Article pubs.acs.org/crt
Metabolomic Characterization of Laborers Exposed to Welding Fumes Ching-Hua Kuo,†,‡,○ Kuo-Ching Wang,‡,§,○ Tze-Feng Tian,‡,∥ Mong-Hsun Tsai,⊥ Yin-Mei Chiung,# Chun-Ming Hsiech,# Sung-Jeng Tsai,†,‡ San-Yuan Wang,‡,∥ Dong-Ming Tsai,‡,∥ Chiang-Ching Huang,∇ and Y. Jane Tseng*,†,‡,§,∥ †
Department of Pharmacy, ‡Metabolomics Group, §Graduate Institute of Biomedical Electronics and Bioinformatics, ∥Department of Computer Science and Information Engineering, and ⊥Institute of Biotechnology, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 106, Taiwan # Institute of Occupational Safety and Health, Council of Labor Affairs, No. 99, Lane 407, Hengke Road, New Taipei City, Taiwan ∇ Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611-4402, United States S Supporting Information *
ABSTRACT: The complex composition of welding fumes, multiplicity of molecular targets, diverse cellular effects, and lifestyles associated with laborers vastly complicate the assessment of welding fume exposure. The urinary metabolomic profiles of 35 male welders and 16 male office workers at a Taiwanese shipyard were characterized via 1H NMR spectroscopy and pattern recognition methods. Blood samples for the same 51 individuals were also collected, and the expression levels of the cytokines and other inflammatory markers were examined. This study dichotomized the welding exposure variable into high (welders) versus low (office workers) exposures to examine the differences of continuous outcome markersmetabolites and inflammatory markers between the two groups. Fume particle assessments showed that welders were exposed to different concentrations of chromium, nickel, and manganese particles. Multivariate statistical analysis of urinary metabolomic patterns showed higher levels of glycine, taurine, betaine/TMAO, serine, S-sulfocysteine, hippurate, gluconate, creatinine, and acetone and lower levels of creatine among welders, while only TNF-α was significantly associated with welding fume exposure among all cytokines and other inflammatory markers measured. Of the identified metabolites, the higher levels of glycine, taurine, and betaine among welders were suspected to play some roles in modulating inflammatory and oxidative tissue injury processes. In this metabolomics experiment, we also discovered that the association of the identified metabolites with welding exposure was confounded by smoking, but not with drinking, which is a finding consistent with known modified response of inflammatory markers among smokers. Our results correspond with prior studies that utilized nonmetabolomic analytical techniques and suggest that the metabolomic profiling is an efficient method to characterize the overall effect of welding fume exposure and other confounders.
■
INTRODUCTION Metabolomics with nuclear magnetic resonance (NMR) or gas/ liquid chromatography−mass spectrometry (GC/LC-MS) can detect and quantitatively measure the endogenous and exogenous low molecular weight metabolites in the investigation of metabolic disturbances in cells, tissues, and organisms. Patterns of these large quantities of metabolites are able to provide considerable insight into disregulated metabolic pathways, underlying disease processes, and pathophysiological changes induced by external stimuli such as environmental stress, drugs, nutrition, or genetic modification.1 Because of the analytical advantages of metabolomic analysis, it is unsurprising that metabolomics has already been widely applied in the field of toxicology, with specific regards to studying hepatotoxicity, neurotoxicity, renal toxicity, and pulmonary toxicity.2−5 Moreover, the ability of metabolomic analysis to characterize and capture the overall influence of net © 2012 American Chemical Society
chemical mixtures (including interactive effects) and spatial/ temporal heterogeneity in contaminant distribution on living organisms draws attention to its future value and use in the emerging field of environmental risk assessment.6,7 The effect of air pollutants produced by welding processes on the health of laborers, specifically welders, is a major concern of occupational medicine.8−10 In addition to work-related injuries, welders often suffer from acute and chronic occupational diseases such as hepatitis, dermatitis, neuropathy, and encephalopathy and are often associated with respiratory conditions such as chronic obstructive pulmonary disease, asthma, chronic bronchitis, and pneumonia after long-term exposure.11−16 Welders are also associated with an increased Received: October 27, 2011 Published: January 31, 2012 676
dx.doi.org/10.1021/tx200465e | Chem. Res. Toxicol. 2012, 25, 676−686
Chemical Research in Toxicology
Article
Table 1. Distribution of Participants by Fume Exposure Status and Alcohol and Cigarette Usea welders
controls
smokers
nonsmokers
group C
smokers
group A
group B
group A
group B
nondrinkers
drinkers
nondrinkers
drinkers
nondrinkers
drinkers
nondrinkers
drinkers
5
18
10
2
3
1
6
23
group C
12
6 12
35 a
nonsmokers
16
Group D includes all of the 51 participants.
risk for lung cancer, laryngeal cancer, esophageal cancer, and, potentially, leukemia.17−20 Fumes generated by welding processes contain considerable amounts and various types of gases and particulate dust. Gaseous components of welding fumes commonly contain carbon monoxide, nitrogen oxides, and ozone. Depending on the welding technique utilized, particulate components of welding fumes may also contain metal particulates such as manganese, chromium, nickel, vanadium, and their oxides.21−23 Heavy metal byproducts are also largely present in welding fumes and are potentially harmful when aspirated. Ultrafine particles have unique toxicological characteristics due to their small size. Welding-produced nanoparticulates are able to translocate from the respiratory tract and ultimately become deposited in other extrapulmonary organs to produce their toxic effect.24 These ultrafine particulates and their soluble fractions, as a result, are often associated with occupational diseases observed in welders.25−27 Complex toxicants in welding fumes have a wide range of molecular targets and diverse cellular effects. Heavy metals, such as hexavalent chromium, manganese, and nickel, attack lung epithelial cells and activate mitogen-activated protein kinase.28,29 Gene expression profiling using suppressionsubtractive hybridization and cDNA microarray in rat mononuclear cells in response to welding fume exposure shows that genes associated with the immune response, transcription factors, and tyrosine kinases were altered.30 Welding fumes have also been demonstrated to increase lipid peroxidation in plasma, lung, and liver tissue in rats.31 Analysis of genetic polymorphisms and centromeres in micronuclei showed significantly higher levels of chromosome/genome damage in welders.32 In one study, DNA damage was associated with higher chromium, lead, and nickel blood and urine concentrations.33 In addition, ozone, a strong oxidant produced during welding, generates reactive oxygen species (ROS) and causes oxidative injury in culture cells.34,35 Via the oxidative stress mechanism, welding fumes enhance the inflammatory response36 and induce cytotoxic reactions such as DNA breakdown and cytokine activation that ultimately result in cellular apoptosis and neuronal degeneration.21,31,37 Because of extensive study population heterogeneity and dissimilar site-to-site welding environments, results of existing toxicological studies of human exposure to welding fumes are inherently difficult to interpret.27 The toxicity of welding fumes may be a result of the composite interaction of the biological actions of multiple fume components; therefore, findings regarding the biological mechanisms of action of single components of welding fumes may be inconsistent, incomplete, or contradictory from study to study if only investigated at the epidemiological level. Analyses of disease−toxicant interactions
may also be further confounded by certain lifestyle behaviors such as smoking.38 This study aims to investigate the long-term effect of welding fume exposure on metabolomic profiles. We examined the complex effects of welding fumes via 1H NMR spectroscopy analysis of metabolomic profiles of individuals employed by the shipbuilding company, CSBC Corporation, Taiwan. Fume composition, particle size, and individual immune response were also evaluated. A spot urine sample for 1H NMR analysis and detailed questionnaires evaluating the medical history and existing lifestyle habits such as smoking or alcohol use were obtained from 51 individuals, of which 35 were welders with prior exposure to welding fumes and 16 were office workers with no prior exposure to welding fumes. A multivariate statistical procedure was used to identify metabolites associated with various environmental stimuli such as welding fume exposure, smoking, and alcohol use. Blood samples for the same 51 individuals were also collected, and the expression levels of the cytokines and other inflammatory markers were examined.
■
EXPERIMENTAL PROCEDURES
Study Design and Participants' Profiles. This study was approved by the Institutional Review Board of the Institute of Occupational Safety and Health, Council of Labor Affairs, Taiwan. Written informed consent was obtained from each study participant. A total of 51 full time employees at a shipyard (CSBC Corporation, Keelung, Taiwan) consisting of 35 male welders and 16 male office workers (as controls) aging from 45 to 64 were included in this study. Forty-one out of 51 participants had employment tenures at the shipyard exceeding 20 years. The average length of employment for the remaining 10 participants was 9 years. The 35 participants exposed to welding fumes were all active welders at the shipyard, whereas the 16 controls were located in their respective administration offices away from the welding sites. A self-administered questionnaire was used to collect information on medical history, cardiopulmonary history, smoking history, drinking history (including herbal alcohol consumption), and occupational history. Individuals with a history of respiratory disease and chronic inflammation diseases were preexcluded from this study. Blood and urine samples of all 51 participants were collected for inflammatory biomarker measurement and 1H NMR metabolomic analysis, respectively. Participants were stratified into four groups: (1) group A (10 welders and 6 controls) contained participants without any use of alcohol and cigarettes, (2) group B (2 welders and 6 controls) contained participants who were alcohol users but not smokers, (3) group C (23 welders and 4 controls) contained participants who were smokers (including alcohol drinkers and nondrinkers), and (4) group D (35 welders and 16 controls) contained all participants. See Table 1 for detail numbers in each group. Exposure Assessment. Air samples were collected at the manufacture and assembly areas of the shipyard for assessing fine particle exposure of welders. Welding fume exposure originated from either shielded metal arc welding, manual metal arc welding with 677
dx.doi.org/10.1021/tx200465e | Chem. Res. Toxicol. 2012, 25, 676−686
Chemical Research in Toxicology
Article
Scheme 1. Scheme for 1H NMR Spectral Analysis of Welding Fume Exposure
IOSH new cyclone (IOSH, Taipei, Taiwan) and a Nanoparticle Surface Area Monitor (NSAM) (model 3350, TSI, United States) for lung deposited surface area concentration. Air samples were collected on a 37 mm polyvinylchloride (PVC) or a mixed cellulose ester
E7016 (0.08% C, 0.52% of Si, 0.013% of P, 0.004% of S, and 1.10% of Mn), or metal inert gas with E71T-1 (0.037% C, 0.42% of Si, 0.02% of P, 0.010% of S, and 1.30% of Mn). Fine particulate matter exposure was assessed with “fixed point” and “personal sampling techniques” by 678
dx.doi.org/10.1021/tx200465e | Chem. Res. Toxicol. 2012, 25, 676−686
Chemical Research in Toxicology
Article
Table 2. Welding Fume Component Concentrations Measured by Different Sampling Types in Shipyard Working Environment collection area
manufacture
assembly
sample counts
14
6
sampling type Cr (mg/m3) Ni (mg/m3) Mn (mg/m3)
mean ± SD (range) mean ± SD (range) mean ± SD (range)
fixed point collection
personal sampler collection
fixed point collection
personal sampler collection
0.06 ± 0.02 (0.03−0.11) 0.02 ± 0.02 (0.01−0.04) 0.11 ± 0.11 (0.01−0.26)
0.64 ± 0.35 (0.27−1.26) 0.18 ± 0.23 (0.01−0.96) 0.07 ± 0.05 (0.02−0.19)
0.76 ± 0.97 (0.08−2.59) 0.07 ± 0.04 (0.01−0.14) 1.05 ± 0.94 (0.22−2.29)
2.19 ± 0.88 (1.19−3.43) 0.41 ± 0.30 (0.08−0.91) 1.23 ± 0.77 (0.17−2.26)
(MEC) membrane filter (SKC, United States) and weighed by a 5decimal microbalance (AX-205, Mettler Toledo, Switzerland). The mass concentration was determined as described in a previous publication.39 Inductively coupled plasma mass spectrometry (ICP/MS, 7500CE, Agilent, United States) was utilized to detect chromium, nickel, and manganese concentrations. Babington Nebulizer (PNG1820-60453, Agilent) was used to nebulize the analytes, with 0.3 mm diameter pump component, and a sample introduction speed of 2 mL/min in 0.45 rps. The spray chamber was cooled to 2 °C to prevent larger particles from entering the plasma torch. Inflammatory Marker Measurement. Blood samples and nasal lavage fluids were collected from individuals after overnight fasting before work. Nasal lavage fluids were collected with the procedures described by Naclerio et al.40,41 Nasal lavage fluids were analyzed for granulocyte, monocyte, and lymphocyte differential counts. Real-time polymerase chain reaction (real-time PCR) was performed to detect expression of cytokines including IL-6, IL-8, TNF-α, RRM2, NCF1, and PTGS1 on blood samples. Enzyme-linked immunosorbent assay (ELISA) for IL-6, IL-8, Endo-1, and TNF-α detection was performed by using purified mAb-coated plates. All procedures followed standard protocols provided by R&D Systems (Minneapolis, MN). Cytokine concentrations were measured using a MRX microplate reader (Dynex Technologies, Chantilly, VA) at 450 nm (reference, 540 nm). Quantitative reverse transcription polymerase chain reaction (RTPCR) was performed using 1 μg of total RNA that was reverse transcribed using Superscript II enzyme (Invitrogen, Carlsbad, CA). Following reverse transcription, cDNA samples were used for SYBR Green real-time PCR assay. Real-time PCR was performed using ABI 7300 Real Time PCR System (Foster City, CA) with reagents purchased from Applied Biosystems (Foster City, CA). Primer sequences were designed using Primer Express Software (Applied Biosystems). All data were analyzed using the 7300 Real-Time PCR System (Foster City, CA). Metabolomics Analysis with 1H NMR Spectroscopy in Urine. The overall analysis procedure is shown in Scheme 1. 1H NMR metabolomic profiles of urine samples were analyzed by the following procedures. All subjects were studied after overnight fasting. The urine samples were collected in the same manner and time for the exposed and nonexposed subjects. Ten milliliters of a spot urine sample was collected from each individual in the morning before work. After sample collection, sodium azide was added to the urine samples to reach a final concentration of 10 mM. Urine samples were then centrifuged at 3000 rpm for 15 min. The supernatant was transferred to an Eppendrof and stored at −80 °C before NMR analysis. A test sample of 825 μL for each study participant was prepared using 500 μL of the urine sample, 250 μL of 0.2 M Na2HPO4 (pH 7.4), and 75 μL of sodium 3-trimethylsilyl-1-(2,2,3,3-d4) propionate (TSP) in D2O (final concentration, 0.1 mg/mL). D2O provided an NMR lock signal for the NMR spectrometer. Conventional 1H NMR spectra of the urine samples were obtained from a Bruker Avance 600 spectrometer (Bruker Biospin, Germany) operated at 600.04 MHz at 25 °C. One-dimensional 1H NMR spectra were acquired using a standard NOESYPR1D pulse sequence (recycle delay-90°-t1-90°-tm-90°-acquisition; XWIN-NMR3.5) with a recycle delay time of 2 s and a mixing time of 150 ms. The 90° pulse length was adjusted to ∼12.5 μs at −1 dB, and t1 was set to 3 μs, which provided an acquisition time of 2.72 s. The FIDs were multiplied by an exponential weighting function corresponding to a line broadening of
0.3 Hz, and the data were zero-filled to 64 k data points. All spectra were corrected for phase and baseline distortions and referenced to the internal reference standard TSP (δ1H = 0.0). All spectra were exported and processed by using R statistical package (version 2.9.1). Each 1H NMR spectrum was segmented and binned with a binning size 0.04 ppm in the range of chemical shift δ 0−10 ppm. The integral value of each binned region was calculated, and the integral values contributed to an intensity distribution of the whole spectrum. The peak region of water signal between δ 4.5 and 6.0 ppm was removed, and a total of 212 bins of each spectrum were used for statistical analysis. Identification of Metabolites and Statistical Analysis. The urine metabolomic profiles of group A (participants without alcohol and cigarette consumption) were initially investigated to identify metabolite biomarkers of welding fume exposure and to avoid the potential confounding factors from alcohol and cigarette use. Before metabolite identification, all binned NMR spectral data were logged and normalized with a quantile normalization procedure using the Bioconductor package: affy. Identification of differential chemical signals (i.e., binned variables) was performed using two sample t test between the welders and the controls. To assess the probability of false positive findings, we used the positive False Discovery Rate (pFDR) method proposed by Storey.42 The pFDR method was applied to calculate q values, the pFDR analogue of the p value, for each test statistic using the Bioconductor package: QVALUE. Binned NMR spectral data with p values less than 0.05 and FDR less than 20% were considered significant. Significant metabolites were identified by assigning significant NMR spectral data with respect to the internal reference NMR spectra and using the software, Chenomx (Version 7.0, Chenomx Inc., Canada). The metabolites were confirmed by the 2D NMR method, 1H−1H homonuclear correlation spectroscopy (COSY). Principal component analysis (PCA)43 was performed using the significant variables. PCA is a data reduction technique that aims to identify patterns and similarity among data samples. PCA uses a linear transformation to convert data into new variables called principal components (PC) with each PC orthogonal and uncorrelated to each other. The first PC captures the most variability in the data, and each successive PC has the highest variance unexplained by the preceding PCs. The identified metabolites were also investigated among the welders and controls in group B, group C, and all 51 participants to understand the impacts of alcohol and cigarette use.
■
RESULTS Particle Exposure Assessment. The total welding fume exposure of welders ranged from a minimum value of 0.38 to a maximum value of 2.40 mg/m3 (permissible exposure limit in Taiwan is 10 mg/m3), which was significantly greater than that of the controls (0.05 mg/m3). Table 2 showed the exposure concentrations of chromium, nickel, and manganese particles sampled with fixed point and personal sampling techniques at two different shipyard areas. The particle concentration of chromium, mainly produced from welding the stainless steel materials, was the highest among three types of particles. The particle concentrations of manganese are mainly produced from the welding electrodes E7016 and E71T-1. Fixed point sampling concentration measurements of chromium and nickel ranged from 0.03 to 0.11 and from 0.01 to 0.04 mg/m3, 679
dx.doi.org/10.1021/tx200465e | Chem. Res. Toxicol. 2012, 25, 676−686
Chemical Research in Toxicology
Article
Table 3. Urinary Metabolites That Show Significantly Different Concentration between Welders and Controls without the Use of Alcohol or Cigarettes (Group A)a mean ± SD metabolite creatine (V) taurine trimethylamine-N-oxide/betaine acetone creatinine glycine gluconate hippurate S-sulfocystein serine
intensities among welders 9.9 21.0 102.4 38.1 637.4 39.1 91.6 93.7 37.8 135.5
± ± ± ± ± ± ± ± ± ±
5.4 6.1 30.5 9.5 151.9 8.3 22.4 14.3 8.1 24.2
intensities among controls
decreasing
increasing
± ± ± ± ± ± ± ± ± ±
○○● ○○○ ○○○ ○○○ ○○○ ○○○ ○○○ ○○○ ○○○ ○○○
○○○ ●○○ ●○○ ●○○ ●●○ ●●○ ●●○ ●●○ ●●○ ●●●
18.5 12.9 70.3 29.5 453.5 30.1 62.2 69.6 21.1 84.3
9.6 4.3 20.4 3.4 89.4 2.1 7.7 11.3 7.6 9.1
a
Note the p value of metabolite (welders vs controls). The first three circles indicate decreased concentration in welders, and the last three circles indicate increased concentration in welders. ●, p value
