Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice. Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella-dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella, and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated. Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized.

N⁶-methyladenosine (m⁶A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m⁶A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m⁶A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m⁶A site named SRAMP is established. To depict the sequence context around m⁶A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/.

Motivation: It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. Results: It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. Availability: The online tool is available at http://cmbi.bjmu.edu.cn/misim Contact:cuiqinghua@hsc.pku.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs’ roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA–disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA–disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA–disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA–disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.

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.

The Human microRNA Disease Database (HMDD; available via the Web site at http://cmbi.bjmu.edu.cn/hmdd and http://202.38.126.151/hmdd/tools/hmdd2.html) is a collection of experimentally supported human microRNA (miRNA) and disease associations. Here, we describe the HMDD v2.0 update that presented several novel options for users to facilitate exploration of the data in the database. In the updated database, miRNA–disease association data were annotated in more details. For example, miRNA–disease association data from genetics, epigenetics, circulating miRNAs and miRNA–target interactions were integrated into the database. In addition, HMDD v2.0 presented more data that were generated based on concepts derived from the miRNA–disease association data, including disease spectrum width of miRNAs and miRNA spectrum width of human diseases. Moreover, we provided users a link to download all the data in the HMDD v2.0 and a link to submit novel data into the database. Meanwhile, we also maintained the old version of HMDD. By keeping data sets up-to-date, HMDD should continue to serve as a valuable resource for investigating the roles of miRNAs in human disease.