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

Mind your marker: the effect of common auxotrophic markers on complex traits in yeast

Yeast cells are extensively used as a key model organism owing to their highly conserved genome, metabolic pathways, and cell biology processes. To assist in genetic engineering and analysis, laboratory yeast strains typically harbor auxotrophic selection markers. When uncompensated, auxotrophic markers cause significant phenotypic bias compared to prototrophic strains and have different combinatorial influences on the metabolic network. Here, we used BY4741, a laboratory strain commonly used as a "wild type" strain in yeast studies, to generate a set of revertant strains, containing all possible combinations of four common auxotrophic markers (leu2∆, ura3∆, his3∆1, met15∆). We examined the effect of the auxotrophic combinations on complex phenotypes such as resistance to rapamycin, acetic acid, and ethanol. Among the markers, we found that leucine auxotrophy most significantly affected the phenotype. We analyzed the phenotypic bias caused by auxotrophy at the genomic level using a prototrophic version of a genome-wide deletion library and a decreased mRNA perturbation (DAmP) library. Prototrophy was found to suppress rapamycin sensitivity in many mutants previously annotated for the phenotype, raising a possible need for reevaluation of the findings in a native metabolic context. These results reveal a significant phenotypic bias caused by common auxotrophic markers and support the use of prototrophic wild-type strains in yeast research.

A cell-nonautonomous mechanism of yeast chronological aging regulated by caloric restriction and one-carbon metabolism

Caloric restriction (CR) improves health span and life span of organisms ranging from yeast to mammals. Understanding the mechanisms involved will uncover future interventions for aging-associated diseases. In budding yeast, Saccharomyces cerevisiae, CR is commonly defined by reduced glucose in the growth medium, which extends both replicative and chronological life span (CLS). We found that conditioned media collected from stationary-phase CR cultures extended CLS when supplemented into nonrestricted (NR) cultures, suggesting a potential cell-nonautonomous mechanism of CR-induced life span regulation. Chromatography and untargeted metabolomics of the conditioned media, as well as transcriptional responses associated with the longevity effect, pointed to specific amino acids enriched in the CR conditioned media (CRCM) as functional molecules, with L-serine being a particularly strong candidate. Indeed, supplementing L-serine into NR cultures extended CLS through a mechanism dependent on the one-carbon metabolism pathway, thus implicating this conserved and central metabolic hub in life span regulation.

High-resolution yeast quiescence profiling in human-like media reveals complex influences of auxotrophy and nutrient availability

Yeast cells survive in stationary phase culture by entering quiescence, which is measured by colony-forming capacity upon nutrient re-exposure. Yeast chronological lifespan (CLS) studies, employing the comprehensive collection of gene knockout strains, have correlated weakly between independent laboratories, which is hypothesized to reflect differential interaction between the deleted genes, auxotrophy, media composition, and other assay conditions influencing quiescence. This hypothesis was investigated by high-throughput quiescence profiling of the parental prototrophic strain, from which the gene deletion strain libraries were constructed, and all possible auxotrophic allele combinations in that background. Defined media resembling human cell culture media promoted long-term quiescence and was used to assess effects of glucose, ammonium sulfate, auxotrophic nutrient availability, target of rapamycin signaling, and replication stress. Frequent, high-replicate measurements of colony-forming capacity from cultures aged past 60 days provided profiles of quiescence phenomena such as gasping and hormesis. Media acidification was assayed in parallel to assess correlation. Influences of leucine, methionine, glucose, and ammonium sulfate metabolism were clarified, and a role for lysine metabolism newly characterized, while histidine and uracil perturbations had less impact. Interactions occurred between glucose, ammonium sulfate, auxotrophy, auxotrophic nutrient limitation, aeration, TOR signaling, and/or replication stress. Weak correlation existed between media acidification and maintenance of quiescence. In summary, experimental factors, uncontrolled across previous genome-wide yeast CLS studies, influence quiescence and interact extensively, revealing quiescence as a complex metabolic and developmental process that should be studied in a prototrophic context, omitting ammonium sulfate from defined media, and employing highly replicable protocols.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

BACKGROUND

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.

METHODS

Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.

RESULTS

Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.

CONCLUSIONS

Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Gene-nutrient interaction markedly influences yeast chronological lifespan

PURPOSE

Research into the genetic mechanisms of aging has expanded rapidly over the past two decades. This has in part been the result of the use of model organisms (particularly yeast, worms and flies) and high-throughput technologies, combined with a growing interest in aging research. Despite this progress, widespread consensus regarding the pathways that are fundamental to the modulation of cellular aging and lifespan for all organisms has been limited due to discrepancies between different studies. We have compared results from published genome-wide, chronological lifespan (CLS) screens of individual gene deletion strains in Saccharomyces cerevisiae in order to identify gene deletion strains with consistent influences on longevity as possible indicators of fundamental aging processes from this single-celled, eukaryotic model organism.

METHODS

Three previous reports have described genetic modifiers of chronological aging in the budding yeast (S. cerevisiae) using the yeast gene deletion strain collection. We performed a comparison among the data sets using correlation and decile distribution analysis to describe concordance between screens and identify strains that consistently increased or decreased CLS. We used gene enrichment analysis in an effort to understand the biology underlying genes identified in multiple studies. We attempted to replicate the different experimental conditions employed by the screens to identify potential sources of variability in CLS worth further investigating.

RESULTS

Among 3209 strains present in all three screens, nine deletions strains were in common in the longest-lived decile (2.80%) and thirteen were in common in the shortest-lived decile (4.05%) of all three screens. Similarly, pairwise overlap between screens was low. When the same comparison was extended to three deciles to include more mutants studied in common between the three screens, enrichment of cellular processes based on gene ontology analysis in the long-lived strains remained very limited. To test the hypothesis that different parental strain auxotrophic requirements or media formulations employed by the respective genome-wide screens might contribute to the lack of concordance, different CLS assay conditions were assessed in combination with strains having different ploidy and auxotrophic requirements (all relevant to differences in the way the three genome-wide CLS screens were performed). This limited but systematic analysis of CLS with respect to auxotrophy, ploidy, and media revealed several instances of gene-nutrient interaction.

CONCLUSIONS

There is surprisingly little overlap between the results of three independently performed genome-wide screens of CLS in S. cerevisiae. However, differences in strain genetic background (ploidy and specific auxotrophic requirements) were present, as well as different media and experimental conditions (e.g., aeration and pooled vs. individual culturing), which, along with stochastic effects such as genetic drift or selection of secondary mutations that suppress the loss of function from gene deletion, could in theory account for some of the lack of consensus between results. Considering the lack of overlap in CLS phenotypes among the set of genes reported by all three screens, and the results of a CLS experiment that systematically tested (incorporating extensive controls) for interactions between variables existing between the screens, we propose that discrepancies can be reconciled through deeper understanding of the influence of cell intrinsic factors such as auxotrophic requirements ploidy status, extrinsic factors such as media composition and aeration, as well as interactions that may occur between them, for example as a result of different pooling vs. individually aging cultures. Such factors may have a more significant impact on CLS outcomes than previously realized. Future studies that systematically account for these contextual factors, and can thus clarify the interactions between genetic and nutrient factors that alter CLS phenotypes, should aid more complete understanding of the underlying biology so that genetic principles of CLS in yeast can be extrapolated to differential cellular aging observed in animal models.

Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale

The capacity to map traits over large cohorts of individuals—phenomics—lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker’s yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases

Ribosomal Stalk Protein Silencing Partially Corrects the ΔF508-CFTR Functional Expression Defect

The most common cystic fibrosis (CF) causing mutation, deletion of phenylalanine 508 (ΔF508 or Phe508del), results in functional expression defect of the CF transmembrane conductance regulator (CFTR) at the apical plasma membrane (PM) of secretory epithelia, which is attributed to the degradation of the misfolded channel at the endoplasmic reticulum (ER). Deletion of phenylalanine 670 (ΔF670) in the yeast oligomycin resistance 1 gene (YOR1, an ABC transporter) of Saccharomyces cerevisiae phenocopies the ΔF508-CFTR folding and trafficking defects. Genome-wide phenotypic (phenomic) analysis of the Yor1-ΔF670 biogenesis identified several modifier genes of mRNA processing and translation, which conferred oligomycin resistance to yeast. Silencing of orthologues of these candidate genes enhanced the ΔF508-CFTR functional expression at the apical PM in human CF bronchial epithelia. Although knockdown of RPL12, a component of the ribosomal stalk, attenuated the translational elongation rate, it increased the folding efficiency as well as the conformational stability of the ΔF508-CFTR, manifesting in 3-fold augmented PM density and function of the mutant. Combination of RPL12 knockdown with the corrector drug, VX-809 (lumacaftor) restored the mutant function to ~50% of the wild-type channel in primary CFTR^{ΔF508/ΔF508} human bronchial epithelia. These results and the observation that silencing of other ribosomal stalk proteins partially rescue the loss-of-function phenotype of ΔF508-CFTR suggest that the ribosomal stalk modulates the folding efficiency of the mutant and is a potential therapeutic target for correction of the ΔF508-CFTR folding defect.

Reducing the rate of translational elongation by silencing ribosomal stalk proteins ameliorates the folding and stability defect of the cystic fibrosis mutant protein ΔF508-CFTR, partially restoring the plasma membrane chloride conductance.

Cystic fibrosis (CF) is one of the most common autosomal recessive diseases in Caucasians. It is caused by mutations in the CF transmembrane conductance regulator (CFTR), which functions as an anion channel at the apical plasma membrane of secretory epithelia. The most common CF mutation, a deletion of the phenylalanine residue at position 508 (ΔF508), results in the channel misfolding and subsequent intracellular degradation. Our previous genome-wide phenotypic screens, using a yeast variant, have predicted modifier genes for ΔF508-CFTR biogenesis. Here, we show that silencing of one of these candidate genes, RPL12, a component of the ribosomal stalk, increased the folding and stabilization of ΔF508-CFTR, resulting in its increased plasma membrane expression and function. Our data suggest that reducing the translational elongation rate via RPL12 silencing can partially reverse the ΔF508-CFTR folding defect. Importantly, RPL12 silencing in combination with the corrector drug VX-809 (lumacaftor), increased the mutant function to 50% of the wild-type CFTR channel, suggesting that the ribosomal stalk perturbation may represent a therapeutic target for rescuing the ΔF508-CFTR biogenesis defect.