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Genomics
Article . 2021 . Peer-reviewed
License: Elsevier Non-Commercial
Data sources: Crossref
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Genomics
Article . 2022
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Identification of transfer RNA-derived fragments and their potential roles in aortic dissection

Authors: Xiuxiu, Fu; Xingqiang, He; Yanyan, Yang; Shaoyan, Jiang; Shizhong, Wang; Xingang, Peng; Guozhang, Tang; +5 Authors

Identification of transfer RNA-derived fragments and their potential roles in aortic dissection

Abstract

Emerging evidence suggests that majority of the transfer RNA (tRNA)-derived small RNA, including tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs), play a significant role in the molecular mechanisms underlying some human diseases. However, expression of tRFs/tiRNAs and their potential roles in aortic dissection (AD) remain unclear. This study examined the expression characteristics and explored the functional roles of tRFs/tiRNAs in AD using RNA-sequencing, bioinformatics, real-time quantitative reverse transcription polymerase chain reaction, and loss- and gain-of-function analysis. Results revealed that a total of 41 tRFs/tiRNAs were dysregulated in the AD group compared to the control group. Among them, 12 were upregulated and 29 were downregulated (fold change≥1.5 and p < 0.05). RT-qPCR results revealed that expressions of tRF-1:30-chrM.Met-CAT was significantly upregulated, while that of tRF-54:71-chrM.Trp-TCA and tRF-1:32-chrM.Cys-GCA were notably downregulated; expression patterns were consistent with the RNA sequencing data. Bioinformatic analysis showed that a variety of related pathways might be involved in the pathogenesis of AD. Functionally, tRF-1:30-chrM.Met-CAT could facilitate proliferation, migration, and phenotype switching in vascular smooth muscle cells (VSMCs), which might serve as a significant regulator in the progression of AD. In summary, the study illustrated that tRFs/tiRNAs expressed in AD tissues have potential biological functions and may act as promising biomarkers or therapeutic targets for AD.

Related Organizations
Keywords

Aortic Dissection, RNA, Transfer, Computational Biology, Humans, Real-Time Polymerase Chain Reaction, Biomarkers

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Average
Top 10%
gold