MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR–messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR–mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/
Introduction: Type 2 diabetes (T2D) is a major risk factor for cardiovascular disease (CVD), and CVD remains a leading cause of death among those living with T2D. MicroRNAs (miRs) target messenger RNAs (mRNAs) to alter gene expression, and circulating miRs, which are readily measured from blood, have been studied as potential biomarkers for T2D risk. Our prior analysis of the Diabetes Prevention Program identified a subset of five miRs that were optimal predictors of incident T2D with interactions by trial arm. The purpose of this study was to identify the mRNA and biological pathways targeted by these five miRs to elucidate potential mechanisms of risk and responses to the tested interventions. Methods: We obtained experimentally validated human data from miRTarBase (version 8.0), filtered and subsetted the data using R (version 4.1.1) to identify mRNAs with strong evidence of being regulated by the five miRs. Pathway analysis was employed to assess for overrepresentation of the mRNA targets of miRs using the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation database and clusterProfiler (version 4.2.2) package. Results: The five miRs targeted a total of 167 pathways and 122 genes. Among the identified pathways, nine have known T2D associations: Insulin signaling, Insulin resistance, Diabetic cardiomyopathy, T2D, AGE-RAGE signaling in diabetic complications, HIF-1 signaling, TGF-beta signaling, Fatty acid biosynthesis, and Endocrine resistance. Of these, AGE-RAGE signaling was targeted by all five miRs and HIF-1 signaling was targeted by all except miR-144. A total of 36 genes were targeted in the two pathways. Vascular endothelial growth factor A (VEGFA) was targeted by miR-186, miR-203a, miR-205, and miR-206 in both AGE-RAGE and HIF-1 signaling pathways. Among genes that were targeted by two or more miRs, BCL2 apoptosis regulator (BCL2) and VEGFA have prior genetic associations with risk for T2D. Conclusions: These findings suggest that miR predictors of incident T2D target mRNAs and pathways known to underlie risk for T2D. Future studies can evaluate miRs as potential therapeutic targets for preventing and treating T2D and for informing mechanisms underlying risk reduction interventions, allowing for more precise treatments for individuals.
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