ScreenMill: a freely available software suite for growth measurement, analysis and visualization of high-throughput screen data

Unraveling the biology of a fungal meningitis pathogen using chemical genetics

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths.

Large-scale robot-assisted genome shuffling yields industrial Saccharomyces cerevisiae yeasts with increased ethanol tolerance

BACKGROUND

During the final phases of bioethanol fermentation, yeast cells face high ethanol concentrations. This stress results in slower or arrested fermentations and limits ethanol production. Novel Saccharomyces cerevisiae strains with superior ethanol tolerance may therefore allow increased yield and efficiency. Genome shuffling has emerged as a powerful approach to rapidly enhance complex traits including ethanol tolerance, yet previous efforts have mostly relied on a mutagenized pool of a single strain, which can potentially limit the effectiveness. Here, we explore novel robot-assisted strategies that allow to shuffle the genomes of multiple parental yeasts on an unprecedented scale.

RESULTS

Screening of 318 different yeasts for ethanol accumulation, sporulation efficiency, and genetic relatedness yielded eight heterothallic strains that served as parents for genome shuffling. In a first approach, the parental strains were subjected to multiple consecutive rounds of random genome shuffling with different selection methods, yielding several hybrids that showed increased ethanol tolerance. Interestingly, on average, hybrids from the first generation (F1) showed higher ethanol production than hybrids from the third generation (F3). In a second approach, we applied several successive rounds of robot-assisted targeted genome shuffling, yielding more than 3,000 targeted crosses. Hybrids selected for ethanol tolerance showed increased ethanol tolerance and production as compared to unselected hybrids, and F1 hybrids were on average superior to F3 hybrids. In total, 135 individual F1 and F3 hybrids were tested in small-scale very high gravity fermentations. Eight hybrids demonstrated superior fermentation performance over the commercial biofuel strain Ethanol Red, showing a 2 to 7% increase in maximal ethanol accumulation. In an 8-l pilot-scale test, the best-performing hybrid fermented medium containing 32% (w/v) glucose to dryness, yielding 18.7% (v/v) ethanol with a productivity of 0.90 g ethanol/l/h and a yield of 0.45 g ethanol/g glucose.

CONCLUSIONS

We report the use of several different large-scale genome shuffling strategies to obtain novel hybrids with increased ethanol tolerance and fermentation capacity. Several of the novel hybrids show best-parent heterosis and outperform the commonly used bioethanol strain Ethanol Red, making them interesting candidate strains for industrial production.

Assessing the potential of wild yeasts for bioethanol production

Bioethanol fermentations expose yeasts to a new, complex and challenging fermentation medium with specific inhibitors and sugar mixtures depending on the type of carbon source. It is, therefore, suggested that the natural diversity of yeasts should be further exploited in order to find yeasts with good ethanol yield in stressed fermentation media. In this study, we screened more than 50 yeast isolates of which we selected five isolates with promising features. The species Candida bombi, Wickerhamomyces anomalus and Torulaspora delbrueckii showed better osmo- and hydroxymethylfurfural tolerance than Saccharomyces cerevisiae. However, S. cerevisiae isolates had the highest ethanol yield in fermentation experiments mimicking high gravity fermentations (25 % glucose) and artificial lignocellulose hydrolysates (with a myriad of inhibitors). Interestingly, among two tested S. cerevisiae strains, a wild strain isolated from an oak tree performed better than Ethanol Red, a S. cerevisiae strain which is currently commonly used in industrial bioethanol fermentations. Additionally, a W. anomalus strain isolated from sugar beet thick juice was found to have a comparable ethanol yield, but needed longer fermentation time. Other non-Saccharomyces yeasts yielded lower ethanol amounts.

Phenotypic evaluation of natural and industrial Saccharomyces yeasts for different traits desirable in industrial bioethanol production

Saccharomyces cerevisiae is the organism of choice for many food and beverage fermentations because it thrives in high-sugar and high-ethanol conditions. However, the conditions encountered in bioethanol fermentation pose specific challenges, including extremely high sugar and ethanol concentrations, high temperature, and the presence of specific toxic compounds. It is generally considered that exploring the natural biodiversity of Saccharomyces strains may be an interesting route to find superior bioethanol strains and may also improve our understanding of the challenges faced by yeast cells during bioethanol fermentation. In this study, we phenotypically evaluated a large collection of diverse Saccharomyces strains on six selective traits relevant for bioethanol production with increasing stress intensity. Our results demonstrate a remarkably large phenotypic diversity among different Saccharomyces species and among S. cerevisiae strains from different origins. Currently applied bioethanol strains showed a high tolerance to many of these relevant traits, but several other natural and industrial S. cerevisiae strains outcompeted the bioethanol strains for specific traits. These multitolerant strains performed well in fermentation experiments mimicking industrial bioethanol production. Together, our results illustrate the potential of phenotyping the natural biodiversity of yeasts to find superior industrial strains that may be used in bioethanol production or can be used as a basis for further strain improvement through genetic engineering, experimental evolution, or breeding. Additionally, our study provides a basis for new insights into the relationships between tolerance to different stressors.

Quantitative analysis of colony morphology in yeast

Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism's virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.

Characterization of yeast mutants lacking alkaline ceramidases YPC1 and YDC1

Humans and yeast possess alkaline ceramidases located in the early secretory pathway. Single deletions of the highly homologous yeast alkaline ceramidases YPC1 and YDC1 have very little genetic interactions or phenotypes. Here, we performed chemical-genetic screens to find deletions/conditions that would alter the growth of ypc1∆ydc1∆ double mutants. These screens were essentially negative, demonstrating that ceramidase activity is not required for cell growth even under genetic stresses. A previously reported protein targeting defect of ypc1∆ could not be reproduced and reported abnormalities in sphingolipid biosynthesis detected by metabolic labeling do not alter the mass spectrometric lipid profile of ypc1∆ydc1∆ cells. Ceramides of ypc1∆ydc1∆ remained normal even in presence of aureobasidin A, an inhibitor of inositolphosphorylceramide synthase. Moreover, in caloric restriction conditions Ypc1p reduces chronological life span. A novel finding is that, when working backwards as a ceramide synthase in vivo, Ypc1p prefers C24 and C26 fatty acids as substrates, whereas it prefers C16:0, when solubilized in detergent and working in vitro. Therefore, its physiological activity may not only concern the minor ceramides containing C14 and C16. Intriguingly, so far the sole discernable benefit of conserving YPC1 for yeast resides with its ability to convey relative resistance toward H2O2.

Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases

Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets.

Here we introduce our Yeast Augmented Network Analysis (YANA) approach and test it with the X-linked spinal muscular atrophy (SMA) disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish) SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

gitter: a robust and accurate method for quantification of colony sizes from plate images

Colony-based screens that quantify the fitness of clonal populations on solid agar plates are perhaps the most important source of genome-scale functional information in microorganisms. The images of ordered arrays of mutants produced by such experiments can be difficult to process because of laboratory-specific plate features, morphed colonies, plate edges, noise, and other artifacts. Most of the tools developed to address this problem are optimized to handle a single setup and do not work out of the box in other settings. We present gitter, an image analysis tool for robust and accurate processing of images from colony-based screens. gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately. We show that gitter produces comparable colony sizes to other tools in simple cases but outperforms them by being able to handle a wider variety of screens and more accurately quantify colony sizes from difficult images. gitter is freely available as an R package from http://cran.r-project.org/web/packages/gitter under the LGPL. Tutorials and demos can be found at http://omarwagih.github.io/gitter.

Estrogen receptor antagonists are anti-cryptococcal agents that directly bind EF hand proteins and synergize with fluconazole in vivo

Cryptococcosis is an infectious disease of global significance for which new therapies are needed. Repurposing previously developed drugs for new indications can expedite the translation of new therapies from bench to beside. Here, we characterized the anti-cryptococcal activity and antifungal mechanism of estrogen receptor antagonists related to the breast cancer drugs tamoxifen and toremifene. Tamoxifen and toremifene are fungicidal and synergize with fluconazole and amphotericin B in vitro. In a mouse model of disseminated cryptococcosis, tamoxifen at concentrations achievable in humans combines with fluconazole to decrease brain burden by ~1 log₁₀. In addition, these drugs inhibit the growth of Cryptococcus neoformans within macrophages, a niche not accessible by current antifungal drugs. Toremifene and tamoxifen directly bind to the essential EF hand protein calmodulin, as determined by thermal shift assays with purified C. neoformans calmodulin (Cam1), prevent Cam1 from binding to its well-characterized substrate calcineurin (Cna1), and block Cna1 activation. In whole cells, toremifene and tamoxifen block the calcineurin-dependent nuclear localization of the transcription factor Crz1. A large-scale chemical genetic screen with a library of C. neoformans deletion mutants identified a second EF hand-containing protein, which we have named calmodulin-like protein 1 (CNAG_05655), as a potential target, and further analysis showed that toremifene directly binds Cml1 and modulates its ability to bind and activate Cna1. Importantly, tamoxifen analogs (idoxifene and methylene-idoxifene) with increased calmodulin antagonism display improved anti-cryptococcal activity, indicating that calmodulin inhibition can be used to guide a systematic optimization of the anti-cryptococcal activity of the triphenylethylene scaffold.

Worldwide, cryptococcosis affects approximately 1 million people annually and kills more HIV/AIDS patients per year than tuberculosis. The gold standard therapy for cryptococcosis is amphotericin B plus 5-flucytosine, but this regimen is not readily available in regions where resources are limited and where the burden of disease is highest. Herein, we show that molecules related to the breast cancer drug tamoxifen are fungicidal for Cryptococcus and display a number of pharmacological properties desirable for an anti-cryptococcal drug, including synergistic fungicidal activity with fluconazole in vitro and in vivo, oral bioavailability, and activity within macrophages. We have also demonstrated that this class of molecules targets calmodulin as part of their mechanism of action and that tamoxifen analogs with increased calmodulin antagonism have improved anti-cryptococcal activity. Taken together, these results indicate that tamoxifen is a pharmacologically attractive scaffold for the development of new anti-cryptococcal drugs and provide a mechanistic basis for its further optimization.

Balony: a software package for analysis of data generated by synthetic genetic array experiments

BACKGROUND

Synthetic Genetic Array (SGA) analysis is a procedure which has been developed to allow the systematic examination of large numbers of double mutants in the yeast Saccharomyces cerevisiae. The aim of these experiments is to identify genetic interactions between pairs of genes. These experiments generate a number of images of ordered arrays of yeast colonies which must be analyzed in order to quantify the extent of the genetic interactions. We have designed software that is able to analyze virtually any image of regularly arrayed colonies and allows the user significant flexibility over the analysis procedure.

RESULTS

"Balony" is freely available software which enables the extraction of quantitative data from array-based genetic screens. The program follows a multi-step process, beginning with the optional preparation of plate images from single or composite images. Next, the colonies are identified on a plate and the pixel area of each is measured. This is followed by a scoring module which normalizes data and pairs control and experimental data files. The final step is analysis of the scored data, where the strength and reproducibility of genetic interactions can be visualized and cross-referenced with information on each gene to provide biological insights into the results of the screen.

CONCLUSIONS

Analysis of SGA screens with Balony can be either automated or highly interactive, enabling the user to customize the process to their specific needs. Quantitative data can be extracted at each stage for external analysis if required. Beyond SGA, this software can be used for analyzing many types of plate-based high-throughput screens.

SGAtools: one-stop analysis and visualization of array-based genetic interaction screens

Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.

Approaching the functional annotation of fungal virulence factors using cross-species genetic interaction profiling

In many human fungal pathogens, genes required for disease remain largely unannotated, limiting the impact of virulence gene discovery efforts. We tested the utility of a cross-species genetic interaction profiling approach to obtain clues to the molecular function of unannotated pathogenicity factors in the human pathogen Cryptococcus neoformans. This approach involves expression of C. neoformans genes of interest in each member of the Saccharomyces cerevisiae gene deletion library, quantification of their impact on growth, and calculation of the cross-species genetic interaction profiles. To develop functional predictions, we computed and analyzed the correlations of these profiles with existing genetic interaction profiles of S. cerevisiae deletion mutants. For C. neoformans LIV7, which has no S. cerevisiae ortholog, this profiling approach predicted an unanticipated role in the Golgi apparatus. Validation studies in C. neoformans demonstrated that Liv7 is a functional Golgi factor where it promotes the suppression of the exposure of a specific immunostimulatory molecule, mannose, on the cell surface, thereby inhibiting phagocytosis. The genetic interaction profile of another pathogenicity gene that lacks an S. cerevisiae ortholog, LIV6, strongly predicted a role in endosome function. This prediction was also supported by studies of the corresponding C. neoformans null mutant. Our results demonstrate the utility of quantitative cross-species genetic interaction profiling for the functional annotation of fungal pathogenicity proteins of unknown function including, surprisingly, those that are not conserved in sequence across fungi.

HIV/AIDS patients, cancer chemotherapy patients, and organ transplant recipients are highly susceptible to infection by opportunistic fungal pathogens, organisms common in the environment that are harmless to normal individuals. Understanding how these pathogens cause disease requires the identification of genes required for virulence and the determination of their molecular function. Our work addresses the latter problem using the yeast Cryptococcus neoformans, which is estimated to cause 600,000 deaths annually worldwide in the HIV/AIDS population. We describe a method for determining gene function in which C. neoformans genes are expressed in deletion mutants of all nonessential genes of the well-studied model yeast S. cerevisiae. By examining the impact on growth (enhancement or suppression) we generated “cross-species” genetic interaction profiles. We compared these profiles to the published genetic interaction profiles of S. cerevisiae deletion mutants to identify those with correlated patterns of genetic interactions. We hypothesized that the known functions of S. cerevisiae genes with correlated profiles could predict the function of the pathogen gene. Indeed, experimental tests in C. neoformans for two pathogenicity genes of previously unknown function found the functional predictions obtained from genetic interaction profiles to be accurate, demonstrating the utility of the cross-species approach.

Sec24p and Sec16p cooperate to regulate the GTP cycle of the COPII coat

Vesicle budding from the endoplasmic reticulum (ER) employs a cycle of GTP binding and hydrolysis to regulate assembly of the COPII coat. We have identified a novel mutation (sec24-m11) in the cargo-binding subunit, Sec24p, that specifically impacts the GTP-dependent generation of vesicles in vitro. Using a high-throughput approach, we defined genetic interactions between sec24-m11 and a variety of trafficking components of the early secretory pathway, including the candidate COPII regulators, Sed4p and Sec16p. We defined a fragment of Sec16p that markedly inhibits the Sec23p- and Sec31p-stimulated GTPase activity of Sar1p, and demonstrated that the Sec24p-m11 mutation diminished this inhibitory activity, likely by perturbing the interaction of Sec24p with Sec16p. The consequence of the heightened GTPase activity when Sec24p-m11 is present is the generation of smaller vesicles, leading to accumulation of ER membranes and more stable ER exit sites. We propose that association of Sec24p with Sec16p creates a novel regulatory complex that retards the GTPase activity of the COPII coat to prevent premature vesicle scission, pointing to a fundamental role for GTP hydrolysis in vesicle release rather than in coat assembly/disassembly.

An "exacerbate-reverse" strategy in yeast identifies histone deacetylase inhibition as a correction for cholesterol and sphingolipid transport defects in human Niemann-Pick type C disease

Niemann-Pick type C (NP-C) disease is a fatal lysosomal lipid storage disorder for which no effective therapy exists. A genome-wide, conditional synthetic lethality screen was performed using the yeast model of NP-C disease during anaerobiosis, an auxotrophic condition that requires yeast to utilize exogenous sterol. We identified 12 pathways and 13 genes as modifiers of the absence of the yeast NPC1 ortholog (NCR1) and quantified the impact of loss of these genes on sterol metabolism in ncr1Δ strains grown under viable aerobic conditions. Deletion of components of the yeast NuA4 histone acetyltransferase complex in ncr1Δ strains conferred anaerobic inviability and accumulation of multiple sterol intermediates. Thus, we hypothesize an imbalance in histone acetylation in human NP-C disease. Accordingly, we show that the majority of the 11 histone deacetylase (HDAC) genes are transcriptionally up-regulated in three genetically distinct fibroblast lines derived from patients with NP-C disease. A clinically approved HDAC inhibitor (suberoylanilide hydroxamic acid) reverses the dysregulation of the majority of the HDAC genes. Consequently, three key cellular diagnostic criteria of NP-C disease are dramatically ameliorated as follows: lysosomal accumulation of both cholesterol and sphingolipids and defective esterification of LDL-derived cholesterol. These data suggest HDAC inhibition as a candidate therapy for NP-C disease. We conclude that pathways that exacerbate lethality in a model organism can be reversed in human cells as a novel therapeutic strategy. This "exacerbate-reverse" approach can potentially be utilized in any model organism for any disease.

Srs2 overexpression reveals a helicase-independent role at replication forks that requires diverse cell functions

Srs2 is a 3'-5' DNA helicase that regulates many aspects of DNA metabolism in Saccharomyces cerevisiae. It is best known for its ability to counteract homologous recombination by dismantling Rad51 filaments, but is also involved in checkpoint activation, adaptation and recovery, and in resolution of late recombination intermediates. To further address its biological roles and uncover new genetic interactions, we examined the consequences of overexpressing SRS2 as well as two helicase-dead mutants, srs2-K41A and srs2-K41R, in the collection of 4827 yeast haploid deletion mutants. We identified 274 genes affecting a large variety of cellular functions that are required for cell growth when SRS2 or its mutants are overexpressed. Further analysis of these interactions reveals that Srs2 acts independently of its helicase function at replication forks likely through its recruitment by the sumoylated PCNA replication clamp. This helicase-independent function is responsible for the negative interactions with DNA metabolism genes and for the toxicity of SRS2 overexpression in many of the diverse cellular pathways revealed in our screens.

Selective ploidy ablation, a high-throughput plasmid transfer protocol, identifies new genes affecting topoisomerase I-induced DNA damage

We have streamlined the process of transferring plasmids into any yeast strain library by developing a novel mating-based, high-throughput method called selective ploidy ablation (SPA). SPA uses a universal plasmid donor strain that contains conditional centromeres on every chromosome. The plasmid-bearing donor is mated to a recipient, followed by removal of all donor-strain chromosomes, producing a haploid strain containing the transferred plasmid. As proof of principle, we used SPA to transfer plasmids containing wild-type and mutant alleles of DNA topoisomerase I (TOP1) into the haploid yeast gene-disruption library. Overexpression of Top1 identified only one sensitive mutation, rpa34, while overexpression of top1-T(722)A allele, a camptothecin mimetic, identified 190 sensitive gene-disruption strains along with rpa34. In addition to known camptothecin-sensitive strains, this set contained mutations in genes involved in the Rpd3 histone deacetylase complex, the kinetochore, and vesicle trafficking. We further show that mutations in several ESCRT vesicle trafficking components increase Top1 levels, which is dependent on SUMO modification. These findings demonstrate the utility of the SPA technique to introduce plasmids into the haploid gene-disruption library to discover new interacting pathways.

Measuring growth rate in high-throughput growth phenotyping

Growth rate is an important variable and parameter in biology with a central role in evolutionary, functional genomics, and systems biology studies. In this review the pros and cons of the different technologies presently available for high-throughput measurements of growth rate are discussed. Growth rate can be measured in liquid microcultivation of individual strains, in competition between strains, as growing colonies on agar, as division of individual cells, and estimated from molecular reporters. Irrespective of methodology, statistical issues such as spatial biases and batch effects are crucial to investigate and correct for to ensure low false discovery rates. The rather low correlations between studies indicate that cross-laboratory comparison and standardization are pressing issue to assure high-quality and comparable growth-rate data.