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Kaposi’s sarcoma-associated herpesvirus (KSHV) is causally linked to several acquired immune deficiency syndrome related malignancies including Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and a subset of multicentric Castleman’s disease1. Control of viral lytic replication is essential for KSHV latency, evasion of host immune system, and induction of tumors1. Here, we show that deletion of a cluster of 14 microRNAs (miRs) from KSHV genome significantly enhances viral lytic replication as a result of reduced NF-κB activity. The miR cluster regulates NF-κB pathway by reducing the expression of IκBα protein, an inhibitor of the NF-κB complexes. Computational and miR seed mutagenesis analyses identify KSHV miR-K1 that directly mediates IκBα ?protein level by targeting the 3’UTR of its transcript. Expression of miR-K1 is sufficient to rescue the NF-κB activity and inhibit viral lytic replication while inhibition of miR-K1 in KSHV-infected PEL cells has the opposite effects. Thus, KSHV encodes a miR to control viral replication by activating NF-κB pathway. These results illustrate an important role for KSHV miRs in regulating viral latency and lytic replication by manipulating a host survival pathway.

BackgroundThe identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.Methodology/Principal FindingsIt is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.ConclusionsThe superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred.