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Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics

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Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics Article Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics Jessica C.S. Brown,1,6 Justin Nelson,2 Benjamin VanderSluis,2 Raamesh Deshpande,2 Arielle Butts,3 Sarah Kagan,4 Itzhack Polacheck,4 Damian J. Krysan,3,5 Chad L. Myers,2,* and Hiten D. Madhani1,* 1Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA 2Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA 3Department of Chemistry, University of Rochester Medical Center, Rochester, NY 14643, USA 4Department of Clinical Microbiology and Infection Diseases, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Israel 5Departments of Pediatrics and Microbiology/Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY 14643, USA 6Present address: Department of Pathology, Division of Microbiology and Immunology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA *Correspondence: [email protected] (C.L.M.), [email protected] (H.D.M.) http://dx.doi.org/10.1016/j.cell.2014.10.044 SUMMARY infected with HIV (Mandell et al., 2010). Compounding the clinical challenge is the slow pace of antifungal drug development: only The fungal meningitis pathogen Cryptococcus neo- a single new class of drugs (the echinocandins) has been formans is a central driver of mortality in HIV/AIDS. approved for use in the United States in the last 30 years (Butts We report a genome-scale chemical genetic data and Krysan, 2012; Mandell et al., 2010; Roemer et al., 2011). map for this pathogen that quantifies the impact of Fungal infections are estimated to cause 50% of deaths 439 small-molecule challenges on 1,448 gene knock- related to AIDS and have been termed a ‘‘neglected epidemic’’ outs. We identified chemical phenotypes for 83% of (Armstrong-James et al., 2014). The fungus chiefly responsible for deaths in this population is C. neoformans (Armstrong-James mutants screened and at least one genetic response C. neoformans et al., 2014). C. neoformans is an encapsulated basidiomycetous for each compound. chemical-genetic haploid yeast distantly related to Saccharomyces cerevisiae and responses are largely distinct from orthologous pub- Schizosaccharomyces pombe. A 2009 CDC study estimated lished profiles of Saccharomyces cerevisiae, demon- that 1 million infections and 600,000 deaths annually are strating the importance of pathogen-centered caused by C. neoformans, exceeding the estimated worldwide studies. We used the chemical-genetic matrix to pre- death toll from breast cancer (Lozano et al., 2012; Park et al., dict novel pathogenicity genes, infer compound 2009). C. neoformans is widespread in the environment and mode of action, and to develop an algorithm, O2M, exposure occurs through inhalation of desiccated yeast or that predicts antifungal synergies. These predictions spores (Heitman et al., 2011). In immunocompromised patients, were experimentally validated, thereby identifying C. neoformans replicates and disseminates, causing meningo- virulence genes, a molecule that triggers G2/M arrest encephalitis that is lethal without treatment (Heitman et al., 2011). Induction therapy involves flucytosine and intravenous in- and inhibits the Cdc25 phosphatase, and many com- fusions of amphtotericin B (Loyse et al., 2013). Both drugs are pounds that synergize with the antifungal drug flu- highly toxic, difficult to administer, and neither is readily available conazole. Our work establishes a chemical-genetic in the areas with the highest rates of disease. The current recom- foundation for approaching an infection responsible mendation for Cryptococcosis treatment is at least a year of ther- for greater than one-third of AIDS-related deaths. apy, which is difficult to accomplish in resource-limited settings (WHO, 2011). Thus, as is the case with infections caused by INTRODUCTION other fungal pathogens, effective treatment of cryptococcal in- fections is limited by the efficacy, toxicity, and availability of cur- Invasive fungal infections are notoriously difficult to diagnose rent pharmaceuticals. and treat, resulting in high mortality rates, even with state-of- We implemented chemogenomic profiling to approach the the art treatments. The three most common pathogenic agents challenges of therapeutic development in C. neoformans. This are Cryptococcus neoformans, Candida albicans, and Asper- method involves the systematic measurement of the impact of gillus fumigatus (Mandell et al., 2010). These organisms are compounds on the growth of defined null mutants to produce opportunistic fungi that prey on individuals with varying degrees a chemical-genetic map. Such a map represents a quantitative of immune deficiency. Susceptible patient populations include description composed of numerical scores indicative of the premature infants, diabetics, individuals with liver disease, growth behavior of each knockout mutant under each chemical chemotherapy patients, organ transplant recipients, and those condition. Cluster analysis of the growth scores for large 1168 Cell 159, 1168–1187, November 20, 2014 ª2014 Elsevier Inc. numbers of mutants under many chemical conditions can reveal each small molecule, we determined an approximate minimum genes that function in the same pathway and even those whose inhibitory concentration (MIC) in agar, then measured growth products are part of the same protein complex (Collins et al., of the knockout collection on each small molecule at 50%, 2007; Parsons et al., 2004; Parsons et al., 2006). In addition, 25%, and 12.5% MIC using high density agar plate colony arrays the identity of genes whose mutation produce resistance or and a robotic replicator. We then measured the size of each col- sensitivity is useful for uncovering compound mode of action ony using flatbed scanning and colony measurement software (MOA) (Hillenmeyer et al., 2008; Jiang et al., 2008; Nichols (Dittmar et al., 2010). We performed a minimum of four replicate et al., 2011; Parsons et al., 2006; Xu et al., 2007; Xu et al., colony measurements for each mutant-condition pair. Plate- 2009). Large-scale studies have been restricted to model organ- based assays are subject to known nonbiological effects, such isms for which gene deletion collections have been constructed, as spatial patterns. To mitigate these errors, a series of correc- namely S. cerevisiae, S. pombe, and Escherichia coli K12 (Hillen- tive measures were implemented using approaches described meyer et al., 2008; Nichols et al., 2011; Parsons et al., 2006). previously, including manual filtration of noisy data, spatial effect However, as none of these are pathogens, the extent to which normalization and machine learning-based batch correction the resulting insights translate to pathogenic organisms is un- (Baryshnikova et al., 2010). In addition, the data for each deletion known. A variation on chemogenomic profiling, chemically- mutant and compound was centered and normalized. Each induced haploinsufficiency, was first developed using a diploid mutant-small molecule combination was assigned a score with heterozygote gene deletion library S. cerevisiae to identify com- positive scores representing relative resistance and negative pound MOA. This method, which identifies genes that impact scores representing compound sensitivity (Table S2). A global compound sensitivity based on a two-fold gene dosage change, summary of the processed data organized by hierarchical clus- is suited for diploid organisms and has been used in the path- tering is shown in Figure 1A. ogen C. albicans (Jiang et al., 2008; Xu et al., 2007; Xu et al., The importance and validity of the computational corrections 2009). is shown in Figures 1B and S1. We estimated how reproducible We report here the generation of a large-scale chemogenomic the chemical-genetic profiles were by calculating the correlation map for C. neoformans using defined, commonly available scores for data obtained for different concentrations of the same knockout mutants, assessments of data quality, and extensive small molecule (purple). This measures the degree of overlap experimental verification. Comparisons of the C. neoformans between the overall chemical-genetic profiles, which are them- profile with two large-scale published profiles from selves each composed of a score for each mutant-small mole- S. cerevisiae revealed that for most types of compounds, the cule combination. We found significant correlation (p = 2.67 3 chemical-genetic interactions are distinct even among ortholo- 10À176) between data obtained for different concentrations of gous genes, emphasizing the importance of pathogen-focused the same small molecule compared to those between profiles investigation. We used nearest-neighbor analysis to predict generated by data set randomization, suggesting significant new genes involved in polysaccharide capsule formation and reproducibility. Moreover, correlation scores between chemi- infectivity, which we validated through experiment. We also uti- cal-genetic profiles of different concentrations of different com- lized genetic responses to predict the G2/M phase of the cell pounds (gray) are centered at approximately 0 (Figure 1B). This cycle and the Cdc25 phosphatase as targets of a thiazolidone- difference in correlation scores is apparent even when 2,4-dione derivative, which we confirmed in vivo and in vitro. comparing experiments performed on the same day, when Finally, because of the unmet need for improved antifungal spurious
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