Identification of miRNA regulatory pathways in complex diseases

Official URL: http://risc01.sabanciuniv.edu/record=b1817406 (Table of Contents)

MicroRNAs, small endogenous non-coding RNAs are one of the most important components in the cell and they play a critical role in many cellular processes and have been linked to the control of signal transduction pathways. Identifying disease related miRNAs and using that knowledge to understand the disease pathogenesis at the molecular level, new molecular tools can be designed for reducing the time and cost of diagnosis, treatment and prevention. Computational models have become very useful and practical in terms of discovering new miRNA disease associations to be used in experimental validations. Omics studies demonstrated that changes in miRNA profiles of various tissues correlate with many complex diseases, such as Alzheimer’s, Parkinson’s or Huntington’s and various cancers. The aim of our study was to identify the potential active TF-miRNA-gene regulatory pathways involved in complex diseases Huntington’s and Parkinson’s, via integrating miRNA and gene expression profiles with known experimentally verified miRNAs/genes and directed signaling network. We downloaded the miRNA and gene expression profiles from gene expression omnibus (GEO) database. We derived the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs). SIGNOR database of causal relationships between signaling entities is used vi as a signed directed network and TF-miRNA-gene bidirectional regulatory network is constructed. Then, DEGs and DEmiRs are mapped to the TF-miRNA-gene regulatory network. We connected the mapped DEGs and DEmiR nodes with their third-degree neighbors, hence, the potential regulatory TF-miRNA-gene subnetwork was built. By using BFS algorithm, the potential disease related TF-miRNA-gene regulatory pathways were identified. In this study, we analyzed Huntington’s and Parkinson’s related mRNA and miRNA expression profiles with transcription factors (TF) and miRNAs known to be related to diseases. miRNA-TF-gene regulatory mechanisms and disease specific TF and miRNA regulatory pathways were aimed to be identified systematically. This study provides bioinformatic support for further research on the molecular mechanism of complex diseases.