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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,
1
Phillip W. Clapp,
2
Meghan E. Rebuli,
2
Erica A. Pawlak,
3
Ellen Glista-Baker,
3
Neal L. Benowitz,
4
Rebecca C. Fry,
1,2
and Ilona Jaspers
2,3,4
1
Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North
Carolina, Chapel Hill, North Carolina;
2
Curriculum in Toxicology, School of Medicine, University of North Carolina, Chapel
Hill, North Carolina;
3
Center for Environmental Medicine, Asthma, and Lung Biology, School of Medicine, University of
North Carolina, Chapel Hill, North Carolina; and
4
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 (n⫽53) were also decreased in e-cigarette smokers. Additionally,
vaping e-cigarettes was associated with suppression of a large number
of unique genes (n⫽305). 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: ilona_jaspers@med.unc.edu).
Am J Physiol Lung Cell Mol Physiol 311: L135–L144, 2016.
First published June 10, 2016; doi:10.1152/ajplung.00170.2016.
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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.
MATERIALS AND METHODS
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
1
) 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, n⫽9 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
website).
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 n⫽597 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 (n⫽13) Cigarette Smokers (n⫽14) E-Cigarette Users (n⫽12)
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.
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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 P⬍0.05 with a false discovery
corrected q⬍0.1.
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 P⬍0.05. 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 P⬍0.01. 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 ⱖ2orⱕ⫺2 corresponding to
aPvalue of 0.05. For this reason statistical significance was set at
z⬎|2|.
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
t-test.
RESULTS
Description of study subjects. Among the individuals in-
cluded in this study (n⫽39), about an equal number were
nonsmokers (n⫽13; 33.3%), cigarette smokers (n⫽14;
35.8%), and e-cigarette users (n⫽12; 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 (P⬍0.05). Similarly, serum cotinine levels
were significantly correlated with e-cigarette puffs per day
(P⬍0.01), 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
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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
0
200
400
600
800
Cigarette/day
Serum Cotinine ng/ml
Cigarette Smokers
0 5 10 15 20 25
0
200
400
600
800
Cigarette/day
NNAL/creatinine pg/ml
Cigarette Smokers
0 200 400 600 800
0
200
400
600
E-cig Users
Puffs/day
Serum Cotinine ng/ml
0 200 400 600 800
0
200
400
600
800
Puffs/day
NNAL/creatinine pg/ml
E-cig Users
BA
DC
r2=0.5
p<0.05
r2=0.46
p<0.05
r2=0.62
p<0.01
r2=0.01
NS
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.
CS
EC
0
100
200
300
400
Number of Genes
053 305
BA
CS EC
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
users.
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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.
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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).
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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).
DISCUSSION
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
capabilities.
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.
Nonsmokers
E-Cigarette Users vs.
Nonsmokers
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).
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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 (n⫽9) of our e-cigarette users smoked
cigarettes prior to switching to e-cigarettes, while the remain-
der (n⫽3) 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
CS
EC
-0.6
-0.4
-0.2
0.0
CSF1 levels (fold change)
*
*
CS
E
C
-2.0
-1.5
-1.0
-0.5
0.0
CCL26 levels (fold change)
*
AB
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, P⬍0.05.
L142 GENE EXPRESSION IN NASAL EPITHELIAL CELLS FROM E-CIGARETTE USERS
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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.
ACKNOWLEDGMENTS
Dr. Glista-Baker is currently at SC Johnson, Racine, Wisconsin.
GRANTS
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.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
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.
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