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Post-transcriptional Regulation of PCSK9 by miR-191, miR-222, and miR-224

Overview of attention for article published in Frontiers in Genetics, November 2017
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Title
Post-transcriptional Regulation of PCSK9 by miR-191, miR-222, and miR-224
Published in
Frontiers in Genetics, November 2017
DOI 10.3389/fgene.2017.00189
Pubmed ID
Authors

Parisa Naeli, Fatemeh Mirzadeh Azad, Mahshid Malakootian, Nabil G. Seidah, Seyed J. Mowla

Abstract

Since proprotein convertase subtilisin kexin 9 (PCSK9) discovery, a gene involved in LDL metabolism regulation and cardiovascular diseases (CVD), many therapeutic strategies have been introduced for direct targeting of PCSK9. The main goal of these strategies has been to reduce PCSK9 protein level either by application of antibodies or inhibition of its production. In this study, we have tried to discover microRNAs (miRNAs) which can target, and hence regulate, PCSK9 expression. Using bioinformatics tools, we selected three microRNAs with binding sites on 3'-UTR of PCSK9. The expression level of these miRNAs was examined in three different cell lines using real-time RT-PCR. We observed a reciprocal expression pattern between expression level of miR-191, miR-222, and miR-224 with that of PCSK9. Accordingly, the expression levels were highest in Huh7 cells which expressed the lowest level of PCSK9, compared to HepG2 and A549 cell lines. PCSK9 mRNA level also showed a significant decline in HepG2 cells transfected with the vectors overexpressing the aforementioned miRNAs. Furthermore, the miRNAs target sites were cloned in psiCHECK-2 vector, and a direct interaction of the miRNAs and the PCSK9 3'-UTR putative target sites was investigated by means of luciferase assay. Our findings revealed that miR-191, miR-222, and miR-224 can directly interact with PCSK9 3'-UTR and regulate its expression. In conclusion, our data introduces a role for miRNAs to regulate PCSK9 expression.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 20%
Student > Ph. D. Student 6 17%
Researcher 5 14%
Student > Bachelor 3 9%
Professor 2 6%
Other 3 9%
Unknown 9 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 23%
Pharmacology, Toxicology and Pharmaceutical Science 4 11%
Agricultural and Biological Sciences 4 11%
Medicine and Dentistry 3 9%
Nursing and Health Professions 1 3%
Other 2 6%
Unknown 13 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 December 2017.
All research outputs
#17,920,654
of 23,008,860 outputs
Outputs from Frontiers in Genetics
#6,162
of 12,067 outputs
Outputs of similar age
#305,918
of 438,449 outputs
Outputs of similar age from Frontiers in Genetics
#60
of 82 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,067 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.