Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection

Abstract

BackgroundGenome-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.ResultsThe 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.ConclusionsThese 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.

Transparency in metabolic network reconstruction enables scalable biological discovery

“…It can be surprisingly difficult to generate a reference set of essential genes that can be used for evaluating model development. Even in genetic model organisms like Saccharomyces cerevesiae , recent work has highlighted that genes can be conditionally essential [30]; that the deletion of a single gene makes additional mutations more likely [31]; and that auxotrophic markers in laboratory strains meaningfully affect metabolic phenotype [32]. Thus, a model that has high predictive accuracy for one set of “essential” genes may in fact be over-fit, and have unexpectedly limited predictive ability of gene essentiality in non-reference environments.…”

How close are we to complete annotation of metabolomes?

“…Unlike other stable isotope labelling methods, rather than utilising natural abundance and 98–99% enrichment for the control and experimental populations, respectively [44–48], IROA uses an enrichment level of 95% and 5% 13 C. This leads to more observable isotopic peaks in the mass spectra in predictable and diagnostic patterns. Recent studies have demonstrated the promise of IROA for metabolic phenotyping in model organisms, including for prototrophic S. cerevisiae [17, 49] and C. elegans [43]; the latter was grown in liquid culture with 13 C-labeled E. coli that was first grown in M9 minimal media on either 95% or 5% 13 C glucose, creating labelled C. elegans . These 95% 13 C and 5% 13 C glucose labelling experiments, when extracted and combined, show distinctive IROA patterns: 12 C -derived molecules, 13 C-derived molecules, artifacts (lack IROA patterns) and derivatives of exogenously applied compounds.…”

Mitochondrial control through nutritionally regulated global histone H3 lysine-4 demethylation

“…The prototrophic S288C strain RCY308 was cultured and metabolites were prepared according to established procedures40. Yeast metabolite extracts were reconstituted to a concentration of 0.6 OD 600 of yeast in 40 uL of HPLC-grade H 2 O by vortexing for 1 minute.…”