Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

Rajput, A.; Tsunemoto, H.; Sastry, A. V.; Szubin, R.; Rychel, K.; Sugie, J.; Pogliano, J.; Palsson, B.

doi:10.1101/2021.07.28.454220

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bioRxiv · 10.1101/2021.07.28.454220

Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

Rajput, A.·Tsunemoto, H.·Sastry, A. V.·Szubin, R.·Rychel, K.·Sugie, J.·Pogliano, J.·Palsson, B.

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Abstract

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa plays a critical role in coordinating numerous cellular processes. We extracted and quality controlled all publicly available RNA-sequencing datasets for P. aeruginosa to find 281 high-quality transcriptomes. We produced 83 new RNAseq data sets under critical conditions to generate a comprehensive compendium of 364 transcriptomes. We used this compendium to reconstruct the TRN of P. aeruginosa using independent component analysis (ICA). We identified 104 independently modulated sets of genes (called iModulons), among which 81 (78%) reflect the effects of known transcriptional regulators. We show that iModulons: 1) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs); 2) show increased expression of the BGCs and associated secretion systems in conditions that emulate cystic fibrosis (CF); 3) show the presence of a novel BGC named RiPP (bacteriocin producer) which might have a role in worsening CF outcomes; 4) exhibit the interplay of amino acid metabolism regulation and central metabolism across carbon sources, and 5) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compare the iModulons of P. aeruginosa with those of E. coli to observe conserved regulons across two gram negative species. This comprehensive TRN framework covers almost every aspect of the transcriptional regulatory machinery in P. aeruginosa, and thus could prove foundational for future research of its physiological functions.

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Rajput, A., Tsunemoto, H., Sastry, A. V., Szubin, R., Rychel, K., Sugie, J., Pogliano, J., Palsson, B.. 2021-07-28. Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators. https://doi.org/10.1101/2021.07.28.454220

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