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

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 metaanalysis 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 the 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 the 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.

The sbv IMPROVER Systems Toxicology computational challenge: Identification of human and species-independent blood response markers as predictors of smoking exposure and cessation status

“…These genes overlapped remarkably with genes present in previously published blood-based smoking gene signatures derived from the same datasets, constituting an independent confirmation of a signature proposed by Martin et al [27] and signatures from a meta-analysis of six studies that included a total of 10,233 subjects [50]. Many genes of the consensus signature are reported to undergo differential regulation at the mRNA and/or DNA methylation level following exposure to cigarette smoke (references in Supplementary table 7).…”

“…Therefore, the consensus blood smoking signature may not only include genes that reflect a response to smoke exposure, but also genes that may play roles in smoke-related disease pathogenesis in the long term. This hypothesis was supported by Huan et al who found significant associations between their whole blood-based cigarette smoking signature and human complex diseases and traits [50]. …”

Smoking affects gene expression in blood of patients with ischemic stroke

“…A previous transcriptome meta‐analysis reported the top 25 smoking‐related genes associated with current smokers versus never smokers . Comparing our 100 smoking genes identified in C‐SM versus C‐NSM with the top 25 smoking‐related genes identified in this previous meta‐analysis ( n = 25), we found three genes (GPR15, LRRN3, and CLDND1) overlapped, which is highly significant based on a hypergeometric probability test ( P < 0.0002).…”

“…GPR15 is an orphan receptor that is involved in the regulation of the innate immunity and T‐cell trafficking . GPR15 and LRRN3 have DNA methylation loci in their promoter regions that are reported to be hypomethylated among smokers which might indicate smoking‐induced epigenetic changes . LRRN3 is an inflammatory regulatory gene related to T‐cell function and immunosenescence whose expression declines with age and smoking, perhaps resulting in a dysregulated immune system prior to and after stroke.…”

Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect

“…lithium users) and non-lithium users included the following variables: BD diagnosis, age, sex, tobacco use, assessment group, RIN, sequencing plate, and sequencing metric PCs 1 through 3. Tobacco use was included because of its well-characterized effect on whole blood gene expression 30 . An overview of covariates can be found in Table S1.…”