Allelic diversity in the transcriptomes of contrasting rust-infected genotypes of Lathyrus sativus, a lasting resource for smart breeding

Abstract

BackgroundGrass pea (Lathyrus sativus L.) is a valuable resource for potentially durable partial resistance to rust. To gain insight into the resistance mechanism and identify potential resistance genes, we generated the first comprehensive transcriptome assemblies from control and Uromyces pisi inoculated leafs of a susceptible and a partially rust-resistant grass pea genotype by RNA-seq.Results134,914 contigs, shared by both libraries, were used to analyse their differential expression in response to rust infection. Functional annotation grouped 60.4% of the contigs present in plant databases (37.8% of total) to 33 main functional categories, being “protein”, “RNA”, “signalling”, “transport” and “stress” the most represented. Transcription profiles revealed considerable differences in regulation of major phytohormone signalling pathways: whereas Salicylic and Abscisic Acid pathways were up-regulated in the resistant genotype, Jasmonate and Ethylene pathways were down-regulated in the susceptible one. As potential Resistance-genes we identified a mildew resistance locus O (MLO)-like gene, and MLO-related transcripts. Also, several pathogenesis-related genes were up-regulated in the resistant and exclusively down regulated in the susceptible genotype. Pathogen effectors identified in both inoculated libraries, as e.g. the rust Rtp1 transcript, may be responsible for the down-regulation of defence-related transcripts. The two genotypes contained 4,892 polymorphic contigs with SNPs unevenly distributed between different functional categories. Protein degradation (29.7%) and signalling receptor kinases (8.2%) were the most diverged, illustrating evolutionary adaptation of grass pea to the host/pathogens arms race.ConclusionsThe vast array of novel, resistance-related genomic information we present here provides a highly valuable resource for future smart breeding approaches in this hitherto under-researched, valuable legume crop.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0376-2) contains supplementary material, which is available to authorized users.

Grain Legumes

“…In addition, expression analysis using RNA-sequencing was also employed in grass pea to tackle the molecular mechanisms underlying prehaustorial rust resistance (Almeida et al 2014b). These authors identified several pathogenesis-related proteins as possibly involved in grass pea resistance to rust, that included some regulated by the well-studied mildew resistance locus O (MLO) gene.…”

Genome-wide identification and comparison of legume MLO gene family

“…A clade V MLO gene, from pepper, CaMLO2 , has also been associated with susceptibility to bacterial and oomycete pathogens, and to drought 19 20 . In addition, the expression of some Lathyrus sativus MLO transcripts were induced shortly after rust infection in resistant genotypes, which might suggest their involvement in rust resistance 21 . Although the exact role of clade V MLOs is still unclear, they might interfere with the plant immune response to stresses.…”

Lathyrus sativus transcriptome resistance response to Ascochyta lathyri investigated by deepSuperSAGE analysis

“…Of these 205,691 tags were derived from L. sativus inoculated with A. lathyri and 193,957 tags from control plants. These tags corresponded to 14,386 unique sequences (UniTags) of which 13,773 (95.7%) were successfully annotated to the L. sativus reference dataset (Almeida et al, 2014 ).…”

“…A multi-step BLAST procedure was used to annotate the UniTag reads to ensure an unambiguous assignment to their corresponding transcript and to eliminate any remaining adaptor sequences. Reference datasets were generated by own de-novo-assembly (Almeida et al, 2014 ) and downloaded from the publicly accessible Fabaceae databases using the nucleotide database from the National Center for Biotechnology Information (NCBI). UniTag reads were successively aligned against these reference datasets in the following order: (1) 26 bp de-novo-assembly dataset with a minimum BLAST-score of 42; (2) UniTags which did not attain the specified BLAST score in the previous step were aligned against the complete NCBI dataset with the same required BLAST score of 42 or above.…”