Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

Abstract

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Gene-nutrient interaction markedly influences yeast chronological lifespan

“…The S. cerevisiae YGDS has contributed greatly to understanding that gene interaction is ubiquitous; i.e., the knowledge that networks of genes contribute to every phenotype is a direct result of widespread use of the YGDS library (Costanzo and others 2010; Giaever and Nislow 2014; Hartman and others 2015). Gene interaction in the YGDS is usually reported in terms of surprising changes in cell proliferation of deletions mutants relative to reference strain, in response to genetic (e.g., double mutants or expression of heterologous gene) or chemical perturbation (Mani and others 2008).…”

“…One way of quantifying gene interaction involves a dose response to perturbations such as concentrations of a drug, levels of gene expression, or both (Hartman IV 2007; Hartman IV and Tippery 2004; Louie and others 2012), which can be seen as analogous to CLS, where the perturbation is time (Figure 5). Gene interaction effects are observed extensively with respect to different yeast media (Hartman and others 2015), which is likely to also be true for CLS - a more complicated phenotype that scores cell proliferation as a function of age. Thus media recipes appear to have a greater effect on CLS than the simple logarithmic growth phenotype (Burtner and others 2009a; Murakami and Kaeberlein 2009; Murakami and others 2008).…”

“…Toward the goal of extrapolating observations about yeast chronological aging to human cells, and given the potential influence of media on CLS, it is conceivable that the use of yeast media that more closely resembles human tissue culture media could help to reduce gene × media interaction as a confounding factor thwarting the validation of yeast CLS findings for human cells (Hartman and others 2015). …”

A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

“…Interaction scores were calculated by departure of the observed CPP for each YKO/KD strain from that expected based on the observed phenotypes for the reference strain treated and untreated with drug and the YKO/KD strain in the absence of drug, incorporating multiple drug concentrations, 768 replicate reference strain control cultures, and summarized by linear regression as z-scores [6-8,30,34,38]. Gene interaction scores with absolute value greater than two were selected for global analysis and termed deletion enhancers (z-score_L ≥ 2 or z-score_K ≤ −2) or deletion suppressors (z-score_L ≤ −2 or z-score_K ≥ 2) of drug cytotoxicity, revealing functions that buffer or promote drug cytotoxicity, respectively [39] (Fig.…”

“…Saccharomyces cerevisiae does not have a dCK homolog and is thus naturally resistant to gemcitabine and cytarabine. To examine the gene-drug interaction networks for gemcitabine and cytarabine in yeast, we introduced human dCK into the yeast knockout and knockdown ( YKO/KD ) library by the synthetic genetic array ( SGA ) method [27-29], and conducted phenomic analysis on the resulting double mutant library by quantitative high throughput cell array phenotyping ( Q-HTCP ) [6-8,30], using multiple growth inhibitory concentrations of gemcitabine or cytarabine (Fig. 1B).…”