Electronic cigarettes (e-cig) and heated tobacco products (HTP) are often used as smoking cessation aids, while the harm reduction effects of these alternatives to cigarettes are still the subject of controversial debate, in particular regarding their carcinogenic potential. The objective of this study is to compare the effects of e-cig, HTP and conventional cigarette emissions on the generation of oxidative stress and genetic and epigenetic lesions in human bronchial epithelial BEAS-2B cells. Our results show that HTP were less cytotoxic than conventional cigarettes while e-cig were not substantially cytotoxic in BEAS-2B cells. E-cig had no significant effect on the Nrf2 pathway, whereas HTP and cigarettes increased the binding activity of Nrf2 to antioxidant response elements and the expression of its downstream targets HMOX1 and NQO1. Concordantly, only HTP and cigarettes induced oxidative DNA damage and significantly increased DNA strand breaks and chromosomal aberrations. Neither histone modulations nor global DNA methylation changes were found after acute exposure, regardless of the type of emissions. In conclusion, this study reveals that HTP, unlike e-cig, elicit a biological response very similar to that of cigarettes, but only after a more intensive exposure: both tobacco products induce cytotoxicity, Nrf2-dependent oxidative stress and genetic lesions in human epithelial pulmonary cells. Therefore, the health risk of HTP should not be underestimated and animal studies are required in order to determine the tumorigenic potential of these emerging products.

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models’ reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

Smoking is an established risk factor for various pathologies including lung cancer. Electronic cigarettes (e-cigs) and heated tobacco products (HTPs) have appeared on the market in recent years, but their safety or, conversely, their toxicity has not yet been demonstrated. This study aimed to compare the metabolome of human lung epithelial cells exposed to emissions of e-cigs, HTPs, or 3R4F cigarettes in order to highlight potential early markers of toxicity. BEAS-2B cells were cultured at the air–liquid interface and exposed to short-term emissions from e-cigs set up at low or medium power, HTPs, or 3R4F cigarettes. Untargeted metabolomic analyses were performed using liquid chromatography coupled with mass spectrometry. Compared to unexposed cells, both 3R4F cigarette and HTP emissions affected the profiles of exogenous compounds, one of which is carcinogenic, as well as those of endogenous metabolites from various pathways including oxidative stress, energy metabolism, and lipid metabolism. However, these effects were observed at lower doses for cigarettes (2 and 4 puffs) than for HTPs (60 and 120 puffs). No difference was observed after e-cig exposure, regardless of the power conditions. These results suggest a lower acute toxicity of e-cig emissions compared to cigarettes and HTPs in BEAS-2B cells. The pathways deregulated by HTP emissions are also described to be altered in respiratory diseases, emphasizing that the toxicity of HTPs should not be underestimated.

MicroRNAs are epigenetic regulatory factors capable of silencing the expression of target genes and might mediate the effects of air pollution on health. The objective of the present population-based study was to investigate the association between microRNA expression and long-term, residential exposure to atmospheric PM₁₀ and NO₂. We included 998 non-smoking adult participants from the cross-sectional ELISABET survey (2010-2014) in the Lille urban area of France. The mean residential annual pollution levels were estimated with an atmospheric dispersion modelling system. Ten microRNAs were selected on the basis of the literature data, together with two housekeeping microRNAs (miR-93-5p and miR-191-5p) and were quantified with RT-qPCRs. Multivariate linear regression models were used to study the association between microRNAs and air pollution. The threshold for statistical significance (after correction for the FDR) was set to p < 0.1. The mean annual exposure between 2011 and the year of inclusion was 26.4 ± 2.0 µg/m³ for PM₁₀ and 24.7 ± 5.1 µg/m³ for NO₂. Each 2 µg/m³ increment in PM₁₀ exposure was associated with an 8.6% increment (95%CI [3.1; 14.3]; p_FDR = 0.019) in miR-451a expression. A 5 µg/m³ increment in NO₂ exposure was associated with a 5.3% increment ([0.7; 10]; p_FDR = 0.056) in miR451a expression, a 3.6% decrement (95%CI [-6.1; -1.1]; p_FDR = 0.052) in miR-223-3p expression, a 3.8% decrement (95%CI[-6.8; -0.7]; p_FDR = 0.079) in miR-28-3p expression, a 4.3% decrement (95%CI [-7.7; -0.8]; p_FDR = 0.055) in miR-146a-5p expression, and a 4.0% decrement (95% CI[-7.4; -0.4]; p_FDR = 0.059) in miR-23a-5p expression. The difference between the two housekeeping microRNAs miR-93-5p and miR-191-5p was also associated with PM₁₀ and NO₂ exposure. Our results suggest that circulating miRNAs are potentially valuable biomarkers of the effects of air pollution.