Chronic exposure to environmental pollutants is often associated with systemic inflammation. As such, cigarette smoking contributes to inflammation and lung diseases by inducing senescence of pulmonary cells such as pneumocytes, fibroblasts, and endothelial cells. Yet, how smoking worsens evolution of chronic inflammatory disorders associated with Th17 lymphocytes, such as rheumatoid arthritis, psoriasis, Crohn's disease, and multiple sclerosis, is largely unknown. Results from human studies show an increase in inflammatory CD4 + Th17 lymphocytes at blood-and pulmonary level in smokers. The aim of the study was to evaluate the sensitivity of CD4 + Th17 lymphocytes to cigarette smoke-induced senescence. Mucosa-homing CCR6 + Th17-were compared to CCR6 neg -and regulatory T peripheral lymphocytes after exposure to cigarette smoke extract (CSE). Senescence sensitivity of CSE-exposed cells was assessed by determination of various senescence biomarkers (β-galactosidase activity, p16 Ink4a -and p21 expression) and cytokines production. CCR6 + Th17 cells showed a higher sensitivity to CSE-induced senescence compared to controls, which is associated to oxidative stress and higher VeGfα secretion. Pharmacological targeting of ROS-and ERK1/2 signalling pathways prevented CSEinduced senescence of CCR6 + Th17 lymphocytes as well as VEGFα secretion. Altogether, these results identify mechanisms by which pro-oxidant environmental pollutants contribute to pro-angiogenic and pathogenic CCR6 + Th17 cells, therefore potential targets for therapeutic purposes.open Scientific RepoRtS | (2020) 10:6488 | https://doi.Statistical analysis. The sample sizes were dependent on the experimental question and are shown in the related figures. 32 individual healthy donors were studied for all experiments shown in this manuscript. At least, 4individual healthy donors were used for the study of each marker of senescence. Each donor was used to test two different biomarkers at least, depending on the yield of T cells sorting. Statistical analysis tests were performed using Prism version 5.04 (GraphPad, La Jolla, CA, USA) and data are represented as mean standard error of the mean (SEM). As the gaussian distribution of the different biomarkers could not be checked, non-parametric tests were used to address their statistical relevance. The Mann-Whitney test was used to compare the means of two groups of ordinal (non-parametric) data, and the Kruskal-Wallis test (non-parametric test) was used to compare between the 3 groups.