Abstract
Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path "smoking→gene expression→plaques". Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the "smoking→gene expression→plaques" causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms
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Atherosclerosis / etiology
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Atherosclerosis / genetics*
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Atherosclerosis / metabolism
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Atherosclerosis / pathology
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Carotid Arteries / metabolism*
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Carotid Arteries / pathology
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Cation Transport Proteins / genetics
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Cation Transport Proteins / metabolism
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Female
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Gene Expression Profiling
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Gene Expression*
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Humans
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Intercellular Signaling Peptides and Proteins / genetics
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Intercellular Signaling Peptides and Proteins / metabolism
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Likelihood Functions
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Male
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Middle Aged
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Models, Genetic*
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Monocytes / chemistry
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Monocytes / metabolism*
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Monocytes / pathology
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Multigene Family
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PPAR gamma / genetics
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PPAR gamma / metabolism
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Plaque, Atherosclerotic / genetics*
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Plaque, Atherosclerotic / metabolism
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Plaque, Atherosclerotic / pathology
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Pregnancy
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Risk Factors
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Smoking / adverse effects
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Smoking / genetics*
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Smoking / metabolism
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Smoking / pathology
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Transcriptome
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Tumor Suppressor Proteins / genetics
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Tumor Suppressor Proteins / metabolism
Substances
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Cation Transport Proteins
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Intercellular Signaling Peptides and Proteins
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PPAR gamma
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SASH1 protein, human
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SLC39A8 protein, human
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Tumor Suppressor Proteins
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growth arrest-specific protein 6
Grants and funding
The Gutenberg Health Study is funded through the government of Rheinland-Pfalz (“Stiftung Rheinland Pfalz für Innovation”, contract number AZ 961-386261/733), the research programs “Wissen schafft Zukunft” and “Schwerpunkt Vaskuläre Prävention” of the Johannes Gutenberg-University of Mainz and its contract with Boehringer Ingelheim and PHILIPS Medical Systems including an unrestricted grant for the Gutenberg Health Study. Specifically, the research reported in this article was supported by the National Genome Network “NGFNplus” by the Federal Ministry of Education and Research, Germany (contract number project A3 01GS0833) and by a joint funding from the Federal Ministry of Education and Research, Germany (contract BMBF 01KU0908A) and from the Agence Nationale de la Recherche, France (contract ANR 09 GENO 106 01) for the project CARDomics. Statistical analyses benefit from the C2BIG computing centre funded by the Fondation pour la Recherche Médicale and the Région Ile-de-France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.