miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks
A peer-reviewed article of this Preprint also exists.
Author and article information
Abstract
Background. MiRNA regulates cellular processes through acting on specific target genes. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, are often organized into pathways among genes and gene products. Large databases on protein-protein interactions (PPIs) are available. Here, we have integrated the information mentioned above to build a web service platform, miRNA Disease Regulatory Network, or miRDRN, for users to construct disease and tissue-specific miRNA-protein regulatory networks. Methods. Data on human protein interaction, disease-associated miRNA, tumor-associated gene, miRNA targeted gene, molecular interaction and reaction network or pathway, gene ontology, gene annotation and gene product information, and gene expression were collected from publicly available databases and integrated. A complete set of regulatory sub-pathways (RSPs) having the form (M, T, G1, G2) were built from the integrated data and stored in the database part of miRDRN, where M is a disease-associated miRNA, T is its regulatory target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results. A web service platform, miRDRN ( http://mirdrn.ncu.edu.tw/mirdrn/), was built to allow users to retrieve a disease and tissue-specific subset of RSPs, from which a miRNA regulatory network is constructed. miRDRN is a database that currently contains 6,973,875 p-valued sub-pathways associated with 119 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes, and a web tool that facilitates the construction and visualization of disease and tissue-specific miRNA-protein regulatory networks, for exploring single diseases, or for exploring the comorbidity of disease-pairs. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes (AD-T2D), in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.
Cite this as
2019. miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks. PeerJ Preprints 7:e27699v1 https://doi.org/10.7287/peerj.preprints.27699v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This is a submission to PeerJ for review.
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Hsueh-Chuan Liu conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Yi-Shian Peng authored or reviewed drafts of the paper, approved the final draft, provided material and did literature search for discussion in the manuscript related to AD, T2D, and anti-AD drugs.
Hoong-Chien Lee conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The source code and data of miRDRN, in compressed zip format, is deposited at and freely downloadable from the public repository Github: https://github.com/o2snow/2019MIRDRN
Funding
This work was supported by the Ministry of Science and Technology, Republic of China (No. MOST-105-2314-B-033-001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
