Transcriptomic analyses of human bronchial epithelial cells BEAS-2B exposed to atmospheric fine particulate matter PM2.5

Accepted Manuscript Transcriptomic analyses of human bronchial epithelial cells BEAS-2B exposed to atmospheric fine particulate matter PM2.5

Yang Li, Junchao Duan, Man Yang, Yanbo Li, Li Jing, Yang Yu, Ji Wang, Zhiwei Sun PII: DOI: Reference:

S0887-2333(17)30097-8 doi: 10.1016/j.tiv.2017.04.014 TIV 3978

To appear in:

Toxicology in Vitro

Received date: Revised date: Accepted date:

25 November 2016 5 April 2017 11 April 2017

Please cite this article as: Yang Li, Junchao Duan, Man Yang, Yanbo Li, Li Jing, Yang Yu, Ji Wang, Zhiwei Sun , Transcriptomic analyses of human bronchial epithelial cells BEAS-2B exposed to atmospheric fine particulate matter PM2.5. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Tiv(2017), doi: 10.1016/j.tiv.2017.04.014

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ACCEPTED MANUSCRIPT

Transcriptomic Analyses of Human Bronchial Epithelial Cells

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BEAS-2B Exposed to Atmospheric Fine Particulate Matter PM2.5

Yang Li a, b, Junchao Duan a, b, Man Yang a, b, Yanbo Li a, b, Li Jing a, b, Yang Yu a, b, Ji

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Wang a, b, *, Zhiwei Sun a, b, *

School of Public Health, Capital Medical University, Beijing, 100069, P.R. China

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Beijing Key Laboratory of Environmental Toxicology, Capital Medical University,

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Beijing, 100069, P.R. China

* Corresponding author: Zhiwei Sun, Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069,

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P.R. China. Tel: +86 010 83911507. Fax: +86 010 83911507. E-mail: [email protected]

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Ji Wang, Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China. Tel: +86 010 83911775. E-mail: [email protected]

ACCEPTED MANUSCRIPT Transcriptomic Analyses of Human Bronchial Epithelial Cells BEAS-2B Exposed to Atmospheric Fine Particulate Matter PM2.5 Abstract Respiratory exposure is the major route of atmospheric PM2.5 entering the human

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body. Epidemiological studies have indicated that exposure to PM2.5 is associated with increased risk of pulmonary diseases, but the underlying mechanisms remain

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less clear. In this study, human bronchial epithelial cells (BEAS-2B) were used to

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investigate the toxic effect and gene expression changes induced by PM2.5 collected from Beijing, China, based on microarray and following bioinformatic analyses.

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Gene ontology (GO) analysis indicated that PM2.5 caused significant changes in gene expression patterns related to a series of important functions, covering gene

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transcription, signal transduction, cell proliferation, cellular metabolic processes, immune response, etc. Additionally, pathway analysis and signal-net analysis showed that PI3K/Akt, MAPK, and TNF signaling pathways were the most

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prominently significant pathways affected by PM2.5, which play key roles in

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regulating cell proliferation, cell differentiation, cytoskeleton regulation, and inflammatory response. Finally, for the purpose of verifing the accuracy of microarray analysis, qRT-PCR was used to dectect the expression of part key genes

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in the above signaling pathways, which were selected from the signal-net. Our study provided a large amount of information on the molecular mechanism that underling

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PM2.5 caused pulmonary diseases, and follow-up researches are still needed for further exploration. Keywords: PM2.5, BEAS-2B cells, microarray analysis, toxicity, cell proliferation. Abbreviations PM, particulate matter; GO, gene ontology; VOCs, volatile organic compounds; PAHs, polycyclic aromatic hydrocarbons; TEM, transmission electron microscope; OC, organic carbon; EC, elemental carbon;

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CCK-8, cell counting kit-8; FCM, flow cytometry; ROS, reactive oxygen species.

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1. Introduction In recent years, atmospheric particulate matters (PM) were gradually proved to be an important environmental health risk factor to individuals, especially in developing countries with severe air pollution (Chen et al., 2016). In 2013, the International Agency for Research on Cancer (IARC) of WHO classified PM as one of Group 1

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carcinogens (WHO, 2013). In addition, PM exposure has also been linked to a series of pulmonary and cardiovascular diseases (Pelucchi et al., 2009; Zanobetti et al.,

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2010). Actually, PM is a complex mixture of solid particles with different sizes,

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which absorbs a variety of chemicals and biological elements, such as organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), heavy metals,

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bacteria, viruses, etc (Pope and Dockery, 2006). According to their aerodynamic diameters, inhaled PM can be divided into coarse particles PM10 (diameter ≤10 μm)

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and fine particles PM2.5 (diameter ≤ 2.5 μm) (Manzano-León et al., 2016). Compared with PM10, PM2.5 could deposit in the deeper sites of respiratory system and produce more profound adverse effect (Dockery, 2009). It has been reported that short-term

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exposure to PM2.5 led to respiratory irritation symptoms, acute changes in lung

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function, bronchitis, and excess mortality (Ouyang, 2013; Ding et al., 2014; Li et al., 2010). For long-term exposure, PM2.5 could contribute to chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis, even pulmonary tumor

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(Holguin, 2008; Iskandar et al., 2012; Kim et al., 2015). While results from European

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cohort studies in ESCAPE (European Study of Cohorts for Air Pollution Effects) project did not show consistent associations between very low level atmospheric PM2.5 exposure and changes of lung function as well as prevalence of chronic bronchitis symptoms in adult European population (Cai et al., 2014; Adam et al., 2015). As a developing country, China is experiencing a big challenge in terms of environmental pollution for the last decade. Moreover, high level polluted environments have been reported to significantly increase the PM2.5 nucleation (Kumar et al., 2014). Beijing, the capital of China, is one of the cities which suffering

ACCEPTED MANUSCRIPT from serious PM2.5 contamination. According to the Beijing environmental statement (Beijing Municipal Environmental Protection Bureau, 2015), the number of PM2.5 severe polluted days (PM2.5 concentration ≥ 200 μg/m3) in 2015 accounted for 42 days in Beijing; And the annual mean concentration of PM2.5 was 80.6 μg/m3, decreased by 6.2% compared with the monitoring data in 2014, while which was still 8-fold higher

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than the WHO air quality guideline (AQG) value (10 μg/m3). PM2.5, due to their small particle size, could suspend in outdoor and indoor air for a long time. Thus,

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respiratory exposure is the primary way for PM2.5 to enter the body. Lung, possessing a huge epithelial surface area, is the main organ which directly interacts with PM2.5.

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Moreover, considering the high deposition efficiency and low clearance efficiency of PM2.5 in alveolus, its influence on pulmonary diseases has caused great health

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concerns (Chen et al., 2016). In the past few years, a large number of studies have been conducted to investigate the adverse health effects of PM2.5 and its related

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mechanisms. It is shown that the size and chemical compositions of PM2.5 collected at different locations and in different seasons were diverse, and the exhibited toxic

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effects were also different (Manzano-León et al., 2016; Bell et al., 2007). PAHs,

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metals, and ultrafine particles are the critical active components of PM2.5, which could induce oxidative stress, endoplasmic reticulum stress, and inflammation, then lead to the ultrastructural damage as well as DNA damage in tissues and cells (Thomson et

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al., 2015; Zou et al., 2016; Chu et al., 2015). However, the complicated biological mechanism by which PM2.5 induced pulmonary toxicity is still far from being well

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understood.

Systemic study on changes of the global gene expression profiles is conducive to a better understanding of the potential molecular mechanisms underlying the pulmonary toxicity of a complex mixture of PM2.5. Additionally, short-term in vitro alternative assay, rather than costly long-term in vivo assay, is nowadays highly promoted and is of more benefit to mechanism studies (Vaccari et al., 2015). Thus, in the present study, in order to screen out the significantly changed genes, cell functions and signaling pathways of the PM2.5 exposure, BEAS-2B human bronchial epithelial cells were chosen as the tested cell model. Microarray analysis was conducted, followed by

ACCEPTED MANUSCRIPT bioinformatics analysis, including differential gene expression analysis, GO analysis, significant pathway analysis, and signal-net analysis. Lastly, qRT-PCR analysis was performed to verify the microarray data. 2. Material and methods 2.1. Particle sample collection and extraction

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PM2.5 tested in this study was collected using a TH-1000C II air sampler (Tianhong, Wuhan,

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China) with a flow rate of 1.05 m3/min for 24 h from ambient air in Capital Medical University, Beijing, China in December, 2015. Quartz fiber filters (8×10 in, Pall, USA) covered with PM2.5

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samples were cutted into small pieces, immerged in high purity water, and after 30-min ultrasonic disruption with a probe sonicator, the extracted suspension was filtered using 8-layer

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sterile gauze to remove the fiber fragments. After that, the PM2.5 suspension was frozen at -80 ºC, and freeze dried using a vacuum freeze drying machine. For the preparation of PM2.5 stock

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solution, the freeze dried PM2.5 samples were weighted and resuspended into 0.9% saline, then stored in fridge at 4 ºC. The concentration of the stock solution was 16 mg/mL.

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2.2. Physicochemical characterization of PM2.5

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For physicochemical characterization, PM2.5 was collected on quartz fiber filters (47 mm, Pall, USA) and polytetrafluoroethylene (PTFE) filters (47 mm, Tisch, USA) through two low-volume sequential PM samplers (PMS-104, APM Engineering Co. Ltd., Korean). The morphology of

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PM2.5 was observed using transmission electron microscope (TEM) (JEOL JEM2100, Japan). A zeta electric potential granulometer (Malvern Nano-ZS90, UK) was used to determine the

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hydrodynamic size and zeta potential of PM2.5 in saline and DMEM culture medium (Gibco, USA). Element contents of PM2.5 collected on PTFE filters were detected by WDXRF spectrometer (Model RIX 3000, RIGAKU Co. Japan) using EDXRF coupled with fundamental parameter quantification technique. Carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were detected through thermal/optical carbon aerosol analyzer (Model 2001A, Sunset Laboratory, Forest Grove, USA). The types and content of PAHs in PM2.5 collected on Quartz fiber filters were analyzed by a GC-MS instrument (7890A-5975C: Agilent, USA) with an electron ionization (EI) ion source.

ACCEPTED MANUSCRIPT 2.3. Cell culture and Particle exposure The human bronchial epithelial cell line, BEAS-2B, was purchased from the Cell Resource Center, Shanghai Institutes for Biological Sciences (SIBS, China). The cells were maintained in DMEM culture medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA), 100 U/mL penicillin and 100 μg/mL streptomycin, and cultured at 37 ºC in 5% CO2 humidified environment.

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For experiments, the cells were seeded in culture plates at density of 1×105 cells/mL and

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allowed to attach for 24 h, then treated with PM2.5 suspended in DMEM culture medium of certain concentrations for another 24 h. Suspension of PM2.5 was dispersed by sonicator (160 W,

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20 kHz, 5 min) (Bioruptor UDC-200, Belgium), and diluted to various concentrations, then added to BEAS-2B cells immediately. In this study, 96, 24 and 6 well culture plates and 9 cm

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petri dish were used, the bottom area of which were 0.32, 2, 9.6 and 49 cm2, respectively. To ensure each unit area of cells exposed to the same amount of PM2.5, the exposure volume of

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PM2.5 suspension was calculated according to the bottom area of cell culture plate. The final volume of PM2.5 suspension added in 96, 24 and 6 well culture plates and 9 cm petri dish were

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PM2.5 were used as control group.

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0.1, 0.625, 3, and 15.3 mL, respectively. Cells maintained in DMEM culture medium without

2.4. Assessment of cell viability

The effect of PM2.5 on cell viability was detected by WST-8 cell counting kit (CCK-8)

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according to the manufacture’s instruction. BEAS-2B cells were exposed to 6.25, 12.5, 25, 50, 100, 200, and 400 μg/mL of PM2.5 for 24 h. Then 10 μL CCK-8 solution was added into each

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well, and the cells were incubated for an additional 1 h at 37 ºC. Optical density at 450 nm was detected by microplate reader (Themo Multiscan MK3, USA).

2.5. Giemsa stain

Giemsa stain, a complex dye, mainly stains chromatin in nucleus blue and components in cytoplasm pink or red. After treated with 50 μg/mL of PM2.5 for 24 h, the cells were washed twice, and then stained by Giemsa staining kit (Maxim, China) according to the manufacturer's instruction. The cellular morphological changes were observed under optical microscope (Olympus IX81, Japan).

ACCEPTED MANUSCRIPT 2.6. Assessment of cellular adhesion and internalization of PM2.5 Flow cytometry (FCM) was used to reflect the intracellular PM2.5 level. BEAS-2B cells were harvested after 24 h of exposure to 50 μg/mL of PM2.5. At least 1×104 cells were collected and the light side scatter was analyzed by FCM (Becton Dickinson, USA).

2.7. Total RNA extraction and microarray assay

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Gene expression was analyzed using BEAS-2B cells exposed to 50 μg/mL of PM2.5 through microarray analysis, unexposed cells were used as normal control. Each group had three replicate

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samples, and for each sample, the experiment was performed in triplicated as technical replicate. For Affymetrix microarray profiling, the total RNA was isolated from 1×106 cells of control and

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PM2.5 treated groups using TRIzol reagent (Invitrogen, Carlsbad, Canada) and purified with an RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The

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amount and quality of RNA were determined using a UV-Vis Spectrophotometer (Thermo, NanoDrop 2000, USA) at an absorbance of 260 nm. The mRNA expression profiling was

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measured using Human Transcriptome Array 2.0 (Affymetrix GeneChip, USA), which contains 44 699 gene-level probe sets. The microarray analysis was performed using Affymetrix

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Expression Console Software (version 1.2.1). Raw data (CEL files) were normalized at the

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transcript level using the robust multi-array average method (RMA workflow). The median summarization of transcript expressions was calculated. The gene-level data were then filtered to include only those probe sets that are in the ‘core’ metaprobe list, which represents RefSeq genes.

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Data of microarray analysis (ECL files) discussed in this article has deposited in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository and are through

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available

the

accession

number

GSE93329

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93329).

2.8. Bioinformation analysis For the microarray data analysis, differentially expressed genes were identified based on the random variance model (RVM) t-test. And the differentially expressed genes were considered to be up- or down-regulated with at least p < 0.05. Genes with similar expression patterns often facilitate the overlapping functions. Accordingly, the cluster analysis of gene expression patterns was analyzed using Cluster and Java Treeview software. GO analysis was applied to analyze the main function of the differentially expressed genes

ACCEPTED MANUSCRIPT according to the GO which is the key functional classification of the National Center for Biotechnology Information (NCBI), which can organize genes into hierarchical categories and uncover the gene regulatory network on the basis of biological processes and molecular function. Specifically, a two-sided Fisher’s exact test and chi-square test were used to classify the GO category, and the false discovery rate (FDR) was calculated to correct the P-value; the smaller the

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FDR, the small the error in judging the P-value. Pathway analysis was used to find out the significant pathways of the differential genes

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according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), Biocarta and Reatome

threshold of significance was considered as p < 0.05.

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databases. Fisher’s exact test was performed to select the statistically significant pathway, and the

Gene signal transduction networks (Signal-net), based on the KEGG database about the

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interactions between different gene products and the theory of network biology, were established to illustrate the inter-gene signaling between differentially expressed genes. Networks were stored

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and presented as graphs, where nodes are mainly genes and edges represent relation types between the nodes, such as activation or phosphorylation. The degree was defined as the link number of

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one node with all of the other nodes. Genes with higher degrees occupied more important

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positions within the network. In addition, the properties of genes were described by Betweenness Centrality (BC) measures, reflecting the intermediary capacity of a node to modulate other interactions between nodes. Finally, the purpose of the signal transduction network analysis was to

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locate core key regulatory genes that had a stronger capacity to modulate adjacent genes.

2.9. Quantitative RT-PCR analysis

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Total RNA was extracted from 1×106 cells of control and PM2.5 treated groups using TRIzol reagent (Invitrogen, Carlsbad, Canada) according to the manufacturer’s protocol. Equal amounts of total RNA from each sample were reverse transcribed using Thermoscript reverse transcriptase (Invitrogen) using oligo (dT) and random hexamer primers. The qRT-PCR reaction was monitored using the ABI PRISM 7500 Sequence DetectionSystem (Applied Biosystems, CA, USA) and was run with three biological repeats and three duplicated repeats. All levels were normalized to β-actin (18S levels were similar) and fold induction was calculated by setting control conditions to 1.

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Statistical analysis

Statistical analysis was performed using SPSS 16.0 software (SPSS, Chicago, IL, USA). Data were expressed as mean ± S.D. Independent sample t-test was taken to compare the differences between two groups; One-way analysis of variance (ANOVA) was used in the comparison of multiple groups. Pairwise comparison in multiple groups was conducted with least significant

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difference (LSD) test. Differences were considered statistically significant at p < 0.05.

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3. Results

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3.1. Physicochemical Characterization of PM2.5

The morphological characterization of PM2.5 obtained from TEM was shown in

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Fig. 1. PM2.5 appeared as different sizes of agglomerates, which were mainly

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constituted by regular or irregular-shaped ultrafine particles and fine particles. The hydrodynamic diameter and Zeta potential of PM2.5 measured in saline and in

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DMEM culture medium were shown in Table 1. The hydrodynamic diameter of

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PM2.5 suspended in saline and DMEM were 1.3 and 1.6 μm, respectively, and PM2.5 suspended in DMEM exhibited a smaller PDI value. In general, PDI value is

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between 0.1 and 1. The smaller the PDI value, the better the monodispersity of

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suspended particles. Thus, PM2.5 suspended in DMEM showed better dispersibility than suspended in saline. This might be due to the amino acids and small molecular proteins contained in DMEM could be absorbed on the surface of the ultrafine and fine particles, which made the particles highly negatively charged (−32.03 mV). If the absolute value of Zeta potential is greater than 30, there will be enough electrostatic repulsion between particles to reduce the possibility of aggregation between each other.

ACCEPTED MANUSCRIPT Results of elements and PAHs analysis were shown in Supplementary material. The average concentrations of organic carbon (OC) and elemental carbon (EC) in PM2.5 were 225.08 and 29.57 mg/g, and the OC/EC ratio was 7.61 (Table S1), which exceeding 2 was an indicator for the existence of secondary organic carbon. S, Si, K,

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Fe and Cl were the most abundant elements among the 28 inorganic elements detected

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in the PM2.5 (Table S2). Toxic heavy metals and toxic non-metal, including Zn, Pb,

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Mn, Cu, As, Ni and Cr, were detected in this PM sample. Among the 17 kinds of PAHs, 15 kinds of PAHs were detected (Table S3). The total PAHs concentration was

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464.73 µg/g. Benzo[123-cd]pyrene (46.54 µg/g), Benzo[b]fluoranthene (44.12 µg/g),

PAHs.

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3.2. Cytotoxicity induced by PM2.5

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and Benzo[ghi]pyrene (42.59 µg/g ) presented at a high concentration level among the

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To evaluate the possible cytotoxicity of PM2.5 on BEAS-2B cells, cell viability was measured after exposed the cells to 6.25, 12.5, 25, 50, 100, 200 and 400 μg/mL

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of PM2.5, which were equivalent to 1.953, 3.9, 7.813, 15.625, 31.25, 62.5 and 125

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μg/cm2. As shown in Fig. 2A, the cell viability decreased gradually in a dose-dependent manner with the concentration increased. Lower dose PM2.5, including 6.25 μg/mL, 12.5 μg/mL and 25 μg/mL, did not affect the cell viability obviously, while as the dose raised up to 50 μg/mL, the cell viability lost nearly 10%, which was reduced significantly compared with that in control group. Therefore, 50 μg/mL was chosen as the experimental dose for the following analysis.

ACCEPTED MANUSCRIPT 3.3. Cell morphological changes induced by PM2.5 Morphological changes of BEAS-2B cells were detected by Giemsa staining. As shown in Fig. 2B, after PM2.5 exposure, the cell morphology changed obviously, most of the cells possessed smaller volume and slender shape compared with cells in

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control group. Furthermore, megakaryocytes, binucleated and multinucleated cells

3.4. Cellular adhesion and internalization of PM2.5

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were observed in PM2.5 treated group.

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The intensity of light side scatter detected by FCM, a parameter reflecting the

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smooth degree of cell surface and the complexity of the internal structure of cells, was often used in nanotoxicology research to report the cytoplasmic concentration of

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nanoparticles (Li et al., 2016). Therefore, in this study we introduced this parameter to determine the cell surface adhesion and internalization of PM2.5 qualitatively to some

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degree. As shown in Fig. 2C, compared with control, the light side scatter intensity elevated obviously in PM2.5 treated group, which indicated that PM2.5 could penetrate

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into the cells or adhere to the cell surface. 3.5. Differential gene expression induced by PM2.5

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To investigate the possible gene expression change in BEAS-2B cells induced by PM2.5, we performed a genome-wide transcriptional analysis using the Affymetrix GeneChip (Human Transcriptome Array 2.0) which contains 44 699 gene-level probe sets. The results manifested that compared with the control group, 1636 significant differentially expressed genes were changed triggered by PM2.5, including 867 genes that were up-regulated and 769 genes that were down-regulated. The top 20 differentially expressed up-regulated and down-regulated genes between control

ACCEPTED MANUSCRIPT group and PM2.5 treated group were listed in Tables 2 and 3, respectively. 3.6. GO analysis of differential gene expression induced by PM2.5 GO analysis was introduced to find the main gene functions affected by PM2.5. An interaction network of significant GO terms was assembled into a GO map to describe

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the prominent functional categories. As manifested in Fig. 3A, the top 15 significant

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up-regulated GOs induced by PM2.5 were positive regulation of transcription from

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RNA polymerase II promoter, signal transduction, innate immune response, DNA-dependent transcription, viral reproduction, transcription from RNA polymerase

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II promoter, protein phosphorylation, negative regulation of transcription from RNA

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polymerase II promoter, blood coagulation, neurotrophin TRK receptor signaling pathway, negative regulation of cell proliferation, small molecule metabolic process,

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apoptotic process, intracellular signal transduction, and regulation of transcription

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from RNA polymerase II promoter. As manifested in Fig. 3B, the top 15 significant down-regulated GOs induced by PM2.5 were mitotic cell cycle, cell division,

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respiratory electron transport chain, cellular metabolic process, translation, gene expression, mitosis, nucleosome assembly, RNA metabolic process, small molecule

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metabolic process, viral reproduction, DNA-dependent transcription, cellular protein metabolic process, mRNA metabolic process, and mitochondrial electron transport. 3.7. Pathway analysis of differential gene expression induced by PM2.5 The significant pathways were then analyzed according to the functions and interactions of differential genes based on KEGG database. As manifested in Fig. 4, the top 10 significant up-regulation pathways induced by PM2.5 were HTLV-I infection, chemokine signaling pathway, herpes simplex infection, insulin signaling

ACCEPTED MANUSCRIPT pathway, endocytosis, TNF signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, pertussis, and pathways in cancer. Meanwhile, the top 10 significant down-regulation pathways induced by PM2.5 were involved in systemic lupus

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alcoholism,

ribosome,

alzheimer's

disease,

oxidative

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phosphorylation, huntington's disease, parkinson's disease, non-alcoholic fatty liver

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disease (NAFLD), metabolic pathways, and viral carcinogenesis. The summaries of

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genes which were involved in the significant up- and down-regulation pathways were listed in Tables 4 and 5, respectively.

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3.8. Signal-net analysis revealed the key genes triggered by PM2.5

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Based on the significant GO and pathway analysis, Signal-net analysis was used to screen the key genes involved in PM2.5-induced toxicity in BEAS-2B cells. Result

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manifested that there was a total number of 156 key genes which were involved in the

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transduction network. Fig. 5 showed the interaction relationship of 46 genes with degree greater than or equal to 5, and Table 6 listed 20 genes with degree greater than

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or equal to 8 involved in the single-net, which were pik3r2 (homo sapiens phosphoinositide-3-kinase, regulatory subunit 2), rela (homo sapiens v-rel

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reticuloendotheliosis viral oncogene homolog A), arrb2 (homo sapiens arrestin, beta 2), plc (homo sapiens phospholipase C) family, rac2 (homo sapiens ras-related C3 botulinum toxin substrate 2), akt (homo sapiens v-akt murine thymoma viral oncogene homolog) family, rac3 (Homo sapiens ras-related C3 botulinum toxin substrate 3), fgfr1 (Homo sapiens fibroblast growth factor receptor 1), itpa (Homo sapiens inosine triphosphatase), hif1a (Homo sapiens hypoxia inducible factor 1,

ACCEPTED MANUSCRIPT alpha subunit), plcd3 (Homo sapiens phospholipase C, delta 3), adcy (Homo sapiens adenylate cyclase) family, dgka (Homo sapiens diacylglycerol kinase) family, and stat3 (Homo sapiens signal transducer and activator of transcription 3). 3.9. Quantitative RT-PCR verification

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To verify the microarray analysis results, the expression of 6 significantly

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up-regulated genes and 5 down-regulated genes selected from microarray data were

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verified using qRT-PCR. These selected genes related to various pathways or cell processes, including antioxidant response gene (gpx5), inflammatory signaling genes

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(cxcl1 and tnfaip2), PI3K/Akt signaling pathway (pik3r2, akt1, and akt2), regulation

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of cytoskeleton (gsk3b), mitotic cell cycle (mad2l1 and gadd45b), exit from mitosis (ube2c and ube2s). As shown in Fig. 6, the expression trend of these 11 genes was

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similar with that manifested in microarray analysis. Very good consistency was

4. Discussion

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obtained between microarray analysis and qRT-PCR verification.

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Respiratory exposure is the major route for atmospheric PM2.5 entering the human body. Epidemiological studies have indicated that exposure to PM2.5 is associated

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with increased risk of pulmonary diseases, but the underlying mechanisms remain unclear (Donaldson and Seaton, 2012). In the present study, human bronchial epithelial cells (BEAS-2B) were used to test the toxicity of PM2.5 collected from Beijing, China during the period of December, 2015. Firstly, cell viability, cell morphological changes, and the cellular adhesion and internalization of PM2.5 were detected at the preliminary study of the toxic effects of PM2.5. Then genome-wide

ACCEPTED MANUSCRIPT transcriptional analysis and bioinformatics analysis were performed, including the gene expression profile analysis, GO analysis, pathway analysis, and signal-net analysis, to make a more comprehensive understanding of mechanisms leading to the adverse effects of PM2.5.

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Based on the result of Fig. 2A, the cell viability of BEAS-2B cells reduced

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significantly from 50 μg/mL of PM2.5 treated group, and at this dosage level the cells

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could still maintain more than 90% of the survival rate. Thus, 50 μg/mL, equivalent of 15.625 μg/cm2, was chosen as the experimental dose in the present study. As

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calculated by Multiple-path Particle Dosimetry (MPPD) Model software (Zhou et al.,

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2016), the predicted concentration of PM2.5 deposited on the surface of tracheal-bronchial epithelium was 104 μg/m2 after 24 h exposure at the real daily

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concentration of 147.59 μg/m3 in Beijing, China during December 2015. Baseline set

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of MPPD inputs was shown in Table S4. The experimental dose used in this study was about 1500 times of the predicted deposition concentration.

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In the following morphological observation, an obvious phenomenon was found

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in PM2.5 treated group that is a few megakaryocytes, binucleated cells, and multinucleated cells occurred among numerous mononuclear cells (Fig. 2B). Longhin et al. (2013) have reported similar findings that PM2.5 could increase the number of double nuclei and micronuclei cells in BEAS-2B cell line through G2 arrest and sever mitotic spindle aberration. Thus, it could be presumed that PM2.5 in this study might act in a similar way. Further gene regulation mechanisms of the above phenomenon caused by PM2.5 were explored in the next microarray analysis. Besides, under the

ACCEPTED MANUSCRIPT optical microscope we also found many granular or agglomerate particles with dark brown colour persisting ether inside the cytoplasma or outside on the cell surface (Fig. 2B). Thus, a semi-quantitative assessment was performed using FCM to detect the cellular adhesion and internalization of PM2.5. The above mentioned observation was

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confirmed by the elevated intensity of light side scatter, as shown in Fig. 2C. It means

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that PM2.5 did enter the cells or adhere to the cell surface, which could further produce

Results

of

microarray

analysis

and

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cytotoxic effects to BEAS-2B cells.

following

bioinformatics

analysis

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demonstrated that PM2.5 could affect the expression of many kinds of genes with

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important functions in BEAS-2B cells (Table 2 and 3). GO analysis was used to classify differentially expressed genes into hierarchical categories according to their

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pertinent biological processes (Hu et al., 2016). As shown in Fig. 3, the five functions

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mainly affected were: gene transcription (up-regulation), signal transduction (up-regulation), cell proliferation (down-regulation), cellular metabolic processes

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(down-regulation), and immune response (up-regulation). Based on the KEGG

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database, the significantly changed pathways were classified by pathway analysis (Fig. 4 and Table 4 and 5). PI3K/Akt, MAPK, and TNF signaling pathways were three prominently significant up-regulated pathways affected by PM2.5, which played key regulatory roles in cell proliferation, cell differentiation, cytoskeleton regulation, and inflammatory response. Oxidative phosphorylation, ribosome and metabolic pathways were the three significantly down-regulated pathways, and were ralated to the energy supply and material metabolism. Signal-net analysis manifested the inter-gene

ACCEPTED MANUSCRIPT signaling between the differentially expressed genes. Among the genes listed in Table 6, pik3r2, akt1, and akt2 were involved in PI3K/Akt and TNF signaling pathway; rela was involved in TNF and MAPK signaling pathway; arrb2, rac2, rac3, and fgfr1 were involved in MAPK signaling pathway; plcg1, hif1a, stat3 were involved in Pathways

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in cancer; plcb3, adcy3, adcy6 were involved in chemokine signaling pathway; plcb4,

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plcd3, dgka, dgkz were involved in metabolic pathways. Therefore, according to the

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above results, it could be concluded that PM2.5 mainly affected the pathways associated with cell proliferation and differentiation, cell metabolism, and

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inflammatory response.

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Other researchers using microarray analysis have reported similar results. But due to the different periods and locations of PM2.5 collection, the physical and chemical

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properties of PM2.5 samples could be different, thus there were still many functions

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and pathways affected by PM2.5 that were diverse with each other. As reported, PM2.5 collected in March, 2012 in Wuhan, China mainly affected genes related to

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inflammatory and immune response, response to oxidative stress, response to DNA

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damage stimulus, signal transduction and cell cycle (Ding et al., 2014). While PM2.5 collected in January, 2013 in Beijing, China induced the abnormal expression of genes involved in responses to xenobtiotic stimuli, metabolic response, and inflammatory and immune response pathways such as MAPK signaling, NF-κB signaling and cytokine-cytokine receptor interaction (Zhou et al., 2015). Longhin et al. (2016) sampled winter PM2.5 (wPM) and summer PM10 (sPM) in Milan, Italy, and found that both PM affected a set of genes associated with antioxidant responses,

ACCEPTED MANUSCRIPT cancer development, extracellular matrix remodeling and cytoskeleton organization, while there were also many differences between the two kinds of PM, especially in the pro-inflammatory response and epigenetic regulation. The author suggested that sPM effects may be related to biological and inorganic components, and wPM

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apparently related to the high content of organic compounds.

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Excessive reactive oxygen species (ROS) induced by PAHs, trace metals, and

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ultrafine particles has been proposed as one of the important mechanisms for the toxicity of PM2.5 (Shafer et al., 2010; Hamad et al., 2016). Over consumption of

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cellular antioxidants would lead to sustained oxidative stress, lipid peroxidation, and

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following pro-inflammatory response (Kioumourtzoglou et al., 2013). Consequently, gene expression and signal transduction would be affected, and biological

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macromolecules as well as subcellular structures would be damaged (Chen et al.,

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2016). The present study showed that the expression of antioxidant response gene (gpx5) and inflammatory signaling genes (cxcl1 and tnfaip2) increased obviously

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induced by PM2.5 (Fig. 6), which indicated that cells in PM2.5 treated group might

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undergo oxidative stress and initiate inflammatory signaling transduction. Additionally, the expression of genes in PI3K/Akt signaling pathway (pik3r2, akt1, akt2) were up-regulated, and key genes in regulation of cytoskeleton (gsk3b), mitotic cell cycle (mad2l1 and gadd45b), and exit from mitosis (ube2c and ube2s) were down regulated (Fig. 6). It was reported that PI3K/Akt signaling pathway could be activated by ROS, which is closely related to cell proliferation and differentiation, and Akt could further regulate the microtubule dynamics via the inhibition of GSK-3 (Apopa

ACCEPTED MANUSCRIPT et al., 2009). Therefore, this abnormal gene expression indicated that PM2.5 might lead to abnormal regulation of cell proliferation, cytoskeleton dynamics, and mitosis process through oxidative stress, which could be partially responsible for the phenomenon of megakaryocytes, binucleated and multinucleated cells observed in

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this study. Based on the result of PCR verification, we can deduce that PM2.5 used in

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this study could induce oxidative stress in BEAS-2B cells, affect the expression of

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cell proliferation and mitosis related genes, and activate immune response related pathways, which finally resulted in the abnormal cell proliferation and inflammatory

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response.

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In conclusion, cytotoxicity and changes of gene expression profile induced by PM2.5 in BEAS-2B cell line were investigated. A dose-dependent cytotoxicity and

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abnormal morphological changes induced by PM2.5 were confirmed. Result of

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microarray analysis manifested that PM2.5 affected the expression of 1636 genes, including 867 genes that were up-regulated and 769 genes that were down-regulated.

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The bioinformatics analysis results indicated that PM2.5 caused significant changes in

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gene expression patterns related to a series of important functions, covering gene transcription, signal transduction, cell proliferation, cellular metabolic processes, immune response, etc. PI3K/Akt, MAPK, and TNF signaling pathways were the most prominent significant pathways affected by PM2.5, which play key regulatory roles in cell proliferation, cell differentiation, cytoskeleton regulation, and inflammatory response. Result of qRT-PCR further verified that PM2.5 could induce abnormal expression of genes associated with antioxidant and inflammatory response, cell

ACCEPTED MANUSCRIPT proliferation, cytoskeleton and mitosis regulation in BEAS-2B cells. Further exploration is needed to investigate the specific toxic mechanism induced by PM2.5.

Acknowledgments

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This work was supported by the Beijing Natural Science Foundation Program and Scientific Research Key Program of Beijing Municipal Commission of Education

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(KZ201410025022), the National Natural Science Foundation of China (NSFC)

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(81673204 and 81602875), the Training Programme Foundation for the Talents by

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the Beijing Ministry of Education (Grant No. 2014000020124G157), and the Natural Science Foundation of Capital Medical University (2016ZR02). The authors thank

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Weiping Tang of Cnkingbio biotechnology Co. Ltd for bioinformatics assistance.

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ACCEPTED MANUSCRIPT Fig. 1. TEM images of PM2.5. Fig. 2. Cytotoxicity and internalization of PM2.5 in BEAS-2B cells. (A) Cell viability was measured after the cells exposure to PM2.5 with concentrations from 6.25 to 400 μg/mL for 24 h. The cell viability decreased gradually in a dose-dependent manner. Data are expressed as means ± SD from three independent experiments. Each group

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had five replicate wells. *p < 0.05 PM2.5 treated groups compared with control group using ANOVA. (B) Cell morphological changes were observed after the cells

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exposure to 50 μg/mL of PM2.5 for 24 h. (a) Control; (b) PM2.5 treated group. As

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shown, the cell morphology in PM2.5 treated group changed obviously, meanwhile, megakaryocytes (blue arrow), binucleated (yellow arrow) and multinucleated cells

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(red arrow) were observed. These phenomena were observed from three independent experiments. Each group had three replicate wells. (C) Qualitative detection of the

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internalization of PM2.5. (a) After exposure to 50 μg/mL of PM2.5 for 24 h, the light side scatter intensity of BEAS-2B cells was measured by FCM. The corresponding

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value of side scatter intensity was shown in (b). Data are expressed as means ± SD

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from three independent experiments. Each group had three replicate wells. *p < 0.05 PM2.5 treated group compared with control group using independent sample t-test.

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Fig. 3. Significantly changed up- and down-regulation GOs of differentially expressed genes induced by PM2.5 in BEAS-2B cells. (A) Significant up-regulation GOs; (B) Significant down-regulation GOs. The y axis shows the GO category and the x axis,

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−lg (P-value). The larger −lg P indicates a smaller P-value. Fig. 4. Significantly changed pathways of differentially expressed up- and down-regulated genes based on the KEGG database. (A) Significant up-regulation pathways; (B) Significant down-regulation pathways. −lg P, negative logarithm of the P value. X-axis denotes that a larger number corresponds to a smaller P-value. Fig. 5. Signal-net of PM2.5-induced toxicity in BEAS-2B cells. The red circle represented the up-regulated genes and the blue circles down-regulated genes. The

ACCEPTED MANUSCRIPT area of the circle represented the degree of genes. Interaction between the genes is shown as: a activation, a(u) activation (ubiquination), u ubiquination, inh u inhibition (ubiquination), b binding/association, p phosphorylation, inh(−p) inhibition (dephosphorylation), a(+p) activation (phosphorylation), ind indirect effect, inh inhibition, disso dissociation, ex expression, c compound, a(ind) activation (indirect

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effect), p(ind) phosphorylation (indirect effect), inh(+p) inhibition (phosphorylation), m missing interaction, and inh(ind) inhibition (indirect effect).

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Fig. 6. Correlation between microarray data and qRT-PCR data. Very good

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consistency was obtained between microarray analysis and qRT-PCR verification. The fold changes were calculated by using 2-∆∆Ct method comparing PM2.5 treated

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group to contral. Data are expressed as means ± SD from three independent experiments. Each group had three replicate wells, and for each sample the

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experiment was performed in triplicated as technical replicate.

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Fig. 5

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Fig. 6

ACCEPTED MANUSCRIPT Table 1 Hydrodynamic size and Zeta potential of PM2.5 Hydrodynamic size (nm)

PDI

Zeta potential (mV)

Saline

1351±231

0.688±0.034

-20.40±0.80

DMEM culture medium

1647±317

0.428±0.053

-32.03±0.21

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Dispersion medium

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Table 2 The top 20 differentially expressed up-regulated genes between PM2.5 and control group in BEAS-2B cells Geom mean of intensities Gene name

C S U

Fold-change

p-value

29.9

15

0.0000886

1818.47

238.29

7.63

SLC7A5

36809.42

5141.49

7.16

SF3B3

5133.02

865.66

B4GALT5

919.77

SECTM1

411.7

PM treated group

contral group

SHISA2

448.51

MMP2

N A

Related significant GO --

0.0020676

extracellular matrix organization; cellular protein metabolic process; response to hypoxia

0.0003827

cell differentiation; leukocyte migration; transmembrane transport

5.93

0.0339342

protein complex assembly; gene expression; RNA splicing

159.32

5.77

0.0252587

small molecule metabolic process; carbohydrate metabolic process; post-translational protein modification

71.88

5.73

0.0000079

signal transduction; immune response positive regulation of I-kappaB kinase/NF-kappaB cascade;

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C3

728.46

138.39

5.26

0.002729

signal transduction; innate immune response; inflammatory response; complement activation, classical pathway

KDELR1

10823.24

2086.24

5.19

0.0009658

intracellular protein transport; protein transport; vesicle-mediated transport

CCNI

1472.27

298.48

4.93

0.0164666

WASF2

1494.8

319.05

4.69

0.0325115

PORCN

420.82

96.66

4.35

NPIPB5

812.89

204.05

3.98

CYP1B1

177.14

46.27

3.83

EGR1

118.4

C C

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515.59

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N A

regulation of cell cycle;

innate immune response; actin cytoskeleton organization

0.0002093

Wnt receptor signaling pathway;

0.0002145

--

0.0000006

negative regulation of cell proliferation; small molecule metabolic process; cell adhesion; cellular response to hydrogen peroxide; positive regulation of apoptotic process; xenobiotic metabolic process

31.29

3.78

0.0007529

transcription from RNA polymerase II promoter; positive regulation of transcription, DNA-dependent; cytokine-mediated signaling pathway; type I interferon-mediated signaling pathway; negative regulation of apoptotic process; cellular response to mechanical stimulus

137.15

3.76

0.0010424

--

A SEPN1

T P

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IGHD2-21

454.58

122.39

3.71

0.0068171

--

NPIPB11

451.81

125.4

3.6

0.0007456

--

SLC43A3

1630.2

455.88

3.58

0.0195012

transmembrane transport

NPIPB3

591.97

169.66

3.49

0.0003826

CHD4

474.13

136.02

3.49

0.0392408

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transcription, DNA-dependent; regulation of transcription from RNA polymerase II promoter; regulation of transcription, DNA-dependent; metabolic process; chromatin organization; ATP-dependent chromatin remodeling

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Table 3 The top 20 differentially expressed down-regulated genes between PM2.5 and control group in BEAS-2B cells Geom mean of intensities Gene name

Fold-change

p-value

PM treated group

contral group

HIST1H2BI

1128.63

16705.96

0.068

0.0029902

TMSB4XP4

95.77

707.1

0.14

0.0102454

HIST1H3F

12186.43

84513.79

0.14

DKK1

192.34

1297.96

HIST1H2AB

23947.89

125403.05

HIST1H2BO

7232.56

39091.74

MRPL47

68.29

CKS2

12010.52

HIST1H1D MED21

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N A

T P

Related significant GO --

actin cytoskeleton organization

0.0199018

--

M

0.0000104

mesoderm formation

0.19

0.0097029

biological_process

0.19

0.0120396

nucleosome assembly; chromatin organization

333.46

0.2

0.0103041

biological_process; mitochondrial translation

58414.09

0.21

0.0006538

regulation of cyclin-dependent protein serine/threonine kinase activity

96.96

458.51

0.21

0.0114636

nucleosome assembly; nucleosome positioning

59.77

272.28

0.22

0.0016049

stem cell maintenance

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DPM1

3176.06

14512.47

0.22

0.0107305

cellular protein metabolic process; metabolic process; GDP-mannose metabolic process

CSE1L

167.91

715.15

0.23

0.0132706

cell proliferation; apoptotic process; protein export from nucleus

HIST1H3H

159.41

678.37

0.23

0.0141266

HIST1H4F

54.41

234.19

0.23

0.017703

HIST1H2BB

171.21

655.67

0.26

SRGN

2787.14

10482.16

0.27

CCT2

273.71

994.35

COX7B

935.29

3202.42

CENPQ

61.95

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ARL6IP1

224.6

A

C C

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N A

---

0.0447085

nucleosome assembly; chromatin organization

0.019561

--

M

0.0405297

cellular protein metabolic process; protein folding

0.29

0.0006418

respiratory electron transport chain; cellular metabolic process; small molecule metabolic process

217.03

0.29

0.002651

mitotic cell cycle; nucleosome assembly; CENP-A containing nucleosome assembly at centromere

780.36

0.29

0.0208177

cotranslational protein targeting to membrane

0.28

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Table 4 The summary of genes involved in 10 up-regulation significant pathways pathway name

Count

p-value

Gene Symbol

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PDGFB, BAX, RELA, TLN1, HLA-E, IL6, VAC14, LTBR, AKT1, EGR1, NFKB2, ELK1, 1.16279E-16 CRTC3, ICAM1, FOSL1, FZD7, DVL2, HLA-A, CRTC2, TCF3, HLA-C, BCL2L1, ADCY3, KAT2A, ADCY6, TNFRSF1A, ZFP36, POLE, PIK3R2, AKT2

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GNG4, STAT2, CXCL2, GRK6, PLCB3, ADRBK1, ARRB2, CXCL1, CXCL16, ADCY3, 1.55751E-15 PIK3R2, PTK2B, SHC1, STAT3, ADCY6, AKT1, CCL20, RAC2, GNAI2, ARRB1, PXN, GSK3A, RELA, PREX1, AKT2

Herpes simplex infection

22

CSNK2A2, HLA-E, STAT2, IFNA17, PVRL2, HLA-A, C3, HCFC1, CD74, MAVS, 1.17846E-12 HLA-C, TAP1, UBE2R2, POLR2A, TNFRSF1A, PML, IRF3, SOCS3, RELA, IKBKE, IL6, NXF1

Insulin signaling pathway

19

3.8328E-12

IRS2, MKNK2, PIK3R2, FASN, ARAF, RPTOR, FLOT1, AKT2, TSC2, SOCS3, G6PC3, GYS1, FLOT2, SHC1, ELK1, INPPL1, PTPRF, AKT1, SREBF1

Endocytosis

21

4.46651E-11

HLA-E, RAB5B, PLD2, ARRB1, AP2A1, HLA-C, ARRB2, ADRBK1, PIP5K1C, HLA-A, GRK6, GIT1, SH3GL1, ARAP3, EHD2, HGS, ADRB2, VPS37B, PML, DNM1, EPN1

15

7.94745E-10

CXCL2, CXCL1, TNFRSF1A, PIK3R2, TNFAIP3, CCL20, CSF1, AKT1, BCL3, ICAM1, JUNB, SOCS3, RELA, IL6, AKT2

21

ARRB2, NFKB2, FGFR1, DUSP4, RAC2, MKNK2, CD14, MAP3K12, MAP3K11, AKT1, 3.64349E-09 TNFRSF1A, RELA, CACNB3, PDGFB, ELK1, TAOK2, ARRB1, CDC25B, AKT2, MAPKAPK2, MAP3K6

HTLV-I infection

Chemokine signaling pathway

TNF signaling pathway

MAPK signaling pathway

C S U

N A

T P E

C C

A

D E

M

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ACCEPTED MANUSCRIPT

PI3K-Akt signaling pathway

24

PKN1, G6PC3, TSC2, CRTC2, ITGA11, PPP2R5B, ITGA3, BCL2L1, IL6, PIK3R2, GNG4, 6.41928E-09 AKT2, PKN3, RPTOR, VEGFA, ITGB4, PDGFB, CSF1, ITGA5, RELA, AKT1, IFNA17, FGFR1, GYS1

Pertussis

12

7.90005E-09 GNAI2, CD14, C1R, RELA, C3, IRF3, C4B, IRF1, IL6, ITGA5, SERPING1, C1S

Pathways in cancer

23

1.07493E-08

T P

I R

NFKB2, IL6, PML, DVL2, BAX, VEGFA, FGFR1, ITGA3, AKT1, ARAF, PLCG1, JUP, PIK3R2, RELA, BCL2L1, RALGDS, MMP2, STAT3, PDGFB, FZD7, AKT2, ERBB2, RAC2

C S U

N A

D E

M

T P E

C C

A

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Table 5 The summary of genes involved in 10 down-regulation significant pathways pathway name

Count

Systemic lupus erythematosus

30

T P

I R

p-value

Gene Symbol

1.25344E-25

HIST1H2AB, HIST1H2BE, HIST1H2AM, HIST1H4B, HIST1H4F, HIST1H2BN, H2AFZ, HIST1H2BF, HIST1H2BM, SNRPD1, HIST2H3A, HIST1H3F, HIST1H2AL, HIST1H2BB, HIST1H2BI, HIST1H2AK, HIST1H2BJ, HIST1H2AG, HIST1H2AH, HIST3H3, HIST1H3D, HIST1H2BO, HIST1H4A, HIST1H3E, HIST3H2BB, HIST1H3J, HIST2H3D, HIST1H3H, HIST1H4I, HIST1H3B

C S U

N A

Alcoholism

Ribosome

32

2.20204E-24

2.29413E-23

RPL27, MRPL12, RPL10A, MRPL21, RPL29, RPS18, RPS5, MRPS18C, RPS14, MRPL35, RPS27A, RPL14, RPSA, MRPL14, RPL26L1, RPL24, FAU, RPS27L, RPS12, MRPL24, MRPS12, MRPL20, RSL24D1, MRPL13, RPL22L1, MRPL36, RPL7, MRPL33

D E

T P E

A

C C

28

M

HIST1H2BN, HIST1H2BO, HIST1H2AM, HIST1H2AG, HIST1H2BM, HIST1H2BF, HIST1H4I, HIST3H3, HIST1H2BE, HIST1H3F, H2AFZ, HIST1H4B, HIST1H4F, HIST1H2BI, HIST1H3E, CALM2, HAT1, HIST1H2AH, HIST1H2AK, HIST1H2AB, HIST1H2AL, HIST2H3A, HIST1H2BB, PPP1CB, HIST1H2BJ, HIST2H3D, HIST1H4A, HIST1H3B, HIST1H3D, HIST1H3J, HIST1H3H, HIST3H2BB

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Alzheimer's disease

30

Oxidative phosphorylation

Huntington's disease

27

29

5.7866E-23

NDUFA4, NDUFS6, UQCRHL, COX7B, ATP5G1, NDUFS4, NDUFA1, NDUFS5, FADD, CALM2, NDUFV1, NDUFB2, NDUFB1, GSK3B, COX5B, NDUFB7, ATP5H, ATP5E, COX7A2, ATP5C1, COX7C, NDUFA5, UQCRB, NDUFA11, NDUFB3, COX6C, UQCRQ, UQCRH, UQCR10, PLCB4

2.70227E-22

NDUFB2, COX7C, NDUFB7, NDUFB3, COX6C, ATP5H, COX5B, UQCRB, UQCRHL, NDUFV1, UQCRH, UQCR10, NDUFA1, NDUFA11, COX7A2, NDUFS6, NDUFB1, UQCRQ, NDUFS4, NDUFA4, ATP5C1, ATP5G1, ATP5E, NDUFS5, ATP5L, NDUFA5, COX7B

1.05009E-20

UQCRB, POLR2I, NDUFB2, NDUFS6, NDUFA11, COX7C, NDUFA1, COX7A2, NDUFA4, POLR2K, NDUFB3, NDUFB1, COX7B, UQCR10, ATP5C1, NDUFB7, NDUFS5, NDUFA5, COX5B, ATP5E, PLCB4, NDUFS4, NDUFV1, ATP5H, UQCRQ, ATP5G1, UQCRH, UQCRHL, COX6C

D E

26

A

C C

Non-alcoholic fatty liver disease (NAFLD)

25

I R

C S U

N A

M

3.28456E-20

NDUFS6, COX7B, UQCRHL, NDUFB7, COX7A2, NDUFA4, UQCRH, ATP5H, NDUFB2, NDUFV1, NDUFA1, UQCRB, NDUFS5, COX7C, NDUFA5, NDUFB3, NDUFB1, NDUFS4, COX5B, NDUFA11, ATP5E, UQCRQ, ATP5G1, UQCR10, COX6C, ATP5C1

1.97614E-18

ITCH, NDUFB7, COX5B, NDUFS6, COX7B, NDUFS5, AKT3, NDUFA1, COX6C, COX7C, UQCRQ, NDUFS4, NDUFB2, UQCRB, GSK3B, COX7A2, NDUFA4, NDUFA11, NDUFB3, UQCR10, UQCRH, UQCRHL, NDUFV1, NDUFB1, NDUFA5

T P E

Parkinson's disease

T P

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Metabolic pathways

57

Viral carcinogenesis

20

6.14102E-14

1.82478E-10

D E

PIK3C2A, PANK3, ZNRD1, ATP5C1, UQCRHL, ASL, HMBS, MAN2A1, NDUFB2, COX6C, UQCRB, BDH2, PGP, DCTPP1, NDUFV1, UQCRH, PMVK, COX7B, NDUFS6, NT5C, ITPA, NDUFB7, COX5B, GUK1, NDUFA1, PIK3C3,ADA, GLS, PIGW, POLR2I, ATP5G1, PLCB4,NDUFS4, NDUFA11, NDUFB3, EPT1, PAFAH1B3, POLR2K, UAP1, HYI, PYCR1, UQCR10, ATP5L, DHODH, SUCLG2, NDUFB1, NDUFA4, CRLS1, COX7C, GAMT, DPM1, ATP5E, UQCRQ, ACADVL, NDUFS5, ATP5H, NDUFA5

T P

I R

C S U

HIST3H2BB, HIST1H2BO, HIST1H2BM, HIST1H4F, DLG1, HIST1H2BF, GTF2A2, HIST1H2BJ, HIST1H4I, HIST1H2BB, HIST1H2BN, HIST1H4B, PMAIP1, HIST1H2BE, UBE3A, RBL1, GTF2H2, HIST1H4A, MDM2, HIST1H2BI

N A

M

T P E

C C

A

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Table 6 The top 20 genes ranked by degree over 8 after analysis of Signal-net Gene name PIK3R2

RELA

Description Homo sapiens phosphoinositide-3-kinase, regulatory subunit 2 (beta) (PIK3R2), transcript variant 1, mRNA.

T P

degree

indegree

outdegree

up

23

14

16

up

22

12

11

up

17

1

17

I R

C S U

Homo sapiens v-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELA), transcript variant 2, mRNA.

style

N A

ARRB2

Homo sapiens arrestin, beta 2 (ARRB2), transcript variant 1, mRNA.

PLCG1

Homo sapiens phospholipase C, gamma 1 (PLCG1), transcript variant 1, mRNA.

up

15

14

11

PLCB3

Homo sapiens phospholipase C, beta 3 (phosphatidylinositol-specific) (PLCB3), transcript variant 1, mRNA.

up

15

15

11

PLCB4

Homo sapiens phospholipase C, beta 4 (PLCB4), transcript variant 1, mRNA.

down

15

15

11

RAC2

Homo sapiens ras-related C3 botulinum toxin substrate 2 (rho family, small GTP binding protein Rac2) (RAC2), mRNA.

up

14

9

5

AKT2

Homo sapiens v-akt murine thymoma viral oncogene homolog 2 (AKT2), transcript variant 1, mRNA.

up

14

4

10

AKT3

Homo sapiens v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma) (AKT3), transcript variant 3, mRNA.

down

14

4

10

AKT1

Homo sapiens v-akt murine thymoma viral oncogene homolog 1 (AKT1), transcript variant 3, mRNA.

up

14

4

10

D E

M

T P E

C C

A

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RAC3

Homo sapiens ras-related C3 botulinum toxin substrate 3 (rho family, small GTP binding protein Rac3) (RAC3), mRNA.

FGFR1

Homo sapiens fibroblast growth factor receptor 1 (FGFR1), transcript variant 14, mRNA.

down

12

8

4

up

11

5

6

down

11

11

11

down

10

5

5

up

10

10

10

up

9

9

6

T P

ITPA

Homo sapiens inosine triphosphatase (nucleoside triphosphate pyrophosphatase) (ITPA), transcript variant 1, mRNA.

HIF1A

Homo sapiens hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) (HIF1A), transcript variant 3, mRNA.

PLCD3

Homo sapiens phospholipase C, delta 3 (PLCD3), mRNA.

ADCY3

Homo sapiens adenylate cyclase 3 (ADCY3), mRNA.

ADCY6

Homo sapiens adenylate cyclase 6 (ADCY6), transcript variant 2, mRNA.

up

9

9

6

DGKA

Homo sapiens diacylglycerol kinase, alpha 80kDa (DGKA), transcript variant 3, mRNA.

up

9

9

9

DGKZ

Homo sapiens diacylglycerol kinase, zeta (DGKZ), transcript variant 1, mRNA.

up

9

9

9

STAT3

Homo sapiens signal transducer and activator of transcription 3 (acute-phase response factor) (STAT3), transcript variant 2, mRNA.

up

8

4

4

D E

SC

U N

A M

PT

E C

C A

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I R

ACCEPTED MANUSCRIPT Highlights

The changes of gene expression profile induced by PM2.5 were investigated. BEAS-2B cells were treated with 15.625 µg/cm2 of PM2.5 for 24 h.

PT

Microarray analysis and bioinformatics analysis were introduced. Genes associated with antioxidant and inflammatory response were up-regulated.

AC

CE

PT E

D

MA

NU

SC

RI

Genes related to cell proliferation and mitosis regulation were down-regulated.

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