bioRxivBioinformaticsPreprint
Transcriptional reprogramming during effector/flg22-triggered immune is independent of defense phytohormone signaling networks
Abstract
Plants rely on the innate immune system to sense and respond to a wide range of lifestyle pathogens and to facilitate their survival in natural ecosystems. Pathogen-associated molecular patterns (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) are designated as a two-branched system of innate immunity. Although PTI/ETI share a series of downstream molecular events, systematic analysis of convergent and divergent signaling in PTI/ETI is currently lacking. The phytohormones salicylic acid (SA) and jasmonic acid (JA) are considered to constitute the hormonal backbone of plant immunity, are functionally antagonistic, and play essential roles in defending against biotrophic and necrotrophic pathogens, respectively. However, the distinct performance of two phytohormones in PTI/ETI remains unclear. Here, we systemically investigate and validate the reprogramming of molecular networks during PTI and ETI. Using publicly available Arabidopsis RNA sequence data from 560 samples, we construct a co-expression network under Mock conditions and then explore the differential expression/co-expression changes during PTI, ETI, and Pto DC3000 infection. During PTI and ETI, one-third of genes in the Arabidopsis genome exhibit the same directional differential expression in a manner independent of JA/ethylene/PAD4/SA signaling but show differential co-expression patterns. However, the defense phytohormone network is required for defense against Pto DC3000 infection. We also exhibit the use of this network in prioritizing genes that functioned closely with the proteins directly targeted by elicitors. Overall, this study will deepen our understanding of plant transcriptome in plant immunity and provide new insights into the mode of action of elicitors.
- organism: Arabidopsis
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 4
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