The trend of e-cigarette use among teens is ever increasing. Here we show the dysbiotic oral microbial ecology in e-cigarette users influencing the local host immune environment compared with non-smoker controls and cigarette smokers. Using 16S rRNA high-throughput sequencing, we evaluated 119 human participants, 40 in each of the three cohorts, and found significantly altered beta-diversity in e-cigarette users (p = 0.006) when compared with never smokers or tobacco cigarette smokers. The abundance of Porphyromonas and Veillonella (p = 0.008) was higher among vapers. Interleukin (IL)-6 and IL-1β were highly elevated in e-cigarette users when compared with non-users. Epithelial cell-exposed e-cigarette aerosols were more susceptible for infection. In vitro infection model of premalignant Leuk-1 and malignant cell lines exposed to e-cigarette aerosol and challenged by Porphyromonas gingivalis and Fusobacterium nucleatum resulted in elevated inflammatory response. Our findings for the first time demonstrate that e-cigarette users are more prone to infection.

Here, we report the in vivo effects of e-cigarette aerosol and its influence on human salivary microbiome and immune health. Furthermore, we evaluate in vitro the influence of e-cigarette aerosols on infection efficiency of oral pathogens in pre-cancerous and cancer cell lines using a novel e-cigarette aerosol-generating machine and pro-inflammatory immune mediators.

The lucrative e-cigarette vaping delivery systems, available in a variety of flavors and concentrations, can pose a substantial threat to human health ().have shown that e-liquids induce inflammatory responses and alter innate immune defenses in primary airway epithelial cells. The findings ofindicated that exposure of C57BL/6 mice to e-cigarette aerosols for 2 weeks can cause impairment of pulmonary bacterial and viral clearance. Accordingly, in vitro studies can be extrapolated to recognize responses by electronic nicotine delivery system users, however, without the clinical evidence of smoking history (). So, to understand the comprehensive health effects of vaping it is essential to conduct clinical research in conjunction with in vitro e-cigarette aerosol-exposed model. This will further help us to associate and understand the impact of vaping on human health by gauging the positive and negative controls.

The oral cavity is a gateway and a permanent or makeshift harbor for numerous microbial species to colonize the respiratory and gastrointestinal (GI) tracts (). The multifactorial pathogenicity of chronic periodontal disease involves a complex interaction of microbial pathogens, host immune response, and genetic and environmental factors, stimulating the host tissue destruction and bone loss (). Certain oral bacteria, Porphyromonas gingivalis and Fusobacterium nucleatum, are major perpetrators in periodontal destruction, highly associated with disease progression (). The release of host immune mediators as a primary response to these opportunistic pathogens (pathobionts) and its metabolites favors the disease state. The dysbiosis in microbial communities due to impaired homeostasis as a result of environmental and host metabolic factors can contribute to oral diseases, including dental caries, periodontitis, halitosis, or other medical ailments, such as diabetes, cardiovascular diseases, and cancers (). Elaborate studies have shown the toxicity of conventional tobacco product consumption to periodontal health (), albeit with very limited understanding on e-cigarette's effect (). The e-cigarette aerosol interaction with the host ensues largely in the oral cavity and lungs, where the exposure to nicotine and toxic metabolites of the e-liquid components is the highest and can escalate the host susceptibility to infections (). Recent reports associate e-cigarette vaping with vascular endothelial dysfunction causing blood-brain barrier damage with higher risk of cerebrovascular diseases (). Hence, it is imperative to elucidate the detrimental effect of e-liquids on the host inflammatory responses, which is highly challenging and conflicting.

Unlike traditional cigarettes with tobacco filling, which approximates 24 mg of nicotine per pack (1.2 mg/cigarette), e-cigarette liquid contains nicotine () that varies between 6 and 48 mg/mL () and is not meant to be smoked completely in one sitting (). Each nicotine cartridge in an e-cigarette can provide on an average 200 puffs equivalent to one to three packs of cigarettes. The nicotine absorption in the body from e-cigarette use depends on the nicotine concentration in the e-liquid, its aerosol mass quantity and deposition, the chemical form of the nicotine, as well as the vaping volume, frequency, and timing (). As per a 2014 report, the US FDA detected in one of the cartridges the presence of diethylene glycol, a toxic liquid used in explosives and as an antifreeze agent, in addition to, cancer-causing agents, such as tobacco-specific nitrosamines, aldehydes, metals, volatile organic compounds, phenolic compounds, polycyclic aromatic hydrocarbons, flavors, solvent carriers, tobacco alkaloids, and drugs (amino tadalafil and rimonabant) (). Moreover, recent studies showed that nicotine delivery and emission of some toxicant levels from the latest generation of e-cigarettes were comparable to those from tobacco smoke ().

Electronic cigarettes (e-cigarettes), non-combustible battery-operated devices, are considered to be a safe and healthier alternative to conventional combustible cigarette smoke (). However, according to the Centers for Disease Control and Prevention (CDC), the US Food and Drug Administration (FDA), and the National Cancer Institute analysis of the 2011–2018 National Youth Tobacco Surveys data, to estimate tobacco product use in US middle and high school students, it was reported that in 2018, the current use of any tobacco product was 27.1% in high school students (4.04 million) and 7.2% in middle school students (840,000); e-cigarettes were the most commonly used product among high school (20.8%; 3.05 million) and middle school (4.9%; 570,000) students (). Interestingly, e-cigarette vaping swelled by 77.8% (from 11.7% to 20.8%) among high school students and 48.5% (from 3.3% to 4.9%) among middle school students in the year 2017–2018 (). These figures are of great concern due to the mounting popularity of e-cigarette usage among teenagers having never smoked any combustible products.

To affirm that the e-cigarette aerosol influenced the rate of microbial infection in vitro, FaDu cells were co-cultured with either P. gingivalis or F. nucleatum pre-labeled with fluorescein isothiocyanate and then exposed to air or e-cigarette aerosol, and the infected FaDu cell population was evaluated by flow cytometry. It was observed that P. gingivalis and F. nucleatum infection in Fadu cells with e-cigarette aerosol contact increased significantly (p < 0.05) by about 65% and 16%, respectively, compared with air only ( Figures S5 A and S5B). Furthermore, FaDu cells were also infected with E.coli GFP in a 1:50 ratio after treating with either air or e-cigarette aerosol. Infection efficiency determined by GFP expression with flow cytometry showed a significant elevation (p < 0.048) of almost 21% in FaDu cells exposed to e-cigarette aerosol than in those exposed only to air ( Figure S5 C).

Our in vitro results clearly indicated that e-cigarette aerosols altered the cytokine concentration at mRNA and protein levels in the presence of periodontal pathogens, as was also confirmed with E. coli GFP.

In addition, when another cell line Leuk-1 was tested, up-regulated IL-8 level protein upon e-cigarette aerosol exposure was observed. Conversely, co-infection of Leuk-1 with P. gingivalis did not show a similar effect, where TNF-α levels were not altered ( Figure S4 A). However, infection with F. nucleatum showed higher protein concentration of both TNF-α and IL-8 in e-cigarette aerosol-exposed cells ( Figure S4 B). Leuk-1 cells co-infected with E.coli GFP in the presence of e-cigarette aerosols significantly up-regulated IL-8 protein. However, the TNF-α protein levels could not be detected with ELISA due to the very low levels of expression ( Figure S4 C).

Fadu cells infected with P. gingivalis, showed significant up-regulation of IL-8 protein in the presence of e-cigarette aerosol when compared with air exposure. However, TNF-α protein levels did not significantly change in the cells infected with P. gingivalis and F. nucleatum ( Figures 6 A and 6B ). On the contrary, under similar conditions with F. nucleatum, IL-8 and TNF-α protein levels were differentially up-regulated when compared with air only. However, these protein cytokine levels were not in the detectable range with E. coli GFP (data not shown).

Cytokine protein levels were up-regulated with e-cigarette aerosol exposure: (A) IL-8 only with P. gingivalis and (B) TNF-α only with F. nucleatum. Data are represented as mean ± SEM. (*p < 0.05, ***p < 0.001, ****p < 0.0001).

The findings suggested augmented cytokine mRNA expression in Fadu as well as Leuk-1 cells upon e-cigarette aerosol exposure, and even more so when co-infected with bacteria, indicating increased susceptibility to infection in cells when exposed to e-cigarette aerosols.

Furthermore, we examined whether a similar influence on cytokines is observed when Fadu cells were infected with the classical microbe, E. coli GFP ( Figure 5 C). Upon co-infection with this bacteria, the e-cigarette aerosol-exposed Fadu cells exhibited significantly higher mRNA levels of IL-1β and IFN-γ in contrast to air-exposed bacterial co-infected cells. In parallel, the IL-8 mRNA expression was up-regulated significantly in Fadu cells exposed to e-cigarette aerosol and E. coli GFP when compared with cells exposed singly to either air or e-cigarette. TNF-α, IL-8, IFN-γ, IL-1β, and IL-6 mRNA expression was also measured in Leuk-1 cells in the presence of E. coli GFP, and e-cigarette aerosols caused a significant rise in cytokine levels ( Figure S3 C).

Fadu and Leuk-1 cells were exposed to e-cigarette aerosol or air for 40 min followed by infection with P. gingivalis and F. nucleatum for 2 h ( Figures 5 A and 5B ). Initially, we exposed malignant Fadu cells to either air or e-cigarette aerosols followed by infection with each of these bacteria. We quantified the mRNA levels of the cytokines, IL-1β, TNF-α, IFN-γ, IL-6, and IL-8 by qPCR. A significant rise in all the five cytokine mRNA levels was detected in e-cigarette aerosol-exposed Fadu cells co-infected with P. gingivalis when compared with those exposed to only air co-infected with same bacteria. Intriguingly, TNF-α showed almost a 30-fold increase in expression when compared with the cells exposed only to air. Similarly, higher mRNA levels of cytokines were observed when e-cigarette aerosol-exposed Fadu cells were co-infected with F. nucleatum. The expansion in mRNA expression of IFN-γ was highly significant, followed by IL-6, IL-8, and IL-1β, and moderate for TNF-α upon e-cigarette aerosol exposure for Fadu cells. We further assessed the mRNA levels of TNF-α and IL-8 in the Leuk-1 cell line infected with P. gingivalis and F. nucleatum and observed results similar to that with Fadu cell line ( Figures S3 A and S3B). Significantly high mRNA expression of IL-8 was observed in e-cigarette aerosol-exposed cells with both bacteria.

(A–C) Significant increase in expression of all cytokine mRNAs was observed with (A) P. gingivalis (B) F. nucleatum, and (C) E. coli GFP. Cells exposed to only air or e-cigarette aerosol were used as controls. Data are represented as mean ± SEM. (*p < 0.05, **p < 0.01, ***p < 0.001).

mRNA Expression Levels of Various Cytokines, TNF-α, IL-8, IFN-γ, IL-1β, and IL-6 in Fadu Cells in the Presence of Bacteria and e-Cigarette Aerosols as Determined by qPCR.

Figure 5 mRNA Expression Levels of Various Cytokines, TNF-α, IL-8, IFN-γ, IL-1β, and IL-6 in Fadu Cells in the Presence of Bacteria and e-Cigarette Aerosols as Determined by qPCR.

To confirm and expand the relevance of the aforementioned in vivo findings, we next performed in vitro studies using the premalignant (Leuk-1) and malignant (Fadu) cell lines exposed to e-cigarette aerosols. We evaluated the altered pro-inflammatory cytokine response at mRNA and protein levels when co-cultured with the periodontal pathogens, P. gingivalis and F. nucleatum, the taxa observed to be expanded in vivo in the saliva of ES, and with E. coli GFP, one of the most established models of infection.

To explore the salivary inflammatory markers in all participants, 39 in NS cohort and 40 in each of ES and CS cohorts, we analyzed 10 different cytokines using a human multiplex immune assay pro-inflammatory panel. Salivary interleukin (IL)-6 and IL-1β were elevated, although not significant in ES when compared with NS and CS ( Figure 4 A). Significant reduction in levels of interferon (IFN)-γ and IL-4 were observed in traditional CS compared with NS. Tumor necrosis factor (TNF)-α concentrations were altered significantly in non-smokers (p < 0.05) and considerably in ES (p < 0.1) compared with regular CS. However, other salivary cytokines showed no significant difference among cohorts. We further evaluated the association between inflammatory cytokines and salivary bacteria. Spearman correlation analysis indicated Porphyromonas, Haemophilus, Catonella, and Niesseria to be positively correlated with cytokines IL-2, IL-13, IL-8, and IL-1β ( Figure 4 B). In addition, Lachnoanaerobaculum and Stomatobaculum displayed positive correlation toward inflammatory immune indicators such as IL-2, IL-4, IL-8, IL-13, IL-10, IL-12p70, and IFN-γ ( Figure 4 B). Positive correlations were observed among Parvimonas, Peptostreptococcus, and Mogibacterium with IL-8 and IL-1β, and significant correlations were observed in Fusobacterium, Johnsonella, and Catonella with IL-1β. On the contrary, among a few taxa inverse correlation was observed, i.e., Desulfovibrio, Dialister, Helicobacter, Peptostreptococcus, Olsenella, and Treponema with TNF-α; Bacteroides with IL-6; Tannerella with TNF-α, IL-4, and IFN-γ; and Corynebacterium and Olsenella with IL-8.

(A and B) (A) Levels of 10 different pro-inflammatory salivary cytokines and chemokines in three cohorts, NS, ES, and CS. Data are represented as mean ± SEM. ( # p < 0.1, *p < 0.05). (B) Correlation matrices indicate association of cytokines with the bacterial taxa. Values are represented by colors from blue (negative correlation) to red (positive correlation). Clustering is based on the Spearman rank correlation similarity between cytokines and microbial genera. (*p < 0.05, **p < 0.01)

A heat tree illustrates the relationships of species-specific OTUs in the NS, e-cigarette vapers, and CS ( Figure S1 ). Streptococcus oralis subsp. tigurinus clade 071, Porphyromonas pasteri, Fusobacterium periodonticum, and Oribacterium parvum were depleted significantly in the ES and CS cohorts ( Figures S2 A and S2B). On the contrary, abundance of Veillonella rogosae, Granulicatella adiacens, and Prevotella sp. HMT 317 was higher in the NS cohort. Veillonella dispar, Porphyromonas endodontalis, Fusobacterium nucleatum subsp. vincentii, Prevotella oris, and Parvimonas micra predominated substantially in the ES and CS cohorts ( Figures S2 A and S2B). Veillonella atypica, Megasphaera micronuciformis, Streptococcus parasanguinis clade 411, Prevotella sp. HMT 311, and Actinomyces lingnae, although higher in the ES cohort, significantly expanded in the CS cohort ( Figure S2 C). P. gingivalis, Alloprevotella tannerae, Dialister invisus, Corynebacterium durum, and Leptotrichia wadei levels were proliferated in the ES cohort, whereas Streptococcus salivarius was higher in the CS cohort.

The salivary microbiome in the three cohorts was significantly dominated by eight taxa; Streptococcus, Veillonella, Prevotella, Neisseria, Haemophilus, Porphyromonas, Rothia, and Fusobacterium, which constituted 79.15% of all the sequences ( Figures 3 A and 3B ). Other differentially abundant taxa identified were Leptotrichia, Gemella, and Capnocytophaga ( Figures 3 A and 3B). A hierarchical cluster analysis showed stratification of taxa into four clusters ( Figure 3 C). Interestingly, the ES salivary microbiota that harbored Cluster Ib and Cluster IIb showed resemblance to those taxa distinct in NS and CS cohorts, respectively ( Figure 3 C). A similar pattern was also observed in the weighted PCoA plot ( Figure 1 C). Further analyses revealed these similarities to be closely associated with the nicotine intake (smoking status) by the ES participants. Cumulatively, the taxa observed to be proliferated were Actinomyces in the ES cohort, TM7 in the ES and NS cohorts, and Granulicatella in the NS cohort. Moreover, Neisseria and Fusobacterium were significantly associated with the ES and NS cohorts compared with the CS cohort, p < 0.0001, whereas the opportunistic pathogens, Streptococcus (p < 0.01), Prevotella (p = 0.01), and Rothia (p = 0.002) were differentially enriched in the CS cohort. Veillonella levels increased significantly by ∼4% in ES (p = 0.008) and ∼4.5% (p = 0.001) in CS cohorts than NS cohorts. These findings suggest microbial dysbiosis in non-combustible vapers and combustible smokers.

(C) Top 20 taxa in the saliva of the subject population across NS, ES, and CS cohorts; rectangular boxes represents two clusters where several taxa in the samples of ES cohort overlap with the samples of NS and CS cohorts.

(B) Heat tree illustrates the relationship of OTUs up to genus level. Colored branch of the tree denotes significance based on the color of individual cohorts.

The relative abundance of salivary microbiome differed between non-combustible vapers and combustible smokers when compared with healthy non-smoking controls. The five most abundant taxa observed were Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria, which accounted for 97.5% of the total sequences ( Figure 2 E). Intriguingly, Proteobacteria predominated the ES cohort with significantly lower levels in the CS cohort (p < 0.0001). The CS and ES showed significantly elevated salivary Actinobacteria compared with NS (p < 0.0001 and p < 0.01, respectively). However, Firmicutes were highly enriched in the saliva of the CS cohort compared with those of the ES (p < 0.01) and NS (p < 0.0001) cohorts. On the contrary, Fusobacteria exhibited significant depletion in the CS cohort when compared with the ES (p < 0.024) and NS (p < 0.001) cohorts. Another predominant taxon, Spirochaetes, proliferated in the CS cohort (p < 0.005).

The alpha diversity indices, such as observed OTUs, Shannon index, and phylogenetic diversity were computed to determine the microbial diversity within the three groups ( Figure 2 D). We observed a significantly higher number of observed OTUs (p = 0.023) and phylogenetic diversity (p = 0.002) in non-combustible vapers when compared with NS. No significant changes were detected in the diversity indices of the CS cohort when compared with NS and ES users. All the analysis was done using Mann-Whitney test with a confidence level of 99% (p ≤ 0.01) in each index.

Principal coordinate analyses (PCoA) were performed to determine the overall microbiome composition in the three cohorts. Based on the weighted UniFrac distance matrix generated from all the samples in each cohort, we observed significant differences (p < 0.05, PERMANOVA) in the microbial composition between the three cohorts ( Figure 1 C) and additionally, between each cohort in an independent combinatorial comparison ( Figures 1 D–1F). These results suggest that the microbiome structure in each of the three cohorts was distinct. Interestingly, a few of the individual ES cohort samples showed an overlap with some samples in the NS ( Figure 1 D) and CS ( Figure 1 E) cohorts, although they were significantly distinct microbiomes. These findings were reiterated by double PCoA that establishes differences in taxonomic signatures ( Figure 2 C) in context to breath CO levels ( Figure 2 A) and salivary cotinine levels ( Figure 2 B) of the participants.

(A–E) Double PCoA plots showing taxa correlating to the smoking status, in terms of (A) breath CO levels (in percentage), (B) cotinine levels, and (C) phyla taxonomy in the non-smokers (NS), e-cigarette users (ES), and cigarette smokers (CS); non-smoker (NonS): 0–6 ppm, borderline (BdL): 7–9 ppm, low addicted smoker (LaS): 10–15 ppm, moderate addicted smoker (MaS): 16–25 ppm, heavily addicted smoker (HaS): 26–35 ppm, and very heavily addicted smoker (VHaS): 36+ ppm, data not available (na). (D) Alpha diversity as measured by observed species, Shannon diversity index, and phylogenetic diversity (PD) of the salivary microbiome in NS (green), ES (blue), and CS (red) cohorts. The line inside the box represents the median, whereas the whiskers represent the lowest and highest values within the interquartile range. Outliers' individual samples are shown as dots. Analysis was done using Mann-Whitney test. (E) Phylum-level relative abundances of the salivary microbiota based on taxonomic inference of 16S rRNA sequences. Actinobacteria, Firmicutes, Fusobacteria, Proteobacteria, and Spirochaetes were significantly altered. (D and E) Data are represented as mean ± SEM. (*p < 0.05, **p < 0.01, ***p < 0.001).

The salivary microbiome in 119 participants in the NS, ES, and CS groups was analyzed using 8,254,494 high-quality filtered 16S sequences (mean 68,787 ± 19,758 SD). The salivary microbiome was composed of 11 phyla, 22 classes, 33 orders, 55 families, 99 genera, 162 species, and 911 operational taxonomic units (OTUs).

To assess the in vivo influence of smoking on microbial profiles of recruited participants, we stratified 119 subjects according to their smoking status into each of the three cohorts (never smokers [NS, n = 39], e-cigarette users [ES, n = 40], and regular cigarette smokers [CS, n = 40]). The demographic details of all the subjects and smoking history were collected at the baseline for the ES and CS groups ( Table S1 ). The ES and CS cohorts had a nearly equal percentage of male population (∼77%–80%). On the other hand, females were higher at 43.6% in the NS cohort. Participants exclusively using e-cigarettes vaped on an average 0.5 e-cigarettes per day, whereas participants exclusively using combustible cigarettes smoked an average of 11 cigarettes per day. There was no significant change in the salivary flow rate across the participants in different cohorts. The severity index of periodontal disease or infection was significantly higher among CS (72.5%), followed by ES (42.5%), and non-smokers (28.2%), as also reflected by the calculated mean pocket depth among cohorts. This was confirmed by the values of bleeding on probing (BoP), one of the markers for inflammation, elevated in CS and receding in e-cigarette vapers and non-smokers, although with no statistical significance. To determine the participants' smoking status, exhaled breath carbon monoxide (CO) levels (ppm) and salivary cotinine levels (ng/mL) were evaluated. Figures 1 A and 1B, respectively, illustrate the cohort-based distribution of the smoking biomarkers for CO levels in exhaled breath and salivary cotinine levels in the participants. The CO and the salivary cotinine levels were the lowest in non-smokers and the highest among traditional cigarette users ( Table S1 ).

(C–F) Weighted UniFrac 3D PCoA plots illustrating beta diversity of salivary bacterial taxa across all the samples. (C) Three cohorts, NS, ES, and CS (p < 0.001); (D) between NS and ES cohorts (p = 0.005); (E) between ES and CS cohorts (p = 0.006); and (F) between NS and CS cohorts (p < 0.001). Green, never smokers (NS); blue, e-cigarette vapers (ES); and red, cigarette smokers (CS). Ellipses indicate 95% confidence interval.

(A) Levels of breath carbon monoxide (ppm) across the subjects (in percentage) in the non-smokers (NS), e-cigarette users (ES), and cigarette smokers (CS); non-smoker (NONS): 0–6 ppm, borderline (BdL): 7–9 ppm, low addicted smoker (LAS): 10–15 ppm, moderate addicted smoker (MAS): 16–25 ppm, heavily addicted smoker (HAS): 26–35 ppm, and very heavily addicted smoker (VHAS): 36+ ppm.

Discussion

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Chapple I. Molecular aspects of the pathogenesis of periodontitis. The periodontal microbes in tissues likewise stimulate chemokine IL-8 production, which induces a signal for accumulation and activation of neutrophils in local sites (). Importantly, IL-8 levels were expanded in Fadu cells after co-infection with E. coli GFP, P. gingivalis, or F. nucleatum and increased maximally in cells treated with bacteria and exposed to e-cigarette aerosols. Also, our results have shown an up-regulation of IFN-γ upon co-incubation with e-cigarette aerosols and E. coli GFP when compared with air only, e-cigarette only, and combination of air and bacteria. IFN-γ is a key player in immune responses in periodontal disease () and in the activation of immunomodulation in mesenchymal stem cells. In vivo studies reported that mice, in the absence of IFN-γ, showed decreased bone loss after P. gingivalis infection in oral cavity (). The limitations of this experimental design are that all in vitro experiments were done on cell culture models using oral pathogens, and it will be valuable to include primary cell or 3D oral tissue models and other canonical intracellular pathogen (e.g., Listeria) in our future experiments.