Bioinformatics methodologies for coeliac disease and its comorbidities.

Area of Science:

• Bioinformatics
• Genomics
• Immunology

Background:

• Coeliac disease (CD) is a multifactorial condition influenced by nutrition, immunity, and genetics.
• Numerous autoimmune diseases are known comorbidities of CD, suggesting complex interrelationships.

Purpose of the Study:

• To develop and apply an integrated bioinformatics pipeline for analyzing gene expression data in CD and its comorbidities.
• To evaluate the interconnections among CD and associated pathologies using computational approaches.

Main Methods:

• Surveyed public gene expression data for CD and comorbidities.
• Developed an R-language pipeline integrating gene expression, cell-type data, and literature-based comorbidities.
• Utilized gene set enrichment analysis and semantic similarity for data integration and analysis.

Main Results:

• Identified common differentially expressed genes, Gene Ontology terms, and pathways between CD and its comorbidities.
• Quantified the closeness of selected pathologies using Disease Ontology terms.
• The R pipeline is publicly available for further research.

Conclusions:

• The developed bioinformatics pipeline offers a comprehensive approach to analyze CD and its comorbidities.
• Findings highlight shared molecular mechanisms and disease relationships, valuable for clinicians and researchers.
• Facilitates detailed pathology analysis, comparison with comorbidities, and exploration of other diseases.