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Related Experiment Video

Updated: Aug 27, 2025

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis
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Integrative functional analysis uncovers metabolic differences between Candida species.

Neelu Begum1, Sunjae Lee1, Theo John Portlock2

  • 1Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK.

Communications Biology
|September 27, 2022
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Summary

This study reveals metabolic diversity in Candida species, particularly the AGAu cluster (Candida albicans, Candida glabrata, and Candida auris). Understanding these metabolic pathways offers insights into fungal pathogenesis and potential anti-fungal targets.

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Area of Science:

  • Microbiology
  • Mycology
  • Metabolic Engineering

Background:

  • Candida species are key components of the human mycobiome.
  • Their metabolic processes are crucial for understanding pathogenesis.
  • Existing databases lack comprehensive protein-encoding gene annotations for Candida.

Purpose of the Study:

  • To develop the BioFung database for efficient protein-encoding gene annotation in Candida species.
  • To analyze carbohydrate-active enzyme (CAZyme) profiles and uncover metabolic plasticity.
  • To identify key metabolic pathways and potential anti-fungal targets in pathogenic Candida species.

Main Methods:

  • Genomic analysis using the newly developed BioFung database.
  • Carbohydrate-active enzyme (CAZyme) profiling.
  • Functional analysis of amino acid metabolism.
  • Metabolomics and gene expression validation.

Main Results:

  • BioFung database provides efficient protein-encoding gene annotation.
  • Identified core and accessory CAZyme features indicating Candida species' plasticity.
  • All Candida species utilize amino acid metabolism, but AGAu cluster (C. albicans, C. glabrata, C. auris) specifically uses arginine, cysteine, and methionine metabolism.
  • Discovered critical metabolic pathways (CAZyme, polyamine, choline, fatty acid biosynthesis) in AGAu species with anti-fungal potential.

Conclusions:

  • Metabolic diversity, especially within the AGAu cluster, underlies Candida's ability to dominate the mycobiome and cause disease.
  • The identified metabolic pathways and biomarkers present potential anti-fungal targets.
  • The BioFung database and CAZyme analysis offer valuable tools for studying Candida metabolism.