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Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
Joseph J Nalluri1, Pratip Rana2, Debmalya Barh3,4,5
1Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA. nallurijj@vcu.edu.
Abstract:
In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.
Insights
This study introduces a new method using information diffusion theory to identify critical microRNAs (miRNAs) involved in disease. The framework pinpoints causal miRNAs in alcohol dependence, aiding disease understanding and treatment strategies.
Area of Science:
- Molecular Biology
- Genetics
- Computational Biology
Background:
- MicroRNAs (miRNAs) are key regulators of biological processes, influencing gene expression and disease.
- Understanding miRNA-disease associations is crucial, but miRNA-miRNA network regulation in diseases remains complex.
Purpose of the Study:
- To develop a computational framework for identifying causal miRNAs in disease by analyzing miRNA-miRNA regulatory networks.
- To quantify influence diffusion within these networks using information diffusion theory.
Main Methods:
- Applied information diffusion theory to model miRNA-miRNA regulatory networks across various diseases.
- Identified critical, disease-specific miRNAs acting as causal agents in signaling pathways.
- Validated the framework against existing computational tools and a miRNA expression dataset for alcohol dependence.
Main Results:
- The proposed methodology successfully identified critical disease-specific miRNAs.
- Causal miRNAs for alcohol dependence were pinpointed and validated by expression patterns in early disease stages.
- The framework provides a robust tool for dissecting miRNA regulatory roles in disease.
Conclusions:
- The developed computational framework effectively identifies causal miRNAs implicated in disease progression.
- This approach offers valuable insights into miRNA-miRNA interactions and their role in pathophysiology.
- The tool is available online to facilitate research in miRNA-disease associations.
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