Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL Contact:  ogt@genomics.princeton.edu Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement

DNA microarray analysis of gene expression in steady-state chemostat cultures limited for potassium revealed a surprising connection between potassium and ammonium: potassium limits growth only when ammonium is the nitrogen source. Under potassium limitation, ammonium appears to be toxic for Saccharomyces cerevisiae. This ammonium toxicity, which appears to occur by leakage of ammonium through potassium channels, is recapitulated under high-potassium conditions by over-expression of ammonium transporters. Although ammonium toxicity is well established in metazoans, it has never been reported for yeast. To characterize the response to ammonium toxicity, we examined the filtrates of these cultures for compounds whose excretion might serve to detoxify the ammonium (such as urea in mammals). Using liquid chromatography–tandem mass spectrometry to assay for a wide array of metabolites, we detected excreted amino acids. The amounts of amino acids excreted increased in relation to the severity of growth impairment by ammonium, suggesting that amino acid excretion is used by yeast for ammonium detoxification.

Mitochondria are central to many cellular processes including respiration, ion homeostasis, and apoptosis. Using computational predictions combined with traditional quantitative experiments, we have identified 100 proteins whose deficiency alters mitochondrial biogenesis and inheritance in Saccharomyces cerevisiae. In addition, we used computational predictions to perform targeted double-mutant analysis detecting another nine genes with synthetic defects in mitochondrial biogenesis. This represents an increase of about 25% over previously known participants. Nearly half of these newly characterized proteins are conserved in mammals, including several orthologs known to be involved in human disease. Mutations in many of these genes demonstrate statistically significant mitochondrial transmission phenotypes more subtle than could be detected by traditional genetic screens or high-throughput techniques, and 47 have not been previously localized to mitochondria. We further characterized a subset of these genes using growth profiling and dual immunofluorescence, which identified genes specifically required for aerobic respiration and an uncharacterized cytoplasmic protein required for normal mitochondrial motility. Our results demonstrate that by leveraging computational analysis to direct quantitative experimental assays, we have characterized mutants with subtle mitochondrial defects whose phenotypes were undetected by high-throughput methods.

Genome-wide gene-expression studies have shown that hundreds of yeast genes are induced or repressed transiently by changes in temperature; many are annotated to stress response on this basis. To obtain a genome-scale assessment of which genes are functionally important for innate and/or acquired thermotolerance, we combined the use of a barcoded pool of ∼4,800 nonessential, prototrophic Saccharomyces cerevisiae deletion strains with Illuminabased deep-sequencing technology. As reported in other recent studies that have used deletion mutants to study stress responses, we observed that gene deletions resulting in the highest thermosensitivity generally are not the same as those transcriptionally induced in response to heat stress. Functional analysis of identified genes revealed that metabolism, cellular signaling, and chromatin regulation play roles in regulating thermotolerance and in acquired thermotolerance. However, for most of the genes identified, the molecular mechanism behind this action remains unclear. In fact, a large fraction of identified genes are annotated as having unknown functions, further underscoring our incomplete understanding of the response to heat shock. We suggest that survival after heat shock depends on a small number of genes that function in assessing the metabolic health of the cell and/or regulate its growth in a changing environment.