The gut microbiota: a major player in the toxicity of environmental pollutants?

Exposure to environmental chemicals has been linked to various health disorders, including obesity, type 2 diabetes, cancer and dysregulation of the immune and reproductive systems, whereas the gastrointestinal microbiota critically contributes to a variety of host metabolic and immune functions. We aimed to evaluate the bidirectional relationship between gut bacteria and environmental pollutants and to assess the toxicological relevance of the bacteria–xenobiotic interplay for the host. We examined studies using isolated bacteria, faecal or caecal suspensions—germ-free or antibiotic-treated animals—as well as animals reassociated with a microbiota exposed to environmental chemicals. The literature indicates that gut microbes have an extensive capacity to metabolise environmental chemicals that can be classified in five core enzymatic families (azoreductases, nitroreductases, β-glucuronidases, sulfatases and β-lyases) unequivocally involved in the metabolism of >30 environmental contaminants. There is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host. Conversely, environmental contaminants from various chemical families have been shown to alter the composition and/or the metabolic activity of the gastrointestinal bacteria, which may be an important factor contributing to shape an individual’s microbiotype. The physiological consequences of these alterations have not been studied in details but pollutant-induced alterations of the gut bacteria are likely to contribute to their toxicity. In conclusion, there is a body of evidence suggesting that gut microbiota are a major, yet underestimated element that must be considered to fully evaluate the toxicity of environmental contaminants

Genome-wide association studies identify CHRNA5/3 and HTR4 in the development of airflow obstruction

RATIONALE: Genome-wide association studies (GWAS) have identified loci influencing lung function, but fewer genes influencing chronic obstructive pulmonary disease (COPD) are known.OBJECTIVES: Perform meta-analyses of GWAS for airflow obstruction, a key pathophysiologic characteristic of COPD assessed by spirometry, in population-based cohorts examining all participants, ever smokers, never smokers, asthma-free participants, and more severe cases.METHODS: Fifteen cohorts were studied for discovery (3,368 affected; 29,507 unaffected), and a population-based family study and a meta-analysis of case-control studies were used for replication and regional follow-up (3,837 cases; 4,479 control subjects). Airflow obstruction was defined as FEV(1) and its ratio to FVC (FEV(1)/FVC) both less than their respective lower limits of normal as determined by published reference equations.MEASUREMENTS AND MAIN RESULTS: The discovery meta-analyses identified one region on chromosome 15q25.1 meeting genome-wide significance in ever smokers that includes AGPHD1, IREB2, and CHRNA5/CHRNA3 genes. The region was also modestly associated among never smokers. Gene expression studies confirmed the presence of CHRNA5/3 in lung, airway smooth muscle, and bronchial epithelial cells. A single-nucleotide polymorphism in HTR4, a gene previously related to FEV(1)/FVC, achieved genome-wide statistical significance in combined meta-analysis. Top single-nucleotide polymorphisms in ADAM19, RARB, PPAP2B, and ADAMTS19 were nominally replicated in the COPD meta-analysis.CONCLUSIONS: These results suggest an important role for the CHRNA5/3 region as a genetic risk factor for airflow obstruction that may be independent of smoking and implicate the HTR4 gene in the etiology of airflow obstruction.</p

Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits. Cross-ancestry genome-wide association analyses in individuals of European and East Asian ancestry identify 11 new risk loci for intracranial aneurysms and highlight a polygenic architecture explaining a substantial fraction of disease heritability

Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity

Background: Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics. Methods: A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients. Results: Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20-1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01-1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59-0.67) to 0.65 (95% CI, 0.62-0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=-4.82×10-3per year [95% CI, -6.49×10-3to -3.14×10-3]; P=1.82×10-8), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86-0.98]; P=0.0041). Conclusions: The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH