MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

No abstract

Negative checkpoint regulators (NCRs) temper the T cell immune response to self-antigens and limit the development of autoimmunity. Unlike all other NCRs that are expressed on activated T lymphocytes, V-type immunoglobulin domain-containing suppressor of T cell activation (VISTA) is expressed on naïve T cells. We report an unexpected heterogeneity within the naïve T cell compartment in mice, where loss of VISTA disrupted the major quiescent naïve T cell subset and enhanced self-reactivity. Agonistic VISTA engagement increased T cell tolerance by promoting antigen-induced peripheral T cell deletion. Although a critical player in naïve T cell homeostasis, the ability of VISTA to restrain naïve T cell responses was lost under inflammatory conditions. VISTA is therefore a distinctive NCR of naïve T cells that is critical for steady-state maintenance of quiescence and peripheral tolerance.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.