Chemical-genetic Interactions Research Articles

An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.

Chemical-genetics (C-G) experiments can be used to identify interactions between inhibitory compounds and bacterial genes, potentially revealing the targets of drugs, or other functionally interacting genes and pathways. C-G experiments involve constructing a library of hypomorphic strains with essential genes that can be knocked-down, treating it with an inhibitory compound, and using high-throughput sequencing to quantify changes in relative abundance of individual mutants. The hypothesis is that, if the target of a drug or other genes in the same pathway are present in the library, such genes will display an excessive fitness defect due to the synergy between the dual stresses of protein depletion and antibiotic exposure. While assays at a single drug concentration are susceptible to noise and can yield false-positive interactions, improved detection can be achieved by requiring that the synergy between gene and drug be concentration-dependent. We present a novel statistical method based on Linear Mixed Models, called CGA-LMM, for analyzing C-G data. The approach is designed to capture the dependence of the abundance of each gene in the hypomorph library on increasing concentrations of drug through slope coefficients. To determine which genes represent candidate interactions, CGA-LMM uses a conservative population-based approach in which genes with negative slopes are considered significant only if they are outliers with respect to the rest of the population (assuming that most genes in the library do not interact with a given inhibitor). We applied the method to analyze 3 independent hypomorph libraries of M. tuberculosis for interactions with antibiotics with anti-tubercular activity, and we identify known target genes or expected interactions for 7 out of 9 drugs where relevant interacting genes are known.

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical–genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical–genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Oncogene-regulated release of extracellular vesicles.

Oncogenes can alter metabolism by changing the balance between anabolic and catabolic processes. However, how oncogenes regulate tumor cell biomass remains poorly understood. Using isogenic MCF10A cells transformed with nine different oncogenes, we show that specific oncogenes reduce the biomass of cancer cells by promoting extracellular vesicle (EV) release. While MYC and AURKB elicited the highest number of EVs, each oncogene selectively altered the protein composition of released EVs. Likewise, oncogenes alter secreted miRNAs. MYC-overexpressing cells require ceramide, whereas AURKB requires ESCRT to release high levels of EVs. We identify an inverse relationship between MYC upregulation and activation of the RAS/MEK/ERK signaling pathway for regulating EV release in some tumor cells. Finally, lysosome genes and activity are downregulated in the context of MYC and AURKB, suggesting that cellular contents, instead of being degraded, were released via EVs. Thus, oncogene-mediated biomass regulation via differential EV release is a new metabolic phenotype.

Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated RNAP mechanism, and RNAP mutations drive Escherichia coli adaptation to many health- and industry-relevant environments, yet a paucity of systematic analyses hampers our understanding of the fitness trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and prevent killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships invivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.

12621 Targeted Chemical-Genetic Screen Platform for Identifying Drug Modes-of-Action

ABSTRACT IMPACT: The key to advancing precision medicine is to deepen our understanding of drug modes-of-action (MOA). This project aims to develop a novel method for predicting MOA of potential drug compounds, providing an experimental and computational platform for more efficient drug discovery. OBJECTIVES/GOALS: To develop (1) a targeted CRISPR-Cas9 chemical-genetic screen approach, and (2) a computational method to predict drug mode-of-action from chemical-genetic interaction profiles. METHODS/STUDY POPULATION: Screening drugs against a gene deletion library can identify knockouts that modulate drug sensitivity. These chemical-genetic interaction (CGI) screens can be performed in human cell lines using a pooled lentiviral CRISPR-Cas9 approach to assess drug sensitivity/resistance of single-gene knockouts across the human genome. A targeted, rather than genome-wide, library can enable scaling these screens across many drugs.CGI profiles can be derived from phenotypic screen readouts. These profiles are analogous to genetic interaction (GI) profiles, which represent sensitivity/resistance of gene knockouts to a second gene knockout rather than a drug. To computationally predict a drug’s genetic target, we leverage the property that a drug’s CGI profile will be similar to its target’s GI profile. RESULTS/ANTICIPATED RESULTS: Five proof-of-principle screens will be conducted with compounds that have existing genome-wide profiles and well-characterized MOA. I will generate CGI profiles for these five compounds and identify genes that are drug-sensitizers or drug-suppressors. I will then evaluate whether targeted library screens can recapitulate the CGIs found in genome-wide screens. Finally, I will develop a computational tool to integrate these CGI profiles with GI profiles (derived from another project) to predict gene-level and bioprocess-level drug targets. These predictions (from both targeted and genome-wide profiles) will be benchmarked against a drug-target and drug-bioprocess standard. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work will develop a scalable, targeted chemical-genetic screen approach to discovering how putative therapeutics work. The targeted screen workflow provides a method for higher-throughput drug screening. The computational pipeline provides a powerful tool for exploring the MOA of uncharacterized drugs or repurposing FDA-approved drugs.

Machine-Learning and Chemicogenomics Approach Defines and Predicts Cross-Talk of Hippo and MAPK Pathways.

Hippo pathway dysregulation occurs in multiple cancers through genetic and nongenetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RAS, defining tumors that are Hippo pathway-dependent is far more complex due to the lack of hotspot genetic alterations. Here, we developed a machine-learning framework to identify a robust, cancer type-agnostic gene expression signature to quantitate Hippo pathway activity and cross-talk as well as predict YAP/TEAD dependency across cancers. Further, through chemical genetic interaction screens and multiomics analyses, we discover a direct interaction between MAPK signaling and TEAD stability such that knockdown of YAP combined with MEK inhibition results in robust inhibition of tumor cell growth in Hippo dysregulated tumors. This multifaceted approach underscores how computational models combined with experimental studies can inform precision medicine approaches including predictive diagnostics and combination strategies. SIGNIFICANCE: An integrated chemicogenomics strategy was developed to identify a lineage-independent signature for the Hippo pathway in cancers. Evaluating transcriptional profiles using a machine-learning method led to identification of a relationship between YAP/TAZ dependency and MAPK pathway activity. The results help to nominate potential combination therapies with Hippo pathway inhibition.This article is highlighted in the In This Issue feature, p. 521.

Chemical-Genetic Interactions of Bacopa monnieri Constituents in Cells Deficient for the DNA Repair Endonuclease RAD1 Appear Linked to Vacuolar Disruption.

Colorectal cancer is a common cancer worldwide and reduced expression of the DNA repair endonuclease XPF (xeroderma pigmentosum complementation group F) is associated with colorectal cancer. Bacopa monnieri extracts were previously found to exhibit chemical-genetic synthetic lethal effects in a Saccharomyces cerevisiae model of colorectal cancer lacking Rad1p, a structural and functional homologue of human XPF. However, the mechanisms for B. monnieri extracts to limit proliferation and promote an apoptosis-like event in RAD1 deleted yeast was not elucidated. Our current analysis has revealed that B. monnieri extracts have the capacity to promote mutations in rad1∆ cells. In addition, the effects of B. monnieri extracts on rad1∆ yeast is linked to disruption of the vacuole, similar to the mammalian lysosome. The absence of RAD1 in yeast sensitizes cells to the effects of vacuole disruption and the release of proteases. The combined effect of increased DNA mutations and release of vacuolar contents appears to induce an apoptosis-like event that is dependent on the meta-caspase Yca1p. The toxicity of B. monnieri extracts is linked to sterol content, suggesting saponins may be involved in limiting the proliferation of yeast cells. Analysis of major constituents from B. monnieri identified a chemical-genetic interaction between bacopasaponin C and rad1∆ yeast. Bacopasaponin C may have potential as a drug candidate or serve as a model for the development of analogs for the treatment of colorectal cancer.

Chemical-Genetic Interactions as a Means to Characterize Drug Synergy.

The combination of model organisms and comprehensive genome-wide screens has provided a wealth of data into the structure and regulation of the genome, gene-environment interactions, and more recently, into the mechanism of action of human therapeutics. The success of these studies relies, in part, onthe ability to quantify the combined effects of multifactorial biological interactions. In this review, we explore the history and rationale behind genetic and chemical-genetic interactions with an emphasis on the phenomena of drug synergy and then briefly describe the theoretical models that we can leverage to investigate the synergy between compounds. In addition to reviewing the literature, we also provide a reference list including many of the most important studies in this field. The concept of chemical genetics interactions derives from classical studies of synthetic lethality and functional genomics. These techniques have recently graduated from the research lab to the clinic, and a better understanding of the basic principles can help accelerate this translation.

Using Proteolytic Hypomorphs to Detect Small Molecule Mechanism of Action.

With increasing prevalence of antimicrobial resistance, a fundamental goal of antibiotic discovery is to uncover new small molecules that prevent growth of pathogenic bacteria through diverse mechanisms of action. This goal is particularly pertinent for tuberculosis, caused by Mycobacterium tuberculosis. In this chapter, we describe the application of a chemical-genetic method, PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), for sensitively detecting small molecule bioactivity using a pooled panel of hypomorphs (strains depleted in a particular essential gene) of M. tuberculosis. We describe statistical and heuristic approaches to assign small molecule mechanism of action from the resulting chemical-genetic interaction profiles.

Chemical-Genetic Interactions with the Proline Analog L-Azetidine-2-Carboxylic Acid in Saccharomyces cerevisiae.

Non-proteinogenic amino acids, such as the proline analog L-azetidine-2-carboxylic acid (AZC), are detrimental to cells because they are mis-incorporated into proteins and lead to proteotoxic stress. Our goal was to identify genes that show chemical-genetic interactions with AZC in Saccharomyces cerevisiae and thus also potentially define the pathways cells use to cope with amino acid mis-incorporation. Screening the yeast deletion and temperature sensitive collections, we found 72 alleles with negative chemical-genetic interactions with AZC treatment and 12 alleles that suppress AZC toxicity. Many of the genes with negative chemical-genetic interactions are involved in protein quality control pathways through the proteasome. Genes involved in actin cytoskeleton organization and endocytosis also had negative chemical-genetic interactions with AZC. Related to this, the number of actin patches per cell increases upon AZC treatment. Many of the same cellular processes were identified to have interactions with proteotoxic stress caused by two other amino acid analogs, canavanine and thialysine, or a mistranslating tRNA variant that mis-incorporates serine at proline codons. Alleles that suppressed AZC-induced toxicity functioned through the amino acid sensing TOR pathway or controlled amino acid permeases required for AZC uptake. Further suggesting the potential of genetic changes to influence the cellular response to proteotoxic stress, overexpressing many of the genes that had a negative chemical-genetic interaction with AZC suppressed AZC toxicity.

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells.

Chemogenomic screening identifies the Hsp70 co-chaperone DNAJA1 as a hub for anticancer drug resistance

Heat shock protein 70 (Hsp70) is an important molecular chaperone that regulates oncoprotein stability and tumorigenesis. However, attempts to develop anti-chaperone drugs targeting molecules such as Hsp70 have been hampered by toxicity issues. Hsp70 is regulated by a suite of co-chaperone molecules that bring “clients” to the primary chaperone for efficient folding. Rather than targeting Hsp70 itself, here we have examined the feasibility of inhibiting the Hsp70 co-chaperone DNAJA1 as a novel anticancer strategy. We found DNAJA1 to be upregulated in a variety of cancers, suggesting a role in malignancy. To confirm this role, we screened the NIH Approved Oncology collection for chemical-genetic interactions with loss of DNAJA1 in cancer. 41 compounds showed strong synergy with DNAJA1 loss, whereas 18 dramatically lost potency. Several hits were validated using a DNAJA1 inhibitor (116-9e) in castration-resistant prostate cancer cell (CRPC) and spheroid models. Taken together, these results confirm that DNAJA1 is a hub for anticancer drug resistance and that DNAJA1 inhibition is a potent strategy to sensitize cancer cells to current and future therapeutics. The large change in drug efficacy linked to DNAJA1 suggests a personalized medicine approach where tumor DNAJA1 status may be used to optimize therapeutic strategy.

Pharmaceutical-Grade Rigosertib Is a Microtubule-Destabilizing Agent

We recently used CRISPRi/a-based chemical-genetic screens and cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. Reddy and co-workers (Baker etal., 2020, this issue of Molecular Cell) suggest that a contaminating degradation product in commercial formulations of rigosertib is responsible for the microtubule-destabilizing activity. Here, we demonstrate that cells treated with pharmaceutical-grade rigosertib (>99.9% purity) or commercially obtained rigosertib have qualitatively indistinguishable phenotypes across multiple assays. The two formulations have indistinguishable chemical-genetic interactions with genes that modulate microtubule stability, both destabilize microtubules in cells and invitro, and expression of a rationally designed tubulin mutant with a mutation in the rigosertib binding site (L240F TUBB) allows cells to proliferate in the presence of either formulation. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents has been confirmed independently. Thus, rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

Genetic and Chemical-Genetic Interactions Map Biogenesis and Permeability Determinants of the Outer Membrane of Escherichia coli.

Gram-negative bacteria are intrinsically resistant to many antibiotics due to their outer membrane barrier. Although the outer membrane has been studied for decades, there is much to uncover about the biology and permeability of this complex structure. Investigating synthetic genetic interactions can reveal a great deal of information about genetic function and pathway interconnectivity. Here, we performed synthetic genetic arrays (SGAs) in Escherichia coli by crossing a subset of gene deletion strains implicated in outer membrane permeability with nonessential gene and small RNA (sRNA) deletion collections. Some 155,400 double-deletion strains were grown on rich microbiological medium with and without subinhibitory concentrations of two antibiotics excluded by the outer membrane, vancomycin and rifampin, to probe both genetic interactions and permeability. The genetic interactions of interest were synthetic sick or lethal (SSL) gene deletions that were detrimental to the cell in combination but had a negligible impact on viability individually. On average, there were ∼30, ∼36, and ∼40 SSL interactions per gene under no-drug, rifampin, and vancomycin conditions, respectively; however, many of these involved frequent interactors. Our data sets have been compiled into an interactive database called the Outer Membrane Interaction (OMI) Explorer, where genetic interactions can be searched, visualized across the genome, compared between conditions, and enriched for gene ontology (GO) terms. A set of SSL interactions revealed connectivity and permeability links between enterobacterial common antigen (ECA) and lipopolysaccharide (LPS) of the outer membrane. This data set provides a novel platform to generate hypotheses about outer membrane biology and permeability.IMPORTANCE Gram-negative bacteria are a major concern for public health, particularly due to the rise of antibiotic resistance. It is important to understand the biology and permeability of the outer membrane of these bacteria in order to increase the efficacy of antibiotics that have difficulty penetrating this structure. Here, we studied the genetic interactions of a subset of outer membrane-related gene deletions in the model Gram-negative bacterium E. coli We systematically combined these mutants with 3,985 nonessential gene and small RNA deletion mutations in the genome. We examined the viability of these double-deletion strains and probed their permeability characteristics using two antibiotics that have difficulty crossing the outer membrane barrier. An understanding of the genetic basis for outer membrane integrity can assist in the development of new antibiotics with favorable permeability properties and the discovery of compounds capable of increasing outer membrane permeability to enhance the activity of existing antibiotics.

Chemogenomic profiling to understand the antifungal action of a bioactive aurone compound.

Every year, more than 250,000 invasive candidiasis infections are reported with 50,000 deaths worldwide. The limited number of antifungal agents necessitates the need for alternative antifungals with potential novel targets. The 2-benzylidenebenzofuran-3-(2H)-ones have become an attractive scaffold for antifungal drug design. This study aimed to determine the antifungal activity of a synthetic aurone compound and characterize its mode of action. Using the broth microdilution method, aurone SH1009 exhibited inhibition against C. albicans, including resistant isolates, as well as C. glabrata, and C. tropicalis with IC50 values of 4-29 μM. Cytotoxicity assays using human THP-1, HepG2, and A549 human cell lines showed selective toxicity toward fungal cells. The mode of action for SH1009 was characterized using chemical-genetic interaction via haploinsufficiency (HIP) and homozygous (HOP) profiling of a uniquely barcoded Saccharomyces cerevisiae mutant collection. Approximately 5300 mutants were competitively treated with SH1009 followed by DNA extraction, amplification of unique barcodes, and quantification of each mutant using multiplexed next-generation sequencing. Barcode post-sequencing analysis revealed 238 sensitive and resistant mutants that significantly (FDR P values ≤ 0.05) responded to aurone SH1009. The enrichment analysis of KEGG pathways and gene ontology demonstrated the cell cycle pathway as the most significantly enriched pathway along with DNA replication, cell division, actin cytoskeleton organization, and endocytosis. Phenotypic studies of these significantly enriched responses were validated in C. albicans. Flow cytometric analysis of SH1009-treated C. albicans revealed a significant accumulation of cells in G1 phase, indicating cell cycle arrest. Fluorescence microscopy detected abnormally interrupted actin dynamics, resulting in enlarged, unbudded cells. RT-qPCR confirmed the effects of SH1009 in differentially expressed cell cycle, actin polymerization, and signal transduction genes. These findings indicate the target of SH1009 as a cell cycle-dependent organization of the actin cytoskeleton, suggesting a novel mode of action of the aurone compound as an antifungal inhibitor.

Mode of action of nisin on Escherichia coli.

Nisin is a class I polycyclic bacteriocin produced by the bacterium Lactococcus lactis, which is used extensively as a food additive to inhibit the growth of foodborne Gram-positive bacteria. Nisin also inhibits growth of Gram-negative bacteria when combined with membrane-disrupting chelators such as citric acid. To gain insight into nisin's mode of action, we analyzed chemical-genetic interactions and identified nisin-sensitive Escherichia coli strains in the Keio library of knockout mutants. The most sensitive mutants fell into two main groups. The first group accords with the previously proposed mode of action based on studies with Gram-positive bacteria, whereby nisin interacts with factors involved in cell wall, membrane, envelope biogenesis. We identified an additional, novel mode of action for nisin based on the second group of sensitive mutants that involves cell cycle and DNA replication, recombination, and repair. Further analyses supported these two distinct modes of action.

Decyl Gallate as a Possible Inhibitor of N-Glycosylation Process in Paracoccidioides lutzii.

The available antifungal therapeutic arsenal is limited. The search for alternative drugs with fewer side effects and new targets remains a major challenge. Decyl gallate (G14) is a derivative of gallic acid with a range of biological activities and broad-spectrum antifungal activity. Previously, our group demonstrated the promising anti-Paracoccidioides activity of G14. In this work, to evaluate the antifungal characteristics of G14 for Paracoccidioides lutzii, a chemical-genetic interaction analysis was conducted on a Saccharomyces cerevisiae model. N-glycosylation and/or the unfolded protein response pathway was identified as a high-confidence process for drug target prediction. The overactivation of unfolded protein response (UPR) signaling was confirmed using this model with IRE1/ATF6/PERK genes tagged with green fluorescent protein (GFP). In P. lutzii, this prediction was confirmed by the low activity of glycosylated enzymes [α-(1,3)-glucanase, N-acetyl-β-d-glucosaminidase (NAGase), and α-(1,4)-amylase], by hyperexpression of genes involved with the UPR and glycosylated enzymes, and by the reduction in the amounts of glycosylated proteins and chitin. All of these components are involved in fungal cell wall integrity and are dependent on the N-glycosylation process. This loss of integrity was confirmed by the reduction in mitochondrial activity, impaired budding, enhancement of wall permeability, and a decrease in viability. These events led to a reduction of the ability of fungi to adhere on human lung epithelial cells (A549) in vitro Therefore, G14 may have an important role in balancing the inflammatory reaction caused by fungal infection, without interfering with the microbicidal activity of nitric oxide. This work provides new information on the activity of G14, a potential anti-Paracoccidioides compound.

Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes.

New antibiotics are needed to combat rising levels of resistance, with new Mycobacterium tuberculosis (Mtb) drugs having the highest priority. However, conventional whole-cell and biochemical antibiotic screens have failed. Here we develop a strategy termed PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), in which we screen compounds against pools of strains depleted of essential bacterial targets. We engineered strains that target 474 essential Mtb genes and screened pools of 100-150 strains against activity-enriched and unbiased compound libraries, probing more than 8.5million chemical-genetic interactions. Primary screens identified over tenfold more hits than screening wild-type Mtb alone, with chemical-genetic interactions providing immediate, direct target insights. We identified over 40 compounds that target DNA gyrase, the cell wall, tryptophan, folate biosynthesis and RNA polymerase, as well as inhibitors that target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating the ability of PROSPECT to yield inhibitors against targets that would have eluded conventional drug discovery.

Chemical-genetic interaction landscape of mono-(2-ethylhexyl)-phthalate using chemogenomic profiling in yeast

Integration of chemical-genetic interaction data with biological functions provides a mechanistic understanding of how toxic compounds affect cells. Mono-(2-ethylhexyl)-phthalate (MEHP) is an active metabolite of di-(2-ethylhexyl)-phthalate (DEHP), a commonly used plasticizer. MEHP adversely affects human health causing hepatotoxicity and reproductive toxicity. How MEHP affects cellular physiology is not fully understood. We utilized a genome-wide competitive fitness-based assay called ‘chemogenomic profiling’ to determine the genetic interaction map of MEHP in Saccharomyces cerevisiae. Gene Ontology enrichment analysis of 218 genes that provide resistance to MEHP indicated that MEHP affects seven cellular processes namely: (1) cellular amino acid biosynthetic process, (2) sterol biosynthetic process, (3) cellular transport, (4) transcriptional and translational regulation, (5) protein glycosylation, (6) cytokinesis and cell morphogenesis and (7) ionic homeostasis. We show that MEHP protects yeast cells from membrane perturbing agents such as amphotericin B, dihydrosphingosine and phytosphingosine. Moreover, we also demonstrate that MEHP compromises the integrity of the yeast plasma membrane and cell wall. Our work provides a basis for further investigation of MEHP toxicity in humans.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments.

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.