Temporal variability of global DNA methylation and hydroxymethylation in buccal cells of healthy adults: Association with air pollution

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Temporal variability of global DNA methylation and hydroxymethylation in buccal cells of healthy adults: Association with air pollution Siemon De Nysa, Radu-Corneliu Ducab, Tim Nawrotb,c, Peter Hoetb, Bart Van Meerbeeka, ⁎ Kirsten L. Van Landuyta, Lode Godderisb,d, a

KU Leuven (University of Leuven), Department of Oral Health Sciences, BIOMAT & University Hospitals Leuven (UZ Leuven), Dentistry, Leuven, Belgium Environment and Health, Department of Public Health and Primary Care, KU Leuven (University of Leuven), Kapucijnenvoer 35, 3000 Leuven, Belgium c Centre for Environmental Sciences, Hasselt University, Belgium d IDEWE, External Service for Prevention and Protection at Work, Heverlee, Belgium b

A R T I C L E I N F O

A B S T R A C T

Keywords: Global DNA methylation Global DNA hydroxymethylation Epidemiology Buccal mucosa Particulate matter

Background: Epigenetic changes, such as DNA methylation, are observed in response to environmental exposure and in the development of several chronic diseases. Consequently, DNA methylation alterations might serve as indicators of early effects. In this context, the aim of this study was to assess the temporal variability of global DNA methylation and hydroxymethylation levels in buccal cells from healthy adult volunteers. Methods: Global DNA methylation (%5mdC) and hydroxymethylation (%5hmdC) levels in human buccal cells, collected from 26 healthy adults at different time points, were quantified by UPLC-MS/MS. Associations between %5mdC and %5hmdC, respectively, and short-term exposure (1–7 days) to air pollutants PM2.5 and PM10 were tested with mixed-effects models including various covariates. Results/Discussion: Dynamic short-term changes in DNA methylation and hydroxymethylation levels in buccal cells were observed, which were inversely associated with exposure to PM2.5 and PM10. An IQR increase in PM2.5 over a 7-day moving average period was significantly associated with a decrease of − 1.47% (−1.74%, − 1.20%) and −0.043% (−0.054%, −0.032%) in %5mdC and %5hmdC, respectively. Likewise, for PM10, a decrease of − 1.42% (−1.70, −1.13) and −0.040% (−0.051%, −0.028%) was observed. Conclusion: Global DNA methylation and hydroxymethylatation varied over a time period of three weeks. The observed temporal variability was associated with exposure to ambient PM2.5 and PM10 levels. This should be taken into account when interpreting epigenetic alterations in buccal cells.

1. Introduction Since it is not always possible to obtain a sample of the target tissue of choice without invasive procedures that require trained personnel (Hansen et al., 2007; Langie et al., 2017), there is an increasing need to identify a suitable surrogate tissue. Large-scale human biomonitoring studies (HBM) often include hundreds of participants. Hence, the surrogate tissue needs to be easy to collect and the procedure should be more cost-efficient for repeated sampling. HBM studies routinely use peripheral blood and/or urine as target tissue, but also saliva has shown its value in exposure assessment (Angerer et al., 2007; Langie et al., 2017). Buccal cells are an easy accessible, reliable and non-invasive source of DNA. Furthermore, the collection of buccal cell samples is straightforward, inexpensive and does not bring discomfort to the patient

(Burger et al., 2005; Milne et al., 2006). It was already proposed that for non-blood based diseases or phenotypes, buccal cells are a more informative tissue for HBMs than blood cells since buccal cells are more likely to display dynamics that are more representative of other tissues than blood (Lowe et al., 2013; Teschendorff et al., 2015). Additionally, buccal cells are becoming increasingly used for genetic and forensic endpoints including DNA damage (Bolognesi et al., 2015; Holland et al., 2008; Jovanovich et al., 2015), as well as for epigenetic endpoints, in newsborns and very young children (Hagerty et al., 2016; Jiang et al., 2015; Novakovic et al., 2014; Pauwels et al., 2017; Torrone et al., 2012; Verma et al., 2014). It is nowadays of major interest to assess the potential of DNA methylation and hydroxymethylation as biomarkers (Leenen et al., 2016). Hence, studies have led to an increase in knowledge of the DNA methylome in pathophysiological conditions, highlighting the importance of DNA methylation with regard to

⁎ Corresponding author at: Environment and Health, Department of Public Health and Primary Care, KU Leuven (University of Leuven), Kapucijnenvoer 35, 3000 Leuven, Belgium & IDEWE, External service for prevention and protection at work, Heverlee, Belgium. E-mail address: [email protected] (L. Godderis).

https://doi.org/10.1016/j.envint.2017.11.002 Received 20 July 2017; Received in revised form 11 October 2017; Accepted 2 November 2017 0160-4120/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: De Nys, S., Environment International (2017), https://doi.org/10.1016/j.envint.2017.11.002

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Samples were stored at −80 °C before DNA extraction. The turnover time of the surface layer of the oral mucosal epithelium is about 2.7 h and the whole epithelium is replaced after approximately 4.5 days (Dawes, 2003). Thus, it is highly unlikely that the weekly collection of buccal swabs as such can induce an effect on DNA methylation, neither directly by mechanical forces applied onto the cheek, nor indirectly by disrupting the oral epithelium, since buccal swabs contain mainly exfoliated cells.

underlying mechanisms of diseases (Lovinsky-Desir and Miller, 2012; Sanchez-Mut et al., 2016; Witte et al., 2014). More recently, a great breakthrough was achieved by the discovery of a novel epigenetic mark, namely 5-hydroxymethylcytosine (5hmC), formed by the enzymatic oxidation of 5-methylcytosine (5mC) (Tahiliani et al., 2009). It was first thought that 5hmC was only an intermediate in DNA demethylation pathway, but there is an increase in evidence that also supports its role as a regulator of gene expression (Bachman et al., 2015). Even if these findings are crucial for the suitability of using DNA methylation and hydroxymethylation as biomarkers, little is known about the temporal behavior of the DNA methylome under normal physiological conditions. Almost every biomonitoring study assessed DNA methylation in samples collected at a single-time point, and thus accounting for the inter-individual variability (Giuliani et al., 2016; Talens et al., 2012). In contrast, no information about temporal trends or the intra-individual variability were provided (Yamada and Chong, 2016). Furthermore, DNA methylation changes were associated with changes in gene expression induced by environmental exposures such as air pollution (Leenen et al., 2016). In this sense, it has already been shown that particulate air pollution is associated with oxidative stress (Grevendonk et al., 2016; Li et al., 2016) that may further affect both global and gene-specific DNA methylation in blood and placental tissue (Baccarelli et al., 2009; Janssen et al., 2015; Madrigano et al., 2011). Nevertheless, associations between environmental exposures and global DNA (hydroxy)methylation levels in surrogate tissues such as buccal cells have never been investigated. In this context, the objectives of this study were to evaluate whether global DNA (hydroxy)methylation levels in buccal cells of healthy adult individuals are dynamic on short-term and whether these levels are influenced by factors such as life-style and pollution.

2.3. DNA extraction DNA was extracted using the Gentra Puregene® Buccal Cell Kit (QIAGEN), according to the manufacturer's protocol with minor adjustments. An extra protein precipitation and DNA washing step was performed to improve DNA purity. Each sample was extracted separately. DNA concentration and A260/A280 ratio were determined using the GeneQuant™ 100 (GE Healthcare, Diegem, Belgium). A minimum of 0.75 μg DNA was needed in order to perform ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis of global DNA methylation and hydroxymethylation levels. Sufficient DNA for all samples of all time points was extracted. The average A260/ A280 ratio was 1.53 ± 0.25. Samples were stored at − 80 °C before analysis. 2.4. DNA methylation and hydroxymethylation analysis Samples were prepared according to the method described by Godderis et al. (Godderis et al., 2014) with minor changes and adaptations. Briefly, isolated DNA (1 μg) was enzymatically hydrolyzed to individual deoxyribonucleosides by a simple one-step DNA hydrolysis procedure. A digest mix was prepared by adding phosphodiesterase I, alkaline phosphatase and benzonase® Nuclease to Tris-HCl buffer. Extracted DNA was spiked with a mixture containing the internal standards (IS), dried and then hydrolyzed at 37 °C for at least 8 h in presence of 10 μL digest mix. After hydrolysis, 490 μL acetonitrile (ACN) was added to each sample, to a total volume of 500 μL. Daylight was avoided at maximum over the entire sample preparation procedure, in order to minimize potential deamination of the target compounds. An ultra-performance UPLC-MS/MS method was used for the identification and quantification of 2′-deoxycytidine (2dC), 5-methyl-2′-deoxycytidine (5mdC) and 5-hydroxymethyl-2′-deoxycytidine (5hmdC) as described in Cardenas et al. (2017). A 20 μL aliquot was injected on a hydrophilic interaction liquid chromatography (HILIC) column (Phenomenex® Kinetex 2.6 μm Hilic, 50 mm × 4.6 mm), held at a temperature of 60 °C. The mobile phase used for the chromatographic separation was a mixture of 20 mM Ammonium Format Buffer pH 3 (A) and ACN (B) using the following gradient: the program starts at 7%A, was increased linearly to 20%A for 2.2 min, then was hold from 2.2 to 2.4 min at 20%A, brought back to the initial status from 2.4 to 2.6 min and allowed to equilibrate for one minute prior to the next injection. A flow rate of 0.4 mL/min was applied. The analyses were performed using electrospray ionization (ESI) in positive mode and the compounds were determined using multiple reactions monitoring (MRM), with argon as the collision gas. Stock solutions of 2dC, 5mdC, 5hmdC and [15N3]-2dC were prepared by dissolution of commercial solid reference standards in water. The stock solutions were used to prepare the calibration standards. To compensate for the matrix effects, the validation was conducted using an artificial matrix simulating a mammalian DNA hydrolysate comprising three 2′-deoxyrubonuleosides (2′-deoxiguanisine, 2′-deoxyadenosine, and thymidine). The correlation coefficients, R2, of the regression equations exceeded the value of 0.98, demonstrating a good correlation between the measured response (peak area) and the concentration of the target compounds. The limits of quantification (5mdC: 0.482 ng/mL, 5hmdC: 0.023 ng/mL, and 2dC: 1.856 ng/mL) were determined based on the lowest calibration levels analyzed in five

2. Materials and methods 2.1. Study design A group of 26 Caucasian persons were enrolled in this study. Study participants were recruited among students (n = 23) and researchers (n = 3) from the University of Leuven, and were not occupationally exposed to air pollutants. Most volunteers resided in Leuven. The volunteers were not allowed to eat or brush their teeth 2 h before sample collection in order to minimize the loss of cells on the buccal mucosa. All participants received information about the purpose and objectives of the study, gave written informed consent and filled in a short ‘lifestyle’ questionnaire (Godderis et al., 2012). Several factors that are known for their potential influence on the epigenome, including sex, alcohol consumption and smoking behavior were assessed using a standardized questionnaire (Christensen and Marsit, 2011; Hagerty et al., 2016). Also aging is known to alter DNA methylation (Bollati et al., 2009; Jones et al., 2015). Therefore, only volunteers aged between 18 and 27 years were included. The study was approved by the Commission for Medical Ethics of University Hospitals Leuven (reference number: S57170) and registered at ClinicalTrials.gov (ID: NCT02297009). 2.2. Sample collection Buccal cells were collected using a cytobrush, provided in the DNA extraction kit (QIAGEN, Venlo, The Netherlands). Three samples were taken at four different time points separated by one week starting from 9 February 2015 until 9 March 2015. All samplings were scheduled between 8:00 h and 12:00 h. The cytobrush was twirled and rubbed for 30 s against the buccal mucosa from the right inner cheek, when at the same time counter pressure was applied on the outer cheek. The brush was separated from the stick with sterile scissors in a 1.5 mL Eppendorf. 2

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replicates. Furthermore, quality controls, consisting of three different concentration levels for each compound, were analyzed in five replicates to account for intra- and inter-variability within and between different batches. Following performance criteria were set for proper validation: accuracy within the interval 85–115% of the target level and repeatability with a relative standard deviation lower than 15%. More extensive details about method validation have been published elsewhere (Supplemental materials, Cardenas et al., 2017). The absolute concentrations for 5mdC, 2dC and 5hmdC, expressed in units of nanograms per milliliter, were derived by interpolating from the established concentration curves for 5mdC, 2dC and 5hmdC, respectively. Global DNA methylation (%5mdC) is expressed as a percentage of the ratio 5mdC versus the sum of 5mdC, 2dC and 5hmdC (%5mdC = 5mdC/(5mdC + 2dC + 5hmdC)). Global DNA hydroxymethylation (%5hmdC) is expressed as a percentage of the ratio 5hmdC versus the sum of 5mdC, 2dC and 5hmdC (%5hmdC = 5hmdC/ (5mdC + 2dC + 5hmdC)).

Table 1 Descriptive statistics of global DNA methylation and hydroxymethylation levels

Percentiles

DNA methylation (%) 25th 50th 75th

DNA hydroxymethylation (%) 25th 50th 75th

Day Day Day Day

4.66 3.11 3.59 5.68

0.141 0.085 0.096 0.155

0 7 14 21

5.23 3.64 4.69 6.11

5.72 4.42 5.44 7.13

0.171 0.106 0.125 0.181

0.208 0.141 0.166 0.230

%5hmdC levels per interquartile range (IQR) increase in pollutant concentration with the indication of the 95% confidence interval (CI). R 3.3.0 software (R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analyses. Level of significance was set at α < 0.05. 3. Results

2.5. Exposure assessment

3.1. Study population

In Belgium, several ambient air pollutants including PM2.5 and PM10 are continuously measured by a network of automatic monitoring sites (http://www.irceline.be). Since the study participants studied and/or worked in Leuven, Belgium, measurements were taken from an official background monitoring station located in Aarschot, located approximately 15 km bird's eye view from the study region. Averaged daily concentrations from 0:00 h till 24:00 h were calculated and expressed in μg/m3. Buccal cells are extremely suited for short-term exposure since they are directly exposed and have a relative fast turnover time. Therefore, we only assessed associations on short-term. For all pollutants, 1-, 2-, 3-, 5-, and 7-day moving average exposures were calculated based on the daily means. The moving average is the mean exposure during the respective time window before sampling.

A total of 26 healthy volunteers (13 males and 13 females) enrolled in this study. Median age was 23 years (range 19–24 years) for males and 22 years (range 18–27 years) for females. No smokers were included in this study. 3.2. DNA methylation and hydroxymethylation variability The median %5mdC and %5hmdC levels varied between 3.64% and 6.11%, and 0.106% and 0.181%, respectively (Table 1, Fig. 1). Furthermore, upon statistical analysis, the first mixed-effects model showed that there were statistically significant main effects of time (Χ2(1) = 26.23, p < 0.0001), and significant random effects (Χ2(1) = 5.86 p = 0.0155) on global DNA methylation levels. The same effects were found to be statistically significant (time: Χ2(1) = 5.56, p = 0.0184; random effects: Χ2(1) = 11.26, p = 0.0008) for DNA hydroxymethylation levels. No significant effects were observed for other included covariates. Total variance was decomposed in two components: intra-individual variance, which is due to changes in DNA methylation between the successive time points and inter-individual variance in DNA methylation, which represent differences in DNA methylation between the study participants. The temporal intra-individual variability was expressed by the ICC, which was 0.082 and 0.104 for global DNA methylation and DNA hydroxymethylation, indicating that the within-subject variation was approximately 16 and 9 times higher than the between-subject variation.

2.6. Statistical analysis First, regression analyses were performed to assess the short-term variability of global DNA methylation and hydroxymethylation levels in human buccal cells. Therefore, a linear mixed-effects model was fitted by maximum likelihood for DNA methylation and hydroxymethylation, respectively, with one random intercept to capture the correlation among measurements within the same subject. The following a priori chosen confounders, based on literature, were included in the model regardless of their statistical significance to correct for possible confounding: a linear term for time, sex, age and weekly alcohol consumption (expressed in g/week). The following model was fitted:

Yij = β0 + μ0 + β1 X1 + β2 X2 + β3 X3 + β4 X 4 + εij,

3.3. Exposure data

where Yij is the measured value of DNA methylation or hydroxymethylation of subject j at time point i; β0 is the overall intercept, μ0 is the random intercept, and X1–X4 are the confounding factors. Next, regression analyses were performed to assess the influence of air pollutants on DNA methylation and hydroxymethylation, respectively. Therefore, separate unadjusted linear mixed-effects models were created for each pollutant and for each moving average time window. The following model was fitted:

An overview of the pollutants measured during the study period is given in Fig. 2. Statistically significant differences were found between the different samping periods (p < 0.0001) for all moving averages, with an increase in the second sampling period followed by a gradual decrease in pollutant levels in the end (Table S1). In general, exposure levels varied between 5.0 and 52.0 μg/m3 and between 6.0 and 59.0 μg/m3 for PM2.5 and PM10, respectively. Various significant effects of air pollutants on DNA (hydroxy)methylation were detected across all the different moving averages (Table 2). Overall, exposures to PM2.5 and PM10 were associated with a decrease in both %5mdC and %5hmdC levels during all investigated time windows. The effect was the strongest for 7-day moving averages, where an IQR increase of PM2.5 (14.1 μg/m3) and PM10 (15.9 μg/m3) were associated with a decrease of −1.47% (95% CI: −1.74%, − 1.20%) and − 1.42% (95% CI: −1.70%, − 1.13%) DNA methylation, respectively (Figs. 3A and 4A). Likewise, for DNA hydroxymethylation, the effect was the strongest for the 7-day moving

Yij = β0 + μ0 + β1 pollutant + β2 age + β3 sex + εij, where Yij is again the measured value of DNA methylation or hydroxymethylation of subject j at time point i; β0 is the overall intercept, μ0 is the random intercept and β1, β2, and β3 the estimated effect of the respective air pollutant, age and sex respectively. Furthermore, a sensitivity analysis was performed to adjust for the average temperature during the respective moving averages and weekly alcohol consumption and a linear term for time, sex and age as additional covariates. Results were expressed as estimated percent change (β1) in %5mdC and 3

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Fig. 1. Bean plots of the distribution of DNA methylation (%5mdC) levels (A) and DNA hydroxymethylation (%5hmdC) levels (B). (red lines represent the mean levels at sample collection and dotted line represents overall mean) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

individual variability of genomic DNA methylation and hydroxymethylation levels in buccal cells of healthy adult individuals. Furthermore, associations between short-term exposure to air pollutants and these respective levels across multiple moving averages were observed. An increase in environmental pollution was associated with decreased DNA methylation and hydroxymethylation levels. This study reveals that DNA methylation is a dynamic epigenetic mechanism and that both methylation and hydroxymethylation levels can change weekly in healthy adults depending on environmental exposure. Longterm dynamic changes in DNA methylation and hydroxymethylation have already been observed under normal physiological conditions, like aging (Pal and Tyler, 2016), as a result of environmental adaptations allowing transient changes and long-term alterations of the blood cell's transcriptome (Leenen et al., 2016). However, this is, to the best of our knowledge, the first time that associations of short-term exposure to air pollutants with both global DNA methylation and hydroxymethylation in buccal cells is characterized in a longitudinal study design. These results confirm the findings of other studies, in which similar associations between particulate air pollution and global, but as well gene-specific, DNA methylation were found, and this for both shortterm and long-term exposure. Madrigano et al. found a decrease in methylation of repeated elements LINE-1 and Alu, which are both used as a surrogate for global methylation, after prolonged exposure over 28–60 days and 45–90 days, respectively, to black carbon (BC) and over

average, where an IQR increase was associated with a decrease of − 0.043% (95% CI: −0.054%, −0.032%) and − 0.040% (95% CI: -0.051%, − 0.028%), for PM2.5 and PM10 respectively (Figs. 3B and 4B). In a sensitivity analysis we additionally adjusted for ambient temperature averaged over the respective moving averages, weekly alcohol consumption and a linear term for time. This additional adjustement had an influence on the previous reported estimates. For DNA methylation, the effect of PM2.5 decreased for 1-, 2- and 7-day moving averages and increased for 3- and 5-day, while the effect of PM10 increased for the 5- and 7-day moving averages. In contrast, for DNA hydroxymethylation, the effect of both PM2.5 and PM10 only decreased for 1-day moving average. Furthermore, the strongest effects for PM2.5 were observed for the 5-day moving average (−1.74% (95% CI: − 2.17%, − 1.31%) and − 0.064% (95% CI: −0.081%, −0.047%) for DNA methylation and hydroxymethylation respectively). Likewise, the strongest effects for PM10 were also observed for the 5-day moving average (−1.81% (95% CI: −2.35%, −1.27%) and − 0.067% (95% CI: − 0.089%, − 0.046%) for DNA methylation and hydroxymethylation respectively). 4. Discussion This study shows the existence of temporal intra- and inter-

Fig. 2. Levels of PM exposure during the entire study period. (black line represents PM2.5 and dotted line represents PM10 levels)

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Table 2 Effects of moving average PM2.5 and PM10 exposures on global DNA methylation and hydroxymethylation levels. Model 1 was adjusted for particulate matter, age and sex. Model 2 was additionally adjusted for average ambient temperature during the respective moving average, weekly alcohol consumption and a linear term for time, age and sex.a μg/m3,b Estimated change in DNA methylation (%) and hydroxymethylation (%) per IQR increase in pollutant concentration. (* p < 0.05, ** p < 0.01, *** p < 0.001) IQRa

PM2.5

Model 1

Model 2

PM10

Model 1

Model 2

1-d 2-d 3-d 5-d 7-d 1-d 2-d 3-d 5-d 7-d 1-d 2-d 3-d 5-d 7-d 1-d 2-d 3-d 5-d 7-d

Estimateb (95% CI)

20.0 13.0 17.0 15.6 14.1 20.0 13.0 17.0 15.6 14.1 20.5 14.0 17.0 18.2 15.9 20.5 14.0 17.0 18.2 15.9

DNA methylation

DNA hydroxymethylation

− 0.85 − 0.84 − 1.14 − 1.29 − 1.47 − 0.54 − 0.76 − 1.21 − 1.74 − 1.45 − 0.73 − 0.72 − 0.90 − 1.28 − 1.42 − 0.38 − 0.60 − 0.86 − 1.81 − 1.56

− 0.024 − 0.024 − 0.032 − 0.037 − 0.043 − 0.022 − 0.027 − 0.043 − 0.064 − 0.049 − 0.019 − 0.020 − 0.024 − 0.035 − 0.040 − 0.017 − 0.022 − 0.032 − 0.067 − 0.052

(− 1.11, (− 1.04, (− 1.43, (− 1.57, (− 1.74, (− 0.86, (− 1.01, (− 1.60, (− 2.17, (− 1.77, (− 0.97, (− 0.93, (− 1.18, (− 1.60, (− 1.70, (− 0.70, (− 0.86, (− 1.25, (− 2.35, (− 1.96,

− 0.60)*** − 0.64)*** − 0.86)*** − 1.01)*** − 1.20)*** − 0.22)*** − 0.52)*** − 0.82)*** − 1.31)*** − 1.12)*** − 0.48)*** − 0.52)*** − 0.63)*** − 0.97)*** − 1.13)*** − 0.06)* − 0.34)*** − 0.46)*** − 1.27)*** − 1.16)***

(− 0.034, (− 0.032, (− 0.043, (− 0.048, (− 0.054, (− 0.035, (− 0.037, (− 0.059, (− 0.081, (− 0.062, (− 0.029, (− 0.028, (− 0.035, (− 0.048, (− 0.051, (− 0.030, (− 0.032, (− 0.048, (− 0.089, (− 0.069,

−0.013)*** −0.016)*** −0.020)*** −0.025)*** −0.032)*** −0.009)** −0.017)*** −0.028)*** −0.047)*** −0.036)*** −0.010)** −0.012)*** −0.013)*** −0.022)*** −0.028)*** −0.004)* −0.011)*** −0.017)*** −0.046)*** −0.036)***

To date, there is still no evidence that epigenetic changes are the direct cause of disease, but they may play important roles in the disease pathogenesis. A more in-depth approach is necessary to elucidate whether the observed changes are only an adaptive response to the changing environmental conditions, or whether these changes are involved in disease development. A recent study in Sweden showed that long-term exposure to PM was associated with a higher incidence of cardiovascular diseases (Stockfelt et al., 2017). This suggests that the small rapid evolving methylation changes observed in this study may seem clinically irrelevant on short-term, but can have an important influence on a longer term. Therefore, we suggest that assessing genespecific DNA methylation and gene-expression patterns in buccal cells can provide more insight about potential clinical consequences of the continuous exposure to air pollutants. It has already been shown that the gene-expression profile of the buccal mucosa in non-smoking women exposed to household air pollutants is partly similar to the molecular response to tobacco smoke, thereby lending biologic plausibility to prior epidemiological studies that have linked this exposure to lung cancer risk (Wang et al., 2015). In contrast to blood samples, buccal cells may be a more suited target tissue, as they are directly exposed to pollutants and easier to

90 days to sulfates (SO4), two components of PM2.5. Nevertheless, no significant assaciations were found for PM2.5 (Madrigano et al., 2011). The same study cohort was used in a study from Baccarelli et al., 2009. Stronger effects of PM2.5 exposure on LINE-1, but not on Alu, methylation were observed for shorter moving averages (2–7 days, strongest for 7-day moving average). In contrast to these studies, global DNA methylation and hydroxymethylation were directly assessed in this study using a well-validated UPLC-MS/MS method that can accurately reflect differences in global (hydroxy)methylation levels. This method provides a true genome-wide coverage, in contrast to surrogate markers for DNA methylation which cover only very distinct genomic positions (Cardenas et al., 2017). In this sense, similar trends were observed in a study of Janssen et al. (2013) that assessed the association between exposure to air pollutants during whole pregnancy and placental genomic DNA methylation. A 5 μg/m3 increase in PM2.5 was associated with a decrease in methylation of − 1.08% (− 1.80; − 0.36) in early pregnancy during the implantation period (Janssen et al., 2013). In this study, a 5 μg/m3 increase in PM2.5 was associated with a decrease of − 0.52% (95% CI: -0.62%, − 0.43%) and − 0.015% (95% CI: -0.019%, − 0.011%) in DNA methylation and hydroxymethylation levels, respectively.

Fig. 3. Correlation between DNA methylation (A) and hydroxymethylation (B) levels and 7-day PM2.5 exposure, respectively. (○: day 0, : day 7, : day 14, : day 21, solid line represents fitted regression line)

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Fig. 4. Correlation between DNA methylation (A) and hydroxymethylation (B) levels and 7-day PM10 exposure, respectively. (○: day 0, : day 7, : day 14, : day 21, solid line represents fitted regression line)

downstream PCR amplification as shown by Mahfuz et al. who reported the presence of Streptococcus parasanguinis in human buccal DNA samples (Mahfuz et al., 2013). However, it is unclear whether the presence of bacteria influences the DNA methylation and hydroxymethylation marks as such, or that it interferes with the LC-MS/MS analysis. Furthermore, DNA methylation and hydroxymethylation are also influenced by nutrition, which provides nutrients that play a role as essential co-factors (Pauwels et al., 2016a, 2016b). Other limitations of our study need to be further addressed. Firstly, the limited number of volunteers consisted mainly of students and form thus a more homogenous population, which makes it difficult to extrapolate these findings to a broader population. Secondly, our exposure contrast is not based on personal exposure sampling. However, the temporal variation is much larger than the spatial contrasts within the study area of our participants. Therefore, exposure levels from one central monitoring station were assigned to all study participants. This approach was supported by the proximity of the monitoring station to the study region, and by the fact that these values correlated strongly with the average exposure values in Flanders for the respective duration of the study, supporting a homogenous spatial distribution. No further assessment was made about the time spent indoor and outdoor. Indoor exposure to air pollutants with outdoor origin is depending on the home characteristics (Meier et al., 2015). Thirdly, additional lifestyle factors such as exposure to second-hand smoke, physical activity or nutrition were also not taken into account. No information about exposure to second-hand smoke was obtained via the questionnaire although this was not expected since only non-smokers were included. Moreover, Belgian legislation prohibits smoking in public places that are frequently visited among students such as bars, restricting the possibility that the volunteers were exposed. Physical exercise, both acute and chronic, can significantly impact DNA methylation in a highly tissueand gene-specific manner (Voisin et al., 2015). It was shown that a physically active livestyle was associated with increased global DNA methylation levels in blood (White et al., 2013). Up till now, it is unknown if exercise influence DNA methylation levels in buccal cells. Also nutrition has been linked with changes in gene-specific DNA methylation (RXRA and LEP) in cord blood (Pauwels et al., 2016b). However, differences in target tissue make it difficult to make direct comparisons between blood and buccal cells. Furthermore, it was shown that there is little or no variability in diet on long term (Pauwels et al., 2016a). Hence, this was also not expected on the short-term of this study.

sample. Compared to other often used tissues in epidemiological studies, buccal cells are a more homogenous cell population, which decreases the variability, and therefore may be a better alternative than blood samples for studies with a repeated measures design, especially in young children (Bessonneau et al., 2017; Burger et al., 2005). This approach may increase participation and improve the overall feasibility (Langie et al., 2017). Moreover, one study reported that for non-blood based diseases, buccal cells are a more informative tissue compared to blood samples (Lowe et al., 2013). Therefore, we propose buccal cells as a promising surrogate tissue for epidemiological studies. The effects observed in the current study are identical for both DNA methylation and hydroxymethylation, which is supported by the observed significant positive association between %5mdC and %5hmdC levels (r = 0.772, p < 0.0001). In a previous study, we showed a similar positive association (r = 0.72, p = 0.004) in DNA obtained from saliva (Godderis et al., 2014). The same positive trends were found in human blood DNA from two time points (r = 0.32, p = 0.03; r = 0.54, p < 0.001) by Tellez-Plaza et al. (Tellez-Plaza et al., 2014). More research is needed to determine which of the two parameters may serve as the best biomarker for epigenetic effects of environmental exposure or diseases. In this study, important rapidly changing cofounding parameters that can influence DNA methylation, including air pollution, ambient temperature and weekly alcohol consumption, were taken into account. Since it is known that global DNA methylation levels decrease with aging (Fuke et al., 2004), we recruited participants in a young adult group (22.3 ± 1.9 years). Nevertheless, age was included as a confounder despite this narrow range and the short-term exposure period. Furthermore, the particular life style of students might have an influence as well. The average student does not have a standard 24-h regime of activities during daytime alternated with a normal sleep pattern. In addition, students are known to consume high amounts of alcohol on regular basis. A recent study showed that approximately 50% of Flemish students in higher education consumed alcohol (beer) several times a week during the academic year. For male students, this percentage was almost 75%. Moreover, consumption of alcoholic beverages was associated with increased smoking habits (Rosiers et al., 2014). All these factors should be taken into account when extrapolating the findings of this study to a broader population. DNA methylation is not unique for humans. Bacterial DNA methylation occurs on both adenine and cytosine residues (Casadesús, 2016). The oral hygiene, and thus also the bacterial composition in the oral cavity of the volunteers could play a role. However, participants in our study were not allowed to eat or drink 2 h prior sample collection in order to prevent bacterial contamination of samples. Furthermore, samples were stored at −80 °C, which inhibited bacterial growth. Food residues can contaminate samples which can interfere with further

5. Conclusion In this study, the dynamic change of global DNA methylation and DNA hydroxymethylation levels in human buccal cells over a time period of three weeks was shown. This temporal variability was 6

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associated with short-term exposure to particulate air pollution. In addition, the obtained reference levels are particularly interesting for future studies in which these levels can serve to investigate the potential influence of a particular intervention or exposure, and in which air pollution should be taken into account as a covariate. As a final conclusion, the potential use of buccal cells as a target tissue in epidemiological studies is promising and should be considered for future research, especially when conducted in children.

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