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. 2017 Oct;10(5):e001678.
doi: 10.1161/CIRCGENETICS.116.001678.

MicroRNA Signature of Cigarette Smoking and Evidence for a Putative Causal Role of MicroRNAs in Smoking-Related Inflammation and Target Organ Damage

MicroRNA Signature of Cigarette Smoking and Evidence for a Putative Causal Role of MicroRNAs in Smoking-Related Inflammation and Target Organ Damage

Christine M Willinger et al. Circ Cardiovasc Genet. 2017 Oct.

Abstract

Background: Cigarette smoking increases risk for multiple diseases. MicroRNAs regulate gene expression and may play a role in smoking-induced target organ damage. We sought to describe a microRNA signature of cigarette smoking and relate it to smoking-associated clinical phenotypes, gene expression, and lung inflammatory signaling.

Methods and results: Expression profiling of 283 microRNAs was conducted on whole blood-derived RNA from 5023 Framingham Heart Study participants (54.0% women; mean age, 55±13 years) using TaqMan assays and high-throughput reverse transcription quantitative polymerase chain reaction. Associations of microRNA expression with smoking status and associations of smoking-related microRNAs with inflammatory biomarkers and pulmonary function were tested with linear mixed effects models. We identified a 6-microRNA signature of smoking. Five of the 6 smoking-related microRNAs were associated with serum levels of C-reactive protein or interleukin-6; miR-1180 was associated with pulmonary function measures at a marginally significant level. Bioinformatic evaluation of smoking-associated genes coexpressed with the microRNA signature of cigarette smoking revealed enrichment for immune-related pathways. Smoking-associated microRNAs altered expression of selected inflammatory mediators in cell culture gain-of-function assays.

Conclusions: We characterized a novel microRNA signature of cigarette smoking. The top microRNAs were associated with systemic inflammatory markers and reduced pulmonary function, correlated with expression of genes involved in immune function, and were sufficient to modulate inflammatory signaling. Our results highlight smoking-associated microRNAs and are consistent with the hypothesis that smoking-associated microRNAs serve as mediators of smoking-induced inflammation and target organ damage. These findings call for further mechanistic studies to explore the diagnostic and therapeutic use of smoking-related microRNAs.

Keywords: C-reactive protein; inflammation; microRNAs; risk factors; smoking.

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Figures

Figure 1
Figure 1
Network of Gene Ontology Categories for Smoking-Associated mRNAs Coexpressed with Smoking-Associated miRNAs. Smoking-associated gene transcripts that are coexpressed with one or more smoking-associated miRNAs and represented by one or more GO categories. Only gene transcripts belonging to GO categories meeting the Bonferroni-corrected enrichment p-value threshold of p<10−5, are included (see Supplementary Tables 3 and 4 for GO results). Blue lines denote negative correlations, while purple lines indicate positive correlations between miRNA and mRNA expression. This figure was drawn by ProteoLens.
Figure 2
Figure 2
Smoking-Related miRNAs Alter Cytokine Expression in Vitro. Multiplex cytokine bead array analysis of A549 cell culture supernatants after transfection with non-targeting (NT), miR-1180, or miR-1285-3p mimetics. Cells were stimulated 24 hours post-transfection with vehicle or 10 ng/mL rhTNF-α for 6 hours. Squares, triangles, and circles represent data collected from three independent experiments, normalized to the cytokine concentrations in the negative control group (NT vehicle). The * indicates p<0.01 (significant p-values shown) when compared to NT control within a given stimulation group as determined by a two-way ANOVA followed by Dunnett’s test, using raw rather than normalized values.
Figure 3
Figure 3
Proposed Roles for Smoking-Associated miRNAs in Mediating Target Organ Damage. Thick arrows indicate established relationships, while thin arrows indicate observed associations or correlations based on epidemiological analyses (Tables 2–4), bioinformatics approaches (Supplementary Tables 2–5), and functional experiment (Figure 2).

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