited inferences about the phylogeography and phylogenetics of most aquatic insect taxa. Genomic resources are becoming available at an unprecedented rate and scale. Sequencing a single transcriptome (complementary deoxyribonucleic acid [cDNA] library corresponding to all messenger ribonucleic acid [mRNA] expressed at a single point in time) can generate tens of thousands of sets of overlapping DNA segments that together represent a consensus region of DNA (contigs) averaging >500 base pairs [bp] in length. A challenge posed by these large data sets is extraction of markers with population-level utility. We demonstrate proof-of-concept for using comparative transcriptomics to develop primers for multiple nuclear loci with population-level utility in Hesperoperla pacifica efficiently and cost effectively. We created a cDNA library from total RNA and assembled the resulting transcriptomes de novo with the computer program Trinity. We used a Basic Local Alignment Search Tool (BLAST)-based algorithm to identify and filter putative orthologs (homologous gene sequences in different species). Filtering with a minimum sequence length of 600 bp and nucleotide identity of ≥85% yielded >1800 ortholog alignments. We examined some of these alignments for suitability for polymerase chain reaction (PCR) primer design. We screened 40 primers for their ability to amplify the correct-size PCR product and to detect genetic variation in 4 populations across the Great Basin (USA). The large number of loci that can be identified with this technique will lead to more-robust estimates of population parameters and more-rigorous tests of phylogeographic hypotheses than can be done with fewer loci. The advantages of nuclear-sequence-based studies include better comparisons with mitochondrial DNA phylogenies because of shared statistical techniques and models of mutation and the potential for direct comparisons of genetic structure across taxa.