AgingBioinformaticsPeer-reviewed journal article
Genome-wide identification of functional tRNA-derived fragments in Senescence-accelerated mouse prone 8 brain
Shuai ZhangiD, Hejian Li, Ling ZhengiD, Hong LiiD, Chengqiang Feng, Wensheng ZhangiD
Abstract
tRNA-derived fragments (tRFs) have been linked previously to the development of various diseases, such as cancer and viral infection. However, tRFs seem also related to brain aging and related diseases, especially Alzheimer and Parkinson disease. RNA sequencing, a state-of-the-art technology, has allowed for investigation of tRFs in this field. In this study, we investigated the changes of tRFs in the brains of a senescence-accelerated mouse model, senescence-accelerated mouse prone 8 (SAMP8), that show age-dependent deficits in learning and memory; and a control model, senescence-accelerated mouse resistant 1 (SAMR1), with normal aging, both at 7 months of age. A total of 570 tRF transcripts were discovered. Among these transcripts, 8, including 3 upregulated and 5 downregulated transcripts, were differentially expressed in the SAMP8 mice. Then, we obtained 110 potential target genes in a miRNA-like pattern. GO survey implicated these target genes in the function of various aspects, e.g. postsynaptic density (GO: 0014069). Furthermore, we assessed in detail those tRFs whose miRNA-like pattern was most likely to affect the progression of either Alzheimer and Parkinson disease, such as AS-tDR-011775 acting on Mobp and Park2. In fact, we found the tRFs to be involved in the regulation of gene expression by means other than the miRNA-like pattern. Therefore, these 8 dysregulated tRFs may hold consequences far into the future and can be attractive biomarkers and valid targets. In brief, our study is the first to provide a comprehensive analysis on tRFs in SAMP8 mouse brain, and this breakthrough identified promising new targets for preventing the age-related changes of brain and the therapeutic intervention of Alzheimers and Parkinsons.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 62
- FWCI
- 2.57
- Field percentile
- top 9%
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