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

Abstract

The 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 genes in yeast. The landscapes can be decomposed into two principal topologies: the fitness effects of protein shortage and 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, 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.\n\nHighlights- Expression fitness landscapes in mean-noise space allow quantitative independent assessment of the effects of noise and mean expression on fitness for 33 genes\n- Landscapes are described by a combination of just two principal topologies: fitness defects due protein shortage, or due to protein surplus\n- Direct evidence that high expression noise is detrimental to fitness for 50% of genes\n- Mean expression and noise have equivalent impact on organismal fitness\n- Landscapes created by sensitivities to protein shortage and surplus facilitate independent evolution of gene expression noise via an epistatic ratchet