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Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.

ABSTRACT: 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.

SUBMITTER: Rajput A

PROVIDER: S-EPMC9023270 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The transcriptomes of Pseudomonas aeruginosa clone C isolates NN2 and SG17M during the mid-exponential and early stationary phases of planktonic growth were evaluated by direct RNA sequencing on the nanopore platform and compared with established short-read cDNA sequencing on the Illumina platform. Fifty to ninety percent of the sense RNAs turned out to be rRNA molecules, followed by similar proportions of mRNA transcripts and noncoding RNAs. The two platforms detected similar proportions of uncharged tRNAs and 29 yet-undescribed antisense tRNAs. For example, the rarest arginine codon was paired with the most abundant tRNAArg, and the tRNAArg gene is missing for the most frequent arginine codon. More than 90% of the antisense RNA molecules were complementary to a coding sequence. The antisense RNAs were evenly distributed in the genomes. Direct RNA sequencing identified more than 4,000 distinct nonoverlapping antisense RNAs during exponential and stationary growth. Besides highly expressed small antisense RNAs less than 200 bases in size, a population of longer antisense RNAs was sequenced that covered a broad range (a few hundred to thousands of bases) and could be complementary to a contig of several genes. In summary, direct RNA sequencing identified yet-undescribed RNA molecules and an unexpected composition of the pools of tRNAs and sense and antisense RNAs. IMPORTANCE Genome-wide gene expression of bacteria is commonly studied by high-throughput sequencing of size-selected cDNA fragment libraries of reverse-transcribed RNA preparations. However, the depletion of rRNAs, enzymatic reverse transcription, and the fragmentation, size selection, and amplification during library preparation lead to inevitable losses of information about the initial composition of the RNA pool. We demonstrate that direct RNA sequencing on the Nanopore platform can overcome these limitations. Nanopore sequencing of total RNA yielded novel insights into the Pseudomonas aeruginosa transcriptome that-if replicated in other species-will change our view of the bacterial RNA world. The discovery of sense-antisense pairs of transfer-messenger RNA (tmRNA), tRNAs, and mRNAs indicates a further and unknown level of gene regulation in bacteria.

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Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.

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