Uncovering coordinated pathway interactions through gene co-differential expression in yeast

Area of Science:

• Systems biology
• Molecular and cellular biology
• Bioinformatics

Background:

• Traditional gene co-expression networks often include spurious associations.
• They struggle to identify genes with context-specific expression patterns.
• Understanding gene and pathway responses in specific contexts is crucial for biological discovery.

Purpose of the Study:

• To construct a global co-differential expression network for Saccharomyces cerevisiae (YeastCoDEGNet).
• To identify gene and pathway modules based on shared context-specific responses.
• To improve the identification of functionally related genes and pathways under varying conditions.

Main Methods:

• Reanalyzed 143 curated microarray experiments (425 comparisons) in yeast.
• Constructed a co-differential expression network where connections represent shared context-specific responses.
• Built a cross-pathway coordination network based on pathway pair responses.

Main Results:

• YeastCoDEGNet identified distinct gene groups enriched in essential/nonessential genes and metabolic/nonmetabolic processes.
• Gene features like responsiveness and essentiality clustered positionally on chromosomes.
• The cross-pathway network revealed novel functional links, highlighting coordination in central metabolism, protein synthesis, and gene regulation.

Conclusions:

• YeastCoDEGNet offers a robust framework for analyzing context-specific gene expression dynamics.
• This network improves the identification of functional gene relationships and pathway coordination.
• The findings provide a deeper understanding of how genes and pathways adapt to perturbations.