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JGM, Pseudomonas aeruginosa, Genomics, Base Sequence

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mSystems. 2023;8(1):e0034222.







This work was supported by grants from the Gordon and Betty Moore Foundation (GBMF4552 to C.S.G.), the Cystic Fibrosis Foundation (HOGAN19GO to D.A.H., GREENE21GO to C.S.G., and STANTO19R0 to S.L.N.), and the Flatley Foundation. Finally, this work was supported by the National Institutes of Health (NIH) through awards NIDDK P30-DK117469, P30 DK117469, and R01 HL151385.


Pseudomonas aeruginosa is an opportunistic pathogen that causes difficult-to-treat infections. Two well-studied divergent P. aeruginosa strain types, PAO1 and PA14, have significant genomic heterogeneity, including diverse accessory genes present in only some strains. Genome content comparisons find core genes that are conserved across both PAO1 and PA14 strains and accessory genes that are present in only a subset of PAO1 and PA14 strains. Here, we use recently assembled transcriptome compendia of publicly available P. aeruginosa RNA sequencing (RNA-seq) samples to create two smaller compendia consisting of only strain PAO1 or strain PA14 samples with each aligned to their cognate reference genome. We confirmed strain annotations and identified other samples for inclusion by assessing each sample's median expression of PAO1-only or PA14-only accessory genes. We then compared the patterns of core gene expression in each strain. To do so, we developed a method by which we analyzed genes in terms of which genes showed similar expression patterns across strain types. We found that some core genes had consistent correlated expression patterns across both compendia, while others were less stable in an interstrain comparison. For each accessory gene, we also determined core genes with correlated expression patterns. We found that stable core genes had fewer coexpressed neighbors that were accessory genes. Overall, this approach for analyzing expression patterns across strain types can be extended to other groups of genes, like phage genes, or applied for analyzing patterns beyond groups of strains, such as samples with different traits, to reveal a deeper understanding of regulation.


This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.