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PDF file, 59K.

XLS file, 4065K, This table contains information of all significant yeast synthetic sick or lethal interactions (Costanzo et al. 2010) between genes that are conserved in human based on InParanoid7 (Ostlund et al. 2010).

XLS file, 258K, The orthology between yeast and human genes from InParanoid7 (Ostlund et al. 2010) has been listed in this table.

XLS file, 326K, This table contains filtered set of interactions between conserved genes that satisfy the following prioritization criteria: (i) epsilon < -0.2 or epsilon < -0.08 for both AB-BA interaction in cases where both have been screened. (see Methods - Processing yeast genetic interaction data) (ii) Only 1-1 orthologs are considered. The information is arranged the same order as Supplementary Table S3.

PDF file, 464K, Genetic interaction test results for all interactions tested for this study.

XLS file, 55K, This table contains significant genetic interactions for rpt1-1, a temperature sensitive allele of RPT1.

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.

No Abstract.

Environmental impacts on gene networks A phenotype can be affected by genes interacting with other genes, the environment, or both other genes and the environment (a differential interaction). To better understand how these interactions function in yeast, Costanzo et al. mapped gene-gene interactions using single- and double-mutant deletions and temperature-sensitive alleles under 14 environmental conditions. Many deleted or temperature-sensitive nonessential genes affected yeast fitness both positively and negatively under at least one of the environmental conditions tested. In these cases, up to 24% of yeast genes were affected. A minority of these differential interactions point to previously unknown genetic connections across functional networks, informing on how genetic architecture responds to environmental variation. Science , this issue p. eabf8424

Multitasking is the ability to perform more than one task simultaneously. The need to multitask is common in many industries especially within the military with tasks such as air traffic controller...

Introduction: Sclerosing Angiomatoid Nodular Transformation of the spleen is a benign vascular lesion with no known etiology. Presentation of Case: We report a new case in a symptomatic twenty-one-year old female with thrombocytopenia and a hypervascular splenic mass discovered on ultrasound. Two MRIs were performed prior to hand-assisted laparoscopic splenectomy. The specimen was sent for histopathologic analysis with confirmation of final diagnosis from an outside facility. Discussion: Sclerosing Angiomatoid Nodular Transformation of the spleen is most often discovered incidentally as a solitary splenic mass. The presence of a spoke-wheel pattern should alert the radiologist to this as a possibility. Conclusion: Ultrasound and MR imaging findings can be used to accurately diagnose cases of splenic Sclerosing Angiomatoid Nodular Transformation. Susceptibility artifact within the lesion may be directly related to the amount of iron deposition. Keywords: Sclerosing angiomatoid nodular transformation, SANT, Spleen

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. Multiplexed sequencing of barcoded mutant collections enables high-throughput, fitness-based condition profiling. BEAN-counter is a computational pipeline for quantifying mutant sensitivity or resistance for a few to thousands of conditions.

Prospecting for antifungal molecules Marine bacteria produce a plethora of natural products that often have unusual chemical structures and corresponding reactivity, which sometimes translate into a valuable biological function. Zhang et al. used a metabolomic screen to zero in on microbial strains from the microbiome of a sea squirt that produces a high diversity of chemical structures. They then screened these molecules for inhibition of fungi (see the Perspective by Cowen). A polycyclic molecule dubbed turbinmicin possessed potent antifungal activity against the multidrug-resistant fungal pathogens Candida auris and Aspergillus fumigatus . Preliminary mechanism-of-action and mouse toxicity studies suggest that this molecule works though a fungus-specific pathway and is well tolerated at therapeutic doses. Science , this issue p. 974 ; see also p. 906

Toxins have been proposed to facilitate fungal root infection by creating regions of readily-penetrated necrotic tissue when applied externally to intact roots. Isolates of the charcoal rot disease fungus, Macrophomina phaseolina, from soybean plants in Mississippi produced a phytotoxic toxin, (−)-botryodiplodin, but no detectable phaseolinone, a toxin previously proposed to play a role in the root infection mechanism. This study was undertaken to determine if (−)-botryodiplodin induces toxic responses of the types that could facilitate root infection. (±)-Botryodiplodin prepared by chemical synthesis caused phytotoxic effects identical to those observed with (−)-botryodiplodin preparations from M. phaseolina culture filtrates, consistent with fungus-induced phytotoxicity being due to (−)-botryodiplodin, not phaseolinone or other unknown impurities. Soybean leaf disc cultures of Saline cultivar were more susceptible to (±)-botryodiplodin phytotoxicity than were cultures of two charcoal rot-resistant genotypes, DS97-84-1 and DT97-4290. (±)-Botryodiplodin caused similar phytotoxicity in actively growing duckweed (Lemna pausicostata) plantlet cultures, but at much lower concentrations. In soybean seedlings growing in hydroponic culture, (±)-botryodiplodin added to culture medium inhibited lateral and tap root growth, and caused loss of root caps and normal root tip cellular structure. Thus, botryodiplodin applied externally to undisturbed soybean roots induced phytotoxic responses of types expected to facilitate fungal root infection.

Previously, our lab demonstrated a 6 h performance benefit after a single 30-minute transcranial direct current stimulation (tDCS) session. Medical research with tDCS shows beneficial effects of repeated exposures; therefore, we hypothesize that repeated exposures will extend to healthy populations. Four groups of twelve participants in each received either active stimulation at 1800 and sham at 0400 h, sham at 1800 and active at 0400 h, active stimulation at both 1800 and 0400 h, or sham at 1800 and 0400 h during 36 h of continued wakefulness. Every two hours beginning at 1800 and ending at 1900 h the next day, participants completed 4 cognitive tasks along with subjective questionnaires. Contrary to our hypothesis, repeating stimulation did not have an additive benefit to cognitive performance. However, participants did report feeling significantly less fatigued on the subjective questionnaires compared to the other groups; suggesting possible additive effects for improving subjective mood.

Advances in genomics and metabolomics have made clear in recent years that microbial biosynthetic capacities on Earth far exceed previous expectations. This is attributable, in part, to the realization that most microbial natural product (NP) producers harbor biosynthetic machineries not readily amenable to classical laboratory fermentation conditions. Such “cryptic” or dormant biosynthetic gene clusters (BGCs) encode for a vast assortment of potentially new antibiotics and, as such, have become extremely attractive targets for activation under controlled laboratory conditions. We report here that co-culturing of a Rhodococcus sp. and a Micromonospora sp. affords keyicin, a new and otherwise unattainable bis-nitroglycosylated anthracycline whose mechanism of action (MOA) appears to deviate from those of other anthracyclines. The structure of keyicin was elucidated using high resolution MS and NMR technologies, as well as detailed molecular modeling studies. Sequencing of the keyicin BGC (within the Micromonospora genome) enabled both structural and genomic comparisons to other anthracycline-producing systems informing efforts to characterize keyicin. The new NP was found to be selectively active against Gram-positive bacteria including both Rhodococcus sp. and Mycobacterium sp. E. coli-based chemical genomics studies revealed that keyicin’s MOA, in contrast to many other anthracyclines, does not invoke nucleic acid damage.

Background To assess the efficacy of using transcranial direct current stimulation (tDCS) to remediate the deleterious effects of fatigue induced by sleep deprivation and compare these results to caffeine, a commonly used fatigue countermeasure. Objective/Hypothesis Based on previous research, tDCS of the dorsolateral prefrontal cortex (DLPFC) can modulate attention and arousal. The authors hypothesize that tDCS can be an effective fatigue countermeasure. Methods Five groups of ten participants each received either active tDCS and placebo gum at 1800, caffeine gum with sham tDCS at 1800, active tDCS and placebo gum at 0400, caffeine gum with sham tDCS at 0400, or sham tDCS with placebo gum at 1800 and 0400 during 36-h of sustained wakefulness. Participants completed a vigilance task, working memory task, psychomotor vigilance task (PVT), and a procedural game beginning at 1800 h and continued every two hours throughout the night until 1900 the next day. Results tDCS dosed at 1800 provided 6 h of improved attentional accuracy and reaction times compared to the control group. Caffeine did not produce an effect. Both tDCS groups also had an improved effect on mood. Participants receiving tDCS reported feeling more vigor, less fatigue, and less bored throughout the night compared to the control and caffeine groups. Conclusions We believe tDCS could be a powerful fatigue countermeasure. The effects appear to be comparable or possibly more beneficial than caffeine because they are longer lasting and mood remains more positive.

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

Human multitasking performance is important in many areas, such as defense, medicine, and everyday life. However, multitasking can be difficult to research due to a lack of objective metrics. This gap is remedied by applying information theory-based models to multitasking systems. The human operator informatic model is a throughput model that has been successfully applied to the multiple attribute task battery (MATB) software. In this study, auditory and haptic cueing were applied to the monitoring and targeting components of MATB, respectively. Interestingly, overall information throughput was not significantly affected by the cueing. These results can be traced to a mathematical change in operator strategy. In the presence of multisensory cueing, operators responded at higher information rates to the monitoring and targeting components; however, this came at a proportional cost to the communications and resource components. We propose that each operator has an information throughput capacity—an asymptotic limit to the amount of information he/she can process. This theoretical limit is analogous to the “channel capacity” for single tasking proposed by Miller in 1956.

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes., Author summary Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

The economy of a nation is driven by a robust securities market. The growth of a nation is indubitably based on the strength and stability of its secondary market systems and intermediaries. The mobilization of funds and its flow into diverse sectors of the economy in a regulated manner signifies dynamism and progress. The Egyptian economy has been in a trajectory of progress right since the establishment of its secondary market and its stock index EGX 30 in 2009. The Egyptian pound (EGP) has been focus of Egyptian monetary policy due to undue stress on the pound during the recent years. The Central Bank of Egypt (CBE) has been in the forefront of all monetary measures to stabilise the pound. The growth and development of a nation is charted by the changing economic and business environment. Any change in the foreign exchange market is sure to leave its footprint in the secondary market. All researchers in the field of foreign exchange management have been intrigued by the relationship between secondary market and forex market. Many an investigation has been undertaken to find if there is a significant relation between stock prices and exchange rates. The recent transition in Egyptian economy to a floating rate mechanism and efforts to stabilize the pound have attracted researchers to find out the effects of such a monetary change. The relationship between securities market and forex market has to be given a serious thought before any decision pertaining to forex market policy and regulation. This study analyses the dynamic relationship between stock market and exchange rate in Egypt using Engle-Granger cointegration methodology and Granger causality test.

Background: Dorsolateral prefrontal cortex (DLPFC) low frequency repetitive transcranial magnetic stimulation (LF-rTMS) has shown promise as a treatment and investigative tool in the medical and research communities. Researchers have made significant progress elucidating DLPFC LF-rTMS effects-primarily in individuals with psychiatric disorders. However, more efforts investigating underlying molecular changes and establishing links to functional and behavioral outcomes in healthy humans are needed. Objective: We aimed to quantify neuromolecular changes and relate these to functional changes following a single session of DLPFC LF-rTMS in healthy participants. Methods: Eleven participants received sham-controlled neuronavigated 1 Hz rTMS to the region most activated by a 7-letter Sternberg working memory task (SWMT) within the left DLPFC. We quantified SWMT performance, functional magnetic resonance activation and proton Magnetic resonance spectroscopy (MRS) neurometabolite measure changes before and after stimulation. Results: A single LF-rTMS session was not sufficient to change DLPFC neurometabolite levels and these changes did not correlate with DLPFC activation changes. Real rTMS, however, significantly altered neurometabolite correlations (compared to sham rTMS), both with baseline levels and between the metabolites themselves. Additionally, real rTMS was associated with diminished reaction time (RT) performance improvements and increased activation within the motor, somatosensory and lateral occipital cortices. Conclusion: These results show that a single session of LF-rTMS is sufficient to influence metabolite relationships and causes widespread activation in healthy humans. Investigating correlational relationships may provide insight into mechanisms underlying LF-rTMS.

A polyether antibiotic, ecteinamycin (1), was isolated from a marine Actinomadura sp., cultivated from the ascidian Ecteinascidia turbinata. 13C enrichment, high resolution NMR spectroscopy, and molecular modeling enabled elucidation of the structure of 1, which was validated on the basis of comparisons with its recently reported crystal structure. Importantly, ecteinamycin demonstrated potent activity against the toxigenic strain of Clostridium difficile NAP1/B1/027 (MIC = 59 ng/μL), as well as other toxigenic and nontoxigenic C. difficile isolates both in vitro and in vivo. Additionally, chemical genomics studies using Escherichia coli barcoded deletion mutants led to the identification of sensitive mutants such as trkA and kdpD involved in potassium cation transport and homeostasis supporting a mechanistic proposal that ecteinamycin acts as an ionophore antibiotic. This is the first antibacterial agent whose mechanism of action has been studied using E. coli chemical genomics. On the basis of these data, we propose ecteinamycin as an ionophore antibiotic that causes C. difficile detoxification and cell death via potassium transport dysregulation.

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

The metalloid tellurite is highly toxic to microorganisms. Several mechanisms of action have been proposed, including thiol depletion and generation of hydrogen peroxide and superoxide, but none of them can fully explain its toxicity. Here we use a combination of directed evolution and chemical and biochemical approaches to demonstrate that tellurite inhibits heme biosynthesis, leading to the accumulation of intermediates of this pathway and hydroxyl radical. Unexpectedly, the development of tellurite resistance is accompanied by increased susceptibility to hydrogen peroxide. Furthermore, we show that the heme precursor 5-aminolevulinic acid, which is used as an antimicrobial agent in photodynamic therapy, potentiates tellurite toxicity. Our results define a mechanism of tellurite toxicity and warrant further research on the potential use of the combination of tellurite and 5-aminolevulinic acid in antimicrobial therapy., The mechanisms of action of the antibacterial metalloid tellurite are unclear. Here, the authors show that tellurite induces an accumulation of hydroxyl radical and intermediates of heme biosynthesis in E. coli, and that the heme precursor 5-aminolevulinic acid potentiates tellurite toxicity.

The drug discovery process can be significantly improved through understanding how the structure of chemical compounds relates to their function. A common paradigm that has been used to filter and prioritize compounds is ligand-based virtual screening, where large libraries of compounds are queried for high structural similarity to a target molecule, with the assumption that structural similarity is predictive of similar biological activity. Although the chemical informatics community has already proposed a wide range of structure descriptors and similarity coefficients, a major challenge has been the lack of systematic and unbiased benchmarks for biological activity that covers a broad range of targets to definitively assess the performance of the alternative approaches. We leveraged a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae that our labs have recently generated, covering more than 13,000 compounds from the RIKEN NPDepo and several NCI, NIH, and GlaxoSmithKline (GSK) compound collections. Supportive of the idea that chemical-genetic interaction data provide an unbiased proxy for biological functions, we found that many commonly used structural similarity measures were able to predict the compounds that exhibited similar chemical-genetic interaction profiles, although these measures did exhibit significant differences in performance. Using the chemical-genetic interaction profiles as a basis for our evaluation, we performed a systematic benchmarking of 10 different structure descriptors, each combined with 12 different similarity coefficients. We found that the All-Shortest Path (ASP) structure descriptor paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several different compound collections. We further describe a machine learning approach that improves the ability of the ASP metric to capture biological activity. We used the ASP fingerprints as input for several supervised machine learning models and the chemical-genetic interaction profiles as the standard for learning. We found that the predictive power of the ASP fingerprints (as well as several other descriptors) could be substantially improved by using support vector machines. For example, on held-out data, we measured a 5-fold improvement in the recall of biologically similar compounds at a precision of 50% based upon the ASP fingerprints. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining chemical structure descriptors can be a powerful approach to improving prediction of biological function from structure. Keywords: chemical-genetic network, chemical structure, molecular descriptor, structural similarity, virtual screening, and machine learning

Summary Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation MOSAIC is available at http://mosaic.cs.umn.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Chemical-genetic interactions – observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes – contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

The permeation of antibiotics through bacterial membranes to their target site is a crucial determinant of drug activity but in many cases remains poorly understood. During screening efforts to discover new broad-spectrum antibiotic compounds from marine sponge samples, we identified a new analog of the peptidyl nucleoside antibiotic blasticidin S that exhibited up to 16-fold-improved potency against a range of laboratory and clinical bacterial strains which we named P10. Whole-genome sequencing of laboratory-evolved strains of Staphylococcus aureus resistant to blasticidin S and P10, combined with genome-wide assessment of the fitness of barcoded Escherichia coli knockout strains in the presence of the antibiotics, revealed that restriction of cellular access was a key feature in the development of resistance to this class of drug. In particular, the gene encoding the well-characterized multidrug efflux pump NorA was found to be mutated in 69% of all S. aureus isolates resistant to blasticidin S or P10. Unexpectedly, resistance was associated with inactivation of norA , suggesting that the NorA transporter facilitates cellular entry of peptidyl nucleosides in addition to its known role in the efflux of diverse compounds, including fluoroquinolone antibiotics.