ABSTRACT
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 â¼100 genes at â¼100 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.
