Blog
About

34
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dynamics of tRNA fragments and their targets in aging mammalian brain

      1 , a , 1

      F1000Research

      F1000Research

      transfer RNA, rat brain, rat cortex, tRNA fragments, aging, non-coding RNA

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background: The progress of next-generation sequencing technologies has unveiled various non-coding RNAs that have previously been considered products of random degradation and attracted only minimal interest. Among small RNA families, microRNA (miRNAs) have traditionally been considered key post-transcriptional regulators. However, recent studies have reported evidence for widespread presence of fragments of tRNA molecules (tRFs) across a range of organisms and tissues, and of tRF involvement in Argonaute complexes.  Methods:To elucidate potential tRF functionality, we compared available RNA sequencing datasets derived from the brains of young, mid-aged and old rats. Using sliding 7-mer windows along a tRF, we searched for putative seed sequences with high numbers of conserved complementary sites within 3' UTRs of 23 vertebrate genomes. We analyzed Gene Ontology term enrichment of predicted tRF targets and compared their transcript levels with targets of miRNAs in the context of age.  Results and Discussion: We detected tRFs originating from 3’- and 5’-ends of tRNAs in rat brains at significant levels. These fragments showed dynamic changes: 3’ tRFs monotonously increased with age, while 5’ tRFs displayed less consistent patterns. Furthermore, 3’ tRFs showed a narrow size range compared to 5’ tRFs, suggesting a difference in their biogenesis mechanisms. Similar to our earlier results in Drosophila and compatible with other experimental findings, we found “seed” sequence locations on both ends of different tRFs. Putative targets of these fragments were found to be enriched in neuronal and developmental functions. Comparison of tRFs and miRNAs increasing in abundance with age revealed small, but distinct changes in brain target transcript levels for these two types of small RNA, with the higher proportion of tRF targets decreasing with age. We also illustrated the utility of tRF analysis for annotating tRNA genes in sequenced genomes.

          Related collections

          Most cited references 49

          • Record: found
          • Abstract: found
          • Article: not found

          MicroRNAs: target recognition and regulatory functions.

           David Bartel (2009)
          MicroRNAs (miRNAs) are endogenous approximately 23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

            MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              MicroRNA targeting specificity in mammals: determinants beyond seed pairing.

              Mammalian microRNAs (miRNAs) pair to 3'UTRs of mRNAs to direct their posttranscriptional repression. Important for target recognition are approximately 7 nt sites that match the seed region of the miRNA. However, these seed matches are not always sufficient for repression, indicating that other characteristics help specify targeting. By combining computational and experimental approaches, we uncovered five general features of site context that boost site efficacy: AU-rich nucleotide composition near the site, proximity to sites for coexpressed miRNAs (which leads to cooperative action), proximity to residues pairing to miRNA nucleotides 13-16, positioning within the 3'UTR at least 15 nt from the stop codon, and positioning away from the center of long UTRs. A model combining these context determinants quantitatively predicts site performance both for exogenously added miRNAs and for endogenous miRNA-message interactions. Because it predicts site efficacy without recourse to evolutionary conservation, the model also identifies effective nonconserved sites and siRNA off-targets.
                Bookmark

                Author and article information

                Affiliations
                [1 ]Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, USA
                [1 ]Institute of Medical Microbiology and Parasitology (IMPaM), Faculty of Medicine, University of Buenos Aires (UBA) and National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
                [1 ]Center for Personalized Medicine (CPM), Spatial Sciences Institute, Children's Hospital Los Angeles, University of Southern California , Los Angeles, CA, USA
                Author notes

                SK participated in the design of the study, analyzed the data, and drafted the manuscript. AG conceived the study, oversaw its design, execution and coordination, and drafted and finalized the manuscript. All authors were involved in the revision of the draft manuscript and have agreed to the final content.

                Competing interests: No competing interests were disclosed.

                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                24 November 2016
                2016
                : 5
                5224686 10.12688/f1000research.10116.1
                Copyright: © 2016 Karaiskos S and Grigoriev A

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Product
                Funding
                Funded by: National Science Foundation
                Award ID: DBI-1458202
                This work was in part supported by the National Science Foundation to AG [DBI-1458202].
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles
                Bioinformatics
                Neurodevelopment

                Comments

                Comment on this article