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Ecotoxicology and Human Environmental Health

Personal ozone exposure and respiratory inflammatory response: the role of DNA methylation in the arginase–nitric oxide synthase pathway Yue Niu, Renjie Chen, Yongjie Xia, Jing Cai, Zhijing Lin, Cong Liu, Chen Chen, Li Peng, Zhuohui Zhao, Wen-Hao Zhou, Jianmin Chen, and Haidong Kan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01295 • Publication Date (Web): 09 Jul 2018 Downloaded from http://pubs.acs.org on July 10, 2018

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Personal ozone exposure and respiratory inflammatory response: the role of DNA methylation in the arginase–nitric oxide synthase pathway

Yue Niu1,†, Renjie Chen1,2,†, Yongjie Xia1, Jing Cai1, Zhijing Lin1, Cong Liu1, Chen Chen1, Li Peng2, Zhuohui Zhao1, Wenhao Zhou3, Jianmin Chen4, Haidong Kan1,5,*



These authors contributed equally to this work.

1

School of Public Health, Key Lab of Public Health Safety of the Ministry of

Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China; 2

Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030,

China; 3

Department of Neonates, Children's Hospital, Fudan University, Shanghai

201102, China; 4

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention

(LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; 5

Key Laboratory of Reproduction Regulation of National Population and

Family Planning Commission, Shanghai Institute of Planned Parenthood Research, Institute of Reproduction and Development, Fudan University,

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Shanghai 200032, China.

*Correspondence: Haidong Kan, Department of Environmental Health, School of Public Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. E-mail: [email protected].

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Abstract (200 words)

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Little is known regarding the molecular mechanisms behind respiratory

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inflammatory response induced by ozone. We performed a longitudinal panel

4

study with four repeated measurements among 43 young adults in Shanghai,

5

China from May to October in 2016. We collected buccal samples and

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measured the fractional exhaled nitric oxide (FeNO) after 3-day personal

7

ozone monitoring. In buccal samples, we measured concentrations of

8

inducible nitric oxide synthase (iNOS) and arginase (ARG), and DNA

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methylation of NOS2A and ARG2. We used linear mixed-effect models to

10

analyze the effects of ozone on FeNO, two enzymes and their DNA

11

methylation. A 10 ppb increase in ozone (lag 0−8 h) was significantly

12

associated with a 3.89% increase in FeNO, a 36.33% increase in iNOS, and a

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decrease of 0.36 in the average methylation (%5mC) of NOS2A. Ozone was

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associated with decreased ARG and elevated ARG2 methylation, but the

15

associations were not significant. These effects were more pronounced among

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allergic subjects than healthy subjects. The effects were much stronger when

17

using personal exposure monitoring than fixed-site measurements. Our study

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demonstrated that personal short-term exposure to ozone may result in acute

19

respiratory inflammation, which may be mainly modulated by NOS2A

20

hypomethylation in the arginase–nitric oxide synthase pathway.

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Key words: ozone; personal exposure; exhaled nitric oxide; respiratory

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inflammation; DNA methylation

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1 Introduction

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Ambient ozone is the main component of photochemical air pollution, and

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it frequently exceeds the health-based standard worldwide, especially in warm

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or hot seasons.1 Ozone air pollution arouses increasing public health concerns

27

in recent decades because of accelerated urban motorization. Except for

28

particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5), ozone

29

was the sole air pollutant considered as a risk factor in the Global Burden of

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Disease Study due to its independent effects on respiratory mortality in the

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well-known American Cancer Society Cohort Study.2 For the short-term

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exposure, a large body of epidemiological studies have also shown effects of

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ozone on various clinical and subclinical outcomes in the respiratory system.3

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Inflammatory response was central in the pathophysiologic process

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whereby airborne toxicants damage the respiratory tract.4, 5 Epidemiological

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studies have associated ozone exposure with elevated levels of the fractional

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exhaled nitric oxide (FeNO). It is a well-established noninvasive marker of

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respiratory inflammation,6, 7 and was strongly recommended by the American

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Thoracic Society Committee.8 However, the underlying mechanisms behind

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this association remain to be elucidated. The exhaled nitric oxide (NO) is

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mainly synthesized from L-arginine by inducible nitric oxide synthase (iNOS,

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mainly encoded by NOS2A in the respiratory tract), which can be inhibited by

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the arginase (ARG, mainly encoded by ARG2 in the respiratory tract) by

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competing for a common substrate.9 Consequently, the arginase–nitric oxide

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synthase pathway represents an important mechanism for human respiratory

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inflammation. Epigenetics, such as DNA methylation, may serve as a bridge

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between airborne toxicants and adverse health outcomes.10, 11 In most cases,

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hypermethylation in promoter regions of genes can down-regulate the levels of

49

protein by suppressing gene expression, and hypomethylation can up-regulate

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the levels.12 However, little is known regarding whether NOS2A and ARG2

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methylation modulates respiratory inflammatory response induced by ambient

52

ozone pollution.

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Therefore, we performed a longitudinal panel study to evaluate the roles

54

of NOS2A and ARG2 methylation in respiratory inflammatory response

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induced by ambient ozone pollution among a group of allergic and healthy

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subjects. Considering the large spatial-temporal variations in ozone

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concentrations, its exposure was directly monitored at the individual level.

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2 Methods

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2.1 Subjects and Study Design

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A longitudinal panel study with 4 follow-ups was conducted in Shanghai,

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China, from May 29 to October 12, 2016. We initially recruited 43 nonsmoking

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college students from the medical (Fenglin) campus of Fudan University. Three

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asthmatic patients were excluded to avoid possible influences of asthmatic

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attacks on our findings. We finally included19 allergic and 21 healthy subjects

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in this study. The allergic subjects were defined as those who have a history of

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respiratory allergy but not receive any medication treatments (e.g., intranasal

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corticosteroids and oral antihistamines) at enrollment. The allergic status was

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determined by whether having a self-reported physician-diagnosed allergic

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disease (i.e., allergic rhinitis, atopy, or both) and whether having allergic

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symptoms according to a modified questionnaire based on the core

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questionnaires of the International Study of Asthma and Allergies in Childhood.

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Eligible participants were required to abstain from any alcoholic beverage,

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medications and dietary supplements during the study period.

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Four follow-ups were arranged during the periods from May 29 to June 22,

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June 28 to July 23, July 31 to August 31, and August 31 to October 12,

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respectively. In each round of follow-ups, we randomly divided these

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participants into 6 subgroups (6–8 participants for each) and scheduled

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follow-ups at different days to expand natural variations in the measurements

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of exposure-response data. For each follow-up, physical examinations were

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arranged at the same daytime (between 4:00 p.m. and 5:00 p.m.) immediately

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after a consecutive 3-day (from 8:00 a.m. to 6:00 p.m.) personal ozone

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monitoring to control for possible circadian rhythms. At baseline, demographic

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characteristics, including age, gender, height and weight, were collected

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through a general questionnaire. During the study period, participants were

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asked to record whether they were experiencing a disease (for example, a

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cold), and whether they were suffering any allergic symptoms through a

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symptom questionnaire.

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The Institutional Review Board of the School of Public Health, Fudan

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University approved the study protocol (NO. 2014-07-0523), and all

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participants signed the informed consent at the enrollment.

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2.2 Exposure Measurements

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Personal exposure to ozone was measured in real time by the Personal

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Ozone Monitor (POM, 2B Technologies, Boulder, Colorado, USA) base on the

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method of ultraviolet absorption. This device showed strong inter-unit

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precisions and good agreements with a fixed-site ozone analyzer (Model 49i,

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Thermo Fisher Scientific Inc., MA, USA).13 For each round of 3-day exposure

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measurement, participants were instructed to carry a POM during daytime only

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(from 8:00 a.m. to 6:00 p.m.). The quality assurance and quality control

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procedures were strictly followed when conducting personal ozone monitoring.

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More details about personal monitoring can be found in our recent

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publication.13 We calculated the maximum 8-h (from 10:00 a.m. to 6:00 p.m.)

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average concentrations as the daily mean concentrations and numbered them

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as lag 1 d (the previous day) and lag 2 d (the previous 2 day), respectively. We

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also averaged the concentrations of 0−2 h, 3−5 h, 6−8 h and 0−8 h (lag 0 d)

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preceding physical examinations. In addition, we placed a HOBO data logger

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(Onset Computer Corporation, Pocasset, Massachusetts, USA) to measure

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personal temperature and relative humidity in real time.

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To allow for a comparison with measurements from fixed-site monitoring,

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we obtained hourly ozone concentrations from the nearest state-controlled

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monitoring station, which is about 3 kilometers away from the campus. They

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were measured by the method of ultraviolet absorption. To allow for the

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adjustment of other air pollutants in a sensitivity analysis, we also collected

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daily concentrations of PM2.5, sulfur dioxide (SO2), nitrogen dioxide (NO2), and

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carbon monoxide (CO) from the same monitoring station.

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2.3 Physical Examinations and Lab Analyses

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FeNO levels were tested by a trained staff using a portable NIOX MINO

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machine (Aerocrine AB, Solna, Sweden) according to the standardized

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procedures proposed by the American Thoracic Society and the European

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Respiratory Society.14 Briefly, all subjects inhaled NO-free air through the filter

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(built-in NO scrubbers) to close to total lung capacity and exhaled air at a flow

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rate of 50 ± 5 ml/s into the measurement chamber containing the sensor.

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Foods, beverages and intense exercises were not allowed at least within 1 h

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before the test.

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Buccal samples were collected after FeNO tests using Isohelix

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SK-1S/MS-01 Buccal Swabs (Cell Projects, Kent, UK). Specifically, subjects

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were requested to rinse the mouth using purified water before sampling. Then,

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they were instructed to rub firmly against the inside of their cheeks with sterile

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swabs for 1 minute. Upon completion, the swabs were immediately placed into

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microcentrifuge tubes containing 1200 µL of phosphate buffer solution and the

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buccal-cell suspensions were frozen at -80℃ till use.

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The levels of iNOS and ARG protein in buccal samples were detected by

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the method of simple Western Blot. All procedures were performed with

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manufacturers reagents (Proteinsimplem, San Jose, California, USA)

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according to the user manual. Briefly, buccal samples were mixed with

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fluorescent master mix prior to tests. Then, prepared samples, blocking

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reagent,

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substrate and wash buffer were dispensed into designated wells in the

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manufacturer-provided microplate. The separation and immunodetection were

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performed automatically using default settings. The level of protein was

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analyzed by the inbuilt Compass software and was expressed as a ratio

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relative to a reference protein (β-actin).

primary

antibodies,

secondary

antibodies,

chemiluminescent

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Genomic DNA was extracted from buccal samples using the QIAmp DNA

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Mini Kit (Qiagen, Hilden, Germany) and was quantified using a NanoDrop

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spectrophotometer (Thermo Scientific, Waltham, Massachusetts, USA). The

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extracted DNA (2 µg) were bisulfite converted and purified using the EpiTect

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Fast DNA Bisulfite Kit (Qiagen, Hilden, Germany) according to manufacturer’s

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recommended protocol. The purified DNA were eluted in Buffer EB solution

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and stored at -80℃ for DNA methylation analysis. We selected 3 CpG loci in

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the promoter region of NOS2A gene and 3 CpG loci in the promoter region of

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ARG2 gene according to a previous study (see Table S1).9 Polymerase chain

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reaction (PCR) primers were designed for the targeted gene regions using the

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Pyromark Assay Design SW 2.0 Software (Qiagen, Hilden, Germany).

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Methylation levels of each targeted CpG were examined by the methods of

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bisulfite-PCR

pyrosequencing

assay

using

the

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Pyromark

Q96

MD

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pyrosequencing instrument (Qiagen, Hilden, Germany) following standard

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protocols. The output from pyrosequencing was expressed as the proportion of

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methylated cytosines over the sum of methylated and unmethylated cytosines,

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that is, the percentage of 5-methylcytosine (%5mC).

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2.4 Statistical Analyses

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Exposure and health data were merged by the time of physical

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examinations (rounded to the integer hour). We applied the linear mixed-effect

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(LME) model to analyze the associations of ozone exposure with FeNO, two

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enzymes and their DNA methylation. This model automatically allows each

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subject to serve as his or her own control over time by introducing a

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random-effect intercept, which reflect each individual's baseline.15 The levels

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of FeNO, iNOS and ARG were natural log-transformed before statistical

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analyses because of their almost log-normal distributions, while methylation

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data was directly entered the models because they were normally distributed.

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In the main model, personal ozone concentration was introduced as a

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fixed-effect term and an identity number for each subject was added as a

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random-effect intercept to account for correlations among multiple repeated

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measurements per person. We also included several covariates in the main

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models: (1) a natural cubic smooth function of the day within the study with 3

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degrees of freedom to account for any unmeasured time trends; (2) personal

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temperature and relative humidity that were simultaneously measured to

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control for the potential confounding effects of weather conditions;16 (3)

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individual characteristics, including age, gender, body mass index (BMI),

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allergic status (as a binary variable) and whether having a cold during 3-day

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follow-up; and (4) the plate number (only for the methylation data). Various

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lags of exposure were introduced into the main model one at a time to

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investigate the lag pattern in the effects of ozone. We further performed

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stratification analysis by allergic status using the afore-mentioned main models

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to compare respiratory response to ozone in allergic and healthy adults.

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In addition, we reran the main models using the ozone concentrations

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measured by the nearest fixed-site monitor in place of personal exposure. We

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calculated the marginal R square (RM2) and the Akaike Information Criterion

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(AIC) values to compare the model fit. Higher RM2 and lower AIC mean better

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model fit.

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At last, we performed a sensitivity analysis to evaluate whether our results

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were confounded by the simultaneous exposure to other air pollutants. We fit

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multiple two-pollutant models by introducing the present-day concentrations of

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PM2.5, SO2, NO2 and CO into the main model one at a time.

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All models were fitted in R software (V.3.4.0, R Foundation for Statistical

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Computing, Vienna, Austria) with the LME using “lme4” package. p-values less

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than 0.05 were considered statistically significant. The estimated effects on

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FeNO, iNOS and ARG were presented as the percent changes and their 95%

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confidence intervals (CIs) associated with a 10 ppb increase in ozone

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concentrations. The estimated effects on NOS2A and ARG2 methylation were

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presented as the absolute changes and their 95% CIs in %5mC associated

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with a 10 ppb increase in ozone concentrations.

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3 Results

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3.1 Descriptive statistics

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We finally included 40 participants (30 females and 10 males) into analyses,

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with a mean age of 24 years and an average BMI of 21 kg/m2. Twenty-seven

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participants completed scheduled 4 rounds of follow-ups. Eleven participants

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missed one follow-up due to personal reasons (e.g., schedule conflict). Two

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participants missed 2 rounds of follow-ups because they declined frequent

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physical examinations. They all declared no use of alcohol, medications and

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dietary supplements and no suffering from apparent allergic symptoms during

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the study period.

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As shown in Table 1, during the study period, the average ozone exposure

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at the individual level varied from 18 ± 20 ppb to 23 ± 17 ppb by different lag

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periods. Meanwhile, ozone concentrations measured by the nearest fixed-site

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monitor were almost twice as high as personal measurements, with an

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average of 46 ± 18 ppb, which is close to the guideline recommended by the

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World Health Organization (100 µg/m3, 47 ppb), but is below National Ambient

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Air Quality Standard of the United States (70 ppb). The correlations between

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personal ozone exposures and ambient ozone concentrations as well as the

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distributions of correlations have been described in detail in our recent

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publication.13

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In total, we collected 145 FeNO measurements and 145 buccal samples.

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As summarized in Table 1, FeNO measurements varied appreciably with a

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mean of 11 ± 7 ppb. The levels of iNOS and ARG relative to β-actin varied

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considerably and the averages were 0.3 ± 0.8 and 20.6 ± 32.5, respectively.

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Because methylation levels at 3 loci in both genes were highly correlated, we

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averaged them before entering statistical analyses. In general, the NOS2A

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was heavily methylated [73.83 ± 11.51 on average (%5mC)] and ARG2 was

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mildly methylated [2.88 ± 1.95 on average (%5mC)].

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3.2 Regression results

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Table 2 shows the changes in FeNO, iNOS protein, ARG protein, and

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DNA methylation of NOS2A and ARG2 associated with ozone exposure at

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different lag periods. We observed that FeNO increased immediately after two

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hours of ozone exposure, then attenuated at longer lag hours. iNOS

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significantly increased from lags 2 h to 8 h. Similarly, NOS2A methylation

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showed a significantly inverse association with ozone from lags 2 h to 8 h.

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Ozone exposure was inversely associated with ARG and positively associated

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with ARG2 methylation, but the associations were not significant. The

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associations are somewhat larger when using lag 0-8 h than lag 1 d and 2 d.

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Therefore, we used the lags 0 to 8 h in subsequent analyses. At this specific

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lag period, an increase of 10 ppb in ozone was associated with a 3.89% (95%

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CI: 0.45%, 7.46%) increase in FeNO, a 36.33% (95% CI: 11.04%, 67.37%)

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increase in iNOS, a 7.15% (95% CI: -9.43%, 21.22%) decrease in ARG, a

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decrease of 0.36 (95% CI: 0.06, 0.67) in the average methylation (%5mC) of

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NOS2A gene, and an increase of 0.18 (95% CI: -0.11, 0.48) in the average

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methylation (%5mC) of ARG2 gene.

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In stratification analysis (see Table 3), the increment in FeNO was larger

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among allergic subjects than among healthy subjects. Accordingly, the positive

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association with iNOS and the negative association with NOS2A methylation

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remained significant among allergic subgroup. Notably, there was a positive

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but non-significant association with iNOS and a negative but non-significant

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association with NOS2A methylation in the healthy subgroup. The effects of

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ozone on ARG and ARG2 methylation were not statistically significant in both

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subgroups.

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As shown in Table 4, the use of personal measurements yielded much

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larger effect estimates than the use of fixed-site measurements at the lag 0−8

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h. Meanwhile, the models using personal measurements had an average of

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9.6% higher RM2 and 4.6% lower AIC values than the models using fixed-site

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measurements.

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In the sensitivity analysis, the associations of ozone with FeNO, iNOS and

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NOS2A methylation were not appreciably changed after controlling for other

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air pollutants in two-pollutant models (see Table S2–S5).

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4 Discussion

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The present investigation has the advantage of using personal real-time

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monitoring of ozone in a repeated panel study, which allows for an accurate

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assessment on its respiratory effects. We found that a short-term exposure to

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ozone could induce an acute increase in FeNO levels. This increment was

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accompanied by alterations in DNA methylation in the arginase–nitric oxide

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synthase pathway that leads to the production of NO. We further found much

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stronger effects of ozone using personal than fixed-site measurements. To our

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knowledge, this is the first epidemiological study to explore the potential

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molecular biological mechanisms underlying the respiratory inflammation

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induced by ambient ozone exposure.

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In the present study, we found acute respiratory inflammatory response

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represented by increased FeNO immediately after hours’ exposure to low-level

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ozone, which was consistent with the broad literatures. For example, a panel

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study in Changsha, China, estimated that 24-h ozone exposure levels for

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white-collar workers ranged from 1.4 ppb to 19.4 ppb. Within this range, they

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found per 10 ppb increase in ozone was associated with a 18.1% (95% CI:

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4.5%, 33.5%) increase in FeNO.17 A panel study in Greece also showed that a

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very low personal exposure to ozone (5–11 µg/m3) may result in a significant

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increase in FeNO.18 Likewise, a longitudinal study based on fixed-site

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monitoring observed a significant increase in FeNO following exposure to low

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levels of ambient ozone (31.6 ppb).19

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The molecular biological pathway whereby an inhalation of ozone induces

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the production of NO remained unknown. The arginase–nitric oxide synthase

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pathway was considered biologically responsible for the NO production, and

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there was a potential that DNA methylation may modulate this pathway.20 Our

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results indicated that a short-term exposure to ozone resulted in an immediate

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decrease in NOS2A methylation and an increase in iNOS protein. The

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hypothesized pathway is that oxidative stress induced by ozone inhalation may

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cause DNA lesions, subsequently lead to NOS2A hypomethylation by

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interfering DNA methyltransferases binding to damaged DNA,21 and further

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enhance the expression of iNOS.12 Although no studies to date have directly

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evaluated the influences of ozone on NOS2A methylation, similar associations

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have been established between particulate air pollution and NOS2A

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hypomethylation.10, 11, 22 These findings provided the biological plausibility that

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NOS2A hypomethylation may modulate the process of NO production

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stimulated by an acute ozone exposure.

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ARG is another important enzyme that inhibits the function of iNOS in the

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arginase–nitric oxide synthase pathway. Theoretically, low expression of ARG

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protein caused by hypermethylation of ARG2 may ensure a sufficient

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L-arginine supply by reducing the hydrolysis process, resulting in accelerated

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production of NO.20 Few studies have explored the role of ARG or its DNA

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methylation in elevated FeNO levels by air pollutants. In the present study, we

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observed inverse associations of ozone with ARG and positive associations

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with ARG2 methylation, but all the associations were not statistically significant.

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Likewise, Jung et al found positive but non-significant associations of PM2.5

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with ARG2 methylation among a group of urban children.23 Paradoxically,

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another human-based study associated ARG2 hypermethylation with

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decreased FeNO,9 revealing the complexity inherent in the arginase–nitric

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oxide synthase pathway. There exist other unknown regulating factors in NO

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production.

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It is noteworthy that the inflammation-related response to ozone differed

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greatly by the allergic status. We observed significantly elevated FeNO levels,

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increased iNOS concentrations and decreased NOS2A methylation following

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ozone exposure in allergic subjects rather than in healthy subjects. This finding

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indicated that people with pre-existing respiratory allergic diseases were more

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susceptible to the airway inflammatory response induced by ozone exposure.

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These findings were biologically plausible because FeNO is inherently a

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biomarker of eosinophilic inflammation and allergic subjects (allergic rhinitis

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and atopy in this study) were characterized by eosinophilic inflammation in the

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airway. Nevertheless, it should be noted that a prior study still reported evident

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associations between ozone and FeNO in non-asthmatic children.19 This

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inconsistency may be caused by different study designs, sample sizes and

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populations.

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We found much stronger associations of ozone using personal than

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fixed-site

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misclassification leads to substantial attenuation of the associations. The

measurements,

suggesting

that

nondifferential

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better model fit for personal-exposure models than for ambient-exposure

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models further revealed that the use of direct personal measurements could

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more effectively predict human health response. This reflects the fact that

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ozone is highly spatially heterogenous due to its chemical reactions with NO,24

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as well as the fact that its concentrations can differ much between outdoors

336

and indoors due to the rapid surface adsorption in indoor environments.25, 26

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Therefore, our findings demonstrated that ambient ozone measurements from

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fixed-site monitors was not a perfect surrogate of personal exposure to ozone

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in epidemiological studies. Nevertheless, it should be acknowledged that most

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previous epidemiological studies have associated health outcomes with ozone

341

measured based on fixed-site monitoring or residential modelling, which may

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have led to considerable underestimation of its health effects and

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corresponding disease burden.

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Our study has some limitations. First, the relatively small sample size

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limited the power and precision of this study, and consequently some important

346

associations might have been missed. Second, the enrollment of college

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students reduced the potential of uncontrolled confounding (such as smoking,

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alcohol drinking, indoor air pollution, and dietary structures), but may limit the

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generalizability to other populations and contexts. Third, we were unable to

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evaluate causality and temporal associations from ozone exposure to NO

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production through DNA methylation and protein expression because buccal

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sample collection and FeNO tests were simultaneously conducted. Fourth,

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although our results were robust to the adjustment of other air pollutants

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measured by a fixed-site station, their confounding effects cannot be excluded

355

because of the lack of personal monitoring data.

356

In summary, our study demonstrated that short-term exposure to ozone

357

may result in acute respiratory inflammation. Our results further offered a new

358

pathophysiologic insight that this inflammatory response may be mainly

359

modulated by NOS2A hypomethylation in the arginase–nitric oxide synthase

360

pathway. Further research with larger sample size and controlled-exposure

361

design in various subpopulations is warranted to confirm our findings.

362

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Acknowledgments: We appreciate the contributions of all volunteers in this

364

study. This work was funded by the National Natural Science Foundation of

365

China (91643205 and 91743111), China Medical Board Collaborating Program

366

(16-250), the Research Program of Shanghai Environmental Protection

367

Bureau (2016-11), and Shanghai Key Laboratory of Meteorology and Health

368

(QXJK201702).

369 370

Supporting Information

371

This information is available free of charge via the Internet at

372

http://pubs.acs.org/. Table S1. Primer sequences for NOS2A and ARG2 genes.

373

Table S2–S5. Changes in FeNO, iNOS, ARG, NOS2A and ARG2 methylation

374

associated with a 10 ppb increase in personal ozone levels at different lag

375

periods, adjusting for other air pollutants.

376 377

Notes: The authors declared no conflicts of interests.

378 379

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Table 1. Descriptive statistics of personal and ambient ozone concentrations at different lag periods, FeNO, iNOS, ARG, and methylation levels of NOS2A and ARG2 genes. Lag period

Mean

SD

Min

P25

Median

P75

Max

0−2 h

22.23

14.16

4.50

12.28

19.82

28.13

80.27

3−5 h

22.70

17.36

4.50

11.18

18.17

30.31

103.76

6−8 h

17.97

20.45

4.50

7.58

13.32

21.29

201.95

0−8 h

21.01

14.71

4.50

11.54

17.43

26.14

104.27

1d

19.42

11.24

4.50

10.93

18.38

25.94

76.19

2d

19.57

11.53

4.50

10.67

17.26

25.65

67.94

Ambient ozone (ppb) a

0−8 h

46.15

17.62

11.63

36.40

47.20

58.41

86.45

FeNO (ppb)

-

11.34

7.16

4.00

7.00

9.00

12.00

43.00

iNOS / β-actin

-

0.32

0.78