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Expression profiling of chickpea genes differentially regulated during a resistance response to Ascochyta rabiei.

Using microarray technology and a set of chickpea (Cicer arietinum L.) unigenes, grasspea (Lathyrus sativus L.) expressed sequence tags (ESTs) and lentil (Lens culinaris Med.) resistance gene analogues, the ascochyta blight (Ascochyta rabiei (Pass.) L.) resistance response was studied in four chickpea genotypes, including resistant, moderately resistant, susceptible and wild relative (Cicer echinospermum L.) genotypes. The experimental system minimized environmental effects and was conducted in reference design, in which samples from mock-inoculated controls acted as reference against post-inoculation samples. Robust data quality was achieved through the use of three biological replicates (including a dye swap), the inclusion of negative controls and strict selection criteria for differentially expressed genes, including a fold change cut-off determined by self-self hybridizations, Student's t-test and multiple testing correction (P < 0.05). Microarray observations were also validated by quantitative reverse transcriptase-polymerase chain reaction (RT-PCR). The time course expression patterns of 756 microarray features resulted in the differential expression of 97 genes in at least one genotype at one time point. k-means clustering grouped the genes into clusters of similar observations for each genotype, and comparisons between A. rabiei-resistant and A. rabiei-susceptible genotypes revealed potential gene 'signatures' predictive of effective A. rabiei resistance. These genes included several pathogenesis-related proteins, SNAKIN2 antimicrobial peptide, proline-rich protein, disease resistance response protein DRRG49-C, environmental stress-inducible protein, leucine-zipper protein, polymorphic antigen membrane protein, Ca-binding protein and several unknown proteins. The potential involvement of these genes and their pathways of induction are discussed. This study represents the first large-scale gene expression profiling in chickpea, and future work will focus on the functional validation of the genes of interest.