bioRxivMicrobiologyPreprint
Signatures of antibiotic tolerance and persistence in response to divergent stresses
H. Wang, G. McElfresh, N. Wijesuriya, A. Podgorny, A. D. Hecht, C. J. Ray
Abstract
In an environment with overly abundant lactose, a strain of the Gram-negative bacterium E. coli induces a persister-enriched phenotype and a heterogeneous pattern of growth rates. In high lactose conditions, the majority of cells are fast-growing, while a minority stochastically switch to a slow-growing, persister-prone phenotype that has higher ampicillin tolerance. Previously, bulk bacterial RNA-seq demonstrated broad changes in gene expression profiles for cells cultured in different lactose conditions, revealing multiple pathway regulatory regime switches enhancing its survivability for counteracting osmotic pressure in high lactose conditions with overflow metabolism. We hypothesized that a set of unique gene regulatory signatures underlies antibiotic tolerance in the high lactose condition. To further understand the gene regulatory regime in slow-growing cells, the subpopulation of persister-prone cells was enriched with ampicillin treatment. The resulting culture was collected for transcriptomic analysis. The transcriptomic data were then analyzed for differentially expressed genes, signature genes, GO term enrichment, pathway enrichment, and flux balance analysis. Our results show that under opposing stresses, the cells have similar and divergent responses. Cells exhibit upregulated assimilation pathways and downregulated biosynthesis pathways when encountering stresses. Post ampicillin treatment, cells in both high and low lactose conditions exhibit downregulated central metabolism to reduce growth. In the high-lactose concentration medium after ampicillin treatment, persisters may arise due to ferric imbalance-induced cell growth arrest and gene regulation due to ssrA-mediated downregulation-induced error-prone transcription.
- organism: E. coli
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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