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Empirical noise-mean fitness landscapes and the evolution of gene expression

By Jörn M. Schmiedel, Lucas B. Carey, Ben Lehner

Posted 05 Oct 2018
bioRxiv DOI: 10.1101/436170

The fitness effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify, in part due to the mechanistic coupling of noise to mean expression. To independently evaluate the effects of changes in expression mean and noise we determined the fitness landscapes in mean-noise expression space for 33 native genes in yeast. The landscapes can be decomposed into two principal components, or topologies. The first being the fitness defects due to protein shortage; the second fitness defects due to protein surplus. For most genes, the fitness impact of sustained (mean) and short-lived (noise) deviations away from the expression optimum are linked and of similar magnitude. Sensitivity to both protein shortage and surplus creates a fitness landscape in which an epistatic ratchet uncouples the evolution of noise from mean expression, thus promoting noise minimization. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.

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