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A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards
Stephanie L Hickey1, Alexander McKim2,3, Christopher A Mancuso2,4
1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.
Abstract:
Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation "component") could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.
Insights
This study introduces a computational method to identify genes and drugs for complex diseases, focusing on chronic inflammation. It aims to improve understanding and treatment of diseases linked by shared inflammatory pathways.
Area of Science:
- Biomedical Informatics
- Genetics
- Pharmacology
Background:
- Complex diseases share phenotypes like inflammation, complicating treatment.
- Identifying shared molecular underpinnings across diseases is crucial for therapeutic advancement.
- Chronic inflammation is implicated in numerous conditions, including heart disease, cancer, and neurodegenerative disorders.
Purpose of the Study:
- To develop a computational approach for isolating disease-specific gene signatures.
- To identify genes and pathways associated with the chronic inflammation phenotype across diverse complex diseases.
- To discover potential drug targets for treating inflammation-related conditions.
Main Methods:
- Integration of gene interaction networks.
- Utilizing disease-/trait-gene association data.
- Incorporating drug-target information and employing SAveRUNNER for drug prioritization.
Main Results:
- Successfully isolated gene signatures specific to chronic inflammation.
- Identified key genes and pathways underlying inflammation across multiple diseases.
- Prioritized potential drugs for targeting inflammation.
Conclusions:
- The computational approach effectively identifies disease phenotypes and potential therapeutics.
- This method offers a novel strategy for drug discovery in complex diseases.
- Targeting shared inflammatory pathways presents a promising therapeutic avenue.
