Gene communities in co-expression networks across different tissues

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Tissue-specific gene expression data, such as from the Genotype-Tissue Expression (GTEx) Consortium, enables comparisons of gene co-expression patterns across different human tissues.
• Gene co-expression networks are valuable for identifying groups of genes with similar expression, potentially indicating shared biological functions, responses to stimuli, or regulatory variations.

Purpose of the Study:

• To develop and apply a multilayer network analysis framework for comparing gene co-expression patterns across multiple exocrine gland tissues.
• To identify groups of genes (communities) that exhibit similar co-expression across tissues (generalist communities) or within a single tissue (specialist communities).

Main Methods:

• Construction of a multilayer network where each layer represents a tissue-specific gene co-expression network from four exocrine gland tissues.
• Development of a novel multilayer community detection method specifically designed for correlation matrix input, incorporating a null model.
• Application of the method to identify gene communities based on shared co-expression patterns across the network layers.

Main Results:

• The analysis identified five 'generalist' communities of genes co-expressed across multiple tissues and two 'specialist' communities co-expressed within a single tissue.
• Gene co-expression communities were found to exhibit significant physical clustering on chromosomes 1 and 11, suggesting shared regulatory elements.
• Specific gene groups, such as KRTAP3-1/3/5 in skin and pancreas, and CELA3A/CELA3B in the pancreas, showed evidence of shared regulatory elements or expression quantitative trait loci.

Conclusions:

• The developed multilayer community detection method effectively extracts biologically relevant gene communities from correlation matrix data.
• The findings highlight the utility of multilayer network analysis for understanding tissue-specific gene regulation and function.
• The identification of gene clustering and shared regulatory elements provides a foundation for further investigation into gene function and interaction networks.