miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

Abstract

Background: MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

Methods: Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G ₁, G ₂) was built from the integrated data and stored, where T is a miRNA-associated target gene, G ₁ (G ₂) is a gene/protein interacting with T (G ₁). Each sequence (T, G ₁, G ₂) 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. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. 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, 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.

Keywords: Alzheimer’s disease; Colorectal cancer; Comorbidity gene; Database and web service tool; Disease and tissue-specific miRNA-protein regulatory network; Disease-miRNA association; Target-specific regulatory pathway; Type 2 diabetes; anti-AD BACE1 inhibitor drug; miRNA-target association.

Figure 1. Regulatory sub-pathways.

In the linked sequence (M, T, G₁, G₂), called a miRNA-specific regulatory sub-pathway (MRSP), M is a miRNA, T is its regulatory target gene, G₁ is a protein interacting (according to PPI data) with T, and G₂ is a protein interacting with G₁. In the text the sequence (T, G₁, G₂) is called a target-specific regulatory sub-pathway, or simply, regulatory sub-pathway (RSP).

Figure 2. Schematic construction of disease-specific miRNA regulatory network (RRN).

For a given disease there may be more than one miRNA associated with it (A), and each disease-associated miRNA may have one or more target genes (B). After all the miRNA-specific RSPs having the from (M, T, G₁, G₂) are constructed (in the case of miRDRN utilization, retrieved from its database), an RRN is built from entire set of MRSPs by linking all unlinked pairs of genes/proteins if they have interaction according to BioGRID.

Figure 3. Entry interface of miRDRN.

User may select “Single Search” to explore a single disease, miRNA, or siRNA, or “Comorbidity Search” to explore a disease-pair.

Figure 4. Query interface of Single Search.

User is required to select a disease (or miRNA/siRNA) and other filters/options. As shown in the figure, the disease “colorectal neoplasms” with the optional tissue type “colorectal tumor” are selected. Other selections require target experimental validation to be “positive” and “direct,” targets restricted to be cancer related genes, pathway ranked by Jaccard scores on molecular functions (MF), and p-value less than 0.001.

Figure 5. Result interface on miRNAs and target genes (for colorectal neoplasms/colorectal tumor).

Search result, based on query input shown in Fig. 4, on miRNAs and literature source (blue area) and target genes (green). For each gene the gene symbol and its OMIM id are given, as well as information on whether the protein it encodes has a cancerous protein tag: CRG, cancer related gene; OCG, oncogene; TSG, tumor suppressor gene.

Figure 6. Result interface on target-specific RSPs (for colorectal neoplasms/colorectal tumor).

RSPs are listed in descending order (column 1) by p-value (column 6). Columns 2–4 give the symbols of genes in the sequence (T, G₁, G₂). Column 5 gives known pathways, such as a KEGG pathway, of which (T, G₁, G₂) is a part. On first appearance, all RSPs (2,111 in this example) are listed on multiple pages. Three options allow restricting the output to a smaller set: “Gene filter,” where user can restrict the set to only those RSPs containing a specified gene, similarly “KEGG filter,” and “Show top … sub-pathways,” where user can ask for only the top-N RSPs having the smallest p-values be listed and used for network construction.

Figure 7. Result on comorbidity genes in Alzheimer’s disease-Type 2 diabetes comorbidity search.

Genes common to some RSPs of both diseases are listed, together with information on cancer genes status, OMIM Id, and KEGG pathway.

Figure 8. Display of a sub-RRN built from a subset of RSPs determined by the user using options available in the interface shown in Fig. 6.

The option “Show top-70” RSPs (by p-value) was used. When the mouse is placed on a node (in this case the gene IRS1) in the displayed network, a small pop-up window opens to show the name of the node/gene and the number of other nodes it is linked to, and annotation on the node from GO, OMIM, KEGG, and GeneBank databases.

Figure 9. A partial miRNA regulatory network (RRN) for colorectal neoplasm.

The RRN is constructed from the top 70 RSPs by p-value for colorectal neoplasm, tissue type, colorectal tumor. A link indicates a miRNA-target relation or a PPI; red circle, miRNA; blue circle, miRNA target gene; yellow circle, non-target gene; diamond, oncogene; triangle, tumor suppressor gene.

Figure 10. The sub-RRN network-1.

This largest connected sub-RRN for colorectal neoplasm (constructed from the top 70 RSPs by p-value), containing six miRNAs targeting four genes connected to 52 other genes, is itself composed of two parts, one 28 nodes (five miRNAs targeting three genes) and the other 34 nodes (one miRNA targeting one gene), connected by a single link.

Figure 11. A sub-RRN of CRC obtained by using TNK2 as a gene filter.

The RRN contains the target gene AXL regulated by three miRNAs, hsa-mir-199b, hsa-mir-34a, hsa-mir-199a, and linked by PPI to TNK2, itself linked by PPI to four other genes AXL(OCG), MAGI3, HSP90AB2P, MERTK(OCG), KAT8.

Figure 12. A miRNA regulatory sub-network centered on the AD-associated gene BACE1.

The genes PSEN1, NCSTN, RANBP9, PLSCR1, MMP2, and FURIN are shown to be immediately downstream to, that is, have level 1 PPI with, BACE1.