Abstract
Fine particulate matter (PM[2.5]) pollution remains a major threat to
public health. As the physical barrier against inhaled air pollutants,
airway epithelium is a primary target for PM[2.5] and influenza
viruses, two major environmental insults. Recent studies have shown
that PM[2.5] and influenza viruses may interact to aggravate airway
inflammation, an essential event in the pathogenesis of diverse
pulmonary diseases. Airway epithelium plays a critical role in lung
health and disorders. Thus far, the mechanisms for the interactive
effect of PM[2.5] and the influenza virus on gene transcription of
airway epithelial cells have not been fully uncovered. In this present
pilot study, the transcriptome sequencing approach was introduced to
identify responsive genes following individual and co-exposure to
PM[2.5] and influenza A (H3N2) viruses in a human bronchial epithelial
cell line (BEAS-2B). Enrichment analysis revealed the function of
differentially expressed genes (DEGs). Specifically, the DEGs enriched
in the xenobiotic metabolism by the cytochrome P450 pathway were linked
to PM[2.5] exposure. In contrast, the DEGs enriched in environmental
information processing and human diseases, such as viral protein
interaction with cytokines and cytokine receptors and epithelial cell
signaling in bacterial infection, were significantly related to H3N2
exposure. Meanwhile, co-exposure to PM[2.5] and H3N2 affected G
protein-coupled receptors on the cell surface. Thus, the results from
this study provides insights into PM[2.5]- and influenza virus-induced
airway inflammation and potential mechanisms.
Subject terms: Microbiology, Environmental sciences, Molecular medicine
Introduction
Particulate matter (PM) in ambient air with an aerodynamic diameter of
less than or equal to 2.5 μm is referred to as PM[2.5]. Due to its
small size and high specific surface area, PM[2.5] can absorb diverse
harmful substances from the air, including microbes, transition metals,
and polycyclic aromatic hydrocarbons (PAH), then enters the lungs and
other remote regions through the circulatory system^[38]1–[39]3. Even
though air pollution mitigation measures have achieved impressive gains
in China in the last several years, PM[2.5] remains a significant
threat to human health^[40]4. A recent study has demonstrated that
PM[2.5] pollution still causes around 4.23 million deaths globally each
year^[41]5. Previous study has shown that PM[2.5] accounts for 96% of
PM seen in human lung parenchyma^[42]6, directly causing or aggravating
respiratory diseases. Long-term PM[2.5] exposure has been associated
with higher hospitalization and mortality for pneumonia, lung cancer,
cardiovascular disorders, and neurological diseases^[43]7–[44]10. A
case-crossover study of 40,002 people in Guangzhou found that each
10 μg/m^3 increase in PM[2.5] concentration was related to a 1.6%
increase in chronic obstructive pulmonary disease (COPD)
hospitalization^[45]11. The interaction of PM[2.5] and the influenza
virus has been reported in multiple epidemiological investigations,
showing a correlation between exposure to PM[2.5] and higher
hospitalization and mortality rates by respiratory viral
infections^[46]12–[47]14.
Influenza is a devastating respiratory viral disease that infects
around one billion people each year and kills 500,000^[48]15. The most
prevalent kind of influenza virus is influenza A virus (IAV), with H1N1
and H3N2 as the main subtypes causing influenza, pneumonia, and acute
respiratory distress syndrome^[49]16. Incubation time for influenza
viruses ranges from one to three days following infection of the host,
and the development of influenza symptoms is largely dependent on the
body's innate immune system's capacity to eliminate the virus^[50]17.
Previous study has demonstrated that air pollution influences viral
infection through modification of the viral life cycle or the intensity
of the host's innate and adaptive immune response after
infection^[51]18. Epidemiological investigations and experimental
studies have demonstrated that exposure to environmental PM can result
in the exacerbation of adverse health effects linked with respiratory
viral infection in humans, and that metals in PM can interact with
respiratory viruses via complicated modes of action leading to serious
harm in humans^[52]19,[53]20. For example, ozone (O[3]) exposure
promotes the production of protein hydrolases, which more efficiently
activate influenza virus particles, resulting in greater IAV
infection^[54]21. PM[10] increases H5N1 influenza virus infection in
A549 cells (a human alveolar epithelial cell line) by modulating the
innate immune response^[55]22. PM[2.5] poses a negative impact on the
innate immune system of the lung by altering the function of bronchial
epithelial cells' mucus cilia, hindering alveolar macrophages' ability
to destroy pathogens, reducing the natural killer (NK) cell response,
and causing airway epithelial cell dysfunction^[56]23–[57]25. By
inhibiting lipopolysaccharide (LPS)-induced activation of the NLRP3
(NOD-, LRR- and pyrin domain-containing protein 3) inflammasome and
production of interferon-β (IFN-β) during influenza infection, PM[2.5]
could also change the inflammatory responses of macrophages^[58]26.
Taken together, these studies suggest that air pollutants can increase
influenza virus infectivity and disease severity by inducing
inflammation, suppressing host innate and adaptive immunity, reducing
antiviral ability, and increasing virus replication.
Airway epithelial cells, a physical barrier against inhaled air
pollutants, are the primary targets of environmental hazards including
PM[2.5] and influenza viruses^[59]27. In this study, we used a normal
human bronchial epithelial cell line (BEAS-2B) as an in vitro model and
transcriptomic approach to examine the joint effects of PM[2.5] and
H3N2 on the transcriptome of human airway epithelium to provide
insights into the mechanisms for interactions of ambient PM[2.5] and
H3N2.
Results
Metallic elements of PM[2.5]
ICP-MS was used to detect the content of various metallic elements in
PM[2.5]. As shown in Fig. [60]1, The top 10 metallic elements in terms
of concentration in the PM[2.5] samples utilized in this study were Al,
Fe, Mg, Ca, Cu, Zn, Bi, Sc, Y, In, Ti. Of them, Al, Fe, Ca, and Mg are
crustal elements, implying that road and construction specks of dust
may have the greatest impact on air quality in the winter and spring
seasons of Xinxiang, where the study was performed.
Figure 1.
Figure 1
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The top 10 Metallic elements composition of PM[2.5] (
[MATH: X¯ :MATH]
± SD, n = 3).
Cytotoxicity of PM[2.5] exposure and H3N2 infection on BEAS-2B cells
CCK-8 assay was used to evaluate the cytotoxicity of PM[2.5] exposure
and H3N2 virus infection on BEAS-2B cells, which showed that exposure
to PM[2.5] (0–100 μg/mL) and H3N2 virus (MOI = 1) had minimal effect on
cell viability (Fig. [62]2).
Figure 2.
Figure 2
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PM[2.5] and H3N2 virus exposure caused damage in BEAS-2B cells: Cell
viability (
[MATH: X¯ :MATH]
± SD, n = 3).
Influence of PM[2.5] on infectivity of H3N2 virus on BEAS-2B cells
To assess whether exposure to PM[2.5] could affect the ability of the
H3N2 virus to infiltrate BEAS-2B, we first exposed them to PM[2.5]
(12.5 g/mL) for 4 h before infecting them with H3N2 (MOI = 1). As shown
in Fig. [64]3A and [65]B, co-exposure of BEAS-2B cells to PM[2.5] and
H3N2 virus increased 65.9% of virus-infected cells compared to H3N2
group alone (P < 0.05). It suggests that exposure to PM[2.5] can
increase the susceptibility of respiratory epithelial cells to IAV in
co-exposure. This is consistent with what we found in our previous
study^[66]28.
Figure 3.
[67]Figure 3
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Influence of PM[2.5] on infectivity of H3N2 virus BEAS-2B cells. (A)
Cell morphology after IPMA. (B) PM[2.5] exposure enhances H3N2
infectivity in BEAS-2B cells (n = 3, *P < 0.05, compared to H3N2 virus
exposure).
Analyses of gene expression and correlations
The distribution of the gene expression data for all samples is
displayed in boxplots. Gene expression levels of all samples were
essentially the same after normalization, indicating that batch effect
and systematic bias were not significant (Fig. [69]4A). The results of
the principal component analysis (PCA) revealed that the control (MOCK)
group, H3N2 group, PM[2.5] group, and the co-exposure group
(PM[2.5] + H3N2) were well separated, indicating that there was a high
degree of similarity among the samples in the same group and
differences among different groups (Fig. [70]4B). As shown in
Fig. [71]4C, the correlation coefficient between the samples was
calculated by Pearson correlation analysis, and the closer the
correlation coefficient is to 1, the higher the similarity between the
samples, and the smaller the differences between the samples. The
results show that biological experimental operations of the samples are
highly repeatable and that samples in the same experimental group have
a high degree of similarity.
Figure 4.
[72]Figure 4
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Gene expression level, principal component analysis, and correlations
among samples. (A) The distribution of expression data from all the
samples. (B) Principal component analysis (PCA). (C) The correlations
among samples were analyzed by Pearson correlation.
Screen of DEGs
In PM[2.5] group, 53 DEGs were detected in contrast to the control
group, of which 30 were up-regulated and 23 were down-regulated. In
H3N2 group, 54 DEGs were detected with 21 up-regulated and 33
down-regulated. In the co-exposure group, 97 DEGs were discovered
compared with the control group, of which 45 were up-regulated and 52
down-regulated. Moreover, 52 DEGs were found between the co-exposure
group and PM[2.5] group, of which 22 were up-regulated and 30
down-regulated. 47 DEGs were found between the co-exposure group and
H3N2 groups, of which 32 were up-regulated and 15 down-regulated
(Fig. [74]5A–C). These DEGs could be divided into multiple sub-groups
through a heat map of hierarchical cluster analysis (Fig. [75]5D–F).
The top 10 DEGs in each comparison group are displayed in Tables [76]1,
[77]2, [78]3.
Figure 5.
[79]Figure 5
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Volcanic map of DEGs expression profiles among (A) Co-exposure and
MOCK. (B) Co-exposure and PM[2.5] exposure. (C) Co-exposure and H3N2
virus exposure. And hierarchical cluster analysis heat map of DEGs
expression profiles among (D) Co-exposure and MOCK. (E) Co-exposure and
PM[2.5] exposure. (F) Co-exposure and H3N2 virus exposure. (P < 0.05,
|log[2]FC|> 1).
Table 1.
Top 10 DEGs between co-exposure group and MOCK group.
Gene ID Log[2] fold change P-value Regulation Description
MYT1 4.303932 0.01205 Up Myelin transcription factor 1
NLRC3 4.008194 0.026464 Up NLR family CARD domain containing 3
GCM1 − 3.88936 0.033104 Down Glial cells missing transcription factor
1
CXCR4 − 3.89126 0.033771 Down C-X-C motif chemokine receptor 4
FAM156B − 4.05245 0.022163 Down Family with sequence similarity 156
member B
ANKRD34B − 4.05414 0.022075 Down Ankyrin repeat domain 34B
ADAM20 − 4.05763 0.022873 Down ADAM metallopeptidase domain 20
SPAG17 − 4.21283 0.014399 Down Sperm associated antigen 17
ATP6V1C2 − 4.69288 0.002946 Down ATPase H + transporting V1 subunit C2
SLC9A4 − 4.89332 0.001712 Down Solute carrier family 9 member A4
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Table 2.
Top 10 DEGs between co-exposure group and PM[2.5] exposure group.
Gene ID Log[2] fold change P-value Regulation Description
TBC1D3H 4.894203 0.002049 Up TBC1 domain family member 3H
ZBP1 4.351898 0.010975 Up Z-DNA binding protein 1
STEAP4 3.523135 0.025576 Up STEAP4 metalloreductase
GPR83 3.514048 0.027748 Up G protein-coupled receptor 83
SOWAHB − 3.56796 0.03485 Down Sosondowah ankyrin repeat domain family
member B
PTTG2 − 3.81632 0.036827 Down Pituitary tumor-transforming 2
ADAM20 − 3.99146 0.023591 Down ADAM metallopeptidase domain 20
B3GAT2 − 4.13201 0.016485 Down Beta-1,3-glucuronyltransferase 2
QPRT − 4.26867 0.012643 Down Quinolinate phosphoribosyltransferase
ATP6V1C2 − 4.62835 0.005014 Down ATPase H^+ transporting V1 subunit C2
[82]Open in a new tab
Table 3.
Top 10 DEGs between co-exposure group and H3N2 exposure group.
Gene ID Log[2] fold change P-value Regulation Description
TBC1D3H 4.865379 0.002583 Up TBC1 domain family member 3H
MYT1 4.344997 0.01179 Up Myelin transcription factor 1
KCNK13 3.616799 0.023439 Up Potassium two pore domain channel subfamily
K member 13
CD38 3.59755 0.026918 Up CD38 molecule
LUM 3.503359 0.041209 Up Lumican
HCRTR1 − 3.46801 0.032 Down Hypocretin receptor 1
ANKRD34B − 3.84559 0.037877 Down Ankyrin repeat domain 34B
SPEM2 − 3.84969 0.040319 Down SPEM family member 2
MTNR1A − 4.02484 0.02681 Down Melatonin receptor 1A
ATP6V1C2 − 4.16972 0.017146 Down ATPase H^+ transporting V1 subunit C2
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GO analysis of DEGs
The DEGs were further analyzed by GO enrichment analysis for their
expression and functions. Hypergeometric test between the co-exposure
group and the control group revealed 251 significant enrichment
sub-classes related to the biological process (73.71%), molecular
function (17.53%), and cellular components (7.37%), such as
extracellular matrix organization, cellular protein metabolism, G
protein-coupled receptor signaling pathway, and peptide hormone binding
(Fig. [84]6A).
Figure 6.
[85]Figure 6
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GO enrichment analysis of DEGs between (A) Co-exposure and MOCK. (B)
Co-exposure and PM[2.5] exposure. (C) Co-exposure and H3N2 virus
exposure (Top 30 GO term).
Between the co-exposure group and PM[2.5] group, 103 enrichment
sub-classes were identified, which were related to the biological
process (59.22%), molecular function (25.24%), and cellular component
(15.54%), such as transmembrane signaling receptor activity, activation
of GTPase activity, positive regulation of cell migration,
intracellular protein transport, and plasma membrane (Fig. [87]6B).
Between the co-exposure group and H3N2 group, 119 enrichment
sub-classes were identified, which were associated with the biological
process accounting (68.07%), cellular component (8.10%), and molecular
function (23.53%), such as extracellular matrix organization, cellular
protein metabolism, G protein-coupled receptor signaling pathway, and
peptide hormone binding (Fig. [88]6C).
KEGG pathway analysis of DEGs
Analysis of DEG-associated pathways was performed using the KEGG
database to study the potential mechanism of the joint biological
effects of PM[2.5] exposure and H3N2 infection. It was revealed that
biological systems related to DEGs between the co-exposure and the
control groups included the endocrine system, digestive system, and
immune system. Related metabolic processes including lipid metabolism,
carbon metabolism, etc. DEG-related human diseases include viral,
bacterial, and immune diseases. DEG-related environmental information
processing included signal transduction, signal molecules and
interaction, and cellular community-eukaryotes of cellular processes
(Fig. [89]7A). Moreover, DEGs were significantly enriched in several
related pathways (levle3), including the calcium signaling pathway,
neuroactive ligand-receptor interaction, complement and coagulation
cascades, long-term potentiation, glucagon signaling pathway, and
chemical carcinogenesis, among others. (Fig. [90]7B).
Figure 7.
[91]Figure 7
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KEGG pathway classification (upper panel) and enrichment analysis
(lower panel) of DEGs between Co-exposure and MOCK (A and B),
Co-exposure and PM[2.5] exposure (C and D), and Co-exposure and H3N2
virus exposure (E and F).
The biological systems associated with DEGs between the co-exposure and
PM[2.5] groups included the immune system and endocrine system.
DEG-associated human diseases included viral, bacterial, immune, and
cancer. DEG-associated environmental information processing included
signal transduction, signal molecules and interaction (Fig. [93]7C).
Moreover, DEGs were significantly enriched in several related pathways
(levle3), including the cytokine-cytokine receptor interaction, viral
protein interaction with cytokine and cytokine receptors and epithelial
cell signaling in Helicobacter pylori infection (Fig. [94]7D).
Biological systems associated with DEGs between the co-exposure and
H3N2 groups included the immune system and the endocrine system.
DEG-associated human diseases included viral, bacterial, immune, and
cancer. In addition, DEG-associated environmental information
processing included signal transduction, and signal molecules and
interaction (Fig. [95]7E). DEGs were significantly enriched in several
related pathways (levle3), including neuroactive ligand-receptor
interaction, metabolism of xenobiotics by cytochrome P450, complement
and coagulation cascades, chemical carcinogenesis, rheumatoid
arthritis, and systemic lupus erythematosus, et al. (Fig. [96]7F).
It is noteworthy that some differential genes and pathways were
identified and enriched only in the co-exposure group through the
discovery of DEGs and KEGG enrichment, KEGG pathways such as calcium
signaling pathway, long-term potentiation, etc., and related DEGs
including Adrenoceptor Alpha 1 Beta (ADRA1B), Adrenoceptor Beta 1
(ADRB1), Calcium/Calmodulin Dependent Protein Kinase II Beta (CAMK2B),
etc., implying that the co-exposure of PM[2.5] and H3N2 is more than
just an additive effect.
Gene set enrichment analysis (GESA) of DEGs
The samples were split into two groups in the gene expression matrix
that served as the input for the GSEA analysis. Then, the genes in
these two groups were all listed based on their values generated from
GSEA analysis from large to small, respectively. Here, we can tell
whether a pathway is activated or inhibited by simply looking at the
value of its corresponding gene in the list. The gene with a large
value was putatively considered as up-regulated. On the contrary, the
gene with a smaller value was considered down-regulated.
Compared with the control group, the up-regulated pathways in the
co-exposure group included drug metabolism cytochrome P450, DNA
replication, metabolism of xenobiotics by cytochrome P450, small cell
lung cancer, base excision repair, autoimmune thyroid disease, antigen
processing and presentation, and type I diabetes mellitus, the
down-regulated pathways include steroid biosynthesis, biosynthesis of
unsaturated fatty acids, and cytokine-cytokine receptor interaction
(Fig. [97]8A).
Figure 8.
[98]Figure 8
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GSEA of DEGs between (A) Co-exposure and MOCK. (B) Co-exposure and
PM[2.5] exposure. (C) Co-exposure and H3N2 virus exposure (P < 0.05).
Compared with PM[2.5] group, the up-regulated pathways in the
co-exposure group included the chemokine signaling pathway, antigen
processing and presentation, the down-regulated pathways include base
excision repair, steroid biosynthesis, circadian rhythm mammal, insulin
signaling pathway, JAK-STAT signaling pathway, and peroxisome
(Fig. [100]8B).
Compared with H3N2 group, the upregulated pathways in the co-exposure
group included metabolism of xenobiotics by cytochrome P450, drug
metabolism by cytochrome P450, antigen processing and presentation, and
TGF -beta signaling pathway, the down-regulated pathways include
epithelial cell signaling in helicobacter pylori infection, tight
junction, and pathogenic Escherichia coli infection (Fig. [101]8C).
Verification of DEGs using RT-PCR
The expression of some representative DEGs identified by transcriptomic
methods within different groups was verified using RT-PCR. It was shown
that mRNA levels of some DEGs including CYP1A1, CYPIB1 and Aldehyde
Dehydrogenase 3 Family Member A1 (ALDH3A1) in the co-exposure group
were significantly higher than those in the control group and H3N2
group. Such results provide clues for exploring the mode of action of
PM[2.5] in combined exposures.
The mRNA level of Interleukin 21 Receptor (IL21R) was significantly
higher in the PM[2.5] exposure group and significantly lower in the
H3N2 and co-exposure groups. Furthermore, the mRNA level of ADRB1 was
significantly lower in the co-exposure group, but not in the exposure
alone group (P < 0.05), and CAMK2B was similar, with a tendency of
lower expression in the co-exposure group. (Fig. [102]9). These
findings were in support of the results from RNA-seq, as depicted
above.
Figure 9.
Figure 9
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mRNA expression analysis. Detection of mRNA expression in BEAS-2B cells
by RT-PCR (n = 3, *P < 0.05, compared to MOCK).
Discussion
Even though PM[2.5] air pollution has been significantly reduced in
recent years, it remains a major threat to public health. A previous
study has reported that PM[2.5] has an impact on the infection and
severity of infectious diseases^[104]29. In this study, we examined the
joint effect of PM[2.5] and H3N2 on gene transcription in human
bronchial epithelial cells using RNA-seq. The results suggest that
pleiotropic genes and pathways are involved in the promotive effect of
PM[2.5] on H3N2 infection of human bronchial epithelial cells.
To explore the effect of PM[2.5] exposure on H3N2 infection of BEAS-2B
cells and the possible underlying mechanisms, we first examined the
differential effect of the co-exposure group and PM[2.5] group on gene
expression, 22 DEGs are found up-regulated and 30 DEGs down-regulated
between these two groups. Analysis of the related functions of these
DEGs suggest H3N2 exposure may facilitate viral protein interaction
with cytokine and cytokine receptor, and epithelial cell signaling in
Helicobacter pylori infection in the co-exposure group are
significantly enriched pathways. Whether H3N2 exposure modulates the
effect of PM[2.5] on BEAS-2B cells through signaling molecules and
interaction of environmental information processing and bacterial
infectious diseases is currently under the assumption.
Previous studies have reported the interaction between viral and
bacterial infections. TLR4 is proposed as unable to recognize viruses,
but RSV infection has been shown to upregulate TLR4 expression, which
increases inflammatory signaling and makes the respiratory system more
sensitive to LPS, the major surface membrane component present in
almost all Gram-negative bacteria and also a common biological
component absorbed on PM[2.5].^[105]30 Influenza infection followed by
secondary bacterial pneumonia is associated with significant mortality
and mortality. The susceptibility to bacterial infection may be
increased by viral pathogen-associated molecular patterns (PAMP)
desensitization to TLRs. Desensitization results in reduced chemokine
production and NF-κB activation^[106]31. The hygiene hypothesis for
asthma pathogenesis is also based on the state of tolerance after
repeated PAMP exposure, early exposure to viral PAMP may lessen the
risk of developing high inflammation later in life^[107]32. In another
of our previous experiments^[108]28, the effects of PM[2.5] exposure on
influenza virus (H3N2) infection and downstream regulation of
inflammatory and antiviral immune responses were investigated, also
using the human bronchial epithelial cell line BEAS-2B. The results
showed that exposure to PM[2.5] alone increased the production of
pro-inflammatory cytokines, including interleukin-6 (IL-6) and IL-8,
but decreased the production of the antiviral cytokine interferon-β
(IFN-β) in BEAS-2B cells. In contrast, exposure to H3N2 alone increased
the production of IL-6, IL-8, and IFN-β. In this study, PM[2.5]
exposure significantly increased IL21R expression, whereas IL21R
expression was decreased in both groups with H3N2 exposure. A recent
study has shown that IL-21R signaling suppresses IL-17^+ gamma delta T
cell responses and production of IL-17 related cytokines in the lung at
steady state and after influenza A virus infection^[109]33. These
findings suggest that H3N2 may influence the effects on cells via
signaling molecules and environmental information processing with
bacterial infectious diseases interactions of viral proteins with
cytokines and cytokine receptors, and epithelial cell signaling in
combined exposures.
Meanwhile, we identified 32 up-regulated and 15 down-regulated DEGs
between the co-exposure group and the H3N2 group. The metabolism of
xenobiotics by cytochrome P450, complement and coagulation cascades,
and chemical carcinogenesis are significantly enriched pathways
associated with these DEGs. Whether PM[2.5] exposure modulates the
response of BEAS-2B cells to H3N2 through the pathways regulated by
cytochrome P450 metabolism, respiratory immune system, or
cancer-related diseases, needs to be clarified in the future.
Moreover, the expression of some DEGs in BEAS-2B cells exposed to
PM[2.5] and H3N2 are examined using RT-PCR. Similar to the results from
RNA-seq, mRNA levels of CYP1A1 and ALDH3A1 in the co-exposure group,
those of CYP1B1 and ALDH3A1 H3N2 group are significantly higher than
those in the control group, respectively. KEGG indicates that the
pathway related to ALDH3A1 is associated with the metabolism of
xenobiotics by cytochrome P450 and chemical carcinogenesis.
Additionally, differential expression of CYP1A1 and CYP1B1 is detected
between the co-exposure group and control group or H3N2 group,
respectively. The CYP1 (cytochrome P450 1) family has two significant
subtypes, CYP1A1 and CYP1B1, which are abundant in lung tissues. CYP1A1
and CYP1B1 participate in the metabolism of lung polycyclic aromatic
hydrocarbons (PAHs) as PAHs-sensitive genes and can be activated by
PAHs in the lung through the aryl hydrocarbon receptor
(AhR)^[110]34–[111]36. CYP1A1 is most abundant in alveolar type II
cells and endothelial cells^[112]37, while CYP1B1 is most abundant in
airway epithelial cells^[113]35. Interestingly, PAH content contributes
to PM[2.5] toxicity, and recent research has shown that PAHs in PM are
a powerful mediator of health effects^[114]38–[115]41. Mice exposed to
high PM concentrations in Fresno, California were found to increase
CYP1A1 expression in pulmonary tissues, including pulmonary blood
vessels, parenchyma tissue, and airways^[116]42. Another study found
that CYP1B1, as an enzyme with a unique tumor-specific expression
pattern, can bioactivate a wide range of carcinogenic compounds.
Inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and AhR
ligands co-regulate CYP1B1 expression and change the metabolism of
exogenous carcinogens, increase the biological activity of promutagens,
such as benzo[a]pyrene (BaP) in epithelial cells^[117]43. Recent
studies have shown that CYP450 is able to influence macrophage
inflammatory signaling through the PPARa axis, which may explain how
PM[2.5] affects the role of H3N2 viruses in co-exposure^[118]44. These
findings imply that the action pattern of PM[2.5] may be closely
related to the metabolism of cytochrome P450 in the context of
co-exposure to H3N2 and PM[2.5].
Notably, this study discovered that some differential genes and
pathways were identified and enriched only in the common exposure
group, KEGG pathways such as calcium signaling pathway, long-term
potentiation, and related DEGs including ADRA1B, ADRB1, CAMK2B, and so
on, and the results of RT-PCR assay confirmed the DEGs differences
among them. ADRA1B and ADRB1 belong to the G protein-coupled receptor
adrenergic receptor group and GO annotations for this gene include G
protein-coupled receptor activity α1 adrenergic receptor activity, and
β adrenergic receptor activity. Calmodulin-dependent kinases (CaMK) are
a family of serine/threonine kinases that mediate many of the second
messenger effects of Ca^2+. Recent studies have demonstrated that
CAMK2B expression is modified in neuropsychiatric illnesses and
potentially affects synaptic plasticity^[119]45. These findings suggest
that co-exposure to PM[2.5] and H3N2 may be able to affect the
calcium-sensing receptor on the cell surface by modulating Ca^2+,
which, as a typical nutrient-sensing G-protein-coupled receptor, is
activated and modulated by a wide range of endogenous or exogenous
substances (e.g., cations, amino acids, polyamines, aminoglycoside
antibiotics, etc.), resulting in an "additional" effect^[120]46. Its
specific mechanisms will be investigated further.
In conclusion, PM and H3N2 still pose serious risks to public health,
but the mechanisms of their combined action remain largely unknown.
This study profiles the transcriptome of human bronchial epithelial
cells exposed to PM[2.5] influenza virus (H3N2) by RNA-Seq. The results
indicate that PM[2.5] exposure disrupts the expression of CYP-coding
genes, further altering the body's metabolism of exogenous harmful
substances, and leading to the intensification of H3N2 invasion of
BEAS-2B cells. Meanwhile, by working as a PAMP, H3N2 exposure might
influence the immune system's response to PM[2.5] that contains
bacterial pathogens or LPS. We also found that the combined effects of
PM[2.5] and H3N2 are not simply additive and that the combined exposure
of the two may have an "additional" effect by modulating Ca^2+ to
affect G protein-coupled receptors on the cell surface. The exploration
of the joint effects of PM[2.5] and H3N2 may provide insights into the
pathophysiological basis of the interaction between PM and influenza
virus, as well as for developing efficient strategies to prevent the
adverse respiratory effects caused by PM[2.5] and H3N2 viruses.
Materials and methods
PM[2.5] collection and suspension preparation
Quartz sampling filters were used to collect PM[2.5] in January and
March 2021 using a TischTE-6070 high-flow particle sampler (Tisch
Environmental, USA) with a flow rate of 1.13 m^3/min. After sample
collection, the quartz sampling filter was submerged in a Petri dish
10 cm in diameter filled with ultrapure water and sonicated three times
for five minutes each. The suspension was filtered through six layers
of gauze and then underwent lyophilization to collect PM[2.5] powder
using a vacuum freeze drier (Christ, Germany) for 24 h. PM[2.5] was
thoroughly suspended in phosphate-buffered saline (PBS, Solarbio Life
Sciences, China) solution at a final concentration of 1 mg/mL. The
suspension was aliquoted and stored in a -80 freezer. Before use,
PM[2.5] suspension was vortexed for homogenization.
Analysis of PM[2.5] component
1.0 mg of PM[2.5] was digested in the mixture of 65% HNO[3] and 30%
H[2]O[2] (3:2). The digestion condition in the microwave was 160 °C for
20 min and 140 °C for another 2 h. Metals in PM[2.5] including
magnesium (Mg), aluminum (Al), calcium (Ca), manganese (Mn), barium
(Ba), copper (Cu), zinc (Zn), strontium (Sr), tin (Sn), lead (Pb), etc.
were measured with inductively coupled plasma-mass spectrometry
(ICP-MS, NCS testing technology, China).
Cell culture and treatment
BEAS-2B cells (ATCC, Rockville, Maryland, USA) were used as the in
vitro cell model and cultured in a cell incubator at 37 °C with 5%
CO[2]in Dulbecco’s Modified Eagle Medium (DMEM) with 10% fetal bovine
serum (FBS), 100 U/mL penicillin, and 100 μg/mL streptomycin. Cells
were grown in 12-well plates at a density of 10^5/mL. Upon 90%
confluence, the cells were subjected to the following treatments:
serum-free medium (control group, mock), PM[2.5] (12.5 μg/mL), H3N2
(MOI = 1), or co-exposure of PM[2.5] (12.5 μg/mL) and H3N2 (MOI = 1).
Cytotoxicity assay
Cell counting kit-8 (CCK-8, Shanghai Omo Biotech) was used to evaluate
the cytotoxicity of PM[2.5] and H3N2, respectively. In detail, 100 μL
of 10^5 cells /mL BEAS-2B cells were seeded into each well of a
96-wells plate and cultured at 37 °C in an incubator. Upon 90%
confluence, the cells were incubated with different concentrations of
PM[2.5] (0, 6.25, 12.5, 25, 50, 100 μg/mL), H3N2 (1 MOI), and
co-exposure of PM[2.5] (12.5 μg/mL) and H3N2 (MOI = 1) for 24 h,
respectively. The optical density at 450 nm was measured with an
enzyme-labeled instrument (PerkinElmer, USA), and the cytotoxicity was
calculated according to the manufacturer’s instructions.
Immunoperoxidase monolayer cell assay (IPMA)
IPMA with 3-Amino-9-Ethylcarbazole (AEC) peroxidase substrate as the
chromogenic solution was used to visualize H3N2-infected BEAS-2B cells.
The 12-well plate was cleaned once with PBS before addition of 5%
skimmed milk to each well, and the wells were then blocked in a
thermostat at 37 °C for 1 h. After blocking, monoclonal antibodies
against hemagglutinin of H3N2 were incubated at 37 °C for 1 h, and then
washed five times with PBS containing 0.05% Tween-20 (PBST). Goat
anti-mouse IgG-horseradish peroxidase (HRP) antibody (Beyotime, China)
was added and incubated for another 1 h at 37 °C. AEC chromogenic
solution (Affinity Biosciences, USA) was applied to identify the
infected cells. The number of infected cells was counted under a
microscope (Leica, Germany) which showing a brownish-red color after
treatment with AEC chromogenic solution were observed and counted. The
average number of H3N2-infected cells per microscopic field was
determined according to a protocol for a systematic randomization
procedure^[121]47, and 8 images were recorded for each experimental
group, from 3 independent experiments.
RNA isolation and library preparation
Total RNA was extracted using the miRNA Isolation Kit (mirVana™,
Ambion-1561) according to the manufacturer's instructions. The NanoDrop
2000 spectrophotometer was used to assess the purity and quantity of
RNA (Thermo Scientific, USA). The Agilent 2100 Bioanalyzer was used to
evaluate the integrity of the RNA (Agilent Technologies, Santa Clara,
CA, USA). The mRNA libraries were then created using the TruSeq
Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) for
carrying out the transcriptome sequencing and analysis (OE Biotech Co.,
Ltd., Shanghai, China).
Quality control and RNA sequencing
PCA was used to analyze gene expression data and used to measure the
distance between the samples in order to identify sample similarities.
Pearson correlation analysis was used to calculate the correlation
coefficients between samples, and the correlation between samples
represents the degree of similarity between samples, and the similarity
of samples from various treatments or tissues in terms of expression
levels. The correlation of biological duplicates can be used to not
only examine the reproducibility of biological experimental
manipulations but also to evaluate the dependability of differentially
expressed genes and to aid in the screening of aberrant samples.
On the Illumina HiSeq X Ten platform, the mRNA libraries were
sequenced, and 150 bp paired-end reads were generated. The HiSeq X Ten
System was specially created for population-scale whole-genome
sequencing. Each HiSeq X System is capable of 30-fold or greater
coverage of human genome sequencing. Trimmomatic^[122]48 was utilized
for the processing of raw data (raw reads). To get the clean reads, the
low-quality reads and reads containing ploy-N were eliminated. The
human genome (GRCh38) was then mapped using HISAT2^[123]49 using the
clean reads. FPKM^[124]50 of each gene was calculated using
Cufflinks^[125]51, and the read counts of each gene were obtained by
HTSeqcount^[126]52. Differential expression analysis was performed
using the DESeq (2012) R package.
Differential expression gene (DEG) analysis
Only genes with count mean values larger than 2 were kept for the
subsequent study after the genes had initially been filtered based on
the count's mean value. The DESeq software was used to normalize the
counts of each sample gene (the BaseMean value was used to estimate the
expression), the difference fold was calculated and the NB (negative
binomial distribution test) was used to test the significance of
differences. Finally, the differential protein-coding genes were
screened based on the fold and significance test results. For screening
differences, the default parameters were P < 0.05 and
|Log[2]foldchange|> 1. The expression pattern of genes in different
groups and samples was displayed using hierarchical cluster analysis of
DEGs.
GO and KEGG enrichment analysis
R software was used to analyze the DEGs' Gene Ontology (GO) enrichment
and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
based on the hypergeometric distribution. GO enrichment and
KEGG^[127]53 pathway enrichment analysis of DEGs were performed
respectively using R based on the hypergeometric distribution.
Biological process, cellular composition, and molecular function, the
three categories of GO functional annotations, were all covered. The
pathway diagram was derived from the KEGG database.
Gene set enrichment analysis
For Gene set enrichment analysis (GSEA), the GSEA software (version
3.0) was used to divide the samples into two groups according to the
presence or absence of PM[2.5] exposure and download the samples from
Molecular Signatures Database
([128]http://www.gsea-msigdb.org/gsea/downloads.jsp)^[129]54, the
c2.cp.kegg.v7.4.symbols.gmt subset was downloaded to evaluate relevant
pathways and molecular mechanisms based on gene expression profiles and
phenotypic groupings, setting a minimum gene set of 5 and a maximum
gene set of 5000. One thousand resamples with P < 0.05 was considered
statistically significant.
Real-time quantitative reverse transcription polymerase chain reaction
(RT-PCR)
Each RT reaction contained 10 μL of 5 × TransScript All-in-one SuperMix
for qPCR, 2 μL of 0.5 μg RNA, and 0.5 μL of gDNA Remover. In a
GeneAmp^® PCR System 9700 (Applied Biosystems, USA), reactions were
carried out for 15 min at 42 °C and 5 s at 85 °C. After being diluted
10 times in nuclease-free water, the 10 μL RT reaction mix was kept at
-20 °C.
RT-PCR was carried out using the LightCycler^® 480 II Real-time PCR
Instrument (Roche, Switzerland) and a 10 μL PCR reaction mixture that
contained 1 μL of cDNA, 5 μL of 2 × PerfectStart™ Green qPCR SuperMix,
0.2 μL of forward primer, 0.2 μL of reverse primer, and 3.6 μL of
nuclease-free water. In a 384-well optical plate (Roche, Switzerland),
reactions were incubated for 30 s at 94 °C, then underwent 45 cycles of
5 s at 94 °C and 30 s at 60 °C. Each sample was run in triplicate for
analysis. Melting curve analysis was performed following the PCR cycles
to confirm the precise generation of the desired PCR product. mRNA
sequences obtained from the NCBI database served as the basis for the
design of primer sequences. The primer sequences are shown in Table
[130]4. The expression levels of mRNAs were normalized to ACTB
(β-actin) and calculated using the 2-ΔΔCt method.
Table 4.
RT-PCR primers.
Gene Forward primer(5′ → 3′) Reverse primer(5′ → 3′)
ACTB CATTCCAAATATGAGATGCGTT TACACGAAAGCAATGCTATCAC
ADRB1 GCACAGCAGATAGAAAGACTT ATTGACAGAGTCACATGTCAC
ALDH3A1 GCAACGACAAGGTGATTAAGAA GGTGATGTGGACGATGAC
CAMK2B GTTTGAGCCTGAAGCACTG GATCGGCTTGCTGTTCTT
CYP1A1 CCTCCAAGATCCCTACACT CCCTGATTACCCAGAATACCA
CYP1B1 TCGAGTGGGAGTTAAAGCTTC TAGGGCAAGACGTCAACAG
IL21R TCGGGTTGGAAGTCAGCA GCATTCTCTCAGCTACCTC
[131]Open in a new tab
Statistical analysis
SPSS 22.0 (IBM, USA) and GraphPad Prism8 (GraphPad Software, USA)
statistical software was used to process the experimental data, analyze
the variables, and create graphs. Data with a normal distribution were
expressed as mean and standard deviation (
[MATH: X¯ :MATH]
± SD). ANOVA was applied to compare the cell viability of each group,
and the Games-Howell method was used to compare the groups pairwise.
The t-test of two independent samples was used to compare the numbers
of virus-infected cells between the combined group and H3N2 virus group
in the IPMA assay, with α = 0.05.
Acknowledgements