# Innovative graph analysis method to assess gene expression modulation after fine particles exposures of 3D human airway epithelia

Philomène Despréaux, Capucine Jeanton, Dorota Desaulle, Margueritta Al Zallouha, Anthony Verdin, Isabelle Momas, Sophie Achard

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## ► To cite this version:

Philomène Despréaux, Capucine Jeanton, Dorota Desaulle, Margueritta Al Zallouha, Anthony Verdin, et al. Innovative graph analysis method to assess gene expression modulation after fine particles exposures of 3D human airway epithelia. Environmental Research, 2023, 221 (04), pp.115296. 10.1016/j.envres.2023.115296. hal-04394320

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Distributed under a Creative Commons Attribution 4.0 International License
1 Innovative graph analysis method to assess gene expression modulation after fine

2 particles exposures of 3D human airway epithelia

3 Philomène Despréauxª, Capucine Jeantonª, Dorota Desaulleᵇ§, Margueritta Al Zallouhaª§,

4 Anthony Verdinᶜ§, Isabelle Momasª, Sophie Achardª*§

5
6 a Université Paris Cité, Faculté de Pharmacie, CRESS INSERM UMR 1153, équipe HERA

(Health Environmental Risk Assessment), Paris, France

8 b Université Paris Cité, Faculté de Pharmacie, UR 7537 - BioSTM (Biostatistique, Traitement et

9 Modélisation des données biologiques), Paris, France

10 c Université du Littoral Côte d'Opale, Unité de Chimie Environnementale et Interactions sur le

11 Vivant (UCEIV) UR4492, SFR Condorcet CNRS 3417, Dunkerque, France

12
13 *Corresponding author – email address: sophie.achard@u-paris.fr

14
15 $ORCID number: S. Achard 0000-0003-4991-5888

16 M. Al Zallouha 0000-0001-7876-6725

17 D. Desaulle 0000-0002-3419-9447

18 I. Momas 0000-0003-4344-3787

19 A. Verdin 0000-0003-3438-9854

20
21 **Abstract**

22 Environmental particles have dramatic consequences for health, especially for the most
23 vulnerable people, such as asthmatics. To better understand the impact on gene expression
24 modulation of fine particles (PM2.5-0.3) from different emission sources, a 3D-airway model, a
25 human bronchial epithelium (MucilAir-HF™) reconstructed from primary cells from healthy
26 (EpiH) or asthmatic (EpiA) donors, was used. Repeated air-liquid exposures were performed, and
27 epithelia were sacrificed to extract RNAs and assess gene expression. Data were analyzed
28 according to the emission sources, physiological status, and exposure doses using a recent model
29 consisting in a graph analysis on pairwise expression ratio. The results were compared with those
30 from the classical ΔΔCt method. The graph analysis method proved to have better statistical
31 properties than the classical ΔΔCt method and demonstrated that repeated PM2.5-0.3 exposures
32 induced a dose-dependent up-regulation of the metabolic gene (CYP1B1) and a down-regulation
33 of the inflammation gene (CXCL10). These modulations were greater for “industrial” than for
34 “urban traffic” fine particles, and the effects were found to be greater after exposure of EpiA
35 than EpiH, thus emphasizing the importance of the epithelium’s physiological status in sensitivity
36 to particles. Our study is original in terms of the experimental conditions and the graphical
37 statistical analysis model established. The results highlight the importance of particle chemistry
38 on the modulation of cellular and molecular responses, which may vary according to the
39 individual’s vulnerability.

40

41 **Keywords**

42 Fine particles

43 Repeated air-liquid exposures

44 Healthy or Asthma epithelium

45 Innovative statistical analysis to assess qPCR data

46 Industrial or Urban PM sources
47 **Abbreviations**

48 AhR, aryl hydrocarbon receptor; ALI, air-liquid interface; ARN18S, 18S ribosomal RNA;
49 CREBBP, CREB (c-AMP response element-binding protein) binding protein; Ct, cycle threshold;
50 Epi (H) or (A), CXCLx, C-X-C motif chemokine ligand x (CXCL10, CXCL2, ...); CYP,
51 cytochrome (CYP1A1, CYP1B1, CYP2B6); cDNA, complementary deoxyribonucleic acid; EGF,
52 epidermal growth factor; EpiH or EpiA, reconstituted epithelium Healthy or Asthma; HF,
53 human airway fibroblasts; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GM-CSF,
54 Granulocyte-macrophage colony-stimulating factor; HDAC, histone deacetylase (HDAC1,
55 HDAC2, HDAC3, HDAC6); HMOX, heme oxygenase; IL, interleukine (IL-6, IL-1β, ...);
56 HPRT, hypoxanthine phosphoribosyltransferase; MUC, mucins (MUC5B, MUC5AC); NF-κB,
57 nuclear factor-kappa B; NOS, nitric oxide synthase; Nrf2, Nuclear Factor Erythroid 2-related
58 factor 2; PAH, polycyclic aromatic hydrocarbon; PM, particulate matter; PMInd or PMTraf,
59 particles from Industrial or Traffic emission sources; PM2.5, fine particles; RNA, ribonucleic
60 acid; RPL13, ribosomal protein L13A; RT-qPCR, reverse transcription quantitative polymerase
61 chain reaction; SOD, superoxide dismutase; TEER, transepithelial electric resistance; TET1, Tet
62 (ten-eleven translocation) methylcytosine dioxygenase 1; TGF-β, transforming growth factor-β;
63 3D, three-dimensional; TLR, toll-like receptors; TNF-α, tumor Necrosis Factor.

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## 1. Introduction

Environmental particles (PM) represented a complex and heterogeneous mixture of air pollutants. Among them, fine particles (PM2.5), characterized by an aerodynamic diameter of less than or equal to 2.5 µm, are constituted of a carbonaceous core on which inorganic compounds (e.g., metals and salts), organic compounds (e.g., polycyclic aromatic hydrocarbons: PAHs), and biological contaminants (e.g., pollen, bacteria, and fungi) are adsorbed. These compounds, in relation to emission sources, contribute to PM's high surface reactivity. Once inhaled, PM2.5 penetrate in the airway lower regions to reach the bronchial epithelium. Carbonaceous core and compounds such as PAHs or metals may interact with epithelial cells to cause numerous biological reactions. To limit such deleterious mechanisms, airway epithelium rapidly establishes a defense system. Insoluble compounds can be handled by mucociliary function, while some compounds, such as PAHs, can be metabolized to make them more water-soluble. Activation of some receptors such as aryl hydrocarbon receptors (AhR) leads to the transcription of metabolic enzymes, such as cytochromes P450, promoting metabolic activities (Øvrevik et al., 2015; Chen et al., 2021). However, PAH metabolization can induce free radicals formation responsible for an oxidative stress (Øvrevik, 2019). To limit such deleterious mechanisms, antioxidant enzymes, such as superoxide dismutase (SOD) or heme oxygenase (HMOX), allowing elimination of excess radical species, are produced (Zelko et al., 2002). Nevertheless, repeated exposures to low doses of PM over prolonged periods of time, defense system can become overwhelmed and ineffective, resulting in cellular and molecular damages. Thus, many nuclear transcription factors regulating the expression of genes involved in a variety of cellular processes such as cell growth, mucus production, and inflammation could be activated and lead to various respiratory pathologies.

Cell-signaling protein activation, such as NF-κB, TLRs, and Nrf2, can cause increased pro-inflammatory cytokines expression, for instance TNF-α, IL-6, or IL-1β (Kaspar et al., 2009; Lee & Yang, 2012; Mishra et al., 2018). Up-regulation of the epidermal growth factor (EGF) can lead
to goblet cell proliferation and therefore increased secretion of major mucins like MUC5B and MUC5AC (Casalino-Matsuda et al., 2006, 2009). Transforming growth factor-β (TGF-β) excretion by epithelial cells can lead to tissue remodeling (Halwani et al., 2011; Latella, 2018) and ciliophagy, notably by increasing the mRNA expression levels of autophagy-associated proteins such as Beclin1 (Kiyono et al., 2009; McAlinden et al., 2019). Finally, epigenetic mechanisms, especially DNA methylation and histone modifications, seem to regulate certain cellular signaling pathways, as those described above, and more specifically inflammation development and maintenance in chronic inflammatory pathologies (Potaczek et al., 2020). The biological responses described above are largely associated with the chemical part of PM, and will be differentially modulated according to the vulnerability of people, such as those suffering from a respiratory pathology, such as asthma. To improve the understanding of cellular and molecular mechanisms taking place in the bronchial epithelium following PM exposures, modulation of the expression of certain genes involved in such mechanisms can be evaluated. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the method of choice for such assessments because it has high sensitivity, relatively low cost and high adaptability to a wide range of experimental designs. Data obtained after qPCR are classically analyzed using the ΔΔCt method (Livak & Schmittgen, 2001). To ensure data reliability, this method is based on a normalization using an endogenous control. Thus, in each sample, each target gene expression level is normalized by the expression level of one housekeeping gene. A housekeeping gene's expression level does not vary under different experimental conditions. However, the strong assumption that such genes' expression levels are not affected by the condition(s) tested is not always verified (Ling & Salvaterra, 2011; Radonić et al., 2004; Xu et al., 2015). Several methods described in the literature could circumvent this limitation, including one based on graph analysis on pairwise expression ratio (Curis et al., 2019). This method, combined with a statistical procedure that controls the type I error, compares each target gene to all other genes, thus avoiding the difficulties associated with unexpected variations in the expression level of the housekeeping
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gene. This procedure uses an equivalence test to prove the hypothesis that a given pair of genes undergoes the same change in expression under two different conditions.

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In this work, our objectives were to assess the modulation of gene expression after repeated exposure of human epithelia to PM using RT-qPCR and applying an innovative graph analysis method to analyze the different modulations observed. The strengths of our study were to have used an *in vitro* model respecting the main route of exposure to PM, a reconstructed human epithelium of bronchial origin (MucilAir-HF™), and to have considered the notion of individual vulnerability to PM2.5-0.3 from two different emission sources by using epithelia reconstituted from cells of healthy and asthmatic donors. Finally, the novelty of this work lies in the use of two different methods of gene expression analysis, graph analysis on pairwise expression *ratio* and the classical ΔΔCt method.

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## 2. Material and methods

### 2.1. Collection and physicochemical characterization of fine particles (PM<sub>2.5-0.3</sub>)

PM was sampled from February to June 2017 in two sites in northern France: Grande Synthe, for industrial activities (PMInd), and Roubaix, an urban area with dense road traffic (PMTraf). A five stages plus back-up cascade impactor (model 235 TFIA-2, Staplex®, USA) was used to collect particles (Cazier et al., 2016). The resulting PM<sub>2.5-0.3</sub> was carefully collected and stored at -20°C until use. After collection PM<sub>2.5-0.3</sub> were carefully stored at -20°C until use, conditions ensuring their chemical composition stability for several years (Dergham et al., 2015; Cazier et al., 2016).

Chemical analyses were performed using inductively coupled plasma-atomic emission spectrometry (iCAP 6000 Series, ThermoScientific) for metals, and gas chromatography coupled to mass spectrometry after Soxhlet extraction (CP 3800/1200MS, Varian) for organic compounds. The main results discussed in this work are presented in Table 1 (All data concerning the physicochemical characterization of PM<sub>2.5-0.3</sub> are in supplementary data Table S1).

### 2.2. 3D *in vitro* model of reconstituted human bronchial epithelia (MucilAir-HF™)

The 3D *in vitro* model used was reconstituted human bronchial epithelia co-cultured with human airway fibroblasts (HF) (MucilAir-HF™, Epithelix®, Geneva, Switzerland). Epithelia were reconstituted from human bronchial primary cells obtained from biopsies of nonsmoker donors - without reported respiratory pathologies (EpiH, one donor) and with asthma (EpiA, one donor).

The supplier obtained the donors' signed, informed consent and ethical approval. Donors' characteristics provided by Epithelix® are summarized in Table S2 (supplementary data).

The primary cells from biopsies were differentiated at the air-liquid interface (ALI) leading to the progressive reconstruction of a pseudostratified epithelium, physiologically close to human bronchial epithelium, composed of basal, ciliated, and goblet cells, and characterized by tight junctions, metabolic activity, ciliary beats, and mucus secretion. Reconstituted epithelia (Epi) were maintained in small culture chambers (0.33 cm² insert surface, 0.4 µm pore size, Sigma-Aldrich, St Louis, MO, USA) at the ALI in an incubator (37°C, 5% CO₂ and 95% constant relative humidity,
MCO-18AIC, Sanyo, Japan) with 700 µL of MucilAir nutrient medium (Epithelix®, Geneva, Switzerland) in the basal side. The culture medium was changed every two or three days to maintain tissue homeostasis. Once a week, the apical side was washed with nutrient medium to remove mucus and surface desquamating cells, and the tissue integrity was assessed by measuring the transepithelial electrical resistance (TEER) using a Voltohmometer (EVOM², World Precision Instruments, USA). According to Epithelix®, tissue integrity is considered good if the TEER value is greater than or equal to 200 Ω·cm². Whatever our experimental conditions, the TEER values measured were greater than or equal to 200 Ω·cm² indicating no toxic effect of PM.

## 2.3. Epithelia exposures

Epithelia were exposed to PM₀.₃–₂.₅ at the ALI by depositing a 10µL drop of PMInd or PMTraf at concentrations of 45 or 90 µg/cm² on the apical side, as described by Achard et al., 2019. Particles, at the target concentrations, were suspended in the nutrient medium then dispersed using an ultrasonic bath (M1800, Bransonic, USA) for at least 20 minutes to avoid the presence of particulate aggregates. After deposition, PM₂.₅-₀.₃ was rapidly distributed across the entire epithelia surface by ciliary beats allowing homogeneous tissue exposure. Exposures were performed twice a week for three consecutive weeks. Duplicates were considered for each experimental condition and exposure time. Two control groups were considered: Incubator Control, epithelia maintained without exposure in the incubator during one, two, or three weeks; Medium Control, epithelia repeatedly exposed to nutrient medium without PM₂.₅-₀.₃ during one, two, or three weeks.

## 2.4. Gene expression evaluation

At the end of each week, two of the six epithelia exposed per condition were sacrificed to extract total RNA using NucleoSpin® TriPrep (Macherey-Nagel, Germany). The extracted RNA was quantified using Qubit® 2.0 Fluorometer (model QFX, Denovix, USA). RT of total RNA was performed using iScript™ cDNA Synthesis (Bio-Rad, USA). Expressions of four candidate housekeeping genes and of target genes involved in inflammation, oxidative stress, cell growth,
autophagy, mucus production, metabolism, and epigenetic mechanisms (Table S3 - supplementary data) were assessed by qPCR performed using iQ™ SYBR® Green Supermix (Bio-Rad, USA) and specific primers following manufacturer recommendations. Briefly, cDNA was amplified using a thermocycler (CFX Connect Optics Module, Bio-Rad, USA) under the following conditions: 30 seconds at 98°C; 40 cycles of 5 seconds at 95°C, 10 seconds at 60°C, and 10 seconds at 72°C; 1 minute at 65°C; and 50 seconds at 95°C.

Gene expression modulation quantification used the cycle threshold (Ct) values corresponding to the number of cycles needed for the fluorescence signal to reach a specific threshold level of detection. Ct values were inversely correlated with the amount of template nucleic acid present in the reaction (Walker, 2002).

## 2.5. Gene expression and statistical analyses

Two methods were investigated to analyse Ct values obtained by RT-qPCR: the classical ΔΔCt method (Livak & Schmittgen, 2001) and the recent method using graph analysis based on pairwise expression *ratio* comparisons (Curis et al., 2019).

Given the low number of replicates for performing Student tests, the statistical analyses were performed “all exposure times combined” for each experimental condition; estimation of Cts variability with n=6.

Statistical analyses were performed using R (version 4.1.3, R Core Team (2022)). A Student test under Gaussian assumption was performed to evaluate whether the expressions between two genes differ in exposed and unexposed conditions.

### 2.5.1. Classical ΔΔCt method

The classical ΔΔCt method is based on the relative expression of each gene normalized to one housekeeping gene. To ensure accuracy, four housekeeping genes were tested (*GAPDH*, *HPRT*, *ARN18S*, and *RPL13*) (Radonić et al., 2004).
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Relative normalization of target nucleic acid in the sample was calculated as: ΔCt = Ct (target gene) - Ct (housekeeping gene). The difference between target mRNA levels in exposed and unexposed epithelia was considered as ΔΔCt = ΔCt (exposed) - ΔCt (unexposed).

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The classical ΔΔCt method results were represented as heatmaps generated by Morpheus software (Morpheus, https://software.broadinstitute.org/morpheus) from -ΔΔCt values calculated with validated housekeeping genes. Heatmaps' color-coded representations allow visualization of the direction of gene expression modulation: red indicates an up-regulation (-ΔΔCt > 1) and blue a down-regulation (-ΔΔCt < -1). The -ΔΔCt values, between -1 and 1 (white), indicate no gene expression modulation.

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For each target gene, only comparisons with *HPRT* and *GAPDH* were considered, and the false discovery rate was used to take into account the multiplicity of comparisons (Benjamini & Hochberg, 1995). P-adjusted < 0.05 was considered significant.

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### 2.5.2. Graph analysis on pairwise expression ratio method

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The graph analysis on pairwise expression *ratio* method compares each RNA amount to all the other RNA amounts in each experimental condition.

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In this analysis, the housekeeping genes were treated in the same way as the target genes. The logarithm of *ratio* of two RNA amounts was equivalently quantified as the Ct difference for the corresponding genes. More precisely, for any pair of gene x and y, the Ct differences [Ct (gene x) - Ct (gene y)] were calculated for each exposed and unexposed epithelium. An individual statistical test then evaluated whether the expressions between the two genes x and y differed between exposed and unexposed conditions, i.e., the alternative hypothesis. The null hypothesis states no difference between conditions. The corresponding test results were converted into a graph - a network-based visualization - using an R package, SARP.compo, developed by Curis.<fnref n="1" />

On the graphs, each gene is represented by a node (orange for housekeeping genes, green for target genes). Two nodes can be connected or not by an edge. If the change in the *ratio* of

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<footnote n="1">¹ Emmanuel Curis (2021). SARP.compo: Network-Based Interpretation of Changes in Compositional Data. R package version 0.1.5. https://CRAN.R-project.org/package=SARP.compo</footnote>
corresponding RNA amounts between conditions is not significant, the two nodes are connected by an edge. If the *ratio* change is significant, the edge between the two nodes is absent.

Graph interpretation is as follows: i/Connected graph: If all nodes are connected, there is no significant difference in the gene expression in exposed epithelia and unexposed epithelia; ii/Limit of differential expression: If one gene is connected to one or very few other genes, it is considered as being at the limit of differential expression between exposed and unexposed conditions; iii/Disjoint subgraphs: If a node or set of nodes is not connected by an edge to another node or set of nodes, the gene expressions in each node or set of nodes are considered significantly different in the exposed and unexposed conditions.

Simulations were performed for each graph construction to calibrate the choice of significance level for individual *ratio* tests. The objective was to maintain the risk of obtaining disjoint subgraphs by error (overall type I error) below 0.05.
241 **3. Results**

242 **3.1. Housekeeping genes selection**

243 Quantitative real-time PCR was used to determine the mRNA transcript profiles of four
244 housekeeping genes - *GAPDH*, *HPRT*, *ARN18S* and *RPL13* - to select the most suitable for our
245 experimental conditions (unexposed and exposed epithelia). To evaluate the different RNA
246 transcription levels, the Ct values were compared directly (supplementary data Figure S1). First,
247 for each housekeeping gene, the transcription range - the difference between the lowest RNA
248 transcription (high Ct value) and the highest RNA transcription (low Ct value) - was evaluated in
249 all epithelia. To evaluate the stability of candidate RNA transcription depending on the test
250 conditions, the average RNA transcription levels in unexposed and exposed epithelia were
251 compared.

252 The *ARN18S* expression level was too high compared to those of the genes of interest in our
253 study. *HPRT* had the lowest transcription range (1.9 Ct value) of the candidate housekeeping
254 genes, thus indicating its constant RNA transcription. When comparing experimental conditions,
255 the *HPRT* RNA transcription level was the same for unexposed and exposed epithelia, thus
256 indicating that PM2.5-0.3 exposure had no influence on *HPRT* expression. *GAPDH* and *RPL13*
257 had the same transcription range (5 Ct value). However, *GAPDH* expression was the least
258 impacted by experimental conditions, as indicated by the average gene expression levels between
259 unexposed and exposed conditions.

260 Thus, to continue the analysis, the two housekeeping genes *HPRT* and *GAPDH* were chosen.

261 **3.2. Control groups validation**

262 Two control groups were envisaged: Incubator Control (epithelia maintained in incubator
263 without exposure) and Medium Control (epithelia exposed to 10µL of medium). To determine
264 whether the deposition of 10µL of medium on the epithelium's apical side impacted tissue
265 integrity, TEER values obtained in both control groups were compared over a four-week period
266 (week 0: week before exposure; weeks 1, 2 and 3: weeks after exposure, or not) (supplementary
267 data Figure S2). No significant difference was observed after comparison of Medium Control
268 versus Incubator Control.

269 Similarly, to verify that repeated exposure by deposition of medium on apical surface did not
270 modulate gene expression, the graph analysis on pairwise expression *ratio* was applied for EpiH
271 (Fig. 1.A) and EpiA (Fig. 1.B). For the two epithelia (EpiH and EpiA), networks obtained by
272 graph analysis on pairwise expression *ratio* display one “connected graph” indicating no
273 significant gene modulation between the two control groups. *GM-CSF*'s connection to the other
274 genes in the EpiH group through only one edge put it at the limit of differential expression.
275 However, that gene's modulation was not significant. These results allowed us to combine the
276 two control groups to form one single control group “CONT” to increase the analyses' statistical
277 power.

278 **3.3. Modulation of epithelia gene expression after repeated PM2.5-0.3 exposures**

279 After epithelia exposures to PMInd and PMTraf, a graph analysis on pairwise expression *ratio* was
280 considered to assess gene expression modulation for EpiH (Fig. 2) and EpiA (Fig. 3). These
281 results were then compared to those from the classical ΔΔCt method after constructing heatmaps
282 using the -ΔΔCt values obtained for the domestic genes *HPRT* or *GAPDH*.

283 **3.3.1. EpiH (Healthy epithelia) exposures**

284 Following exposures to PMInd or PMTraf at 45 µg/cm², the graph analysis method showed
285 connected graphs indicating no significant difference in the gene expression of the exposed and
286 CONT groups, whereas some gene expressions appeared significantly modulated in the classical
287 ΔΔCt method, regardless of the housekeeping gene used (Fig. 2.A and Fig. 2.C). At 90 µg/cm²,
288 regardless of the analysis method used, one gene for PMInd (Fig. 2.B) and three genes for
289 PMTraf (Fig. 2.D) were significantly modulated. One common gene, *CYP1B1*, was up-regulated
290 for both PM types.

291 After PMInd exposures, the classical method indicated that *CYP1B1* was always up-regulated
292 (Fig. 2.A and Fig. 2.B), whereas this overexpression appeared at the higher concentration with the
graph analysis method (Fig. 2.B). Whatever the analysis method used, *CYP1B1* was gradually overexpressed after PMTraf exposures from 45 to 90 µg/cm². It was important to note that no significant modulation of *AbR* expression was observed, whatever the experimental conditions, whereas this gene is linked to *CYP1B1* expression, involved in the metabolism of PAHs. Furthermore, the graph analysis method gave a greater impact of PMTraf on EpiH than PMInd, whereas the classical analysis method suggested the opposite, gave a greater impact of PMInd than PMTraf.

Considering the effects of PM2.5-0.3 according to their sources, the graph analysis method indicated that after PMInd exposures at 45 µg/cm², three genes were at the limit of differential expression (*CYP1B1*, *NOS2*, and *CYP2B6*), whereas four genes (*CYP1B1*, *NOS2*, *GM-CSF*, and *TET1*) were up-regulated, but not significantly, with the classical analysis, whatever the housekeeping gene used (Fig. 2.A). At the 90 µg/cm² of PMInd, both methods showed that *CYP1B1* was expressed differently (up-regulation) in EpiH. Another metabolism gene, *CYP1A1*, was also up-regulated, but it could not be included in our graphs or heatmaps because the Ct values for the control groups (unexposed) were above 40, which is too high for reliability. However, following repeated exposures to PMInd, Ct values were around 25, indicating an increase in expression of this gene following exposure. This result was also observed for the other experimental conditions (EpiH exposed to PM Traf and EpiA exposed to PMInd or PMTraf). Graph analysis revealed that one inflammation gene, *CXCL10*, was at the limit of differential expression after exposure. However, although there was a tendency for the expression to decrease, the classical ΔΔCt method (Fig. 2.B) revealed no significant modulation.

For PMTraf exposures at 45 µg/cm², graph analysis indicated one gene cluster, *MUC5AC*, *GM-CSF*, *NOS2*, *CYP1B1*, and *MUC5B*, showing a tendency to disconnect from the main network (Fig. 2.C). Only *CYP1B1* was significantly disjointed from the gene cluster after exposures at 90 µg/cm². Two other genes were found to be differentially expressed at this concentration, *CXCL10* and *HDAC6*, which are involved in inflammatory and epigenetic mechanisms,
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respectively (Fig. 2.D). No significant modulation was observed with the classical ΔΔCt method, whatever the housekeeping gene used.

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### 3.3.2. EpiA (Asthma epithelia) exposures

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Two general observations could be made from the results presented below. The first was that gene expression modulation was greater after exposures to fine particles of EpiA (Fig.3). than EpiH (Fig.2). The second was that, for all experimental conditions tested, the graph analysis highlighted the formation of numerous gene clusters with one gene, *CYP1B1*, significantly disconnected from the main network. Finally, while the network analysis did not show this, the classical method with housekeeping gene *GAPDH* indicated that, for both types of particles tested at 45 µg/cm², genes involved in inflammation (*CXCL2*, *CXCL10*, and *TLR4*), metabolism (*AbR*, *CYP1B1*, and *CYP2B6*), and epigenetic mechanisms (*HDAC1*, *HDAC2*, *HDAC3*, *HDAC6*, and *CREBBP*) were significantly modulated.

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After repeated exposures to PMInd at 45 µg/cm², graph analysis highlighted one disjoint gene, *CYP1B1*. That is consistent with classical ΔΔCt method results, regardless of the housekeeping gene used (Fig. 3.A). When the exposure concentration was increased, gene expression modulation became more important. Indeed, after repeated exposures to PMInd at 90 µg/cm², subgraphs appeared (Fig. 3.B). A network containing the two housekeeping genes and the majority of the target genes was shown; three genes, *MUC5AC*, *MIF*, and *GM-CSF*, were at the limit of disjunction. A subgraph consisting of three inflammation genes, *CXCL2*, *IL-6*, and *TLR4* - indicating these genes were coregulated - was also observed. Finally, two genes fully disjoint from the other subgraphs, *CYP1B1* and *CXCL10*, were also revealed (Fig. 3.B). Consistent results were obtained with the classical ΔΔCt method regardless of the housekeeping gene used. Indeed, *CYP1B1*, *CXCL10*, *CXCL2*, *IL-6*, and *TLR4* were significantly modulated following exposure to PMInd at 90 µg/cm². *GM-CSF*, which was at the limit of differential expression when using graph analysis, was significantly up-regulated when the classical ΔΔCt method was applied (Fig. 3.B).

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For PMTraf exposures at 45 µg/cm², *CYP1B1* and *GM-CSF* were fully disjoint, thus indicating their differential expression between unexposed and exposed conditions. The classical ΔΔCt method supported these observations: these genes were significantly up-regulated, *CYP1B1* with *HPRT* analysis and *GM-CSF*, regardless of the housekeeping gene used (Fig. 3.C). Graph analysis indicated that two other genes were at the limit of differential expression: one metabolic gene, *CYP2B6*, and one involved in mucus production, *MUC5B*. Only *CYP2B6* appeared significantly down-regulated with the classical ΔΔCt method, regardless of the housekeeping gene used (Fig. 3.C). PMTraf tested at 90 µg/cm² revealed significantly differential expression of *CYP1B1* and *CXCL10* in the graph analysis. The classical ΔΔCt method consolidates these results, with *CXCL10* significantly down-regulated regardless of the housekeeping gene used and *CYP1B1* significantly up-regulated only with the *HPRT* analysis. A gene cluster consisting of *MUC5AC*, *HDAC6*, *HMOX1*, and *MUC5B* was slightly disconnected from the main network. Only *HMOX1*, involved in antioxidant response, did not stand out as significantly modulated in the classical method. The other three were, but only after analysis using *HPRT* as the housekeeping gene (Fig. 3.D).
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## 4. Discussion

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Many recent studies have described the harmful effects on human health of fine particles (PM2.5) initiating or promoting numerous pathologies as respiratory, cardiovascular, or neurodegenerative diseases, and cancers (Lu et al., 2021; Thangavel et al., 2022). However, due to the complex chemical and biological composition of PM2.5, the significant spatiotemporal variation with emission sources, and human intra- or inter-individual variability, the health outcomes remain difficult to determine.

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In the present study, we evaluated the impact of repeated exposures to PM2.5-0.3 of various chemical compositions on gene expression modulation in reconstructed bronchial epithelium of different physiological statuses. The study's originality is in establishing a graph analysis on pairwise expression ratio which allow to take into account the existing variabilities between the biological samples, for example between healthy and asthmatic subjects, or between various experimental situations, such as the origin of particles. The statistical significance of the expression modulation of certain genes after exposures could be demonstrated with the graph analysis method and, corroborated using the classical ΔΔCt method. Associations have been demonstrated between gene expression modulation and, particle chemistry in relation to emission sources and epithelium's physiological status in relation to individuals' vulnerability. However, the sampling process presents a limitation by obliging us to make grouping choices and work "all exposure times combined."

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Numerous experimental studies have been conducted to improve knowledge of PM2.5 impact on human respiratory health. As a replacement for animal studies and in accordance with the 3Rs principle (Replace, Reduce, and Refine) (Russell & Burch, 1959), a need for *in vitro* models was felt. The literature was thus enriched by numerous *in vitro* studies. The most frequently used *in vitro* models to study the impact of PM2.5 on airways are monolayer cell cultures including primary cells as hAECs (Park et al., 2021), bronchial cell lines such as BEAS-2B (Abbas et al., 2019; Dergham et al., 2015), 16HBE (Jia et al., 2021), and Calu-3 (Hammond et al., 2022), or alveolar

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cell lines like A549 (Chen et al., 2018; Kouassi et al., 2010). Although these models have many advantages, such as human origin and, ease of obtainment and use, they are monocellular-monolayer models maintained in submerged culture that do not consider the interactions between cells and the airways physiology to reflect the normal route of exposure. Such models cannot be exposed at the air-liquid interface (ALI) beyond 60 minutes, as drying of the apical side leads to cell death (Bardet et al., 2014; Persoz et al., 2010; personal data). Given these limitations, other *in vitro* models have been developed to increase exposure times. Co-cultures, consisting of human blood monocyte-derived macrophages (THP1) with bronchial epithelial cells (BEAS-2B) (Persoz et al, 2012) or alveolar epithelial cells (A549) (Longhin et al., 2013; Wottrich et al., 2004) appear to be more robust than monocultures for pollutant impact studies but remain difficult to implement. Thus, *in vitro* impacts' studies of gaseous or particulate aerosols seems limited by the availability of relevant *in vitro* models reflecting the inhalation exposure route with a maintain of the biological activities over time. Hence, over the past 20 years, primary cells grown at the ALI have been developed to reconstruct human airway epithelium (Anderson et al., 2013). These models show polarized mucociliary differentiation, essential function for applying ALI exposures (Baxter et al., 2015; Bisig et al., 2018; Rossner et al., 2019). Unfortunately, the use of these 3D models to assess the impact of pollutants on the respiratory epithelium is not very common. In this context, our approach is original and innovative.

The impact of repeated exposures to fine particles (PM<sub>2.5</sub>-<sub>0.3</sub>) from different emission sources on the expression modulation of genes involved in various signaling pathways, in relation to chronic respiratory diseases was researched in this work. This study was conducted on a 3D *in vitro* model of reconstituted human bronchial epithelium (MucilAir-HF™) with two different physiological statuses: “healthy” and “asthma.” The MucilAir-HF™ model is composed of ciliated, mucus-secreting cells and basal cells, thus allowing a closer approximation of human respiratory tract physiology. The presence of mucus on the apical side enables repeated long-term ALI exposures without tissue drying (Baxter et al., 2015; Cervena et al., 2019; Frieke Kuper et al., 2015; Kooter
et al., 2016; Zavala et al., 2016). ALI exposure consists in applying an aerosol directly on the apical side of cells or tissues maintained at the air-liquid interface on a transwell porous membrane, using different ALI exposure devices. Mülhopt *et al.* developed a device using Vitrocell® exposure modules connected to an aerosol generation system. Thus, thanks to a continuous air flow, the suspension of particles generated was deposited on the surface of tissues.

Quantification of the deposited particles was made possible by a quartz crystal microbalance integrated into an exposure module (Mülhopt et al., 2016). Gualtieri *et al.* used an exposure system composed of Cultex RFS modules placed in a mobile laboratory. A complex unmodified gas-particle mixture collected directly from the native atmosphere was deposited, thanks to a continuous air flow, onto cells grown at the air-liquid interface (Gualtieri et al., 2018). In our study, due to the limited amount of particles collected, epithelia were exposed to a suspension of PM<sub>2.5-0.3</sub> deposited directly on the apical surface. Ciliary beating allowed rapid distribution of PM<sub>2.5-0.3</sub> over the entire epithelial surface ensuring homogeneous tissue exposure. The small volume (10µL) deposited on the apical surface was negligible and evaporated rapidly once at 37°C in the incubator, thus maintaining culture and exposure conditions at the air-liquid interface. Note that if mucociliary function allows homogeneous distribution of PM on the epithelium surface, it also leads to the elimination of excess PM. Thus, 2 hours after deposition, we observed that a quantity of PM, estimated at 80% of the initial deposit, was found against the walls of inserts. This phenomenon led us to work with higher PM concentrations than in some studies conducted on monocultures (Gualtieri et al., 2011; Dergham et al., 2015; Abbas et al., 2019; Badran et al., 2020).

To evaluate the effects of PM<sub>2.5-0.3</sub> on the expression modulation of genes involved in inflammation, mucus overproduction, tissue remodeling, metabolism, epigenetic mechanisms, a graph analysis on pairwise expression *ratio* method was applied. The data obtained with this method were compared to the classical ΔΔCt method results and discussed according to the type of particles in relation with the emission sources, exposure level, and physiological status of the
epithelium. The major results showed dose-dependent gene expression modulation after repeated exposures to PM2.5-0.3, regardless of the physiological status of epithelia, EpiH or EpiA, mainly for the genes involved in PAHs metabolism and inflammation. This modulation was higher in EpiA than in EpiH. However, our results did not show that repeated exposures could lead to a significant modulation of the expression of specific genes such as mucus overproduction or tissue remodeling, involved in development (after exposures to EpiH) or exacerbation (after exposure to EpiA) of asthma.

Whatever the PM2.5-0.3 emission sources - industrial or urban traffic sites - an up-regulation of two metabolic genes, *CYP1A1* and *CYP1B1*, was observed after repeated EpiH and EpiA exposures. These overexpressions were higher after PMInd than PMTraf exposures. The results were consistent with the concentration of PAHs in PM, namely 136 µg/g for PMInd versus 18 µg/g for PMTraf (Table S1). Although this observation has never been described with a human bronchial reconstituted epithelium model, some studies using bronchial cell lines such as BEAS-2B (Borgie et al., 2015) and 16HBE (Zhou et al., 2015) showed such induction of metabolism genes (*CYP1A1*, *CYP1B1*) linked to the presence of PAHs on the fine particles surface.

Similarly, exposure to PM2.5-0.3 resulted in decreased *CXCL10* expression in EpiH and EpiA. Meldrum et al. demonstrated that exposure to diesel exhaust particulate extract, containing PAHs, induced an attenuation in *CXCL10* expression in human primary bronchial epithelial cells (Meldrum et al., 2017). *CXCL10* is a chemokine that enables the recruitment of Th1-type CD4+ T cells (Griffith et al., 2014). Modulation of its expression in the airways may be a mechanism by which pollutants can modify respiratory activity by counterbalancing the Th2-type immunity seen in allergic asthma, for example. In our study, the down-regulation of *CXCL10* may suggest that PM2.5-0.3 is involved in modifying the pulmonary immune response by preventing Th1-type CD4+ cells recruitment to the bronchial epithelium.

Interestingly, the regulation of *CYP1B1*, *CYP1A1*, and *CXCL10* expressions are often described as related to the activation of a cytosolic transcription factor, AhR. AhR plays a role in the
regulation of metabolism, immunity, and cellular differentiation (Esser, 2016; Gutiérrez-Vázquez & Quintana, 2018; Kawajiri & Fujii-Kuriyama, 2017, Borgie et al., 2015). This transcription factor is, as a tetrameric complex, kept inactive by its binding to several chaperone proteins. It can be activated in response to endogenous ligands or exogenous xenobiotic ligands such as PAHs. In our experimental conditions, no AhR expression modulation was observed after PM2.5 exposures. This can be explained by the early induction of the expression of this transcription factor following environmental exposure. Gupta *et al.*, demonstrated AhR involvement in early responses with an activation of AhR-dependent pathways 30 minutes after exposure of bronchial cells (BEAS-2B) to wood smoke particles at 100 µg/mL (Gupta *et al.*, 2021). Bardan *et al.*, demonstrated exposure of BEAS-2B cells to PM2.5 at 3 or 12 µg/cm² induced a significant increase of *AbR* expression after 6 and 24 hours (Badran *et al.*, 2020). Gualtieri *et al.*, reported that the expression of *CYP1A1*, an AhR-regulated gene, was significantly increased in BEAS-2B cells after 24 hours of PM2.5 exposure at 0.5, 2.5 or 5 µg/cm², but *AbR* level was rather decrease after 24 hours of exposure (Gualtieri *et al.*, 2011). In our study, the presence of PAHs on PM2.5-0.3 probably lead to early activation of AhR by PAH ligand binding, allowing translocation of AhR to the nucleus and generating subsequent biological responses. Upon ligand binding, AhR activates the transcription of metabolic genes such as *CYP1B1* and *CYP1A1* that can catalyze the biotransformation of various xenobiotics generating metabolites that can be highly reactive (Petrulis & Perdew, 2002; Shimada, 2006). Besides, recent studies demonstrated that AhR activation decreases *CXCL10* expression (Rael *et al.*, 2018; Zablocki-Thomas *et al.*, 2020), but the mechanisms involved remain unclear. Therefore, the main effects observed in our study seem to be related to the presence of PAHs on the surface of PM2.5, likely *via* the AhR-activation pathway. However, we did not find this modulation after analysis with the graphic and classical methods, probably because we worked "all exposure times combined" to increase the statistical power of our analyses.
Another inflammation biomarker, GM-CSF, is of particular interest. GM-CSF is a monomeric glycoprotein that functions as a Th2 cytokine stimulating the production of granulocytes (neutrophils, eosinophils, and basophils). Recent studies show that GM-CSF is a critical factor regulating pulmonary accumulation of eosinophils and neutrophils; an important mechanism which facilitates allergen sensitization, airway eosinophilia, and thus asthma exacerbation (Kim et al., 2020; Nakagome & Nagata, 2018; Nobs et al., 2021). In our study, a significant differential expression of GM-CSF was observed after exposures of EpiA to PMTraf at 45 µg/cm², regardless of the analysis method used. However, no expression modulation was observed at 90 µg/cm². Concerning PMInd, although graph analysis on pairwise expression ratio did not allow a differential expression of GM-CSF to be observed, this gene was at the limit of disjunction from the network after exposures of 90 µg/cm². A significant dose-dependent up-regulation was observed when the classical ΔΔCt method was applied. Comparatively no significant modulation of GM-CSF expression was observed after EpiH exposure to PMInd or PMTraf. Our results suggest that PM2.5-0.3 induces a greater inflammatory response in EpiA than in EpiH. Sotty et al. corroborate that when using healthy and diseased organotypic mucociliary-phenotype models to study the impact of fine and ultrafine particles. They demonstrate that GM-CSF secretion is significantly increased in asthma-diseased human bronchial epithelial cells after acute or repeated exposure to ultrafine particles while observing no secretion modulation in healthy cells. Those authors conclude that asthmatic cells are more sensitive than healthy cells to particles (Sotty et al., 2019).

Comparing the amount of corresponding RNA transcripts present in the samples is often mentioned as one of the main difficulties in comparing gene expression levels. To determine whether the amount of target RNA changes between several conditions, RTqPCR is the method of choice. This method is applicable to all experimental conditions, is very sensitive, and provides Ct values which are assumed to be proportional to the target RNA amounts. However, RTqPCR's main limitation is that any change in any RNA amount will modify the total RNA
515 (several thousands of different RNAs) quantification. Thus, changes in quantified amounts are 516 not directly understandable. To interpret the results, the ΔΔCt method is classically used (Livak & 517 Schmittgen, 2001). This method is based on choosing a housekeeping gene, which is supposed to 518 have the same expression level in all samples and is used as an internal control in each sample. It 519 is an easy method to use and allows gene expression modulation, up- or down-regulated or 520 unchanged compared to the control condition, to be presented graphically or in heatmaps, as in 521 our study. When applying the classical ΔΔCt method, we often found significant gene expression 522 modulation following repeated PM2.5-0.3 exposures, whatever the particles type and epithelium 523 status, but several limitations are noted. This method relies on comparing target genes with a 524 housekeeping gene, thus it does not allow classification of genes into groups and identification of 525 co-regulations. Furthermore, choosing the housekeeping gene is a delicate issue. Indeed, the 526 supposition that the housekeeping gene's expression level is the same in all samples and 527 conditions is rarely verified. Several studies show the expression of some commonly used 528 housekeeping genes in humans to be modified depending on the experimental conditions tested 529 (Gu et al., 2021; Panagodimou et al., 2022; Raaijmakers et al., 2002). To limit the impact of 530 unexpected variations in housekeeping genes, data normalization by geometric averaging of 531 several housekeeping genes is recommended (Vandesompele et al., 2002). In our study, we chose 532 to introduce two housekeeping genes, *HPRT* and *GAPDH*, and the significance of the results 533 obtained differed depending on the gene used in the classical ΔΔCt analysis. The classical ΔΔCt 534 method allow to visualize the direction of the gene expression modulation (up or down- 535 regulation) but results remain statistically weak. 536

536 To optimize our results analysis, we implemented, in addition to the ΔΔCt method, a graph 537 analysis on pairwise expression *ratio* (Curis et al., 2019). This method is more robust than, and can 538 be seen as a generalization of, the classical ΔΔCt method. Housekeeping genes are considered in 539 the same way as target genes. Individual tests are performed to determine whether a given pair of 540 genes experience the same expression change in two different experimental conditions. If the
logarithm of *ratio* of two RNA amounts does not change (the test is not significant), both target genes share similar expression actions between the tested conditions. Groups of genes of homogeneous behavior are built, thus allowing easier graphical interpretation. In our study, graph analysis method results indicated some genes, such as those involved in metabolism and inflammation, were significantly modulated (up- or down-regulated), as corroborated by the classical analysis. To improve our results, we should consider repeating the experiments using more samples for each experimental condition. In this way, the statistical power would be increased, and it would be possible to highlight subtler modulations in gene regulation

## Conclusion

The originality of this work was to apply a recent graph analysis method to assess gene expression modulation of bronchial epithelium reconstituted from healthy or asthmatic donors, after exposures to fine particles from different emission sources. The graphical analysis on pairwise expression *ratio* method has better statistical properties than the classical ΔΔCt method. Indeed, unlike the classical method, it accounts for co-expressions between all genes and gives an overview of all gene associations related to the comparison of the experimental conditions. This methodology is still very little used and needs to be refined to answer more specific questions arising from our study, for example, by modeling gene expressions over exposure time to a greater understanding of their trajectory.
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796
797 **Declaration of competing interest**

798 The authors declare no conflict of interest.

799

800 **CRediT authorship contribution statement**

801 Philomène Despréaux: Conceptualization, Methodology, Formal analysis, Writing—original draft;

802 Capucine Jeanton: Methodology, Formal analysis; Dorota Desaulle: Methodology, Formal analysis, Proof-reading; Margueritta Al Zallouha: Methodology, Formal analysis, Proof-reading;

803 Anthony Verdin: Proof-reading; Isabelle Momas: Supervision, Proof-reading; Sophie Achard: Conceptualization, Methodology, Formal analysis, Investigation, Writing—original draft, Visualization, Resources, Data curation, Funding acquisition, and Supervision.

804 Anthony Verdin: Proof-reading; Isabelle Momas: Supervision, Proof-reading; Sophie Achard: Conceptualization, Methodology, Formal analysis, Investigation, Writing—original draft, Visualization, Resources, Data curation, Funding acquisition, and Supervision.

805

806

807

808 **Funding**

809 Collection and characterization of fine particles were supported by the French National Agency for Food, Environment, and Occupational Health Safety (ANSES, EST01-60). Philomène Despréaux received a doctoral grant from the French Environment and Energy Management Agency (ADEME) and the Air Quality Research Network Paris Region (DimQi²).

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813

814 **Acknowledgments**

815 The authors would like to thank Dr Emmanuel Curis and Dr Anne-Laure Schang for their helpful discussions and advice. They would also like to thank The Proofreading Company Limited Tate & Clayburn, for their proofreading services

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821

822
823 Tables

824 Table 1

825 Concentrations of total Organic compounds (PAHs) in fine particles (PM<sub>2.5-0.5</sub>) from

826 Urban (PMTraf) and Industrial (PMInd) sites.

<table><thead><tr><th>Organic compounds (PAHs)</th><th>m/z</th><th>PMTraf (µg/g)</th><th>PMInd (µg/g)</th><th>PMInd/PMTraf</th></tr></thead><tbody><tr><td>Naphtalene</td><td>128</td><td>0.24</td><td>1.07</td><td>4.54</td></tr><tr><td>Acenaphtylene</td><td>152</td><td>0.12</td><td>0.18</td><td>1.51</td></tr><tr><td>Acenaphtene</td><td>154</td><td>0.00</td><td>0.00</td><td>-</td></tr><tr><td>Fluorene</td><td>166</td><td>0.09</td><td>0.31</td><td>3.59</td></tr><tr><td>Phenanthrene</td><td>178</td><td>0.48</td><td>1.92</td><td>4.00</td></tr><tr><td>Anthracene</td><td>178</td><td>0.18</td><td>0.56</td><td>3.14</td></tr><tr><td>Fluoranthene</td><td>202</td><td>0.86</td><td>6.00</td><td>7.01</td></tr><tr><td>Pyrene</td><td>202</td><td>0.83</td><td>5.24</td><td>6.28</td></tr><tr><td>Benzo[a]Anthracene</td><td>228</td><td>0.65</td><td>10.14</td><td>15.57</td></tr><tr><td>Chrysene</td><td>228</td><td>2.60</td><td>20.54</td><td>7.92</td></tr><tr><td>Benzo[b]Fluoranthene</td><td>252</td><td>2.33</td><td>19.99</td><td>8.58</td></tr><tr><td>Benzo[k]Fluoranthene</td><td>252</td><td>0.56</td><td>7.08</td><td>12.69</td></tr><tr><td>Benzo[a]Pyrene</td><td>252</td><td>1.70</td><td>15.47</td><td>9.08</td></tr><tr><td>Indeno[1,2,3-c,d]Pyrene</td><td>276</td><td>4.18</td><td>16.93</td><td>4.05</td></tr><tr><td>Dibenzo[a,h]Anthracene</td><td>278</td><td>2.57</td><td>18.18</td><td>7.07</td></tr><tr><td>Benzo[g,h,i]Perylene</td><td>276</td><td>2.96</td><td>12.77</td><td>4.31</td></tr></tbody><tfoot><tr><td>Total</td><td></td><td>17.77</td><td>136.39</td><td>7.68</td></tr></tfoot></table>

827

828 Legends of the figures

829 Figure 1.

830 Comparison of gene expression in Incubator Control group and Medium Control group in A.

831 EpiH (Healthy epithelia) and B. EpiA (Asthma epithelia) using graph analysis on pairwise expression *ratio*. Each graph was constructed with n = 6.

833

834 Figure 2.

835 Determination of gene expression modulation after exposure of EpiH to: A. PMInd at 45 µg/cm²; B. PMInd at 90 µg/cm²; C. PMTraf at 45 µg/cm²; D. PMTraf at 90 µg/cm², compared to the CONT group, using graph analysis on pairwise expression *ratio* and heatmap constructions

837
838

with *HPRT* or *GAPDH*. For both analyses, representations were constructed with at least five
epithelia in each group. *P-adjusted* was considered statistically significant when *P* < 0.05.

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**Figure 3.**

842

Determination of gene expression modulation after exposure of EpiH to: **A.** PMInd at 45 µg/cm²; **B.** PMInd at 90 µg/cm²; **C.** PMTraf at 45 µg/cm²; **D.** PMTraf at 90 µg/cm², compared to the CONT group, using graph analysis on pairwise expression *ratio* and heatmap constructions with *HPRT* or *GAPDH*. For both analyses, representations were constructed with at least six epithelia in each group. *P* was considered statistically significant when *P* < 0.05.

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<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>
Graphical abstract

<figure><img src="image_6.png" /><figcaption>Fine particles
Emission sources</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Inhalation
Human exposure</figcaption></figure>

Human bronchial epithelium
reconstructed from
Healthy or Asthma primary cells

<figure><img src="image_8.png" /><figcaption>*In vitro*
Air-liquid exposure</figcaption></figure>

Graph analysis on pairwise
Gene expression modulation