Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection
ABSTRACT The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agricultura...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2023-10-01
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| Series: | mSystems |
| Subjects: | |
| Online Access: | https://journals.asm.org/doi/10.1128/msystems.00437-23 |
| _version_ | 1827755088184082432 |
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| author | Heera Bajpe Kevin Rychel Cameron R. Lamoureux Anand V. Sastry Bernhard O. Palsson |
| author_facet | Heera Bajpe Kevin Rychel Cameron R. Lamoureux Anand V. Sastry Bernhard O. Palsson |
| author_sort | Heera Bajpe |
| collection | DOAJ |
| description | ABSTRACT The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 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 of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we (i) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, (ii) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and (iii) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae. IMPORTANCE Pseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis 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. |
| first_indexed | 2024-03-11T07:58:22Z |
| format | Article |
| id | doaj.art-03434c73c09b45f4ba4bb8c89908a950 |
| institution | Directory Open Access Journal |
| issn | 2379-5077 |
| language | English |
| last_indexed | 2024-03-11T07:58:22Z |
| publishDate | 2023-10-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj.art-03434c73c09b45f4ba4bb8c89908a9502023-11-17T03:22:37ZengAmerican Society for MicrobiologymSystems2379-50772023-10-018510.1128/msystems.00437-23Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infectionHeera Bajpe0Kevin Rychel1Cameron R. Lamoureux2Anand V. Sastry3Bernhard O. Palsson4Department of Bioengineering, University of California San Diego , La Jolla, California, USADepartment of Bioengineering, University of California San Diego , La Jolla, California, USADepartment of Bioengineering, University of California San Diego , La Jolla, California, USADepartment of Bioengineering, University of California San Diego , La Jolla, California, USADepartment of Bioengineering, University of California San Diego , La Jolla, California, USAABSTRACT The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 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 of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we (i) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, (ii) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and (iii) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae. IMPORTANCE Pseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis 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.https://journals.asm.org/doi/10.1128/msystems.00437-23Pseudomonas syringaeindependent component analysistranscriptomicsgene regulationdata miningmicrobial interactions |
| spellingShingle | Heera Bajpe Kevin Rychel Cameron R. Lamoureux Anand V. Sastry Bernhard O. Palsson Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection mSystems Pseudomonas syringae independent component analysis transcriptomics gene regulation data mining microbial interactions |
| title | Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection |
| title_full | Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection |
| title_fullStr | Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection |
| title_full_unstemmed | Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection |
| title_short | Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection |
| title_sort | machine learning uncovers the pseudomonas syringae transcriptome in microbial communities and during infection |
| topic | Pseudomonas syringae independent component analysis transcriptomics gene regulation data mining microbial interactions |
| url | https://journals.asm.org/doi/10.1128/msystems.00437-23 |
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