» Articles » PMID: 27776116

General Rules for Functional MicroRNA Targeting

Overview
Journal Nat Genet
Specialty Genetics
Date 2016 Nov 8
PMID 27776116
Citations 74
Authors
Affiliations
Soon will be listed here.
Abstract

The functional rules for microRNA (miRNA) targeting remain controversial despite their biological importance because only a small fraction of distinct interactions, called site types, have been examined among an astronomical number of site types that can occur between miRNAs and their target mRNAs. To systematically discover functional site types and to evaluate the contradicting rules reported previously, we used large-scale transcriptome data and statistically examined whether each of approximately 2 billion site types is enriched in differentially downregulated mRNAs responding to overexpressed miRNAs. Accordingly, we identified seven non-canonical functional site types, most of which are novel, in addition to four canonical site types, while also removing numerous false positives reported by previous studies. Extensive experimental validation and significantly elevated 3' UTR sequence conservation indicate that these non-canonical site types may have biologically relevant roles. Our expanded catalog of functional site types suggests that the gene regulatory network controlled by miRNAs may be far more complex than currently understood.

Citing Articles

Mammalian piRNA target prediction using a hierarchical attention model.

Zhang T, Chen L, Zhu H, Wong G BMC Bioinformatics. 2025; 26(1):50.

PMID: 39934678 PMC: 11817350. DOI: 10.1186/s12859-025-06068-6.


miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.

Wu X, Zhang L, Tong X, Wang Y, Zhang Z, Kong X Brief Bioinform. 2024; 26(1).

PMID: 39592153 PMC: 11596087. DOI: 10.1093/bib/bbae616.


Advancing microRNA target site prediction with transformer and base-pairing patterns.

Bi Y, Li F, Wang C, Pan T, Davidovich C, Webb G Nucleic Acids Res. 2024; 52(19):11455-11465.

PMID: 39271121 PMC: 11514461. DOI: 10.1093/nar/gkae782.


PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction.

Uthayopas K, de Sa A, Alavi A, Pires D, Ascher D Comput Struct Biotechnol J. 2024; 23:3030-3039.

PMID: 39175797 PMC: 11340604. DOI: 10.1016/j.csbj.2024.06.030.


Circulating microRNA Profiles Identify a Patient Subgroup with High Inflammation and Severe Symptoms in Schizophrenia Experiencing Acute Psychosis.

Miyano T, Mikkaichi T, Nakamura K, Yoshigae Y, Abernathy K, Ogura Y Int J Mol Sci. 2024; 25(8).

PMID: 38673876 PMC: 11050142. DOI: 10.3390/ijms25084291.


References
1.
Helwak A, Kudla G, Dudnakova T, Tollervey D . Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 2013; 153(3):654-65. PMC: 3650559. DOI: 10.1016/j.cell.2013.03.043. View

2.
Nicolas F, Pais H, Schwach F, Lindow M, Kauppinen S, Moulton V . Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140. RNA. 2008; 14(12):2513-20. PMC: 2590970. DOI: 10.1261/rna.1221108. View

3.
Majoros W, Lekprasert P, Mukherjee N, Skalsky R, Corcoran D, Cullen B . MicroRNA target site identification by integrating sequence and binding information. Nat Methods. 2013; 10(7):630-3. PMC: 3818907. DOI: 10.1038/nmeth.2489. View

4.
Schirle N, Sheu-Gruttadauria J, MacRae I . Structural basis for microRNA targeting. Science. 2014; 346(6209):608-13. PMC: 4313529. DOI: 10.1126/science.1258040. View

5.
Vigorito E, Perks K, Abreu-Goodger C, Bunting S, Xiang Z, Kohlhaas S . microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity. 2007; 27(6):847-59. PMC: 4135426. DOI: 10.1016/j.immuni.2007.10.009. View