bioRxivMicrobiologyPreprint
Diverse transcriptomic effects of evolved imipenem-relebactam resistance in Pseudomonas aeruginosa
Adeline SupandyiD, Ava J DorazioiD, Kevin M SquiresiD, Ellen G KlineiD, Ryan K. ShieldsiD, Daria Van TyneiD
Abstract
Imipenem-relebactam (Imi/Rel) is a {beta}-lactam/{beta}-lactamase inhibitor combination used for the treatment of multidrug-resistant Pseudomonas aeruginosa infections. We previously reported that treatment-emergent resistance to Imi/Rel is associated with mutations in the AmpC beta-lactamase and/or the MexAB-OprM and MexEF-OprN efflux operons. However, the impact of these mutations on bacterial gene expression has not been explored extensively, particularly among P. aeruginosa from patients treated with Imi/Rel. To determine the effect of treatment-emergent Imi/Rel resistance on P. aeruginosa global transcription, we performed RNA sequencing on paired P. aeruginosa clinical isolates from six patients collected before and after Imi/Rel treatment. Transcriptional responses varied substantially, with no conserved changes in gene expression identified across all six patients. Three isolate pairs showed significant upregulation of previously characterized Imi/Rel resistance-associated genes in the treatment-emergent resistant isolate, while two resistant isolates displayed significant downregulation of ampC. Comparisons of the top 10 differentially regulated genes in the resistant isolate from each patient revealed only one gene that was shared between all six patients. Pathway enrichment analysis using Clusters of Orthologous Genes (COG) categories suggested that Imi/Rel exposure impacts transcription of genes involved in translation and metabolism, but these changes are highly variable between patients and isolates. Overall, we find that the transcriptional response of clinical P. aeruginosa to Imi/Rel exposure appears to be highly diverse and likely dependent on the genetic background of the infecting P. aeruginosa strain.
- organism: bacteria (other)
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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