J Proteome Res. 2020 Feb 10.
Peyronellaea pinodes causes Ascochyta blight, one of the major diseases in pea worldwide. Cultivated pea plants have a low resistance to this disease. Although quantitative trait loci (QTLs) involved in the resistance to Ascochyta blight have been identified, the specific genes associated with these QTLs remain unknown, which makes marker-assisted selection difficult. Complex traits alter proteins and their abundance. Quantitative estimation of proteins in pea might therefore be useful in selecting potential markers for breeding. In this work, we developed a strategy using a combination of shotgun proteomics (viz., high performance liquid chromatography-mass spectrometry data-dependent acquisition) and data-independent acquisition (DIA) analysis, to identify putative protein markers associated with resistance to Ascochyta blight and explored its use for breeding selection. For this purpose, an initial list of target peptides based on proteins closely related to resistance to P. pinodes was compiled by using two genotypes with contrasting responses to the disease. Then, targeted data analysis (viz., shotgun proteomics-DIA) was used for constitutive quantification of the target peptides in a representative number of the recombinant inbred line population segregated for resistance as derived from a cross between the two genotypes. Finally, a peptide panel of potential markers for resistance to P. pinodes was built. The results thus obtained are discussed and compared with those of previous gene expression studies using the same parental pea genotypes responding to the pathogen. Also, a molecular defense mechanism against Ascochyta blight in pea is proposed. To the authors' knowledge, this is the first time a targeted proteomics approach based on data analysis has been used to identify peptides associated with resistance to this disease.
Keywords: Ascochyta blight; data-independent acquisition (DIA); pea; peptide markers; resistance; shotgun-data-dependent acquisition (DDA); targeted proteomics analysis