Research Article

Woodsmoke particle exposure prior to SARS-CoV-2 infection alters antiviral response gene expression in human nasal epithelial cells in a sex-dependent manner

Published Online:


Inhalational exposure to particulate matter (PM) derived from natural or anthropogenic sources alters gene expression in the airways and increases susceptibility to respiratory viral infection. Woodsmoke-derived ambient PM from wildfire events during 2020 was associated with higher COVID-19 case rates in the western United States. We hypothesized that exposure to suspensions of woodsmoke particles (WSPs) or diesel exhaust particles (DEPs) prior to SARS-CoV-2 infection would alter host immune gene expression at the transcript level. Primary human nasal epithelial cells (hNECs) from both sexes were exposed to WSPs or DEPs (22 μg/cm2) for 2 h, followed by infection with SARS-CoV-2 at a multiplicity of infection of 0.5. Forty-six genes related to SARS-CoV-2 entry and host response were assessed. Particle exposure alone minimally affected gene expression, whereas SARS-CoV-2 infection alone induced a robust transcriptional response in hNECs, upregulating type I and III interferons, interferon-stimulated genes, and chemokines by 72 h postinfection (p.i.). This upregulation was higher overall in cells from male donors. However, exposure to WSPs prior to infection dampened expression of antiviral, interferon, and chemokine mRNAs. Sex stratification of these results revealed that WSP exposure downregulated gene expression in cells from females more so than males. We next hypothesized that hNECs exposed to particles would have increased apical viral loads compared with unexposed cells. Although apical viral load was correlated to expression of host response genes, viral titer did not differ between groups. These data indicate that WSPs alter epithelial immune responses in a sex-dependent manner, potentially suppressing host defense to SARS-CoV-2 infection.


Wildfires contribute significantly to air pollution and ambient particulate matter (PM) (1, 2). During the 2020 fire season, large, populous regions of the western United States were exposed to unhealthy or hazardous air quality from woodsmoke-derived PM (3). Studies have shown that wildland firefighters can be exposed to respirable particulate matter at concentrations >1 mg/m3 over the course of their work shift with maximum exposures reaching >2.5 mg/m3 (46). Particulate air pollution released from burning wildlands is associated with negative respiratory and cardiovascular health outcomes (reviewed in refs. 710), the toxicity of which depends heavily on the type of biomass burned and the burn temperature (11). Epidemiological studies examining the health effects of wildfires showed an association between PM from wildfires and increased respiratory hospitalizations across 16 western states (12). A similar health effects study in California showed that women were more likely than men to visit the hospital for asthma- or hypertension-related reasons due to an increase in wildfire-generated PM (13), suggesting sex-dependent effects.

Coinciding with severe wildfires was the global coronavirus disease 2019 (COVID-19) pandemic that is, to date, responsible for over 4.9 million deaths worldwide (14). Sex has been found to affect COVID-19 outcomes with males more likely than females to develop severe or fatal cases of the disease (1517). SARS-CoV-2, the etiologic agent behind COVID-19, primarily affects the respiratory system (18) and exhibits tropism for cells of the upper airways, with nasal epithelial cells most susceptible to infection (19). Primary human nasal epithelial cells (hNECs) grown in vitro at air-liquid interface (ALI) mimic in vivo differentiation patterns, evidenced by expression of mucins, presence of beating cilia, and tight junction formation (20, 21). Because the nasal epithelium expresses the SARS-CoV-2 viral entry factors angiotensin converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) in ciliated and secretory cells (22), the differentiated hNEC model is a suitable in vitro culture system to study SARS-CoV-2 pathogenesis. Along with biological aerosols, the nasal epithelium is exposed to airborne particulates, gaseous pollution, and allergens in vivo. Thus, in addition to being a useful model for studying respiratory viral infection (23, 24), hNECs demonstrate utility for toxicological studies involving aerosolized (25, 26) and gaseous (27) toxicants.

Exposure to air pollution is known to alter susceptibility to respiratory viral infection (reviewed in refs. 28 and 29). In vitro models of respiratory epithelium treated with diesel exhaust particles (DEPs) prior to influenza infection demonstrated increases in viral attachment and the number of virus-infected cells relative to untreated cells (23). Red oak woodsmoke exposure followed by live attenuated influenza virus (LAIV) inoculation suppressed expression of host defense genes in women and upregulated many proinflammatory genes in men (30). Numerous epidemiological studies from around the world have found correlations between ambient air pollution levels and COVID-19 case number or case fatality rate (3135). Recently, two studies found positive associations between ambient woodsmoke particles (WSPs) and COVID-19 cases and deaths in the western United States (36, 37).

The present study examined the interactive effects of sex, exposure to WSPs, and SARS-CoV-2 infection on gene expression in hNECs. To do this, hNECs from male and female healthy human donors were exposed to aqueous suspensions of DEPs or WSPs derived from burned eucalyptus or red oak. Particle exposures occurred prior to and during infection with SARS-CoV-2 and sampling occurred at 0, 24, and 72 h postinfection (p.i.). We measured expression of a panel of 46 genes related to respiratory viral infection and host immune response, including the SARS-CoV-2 entry factor (ACE2), several airway proteases, interferons, interferon-stimulated genes (ISGs), chemokines, transcription factors, pathogen recognition receptors, mucins, and surfactants. In addition, the effects of particle exposure on viral load were assessed by measuring viral titers in apical washes collected from hNECs in the various exposure groups.


Primary Nasal Epithelial Cell Donors

Collection of primary hNECs from adults was performed as previously described (21). Superficial nasal epithelial scrape biopsies were obtained from healthy, nonsmoking male and female adults with a Rhino-Pro curette (Arlington Scientific, Inc. 96–0900) per protocols approved by the University of North Carolina School of Medicine Institutional Review Board for Biomedical Research (protocol numbers 05–2528, 09–0716, 11–1363). Written informed consent was obtained from all study participants. hNECs from an equal number of male and female donors were used for each experiment. Demographic information about the donors used for each exposure including age, body mass index (BMI), and race is provided in Table 1. Nasal biopsies were stored in RPMI-1640 medium (Gibco, Cat. No. 11875-093) on ice until further processing.

Table 1. Demographic information about hNEC donors

DEPs (n = 6)
Eucalyptus WSPs (n = 6)
Red Oak WSPs (n = 6)
Males (n = 3)Females (n = 3)Aggregate (males and females) Males (n = 3)Females (n = 3)Aggregate (males and females) Males (n = 3)Females (n = 3)Aggregate (males and females) 
Age, yr32 ± 5.733.0 ± 6.132.5 ± 3.723.7 ± 2.923.3 ± 2.823.5 ± 1.820.7 ± 1.229.3 ± 5.025.0 ± 3.0
BMI, kg/m227.8 ± 3.126.5 ± 1.427.1 ± 1.623.5 ± 0.829.9 ± 6.126.7 ± 3.124.3 ± 1.526.9 ± 3.825.6 ± 1.9
Race: White/Black/Asian2/1/03/0/05/1/01/0/22/1/03/1/20/0/31/1/11/1/4

Age and BMI values are means ± SE. BMI, body mass index; DEPs, diesel exhaust particles; WSPs, woodsmoke particles.

Expansion and Culture of hNECs

Culture of hNECs was performed as previously described (21, 26). Cells from nasal biopsy were expanded at passage 0 on a 12-well, PureCol-coated (Advanced Biomatrix, 5005-100ML) cell culture plate (Costar 3512) in PneumaCult-Ex Plus Medium (StemCell Technologies, Inc., Cat. No. 05041 and 05042) supplemented with hydrocortisone (StemCell Technologies, Inc., Cat. No. 07925), antibiotic antimycotic solution (Sigma, Cat. No. A5955), and gentamicin reagent solution (Gibco, Cat. No. 15750-060). Cells were passaged and further expanded in 25 cm2 tissue culture flasks (Corning, Cat. No. 430639) until passage 2. hNECs were then seeded on 12-mm transwell inserts with 0.4-μm pores (Costar, Cat. No. 3460) coated with human placental collagen (Sigma, Cat. No. C7521-10MG) at a density of 203,000–333,000 cells per well and maintained in PneumaCult-Ex Plus Medium. Once confluency was reached on the transwells, the cultures were taken to air-liquid interface (ALI) and the apical medium was permanently removed, whereas the basolateral medium was switched for PneumaCult ALI Medium (StemCell Technologies, Inc., Cat. No. 05002, 05003, and 05006), supplemented with 1% pen strep (Gibco, Cat. No. 15140-122), hydrocortisone (StemCell Technologies, Inc., Cat. No. 07925), and heparin (StemCell Technologies, Inc., Cat. No. 07980). After this point, three times per week, the basolateral medium was changed and the apical surfaces of the cultures were washed with 37°C HBSS + CaCl2, + MgCl2 (HBSS++; Gibco, Cat. No. 14025-092). Mucociliary differentiation of the cultures was achieved after 4–6 wk of ALI conditions. At the time of exposure, cultures were at ALI for 5.29–9.14 wk.

Diesel Exhaust Particle (DEP) Suspension Preparation

Whole diesel exhaust particle material from an automobile engine was collected as described by Sagai et al. (38). The DEPs were generated using an Isuzu Automobile Co. 4JB1-type light duty 4 cylinder diesel engine (2,740 mL). The engine was operated under a load of 6 kg·m of torque at 2,000 rpm. Particles were collected “cold” at a sampling temperature of 50°C from glass fiber filters and the stainless-steel walls of the collection duct (38). Twenty-five milligrams of the DEPs was diluted in 5 mL of warmed (37°C) phenol red-free MEM basal medium (Gibco, Cat. No. 51200-038). The suspension was sonicated with a Fisher Sonic Dismembrator Model 500 with a microprobe tip for two 1-min cycles. During each cycle, the probe was moved up and down in the suspension, and sonication alternated between 30% output for 0.5 s and 0% output for 0.5 s. After each cycle, the suspension was mixed by inversion. An additional 20 mL of warmed (37°C) medium was then added to the suspension to achieve a final concentration of 1 mg/mL. Aliquots of the suspension were snap frozen in liquid nitrogen and stored at −80°C for future use.

Woodsmoke Particle Suspension Preparation

Woodsmoke generated from eucalyptus (Eucalyptus globulus) and red oak (Quercus rubra) were each collected as previously described by Kim et al. (11). Briefly, eucalyptus or red oak was burned in a quartz tube furnace at 640°C and smoke was collected in a series of cryogenic traps. The resulting woodsmoke particle condensates were then collected in acetone and concentrated with a rotary evaporator. Finally, the particles were dried and the solid PM was resuspended in Dulbecco’s PBS (Gibco, Cat. No. 14200-075) at 2 mg/mL and frozen at −20°C. Prior to exposure, aliquots were sonicated in a water bath sonicator (Sinosonic Industrial Co. Ltd., Taiwan, Model B200) at 40 kHz for 4.75 min.

Particle Size Measurements

Chemical composition analyses of particles used here from previously published studies are presented in Supplemental Table S1 (all Supplemental material is available at Particle size distributions of the three particle suspensions were determined by diluting an aliquot of each to 50 μg/mL in ddH2O. The diluted suspensions were run through a BD FACSVerse 2013 Flow Cytometer for size measurement and compared with size calibration standards (Thermo Fisher, Cat. No. F13838) of 1.0, 2.0, 4.0, 6.0, 10.0, and 15.0 μm in diameter. Graphs of particle size distribution overlaid with the standard sizes are shown in Supplemental Fig. S1.

Exposure of hNECs to DEPs or WSPs

A pictorial depiction of the exposure and infection scheme is provided in Fig. 1. Prior to exposure, the apical surface of each culture was washed with 100 μL of warmed (37°C) HBSS++ and basolateral medium was replaced with 1.0 mL of 37°C PneumaCult ALI Medium. Warm ALI Medium was used as the control exposure and as the vehicle for particle exposures. hNECs from three male and three female donors were used for each type of exposure (DEPs, eucalyptus WSPs, and red oak WSPs). Separate cultures from the same donor were used in multiple groups in some cases. Particle stock aliquots were diluted in ALI medium and applied to the apical surface of the experimental wells at a concentration of 165.7 μg/mL in 150-μL apical volume. This corresponds to a dosage of 22 μg/cm2, which we have studied previously (23). Control wells received 150-μL ALI medium apically. Cultures were then returned to the incubator (37°C, 5% CO2) for 2 h.

Figure 1.

Figure 1.Experimental design scheme. Differentiated hNECs from males and females grown at ALI were exposed to 22 μg/cm2 DEPs, eucalyptus WSPs, or red oak WSPs (or control) for 2 h. At the end of the exposure period, cells were infected with SARS-CoV-2 at an MOI of 0.5 (or mock infected with vehicle) for 2 h. Excess virus and residual particles were then removed and the apical surface was washed. A second apical wash and cell lysis were performed immediately or 24 or 72 h later. Apical washes were used to determine viral titers and RNA was purified from cell lysates and used for RT-qPCR to assess altered gene expression in a panel of 48 genes. ALI, air-liquid interface; DEPs, diesel exhaust particles; MOI, multiplicity of infection; WSPs, woodsmoke particles. [Created with]

Infection with SARS-CoV-2 or Mock

At the end of the 2-h exposure, half the wells exposed to particle and half the control wells were apically infected with SARS-CoV-2 derived from clinical isolate WA1 (19) in high-glucose DMEM (Gibco, Cat. No. 11995-065) with 5% heat-inactivated fetal bovine serum, 1% l-glutamine diluted in ALI medium at a multiplicity of infection (MOI) of 0.5 in 100 μL. The other half of the cultures were mock infected with 100 μL of high-glucose DMEM with 5% heat-inactivated fetal bovine serum and 5% l-glutamine diluted in ALI medium. To avoid damaging or disturbing the cell monolayer, particle suspensions were not removed before addition of viral inoculum or vehicle. Total apical volume during viral infection was thus 250 μL. Cultures were then returned to the incubator (37°C, 5% CO2) for 2 h.

Sample Collection

After the 2-h infection, cells were checked under the microscope for signs of cell death. The apical liquid was carefully removed from every well. Cultures were then washed with 200-μL 37°C HBSS++ and returned to the incubator until collection. At the time of collection (0, 24, or 72 h p.i.), 100-μL 37°C HBSS++ was added to the apical surface of each culture, and cells were returned to the incubator for 15 min. Apical washes were then carefully collected and analyzed for viral titer. Cells were lysed using 350-μL cold TRIzol reagent (Life Technologies, Cat. No. 15596018) for subsequent gene expression analysis.

Determination of Viral Titer

Fifty microliters of the apical wash were mixed with 450 μL of medium (DMEM + 5% FBS + 1% l-glutamine) followed by 10-fold serial dilutions resulting in a dilution series of 10−1 to 10−6. Two hundred microliters of each dilution was added to plated Vero E6 cells (C1008, ATCC) and incubated at 37°C. Plates were rocked every 15 min to ensure even distribution of the virus over the surface of the well. After 1 h, 2 mL of overlay (50:50 mixture of 2.5% carboxymethylcellulose and 2× MEM α containing 6% FBS + 2% penicillin/streptomycin + 2% l-glutamine + 2% HEPES) was added to each well. Plates were incubated at 37°C, 5% CO2 for 4 days, and then fixed with 2 mL of 4% paraformaldehyde left on overnight. Following removal of the fixative, wells were rinsed with water to remove residual overlay and then stained with 0.25% crystal violet. Visible plaques were counted and averaged between two technical replicate wells. Viral titers were calculated as plaque-forming units (pfu) per mL. The limit of detection for the assay was determined to be 12.5 pfu/wash, and samples that yielded no plaques were assigned a value of 6.25, half of the limit of detection.

RNA Extraction from Whole Cell Lysates in TRIzol

Whole cell lysates in TRIzol reagent were thawed on ice. An additional 650-μL cold TRIzol was added to each sample to facilitate RNA collection. Two hundred microliters of chloroform was added to each tube, and tubes were shaken vigorously and incubated at room temperature for 3 min. Samples were then centrifuged for 15 min at 12,000 g at 4°C. The aqueous phase containing RNA was then carefully removed from each sample and transferred to new microcentrifuge tubes. One volume of 100% ethanol was added per volume of aqueous phase removed, and samples were vortexed. Samples were further processed with the Zymo RNA Clean and Concentrator Kit (Zymo, Cat. No. R1016) according to the manufacturer’s instructions. Eluted RNA was stored at −80°C until use.

Generation of cDNA and Quantification of Gene Expression of 48 Genes by qPCR

RNA concentration and purity were measured using a CLARIOstar plate reader and an LVis Plate (BMG LABTECH). For each sample, 800 ng of RNA was used to generate cDNA in a reaction volume of 25 μL. The final concentrations of reagents in each reaction were as follows: 0.50 mM dNTPs (Promega, Cat. No. U151B), 1.00 U/μL RNasin Ribonuclease Inhibitor (Promega, Cat. No. N211A), 10.0 U/μL M-MLV Reverse Transcriptase (Invitrogen, Cat. No. 28025-013), 0.10 μg/μL Random Primers (Invitrogen, Cat. No. 58875), 50.0 mM KCl, 0.25 mM MgCl2, 20.0 mM Tris-HCl, and 0.01 mg/mL BSA. PCR was performed in 96-well plates (Thermo, Cat. No. AB-0600 and AB-0851) for one cycle (25°C for 10 min, 37°C for 50 min, 70°C for 15 min, followed by 4°C infinite hold). Samples were submitted to the UNC School of Medicine Center for Gastrointestinal Biology and Disease Advanced Analytics Core for high-throughput qPCR gene expression analysis. Gene expression of a panel of 48 genes (including two reference genes) was assayed in a Fluidigm BioMark HD system using TaqMan primers and probes (Table 2). Duplicate Ct values were measured for each sample/gene combination and averaged for further analysis. Gene expression was calculated using the ΔΔCt method with normalization to the geometric mean of expression of the two reference genes (ACTB and GAPDH). Two samples (out of 216) showing poor amplification across the panel (i.e., comparable to the no-template controls) were excluded from the data set and not further analyzed.

Table 2. Genes assayed, grouped by functional categories

Functional CategoryGene NameEncoded ProteinTaqMan Probe Assay ID
Viral entry factor (VEF)ACE2Angiotensin converting enzyme 2Hs01085331_m1
Airway proteasesCTSBCathepsin BHs00157194_m1
CTSLCathepsin LHs00964651_m1
MMP7Matrix metallopeptidase 7Hs01042796_m1
ST14ST14 transmembrane serine protease matriptaseHs01058386_m1
TMPRSS11DTransmembrane serine protease 11 DHs00975370_m1
TMPRSS2Transmembrane serine protease 2Hs05024838_m1
Antiviral defenseIFIT1Interferon induced protein with tetratricopeptide repeats 1Hs03027069_s1
IFITM3Interferon induced transmembrane protein 3Hs03057129_s1
IFNA1Interferon α1Hs04189288_g1
IFNA2Interferon αHs00265051_s1
IFNB1Interferon β1Hs00265051_s2
IFNGInterferon γHs00265051_s3
IFNL1Interferon λ1Hs00265051_s4
IFNL2Interferon λ2Hs00265051_s5
MX1MX dynamin like GTPase 1Hs00265051_s7
SOCS3Suppressor of cytokine signaling 3Hs00265051_s8
Cell signaling/immune cell recruitmentCCL2C-C motif chemokine ligand 2; MCP-1Hs00265051_s9
CCL3C-C motif chemokine ligand 3; MIP-1-αHs00265051_s10
CCL5C-C motif chemokine ligand 5; RANTESHs00265051_s11
CSF2Colony stimulating factor 2; GM-CSFHs00265051_s12
CXCL10C-X-C motif chemokine ligand 10; IP-10Hs00265051_s13
CXCL11C-X-C motif chemokine ligand 11Hs00265051_s14
CXCL8C-X-C motif chemokine ligand 8; IL-8Hs00265051_s15
CXCL9C-X-C motif chemokine ligand 9; MIGHs00265051_s16
IL15Interleukin 15Hs00265051_s17
IL1BInterleukin 1βHs00265051_s18
IL6Interleukin 6Hs00265051_s19
TNFTumor necrosis factorHs00265051_s20
MucinsMUC5ACMucin 5AC, oligomeric mucus/gel-formingHs00265051_s21
MUC5BMucin 5B, oligomeric mucus/gel-formingHs00265051_s22
SurfactantSFTPA1Surfactant protein A1Hs00265051_s23
SFTPDSurfactant protein DHs00265051_s24
Transcription factorsIRF1Interferon regulatory factor 1Hs00265051_s25
IRF3Interferon regulatory factor 3Hs00265051_s26
IRF7Interferon regulatory factor 7Hs00265051_s27
NFKB1Nuclear factor-κB subunit 1Hs00265051_s28
STAT1Signal transducer and activator of transcription 1Hs00265051_s29
STAT2Signal transducer and activator of transcription 2Hs00265051_s30
STAT3Signal transducer and activator of transcription 3Hs00265051_s31
Viral recognitionDDX58DExD/H-box helicase 58; RIG-IHs00265051_s32
TLR3Toll like receptor 3Hs00265051_s33
TLR7Toll like receptor 7Hs00265051_s34
TLR9Toll like receptor 9Hs00265051_s35
Viral genesnCoVN1SARS-CoV-2 nucleocapsidIDT Cat. No. 10006713
nCoVN2SARS-CoV-2 nucleocapsidIDT Cat. No. 10006713
Reference genesGAPDHGlyceraldehyde-3-phosphate dehydrogenaseHs00265051_s36
ACTBActin βHs00265051_s37
RPP30Ribonuclease P/MRP subunit P30IDT Cat. No. 10006713

Assay identifiers are listed for TaqMan primer/probe sets purchased from thermo fisher (or IDT where indicated).

Quantification of SARS-CoV-2 N1 and N2 Gene Expression by qPCR

Expression of viral SARS-CoV-2 N1 and N2 genes was also quantified and normalized to human RNase P gene expression using the Integrated DNA Technologies 2019-nCoV RUO Kit (IDT 10006713). For a single reaction, 6.5-μL nuclease-free water, 1.5 μL of one primer/probe mix, and 10 μL of TaqMan Universal Master Mix II, with UNG (Thermo Fisher, Cat. No. 4440038) were mixed and added to every well of a Sapphire 96-well PCR Microplate (Greiner Bio-One, Cat. No. 652260). cDNA was then added to each well (2 μL) for a total volume of 20 μL per reaction. The plate was sealed with a plate film (Thermo Fisher, Cat. No. 4311971) and centrifuged for 5 min at 500 g at room temperature. RT-qPCR was performed on a QuantStudio 3 using the following reaction conditions: hold 50°C for 2 min then hold 95°C for 10 min and cycle through 95°C for 15 s and 60°C for 1 min for 40 cycles. Transitions between temperatures occurred at 1.6°C/s. The two samples excluded from the Fluidigm PCR data were also excluded here. Results were collected as Ct and analyzed with the ΔΔCt method, normalized to expression of human RNase P.

Statistical Analysis

Analysis was carried out using SAS PROC MIXED as a full factorial design, with sex (male, M, or female, F), particle treatment (control, DEPs, eucalyptus WSPs, or red oak WSPs), virus or no virus, and duration (0, 24, or 72 h), as well as all their interactions. Donor was fit as a random effect. Preplanned hypothesis tests for differences between marginal means were carried out as t tests with the LSMESTIMATE command. Sex-specific means were calculated for each combination of particle treatment, virus, and duration and differences were tested using a t test with the LSMESTIMATE command. Correction for multiple comparisons was performed across all statistical tests for the entire experiment using the “qvalue” R package (v. 2.22.0), with a false-discovery rate q value threshold of 0.05, assuming pi0 = 1 (equivalent to Benjamini-Hochberg correction). The resultant P value for statistical significance was P ≤ 0.00369. Viral titer data were analyzed using GraphPad Prism, v. 8.4.0. Unpaired t tests (with Welch’s correction when appropriate) were used to evaluate differences in log10-transformed data.


Particle Exposure Alone Has Modest Effects on Expression of Antiviral Host Response Genes

First, we assessed how exposure to particles alone without subsequent viral infection would affect expression of host response genes in our panel. hNECs from male and female donors were exposed to one of three particle suspensions (DEPs, eucalyptus WSPs, or red oak WSPs) or control for 2 h, followed by a “mock” infection for 2 h. Results are shown graphically in Fig. 2 and statistically significant results are reported in Table 3. At 0 h after mock infection, exposure to both types of WSPs increased expression of IL6 and eucalyptus WSPs also upregulated expression of IL1B. Further, DEPs and red oak WSPs significantly decreased expression of IFNG at 0 h p.i. (data for eucalyptus WSPs not shown due to missing data points). By 24 h p.i., both eucalyptus WSPs and red oak WSPs further upregulated IL1B expression, whereas IL6 was no longer upregulated. Overall, by 24 and 72 h p.i., particle treatment in the absence of infection had little effect on expression of the genes in our panel.

Figure 2.

Figure 2.Effects of particle exposure on gene expression (ΔΔCt) in uninfected hNECs at 0, 24, and 72 h after (mock) infection. Corresponds to 2, 26, and 74 h postexposure to particles. Gene categories are color-coded at the top, with “VEF” an abbreviation for “viral entry factor” and “Surf.” an abbreviation for “surfactant.” Graphed as means with black bars representing standard error. Males and females are combined (n = 6 biological replicates for each bar). *Statistically significant changes in gene expression (q ≤ 0.05).

Table 3. Statistically significant particle-induced effects on gene expression in hNECs at 0, 24, and 72 h after (mock) infection

TimeGeneCategoryParticleSexFold InductionP Value
0 hIFNGAntiviral defenseDEPsCombined0.062.30E-04
TLR3Viral recognitionDEPsCombined0.644.35E-04
LTFAntiviral defenseEucalyptus WSPsCombined0.457.36E-04
IL1BImmune cell recruit.Eucalyptus WSPsCombined2.531.55E-03
IL6Immune cell recruit.Eucalyptus WSPsCombined5.296.63E-05
TLR3Viral recognitionEucalyptus WSPsCombined0.605.88E-05
IFNGAntiviral defenseRed oak WSPsCombined0.083.81E-04
IL6Immune cell recruit.Red oak WSPsCombined5.535.08E-05
TLR3Viral recognitionRed oak WSPsCombined0.566.43E-06
24 hCTSBProteaseEucalyptus WSPsCombined0.782.04E-03
CCL2Immune cell recruit.Eucalyptus WSPsCombined0.258.53E-04
IL1BImmune cell recruit.Eucalyptus WSPsCombined2.659.03E-04
TMPRSS11DProteaseRed oak WSPsCombined2.152.83E-03
IL1BImmune cell recruit.Red oak WSPsCombined6.708.28E-10
72 hMMP7ProteaseRed oak WSPsCombined0.393.32E-04
IL1BImmune cell recruit.Red oak WSPsCombined2.422.73E-03

n = 6 (3 M, 3 F) biological replicates per measurement. DEPs, diesel exhaust particles; F, female; M, male; WSPs, woodsmoke particles.

SARS-CoV-2 Infection Greatly Affects Expression of Antiviral Host Response Genes in hNECs

To assess how particle exposure affects expression of antiviral host defense genes in the presence of an infection, we next needed to measure the independent effects of SARS-CoV-2 infection on gene expression. Thus, hNECs from male and female donors that were not exposed to any particles were infected with SARS-CoV-2 (or mock infected with vehicle). Virus-induced changes in gene expression in hNECs at 0, 24, and 72 h p.i. are shown in Fig. 3 and fold-inductions and P values are tabulated in Table 4. By 24 h p.i., the type III IFNs (IFNL1 and IFNL2) were upregulated in hNECs from male and female donors, with statistically significant upregulation of both genes in males. Expression of IFNL1 and IFNL2 was even more highly upregulated at 72 h p.i. and reached statistical significance in both sexes. In addition, by 72 h p.i., infection had upregulated mRNAs of type I interferons, ISGs, chemokines, transcription factors, and viral recognition receptors. In most instances, gene expression in hNECs from males was more highly induced by infection than in hNECs from females, suggesting an overall more robust epithelial response to SARS-CoV-2 in hNECs from male donors. For each gene that was differentially expressed in infected cells from both sexes, the ratio of expression in males and females was calculated. Indeed, on average, the level of virus-induced gene expression in hNECs from males was 2.08 times (95% CI: ± 0.57) that of hNECs from females. We also assessed whether baseline differences in gene expression existed between the sexes in uninfected cells. There were no statistically significant differences in baseline gene expression between hNECs from males and females at 24 and 72 h after mock infection (data not shown).

Figure 3.

Figure 3.Gene expression in infected hNECs from males and females relative to uninfected controls at 0, 24, and 72 h p.i. Graphed as average (n = 9 biological replicates for each bar) with standard error. *Statistically significant (q ≤ 0.05) changes in gene expression. #Statistically significant difference in gene expression between males and females. p.i., postinfection.

Table 4. Statistically significant virus-induced changes in gene expression in hNECs from males and females at 0, 24, and 72 h p.i. with SARS-CoV-2

TimeGeneFunctionSexFold InductionP Value
0 hTMPRSS11DProteaseCombined0.582.43E-03
IFNGAntiviral defenseCombined0.163.69E-03
IFNGAntiviral defenseM0.029.74E-05
IFNGAntiviral defense(#) M vs. F0.014.10E-04
24 hIFNL1Antiviral defenseCombined8.711.46E-04
IFNL1Antiviral defenseM20.681.92E-04
IFNL2Antiviral defenseCombined19.873.34E-05
IFNL2Antiviral defenseM20.862.07E-03
STAT3Transcription factorCombined0.852.01E-03
STAT3Transcription factorF0.801.63E-03
72 hACE2Viral entry factorCombined2.421.00E-15
ACE2Viral entry factorM3.005.50E-13
ACE2Viral entry factorF1.951.89E-06
IFIT1Antiviral defenseCombined26.041.00E-15
IFIT1Antiviral defenseM34.121.00E-15
IFIT1Antiviral defenseF19.871.00E-15
IFITM3Antiviral defenseCombined3.901.00E-15
IFITM3Antiviral defenseM4.691.00E-15
IFITM3Antiviral defenseF3.251.00E-15
IFNB1Antiviral defenseCombined21.331.00E-15
IFNB1Antiviral defenseM25.401.00E-15
IFNB1Antiviral defenseF17.911.00E-15
IFNL1Antiviral defenseCombined6,936.061.00E-15
IFNL1Antiviral defenseM17,769.311.00E-15
IFNL1Antiviral defenseF2,707.411.00E-15
IFNL2Antiviral defenseCombined3,694.081.00E-15
IFNL2Antiviral defenseM4,439.863.20E-14
IFNL2Antiviral defenseF3,073.571.60E-14
LTFAntiviral defenseCombined0.531.75E-04
LTFAntiviral defenseF0.481.62E-03
MX1Antiviral defenseCombined6.401.00E-15
MX1Antiviral defenseM7.951.00E-15
MX1Antiviral defenseF5.161.00E-15
CCL3Immune cell recruit.Combined23.011.00E-07
CCL3Immune cell recruit.M39.812.81E-05
CCL3Immune cell recruit.F13.313.10E-04
CCL5Immune cell recruit.Combined50.331.00E-15
CCL5Immune cell recruit.M80.161.00E-15
CCL5Immune cell recruit.F31.601.00E-15
CXCL10Immune cell recruit.Combined407.911.00E-15
CXCL10Immune cell recruit.M690.691.00E-15
CXCL10Immune cell recruit.F240.901.00E-15
CXCL11Immune cell recruit.Combined466.261.00E-15
CXCL11Immune cell recruit.M830.131.00E-15
CXCL11Immune cell recruit.F261.911.00E-15
CXCL8Immune cell recruit.Combined4.981.00E-15
CXCL8Immune cell recruit.M5.978.79E-12
CXCL8Immune cell recruit.F4.169.03E-09
CXCL9Immune cell recruit.Combined101.791.00E-15
CXCL9Immune cell recruit.M180.871.00E-15
CXCL9Immune cell recruit.F57.292.53E-11
IL6Immune cell recruit.Combined48.591.00E-15
IL6Immune cell recruit.M65.441.00E-15
IL6Immune cell recruit.F36.081.00E-15
TNFImmune cell recruit.Combined9.226.90E-14
TNFImmune cell recruit.M14.858.81E-11
TNFImmune cell recruit.F5.727.13E-06
IRF1Transcription factorCombined1.493.15E-07
IRF1Transcription factorM1.656.01E-06
IRF3Transcription factorCombined0.831.10E-03
IRF7Transcription factorCombined4.511.00E-15
IRF7Transcription factorM5.341.00E-15
IRF7Transcription factorF3.801.00E-15
STAT1Transcription factorCombined3.401.00E-15
STAT1Transcription factorM4.001.00E-15
STAT1Transcription factorF2.891.00E-15
STAT2Transcription factorCombined1.541.03E-07
STAT2Transcription factorM1.851.23E-07
STAT3Transcription factorCombined0.762.63E-07
STAT3Transcription factorM0.763.35E-04
STAT3Transcription factorF0.761.17E-04
DDX58Viral recognitionCombined6.411.00E-15
DDX58Viral recognitionM7.881.00E-15
DDX58Viral recognitionF5.211.00E-15
TLR3Viral recognitionCombined1.491.30E-05
TLR3Viral recognitionM1.678.77E-05
TLR7Viral recognitionCombined4.581.36E-05
TLR7Viral recognitionM9.121.06E-05
TLR9Viral recognitionCombined1.479.94E-04

n = 9 biological replicates for individual sex effects (M or F) and n = 18 for combined effects. F, female; M, male; p.i., postinfection.

Woodsmoke Particles Affect Expression of Virus-Induced Genes in hNECs Infected with SARS-CoV-2

We hypothesized that exposure to particles would dampen expression of crucial antiviral host response genes on subsequent SARS-CoV-2 infection. To test this, hNECs from male and female donors were exposed to control or DEPs, eucalyptus WSPs, or red oak WSPs for 2 h, followed by infection with SARS-CoV-2. Overall, red oak WSPs caused more statistically significant changes in virus-induced gene expression than the other particles (Table 5). DEPs had very few effects on virus-induced gene expression at all time points. In general, the number of statistically significant effects on gene expression increased with duration of infection in the WSP-exposed groups. More specifically, at 0 h p.i., both types of WSPs increased IL1B and IL6 expression compared with unexposed, infected cells, with red oak WSP exposure generating more potent upregulation of IL6. By 24 h p.i, all three types of particles upregulated IL1B to similar degrees and red oak WSPs downregulated MX1 and STAT2. At 72 h p.i., WSP exposures, especially from red oak, decreased expression of several genes, including IFNB1, CCL3, CCL5, CXCL10, and CXCL11 (Fig. 4). Red oak WSPs also decreased expression of IFNL1 and IFNL2, albeit not statistically significantly. Other genes that are important for the antiviral response group were also downregulated by WSPs, such as IFIT1, IFITM3, MX1, IRF7, STAT1, STAT2, DDX58, and MMP7. Thus, exposure to WSPs prior to infection with SARS-CoV-2 suppressed IFN-dependent immune gene expression.

Figure 4.

Figure 4.Effects of particle exposure (DEPs and WSPs) on virus-induced gene expression in infected hNECs at 72 h p.i. Data graphed as means with black bars representing standard error. Males and females are combined for n = 6 biological replicates per bar. *Statistically significant changes in gene expression (q ≤ 0.05). DEPs, diesel exhaust particles; p.i., postinfection; WSPs, woodsmoke particles.

Table 5. Statistically significant effects of particle exposure on virus-induced gene expression in hNECs at 0, 24, and 72 h p.i.

TimeGeneFunctionParticleSexFold InductionP Value
0 hIRF1Transcription factorDEPsCombined0.643.61E-05
IL1BImmune cell recruit.Eucalyptus WSPsCombined2.551.38E-03
IL6Immune cell recruit.Eucalyptus WSPsCombined4.027.84E-04
CTSBProteaseRed oak WSPsCombined0.781.89E-03
IL1BImmune cell recruit.Red oak WSPsCombined2.501.97E-03
IL6Immune cell recruit.Red oak WSPsCombined8.525.59E-07
STAT2Transcription factorRed oak WSPsCombined0.723.40E-03
24 hIL1BImmune cell recruit.DEPsCombined3.532.78E-05
IL1BImmune cell recruit.Eucalyptus WSPsCombined3.691.13E-05
CTSBProteaseRed oak WSPsCombined0.741.45E-04
FURINProteaseRed oak WSPsCombined0.712.30E-04
MMP7ProteaseRed oak WSPsCombined0.417.24E-04
MX1Antiviral defenseRed oak WSPsCombined0.661.68E-03
IL1BImmune cell recruit.Red oak WSPsCombined3.306.47E-05
STAT2Transcription factorRed oak WSPsCombined0.698.85E-04
72 hMMP7ProteaseEucalyptus WSPsCombined0.473.38E-03
IFITM3Antiviral defenseEucalyptus WSPsCombined0.642.08E-04
MX1Antiviral defenseEucalyptus WSPsCombined0.543.55E-06
IL1BImmune cell recruit.Eucalyptus WSPsCombined2.383.20E-03
IRF7Transcription factorEucalyptus WSPsCombined0.656.52E-04
STAT1Transcription factorEucalyptus WSPsCombined0.604.38E-06
STAT2Transcription factorEucalyptus WSPsCombined0.722.85E-03
MMP7ProteaseRed oak WSPsCombined0.291.65E-05
IFIT1Antiviral defenseRed oak WSPsCombined0.355.96E-06
IFITM3Antiviral defenseRed oak WSPsCombined0.572.03E-05
IFNB1Antiviral defenseRed oak WSPsCombined0.242.10E-06
MX1Antiviral defenseRed oak WSPsCombined0.491.46E-06
CCL3Immune cell recruit.Red oak WSPsCombined0.132.30E-03
CCL5Immune cell recruit.Red oak WSPsCombined0.291.15E-03
CXCL10Immune cell recruit.Red oak WSPsCombined0.199.47E-04
CXCL11Immune cell recruit.Red oak WSPsCombined0.181.13E-03
IRF7Transcription factorRed oak WSPsCombined0.651.90E-03
STAT1Transcription factorRed oak WSPsCombined0.671.07E-03
DDX58Viral recognitionRed oak WSPsCombined0.581.08E-03

n = 6 (3 M, 3 F) biological replicates per measurement. DEPs, diesel exhaust particles; F, female; M, male; p.i., postinfection; WSPs, woodsmoke particles. #Significantly different between hNECs from male and female donors.

Woodsmoke Particle Effects on Gene Expression in Infected hNECs Are Sex-Specific

Because the virus-induced effects on gene expression were sex-dependent (Fig. 3), we next assessed whether gene expression changes in cells exposed to particles prior to infection were also sex-dependent. Few sex-specific changes were observed at 0 and 24 h p.i. (Table 6). However, at 72 h p.i., WSPs from eucalyptus and red oak modified virus-induced expression of more genes in hNECs from female donors than from male donors (Fig. 5). At this time point, WSPs from both eucalyptus and red oak caused statistically significant downregulation of IFITM3, MX1, IRF7, and STAT1 in hNECs from females. In addition, red oak WSPs caused a statistically significant decline in MX1 expression in hNECs from females versus males. These results suggest that WSP exposure, especially from red oak, dampens expression of antiviral genes in hNECs from females during SARS-CoV-2 infection, with lesser effects on hNECS from males.

Figure 5.

Figure 5.Effects of particle exposure on virus-induced gene expression in infected hNECs from males or females at 72 h postinfection. Graphed as means with black bars representing standard error, n = 3 biological replicates per bar. *Statistically significant changes in gene expression. #Statistically significant differences in expression between males and females (q ≤ 0.05).

Table 6. Statistically significant, sex-disaggregated effects of particle exposure on virus-induced gene expression in infected hNECs at 0, 24, and 72 h p.i.

TimeGeneFunctionParticleSexFold InductionP Value
0 hIRF1Transcription factorDEPsM0.594.62E-04
IL6Immune cell recruit.Red oak WSPsM8.772.41E-04
IL6Immune cell recruit.Red oak WSPsF8.273.87E-04
TLR3Viral recognitionRed oak WSPsM0.582.20E-03
24 hIL1BImmune cell recruit.DEPsM4.961.57E-04
IL1BImmune cell recruit.Eucalyptus WSPsF4.692.17E-04
FURINProteaseRed oak WSPsF0.672.09E-03
IL1BImmune cell recruit.Red oak WSPsF3.403.54E-03
72 hIFITM3Antiviral defenseEucalyptus WSPsF0.565.88E-04
MX1Antiviral defenseEucalyptus WSPsF0.438.25E-06
IRF7Transcription factorEucalyptus WSPsF0.523.29E-04
STAT1Transcription factorEucalyptus WSPsF0.497.24E-06
STAT2Transcription factorEucalyptus WSPsF0.621.97E-03
MMP7ProteaseRed oak WSPsM0.224.72E-05
IFIT1Antiviral defenseRed oak WSPsF0.181.63E-06
IFITM3Antiviral defenseRed oak WSPsF0.419.25E-06
IFNB1Antiviral defenseRed oak WSPsF0.136.06E-06
MX1Antiviral defenseRed oak WSPsF0.323.90E-07
MX1Antiviral defenseRed oak WSPs(#) M vs. F2.352.96E-03
CCL3Immune cell recruit.Red oak WSPsM0.071.60E-03
CXCL10Immune cell recruit.Red oak WSPsF0.091.35E-03
CXCL11Immune cell recruit.Red oak WSPsF0.081.05E-03
IRF7Transcription factorRed oak WSPsF0.451.98E-04
STAT1Transcription factorRed oak WSPsF0.523.37E-04
DDX58Viral recognitionRed oak WSPsF0.403.60E-04

n = 3 biological replicates per measurement. DEPs, diesel exhaust particles; F, female; M, male; p.i., postinfection; WSPs, woodsmoke particles. #Significantly different between hNECs from male and female donors.

Particle Exposure Does Not Affect Viral Load in hNECs

Previously, we found that exposing hNECs and other airway epithelial cells to DEPs prior to infection with influenza A enhanced viral replication and susceptibility to viral infection (23). Because WSP exposure altered expression of antiviral genes in the present study, we assessed whether viral replication and release were also altered by WSP exposure. Apical viral loads for the hNECs exposed to particles and their respective controls at 0, 24, and 72 h p.i. are shown in Fig. 6, AC. The amount of infectious virus recovered from apical washes increased with duration of infection (Fig. 6D), suggesting increased viral replication and apical secretion over time, consistent with our previous study (19). However, exposure to particles regardless of type had no effect on viral loads in apical washes (Fig. 6, AD).

Figure 6.

Figure 6.SARS-CoV-2 viral titers in hNEC cultures at 0, 24, and 72 h p.i. hNECs from male and female donors were exposed to particles (DEPs or WSPs from flaming eucalyptus or red oak, at 22 μg/cm2) or control for 2 h, then infected with SARS-CoV-2 at a multiplicity of infection (MOI) of 0.5. At 0, 24, or 72 h postinfection, the apical washes were collected and used for approximating viral titer. Titers from individual particle exposures with respective controls for DEPs, eucalyptus WSPs, and red oak WSPs are shown in AC, respectively. Black symbols indicate sex-specific means with standard error bars (n = 3 biological replicates each for males and females). D: aggregated viral titers recovered from hNECs exposed to vehicle or a particle. Standard error is shown (n = 9 biological replicates for each bar). Unpaired t tests with Welch’s correction were used to determine (sex aggregated) differences in viral titer between time points, with ***P = 0.0001, ****P < 0.0001. DEPs, diesel exhaust particles; p.i., postinfection; WSPs, woodsmoke particles.

The relationship between the expression level of each gene (relative to reference genes) and the viral titer recovered from respective samples is shown in Fig. 7. As expected, expression levels of SARS-CoV-2 N1 and N2 genes are highly correlated with viral load recovered (Pearson’s r = 0.91 for both). This indicates that apical release of infectious viral particles is highly correlated with viral mRNA levels. The following genes are also correlated with viral titer, with a statistically significant Pearson’s r > 0.70: ACE2, IFIT1, IFITM3, IFNB1, IFNL1, IFNL2, MX1, CCL5, CXCL10, IRF7, STAT1, DDX58, and TLR9. In contrast, TMPRSS2 and IL1B both appear to be negatively correlated with viral titer.

Figure 7.

Figure 7.Relationship between gene expression relative to reference genes (−ΔCt) and log10(viral titer) in infected cells. Colors behind gene names correspond to functional categories presented in Table 2. Statistical significance is indicated next to the coefficient of determination (R2): *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.


During 2020, air quality reached unhealthy and hazardous levels in the western United States due to wildfires, which coincided with the spread of COVID-19. Epidemiological evidence has shown that worsened air quality from PM is associated with increased COVID-19 case rate and case fatality rate around the world (3137, 39, 40). Toxicological studies have indicated that PM exposure affects the host defense response of the airways on viral infection. In the present study, we hypothesized that exposing hNECs to PM derived from diesel exhaust and woodsmoke would alter the expression of host antiviral response genes on subsequent infection with SARS-CoV-2. We also hypothesized that these effects would be sex-dependent.

Exposure to DEPs or WSPs in the absence of infection had few effects on expression of genes in our panel, besides upregulation of proinflammatory IL6 and IL1B and downregulation of IFNG. Some studies of WSP exposure in human volunteers did not show significant proinflammatory changes in the airway (4143) whereas others found signs of pulmonary or systemic inflammation following WSP exposure (6, 44). Proinflammatory effects of WSPs on epithelial cells in vitro have been mild and inconsistent in past studies (4547). These discrepancies could be due, in part, to differences in exposure paradigms, fuel types, and burn conditions across studies. Indeed, exposure to aerosolized particles versus particle suspensions alters the toxicological outcomes in vitro (48). Kim et al. reported that both biomass fuel source and burn temperature affected chemical composition and thus toxicity of WSPs in an in vivo mouse exposure (11). In addition, particle size is an important consideration in in vitro exposures to particles. Pulmonary toxicity is thought to be inversely related to particle size since smaller particles have a higher surface area to mass ratio (4951). Moreover, particles differentially deposit in the respiratory tract based on particle size (52), which is another factor to be considered when modeling inhalational toxicity. As shown in Supplemental Fig. S1, there were differences in the particle size distributions for the DEPs and WSPs used in the present study, meaning different numbers of particles were delivered per unit mass in the three exposure groups. This should be acknowledged as a limitation to making direct comparisons between the effects induced by each of the three particle types. However, differences in chemical compositions of the particles (Supplemental Table S1) may also contribute to their differential effects. Computational clustering analyses have revealed certain chemical groups in biomass smoke are linked to enhanced or repressed toxicity (53) and may therefore be an approach to further delineate which chemical signatures are drivers of the effects on antiviral host defense responses.

Our data indicate that SARS-CoV-2-induced gene expression changes in hNECs are sex-dependent, alone and in the context of WSP exposure (Figs. 3 and 5). In response to infection, expression of many of the genes in our panel increased, matching previously reported findings about the cellular responses to SARS-CoV-2 infection. Induction of type I and type III interferons is well-documented in the epithelial cell response to SARS-CoV-2 infection [(54, 55) reviewed in Refs. 56 and 57]. We observed significant upregulation of IFNB1, IFNL1, and IFNL2 mRNA by 72 h p.i., whereas IFNA1 and IFNA2 were not induced. Accordingly, several interferon-stimulated genes with antiviral function (IFIT1, IFITM3, MX1) and related transcription factors (IRF1, IRF7, STAT1, STAT2; reviewed in Ref. 58) were also induced in infected hNECs from one or both sexes. In addition to activating the interferon response pathway, SARS-CoV-2 is known to activate NF-κB transcription factors and result in upregulation of genes which promote leukocyte chemotaxis [(55, 59), reviewed in Refs. 6063]. Although in our model, SARS-CoV-2 infection induced expression of many of these chemokines in both sexes, by 72 h p.i., hNECs from males displayed greater upregulation of antiviral and immune signaling gene expression than hNECs from females. In contrast, in our previous study examining nasal mucosal immune responses to inoculation with live attenuated influenza virus (LAIV) vaccine, Rebuli et al. observed a more robust antiviral and inflammatory response in female subjects exposed to LAIV compared with male subjects (30). In that study, it was hypothesized that the seemingly larger upregulation of genes involved with antiviral defense and immune cell recruitment in females could reflect differential baseline gene expression levels between the sexes (30). However, in the data presented here, no differences in baseline gene expression were observed between the sexes at 24 and 72 h p.i. (data not shown). This previous in vivo human study also revealed that exposure to woodsmoke (500 μg/m3) for 2 h prior to inoculation with LAIV resulted in upregulation of inflammatory gene expression in males and suppression of antiviral defense genes in females (30). The data presented here showed a similar, sex-dependent response to woodsmoke exposure in the context of infection. It is worth noting that the particles used in our study were not removed from the cells prior to addition of the virus; however, we do not expect they interfered with viral infection since apical viral loads and viral gene expression did not differ between exposed and unexposed groups. Suppression of genes involved in the interferon response pathway was more frequent and greater in magnitude in hNECs from females versus males treated with WSPs before SARS-CoV-2 infection. Signaling molecules involved in recruitment of immune cells were also generally more downregulated in hNECs from females compared with males. These findings suggest that WSP exposure may dampen antiviral responses in females. Furthermore, since many of the genes assayed in this study are involved in general antiviral host defense, these results may translate to other viral pathogens of public health importance. Recently, urban PM was shown to impair antiviral properties of airway epithelial cultures toward SARS-CoV-2 and 229E-CoV, which causes the common cold (64).

Although our data did not show significant differences in viral titers based on sex or particle exposure, gene expression correlated significantly with viral titers and uncovered positive and negative associations with immune and inflammatory genes. Several of these positive correlations to antiviral genes (i.e., IFNB1, IFNL1, IFNL2, IFIT1, IFITM3, ACE2, MX1, STAT1, DDX58, and CXCL10) have been previously reported (54, 55). Expression of TMPRSS2, a protease that is crucial for SARS-CoV-2 entry (65), was negatively correlated with viral titer, which was also shown by Lieberman et al. (55). Interestingly, IL1B expression was negatively correlated with viral titer in our model, and expression of IL6, TNF, and CXCL8 showed weak positive or no associations with viral titer (r of 0.42, 0.14, and 0.28, respectively). These findings may be indicative of viral evasion of proinflammatory cytokine induction but indicate that the gene expression response to SARS-CoV-2 infection in our nasal epithelial model is dominated by the IFN response.

The fact that there were no differences in viral load recovered from exposed and unexposed hNECs, even at 72 h p.i., points at some potential limitations of the data presented here. The first is that the changes observed in gene expression at the transcript level may not translate into functional differences at the tissue level. Although IFIT1, IFITM3, IFNB1, IFNL1, IFNL2, MX1, CXCL10, DDX58, and other crucial genes for the antiviral response were all downregulated by particle treatments (in hNECs from females), further investigation is necessary to determine whether these changes result in host defense decrements in vivo. The SARS-CoV-2-induced interferon response has been shown to be ineffective in controlling viral replication in another study of human airway epithelium (66). Although the respiratory epithelium represents the first line of defense to inhaled pollution and pathogens, clearance of infection and inhaled debris relies heavily on recruitment and activation of immune cells. In our study, particle treatment prior to infection decreased expression of several important chemokines by 72 h p.i. (Tables 5 and 6). It is possible that in vivo, the WSP-induced reduction in expression of CCL3, CCL5, CXCL10, CXCL11, CXCL9, IL6, and TNF, which are chemoattractants for innate and adaptive immune cells, would result in a more widespread and lasting infection and delay nasal mucosal antibody production. In vivo exposures of mice to diesel exhaust prior to respiratory viral infection increased viral titers and viral mRNA collected from whole lungs (67, 68). Management of viral load mediated by immune cells is not captured in our monoculture model. Finally, many groups have reported effective evasion of interferon and NF-κB pathway activation by SARS-CoV-2 (6972). Indeed, only a small fraction of infected epithelial cells express the majority of interferons and ISGs (54) suggesting the virus evades or inhibits antiviral responses in most cells it infects. Figures 3 and 6 suggest that viral replication and release were underway by 24 h p.i., though ISGs and proinflammatory responses were not yet induced by that time point. The kinetic delay in cellular responses relative to viral replication as well as antiviral evasion by SARS-CoV-2 likely significantly influence the effects of coexposure to inhaled pollution on host responses. A longer in vitro study that captures the recovery phase after peak antiviral activity (which occurred at our final time point, 72 h p.i., in this model) would be informative.

Further work is necessary to elucidate the effects of WSP exposure on SARS-CoV-2 infection, especially in bronchial and small airway epithelial cells and airway macrophages, and with particles derived from other types of biomass or biomass mixtures. Exposure to red oak WSPs prior to SARS-CoV-2 infection dampens expression of antiviral and host defense genes in nasal epithelial cells. These effects are sex-dependent, with overall greater downregulation of genes in females than in males. Men have been found to be more susceptible to severe and fatal cases of COVID-19 (17). It is possible that wildfire-derived PM could increase COVID-19 morbidity in exposed females, but additional epidemiological studies are needed. The impact of wildfire smoke on public health in the United States and abroad is expected to increase as wildfire seasons become more intense and the population exposed to wildfire smoke continues to rise (8). As viral pandemics and wildfire exposures continue to be concurrent respiratory health risks, it is important to understand their potential synergisms and interactions. This will inform strategies for mitigating risk, especially for subpopulations already susceptible to respiratory infections.


Data will be made available on reasonable request.


Supplemental Table S1, Supplemental Fig. S1, and SAS and R codes used for data processing, statistical analysis, and data visualization:


Funding was provided by NIH Grants R01 ES031173, T32 ES007126, and P30 DK034987.


No conflicts of interest, financial or otherwise, are declared by the authors.


S.A.B., M.T.H., and I.J. conceived and designed research; S.A.B. and S.T.-B. performed experiments; S.A.B. and G.T.B. analyzed data; S.A.B., N.E.A. and I.J. interpreted results of experiments; S.A.B. and G.T.B. prepared figures; S.A.B., S.T.-B., and I.J. drafted manuscript; S.A.B., G.T.B., N.E.A., M.T.H., and I.J. edited and revised manuscript; S.A.B., G.T.B., S.T.-B., N.E.A., M.T.H., and I.J. approved final version of manuscript.


The authors acknowledge and thank the study coordinators Noelle Knight, Carole Robinette, and Martha Almond for recruiting hNEC donors and retrieving scrape biopsies. The authors thank Shaun McCullough for the generous donation of DEPs and Eva Vitucci for help in making the DEP preparation and determining particle sizes. The authors also thank Yong Ho Kim and Ian Gilmour for the generous contribution of WSP samples. Finally, the authors thank the Advanced Analytics Core for help and contributions.

A preprint is available at


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