Comparative gene expression analysis by differential clustering approach: application to the Candida albicans...

Area of Science:

• Comparative genomics
• Systems biology
• Molecular evolution

Background:

• Genome-wide gene expression differences drive phenotypic variation between related species.
• Existing methods for characterizing these differences at a large scale are limited.

Purpose of the Study:

• To develop and apply a novel algorithm for identifying conserved and diverged gene co-expression patterns across species.
• To systematically compare the transcriptional programs of *Candida albicans* and *Saccharomyces cerevisiae*.

Main Methods:

• Introduction of the "differential clustering algorithm" for analyzing gene co-expression.
• Application of the algorithm at multiple organizational levels, from pairwise to higher-order gene group correlations.
• Systematic comparison of gene expression between *Candida albicans* and *Saccharomyces cerevisiae*.

Main Results:

• The differential clustering algorithm successfully revealed conserved and diverged co-expression patterns.
• Significant differences were identified in the requirement for mitochondrial function between the two yeast species.
• Distinct regulation patterns were observed for cell cycle and amino acid metabolic genes, potentially linked to cis-regulatory elements.

Conclusions:

• The differential clustering algorithm provides a robust framework for comparative gene expression analysis.
• The findings offer insights into the distinct biological adaptations of *Candida albicans* and *Saccharomyces cerevisiae*.
• The study generates hypotheses for uncharacterized genes and regulatory elements in *Candida albicans*.