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Deep Sequencing Reveals Novel MicroRNAs and Regulation of MicroRNA Expression during Cell Senescence

Overview of attention for article published in PLOS ONE, May 2011
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Title
Deep Sequencing Reveals Novel MicroRNAs and Regulation of MicroRNA Expression during Cell Senescence
Published in
PLOS ONE, May 2011
DOI 10.1371/journal.pone.0020509
Pubmed ID
Authors

Joseph M. Dhahbi, Hani Atamna, Dario Boffelli, Wendy Magis, Stephen R. Spindler, David I. K. Martin

Abstract

In cell senescence, cultured cells cease proliferating and acquire aberrant gene expression patterns. MicroRNAs (miRNAs) modulate gene expression through translational repression or mRNA degradation and have been implicated in senescence. We used deep sequencing to carry out a comprehensive survey of miRNA expression and involvement in cell senescence. Informatic analysis of small RNA sequence datasets from young and senescent IMR90 human fibroblasts identifies many miRNAs that are regulated (either up or down) with cell senescence. Comparison with mRNA expression profiles reveals potential mRNA targets of these senescence-regulated miRNAs. The target mRNAs are enriched for genes involved in biological processes associated with cell senescence. This result greatly extends existing information on the role of miRNAs in cell senescence and is consistent with miRNAs having a causal role in the process.

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Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Germany 2 2%
Denmark 2 2%
Brazil 2 2%
Austria 1 <1%
Unknown 117 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 28%
Student > Ph. D. Student 33 26%
Student > Master 13 10%
Professor > Associate Professor 9 7%
Professor 5 4%
Other 17 13%
Unknown 14 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 54%
Biochemistry, Genetics and Molecular Biology 21 17%
Medicine and Dentistry 10 8%
Veterinary Science and Veterinary Medicine 3 2%
Computer Science 2 2%
Other 8 6%
Unknown 14 11%