Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers
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Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers. / Verdugo, Ricardo A; Zeller, Tanja; Rotival, Maxime; Wild, Philipp S; Münzel, Thomas; Lackner, Karl J; Weidmann, Henri; Ninio, Ewa; Trégouët, David-Alexandre; Cambien, François; Blankenberg, Stefan; Tiret, Laurence.
in: PLOS ONE, Jahrgang 8, Nr. 1, 2013, S. e50888.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers
AU - Verdugo, Ricardo A
AU - Zeller, Tanja
AU - Rotival, Maxime
AU - Wild, Philipp S
AU - Münzel, Thomas
AU - Lackner, Karl J
AU - Weidmann, Henri
AU - Ninio, Ewa
AU - Trégouët, David-Alexandre
AU - Cambien, François
AU - Blankenberg, Stefan
AU - Tiret, Laurence
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Algorithms
KW - Atherosclerosis
KW - Carotid Arteries
KW - Cation Transport Proteins
KW - Female
KW - Gene Expression
KW - Gene Expression Profiling
KW - Humans
KW - Intercellular Signaling Peptides and Proteins
KW - Likelihood Functions
KW - Male
KW - Middle Aged
KW - Models, Genetic
KW - Monocytes
KW - Multigene Family
KW - PPAR gamma
KW - Plaque, Atherosclerotic
KW - Pregnancy
KW - Risk Factors
KW - Smoking
KW - Transcriptome
KW - Tumor Suppressor Proteins
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1371/journal.pone.0050888
DO - 10.1371/journal.pone.0050888
M3 - SCORING: Journal article
C2 - 23372645
VL - 8
SP - e50888
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 1
ER -
