Fine Particulate Matter Constituents, Nitric Oxide Synthase DNA

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Fine particulate matter constituents, nitric oxide synthase DNA methylation and exhaled nitric oxide Renjie Chen, Liping Qiao, Huichu Li, Yan Zhao, Yunhui Zhang, Wenxi Xu, Cuicui Wang, Hongli Wang, Zhuohui Zhao, Xiaohui Xu, Hui Hu, and Haidong Kan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02527 • Publication Date (Web): 15 Sep 2015 Downloaded from http://pubs.acs.org on September 17, 2015

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Environmental Science & Technology

Fine particulate matter constituents, nitric nitric oxide synthase DNA methylation and exhaled nitric oxide

Renjie Chen,†‡¶ Liping Qiao,§¶ Huichu Li,† Yan Zhao,† Yunhui Zhang,† Wenxi Xu,

∥

Cuicui Wang,† Hongli Wang,

§

Zhuohui Zhao,† Xiaohui Xu,⊥ Hui Hu,#

Haidong Kan†‡*

†

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

Education, & Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China ‡

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention

(LAP3), Fudan University, Shanghai, China §

State Environmental Protection Key Lab of the Formation and Prevention of

Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, China ∥

Huangpu District Center for Disease Control and Prevention, Shanghai,

China ⊥

Department of Epidemiology & Biostatistics, Texas A&M School of Public

Health, Texas, USA. #

Department of epidemiology, College of Public Health and Health

Professionals, College of Medicine, University of Florida, Gainesville, Florida, USA. 1

ACS Paragon Plus Environment


¶

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Renjie Chen and Liping Qiao contributed equally to this work.

*Corresponding

author:

Tel/fax:

+86

(21)

54237908;

e-mail:

[email protected]; mail: P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China.

2


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ABSTRACT (199 words)

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It remains unknown how fine particulate matter (PM2.5) constituents affect

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differently the fractional concentration of exhaled nitric oxide (FeNO, a

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biomarker of airway inflammation) and the DNA methylation of its encoding

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gene (NOS2A). We aimed to investigate the short-term effects of PM2.5

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constituents on NOS2A methylation and FeNO. We designed a longitudinal

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study among chronic obstructive pulmonary disease (COPD) patients with 6

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repeated health measurements in Shanghai, China. We applied linear

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mixed-effect models to evaluate the associations. We observed that the

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inverse association between PM2.5 and methylation at position 1 was limited

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within 24 hours, and the positive association between PM2.5 and FeNO was

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the strongest at lag 1 day. Organic carbon, element carbon, NO3- and NH4+

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were robustly and significantly associated with decreased methylation and

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elevated FeNO. An interquartile range increase in total PM2.5 and the four

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constituents was associated with decreases of 1.19, 1.63, 1.62, 1.17 and 1.14

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in

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13.30%,16.93%, 8.97%, 18.26% and 11.42% in FeNO, respectively. Our

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results indicated that organic carbon, element carbon, NO3- and NH4+ might be

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mainly responsible for the effects of PM2.5 on the decreased NOS2A DNA

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methylation and elevated FeNO in COPD patients.

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Key words: fine particulate matter; constituent; DNA methylation; airway

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inflammation

percent

methylation

of

NOS2A,

respectively,

3


and

increases

of


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Main text (3973 words)

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INTRODUCTION

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A large number of epidemiological studies have reported the hazardous

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respiratory effects of short-term exposure to fine particulate matter

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(aerodynamic diameter less than 2.5 micrometer, PM2.5) air pollution, including

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increased risks of respiratory mortality and hospitalization.1 Elevated

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inflammation in the respiratory tract is one of the key mechanisms involved in

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the development of PM2.5-related outcomes.2 As a well-known noninvasive

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biomarker for assessing airway inflammation, fractional concentration of

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exhaled nitric oxide (FeNO) has been positively linked to an exposure to PM2.5

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in recent human studies.2-5 These findings suggest that FeNO is a useful

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intermediary phenotype in the pathophysiological process of airway

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inflammation triggered by PM2.5.3

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The underlying mechanisms linking PM2.5 and FeNO are not fully

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characterized. There is increasing evidence that epigenetic alternations,

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typically DNA methylation, can change the expression and function of a gene

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under exogenous stimuli without changing in its DNA sequence.6 Inducible

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nitric oxide synthase (iNOS; encoded by nitric oxide synthase isoform 2A

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[NOS2A], Genbank accession number AF017634) is the major enzyme

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responsible for nitric oxide synthesis in the respiratory tract.7 Hypomethylation

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in the promoter of NOS2A gene was previously shown to be inversely related

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to iNOS.8, 9 Several prior studies have revealed a potential downward influence 4


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of PM2.5 on the promoter DNA methylation of NOS2A,3, 10, 11 but the time-lag

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patterns of PM2.5, NOS2A methylation and FeNO are largely unclear.

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Furthermore, PM2.5 has a very complex chemical composition, which raises

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the question of how these constituents affect differently the levels of NOS2A

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DNA methylation and FeNO. One investigation in the Boston area Normative

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Aging Study evaluated the association between two components (black carbon

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and sulfate) and global DNA methylation.6 However, this knowledge remains

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quite limited. Therefore, we aimed to investigate the short-term effects of PM2.5

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constituents on the DNA methylation of NOS2A promoter and FeNO in

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Shanghai, a Chinese city with high PM2.5 levels. We tested these hypotheses

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in a panel of chronic obstructive pulmonary disease (COPD) patients because

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respiratory inflammation plays an important role in the development and

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exacerbation of this disease.

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MATERIALS AND METHODS

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Design and population. population. This is a longitudinal panel study with repeated

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measurements on the exposure and health. We initially recruited 30 retired

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COPD patients from a central urban community in Shanghai with a total area

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of 1.9 km2. All COPD diagnoses were confirmed by physicians before they

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were finally included in this study. To reduce the influence of respiratory

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medication on our results, we only included the stable patients with

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mild-to-moderate COPD in this study according to the classification of Global 5



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Initiative for Chronic Obstructive Lung Disease based on the baseline test of

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spirometry.12 We excluded those who were current active or passive smokers

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(living with a current smoker); former smokers (quit smoking for at least 3

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years); consumed any alcohol; or had severe comorbidities or inflammatory

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diseases. To reduce the inherent seasonal variations of FeNO and DNA methylation

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13,

six weekly follow-up visits were scheduled from May 27th to July 5th, 2014.

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To fully capture the variations in the exposure and biomarkers, we arranged

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health examinations on various weekdays (i.e., Tuesday, Thursday, and

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Saturday). For each patient, physical examinations were scheduled at the

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same time (1:30 p.m. to 2:30 p.m.) on the same day of the week in order to

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control for possible circadian rhythm and day-of-week effects. Data on

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individual demographic and medical characteristics, such as age, gender,

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height, weight, education attainment, income, medication use, duration of

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COPD and chronic comorbidities, were collected during the baseline visit.

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Study subjects were asked to record any use of medications, acute

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exacerbation of COPD, and whether they went out of the central urban areas

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during the study period. The Institutional Review Board in the School of Public

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Health at Fudan University approved the study protocol. We obtained written

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consent forms from all subjects.

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FeNO measurements. measurements. All physical examinations were conducted at the

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Community Health Center. We measured FeNO levels using a portable NIOX 6


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

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procedures by the American Thoracic Society and the European Respiratory

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Society. A maximum of 5 repeated tests with 5 minutes rest between testes

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were allowed for subjects who could not complete the test at the first time.

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

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hour before the FeNO measurements.

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Collection of buccal samples. Buccal samples were collected directly after the

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FeNO test. Specifically, the subjects were asked to rinse the mouth using

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purified water. They were provided with two tooth-brushes and instructed to

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brush the teeth using the first one. Then, they were instructed to moderately

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brush the bilateral buccal mucosa 20 times at each side with the second

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toothbrush. The brush was then placed in a container that was filled with 10-ml

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phosphate buffer solution (Recipe: NaCl 137mmol/L, KCl 2.7mmol/L, Na2HPO4

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10 mmol/L and KH2PO4 4.2mmol/L). At last, patients swished liquid (10 ml)

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throughout their mouths and expelled the fluid into the container. Buccal-cell

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suspensions were immediately centrifuged at 1,000 r/min in the laboratory,

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and the pellets were stored frozen at –80°C until used for DNA extraction.

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DNA methylation. Genomic DNA was extracted using the QIAmp DNA Mini Kit

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(Qiagen, Hilden, Germany) according to the manufacturer’s instructions.

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Purified DNA was quantified using a ND1000 spectrophotometer (Nanodrop,

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Wilmington, DE, USA) and about 300 ng DNA was bisulfite modified using the

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EZ Methylation Gold-Kit (Zymo Research, Orange, CA, USA). Final elution 7



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was performed with a 10 μl M-Elution Buffer.

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We examined three CpG loci located in NOS2A according to previous

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studies. To be specific, positions 1 and 2 were in a non-CpG island region of

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the promoter because the promoter was previously shown to be inversely

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related to iNOS.9,

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between exon 1 and exon 2.10 We performed DNA methylation analyses using

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a bisulfite-PCR and pyrosequencing assay. We evaluated the levels of DNA

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methylation at 2 loci (position 1 and 2) in the non-CpG island regions of

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NOS2A promoter, which have been negatively associated with PM2.5 exposure

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in previous studies.10, 14 The detailed method, location of the gene promoter,

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amplified regions, and CpG sites that were evaluated have been published

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previously.10 Methylation level of each CpG dinucleotide was expressed as

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methylated cytosines over the sum of methylated and unmethylated cytosines,

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i.e., the percentage of 5-methylcytosine (%5mC). Each sample was tested in

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triplicate and the average was used for statistical analysis. Ten controls were

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included in every pyrosequencing run. To ensure that pyrosequencing was

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sequencing the correct pattern, two wells were filled with oligonucleotide with

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known sequence. To verify bisulfite conversion efficiency, built-in controls were

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used in every assay. Moreover, a human unmethylated (0%) standard and fully

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methylated (100%) standard were used as sample controls.

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Environmental data. Real-time concentrations of PM2.5 and its constituents

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were measured by a fixed-site monitor located on the rooftop of a five-story

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Position 3 was located in a non-CpG island region

8


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building (about 15 m high above the ground) at Shanghai Academy of

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Environmental Sciences, which was about 4 km away from the community

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where the subjects resided. Both the sites were not in direct vicinity of major

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sources of air pollution including main roads. The mass concentration of PM2.5

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was measured by an online particulate monitor (FH 62 C14 series, Thermo

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Fisher Scientific Inc.) using beta attenuation techniques equipped with a

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verified PM2.5 cyclone. The carbonaceous concentrations [i.e., organic carbon

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(OC) and elemental carbon (EC)] in PM2.5 were measured by a

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semicontinuous OC/EC analyzer (model RT-4, Sunset Laboratory Inc.)

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equipped with a PM2.5 cyclone and an upstream parallel-plate organic denuder

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(Sunset Laboratory Inc.). The concentrations of 8 major water-soluble

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inorganic ions in PM2.5 (Cl−, NO3−, SO42−, NH4+, Na+, K+, Mg2+, Ca2+) were

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measured by a commercial instrument for online monitoring of aerosols and

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gases (MARGA, model ADI 2080, Applikon Analytical B.V.). The principle and

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operation of this instrument has been given in detail elsewhere15,

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obtained daily mean temperature and mean relative humidity from Shanghai

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Meteorological Bureau to allow for controlling for the confounding effects of

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weather conditions.

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Statistical analyses. Before statistical analyses, FeNO levels were natural

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log-transformed because they were not normally distributed, but such

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transformation was not required for the NOS2A promoter methylation because

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the data were normally distributed. Environmental and individual data were 9


16.

We


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merged by the time of physical examinations (rounded to the integer hour).

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We applied the linear mixed-effect model to evaluate the FeNO-PM2.5

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association and methylation-PM2.5 association.11 In the basic model, PM2.5 and

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its components were incorporated as the fixed-effect terms one at a time; a

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random intercept for each patient was added to account for correlations among

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multiple repeated measurements collected per person. We also included

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several covariates as fixed-effect terms: (1) an indicator variable of “week” of

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physical examinations to exclude any unknown weekly time trends in the levels

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of FeNO and DNA methylation; (2) an indicator variable of “day of the week” to

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exclude any variations of the response variables in one week; (3) the moving

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average of mean temperature and relative humidity on the present day and

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previous 3 days to control for the confounding effects of weather conditions 17;

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and (4) individual characteristics including age, gender, body mass index,

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education, duration of COPD and chronic comorbidities.

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In addition to the above basic single-constituent model, we also built a

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“constituent-PM2.5 adjustment model” after controlling for the confounding

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effects of total PM2.5 mass, as well as a “constituent-residual model” after

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accounting for the collinearity between a constituent and total PM2.5.18 For the

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third model, we firstly obtained the residual of each constituent by establishing

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a linear regression model between total PM2.5 and a constituent, and then

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introduced the residual into the basic model replacing the individual

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constituents. The constituent residual can be interpreted as a crude measure 10


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of the “independent” contribution of each constituent to the effects of PM2.5

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after excluding its collinearity of the remaining constituents.19

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In order to fully investigate the time-lag patterns for the effects of PM2.5, we

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examined the above models using multiple periods preceding the time of

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physical examinations, i.e., single lags of 0–6 hour (h), 7–12 h, 13–24 h, 0–24 h

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(0 d), 25–48 h (1 d), 2 d and 3–7 d.

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As an additional analysis, we included a multiplicative interaction term for

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PM2.5 or its constituent and methylation in single-pollutant model or each

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single-constituent models to examine whether the association between PM2.5

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(or its constituents) and FeNO can be modified by NOS2A methylation.3

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The statistical tests were two-sided, and values of P

Fine Particulate Matter Constituents, Nitric Oxide Synthase DNA

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