Transcriptome de novo assembly from next-generation sequencing and comparative analyses in the hexaploid salt marsh species Spartina maritima and Spartina alterniflora (Poaceae)

Plateforme BioGenouest 'Environmental and Functional Genomics'; Genouest Bioinformatics PlateformInternational audienceSpartina species have a critical ecological role in salt marshes and represent an excellent system to investigate recurrent polyploid speciation. Using the 454 GS-FLX pyrosequencer, we assembled and annotated the first reference transcriptome (from roots and leaves) for two related hexaploid Spartina species that hybridize in Western Europe, the East American invasive Spartina alterniflora and the Euro-African S. maritima. The de novo read assembly generated 38 478 consensus sequences and 99% found an annotation using Poaceae databases, representing a total of 16 753 non-redundant genes. Spartina expressed sequence tags were mapped onto the Sorghum bicolor genome, where they were distributed among the subtelomeric arms of the 10 S. bicolor chromosomes, with high gene density correlation. Normalization of the complementary DNA library improved the number of annotated genes. Ecologically relevant genes were identified among GO biological function categories in salt and heavy metal stress response, C4 photosynthesis and in lignin and cellulose metabolism. Expression of some of these genes had been found to be altered by hybridization and genome duplication in a previous microarray-based study in Spartina. As these species are hexaploid, up to three duplicated homoeologs may be expected per locus. When analyzing sequence polymorphism at four different loci in S. maritima and S. alterniflora, we found up to four haplotypes per locus, suggesting the presence of two expressed homoeologous sequences with one or two allelic variants each. This reference transcriptome will allow analysis of specific Spartina genes of ecological or evolutionary interest, estimation of homoeologous gene expression variation using RNAseq and further gene expression evolution analyses in natural populations