Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions

BACKGROUND: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0x10(-3) (Bonferroni correction for 50 tests). RESULTS: We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P= 1.3x10(-16)) in comparison with 5% in ever-smokers (P= 2.5x10(-4)), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value= 8.7x10(-5)). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.Peer reviewe

Angiographic performance of everolimus-eluting stents for the treatment of coronary in-stent restenosis in daily practice

Objectives: The present study aims to analyze the angiographic anti-restenotic performance of durable polymer everolimus-eluting stents (EES) for the treatment of in-stent restenosis (ISR) in daily practice.Background: Randomized data is available supporting the use of drug-coated balloons and drug-eluting stents for the treatment of ISR; however, additional real-world data including angiographic follow-up is needed.Methods: Patients who underwent EES-implantation for the treatment of drug-eluting stent ISR and attended for a 6–8 months angiographic surveillance were analyzed. Off-line assessment of the angiograms was conducted at a central quantitative coronary angiographic core laboratory. Results: A total of 426 patients with ISR were treated with EES and had undergone angiographic follow-up. The mean age was 66.8 ± 9.9 years and 27.5% suffered from diabetes. A total of 459 lesions were treated. The diameter stenosis decreased from 64.3 ± 19.1% (preprocedural) to 12.0 ± 6.4% (postprocedural). At 6–8 months angiographic follow-up, the in-segment diameter stenosis was 38.3 ± 21.7% and the in-stent late luminal loss was 0.54 ± 0.74 mm in the treated area analysis. The rate of recurrent binary restenosis was 25.7%.Conclusions: In the setting of ISR, the angiographic anti-restenotic efficacy of stenting with EES is comparable to that observed in randomized clinical trials and less favorable than its performance in patients undergoing stenting for de novo disease.</div

Genetic and modifiable risk factors combine multiplicatively in common disease.

BackgroundThe joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized.ObjectivesWe modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors.MethodsWe analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions.ResultsIn UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p  0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer.ConclusionsAlleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed

The integrin ligand SVEP1 regulates GPCR-mediated vasoconstriction via integrins alpha 9 beta 1 and alpha 4 beta 1

Background and Purpose: Vascular tone is regulated by the relative contractile state of vascular smooth muscle cells (VSMCs). Several integrins directly modulate VSMC contraction by regulating calcium influx through L-type voltage-gated Ca2+ channels (VGCCs). Genetic variants in ITGA9, which encodes the α9 subunit of integrin α9β1, and SVEP1, a ligand for integrin α9β1, associate with elevated blood pressure; however, neither SVEP1 nor integrin α9β1 has reported roles in vasoregulation. We determined whether SVEP1 and integrin α9β1 can regulate VSMC contraction. Experimental Approach: SVEP1 and integrin binding were confirmed by immunoprecipitation and cell binding assays. Human induced pluripotent stem cell-derived VSMCs were used in in vitro [Ca2+]i studies, and aortas from a Svep1+/− knockout mouse model were used in wire myography to measure vessel contraction. Key Results: We confirmed the ligation of SVEP1 to integrin α9β1 and additionally found SVEP1 to directly bind to integrin α4β1. Inhibition of SVEP1, integrin α4β1 or α9β1 significantly enhanced [Ca2+]i levels in isolated VSMCs to Gαq/11-vasoconstrictors. This response was confirmed in whole vessels where a greater contraction to U46619 was seen in vessels from Svep1+/− mice compared to littermate controls or when integrin α4β1 or α9β1 was inhibited. Inhibition studies suggested that this effect was mediated via VGCCs, PKC and Rho A/Rho kinase dependent mechanisms. Conclusions and Implications: Our studies reveal a novel role for SVEP1 and the integrins α4β1 and α9β1 in reducing VSMC contractility. This could provide an explanation for the genetic associations with blood pressure risk at the SVEP1 and ITGA9 loci

Association sub-loci signal for the TGFB1 locus.

<p>The three lead SNPs are shown with the corresponding high-LD blocks (SNPs within r2>0.2.) depicted in orange, red and green. Independent sub-loci were identified with the GCTA conditional analysis tool (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182999#sec002" target="_blank">methods</a>). The LD between the lead SNPs indicated and under r²<0.1. The three individual LocusZoom plots are found in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182999#pone.0182999.s002" target="_blank">S2 Fig</a>.</p

Schematic approach.

<p>1) Identification of reported adverse effect of GA 2) Identify genes reported to interact with GA. 3) Establish a link between the genes identified in 2. and the adverse effect identified in 1).</p

TGFB1 locus.

<p>TGFB1 locus.</p

GA interacts with CCR, TGFB1, IFNAR1 and HLA-DRB1 (solid line).

<p>Moreover, it is known that GA affects CAD (Coronary Artery Disease) risk (dashed line). In this work, we searched for SNPs associated with CAD in the gene regions representing the GA off target effects (dotted lines). We found a genome-wide significant association for the TGFB1 locus with a p-value of 1.58 × 10⁻¹² (red dotted line). n.s.: non-significant; TGFB1: Transforming Growth Factor, Beta 1; CCR5: Chemokine (C-C Motif) Receptor 5 (Gene/Pseudogene); IFNAR1: Interferon (Alpha, Beta And Omega) Receptor 1; HLA-DRB1: Major Histocompatibility Complex, Class II, DR Beta 1.</p