Utilising established SDL-screening methods as a tool for the functional genomic characterisation of model and non-model organisms

Regulation of meiotic gene expression is functional in the human fungal pathogen Candida glabrata

The human fungal pathogen Candida glabrata is closely related to the budding yeast Saccharomyces cerevisiae. The sexual cycle in S. cerevisiae has been extensively characterized. Haploid cells 'a' and alpha secrete pheromones involved in mating of the opposite cell type leading to the formation of a diploid cell. Under harsh conditions, diploid cells undergo meiosis for the formation of four haploid spores. In C. glabrata, cells are also found as 'a' and alpha and this organism possesses most S. cerevisiae homologous genes involved in meiosis and mating. However, mating has never been observed in C. glabrata. In S. cerevisiae, the non-essential UME6 gene is involved in controlling the expression of meiotic genes. We have previously shown that Zcf11, a putative homolog of Ume6, is encoded by an essential gene but its function is unknown. Here, we show that the expression of UME6 in C. glabrata can partially complement a Zcf11 knock-down and that these factors recognize the same DNA sequence. Importantly, expression profiling using a Zcf11 knock-down strain revealed that this factor is a negative regulator of meiotic genes expression as well as some genes involved in mating. Thus, regulation of the expression of meiotic genes is functional in this organism reinforcing the view that C. glabrata may have a sexual cycle under specific conditions.

Using Synthetic Genetic Interactions in Candida glabrata as a Novel Method to Detect Genes with Roles in Antifungal Drug Resistance

Synthetic genetic interaction analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the interactions between genes, categorize into biological pathways, and characterize genes of unknown function. It has been extensively employed in model organisms for fundamental, systems-level assessment of the interactions between genes. However, more recently, genetic interaction mapping has been applied to fungal pathogens and has been instrumental for the study of clinically important infectious organisms. This protocol herein explains in the detail the methodology and analysis that can be employed to develop interaction maps in microbial pathogens. Such techniques can aid in bridging our understanding of complex genetic networks, with applications to diverse microbial pathogens to further our understanding of virulence, the use of antimicrobial therapies, and host-pathogen interactions.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Comprehensive genetic analysis of adhesin proteins and their role in virulence of Candida albicans

Candida albicans is a microbial fungus that exists as a commensal member of the human microbiome and an opportunistic pathogen. Cell surface-associated adhesin proteins play a crucial role in C. albicans' ability to undergo cellular morphogenesis, develop robust biofilms, colonize, and cause infection in a host. However, a comprehensive analysis of the role and relationships between these adhesins has not been explored. We previously established a CRISPR-based platform for efficient generation of single- and double-gene deletions in C. albicans, which was used to construct a library of 144 mutants, comprising 12 unique adhesin genes deleted singly, and every possible combination of double deletions. Here, we exploit this adhesin mutant library to explore the role of adhesin proteins in C. albicans virulence. We perform a comprehensive, high-throughput screen of this library, using Caenorhabditis elegans as a simplified model host system, which identified mutants critical for virulence and significant genetic interactions. We perform follow-up analysis to assess the ability of high- and low-virulence strains to undergo cellular morphogenesis and form biofilms in vitro, as well as to colonize the C. elegans host. We further perform genetic interaction analysis to identify novel significant negative genetic interactions between adhesin mutants, whereby combinatorial perturbation of these genes significantly impairs virulence, more than expected based on virulence of the single mutant constituent strains. Together, this study yields important new insight into the role of adhesins, singly and in combinations, in mediating diverse facets of virulence of this critical fungal pathogen.

Attenuating the emergence of anti-fungal drug resistance by harnessing synthetic lethal interactions in a model organism

Drug resistance is a rapidly emerging concern, thus prompting the development of novel therapeutics or combinatorial therapy. Currently, combinatorial therapy targets are based on knowledge of drug mode of action and/or resistance mechanisms, constraining the number of target proteins. Unbiased genome-wide screens could reveal novel genetic components within interaction networks as potential targets in combination therapies. Testing this, in the context of antimicrobial resistance, we implemented an unbiased genome-wide screen, performed in Saccharomyces cerevisiae expressing a Candida glabrata PDR1+ gain-of-function allele. Gain-of-function mutations in this gene are the principal mediators of fluconazole resistance in this human fungal pathogen. Eighteen synthetically lethal S. cerevisiae genetic mutants were identified in cells expressing C. glabrata PDR1+. One mutant, lacking the histone acetyltransferase Gcn5, was investigated further. Deletion or drug-mediated inhibition of Gcn5 caused a lethal phenotype in C. glabrata cells expressing PDR1+ alleles. Moreover, deletion or drug-mediated inactivation of Gcn5, inhibited the emergence of fluconazole-resistant C. glabrata isolates in evolution experiments. Thus, taken together, the data generated in this study provides proof of concept that synthetically lethal genetic screens can identify novel candidate proteins that when therapeutically targeted could allow effective treatment of drug-resistant infections.