RIP-Chip analysis supports different roles for AGO2 and GW182 proteins in recruiting and processing microRNA targets.
Argonaute Proteins
/ genetics
Autoantigens
/ genetics
Binding Sites
Chromatin Immunoprecipitation
/ methods
Gene Expression Profiling
Gene Expression Regulation
Humans
MCF-7 Cells
MicroRNAs
/ genetics
Open Reading Frames
/ genetics
RNA, Messenger
/ genetics
RNA-Binding Proteins
/ genetics
Support Vector Machine
RIP-Chip data analysis
RISC proteins AGO2 and GW182
microRNA regulatory activity
microRNA target prediction
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
18 Apr 2019
18 Apr 2019
Historique:
entrez:
20
4
2019
pubmed:
20
4
2019
medline:
15
6
2019
Statut:
epublish
Résumé
MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. To discern further RISC functions, we analyzed the activities of two RISC proteins, AGO2 and GW182, in the MCF-7 human breast cancer cell line. We performed three RIP-Chip experiments using either anti-AGO2 or anti-GW182 antibodies and compiled a data set made up of the miRNA and mRNA expression profiles of three samples for each experiment. Specifically, we analyzed the input sample, the immunoprecipitated fraction and the unbound sample resulting from the RIP experiment. We used the expression profile of the input sample to compute several variables, using formulae capable of integrating the information on miRNA binding sites, both in the 3'UTR and coding regions, with miRNA and mRNA expression level profiles. We compared immunoprecipitated vs unbound samples to determine the enriched or underrepresented genes in the immunoprecipitated fractions, independently for AGO2 and GW182 related samples. For each of the two proteins, we trained and tested several support vector machine algorithms capable of distinguishing the enriched from the underrepresented genes that were experimentally detected. The most efficient algorithm for distinguishing the enriched genes in AGO2 immunoprecipitated samples was trained by using variables involving the number of binding sites in both the 3'UTR and coding region, integrated with the miRNA expression profile, as expected for miRNA targets. On the other hand, we found that the best variable for distinguishing the enriched genes in the GW182 immunoprecipitated samples was the length of the coding region. Due to the major role of GW182 in GW/P-bodies, our data suggests that the AGO2-GW182 RISC recruits genes based on miRNA binding sites in the 3'UTR and coding region, but only the longer mRNAs probably remain sequestered in GW/P-bodies, functioning as a repository for translationally silenced RNAs.
Sections du résumé
BACKGROUND
BACKGROUND
MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. To discern further RISC functions, we analyzed the activities of two RISC proteins, AGO2 and GW182, in the MCF-7 human breast cancer cell line.
METHODS
METHODS
We performed three RIP-Chip experiments using either anti-AGO2 or anti-GW182 antibodies and compiled a data set made up of the miRNA and mRNA expression profiles of three samples for each experiment. Specifically, we analyzed the input sample, the immunoprecipitated fraction and the unbound sample resulting from the RIP experiment. We used the expression profile of the input sample to compute several variables, using formulae capable of integrating the information on miRNA binding sites, both in the 3'UTR and coding regions, with miRNA and mRNA expression level profiles. We compared immunoprecipitated vs unbound samples to determine the enriched or underrepresented genes in the immunoprecipitated fractions, independently for AGO2 and GW182 related samples.
RESULTS
RESULTS
For each of the two proteins, we trained and tested several support vector machine algorithms capable of distinguishing the enriched from the underrepresented genes that were experimentally detected. The most efficient algorithm for distinguishing the enriched genes in AGO2 immunoprecipitated samples was trained by using variables involving the number of binding sites in both the 3'UTR and coding region, integrated with the miRNA expression profile, as expected for miRNA targets. On the other hand, we found that the best variable for distinguishing the enriched genes in the GW182 immunoprecipitated samples was the length of the coding region.
CONCLUSIONS
CONCLUSIONS
Due to the major role of GW182 in GW/P-bodies, our data suggests that the AGO2-GW182 RISC recruits genes based on miRNA binding sites in the 3'UTR and coding region, but only the longer mRNAs probably remain sequestered in GW/P-bodies, functioning as a repository for translationally silenced RNAs.
Identifiants
pubmed: 30999843
doi: 10.1186/s12859-019-2683-y
pii: 10.1186/s12859-019-2683-y
pmc: PMC6471694
doi:
Substances chimiques
AGO2 protein, human
0
Argonaute Proteins
0
Autoantigens
0
MicroRNAs
0
RNA, Messenger
0
RNA-Binding Proteins
0
TNRC6A protein, human
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
120Références
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21
pubmed: 11309499
J Mol Med (Berl). 2003 Dec;81(12):811-8
pubmed: 14598044
Genome Biol. 2003;5(1):R1
pubmed: 14709173
Science. 2004 Sep 3;305(5689):1437-41
pubmed: 15284456
Cell. 2005 Jan 14;120(1):15-20
pubmed: 15652477
Nat Methods. 2007 Sep;4(9):721-6
pubmed: 17694064
Nat Genet. 2007 Oct;39(10):1278-84
pubmed: 17893677
Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19291-6
pubmed: 18042700
RNA. 2008 Dec;14(12):2580-96
pubmed: 18978028
RNA. 2009 Aug;15(8):1433-42
pubmed: 19535464
Nucleic Acids Res. 2009 Nov;37(20):e137
pubmed: 19734348
J Cell Sci. 2009 Oct 15;122(Pt 20):3619-26
pubmed: 19812307
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W352-9
pubmed: 20484379
J Cell Sci. 2010 Jun 1;123(Pt 11):1819-23
pubmed: 20484662
RNA Biol. 2011 Jan-Feb;8(1):158-77
pubmed: 21282978
BMC Bioinformatics. 2011 Mar 17;12:77
pubmed: 21414208
Cell. 2011 Aug 5;146(3):353-8
pubmed: 21802130
Genes Dev. 2012 Apr 1;26(7):693-704
pubmed: 22474261
RNA Biol. 2012 Aug;9(8):1066-75
pubmed: 22858679
Bioinformatics. 2013 Jan 1;29(1):77-83
pubmed: 23104891
PLoS Comput Biol. 2012;8(12):e1002830
pubmed: 23284279
Biochem Soc Trans. 2013 Aug;41(4):855-60
pubmed: 23863144
J Biol Chem. 2013 Sep 20;288(38):27480-93
pubmed: 23921383
Proc Natl Acad Sci U S A. 2013 Oct 1;110(40):E3770-9
pubmed: 24043833
Mol Cell. 2013 Oct 10;52(1):113-23
pubmed: 24055343
RNA Biol. 2014;11(1):18-24
pubmed: 24384674
Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):11841-5
pubmed: 26351695
Sci Rep. 2015 Dec 07;5:17868
pubmed: 26639163
RNA. 2016 Jul;22(7):1085-98
pubmed: 27198507
PLoS One. 2016 Aug 12;11(8):e0161165
pubmed: 27518285
Mol Cell. 2017 Aug 17;67(4):646-658.e3
pubmed: 28781232
Cell Rep. 2017 Aug 15;20(7):1543-1552
pubmed: 28813667