Influence of aging in the modulation of epigenetic biomarkers of carcinogenesis after exposure to air pollution

Experimental Gerontology 110 (2018) 125–132

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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero

Influence of aging in the modulation of epigenetic biomarkers of carcinogenesis after exposure to air pollution

T

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Bertrand Fougèrea,b, , Yann Landkoczb, Capucine Lepersb, Perrine J. Martinb, Lucie Armandb, Nicolas Grossinc, Anthony Verdinb, Eric Boulangerc,e, Pierre Gossetb,d, François Sichelf, Pirouz Shiralib, Sylvain Billetb a

GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Gerontology Clinic, Faculté Libre de Médecine, Lille 59000, France Unité de Chimie Environnementale et Interactions sur le Vivant (EA4492), Université du Littoral Côte d'Opale, Dunkerque 59140, France c University of Lille 2, Biology of Aging (EA2693), Lille 59000, France d GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Department of Anatomy and Pathology, Faculté Libre de Médecine, Lille 59000, France e Gerontology Clinic, Les Bateliers Geriatric Hospital, Lille University Hospital, Lille 59000, France f Normandie Univ, UNICAEN, ABTE EA4651, Centre de Lutte Contre le Cancer François Baclesse, Caen 14000, France b

A R T I C LE I N FO

A B S T R A C T

Section Editor: Werner Zwerschke

Background: Classified as carcinogenic to humans by the IARC in 2013, fine air particulate matter (PM2.5) can be inhaled and retained into the lung or reach the systemic circulation. This can cause or exacerbate numerous pathologies to which the elderly are often more sensitive. Methods: In order to estimate the influence of age on the development of early cellular epigenetic alterations involved in carcinogenesis, peripheral blood mononuclear cells sampled from 90 patients from three age classes (25–30, 50–55 and 75–80 years old) were ex vivo exposed to urban PM2.5. Results: Particles exposure led to variations in telomerase activity and telomeres length in all age groups without any influence of age. Conversely, P16INK4A gene expression increased significantly with age after exposure to PM2.5. Age could enhance MGMT gene expression after exposure to particles, by decreasing the level of promoter methylation in the oldest people. Conclusion: Hence, our results demonstrated several tendencies in cells modification depending on age, even if all epigenetic assays were carried out after a limited exposure time allowing only one or two cell cycles. Since lung cancer symptoms appear only at an advanced stage, our results underline the needs for further investigation on the studied biomarkers for early diagnosis of carcinogenesis to improve survival.

Keywords: PM2.5 PBMC Biomarkers Epigenotoxicity Aging

1. Background Cancer is the leading cause of death between the ages of 60 and 79. More than 50% of all cancers and > 70% of cancer-related deaths occur after 65 years (Howlader et al., 2013). This public health problem is growing due to population aging and increased life expectancy. Lung cancer represents the first cause of death worldwide. Its incidence is steadily increasing among women (3 fold in 20 years), in connection with the rise in smoking, which is the first risk factor. The prognosis remains gloomy with only 14% of 5-years survival, presumably as the first symptoms, thus diagnosis, appear only at an advanced stage of the

disease. Therefore, the objective is to propose new markers allowing an earlier diagnosis to improve survival. Numerous degenerative pathologies such as cancer are influenced or directly caused by genetic and epigenetic modifications that are also found in the biology of aging. The link between aging and cancer is complex, because senescence may, in some cases, protect cells from malignant transformation by triggering apoptosis and, in other cases, increase the carcinogenic risk with the appearance of genetic mutations (Hughes et al., 2002). The cellular senescence is characterized by a permanent cell cycle arrest after a number of divisions, and the appearance of a senescent phenotype involving important cellular

Abbreviations: AGT, O6-AlkylGuanine DNA alkylTransferase; DLPCB, Dioxin Like PolyChloroBiphenyl; EDTA, EthyleneDiamineTetraAcetic acid; FBS, Fetal Bovine Serum; GC/MS, Gas Chromatography/Mass Spectrometry; HRGC/HRMS, High Resolution Gas Chromatography/High Resolution Mass Spectrometry; IARC, International Agency for Research on Cancer; ICP/ MS, Inductive Coupled Plasma/Mass Spectrometry; LDH, lactate deshydrogenase; MGMT, MethylGuanine-DNA MethylTransferase; P16INK4A, P16 Inhibitor Kinase 4A; PBS, Phosphate Buffer Saline; SEM, Scanning Electron Microscopy; PAHs, polycyclic aromatic hydrocarbons; PM, particulate matter; PCDD/F, PolyChloroDibenzo Dioxins/Furans; PCR, Polymerase Chain Reaction; ROS, Reactive Oxygen Species; RPMI, Roswell Park Memorial Institute medium; RT, Reverse Transcription; SD, Standard Deviation; TSG, tumor suppressor genes ⁎ Corresponding author at: Saint Louis University School of Medicine, Division of Geriatric Medicine, 1402 South Grand Boulevard, Room M238, St Louis, MO 63104, USA. E-mail address: [email protected] (B. Fougère). https://doi.org/10.1016/j.exger.2018.05.018 Received 21 February 2018; Received in revised form 11 May 2018; Accepted 25 May 2018 Available online 31 May 2018 0531-5565/ © 2018 Elsevier Inc. All rights reserved.

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response thanks to the recruitment of people from contrasted ages.

reorganizations and gene expression changes. This replicative senescence comes along with the shortening of telomeres which can be counterbalanced by telomerase activation. Cancer cells exhibit high telomerase activity associated with telomeres presenting frequent cytogenetic abnormalities (Gisselsson et al., 2001). Telomerase activation by the onset of mutations can induce immortal cancerous clone, leading to cellular immortalization. Conversely, a lowered activity leads to a premature telomere shortening, up to the entrance into senescence phase and cell proliferation arrest. The effect of pollutants on the length of telomeres is relatively little documented, and the results of studies are sometimes contradictory. Some epigenetic modifications described in aging are features of cancer (i.e. decrease in global DNA methylation; increase in promoterspecific CpG islands methylation) (Fraga et al., 2007). Methylation profile of many crucial genes promoters is altered during tumorigenesis, leading on one hand to some oncogenes activation through promoter hypomethylation. On the other hand, promoter hypermethylation leads to extinction of tumor suppressor genes (TSG) (e.g. P16INK4A) and DNA repair genes (e.g. O6-MethylGuanine-DNA MethylTransferase, MGMT). P16INK4A protein inhibits cyclinD-dependant phosphorylation of Rb, thus preventing G1-S transition. P16INK4A is often silenced during carcinogenesis by promoter hypermethylation in CpG islands, resulting in the loss of P16INK4A activity and the consecutive deregulation of cell proliferation. The O6-AlkylGuanine DNA alkylTransferase (AGT) protein, encoded by MGMT gene, is a DNA repair enzyme which fixes O6methylguanine, a major mutagenic and cytotoxic DNA lesion produced by various endogenous and exogenous methylating agents (Shiraishi et al., 2000). MGMT gene promoter is hypermethylated in approximately 20% of lung cancers (Zöchbauer-Müller et al., 2001). Furthermore, the AGT activity decreases with age (Aoki et al., 1993). Finally, exposure to some environmental toxins (e.g. As, Cr, Ni, radon, smoky coal emissions, urban particulate matter (PM), and metal-rich PM2.5) may lead to the inactivation of these genes by promoter hypermethylation (Soberanes et al., 2012). Air pollution and particulate matter itself were recently recognized as carcinogenic to humans by the IARC (group 1) (Loomis et al., 2013). The elderly people experience a complex relationship to the environment since they are more sensitive to changes in the environment, such as climate change with temperature increase, and exposure to toxins and infectious agents. This greater sensitivity could result from a lower physiological reserve capacity and a more slowly responding immune system. Their higher disease burden than people at younger ages makes specific organ systems less able to tolerate stress (Carnes et al., 2014). The elderly adults then suffer adverse health effects, with increased numbers of hospitalizations for cardiorespiratory diseases at lower concentrations of pollutants (Bentayeb et al., 2012). Although health effects of air pollution affect all age groups, there have been recent calls for a focus on air pollution research in the elderly population as they are the principal group at risk and are particularly vulnerable to air pollutants (Argacha et al., 2016; Fougère et al., 2015; Sandström et al., 2003; Wen and Gu, 2012). Among air pollutants, PM2.5 refers to particles with an aerodynamic diameter lower than 2.5 μm, thus able to penetrate deeply into the lung and to interact with the alveolo-capillary barrier. PM2.5 or its soluble constituents may also join the systemic circulation, and affect other target organs and then lead to pro-inflammatory effects, oxidative stress, and genetic alterations (Nemmar et al., 2002). The aim of this study was to estimate the influence of age on the appearance of early cellular epigenetic alterations potentially involved in carcinogenesis following cell exposure to urban air pollution PM (Fig. 1). PM2.5 was collected in a French seaside city characterized by important industrial activities and heavy motor vehicle traffic. Peripheral blood mononuclear cells (PBMC) were sampled from three age classes before ex vivo exposure to PM2.5. Telomerase activity and gene expression modulation of P16INK4A and MGMT were then analyzed, in order to: (i) determine the impact of air pollution on early events of carcinogenesis; (ii) investigate the influence of age on the cell biological

2. Methods 2.1. PM sampling, physical and chemical characteristics PM was collected in Dunkerque (51°04′N; 2°38′E), an industrialized French seaside City located on the southern coast of the North Sea, using high volume cascade impactor. PM size distribution, evaluated by Scanning Electron Microscopy (SEM), showed size ranging from 0.33 μm to 5 μm with 95% of PM2.5. ICP/MS was used to quantify metals. GC/MS allowed to identify PAHs coated onto PM. Dioxins/ Furans (PCDD/F) and Polychlorobiphenyls (DLPCB) were analyzed by HRGC/HRMS as previously described (Billet et al., 2008) (Table 1). 2.2. Patients PBMC isolation and exposure to air pollution 2.2.1. Blood sampling This study was approved by the regional ethical committee (i.e. Comité de protection des personnes, 20th December 2011, ECH 11/03, Lille, France). A total of 90 healthy volunteers (male and female) in three age classes (25–30, 50–55 and 75–80 years old; n = 30/age class) were recruited at the Saint-Philibert and the Saint-Vincent Hospitals (Lille, France). Their informed written consents were obtained prior to blood sampling. The exclusion criteria were the following: smoking or smoking cessation < 10 years, corticosteroids or immunosuppressive treatment, radiotherapy, chemotherapy, workers in metallurgy, petrochemical industry or painters. A complete blood count was performed by Sysmex machine, automatically calibrated at every ignition. 2.2.2. PBMC isolation and culture Whole blood samples were first diluted 1:1 in PBS (EDTA, 1 mM). PBMCs were then isolated by density gradient centrifugation using Ficoll hypaque solution (Amersham). Isolated cells were cultured under agitation in sterile plastic culture tubes (Dutscher), in RPMI containing: 20% FBS, L-glutamin (2 mM), phytohaemagglutinin (E and L) (5 μg/ mL), penicillin (1 IU/mL), and streptomycin (1 μg/mL) (InVitrogen). Cells were maintained at 37 °C, in a humidified atmosphere containing 5% CO2. 2.2.3. Cytotoxicity dose-response relationship In order to determine the exposure concentration to use for the study of epigenotoxic effects, two cytotoxicity tests of PM2.5 were preliminary performed in 96-well microplate. Isolated PBMCs were exposed at a density of 2.104 cells/mL of RPMI to 0, 5, 15, 45 or 135 μg PM2.5/mL, during 24, 48, or 72 h. Accordingly, at each time of exposure, PBMCs cultured in 16 wells were unexposed, while PBMCs cultured in 8 wells/concentration were exposed. Unexposed cells were used as negative controls (100% viability) and Triton X-100 2% (v/v)exposed cells as positive controls (100% mortality). Cytotoxicity was evaluated by studying extracellular Lactate DeHydrogenase (LDH) release in cell-free culture supernatants (Cytotoxicity Detection Kit LDH, Roche Diagnostics) and by studying Mitochondrial DeHydrogenase activity in cells (Cell Proliferation Reagent WST-1, Roche Diagnostics), according to the manufacturer's instructions. 2.2.4. PBMC exposure About 5 million PBMC suspension were exposed or not to collected PM2.5 during 72 h at a concentration of 45 μg PM2.5/mL, according to the cytotoxicity results. After 72 h of incubation, cells were centrifuged (500g; 10 min; 4 °C). Cell pellets were then washed twice in 5 mL of cold PBS (0.01 M; pH = 7.2), and aliquots were quickly frozen at −80 °C. 2.2.5. Telomerase activity measurement Telomerase activity was assessed using TRAPEZE RT Telomerase detection kit (Merck-Millipore, France), according to manufacturer's 126

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Fig. 1. Experimental protocol: Genetic and epigenetic biomarkers of air pollution toxicity. PBMC, peripheral blood mononuclear cells.

2.2.8. Gene expression determination Total RNA extraction, including a gDNA elimination step, and subsequent cDNA synthesis from mRNA were carried out using TaqMan® Gene expression Cell-to-CT kit (Ambion, Invitrogen-Life Technologies), following the manufacturer's instructions. cDNA coding for P16, MGMT and 18S rRNA (endogenous control) were then quantified using TaqMan® Gene Expression Assay (Applied Biosystems) combined with TaqMan® specific probe primers sets (P16, Hs00923894_m1; MGMT, Hs01037698_m1; 18S rRNA, Hs99999901_s1). Amplification was realized through 40 cycles, using 7500 Fast Real-Time PCR System. Ct was determined with Sequence Detection Software v2.0.3 and relative quantification (RQ) between exposed and unexposed cells was achieved for each exposure time using the 2−ΔΔCT method (Livak and Schmittgen, 2001).

instructions. Briefly, real-time PCR was used to quantify telomere-repeats amplified by telomerases in a first incubation stage. PCR was carried out using 7500 Fast Real-Time PCR System (Applied Biosystems, Life technologies). Total protein content in cellular lysates was determined using bicinchoninic acid. Results were expressed as mean value ± SD of Log(telomeric-DNA copies)/protein content. 2.2.6. Telomere Length After DNA extraction from cells pellets (106 cells) (Blood & Cell Culture DNA Mini Kit, QIAGEN), the size of telomeres was measured by qPCR using 7500 Fast Real-Time PCR System (Cawthon, 2009) (Supplementary Table 1). After PCR, cycle threshold (Ct) was determined with Sequence Detection Software v2.0.3 (Applied Biosystems). The ratio of the length of samples was calculated by the technique of ΔΔCt procedure, where ΔΔCt = ΔCtexposed − ΔCtunexposed and ΔCt = Cttelomere − Ct36B4.

2.3. Statistical analysis

2.2.7. Specific methylation The evaluation of P16INK4A and MGMT gene promoter methylation was adapted from the methylation-specific PCR protocol (Herman et al., 1996). Briefly, gDNA was extracted using DNeasy Blood & Tissue kit (Qiagen) then treated with bisulfite using Cell-to-CpG™ kit (Life Technologies). The gDNA concentration in the solutions was quantified, using μDrop plate of the Multiskan Go reader (Thermo Fisher Scientific). The PCR protocol was adapted to the target (Supplementary Table 2). Specificity of the signal was systematically verified determining melting curves. The values of Ct were obtained using the Sequence Detection software v.2.0.3 and the samples comparison according to the formula ΔCttarget = Ctmethylated − Ctunmethylated.

For all the biological parameters, normality was assessed by using the Shapiro-wilk test (p > 0.2). As all the parameters were not normally distributed, non-parametric tests were used for the statistical analysis. Results of complete blood count and biological parameters (i.e. telomerase activity, telomere length, P16INK4A and MGMT methylation and gene expression), were expressed by the median and interquartile distribution [Q1; Q3] of thirty biological replicates and interclass comparisons were achieved using Mann-Whitney U test (p < 0.05). The results of all the biological parameters were normalized using the group of unexposed PBMCs isolated from the 25–30 years old volunteers as a reference. Since each exposure sample was compared to his own unexposed sample, intra-class comparisons were 127

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3.2. Exposure to air pollution activates telomerase but telomere length decreases with age

Table 1 Chemical characteristics of collected PM2.5. Metals (μg/g PM2.5) Aluminium Chromium Copper Iron Lead Manganese Nickel Titanium Zinc Total metals

Before studying epigenetic effects of PM2.5, the dose-cytotoxicity relationship of particles was analyzed. Extracellular LDH activity measured in PBMCs exposed to increased concentration of air pollution particulate matter (PM2.5), during 48 and 72 h showed a significant 10% decrease of viability when exposed to 45 μg/mL of PM2.5 (Table 3). This concentration of exposure has thus been chosen for the following of the study. DHm activity showed no significant adverse effects of exposure to PM2.5 (data not shown). Indirect genotoxicity was next evaluated by measuring of telomerase activity which is implicated in telomeres preservation and is associated with cellular immortalization in lung cancer (Young, 2010). After 72 h of PBMC culture in absence of PM2.5, telomerase activity was not significantly different between the three age groups (Fig. 2A). When cells were exposed to PM2.5, the activity was significantly higher than in unexposed cells in the three age classes, but without any difference in relation to aging. In agreement, an increase of telomeres length after exposure to particles is observed and Spearman coefficient between telomerase activity and telomere length exhibit values ranging between 0.521 and 0.745 (p < 0.05) in the three different conditions (Supplementary Table 3). At the basal level, in the absence of PM2.5, telomeres length tended to decrease with age, but without any significant difference. Observing each group separately, telomeres length in PBMCs tended to increase after exposure to PM2.5 (Fig. 2B).

14,818 89 322 30,513 144 3503 73 412 1602 139,732

Ions (mg/g PM2.5) Ammonium Nitrate Sulfate Total ions

14.22 180.07 32.25 394.62

Polycyclic aromatic hydrocarbons (PAHs) (μg/g PM2.5) Dibenzo(ah)anthracene Benzo(a)pyrene Benzo(a)anthracene Indeno(123cd)pyrene Benzo(k)fluoranthene Benzo(b)fluoranthene Total PAHs

2.01 3.16 3.25 5.27 5.28 9.55 47.52

3.3. Air pollution down-regulates P16INK4A gene expression Dioxins, Furans and DL-PCB (ng/g PM2.5)

3. Results

P16INK4A can be activated in response to telomeres dysfunction (Jacobs and de Lange, 2004). No significant difference in P16INK4A gene promoter methylation was found according to age, in presence or absence of PM2.5 (Fig. 3A). Nevertheless, promoter methylation significantly increased after PBMC exposure to air pollution particulate matter. To confirm the impact of promoter methylation, P16INK4A gene expression was analyzed. In absence of PM2.5 exposure, P16INK4A gene expression did not significantly vary with age (Fig. 3B), showing no replicative senescence of cultured PBMCs even in 75–80 yo group. After exposure to PM2.5, a significant decrease in P16INK4A gene expression was shown only in exposed cells in 25–30 yo age group (RQ = 0.45). After exposure, significant increases with age were observed, between age 25–30 and 50–55 yo; and between 25–30 and 75–80 yo. Negative but not significant Spearman correlation between methylation and gene expression was determined in all age groups according to the exposure (Supplementary Table 3).

3.1. PM2.5 characterization and relative aging stability of complete blood count

3.4. Age modulates MGMT gene expression

Dioxins (PCDD) I-TEQ NATO (PCDD) Furans (PCDF) I-TEQ NATO (PCDF) Dioxin-like Polychlorobiphenyls (DL-PCB) TE WHO (DL-PCB)

1.6752 0.006–0.085 0.4998 0.008–0.040 11.057 0.001–0.057

achieved using Wilcoxon rank test for paired samples (p < 0.05). Correlation between gene expression level of P16 and MGMT and methylation level of their respective promoter, on the one hand, and between telomerase activity and telomeres length, on the other hand, were performed using Spearman test (p < 0.05). All statistical analyses were done using SPSS software v.18.

In absence of PM2.5, the methylation of MGMT gene promoter was significantly higher in PBMCs from the 75–80 yo class than in 25–30 and 50–55 yo classes (Fig. 3C). The exposure to PM2.5 did not modify the methylation in 25–30 and 50–55 yo groups. On the contrary, in the elderly, even if the MGMT promoter methylation stayed at a higher level than in the young and medium groups, it decreased significantly by 50% after exposure. Gene expression is consequently significantly decreased in the oldest group (Fig. 3D). Spearman coefficient between methylation and gene expression was not significant in all age groups (data not shown). Observing each group individually, MGMT gene expression did not vary after exposure to PM2.5 in 25–30 and 50–55 yo groups (RQ = 1.04 and 0.97 vs unexposed cells, respectively). On the contrary, in 75–80 yo group, MGMT gene expression increased significantly (RQ = 2.59 vs unexposed cells). The absolute value of MGMT gene expression in the 75–80 yo group after exposure to PM2.5 remained lower than in the 25–30 yo age class (RQ = 0.76).

According to their characterization, collected particles were representative of urban air pollution, including the presence of metals, diesel exhausts particles and secondary aerosols like nitrates and sulfates (Table 1). Before ex vivo exposure to PM2.5, PBMCs were sampled from three age classes. All measured parameters were in the expected standards without any significant differences among the three age groups in complete blood count parameters (platelets, leukocytes, leukocytes with neutrophils, eosinophils, basophils, and monocytes), except for red blood cells, hemoglobin and lymphocytes (Table 2). Hemoglobin decreased significantly in 75–80 years old age class (12.30 g/ dL vs 13.56 and 13.27 g/dL in young and medium age classes), as already described by Hawkins (Hawkins et al., 1954). Lymphocytes declined significantly with age, especially in the elderly (p < 0.05), but stayed in the expected standards (1.82 ∗ 109/L) (Tollerud et al., 1989).

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Table 2 Complete blood count values in the three age classes. Values are depicted as median and interquartile range [Q1–Q3]. Statistical analysis: Inter-class comparisons: Mann-Whitney U test (a: p < 0.05 between 25 and 30 years old (yo) age class and 50–55 yo age class, b: p < 0.05 between 25 and 30 yo age class and 75–80 yo age class, c: p < 0.05 between 50 and 55 yo age class and 75–80 yo age class). Median [Q1–Q3]

25–30 yo (n = 30) 12

Red blood cells (10 /L) Hemoglobin (g/dL) Blood platelets (109/L) Leukocytes (109/L) Neutrophils (109/L) Eosinophils (109/L) Basophils (109/L) Lymphocytes (109/L) Monocytes (109/L)

4.71 [4.53–4.86] 13.6 [13–14.3]b 247 [209–275] 7.50 [5.4–8.6] 4.15 [3.03–5.38] 0.1 [0.1–0.2] 0.0 [0.0–0.0] 2.1 [1.53–2.68]b 0.5 [0.43–0.78]

a,b

50–55 yo (n = 30) a,c

4.56 [4.53–4.64] 13.4 [12.8–13.98]c 228 [208–283] 6.75 [5.65–7.7] 4.00 [3.18–4.85] 0.2 [0.1–0.2] 0.0 [0.0–0.0] 1.95 [1.48–2.23]c 0.45 [0.40–0.60]

75–80 yo (n = 30) b,c

3.78 [3.44–4.04] 10.95 [10.13–1.88]b,c 232 [193–303] 7.05 [5.88–10.2] 4.30 [3.25–6.10] 0.1 [0.1–0.3] 0.0 [0.0–0.0] 1.5 [1.05–1.80]b,c 0.6 [0.50–0.75]

Normal values 4.0–5.8 12–18 150–400 4–10 1.5–7 0.05–0.5 < 0.1 1.5–4 0.2–1

Table 3 Cytotoxicity of PM2.5. Extracellular lactate deshydrogenase (LDH) activity in PBMCs exposed to increased concentration of air pollution particulate matter (PM2.5), during 24, 48 and 72 h. Values are depicted as median and interquartile range [Q1–Q3] (Mann-Whitney U test versus control: in bold, p < 0.05). PM2.5 (μg/mL)

0

5

15

45

135

24 h 48 h 72 h

100.0 [98.1–100.7] 98.7 [98.3–100.2] 100.0 [98.1–106.6]

102.4 [96.6–103.6] 96.8 [96.4–100.4] 100.9 [91.6–112.7]

98.2 [94.1–100.1] 96.1 [95.2–100.2] 98.5 [97.0–100.0]

100.0 [98.9–100.3] 92.7 [91.4–94.1] 89.7 [83.1–96.5]

98.2 [95.7–99.7] 73.1 [71.2–78.5] 85.6 [82.0–89.3]

by genetic and epigenetic modifications that are also found in the biology of aging. Air pollution and PM2.5 were recently recognized as lung carcinogenic to human by IARC. The aim of this study was to estimate the influence of age on the appearance of early cellular alterations potentially involved in carcinogenesis following cells ex vivo exposure to urban air pollution PM2.5. As first investigated biomarkers, we measured telomerase activity and telomere length in leukocytes arising from the three classes of age and exposed or not to PM2.5 (Fig. 2). Increasing evidence has linked environmental and occupational pollutants with telomere length that can be influenced by telomerase activity, but the results are contradictory (Zhang et al., 2013). Here we showed that relative telomerase activity is significantly higher in PM2.5 exposed cells than in unexposed cells in the three age classes, without any difference in relation to aging. This can be due in part to transition metals present in PM composition, like iron, which are known to cause an oxidative stress through the involvement of catalytically active prooxidant oxides (Dergham et al., 2012). This oxidative stress was indeed correlated to increased telomerase activity in dogs' epithelial cells (Babizhayev and Yegorov, 2014). Even non-significant, leukocyte telomere length decreases with age, in concordance with a high number of publications (reviewed in Müezzinler et al., 2013). On the contrary, we see slight increases of telomere length in the presence of PM2.5, whatever the age. The absence of statistical significance could probably be explained by the heterogeneity of PBMCs' cell population, (Lin et al., 2010). Our results are however in concordance with studies showing that telomere length can be affected by various environmental and lifestyle factors (Müezzinler et al., 2013) and can be associated with aging speeded up through inflammation and oxidative stress induced by environmental factors (Mitchell et al., 2014; von Zglinicki, 2002). Moreover, exposure to metal-rich particles, like in steelworker, leads to an increase of telomeres length in leukocytes (Dioni et al., 2011). This tendency to a telomeric extension could suggest the beginning of cell immortalization due to toxic exposure. Time of exposure, rather short (72 h), allowed only some division cycles of lymphocytes. In our study, telomere length and telomerase activity cannot be correlated depending on age category. This is in concordance with the studies of Lin et al. showing that telomere length change and telomerase activity is cell-dependent (Lin et al., 2015, 2016). But interestingly, we show for the first time that the

Fig. 2. Telomerase activity in unexposed or exposed PBMCs during 72 h to 45 μg/mL of fine urban particulate matter PM2.5. (A) Relative telomerase activity, (B) relative telomeres length (intra-class: Wilcoxon test: §: p < 0.05; §§: p < 0.01). Results are expressed as median and interquartile range [Q1;Q3] (n = 30) relatively to 25–30 years old (yo) unexposed cells.

4. Discussion New noninvasive blood markers of lung cancer have to be proposed due to high incidence and gloomy prognostic of this pathology. Since the actual symptoms appear only at an advanced stage of the disease, one of the objectives of cancer research is to propose new tumor markers allowing an earlier diagnosis to improve survival. Numerous degenerative pathologies such as cancer are influenced or directly caused 129

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Fig. 3. P16INK4A and MGMT gene expression modulation in unexposed or exposed PBMCs during 72 h to 45 μg/mL of fine urban particulate matter. (A) Relative methylation levels of P16INK4A gene promoter; (B) relative P16INK4A gene expression; (C) relative methylation levels of MGMT gene promoter; (D) relative MGMT gene expression (inter-class: Mann-Whitney U test: **: p < 0.01; intra-class: Wilcoxon test: §: p < 0.05; §§: p < 0.01). Results are expressed as median and interquartile range [Q1;Q3] (n = 30) relatively to 25–30 years old (yo) unexposed cells.

Consecutive to TP53 inactivation, P16INK4A is upregulated as shown during replicative senescence (Alcorta et al., 1996; Hara et al., 1996; Palmero et al., 1997). This activation can also be induced in answer to DNA damage and could explain our obtained results in the oldest groups. The promoter of the DNA repair gene MGMT was described as being hypermethylated on CpG island in approximately 20% of lung cancers (Zöchbauer-Müller et al., 2001). However, there are currently only a few data about changes in MGMT activity due to aging and particulate matter exposure. Global MGMT activity seems to decrease with aging, but the gene is still expressed at appreciable levels throughout life in limited tissues like lung (Nakatsuru et al., 1994). Here, aging could reinforce the increase of MGMT gene expression due to PM2.5. These results are in agreement with two other studies of MGMT methylation profile carried out on patients of > 65 or 70 years old with a glioblastoma (Yin et al., 2014). These studies encouraged routine testing of MGMT promoter status especially in elderly glioblastoma patients by indicating a direct linkage between biomarker test and individual treatment decision. This hypermethylation associated with a decrease of gene expression may cause deregulations of cellular cycle. Only a few studies report the impact of environmental pollutants on MGMT methylation, but it seems that it can be increased by nickel (Ji et al., 2008) and radon (Su et al., 2006), and on the contrary decreased by PAHs, detected in PM2.5 in our study (Duan et al., 2013). Nevertheless, after exposure to PM2.5, methylation of the promoter and MGMT gene expression present no significant differences between the three age groups. Age seemed to influence methylation profile and MGMT gene expression after exposure to particles since significant differences could be found only in the elderly group. Despite the strengths of our work, there were several limitations. This is a pilot study due to the relatively limited number of volunteers. The choice to recruit ninety people in three different age groups indeed divides our sample and decreases the statistical power of our analyses. Some small variations may be statistically significant by having the opportunity to recruit more volunteers. Moreover, this study was focused on the in vitro impact of air pollution and the proposed markers could not be used for diagnosis, but they help in the understanding of

increase of telomere length and telomerase activity after exposure to particles exhibits Spearman coefficient values ranging between 0.521 and 0.745 (p < 0.05), suggesting a correlation between both. An important characteristic of cancer development and progression is the modification of DNA methylation patterns, characterized by the hypermethylation of specific genes simultaneously with a global decrease in the abundance of 5-methylcytosine. In some cancers, CpG islands (DNA regions with a high frequency of CpG dinucleotides), located especially in promoter regions, are frequently hypermethylated, repressing expression of tumor suppressor genes such as P16INK4A or MGMT (Esteller, 2007). Here we observed that P16INK4A promoter methylation significantly increased after PBMC exposure to air pollution particulate matter (Fig. 2A). This is coherent with previous studies describing a hypermethylation after exposure to coal combustion products, urban PM2.5 (Soberanes et al., 2012) or metal-rich particles (Hou et al., 2011). Our study also highlighted a P16INK4A promoter hypermethylation associated with exposure to PM2.5 in all age groups. This hypermethylation, observed in most of the cancers, would involve a signalization pathway activated by ROS (Govindarajan et al., 2002; Soberanes et al., 2012). In our experiment, due to its promoter hypermethylation, P16INK4A gene expression decreased, but only in young people's cells. In the other age groups, PBMC exposure to PM2.5 did not significantly modify P16INK4A gene expression, despite its significant promoter hypermethylation in all age groups. This absence of gene modulation in the oldest people's cells could be linked to other mechanisms of regulation. An antagonism was described between senescence and oncogenesis since progressive DNA damages could initiate tumorigenesis, on the one hand, but they are also considered to be prime instigators of natural aging, on the other hand (Mancini et al., 2012). Cellular senescence is considered as a robust physiological antitumor response, which counteracts oncogenic insults, while the age could favor its emergence by two mechanisms: the accumulation of mutations which can lead to the inactivation of tumor suppressor genes and the reduction of the controls normally provided by the microenvironment tissue caused by the aging of the cells (Rufini et al., 2013). The main tumor suppressor gene, TP53 undergoes a lower transcriptional activity with age (Feng et al., 2007; Wu and Prives, 2018). 130

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the underlying biological mechanisms. Finally, the studied cells are all PBMCs. The heterogeneity of this cell population is known in the elderly people and could mitigate observable changes (Lin et al., 2010). A focus on T cells could provide new information on how air pollution affects the immune response. However, it is noteworthy that this study was the first investigating some underlying mechanisms implied in the role of aging in the sensitivity of the elderly to air pollution. As recently proposed by Dent et al., telomeres length in leukocytes could be associated with the newly developed biomarker-based frailty index FI-LAB (Dent et al., 2018).

All authors made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; BF, PJM, FS, and SB been involved in drafting the manuscript or revising it critically for important intellectual content; BF, PJM, FS, and SB given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content; and all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

5. Conclusion

Acknowledgements

In conclusion, our study showed for the first time that air pollution PM2.5 could induce epigenetic modifications susceptible to favor tumorigenesis, depending on age status. In our experimental ex vivo study, age could modulate in some cases these mechanisms of action. Despite the known antagonism between senescence and oncogenesis, measured for example through P16INK4A expression, age could enhance MGMT gene expression after exposure to particles, as shown by the decreased level of promoter methylation in the oldest group. Furthermore, all assays were done after a limited time of exposure allowing only one or two cell cycles. Several tendencies of cell modifications could be observed. To improve the knowledge of age influence on the observed effects, it could be interesting to extend time exposure of PBMCs to detect stable or amplified alterations, like telomeres length, and estimate with more sensitivity the influence of age on changes connected to the toxicity of urban PM.

The authors would like to thank the Centre Commun de Mesures (ULCO) and MicroPolluants Technologie SA for chemical characterization of PM. They also thank Dr. John M. Halket for the reading and English language corrections of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.exger.2018.05.018. References Alcorta, D.A., Xiong, Y., Phelps, D., Hannon, G., Beach, D., Barrett, J.C., 1996. Involvement of the cyclin-dependent kinase inhibitor p16 (INK4a) in replicative senescence of normal human fibroblasts. Proc. Natl. Acad. Sci. U. S. A. 93, 13742–13747. Aoki, K., Nakatsuru, Y., Sakurai, J., Sato, A., Masahito, P., Ishikawa, T., 1993. Age dependence of O6-methylguanine-DNA methyltransferase activity and its depletion after carcinogen treatment in the teleost medaka (Oryzias latipes). Mutat. Res. 293, 225–231. Argacha, J.F., Collart, P., Wauters, A., Kayaert, P., Lochy, S., Schoors, D., Sonck, J., de Vos, T., Forton, M., Brasseur, O., Beauloye, C., Gevaert, S., Evrard, P., Coppieters, Y., Sinnaeve, P., Claeys, M.J., 2016. Air pollution and ST-elevation myocardial infarction: a case-crossover study of the Belgian STEMI registry 2009–2013. Int. J. Cardiol. 223, 300–305. http://dx.doi.org/10.1016/j.ijcard.2016.07.191. Babizhayev, M.A., Yegorov, Y.E., 2014. Biomarkers of oxidative stress and cataract. Novel drug delivery therapeutic strategies targeting telomere reduction and the expression of telomerase activity in the lens epithelial cells with N-acetylcarnosine lubricant eye drops: anti-cataract which helps to prevent and treat cataracts in the eyes of dogs and other animals. Curr. Drug Deliv. 11, 24–61. Bentayeb, M., Simoni, M., Baiz, N., Norback, D., Baldacci, S., Maio, S., Viegi, G., AnnesiMaesano, I., Geriatric Study in Europe on Health Effects of Air Quality in Nursing Homes GERIE Group, 2012. Adverse respiratory effects of outdoor air pollution in the elderly. Int. J. Tuberc. Lung Dis. 16, 1149–1161. http://dx.doi.org/10.5588/ijtld.11. 0666. (review article). Billet, S., Abbas, I., Le Goff, J., Verdin, A., André, V., Lafargue, P.-E., Hachimi, A., Cazier, F., Sichel, F., Shirali, P., Garçon, G., 2008. Genotoxic potential of polycyclic aromatic hydrocarbons-coated onto airborne particulate matter (PM 2.5) in human lung epithelial A549 cells. Cancer Lett. 270, 144–155. http://dx.doi.org/10.1016/j.canlet. 2008.04.044. Carnes, B.A., Staats, D., Willcox, B.J., 2014. Impact of climate change on elder health. J. Gerontol. A Biol. Sci. Med. Sci. 69, 1087–1091. http://dx.doi.org/10.1093/gerona/ glt159. Cawthon, R.M., 2009. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 37, e21. http://dx.doi.org/10.1093/ nar/gkn1027. Dent, E., Hoogendijk, E.O., Moldovan, M., 2018. Frailty index from routine laboratory measurements correlates with leukocyte telomere length. Geriatr Gerontol Int 18, 654–655. http://dx.doi.org/10.1111/ggi.13257. Dergham, M., Lepers, C., Verdin, A., Billet, S., Cazier, F., Courcot, D., Shirali, P., Garçon, G., 2012. Prooxidant and proinflammatory potency of air pollution particulate matter (PM₂.₅₋₀.₃) produced in rural, urban, or industrial surroundings in human bronchial epithelial cells (BEAS-2B). Chem. Res. Toxicol. 25, 904–919. http://dx.doi.org/10. 1021/tx200529v. Dioni, L., Hoxha, M., Nordio, F., Bonzini, M., Tarantini, L., Albetti, B., Savarese, A., Schwartz, J., Bertazzi, P.A., Apostoli, P., Hou, L., Baccarelli, A., 2011. Effects of shortterm exposure to inhalable particulate matter on telomere length, telomerase expression, and telomerase methylation in steel workers. Environ. Health Perspect. 119, 622–627. http://dx.doi.org/10.1289/ehp.1002486. Duan, H., He, Z., Ma, J., Zhang, B., Sheng, Z., Bin, P., Cheng, J., Niu, Y., Dong, H., Lin, H., Dai, Y., Zhu, B., Chen, W., Xiao, Y., Zheng, Y., 2013. Global and MGMT promoter hypomethylation independently associated with genomic instability of lymphocytes

Ethics approval and consent to participate This study was approved by the regional ethical committee (i.e. Comité de protection des personnes, 20th December 2011, ECH 11/03, Lille, France). Informed written consents were obtained from all volunteers prior to blood sampling. Consent for publication Not applicable. Availability of data and material All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interests All authors declare no actual or potential competing financial interests. Funding This work was supported by the Institut National du Cancer (INCa; Grant number 2010-368), the Hauts-de-France Region (Grant number 14003399) and the French Agency of the Environment and Energy (ADEME; Grant number 1494c0082-83-84). BF benefited from grants from the Lille Métropole Communauté Urbaine. CL benefited from grants from the Région Nord-Pas de Calais and the Syndicat Mixte de la Côte d'Opale (Grant number 2010_09206). This work is a contribution to the CPER research project CLIMIBIO. The authors thank the French Ministère de l'Enseignement Supérieur et de la Recherche, the Hauts-deFrance Region and the European Funds for Regional Economical Development for their financial support to this project. 131

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