bioRxivEcologyPreprint
Multidimensional plasticity of gene expression underlying higher macrolide tolerance in saline and warm environments
Abstract
Organisms are routinely exposed to multiple environmental stresses, increasingly intensified by human activities. While adaptation occurs over the scale of at least a few generations, phenotypic plasticity enables rapid adjustment to environmental changes. Gene expression is a central plastic trait that mediates phenotypic change, yet how synergistic or antagonistic fitness effects arise from interactions among transcriptional responses remains poorly understood. Here, we introduce a general framework to classify interactions at the gene-expression level, and discuss their evolutionary consequences in the pressing context of antibiotic resistance. We analyzed the transcriptional response of Escherichia coli to azithromycin (AZI) across two salinity and temperature conditions. De novo and antagonistic interactions were prevalent, with evidence of cross-regulation between salt and AZI. High salinity increased tolerance by two orders of magnitude and, similarly to AZI, promoted a metabolic shift from carbon to nitrogen, potentially facilitating the clearance of macrolide-induced misfolded proteins. Reduced temperature, which cancelled the salinity protective effect, enhanced carbon metabolism and counteracted this shift. Salinity additionally restored stress-response pathways, largely repressed by AZI. Third-order interactions attenuated the contribution of salinity relative to AZI, but the number of affected genes declined exponentially with interaction order, suggesting that higher-order interactions at the gene-expression level should play a minor role in the responses to multiple stressors. By modulating transcriptional responses to AZI, simple environmental parameters ultimately reshaped the adaptive landscape of antibiotic resistance, altering the spectrum of resistance mutations likely be fixed.
- organism: E. coli
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- approach: statistical model
- Citations
- 0
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