A network biology workflow to study transcriptomics data of the diabetic liver

Area of Science:

• Systems biology
• Genomics
• Bioinformatics

Background:

• Transcriptomics data is increasingly available, necessitating advanced analysis methods.
• Understanding gene expression at the process level is crucial for deciphering complex diseases like type 2 diabetes mellitus.
• The liver plays a central role in metabolic diseases, and diabetic patients often develop non-alcoholic fatty liver disease.

Purpose of the Study:

• To develop a comprehensive network biology workflow for integrating differential gene expression data with pathway information.
• To build a network of interconnected pathways in the human diabetic liver.
• To gain deeper insights into the concerted action of disease-related processes in type 2 diabetes mellitus.

Main Methods:

• Selected a publicly available liver transcriptome dataset comparing lean/healthy versus obese/insulin-resistant subjects.
• Performed pathway analysis using the WikiPathways human pathway collection.
• Integrated data with transcription factor-gene interactions (ENCODE) and drug-target interactions (DrugBank).

Main Results:

• Identified seven significantly altered pathways, merged into a network of 408 gene products, 38 metabolites, and 5 pathway nodes.
• Highlighted 17 nodes present in multiple pathways, revealing inter-pathway connections.
• Identified new regulatory links via transcription factor-gene interactions and potential drug targets.

Conclusions:

• The network biology workflow effectively visualizes the rewiring of the diabetic liver.
• Integration of diverse data sources (gene expression, pathways, regulatory elements, drugs) enhances understanding of disease processes.
• The approach provides a resource for generating new hypotheses and is applicable across different research fields.