miRBase: tools for microRNA genomicsmiRBase is the central online repository for microRNA (miRNA) nomenclature, sequence data, annotation and target prediction. The current release (10.0) contains 5071 miRNA loci from 58 species, expressing 5922 distinct mature miRNA sequences: a growth of over 2000 sequences in the past 2 years. miRBase provides a range of data to facilitate studies of miRNA genomics: all miRNAs are mapped to their genomic coordinates. Clusters of miRNA sequences in the genome are highlighted, and can be defined and retrieved with any inter-miRNA distance. The overlap of miRNA sequences with annotated transcripts, both protein- and non-coding, are described. Finally, graphical views of the locations of a wide range of genomic features in model organisms allow for the first time the prediction of the likely boundaries of many miRNA primary transcripts. miRBase is available at http://microrna.sanger.ac.uk/.
Genomic analysis of human microRNA transcriptsHarpreet K. Saini, Sam Griffiths‐Jones, Anton J. Enright|Proceedings of the National Academy of Sciences|2007 MicroRNAs (miRNAs) are important genetic regulators of development, differentiation, growth, and metabolism. The mammalian genome encodes approximately 500 known miRNA genes. Approximately 50% are expressed from non-protein-coding transcripts, whereas the rest are located mostly in the introns of coding genes. Intronic miRNAs are generally transcribed coincidentally with their host genes. However, the nature of the primary transcript of intergenic miRNAs is largely unknown. We have performed a large-scale analysis of transcription start sites, polyadenylation signals, CpG islands, EST data, transcription factor-binding sites, and expression ditag data surrounding intergenic miRNAs in the human genome to improve our understanding of the structure of their primary transcripts. We show that a significant fraction of primary transcripts of intergenic miRNAs are 3-4 kb in length, with clearly defined 5' and 3' boundaries. We provide strong evidence for the complete transcript structure of a small number of human miRNAs.
Extracellular Vesicles from Neural Stem Cells Transfer IFN-γ via Ifngr1 to Activate Stat1 Signaling in Target CellsMalignant Germ Cell Tumors Display Common MicroRNA Profiles Resulting in Global Changes in Expression of Messenger RNA TargetsDespite their extensive clinical and pathologic heterogeneity, all malignant germ cell tumors (GCT) are thought to originate from primordial germ cells. However, no common biological abnormalities have been identified to date. We profiled 615 microRNAs (miRNA) in pediatric malignant GCTs, controls, and GCT cell lines (48 samples in total) and re-analyzed available miRNA expression data in adult gonadal malignant GCTs. We applied the bioinformatic algorithm Sylamer to identify miRNAs that are of biological importance by inducing global shifts in mRNA levels. The most significant differentially expressed miRNAs in malignant GCTs were all from the miR-371-373 and miR-302 clusters (adjusted P < 0.00005), which were overexpressed regardless of histologic subtype [yolk sac tumor (YST)/seminoma/embryonal carcinoma (EC)], site (gonadal/extragonadal), or patient age (pediatric/adult). Sylamer revealed that the hexamer GCACTT, complementary to the 2- to 7-nucleotide miRNA seed AAGUGC shared by six members of the miR-371-373 and miR-302 clusters, was the only sequence significantly enriched in the 3'-untranslated region of mRNAs downregulated in pediatric malignant GCTs (as a group), YSTs and ECs, and in adult YSTs (all versus nonmalignant tissue controls; P < 0.05). For the pediatric samples, downregulated genes containing the 3'-untranslated region GCACTT showed significant overrepresentation of Gene Ontology terms related to cancer-associated processes, whereas for downregulated genes lacking GCACTT, Gene Ontology terms generally represented metabolic processes only, with few genes per term (adjusted P < 0.05). We conclude that the miR-371-373 and miR-302 clusters are universally overexpressed in malignant GCTs and coordinately downregulate mRNAs involved in biologically significant pathways.
Computational Prediction of Protein–Protein Interactions