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Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network

Overview of attention for article published in BMC Genomics, August 2016
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Title
Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2909-6
Pubmed ID
Authors

Jin Li, Ying Wang, Lei Wang, Xuefeng Dai, Wang Cong, Weixing Feng, Chengzhen Xu, Yulin Deng, Yue Wang, Todd C. Skaar, Hong Liang, Yunlong Liu

Abstract

In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks. In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways. Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 17%
Researcher 4 17%
Other 3 13%
Professor 2 9%
Professor > Associate Professor 2 9%
Other 3 13%
Unknown 5 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 17%
Engineering 3 13%
Medicine and Dentistry 3 13%
Business, Management and Accounting 2 9%
Veterinary Science and Veterinary Medicine 2 9%
Other 4 17%
Unknown 5 22%
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 25 August 2016.
All research outputs
#18,467,727
of 22,883,326 outputs
Outputs from BMC Genomics
#8,197
of 10,668 outputs
Outputs of similar age
#263,392
of 343,744 outputs
Outputs of similar age from BMC Genomics
#212
of 273 outputs
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So far Altmetric has tracked 10,668 research outputs from this source. They receive a mean Attention Score of 4.7. 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 273 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.