Pseudomonas syringae Virulence Regulatory Network construction
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA575032 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10207266_1.fastq.gz | Fastqsanger.gz | |||
| SRR10207266_2.fastq.gz | Fastqsanger.gz | |||
| SRR10207267_1.fastq.gz | Fastqsanger.gz | |||
| SRR10207267_2.fastq.gz | Fastqsanger.gz | |||
| SRR10207268_1.fastq.gz | Fastqsanger.gz |
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Project description:Pseudomonas syringae, a Gram-negative plant pathogen, infects more than 50 crops with its type III secretion system (T3SS) and causes severe economic losses around the world. Although the mechanisms of virulence-associated regulators of P. syringae T3SS have been studied for decades, the crosstalk and network underlying these regulators are still elusive. Previously, we have individually studied a group of T3SS regulators, including AefR, HrpS, and RhpRS. In the present study, we found 4 new T3SS regulator genes (envZ, ompR, tsiS and phoQ) via transposon-mediated mutagenesis. Two-component systems EnvZ and TsiS natively regulate T3SS. In order to uncover the crosstalk between 16 virulence-associated regulators, (including AefR, AlgU, CvsR, GacA, HrpL, HrpR, HrpS, MgrA, OmpR, PhoP, PilR, PsrA, RhpR, RpoN, TsiR and Vfr) in P. syringae, we mapped an intricate network named PSVnet (Pseudomonas syringae Virulence Regulatory Network) by combining differentially expression genes in RNA-seq and binding loci in ChIP-seq of all regulators.
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Project description:Bacteria relies on two-component systems (TCSs) to respond to a wide range of stimuli or environmental cues for their survival and virulence. However, the functions and synergistic actions of TCSs in genomic level remains unclear. Here, in model phytopathogen Pseudomonas syringae, by integrating multiomics data, we developed a network-based PSTCSome (Pseudomonas syringae two-component systems regulome) to identify functions and crosstalk among global TCSs under either virulence suppressing (King’s B medium, KB) or activating conditions (minimal medium, MM). Transcriptome profiling identifies 2,099 differentially expressed genes (DEGs) in KB and 1,250 DEGs in MM, while ChIP-seq identifies 1,628 target genes across the whole genome. The multiomics results are applied to perform not only gene ontology (GO) analysis to detect the biological processes that TCSs involved in, but also subnetworks and co-expression analysis of 8 virulence-related pathways to decipher the TCSs regulated pathogenic behaviors. The following phenotypic experiments newly confirmed 8 TCSs that regulates type III secretion system (T3SS) (PSPPH_0253, PSPPH_2606, PSPPH_4416, and PSPPH_4451) and surface attachment (PSPPH_0253, PSPPH_3041, PSPPH_3473, PSPPH_3736, PSPPH_4001). We then compute 259 functional genes in KB and 161 in MM for those cluster TCSs. Analysis of cluster TCSs regulons led to the identification of either novel functions or 2 master regulatory TCSs (RhpS/RhpR and PSPPH_4827/4828) toward virulence. Our results show that TCSs in P. syringae dynamically adjust their regulatory networks by sensing the external environment, and then switch bacteria between pathogenic and non-pathogenic states in a sophisticated way. Furthermore, we present an online platform of PSTCSome to facilitate updating, network visualization and user-customized analyses.
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