New insights into azelaic acid-induced resistance against Alternaria Solani in tomato plants.

BACKGROUND: The effect of azelaic acid (Aza) on the response of tomato plants to Alternaria solani was investigated in this study. After being treated with Aza, tomato plants were infected with A. solani, and their antioxidant, biochemical, and molecular responses were analyzed. RESULTS: The results demonstrated that H2O2 and MDA accumulation increased in control plants after pathogen infection. Aza-treated plants exhibited a remarkable rise in peroxidase (POD) and catalase (CAT) activities during the initial stages of A. solani infection. Gene expression analysis revealed that both Aza treatment and pathogen infection altered the expression patterns of the SlNPR1, SlERF2, SlPR1, and SlPDF1.2 genes. The expression of SlPDF1.2, a marker gene for the jasmonic acid/ethylene (JA/ET) signaling pathway, showed a remarkable increase of 4.2-fold upon pathogen infection. In contrast, for the SlNPR1, a key gene in salicylic acid (SA) pathway, this increased expression was recorded with a delay at 96 hpi. Also, the phytohormone analysis showed significantly increased SA accumulation in plant tissues with disease development. It was also revealed that tissue accumulation of JA in Aza-treated plants was increased following pathogen infection, while it was not increased in plants without pathogen inoculation. CONCLUSION: The results suggest that the resistance induced by Aza is mainly a result of modulations in both SA and JA pathways following complex antioxidant and molecular defense responses in tomato plants during A. solani infection. These findings provide novel information regarding inducing mechanisms of azelaic acid which would add to the current body of knowledge of SAR induction in plants as result of Aza application.

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets.

A Gene Regulatory Network (GRN) is a collection of interactions between molecular regulators and their targets in cells governing gene expression level. Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine the impact of transcript data platforms and normalization procedures on describing associations in GRNs. In this study exploiting publically available microarray and RNA-Seq datasets and a gold standard of transcriptional interactions in Arabidopsis, we performed a comparison between six GRNs derived by RNA-Seq and microarray data and different normalization procedures. As a result we observed that compared algorithms were highly data-specific and Networks reconstructed by RNA-Seq data revealed a considerable accuracy against corresponding networks captured by microarrays. Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network. Taken together transcriptional regulatory networks obtained by Robust Multiarray Averaging (RMA) and Variance-Stabilizing Transformed (VST) normalized data demonstrated predicting higher rate of true edges over the rest of methods used in this comparison.