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Characterizing the effect of heavy metal contamination on marine mussels using metabolomics
Marine Pollution Bulletin 64 (2012) 1874–1879
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Characterizing the effect of heavy metal contamination on marine mussels using metabolomics Yong-Kook Kwon a,b, Young-Sang Jung a, Jong-Chul Park a,b, Jungju Seo a, Man-Sik Choi b,c, Geum-Sook Hwang a,b,⇑ a b c
Seoul Center, Korea Basic Science Institute, Seoul 136-713, Republic of Korea Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 305-764, Republic of Korea Department of Marine Environmental Sciences, Chungnam National University, Daejeon 305-764, Republic of Korea
a r t i c l e Keywords: Marine mussel Heavy metal Contamination Metabolomics NMR Multivariate analysis
i n f o
a b s t r a c t Marine mussels (Mytilus) are widely used as bioindicators to measure pollution in marine environments. In this study, 1H NMR spectroscopy and multivariate statistical analyses were used to differentiate mussel groups from a heavy metal-polluted area (Onsan Bay) and a clean area (Dokdo area). Principal component analysis and orthogonal projection to latent structure-discriminant analysis revealed significant separation between extracts of mussels from Onsan Bay and from the Dokdo area. Organic osmolytes (betaine and taurine) and free amino acids (alanine, arginine, glutamine, phenylalanine, and threonine) were more highly accumulated in Onsan Bay mussels compared with Dokdo mussels. These results demonstrate that NMR-based metabolomics can be used as an efficient method for characterizing heavy metal contamination derived from polluted area compared to clean area and to identify metabolites related to environments that are contaminated with heavy metals. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Metal pollution is a threat to marine environments. Many animal and plant species have been proposed as indicator species for monitoring marine pollution. They are generally termed biomonitors, and most current research focuses on sedentary organisms (Besada et al., 2011). For example, marine mussels, ubiquitous and sedentary filter-feeders that inhabit coastal and estuarine habitats (Dyrynda et al., 1998), have been widely studied and are frequently used in marine environmental toxicology studies (Besada et al., 2011; Dyrynda et al., 1998; Hines et al., 2007; Robertson, 2010; Wang et al., 2008; Wu and Wang, 2010; Zimmer et al., 2010). To characterize biological stresses induced by metals, specific responses based on selected biomarkers have been measured using methods such as the lysosomal membrane stability assay (Rank et al., 2007), glutathione peroxidase assay (de Almeida et al., 2004), comet assay (De Andrade et al., 2004), and phagocytosis assay (Dyrynda et al., 1998). In addition to these traditional targeted approaches to studying environmental toxicology, non-targeted approaches such as genomics, transcriptomics, proteomics, and metabolomics are also available. In particular, metabolomics has shown considerable potential as a tool for environmental toxicology. Recently, a few studies have applied ⇑ Corresponding author at: Seoul Center, Korea Basic Science Institute, Seoul 136713, Republic of Korea. Tel.: +82 2 920 0737; fax: +82 2 920 0709. E-mail address: [email protected] (G.-S. Hwang). 0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2012.06.012
NMR-based metabolomics to investigations of the toxicological effects of environmental metals as stressors on organism health and metabolism in marine environments. Liu et al. determined the most sensitive species of clam to use as a sentinel organism for monitoring metal pollution by comparing metabolic profiles of adductor muscle tissue extracts from three clam species, and characterized the metabolic responses to acute waterborne mercury exposure (Liu et al., 2011). Wu and Wang investigated the toxicological effects of cadmium exposure in both digestive gland and adductor muscle tissues, and changes in the metabolic profiles of soft tissues in response to copper and cadmium (Wu and Wang, 2010). Zhang et al. characterized the metabolic responses of the Manila clam after exposure to copper (Zhang et al., 2011). In this study, we applied NMR-based metabolomics to investigate metabolic responses of mussels (Mytilus edulis) to heavy metals, by comparing metabolites of mussels from Onsan Bay with metabolites of mussels from the Dokdo area. Onsan Bay is surrounded by several industrial complexes and a large commercial harbor, and thus is heavily polluted. In contrast, the Dokdo area is known as a pristine island with low contamination. We performed multivariate statistical analyses to characterize the metabolite profiles in response to heavy metals, in order to better understand their toxic effects in soft tissues. The aims of this study were to demonstrate the potential of the NMR-based metabolomics approach for studying marine environmental toxicology and to identify metabolic biomarkers of toxic effects.
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Fig. 1. Map showing the locations of sampling sites. Images were captured from Google Earth and edited (Dokdo area: 37°240 28.300 N, 131°860 21.200 E; Onsan Bay: 35°270 48.7600 N, 129°210 2.9300 E).
2. Materials and methods 2.1. Sample collection and treatment A total of 42 mussel samples were collected from two regions of Korea, each 21 samples from Dokdo area and from Onsan Bay, in August 2009 (Fig. 1). The specimens were rope grown and were of consistent size throughout the population. To allow evacuation of the gut, collected mussels were maintained at ambient
temperature and salinity, and were kept for 12 h in a plastic aquarium containing seawater from Onsan Bay and the Dokdo area, respectively. The mantle tissues and soft tissues were then rapidly dissected and stored at 80 °C until NMR analysis. 2.2. Metabolite extraction Polar metabolites were extracted from mantle tissues of mussels using methanol (Bligh and Dyer, 1959). From each sample,
Fig. 2. Representative 600 MHz 1H NMR spectra for mantle tissues from marine mussels (Mytilus) from Onsan Bay (A) and the Dokdo area (B) Key: (1) leucine, (2) isoleucine, (3) valine, (4) threonine, (5) alanine, (6) arginine, (7) acetate, (8) acetoacetate, (9) succinate, (10) glutamate, (11) glutamine, (12) b-alanine, (13) aspartate, (14) taurine, (15) betaine, (16) inosine, (17) phenylalanine, (18) tyrosine, (19) adenine, (20) fumarate, and (21–24) unknown.
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about 200 mg of mantle tissue were placed in a 1.5-ml tube containing 2.8 mm zirconium oxide beads. Each sample was mixed with 350 ll of methanol (d4) and 150 ll of sodium phosphate buffer (0.2 M, pH 7.0), and homogenized twice at 5000 rpm for 20 s using a tissue grinder (Precellys 24, Bertin Technologies, Ampere Montigny-le-Bretonneux, France). After homogenization, 210 ll of methanol, 90 ll of sodium phosphate buffer, and 400 ll of chloroform were added to the tube. The mixture was vortexed for 1 min, followed by centrifugation at 14,240g for 10 min at 4 °C. The upper layer was transferred in 630-ll aliquots to new 1.5-ml tubes and mixed with 70 ll of 0.25 mM sodium 2,2-dimethyl-2silapentane-5-sulfonate (DSS) in deuterium oxide (D2O). A 600-ll aliquot of each prepared sample was transferred to a 5-mm NMR tube (Wilmad, Buena, NJ).
Institute, Seoul, Korea. The concentrations obtained for the standard reference materials were always within a 95% confidence interval of the certified values. The analytical values for each metal concentration in a mussel were referenced to the dry weight of its soft tissues. 2.7. Statistical methods Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were performed using SIMCA-P version 12.0 (Umetrics, Umea, Sweden). To screen the metabolites that contributed to separating the two groups, an S-plot was generated from the OPLS-DA model. Two vectors, p and p(corr), were combined in one plot; p represents the covariance, and p(corr) gives the correlation for variables with
2.3. 1H NMR spectroscopy The 1H NMR spectra were acquired on a VNMRS 600-MHz NMR using a triple-resonance HCN salt-tolerant cold probe (Agilent Technologies, Palo Alto, CA). A NOESY-PRESAT pulse sequence was applied to suppress the residual water signal. The D2O and DSS provided a field frequency lock and a chemical shift reference (1H, d 0.00), respectively. For each sample, 64 transients were collected into 32 K data points using a spectral width of 9615.4 Hz with a relaxation delay of 2.0 s, an acquisition time of 4.00 s, and a mixing time of 100 ms. 2.4. NMR data preprocessing All NMR spectra were phased and baseline corrected using Chenomx NMR suite ver. 6.0 (Chenomx Inc., Edmonton, AB, Canada). The regions corresponding to solvent and DSS (4.75–5.12, 3.30– 3.33, and 0.0–0.7 ppm) were excluded, and the remaining spectral regions were divided into 0.01-ppm bins. All spectra were aligned using the correlation optimized warping method (Skov et al., 2006) in MATLAB (R2006a, Mathworks Inc., Natick, MA). The aligned data sets were Pareto scaled for multivariate analysis (Loo et al., 2009). 2.5. Targeted metabolite profiling Metabolites were identified using Chenomx Profiler, a module of Chenomx NMR Suite ver. 6.0. All standard NMR spectra used for metabolite identification were commercially available (Chenomx Inc.). Quantification was achieved with the 600-MHz library from Chenomx NMR Suite 6.0, which uses the concentration of a known DSS signal to determine the concentrations of individual metabolites (Jung et al., 2010). 2.6. Heavy metal analysis Mussel samples were prepared for heavy metal analysis by pooling all of the soft tissue from a whole mussel and drying a suitable amount of material for sample digestion. Dried mussel tissues were placed in a 30-ml acid-washed Teflon vial for acid digestion with 3–5 ml of concentrated nitric acid (Merck, Suprapur) at around 180 °C. The digests were gently evaporated on a hot plate at 180 °C, to produce a final liquid suitable for ICP-MS analysis. For quality assurance and quality control, three sample blanks were processed with each batch of 20 samples prepared for digestion. The two standard reference materials, oyster tissue (SRM 1566a) and lobster hepatopancreas (TORT-2), were provided by the National Bureau Standards, USA and the National Research Council, Canada, respectively. The concentrations of heavy metals such as Zn, Cu, Cd, and Pb in the biological samples were determined using an inductively coupled plasma-mass spectrometer (ICP-MS, Elan 6100, PerkinElmer, USA) at the Korea Basic Science
Fig. 3. PCA score plot (A) for mantle tissues from marine mussels (Mytilus) from Onsan Bay (d) and the Dokdo area (s). The proportions of variation explained by PC1 and PC2 were 32.3 and 10.8%, respectively. Loading plots for PC1 (B) and PC2 (C) are shown. Key to metabolites: (1) unknown, (2) succinate, (3) betaine, (4) inosine, (5) alanine, (6) unknown, and (7) phenylalanine.
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respect to the component (Chen et al., 2009; Jung et al., 2010). Jackknife cross-validation of a PLS-DA model and a Wilcoxon rank sum test were performed using R (ver. 2.12.1). To estimate the magnitude of a treatment effect, the effect size (Cohen’s d value) was determined using R (ver. 2.12.1) (Rosnow and Rosenthal, 1996).
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the differentiation, we constructed an S-plot from the OPLS-DA between the two mussel groups. The mussels from Onsan Bay were characterized by higher levels of alanine (1.475, 1.485 ppm), betaine (3.86, 3.275 ppm), inosine (8.235 ppm), and unknown compounds (3.5, 3.505 ppm), whereas unknown compounds (3.375, 3.325, 3.305 ppm) were more abundant in extracts from Dokdo area mussels (Fig. 4B).
3. Results 3.3. Metabolite profiling of mussels 3.1. 1H NMR spectroscopy of mussel tissue extracts Representative 1H NMR spectra of mussel tissue extracts from Onsan Bay and the Dokdo area are shown in Fig. 2. Although several metabolites were observed, all of the spectra were dominated by organic osmolytes such as betaine and taurine. The other observed metabolites included amino acids (e.g., alanine, glycine, isoleucine, leucine, and valine), organic acids (e.g., acetate and fumarate), carbohydrates (e.g., xylose), and Krebs cycle intermediates (e.g., succinate).
The metabolite quantification showed significant differences in metabolite ratios between the mussels from Onsan Bay and those from the Dokdo area (Table 1). The levels of acetate, acetoacetate, alanine, arginine, betaine, glutamine, inosine, phenylalanine, succinate, taurine, threonine, and tyrosine were significantly higher in Onsan Bay mussels compared with Dokdo area mussels (p < 0.05). Acetate, alanine, arginine, betaine, inosine, phenylalanine, succinate, taurine, and threonine led to the differentiation between the two groups (Cohen’s d > 0.8).
3.2. Pattern recognition analysis for mussels
3.4. Concentrations of heavy metals in mussels
1
The PCA plot (Fig. 3A) of H NMR spectra of mussel shows that mussels from the Dokdo area were distinct from Onsan Bay mussels. All of the samples clearly separated into their respective groups. PC1 (32.3% of variation) and PC2 (10.8% of variation) showed significant differences (p < 0.05) between the two groups. Cohen’s d values for PC1 and PC2 were 2.614 and 0.899, respectively. The loading plots for PC1 and PC2 (Fig. 3B and C) exhibited significant changes in the levels of inosine, adenine, phenylalanine, betaine, succinate, and two unknown metabolite peaks (at 1.1, 1.45 ppm), indicating that these metabolites were responsible for the separation between the two mussel groups. The OPLS-DA score plot (Fig. 4A) derived from 1H NMR spectra of the mussels also revealed a clear differentiation between Dokdo area mussels and Onsan Bay mussels. The OPLS-DA model was established using two components and had R2 and Q2 values of 0.482 and 0.962, respectively. The model parameters for the explained variation, R2, and the predictive capability, Q2, were significantly high (R2 > 0.4, Q2 > 0.5) for the mussel tissue extracts, indicating that the modeling results were excellent. To test the validity of the OPLS-DA model, we performed a jackknife cross-validation test in a PLS-DA model having the same number of components as the OPLS-DA model. The cross-validation had an accuracy of 100%. To further understand the variables that contributed to
The heavy metals Zn, Cu, Cd, and Pb were detected most frequently in mussels from Onsan Bay and the Dokdo area (Table 2). The levels of Zn, Cu, and Pb were significantly higher in mussels from Onsan Bay (p < 0.003, Cohen’s d > 1), whereas mussels from the Dokdo area had slightly higher Cd levels, despite the higher Cd concentration in Onsan Bay waters than in Dokdo area waters (Table 3).
4. Discussion The metabolite profiles of mussels from Onsan Bay, a heavy metal-polluted environment, and from the Dokdo area, which has not been exposed to artificial contamination, were investigated using NMR-based metabolomics coupled with multivariate analysis. An OPLS-DA plot showed clear differentiation between mussels from the two regions. Water from Onsan Bay is highly polluted by soluble heavy metals emitted from multiple industrial facilities and a large commercial harbor (Kang et al., 1999). Zn and Pb levels in the sea water of Onsan Bay were 29 and 18 times the levels in the Dokdo area, respectively (Table 3). Similarly, mussels from Onsan Bay had Zn and Pb levels that were 2.5 and 4 times the respective levels in mussels from the Dokdo area (Table 2). The ICP-MS
Fig. 4. OPLS-DA score plot (A) for mantle tissues from marine mussels (Mytilus) from Onsan Bay (d) and the Dokdo area (s) (R2 = 0.482, Q2 = 0.914). S-plot (B) generated from the OPLS-DA model. The range of the variables selected is highlighted with dotted rectangles. Cutoff values for the covariance of p P |0.1| and for the correlation of p(corr) P |0.5| were used. The variables in the dotted rectangles represent the metabolites that were responsible for the differentiation in the OPLS-DA score plot.
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Table 1 Concentrations, chemical shifts, p values from Wilcoxon rank sum tests, and Cohen’s d values for metabolites in mantle tissues of marine mussels (Mytilus) from Onsan Bay and the Dokdo area. Metabolite
Chemical shift (d)
Dokdo area (lm/l)
Onsan Bay (lm/l)
p-Value
Cohen’s d
Ratio (Onsan Bay/Dokdo area)
Acetate Acetoacetate Adenine Alanine Arginine Aspartate Betaine Fumarate Glucose Glutamate Glutamine Inosine Isoleucine Leucine Phenylalanine Succinate Taurine Threonine Tyrosine Valine b-Alanine
1.91(s) 3.44(s), 2.27(s) 8.20(s), 8.18(s) 3.78(m), 1.47(d) 3.76(t), 3.25(t), 1.92(m), 1.70(m) 3.89(dd), 2.81(dd), 2.68(dd) 3.89(s), 3.26(s) 6.51(s) 5.22(d), 4.63(d), 3.9(dd), 3.72(m), 3.48(m) 3.22(dd) 3.76(t), 2.36(m), 2.10(m) 2.46(m), 2.41(m), 2.13(m) 8.34(s), 8.20(s), 6.04(d), 4.46(m) 3.68(d), 1.98(m), 1.28(m), 1.02(d), 0.94(t) 3.73(t), 1.68(m), 0.98(d), 0.96(d) 7.41(t), 7.34(m), 3.99(m), 3.27(m), 3.11(m) 2.40(s) 3.39(t), 3.19(t) 4.22(m), 3.59(d), 1.33(d) 7.18(d), 6.89(d), 3.93(m), 3.18(m), 3.05(m) 3.61(d), 2.27(m), 1.05(d), 1.00(d) 3.17(t), 2.54(t)
99.25 166.10 238.20 936.37 70.87 601.76 7803.05 14.83 1865.25 763.75 222.95 29.92 53.65 81.04 29.04 226.37 5339.69 186.37 158.48 95.69 156.57
180.86 212.96 263.10 1561.39 298.82 473.31 11,012.51 15.55 1598.05 985.06 422.82 396.77 63.73 116.36 118.56 637.15 7735.18 351.22 258.80 109.89 112.93
0.001 0.022 0.852 0.001 <0.0001 0.334 0.007 0.308 0.265 0.056 0.012 <0.0001 0.155 0.063 <0.0001 <0.0001 0.004 0.003 0.001 0.403 0.222
1.092 0.377 0.163 0.990 1.205 0.486 0.899 0.055 0.333 0.523 0.594 2.197 0.277 0.708 1.619 1.761 0.932 0.937 0.310 0.223 0.457
1.822 1.282 1.105 1.667 4.210 0.787 1.411 1.048 0.857 1.290 1.896 13.262 1.188 1.436 4.083 2.815 1.449 1.884 1.633 1.148 0.721
The levels were estimated from the relative intensities of H NMR spectra of tissue extracts following spectral normalization. (s) singlet, (d) doublet, (t) triplet, (m) multiplet.
Table 2 Quantification of heavy metals in soft tissues of marine mussels (Mytilus) from Onsan Bay and the Dokdo area. Heavy metal
Onsan Bay (lg/g)
Dokdo area (lg/g)
Ratio (Onsan Bay/Dokdo area)
p-Value
Cohen’s d
Zn Cu Pb Cd
192.97 ± 80.04 3.56 ± 1.04 5.07 ± 2.04 2.60 ± 1.15
71.51 ± 8.65 2.64 ± 0.43 1.33 ± 0.62 5.58 ± 3.65
2.90 1.38 4.07 0.40
<0.0001 0.0005 <0.0001 0.0229
2.106 1.289 2.617 0.768
Data are given as the mean ± standard deviation. p-Values were calculated using the Wilcoxon rank sum test.
results indicated that mussels from Onsan Bay were significantly affected by heavy metal pollution. Major metabolic differences between the two mussel groups were characterized by higher levels of acetate, acetoacetate, alanine, arginine, betaine, glutamine, inosine, phenylalanine, succinate, taurine, threonine, and tyrosine in mussels from Onsan Bay than in mussels from the Dokdo area. Osmolytes such betaine, taurine, hypotaurine, and alanine are small organic molecules that participate in osmotic regulation in marine organisms via various metabolic pathways (Preston, 1993). In previous studies, marine invertebrates that were exposed to heavy metals accumulated the organic osmolytes betaine and taurine (Zhang et al., 2011). Free amino acids, including alanine, arginine, glutamine, leucine, phenylalanine, and threonine, were present in higher concentrations in mussels from Onsan Bay. Recent studies have reported that some marine mollusks use high intracellular levels of free amino acids
Table 3 Concentrations of soluble heavy metals in the two regions. Heavy metal
Onsan Bay (lg/l)
Dokdo area (lg/l)
Ratio (Onsan Bay/Dokdo area)
Zn Cu Pb Cd
3.23 ± 1.79 0.79 ± 0.48 0.42 ± 0.33 0.045 ± 0.03
0.11 ± 0.016 0.11 ± 0.015 0.023 ± 0.0055 0.016 ± 0.0017
29.53 6.96 18.2 2.74
Data are given as the mean ± standard deviation. ⁄ Concentrations of soluble heavy metals in Onsan Bay are from another study (Kim et al., 2004), and values for the Dokdo area are from the KORDI report in 2007.
to balance intracellular osmolarity with the environmental osmolarity, and these pools of oxidizable amino acids are also used extensively in cellular energy metabolism (Viant et al., 2003). High levels of alanine have been observed in various invertebrate species, such as Deroceras species (Storey et al., 2007), Crassostrea gigas (Michaelidis et al., 2005), crayfish (Abe, 2002), Patella caerulea (Santini et al., 2001), Macoma balthica (Ahmad and Chaplin, 1984), Mytilus galloprovincialis (Bacchiocchi and Principato, 2000; Isani et al., 1995), Mytilus edulis (Tuffnail et al., 2009), and Limulus polyphemus (Carlsson and Gade, 1986), when subjected to hypoxic conditions. These previous studies demonstrated that increases in alanine caused by anoxia are associated with increases in succinate. It has been reported that alanine and succinate accumulate in the abiotic environment (De Zwaan et al., 1976). In the present study, our results showed a similar phenomenon regarding the levels of alanine and succinate in mussels. Mussels that grew in Onsan Bay, a metal-polluted environment, accumulated succinate and metabolites related to osmolarity, suggesting that abiotic stress and metal pollution can affect pathways related to osmolytes in mussels. Our results demonstrate that invertebrates living in the sea experience metabolite changes in response to heavy metal pollution. 5. Conclusions We investigated differences in metabolite patterns between mussels from Onsan Bay, a heavy metal-polluted area, and the Dokdo area, a non-polluted area. Our study illustrates that NMRbased metabolomics can be an efficient method for characterizing
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heavy metal contamination in different environments and for identifying metabolic changes related to heavy metal contamination. This study found higher concentrations of organic osmolytes and free amino acids in mussels from Onsan Bay compared with mussels from the Dokdo area. Our study demonstrates that the major metabolites related to heavy metal contamination can be quantitatively evaluated using metabolite profiling. Further studies are required to understand the responses of mussels exposed to single metal contamination. Acknowledgments This study was supported by a grant from the Korea Basic Science Institute (C32700) and Graduate School of Analytical Science and Technology and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (20100024645). References Abe, H., 2002. Distribution, metabolism and physiological functions of free d-amino acids in aquatic invertebrates. Nippon Suisan Gakk. 68, 516–525. Ahmad, F.A., Chaplin, A.E., 1984. Anaerobic metabolism of bivalve molluscs during exposure to air. Biochem. Syst. Ecol. 12, 85–88. Bacchiocchi, S., Principato, G., 2000. Mitochondrial contribution to metabolic changes in the digestive gland of Mytilus galloprovincialis during anaerobiosis. J. Exp. Zool. 286, 107–113. Besada, V., Andrade, J.M., Schultze, F., Gonzalez, J.J., 2011. Comparison of the 2000 and 2005 spatial distributions of heavy metals in wild mussels from the NorthAtlantic Spanish coast. Ecotoxicol. Environ. Saf. 74, 373–381. Bligh, E.G., Dyer, W.J., 1959. A rapid method of total lipid extraction and purification. Can. J. Biochem. Phys. 37, 911–917. Carlsson, K.H., Gade, G., 1986. Metabolic adaptation of the horseshoe crab, Limulus polyphemus, during exercise and environmental hypoxia and subsequent recovery. Biol. Bull. 171, 217. Chen, J., Wang, W., Lv, S., Yin, P., Zhao, X., Lu, X., Zhang, F., Xu, G., 2009. Metabolomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Anal. Chim. Acta 650, 3–9. De Almeida, E.A., Miyamoto, S., Bainy, A.C.D., de Medeiros, M.H.G., Di Mascio, P., 2004. Protective effect of phospholipid hydroperoxide glutathione peroxidase (PHGPx) against lipid peroxidation in mussels Perna perna exposed to different metals. Mar. Pollut. Bull. 49, 386–392. De Andrade, V.M., Da Silva, J., Da Silva, F.R., Heuser, V.D., Dias, J.F., Yoneama, M.L., De Freitas, T.R.O., 2004. Fish as bioindicators to assess the effects of pollution in two southern Brazilian rivers using the Comet assay and micronucleus test. Environ. Mol. Mutagen. 44, 459–468. De Zwaan, A., Kluytmans, J., Zandee, D., 1976. Facultative anaerobiosis in molluscs. Biochem. Soc. Symp. 41, 133–168. Dyrynda, E.A., Pipe, R.K., Burt, G.R., Ratcliffe, N.A., 1998. Modulations in the immune defences of mussels (Mytilus edulis) from contaminated sites in the UK. Aquat. Toxicol. 42, 169–185. Hines, A., Yeung, W.H., Craft, J., Brown, M., Kennedy, J., Bignell, J., Stentiford, G.D., Viant, M.R., 2007. Comparison of histological, genetic, metabolomics, and lipid-
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based methods for sex determination in marine mussels. Anal. Biochem. 369, 175–186. Isani, G., Cattani, O., Zurzolo, M., Pagnucco, C., Cortesi, P., 1995. Energy-metabolism of the mussel, Mytilus galloprovincialis, during long-term anoxia. Comp. Biochem. Phys. B 110, 103–113. Jung, Y., Lee, J., Kwon, J., Lee, K.S., Ryu, D.H., Hwang, G.S., 2010. Discrimination of the geographical origin of beef by 1H NMR-based metabolomics. J. Agric. Food Chem. 58, 10458–10466. Kang, S.G., Choi, M.S., Oh, I.S., Wright, D.A., Koh, C.H., 1999. Assessment of metal pollution in Onsan Bay, Korea using Asian periwinkle Littorina brevicula as a biomonitor. Sci. Total Environ. 234, 127–137. Kim, C.K., Choi, M.S., Lee, C.-B., 2004. Accumulation and release of heavy metals (Cu, Zn, Cd and Pb) in the mussel, Mytilus galloprovincialis; reciprocal transplantation experiment. J. Kor. Soc. Oceanogr. 39, 197–206. Liu, X., Yang, C., Zhang, L., Li, L., Liu, S., Yu, J., You, L., Zhou, D., Xia, C., Zhao, J., 2011. Metabolic profiling of cadmium-induced effects in one pioneer intertidal halophyte Suaeda salsa by NMR-based metabolomics. Ecotoxicology 20, 1422– 1431. Loo, R.L., Coen, M., Ebbels, T., Cloarec, O., Maibaum, E., Bictash, M., Yap, I., Elliott, P., Stamler, J., Nicholson, J.K., Holmes, E., 2009. Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies. Anal. Chem. 81, 5119–5129. Michaelidis, B., Haas, D., Grieshaber, M.K., 2005. Extracellular and intracellular acidbase status with regard to the energy metabolism in the oyster Crassostrea gigas during exposure to air. Physiol. Biochem. Zool. 78, 373–383. Preston, R.L., 1993. Transport of amino-acids by marine-invertebrates. J. Exp. Zool. 265, 410–421. Rank, J., Lehtonen, K.K., Strand, J., Laursen, M., 2007. DNA damage, acetylcholinesterase activity and lysosomal stability in native and transplanted mussels (Mytilus edulis) in areas close to coastal chemical dumping sites in Denmark. Aquat. Toxicol. 84, 50–61. Robertson, D.G., 2010. Muscling mussels: metabolomic evaluation of toxicity. Toxicol. Sci. 115, 305–306. Rosnow, R.L., Rosenthal, R., 1996. Computing contrasts, effect sizes, and counternulls on other people’s published data: general procedures for research consumers. Psychol. Methods 1, 331–340. Santini, G., Bruschini, C., Pazzagli, L., Pieraccini, G., Moneti, G., Chelazzi, G., 2001. Metabolic responses of the limpet Patella caerulea (L.) to anoxia and dehydration. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 130, 1–8. Skov, T., Van Den Berg, F., Tomasi, G., Bro, R., 2006. Automated alignment of chromatographic data. J. Chemometr. 20, 484–497. Storey, K.B., Storey, J.M., Churchill, T.A., 2007. Freezing and anoxia tolerance of slugs: a metabolic perspective. J. Comp. Physiol. B 177, 833–840. Tuffnail, W., Mills, G., Cary, P., Greenwood, R., 2009. An environmental 1H NMR metabolomic study of the exposure of the marine mussel Mytilus edulis to atrazine, lindane, hypoxia and starvation. Metabolomics 5, 33–43. Viant, M.R., Rosenblum, E.S., Tieerdema, R.S., 2003. NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. Environ. Sci. Technol. 37, 4982–4989. Wang, Z., Wu, H., Chen, J., Zhang, J., Yao, Y., Chen, G.Q., 2008. A novel self-cleaving phasin tag for purification of recombinant proteins based on hydrophobic polyhydroxyalkanoate nanoparticles. Lab. Chip. 8, 1957–1962. Wu, H.F., Wang, W.X., 2010. NMR-based metabolomic studies on the toxicological effects of cadmium and copper on green mussels Perna viridis. Aquat. Toxicol. 100, 339–345. Zhang, L., Liu, X., You, L., Zhou, D., Wu, H., Li, L., Zhao, J., Feng, J., Yu, J., 2011. Metabolic responses in gills of Manila clam Ruditapes philippinarum exposed to copper using NMR-based metabolomics. Mar. Environ. Res. 72, 33–39. Zimmer, L.A., Asmund, G., Johansen, P., Mortensen, J., Hansen, B.W., 2010. Pollution from mining in South Greenland: uptake and release of Pb by blue mussels (Mytilus edulis L.) documented by transplantation experiments. Polar Biol., 1–9.
Contents lists available at SciVerse ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Characterizing the effect of heavy metal contamination on marine mussels using metabolomics Yong-Kook Kwon a,b, Young-Sang Jung a, Jong-Chul Park a,b, Jungju Seo a, Man-Sik Choi b,c, Geum-Sook Hwang a,b,⇑ a b c
Seoul Center, Korea Basic Science Institute, Seoul 136-713, Republic of Korea Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 305-764, Republic of Korea Department of Marine Environmental Sciences, Chungnam National University, Daejeon 305-764, Republic of Korea
a r t i c l e Keywords: Marine mussel Heavy metal Contamination Metabolomics NMR Multivariate analysis
i n f o
a b s t r a c t Marine mussels (Mytilus) are widely used as bioindicators to measure pollution in marine environments. In this study, 1H NMR spectroscopy and multivariate statistical analyses were used to differentiate mussel groups from a heavy metal-polluted area (Onsan Bay) and a clean area (Dokdo area). Principal component analysis and orthogonal projection to latent structure-discriminant analysis revealed significant separation between extracts of mussels from Onsan Bay and from the Dokdo area. Organic osmolytes (betaine and taurine) and free amino acids (alanine, arginine, glutamine, phenylalanine, and threonine) were more highly accumulated in Onsan Bay mussels compared with Dokdo mussels. These results demonstrate that NMR-based metabolomics can be used as an efficient method for characterizing heavy metal contamination derived from polluted area compared to clean area and to identify metabolites related to environments that are contaminated with heavy metals. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Metal pollution is a threat to marine environments. Many animal and plant species have been proposed as indicator species for monitoring marine pollution. They are generally termed biomonitors, and most current research focuses on sedentary organisms (Besada et al., 2011). For example, marine mussels, ubiquitous and sedentary filter-feeders that inhabit coastal and estuarine habitats (Dyrynda et al., 1998), have been widely studied and are frequently used in marine environmental toxicology studies (Besada et al., 2011; Dyrynda et al., 1998; Hines et al., 2007; Robertson, 2010; Wang et al., 2008; Wu and Wang, 2010; Zimmer et al., 2010). To characterize biological stresses induced by metals, specific responses based on selected biomarkers have been measured using methods such as the lysosomal membrane stability assay (Rank et al., 2007), glutathione peroxidase assay (de Almeida et al., 2004), comet assay (De Andrade et al., 2004), and phagocytosis assay (Dyrynda et al., 1998). In addition to these traditional targeted approaches to studying environmental toxicology, non-targeted approaches such as genomics, transcriptomics, proteomics, and metabolomics are also available. In particular, metabolomics has shown considerable potential as a tool for environmental toxicology. Recently, a few studies have applied ⇑ Corresponding author at: Seoul Center, Korea Basic Science Institute, Seoul 136713, Republic of Korea. Tel.: +82 2 920 0737; fax: +82 2 920 0709. E-mail address: [email protected] (G.-S. Hwang). 0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2012.06.012
NMR-based metabolomics to investigations of the toxicological effects of environmental metals as stressors on organism health and metabolism in marine environments. Liu et al. determined the most sensitive species of clam to use as a sentinel organism for monitoring metal pollution by comparing metabolic profiles of adductor muscle tissue extracts from three clam species, and characterized the metabolic responses to acute waterborne mercury exposure (Liu et al., 2011). Wu and Wang investigated the toxicological effects of cadmium exposure in both digestive gland and adductor muscle tissues, and changes in the metabolic profiles of soft tissues in response to copper and cadmium (Wu and Wang, 2010). Zhang et al. characterized the metabolic responses of the Manila clam after exposure to copper (Zhang et al., 2011). In this study, we applied NMR-based metabolomics to investigate metabolic responses of mussels (Mytilus edulis) to heavy metals, by comparing metabolites of mussels from Onsan Bay with metabolites of mussels from the Dokdo area. Onsan Bay is surrounded by several industrial complexes and a large commercial harbor, and thus is heavily polluted. In contrast, the Dokdo area is known as a pristine island with low contamination. We performed multivariate statistical analyses to characterize the metabolite profiles in response to heavy metals, in order to better understand their toxic effects in soft tissues. The aims of this study were to demonstrate the potential of the NMR-based metabolomics approach for studying marine environmental toxicology and to identify metabolic biomarkers of toxic effects.
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Fig. 1. Map showing the locations of sampling sites. Images were captured from Google Earth and edited (Dokdo area: 37°240 28.300 N, 131°860 21.200 E; Onsan Bay: 35°270 48.7600 N, 129°210 2.9300 E).
2. Materials and methods 2.1. Sample collection and treatment A total of 42 mussel samples were collected from two regions of Korea, each 21 samples from Dokdo area and from Onsan Bay, in August 2009 (Fig. 1). The specimens were rope grown and were of consistent size throughout the population. To allow evacuation of the gut, collected mussels were maintained at ambient
temperature and salinity, and were kept for 12 h in a plastic aquarium containing seawater from Onsan Bay and the Dokdo area, respectively. The mantle tissues and soft tissues were then rapidly dissected and stored at 80 °C until NMR analysis. 2.2. Metabolite extraction Polar metabolites were extracted from mantle tissues of mussels using methanol (Bligh and Dyer, 1959). From each sample,
Fig. 2. Representative 600 MHz 1H NMR spectra for mantle tissues from marine mussels (Mytilus) from Onsan Bay (A) and the Dokdo area (B) Key: (1) leucine, (2) isoleucine, (3) valine, (4) threonine, (5) alanine, (6) arginine, (7) acetate, (8) acetoacetate, (9) succinate, (10) glutamate, (11) glutamine, (12) b-alanine, (13) aspartate, (14) taurine, (15) betaine, (16) inosine, (17) phenylalanine, (18) tyrosine, (19) adenine, (20) fumarate, and (21–24) unknown.
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about 200 mg of mantle tissue were placed in a 1.5-ml tube containing 2.8 mm zirconium oxide beads. Each sample was mixed with 350 ll of methanol (d4) and 150 ll of sodium phosphate buffer (0.2 M, pH 7.0), and homogenized twice at 5000 rpm for 20 s using a tissue grinder (Precellys 24, Bertin Technologies, Ampere Montigny-le-Bretonneux, France). After homogenization, 210 ll of methanol, 90 ll of sodium phosphate buffer, and 400 ll of chloroform were added to the tube. The mixture was vortexed for 1 min, followed by centrifugation at 14,240g for 10 min at 4 °C. The upper layer was transferred in 630-ll aliquots to new 1.5-ml tubes and mixed with 70 ll of 0.25 mM sodium 2,2-dimethyl-2silapentane-5-sulfonate (DSS) in deuterium oxide (D2O). A 600-ll aliquot of each prepared sample was transferred to a 5-mm NMR tube (Wilmad, Buena, NJ).
Institute, Seoul, Korea. The concentrations obtained for the standard reference materials were always within a 95% confidence interval of the certified values. The analytical values for each metal concentration in a mussel were referenced to the dry weight of its soft tissues. 2.7. Statistical methods Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were performed using SIMCA-P version 12.0 (Umetrics, Umea, Sweden). To screen the metabolites that contributed to separating the two groups, an S-plot was generated from the OPLS-DA model. Two vectors, p and p(corr), were combined in one plot; p represents the covariance, and p(corr) gives the correlation for variables with
2.3. 1H NMR spectroscopy The 1H NMR spectra were acquired on a VNMRS 600-MHz NMR using a triple-resonance HCN salt-tolerant cold probe (Agilent Technologies, Palo Alto, CA). A NOESY-PRESAT pulse sequence was applied to suppress the residual water signal. The D2O and DSS provided a field frequency lock and a chemical shift reference (1H, d 0.00), respectively. For each sample, 64 transients were collected into 32 K data points using a spectral width of 9615.4 Hz with a relaxation delay of 2.0 s, an acquisition time of 4.00 s, and a mixing time of 100 ms. 2.4. NMR data preprocessing All NMR spectra were phased and baseline corrected using Chenomx NMR suite ver. 6.0 (Chenomx Inc., Edmonton, AB, Canada). The regions corresponding to solvent and DSS (4.75–5.12, 3.30– 3.33, and 0.0–0.7 ppm) were excluded, and the remaining spectral regions were divided into 0.01-ppm bins. All spectra were aligned using the correlation optimized warping method (Skov et al., 2006) in MATLAB (R2006a, Mathworks Inc., Natick, MA). The aligned data sets were Pareto scaled for multivariate analysis (Loo et al., 2009). 2.5. Targeted metabolite profiling Metabolites were identified using Chenomx Profiler, a module of Chenomx NMR Suite ver. 6.0. All standard NMR spectra used for metabolite identification were commercially available (Chenomx Inc.). Quantification was achieved with the 600-MHz library from Chenomx NMR Suite 6.0, which uses the concentration of a known DSS signal to determine the concentrations of individual metabolites (Jung et al., 2010). 2.6. Heavy metal analysis Mussel samples were prepared for heavy metal analysis by pooling all of the soft tissue from a whole mussel and drying a suitable amount of material for sample digestion. Dried mussel tissues were placed in a 30-ml acid-washed Teflon vial for acid digestion with 3–5 ml of concentrated nitric acid (Merck, Suprapur) at around 180 °C. The digests were gently evaporated on a hot plate at 180 °C, to produce a final liquid suitable for ICP-MS analysis. For quality assurance and quality control, three sample blanks were processed with each batch of 20 samples prepared for digestion. The two standard reference materials, oyster tissue (SRM 1566a) and lobster hepatopancreas (TORT-2), were provided by the National Bureau Standards, USA and the National Research Council, Canada, respectively. The concentrations of heavy metals such as Zn, Cu, Cd, and Pb in the biological samples were determined using an inductively coupled plasma-mass spectrometer (ICP-MS, Elan 6100, PerkinElmer, USA) at the Korea Basic Science
Fig. 3. PCA score plot (A) for mantle tissues from marine mussels (Mytilus) from Onsan Bay (d) and the Dokdo area (s). The proportions of variation explained by PC1 and PC2 were 32.3 and 10.8%, respectively. Loading plots for PC1 (B) and PC2 (C) are shown. Key to metabolites: (1) unknown, (2) succinate, (3) betaine, (4) inosine, (5) alanine, (6) unknown, and (7) phenylalanine.
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respect to the component (Chen et al., 2009; Jung et al., 2010). Jackknife cross-validation of a PLS-DA model and a Wilcoxon rank sum test were performed using R (ver. 2.12.1). To estimate the magnitude of a treatment effect, the effect size (Cohen’s d value) was determined using R (ver. 2.12.1) (Rosnow and Rosenthal, 1996).
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the differentiation, we constructed an S-plot from the OPLS-DA between the two mussel groups. The mussels from Onsan Bay were characterized by higher levels of alanine (1.475, 1.485 ppm), betaine (3.86, 3.275 ppm), inosine (8.235 ppm), and unknown compounds (3.5, 3.505 ppm), whereas unknown compounds (3.375, 3.325, 3.305 ppm) were more abundant in extracts from Dokdo area mussels (Fig. 4B).
3. Results 3.3. Metabolite profiling of mussels 3.1. 1H NMR spectroscopy of mussel tissue extracts Representative 1H NMR spectra of mussel tissue extracts from Onsan Bay and the Dokdo area are shown in Fig. 2. Although several metabolites were observed, all of the spectra were dominated by organic osmolytes such as betaine and taurine. The other observed metabolites included amino acids (e.g., alanine, glycine, isoleucine, leucine, and valine), organic acids (e.g., acetate and fumarate), carbohydrates (e.g., xylose), and Krebs cycle intermediates (e.g., succinate).
The metabolite quantification showed significant differences in metabolite ratios between the mussels from Onsan Bay and those from the Dokdo area (Table 1). The levels of acetate, acetoacetate, alanine, arginine, betaine, glutamine, inosine, phenylalanine, succinate, taurine, threonine, and tyrosine were significantly higher in Onsan Bay mussels compared with Dokdo area mussels (p < 0.05). Acetate, alanine, arginine, betaine, inosine, phenylalanine, succinate, taurine, and threonine led to the differentiation between the two groups (Cohen’s d > 0.8).
3.2. Pattern recognition analysis for mussels
3.4. Concentrations of heavy metals in mussels
1
The PCA plot (Fig. 3A) of H NMR spectra of mussel shows that mussels from the Dokdo area were distinct from Onsan Bay mussels. All of the samples clearly separated into their respective groups. PC1 (32.3% of variation) and PC2 (10.8% of variation) showed significant differences (p < 0.05) between the two groups. Cohen’s d values for PC1 and PC2 were 2.614 and 0.899, respectively. The loading plots for PC1 and PC2 (Fig. 3B and C) exhibited significant changes in the levels of inosine, adenine, phenylalanine, betaine, succinate, and two unknown metabolite peaks (at 1.1, 1.45 ppm), indicating that these metabolites were responsible for the separation between the two mussel groups. The OPLS-DA score plot (Fig. 4A) derived from 1H NMR spectra of the mussels also revealed a clear differentiation between Dokdo area mussels and Onsan Bay mussels. The OPLS-DA model was established using two components and had R2 and Q2 values of 0.482 and 0.962, respectively. The model parameters for the explained variation, R2, and the predictive capability, Q2, were significantly high (R2 > 0.4, Q2 > 0.5) for the mussel tissue extracts, indicating that the modeling results were excellent. To test the validity of the OPLS-DA model, we performed a jackknife cross-validation test in a PLS-DA model having the same number of components as the OPLS-DA model. The cross-validation had an accuracy of 100%. To further understand the variables that contributed to
The heavy metals Zn, Cu, Cd, and Pb were detected most frequently in mussels from Onsan Bay and the Dokdo area (Table 2). The levels of Zn, Cu, and Pb were significantly higher in mussels from Onsan Bay (p < 0.003, Cohen’s d > 1), whereas mussels from the Dokdo area had slightly higher Cd levels, despite the higher Cd concentration in Onsan Bay waters than in Dokdo area waters (Table 3).
4. Discussion The metabolite profiles of mussels from Onsan Bay, a heavy metal-polluted environment, and from the Dokdo area, which has not been exposed to artificial contamination, were investigated using NMR-based metabolomics coupled with multivariate analysis. An OPLS-DA plot showed clear differentiation between mussels from the two regions. Water from Onsan Bay is highly polluted by soluble heavy metals emitted from multiple industrial facilities and a large commercial harbor (Kang et al., 1999). Zn and Pb levels in the sea water of Onsan Bay were 29 and 18 times the levels in the Dokdo area, respectively (Table 3). Similarly, mussels from Onsan Bay had Zn and Pb levels that were 2.5 and 4 times the respective levels in mussels from the Dokdo area (Table 2). The ICP-MS
Fig. 4. OPLS-DA score plot (A) for mantle tissues from marine mussels (Mytilus) from Onsan Bay (d) and the Dokdo area (s) (R2 = 0.482, Q2 = 0.914). S-plot (B) generated from the OPLS-DA model. The range of the variables selected is highlighted with dotted rectangles. Cutoff values for the covariance of p P |0.1| and for the correlation of p(corr) P |0.5| were used. The variables in the dotted rectangles represent the metabolites that were responsible for the differentiation in the OPLS-DA score plot.
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Table 1 Concentrations, chemical shifts, p values from Wilcoxon rank sum tests, and Cohen’s d values for metabolites in mantle tissues of marine mussels (Mytilus) from Onsan Bay and the Dokdo area. Metabolite
Chemical shift (d)
Dokdo area (lm/l)
Onsan Bay (lm/l)
p-Value
Cohen’s d
Ratio (Onsan Bay/Dokdo area)
Acetate Acetoacetate Adenine Alanine Arginine Aspartate Betaine Fumarate Glucose Glutamate Glutamine Inosine Isoleucine Leucine Phenylalanine Succinate Taurine Threonine Tyrosine Valine b-Alanine
1.91(s) 3.44(s), 2.27(s) 8.20(s), 8.18(s) 3.78(m), 1.47(d) 3.76(t), 3.25(t), 1.92(m), 1.70(m) 3.89(dd), 2.81(dd), 2.68(dd) 3.89(s), 3.26(s) 6.51(s) 5.22(d), 4.63(d), 3.9(dd), 3.72(m), 3.48(m) 3.22(dd) 3.76(t), 2.36(m), 2.10(m) 2.46(m), 2.41(m), 2.13(m) 8.34(s), 8.20(s), 6.04(d), 4.46(m) 3.68(d), 1.98(m), 1.28(m), 1.02(d), 0.94(t) 3.73(t), 1.68(m), 0.98(d), 0.96(d) 7.41(t), 7.34(m), 3.99(m), 3.27(m), 3.11(m) 2.40(s) 3.39(t), 3.19(t) 4.22(m), 3.59(d), 1.33(d) 7.18(d), 6.89(d), 3.93(m), 3.18(m), 3.05(m) 3.61(d), 2.27(m), 1.05(d), 1.00(d) 3.17(t), 2.54(t)
99.25 166.10 238.20 936.37 70.87 601.76 7803.05 14.83 1865.25 763.75 222.95 29.92 53.65 81.04 29.04 226.37 5339.69 186.37 158.48 95.69 156.57
180.86 212.96 263.10 1561.39 298.82 473.31 11,012.51 15.55 1598.05 985.06 422.82 396.77 63.73 116.36 118.56 637.15 7735.18 351.22 258.80 109.89 112.93
0.001 0.022 0.852 0.001 <0.0001 0.334 0.007 0.308 0.265 0.056 0.012 <0.0001 0.155 0.063 <0.0001 <0.0001 0.004 0.003 0.001 0.403 0.222
1.092 0.377 0.163 0.990 1.205 0.486 0.899 0.055 0.333 0.523 0.594 2.197 0.277 0.708 1.619 1.761 0.932 0.937 0.310 0.223 0.457
1.822 1.282 1.105 1.667 4.210 0.787 1.411 1.048 0.857 1.290 1.896 13.262 1.188 1.436 4.083 2.815 1.449 1.884 1.633 1.148 0.721
The levels were estimated from the relative intensities of H NMR spectra of tissue extracts following spectral normalization. (s) singlet, (d) doublet, (t) triplet, (m) multiplet.
Table 2 Quantification of heavy metals in soft tissues of marine mussels (Mytilus) from Onsan Bay and the Dokdo area. Heavy metal
Onsan Bay (lg/g)
Dokdo area (lg/g)
Ratio (Onsan Bay/Dokdo area)
p-Value
Cohen’s d
Zn Cu Pb Cd
192.97 ± 80.04 3.56 ± 1.04 5.07 ± 2.04 2.60 ± 1.15
71.51 ± 8.65 2.64 ± 0.43 1.33 ± 0.62 5.58 ± 3.65
2.90 1.38 4.07 0.40
<0.0001 0.0005 <0.0001 0.0229
2.106 1.289 2.617 0.768
Data are given as the mean ± standard deviation. p-Values were calculated using the Wilcoxon rank sum test.
results indicated that mussels from Onsan Bay were significantly affected by heavy metal pollution. Major metabolic differences between the two mussel groups were characterized by higher levels of acetate, acetoacetate, alanine, arginine, betaine, glutamine, inosine, phenylalanine, succinate, taurine, threonine, and tyrosine in mussels from Onsan Bay than in mussels from the Dokdo area. Osmolytes such betaine, taurine, hypotaurine, and alanine are small organic molecules that participate in osmotic regulation in marine organisms via various metabolic pathways (Preston, 1993). In previous studies, marine invertebrates that were exposed to heavy metals accumulated the organic osmolytes betaine and taurine (Zhang et al., 2011). Free amino acids, including alanine, arginine, glutamine, leucine, phenylalanine, and threonine, were present in higher concentrations in mussels from Onsan Bay. Recent studies have reported that some marine mollusks use high intracellular levels of free amino acids
Table 3 Concentrations of soluble heavy metals in the two regions. Heavy metal
Onsan Bay (lg/l)
Dokdo area (lg/l)
Ratio (Onsan Bay/Dokdo area)
Zn Cu Pb Cd
3.23 ± 1.79 0.79 ± 0.48 0.42 ± 0.33 0.045 ± 0.03
0.11 ± 0.016 0.11 ± 0.015 0.023 ± 0.0055 0.016 ± 0.0017
29.53 6.96 18.2 2.74
Data are given as the mean ± standard deviation. ⁄ Concentrations of soluble heavy metals in Onsan Bay are from another study (Kim et al., 2004), and values for the Dokdo area are from the KORDI report in 2007.
to balance intracellular osmolarity with the environmental osmolarity, and these pools of oxidizable amino acids are also used extensively in cellular energy metabolism (Viant et al., 2003). High levels of alanine have been observed in various invertebrate species, such as Deroceras species (Storey et al., 2007), Crassostrea gigas (Michaelidis et al., 2005), crayfish (Abe, 2002), Patella caerulea (Santini et al., 2001), Macoma balthica (Ahmad and Chaplin, 1984), Mytilus galloprovincialis (Bacchiocchi and Principato, 2000; Isani et al., 1995), Mytilus edulis (Tuffnail et al., 2009), and Limulus polyphemus (Carlsson and Gade, 1986), when subjected to hypoxic conditions. These previous studies demonstrated that increases in alanine caused by anoxia are associated with increases in succinate. It has been reported that alanine and succinate accumulate in the abiotic environment (De Zwaan et al., 1976). In the present study, our results showed a similar phenomenon regarding the levels of alanine and succinate in mussels. Mussels that grew in Onsan Bay, a metal-polluted environment, accumulated succinate and metabolites related to osmolarity, suggesting that abiotic stress and metal pollution can affect pathways related to osmolytes in mussels. Our results demonstrate that invertebrates living in the sea experience metabolite changes in response to heavy metal pollution. 5. Conclusions We investigated differences in metabolite patterns between mussels from Onsan Bay, a heavy metal-polluted area, and the Dokdo area, a non-polluted area. Our study illustrates that NMRbased metabolomics can be an efficient method for characterizing
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