Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation. [Display omitted] •Multiplexed approach unravels causal effects of gene expression levels on fitness•Expression of many yeast genes altered at high resolution across a 500-fold range•Condition-specific effect of expression on fitness reveals biological function•Wild-type expression is optimal for fitness in one condition, but not in another How does gene expression variation affect fitness? Systematic probing informs gene function, optimality of wild-type expression, and the impact of noise.