Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.
Akanksha Rajput, Hannah Tsunemoto, Anand V Sastry, Richard Szubin, Kevin Rychel, Joseph Sugie, Joe Pogliano, Bernhard O Palsson
Author Information
Akanksha Rajput: Department of Bioengineering, University of California, San Diego, La Jolla, USA.
Hannah Tsunemoto: Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Anand V Sastry: Department of Bioengineering, University of California, San Diego, La Jolla, USA.
Richard Szubin: Department of Bioengineering, University of California, San Diego, La Jolla, USA.
Kevin Rychel: Department of Bioengineering, University of California, San Diego, La Jolla, USA. ORCID
Joseph Sugie: Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Joe Pogliano: Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Bernhard O Palsson: Department of Bioengineering, University of California, San Diego, La Jolla, USA. ORCID
The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.
