Gene co-expression networks are commonly used to identify functionally related genes, but they often suffer from spurious associations and fail to capture genes with restricted, context-specific responses. To address these limitations, we constructed YeastCoDEGNet, a global co-differential expression network in Saccharomyces cerevisiae, by reanalyzing microarray data from 143 carefully curated experiments comprising 425 comparisons. In this network, gene connections are defined by shared context-specific responses, enabling the identification of distinct topological groups enriched in either essential or nonessential genes, often associated with metabolic or nonmetabolic biological processes, respectively. We further characterized each gene by its responsiveness, essentiality, and number of co-differentially expressed partners, uncovering positional clustering of these features across chromosomal locations. To explore higher-order functional organization, we built a cross-pathway coordination network based on context-specific responses between pathway pairs, revealing modules of functionally related pathways. This network not only recovered well-known associations but also identified novel links, with particularly strong coordination observed among pathways involved in central carbon metabolism, amino acid and antioxidant processes, protein synthesis, trafficking, degradation, and gene regulation. By capturing context-specific gene expression dynamics, YeastCoDEGNet provides a powerful framework for studying how genes and pathways adapt to genetic and environmental perturbations.

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Journal Article

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Nasar MI, Sabih Ur Rehman S, Ott S, Alam MT

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