Architecture of genome-wide transcriptional regulatory network reveals dynamic functions and evolutionary trajectories in Pseudomonas syringae
Sun, Y.; Li, J.; Huang, J.; Li, S.; Li, Y.; Deng, X.
Show abstract
The model Gram-negative plant pathogen Pseudomonas syringae utilises hundreds of transcription factors (TFs) to regulate its functional processes, including virulence and metabolic pathways that control its ability to infect host plants. Although the molecular mechanisms of regulators have been studied for decades, a comprehensive understanding of genome-wide TFs in Psph 1448A remains limited. Here, we investigated the binding characteristics of 170 of 301 annotated TFs through ChIP-seq. Fifty-four TFs, 62 TFs and 147 TFs were identified in top-level, middle-level and bottom-level, reflecting multiple higher-order network structures and direction of information-flow. More than forty thousand TF-pairs were classified into 13 three-node submodules which revealed the regulatory diversity of TFs in Psph 1448A regulatory network. We found that bottom-level TFs performed high co-associated scores to their target genes. Functional categories of TFs at three levels encompassed various regulatory pathways. Three and 25 master TFs were identified to involve in virulence and metabolic regulation, respectively. Evolutionary analysis and topological modularity network revealed functional variability and various conservation of TFs in P. syringae (Psph 1448A, Pst DC3000, Pss B728a and Psa C48). Overall, our findings demonstrated the global transcriptional regulatory network of genome-wide TFs in Psph 1448A. This knowledge can advance the development of effective treatment and prevention strategies for related infectious diseases.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Global transcription factors analyses reveal hierarchy and synergism of regulatory networks and master virulence regulators in Pseudomonas aeruginosa 94%
- Structure-guided secretome analysis of gall-forming microbes offers insights into effector diversity and evolution 93%
- The Rhizobial effector NopT targets Nod factor receptors to regulate symbiosis in Lotus japonicus 93%
Similar papers in this journal
- Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius 93%
- Repertoire and abundance of secreted virulence factors shape the pathogenic capacity of Pseudomonas syringae pv. aptata 93%
- Genome recombination-mediated tRNA up-regulation conducts general antibiotic resistance of bacteria at early stage 92%
Similar papers in this journal
- Polarity-dependent expression and localization of secretory glucoamylase mRNA in filamentous fungal cells 91%
- HsbA represses stationary phase biofilm formation in Pseudomonas putida 91%
- Alanine and glutamate catabolism ensure proper sporulation by preventing premature germination and providing energy respectively 91%
Similar papers in this journal
- A systems biology approach to disentangle the direct and indirect effects of global transcription factors on gene expression in Escherichia coli 93%
- Comparative phylogenomic analysis reveals evolutionary genomic changes and novel toxin families in endophytic Liberibacter pathogens 92%
- Characterization of TelE, an LXG effector exhibiting a conserved C-terminal glycine zipper motif required for toxicity 92%
Similar papers in this journal
- The COMPASS-like complex modulates fungal development and pathogenesis by regulating H3K4me3-mediated targeted gene expression in Magnaporthe oryzae 95%
- Genome-wide alternative splicing profiling in the fungal plant pathogen Sclerotinia sclerotiorum during the colonization of diverse host families 93%
- Show me your secret(ed) weapons: a multifaceted approach reveals novel type III-secreted effectors of a plant pathogenic bacterium 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.