Vaping versus Smoking: A Quest for Long-term impact in a mouse model

Vaping versus Smoking: A Quest for Long-term impact in a mouse model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vaping versus Smoking: A Quest for Long-term impact in a mouse model Layal Massara, Anaïs Ollivier, Romain Dussautoir, Gwenola Kervoaze, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4926091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Most smokers consider that electronic cigarettes are safer than tobacco and are marketed as safe products. Nevertheless, recent reports show the exposure to high levels of electronic cigarette vapors (ECV) activates lung cells and triggers lung inflammation and structural alterations after chronic exposure. In order to assess the potential harmful effect of moderate exposure to ECV, we investigated in mice, its effect on lung and systemic inflammation and on lung structure and function. Methods To reproduce closely the situation frequently encountered in human, we exposed mice during 1h/day during 3 or 6 months with two levels of electronic cigarette power in comparison with mice exposed to cigarette smoke (CS). Lung and systemic inflammation was evaluated by measuring cell recruitment and activation as well as cytokine concentrations. Lung transcriptome, respiratory function and body weight were also measured. Results Our data revealed that chronic exposure to moderate levels of ECV increased specifically lung inflammation including NK cells and T lymphocyte recruitment and the production of CXCL1 and CXCL2 chemokines as well as IL-22 after 3 months, these effects being different from the profile induced by CS. Surprisingly, there is no strong overlap between the impact of the 3 types of emissions on lung transcriptome. Modulation of pro-inflammatory pathways are limited to mice exposed to e-cig set to low power. In contrast, alteration of respiratory function is observed in high-power ECV-exposed mice but not at low power, with a different profile than in CS-exposed mice. Conclusion Subchronic (or mid-term) exposure to ECV might alter the respiratory function independently of the inflammatory response and in a different manner than CS. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Over the past few years, new methods of delivering nicotine have emerged, and many users prefer electronic cigarettes (e-cigs) to traditional tobacco cigarettes. Invented in 2003, e-cigs have revolutionized the tobacco industry. They were introduced as a safe alternative to conventional cigarettes, and designed with the intent to provide smokers the satisfaction of conventional cigarettes without major side effects. E-cig vapors (ECV) are generated by non-combustion heating and aerosolization of an e-liquid commonly composed of glycerol, propylene glycol, nicotine and flavorings [ 1 ]. In addition to nicotine, ECV contain a variety of substances, depending on the composition of the e-liquids, the duration and volume of the puff and the power of the e-cig [ 2 , 3 ]. In particular, ECV may contain low concentrations of toxic substances, some of which are also present in combustible cigarette smoke (CS) such as carbonyl compounds, polycyclic aromatic hydrocarbons and metals [ 4 – 6 ]. However, the concentration of these compounds is also strongly dependent of the power settings used for the e-cig. Supporting this, recent data demonstrated that ECV induced an inflammatory reaction both in vitro , on epithelial cells [ 3 , 7 ] and in vivo in mice and in smokers [ 1 , 5 ]. To date, only a handful of in vivo toxicological studies have been performed on ECV. An in vivo study conducted in a mouse model of intense exposure (5 hours per day for 3 successive days) suggested that ECV increased pro-inflammatory cytokines and diminished lung glutathione levels which are critical in maintaining cellular redox balance [ 8 ]. In another study, e-cig exposure for 2 weeks resulted in immunomodulatory effects similar to those observed after exposure to CS, with impaired pulmonary anti-microbial defenses [ 9 ]. Furthermore, the chronic inhalation of ECV (1-hour daily for 4 months) has been reported to induce airway hyper-reactivity and air space enlargement in exposed mice, some of these effects being nicotine-dependent [ 10 ]. Altogether, these data confirm that inhalation of ECV could have an impact on the inflammatory and immune response of the lungs, two stages that could constitute the background for the development of cancers or chronic bronchitis as shown with the conventional cigarette. Nowadays, a comprehensive understanding of the long-term toxicological and immunologic consequences associated with e-cig use compared with CS are lacking. Long-term effects of ECV and the specific role of nicotine on lung parenchyma have been assessed by Roxlau ET et al [ 11 ]. Using an intense whole-body exposure protocol of 6 hours per day for 8 months, the authors showed that airway inflammation following ECV inhalation, characterized by lymphocyte recruitment and significant changes in lung structure and function, was close to mild tobacco smoke-induced alterations. However, it remains unclear if a moderate ECV exposure protocol in a more physiologic context might have similar effects. This is why we used a moderate exposure protocol (1 hour/day) to low and high power (18 W and 30 W) e-cig in nose-only exposed mice. Using this protocol, we previously reported that moderate exposure to the high-power e-cig aerosol induced oxidative DNA damage in the lung and the liver of exposed mice similarly to the effect of 3R4F cigarette smoke exposure [ 12 ]. In the present study, we compared the impact on lung function, body weight and immune response of chronic exposure (either 3 or 6 months) to ECV generated at two different powers with that of conventional cigarette (3R4F) smoke (Fig. 1 ). Methods In vivo model Experiments were conducted on spf male BALB/c mice (Janvier Labs, Le Genest-Saint-Isle, France), 9 weeks old, 8 animals/group for each time points (3 and 6 month exposure) dispatched in 2 cages. The housing procedure respects the classical procedures with light and temperature control, free access to food and water and environmental enrichment. The number was determined according to our previous experiments with CS-exposed mice. The primary outcome measures were the alteration of the respiratory function and of the body weight. This mouse strain is described as sufficiently sensitive to oxidative stress and chemical induction of lung cancers [ 13 ]. 13 Animal procedures were in agreement with European directive 2010/63/EU for the protection of animals used for scientific purposes and obtained the Ethical Committee on Animal Experimentation (CEEA 75) approval (ref APAFIS #10363-2017062615002072v2). Animal randomisation was performed by the responsible of the animal facility at the delivery of the mice. Animal body weights were recorded on Monday of each weak while clinical signs were monitored daily. E-cigarettes and conventional cigarette We chose the third generation “ModBox” model from NHOSS® (Innova, Bondues, France), used with the “Air Tank” clearomiser equipped with a 0.5 Ω kanthal coil and with a partially closed air flow. For experiments, we chose two power settings for the Modbox model: a “low” power of 18 W and a “high” power of 30 W. For the e-liquid, we chose the best-selling NHOSS® brand containing 65% propylene glycol, 35% glycerine, 16 mg/mL nicotine and the most common flavor, “blond tobacco”. Conventional 3R4F cigarettes were obtained from the University of Kentucky (Lexington, KY, USA) To avoid chemical cross-contamination, two different pieces of equipment (dilution chamber, tubes, exposure towers and pipes) were used for e-cig and 3R4F exposures (Fig. 1 ). Aerosols from e-cigs and 3R4F cigarette were generated with an InExpose e-cigarette extension system on which we adapted the Modbox and a cigarette smoking robot (SCIREQ®, Emka technologies, Montreal, Quebec, Canada), respectively. Mice were exposed to aerosols by a nose-only tower (InExpose system, SCIREQ®, Emka technologies). In order to perform a comparative toxicological study of ECV and CS, we used the Health Canada Intense puff regime (55 mL puff volume, 2 s puff duration, 30 s puff period). Based on data from the literature and our preliminary study after a 4-day subacute exposure (data not shown), two exposure protocols were applied in this study for both e-cig and 3R4F emissions: a 3-month subchronic and a 6-month chronic exposure, 60 min/day and 5 days/week. For each exposure schedule, an additional group of mice was exposed to fresh conditioned air (negative control) and a period of acclimatisation of one week was performed before the exposure . Sample Collection Mice were euthanized 24 hours after the last exposure for both the protocols of 3 or 6 month exposures. Bronchoalveolar lavage (BAL) fluids, lungs, spleens and blood samples were collected and kept on ice until they were processed. In addition for lungs, one lobe was stored in RNA Later (Ambion,ThermoFisher Scientific, Illkirch, France) for transcriptomic analyses, posterior lobes were fixed with paraformaldehyde (PFA 4%, Labonord, Villeneuve d’Ascq, France) for histopathological analyses, and another lobe was frozen in liquid nitrogen and then stored at − 80°C until further analyses. Broncho-alveolar lavage procedure, lung and spleen processing BALs were performed by instilling 5 aliquots of 0,5 mL (final volume 2,5 mL) of sterile PBS. After centrifugation at 400g for 6 min at 4°C, supernatants (cell-free BAL fluid) from the first two aliquots of 0.5 ml were stored at − 80°C for cytokine analysis (ELISA), and cell pellets were used for flow-cytometry analysis. The left lobe of the lung was mashed with a sterile blade and then digested with collagenase (Collagenase Type VI 17104–019 Gibco by Life technologies, Carlsbad, California United States) at 37°C. After 15 min of digestion, lungs were homogenized with an 18 G needle and further digested for 15 min. After centrifugation at 400g for 6 min at 4◦C, the pellets were resuspended in a 30% Percoll solution (Percoll TM GE Healthcare 17–0891-01, Chicago, IL, United States) and centrifuged at 500g for 15 min. Total spleen cells were also isolated from spleen and centrifuged at 400g for 6 min at 4°C. The lung and spleen pellets were resuspended in red blood cells (RBC) lysis buffer during 5 min at room temperature, to remove erythrocytes. The reaction of RBC lysis was stopped with PBS 2% FBS (Gibco by Life technologies, Carlsbad, California United States). After centrifugation at 400g for 6 min at 4 ◦C, pulmonary and spleen cells were resuspended in PBS 2% FBS, then enumerated and used for flow cytometry. Flow cytometry BAL, lung and spleen total cells were incubated with the appropriate panel of antibodies for 30 min in PBS 2% FCS. Conjugated antibodies were used against mouse CD5 (ref 130–102–574, FITC-conjugated), PBS57-loaded CD1 d Tetramer (NIH facility,PE-conjugated), NK1.1 (ref 130–103–963, PerCp-Cy5.5–conjugated), CD4 (ref 130–102– 411, PE-Cy7-conjugated), CD25 (ref 130–102–550, APC-conjugated), CD69 (ref 561–238, Alexa700-conjugated), TCRγδ (ref 130–104–016, APC-Vio770 conjugated), TCR-β (ref 130– 104–815, V450-conjugated), CD8 (ref 130–109–252, V500-conjugated), CD45 (ref BLE103140, BV605-conjugated), I-Ab (ref 130–102–168, FITC-conjugated), F4/80 (ref 130–102–422,PE conjugated), CD103 (ref 563–637, PerCP-Cy5.5-conjugated), CD11c (ref 558–079, PE Cy7-conjugated), CD86 (ref 560–581, Alexa-700 conjugated), Ly6G (ref 560–600, APC-H7conjugated), CD11b (ref 560–455, V45O conjugated), CD45 (ref 130–402–512, V500 conjugated), Ly6C (ref BLE128036, BV605-conjugated) (BD Biosciences, Franklin Lakes, United States; Biolegend, San Diego, United States and Myltenyi Biotech, Paris, France) and CCR2 (ref FAB 5538A, R&D systems, APC conjugated). Data were acquired on a LSR Fortessa (BD Biosciences, Franklin Lakes, United States) and analyzed using FlowJo™ software v10.2 (Stanford, CA, USA). Gating strategy has been previously described [ 14 ]. Absolute cell numbers were calculated according to the total cell number and the frequency of CD45 + immune cells. Cytokine Assay Levels of IL-1β, IL-6, IL-22, IL-23, IFN-γ, CXCL1, CXCL2, CXCL5, CXCL17, TNF-α and Resistin were determined in BAL, lung and serum by enzyme-linked immunosorbent assay (ELISA) using the manufacturer’s recommendation (R&D systems, Biotechne, Minneapolis, MN, United States). In addition, concentrations of IL-17 and IL-23p19 were measured by an ELISA from Invitrogen (Waltham, MA, United States). Lung Histology Fixed lung lobes were paraffin-embedded and lung sections were stained with hematoxylin-eosin. Lung injury and inflammation were scored based on a scale evaluating bronchial damage, hyperplasia, inflammatory cell influx, alveolar exudates, damage, wall thickness, inflammation, and emphysema, all with a scale from 0 to 4. Moreover, hemorrhage, fibrinoid necrosis, leukocytoclasis and suppuration were noted as present or not (value of 1 and 0, respectively). This allows to generate a global histologic score from 0 to 36. The score was established in a blinded fashion by independent experts (OCV clinical research, Lille). In order to assess the potential development of emphysema, we evaluated the mean linear intercept (MLI) on lung sections by using the Image J software (NIH). Lung function On the day of the sacrifice, each mouse was anaesthetized intraperitoneally with xylazine hydrochloride (15 mg/kg) / ketamine (100 mg/kg), tracheotomized, and cannulated with an 18 G metal cannula (resistance: 0.36–0.40 cmH2O.s/mL). Mice were then connected to a flexiVent FX system (SCIREQ Inc., Montreal, Qc, Canada) and operated by the flexiWare software v7.7 as previously described [ 15 ]. Immediately after connection to the ventilator, set at 150 breaths/min, two deep lung inflations were performed at least 6–12 seconds apart to recruit lung beyond any closed airway and to standardize lung volume history. This was done by inflating the lungs to 30 cm H 2 O over 3 seconds and holding that pressure for another 3 seconds to allow for the lungs to equilibrate after the inflation. Mice were then submitted to a 300 breaths/min hyperventilation in order to eliminate spontaneous breathing before a 150 breaths/min return. Next, the mechanical properties of the mouse respiratory system were assessed at baseline, i.e . before the construction of a full-range pressure-volume (PV) curve. This was done using a sequence of measurements integrated by default in the flexiVent operating software (flexiWare v7.7). The Area between the PV loop inflation and deflation limbs (Hysteresis (H)) A static compliance (Cst) was calculated. Cst, the parameters A (estimate of inspiratory capacity) and K (shape constant) can be extracted from the Salazar-Knowles equation [ 16 ]. Cst reflects the intrinsic elastic properties of the respiratory system ( i.e. lung + chest wall). Transcriptomic analyses Total RNAs were extracted from RNA later treated tissue samples using the miRNeasy mini kit (Qiagen, Courtaboeuf, France) according to the manufacturer’s protocol. The RNA concentration was measured with the Biospecnano spectrophotometer (Shimadzu, Marne-la-Vallée, France). Transcriptomic experiments were carried out using 8x60k OneColor microarrays (Agilent Technologies) coupled to 60-mer oligonucleotides covering the entire mouse genome. Labeling, hybridization and lncRNA detection were carried out according to the manufacturer's instructions (Agilent Technologies). For each microarray, Cyanine 3-coupled lncRNAs were synthesized by the QuickAmp Low Input Kit from 50 ng of total RNA. Spike-in RNAs were added to each tube and used as positive controls for the labeling and amplification steps. Labeled cDNAs were purified and 600 ng of each cDNA was then hybridized to the microarrays according to the manufacturer's instructions. After washing, the microarrays were scanned and the data exported using Agilent Feature Extraction Software© (FE version 10.7.3.1). Results were then interpreted by selecting those mRNAs whose expression was at least significantly 1.5-fold higher or 1.5-fold lower than that of unexposed control mice (p < 0.05). Statistical analyses were performed with the "linear models for microarray data" (limma) package for R, using moderated t statistics with standardized data. Functional analysis of selected deregulated lung RNAs was performed using Ingenuity Pathway Analysis software (Qiagen) by selecting the top 25 of canonical pathways for each exposure condition. Statistical analyses The data are expressed as mean ± Standard Error of the Mean (SEM). Statistical analyses were computed using GraphPad Prism software (5.00 version, GraphPad software, San Diego, USA) in accordance with the size of samples and the nature of the experiment. Values were compared to the controls with a bilateral and non-parametric Mann-Whitney test, or with a one-sample t-test, or a two-way ANOVA test when appropriate. Statistical significance was accepted for an error risk inferior to 5% and is represented as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001. Results 1- Long term e-cigarette exposure impairs clinical parameters a- Subchronic exposure (3-month treatment) We first analyzed the impact of ECV on clinical parameters, including body weight and lung function, and compared it to that of CS. As expected [ 15 , 17 ], mice exposed to CS showed slower weight gain than control mice exposed to fresh conditioned air. This effect was also observed when mice were exposed to ECV with a power of either 18W or 30W (Fig. 2 A). Interestingly, this reduction in weight gain was associated with impaired lung function in mice exposed to 30W ECV and those exposed to CS compared to the control mice (Fig. 2 B). Indeed, the PV-loop curve revealed a shift towards higher volumes for mice exposed to 30W ECV but not with 18W ECV, suggesting the development of some emphysema with the Modbox 30W.We also observed the same trend with CS-exposed mice although at a lower intensity than in 30W ECV-exposed mice. Based on PV-loop curves, we calculated the static compliance (Cst) and the area under the curve (AUC). The Cst tends to be increased both in 30W ECV and CS-exposed mice (Fig. 2 E) whereas the AUC was only increased in CS-exposed mice (Fig. 2 G). Afterwards, we analyzed the impact of ECV exposure on morphological lung structure. We observed an extensive lung remodeling mainly in alveolar walls and bronchial epithelium (Fig. 3 ). Compared to control mice, extensive thickening of respiratory epithelium due to hyperplasia and folding was observed in both ECV- and CS-exposed mice. Atypias like loss of orientation, binucleation or meganucleation were also sporadically observed in 30W ECV-exposed mice. Inflammatory cell recruitment was also detected in both peribronchial areas and alveoli. Additionally, all samples of 30W ECV and CS-exposed mice displayed periarterial edema and alveolar emphysema. MLI measurement exhibited a statistically significant (p < 0.001) difference between control mice and either 30W ECV- or CS-exposed mice but not with 18W ECV-exposed mice (Fig. 2 H). b- Chronic exposure (6-month treatment) After 6 months of exposure, weight gain was substantially reduced in both ECV- and CS-exposed mice. Moreover, this effect tends to be greater in mice exposed to high power ECV compared to CS-exposed mice (Fig. 2 C). Lower weight gain was associated with an emphysematous-type alteration of lung function in mice which is more pronounced in 30W ECV-exposed mice (Fig. 2 D). This was confirmed by a significant increase in Cst, A and AUC in the 30W ECV-exposed mice. Surprisingly, CS-exposed mice exhibited a fibrotic-like alteration of the PV loop curve and no alteration of the K, A and AUC parameters after 6 months of exposure as compared with both air and ECV-exposed mice (Fig. 2 I-K, respectively). The histopathologic analysis revealed an increase in emphysema in both 30W ECV- and CS-exposed mice and a not-significant effect in 18W ECV-exposed mice as compared to air-exposed mice (Fig. 3 ) which were confirmed by MLI measurement (Fig. 2 L). We also observed extensive lung remodeling mainly in alveolar walls and bronchial epithelium, including alveolar wall thickening, hyperplasia of bronchial epithelium and some area of cell desquamation mainly in both 30W ECV- and CS-exposed mice. This was associated with some inflammatory cell infiltrates both in the peribronchial and in alveolar area. 2- E-cigarette vapors induce pulmonary inflammation a- Subchronic exposure (3-month treatment) The inflammatory cell infiltrate was analysed in both lung and spleen by flow cytometry. This study revealed that mice exposed to CS during 3 months exhibited a specific immune cell signature compared to ECV-exposed mice (Fig. 4 ). CS-exposed mice presented reduced number of alveolar macrophages (AM) in lung tissue although these cells expressed higher levels of the activation markers CD86 and CMH class II molecules. Furthermore, we observed a statistically significant (p < 0.01) recruitment of inflammatory monocytes as well as NK and NKT cells in the BAL of CS-exposed mice (Fig. 4 A) while this treatment increased the number of conventional dendritic cells type 1 and 2 (cDC1 and cDC2), CD8 + T Cells, CD4 + T cells, inflammatory monocytes and neutrophils in lung tissue (Fig. 4 B and data not shown). This is not associated with a modulation of the expression of activation markers such as CD86 and I-Ab for monocytes and neutrophils, as well as CD25 and CD69 for lymphocytes (Fig. 4 D). In contrast, ECVs have a limited impact on innate immune cells recruitment in the BAL since they decreased the number of NK and CD8 + T cells whereas only 30W ECV induced a statistically higher expression (p < 0.01) of I-Ab on inflammatory monocytes (Fig. 4 ). In lung tissue, only 18W ECV induced the recruitment of NK cells, CD8 + and CD4 + T cells while exposure to 30W ECV increased the expression of CD25 on CD4 + T cells . In order to decipher the mechanism of the inflammatory cell recruitment, we measured the levels of cytokines in the BAL and lungs of exposed mice (Fig. 5 ). Exposure to 30W ECV for 3 months was associated with higher levels of IL-22 and the chemokines CXCL1, CXCL2 and in BAL and a trend for CXCL17 concentrations (Fig. 5 A). In mice exposed to CS, we detected higher concentrations of IL-17 and a decrease in Il-1β in the lungs compared with control mice (Fig. 5 B). Meanwhile no effect was observed on Il-17 levels after exposure to ECV in either lung tissue or BAL in contrast with CS-exposed mice. In addition, we further assessed the ability of immune cells to produce Th1 and Th17 cytokines with and without in vitro CD3 activation. Surprisingly, lung cells from CS-exposed mice produced significantly less IL-22 than those of control mice. In contrast, exposure to 30W ECV increased the levels of IL-22 by unstimulated cells but not after anti-CD3 activation while there is no change with 18W ECV. b- Chronic exposure (6-month treatment) After 6 months, mice exposed to ECV exhibited a significant decrease in the number of NK and CD4 + T cells in BAL, as well as a trend to lower number of AMs (Fig. 4 A). Nevertheless, this was associated with a significant increase in activated AM and inflammatory monocytes in both ECV- and CS- exposed mice. In lung tissue, we observed a statistically significant recruitment of CD4 + and CD8 + T cells in all exposed mice (Fig. 4 B). Only exposure to 30W ECV induced significant NK recruitment in lungs with a tend to recruit more NKT and AM. This was associated with higher expression of the activation marker I-Ab in AM and inflammatory monocytes in both the BAL and the lung (Fig. 4 C-D). The same trend was observed in AM and inflammatory monocytes from CS-exposed mice whereas the expression of activation markers was not modulated in conventional T lymphocytes from these mice. Moreover, higher expression of CD25 in NKT cells in the lung of 30W ECV-exposed mice but not in 18W ECV-exposed mice (Fig. 4 C). Interestingly, both exposure to 30W ECV and CS significantly reduce the number of cDC1 in lungs (Supplementary figure S1 ). Regarding the secretion of inflammatory mediators, our data did not reveal an overproduction of cytokines in BAL and lungs from 30W ECV- and CS-exposed mice (Fig. 5 A-B). In 18W ECV-exposed mice, only CXCL17 were significantly induced in the BAL and lungs as compared with controls. In contrast, we observed lower levels of IL-1β and CXCL1 in lungs for all emissions. No significant alteration of the Th1 and Th17 cytokine secretion by lung cells was reported in mice exposed to ECV or CS. E-cigarette vapors induce systemic inflammation. To assess the systemic impact of ECV, we examined the number and phenotype of immune cells in the spleen, as well as their ability to produce Th1 and Th17 cytokines, and cytokine concentrations within the blood. a- Subchronic exposure (3-month treatment) The total cell number of spleen cells are similar among all the groups (58 ± 6, 35.3 ± 4.4, 37.4 ± 3, and 49.1 ± 4.7 x10 6 cells for Air, 18W ECV, 30W ECV and CS, respectively). Exposure to 18 and 30 W ECV was responsible for a decrease in the number of antigen-presenting cells (DC and macrophages) and lymphocytes (CD4 + and CD8 + T cells) in the spleen of ECV-exposed animals (Fig. 6 A). This was associated with an increased expression in CD25 in CD8 + T cells in mice exposed to 30W ECV (Fig. 6 B). In contrast, the spleen of CS-exposed mice was characterized by a statistically significant recruitment of iNKT and a lower number of neutrophils, as well as a trend towards a decreased number of DC. Despite the lack of effect on their numbers, a higher expression of the activation marker I-Ab was observed in macrophages as well as CD25 in CD4 + and CD8 + T Cells after CS exposure (Fig. 6 B). Interestingly, exposure to ECV tended to increase the levels of CXCL2 and TNF-α in blood, whereas the 30W ECV-exposed mice had lower concentrations of CXCL1 (Fig. 7 A). In addition, unstimulated as well as anti-CD3 stimulated spleen cells from 30W ECV- and CS-exposed mice produced significantly more IL-22 than spleen cells from controls (Fig. 7 B). b- Chronic exposure (6-month treatment) After 6 months, ECV-exposed mice presented a statistically significant recruitment of NKT cells in the spleen, regardless of e-cig power. Moreover, only ECV of 18W Modbox increased the recruitment of CD4 + and CD8 + T cells in the spleen (Fig. 6 A). No impact was noticed on cell activation. Surprisingly ECV and CS exposures induced a drop in the neutrophil number in the spleen. This was associated with a non-significant increase in macrophage number. No effect was observed on the number and expression of activation markers by other immune cells in the spleen. Regarding the ability of spleen cells to produce cytokines (Fig. 7 B), IL-22 production consecutive to anti-CD3 stimulation was enhanced in mice exposed to 18W ECV whereas IL-17 and IFN-γ production was unchanged. Surprisingly, serum levels of IL-6, IL-22, TNF-α, CXCL1 and CXCL2 were lower in ECV- and CS-exposed mice whereas there is no change for TNF-α in CS-exposed mice (Fig. 7 A). Only, CS-exposed mice exhibited increased concentrations of resistin (p < 0.0001). We have also analyzed the cotinine levels in order to assess the level of systemic exposure of mice to CS and ECV, as well as to estimate the potential involvement of nicotine in the modulation of the inflammatory reaction. Our data revealed that the cotinine levels are higher in mice exposed to CS and ECV than in mice exposed to air (Supplementary Figure S2 ). However, there is a trend for higher levels of cotinine in CS-exposed mice as compared with 18W and at a lesser degree 30W ECV-exposed mice at both time courses, although the differences are not statistically significant. In summary, exposure to ECV has an ambivalent effect on systemic inflammation, with a drop in blood cytokine concentration, and, at the opposite, some activation and enhanced capacity to produce IL-22 in splenic T lymphocytes. 4- Impact of e-cigarette on lung transcriptome In order to determine and compare the signaling pathways deregulated by exposure to ECV or CS, lung mRNA expression profiles were characterized by pangenomic microarrays in mice exposed to emissions for 3 or 6 months. a- Subchronic exposure (3-month treatment) Exposure to CS for 3 months induced the deregulation of 248 genes (186 up- and 62 down-regulated). Surprisingly, the number of deregulated genes was much higher in 18W ECV-exposed mice (197 with 121 up- and 76 down-regulated) compared to those exposed to 30W ECV (72 with 50 up- and 22 down-regulated) (Fig. 8 A). The fold changes and the adjusted p values were reported in the supplemental Excel table S1 . Overall, 25 genes (Table 1 ) were deregulated by exposure to the three types of emissions, including genes involved in cancer development (increase in Pla1a expression and down-regulation of the tumour suppressor genes Cdkn2a and Ppp2r2c) and cell metabolism ( e.g . increased levels in Fmo3). Moreover, the expression of 16 genes were specifically altered by both 18W and 30W ECV (Fig. 8 B), some of which are involved in the control of oxidative stress (Hmox1, Slc7a11) and cell proliferation (Fosl1). Table 1 Genes commonly deregulated by 3 month subchronic exposure to ECV18W, ECV30W and CS. Fold changes are calculated in comparison with Air mice. The genes in bold are those deregulated both after 3 and 6 month exposure. Gene Symbol Gene Name Fold-Change ECV18W Fold-Change ECV30W Fold-Change CS Atic 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase -1.93 -2.02 -1.89 Calca calcitonin/calcitonin-related polypeptide. alpha 3.04 3.16 3.38 Cckar cholecystokinin A receptor 1.99 2.08 1.92 Cdkn2a cyclin dependent kinase inhibitor 2A -1.70 -1.61 -0.63 Celf3 CUGBP. Elav-like family member 3 1.82 2.30 1.82 Chac1 ChaC. cation transport regulator 1 -3.69 -3.64 -2.53 Fam167b family with sequence similarity 167. member B -1.68 -1.66 -1.82 Fmo3 flavin containing monooxygenase 3 2.37 2.38 1.79 Gbp11 guanylate binding protein 11 -1.59 -1.58 -1.62 Gbp4 guanylate binding protein 4 -1.55 -1.56 -1.68 Gdf15 growth differentiation factor 15 -3.53 -3.58 -2.17 Gm26641 predicted gene. 26641 1.87 2.13 1.94 Gm4610 predicted gene 4610 3.18 3.82 2.94 H1fx H1 histone family. member X 1.57 1.76 1.57 Klhl38 kelch-like 38 2.09 1.75 1.54 Mcpt8 mast cell protease 8 -1.97 -1.61 -1.83 Nupr1 nuclear protein transcription regulator 1 -2.16 -2.34 -2.13 Pla1a phospholipase A1 member A 1.99 2.41 2.32 Ppp2r2c protein phosphatase 2. regulatory subunit B. gamma -1.58 -1.86 -1.83 Scn3b sodium channel. voltage-gated. type III. beta -1.61 -1.56 -1.65 Selenbp2 selenium binding protein 2 1.51 1.54 2.41 Slc38a4 solute carrier family 38. member 4 2.33 2.58 1.78 Syt15 synaptotagmin XV -1.66 -1.72 -1.72 Tcf23 transcription factor 23 2.32 1.92 1.67 Trib3 tribbles pseudokinase 3 -2.12 -2.22 -1.88 Analysis of the pathways affected by CS treatments (Fig. 9 C) revealed that most of the deregulated pathways are involved in the metabolism of xenobiotics (in particular the biotransformation of PAHs with increased expression of Cyp1a1 and Cyp1b1) and oxidative stress (increased levels of Nqo1, Gsta3, Gsto1). Pathways deregulated by 18W ECV are mainly related to inflammation linked to IL-17 signaling (decreased expression of Ccl2 and Il6) (Fig. 9 A), oxidative stress (deregulation of Abcc1, Chac1, Gclc, Hmox1 and Myc) and lipid metabolism (Pla1a, Pmvk, Plcb1, Plcb4). b- Chronic exposure (6-month treatment) While we found an increase in the number of deregulated genes after 6 months of exposure to 18W ECV (431 with 388 up- and 43 down-regulated), the opposite was observed with ECV 30W, which altered the expression of only 40 genes (12 up- and 28 down-regulated) (Fig. 8 C). The fold changes and the adjusted p values were reported in the supplementary Excel table S2 . Thirty-four of them were commonly deregulated by the vapors of the 2 wattages. CS exposures changed the expression of 615 genes (336 up- and 279 down-regulated), including 398 in a specific manner. Furthermore, 216 genes were deregulated in common with 18W ECV. Overall, 23 genes (Table 2 ) were deregulated by exposure to the three types of emissions (Fig. 8 D), including genes involved in inflammatory response (decreased expression of Il6 and Tnip3) and in cancer development (down-regulation of Fosl1 and Sfn and up-regulation of Plk5). Table 2 Genes commonly deregulated by 6 month chronic exposure to ECV18W, ECV30W and CS. Fold changes are calculated in comparison with Air mice. The genes in bold are those deregulated both after 3 and 6 month exposure. Gene Symbol Gene Name Fold-Change ECV18W Fold-Change ECV30W Fold-Change CS Adm2 adrenomedullin 2 -2.15 -1.92 -1.92 Atic 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase -2.04 -2.31 -2.86 Chac1 ChaC. cation transport regulator 1 -2.79 -2.27 -2.45 Diras2 DIRAS family. GTP-binding RAS-like 2 1.71 1.53 1.84 Dpp6 dipeptidylpeptidase 6 2.43 1.72 1.56 Fam161a family with sequence similarity 161. member A 2.17 1.71 2.06 Fmo3 flavin containing monooxygenase 3 2.61 2.45 3.11 Fosl1 fos-like antigen 1 -4.03 -4.10 -3.04 Galnt5 polypeptide N-acetylgalactosaminyltransferase 5 1.76 1.67 1.54 Gdf15 growth differentiation factor 15 -3.45 -2.55 -2.64 Gjb2 gap junction protein. beta 2 -2.18 -2.19 -1.55 Il6 interleukin 6 -2.18 -2.80 -1.73 Itprid1 ITPR interacting domain containing 1 3.45 2.23 1.57 Lrrc26 leucine rich repeat containing 26 3.54 2.11 1.84 Mab21l3 mab-21-like 3 (C. elegans) -2.20 -2.27 -2.34 Mgat3 mannoside acetylglucosaminyltransferase 3 3.08 2.21 2.21 Nupr1 nuclear protein transcription regulator 1 -2.39 -2.59 -4.13 Phgdh 3-phosphoglycerate dehydrogenase -1.57 -1.50 -1.87 Plk5 polo like kinase 5 3.13 1.80 2.38 Rptoros regulatory associated protein of MTOR. complex 1. opposite strand 2.32 1.67 1.79 Sfn stratifin -3.67 -3.88 -2.57 Tnip3 TNFAIP3 interacting protein 3 -1.55 -1.67 -1.70 Trib3 tribbles pseudokinase 3 -1.84 -1.87 -2.17 After 6 months of exposure, the main pathways affected by CS were related to the inflammatory response (e.g. decreased levels of Il6, Il1b and Mmp9) and xenobiotic metabolism (e.g. modulation of Ahrr, Cyp1a1, Cyp1b1 and Gsta3) (Fig. 10 C). The 18W ECV mostly affected nucleotide biosynthesis, metabolism of xenobiotics (Aldh18a1, Aldh1l2 et Aldh3a1) and inflammation (Ccl20, Cxcl6, Il6, Muc5ac) (Fig. 10 A). Altogether, the transcriptomic data showed that mRNA profiles are very different between 3 and 6 months of exposure, for both 18W ECV and CS. Moreover, the impact of 30W ECV on the lung transcriptome appeared to be much lower than that of 18W ECV. We failed to clearly identify signalling pathways altered by 30W ECV due to the low number of modulated genes (Figs. 9 B and 10 B). Discussion The use of electronic nicotine delivery systems, commonly known as e-cig, has gained significant in popularity as an alternative to conventional cigarette or even as a trend among adolescents. While the well-established health risks associated with CS exposure are widely acknowledged, the potential long-term effects of e-cig usage have only recently begun to receive attention. In this study, we aim to investigate the impact of moderate (1h/day) and chronic exposure to e-cig set at 2 different wattages ( i.e . 18W or 30W) in comparison to CS on a range of major physiological parameters. Interestingly, exposure to ECV induced an alteration of lung function but this effect was not related to a chronic lung inflammation as revealed by the data obtained with 30W ECV. Moreover, the impact of ECV on transcriptome, inflammation and lesions within the lung are mostly specific compared to the effect of CS. Exposure to ECV has some major impact on physiological mechanisms including host metabolism and respiratory function although this latter is restricted to 30W ECV. The impact on host metabolism was evidenced by a lower weight gain in mice exposed to ECV. This effect is similar with both low and high power e-cig and does not seem to be related to blood nicotine levels (Supplementary figure S2 ). Furthermore, the impact of CS on weight gain was similar to that of ECV, although blood cotinine concentrations were higher in these mice. We also observed a strong effect of 30W ECV on lung function, with a shift in the PV-loop curve towards an emphysematous phenotype associated with a major impact on static compliance and the area of the curve. The presence of emphysema was confirmed by a significant increase in MLI in 30W ECV-exposed mice, close to the effect observed in CS-exposed mice. However, our protocol of CS exposure has a very different effect on the PV-loop curve with no significant effect on compliance and area under the curve. A previous study has reported a similar impact of ECV on lung function with emphysema and alteration of static compliance, an effect that has been shown to be nicotine-dependent [ 11 ]. In parallel, we also reported on the same mice that the ECV exposure induced important disturbances of proliferation, inflammation, and permeability in both ileum and colon, which reveals that ECV-induced lesions are not restricted to respiratory mucosa [ 18 ]. To determine whether a chronic inflammatory response could be responsible for the altered lung function observed in mice exposed to ECV, we assessed pulmonary and systemic inflammation in these mice by measuring leukocyte recruitment and activation, as well as cytokine concentrations. In contrast to a previous study using an intense ECV exposure (5-6h/day) protocol [ 11 ], we did not detect any effect on neutrophil recruitment within the lung after ECV exposure; the number of these granulocytes was even reduced within the spleen. Nevertheless, we observed a transient increase of neutrophil-active chemokine secretion which suggests that neutrophil recruitment requires persistent chemokine production not detectable after chronic exposure to moderate doses of ECV, such as those used in the present study. However, our protocol of exposure using Modbox 30W was associated with higher activation of antigen-presenting cells such as AM and inflammatory monocytes in the BAL and lung tissue although their numbers were similar. This stimulation was associated with the mobilization of NK cells and T lymphocytes within lung tissue even if activation of these cells could not be observed. An activation of CD4 + T lymphocytes was only detected in 30W ECV- and CS-exposed mice. Altogether, these data showed that exposure to e-cig induced a specific profile of cell recruitment and activation as compared to CS. Moreover, no clear impact was detected on neutrophil count and phenotype in contrast to CS. Regarding cytokine production, we observed some upregulation of neutrophil-chemotactic cytokines such as CXCL1, CXCL2 and CXCL17 in the BAL of mice exposed to 30W ECV for 3 months. This transient upregulation was not associated with neutrophil recruitment, probably because chemokine concentrations are too low, or because of a defect in structural cell activation required for leucocyte diapedesis. While IL-17 levels are unaffected by ECV exposure, IL-22 concentrations are higher in the BAL and in the supernatant of unstimulated lung cells from 30W ECV-exposed mice. Although IL-17 has been identified as a key player in the development of CS-induced COPD, IL-22 production seems to be preferentially targeted by ECV. In the present study, the role of IL-22 in the lung injury remains to be determined although some reports show that IL-22 can induce airway remodeling and can also participate in COPD development [ 19 ]. Interestingly, spleen cells from ECV-exposed mice produced higher levels of IL-22 after anti-CD3 stimulation. Exposure to ECV seems to induce a mobilization of both antigen presenting cells and T lymphocyte in the spleen at 3 months, followed by a replenishment of NKT cells and T cells at 6 months. Some T cell activation was also detected at 3 months of exposure, as evidenced by modulation of CD25 expression and enhanced capacity to produce IL-22. This suggests that the effect of ECV is not restricted to the lung and could induce some recirculation of immune cells towards the lung and/or intestinal mucosa although we did not observe a cytokine response in the blood. In parallel with the modulation of the immune response, ECV exposure affects clinical parameters including body weight and lung function with some similarities to the impact of CS. Previous data confirm this impact of ECV in mice exposed in adulthood [ 20 , 21 ] but also in mice exposed in utero , where both lung dysfunction and structural impairments persist into adulthood. Although the effects observed with our protocol using moderate doses of ECV appear to be close to those following exposure to high concentrations of ECV, the mechanisms underlying these effects are probably different. At high concentrations, ECV induced the recruitment of inflammatory cells such as neutrophils, and the upregulation of metalloproteinases such as MMP9 and MMP12 [ 11 , 21 ]. At lower concentrations, we reported that chronic exposure to ECV did not promote persistent inflammation and MMP expression whereas this treatment enhances the expression of IL-22, an essential cytokine for the maintenance of the epithelium and remodeling of mucosa [ 22 ]. Transcriptomic analyses were carried out to identify the signalling pathways affected by exposure to ECV and attempt to characterize the mechanisms involved in the observed effects of these emissions. The pathways deregulated by CS are deregulated more significantly than by ECV. Unsurprisingly, following exposure to CS, the biological functions most affected are the metabolism of xenobiotics and the inflammatory response. In mice exposed to CS, deregulated genes contribute mainly to the AhR pathway, which is characteristic of a response to exposure to PHAs such as those found in CS [ 3 ]. The overexpressed genes code in particular for the cytochromes CYP1A1 and CYP1B1, enzymes that may be involved in the generation of highly reactive intermediate metabolites capable of inducing DNA damage. In contrast, exposure to ECV did not induce gene expression of these two cytochromes, but rather modulated gene expression of aldehyde dehydrogenases (Aldh18a1, Aldh1l2 et Aldh3a1) possibly involved in the metabolism of carbonyl compounds, and most likely in the biotransformation of propylene glycol, one of the main compounds in e-liquids. Chronic exposure to ECV also altered the expression of gene involved in oxidative stress, a mechanism which can explained the oxidative DNA damage in the lung that we previously reported in this experimental model [ 12 ]. Notably, the absence of activation of the AhR pathway by ECV is consistent with the very low levels of PAHs measured in these emissions [ 4 ]. The involvement of major genes of LXR/RXR pathway, which are also deregulated following exposure to CS and ECV, has already been shown in the exacerbation of macrophage-mediated lung inflammation in cases of CS exposure [ 23 ]. In addition, our data show that the number of deregulated genes was greater after exposure to 18W ECV than 30W ECV. These results are surprising given the lower quantity of toxic compounds measured in 18W ECV compared to 30W ECV. Indeed, we had previously shown that the 18W ECV generated levels of carbonyl compounds that are around 2 times lower than for the ECV 30W [ 4 , 24 ]. This difference in the transcriptomic impact of ECV could be explained by an adaptive mechanism at the higher e-cig power and by the kinetics of the effects, which could differ according to the power of the e-cig. After a short-term exposure (one week), we observed a dose-dependent effect of ECV on transcriptome with more deregulations at the highest e-cig power (data not shown). In ECV-exposed mice, transcriptomic data revealed deregulation of genes involved in lipid metabolism (Pla1a, Pmvk, Plcb1, Plcb4). This effect could explain the altered metabolism and weight gain in exposed mice, and could also contribute to the observed impairment of lung function in these mice. These genes have already been described as being affected in regular e-cig users, which could reflect the excessive lipid load provided by the glycerol contained in e-liquids. This excessive load could then disrupt surfactant secretion in the lungs and impact lung function [ 25 ]. Altogether, our data showed that long term exposure to moderate levels of ECV (1 h / day) can induce some alteration of lung function and host metabolism whereas only limited effect on the inflammatory reaction can be exhibited. Il-22 upregulation is associated with these changes suggesting a physiopathologic role for this cytokine. Our transcriptomic analyses highlighted certain signalling pathways common to exposure to CS and ECV (although the number of genes involved is much lower with ECV), in particular the Th17 pathway involved in the development of chronic inflammation and the progression of COPD. Exposure of C57BL/6 mice to CS for chronic exposure had already demonstrated this increase in Th17 signaling pathway [ 15 , 19 , 26 ]. However, the alteration of IL-22 production had never been demonstrated after exposure to ECV. Overall, the impact on the transcriptome of ECV revealed some alteration in the expression of genes involved in inflammation, oxidative stress, and metabolism of lipids and xenobiotics. These data about ECV associated with its genotoxic effect the lesions in the intestine [ 12 , 18 ] confirm the urgent need of information in the general population about regular use of e-cigarette and the necessity to complete by studying the consequences of e-cigarette smoking in the general population even at moderate levels. Declarations Availability of data and materials: The dataset(s) supporting the conclusions of this article are available within the manuscript or supplementary information files. Acknowledgements We acknowledge Dr Martin Figeac from the Functional and Structural Platform (CHU Lille, France) for transcriptomic analyses and Dr OLivier Molendi-Coste and Hélène Bauderlique from the Bicell platform ( Université de Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41—UAR 2014—PLBS, 59000 Lille, France ) for the flow cytometry analysis. We also thanks the NIH facility for providing murine iNKT tetramer. Conflicts of Interest: The authors declare they have no conflicts of interest related to this work to disclose. Funding This work benefited grant from the IRESP (Institut de recherche en Santé Publique) and the French National Cancer Institute (INCa): Contract No. INCa_11505. Ethics Approval and Consent to Participate This study does not involve human participants. Animal procedures were in agreement with European directive 2010/63/EU for the protection of animals used for scientific purposes and obtained the Ethical Committee on Animal Experimentation of the Hauts de France (CEEA 75) approval (ref APAFIS 10363-2017062615002072v2). Author information Author notes : Nothing to declare Authors and Affiliations 1 Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019-UMR 9017, Center for Infection and Immunity of Lille, Lille, France. 2 Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS - IMPact de l’Environnement Chimique sur la Santé, F-59000, Lille, France 3 Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020 - UMR1277 - Canther - Cancer Heterogeneity, Plasticity and Resistance to Therapies, 59000 Lille, France Contributions Conceptualization, PG, JMLG, SA.; methodology, LM, AO, JMLG, SA, JK, AP, MP and PG.; formal analysis, LM, AO, RD, JMLG, SA, PG.; investigation, LM, AO, RD, GK, JMLG, SA, JK, AP, MP and PG.; writing-original draft, LM, AO, RD, JMLG, SA, PG.; writing-review and editing, all authors.; supervision, PG and SA.; funding acquisition, JMLG, SA, JK, AP, MP and PG. All authors have read and agreed to the published version of the manuscript. References Gotts JE, Jordt SE, McConnell R, Tarran R: What are the respiratory effects of e-cigarettes? Bmj 2019, 366: l5275. 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Harrison OJ, Foley J, Bolognese BJ, Long E, 3rd, Podolin PL, Walsh PT: Airway infiltration of CD4+ CCR6+ Th17 type cells associated with chronic cigarette smoke induced airspace enlargement. Immunol Lett 2008, 121: 13-21. Additional Declarations No competing interests reported. Supplementary Files SupplementalExcelTableS1.xlsx SupplementalExcelTableS2.xlsx SupplementalFigVapingversusSmoking.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4926091","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347729129,"identity":"76219045-5a63-41e6-82c2-1bd0f2db4697","order_by":0,"name":"Layal Massara","email":"","orcid":"","institution":"Univ. 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Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019-UMR 9017, Center for Infection and Immunity of Lille, Lille, France","correspondingAuthor":false,"prefix":"","firstName":"Muriel","middleName":"","lastName":"Pichavant","suffix":""},{"id":347729134,"identity":"38a14ea7-1d46-4557-9d4a-3bd50c713290","order_by":5,"name":"Anne Platel","email":"","orcid":"","institution":"Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS - IMPact de l’Environnement Chimique sur la Santé, F-59000, Lille","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Platel","suffix":""},{"id":347729135,"identity":"22a5e357-bad1-4152-af03-ad987280ddfa","order_by":6,"name":"Jérôme Kluza","email":"","orcid":"","institution":"Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020 - UMR1277 - Canther - Cancer Heterogeneity, Plasticity and Resistance to Therapies, 59000 Lille","correspondingAuthor":false,"prefix":"","firstName":"Jérôme","middleName":"","lastName":"Kluza","suffix":""},{"id":347729136,"identity":"3c0d7081-fa56-4621-acf2-36d201f72e74","order_by":7,"name":"Jean-Marc Lo-Guidice","email":"","orcid":"","institution":"Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS - IMPact de l’Environnement Chimique sur la Santé, F-59000, Lille","correspondingAuthor":false,"prefix":"","firstName":"Jean-Marc","middleName":"","lastName":"Lo-Guidice","suffix":""},{"id":347729137,"identity":"3cc411bc-8277-449d-97b4-ea76c49c5c17","order_by":8,"name":"Sébastien Anthérieu","email":"","orcid":"","institution":"Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS - IMPact de l’Environnement Chimique sur la Santé, F-59000, Lille","correspondingAuthor":false,"prefix":"","firstName":"Sébastien","middleName":"","lastName":"Anthérieu","suffix":""},{"id":347729138,"identity":"42a2e1b3-7962-4c43-b17a-4e464ef1969d","order_by":9,"name":"Philippe Gosset","email":"data:image/png;base64,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","orcid":"","institution":"Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019-UMR 9017, Center for Infection and Immunity of Lille, Lille, France","correspondingAuthor":true,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Gosset","suffix":""}],"badges":[],"createdAt":"2024-08-16 15:55:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4926091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4926091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64628416,"identity":"d5f56da1-3743-40c6-bf3f-f72fbf701880","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":564948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design and major conclusion.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/c59273fba8d633ee6255bf9d.png"},{"id":64628679,"identity":"92031619-a045-4c2e-9910-d7a54addb059","added_by":"auto","created_at":"2024-09-16 19:08:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRespiratory function measurement of mice after ECV 18W, 30W and CS exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) weight evaluation (B and D) PV loop curves (E,F,H,I,J,K)A (estimate of inspiratory capacity), K (shape constant) and static compliance (Cst). A and K parameters and Cst are extracted from the Salazar-Knowles equation and expressed as mean ± SEM (n= 8)\u003c/p\u003e\n\u003cp\u003e(H) Quantification of MLI and epithelial height at bronchiolo-alveolar junctions. Data are expressed as mean ± SEM. For statistical analyses, values are compared to the appropriate air control *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/9acee03293a144fb8cf639aa.png"},{"id":64628417,"identity":"294a4f94-1416-4960-8f88-cb571989819d","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4637718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLung histology after ECV or CS exposure. \u003c/strong\u003eWe have reported representative photos of lung sections in mice exposed to ECV18W, ECV30W and CS for 3 and 6 months at high (x250) and low (x100) magnification. We observed some hyperplasia in bronchial epithelium (arrows), alveolar wall thickening (stars) and some alveolar wall destruction (thunderbolt).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/cbe372a904c39fede9d8f392.png"},{"id":64628426,"identity":"45c82686-9add-4705-8648-6827e710b2e6","added_by":"auto","created_at":"2024-09-16 19:00:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of ECV or CS on cell phenotypes and cell activation \u003c/strong\u003eQuantification by flow cytometry of the number of alveolar macrophages (AM), natural killer T cell) (NKT), natural killer (NK), inflammatory monocytes (inf.monocytes), CD8 T cells and CD4 T cells in the BAL (A) and lung tissue (B). Activation of AM (I-Ab MFI), NKT, NK (CD25 MFI), inflammatory monocytes (Iab MFI), CD8 and CD4 T cells (CD25 MFI) in in the BAL (C) and lung tissue (D). These parameters were analyzed after 3 and 6 months of exposure to ECV 18W, 30W and CS. Data are expressed as mean ± SEM (n = 8). For statistical analyses, values are compared to the appropriate air control *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/5dfe25205a266015c9c06b63.png"},{"id":64628421,"identity":"0d5fb2f3-e918-4fff-bdc7-ead75c79172a","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":303781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytokines variation after ECV or CS exposure within the lung.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcentrations of IL-22, IL-17, IL-1b, CXCL1, CXCL2, CXCL17 were analyzed by ELISA (pg/mL) in the BAL (A) and lung tissue extract (B). IL-22 levels were also evaluated after restimulation of lung cells with or without anti-CD3 (C). These parameters were analyzed after 3 and 6 months of exposure to ECV 18W, 30W and CS. Data are expressed as mean ± SEM (n = 8). For statistical analyses, values are compared to the appropriate air control *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"FIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/fbef4cd93e7ddd22bbad6404.png"},{"id":64628418,"identity":"e3146dbc-5a25-4821-bee9-2e3b02eecf51","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":195150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of ECV or CS impact on cell phenotypes and cell activation in spleen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantification by flow cytometry of the numberof macrophages, natural killer T cell (NKT), Neutrophil, dendritic cell (DC) CD8 T cells and CD4 T cells in spleen (A). Activation of macrophages as well as CD8 and CD4 T cells was evaluated by measurement of I-Ab and CD25 expression on spleen cells, respectively (B). These parameters were analyzed after 3 and 6 months of exposure to ECV 18W, 30W and CS. Data are expressed as mean ± SEM (n = 8). For statistical analyses, values are compared to the appropriate air control *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"FIGURE6.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/6cf86d04d6bba373e653f28d.png"},{"id":64628425,"identity":"9283c002-c253-4d71-98d2-3667f3dc54c4","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":194041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of ECV or CS on sera cytokines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIL-22, IL-6, resistin, CXCL-1 and CXCL2 concentrations were analyzed in sera by ELISA (pg/mL) after exposure to 3 or 6 months to ECV 18w, 30w or CS. IL-22 levels were also evaluated after restimulation of spleen cells with or without anti-CD3 (C). These parameters were analyzed after 3 and 6 months of exposure to ECV 18W, 30W and CS. Data are expressed as mean ± SEM (n = 8). For statistical analyses, values are compared to the appropriate air control *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001 Data are expressed as mean ± SEM.\u003c/p\u003e","description":"","filename":"FIGURE7.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/555f4aef8675a887c979fa15.png"},{"id":64628427,"identity":"c5b2ab32-bd3e-420f-b19d-13035216a40c","added_by":"auto","created_at":"2024-09-16 19:00:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":223984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic impact of ECV or CS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Number of significantly deregulated genes (absolute FC ≥ 1.5 and adjusted p ≤ 0.05) after 3 month subchronic exposure to ECV 18W, ECV 30W or CS. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram representation of differentially expressed genes after 3 month subchronic exposure ECV 18W, ECV 30W or CS for 3 months. \u003cstrong\u003e(C)\u003c/strong\u003e Number of significantly deregulated genes (absolute FC ≥ 1.5 and adjusted p ≤ 0.05) after 6 month chronic exposure to ECV 18W, ECV 30W or CS. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram representation of differentially expressed genes 6 month chronic exposure E ECV 18W, ECV 30W or CS.\u003c/p\u003e","description":"","filename":"FIGURE8.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/f22faaec44b369f224e6f616.png"},{"id":64628680,"identity":"c14709fa-35a2-4521-a1cd-c2cadebcf365","added_by":"auto","created_at":"2024-09-16 19:08:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":926687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop 25 of canonical pathways deregulated after 3 month subchronic exposure to (A) ECV 18W, (B) ECV 30W or (C) CS. \u003c/strong\u003eCanonical pathways are determined by IPA analysis and classified according the p value. The number of deregulated by exposure is indicated for each pathway.\u003c/p\u003e","description":"","filename":"FIGURE9.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/a1e16ee39bbc68c07ab428d6.png"},{"id":64628422,"identity":"12b05252-5d71-4e0b-90e8-188d90d5f558","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":941447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop 25 of canonical pathways deregulated after 6 month chronic exposure to (A) ECV 18W, (B) ECV 30W or (C) CS. \u003c/strong\u003eCanonical pathways are determined by IPA analysis and classified according the p value. The number of deregulated by exposure is indicated for each pathway.\u003c/p\u003e","description":"","filename":"FIGURE10.png","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/5573f5d16ec45bcaf348d0ac.png"},{"id":69840917,"identity":"a5aa1615-d0fb-4826-b269-1a28b9c1d4ff","added_by":"auto","created_at":"2024-11-25 17:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11251719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/07a056f8-b7ec-4e6e-8165-45e5e70b80b0.pdf"},{"id":64628415,"identity":"fadddb11-e179-4974-95a3-a57a6fe3d119","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55097,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalExcelTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/e6aa86d6ce3acc2b797eb6bb.xlsx"},{"id":64628423,"identity":"ed50d368-c1bf-42c1-be83-65b610b79d8c","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1445507,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalExcelTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/8acc5bf0e37e244a21062b1a.xlsx"},{"id":64628420,"identity":"1a3a2a0e-af81-40d6-b98d-79204162b47c","added_by":"auto","created_at":"2024-09-16 19:00:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":89110,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigVapingversusSmoking.docx","url":"https://assets-eu.researchsquare.com/files/rs-4926091/v1/8812965023a4fb9ef66bab51.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vaping versus Smoking: A Quest for Long-term impact in a mouse model","fulltext":[{"header":"Background","content":"\u003cp\u003eOver the past few years, new methods of delivering nicotine have emerged, and many users prefer electronic cigarettes (e-cigs) to traditional tobacco cigarettes. Invented in 2003, e-cigs have revolutionized the tobacco industry. They were introduced as a safe alternative to conventional cigarettes, and designed with the intent to provide smokers the satisfaction of conventional cigarettes without major side effects. E-cig vapors (ECV) are generated by non-combustion heating and aerosolization of an e-liquid commonly composed of glycerol, propylene glycol, nicotine and flavorings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to nicotine, ECV contain a variety of substances, depending on the composition of the e-liquids, the duration and volume of the puff and the power of the e-cig [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In particular, ECV may contain low concentrations of toxic substances, some of which are also present in combustible cigarette smoke (CS) such as carbonyl compounds, polycyclic aromatic hydrocarbons and metals [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the concentration of these compounds is also strongly dependent of the power settings used for the e-cig.\u003c/p\u003e \u003cp\u003eSupporting this, recent data demonstrated that ECV induced an inflammatory reaction both \u003cem\u003ein vitro\u003c/em\u003e, on epithelial cells [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and \u003cem\u003ein vivo\u003c/em\u003e in mice and in smokers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To date, only a handful of \u003cem\u003ein vivo\u003c/em\u003e toxicological studies have been performed on ECV.\u003c/p\u003e \u003cp\u003eAn \u003cem\u003ein vivo\u003c/em\u003e study conducted in a mouse model of intense exposure (5 hours per day for 3 successive days) suggested that ECV increased pro-inflammatory cytokines and diminished lung glutathione levels which are critical in maintaining cellular redox balance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In another study, e-cig exposure for 2 weeks resulted in immunomodulatory effects similar to those observed after exposure to CS, with impaired pulmonary anti-microbial defenses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the chronic inhalation of ECV (1-hour daily for 4 months) has been reported to induce airway hyper-reactivity and air space enlargement in exposed mice, some of these effects being nicotine-dependent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Altogether, these data confirm that inhalation of ECV could have an impact on the inflammatory and immune response of the lungs, two stages that could constitute the background for the development of cancers or chronic bronchitis as shown with the conventional cigarette.\u003c/p\u003e \u003cp\u003eNowadays, a comprehensive understanding of the long-term toxicological and immunologic consequences associated with e-cig use compared with CS are lacking. Long-term effects of ECV and the specific role of nicotine on lung parenchyma have been assessed by Roxlau ET et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Using an intense whole-body exposure protocol of 6 hours per day for 8 months, the authors showed that airway inflammation following ECV inhalation, characterized by lymphocyte recruitment and significant changes in lung structure and function, was close to mild tobacco smoke-induced alterations. However, it remains unclear if a moderate ECV exposure protocol in a more physiologic context might have similar effects. This is why we used a moderate exposure protocol (1 hour/day) to low and high power (18 W and 30 W) e-cig in nose-only exposed mice. Using this protocol, we previously reported that moderate exposure to the high-power e-cig aerosol induced oxidative DNA damage in the lung and the liver of exposed mice similarly to the effect of 3R4F cigarette smoke exposure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the present study, we compared the impact on lung function, body weight and immune response of chronic exposure (either 3 or 6 months) to ECV generated at two different powers with that of conventional cigarette (3R4F) smoke (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003emodel\u003c/b\u003e\u003c/p\u003e \u003cp\u003eExperiments were conducted on spf male BALB/c mice (Janvier Labs, Le Genest-Saint-Isle, France), 9 weeks old, 8 animals/group for each time points (3 and 6 month exposure) dispatched in 2 cages. The housing procedure respects the classical procedures with light and temperature control, free access to food and water and environmental enrichment. The number was determined according to our previous experiments with CS-exposed mice. The primary outcome measures were the alteration of the respiratory function and of the body weight. This mouse strain is described as sufficiently sensitive to oxidative stress and chemical induction of lung cancers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003csup\u003e13\u003c/sup\u003e Animal procedures were in agreement with European directive 2010/63/EU for the protection of animals used for scientific purposes and obtained the Ethical Committee on Animal Experimentation (CEEA 75) approval (ref APAFIS #10363-2017062615002072v2). Animal randomisation was performed by the responsible of the animal facility at the delivery of the mice.\u003c/p\u003e \u003cp\u003eAnimal body weights were recorded on Monday of each weak while clinical signs were monitored daily.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eE-cigarettes and conventional cigarette\u003c/h2\u003e \u003cp\u003eWe chose the third generation \u0026ldquo;ModBox\u0026rdquo; model from NHOSS\u0026reg; (Innova, Bondues, France), used with the \u0026ldquo;Air Tank\u0026rdquo; clearomiser equipped with a 0.5 Ω kanthal coil and with a partially closed air flow. For experiments, we chose two power settings for the Modbox model: a \u0026ldquo;low\u0026rdquo; power of 18 W and a \u0026ldquo;high\u0026rdquo; power of 30 W. For the e-liquid, we chose the best-selling NHOSS\u0026reg; brand containing 65% propylene glycol, 35% glycerine, 16 mg/mL nicotine and the most common flavor, \u0026ldquo;blond tobacco\u0026rdquo;. Conventional 3R4F cigarettes were obtained from the University of Kentucky (Lexington, KY, USA)\u003c/p\u003e \u003cp\u003eTo avoid chemical cross-contamination, two different pieces of equipment (dilution chamber, tubes, exposure towers and pipes) were used for e-cig and 3R4F exposures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Aerosols from e-cigs and 3R4F cigarette were generated with an InExpose e-cigarette extension system on which we adapted the Modbox and a cigarette smoking robot (SCIREQ\u0026reg;, Emka technologies, Montreal, Quebec, Canada), respectively. Mice were exposed to aerosols by a nose-only tower (InExpose system, SCIREQ\u0026reg;, Emka technologies). In order to perform a comparative toxicological study of ECV and CS, we used the Health Canada Intense puff regime (55 mL puff volume, 2 s puff duration, 30 s puff period). Based on data from the literature and our preliminary study after a 4-day subacute exposure (data not shown), two exposure protocols were applied in this study for both e-cig and 3R4F emissions: a 3-month subchronic and a 6-month chronic exposure, 60 min/day and 5 days/week. For each exposure schedule, an additional group of mice was exposed to fresh conditioned air (negative control) and a period of acclimatisation of one week was performed before the exposure .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003eMice were euthanized 24 hours after the last exposure for both the protocols of 3 or 6 month exposures. Bronchoalveolar lavage (BAL) fluids, lungs, spleens and blood samples were collected and kept on ice until they were processed. In addition for lungs, one lobe was stored in RNA Later (Ambion,ThermoFisher Scientific, Illkirch, France) for transcriptomic analyses, posterior lobes were fixed with paraformaldehyde (PFA 4%, Labonord, Villeneuve d\u0026rsquo;Ascq, France) for histopathological analyses, and another lobe was frozen in liquid nitrogen and then stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBroncho-alveolar lavage procedure, lung and spleen processing\u003c/h2\u003e \u003cp\u003eBALs were performed by instilling 5 aliquots of 0,5 mL (final volume 2,5 mL) of sterile PBS. After centrifugation at 400g for 6 min at 4\u0026deg;C, supernatants (cell-free BAL fluid) from the first two aliquots of 0.5 ml were stored at \u0026minus;\u0026thinsp;80\u0026deg;C for cytokine analysis (ELISA), and cell pellets were used for flow-cytometry analysis.\u003c/p\u003e \u003cp\u003eThe left lobe of the lung was mashed with a sterile blade and then digested with collagenase (Collagenase Type VI 17104\u0026ndash;019 Gibco by Life technologies, Carlsbad, California United States) at 37\u0026deg;C. After 15 min of digestion, lungs were homogenized with an 18 G needle and further digested for 15 min. After centrifugation at 400g for 6 min at 4◦C, the pellets were resuspended in a 30% Percoll solution (Percoll TM GE Healthcare 17\u0026ndash;0891-01, Chicago, IL, United States) and centrifuged at 500g for 15 min. Total spleen cells were also isolated from spleen and centrifuged at 400g for 6 min at 4\u0026deg;C. The lung and spleen pellets were resuspended in red blood cells (RBC) lysis buffer during 5 min at room temperature, to remove erythrocytes. The reaction of RBC lysis was stopped with PBS 2% FBS (Gibco by Life technologies, Carlsbad, California United States). After centrifugation at 400g for 6 min at 4 ◦C, pulmonary and spleen cells were resuspended in PBS 2% FBS, then enumerated and used for flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003eBAL, lung and spleen total cells were incubated with the appropriate panel of antibodies for 30 min in PBS 2% FCS. Conjugated antibodies were used against mouse CD5 (ref 130\u0026ndash;102\u0026ndash;574, FITC-conjugated), PBS57-loaded CD1 d Tetramer (NIH facility,PE-conjugated), NK1.1 (ref 130\u0026ndash;103\u0026ndash;963, PerCp-Cy5.5\u0026ndash;conjugated), CD4 (ref 130\u0026ndash;102\u0026ndash; 411, PE-Cy7-conjugated), CD25 (ref 130\u0026ndash;102\u0026ndash;550, APC-conjugated), CD69 (ref 561\u0026ndash;238, Alexa700-conjugated), TCRγδ (ref 130\u0026ndash;104\u0026ndash;016, APC-Vio770 conjugated), TCR-β (ref 130\u0026ndash; 104\u0026ndash;815, V450-conjugated), CD8 (ref 130\u0026ndash;109\u0026ndash;252, V500-conjugated), CD45 (ref BLE103140, BV605-conjugated), I-Ab (ref 130\u0026ndash;102\u0026ndash;168, FITC-conjugated), F4/80 (ref 130\u0026ndash;102\u0026ndash;422,PE conjugated), CD103 (ref 563\u0026ndash;637, PerCP-Cy5.5-conjugated), CD11c (ref 558\u0026ndash;079, PE Cy7-conjugated), CD86 (ref 560\u0026ndash;581, Alexa-700 conjugated), Ly6G (ref 560\u0026ndash;600, APC-H7conjugated), CD11b (ref 560\u0026ndash;455, V45O conjugated), CD45 (ref 130\u0026ndash;402\u0026ndash;512, V500 conjugated), Ly6C (ref BLE128036, BV605-conjugated) (BD Biosciences, Franklin Lakes, United States; Biolegend, San Diego, United States and Myltenyi Biotech, Paris, France) and CCR2 (ref FAB 5538A, R\u0026amp;D systems, APC conjugated).\u003c/p\u003e \u003cp\u003eData were acquired on a LSR Fortessa (BD Biosciences, Franklin Lakes, United States) and analyzed using FlowJo\u0026trade; software v10.2 (Stanford, CA, USA). Gating strategy has been previously described [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Absolute cell numbers were calculated according to the total cell number and the frequency of CD45\u003csup\u003e+\u003c/sup\u003e immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCytokine Assay\u003c/h2\u003e \u003cp\u003eLevels of IL-1β, IL-6, IL-22, IL-23, IFN-γ, CXCL1, CXCL2, CXCL5, CXCL17, TNF-α and Resistin were determined in BAL, lung and serum by enzyme-linked immunosorbent assay (ELISA) using the manufacturer\u0026rsquo;s recommendation (R\u0026amp;D systems, Biotechne, Minneapolis, MN, United States). In addition, concentrations of IL-17 and IL-23p19 were measured by an ELISA from Invitrogen (Waltham, MA, United States).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLung Histology\u003c/h2\u003e \u003cp\u003eFixed lung lobes were paraffin-embedded and lung sections were stained with hematoxylin-eosin. Lung injury and inflammation were scored based on a scale evaluating bronchial damage, hyperplasia, inflammatory cell influx, alveolar exudates, damage, wall thickness, inflammation, and emphysema, all with a scale from 0 to 4. Moreover, hemorrhage, fibrinoid necrosis, leukocytoclasis and suppuration were noted as present or not (value of 1 and 0, respectively). This allows to generate a global histologic score from 0 to 36. The score was established in a blinded fashion by independent experts (OCV clinical research, Lille).\u003c/p\u003e \u003cp\u003eIn order to assess the potential development of emphysema, we evaluated the mean linear intercept (MLI) on lung sections by using the Image J software (NIH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLung function\u003c/h2\u003e \u003cp\u003eOn the day of the sacrifice, each mouse was anaesthetized intraperitoneally with xylazine hydrochloride (15 mg/kg) / ketamine (100 mg/kg), tracheotomized, and cannulated with an 18 G metal cannula (resistance: 0.36\u0026ndash;0.40 cmH2O.s/mL). Mice were then connected to a flexiVent FX system (SCIREQ Inc., Montreal, Qc, Canada) and operated by the flexiWare software v7.7 as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Immediately after connection to the ventilator, set at 150 breaths/min, two deep lung inflations were performed at least 6\u0026ndash;12 seconds apart to recruit lung beyond any closed airway and to standardize lung volume history. This was done by inflating the lungs to 30 cm H\u003csub\u003e2\u003c/sub\u003eO over 3 seconds and holding that pressure for another 3 seconds to allow for the lungs to equilibrate after the inflation. Mice were then submitted to a 300 breaths/min hyperventilation in order to eliminate spontaneous breathing before a 150 breaths/min return. Next, the mechanical properties of the mouse respiratory system were assessed at baseline, \u003cem\u003ei.e\u003c/em\u003e. before the construction of a full-range pressure-volume (PV) curve. This was done using a sequence of measurements integrated by default in the flexiVent operating software (flexiWare v7.7). The Area between the PV loop inflation and deflation limbs (Hysteresis (H)) A static compliance (Cst) was calculated. Cst, the parameters A (estimate of inspiratory capacity) and K (shape constant) can be extracted from the Salazar-Knowles equation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Cst reflects the intrinsic elastic properties of the respiratory system (\u003cem\u003ei.e.\u003c/em\u003e lung\u0026thinsp;+\u0026thinsp;chest wall).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic analyses\u003c/h2\u003e \u003cp\u003eTotal RNAs were extracted from RNA later treated tissue samples using the miRNeasy mini kit (Qiagen, Courtaboeuf, France) according to the manufacturer\u0026rsquo;s protocol. The RNA concentration was measured with the Biospecnano spectrophotometer (Shimadzu, Marne-la-Vall\u0026eacute;e, France). Transcriptomic experiments were carried out using 8x60k OneColor microarrays (Agilent Technologies) coupled to 60-mer oligonucleotides covering the entire mouse genome. Labeling, hybridization and lncRNA detection were carried out according to the manufacturer's instructions (Agilent Technologies). For each microarray, Cyanine 3-coupled lncRNAs were synthesized by the QuickAmp Low Input Kit from 50 ng of total RNA. Spike-in RNAs were added to each tube and used as positive controls for the labeling and amplification steps. Labeled cDNAs were purified and 600 ng of each cDNA was then hybridized to the microarrays according to the manufacturer's instructions. After washing, the microarrays were scanned and the data exported using Agilent Feature Extraction Software\u0026copy; (FE version 10.7.3.1). Results were then interpreted by selecting those mRNAs whose expression was at least significantly 1.5-fold higher or 1.5-fold lower than that of unexposed control mice (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical analyses were performed with the \"linear models for microarray data\" (limma) package for R, using moderated t statistics with standardized data. Functional analysis of selected deregulated lung RNAs was performed using Ingenuity Pathway Analysis software (Qiagen) by selecting the top 25 of canonical pathways for each exposure condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error of the Mean (SEM). Statistical analyses were computed using GraphPad Prism software (5.00 version, GraphPad software, San Diego, USA) in accordance with the size of samples and the nature of the experiment. Values were compared to the controls with a bilateral and non-parametric Mann-Whitney test, or with a one-sample t-test, or a two-way ANOVA test when appropriate. Statistical significance was accepted for an error risk inferior to 5% and is represented as follows: *, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1- Long term e-cigarette exposure impairs clinical parameters\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003ea- Subchronic exposure (3-month treatment)\u003c/h2\u003e \u003cp\u003eWe first analyzed the impact of ECV on clinical parameters, including body weight and lung function, and compared it to that of CS. As expected [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], mice exposed to CS showed slower weight gain than control mice exposed to fresh conditioned air. This effect was also observed when mice were exposed to ECV with a power of either 18W or 30W (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, this reduction in weight gain was associated with impaired lung function in mice exposed to 30W ECV and those exposed to CS compared to the control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Indeed, the PV-loop curve revealed a shift towards higher volumes for mice exposed to 30W ECV but not with 18W ECV, suggesting the development of some emphysema with the Modbox 30W.We also observed the same trend with CS-exposed mice although at a lower intensity than in 30W ECV-exposed mice. Based on PV-loop curves, we calculated the static compliance (Cst) and the area under the curve (AUC). The Cst tends to be increased both in 30W ECV and CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) whereas the AUC was only increased in CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eAfterwards, we analyzed the impact of ECV exposure on morphological lung structure. We observed an extensive lung remodeling mainly in alveolar walls and bronchial epithelium (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to control mice, extensive thickening of respiratory epithelium due to hyperplasia and folding was observed in both ECV- and CS-exposed mice. Atypias like loss of orientation, binucleation or meganucleation were also sporadically observed in 30W ECV-exposed mice. Inflammatory cell recruitment was also detected in both peribronchial areas and alveoli. Additionally, all samples of 30W ECV and CS-exposed mice displayed periarterial edema and alveolar emphysema. MLI measurement exhibited a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) difference between control mice and either 30W ECV- or CS-exposed mice but not with 18W ECV-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eb- Chronic exposure (6-month treatment)\u003c/h2\u003e \u003cp\u003eAfter 6 months of exposure, weight gain was substantially reduced in both ECV- and CS-exposed mice. Moreover, this effect tends to be greater in mice exposed to high power ECV compared to CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eLower weight gain was associated with an emphysematous-type alteration of lung function in mice which is more pronounced in 30W ECV-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This was confirmed by a significant increase in Cst, A and AUC in the 30W ECV-exposed mice. Surprisingly, CS-exposed mice exhibited a fibrotic-like alteration of the PV loop curve and no alteration of the K, A and AUC parameters after 6 months of exposure as compared with both air and ECV-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-K, respectively). The histopathologic analysis revealed an increase in emphysema in both 30W ECV- and CS-exposed mice and a not-significant effect in 18W ECV-exposed mice as compared to air-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) which were confirmed by MLI measurement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). We also observed extensive lung remodeling mainly in alveolar walls and bronchial epithelium, including alveolar wall thickening, hyperplasia of bronchial epithelium and some area of cell desquamation mainly in both 30W ECV- and CS-exposed mice. This was associated with some inflammatory cell infiltrates both in the peribronchial and in alveolar area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2- E-cigarette vapors induce pulmonary inflammation\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003ea- Subchronic exposure (3-month treatment)\u003c/h2\u003e \u003cp\u003eThe inflammatory cell infiltrate was analysed in both lung and spleen by flow cytometry. This study revealed that mice exposed to CS during 3 months exhibited a specific immune cell signature compared to ECV-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). CS-exposed mice presented reduced number of alveolar macrophages (AM) in lung tissue although these cells expressed higher levels of the activation markers CD86 and CMH class II molecules. Furthermore, we observed a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) recruitment of inflammatory monocytes as well as NK and NKT cells in the BAL of CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) while this treatment increased the number of conventional dendritic cells type 1 and 2 (cDC1 and cDC2), CD8\u003csup\u003e+\u003c/sup\u003e T Cells, CD4\u0026thinsp;+\u0026thinsp;T cells, inflammatory monocytes and neutrophils in lung tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and data not shown). This is not associated with a modulation of the expression of activation markers such as CD86 and I-Ab for monocytes and neutrophils, as well as CD25 and CD69 for lymphocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, ECVs have a limited impact on innate immune cells recruitment in the BAL since they decreased the number of NK and CD8\u003csup\u003e+\u003c/sup\u003e T cells whereas only 30W ECV induced a statistically higher expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of I-Ab on inflammatory monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In lung tissue, only 18W ECV induced the recruitment of NK cells, CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003e T cells while exposure to 30W ECV increased the expression of CD25 on CD4\u003csup\u003e+\u003c/sup\u003e T cells .\u003c/p\u003e \u003cp\u003eIn order to decipher the mechanism of the inflammatory cell recruitment, we measured the levels of cytokines in the BAL and lungs of exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Exposure to 30W ECV for 3 months was associated with higher levels of IL-22 and the chemokines CXCL1, CXCL2 and in BAL and a trend for CXCL17 concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In mice exposed to CS, we detected higher concentrations of IL-17 and a decrease in Il-1β in the lungs compared with control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Meanwhile no effect was observed on Il-17 levels after exposure to ECV in either lung tissue or BAL in contrast with CS-exposed mice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we further assessed the ability of immune cells to produce Th1 and Th17 cytokines with and without \u003cem\u003ein vitro\u003c/em\u003e CD3 activation. Surprisingly, lung cells from CS-exposed mice produced significantly less IL-22 than those of control mice. In contrast, exposure to 30W ECV increased the levels of IL-22 by unstimulated cells but not after anti-CD3 activation while there is no change with 18W ECV.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eb- Chronic exposure (6-month treatment)\u003c/h2\u003e \u003cp\u003eAfter 6 months, mice exposed to ECV exhibited a significant decrease in the number of NK and CD4\u003csup\u003e+\u003c/sup\u003e T cells in BAL, as well as a trend to lower number of AMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Nevertheless, this was associated with a significant increase in activated AM and inflammatory monocytes in both ECV- and CS- exposed mice. In lung tissue, we observed a statistically significant recruitment of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in all exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Only exposure to 30W ECV induced significant NK recruitment in lungs with a tend to recruit more NKT and AM. This was associated with higher expression of the activation marker I-Ab in AM and inflammatory monocytes in both the BAL and the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). The same trend was observed in AM and inflammatory monocytes from CS-exposed mice whereas the expression of activation markers was not modulated in conventional T lymphocytes from these mice. Moreover, higher expression of CD25 in NKT cells in the lung of 30W ECV-exposed mice but not in 18W ECV-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Interestingly, both exposure to 30W ECV and CS significantly reduce the number of cDC1 in lungs (Supplementary figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the secretion of inflammatory mediators, our data did not reveal an overproduction of cytokines in BAL and lungs from 30W ECV- and CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). In 18W ECV-exposed mice, only CXCL17 were significantly induced in the BAL and lungs as compared with controls. In contrast, we observed lower levels of IL-1β and CXCL1 in lungs for all emissions.\u003c/p\u003e \u003cp\u003eNo significant alteration of the Th1 and Th17 cytokine secretion by lung cells was reported in mice exposed to ECV or CS.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eE-cigarette vapors induce systemic inflammation.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo assess the systemic impact of ECV, we examined the number and phenotype of immune cells in the spleen, as well as their ability to produce Th1 and Th17 cytokines, and cytokine concentrations within the blood.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ea- Subchronic exposure (3-month treatment)\u003c/h2\u003e \u003cp\u003eThe total cell number of spleen cells are similar among all the groups (58\u0026thinsp;\u0026plusmn;\u0026thinsp;6, 35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4, 37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3, and 49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 x10\u003csup\u003e6\u003c/sup\u003e cells for Air, 18W ECV, 30W ECV and CS, respectively). Exposure to 18 and 30 W ECV was responsible for a decrease in the number of antigen-presenting cells (DC and macrophages) and lymphocytes (CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells) in the spleen of ECV-exposed animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This was associated with an increased expression in CD25 in CD8\u003csup\u003e+\u003c/sup\u003e T cells in mice exposed to 30W ECV (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the spleen of CS-exposed mice was characterized by a statistically significant recruitment of iNKT and a lower number of neutrophils, as well as a trend towards a decreased number of DC. Despite the lack of effect on their numbers, a higher expression of the activation marker I-Ab was observed in macrophages as well as CD25 in CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T Cells after CS exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eInterestingly, exposure to ECV tended to increase the levels of CXCL2 and TNF-α in blood, whereas the 30W ECV-exposed mice had lower concentrations of CXCL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In addition, unstimulated as well as anti-CD3 stimulated spleen cells from 30W ECV- and CS-exposed mice produced significantly more IL-22 than spleen cells from controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eb- Chronic exposure (6-month treatment)\u003c/h2\u003e \u003cp\u003eAfter 6 months, ECV-exposed mice presented a statistically significant recruitment of NKT cells in the spleen, regardless of e-cig power. Moreover, only ECV of 18W Modbox increased the recruitment of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). No impact was noticed on cell activation.\u003c/p\u003e \u003cp\u003eSurprisingly ECV and CS exposures induced a drop in the neutrophil number in the spleen. This was associated with a non-significant increase in macrophage number. No effect was observed on the number and expression of activation markers by other immune cells in the spleen. Regarding the ability of spleen cells to produce cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), IL-22 production consecutive to anti-CD3 stimulation was enhanced in mice exposed to 18W ECV whereas IL-17 and IFN-γ production was unchanged.\u003c/p\u003e \u003cp\u003eSurprisingly, serum levels of IL-6, IL-22, TNF-α, CXCL1 and CXCL2 were lower in ECV- and CS-exposed mice whereas there is no change for TNF-α in CS-exposed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Only, CS-exposed mice exhibited increased concentrations of resistin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eWe have also analyzed the cotinine levels in order to assess the level of systemic exposure of mice to CS and ECV, as well as to estimate the potential involvement of nicotine in the modulation of the inflammatory reaction. Our data revealed that the cotinine levels are higher in mice exposed to CS and ECV than in mice exposed to air (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). However, there is a trend for higher levels of cotinine in CS-exposed mice as compared with 18W and at a lesser degree 30W ECV-exposed mice at both time courses, although the differences are not statistically significant.\u003c/p\u003e \u003cp\u003eIn summary, exposure to ECV has an ambivalent effect on systemic inflammation, with a drop in blood cytokine concentration, and, at the opposite, some activation and enhanced capacity to produce IL-22 in splenic T lymphocytes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4- Impact of e-cigarette on lung transcriptome\u003c/h2\u003e \u003cp\u003eIn order to determine and compare the signaling pathways deregulated by exposure to ECV or CS, lung mRNA expression profiles were characterized by pangenomic microarrays in mice exposed to emissions for 3 or 6 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ea- Subchronic exposure (3-month treatment)\u003c/h2\u003e \u003cp\u003eExposure to CS for 3 months induced the deregulation of 248 genes (186 up- and 62 down-regulated). Surprisingly, the number of deregulated genes was much higher in 18W ECV-exposed mice (197 with 121 up- and 76 down-regulated) compared to those exposed to 30W ECV (72 with 50 up- and 22 down-regulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The fold changes and the adjusted p values were reported in the supplemental Excel table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, 25 genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were deregulated by exposure to the three types of emissions, including genes involved in cancer development (increase in Pla1a expression and down-regulation of the tumour suppressor genes Cdkn2a and Ppp2r2c) and cell metabolism (\u003cem\u003ee.g\u003c/em\u003e. increased levels in Fmo3). Moreover, the expression of 16 genes were specifically altered by both 18W and 30W ECV (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), some of which are involved in the control of oxidative stress (Hmox1, Slc7a11) and cell proliferation (Fosl1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenes commonly deregulated by 3 month subchronic exposure to ECV18W, ECV30W and CS.\u003c/b\u003e Fold changes are calculated in comparison with Air mice. The genes in bold are those deregulated both after 3 and 6 month exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eECV18W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eECV30W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecalcitonin/calcitonin-related polypeptide. alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCckar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echolecystokinin A receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCdkn2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecyclin dependent kinase inhibitor 2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCelf3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUGBP. Elav-like family member 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChac1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChaC. cation transport regulator 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-3.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFam167b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efamily with sequence similarity 167. member B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFmo3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eflavin containing monooxygenase 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGbp11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eguanylate binding protein 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGbp4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eguanylate binding protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGdf15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003egrowth differentiation factor 15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-3.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGm26641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epredicted gene. 26641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGm4610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epredicted gene 4610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1fx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1 histone family. member X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlhl38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekelch-like 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcpt8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emast cell protease 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNupr1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003enuclear protein transcription regulator 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-2.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePla1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephospholipase A1 member A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePpp2r2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein phosphatase 2. regulatory subunit B. gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScn3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esodium channel. voltage-gated. type III. beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenbp2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eselenium binding protein 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlc38a4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esolute carrier family 38. member 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyt15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esynaptotagmin XV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTcf23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etranscription factor 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrib3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003etribbles pseudokinase 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-2.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the pathways affected by CS treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC) revealed that most of the deregulated pathways are involved in the metabolism of xenobiotics (in particular the biotransformation of PAHs with increased expression of Cyp1a1 and Cyp1b1) and oxidative stress (increased levels of Nqo1, Gsta3, Gsto1). Pathways deregulated by 18W ECV are mainly related to inflammation linked to IL-17 signaling (decreased expression of Ccl2 and Il6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), oxidative stress (deregulation of Abcc1, Chac1, Gclc, Hmox1 and Myc) and lipid metabolism (Pla1a, Pmvk, Plcb1, Plcb4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eb- Chronic exposure (6-month treatment)\u003c/h2\u003e \u003cp\u003eWhile we found an increase in the number of deregulated genes after 6 months of exposure to 18W ECV (431 with 388 up- and 43 down-regulated), the opposite was observed with ECV 30W, which altered the expression of only 40 genes (12 up- and 28 down-regulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The fold changes and the adjusted p values were reported in the supplementary Excel table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Thirty-four of them were commonly deregulated by the vapors of the 2 wattages.\u003c/p\u003e \u003cp\u003eCS exposures changed the expression of 615 genes (336 up- and 279 down-regulated), including 398 in a specific manner. Furthermore, 216 genes were deregulated in common with 18W ECV.\u003c/p\u003e \u003cp\u003eOverall, 23 genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were deregulated by exposure to the three types of emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), including genes involved in inflammatory response (decreased expression of Il6 and Tnip3) and in cancer development (down-regulation of Fosl1 and Sfn and up-regulation of Plk5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenes commonly deregulated by 6 month chronic exposure to ECV18W, ECV30W and CS.\u003c/b\u003e Fold changes are calculated in comparison with Air mice. The genes in bold are those deregulated both after 3 and 6 month exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eECV18W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eECV30W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFold-Change\u003c/p\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdm2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadrenomedullin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChac1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChaC. cation transport regulator 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-2.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiras2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIRAS family. GTP-binding RAS-like 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDpp6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edipeptidylpeptidase 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFam161a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efamily with sequence similarity 161. member A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFmo3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eflavin containing monooxygenase 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFosl1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efos-like antigen 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalnt5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epolypeptide N-acetylgalactosaminyltransferase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGdf15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003egrowth differentiation factor 15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-3.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGjb2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egap junction protein. beta 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIl6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einterleukin 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItprid1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITPR interacting domain containing 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLrrc26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eleucine rich repeat containing 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMab21l3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emab-21-like 3 (C. elegans)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMgat3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emannoside acetylglucosaminyltransferase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNupr1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003enuclear protein transcription regulator 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-2.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-4.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhgdh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-phosphoglycerate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlk5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epolo like kinase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRptoros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulatory associated protein of MTOR. complex 1. opposite strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSfn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estratifin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTnip3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNFAIP3 interacting protein 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrib3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003etribbles pseudokinase 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-1.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter 6 months of exposure, the main pathways affected by CS were related to the inflammatory response (e.g. decreased levels of Il6, Il1b and Mmp9) and xenobiotic metabolism (e.g. modulation of Ahrr, Cyp1a1, Cyp1b1 and Gsta3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). The 18W ECV mostly affected nucleotide biosynthesis, metabolism of xenobiotics (Aldh18a1, Aldh1l2 et Aldh3a1) and inflammation (Ccl20, Cxcl6, Il6, Muc5ac) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAltogether, the transcriptomic data showed that mRNA profiles are very different between 3 and 6 months of exposure, for both 18W ECV and CS. Moreover, the impact of 30W ECV on the lung transcriptome appeared to be much lower than that of 18W ECV. We failed to clearly identify signalling pathways altered by 30W ECV due to the low number of modulated genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of electronic nicotine delivery systems, commonly known as e-cig, has gained significant in popularity as an alternative to conventional cigarette or even as a trend among adolescents. While the well-established health risks associated with CS exposure are widely acknowledged, the potential long-term effects of e-cig usage have only recently begun to receive attention. In this study, we aim to investigate the impact of moderate (1h/day) and chronic exposure to e-cig set at 2 different wattages (\u003cem\u003ei.e\u003c/em\u003e. 18W or 30W) in comparison to CS on a range of major physiological parameters. Interestingly, exposure to ECV induced an alteration of lung function but this effect was not related to a chronic lung inflammation as revealed by the data obtained with 30W ECV. Moreover, the impact of ECV on transcriptome, inflammation and lesions within the lung are mostly specific compared to the effect of CS.\u003c/p\u003e \u003cp\u003eExposure to ECV has some major impact on physiological mechanisms including host metabolism and respiratory function although this latter is restricted to 30W ECV. The impact on host metabolism was evidenced by a lower weight gain in mice exposed to ECV. This effect is similar with both low and high power e-cig and does not seem to be related to blood nicotine levels (Supplementary figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Furthermore, the impact of CS on weight gain was similar to that of ECV, although blood cotinine concentrations were higher in these mice. We also observed a strong effect of 30W ECV on lung function, with a shift in the PV-loop curve towards an emphysematous phenotype associated with a major impact on static compliance and the area of the curve. The presence of emphysema was confirmed by a significant increase in MLI in 30W ECV-exposed mice, close to the effect observed in CS-exposed mice. However, our protocol of CS exposure has a very different effect on the PV-loop curve with no significant effect on compliance and area under the curve. A previous study has reported a similar impact of ECV on lung function with emphysema and alteration of static compliance, an effect that has been shown to be nicotine-dependent [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In parallel, we also reported on the same mice that the ECV exposure induced important disturbances of proliferation, inflammation, and permeability in both ileum and colon, which reveals that ECV-induced lesions are not restricted to respiratory mucosa [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo determine whether a chronic inflammatory response could be responsible for the altered lung function observed in mice exposed to ECV, we assessed pulmonary and systemic inflammation in these mice by measuring leukocyte recruitment and activation, as well as cytokine concentrations. In contrast to a previous study using an intense ECV exposure (5-6h/day) protocol [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], we did not detect any effect on neutrophil recruitment within the lung after ECV exposure; the number of these granulocytes was even reduced within the spleen. Nevertheless, we observed a transient increase of neutrophil-active chemokine secretion which suggests that neutrophil recruitment requires persistent chemokine production not detectable after chronic exposure to moderate doses of ECV, such as those used in the present study. However, our protocol of exposure using Modbox 30W was associated with higher activation of antigen-presenting cells such as AM and inflammatory monocytes in the BAL and lung tissue although their numbers were similar. This stimulation was associated with the mobilization of NK cells and T lymphocytes within lung tissue even if activation of these cells could not be observed. An activation of CD4\u003csup\u003e+\u003c/sup\u003e T lymphocytes was only detected in 30W ECV- and CS-exposed mice. Altogether, these data showed that exposure to e-cig induced a specific profile of cell recruitment and activation as compared to CS. Moreover, no clear impact was detected on neutrophil count and phenotype in contrast to CS.\u003c/p\u003e \u003cp\u003eRegarding cytokine production, we observed some upregulation of neutrophil-chemotactic cytokines such as CXCL1, CXCL2 and CXCL17 in the BAL of mice exposed to 30W ECV for 3 months. This transient upregulation was not associated with neutrophil recruitment, probably because chemokine concentrations are too low, or because of a defect in structural cell activation required for leucocyte diapedesis. While IL-17 levels are unaffected by ECV exposure, IL-22 concentrations are higher in the BAL and in the supernatant of unstimulated lung cells from 30W ECV-exposed mice. Although IL-17 has been identified as a key player in the development of CS-induced COPD, IL-22 production seems to be preferentially targeted by ECV. In the present study, the role of IL-22 in the lung injury remains to be determined although some reports show that IL-22 can induce airway remodeling and can also participate in COPD development [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Interestingly, spleen cells from ECV-exposed mice produced higher levels of IL-22 after anti-CD3 stimulation. Exposure to ECV seems to induce a mobilization of both antigen presenting cells and T lymphocyte in the spleen at 3 months, followed by a replenishment of NKT cells and T cells at 6 months. Some T cell activation was also detected at 3 months of exposure, as evidenced by modulation of CD25 expression and enhanced capacity to produce IL-22. This suggests that the effect of ECV is not restricted to the lung and could induce some recirculation of immune cells towards the lung and/or intestinal mucosa although we did not observe a cytokine response in the blood.\u003c/p\u003e \u003cp\u003eIn parallel with the modulation of the immune response, ECV exposure affects clinical parameters including body weight and lung function with some similarities to the impact of CS. Previous data confirm this impact of ECV in mice exposed in adulthood [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] but also in mice exposed \u003cem\u003ein utero\u003c/em\u003e, where both lung dysfunction and structural impairments persist into adulthood. Although the effects observed with our protocol using moderate doses of ECV appear to be close to those following exposure to high concentrations of ECV, the mechanisms underlying these effects are probably different. At high concentrations, ECV induced the recruitment of inflammatory cells such as neutrophils, and the upregulation of metalloproteinases such as MMP9 and MMP12 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At lower concentrations, we reported that chronic exposure to ECV did not promote persistent inflammation and MMP expression whereas this treatment enhances the expression of IL-22, an essential cytokine for the maintenance of the epithelium and remodeling of mucosa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTranscriptomic analyses were carried out to identify the signalling pathways affected by exposure to ECV and attempt to characterize the mechanisms involved in the observed effects of these emissions. The pathways deregulated by CS are deregulated more significantly than by ECV. Unsurprisingly, following exposure to CS, the biological functions most affected are the metabolism of xenobiotics and the inflammatory response. In mice exposed to CS, deregulated genes contribute mainly to the AhR pathway, which is characteristic of a response to exposure to PHAs such as those found in CS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The overexpressed genes code in particular for the cytochromes CYP1A1 and CYP1B1, enzymes that may be involved in the generation of highly reactive intermediate metabolites capable of inducing DNA damage. In contrast, exposure to ECV did not induce gene expression of these two cytochromes, but rather modulated gene expression of aldehyde dehydrogenases (Aldh18a1, Aldh1l2 et Aldh3a1) possibly involved in the metabolism of carbonyl compounds, and most likely in the biotransformation of propylene glycol, one of the main compounds in e-liquids. Chronic exposure to ECV also altered the expression of gene involved in oxidative stress, a mechanism which can explained the oxidative DNA damage in the lung that we previously reported in this experimental model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Notably, the absence of activation of the AhR pathway by ECV is consistent with the very low levels of PAHs measured in these emissions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The involvement of major genes of LXR/RXR pathway, which are also deregulated following exposure to CS and ECV, has already been shown in the exacerbation of macrophage-mediated lung inflammation in cases of CS exposure [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, our data show that the number of deregulated genes was greater after exposure to 18W ECV than 30W ECV. These results are surprising given the lower quantity of toxic compounds measured in 18W ECV compared to 30W ECV. Indeed, we had previously shown that the 18W ECV generated levels of carbonyl compounds that are around 2 times lower than for the ECV 30W [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This difference in the transcriptomic impact of ECV could be explained by an adaptive mechanism at the higher e-cig power and by the kinetics of the effects, which could differ according to the power of the e-cig. After a short-term exposure (one week), we observed a dose-dependent effect of ECV on transcriptome with more deregulations at the highest e-cig power (data not shown).\u003c/p\u003e \u003cp\u003eIn ECV-exposed mice, transcriptomic data revealed deregulation of genes involved in lipid metabolism (Pla1a, Pmvk, Plcb1, Plcb4). This effect could explain the altered metabolism and weight gain in exposed mice, and could also contribute to the observed impairment of lung function in these mice. These genes have already been described as being affected in regular e-cig users, which could reflect the excessive lipid load provided by the glycerol contained in e-liquids. This excessive load could then disrupt surfactant secretion in the lungs and impact lung function [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAltogether, our data showed that long term exposure to moderate levels of ECV (1 h / day) can induce some alteration of lung function and host metabolism whereas only limited effect on the inflammatory reaction can be exhibited. Il-22 upregulation is associated with these changes suggesting a physiopathologic role for this cytokine. Our transcriptomic analyses highlighted certain signalling pathways common to exposure to CS and ECV (although the number of genes involved is much lower with ECV), in particular the Th17 pathway involved in the development of chronic inflammation and the progression of COPD. Exposure of C57BL/6 mice to CS for chronic exposure had already demonstrated this increase in Th17 signaling pathway [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the alteration of IL-22 production had never been demonstrated after exposure to ECV. Overall, the impact on the transcriptome of ECV revealed some alteration in the expression of genes involved in inflammation, oxidative stress, and metabolism of lipids and xenobiotics. These data about ECV associated with its genotoxic effect the lesions in the intestine [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] confirm the urgent need of information in the general population about regular use of e-cigarette and the necessity to complete by studying the consequences of e-cigarette smoking in the general population even at moderate levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article are available within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe acknowledge Dr Martin Figeac from the Functional and Structural Platform (CHU Lille, France) for transcriptomic analyses and Dr OLivier Molendi-Coste and H\u0026eacute;l\u0026egrave;ne Bauderlique from the Bicell platform (\u003c/strong\u003eUniversit\u0026eacute; de Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41\u0026mdash;UAR 2014\u0026mdash;PLBS, 59000 \u0026nbsp;Lille, France\u003cstrong\u003e) for the flow cytometry analysis. We also thanks the NIH facility for providing murine iNKT tetramer.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no conflicts of interest related to this work to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work benefited grant from the IRESP (Institut de recherche en Sant\u0026eacute; Publique) and the French National Cancer Institute (INCa): Contract No. INCa_11505.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve human participants.\u003c/p\u003e\n\u003cp\u003eAnimal procedures were in agreement with European directive 2010/63/EU for the protection of animals used for scientific purposes and obtained the Ethical Committee on Animal Experimentation of the Hauts de France (CEEA 75) approval (ref APAFIS 10363-2017062615002072v2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor notes :\u0026nbsp;\u003c/strong\u003eNothing to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eUniv. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019-UMR 9017, Center for Infection and Immunity of Lille, Lille, France.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eUniv. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS - IMPact de l\u0026rsquo;Environnement Chimique sur la Sant\u0026eacute;, F-59000, Lille, France\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eUniv. Lille, CNRS, Inserm, CHU Lille, UMR9020 - UMR1277 - Canther - Cancer Heterogeneity, Plasticity and Resistance to Therapies, 59000 Lille, France\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, PG, JMLG, SA.; methodology, LM, AO, JMLG, SA, JK, AP, MP and PG.; formal analysis, LM, AO, RD, JMLG, SA, PG.; investigation, LM, AO, RD, GK, JMLG, SA, JK, AP, MP and PG.; writing-original draft, LM, AO, RD, JMLG, SA, PG.; writing-review and editing, all authors.; supervision, PG and SA.; funding acquisition, JMLG, SA, JK, AP, MP and PG. All authors have read and agreed to the published version of the manuscript.\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGotts JE, Jordt SE, McConnell R, Tarran R: \u003cstrong\u003eWhat are the respiratory effects of e-cigarettes?\u003c/strong\u003e \u003cem\u003eBmj \u003c/em\u003e2019, \u003cstrong\u003e366:\u003c/strong\u003el5275.\u003c/li\u003e\n\u003cli\u003eBeauval N, Verri\u0026egrave;le M, Garat A, Fronval I, Dusautoir R, Anth\u0026eacute;rieu S, Gar\u0026ccedil;on G, Lo-Guidice JM, Allorge D, Locoge N: \u003cstrong\u003eInfluence of puffing conditions on the carbonyl composition of e-cigarette aerosols.\u003c/strong\u003e \u003cem\u003eInt J Hyg Environ Health \u003c/em\u003e2019, \u003cstrong\u003e222:\u003c/strong\u003e136-146.\u003c/li\u003e\n\u003cli\u003eDusautoir R, Zarcone G, Verriele M, Gar\u0026ccedil;on G, Fronval I, Beauval N, Allorge D, Riffault V, Locoge N, Lo-Guidice JM, 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MC, Landers CT, Gu BH, Chang CY, Tung HY, You R, Hong MJ, Baghaei N, Song LZ, Porter P, et al: \u003cstrong\u003eElectronic cigarettes disrupt lung lipid homeostasis and innate immunity independent of nicotine.\u003c/strong\u003e \u003cem\u003eJ Clin Invest \u003c/em\u003e2019, \u003cstrong\u003e129:\u003c/strong\u003e4290-4304.\u003c/li\u003e\n\u003cli\u003eHarrison OJ, Foley J, Bolognese BJ, Long E, 3rd, Podolin PL, Walsh PT: \u003cstrong\u003eAirway infiltration of CD4+ CCR6+ Th17 type cells associated with chronic cigarette smoke induced airspace enlargement.\u003c/strong\u003e \u003cem\u003eImmunol Lett \u003c/em\u003e2008, \u003cstrong\u003e121:\u003c/strong\u003e13-21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4926091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4926091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMost smokers consider that electronic cigarettes are safer than tobacco and are marketed as safe products. Nevertheless, recent reports show the exposure to high levels of electronic cigarette vapors (ECV) activates lung cells and triggers lung inflammation and structural alterations after chronic exposure. In order to assess the potential harmful effect of moderate exposure to ECV, we investigated in mice, its effect on lung and systemic inflammation and on lung structure and function.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo reproduce closely the situation frequently encountered in human, we exposed mice during 1h/day during 3 or 6 months with two levels of electronic cigarette power in comparison with mice exposed to cigarette smoke (CS). Lung and systemic inflammation was evaluated by measuring cell recruitment and activation as well as cytokine concentrations. Lung transcriptome, respiratory function and body weight were also measured.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur data revealed that chronic exposure to moderate levels of ECV increased specifically lung inflammation including NK cells and T lymphocyte recruitment and the production of CXCL1 and CXCL2 chemokines as well as IL-22 after 3 months, these effects being different from the profile induced by CS. Surprisingly, there is no strong overlap between the impact of the 3 types of emissions on lung transcriptome. Modulation of pro-inflammatory pathways are limited to mice exposed to e-cig set to low power. In contrast, alteration of respiratory function is observed in high-power ECV-exposed mice but not at low power, with a different profile than in CS-exposed mice.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSubchronic (or mid-term) exposure to ECV might alter the respiratory function independently of the inflammatory response and in a different manner than CS.\u003c/p\u003e","manuscriptTitle":"Vaping versus Smoking: A Quest for Long-term impact in a mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 19:00:15","doi":"10.21203/rs.3.rs-4926091/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a509dad-b11b-457b-930a-96bf63d1be56","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T17:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 19:00:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4926091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4926091","identity":"rs-4926091","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}