4.7 Article

Intraspecies Transcriptional Profiling Reveals Key Regulators of Candida albicans Pathogenic Traits

Journal

MBIO
Volume 12, Issue 2, Pages -

Publisher

AMER SOC MICROBIOLOGY
DOI: 10.1128/mBio.00586-21

Keywords

Candida; coexpression networks; gene expression; transcriptional networks; variation

Categories

Funding

  1. National Institutes of Health [R01AI148788, R01AI141893/R01AI081704]
  2. NIH F31 fellowship [1F31DE02940901]
  3. American Heart Association grant [AHA 20PRE35200201]

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The research on 21 clinical isolates of Candida albicans showed extensive genetic and phenotypic variation, with strain-specific gene expression patterns and linkages between gene expression and phenotypic traits. The study highlights the importance of transcriptional profiling in understanding the genetic basis for phenotypic diversity and identifies new genes and pathways associated with pathogenic processes. These findings underscore the need for personalized treatment strategies based on individual strains of infectious fungi.
The human commensal and opportunistic fungal pathogen Candida albicans displays extensive genetic and phenotypic variation across clinical isolates. Here, we performed RNA sequencing on 21 well-characterized isolates to examine how genetic variation contributes to gene expression differences and to link these differences to phenotypic traits. C. albicans adapts primarily through clonal evolution, and yet hierarchical clustering of gene expression profiles in this set of isolates did not reproduce their phylogenetic relationship. Strikingly, strain-specific gene expression was prevalent in some strain backgrounds. Association of gene expression with phenotypic data by differential analysis, linear correlation, and assembly of gene networks connected both previously characterized and novel genes with 23 C. albicans traits. Construction of de novo gene modules produced a gene atlas incorporating 67% of C. albicans genes and revealed correlations between expression modules and important phenotypes such as systemic virulence. Furthermore, targeted investigation of two modules that have novel roles in growth and filamentation supported our bioinformatic predictions. Together, these studies reveal widespread transcriptional variation across C. albicans isolates and identify genetic and epigenetic links to phenotypic variation based on coexpression network analysis. IMPORTANCE Infectious fungal species are often treated uniformly despite clear evidence of genotypic and phenotypic heterogeneity being widespread across strains. Identifying the genetic basis for this phenotypic diversity is extremely challenging because of the tens or hundreds of thousands of variants that may distinguish two strains. Here, we use transcriptional profiling to determine differences in gene expression that can be linked to phenotypic variation among a set of 21 Candida albicans isolates. Analysis of this transcriptional data set uncovered clear trends in gene expression characteristics for this species and new genes and pathways that were associated with variation in pathogenic processes. Direct investigation confirmed functional predictions for a number of new regulators associated with growth and filamentation, demonstrating the utility of these approaches in linking genes to important phenotypes.

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