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A translational bioinformatic approach in identifying and validating an interaction between Vitamin A and CYP19A1

Overview of attention for article published in BMC Genomics, June 2015
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Title
A translational bioinformatic approach in identifying and validating an interaction between Vitamin A and CYP19A1
Published in
BMC Genomics, June 2015
DOI 10.1186/1471-2164-16-s7-s17
Pubmed ID
Authors

Santosh Philips, Jing Zhou, Zhigao Li, Todd C Skaar, Lang Li

Abstract

One major challenge in personalized medicine research is to identify the environmental factors that can alter drug response, and to investigate their molecular mechanisms. These environmental factors include co-medications, food, and nutrition or dietary supplements. The increasing use of dietary supplements and their potential interactions with cytochrome P450 (CYP450) enzymes is a highly significant personalized medicine research domain, because most of the drugs on the market are metabolized through CYP450 enzymes. Initial bioinformatics analysis revealed a number of regulators of CYP450 enzymes from a human liver bank gene expression quantitative loci data set. Then, a compound-gene network was constructed from the curated literature data. This network consisted of compounds that interact with either CYPs and/or their regulators that influence either their gene expression or activity. We further evaluated this finding in three different cell lines: JEG3, HeLa, and LNCaP cells. From a total of 868 interactions we were able to identify an interesting interaction between retinoic acid (i.e. Vitamin A) and the aromatase gene (i.e. CYP19A1). Our experimental results showed that retinoic acid at physiological concentration significantly influenced CYP19A1 gene expressions. These results suggest that the presence of retinoic acid may alter the efficacy of agents used to suppress aromatase expression.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 17%
Researcher 3 17%
Student > Master 3 17%
Other 2 11%
Student > Bachelor 2 11%
Other 2 11%
Unknown 3 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 22%
Agricultural and Biological Sciences 3 17%
Computer Science 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 2 11%
Medicine and Dentistry 2 11%
Other 2 11%
Unknown 3 17%
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 21 June 2015.
All research outputs
#19,696,602
of 24,220,739 outputs
Outputs from BMC Genomics
#8,486
of 10,923 outputs
Outputs of similar age
#198,024
of 270,910 outputs
Outputs of similar age from BMC Genomics
#198
of 231 outputs
Altmetric has tracked 24,220,739 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,923 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 231 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.