Format

Send to

Choose Destination
Sci Rep. 2017 Aug 15;7(1):8133. doi: 10.1038/s41598-017-08125-4.

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model.

Author information

1
Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA. nallurijj@vcu.edu.
2
Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA.
3
Center for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology, Purba Medinipur, West Bengal, India.
4
Laboratório de Genética Celular e Molecular, Departamento de Biologia Geral, Instituto de Ciências Biológicas (ICB), Universidade Federal de Minas Gerais, Av. Antonio Carlos,6627, Pampulha, Belo Horizonte, Minas Gerais, Brazil.
5
Xcode Life Sciences, 3D Eldorado, 112 Nungambakkam High Road, Nungambakkam, Chennai, Tamil Nadu, 600034, India.
6
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA.
7
Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, Virginia, USA.
8
Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, Maryland, USA.
9
Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA.

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.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center