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A Whole-Blood Transcriptome Meta-Analysis Identifies Gene Expression Signatures of Cigarette Smoking

Overview of attention for article published in Human Molecular Genetics, August 2016
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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Citations

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116 Mendeley
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Title
A Whole-Blood Transcriptome Meta-Analysis Identifies Gene Expression Signatures of Cigarette Smoking
Published in
Human Molecular Genetics, August 2016
DOI 10.1093/hmg/ddw288
Pubmed ID
Authors

Tianxiao Huan, Roby Joehanes, Claudia Schurmann, Katharina Schramm, Luke C. Pilling, Marjolein J. Peters, Reedik Mägi, Dawn DeMeo, George T O'Connor, Luigi Ferrucci, Alexander Teumer, Georg Homuth, Reiner Biffar, Uwe Völker, Christian Herder, Melanie Waldenberger, Annette Peters, Sonja Zeilinger, Andres Metspalu, Albert Hofman, André G. Uitterlinden, Dena G. Hernandez, Andrew B. Singleton, Stefania Bandinelli, Peter J. Munson, Honghuang Lin, Emelia J. Benjamin, Tõnu Esko, Hans J. Grabe, Holger Prokisch, Joyce B.J. van Meurs, David Melzer, Daniel Levy

Abstract

Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.

X Demographics

X Demographics

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 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 <1%
Unknown 115 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 22%
Student > Ph. D. Student 15 13%
Student > Bachelor 14 12%
Student > Master 12 10%
Student > Doctoral Student 9 8%
Other 18 16%
Unknown 23 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 22%
Medicine and Dentistry 22 19%
Agricultural and Biological Sciences 11 9%
Nursing and Health Professions 6 5%
Economics, Econometrics and Finance 4 3%
Other 16 14%
Unknown 31 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 28 June 2018.
All research outputs
#6,491,406
of 24,598,501 outputs
Outputs from Human Molecular Genetics
#3,194
of 8,215 outputs
Outputs of similar age
#95,707
of 344,776 outputs
Outputs of similar age from Human Molecular Genetics
#38
of 106 outputs
Altmetric has tracked 24,598,501 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,215 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,776 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.