Evidence map›Paper›PMID 27557147›Full record

ArticleBMC genomics2016

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

Jin Li, Ying Wang, Lei Wang, Xuefeng Dai, Wang Cong, Weixing Feng, Chengzhen Xu, Yulin Deng, Yue Wang, Todd C Skaar and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Jin LiCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Ying WangCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Lei WangCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Xuefeng DaiNetwork Information Center, Qiqihar University, No.42, Wenhua Street, Qiqihar, Heilongjiang, 161006, China.
Wang CongCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Weixing FengCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Chengzhen XuCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Yulin DengCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China.
Yue WangDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
Todd C SkaarDivision of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
Hong LiangCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China. lh@hrbeu.edu.cn.
Yunlong LiuCollege of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang, 150001, China. yunliu@iupui.edu.
Harbin Engineering University · CNIndiana University – Purdue University Indianapolis · USQiqihar University · CNIndiana University School of Medicine

Funding

Indiana Clinical and Translational Sciences InstituteUL1TR001108 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$23.3M
Regulation of drug metabolizing enzymes by miRNAsR01GM088076 · NIGMS · INDIANA UNIVERSITY INDIANAPOLIS · PI SKAAR, TODD C. · 2009 to 2017
$3.8M
NCATS NIH HHS UL1 TR001108NIGMS NIH HHS R01 GM088076
6 · The paper itself

Abstract

backgroundIn 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.

methodsIn 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.

conclusionsOur 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.

Indexed as

Antibiotics, AntitubercularComputational BiologyGene Expression ProfilingGene Expression RegulationHumansInactivation, MetabolicMicroarray AnalysisMicroRNAsPregnane X ReceptorProtein Interaction MapsReceptors, SteroidRifampinTuberculosisAntibiotics, AntitubercularMicroRNAsPregnane X ReceptorReceptors, SteroidRifampin

Identifiers

PMID27557147
PMCPMC5001204
OpenAlexW2513957010

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.