A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

Abstract

Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acidand jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid-and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

Insights Into the Regulation of the Expression Pattern of Calvin-Benson-Bassham Cycle Enzymes in C3 and C4 Grasses

“…Based on data validated in the model plant A. thaliana , we used a computational approach to identify cis -regulatory elements whose putative trans-acting factors might play a role in C 4 compartmentalization. These data have been used to investigate the putative role of orthologous genes in other crops ( Capote et al, 2018 ; Moon et al, 2018 ; Burgess et al, 2019 ; Zeng et al, 2019 ; DeMers et al, 2020 ; Elzanati et al, 2020 ; Gray et al, 2020 ; Zhou et al, 2020 ), and allow us to generate a compelling hypothesis, as it is expected that similar DNA-binding domains of trans-acting factors would have similar DNA sequence preferences ( Lambert et al, 2019 ). However, several complementary experimental approaches will be needed to provide evidence of functional significance in C 4 plants.…”

Decades of Genetic Research on Soybean mosaic virus Resistance in Soybean

“…Another piece of research also reported that Glyma.14g204700 in L29 is likely to be the Rsv3 gene that confers strain-specific resis-tance to SMV by over-expression and the virus-induced transient silencing of this candidate gene in soybeans [152]. Based on the gene regulatory network using transcriptomic data, Rsv3 causes a defense via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed, including a transcription factor MYC2 encoded by Glyma.07g051500 [153,154].…”

Network Inference of Transcriptional Regulation in Germinating Low Phytic Acid Soybean Seeds

“…DEGs encoding TFs were annotated with the plant TFDB (Jin et al, 2017). For this study, separate network inferences were performed on the three subsets of genotypes using a computational pipeline developed previously (Redekar et al, 2017;DeMers et al, 2020). The pipeline implements the module network approach (Segal et al, 2003), in which genes are clustered into co-expression modules (gene modules) and then gene regulation is inferred between TFs and gene modules.…”