on the human miRNA-disease bipartite graph, we constructed the miRNA-associated disease network (MDN) by giving two diseases an edge if they share at least one common associated miRNA. The MDN network shows cluster structures (Supplementary Figure S2), in which similar diseases are clustered together. All cancers are connected together (Supplementary Figure S2), suggesting that various cancers may share similar associations at the miRNA level, in which some strong onco-miRNAs or miRNA suppressors may play key roles. For example, miR-21 is overexpressed in various cancers from almost all studies, showing a feature of strong onco-miRNAs, whereas miR-125a shows down-regulation in various cancers, suggesting that it is a miRNA suppressor. Similarly, all cardiovascular diseases are also connected together, which may result from some cardiovascular disease related miRNAs, such as miR-1 and miR-133, which play roles in almost all cardiovascular diseases in the MDN. The cancer cluster is clearly separated from the cardiovascular disease cluster (Supplementary Figure S2). These two clusters are connected with each other through only several hub diseases, such as heart failure, cardiac hypertrophy, and skeletal muscle hypertrophy. One exception is neointimal hyperplasia, a cardiovascular disease, which is predominantly connected to the cancer cluster and has only two connections to other cardiovascular diseases. Thus, it may share more common miRNA associations with cancers than with cardiovascular diseases. Diseases in the same cluster are highly interconnected; for example, each node connects to an average of 26 other cancers in the cancer cluster and 9 other cardiovascular diseases in the cardiovascular disease cluster, whereas cancers connect to only 5 cardiovascular diseases on average. Disease-associated miRNAs show various dysfunctions, such as mutation, up-regulation, deleted, and down-regulation. However, it remains unclear whether diseases sharing the same miRNAs have the same miRNA dysfunctions. Because most of the reported miRNA dysfunctions are either up- regulation or down-regulation, we curated most of the dysfunctions in the human miRNA disease database into these two groups. We assigned terms such as ‘‘deleted’’ and ‘‘low expressed’’ into the down- regulation group and terms such as ‘‘overexpressed’’, ‘‘highly expressed’’, and ‘‘over expression’’ into the up-regulation group. We next investigated the dysfunctional patterns within the same cluster as well as between different clusters. We calculated the number of the same dysfunctions (both up-regulation or both down- regulation) and the number of different dysfunctions (one shows up- regulation and the other shows down-regulation) for miRNAs that link diseases within the cancer cluster, or the cardiovascular disease cluster (intra-cluster), and between cancer and cardiovascular disease clusters (inter-cluster). Most of the shared miRNAs show the same dysfunctions in diseases in the same clusters, for example, 82% (578/ 706) of the disease pairs in the cancer cluster show the same dysfunctions and 77% (68/88) of the disease pairs in the cardiovascular disease cluster show the same dysfunctions (Table 1). However, only 54% (92/170) of the paired diseases between the cancer cluster and the cardiovascular disease cluster shows the same dysfunctions (Table 1). Diseases in different clusters thus show a larger fraction of different miRNA dysfunctions (P = 5.97610213, Fisher’s Exact Test). For example, miR-195 is up regulated in all reported cardiovascular diseases and down regulated in all reported cancers. This result suggested that although different classes of diseases associated with common miRNA, the underlying association mechanisms might be different. miRNA tissue specificity and miRNA related diseases It has been reported that tissue-specific miRNAs are often implicated in diseases related to specific tissues[12,13,14]. However, it remains largely unknown whether there is a MicroRNAs and Human Disease Table 1. The miRNA dysfunction pattern of diseases in the same clusters and between clusters. Diseases pairs Num1 Num2 P value In cluster1 In cluster2 Intra-cluster Between-clusters 578 128 68 20 646 148 P = 5.97610213 92 78 Cluster 1 is the cancer cluster. Cluster 2 is the cardiovascular disease cluster. Num1 is the number of the same dysfunction evidences. Num2 is the number of different dysfunction evidences. P value was calculated using Fisher’s Exact Test. doi:10.1371/journal.pone.0003420.t001 correlation between the tissue specificity of a miRNA and the number of diseases associated with it. In order to dissect this question, we first obtained the miRNA expression profiles of 345 miRNAs across 40 normal tissues from a recently published paper[15]. We then used the tissue specificity index t to measure the tissue specificity of a miRNA[16]. The t value ranges from 0 to 1. A higher t value indicates higher tissue specificity of that miRNA. We next classified the human disease related miRNAs into several groups according to the number of diseases in which a miRNA is implicated. We then calculated the average tissue specificity index value for each group of miRNAs. Finally, we observed a negative correlation between the tissue specificity index and the number of diseases in which a miRNA is implicated (Figure 1, R = 20.83, P = 0.058, Spearman’s correlation). Most of the miRNAs associated with a large number of diseases (. = 4) show low tissue specificity index values (Figure 1). However, there are a few outliers; miR-372, miR-373, and miR-206 that are associated with 5, 4, and 4 diseases, whereas they show high tissue specificity index values of 0.86, 0.82, and 0.78, respectively. miR- 372 and miR-373 are specifically expressed in placenta, while miR-206 is specifically expressed in skeletal muscle. We also noted that some miRNAs associated with only one disease show low tissue specificity, which may mainly result from the incompleteness of the miRNA-disease associations, that is, currently only one disease was reported to associate with that miRNA. As the research going on, more diseases would be reported to associate with these low tissue specific miRNAs. This result revealed a potential correlation between miRNA tissue specificity and disease, which may be of value in predicting specific disease- related miRNAs by combining the miRNA tissue specificity values. Thus, if a disease occurs specifically in a given tissue, the miRNAs specifically expressed in that tissue will have a great potential to be related to that disease. However, most of the tissue specific miRNAs have not been reported related to diseases. Using this method, we detected miRNAs that show high tissue specificity (using t. = 0.80 as a cutoff, Supplementary Text S1), which are potentially associated with that tissue-specific diseases. The dissection of these relationships will be valuable for studying the functions of these miRNAs and their mechanisms in diseases and can be used to discovering novel disease-associated miRNAs. miRNA conservation and miRNA related diseases It has been reported that there exists a significant association between a gene’s (protein’s) connectivity and its conservation in yeast Saccharomyces cerevisiae [17] and human [18]. It is expected that if a gene is evolutionarily conserved it would have more connections to other genes and be more lethal when PLoS ONE | www.plosone.org 2 October 2008 | Volume 3 | Issue 10 | e3420