Expression Profiling without Genome Sequence Information in a Non-Model Species, Pandalid Shrimp (Pandalus latirostris), by Next-Generation Sequencing

While the study of phenotypic variation is a central theme in evolutionary biology, the genetic approaches available to understanding this variation are usually limited because of a lack of genomic information in non-model organisms. This study explored the utility of next-generation sequencing (NGS) technologies for studying phenotypic variations between 2 populations of a non-model species, the Hokkai shrimp (Pandalus latirostris; Decapoda, Pandalidae). Before we performed transcriptome analyses using NGS, we examined the genetic and phenotypic differentiation between the populations. Analyses using microsatellite DNA markers suggested that these populations genetically differed from one another and that gene flow is restricted between them. Moreover, the results of our 4-year field observations indicated that the egg traits varied genetically between the populations. Using mRNA extracted from the ovaries of 5 females in each population of Hokkai shrimp, we then performed a transcriptome analysis of the 2 populations. A total of 13.66 gigabases (Gb) of 75-bp reads was obtained. Further, 58,804 and 33,548 contigs for the first and second population, respectively, and 47,467 contigs for both populations were produced by de novo assembly. We detected 552 sequences with the former approach and 702 sequences with the later one; both sets of sequences showed greater than twofold differences in the expression levels between the 2 populations. Twenty-nine sequences were found in both approaches and were considered to be differentially expressed genes. Among them, 9 sequences showed significant similarity to functional genes. The present study showed a de novo assembly approach for the transcriptome of a non-model species using only short-read sequence data, and provides a strategy for identifying sequences showing significantly different expression levels between populations.