Polycyclic aromatic hydrocarbons exposure and their joint effects with age, smoking, and TCL1A variants on mosaic loss of chromosome Y among coke-oven workers

Journal Pre-proof Polycyclic aromatic hydrocarbons exposure and their joint effects with age, smoking, and TCL1A variants on mosaic loss of chromosome Y among coke-oven workers Yuhang Liu, Yansen Bai, Xiulong Wu, Guyanan Li, Wei Wei, Wenshan Fu, Gege Wang, Yue Feng, Hua Meng, Hang Li, Mengying Li, Xin Guan, Xiaomin Zhang, Meian He, Tangchun Wu, Huan Guo PII:

S0269-7491(19)34568-3

DOI:

https://doi.org/10.1016/j.envpol.2019.113655

Reference:

ENPO 113655

To appear in:

Environmental Pollution

Received Date: 13 August 2019 Revised Date:

10 October 2019

Accepted Date: 19 November 2019

Please cite this article as: Liu, Y., Bai, Y., Wu, X., Li, G., Wei, W., Fu, W., Wang, G., Feng, Y., Meng, H., Li, H., Li, M., Guan, X., Zhang, X., He, M., Wu, T., Guo, H., Polycyclic aromatic hydrocarbons exposure and their joint effects with age, smoking, and TCL1A variants on mosaic loss of chromosome Y among coke-oven workers, Environmental Pollution (2019), doi: https://doi.org/10.1016/j.envpol.2019.113655. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

Polycyclic Aromatic Hydrocarbons Exposure and their Joint Effects with Age,

2

Smoking, and TCL1A Variants on Mosaic Loss of Chromosome Y among

3

Coke-oven Workers

4 5

Yuhang Liu1 a, Yansen Bai1 a, Xiulong Wu a, Guyanan Li a, Wei Wei a, Wenshan Fu a,

6

Gege Wang a, Yue Feng a, Hua Meng a, Hang Li a, Mengying Li a, Xin Guan a, Xiaomin

7

Zhang a, Meian He a, Tangchun Wu a, Huan Guo a *

8 9

1

These authors contributed equally to this work.

10

Authors’ affiliations:

11

a

12

Environmental Health (Incubating), School of Public Health, Tongji Medical College,

13

Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China.

Department of Occupational and Environmental Health, State Key Laboratory of

14 15

Declaration of competing financial interest:

16

The authors declare no competing financial interest.

17 18

* Correspondence to:

19

Huan Guo, MD, PhD, Professor, Department of Occupational and Environmental

20

Health, School of Public Health, Tongji Medical College, Huazhong University of

21

Science and Technology, 13 Hangkong Rd, Wuhan 430030, Hubei, China. Tel: 8627-

22

83657914; Fax: 86-27-83657765; E-mail: [email protected]. 1

23

ABSTRACT

24

Mosaic loss of chromosome Y (mLOY) is the most common structure somatic event

25

that related to increased risks of various diseases and mortality. Environmental

26

pollution and genetic susceptibility were important contributors to mLOY. We aimed

27

to explore the associations of polycyclic aromatic hydrocarbons (PAHs) exposure, as

28

well as their joint effects with age, smoking, and genetic variants on peripheral blood

29

mLOY. A total of 1005 male coke-oven workers were included in this study and their

30

internal PAHs exposure levels of 10 urinary PAH metabolites and plasma

31

benzo[a]pyrene-r-7,t-8,t-9,c-10-tetrahydotetrol-albumin (BPDE-Alb) adducts were

32

measured. mLOY was defined by the median log-R ratios (mLRR) of 1480 probes in

33

male-specific region of chromosome-Y from genotyping array. We found that the

34

PAHs exposure levels were linearly associated with mLOY. A 10-fold increase in

35

urinary 1-hydroxynaphthalene (1-OHNa), 1-hydroxyphenanthrene (1-OHPh),

36

2-OHPh, 1-hydroxypyrene (1-OHP), ΣOH-PAHs, and plasma BPDE-Alb adducts

37

could generate 0.0111, 0.0085, 0.0069, 0.0103, 0.0134, and 0.0152 decrease in

38

mLRR-Y, respectively. Additionally, mLOY accelerated with age, smoking pack-years,

39

and TCL1A rs1122138-C allele, and we observed the most severe mLOY among

40

subjects carrying more than 3 of the above risk factors. Our results revealed the linear

41

dose-effect associations between PAHs exposure and mLOY. Elder male smokers

42

carrying rs1122138CC genotype were the most susceptible subpopulations to mLOY,

43

who should be given protections for PAHs exposure induced chromosome-Y

44

aberration.

45

KEYWORDS: Polycyclic aromatic hydrocarbons; mosaic loss of chromosome Y;

2

46

TCL1A; genetic variations; joint effect

3

47

ABBREVIATIONS: BMI, body mass index; BPDE-Alb,

48

benzo[a]pyrene-r-7,t-8,t-9,c-10-tetrahydotetrol-albumin adducts; CIs, Confidence

49

intervals; GC-MS, Gas chromatography-mass; GWAS, genome-wide association

50

study; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; LOD, limit of

51

detection; MAF, minor allele frequencies; mLOY, mosaic loss of chromosome Y;

52

mLRR, median Log R Ratio; mLRR-Y, median Log R Ratio of chromosome Y;

53

OH-PAHs, monohydroxy polycyclic aromatic hydrocarbons; PAH, polycyclic

54

aromatic hydrocarbon; QC, quality control; SNP, single nucleotide polymorphism;

55

TCL1A, T-cell leukemia/lymphoma 1A; ΣOH-PAHs, the sum concentrations of ten

56

OH-PAHs;1-OHNa, 1-hydroxynaphthalene; 2-OHNa, 2-hydroxynaphthalene;

57

2-OHFlu, 2-hydroxyfluprene; 9-OHFlu, 9-hydroxyfluprene; 1-OHPh,

58

1-hydroxyphenanthrene; 2-OHPh, 2-hydroxyphenanthrene; 3-OHPh,

59

3-hydroxyphenanthrene; 4-OHPh, 4-hydroxyphenanthrene; 9-OHPh,

60

9-hydroxyphenanthrene; 1-OHP, 1-hydroxypyrene; 6-OHChr, 6-hydroxychrysene;

61

3-OHBaP, 3-hydroxybenzo[a]pyrene.

62 63

MAIN FINDINGS

64

Increased levels of PAHs exposure were associated with more severe mLOY.

65

There were interactive and joint effects of PAHs, age, smoking pack-years, and

66

TCL1A rs1122138CC on mLOY.

4

67

INTRODUCTION

68

Mosaic loss of chromosome Y (mLOY) is the most frequently detectable

69

structural mosaic event than those observed in autosomes and chromosome X among

70

males (Zhou et al., 2016). It refers to a portion of cells losing the Y chromosome,

71

while the remaining retains the normal. The epidemiological studies had observed

72

preliminary evidence suggesting mLOY in blood was moderately associated with an

73

increased incidence of select solid tumors (Machiela et al., 2017; Loftfield et al., 2019)

74

as well as all-cause and non-hematologic cancer mortality (Forsberg et al., 2014;

75

Loftfield et al., 2018). Recent studies had also revealed the possible relationships

76

between mLOY and occurrences of Alzheimer's disease (Dumanski et al., 2016),

77

cardiovascular events (Haitjema et al., 2017), along with autoimmune diseases

78

(Persani et al., 2012). However, these findings were still preliminary and not validated

79

in a substantially larger independent studies (Zhou et al., 2016).

80

Epidemiologic investigations indicated that mLOY could be modified by

81

genetic and environmental factors. A recent published genome-wide association study

82

(GWAS) had comprehensively reported a series of single nucleotide polymorphisms

83

(SNPs) associated with mLOY based on the UK Biobank cohort of 85,542 men

84

(Wright et al., 2017). These findings provided us with mLOY-related genetic

85

variations. Males could develop aggravated mLOY as they getting old. Besides age,

86

the most confirmed environmental risk factor is cigarette smoking. Dumanski et al.

87

conducted a pooled study of 6014 males in Sweden and proposed current smokers

88

showed a higher level of mLOY than non-current smokers, and smoking pack-years 5

89

tended to be positively associated with mLOY, but this association disappeared after

90

smoking cessation (Dumanski et al., 2015). Another investigation regarding the effect

91

of environmental factors on mLOY indicated that exposure to air particulate matter ≤

92

10 µm (PM10) might exacerbate leukocyte mLOY (Wong et al., 2018). Nevertheless,

93

the relationship of other exposures with mLOY has not been adequately investigated.

94

Polycyclic aromatic hydrocarbons (PAHs), known as their highly toxic, are a

95

series of persistent and pervasive organic pollutants generated mainly from

96

incomplete combustion of organic matter (White et al., 2016; Weinstein et al., 2017).

97

Exposure to PAHs is associated with the development of numerous cancers (Lee et al.,

98

2010) and cardiovascular diseases (Yin et al., 2018). Since PAHs are important

99

genotoxic components common to cigarette smoke and particulate matter, both of

100

which are showed to be associated with increased blood mLOY, we hypothesized here

101

that direct PAHs exposure may have a similarly detrimental effect on chromosome Y.

102

Here, we performed a cross-sectional study including 1005 male workers from a

103

coke-oven plant. For all participants, we detected their urinary concentrations of 10

104

PAH metabolites and plasma levels of

105

benzo[a]pyrene-r-7,t-8,t-9,c-10-tetrahydotetrol-albumin (BPDE-Alb) adducts, as the

106

internal exposure biomarkers for PAHs. We genotyped these subjects by using

107

Illumina SNP array, estimated their levels of blood mLOY, and extracted the

108

genotypes of mLOY-related SNPs according to the previous GWAS report.

109

Additionally, the dose-response relationships between PAHs exposure and mLOY, and

110

their joint effects with age, tobacco smoking, and genetic variants were further 6

111

evaluated among these male subjects.

112

MATERIALS AND METHODS

113

Study Subjects

114

A total of 1628 Han Chinese subjects, who had worked in a coke-oven plant in

115

Wuhan city, Hubei, China for more than one year were recruited in 2010. There were

116

1405 males among this population. These subjects were worked at the top, side, and

117

bottom of the coke-ovens, adjunct workplaces (e.g., the blower operation room and

118

recycling work-shops), or in offices. After excluding male subjects with a history of

119

self-reported cancer and without available urinary samples or qualified DNA, the left

120

1005 males were included in this study. The general information on their demographic

121

characteristics, health status (including the disease histories of cardiovascular disease,

122

benign tumors, and diabetes mellitus), lifestyles (cigarette smoking, alcohol drinking,

123

and physical activity), and occupational history were obtained through face-to-face

124

interviews by using a questionnaire. All participants donated 5 mL of venous blood

125

and 20 mL of first-morning urine, which were stored at -80 °C until laboratory

126

examinations.

127

The anthropometric data, including weight and height, was obtained by direct

128

measurement. The body mass index (BMI) is defined as weight divided by squared

129

height (kg/m2). Subjects who had smoked > 1 cigarettes per day for at least 1 year

130

were defined as current smokers; subjects who had ever smoked and quitted for more

131

than half a year were defined as former smokers; otherwise, they were defined as

132

none smokers (Yuan et al., 1997; Yang et al., 1999). Cigarette smoking history of 7

133

pack-years was calculated through multiplying the packs of cigarettes smoked per day

134

by the years of smoking. Those who had drunk alcohol at least once a week for > 1

135

year were defined as current alcohol drinkers; those who had ever drunk and quitted

136

for more than half a year were defined as former alcohol drinkers; otherwise, they

137

were defined as non-drinkers. Those who spent > 20 minutes/day exercise for ≥ 3

138

times per week were considered as regular physical exercisers; if not, they were

139

considered as non-regular physical exercisers. All subjects provided informed consent

140

and this study was approved by the Ethics Committee of Tongji Medical College,

141

Huazhong University of Science and Technology.

142 143 144

Determination of Urinary PAH Metabolites The concentrations of 12 PAH metabolites, including 1-hydroxynaphthalene

145

(1-OHNa), 2-OHNa, 2-hydroxyfluprene (2-OHFlu), 9-OHFlu,

146

1-hydroxyphenanthrene (1-OHPh), 2-OHPh, 3-OHPh, 4-OHPh, 9-OHPh,

147

1-hydroxypyrene (1-OHP), 6-hydroxychrysene (6-OHChr), and

148

3-hydroxybenzo[a]pyrene (3-OHBaP) were determined by using the Agilent

149

5975B/6890N GC-MS System (Agilent, Santa Clara, CA, USA). Details of the

150

method have been described in our previous studies (Kuang et al., 2013). The

151

6-OHChr and 3-OHBaP were excluded in further analyses since their concentrations

152

were below the limit of detection (LOD) among > 90% samples. The detection rate of

153

the left 10 urinary PAH metabolites ranged from 85.27% to 100%, and their LODs

154

ranged 0.1~1.4 µg/L. Sample with concentration of each PAH metabolite below LOD 8

155

was imputed with half of the LOD. Urinary levels of creatinine (Cr) were measured

156

by an automated clinical chemistry analyzer. Finally, the concentration of each PAH

157

metabolite was calibrated by urinary Cr and presented as µg/mmol Cr. Sum

158

concentrations of 10 PAH metabolites were recorded as ΣOH-PAHs.

159 160

Determination of Plasma BPDE-Alb Adducts

161

The concentrations of plasma BPDE-Alb adducts were detected by using an

162

ELISA method with minor modifications (Chung et al., 2010). The specific processes

163

had been described in our previous studies (Kuang et al., 2013). Each standard and

164

sample was analyzed in duplicate. The plasma concentration of BPDE-Alb adducts

165

was represented as ng/mg albumin. The LOD was 1 ng BPDE-Alb adducts per

166

microgram albumin and the values below LOD were imputed with half of the LOD.

167 168 169

Genotyping and Imputation Peripheral blood DNA was genotyped by using the Illumina Global Screening

170

Array Multi-Disease (GSA-MD) BeadChip array and analyzed by using

171

high-throughput genotyping apparatus (iScan, Illumina). For expecting

172

high-reliability genotyping results, we conducted a preliminary quality control before

173

imputation, excluding SNPs based on the following criteria: (1) with a call rate < 95%,

174

(2) Hardy-Weinberg equilibrium (HWE) P < 10-6, and (3) minor allele count < 1, and

175

excluded subjects with a genotyping missing rate > 10%. The imputation analysis was

176

carried out by using Minimac4 with 1000 Genomes Project ALL Phase 3 Release 9

177

(05/02/2013) as the reference panel. Then, we filtered variants with minor allele

178

frequency (MAF) ≤ 0.05 or with a low imputation quality (R2 ≤ 0.3). The final

179

genotyping data were obtained according to either direct genotyping or imputation.

180 181 182

Estimation of Mosaic Loss of Chromosome Y To estimate the degree of mLOY for each subject, we calculated median log R

183

Ratios (mLRR, i.e. observed intensity/expected intensity) of 1480 probes in the

184

male-specific region of chromosome Y (ChrY: 2655180-59034049, hg19/GRCH37)

185

based on intensity data that recorded by the Genome Studio software. As the total

186

intensity R is proportional to the copy number of chromosome Y, a positive mLRR-Y

187

estimation indicates a gain of copy number of chromosome Y; mLRR-Y estimation

188

close to zero indicates a normal copy number of chromosome Y; a more negative

189

mLRR-Y estimation denotes an increased proportion of leukocytes with mLOY. We

190

applied a rigorous filter criterion by excluding unqualified samples with a call rate <

191

0.97 (n = 25), or with a sex discrepancy (n = 7), or with a Log R Ratio standard

192

deviation on chromosome 1 ≥ 0.28 (n = 19), then the left 954 males were included in

193

the subsequent analysis.

194 195 196

Selection of SNPs Related with mLOY In the GWAS on UK Biobank population, Wright et al. found 19 mLOY-related

197

SNPs (Wright et al., 2017). We excluded 5 variants with minor allele frequency < 0.05

198

in the Chinese population, and 4 indels those failed in imputation were further 10

199

replaced by 4 SNPs with high linkage disequilibrium (r2 > 0.9). Finally, 14 SNPs,

200

including rs2736609, rs4754301, rs9805742, rs10151519, rs1122138, rs12448368,

201

rs11082396, rs13088318, rs6802910, rs56084922, rs13191948, rs381500, rs4721217,

202

and rs6468341, were selected in this study. These SNPs were also chosen in another

203

Chinese study (Qin et al., 2019). Additionally, we extracted all SNPs within the region

204

upstream and downstream 10Kb of TCL1A gene. After excluding those with an MAF

205

< 0.05, HWE P-value < 0.05, or imputation quality (R2) ≤ 0.3, 57 SNPs in TCL1A

206

were chosen for further analysis.

207 208

Expression Quantitative Trait Locus (eQTL) Analysis of Positive SNPs

209

For the TCL1A SNPs those had significant associations with mLRR-Y, we performed

210

the eQTL analysis by using the Genotype-Tissue Expression data (GTEx, version 7,

211

http://www.gtexportal.org/home/), in order to explore their regulation influence on

212

TCL1A expression in the whole blood.

213 214 215

Statistical Analysis We used the one-sample Kolmogorov-Smirnov test to examine the normality of

216

continuous variables. The concentrations of 10 urinary PAH metabolites and the

217

plasma levels of BPDE-Alb adducts were log10 (lg)-transformed to improve their

218

normal distribution. The continuous value of mLRR-Y was used as the dependent

219

variable (y) in the multiple linear regression models to estimate the association

220

coefficient (β) and its 95% confidence interval (95%CI) with each PAH exposure 11

221

biomarker, after adjustment for experimental batch in model 1, or additionally

222

adjusted age, BMI, smoking pack-years, alcohol drinking status (current vs.

223

non-current), and physical activity (yes vs. no) in model 2. In these models, the

224

lg-transformed value of each PAH exposure biomarker was separately included in the

225

regression as the independent variable. The corresponding regression coefficient (β)

226

represents that a 10-fold increase in level of each PAH exposure biomarker was

227

associated with β increase in mLRR-Y. For PAH exposure biomarkers that had

228

significant associations with mLRR-Y (P < 0.05), we further used a restricted cubic

229

spline model with knots at its 25th, 50th, and 75th percentiles, to explore the linear or

230

nonlinear shape of the associations. In addition, a sensitivity analysis by excluding

231

participants with self-report diseases of cardiovascular disease, benign tumor, and

232

diabetes mellitus (n = 79), was also performed.

233

The genotypes of each SNP were added in an additive model to assess their

234

associations with mLRR-Y, with adjustment for age, BMI, smoking pack-years,

235

current alcohol drinking status, physical activity, lg-transformed levels of ΣOH-PAHs

236

and BPDE-Alb adducts, and experimental batch.

237

All study subjects were further categorized into 4 subgroups (Q1 to Q4

238

subgroups, according to the quartile levels of each PAH exposure biomarker. The

239

multiple linear regression models with adjustment for above confounders were used

240

for computing the relative βs and 95%CIs in Q2, Q3, and Q4 subgroups with Q1 as

241

the reference group. The linear trend P-value was also derived by modeling a

242

numerical value for each category in the models. Then, the study participants were 12

243

classified into low (< 75% percentiles of urinary ΣOH-PAHs, Q1 to Q3) and high (≥

244

75% percentiles of urinary ΣOH-PAHs, Q4) PAHs exposure subgroups. We evaluated

245

the interaction effects of age, smoking, and TCL1A rs1122138 with dichotomous

246

PAHs exposure on mLRR-Y respectively, where the multiplicative interaction terms,

247

e.g., age (≤ 45, > 45) × PAHs (low PAHs exposure, high PAHs exposure), smoking

248

status (never and ever smoking) × PAHs (low, and high PAHs exposure), rs1122138

249

genotypes (CC, CA+AA) × PAHs (low, and high exposure), were separately included

250

in the multiple linear regression models, with adjustment for the other confounders.

251

The joint effects of dichotomous PAH exposure (low and high exposure level) with

252

dichotomous age (≤ 45, > 45), smoking (never and ever smoking), and TCL1A

253

rs1122138 (CC, CA+AA) on the level of mLRR-Y were further estimated.

254 255

A two-sided P < 0.05 was defined as statistically significant. All statistical analyses were performed in the R software (version 3.4.1).

256 257

RESULTS

258

Demographic Characteristics

259

Among 1005 participants with genotyping, the mean age was 42.6 ± 8.5

260

years-old and the mean BMI was 24.2 ± 3.0 kg/m2. 681 of them (67.8%) were current

261

smokers and there were 45 former smokers (4.5%). The median smoking pack-years

262

for the ever smokers were 16.9. There were 392 current alcohol drinkers (39.0%) and

263

595 (59.2%) never drinkers; approximately half of participants (49.2%) were regular

264

exercisers (Table 1). Additionally, 6.3% (n = 63) of them had a history of diabetes 13

265

mellitus, 1.1% (n = 11) had self-reported cardiovascular disease, and 1.4% (n = 14)

266

had a benign tumor. There were 954 male subjects had qualified value of mLRR-Y,

267

with mean ± SD equal to -0.0035 ± 0.0500.

268

The distributions for levels of PAH exposure biomarkers were shown in Table

269

S1. The median concentrations of urinary ΣOH-PAHs and plasma BPDE-Alb were

270

12.22 (7.95, 19.29) µg/mmol Cr and 4.25 (3.67, 5.00) ng/mg albumin, respectively.

271

Compared to subjects included (n = 1005, with genotyping), those excluded (n = 400,

272

without genotyping) had a lower BMI, more current alcohol drinkers, and a higher

273

urinary level of 2-OHNa. There were no significant differences in distributions of age,

274

tobacco smoking, physical activity, and levels of other PAH exposure biomarkers

275

between the above two populations.

276 277 278

Associations between Covariates and mLRR-Y Age and smoking pack-years were found to be negatively associated with

279

mLRR-Y [β (95%CI) = -0.0004 (-0.0007 to -0.0001) and -0.0002 (-0.0004 to

280

-5.5E-06), P = 0.020 and 0.044, respectively] (Figure S1). Compared to never

281

smokers, former and current smokers showed decreased mLRR-Y, although the

282

associations did not reach significant level [β (95%CI) = -0.0093 (-0.0234 to 0.0048)

283

and -0.0016 (-0.0079 to 0.0046), P = 0.198 and 0.611, respectively]. We did not

284

observe significant associations between other covariates (BMI, drinking status,

285

physical activity) and mLRR-Y (Table S2).

286

14

287 288

Dose-response Relationships between PAHs Exposure and mLRR-Y

289

The association of each PAH exposure biomarker with mLRR-Y was shown in

290

Table 2. It was shown that an each 10-fold increase in levels of urinary ΣOH-PAHs

291

and plasma BPDE-Alb adducts was associated with a separate 0.0134 or 0.0152

292

reduction in mLRR-Y in males [β (95%CI) = -0.0134 (-0.0227 to -0.0041) and

293

-0.0152 (-0.0296 to -0.0009), P = 0.005 and 0.038, respectively], after adjustment for

294

experimental batch, age, BMI, smoking status, alcohol drinking, and physical activity.

295

For each single urinary PAH metabolite, an each 10-fold increase in urinary 1-OHNa,

296

1-OHPh, 2-OHPh, and 1-OHP level could generate a separate 0.0111, 0.0085, 0.0069,

297

and 0.0103 decrease in mLRR-Y (all P < 0.05). The sensitivity analyses, by excluding

298

participants with a history of diabetes mellitus and self-reported cardiovascular

299

disease or benign tumor, did not materially change the above associations (Table 2).

300

Furthermore, the spline curve analyses did not show non-linear associations of above

301

6 PAH exposure biomarkers with mLRR-Y (all P for non-linear association > 0.05),

302

which confirmed the negative linear dose-response relationships (Figure 1).

303

After categorized all subjects according to the quartile levels of each PAH

304

exposure biomarker, males within the highest quartile (Q4 subgroup) of urinary

305

1-OHNa, 2-OHFlu, 1-OHPh, 2-OHPh, 9-OHPh and ΣOH-PAHs had a separate

306

0.0106, 0.0101, 0.0111, 0.0102, 0.0098, or 0.0119 reduction in mLRR-Y, when

307

compared to those within the lowest subgroup (Q1) of each metabolite (all P < 0.05).

308

The significantly increasing trends in mLRR-Y were found for 1-OHNa, 2-OHFlu, 15

309

1-OHPh, 2-OHPh, 4-OHPh, 9-OHPh, 1-OHP, and ΣOH-PAHs (Ptrend < 0.05), and a

310

marginal trend for BPDE-Alb adducts (Ptrend = 0.051), with the exception of 2-OHNa,

311

9-OHFlu, and 3-OHPh (Ptrend = 0.122, 0.227, and 0.076, respectively) (Table S3).

312 313 314

Association between Selected SNPs with mLRR-Y The general information of 14 selected SNPs was represented in Table 3. Among

315

the study participants, the rs1122138 of TCL1A was associated with leukocyte

316

mLRR-Y in an additive manner, with one allele increase in minor allele A resulting in

317

0.0065 increase of mLRR-Y (SE = 0.0031, P = 0.037), suggesting that the

318

rs1122138A allele is the protective allele associated with lower mLOY. But we didn’t

319

find significant associations for the other 13 SNPs (all P > 0.05) (Table 3). Between

320

the upstream and downstream 10Kb region of TCL1A gene, there were 57 SNPs

321

passed the quality control. Among them, additional 9 SNPs surrounding rs1122138,

322

including rs2296312, rs7359033, rs2296311, rs2887399, rs1957937, rs1123245,

323

rs1984968, rs1984967, and rs8012195, were also significantly associated with

324

increased mLRR-Y (all P < 0.05) (Table S4, Table 4). Additionally, all these 9 SNPs

325

showed high linkage disequilibrium with rs1122138 (all D’ = 1 and r2 = 1, except

326

rs7359033 that had D’ = 1 and r2 = 0.703 with rs1122138) among the study

327

participants (Table S4, Figure S2).

328

In order to test the biological roles of above TCL1A variants, we search for the

329

GTEx database and found that the minor alleles of these 10 TCL1A SNPs could all

330

significantly decrease the expression level of TCL1A in the whole blood samples (all 16

331

P < 0.05) (Table 4, Figure S3).

332

Interactive Effects of Age, Smoking, and TCL1A rs1122138 with PAHs on

333

mLRR-Y

334

The significant interaction between PAHs exposure and age was only marginally

335

shown for urinary 1-OHP (P interaction = 0.052), but not for the others (Figure S4). In the

336

stratification analysis by age, the significant association between 1-OHP and

337

mLRR-Y in leukocytes was predominately shown among the elder participants aged >

338

45 years-old [β (95%CI) = -0.0174 (-0.0302, -0.0059), P = 0.004], but not seen for the

339

young participants aged ≤ 45 years-old [β (95%CI) = -0.0053 (-0.0162, 0.0051), P =

340

0.314]. Also, smoking status and rs1122138 genotypes could not modify the

341

association between each PAH exposure biomarker and mLRR-Y (Figure S4).

342 343 344

Joint Effects of Age, Smoking, and TCL1A rs1122138 with PAHs on mLRR-Y It was shown that, compared to low PAHs exposed young workers, the high

345

PAHs exposed elder workers had the lowest level of mLRR-Y [mean: -0.0181 vs.

346

-0.0012, β (95%CI) = -0.0173 (-0.0270, -0.0067)] (Figure 2A). Compared to never

347

smokers with low PAHs exposure, the ever smokers with high PAHs exposure had the

348

highest mLOY (mean mLRR-Y: -0.0103 vs. -0.0002), with a 0.0078 decrease in

349

mLRR-Y [95%CI (-0.0172, 0.0013)] (Figure 2B).

350

Since the rs1122138A allele had a positive association with mLRR-Y in an

351

additive model, we further sub-grouped the study participants into rs1122138 CA+AA

352

and rs1122138 CC genotype carriers. The high PAHs exposed rs1122138 CC carriers 17

353

showed the lowest level of mLRR-Y among the four subgroups, who had a 0.0140

354

(95%CI: -0.0234 to -0.0049) decreased mLRR-Y than low PAHs exposed rs1122138

355

CA+AA genotype carriers (mean mLRR-Y: -0.0077 vs. 0.0069) (Figure 2C).

356

The joint effects of the above 4 risk factors, including > 45 years-old, ever

357

smoking, carrying rs1122138CC genotype, and high PAHs exposure were further

358

explored. There were 345 subjects (36.2%) carrying more than 3 risk factors, and they

359

showed significant lower mLRR-Y than subjects without any risk factor [β (95%CI) =

360

-0.0185 (-0.0343, -0.0031) for 3 and -0.0274 (-0.0452, -0.0090) for 4 risk factors,

361

respectively]. A significant increase trend in decreasing mLRR-Y was also observed

362

among subjects with an increased number of risk factors (Ptrend = 0.001) (Figure 2D).

363 364 365

DISCUSSION To our knowledge, this was the first study to investigate the associations between

366

PAHs exposure and mLOY in peripheral blood. One of our most noteworthy findings

367

is that the internal exposure levels of PAHs showed significantly linear dose-response

368

relationship with leukocyte mLOY. Consistent with previous studies, our results

369

validated the positive correlations of age and smoking pack-years with mLOY, and

370

also revealed the effects of TCL1A variants, marked by SNP rs1122138 among

371

Chinese population. More importantly, we illustrated the joint effects of PAHs

372

exposure with age, smoking, and TCL1A rs1122138 on elevating leukocyte mLOY.

373 374

Known as highly toxic, PAHs can cause great damage to human health. After absorbing by the human body, PAHs can produce reactive oxygen species (ROS) 18

375

during metabolism in vivo, which can induce oxidative damage to the normal

376

structures and functions of DNA and proteins (Pavanello et al., 2013; Grova et al.,

377

2017). In addition, some PAH metabolites (e.g., BPDE) could directly bind to DNA

378

and form DNA adducts, causing chromosome rearrangement. The oxidative damage

379

and DNA adducts induced by PAHs exposure may be two important mechanisms of

380

PAHs exerting effects on mLOY (Mishra et al., 2016).

381

In the present study, we found a linear dose-response relationship between levels

382

of 1-OHNa, 1-OHPh, 1-OHP along with BPDE-Alb adducts and leukocyte mLOY.

383

Similarly, the previous in vitro experiments indicated that naphthalene exposure could

384

destroy body cells and damage chromosome, leading to the formation of micronucleus,

385

chromosome aberration and recombination (Alegbeleye et al., 2017). Phenanthrene

386

exposure could affect the normal function of aromatic compound receptor (AhR) and

387

result in abnormal expression of corresponding genes, thus breaking the immune

388

system (Liu et al., 2013), the basis for maintaining homeostasis and clearing aberrant

389

cells. Exposure to high level of pyrene had been reported to be associated with

390

impaired structure and function of DNA, further causing chromosome heteroploidy

391

(Idowu et al., 2019). Known as highly carcinogenicity and mutagenicity, BPDE is the

392

ultimate metabolite of benzopyrene. The plasma concentrations of BPDE-Alb adducts

393

were reported to be associated with micronucleus frequency in lymphocytes, which is

394

also a marker of chromosome structural damage like mLOY (Ling et al., 2018). Some

395

epidemiological studies had indicated the effects of PAHs on promoting telomere

396

length shortening (Pavanello et al., 2010; Fu et al., 2018) and decreasing the copy 19

397

number of mitochondrial DNA (Wong et al., 2017), which are also both chromosome

398

damage markers. Although lack of epidemiological replication, Dumanski et al. and

399

Wong et al. illustrated that cigarette smoking and PM10 exposure, in which PAHs

400

existed as both important constituents, could exacerbate leukocyte mLOY, making the

401

current association between PAHs exposure and mLOY feasible (Dumanski et al.,

402

2015; Wong et al., 2018). As a known contributor for mLOY, the effect of age has

403

been widely reported. Our study indicated joint effects on accelerating mLOY among

404

the elder workers (aged > 45) with high PAHs exposure, and there was a marginal

405

interaction of age with 1-OHP on mLOY. During the aging process, human

406

antioxidant capacity is generally weakened and cannot eliminate the excess ROS

407

deriving from PAHs metabolism, thus leading to more severe mLOY

408

(Nikolich-Zugich, 2018). More studies are needed to replicate these findings and

409

uncover the underlying mechanisms.

410

Cigarette smoking was the first environmental factor identified to induce mLOY.

411

In the study, we found a significant correlation of smoking pack-years with increasing

412

mLOY, which was in accordance with the previous findings (Dumanski et al., 2015;

413

Zhou et al., 2016; Wong et al., 2018). In this study, the current and former smokers

414

had lower values of mLRR-Y than never smokers, although the statistical differences

415

were not significant. Also, the effects of cigarette smoking on mLOY may be covered

416

up by high levels of occupational PAHs exposure. The results in present study, along

417

with previous reports, illustrated that mLOY is a novel biomarker for chromosome

418

damage and maybe a potential mediator for carcinogenesis caused by PAHs exposure, 20

419 420

aging, and cigarette smoking. Genetic susceptibility loci of mLOY have also been identified. The first

421

genome-wide association study regarded mLOY as a dichotomous variable and

422

observed the first susceptibility locus of TCL1A gene, marked by rs2887399 (Zhou et

423

al., 2016). A subsequent larger GWAS carried out in the UK Biobank cohort of 85,542

424

males used a quantitative mLRR-Y as a proxy for mLOY, replicated the TCL1A locus

425

(marked by rs1122138, in a complete linkage disequilibrium with rs2887399), and

426

identified additional 18 novel loci of genes functioned in cell proliferation and

427

cell-cycle regulation (Wright et al., 2017). Our study, also adopting a quantitative trait

428

for mLOY by calculating the mLRR-Y, is the first one to validate the mLOY-related

429

TCL1A locus, marked by rs1122138, in a Chinese population. Fine mapping of this

430

region indicated that the major allele of rs1122138 and it is highly linkage

431

disequilibrium 9 SNPs, were associated with increased leukocyte mLOY and high

432

TCL1A expression in whole blood. Overexpression of TCL1A was reported to

433

participate in the development of mature T cell leukemia due to the malignant

434

proliferation of hematopoietic stem cell, and can combine with damaging ATM

435

aberrations to potentiate chromosome fragility and rescue the cell apoptosis (Schrader

436

et al., 2018). When we simultaneously considered all reported risk factors of mLOY,

437

including aged > 45, ever-smoking, carrying rs1122138CC genotype, and exposed to

438

high levels of PAHs, we found that a moderate proportion of subjects (36.2%) had

439

more than 3 risk factors, who were also the most susceptible population to had

440

significant mLOY than those with zero risk score. Further protection strategies should 21

441 442

pay more attention to this subpopulation. There are some strengths in the present study. Firstly, we systematically

443

measured 10 kinds of urinary PAH metabolites and plasma BPDE-Alb adducts, which

444

can provide a comprehensive assessment for total external PAHs exposure levels from

445

diverse aspects. Secondly, our study is the first one to reveal the effect of a typical

446

environmental pollutant on male mLOY in a Chinese population and used a

447

continuous proxy for mLOY in the regression models, where the results are of greater

448

statistical significance and more persuasive. Thirdly, the consistency of findings

449

between the present and previous studies improved the reliability and accuracy of our

450

findings. More importantly, besides PAHs, we simultaneously examined the reported

451

mLOY-related SNPs, which allowed the estimate of joint and interactive effects of

452

PAHs with the known risk factors like age, smoking, and genetic variants, providing

453

scientific evidence for identifying susceptible population as well as putting forward

454

corresponding interventions.

455

However, some limitations should not be neglected. Firstly, the present

456

cross-sectional study is difficult to infer the causality. However, the PAH metabolites

457

were the exposure index and the mLOY in leukocytes was acceptable to be the effect

458

outcome, the dose-response relationships, consistency with previous studies, and

459

biological plausibility lend support for causality. Secondly, urinary levels of PAH

460

metabolites had relatively short half-lives and were detected by using a spot urine

461

instead of multiple points or 24 hours’ urine samples. However, the coke-oven

462

workers are long-termly exposed to PAHs contained combustion emissions 22

463

continuously in their working and daily environment, and the PAH metabolites in a

464

single time point urine were also widely used as internal exposure biomarkers in the

465

environmental epidemiological studies (Kuang et al., 2013; Zhou et al., 2018). In

466

addition, since the urinary levels of PAH metabolites were highly correlated (Table

467

S5), which violates the independence assumption, and since the SNPs were chosen a

468

priori from the large-scale population based GWAS study (Wright et al., 2017), we

469

didn’t control for multiple comparisons for the associations. Thirdly, since coke-oven

470

workers are the typical and occupational population who have been long-term

471

exposure to higher levels of PAHs than the general population (Table S6), they would

472

be more likely to see an effect on mLOY, which minimized the generalization of the

473

findings. Fourthly, insufficient sample size of this study to detect interaction with

474

enough power and the lack of independent replication are also important limitations.

475

Further investigations with larger sample-sized populations are needed to verify the

476

current results. Moreover, chromosome abnormalities like X chromosome loss were

477

also common feature in both males and females and found to be associated with many

478

diseases (Invernizzi et al., 2004; Miozzo et al., 2007), so the effects of PAHs exposure

479

on X chromosome loss still warrant further investigations.

480 481

CONCLUSIONS

482

The present study revealed that exposure to PAHs had a linear dose-dependent

483

effect on elevating mLOY in peripheral blood. We validated the associations of age,

484

smoking pack-years, and TCL1A variants (marked by rs1122138) with mLOY, and 23

485

pointed out the joint effects of the above risk factors. Our findings proposed a

486

susceptible population who should be taken special surveillance and prevention for

487

mLOY related diseases. Extensive investigations are warranted to validate these

488

observations and explore the potential molecular mechanisms.

489 490

DECLARATIONS OF INTEREST

491

The authors declare no competing financial interest.

492 493

FUNDING

494

This work was supported by the funds from the National Natural Science Foundation

495

of China [grant no. 81773398, 81722038], the National Key Research and

496

Development Program of China [grant numbers: 2018YFC2000203 and

497

2015CB553403], and the National Youth Top Talent Support Program of China.

498 499

ACKNOWLEDGMENTS

500

The authors would like to appreciate all participants in this study as well as all

501

volunteers for collecting the samples and questionnaires. Y.L, Y.B., H.G. analyzed

502

data and wrote the paper. X.M., M.H., T.W., and H.G. conducted and designed

503

research. X.W., G.L., W.F., G.W., Y.F., H.M., H.L., and M.L. partially provided

504

essential materials. All authors had access to the data, commented on the report drafts

505

and approved the final submitted version.

506

24

507

SUPPORTING INFORMATION

508

Tables and Figures showing the stratified analysis of PAHs exposure with mLRR-Y,

509

as well as smoking pack-years and age with mLRR-Y. In addition, the associations of

510

TCL1A variants with mLRR-Y, the functional annotations of gene variants were also

511

showed.

25

512

REFERENCES

513

Alegbeleye, O.O., Opeolu, B.O., Jackson, V.A., 2017. Polycyclic Aromatic

514

Hydrocarbons: A Critical Review of Environmental Occurrence and

515

Bioremediation. Environ Manage 60, 758-783.

516

DOI:10.1007/s00267-017-0896-2.

517

Chung, M.K., Riby, J., Li, H., Iavarone, A.T., Williams, E.R., Zheng, Y., Rappaport,

518

S.M., 2010. A sandwich enzyme-linked immunosorbent assay for adducts of

519

polycyclic aromatic hydrocarbons with human serum albumin. Anal Biochem

520

400, 123-129. DOI:10.1016/j.ab.2010.01.018.

521

Dumanski, J.P., Lambert, J.C., Rasi, C., Giedraitis, V., Davies, H., Grenier-Boley, B.,

522

Lindgren, C.M., Campion, D., Dufouil, C., Pasquier, F., Amouyel, P., Lannfelt,

523

L., Ingelsson, M., Kilander, L., Lind, L., Forsberg, L.A., 2016. Mosaic Loss of

524

Chromosome Y in Blood Is Associated with Alzheimer Disease. Am J Hum

525

Genet 98, 1208-1219. DOI:10.1016/j.ajhg.2016.05.014.

526

Dumanski, J.P., Rasi, C., Lonn, M., Davies, H., Ingelsson, M., Giedraitis, V., Lannfelt,

527

L., Magnusson, P.K., Lindgren, C.M., Morris, A.P., Cesarini, D., Johannesson,

528

M., Tiensuu Janson, E., Lind, L., Pedersen, N.L., Ingelsson, E., Forsberg, L.A.,

529

2015. Mutagenesis. Smoking is associated with mosaic loss of chromosome Y.

530

Science 347, 81-83. DOI:10.1126/science.1262092.

531

Forsberg, L.A., Rasi, C., Malmqvist, N., Davies, H., Pasupulati, S., Pakalapati, G.,

532

Sandgren, J., Diaz de Stahl, T., Zaghlool, A., Giedraitis, V., Lannfelt, L., Score, J.,

533

Cross, N.C., Absher, D., Janson, E.T., Lindgren, C.M., Morris, A.P., Ingelsson, E., 26

534

Lind, L., Dumanski, J.P., 2014. Mosaic loss of chromosome Y in peripheral

535

blood is associated with shorter survival and higher risk of cancer. Nat Genet 46,

536

624-628. DOI:10.1038/ng.2966.

537

Fu, W., Chen, Z., Bai, Y., Wu, X., Li, G., Chen, W., Wang, G., Wang, S., Li, X., He,

538

M., Zhang, X., Wu, T., Guo, H., 2018. The interaction effects of polycyclic

539

aromatic hydrocarbons exposure and TERT- CLPTM1L variants on longitudinal

540

telomere length shortening: A prospective cohort study. Environ Pollut 242,

541

2100-2110. DOI:10.1016/j.envpol.2018.05.026.

542

Grova, N., Antignac, J.P., Hardy, E.M., Monteau, F., Pouponneau, K., Le Bizec, B.,

543

Appenzeller, B.M.R., 2017. Identification of new tetrahydroxylated metabolites

544

of Polycyclic Aromatic Hydrocarbons in hair as biomarkers of exposure and

545

signature of DNA adduct levels. Anal Chim Acta 995, 65-76.

546

DOI:10.1016/j.aca.2017.10.002.

547

Haitjema, S., Kofink, D., van Setten, J., van der Laan, S.W., Schoneveld, A.H., Eales,

548

J., Tomaszewski, M., de Jager, S.C.A., Pasterkamp, G., Asselbergs, F.W., den

549

Ruijter, H.M., 2017. Loss of Y Chromosome in Blood Is Associated With Major

550

Cardiovascular Events During Follow-Up in Men After Carotid Endarterectomy.

551

Circ Cardiovasc Genet 10, e001544. DOI:10.1161/circgenetics.116.001544.

552

Idowu, O., Semple, K.T., Ramadass, K., O'Connor, W., Hansbro, P., Thavamani, P.,

553

2019. Beyond the obvious: Environmental health implications of polar

554

polycyclic aromatic hydrocarbons. Environ Int 123, 543-557.

555

DOI:10.1016/j.envint.2018.12.051. 27

556

Invernizzi, P., Miozzo, M., Battezzati, P.M., Bianchi, I., Grati, F.R., Simoni, G., Selmi,

557

C., Watnik, M., Gershwin, M.E., Podda, M., 2004. Frequency of monosomy X in

558

women with primary biliary cirrhosis. Lancet 363, 533-535.

559

DOI:10.1016/s0140-6736(04)15541-4.

560

Kuang, D., Zhang, W., Deng, Q., Zhang, X., Huang, K., Guan, L., Hu, D., Wu, T.,

561

Guo, H., 2013. Dose-response relationships of polycyclic aromatic hydrocarbons

562

exposure and oxidative damage to DNA and lipid in coke oven workers. Environ

563

Sci Technol 47, 7446-7456. DOI:10.1021/es401639x.

564

Lee, K.H., Shu, X.O., Gao, Y.T., Ji, B.T., Yang, G., Blair, A., Rothman, N., Zheng, W.,

565

Chow, W.H., Kang, D., 2010. Breast cancer and urinary biomarkers of polycyclic

566

aromatic hydrocarbon and oxidative stress in the Shanghai Women's Health

567

Study. Cancer Epidemiol Biomarkers Prev 19, 877-883.

568

DOI:10.1158/1055-9965.epi-09-1098.

569

Ling, X., Yang, W., Zou, P., Zhang, G., Wang, Z., Zhang, X., Chen, H., Peng, K., Han,

570

F., Liu, J., Cao, J., Ao, L., 2018. TERT regulates telomere-related senescence and

571

apoptosis through DNA damage response in male germ cells exposed to BPDE in

572

vitro and to B[a]P in vivo. Environ Pollut 235, 836-849.

573

DOI:10.1016/j.envpol.2017.12.099.

574

Liu, J., Zhang, L., Winterroth, L.C., Garcia, M., Weiman, S., Wong, J.W., Sunwoo,

575

J.B., Nadeau, K.C., 2013. Epigenetically mediated pathogenic effects of

576

phenanthrene on regulatory T cells. J Toxicol 2013, 967029.

577

DOI:10.1155/2013/967029. 28

578

Loftfield, E., Zhou, W., Graubard, B.I., Yeager, M., Chanock, S.J., Freedman, N.D.,

579

Machiela, M.J., 2018. Predictors of mosaic chromosome Y loss and associations

580

with

581

DOI:10.1038/s41598-018-30759-1.

mortality

in

the

UK

Biobank.

Sci

Rep

8,

12316.

582

Loftfield, E., Zhou, W., Yeager, M., Chanock, S.J., Freedman, N.D., Machiela, M.J.,

583

2019. Mosaic Y Loss Is Moderately Associated with Solid Tumor Risk. Cancer

584

Res 79, 461-466. DOI:10.1158/0008-5472.can-18-2566.

585

Machiela, M.J., Dagnall, C.L., Pathak, A., Loud, J.T., Chanock, S.J., Greene, M.H.,

586

McGlynn, K.A., Stewart, D.R., 2017. Mosaic chromosome Y loss and testicular

587

germ cell tumor risk. J Hum Genet 62, 637-640. DOI:10.1038/jhg.2017.20.

588

Miozzo, M., Selmi, C., Gentilin, B., Grati, F.R., Sirchia, S., Oertelt, S., Zuin, M.,

589

Gershwin, M.E., Podda, M., Invernizzi, P., 2007. Preferential X chromosome

590

loss but random inactivation characterize primary biliary cirrhosis. Hepatology

591

46, 456-462. DOI:10.1002/hep.21696.

592

Mishra, N., Ayoko, G.A., Morawska, L., 2016. Atmospheric polycyclic aromatic

593

hydrocarbons in the urban environment: Occurrence, toxicity and source

594

apportionment. Environ Pollut 208, 110-117. DOI:10.1016/j.envpol.2015.08.015.

595 596 597

Nikolich-Zugich, J., 2018. The twilight of immunity: emerging concepts in aging of the immune system. Nat Immunol 19, 10-19. DOI:10.1038/s41590-017-0006-x. Pavanello, S., Dioni, L., Hoxha, M., Fedeli, U., Mielzynska-Svach, D., Baccarelli,

598

A.A., 2013. Mitochondrial DNA copy number and exposure to polycyclic

599

aromatic hydrocarbons. Cancer Epidemiol Biomarkers Prev 22, 1722-1729. 29

600 601

DOI:10.1158/1055-9965.epi-13-0118. Pavanello, S., Pesatori, A.C., Dioni, L., Hoxha, M., Bollati, V., Siwinska, E.,

602

Mielzynska, D., Bolognesi, C., Bertazzi, P.A., Baccarelli, A., 2010. Shorter

603

telomere length in peripheral blood lymphocytes of workers exposed to

604

polycyclic aromatic hydrocarbons. Carcinogenesis 31, 216-221.

605

DOI:10.1093/carcin/bgp278.

606

Persani, L., Bonomi, M., Lleo, A., Pasini, S., Civardi, F., Bianchi, I., Campi, I., Finelli,

607

P., Miozzo, M., Castronovo, C., Sirchia, S., Gershwin, M.E., Invernizzi, P., 2012.

608

Increased loss of the Y chromosome in peripheral blood cells in male patients

609

with autoimmune thyroiditis. J Autoimmun 38, J193-196.

610

DOI:10.1016/j.jaut.2011.11.011.

611

Qin, N., Li, N., Wang, C., Pu, Z., Ma, Z., Jin, G., Zhu, M., Dai, M., Hu, Z., Ma, H.,

612

Shen, H., 2019. Association of Mosaic Loss of Chromosome Y with Lung

613

Cancer Risk and Prognosis in a Chinese Population. J Thorac Oncol 14, 37-44.

614

DOI:10.1016/j.jtho.2018.09.013.

615

Schrader, A., Crispatzu, G., Oberbeck, S., Mayer, P., Putzer, S., von Jan, J., Vasyutina,

616

E., Warner, K., Weit, N., Pflug, N., Braun, T., Andersson, E.I., Yadav, B.,

617

Riabinska, A., Maurer, B., Ventura Ferreira, M.S., Beier, F., Altmuller, J., Lanasa,

618

M., Herling, C.D., Haferlach, T., Stilgenbauer, S., Hopfinger, G., Peifer, M.,

619

Brummendorf, T.H., Nurnberg, P., Elenitoba-Johnson, K.S.J., Zha, S., Hallek, M.,

620

Moriggl, R., Reinhardt, H.C., Stern, M.H., Mustjoki, S., Newrzela, S., Frommolt,

621

P., Herling, M., 2018. Actionable perturbations of damage responses by 30

622

TCL1/ATM and epigenetic lesions form the basis of T-PLL. Nat Commun 9, 697.

623

DOI:10.1038/s41467-017-02688-6.

624

Weinstein, J.R., Asteria-Penaloza, R., Diaz-Artiga, A., Davila, G., Hammond, S.K.,

625

Ryde, I.T., Meyer, J.N., Benowitz, N., Thompson, L.M., 2017. Exposure to

626

polycyclic aromatic hydrocarbons and volatile organic compounds among

627

recently pregnant rural Guatemalan women cooking and heating with solid fuels.

628

Int J Hyg Environ Health 220, 726-735. DOI:10.1016/j.ijheh.2017.03.002.

629

White, A.J., Bradshaw, P.T., Herring, A.H., Teitelbaum, S.L., Beyea, J., Stellman, S.D.,

630

Steck, S.E., Mordukhovich, I., Eng, S.M., Engel, L.S., Conway, K., Hatch, M.,

631

Neugut, A.I., Santella, R.M., Gammon, M.D., 2016. Exposure to multiple

632

sources of polycyclic aromatic hydrocarbons and breast cancer incidence.

633

Environ Int 89-90, 185-192. DOI:10.1016/j.envint.2016.02.009.

634

Wong, J.Y.Y., Hu, W., Downward, G.S., Seow, W.J., Bassig, B.A., Ji, B.T., Wei, F.,

635

Wu, G., Li, J., He, J., Liu, C.S., Cheng, W.L., Huang, Y., Yang, K., Chen, Y.,

636

Rothman, N., Vermeulen, R.C., Lan, Q., 2017. Personal exposure to fine

637

particulate matter and benzo[a]pyrene from indoor air pollution and leukocyte

638

mitochondrial DNA copy number in rural China. Carcinogenesis 38, 893-899.

639

DOI:10.1093/carcin/bgx068.

640

Wong, J.Y.Y., Margolis, H.G., Machiela, M., Zhou, W., Odden, M.C., Psaty, B.M.,

641

Robbins, J., Jones, R.R., Rotter, J.I., Chanock, S.J., Rothman, N., Lan, Q., Lee,

642

J.S., 2018. Outdoor air pollution and mosaic loss of chromosome Y in older men

643

from the Cardiovascular Health Study. Environ Int 116, 239-247. 31

644

DOI:10.1016/j.envint.2018.04.030.

645

Wright, D.J., Day, F.R., Kerrison, N.D., Zink, F., Cardona, A., Sulem, P., Thompson,

646

D.J., Sigurjonsdottir, S., Gudbjartsson, D.F., Helgason, A., Chapman, J.R.,

647

Jackson, S.P., Langenberg, C., Wareham, N.J., Scott, R.A., Thorsteindottir, U.,

648

Ong, K.K., Stefansson, K., Perry, J.R.B., 2017. Genetic variants associated with

649

mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer

650

susceptibility. Nat Genet 49, 674-679. DOI:10.1038/ng.3821.

651

Yang, G., Fan, L., Tan, J., Qi, G., Zhang, Y., Samet, J.M., Taylor, C.E., Becker, K., Xu,

652

J., 1999. Smoking in China: findings of the 1996 National Prevalence Survey.

653

JAMA 282, 1247-1253. DOI:10.1001/jama.282.13.1247.

654

Yin, W., Hou, J., Xu, T., Cheng, J., Li, P., Wang, L., Zhang, Y., Wang, X., Hu, C.,

655

Huang, C., Yu, Z., Yuan, J., 2018. Obesity mediated the association of exposure

656

to polycyclic aromatic hydrocarbon with risk of cardiovascular events. Sci Total

657

Environ 616-617, 841-854. DOI:10.1016/j.scitotenv.2017.10.238.

658

Yuan, J.M., Ross, R.K., Gao, Y.T., Henderson, B.E., Yu, M.C., 1997. Follow up study

659

of moderate alcohol intake and mortality among middle aged men in Shanghai,

660

China. BMJ 314, 18-23. DOI:10.1136/bmj.314.7073.18.

661

Zhou, W., Machiela, M.J., Freedman, N.D., Rothman, N., Malats, N., Dagnall, C.,

662

Caporaso, N., Teras, L.T., Gaudet, M.M., Gapstur, S.M., Stevens, V.L., Jacobs,

663

K.B., Sampson, J., Albanes, D., Weinstein, S., Virtamo, J., Berndt, S., Hoover,

664

R.N., Black, A., Silverman, D., Figueroa, J., Garcia-Closas, M., Real, F.X., Earl,

665

J., Marenne, G., Rodriguez-Santiago, B., Karagas, M., Johnson, A., Schwenn, M., 32

666

Wu, X., Gu, J., Ye, Y., Hutchinson, A., Tucker, M., Perez-Jurado, L.A., Dean, M.,

667

Yeager, M., Chanock, S.J., 2016. Mosaic loss of chromosome Y is associated

668

with common variation near TCL1A. Nat Genet 48, 563-568.

669

DOI:10.1038/ng.3545.

670

Zhou, Y., Mu, G., Liu, Y., Xiao, L., Ma, J., Wang, B., Shi, T., Tan, A., Yuan, J., Chen,

671

W., 2018. Urinary polycyclic aromatic hydrocarbon metabolites, Club cell

672

secretory protein and lung function. Environ Int 111, 109-116.

673

DOI:10.1016/j.envint.2017.11.016.

674

33

Table 1. Baseline characteristics of the study population (N = 1005). Variables

Values

Age (mean ± SD, years)

42.6 ± 8.5

2

24.3 ± 3.0

BMI (mean ± SD, kg/m ) Smoking status, n (%) None

279 (27.8)

Former

45 (4.5)

Current

681 (67.8)

Smoking pack-years [median (25th, 75th)]

16.9 (8.4, 26.7)

Drinking status, n (%) None

595 (59.2)

Former

18 (1.8)

Current

392 (39.0)

Physical activity, n (%) No

503 (50.8)

Yes

487 (49.2)

Self-report cardiovascular disease, n (%) No

991 (98.6)

Yes

14 (1.4)

Self-report benign tumor, n (%) No

994 (98.9)

Yes

11 (1.1)

Diabetes mellitus, n (%) No

942 (93.7)

Yes

63 (6.3)

Leukocyte mosaic loss of chromosome Y

a

mLRR-Y -0.0035 ± 0.0500 Note: Continuous variables were presented as mean ± SD or median (25th, 75th percentiles). Categorical variables were presented as n (%). a The 954 subjects passing the QC process were used to calculate the mLRR-Y values.

34

Table 2. Associations of PAH exposure biomarkers with mLRR-Y in blood leukocytes. Model 1 a Model 2 b PAH exposure biomarkers β (95%CI) P β (95%CI)

Sensitivity analysis c P

β (95%CI)

P

1-OHNa

-0.0120 (-0.0194, -0.0047)

0.001

-0.0111 (-0.0187, -0.0036) 0.004

-0.0119 (-0.0198, -0.0040)

0.003

2-OHNa

-0.0069 (-0.0140, 0.0002)

0.057

-0.0054 (-0.0130, 0.0022)

0.164

-0.0065 (-0.0144, 0.0015)

0.113

2-OHFlu

-0.0052 (-0.0117, 0.0014)

0.122

-0.0056 (-0.0122, 0.0010)

0.095

-0.0064 (-0.0133, 0.0005)

0.069

9-OHFlu

-0.0010 (-0.0043, 0.0024)

0.566

-0.0009 (-0.0042, 0.0024)

0.588

-0.0017 (-0.0052, 0.0017)

0.328

1-OHPh

-0.0085 (-0.0144, -0.0027)

0.004

-0.0085 (-0.0143, -0.0026) 0.005

-0.0092 (-0.0154, -0.0031)

0.003

2-OHPh

-0.0066 (-0.0131, -0.0001)

0.047

-0.0069 (-0.0134, -0.0004) 0.037

-0.0061 (-0.0128, 0.0007)

0.079

3-OHPh

-0.0033 (-0.0087, 0.0021)

0.225

-0.0038 (-0.0092, 0.0017)

0.174

-0.0038 (-0.0094, 0.0019)

0.194

4-OHPh

-0.0025 (-0.0059, 0.0010)

0.161

-0.0025 (-0.0059, 0.0010)

0.161

-0.0031 (-0.0067, 0.0005)

0.089

9-OHPh

-0.0059 (-0.0122, 0.0003)

0.064

-0.0057 (-0.0120, 0.0006)

0.074

-0.0049 (-0.0115, 0.0017)

0.143

1-OHP

-0.0108 (-0.0187, -0.0029)

0.008

-0.0103 (-0.0182, -0.0024) 0.011

-0.0099 (-0.0182, -0.0015)

0.021

ΣOH-PAHs

-0.0144 (-0.0237, -0.0051)

0.002

-0.0134 (-0.0227, -0.0041) 0.005

-0.0144 (-0.0241, -0.0046)

0.004

BPDE-Alb adducts

-0.0159 (-0.0302, -0.0015)

0.030

-0.0152 (-0.0296, -0.0009) 0.038

-0.0167 (-0.0319, -0.0015)

0.032

Note: The lg-transformed concentrations of each PAH metabolite and ΣOH-PAHs were separately included in the linear regression models. a

with adjustment for experimental batch.

b

with adjustment for experimental batch, age, BMI, smoking pack-years, alcohol drinking status, and physical activity. Sensitivity analysis (n = 875) after excluding participants with a history of diabetes mellitus, self-reported cardiovascular disease, or benign tumor. The covariates were the same as in model 2.

c

35

Table 3. The characteristics of selected single nucleotide polymorphisms (SNPs) and their associations with mLRR-Y among the study participants. Associations between SNPs and mLRR-Y in this study b a a a a SNP Location ChrPosID Gene EA/OA AA/AB/BB EAF β SE P rs2736609 1q22 chr1:156202640 PMF1, SEMA4A T/C 104/425/425 0.33 -0.0017 0.0021 0.427 NPAT, ATM, -0.0019 0.0020 rs4754301 11q22.3 chr11:108048541 A/G 177/478/299 0.44 0.361 ACAT1 rs9805742 13q14.11 chr13:41593758 WBP4 A/G 788/156/10 0.91 -0.0041 0.0034 0.223 rs10151519 14q32.2 chr14:101175798 DLK1 G/A 463/404/87 0.70 -0.0038 0.0022 0.078 rs1122138 14q32.13 chr14:96180242 TCL1A C/A 748/193/13 0.89 -0.0065 0.0031 0.037 rs12448368 16q23.2 chr16:81044947 CENPN, ATMIN C/T 57/348/549 0.24 -0.0004 0.0023 0.856 rs11082396 18q12.3 chr18:42080720 SETBP1 C/T 44/344/566 0.23 -0.0018 0.0024 0.463 rs13088318 3q12.3 chr3:101242751 SENP7 G/A 105/395/454 0.32 -0.0019 0.0021 0.368 -0.0008 0.0039 0.839 rs6802910 3q25.1 chr3:150016195 TSC22D2 C/A 4/123/827 0.07 rs56084922 5q22.1 chr5:111061883 NREP G/A 81/394/479 0.29 -0.0004 0.0022 0.854 SMPD2, -0.0014 0.0028 rs13191948 6q21 chr6:109634599 C/T 680/254/20 0.85 0.602 CCDC162P rs381500 6q26 chr6:164478388 QKI C/A 292/476/186 0.56 -0.0019 0.0020 0.335 -0.0019 0.0021 0.353 rs4721217 7p22.3 chr7:1973579 MAD1L1 T/C 220/493/241 0.49 rs6468341 8p12 chr8:30279355 RBPMS C/T 635/287/32 0.82 -0.0020 0.0026 0.434 Abbreviations: EA, effect allele; OA, other allele; EAF, effect allele frequency; SE, standard error Note: AA: effect genotype; AB: heterozygote; BB: other genotype a Detail information was extracted from previous GWAS of 85542 population. b Linear regression analyses, with adjustment for age, BMI, smoking pack-years, alcohol drinking status, physical activity, experimental batch, and the lg-transformed concentration of ΣOH-PAHs and BPDE-Alb adducts.

36

Table 4. The associations of TCL1A variants with leukocyte mLRR-Y among the study participants and TCL1A expression levels in the whole blood (based on the GTEx database). Associations with Associations with mLRR-Y selected TCL1A expression Location Gene region AA/AB/BB MAF SNPs a β SE P βb P rs1122138 rs2296312 rs7359033 rs2296311 rs2887399 rs1957937 rs1123245 rs1984968 rs1984967

chr14:9618024 2 chr14:9617832

intron

CC/CA/AA

0.11

0.0066

0.0031

0.037

-0.15

<0.001

intron TT/TC/CC 0.12 0.0070 0.0031 0.025 -0.15 <0.001 3 chr14:9617552 intron TT/TC/CC 0.09 0.0072 0.0034 0.037 -0.14 0.003 5 chr14:9617817 intron GG/GA/AA 0.11 0.0066 0.0031 0.037 -0.15 0.003 3 chr14:9618069 upstream 161 bp GG/GT/TT 0.12 0.0065 0.0031 0.040 -0.10 0.012 5 chr14:9618136 upstream 826 bp AA/AT/TT 0.12 0.0063 0.0031 0.043 -0.15 <0.001 0 chr14:9618152 upstream 991 bp GG/GA/AA 0.12 0.0063 0.0031 0.043 -0.13 0.005 5 chr14:9618158 upstream 1048 AA/AG/GG 0.12 0.0063 0.0031 0.043 -0.15 <0.001 2 bp chr14:9618197 upstream 1438 CC/CT/TT 0.12 0.0063 0.0031 0.043 -0.15 <0.001 2 bp chr14:9618299 upstream 2465 rs8012195 GG/GA/AA 0.12 0.0063 0.0031 0.043 -0.11 0.012 9 bp Abbreviations: MAF, minor allele frequency; SE, standard error Note: AA: effect genotype; AB: heterozygote; BB: other genotype a Linear regression analyses, with adjustment for age, BMI, smoking pack-years, alcohol drinking status, physical activity, experimental batch, and the lg-transformed concentration of ΣOH-PAHs and BPDE-Alb adducts. b The values of β and P were calculated based on the GTEx database.

37

1

FIGURE LEGENDS

2

Figure 1. The associations between PAH exposure biomarkers and leukocyte mLRR-Y

3

based on the restricted cubic spline function.

4

(A) 1-OHNa; (B) 1-OHPh; (C) 2-OHPh; (D) 1-OHP; (E) ΣOH-PAHs; (F) BPDE-Alb

5

Note: The spline plots were drawn by using 3 knots (25th, 50th, 75th percentiles), while

6

the minimum concentration was used as the reference.

7 8

Figure 2. The joint effects of PAHs exposure with age, smoking status, and TCL1A

9

rs1122138 on leukocyte mLRR-Y.

10

Note: The red solid dot and blue line in panels represent β (95% CI), while the

11

mLRR-Y are represented as mean ± SD.

12 13 14

38

Highlights: Increased levels of PAHs exposure were associated with more severe mLOY. PAHs, age, smoking pack-years, and TCL1A rs1122138CC had joint effects on mLOY. Take precautions against mLOY among elder smokers exposed to PAHs and rs1122138CC.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: