An Analysis of Human MicroRNA and Disease Associations Ming Lu1,2., Qipeng Zhang1,2., Min Deng1,2., Jing Miao3, Yanhong Guo 2 , Wei Gao2, Qinghua Cui1,2* 1 Department of Medical Informatics, Peking University Health Science Center, Beijing, China, 2 Ministry of Education Key Laboratory of Molecular Cardiology, Peking University, Beijing, China, 3 Department of Pediatrics, Peking University First Hospital, Beijing, China Abstract It has been reported that increasingly microRNAs are associated with diseases. However, the patterns among the microRNA- disease associations remain largely unclear. In this study, in order to dissect the patterns of microRNA-disease associations, we performed a comprehensive analysis to the human microRNA-disease association data, which is manually collected from publications. We built a human microRNA associated disease network. Interestingly, microRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively. A negative correlation between the tissue- specificity of a microRNA and the number of diseases it associated was uncovered. Furthermore, we observed an association between microRNA conservation and disease. Finally, we uncovered that microRNAs associated with the same disease tend to emerge as predefined microRNA groups. These findings can not only provide help in understanding the associations between microRNAs and human diseases but also suggest a new way to identify novel disease-associated microRNAs. Citation: Lu M, Zhang Q, Deng M, Miao J, Guo Y, et al. (2008) An Analysis of Human MicroRNA and Disease Associations. PLoS ONE 3(10): e3420. doi:10.1371/ journal.pone.0003420 Editor: Mark Isalan, Center for Genomic Regulation, Spain Received July 28, 2008; Accepted September 16, 2008; Published October 15, 2008 Copyright: 2008 Lu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was supported by the National Basic Research program of China (2007CB512100) and the 985 project of Peking University (No. 985-2- 108-121). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Competing Interests: The authors have declared that no competing interests exist. * E-mail: cuiqinghua@bjmu.edu.cn . These authors contributed equally to this work. Introduction MicroRNAs (miRNAs) are a class of small non-coding RNAs (,22 nt) which normally function as negative regulators of target mRNA expression at the posttranscriptional level. They bind to the 39UTR of target mRNAs through base pairing, resulting in target mRNAs cleavage or translation inhibition[1,2,3]. It has also recently been demonstrated that miRNAs may function as positive regulators in some cases[4,5]. It is estimated that 1–4% genes in the human genome are miRNAs and a single miRNA can regulate as many as 200 mRNAs[6]. There is increasing evidence suggesting that miRNAs play critical roles in many key biological processes, such as cell growth, tissue differentiation, cell prolifer- ation, embryonic development, and apoptosis[6]. We previously found that miRNA also play important roles in cellular signaling network[7], cross-species gene expression variation[8], and co- regulation with transcription factors[9]. As such, mutation of miRNAs, dysfunction of miRNA biogenesis and dysregulation of miRNAs and their targets may result in various diseases. Currently, there have been reported ,70 diseases are associated with miRNAs (see our database http://cmbi.bjmu.edu.cn/hmdd.). Many studies have produced a large number of miRNA-disease associations and shown that the mechanisms of miRNAs involved in diseases are very complex. In such a complex case, a comprehensive analysis of these data will do great help in understanding the associations between miRNAs and diseases. Furthermore, a large-scale analysis and integration of these miRNA-disease associations will offer a platform to dissect the patterns of the miRNA and disease associations, even though current known miRNA-disease associations are far from com- pleteness. In this study, we reported our literature-based generation of miRNA-disease associations and the analysis of these data based on bioinformatics. Results The human miRNA disease database We retrieved miRNA-disease associations from ,100 papers and built a human miRNA-associated disease database (HMDD), which contains miRNA names, disease names, dysfunction evidences, and PubMed ID. HMDD is publicly accessible at website: http://cmbi.bjmu.edu.cn/hmdd. This database was built on November 2007 and the data at that time was used in the following analysis. The last update was made on July 2008. The miRNA-disease association data between November 2007 and July 2008 was used to validate the main results in this study. Dysfunctional evidences in the clusters of the miRNA- associated disease network A bipartite graph can be used as a network model to connect two disjoint sets of nodes [10,11]. As shown in Supplementary Figure S1, a bipartite graph contains two sets of nodes. And nodes in the same set are not connected and edges only exist between nodes from different sets. Here, we constructed a bipartite graph consisting of two disjoint sets of nodes based on the associations between a list of miRNAs and a list of human diseases obtained from HMDD. One set contains human miRNA-associated diseases (69 diseases) and the other set contains disease-associated miRNAs (238 miRNAs). Based PLoS ONE | www.plosone.org 1 October 2008 | Volume 3 | Issue 10 | e3420