Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection
- Equal contributors
1 Department of Computer Science and Engineering, University of Minnesota Twin Cities, 200 Union St SE, Minneapolis, MN 55455, USA
2 Department of Biology, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA
3 Department of Plant Biology, University of Minnesota Twin Cities, 1445 Gortner Avenue, Saint Paul, MN 55108, USA
4 Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, H-6701, Szeged, Hungary
5 University of British Columbia, Pharmaceutical Sciences, 2405 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
6 Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
7 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
8 Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
Genome Biology 2014, 15:R64 doi:10.1186/gb-2014-15-4-r64Published: 10 April 2014
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.
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.
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.