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Penicillium expansum  is one of the most destructive post-harvest pathogens that causes fruit spoilage in apple. This study explored the molecular response of apple to this pathogen via microarray data mining based on attribute weighting algorithms (AWAs), differential expression and pathway analyses, and multivariate statistical analysis. A total of 124 probe sets were identified by at least one AWA and these yielded 29 genes with significantly differential expression. Leave-one-out cross-validation showed that the 29-gene signature could differentiate resistant (R) and susceptible (S) cultivars with 98.65% accuracy, with most of the genes showing significant expression changes in these cultivars at 1 week post-inoculation (wpi). Many genes encoding hydrolases, reductases and proteins with zinc-finger domains showed an increased expression in the R cultivars. In particular, disease resistance protein CC-NBS-LRR, resistance-related protein E3-ubiquitin and cuticle wax biosynthesis genes displayed increased expression, especially in R cultivars at 1 and 6 wpi. The secondary metabolite biosynthesis pathway, metabolic pathway and amino acid biosynthesis pathway were commonly upregulated in both R and S cultivars, but with a greater intensity in R cultivars, while the flavonoid biosynthesis pathway was downregulated in the S cultivars at 1 h post-inoculation. This study identifies candidate targets for developing resistant cultivars.

new zealand journal of crop and horticultural science

Exploring the transcriptome signature associated with tolerance to  Penicillium expansum  in apple through feature selection algorithms and differential gene expression analysis