The sub-specific numerical analysis of Candida albicans

Area of Science:

• Microbiology
• Computational Biology
• Biostatistics

Background:

• Candida albicans is a significant human pathogen.
• Understanding the genetic and phenotypic diversity of C. albicans is crucial for effective treatment and control.
• Previous studies have explored C. albicans variability, but comprehensive numerical analysis is needed.

Purpose of the Study:

• To numerically analyze 100 strains of Candida albicans using 96 physiochemical characters.
• To identify distinct groupings and understand the phenotypic relationships within C. albicans populations.
• To investigate the potential for phenotypic switching in C. albicans.

Main Methods:

• Utilized Principal Coordinate Analysis and modified Multidimensional Group Analysis (MGA) for numerical analysis.
• Applied MGA to identify optimal groupings (5, 11, 15, and 17 groups).
• Analyzed distinguishing characters for larger groups, including colonial fringe state, valerate assimilation, and resistance to various agents.

Main Results:

• MGA identified optimal solutions for 5, 11, 15, and 17 groups, with a single large swarm dominating the 5- and 11-group solutions.
• The 17-group solution revealed 10 singleton groups and seven groups with 2 to 32 members.
• Key distinguishing characters included colonial fringe state, valerate assimilation, and resistance to borate, cetrimide, benzalkonium chloride, chlorhexidine, arsenate, and salt.

Conclusions:

• While distinct clusters were observed, small Euclidean distances suggest most C. albicans strains form a single, cohesive swarm.
• The identified groups may represent "preferred phenotypes" that individual C. albicans strains can transition between.
• This finding has implications for understanding C. albicans adaptability and pathogenicity.