ArticlePDF Available

E-cigarette use results in suppression of immune and inflammatory-response genes in nasal epithelial cells similar to cigarette smoke


Abstract and Figures

Exposure to cigarette smoke is known to result in impaired host defense responses and immune suppressive effects. However, the effects of new and emerging tobacco products, such as e-cigarettes, on the immune status of the respiratory epithelium are largely unknown. We conducted a clinical study collecting superficial nasal scrape biopsies, nasal lavage, urine, and serum from non-smokers, cigarette smokers and e-cigarette users and assessed them for changes in immune gene expression profiles. Smoking status was determined based on a smoking history and a 3-4 week smoking diary and confirmed using serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels. Total RNA from nasal scrape biopsies were analyzed using the nCounter® Human Immunology v2 Expression panel. Smoking cigarettes or vaping e-cigarettes resulted in decreased expression of immune-related genes. All genes with decreased expression in cigarette smokers (n=53) were also decreased in e-cigarette smokers. Additionally, vaping e-cigarettes was associated with suppression of in a large number of unique genes (n=305). Furthermore, the e-cigarette users showed a greater suppression of genes common with those changed in cigarette smokers. This was particularly apparent for suppressed expression of transcription factors, such as EGR1, which was functionally associated with decreased expression of 5 target genes in cigarette smokers and 18 target genes in e-cigarette users. Taken together, these data indicate that vaping e-cigarettes is associated with decreased expression of a large number of immune-related genes, which are consistent with immune suppression at the level of the nasal mucosa.
Content may be subject to copyright.
CALL FOR PAPERS Electronic Cigarettes: Not All Good News?
E-cigarette use results in suppression of immune and inflammatory-response
genes in nasal epithelial cells similar to cigarette smoke
Elizabeth M. Martin,
Phillip W. Clapp,
Meghan E. Rebuli,
Erica A. Pawlak,
Ellen Glista-Baker,
Neal L. Benowitz,
Rebecca C. Fry,
and Ilona Jaspers
Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North
Carolina, Chapel Hill, North Carolina;
Curriculum in Toxicology, School of Medicine, University of North Carolina, Chapel
Hill, North Carolina;
Center for Environmental Medicine, Asthma, and Lung Biology, School of Medicine, University of
North Carolina, Chapel Hill, North Carolina; and
Division of Clinical Pharmacology, Departments of Medicine and
Bioengineering & Therapeutic Sciences, University of California San Francisco, San Francisco, California
Submitted 28 April 2016; accepted in final form 6 June 2016
Martin EM, Clapp PW, Rebuli ME, Pawlak EA, Glista-Baker
E, Benowitz NL, Fry RC, Jaspers I. E-cigarette use results in
suppression of immune and inflammatory-response genes in nasal
epithelial cells similar to cigarette smoke. Am J Physiol Lung Cell Mol
Physiol 311: L135–L144, 2016. First published June 10, 2016;
doi:10.1152/ajplung.00170.2016.—Exposure to cigarette smoke is
known to result in impaired host defense responses and immune
suppressive effects. However, the effects of new and emerging to-
bacco products, such as e-cigarettes, on the immune status of the
respiratory epithelium are largely unknown. We conducted a clinical
study collecting superficial nasal scrape biopsies, nasal lavage, urine,
and serum from nonsmokers, cigarette smokers, and e-cigarette users
and assessed them for changes in immune gene expression profiles.
Smoking status was determined based on a smoking history and a 3-
to 4-wk smoking diary and confirmed using serum cotinine and urine
4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels. Total
RNA from nasal scrape biopsies was analyzed using the nCounter
Human Immunology v2 Expression panel. Smoking cigarettes or
vaping e-cigarettes resulted in decreased expression of immune-
related genes. All genes with decreased expression in cigarette smok-
ers (n53) were also decreased in e-cigarette smokers. Additionally,
vaping e-cigarettes was associated with suppression of a large number
of unique genes (n305). Furthermore, the e-cigarette users showed
a greater suppression of genes common with those changed in ciga-
rette smokers. This was particularly apparent for suppressed expres-
sion of transcription factors, such as EGR1, which was functionally
associated with decreased expression of 5 target genes in cigarette
smokers and 18 target genes in e-cigarette users. Taken together, these
data indicate that vaping e-cigarettes is associated with decreased
expression of a large number of immune-related genes, which are
consistent with immune suppression at the level of the nasal mucosa.
e-cigarettes; nasal epithelial cells; gene expression
EXPOSURE TO CIGARETTE SMOKE (CS), via active smoking or
second-hand smoke (SHS) exposure, continues to be the num-
ber one cause of preventable mortality and morbidity world-
wide (48a). A large body of clinical and laboratory data
supports a significant relationship between CS exposure, im-
mune suppression, and increased risk for respiratory viral or
bacterial infection. Even in otherwise healthy subjects, smok-
ing or SHS exposure is associated with enhanced susceptibility
to microbial infections as well as enhanced infection-associ-
ated severity and morbidity (25, 26). Smoking broadly sup-
presses multiple host defense mechanisms including epithelial
cell responses and recruitment and activation of innate immune
cells, such as neutrophils, macrophages, and NK cells. Our
previous work shows that, in the context of viral infections, CS
exposure modifies the ability of epithelial cells to mount an
effective type I interferon response, produce cytokines/chemo-
kines necessary for immune cell activation, and recruit and
activate resident immune cells, effectively compromising in-
nate immune host defense responses (14 –16, 24, 34, 35, 38).
While smoking rates continue to decline in the United
States, the number of e-cigarette users is on the rise. Both the
CDC and the FDA consider e-cigarettes as tobacco products,
and the recently passed “Deeming Rule” deems e-cigarettes as
“tobacco products to be subject to the Federal Food, Drug, and
Cosmetic Act” (5, 9). However, e-cigarettes are often adver-
tised as “less harmful” than conventional cigarettes and the
effects that vaping e-cigarettes may have on respiratory muco-
sal immune responses are completely unknown. A few animal
studies suggest that inhalation of e-cigarette vapor increases
susceptibility to viral and microbial infections (47) or enhances
bacterial growth/biofilm formation (20). E-cigarette vapor is a
complex mixture derived by aerosolizing e-liquids composed
of nicotine, flavoring agents, and humectants, such as propyl-
ene glycol and vegetable glycerin. Vaporizing e-liquids, espe-
cially at higher temperatures than recommended by the man-
ufacturer, may result in the generation of known pulmonary
toxicants such as formaldehyde, acetaldehyde, and acrolein
(10, 11). In addition, some of the potential flavorings contained
in e-cigarettes, such as diacetyl or benzaldehyde, have known
adverse respiratory effects (2, 13, 23, 27, 50). However, the
effects of e-cigarettes on innate immune responses in the
respiratory mucosa of humans are unknown.
In an initial effort to examine the effects of e-cigarettes on
human respiratory innate immune responses, we designed a
clinical study collecting nasal scrape biopsies from human
smokers, nonsmokers, and e-cigarette users to examine differ-
ences in immune gene expression at the level of the epithelium.
Previous studies have shown that exposure-related gene ex-
pression changes in airway epithelial biopsy samples are po-
Address for reprint requests and other correspondence: I. Jaspers, Univ. of
North Carolina at Chapel Hill, 104 Mason Farm Rd., CB#7310, Chapel Hill,
NC 27599-7310 (e-mail:
Am J Physiol Lung Cell Mol Physiol 311: L135–L144, 2016.
First published June 10, 2016; doi:10.1152/ajplung.00170.2016.
1040-0605/16 Copyright ©2016 the American Physiological Society L135
by on January 9, 2017 from
tential biomarkers of disease or underlying adverse health
effects (3, 4, 42– 45). Changes in mRNA expression levels can
indicate disturbances in cellular metabolic pathways leading to
cell death or disease and as such, are valuable predictors of
exposure and/or xenobiotic toxicity. In addition, smoking-
induced gene signatures are very similar in bronchial and nasal
epithelial cells (45) suggesting that the less invasively obtained
nasal tissue is representative and/or an extension of changes
induced in the lower airways. Furthermore, we have previously
demonstrated that in the context of viral infections, the expres-
sion of immune genes was suppressed in nasal epithelial cells
obtained from smokers (24, 38). While we have demonstrated
that CS exposure can alter gene expression in nasal epithelial
cells, our previous studies have been limited to individual gene
analysis. The goals of the present study are to provide a more
comprehensive profile of CS-induced changes to immune gene
expression, a novel assessment of e-cigarettes-induced changes
to immune gene expression, and the first comparison of e-cig-
arette and CS effects on the respiratory innate immune system.
Subject recruitment and sample collection. This was a prospective,
observational cross-sectional study comparing gene expression pro-
files in nonsmokers, cigarette smokers, and e-cigarette users. Subjects
were healthy young adults 18 –50 years of age in three groups: 1)
nonsmokers not regularly exposed to SHS (control group); 2) self-
described active cigarette smokers (smoker group); and 3) self-
described, active e-cigarette users/vapers who had been using e-cig-
arettes regularly for at least 6 mo. Dual users smoking more than 5
cigarettes/wk in addition to using e-cigarettes were excluded from
these studies. The exclusion criteria for this study were current
symptoms of allergic rhinitis, diagnosis or symptoms of asthma,
forced expiratory volume in 1 s (FEV
) less than 75% of predicted at
screen, chronic obstructive pulmonary disorder (COPD), cardiac dis-
ease or any chronic cardiorespiratory condition, bleeding disorders,
immunodeficiency, recent nasal surgery or nasal steroid use, or
current pregnancy. At the initial screen visit, a self-reported smoking/
e-cigarette use history was obtained and subjects were asked to
complete a 3- to 4-wk smoking and e-cigarette use diary prior to the
sample acquisition visit. Subjects were asked to return after 3– 4 wk,
at which point the smoking diary, vital signs, urine, blood, demo-
graphic information, and pregnancy tests (for female subjects) were
collected. For the purposes of analysis, individuals were classified
according self-reported smoking status as current smokers, nonsmok-
ers, or e-cigarette users. Of the e-cigarette users, n9 identified
themselves as former cigarette smokers. For the purpose of this study,
current e-cigarette use was defined as having exclusively or predom-
inantly vaped e-cigarettes for at least 6 mo. In addition, self-classifi-
cation was cross-checked against participants’ diaries as well as
analyses of tobacco and nicotine metabolites in serum and urine to
verify smoking status (see Table 1 and Supplemental Table S1;
Supplemental Material for this article is available online at the Journal
At that point, superficial scrape biopsies of the epithelium in the
inferior surface of the middle nasal turbinate were obtained from each
subject, and epithelial RNA was isolated, similar to our previous
studies (31, 38). Nasal lavage was carried out similar to our previous
studies (16, 33–36) using repetitive spraying of nostrils with sterile
normal saline (0.9%) irrigation solution (4 ml per nostril). Cell-free
nasal lavage fluid (NLF) was obtained by filtration and centrifugation
of the NLF to remove cells and debris as described by us before (13,
29 –32). When sufficient NLF cells were available, cytocentrifuge
slides were prepared as described by us before (13) and stained using
a modified Wright stain for differential cell counts. At least 100 cells
were counted on each slide to quantify the percent neutrophils present
(16, 33–36).
Informed consent was obtained from all subjects, and the protocol
was submitted to and approved by the University of North Carolina at
Chapel Hill Biomedical Institutional Review Board.
Assessment of nicotine and tobacco biomarkers. Serum cotinine
and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL)
levels were assayed by liquid chromatography tandem mass spectrom-
etry using published methods (21, 22). Cotinine is the proximate
metabolite of nicotine and is a biomarker of daily dose of nicotine.
NNAL is a tobacco specific nitrosamine and metabolite of NNK
[nicotine-derived nitrosamine ketone (NNK), also known as 4-(meth-
ylnitrosamino)-1-(3-pyridyl)-1-butanone], which is found in tobacco
smoke but not (or in negligible amounts) in e-cigarette vapor. The
limits of quantification for serum cotinine were 0.02 ng/ml for
nonsmokers and e-cigarette users and 1 ng/ml for cigarette smokers,
while the limit for urine NNAL quantification was 0.25 pg/ml for all
groups. The limit of quantitation was different for nonsmokers/e-
cigarette users and cigarette smokers based on the use of different
levels of internal standards. This is necessary because levels in
cigarette smokers are so much higher (1–200 ng/ml) than in nonsmok-
ers or e-cigarette users (0.05 to 10 ng/ml).
Nanostring-based gene expression analysis. Total RNA isolated
from superficial scrape biopsies was analyzed by using the nCounter
Human Immunology v2 Expression panel from Nanostring (Nanos-
tring, Seattle, WA), which assesses the expression of n597 human
immunology-related genes. Nanostring data were normalized in a
two-step process as per the manufacturer’s recommendation and
processed using Partek Genomic Suite (St. Louis, MO). First, positive
control normalization was performed, where the geometric mean for
each sample’s positive control was calculated. Then a lane normal-
ization factor was calculated by dividing the positive control geomet-
ric mean of each sample by the mean of the geometric means. The
lane normalization factors were then used to adjust each sample
individually. The same protocol was then repeated for housekeeping
genes. Together, these processes control for batch effect and artifact
error. Genes that were expressed below the stated manufacturer
threshold in more than 25% of subjects were excluded from analysis.
Table 1. Subject demographics
Nonsmokers (n13) Cigarette Smokers (n14) E-Cigarette Users (n12)
BMI 28.03 6.48 28.15 6.72 28.73 9.11
Age 30.38 6.84 30.71 5.64 26.33 5.57
Sex, female/male 8/5 8/6 5/7
Ethnicity, White/African American/Asian 9/3/1 6/7/1 7/2/3
Cigarettes per day 0 11.8 5.49 0.21 0.36
E-cigarette puffs per day 0 0 200.66 178.26
Serum cotinine, ng/ml 0.08 0.17 158.95 132.27 174.19 167.90
Urine NNAL, pg/ml 1.16 2.73 377.14 302.27 15.77 18.58
Urine NNAL/creatinine, pg/mg 0.02 0.07 261.76 218.81 8.62 12.78
Values are mean SE.
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
Differential expression was determined between groups using an
analysis of covariance (ANCOVA) controlling for age, race, sex, and
body mass index (BMI). Specifically, differences were tested between
smokers and nonsmokers, e-cigarette users and nonsmokers, and
e-cigarette users and cigarette users. Differential expression was
defined as an ANCOVA overall P0.05 with a false discovery
corrected q0.1.
Pathway analysis and identification of key transcription factors.
Pathway analysis was conducted using Ingenuity Pathway Analysis
(IPA) (Ingenuity Systems, Redwood City, CA). Enriched canonical
pathways were identified via a right-tailed Fisher’s exact test. Statis-
tical significance for canonical pathways was set at P0.05. These
data were subsequently validated in a separate analysis using DAVID.
In addition to canonical pathway analysis, IPA was used to assess
the impact of genes coding for transcription factors that were differ-
entially expressed within our dataset. Specifically, the upstream reg-
ulator module was used to determine the number of transcripts that
were altered within our dataset and controlled by differentially ex-
pressed genes coding for transcription factors. Pvalues for the
upstream regulator module were determined by a two-tailed Fisher’s
exact test and significance was set at P0.01. To validate the
findings of the IPA analysis, a second analysis was conducted using
Genomatix’s Overrepresented Transcription Factor Binding Site tool
(Genomatix Software, Ann Arbor, MI). This analysis determined the
number of genes with binding sites in the promoter region for
differentially expressed genes that code for transcription factors.
Promoter region sequences were defined as 500 base pairs upstream
and 1,000 base pairs downstream of the transcription start site. A
z-score was calculated to determine overrepresented transcription
factor binding sites, with a z-score 2or2 corresponding to
aPvalue of 0.05. For this reason statistical significance was set at
ELISA analysis of CSF-1 and CCL26/eotaxin-3. Cell-free NLF
from all subjects were used to analyze Colony Stimulating Factor 1
(CSF-1) and Chemokine (C-C Motif) Ligand 26 (CCL26/eotaxin-3)
protein levels via commercially available ELISA kits (Meso Scale
Diagnostics, Rockville, MD). Data in the cigarette smokers and
e-cigarette users were assessed as picograms per milliliter cell-free
NLF and normalized to the average level in nonsmokers. The expres-
sion ratio values were converted to fold changes using log2 transfor-
mation, similar to the gene expression data and compared with the
fold change cutoff for nonsmokers [log2(1) 0] by the one-sample
Description of study subjects. Among the individuals in-
cluded in this study (n39), about an equal number were
nonsmokers (n13; 33.3%), cigarette smokers (n14;
35.8%), and e-cigarette users (n12; 30.8%). The average
BMI, age, and sex distribution did not significantly differ
among the different groups (Table 1). Similarly, the average
percent neutrophils present in the NLF did not differ among the
three different groups (nonsmokers 39.66 23.66; cigarette
smokers 52.31 37.72; e-cigarette users 61.35 24.67).
The average number of cigarettes smoked per day in the
smokers category was 12, ranging from 2.5 to 21 cigarettes/
day. In the e-cigarette user category, the average number of
puffs inhaled per day was 200, ranging from 5.8 to 610.7,
illustrating the broad range of users included in this study. Of
the e-cigarette users, nine identified themselves as having
previously smoked cigarettes, while three indicated no prior
cigarette smoking history (Table 1). In addition, five of the
subjects reported occasionally smoking cigarettes (Supplemen-
tal Table S1).
As expected, biochemical markers for nicotine exposure
(cotinine) and tobacco-specific NNAL were at or below the
detection limit in nonsmokers. In smokers, serum cotinine and
urine NNAL levels were significantly correlated with cigarettes
smoked per day (P0.05). Similarly, serum cotinine levels
were significantly correlated with e-cigarette puffs per day
(P0.01), whereas urine NNAL levels were not. Average
urine NNAL levels were significantly lower in e-cigarette users
compared with smokers (Fig. 1). Only one subject had urine
NNAL levels above the cutoff point distinguishing smokers
from nonsmokers of 47 pg/ml (12), while the majority of
e-cigarette users had urine NNAL levels comparable to those
seen in nonsmokers (Supplemental Table S1), suggesting pre-
dominant or exclusive e-cigarette use in these subjects.
Differential expression of genes in nasal biopsy samples in
smokers vs. e-cigarette smokers. Of the 597 genes tested on the
Nanostring nCounter Human Immunology Array, 543 genes
were expressed above background in nasal biopsy samples. A
total of 358 of the 543 detectable genes were differentially
expressed in at least one condition in nasal biopsy samples. Of
these, 53 genes were differentially expressed when comparing
cigarette smokers and nonsmokers (Fig. 2, Aand B). All of the
53 genes that changed in cigarette smokers showed decreased
expression (Supplemental Table S2, Fig. 2B). The top five
genes with changed expression in cigarette smokers were Early
Growth Response 1 (EGR1), Dipeptidyl-Peptidase 4 (DPP4),
Chemokine (C-X-C Motif) Ligand 2 (CXCL2), Chemokine
(C-X3-C Motif) Receptor 1 (CX3CR1), and CD28 Molecule
(CD82). When comparing e-cigarette users with nonsmokers,
358 genes were differentially expressed. As with the cigarette
smokers, all 358 genes that were differentially expressed in
e-cigarette users also showed decreased expression (Supple-
mental Table S2). The top five genes with changed expression
in e-cigarette users were Zinc Finger And BTB Domain Con-
taining 16 (ZBTB16), EGR1, Polymeric Immunoglobulin Re-
ceptor (PIGR), Prostaglandin-Endoperoxide Synthase 2
(PTGS2), and FK506 Binding Protein 5 (FKBP5). All 53 genes
changed in the comparison of cigarette smokers with nonsmok-
ers were also changed in the comparison of e-cigarette smokers
with nonsmokers (Fig. 2B). The extent of change in gene
expression of e-cigarette users was much greater than that of
cigarette smokers. The reduction in gene expression is illus-
trated in the heat map of fold changes induced in e-cigarette
users and cigarette smokers compared with nonsmokers (Fig.
3). Together the data indicate that the gene expression changes
induced by smoking cigarettes or vaping e-cigarettes are con-
sistent with immune suppression and that e-cigarette users had
a large set of unique gene expression changes compared with
nonsmokers and cigarette smokers.
Pathway analysis of cigarette and e-cigarette smokers. Path-
way analysis was conducted for three sets of genes using
DAVID: the 53 common cigarette and e-cigarette responsive
genes (Supplemental Table S2), and the 358 genes representing
the entire e-cigarette response (Supplemental Table S2). The
top 10 canonical pathways representing the gene expression in
each comparison group can be found in Supplemental Table
S3. Four pathways, the cytokine-cytokine receptor interaction,
apoptosis, Toll-like receptor signaling pathway, and NOD-like
receptor signaling pathway, overlapped in both comparison
groups [E-cigarette vs. Nonsmokers and Cigarette Smokers vs.
Nonsmokers (Supplemental Table S3)]. Outside of these four
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
pathways, there was no overlap in canonical pathways repre-
sented in the gene expression changes induced in the two
comparison groups.
Transcription factor analysis of cigarette and e-cigarette
gene expression changes. To further understand the difference
in gene expression changes induced in smokers and e-cigarette
users, we identified functional transcription factor networks
based on differentially expressed genes that code for transcrip-
tion factors using IPA. Additionally, we used Genomatix to
determine whether genes within the gene set also contained
transcription factor binding sites for these transcription factors.
When analyzing the cigarette response, 7 transcription factors
were identified as differentially expressed and statistically
significant in the upstream regulator analysis: EGR1, V-Ets
Avian Erythroblastosis Virus E26 Oncogene Homolog 1
(ETS1), Nuclear Factor Of Kappa Light Polypeptide Gene
Enhancer In B-Cells 1 (NFKB1A), NOTCH1 (NOTCH1), X-
Box Binding Protein 1 (XBP1), B-Cell CLL/Lymphoma 6
(BCL6), and B-Cell CLL/Lymphoma 3 (BCL3) (Supplemental
Table S4). In contrast, 50 transcription factors were identified
as differentially expressed in e-cigarette users and were stati-
cally significant in the upstream regulator analysis (Supple-
mental Table S4). This analysis also suggested that the 7
transcription factors regulated 30 of 53 differentially expressed
genes in the cigarette smokers, and the 50 transcription factors
regulated 262 of 358 genes differentially expressed in the
e-cigarette smokers compared with the nonsmokers group (Fig.
4, Aand B). Focusing on the 7 transcription factors whose
0 5 10 15 20 25
Serum Cotinine ng/ml
Cigarette Smokers
0 5 10 15 20 25
NNAL/creatinine pg/ml
Cigarette Smokers
0 200 400 600 800
E-cig Users
Serum Cotinine ng/ml
0 200 400 600 800
NNAL/creatinine pg/ml
E-cig Users
Fig. 1. Correlation between serum cotinine and
urine NNAL levels and cigarette/e-cigarette
(E-cig) usage in smokers and e-cigarette users.
Serum cotinine (A) and urine NNAL levels (B)
from smokers were correlated with the average
number of cigarettes smoked per day. Serum
cotinine (C) and urine NNAL (D) from e-cig-
arette users were correlated with the average
puffs per day. Pearson correlation coefficient
and Pvalues are depicted. NS, not significant.
Number of Genes
053 305
Fig. 2. Number of genes changed in cigarette smokers
(CS) and e-cigarette (EC) users. A: total number of
genes changed in smokers and e-cigarette users com-
pared with nonsmokers. B: Venn diagram of the genes
unique or common to cigarette smokers and e-cigarette
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
expression was decreased in both cigarette smokers and e-cig-
arette users, 120 genes were regulated by these transcription
factors in e-cigarette users (Fig. 4C) compared with 30 genes
regulated in cigarette smokers (Fig. 4A). Genomatix was used
to determine whether the gene sets identified as changed in
e-cigarette smokers or cigarette smokers compared with non-
smokers were enriched for sequences bound by the predicted
transcription factors. Since Genomatix has a more limited
database, overlap between the IPA and Genomatix analysis
was not complete. In the comparison of cigarette smokers with
nonsmokers, 5 of the 7 identified transcription factors were
represented within the Genomatix family matrix. Of these 5
transcription factors, 4 were significantly enriched in the com-
parison of cigarette smokers with nonsmokers. Similarly, in the
comparison of e-cigarette users with nonsmokers 33 of 50
identified transcription factors were represented within the
Genomatix family matrix, and 31 of these 33 were found to be
significant in the transcription factor binding site analysis
(Supplemental Table S4). These data suggest that the identified
transcription factors likely play an important role in driving the
differential responses seen in e-cigarette users and smokers.
We also found that, for transcription factors whose expres-
sion was changed in both the cigarette smoker and e-cigarette
user groups, the level of suppression was greater in e-cigarette
users than in cigarette smokers for each transcription factor
(Table 2). To determine whether this was functionally associ-
ated with reduced expression of a greater number of target
genes, we focused our analysis on EGR1. EGR1 is an imme-
diate-early gene regulating the transcription of many immune
genes, including cytokines/chemokines, adhesion molecules,
proteases, and autophagy genes. Using Genomatix and IPA,
downregulation of EGR1 was computationally assessed to be
functionally associated with reduced expression of 5 target
genes in cigarette smokers and 18 target genes in e-cigarette
Fig. 3. Comparison of fold change gene expression in cigarette
smokers and e-cigarette users. The level of transcriptional
changes in the 53 genes common to cigarette smokers and
e-cigarette was compared and depicted as the relative fold
change in this heatmap.
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
users (Fig. 5, Aand B, respectively). Genes functionally asso-
ciated in both smokers and e-cigarette users with the sup-
pressed EGR1 expression were CD44 Molecule (CD44), Col-
ony Stimulating Factor 1 (CSF1), Chemokine (C-X-C Motif)
Ligand 2 (CXCL2), BCL2-Like 11 (BCL2L11), and Fas Cell
Surface Death Receptor (FAS).
Confirmation of change in CSF-1 and CCL26 levels in
smokers and e-cigarette users. Based on the functional asso-
ciation analyses between suppressed EGR1 expression and
target genes, we analyzed the levels of CSF-1 in NLF from the
same study subjects as were used in the gene expression
analysis. Since CSF-1 is constitutively expressed by a variety
of epithelial cell types and has been shown to play important
roles in innate immunity, including host defense responses
against fungal, bacterial, and viral infections (6, 18) it repre-
sented a suitable target for confirmatory studies. CSF-1 expres-
sion was significantly decreased in NLF from both cigarette
smokers and e-cigarette users compared with nonsmokers (Fig.
6A). We also analyzed the levels of CCL26/eotaxin-3, a
chemokine expressed by epithelial cells important for the
recruitment of not only for eosinophils, basophils, and T
lymphocytes, but also NK cells (7, 29, 37). Similarly to CSF-1,
Fig. 4. Functional transcription factor networks in cigarette smokers (A) and e-cigarette users (B).
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
expression of CCL26/eotaxin-3 was significantly decreased in
NLF from cigarette smokers, but this suppression did not reach
statistical significance in e-cigarette users (Fig. 6B).
In the study described here we compared immune-related
gene expression changes induced in the nasal mucosa of
cigarette smokers and e-cigarette users, compared with non-
smokers. There were three major observations derived from
this study: First, smoking cigarettes or vaping e-cigarettes
resulted in decreased expression of a large number of immune-
related genes. Second, all of the genes suppressed by cigarette
smoking were also suppressed in nasal biopsies from e-ciga-
rette users. Third, vaping e-cigarettes was associated with a
much greater number of gene expression changes and, in a
gene-by-gene comparison, stronger levels of suppression com-
pared with cigarette smokers. Thus our data indicate that
vaping e-cigarettes is associated with broad gene expression
changes that are consistent with immune suppression at the
level of the nasal mucosa.
The effects of smoking on the respiratory immune response
are very complex and include both activation of proinflamma-
tory pathways and suppression of immune responses in the
respiratory mucosa, leading to increased tissue injury and
enhanced susceptibility to microbial infections (1, 17, 19, 28,
30, 39, 40). The airway epithelium is a key orchestrator of
respiratory immune responses through the expression of cyto-
kines/chemokines, adhesion molecules, surface markers/li-
gands, and antimicrobial peptides/mucins (28). Therefore, we
focused our gene array analyses on changes in a defined set of
immune response genes in nasal biopsies obtained from non-
smokers, cigarette smokers, and e-cigarette users. Our data
demonstrate that immune genes were broadly suppressed in
both cigarette smokers and e-cigarette users compared with
nonsmokers. Within the 597 immune-related genes tested in
our study, the most significantly affected canonical pathway
common to both nasal epithelial biopsies obtained from smok-
ers and e-cigarette users was the cytokine-cytokine receptor
interaction pathway (Supplemental Table S3). In cigarette
smokers this included the suppressed expression of 18 genes
and in e-cigarette users 75 genes related to cytokines/chemo-
kines or their receptors, including the two chemokines CSF-1
and CCL26/eotaxin-3. We demonstrate that baseline levels of
CSF-1, which is important for the recruitment and activation of
innate immune cells, are reduced in the NLF of cigarette
smokers and e-cigarette users compared with nonsmokers.
CSF-1 is a major chemokine and regulatory factor for mono-
nuclear cells (46, 48). It is constitutively expressed by a variety
of epithelial cell types and has been shown to play important
roles in innate immunity, including host defense responses
against fungal, bacterial, and viral infections (4, 15). Similarly,
baseline expression of CCL26/eotaxin-3 was suppressed in
NLF from cigarette smokers, but this reduction did not reach
statistical significance in NLF from e-cigarette users. CCL26/
eotaxin-3 is produced by all epithelial cells lining the respira-
tory tract (37) and recruits and activates eosinophils in the
context of allergic airways disease (29). In addition, CCL26
has been shown to be a potent chemoattractant for nasal NK
cells (7), which we have previously demonstrated to be mod-
ified in cigarette smokers (16). Even though our data do not
demonstrate whether and how the decreased expression of
genes associated with cytokine/chemokine signaling is of bio-
logical significance, it is likely that the smoking- and vaping-
induced reduction of chemokines, such as CSF-1 and CCL26/
eotaxin-3, at the level of the epithelium has functional
consequences related to orchestrating respiratory immune
Table 2. Comparison of mean fold changes in transcription
factor genes differentially expressed in both cigarette
smokers and e-cigarette users
Transcription Factor
Cigarette Smokers vs.
E-Cigarette Users vs.
NFKBIA 1.68542 3.03275
ETS1 1.73855 3.84539
NOTCH1 1.78689 2.61221
BCL3 1.83901 3.05216
XBP1 1.9088 3.00112
BCL6 1.93451 3.33365
EGR1 2.84024 9.64874
Fig. 5. Functional networks regulated by the transcription factor EGR1 in cigarette smokers (A) and e-cigarette users (B).
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
Despite their increased use and popularity, the effects of
e-cigarettes on human health and how they compare with those
induced by cigarette smoking is largely unknown. Our data
indicate that, compared with nonsmokers, nasal biopsies ob-
tained from cigarette smokers presented an overall suppression
of immune-related genes, similar to e-cigarette users. How-
ever, the extent of suppression as well as number of immune-
related genes whose expression was significantly decreased
was six times greater in e-cigarette users than in cigarette
smokers (53 vs. 358). Recent studies suggest that, similar to
CS, immune suppressive effect and increased susceptibility to
microbial infections can also be induced by e-cigarettes. Spe-
cifically, mice exposed to e-cigarette vapor showed impaired
bacterial clearance and enhanced susceptibility to influenza
virus infections (47). In a separate study, exposure to e-ciga-
rette vapor reduced antibacterial host defense responses in
mice, resulting in increased bacterial growth and biofilm for-
mation, which was associated with decreased levels of several
important chemokines/cytokines in the bronchoalveolar lavage
(20). Our data are supportive of the findings in these mouse
studies, indicating that, similar to cigarette smoking, e-ciga-
rette use is associated with a large suppressive effect on the
expression of innate immune-related genes in the nasal mu-
cosa. Therefore, the decreased ability to fight infection and
reduced innate host defense responses associated with cigarette
smoking could also be induced by vaping e-cigarettes and may
be mediated by reduced expression of key immune genes in the
respiratory epithelium.
The majority (n9) of our e-cigarette users smoked
cigarettes prior to switching to e-cigarettes, while the remain-
der (n3) had no prior history of cigarette smoking. E-cig-
arette users and cigarette smokers had similar levels of serum
cotinine, indicating similar daily levels of nicotine exposure.
As expected, e-cigarette users had much lower urine NNAL
levels than smokers, but e-cigarette users’ levels were also
higher than those of nonsmokers. Of the e-cigarette users,
seven subjects had urine NNAL levels similar to those seen in
nonsmokers (urine NNAL 10 pg/ml; Supplemental Table
S1), suggesting that some of the e-cigarette users included in
this study occasionally used some form of tobacco. The small
sample size used in the data presented here does not allow for
a direct comparison among the subgroups of e-cigarette users.
However, it is likely that prior cigarette smoking will result in
gene expression changes that are maintained for long periods
of time after smoking cessation. Previous comparison of gene
expression profiles of bronchial epithelial cells from current,
never, and former smokers suggest smoking induces a broad
range of gene expression changes in bronchial epithelial cells
(43) including enhanced expression of genes associated with
xenobiotic metabolism or redox stress, while suppressing
genes involved in immune responses, such as CX3CL1, which
was also suppressed in our study. Follow-up studies also
indicated that some smoking-induced gene expression changes
are irreversible (45). Thus it is possible that the 53 genes
decreased in both cigarette smokers and e-cigarette users are
derived from the cigarette smoking history common to both
groups, which are not reversed by switching to e-cigarettes.
However, beyond the group of genes shared with cigarette
smokers, e-cigarette users showed 305 unique genes whose
expression was decreased compared with nonsmokers. Based
on the similar serum cotinine levels in cigarette smokers and
e-cigarette users (Table 1), it does not appear that the overall
difference in number and level of gene expression changes is
dependent on nicotine. E-cigarette vapors are a chemical mix-
ture, whose complexity varies based on several different fac-
tors, including the flavoring of the e-juice or e-liquid. We did
not collect detailed information on the preferred flavors used in
our e-cigarette user cohort, but it is unlikely that the effects
shown here can be attributed to a single flavoring chemical, but
possibly a group of flavoring or class of chemicals, like
aldehydes, common to many different vapors.
Among the most striking findings presented here is the
significantly greater number of genes suppressed in e-cigarette
users compared with cigarette smokers, including a greater
number of transcription factors. In addition to inducing a
broader suppression of immune-related genes, we also ob-
served that the level of suppression of common genes was
greater in e-cigarette users compared with cigarette smokers
(Fig. 3). This pattern included the 7 common transcription
factors whose expression was reduced in both cigarette smok-
ers and e-cigarette users. For example, EGR1, which was the
gene with the greatest fold change (FC) observed in nasal
biopsies from smokers (FC ⫽⫺2.84), was decreased almost
10-fold (FC ⫽⫺9.65) in e-cigarette users (Table 2). A com-
putational prediction model of genes regulated by the transcrip-
tion factor EGR1 and represented in this gene expression
platform indicated that a number of genes were functionally
associated with reduced EGR1 expression: specifically, 5 in
cigarette smokers and 18 in e-cigarette users. These data
suggest that the more enhanced suppression of genes encoding
transcription factors in e-cigarette users was also associated
with a greater number of downstream affected genes.
Taken together, the data shown here demonstrate that vaping
e-cigarettes does not reverse smoking-induced gene expression
changes and may result in immunomodulatory effects that go
beyond those induced by smoking cigarettes alone. The poten-
tial underlying mechanisms of these responses are just starting
to emerge as we increase our understanding of the individual
components that comprise e-cigarette vapor. The chemical
components are varied and dependent on the formulation of the
CSF1 levels (fold change)
CCL26 levels (fold change)
Fig. 6. Comparison of fold change CSF-1 levels in cigarette smokers and
e-cigarette users. Levels of CSF-1 were analyzed in NLF from cigarette
smokers and e-cigarette users and normalized to the average level observed in
nonsmokers. Data are expressed as mean SE fold change over nonsmokers.
*Statistically different from nonsmokers, P0.05.
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
e-liquid, the vaporizing device, and the aerosol generation
process itself (8, 10, 11, 49). Recent studies have demonstrated
that, depending on the dose, inhalation of the propylene glycol/
glycerin vehicle vapor alone can generate proinflammatory
responses (49). Vaporization of the humectants contained in
e-cigarettes can also lead to the generation of volatile carbon-
yls, such as formaldehyde, acrolein, and acetaldehyde, which
have known adverse pulmonary health effects (10, 11). Emis-
sion of the carbonyls is greatly dependent on the device used
and increases significantly when the output wattage is in-
creased (10). Interestingly, e-cigarette device wattage can often
be manually adjusted, especially in the more recently devel-
oped devices, thus potentially enhancing the generation of
carbonyls. In addition, the significant number of flavoring
chemicals added to e-cigarettes further enhances the complex-
ities of the inhaled mixture. Despite the common perception
that vaping e-cigarettes is a safe alternative to cigarettes, the
data shown here demonstrate the need for further studies
related to changes in respiratory immune health induced by
vaping e-cigarettes.
Dr. Glista-Baker is currently at SC Johnson, Racine, Wisconsin.
This work was supported by grants from the National Institutes of Health
(T32 ES0070108, T32 ES007126, P50 HL120100, P50 CA180890, P42
ES005948). This work was funded by NIH P50 HL120100. Research reported
in this publication was in part supported by NIH and the FDA Center for
Tobacco Products (CTP). The content is solely the responsibility of the authors
and does not necessarily represent the official views of the National Institutes
of Health or the Food and Drug Administration.
No conflicts of interest, financial or otherwise, are declared by the author(s).
E.M., P.W.C., M.E.R., E.A.P., and E.E.G.-B. performed experiments; E.M.,
P.W.C., M.E.R., E.A.P., N.L.B., R.C.F., and I.J. analyzed data; E.M., M.E.R.,
N.L.B., R.C.F., and I.J. interpreted results of experiments; E.M., M.E.R.,
E.A.P., and I.J. prepared figures; E.M., P.W.C., M.E.R., E.A.P., and I.J. drafted
manuscript; E.M., P.W.C., M.E.R., E.A.P., E.E.G.-B., N.L.B., R.C.F., and I.J.
edited and revised manuscript; M.E.R., E.E.G.-B., N.L.B., R.C.F., and I.J.
approved final version of manuscript; I.J. conception and design of research.
1. Aligne CA, Stoddard JJ. Tobacco and children. An economic evaluation
of the medical effects of parental smoking. Arch Pediatr Adolesc Med 151:
648 –653, 1997.
2. Allen JG, Flanigan SS, LeBlanc M, Vallarino J, MacNaughton P,
Stewart JH, Christiani DC. Flavoring chemicals in E-cigarettes: di-
acetyl, 2,3-pentanedione, and acetoin in a sample of 51 products, including
fruit-, candy-, and cocktail-flavored E-cigarettes. Environ Health Perspect
124: 733–739, 2016.
3. Beane J, Sebastiani P, Liu G, Brody JS, Lenburg ME, Spira A.
Reversible and permanent effects of tobacco smoke exposure on airway
epithelial gene expression. Genome Biol 8: R201, 2007.
4. Campbell JD, McDonough JE, Zeskind JE, Hackett TL, Pechkovsky
DV, Brandsma CA, Suzuki M, Gosselink JV, Liu G, Alekseyev YO,
Xiao J, Zhang X, Hayashi S, Cooper JD, Timens W, Postma DS,
Knight DA, Lenburg ME, Hogg JC, Spira A. A gene expression
signature of emphysema-related lung destruction and its reversal by the
tripeptide GHK. Genome Med 4: 67, 2012.
5. Centers for Disease Control.Emerging tobacco products gaining pop-
ularity among youth. 2013.
p1114-emerging-tobacco-products.html [06/06/2016].
6. Chitu V, Stanley ER. Colony-stimulating factor-1 in immunity and
inflammation. Curr Opin Immunol 18: 39 –48, 2006.
7. El-Shazly AE, Doloriert HC, Bisig B, Lefebvre PP, Delvenne P, Jacobs
N. Novel cooperation between CX3CL1 and CCL26 inducing NK cell
chemotaxis via CX3CR1: a possible mechanism for NK cell infiltration of
the allergic nasal tissue. Clin Exp Allergy 43: 322–331, 2013.
8. Flora JW, Meruva N, Huang CB, Wilkinson CT, Ballentine R, Smith
DC, Werley MS, McKinney WJ. Characterization of potential impurities
and degradation products in electronic cigarette formulations and aerosols.
Regul Toxicol Pharmacol 74: 1–11, 2016.
9. Food and Drug Administration. Deeming tobacco products to be subject
to the federal food, drug, and cosmetic act, as amended by the Family
Smoking Prevention and Tobacco Control Act; Restrictions on the sale
and distribution of tobacco products and required warning statements for
tobacco products. 21 CFR Parts 1100, 1140, and 1143. Federal Register
81, no. 90 (May 10, 2016): 28974.
10. Geiss O, Bianchi I, Barrero-Moreno J. Correlation of volatile carbonyl
yields emitted by e-cigarettes with the temperature of the heating coil and
the perceived sensorial quality of the generated vapours. Int J Hyg Environ
Health 219: 268 –277, 2016.
11. Gillman IG, Kistler KA, Stewart EW, Paolantonio AR. Effect of
variable power levels on the yield of total aerosol mass and formation of
aldehydes in e-cigarette aerosols. Regul Toxicol Pharmacol 75: 58 –65,
12. Goniewicz ML, Eisner MD, Lazcano-Ponce E, Zielinska-Danch W,
Koszowski B, Sobczak A, Havel C, Jacob P, Benowitz NL. Comparison
of urine cotinine and the tobacco-specific nitrosamine metabolite 4-(meth-
ylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and their ratio to discrim-
inate active from passive smoking. Nicotine Tob Res 13: 202–208, 2011.
13. Holden VK, Hines SE. Update on flavoring-induced lung disease. Curr
Opin Pulm Med 22: 158 –164, 2016.
14. Horvath KM, Brighton LE, Herbst M, Noah TL, Jaspers I. Live
attenuated influenza virus (LAIV) induces different mucosal T cell func-
tion in nonsmokers and smokers. Clin Immunol 142: 232–236, 2012.
15. Horvath KM, Brighton LE, Zhang W, Carson JL, Jaspers I. Epithelial
cells from smokers modify dendritic cell responses in the context of
influenza infection. Am J Respir Cell Mol Biol 45: 237–245, 2011.
16. Horvath KM, Herbst M, Zhou H, Zhang H, Noah TL, Jaspers I. Nasal
lavage natural killer cell function is suppressed in smokers after live
attenuated influenza virus. Respir Res 12: 102, 2011.
17. HuangFu WC, Liu J, Harty RN, Fuchs SY. Cigarette smoking products
suppress anti-viral effects of Type I interferon via phosphorylation-
dependent downregulation of its receptor. FEBS Lett 582: 3206 –3210,
18. Hubel K, Dale DC, Liles WC. Therapeutic use of cytokines to modulate
phagocyte function for the treatment of infectious diseases: current status
of granulocyte colony-stimulating factor, granulocyte-macrophage colo-
ny-stimulating factor, macrophage colony-stimulating factor, and interfer-
on-gamma. J Infect Dis 185: 1490 –1501, 2002.
19. Hudy MH, Traves SL, Wiehler S, Proud D. Cigarette smoke modulates
rhinovirus-induced airway epithelial cell chemokine production. Eur Re-
spir J 35: 1256 –1263, 2010.
20. Hwang JH, Lyes M, Sladewski K, Enany S, McEachern E, Mathew
DP, Das S, Moshensky A, Bapat S, Pride DT, Ongkeko WM, Crotty
Alexander LE. Electronic cigarette inhalation alters innate immunity and
airway cytokines while increasing the virulence of colonizing bacteria. J
Mol Med (Berl) 94: 667–679, 2016.
21. Jacob P 3rd, Havel C, Lee DH, Yu L, Eisner MD, Benowitz NL.
Subpicogram per milliliter determination of the tobacco-specific carcino-
gen metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol in human
urine using liquid chromatography-tandem mass spectrometry. Anal Chem
80: 8115–8121, 2008.
22. Jacob P 3rd, Yu L, Duan M, Ramos L, Yturralde O, Benowitz NL.
Determination of the nicotine metabolites cotinine and trans-3=-hydroxy-
cotinine in biologic fluids of smokers and nonsmokers using liquid
chromatography-tandem mass spectrometry: biomarkers for tobacco
smoke exposure and for phenotyping cytochrome P450 2A6 activity. J
Chromatogr B Analyt Technol Biomed Life Sci 879: 267–276, 2011.
23. Jang TY, Park CS, Kim KS, Heo MJ, Kim YH. Benzaldehyde sup-
presses murine allergic asthma and rhinitis. Int Immunopharmacol 22:
444 –450, 2014.
24. Jaspers I, Horvath KM, Zhang W, Brighton LE, Carson JL, Noah TL.
Reduced expression of IRF7 in nasal epithelial cells from smokers after
infection with influenza. Am J Respir Cell Mol Biol 43: 368 –375, 2010.
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
25. Kark JD, Lebiush M. Smoking and epidemic influenza-like illness in
female military recruits: a brief survey. Am J Public Health 71: 530–532,
26. Kark JD, Lebiush M, Rannon L. Cigarette smoking as a risk factor for
epidemic a(h1n1) influenza in young men. N Engl J Med 307: 1042–1046,
27. Kosmider L, Sobczak A, Prokopowicz A, Kurek J, Zaciera M, Knysak
J, Smith D, Goniewicz ML. Cherry-flavoured electronic cigarettes ex-
pose users to the inhalation irritant, benzaldehyde. Thorax 71: 376 –377,
28. Kulkarni R, Antala S, Wang A, Amaral FE, Rampersaud R, Larussa
SJ, Planet PJ, Ratner AJ. Cigarette smoke increases Staphylococcus
aureus biofilm formation via oxidative stress. Infect Immun 80: 3804 –
3811, 2012.
29. Larose MC, Chakir J, Archambault AS, Joubert P, Provost V, Lavio-
lette M, Flamand N. Correlation between CCL26 production by human
bronchial epithelial cells and airway eosinophils: involvement in patients
with severe eosinophilic asthma. J Allergy Clin Immunol 136: 904 –913,
30. Mehta H, Nazzal K, Sadikot RT. Cigarette smoking and innate immu-
nity. Inflamm Res 57: 497–503, 2008.
31. Meyer M, Kesic MJ, Clarke J, Ho E, Simmen RC, Diaz-Sanchez D,
Noah TL, Jaspers I. Sulforaphane induces SLPI secretion in the nasal
mucosa. Respir Med 107: 472–475, 2013.
32. Muller L, Jaspers I. Epithelial cells, the “switchboard” of respiratory
immune defense responses: effects of air pollutants. Swiss Med Wkly 142:
w13653, 2012.
33. Noah TL, Zhang H, Zhou H, Glista-Baker E, Muller L, Bauer RN,
Meyer M, Murphy PC, Jones S, Letang B, Robinette C, Jaspers I.
Effect of broccoli sprouts on nasal response to live attenuated influenza
virus in smokers: a randomized, double-blind study. PLoS One 9: e98671,
34. Noah TL, Zhou H, Jaspers I. Alteration of the nasal responses to
influenza virus by tobacco smoke. Curr Opin Allergy Clin Immunol 12:
24 –31, 2012.
35. Noah TL, Zhou H, Monaco J, Horvath K, Herbst M, Jaspers I.
Tobacco smoke exposure and altered nasal responses to live attenuated
influenza virus. Environ Health Perspect 119: 78 –83, 2011.
36. Noah TL, Zhou H, Zhang H, Horvath K, Robinette C, Kesic M, Meyer
M, Diaz-Sanchez D, Jaspers I. Diesel exhaust exposure and nasal
response to attenuated influenza in normal and allergic volunteers. Am J
Respir Crit Care Med 185: 179 –185, 2012.
37. Paplinska-Goryca M, Nejman-Gryz P, Chazan R, Grubek-Jaworska
H. The expression of the eotaxins IL-6 and CXCL8 in human epithelial
cells from various levels of the respiratory tract. Cell Mol Biol Lett 18:
612–630, 2013.
38. Rager JE, Bauer RN, Muller LL, Smeester L, Carson JL, Brighton
LE, Fry RC, Jaspers I. DNA methylation in nasal epithelial cells from
smokers: identification of ULBP3-related effects. Am J Physiol Lung Cell
Mol Physiol 305: L432–L438, 2013.
39. Razani-Boroujerdi S, Singh SP, Knall C, Hahn FF, Pena-Philippides
JC, Kalra R, Langley RJ, Sopori ML. Chronic nicotine inhibits inflam-
mation and promotes influenza infection. Cell Immunol 230: 1–9, 2004.
40. Robbins CS, Bauer CM, Vujicic N, Gaschler GJ, Lichty BD, Brown
EG, Stampfli MR. Cigarette smoke impacts immune inflammatory re-
sponses to influenza in mice. Am J Respir Crit Care Med 174: 1342–1351,
42. Silverman EK, Spira A, Pare PD. Genetics and genomics of chronic
obstructive pulmonary disease. Proc Am Thorac Soc 6: 539 –542, 2009.
43. Spira A, Beane J, Shah V, Liu G, Schembri F, Yang X, Palma J,
Brody JS. Effects of cigarette smoke on the human airway epithelial cell
transcriptome. Proc Natl Acad Sci USA 101: 10143–10148, 2004.
44. Spira A, Beane JE, Shah V, Steiling K, Liu G, Schembri F, Gilman S,
Dumas YM, Calner P, Sebastiani P, Sridhar S, Beamis J, Lamb C,
Anderson T, Gerry N, Keane J, Lenburg ME, Brody JS. Airway
epithelial gene expression in the diagnostic evaluation of smokers with
suspect lung cancer. Nat Med 13: 361–366, 2007.
45. Sridhar S, Schembri F, Zeskind J, Shah V, Gustafson A, Steiling K,
Liu G, Dumas YM, Zhang X, Brody J, Lenburg M, Spira A. Smoking-
induced gene expression changes in the bronchial airway are reflected in
nasal and buccal epithelium. BMC Genomics 9: 259, 2008.
46. Stanley ER, Guilbert LJ, Tushinski RJ, Bartelmez SH. CSF-1—a
mononuclear phagocyte lineage-specific hemopoietic growth factor. J Cell
Biochem 21: 151–159, 1983.
47. Sussan TE, Gajghate S, Thimmulappa RK, Ma J, Kim JH, Sudini K,
Consolini N, Cormier SA, Lomnicki S, Hasan F, Pekosz A, Biswal S.
Exposure to electronic cigarettes impairs pulmonary anti-bacterial and
anti-viral defenses in a mouse model. PLoS One 10: e0116861, 2015.
48. Tushinski RJ, Stanley ER. The regulation of macrophage protein turn-
over by a colony stimulating factor (CSF-1). J Cell Physiol 116: 67–75,
48a.U.S. Department of Health and Human Services.The Health Conse-
quences of Smoking—50 Years of Progress: a report of the Surgeon
General. A Report of the Surgeon General, 2014. Atlanta, GA: Department
of Health and Human Services, National Center for Chronic Disease
Prevention and Health Promotion, Office on Smoking and Health, 2014.
49. Werley MS, Kirkpatrick DJ, Oldham MJ, Jerome AM, Langston TB,
Lilly PD, Smith DC, McKinney WJ Jr. Toxicological assessment of a
prototype e-cigaret device and three flavor formulations: a 90-day inhala-
tion study in rats. Inhal Toxicol 28: 22–38, 2016.
50. Zaccone EJ, Goldsmith WT, Shimko MJ, Wells JR, Schwegler-Berry
D, Willard PA, Case SL, Thompson JA, Fedan JS. Diacetyl and
2,3-pentanedione exposure of human cultured airway epithelial cells: ion
transport effects and metabolism of butter flavoring agents. Toxicol Appl
Pharmacol 289: 542–549, 2015.
AJP-Lung Cell Mol Physiol doi:10.1152/ajplung.00170.2016
by on January 9, 2017 from
... The findings from the t-cig-exposed cells are consistent with prior findings that t-cig smoking causes a deficiency in host defenses and epithelial barrier breakdown in vivo and in vitro [29][30][31][32][33]. It has been suggested that vaping e-cigs stimulates cytokine release and may elicit cell toxicity and inflammation in the nasal epithelial mucosa [34]. In fact, e-cig usage has been linked to reduction in the expression of immune-related genes [34][35][36]. ...
... It has been suggested that vaping e-cigs stimulates cytokine release and may elicit cell toxicity and inflammation in the nasal epithelial mucosa [34]. In fact, e-cig usage has been linked to reduction in the expression of immune-related genes [34][35][36]. The administration of e-cig fluid, rather than vapor, to primary epithelial cells has been demonstrated to increase inflammation and virus infection susceptibility [37]. ...
Full-text available
Information about the potential oral health effects of vaping from electronic cigarettes (e-cigs) is still sparse and inconsistent. The purpose of this study was to compare the safety and cytotoxicity of e-cig liquid aerosols versus traditional cigarette (t-cig) smoke on human epithelial oral cells. T-cig smoke and e-cig aerosols were generated by a newly developed automated smoking instrument in order to simulate realistic user puffing behaviors. Air–liquid interface transwell cell cultures were exposed to standardized puff topography (puff duration: 2 s, puff volume: 35 mL, puff frequency: 1 puff every 60 s) of reference t-cigs or commercially available e-cigs at different air dilutions. Cell viability, morphology, and death rate were evaluated with MTT and TUNEL assays. The inflammatory cytokine gene expression of inflammatory genes was assessed by quantitative RT-PCR. E-cigs and t-cigs indicated similar adverse effects by enhancing cytotoxicity and cell death in a dose-dependent manner. E-cig aerosol and t-cig smoke treatment expressed upregulation of inflammatory cytokines up to 3.0-fold (p < 0.05). These results indicate that e-cig smoking may contribute to oral tissue–cell damage and tissue inflammation. Our approach allows the production of e-cig aerosol and t-cig smoke in order to identify harmful effects in oral tissues in vitro.
... Although, at much lower levels than found in cigarette smoke, e-cigarette aerosol has been found to contain ultrafine particles and known toxins, such as acetaldehyde, acrolein, toluene, and formaldehyde (Goniewicz et al., 2014). Recently, a growing body of evidence demonstrated that e-cigarette use is associated with numerous short-and longterm deleterious health consequences, including asthma, cardiovascular, oral, brain, kidney, and pulmonary diseases (Antoniewicz et al., 2016;Martin et al., 2016;Osei et al., 2019a;Osei et al., 2019b;Huilgol et al., 2019). ...
Full-text available
Aim: The prevalence of the use of electronic cigarettes (e-cigarettes) has grown rapidly in the past decade in the United States. While numerous studies have demonstrated combustible cigarette is closely associated with an increased risk of arthritis diseases, little is known about the effect of e-cigarette usage on inflammatory arthritis diseases. We aimed to determinate if e-cigarette usage is associated with an increased risk of inflammatory arthritis. Methods: Data were obtained from the Behavioral Risk Factor Surveillance System, which is the largest national telephone-based survey of randomly sampled adults in the United States. A total of 924,882 participants with information on e-cigarette usage and inflammatory arthritis were included. We used multivariable logistic regression to estimate the risk of arthritis associated with e-cigarette usage. Results: Of the 924,882 participants, there were 30,569 (3.3%) current e-cigarette users, and 314,190 (25.9%) reported to have inflammatory arthritis diseases. In the fully adjusted model, we observed that the odds ratio (OR) (95% confidence interval) of inflammatory arthritis diseases was 1.81 (95% CI, 1.70-1.93) for current e-cigarette users compared with never e-cigarette users. The ORs of inflammatory arthritis diseases were 1.31 (95% CI, 1.18-1.47), and 1.55 (95% CI, 1.42-1.69) among sole e-cigarette and dual users compared with never e-cigarette users, respectively. Conclusions: This is the first study to observe a cross-sectional association between e-cigarette usage and inflammatory arthritis diseases, and the findings were consistent in both sole-e-cigarette users and dual users. Our findings provide evidence that e-cigarette usage might be an important risk factor for arthritis diseases, which may have regulatory implications for e-cigarette control.
... MucilAir ™ (Epithelix) primary nasal cells have been directly exposed to ENDS aerosol and 3R4F Kentucky reference cigarette smoke, revealing that ENDS aerosol had reduced effect on gene expression [24,25]. However, other studies have reported extensive reprogramming of gene expression, suggesting that ENDS use is associated with inflammatory responses [28]. Further investigation into the effects of cigarette and ENDS products on gene expression in fully-differentiated primary NHBE cultures are therefore required. ...
Full-text available
Background Acute exposure to cigarette smoke alters gene expression in several biological pathways such as apoptosis, immune response, tumorigenesis and stress response, among others. However, the effects of electronic nicotine delivery systems (ENDS) on early changes in gene expression is relatively unknown. The objective of this study was to evaluate the early toxicogenomic changes using a fully-differentiated primary normal human bronchial epithelial (NHBE) culture model after an acute exposure to cigarette and ENDS preparations. Results RNA sequencing and pathway enrichment analysis identified time and dose dependent changes in gene expression and several canonical pathways when exposed to cigarette preparations compared to vehicle control, including oxidative stress, xenobiotic metabolism, SPINK1 general cancer pathways and mucociliary clearance. No changes were observed with ENDS preparations containing up to 28 µg/mL nicotine. Full model hierarchical clustering revealed that ENDS preparations were similar to vehicle control. Conclusion This study revealed that while an acute exposure to cigarette preparations significantly and differentially regulated many genes and canonical pathways, ENDS preparations containing the same concentration of nicotine had very little effect on gene expression in fully-differentiated primary NHBE cultures.
... When attacked, the airway employs three basic mechanisms to maintain its integrity: mucociliary clearance, cough, and innate immunity. Vaping increases the risk of respiratory infections by downregulating these mechanisms, since it impairs the function of the cilia, reduces the sensitivity of cough receptors, impairs neutrophil and alveolar macrophage function, and modifies the expression of genes and molecules involved in immune reactions [45][46][47][48]. Although these studies were not performed on asthmatic airways, infections are a well-known trigger of asthma exacerbations [49], and vaping may act as a mediator. ...
Full-text available
Adolescence is a period characterized by developmental, psychological, and psychosocial alterations, with a major impact on youths’ attitudes and perceptions. Adolescents with asthma may not comply with treatment and may develop risky behaviors, including smoking, vaping, and other substance use, leading to unanticipated exacerbations and consequences. Vaping has become extremely popular in this age group, and studies have suggested that it has potential adverse effects on asthmatic airways. More well-designed studies are needed to confirm the initial worrying data, and action must be taken by both medical officers and health authorities to deal with the elephant in the room and curb the vaping pandemic. The aim of this paper is to provide a review of the current knowledge regarding the effect of vaping on adolescents with asthma and to propose actions to restrain this fast-growing trend.
Objectives: This work attempts to summarize current knowledge on the effects of cigarettes, electronic nicotine delivery systems and tobacco heating products on miRNA-mediated gene expression regulation and on their possible impact on smoking-related respiratory disease development. Materials and methods: Literature search by terms combination: 'smoking', 'cigarette' 'THP', 'tobacco heating product', 'ENDS', 'electronic nicotine delivery system', 'e-cigarette', electronic cigarette' and 'miRNA-mediated gene expression' has been performed from October 2021 to February 2022. In this systematic review all relevant literature, including clinical trials, cellular and animal-based studies were included. Results: Cigarette smoke (CS) significantly altered transcriptome, including miRNAs expression profile. MiRNA-mediated gene expression is mentioned as one of the mechanisms associated with smoking-related respiratory disease development. Differential expression of miRNAs was reduced in aerosol from e-cigarettes (EC) and tobacco heating products (THP) when compared to CS. However, there was a significant alteration of some miRNAs expression when compared to air-controls in both EC and THP. Discussion: CS negatively affects transcriptome and miRNA-mediated gene expression regulation because of a huge number of hazardous substances which predispose to smoking-related diseases. Despite the reduced effect of ENDS and THP on miRNAs profile compared to CS, differences in expression of miRNAs when compared to air-control were observed, which may be harmful to never-smokers who may perceive such alternative smoking products as non-hazardous. To clearly indicate the role of ENDS and THP in the alteration of miRNA-mediated gene expression and the development of smoking-related respiratory diseases associated with this mechanism, more long-term studies should be performed in the future.
In recent years, new nicotine delivery methods have emerged, and many users are choosing electronic cigarettes (e-cigarettes) over traditional tobacco cigarettes. E-cigarette use is very popular among adolescents, with more than 3.5 million currently using these products in the US. Despite the increased prevalence of e-cigarette use, there is limited knowledge regarding the health impact of e-cigarettes on the general population. Based on published findings by others, E-cigarette is associated with lung injury outbreak, which increased health and safety concerns related to consuming this product. Different components of e-cigarettes, including food-safe liquid solvents and flavorings, can cause health issues related to pneumonia, pulmonary injury, and bronchiolitis. In addition, e-cigarettes contain alarmingly high levels of carcinogens and toxicants that may have long-lasting effects on other organ systems, including the development of neurological manifestations, lung cancer, cardiovascular disorders, and tooth decay. Despite the well- documented potential for harm, e-cigarettes do not appear to increase susceptibility to SARS-CoV- 2 infection. Furthermore, some studies have found that e-cigarette users experience improvements in lung health and minimal adverse effects. Therefore, more studies are needed to provide a definitive conclusion on the long-term safety of e-cigarettes. The purpose of this review is to inform the readers about the possible health-risks associated with the use of e-cigarettes, especially among the group of young and young-adults, from a molecular biology point of view.
This study compared baseline cytokine signatures in upper and lower airway samples and evaluated how groups of co-expressed cytokines change with tobacco product use. Matched nasal lavage fluid (NLF), nasal epithelial lining fluid (NELF), sputum, and circulating serum samples were collected from 14 non-smokers, 13 cigarette smokers, and 17 e-cigarette users. Samples were analyzed for 22 cytokines and cytokine signatures were compared across each sample, followed by identification of cytokine clusters with co-modulation patterns within each type of sample, which were subsequently evaluated for potential alterations associated with tobacco product use using eigenvector analyses. Findings included individual cytokine analyses that were carried out using crude, adjusted, and stratified models. Results were confirmed using a separate validation cohort of similar size and characteristics. Cytokine signatures in the respiratory tract were significantly correlated (NLF, NELF, and sputum) compared to randomly permutated signatures, while serum was not. Cytokines clusters identified in upper and lower airways samples were modified in association with tobacco product use, particularly e-cigarettes, showing significant changes in cytokines involved in host defense and chemotactic immune cell recruitment. Overall, analyses at the individual cytokine-level demonstrated less sensitivity and did not identify biologically meaningful differences between tobacco product use groups, highlighting the utility of cluster-based evaluations. NELF cytokine clusters and associated tobacco product disruptions were confirmed in an independent validation cohort. Hence, novel systems-level patterns uncovered changes induced by tobacco products, in particular, elevations in e-cigarette user nasal eigencytokines and identified that upper airway samples simulate cytokine patterns in lower airways.
Introduction Numerous studies have revealed the relationship between E-cigarettes and asthma but have shown inconsistent results. This study systematically evaluated the potential association between E-cigarette use and asthma in adolescents. Methods PubMed, Embase (Ovid), Cochrane Library, and the China Biological Medicine Database were searched for relevant articles published between database inception and February 28, 2021. The quality of included studies was evaluated using the Agency for Healthcare Research and Quality assessment, and a quantitative meta-analysis was conducted to pool outcomes of ORs with 95% CIs. Results A total of 10 cross-sectional studies incorporating a total of 483,948 participants were included. All the study participants were middle- and high-school students with a mean age of 15–16 years. The median prevalence of ever E-cigarette use was 11.2% (range=2.2%, 45%), and that of current use was 7.5% (range=2.7%, 25%). Overall, E-cigarette use was associated with significantly higher odds of having asthma (pooled OR=1.31, 95% CI=1.22, 1.42) than nonuse, and both current use (OR=1.36, 95% CI=1.26, 1.48) and ever use (OR=1.20, 95% CI=1.12, 1.28) showed similar associations. Discussion This study shows that both current and ever E-cigarette use have significant associations with asthma in adolescents. This knowledge might provide potential evidence for developing primary prevention strategies and serve as a reference for public health policy.
It is currently understood that tobacco smoking is a major cause of pulmonary disease due to pulmonary/lung inflammation. However, due to a highly dynamic market place and an abundance of diverse products, less is known about the effects of e-cigarette (E-cig) use on the lung. In addition, varieties of E-cig liquids (e-liquids), which deliver nicotine and numerous flavor chemicals into the lungs, now number in the 1000s. Thus, a critical need exists for safety evaluations of these E-cig products. Herein, we employed a “2-stage in vivo screening platform” (zebrafish to mouse) to assess the safety profiles of e-liquids. Using the zebrafish, we collected embryo survival data after e-liquid exposure as well as neutrophil migration data, a key hallmark for a pro-inflammatory response. Our data indicate that certain e-liquids induce an inflammatory response in our zebrafish model and that e-liquid exposure alone results in pro-inflammatory lung responses in our C57BL/6J model, data collected from lung staining and ELISA analysis, respectively, in the mouse. Thus, our platform can be used as an initial assessment to ascertain the safety profiles of e-liquid using acute inflammatory responses (zebrafish, Stage 1) as our initial metric followed by chronic studies (C57BL/6J, Stage 2).
Background: Despite recent tobacco control efforts, adolescent vaping remains an epidemic in the United States. The purpose of our study was to understand high school student vaping behaviors using the perceptions of Massachusetts school personnel during the critical window from when the Massachusetts statewide flavor ban legislation was passed in November 2019 through the first year of the COVID-19 pandemic. Methods: High school personnel throughout Massachusetts were invited to complete a cross-sectional survey. Analysis of survey responses was conducted in R. Results: A total of 162 respondents completed the survey representing 137 schools that draw enrollments from 216 (61%) of the 352 cities and towns in Massachusetts. The most popular products that respondents believed their students were using were JUULs (95.7%), other e-cigarettes (85.3%), and disposable vapes (79.6%). Following the flavor ban, the majority (90.7%) did not report an increase in combusted tobacco product use. All participants (100%) reported wanting more access to prevention and treatment resources. Conclusions: Our findings suggest that a comprehensive flavor ban may be an effective tobacco control policy that does not appear to promote student switching from vaping products to combusted tobacco products. These data also indicate that schools report needing additional resources to address the vaping epidemic.
Full-text available
E-liquids generally contain four main components: nicotine, flavours, water and carrier liquids. The carrier liquid dissolves flavours and nicotine and vaporizes at a certain temperature on the atomizer of the e-cigarette. Propylene glycol and glycerol, the principal carriers used in e-liquids, undergo decomposition in contact with the atomizer heating-coil forming volatile carbonyls. Some of these, such as formaldehyde, acetaldehyde and acrolein, are of concern due to their adverse impact on human health when inhaled at sufficient concentrations. The aim of this study was to correlate the yield of volatile carbonyls emitted by e-cigarettes with the temperature of the heating coil. For this purpose, a popular commercial e-liquid was machine-vaped on a third generation e-cigarette which allowed the variation of the output wattage (5-25 watt) and therefore the heat generated on the atomizer heating-coil. The temperature of the heating-coil was determined by infrared thermography and the vapour generated at each temperature underwent subjective sensorial quality evaluation by an experienced vaper. A steep increase in the generated carbonyls was observed when applying a battery-output of at least 15 watt corresponding to 200-250 °C on the heating coil. However, when considering concentrations in each inhaled puff, the short-term indoor air guideline value for formaldehyde was already exceeded at the lowest wattage of 5 watt, which is the wattage applied in most 2nd generation e-cigarettes. Concentrations of acetaldehyde in each puff were several times below the short-term irritation threshold value for humans. Acrolein was only detected from 20 watts upwards. The negative sensorial quality evaluation by the volunteering vaper of the vapour generated at 20 watt demonstrated the unlikelihood that such a wattage would be realistically set by a vaper. This study highlights the importance to develop standardised testing methods for the assessment of carbonyl-emissions and emissions of other potentially harmful compounds from e-cigarettes. The wide variety and variability of products available on the market make the development of such methods and the associated standardised testing conditions particularly demanding.
Full-text available
Electronic (e)-cigarette use is rapidly rising, with 20 % of Americans ages 25–44 now using these drug delivery devices. E-cigarette users expose their airways, cells of host defense, and colonizing bacteria to e-cigarette vapor (EV). Here, we report that exposure of human epithelial cells at the air–liquid interface to fresh EV (vaped from an e-cigarette device) resulted in dose-dependent cell death. After exposure to EV, cells of host defense—epithelial cells, alveolar macrophages, and neutrophils—had reduced antimicrobial activity against Staphylococcus aureus (SA). Mouse inhalation of EV for 1 h daily for 4 weeks led to alterations in inflammatory markers within the airways and elevation of an acute phase reactant in serum. Upon exposure to e-cigarette vapor extract (EVE), airway colonizer SA had increased biofilm formation, adherence and invasion of epithelial cells, resistance to human antimicrobial peptide LL-37, and up-regulation of virulence genes. EVE-exposed SA were more virulent in a mouse model of pneumonia. These data suggest that e-cigarettes may be toxic to airway cells, suppress host defenses, and promote inflammation over time, while also promoting virulence of colonizing bacteria. Key message Acute exposure to e-cigarette vapor (EV) is cytotoxic to airway cells in vitro. Acute exposure to EV decreases macrophage and neutrophil antimicrobial function. Inhalation of EV alters immunomodulating cytokines in the airways of mice. Inhalation of EV leads to increased markers of inflammation in BAL and serum. Staphylococcus aureus become more virulent when exposed to EV.
Full-text available
The study objective was to determine the effect of variable power applied to the atomizer of refillable tank based e-cigarette (EC) devices. Five different devices were evaluated, each at four power levels. Aerosol yield results are reported for each set of 25 EC puffs, as mass/puff, and normalized for the power applied to the coil, in mass/watt. The range of aerosol produced on a per puff basis ranged from 1.5 to 28 mg, and, normalized for power applied to the coil, ranged from 0.27 to 1.1 mg/watt. Aerosol samples were also analyzed for the production of formaldehyde, acetaldehyde, and acrolein, as DNPH derivatives, at each power level. When reported on mass basis, three of the devices showed an increase in total aldehyde yield with increasing power applied to the coil, while two of the devices showed the opposite trend. The mass of formaldehyde, acetaldehyde, and acrolein produced per gram of total aerosol produced ranged from 0.01 to 7.3 mg/g, 0.006 to 5.8 mg/g, and <0.003 to 0.78 mg/g, respectively. These results were used to estimate daily exposure to formaldehyde, acetaldehyde, and acrolein from EC aerosols from specific devices, and were compared to estimated exposure from consumption of cigarettes, to occupational and workplace limits, and to previously reported results from other researchers.
Full-text available
Background: There are over 7,000 e-cigarette flavors currently marketed. Flavoring chemicals gained notoriety in the early 2000's when inhalation exposure of the flavoring chemical diacetyl was found to be associated with a disease that became known as "Popcorn Lung." There has been limited research on flavoring chemicals in e-cigarettes. Objective: To determine if the flavoring chemical diacetyl, and two other high-priority flavoring chemicals 2,3-pentanedione, and acetoin, are present in a convenience sample of flavored e-cigarettes. Methods: We selected 51 types of flavored e-cigarettes sold by leading e-cigarette brands and flavors we deemed were appealing to youth. E-cigarette contents were fully discharged and the air stream was captured and analyzed for total mass of diacetyl, 2,3-pentanedione, and acetoin, according to OSHA Method 1012. Results: At least one flavoring chemical was detected in 47 of 51 unique flavors tested. Diacetyl was detected above the laboratory limit of detection 39 of the 51 flavors tested, ranging from < limit of qualification (LOQ) to 239 μg/e-cigarette. 2,3-pentanedione and acetoin were detected in 23 and 46 of the 51 flavors tested at concentrations up to 64 and 529 μg/e-cigarette, respectively. Conclusion: Due to the associations between diacetyl, bronchiolitis obliterans and other severe respiratory diseases observed in workers, urgent action is recommended to further evaluate this potentially widespread exposure via flavored e-cigarettes.
The Food and Drug Administration (FDA) is issuing this final rule to deem products meeting the statutory definition of "tobacco product,'' except accessories of the newly deemed tobacco products, to be subject to the Federal Food, Drug, and Cosmetic Act (the FD&C Act), as amended by the Family Smoking Prevention and Tobacco Control Act (Tobacco Control Act). The Tobacco Control Act provides FDA authority to regulate cigarettes, cigarette tobacco, roll-your-own tobacco, smokeless tobacco, and any other tobacco products that the Agency by regulation deems to be subject to the law. With this final rule, FDA is extending the Agency's "tobacco product'' authorities in the FD&C Act to all other categories of products that meet the statutory definition of "tobacco product" in the FD&C Act, except accessories of such newly deemed tobacco products. This final rule also prohibits the sale of "covered tobacco products" to individuals under the age of 18 and requires the display of health warnings on cigarette tobacco, roll-your own tobacco, and covered tobacco product packages and in advertisements. FDA is taking this action to reduce the death and disease from tobacco products. In accordance with the Tobacco Control Act, we consider and intend the extension of our authorities over tobacco products and the various requirements and prohibitions established by this rule to be severable.
Many non-cigarette tobacco products, including e-cigarettes, contain various flavourings, such as fruit flavours. Although many flavourings used in e-cigarettes are generally recognised as safe when used in food products, concerns have been raised about the potential inhalation toxicity of these chemicals. Benzaldehyde, which is a key ingredient in natural fruit flavours, has been shown to cause irritation of respiratory airways in animal and occupational exposure studies. Given the potential inhalation toxicity of this compound, we measured benzaldehyde in aerosol generated in a laboratory setting from flavoured e-cigarettes purchased online and detected benzaldehyde in 108 out of 145 products. The highest levels of benzaldehyde were detected in cherry-flavoured products. The benzaldehyde doses inhaled with 30 puffs from flavoured e-cigarettes were often higher than doses inhaled from a conventional cigarette. Levels in cherry-flavoured products were >1000 times lower than doses inhaled in the workplace. While e-cigarettes seem to be a promising harm reduction tool for smokers, findings indicate that using these products could result in repeated inhalation of benzaldehyde, with long-term users risking regular exposure to the substance. Given the uncertainty surrounding adverse health effects stemming from long-term inhalation of flavouring ingredients such as benzaldehyde, clinicians need to be aware of this emerging risk and ask their patients about use of flavoured e-cigarettes.
A prototype electronic cigaret device and three formulations were evaluated in a 90-day rat inhalation study followed by a 42-day recovery period. Animals were randomly assigned to groups for exposure to low-, mid- and high-dose levels of aerosols composed of vehicle (glycerin and propylene glycol mixture); vehicle and 2.0% nicotine; or vehicle, 2.0% nicotine and flavor mixture. Daily targeted aerosol total particulate matter (TPM) doses of 3.2, 9.6 and 32.0 mg/kg/day were achieved by exposure to 1 mg/L aerosol for 16, 48 and 160 min, respectively. Pre-study evaluations included indirect ophthalmoscopy, virology and bacteriological screening. Body weights, clinical observations and food consumption were monitored weekly. Plasma nicotine and cotinine and carboxyhemoglobin levels were measured at days 28 and 90. After days 28, 56 and 90, lung function measurements were obtained. Biological endpoints after 90-day exposure and 42-day recovery period included clinical pathology, urinalysis, bronchoalveolar fluid (BALF) analysis, necropsy and histopathology. Treatment-related effects following 90 days of exposure included changes in body weight, food consumption and respiratory rate. Dose-related decreases in thymus and spleen weights, and increased BALF lactate dehydrogenase, total protein, alveolar macrophages, neutrophils and lung weights were observed. Histopathology evaluations revealed sporadic increases in nasal section 1-4 epithelial hyperplasia and vacuolization. Following the recovery period, effects in the nose and BALF were persistent while other effects were resolved. The no observed effect level based upon body weight decreases is considered to be the mid-dose level for each formulation, equivalent to a daily TPM exposure dose of approximately 9.6 mg/kg/day.
Purpose of review: Since the initial report of bronchiolitis obliterans in microwave popcorn workers, exposures to flavoring substances have been identified in a variety of food and flavor manufacturing facilities and in the consumer market. Attempts to decrease the risk of lung disease have included the use of flavoring substitutes; however, these chemicals may cause similar injury. This article reviews recent flavoring exposures and data on the pathogenesis, clinical characteristics, and surveillance of flavoring-induced lung disease. Recent findings: Diacetyl and 2,3-pentanedione exposures have occurred in food production facilities that make cookies, cereal, chocolate, and coffee. Airborne levels often exceed proposed occupational exposure limits. Cases of biopsy-proven bronchiolitis obliterans in heavy popcorn consumers have also been reported. New data demonstrate the presence of diacetyl and 2,3-pentanedione in flavored nicotine liquids used in electronic nicotine delivery systems. Summary: Diacetyl substitutes cause similar peri-bronchiolar fibrotic lesions in animal studies. Their use may continue to place workers at risk for flavoring-induced lung disease, which may present in forms beyond that of fixed airflow obstruction, contributing to delays in identifying and treating patients with flavoring-induced lung disease. Engineering controls, medical surveillance and personal protective equipment can limit flavorings exposure and risk for lung disease.