Dataset Information

Massively parallel interrogation of the effects of gene expression levels on cellular fitness

ABSTRACT: Data of gene expression levels across individuals, cell types, and disease states is rapidly expanding, yet we have limited understanding of how expression levels impact cellular and organismal phenotypes. Here, we present a massively parallel system for assaying the effect of gene expression levels on cellular fitness in Saccharomyces cerevisiae by systematically altering the expression level of each of ~100 endogenous genes at ~100 distinct expression levels spanning a 500-fold range at high resolution. Our results show that the relationship between expression levels and growth is gene- and environment-specific, with the specific relationship exhibited by each gene being highly informative on its function, stoichiometry within complexes, and interaction with other genes. Notably, in one of the two environmental conditions that we tested, we find that ~20% of the genes have expression levels where fitness is greater than that at wild-type expression levels, indicating that wild-type expression is not optimal for growth in that condition. We find that genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression, suggesting that noise in gene expression is shaped in part by the relationship between expression and fitness. Overall, our study addresses a fundamental gap in our understanding of the functional significance of gene expression regulation and offers a powerful framework for evaluating the phenotypic effects of expression variation.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE83936 | GEO | 2016/08/25

SECONDARY ACCESSION(S): PRJNA329129

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Aneuploidy is a hallmark of tumor cells and yet the precise relationship between aneuploidy and a cell’s proliferative ability, or cellular fitness, has remained elusive. In this study we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic, genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness. We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild-type. Direct competition experiments confirmed that three out of four aneuploid events isolated from evolved populations were themselves sufficient to improve fitness. To expand the scope beyond this small number of exemplars, we created a genome-wide collection of >1,800 diploid yeast strains each containing a different telomeric amplicon (Tamp) ranging in size from 0.4 to 1,000kb. Using pooled competition experiments in nutrient-limited chemostats followed by high-throughput sequencing of strain-identifying barcodes, we determined the fitness effects of these >1,800 Tamps under three different conditions. Our data revealed that the fitness landscape explored by telomeric amplifications is much broader than that explored by single-gene amplifications. As also observed in the evolved clones, we found the fitness effects of most Tamps to be condition specific with a minority showing common effects in all three conditions. By integrating our data with previous work that examined the fitness effects of single-gene amplifications genome wide, we found that a small number of genes within each Tamp are centrally responsible for each Tamp’s fitness effects. Our genome-wide Tamp screen confirmed that telomeric amplifications identified in laboratory-evolved populations generally increased fitness. Our results show that Tamps are mutations that produce large, typically condition-dependent changes in fitness that are important drivers of increased fitness in asexually evolving populations. Each of these arrays is a Comparative Genomic Hybridization experiment to detect copy number differences between a reference strain and a strain of interest.

Dataset Information

Massively parallel interrogation of the effects of gene expression levels on cellular fitness

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets