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Biology subjects

Rescan, M.

Publications and source records attributed to Rescan, M..

2 recordsLinked to original sources

Multidimensional plasticity of gene expression underlying higher macrolide tolerance in saline and warm environments

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.

ecology↗

Predicting population genetic change in an experimental stochastic environment

Most natural environments exhibit a substantial component of random variation. Such environmental noise is expected to cause random fluctuations in natural selection, affecting the predictability of evolution. But despite a long-standing theoretical interest for understanding the population genetic consequences of stochastic environments, there has been a dearth of empirical validation and estimation of the underlying parameters of this theory. Indeed, tracking the genetics of a large number of replicate lines under a controlled level of environmental stochasticity is particularly challenging. Here, we tackled this problem by resorting to an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequency of one of two competing strains through amplicon sequencing of a nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (variance), but also their predictability (autocorrelation), have large impacts on the average selection coefficient. Furthermore, the stochastic variance in population genetic change is substantially higher in a fluctuating environment. Reaction norms of selection coefficients and growth rates of single strains against the environment captured the mean response accurately, but failed to explain the high variance induced by environmental stochasticity. Overall, our results provide exceptional insights on the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

evolutionary biology↗