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Age-Related Argonaute Loading of Ribosomal RNA Fragments

[ Vol. 9 , Issue. 2 ]


Lingyu Guan and Andrey Grigoriev*   Pages 142 - 152 ( 11 )


Background: Accumulating evidence points to the functional roles of rRNA derived Fragments (rRFs), often considered degradation byproducts. Small RNAs, including miRNAs and tRNA-derived Fragments (tRFs), have been implicated in the aging process and we considered rRFs in this context.

Objective: We performed a computational analysis of Argonaute-loaded rRFs in Drosophila melanogaster to study rRF changes with age. We determined rRF abundance in Ago1 and Ago2 at 3 and 30 days to identify Ago1-guided and Ago2-guided fragments. We searched for putative seed sequences in rRFs based on frequent matches of sliding k-mer windows to the conserved regions of 12 Drosophila genomes. We predicted putative targets (containing matches to seeds identified in four rRFs) and studied their functional enrichments using Gene Ontology.

Results: We identified precise cleavage sites of distinct rRF isoforms from both nuclear and mitochondrial rRNAs. The most prominent rRF isoforms were enriched in Ago2 at 3 days and that loading strongly decreased with age. For less abundant rRFs, loading of Ago2-guided rRFs generally increased in Ago2, whereas Ago1-guided rRFs revealed diverse patterns. The distribution of seed matches in targets suggested that rRFs may bind to various conserved regions of many genes, possibly via miRNA-like seed-based mechanisms.

Conclusion: Our observations suggest that rRFs may be functional molecules, with age-dependent Argonaute loading, comparable to that of miRNAs and tRFs. The putative rRF targets showed significant enrichment in developmental processes and biological regulation, similar to tRFs and consistent with a possible involvement of these newly identified small RNAs in the Drosophila aging.


Aging, argonaute, drosophila, ribosomal RNA, rRNA fragments, small RNA.


Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ

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