MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA-disease associations, but few models are available to further prioritize causal miRNA-disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA-disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA-disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA-disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA-disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA-disease associations from general miRNA-disease associations.

Inter-tissue communication (ITC) is critical for maintaining the physiological functions of multiple tissues and is closely related to the onset and development of various complex diseases. Nevertheless, there is no well-organized data resource for known ITC molecules with explicit ITC routes from source tissues to target tissues. To address this issue, in this work, we manually reviewed nearly 190,000 publications and identified 1408 experimentally supported ITC entries in which the ITC molecules, their communication routes, and their functional annotations were included. To facilitate our work, these curated ITC entries were incorporated into a user-friendly database named IntiCom-DB. This database also enables visualization of the expression abundances of ITC proteins and their interaction partners. Finally, bioinformatics analyses on these data revealed common biological characteristics of the ITC molecules. For example, tissue specificity scores of ITC molecules at the protein level are often higher than those at the mRNA level in the target tissues. Moreover, the ITC molecules and their interaction partners are more abundant in both the source tissues and the target tissues. IntiCom-DB is freely available as an online database. As the first comprehensive database of ITC molecules with explicit ITC routes to the best of our knowledge, we hope that IntiCom-DB will benefit future ITC-related studies.

MicroRNAs (miRNAs) regulate multiple transcripts and thus shape the expression landscape of a cell. Information about miRNA expression and distribution across cell types is crucial for the understanding of miRNAs’ functions and their translational applications as biomarkers or therapeutic targets. In this study, we identify cell-type-specific miRNAs by combining multiple correspondence analysis and Gini coefficients to dissect miRNAs’ expression profiles and chromatin activity score profiles, which results in collections of chromatin activity-specific miRNAs in 91 cell types and expression-specific miRNAs in 124 cell types. Moreover, we find that cell-type-specific miRNAs are closely associated with disease miRNAs, such as T-cell-specific miRNAs, which are closely associated with cancer prognosis. Finally, we constructed mirCellType, an online tool based on cell-type-specific miRNA signatures, to dissect the cell type composition of complex samples with miRNA expression profiles.