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Theoretical And Applied Genetics 2016-Oct

Massive analysis of cDNA ends (MACE) reveals a co-segregating candidate gene for LpPg1 stem rust resistance in perennial ryegrass (Lolium perenne).

Vain rekisteröityneet käyttäjät voivat kääntää artikkeleita
Kirjaudu sisään Rekisteröidy
Linkki tallennetaan leikepöydälle
Jens Bojahr
Ottilia Nhengiwa
Nicolas Krezdorn
Björn Rotter
Bernhard Saal
Brigitte Ruge-Wehling
Christine Struck
Peter Winter

Avainsanat

Abstrakti

CONCLUSIONS

Molecular markers including a potential resistance gene co-segregating with the LpPg1 stem rust resistance locus in perennial ryegrass were identified by massive analysis of cDNA ends (MACE) transcriptome profiling. Stem rust caused by Puccinia graminis subsp. graminicola is a severe fungal disease in the forage crop perennial ryegrass and other grasses. The previously identified LpPg1 locus confers efficient resistance against the pathogen. The aim of this study was to identify candidate genes involved in rust resistance and to use them as a resource for the development of molecular markers for LpPg1. To identify such candidates, bulked segregant analysis was combined with NGS-based massive analysis of cDNA ends (MACE) transcriptome profiling. Total RNA was isolated from bulks of infected and non-infected leaf segments from susceptible and resistant genotypes of a full-sibling mapping population and their respective parental lines and MACE was performed. Bioinformatic analysis detected 330 resistance-specific SNPs in 178 transcripts and 341 transcripts that were exclusively expressed in the resistant bulk. The sequences of many of these transcripts were homologous to genes in distinct regions of chromosomes one and four of the model grass Brachypodium distachyon. Of these, 30 were genetically mapped to a 50.8 cM spanning region surrounding the LpPg1 locus. One candidate NBS-LRR gene co-segregated with the resistance locus. Quantitative analysis of gene expression suggests that LpPg1 mediates an efficient resistance mechanism characterized by early recognition of the pathogen, fast defense signaling and rapid induction of antifungal proteins. We demonstrate here that MACE is a cost-efficient, fast and reliable tool that detects polymorphisms for genetic mapping of candidate resistance genes and simultaneously reveals deep insight into the molecular and genetic base of resistance.

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