Effect of aging and dFOXO knockout on small RNA in Ago1 and Ago2 RNA-induced silencing complexes
Ontology highlight
ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE212492 | GEO | 2022/09/04
REPOSITORIES: GEO
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Project description:Drosophila miRNAs show distinct change in isoform distribution pattern with age. Some miRNAs show accumulation of the short isoforms, while other miRNAs show the accumulation of the long isoforms with age. The increase of the long isoforms of some miRNAs reflects increased 2'-O-methylated miRNA isoforms with age. The increase in 2'-O-methylated miRNA isoforms reflected increased Ago2-loading, but not Ago1-loading of specific miRNA isoforms with age. This raised a question on whether there is global shift in small RNA loading pattern between Ago1 and Ago2 with age. To investigate change in small RNA loading pattern between Ago1 and Ago2 with age, we performed small RNA deep-sequencing of Ago1 vs Ago2-IP small RNAs at 3d and 30d in Drosophila. This analysis revealed global increase of miRNA loading into Ago2, but not into Ago1 with age.
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Project description:MicroRNAs 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 complex 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 antibody and compiled a dataset 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 predicted 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 differentially expressed genes, independently for AGO2 and GW182 related samples. For each of the two proteins, we trained and tested several support vector machine algorithms able to predict the differentially expressed genes that were experimentally detected. The most efficient algorithm for predicting the over-expressed 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 predicting over-expressed genes in the GW182 immunoprecipitated sample 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 complex recruits genes based on miRNA binding sites in the 3’UTR and coding region, but only the longer mRNAs remain sequestered in GW/P-bodies, functioning as a repository for translationally silenced RNAs.
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Project description:MicroRNAs 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 complex 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 antibody and compiled a dataset 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 predicted 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 differentially expressed genes, independently for AGO2 and GW182 related samples. For each of the two proteins, we trained and tested several support vector machine algorithms able to predict the differentially expressed genes that were experimentally detected. The most efficient algorithm for predicting the over-expressed 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 predicting over-expressed genes in the GW182 immunoprecipitated sample 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 complex recruits genes based on miRNA binding sites in the 3’UTR and coding region, but only the longer mRNAs remain sequestered in GW/P-bodies, functioning as a repository for translationally silenced RNAs.
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Project description:As a core RISC component, Ago2 associates with miRNAs and target mRNAs. To identify these mRNAs, we ran lysate from HEK293T cells over a FLAG resin from 2 conditions: +FLAG-Ago2, +mock transfection. To identify mRNAs associated with specific miRNAs, we ran lysate from HEK293T cells over a FLAG resin from 2 conditions: +FLAG-Ago2 & miR-1, and +FLAG-Ago2 & miR-124. Set of arrays that are part of repeated experiments Compound Based Treatment: mock transfected Keywords: Biological Replicate Biological Replicate Computed
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