Abstract
Background: Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions. Results: The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current in silico predictions and hitherto unavailable experimental data. Conclusions: These data address a fundamental deficiency in our understanding of the model eukaryote Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.
| Original language | English (US) |
|---|---|
| Article number | R64 |
| Journal | Genome biology |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2014 |
Bibliographical note
Funding Information:BJV and CLM are partially supported by grants from the National Science Foundation (DBI 0953881) and the National Institutes of Health (R01HG005084). BJV was also partially supported by the University of Minnesota Doctoral Dissertation Fellowship. DCH is supported by the National Institutes of Health (R01 GM101091-01) and the National Science Foundation (11122240). BP was supported by the Wellcome Trust and the ‘Lendület Program’ of the Hungarian Academy of Sciences. BS was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TÁMOP 4.2.4. A/2-11-1-2012-0001 'National Excellence Program'. CN acknowledges funding from the CCSRI, grant number 20830. AAC is the Canada Research Chair in Metabolomics for Enzyme Discovery and is supported by the Ontario Early Researcher Award, by the Canadian Institutes for Health Research, and by the Natural Sciences and Engineering Research Council of Canada, and by the Canadian Foundation for Innovation and the Ontario Leader's Opportunity Fund. DCH, CLM, OGT, and AAC were supported by the National Institute of General Medical Sciences (NIGMS) Center of Excellence P50 GM071508. Finally, we thank David Botstein for advice and insight throughout the project.
Publisher Copyright:
© 2014 VanderSluis et al.; licensee BioMed Central Ltd.