bioRxivPlant Biology
Transcriptome analysis reveals robust transcriptional reprogramming during early stages of Cicer-Ascochyta interaction
R. Singh, A. Dwivedi, Y. Singh, K. Kumar, A. Ranjan, P. K. Verma
Abstract
Ascochyta blight (AB) caused by a filamentous fungus Ascochyta rabiei is a major threat to global chickpea production. The mechanisms underlying chickpea response to A. rabiei remain elusive. Here, we investigated the comparative transcriptional dynamics of AB-resistant and susceptible chickpea genotypes upon A. rabiei infection to understand the early host defence response. Our findings revealed that AB-resistant plants underwent rapid and extensive transcriptional reprogramming compared to susceptible host. At early stage (24-hpi), mainly cell wall remodeling and secondary metabolite pathways were highly activated, while DEGs related with signaling components viz. protein kinases, transcription factors, and hormonal pathways show remarkable upsurge at 72-hpi, especially in resistant genotype. Notably, our data suggests imperative role of JA, ET, and ABA signaling in providing immunity against A. rabiei. Furthermore, gene co-expression networks and modules corroborated the importance of cell wall remodeling, signal transduction and phytohormone pathways. The hub genes such as MYB14, PRE6 and MADS-SOC1 discovered in these modules might be the master regulators governing chickpea immunity. Overall, we not only provide novel insights for comprehensive understanding of immune signaling components mediating AB resistance/susceptibility at early Cicer-Ascochyta interactions, but also offer a valuable resource for developing AB-resistant chickpea. HighlightsComparative transcriptomic and co-expression analysis of AB-resistant and susceptible chickpea genotypes reveals high-amplitude transcriptional dynamics in resistant plants, and also identifies TFs, PKs and phytohormone-crosstalk as core regulators for AB-resistance.
- organism: chicken / avian
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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