iTRAQ proteomics reveals the regulatory response to Magnaporthe oryzae in durable resistant vs. susceptible rice genotypes

Abstract

Rice blast disease caused by Magnaporthe oryzae (M. oryzae) is one of the most serious diseases. Although previous research using two-dimensional gel-based proteomics to assess the proteins related to the rice blast resistance had been done, few proteins were identified. Here, we used the iTRAQ method to detect the differentially expressed proteins (DEPs) in the durable resistant rice variety Gangyuan8 (GY8) and the susceptible rice variety Lijiangxintuanheigu (LTH) in response to M. oryzae invasion, and then transcriptome sequencing was used to assist analysis A total of 193 and 672 DEPs were specifically identified in GY8 and LTH, respectively, with only 46 similarly expressed DEPs being shared by GY8 and LTH.39 DEPs involved in plant-pathogen interaction, plant hormone signal transduction, fatty acid metabolism and peroxisome biosynthesis were significantly different between compatible interaction (LTH) and incompatible interaction (GY8). Some proteins participated in peroxide signal transduction and biosynthesis was down-regulated in GY8 but up-regulated in LTH. A lot of genes encoding pathogenesis-related gene (PR), such as chitinase and glucanase, were significantly up-regulated at both the transcriptome and proteome levels at 24 hours post-inoculation in GY8, but up-regulated at the transcriptome level and down-regulated at the proteome level in LTH. Our study reveals that the pathogenassociated molecular pattern (PAMP)-triggered immunity defense system may be activated at the transcriptome level but was inhibited at the protein level in susceptible rice varieties after inoculation. The results may facilitate future studies of the molecular mechanisms of rice blast resistance.

Responses of Phragmites australis to copper stress: A combined analysis of plant morphology, physiology and proteomics

“…The quantitative protein ratios, which require that a protein contains at least one unique peptide, were weighted and normalized by the median ratio in Mascot (Ma et al . 2020). When the difference was >1.2‐fold, it was regarded as a significantly different protein ( P ≤ 0.05).…”

Proteomics and Metabolomics Studies on the Biotic Stress Responses of Rice: an Update

“…Furthermore, proteomics studies have revealed the roles of DUF26, nucleotide binding-leucine rich repeat, PRs, ROS productionscavenging enzymes, heat shock proteins (HSPs), nuclear reorganization-related proteins, TFs, and phytohormone signaling in rice resistance, whereby proteins related to pathogen perception and signal transduction are important during the early stage of infection (for review, see Meng et al 2019). A recent study using iTRAQ found that probenazole-inducible protein 1 (PBZ1) and phenylpropanoid accumulated in both resistant and susceptible cultivars, which was in contrast to reports from previous studies utilizing the 2DE approach (Ma et al 2020b). Interestingly, a metabolomic assay using HPLC identified a rice saponin, Bayogenin 3-O-cellobioside, which is the first saponin found in rice (Norvienyeku et al 2020).…”

“…Moreover, ABA signaling influences the calcium (Ca 2+ ) signaling that is considered as a front line of signaling events and is involved in resistance response of rice against M. oryzae (Wang et al 2019). In a proteomics study of resistant Gangyuan8 (GY8) and susceptible Lijiangxintuanheigu (LTH) cultivars infected by M. oryzae, Ca 2+ − dependent protein kinase, Ca 2+ sensor calmodulin, and calmodulin-like protein were particularly found upregulated in LTH but remained the same in GY8, suggesting that the proteins possibly regulate blast resistance negatively in those cultivars (Ma et al 2020b). Another proteomics study found several Ca 2+ -binding proteins in saliva of phloem-feeding insects and honeydews of BPH and green rice leafhopper, supporting the function of Ca 2+ -binding proteins in counteracting the sieve-tube occlusion defenses in host plants (Will et al 2013;Zhu et al 2020).…”

Retrospective research of rice blast development in Krasnodar region

“…The implementation of the model makes it possible to identify time intervals that contribute to plant infection and the development of a pathogen, as well as to evaluate the intensity of the pathogen exposure during this time period, expressed as a percentage of probability (%). Such time periods are called excess, and the number of excesses during the study period describes the intensity of the manifestations of rice blast [14]. Estimating the number of excesses during the pre-growing and growing periods will help to identify the intensity of the influence of the environment and agroclimatic conditions on the dynamics of the pathogen Pyricularia oryzae Cav.…”