Machine learning uncovers thePseudomonas syringaetranscriptome in microbial communities and during infection

Abstract

AbstractThe transcriptional regulatory network (TRN) of the phytopathogenPseudomonas syringaepv.tomatoDC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agriculturally-significant interactions, a comprehensive understanding of the TRN ofP. syringaeis yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data fromP. syringaeto obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we 1) untangle the complex interspecies interactions betweenP. syringaeand other terrestrial bacteria in cocultures, 2) expand the current understanding of theArabidopsis thaliana-P. syringaeinteraction, and 3) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation inP. syringae.ImportancePseudomonas syringaepv.tomatoDC3000 is a model plant pathogen that infects tomatoes andArabidopsis thaliana. The current understanding of global transcriptional regulation in the pathogen is limited. Here, we applied iModulon analysis to a compendium of RNA-seq data to unravel its transcriptional regulatory network. We characterize each co-regulated gene set, revealing the activity of major regulators across diverse conditions. We provide new insights on the transcriptional dynamics in interactions with the plant immune system and with other bacterial species, such as AlgU-dependent regulation of flagellar genes during plant infection and downregulation of siderophore production in the presence of a siderophore cheater. This study demonstrates the novel application of iModulons in studying temporal dynamics during host-pathogen and microbe-microbe interactions, and reveals specific insights of interest.