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

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Candida Species

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2542))

Abstract

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.

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Author information

Authors and Affiliations

  1. MRC Centre for Medical Mycology, University of Exeter, Exeter, UK

    Jane Usher

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  1. Jane Usher
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Correspondence to Jane Usher .

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Editors and Affiliations

  1. Department of Microbiology and Immunology, Georgetown University, Washington, DC, DC, USA

    Richard Calderone

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Usher, J. (2022). Using Synthetic Genetic Interactions in Candida glabrata as a Novel Method to Detect Genes with Roles in Antifungal Drug Resistance. In: Calderone, R. (eds) Candida Species. Methods in Molecular Biology, vol 2542. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2549-1_7

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  • DOI: https://doi.org/10.1007/978-1-0716-2549-1_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2548-4

  • Online ISBN: 978-1-0716-2549-1

  • eBook Packages: Springer Protocols

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