miRBase entry: hsa-mir-379

Stem-loop hsa-mir-379


Accession
MI0000787
Symbol
HGNC: MIR379
Description
Homo sapiens hsa-mir-379 precursor miRNA
Gene family
MIPF0000126; mir-379

Summary
Caution, this is an AI generated summary based on literature. This may have errors. ?

MIR379 is a member of the δ-like 1 homolog-deiodinase, iodothryonine3 (DLK1-DIO3) cluster and is activated in the epithelial to mesenchymal transition (EMT) of prostate cancer cells [PMC5195822]. It is part of a cluster that includes miR154, miR369, miR376c, miR381, miR382, miR409, and miR410 [PMC10148110]. MIR379 has been studied in various contexts. It has been found to be an imprinted miRNA and a candidate imprinted lncRNA [PMC3743905]. MIR379 levels have been shown to decrease significantly less over time compared to other miRNAs in prostate cancer cells [PMC7486624]. It has also been found to play a role in skeletal muscle differentiation by absorbing other microRNAs such as miR124 [PMC8111742]. In the context of non-alcoholic fatty liver disease (NAFLD), MIR379 levels have been found to correlate positively with ALP, TC, LDL-C and non-HDL-C in early stage patients [PMC9738374]. It has also been proposed as a potential biomarker for diagnosing NAFLD and monitoring disease progression [PMC9738374]. In addition, MIR379 has been shown to regulate target genes involved in various functions related to diabetic nephropathy (DN) [PMC8255808]. Furthermore, computational algorithms predict that MIR379 can bind to the 3' untranslated region of Cyclin B1 mRNA [PMC3707961]. Overall, inhibiting MIR379 may have therapeutic implications for adipose dysfunction and obesity-associated comorbidities such as type 2 diabetes [PMC9535382].

Literature search
48 open access papers mention hsa-mir-379
(227 sentences)

Sequence

93096 reads, 331 reads per million, 119 experiments
agagaUGGUAGACUAUGGAACGUAGGcguuaugauuucugaccUAUGUAACAUGGUCCACUAACUcu
((((.((((.(((((((..(((((((((..........)).)))))))..))))))).)))).))))

Structure
    a    A       GA       -  uuau 
agag UGGU GACUAUG  ACGUAGG cg    g
|||| |||| |||||||  ||||||| ||     
ucUC AUCA CUGGUAC  UGUAUcc gu    a
    A    C       AA       a  cuuu 


Annotation confidence High
Do you think this miRNA is real?
Comments
The mature sequence shown here represents the most commonly cloned form from large-scale cloning studies [4].

Genome context
chr14: 101022066-101022132 [+]
Clustered miRNAs
11 other miRNAs are < 10 kb from hsa-mir-379
Name Accession Chromosome Start End Strand Confidence




Disease association
hsa-mir-379 is associated with one or more human diseases in the Human microRNA Disease Database
Disease Description Category PubMed ID


Database links

Mature hsa-miR-379-5p

Accession MIMAT0000733
Description Homo sapiens hsa-miR-379-5p mature miRNA
Sequence 6 - UGGUAGACUAUGGAACGUAGG - 26
Evidence experimental
cloned [2-4]
Database links
Predicted targets

Mature hsa-miR-379-3p

Accession MIMAT0004690
Description Homo sapiens hsa-miR-379-3p mature miRNA
Sequence 44 - UAUGUAACAUGGUCCACUAACU - 65
Evidence experimental
cloned [4]
Database links
Predicted targets

References

  1. PubMed ID: 17604727
    A mammalian microRNA expression atlas based on small RNA library sequencing
    "Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, Lin C, Socci ND, Hermida L, Fulci V, Chiaretti S, Foa R, Schliwka J, Fuchs U, Novosel A, Muller RU, Schermer B, Bissels U, Inman J, Phan Q, Chien M"
    "Cell (2007) 129:1401-1414

  2. PubMed ID: 15538371
    A pancreatic islet-specific microRNA regulates insulin secretion
    "Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, Stoffel M"
    "Nature (2004) 432:226-230

  3. PubMed ID: 15891114
    Clustering and conservation patterns of human microRNAs
    "Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H"
    "Nucleic Acids Res (2005) 33:2697-2706

  4. PubMed ID: 16274478
    Identification of clustered microRNAs using an ab initio prediction method
    Sewer A, Paul N, Landgraf P, Aravin A, Pfeffer S, Brownstein MJ, Tuschl T, van Nimwegen E, Zavolan M
    BMC Bioinformatics (2005) 6:267