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Metabolic Profile Biomarkers of Metal Contamination in a Sentinel Terrestrial Species Are Applicable Across Multiple Sites J A C O B G . B U N D Y , * ,† H E C T O R C . K E U N , † JASMIN K. SIDHU,† DAVID J. SPURGEON,‡ CLAUS SVENDSEN,‡ PETER KILLE,§ AND A. JOHN MORGAN§ Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Huntingdon PE28 2LS, and Cardiff School of Biosciences, Main Building, Cardiff University, Park Place, Cardiff CF10 3TL, Wales, United Kingdom
In this study, we addressed the question of whether an omic approach could genuinely be useful for biomarker profile analysis across different field sites with different physicochemical characteristics. We collected earthworms (Lumbricus rubellus) from seven sites with very different levels of metal contamination and prevailing soil type and analyzed tissue extracts by 1H nuclear magnetic resonance spectroscopy. Pattern recognition analysis of the data showed that both site- and contaminant-specific effects on the metabolic profiles could be discerned. Zinc was identified as the probable major contaminant causing a metabolic change in the earthworms. Individual sites could be resolved on the basis of NMR spectral profiles by principal component analysis; these site differences may also have been caused by additional abiotic factors such as soil pH. Despite an inevitable degree of confounding between site and contaminant concentrations, it was possible to identify metabolites which were correlated with zinc across all different sites. This study therefore acts as a proof of principle for the use of NMR-based metabolic profiling as a diagnostic tool for ecotoxicological research in polluted field soils.
Introduction The use of biomarkers gives apparently compelling advantages in principle for environmental risk assessment and ecotoxicology. The most significant is that a biomarker response integrates all of the site-specific factors influencing toxicant bioavailability, the net effects of chemical mixtures (including interactive effects), and spatial/temporal heterogeneity in contaminant distribution (1). Thus, a biomarker reports on environmental quality from the organism’s perspective. However, practical issues with the use of * Corresponding author phone: 44 20 75943039; fax: 44 20 75943226; e-mail:
[email protected]. † Imperial College London. ‡ Centre for Ecology and Hydrology. § Cardiff University. 4458
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environmental biomarkers means that, so far, they have not revolutionized the assessment of environmental pollution, a field still dominated by chemical residue analysis (2). One reason is that biomarker responses that are wellbehaved in laboratory exposures are often highly variable in the field, where a large number of abiotic and biotic confounding factors may exist; see, e.g., refs 3-5. It has been proposed that one strategy for overcoming this problem would be to use comprehensive profiling (“omic”) approaches, in which the entire profile serves as a fingerprint of toxicity (6, 7). A battery of biomarkers clearly provides more potential information than a univariate response, and omic profiles can better define an organism’s integrated physiological state (8). Since such approaches provide large datasets, multivariate methods could potentially be used to factor out different confounding contributions. Site-specific variables (e.g., vegetation, soil moisture, pH, substrate, redox potential) will affect resident organisms’ biology, even discounting the effects of chemical contamination. Thus, a strong biomarker profile relationship across widely differing sites would be a validation of the omic profiling approach for assessing environmental pollution. Here we test this approach using metabolomic profiling of autochthonous populations of the earthworm Lumbricus rubellus taken from seven different contaminated sites. Earthworms have been widely used as sentinel organisms in environmental risk and contamination assessment (9), not least because of their keystone ecological status in many soils and their ability to either macroaccumulate metals such as Cd and Pb or regulate the tissue concentrations of others such as Ni and Mn (10). L. rubellus is a litter-inhabiting (epigeic) earthworm commonly found in a wide range of soil types, even shallow disturbed industrial soils, and can therefore be used to compare soil quality across multiple sites. In addition, ongoing research effort, including a publicaccess EST sequencing project, LumbriBASE (11), which has to date yielded 17 225 sequences representing 7629 genes (www.earthworms.org), means that L. rubellus is an excellent subject for future mechanistic toxicity studies. Nontargeted metabolite profile analysis (metabolomics/metabonomics) has been widely used in laboratory-based toxicology (see, e.g., the recent comprehensive review by Robertson (12)). There are also an increasing number of applications demonstrating its utility within the context of environmental contamination and disease (13-18). These include studies on a number of organisms outside the classic laboratory models, including several earthworm species (19-23). Indeed, one of the great advantages of metabolomics (compared to other omic techniques) for environmental studies is that no prior sequence information is necessary, so practically any organism could potentially be profiled (24). However, the applicability of metabolic profiling for pollutant response identification across multiple and diverse sites has not yet been resolved. We collected specimens of L. rubellus from seven geochemically contrasting sites in the United Kingdom with very different levels of metal contamination and analyzed tissue extracts for small-molecule metabolites by 1H nuclear magnetic resonance (NMR) spectroscopy. Thus, we were testing twin and opposing possibilities: either the site effects on the metabolic profiles would be dominant, limiting this approach for future ecotoxicological research, or there would be clear metabolic biomarkers of metal exposure despite all the confounding site effects. 10.1021/es0700303 CCC: $37.00
2007 American Chemical Society Published on Web 05/10/2007
TABLE 1. Measured Properties of the Seven Sites Used for Earthworm Collection, Including Soil Metal Concentrations (mg kg-1 dry weight)a
code position no. of worms pH LOIb [Ca] [Fe] [Mg] [K] [Na] [P] [Pb] [Zn] [Cd] [Cu] [Pb]c [Zn]c [Cd]c [Cu]c BCFd of Pb BCFd of Zn BCFd of Cd BCFd of Cu
Pontcanna Field
Elan River Bank
Cwmystwyth Stream
Cwmystwyth Adit
Cwmystwyth Cottage
Wemyss
Rudry Hollow
PONT 51°29′39′′ N, 3°12′18′′ W 10 6.08 8.13 3.59 39.32 1.67 735 155 800 62 94 0.33 70.2 3.6 0.15 0.00073 46 0.41 18 79 0.49
ELAN 52°20′46′′ N, 3°38′59′′ W 9 4.80 6.91 0.65 80.30 4.11 210 137 400 28 92 0.25 59.2 11 3.4 0.026 175 0.89 6.8 99 0.46
STR 52°21′38′′ N, 3°45′46′′ W 10 4.28 8.64 0.58 83.75 3.94 183 162 512 1380 165 0.32 96.6 192 18 0.13 346 5.1 6.8 79 0.40
ADIT 52°21′27′′ N, 3°45′35′′ W 10 6.06 6.15 5.44 53.15 3.17 156 135 609 4567 3105 4.18 170 57 7.6 0.010 93 7.8 3.2 15 0.49
COT 52°21′26′′ N, 3°45′26′′ W 10 7.11 9.06 63.78 45.46 2.10 178 192 1482 21034 79475 238 468 32 14.9 0.016 44 0.51 0.50 0.26 0.26
WEM 52°20′59′′ N, 3°53′14′′ W 7 4.4 14.65 1.65 54.06 5.41 331 901 659 2629 485 0.32 181 178 29 .066 317 2.2 6.9 78 0.23
RUD 51°34′33′′ N, 3°08′38′′ W 10 5.44 30.59 85.95 38.16 42.77 269 274 687 46841 7067 159 253 186 25 0.65 83 0.15 1.5 1.8 0.25
a Metal concentrations also given for earthworm tissues for four metals (Pb, Zn, Cd, Cu). b Percentage of organic carbon as measured by loss on ignition. c Free ion concentration, calculated using the method of Lofts et al. (41). d Bioconcentration factor (calculated using the total soil concentration).
Materials and Methods Sites. We collected up to 10 adult (clitellate) L. rubellus specimens per site by digging and hand-sorting from seven sites in central Wales, U.K. (Table 1, Figure 1). Soil samples were collected at the same time. Five of the sites were associated with Pb/Zn mines that discontinued production in the period approximately from 1880 to 1920; three of these (STR, ADIT, WEM) were located on relatively base-poor, acidic, geology (as reflected by the presence of the calcifuge plant species Galium saxatile), and two were located on baserich circumneutral soils that reflected either local geology (RUD) or anthropogenic activity (COT). Two sites, one in a nutrient-poor upland grassland area, the other in the vicinity of a limestone outcrop, were chosen as “acidic” (ELAN) and “calcareous” (PONT) uncontaminated reference soils. Sample Collection. The worms were returned to the laboratory in their native soils and then depurated on moist tissue paper in the dark at 15 °C for 48 h. Depuration was undertaken (i) to minimize possible interferences in the NMR spectra and (ii) to allow the reliable measurement of tissue metal concentrations. Worms were then snap-frozen in liquid nitrogen and stored at -80 °C; the weight of individual worms was not recorded. The soil was dried to constant weight at 80 °C prior to analyses. Sample Preparation. The worms were ground at liquid nitrogen temperature using a mortar and pestle, and the frozen ground tissue was lyophilized. Between 10 and 30 mg dry weight of tissue or 1 g of soil was accurately weighed out, and nitric acid digests were analyzed for 10 different metals (K, Na, Mg, Ca, Fe, P, Pb, Cd, Cu, Zn) using a ThermoElemental X-series inductively coupled plasma mass spectrometer. Analyses included appropriate blanks (which were below detection limits for all metals except Cu, maximum 0.06 mg/ L, and Na, maximum 0.7 mg/L) and standard reference materials (light sandy soil BCR CRM 142R, lobster hepatopancreas from the National Bureau of Standards, Canada). Quantified concentrations of stated metals in the standard reference material were typically within 7-8% of the certified
values. Following analysis, values for Cu were corrected for the low level of contamination indicated from blanks. Soil LOI was determined by combustion at 375 °C for 24 h. Between 20 and 30 mg dry weight of tissue was accurately weighed out for each worm for NMR analysis. This was extracted into 2 mL of ice-cold 60% acetonitrile solution by vortexing and centrifuged (16000g, 10 min), and the supernatant was dried in a vacuum concentrator. NMR Spectroscopy. Each sample was rehydrated in 0.6 mL of an NMR buffer, containing 30% D2O, 100 mM phosphate buffer, pH 7.0, and 0.25 mM sodium (trimethylsilyl)[2,2,3,3-2H4]-proprionate (TSP). The rehydrated samples were centrifuged (3 min, 16000g), and 0.55 mL was transferred to a 5 mm NMR tube. Samples were analyzed at 300 K on a Bruker Avance DRX600 spectrometer (Bruker BioSpin, Rheinstetten, Germany), with a field strength of 14.1 T and a proton resonance frequency of 600 MHz, equipped with a broad-band inverse probe. The data were acquired in 32 768 data points across a spectral width of 12 kHz, with an acquisition time of 1.36 s. Spectra were acquired with a onedimensional NOESY sequence for water suppression, with an initial 1.5 s longitudinal relaxation delay. The spectra were processed using iNMR 1.5 (Nucleomatica, Molfetta, Italy). An exponential apodization function of 0.5 Hz was applied to the free induction decay, and the data were zero-filled to 32 768 real points prior to Fourier transformation. The spectra were manually phase-corrected, and the batch-processing function was used to reference each spectrum to TSP at δ ) 0 ppm (parts per million, i.e., fractional frequency difference in hertz × 106) and for baseline correction (first-order polynomial). The spectra were then exported as both fullresolution ASCII files and also data-reduced to 3 Hz (0.005 ppm) bins. Data Analysis. The binned data (regions 9.50-5.00 and 4.50-0.50 ppm) were used for multivariate analysis. The total integral for each sample was normalized to 2000, thus avoiding trivial separations caused by differences in total metabolite concentrations for each sample. The full-resoluVOL. 41, NO. 12, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Results and Discussion
FIGURE 1. Outline maps of the United Kingdom (inset) and of Wales indicating the locations of the seven sampling stations. (Note the close proximities of sites 2, 3, and 4.) The names and U.K. Ordnance Survey map references are given in boxes; the latitude and longitude coordinates of the sites and the soil and earthworm metal data for each site are given in Table 1. tion spectra were overlaid and inspected for regions with resonance frequency shifts (e.g., caused by slight pH differences between samples). The following regions were identified and the bins within each region summed to single variables: 7.89-7.93, 7.845-7.87, 7.095-7.12, 7.08-7.095, and 2.91-2.93 ppm. The data were transformed by log(n + 1), removing the overall correlation between intensity and standard deviation for a series of five technical replicates (made up of an equal mixture of all samples), being a simplified version of the variance-stabilizing transformation described in ref 25. The choice of a total area of 2000 for normalization was made because this gave good results for log transformation with a constant offset of 1. Principal component analysis (PCA) and partial least-squares (PLS) regression were both carried out using PLS Toolbox 4.0 (Eigenvector Research, Wenatchee, WA) running in Matlab 7.3 (The Mathworks, Natick, MA). The NMR spectral data were mean-centered but not otherwise transformed. The metal concentration data were log-transformed and then autoscaled. PLS analyses were cross-validated using data split into 12 random subsets with 20 iterations. The numbers of PLS axes to be fitted were judged both by examining plots of summary statistics and by examining plots of crossvalidated predicted metal concentrations. For repeated-variable analysis, the full spectral resolution data were used. Correlations were calculated between each spectral data point and tissue metal concentrations for each metal in turn, using code written in Matlab. Log-transformed data were used for both datasets. These correlations were then visualized by projecting them as a color scale onto a median spectrum (26, 27). 4460
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Site details are given in Table 1 and Figure 1. The soil contamination resulted in elevated tissue metal concentrations in the earthworms (Table 1; Figure S1, Supporting Information). The high variation in tissue concentrations of essential metals such as Zn is a likely result of the ability of earthworms to sequester high levels of metals in a relatively inert form in phosphate-rich chloragosome granules (28), but could also be affected by incomplete depuration of soil from the gut. The NMR spectra of the tissue extracts show a typically complex mixture of resonances from smallmolecule metabolites (Supporting Information, Figure S2). The highest concentration compound in all worms was 2-hexyl-5-ethyl-3-furansulfonate, an unusual metabolite of unknown function that is apparently unique to earthworms (20). We assigned metabolite resonances on the basis of their chemical shift and multiplicity, having previously used twodimensional spectra to aid assignment. In addition, selected metabolites (adenosine phosphates, scyllo-inositol, phosphocholine) were confirmed by spiking in authentic standards and reacquiring the spectra. Some kind of multivariate pattern-recognition analysis is essential for assessing the complex differences between spectra. We used PCA to visualize the overall patterns in the data. Six axes were sufficient to explain the major variance in the data, on the basis of both scree plots and inspection of scores plots. There were clear differences between sites: a plot of scores for PC 1 vs PC 2 showed that the COT worms were separated along axis 2, whereas the RUD and ADIT worms were partially separated from the other groups along axis 1 (Figure 2). Further differences between sites within the first six PCs are shown in the Supporting Information (Figure S3). We examined the PCA data for any clear relationships to the measured tissue metal concentrations, excluding one of the ELAN samples, which was a clear outlier of all tissue element concentrations (all values lower). This sample was also excluded from all future analyses. PC 2 was significantly correlated with both log [Zn] (R2 ) 0.66, F ) 120, P < 0.001) and log [Cu] (R2 ) 0.42, F ) 44, P < 0.001). Tissue Zn and Cu levels were themselves both strongly correlated (R2 ) 0.69, F ) 137, and P < 0.001 for the log-transformed concentrations); hence, it was not surprising that both were related to PC 2 scores. PC 1 was weakly but significantly correlated with log [Fe] (R2 ) 0.24, F ) 19, P < 0.001), while PC 3 was significantly correlated with log [K] (R2 ) 0.48, F ) 66, P < 0.001). In addition, correlations with soil pH and loss-on-ignition were also calculated against PC scores averaged across all worms for each site. PC 1 was clearly significantly correlated with loss on ignition (R2 ) 0.74) and PC 2 with soil pH (R2 ) 0.75); i.e., these relationships were as strong as the correlations between PC 2 scores and average tissue Zn concentrations (R2 ) 0.74). PCA is unsupervised, i.e., it does not use a priori information about the samples when fitting components, so one can be confident that the differences observed were not the result of overfitting the data. However, because PCA calculates axes that explain as much variance in the dataset as possible, it may fail to pick up biologically significant effects that do not represent a large proportion of variance. Therefore, an additional supervised statistical analysis is very useful. We used PLS regression, because it deals well with highly multivariate and correlated data for predicting continuous variables. It has been widely used in chemometrics and for interpreting omic data (29, 30). Tissue metal concentrations, with the exception of Cu and Zn, were not closely correlated, so it was appropriate to fit independent PLS models for each metal in turn. The strongest model was for Zn, and the next best model was for Cu. However, a joint
FIGURE 2. Principal component analysis of binned NMR spectral data. (A) Scores plot, PC1 v PC2. Different symbols represent worms from different sites. Key: circles, ADIT; squares, COT; triangles, ELAN; inverted triangles, PONT; tilted squares, RUD; plus signs, STR; “bow tie” shapes, WEM. (B) Loadings on axis 1. Key: *, resonances from 2-hexyl-5-ethylfuransulfonate. (C) Loadings on axis 2. Key: +, resonances from histidine; ×, resonances from N-r-methylhistidine.
FIGURE 3. Cross-validated predictions against measured Zn concentrations [log(mg kg-1 of tissue dry weight)] from the partial least-squares model with three latent variables. Different symbols represent worms from different sites. Key: circles, ADIT; squares, COT; triangles, ELAN; inverted triangles, PONT; tilted squares, RUD; plus signs, STR; “bow-tie” shapes, WEM. The dashed line represents the perfect prediction (y ) x). model fitted using both log [Cu] and log [Zn] as the Y matrix gave a clearly better fit for Zn than Cu (data not shown), and hence, it is probable that the apparent relationship with Cu was actually caused by the correlation between tissue Cu and Zn levels. For this reason, we focused on the Zn model. We decided to fit three axes on the basis of cross-validated error plots. The cross-validated predictions showed a good relationship between measured and predicted tissue Zn concentrations (Figure 3). There are two possible outcomes/objectives for a metabolomics analysis. One is a “sample-centered” or patternrecognition approach, looking to cluster samples or use sample profiles to predict specific end points (31, 32). This is clearly immensely valuable for environmental applications, but very often one also wants to identify which metabolites are potential biomarkers. Here, we used variable-by-variable
correlation between NMR data points and an external variable (tissue zinc concentrations), visualized by projecting the magnitude of the correlation as a color scale onto a representative (median) spectrum (27). This provides a rapid and easy way to detect changes by eye (Figure 4), which can then be highlighted for further investigation. Six resonances were identified as correlated with tissue zinc levels: 8.60s, 8.27s, 7.55d, 3.35s, 3.23s, and 2.92s ppm. These were assigned as nuc1, nuc2, tryptophan + uracil, scyllo-inositol, phosphocholine (PCho), and the N-methyl resonance of N-Rmethylhistidine (NAMH), respectively. The major metabolite contributing to “nuc1” was probably AMP; spiking in authentic AMP confirmed that this resonance increased in intensity (but does not rule out the possibility of other nucleotides contributing to this signal). The “nuc2” signal contained contributions from more than one purine nucleotide, including AMP + ADP, as shown by spiking experiments. The assignment of NAMH is tentative, and based on the presence of two resonances from imidazole protons (appearing slightly upfield of the corresponding histidine resonances, Figure S3), which are statistically correlated with the singlet at δ 2.92. This singlet has a relative intensity of 3:1 compared to the aromatic resonances, and the chemical shift is consistent with its belonging to a methyl group from a secondary aliphatic amine. The relationships for these compounds were further investigated by summing the integrals over an appropriate chemical shift range for each resonance (8.58-8.62, 8.268.27, 7.535-7.56, 3.345-3.36, 3.225-3.24, and 2.91-2.93 ppm, respectively) and plotting their distribution against zinc concentrations. The correlations for the integrals 7.535-7.56 and 2.91-2.93 ppm resulted from site-specific differences between the high-zinc COT site and all other sites (vide infra), so results are presented for the other four integral regions only (Figure 4). Clearly there are overall between-site correlations with Zn for all of these, but there is no correlation for each site considered separately. Hence, the question must be addressed of whether the metabolic changes truly represent a response to Zn. Instead, the site-specific differences could be caused by factors such as vegetation cover, which could plausibly result in metabolic changes (33), soil properties, or other unsuspected contaminants that differed between the sites. The best evidence that the observed marker patterns were not caused by site-specific confounding variables would have been within-site correlations between metals and metabolites. There are a number of possible VOL. 41, NO. 12, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Correlation of spectral regions with tissue zinc concentrations is represented as a false-color scale projected onto a typical (median) spectrum, such that those spectral features with the strongest relationship to zinc are shown as red, whereas those with no correlation with zinc are shown as blue. Regions showing peaks with the strongest relationships to zinc: (A) selected aromatic region of the spectrum; (B) selected aliphatic region of the spectrum. Panels C-F show the actual relationships for spectral regions against zinc; the NMR spectroscopic data and Zn concentrations are both shown on a log scale. Key: circles, ADIT; squares, COT; triangles, ELAN; inverted triangles, PONT; tilted squares, RUD; plus signs, STR; “bow tie” shapes, WEM. The solid line represents a linear regression for all data; the dashed line represents linear regression for all data excluding the COT site (points represented by squares). Key: (C) nuc1, AMP + other possible nucleotides; (D) nuc2, AMP + ADP + other possible nucleotides; (E) scyllo-inositol; (F) phosphocholine. reasons why we did not see these within-site correlations, even if the global between-site relationship with Zn was valid. First, only a relatively small number of worms at each site (up to 10) were analyzed. Second, the range of tissue Zn levels for each site was much lower than the overall range considered over all sites. Probably there was simply not enough chemical and biological variability within each site to see strong correlations. An alternative approach would be to sample a large range of sites, ideally measuring the levels of possible confounders as well. This would provide more statistical power in judging whether metabolites were biomarkers of metal exposure or of other site factors. To an extent, we could do this with the current study. The correlation analysis that resulted in the selection of six potential biomarker resonances did not use the site information. As a result, there is a potential danger of interpreting site-related differences as pollutant responses: because the COT site had by far the highest Zn concentrations for both soil and tissue (Table 1), anything that separates the COT site from the others would also appear to be a marker for Zn. Hence, we also examined the data with this site excluded. For four of these resonances, it is clear that even when the high-Zn COT site is excluded, there is still a strong correlation between the metabolite(s) and tissue Zn concentrations (Figure 4). This demonstrates, as proof of principle, that untargeted metabolic profiling can identify biomarkers applicable across different contaminated sites.
Biological Interpretation Ecotoxicogenomics offers the promise that omic profiles contain information about contaminant effects at a molecular level, allowing hypothesis generation for future targeted experiments (34). Metabolite profiling has an important role to play in this, although it is also important to realize that we are still a long way from being able to routinely infer the underlying biochemistry from metabolic data. Extensive 4462
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future work will be required to characterize the effects of additional confounding factors (e.g., seasonality) and to build up a knowledge resource with organisms that are generally less well understood than the classic laboratory models. Thus, it is likely that much of the value of metabolomics in environmental research will be from extending the measured phenotype of an organism, and it is not likely to lead to a full mechanistic understanding when used in isolation. Ad hoc explanations based on metabolomic data alone should be avoided, unless the interpretation is very clear (e.g., oxidative stress leading to an increase in glutathione levels (35)) or there are additional reasons to support a particular interpretation (e.g., extra information on gene transcription or enzyme activities). The fact that we used whole-worm extracts makes it particularly difficult to reach any meaningful conclusion about the mechanistic reasons for the observed metabolic changes, as there is no information about organspecific metabolic changes. It is important for future environmental research to link metabolomics to clear physiological end points. For example, process models, such as dynamic energy budget models, offer significant benefits over single end points in ecotoxicology (36, 37), and it would be extremely interesting to link such models to metabolic data. A previous laboratory-based mesocosm experiment demonstrated that free histidine increased in L. rubellus extracts in response to increased Cu and proposed that this might be a protective mechanism to reduce cytotoxicity directly by chelating intracellular Cu (38). There was no correlation between histidine and Cu (nor any other metal) in the present study, but this could be simply because Cu levels were not high enough to elicit a response. There was a decrease in NAMH at the site with the highest Zn levels in the current study and similarly in a previous field study of Zn pollution (19). It certainly seems that histidine metabolism has a role in L. rubellus response to heavy metals, but the exact details remain to be elucidated; for instance, an increase in histidine-
containing metal-binding proteins could also cause changes in the NMR-visible histidine and NAMH pools. In this context, it may be significant that the variance of histidine was much greater in worms from COT than for any other site. There appeared to be two different cohorts of worms resident at this heavily polluted site, one with high histidine and another with low histidine levels (Supporting Information, Figure S4). If this phenotypic bimodality were confirmed by further largescale analysis of the COT population, then this could suggest a possible genetic basis for these differences. We speculate that there could be an adapted “metal-resistant” ecotype and nonadapted individuals that migrate into the “island” of toxicity (39). The question of how genetic differences between populations may affect metabolomic profiles is very little understood, but will be key to future applications of metabolomics to environmental pollution assessment (40), which will require sampling geographically distinct populations exposed over different time scales.
Acknowledgments We thank NERC for support.
Supporting Information Available Four additional figures showing tissue metal concentrations and BCFs for six additional elements (Figure S1), a typical NMR spectrum of an earthworm tissue extract (Figure S2), site differences as shown by PCA of the NMR data (Figure S3), and histidine levels in worms from the COT site (Figure S4). This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review January 5, 2007. Revised manuscript received March 15, 2007. Accepted March 21, 2007. ES0700303
