Size-dependent effects of low level cadmium and zinc exposure on the metabolome of the Asian clam, Corbicula fluminea

Aquatic Toxicology 105 (2011) 589–599

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Aquatic Toxicology journal homepage: www.elsevier.com/locate/aquatox

Size-dependent effects of low level cadmium and zinc exposure on the metabolome of the Asian clam, Corbicula fluminea Nicole Spann a,∗ , David C. Aldridge a , Julian L. Griffin b , Oliver A.H. Jones b,1 a b

Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, United Kingdom Sanger Building, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, United Kingdom

a r t i c l e

i n f o

Article history: Received 7 June 2011 Received in revised form 18 August 2011 Accepted 19 August 2011 Keywords: Sediment Nuclear magnetic resonance spectroscopy Gas chromatography–mass spectrometry Metabolomics Mixture toxicity Bivalve

a b s t r a c t The toxic effects of low level metal contamination in sediments are currently poorly understood. We exposed different sized Asian clams, Corbicula fluminea, to sediment spiked with environmentally relevant concentrations of either zinc, cadmium or a zinc–cadmium mixture for one week. This freshwater bivalve is well suited for sediment toxicity tests as it lives partly buried in the sediment and utilises sediment particles as a food resource. After one week, the whole tissue composition of low molecular weight metabolites was analysed by nuclear magnetic resonance spectroscopy (NMR) and gas chromatography–mass spectrometry (GC–MS). The condition index (ratio of tissue dry weight to volume inside the shell valves) was also measured. Small and large clams were clearly differentiated by their metabolic composition and the two size classes showed opposite responses to the mixture spiked sediment. No effects of zinc alone on the metabolome were found and cadmium only influenced the smaller size class. The main perturbations were seen in amino acid and energy metabolism, with small clams using amino acids as an energy resource and larger clams primarily drawing on their larger storage reserves of carbohydrates. Our study demonstrates that metabolomics is a useful technique to test for low level toxicity which does not manifest in mortality or condition index changes. The differing effects between the two size classes stress that it is important to consider age/size when conducting metabolomic and ecotoxicology assessments, since testing for the effects on only one size class makes it more difficult to extrapolate laboratory results to the natural environment. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Discharge of toxic metals into the aquatic environment has decreased in Europe over recent decades (Nixon, 2004). However, metal ions rapidly bind to sediments, cannot be degraded and therefore may persist over centuries with the potential of being redistributed to the water column (Macklin et al., 1997). Lethal effects on biota in the field are often obvious, but quantifying the effects of low-level chronic exposure is more difficult. Chronic exposure to pollutants can alter feeding rates, growth and reproduction in bivalves (with associated population level effects) and chronic metal exposure is one of the factors responsible for the decline in freshwater bivalve populations (Naimo, 1995). Typically, a complex mixture of different metals is present in the aquatic

∗ Corresponding author. Tel.: +44 1223 336617; fax: +44 1223 336676. E-mail addresses: [email protected], [email protected] (N. Spann), [email protected] (D.C. Aldridge), [email protected] (J.L. Griffin), [email protected] (O.A.H. Jones). 1 Present address: School of Engineering and Computing Sciences, University of Durham, South Road, Durham DH1 3LE, United Kingdom. 0166-445X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2011.08.010

environment and the effects of exposure to this mixture can be additive or antagonistic (Hemelraad et al., 1987; Keller and Zam, 1991; Shaw et al., 2006). The metals zinc and cadmium often occur together as cadmium is present in zinc minerals and is a by-product of the refining of both zinc and copper (Hem, 1972). Zinc is a biologically essential element, whereas cadmium is not and this leads to different accumulation and depuration kinetics for the two elements in freshwater bivalves (Baudrimont et al., 2003). Metabolomics is the study of small, low-molecular-weight compounds, the metabolome, in a biological sample. Such compounds include amino acids, sugars and lipids amongst others. Characterising the metabolome provides information about the functional status of an organism which can be related to the organism phenotype, including toxic effects induced by metal exposure (Bundy et al., 2009). Being conducted on a lower level of biological organisation as whole organism toxicity endpoints, metabolomic measurements have the power to identify the effects on target organisms exposed to low, but biologically harmful levels. Two of the most commonly used analytical techniques in metabolomics are nuclear magnetic resonance spectroscopy (NMR) and gas chromatography–mass spectrometry (GC–MS) followed by multivariate statistical analysis of the resulting data. NMR has the

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advantage that it requires minimal sample preparation, is fast and non-destructive and can measure a reasonable range of compounds simultaneously. The main disadvantages are that it is not as sensitive as other (MS) techniques and that it can be difficult to identify the compounds in the spectrum. Due to these constraints, samples are often analysed with GC–MS as well which has a better sensitivity than NMR, but requires more sample preparation time. Liquid chromatography–mass spectrometry (LC–MS) is also used for large or very polar metabolites such as lipids and hormones (Jones et al., 2008a). Bivalves are frequently used as biomonitors for metal pollution as they have many advantageous characteristics: they are sedentary, can be long-lived, are easy to sample and handle and have enough tissue for analysis (Elder and Collins, 1991). Metabolomics has already proven to be suitable for environmental toxicology with marine bivalve species. For example metabolomic effects were seen for Mytilus galloprovincialis exposed to nickel and the insecticide chlorpyrifos (Jones et al., 2008b), for Mytilus edulis subjected to copper and pentachlorophenol (Hines et al., 2010) and for Perna viridis exposed to copper and cadmium (Wu and Wang, 2010). Apart from two studies on the crustacean Daphnia magna (Taylor et al., 2009; Vandenbrouck et al., 2010) the use of other freshwater organisms commonly employed in regulatory toxicity testing has been neglected so far, as have freshwater bivalves which nevertheless play an important part in the biomonitoring of pollution. The Asian clam, Corbicula fluminea (Müller, 1774), is native to Southeast Asia but invaded North America in the early 20th century and from there spread to South America and Europe (Cianfanelli et al., 2007; McMahon, 1983). This species is hermaphroditic (Füreder and Pöckl, 2007) and has been used as a test organism in many field and laboratory studies concerning metal pollution of the water column, often as a surrogate species for threatened native mussels (Ingersoll et al., 2007). Sediment tests have also been carried out with this species, but to a smaller extent (Ingersoll et al., 2007). C. fluminea is especially suited for assessing sediment toxicity as it lives partly buried in the sediment and, in addition to filter feeding on planktonic algae, it ingests sediment particles as a food source (Reid et al., 1992). In this study we exposed C. fluminea to sediment spiked with zinc, cadmium and a mixture of the two. We chose environmentally relevant (and thus low) concentrations of the two elements (zinc 350 mg/kg dry weight [dw], cadmium 1.5 mg/kg dw). Individuals were split into two size classes, representing two, separate, year cohorts, as it is well known that metal accumulation and therefore associated effects are influenced by organism age and size (Luoma and Rainbow, 2008) and this should be controlled for in all investigations to reduce variability (Hines et al., 2007). Size/age-dependent sensitivity to pollutants has been shown in freshwater bivalves (Harrison et al., 1984; Hoare and Davenport, 1994; Ringwood, 1993; Wang et al., 2010) and a lack of acknowledgement of size-dependent sensitivity might lead to incorrect estimates of the risk that pollution poses to populations of bivalve species (Markich, 2003). The aims of our study were to, for the first time, measure a metabolic profile of C. fluminea and elucidate how this profile changes when the clams are exposed to zinc and cadmium and how the two metals act in conjunction. Additionally, we wanted to test if small and large sized clams had different responses to metal pollution. A final goal was to compare the sensitivity of the metabolomic analysis to the condition index, a frequently utilised parameter in toxicity tests with bivalves. These objectives will provide answers to the questions if the metabolomic change in C. fluminea can be used for monitoring metal pollution in the natural environment and how important it is to take biological factors such as organism age into account to reduce inter-individual metabolic variability.

2. Materials and methods Asian clams, C. fluminea (obtained via hand sampling and dredging), and sediment (18 L collected with a grab sampler) were obtained from the River Chet in the Norfolk Broads, UK (1 km river stretch: 52◦ 32 28 N, 1◦ 30 51 E to 52◦ 32 55 N, 1◦ 31 36 E, water depth 30–150 cm), during one week in late February and early March 2009. During an acclimatisation period of one week, the mussels were kept in dechlorinated, aerated tap water at a temperature of 15 ◦ C, 12:12 h light:dark photoperiod and fed three times with Nannochloropsis sp. (Reed Mariculture Inc., Campbell, CA, USA) at a concentration of 3.6–7.4 × 107 algae cells per clam (depending on clam size). 2.1. Experimental setup 2.1.1. Sediment The sediment (sieved to <4 mm to remove larger debris) had a water content of 68.8 ± 0.1% (n = 4, mean ± standard deviation, dried at 80 ◦ C until constant weight) and an organic matter content of 16.8 ± 0.5% (n = 4, combusted at 550 ◦ C for 5 h). Homogeneous subsamples were spiked with 50 ml of aqueous solutions of cadmium and zinc chloride (high purity water, 18 M, was used for the control treatment) while continuously mixing the sediment. The added metal concentrations chosen represented values that occur in the River Yare, which lies in the same catchment area (Norfolk Broads) as the sampled River Chet, but has elevated sediment metal levels for several elements (Broads Authority, 2007). Added concentrations were control (high purity water added), 350 mg/kg sediment dw zinc, 1.5 mg/kg cadmium and a mixture of both metals (350 mg/kg zinc plus 1.5 mg/kg cadmium). The actual sediment cadmium and zinc concentrations at the start of the experiment (day 0; ten replicates for each of the first three treatments, nine replicates for the mixture treatment) plus concentrations of copper, chromium, nickel and lead in the ten control aquaria only, were determined in each aquarium. Sediment was analysed by inductively coupled plasma-optical emission spectroscopy (ICP-OES) after acid digestion (hydrogen peroxide, followed by nitric and hydrochloric acid) in digestion blocks at 120 ◦ C (see Table 1). Standard reference material, river sediment LGC6189 (LGC Standards, Teddington, UK), was also analysed and deviations from the certified values were smaller than ±10% for chromium, copper, lead and zinc, −13% for nickel and +46% for cadmium. The values for the four treatments showed that the intended exposure scenarios were achieved (Table 1), but the cadmium concentrations are overestimated. 2.1.2. Aquaria Small 1.3 L aquaria (133 cm2 surface area, 10 cm height), 10 per treatment, were first filled with one of the four previously prepared sediments (2.5 cm layer), carefully covered with dechlorinated tap water (5.5 cm layer) and aerated. The clams were divided into two size classes based on shell length, representing two different year cohorts: small 16.1 ± 2.6 mm (mean ± standard deviation, n = 199) and large clams 24.3 ± 2.6 mm (n = 200). Five clams from each size class were then added to every aquarium. This resulted in a clam density of 753 clams/m2 which is in the same range of values that were found by Müller (2003) for rivers in the same catchment area: 472–1394 clams/m2 . During the experiment the mussels were kept under a 12 h light:12 h dark light regime and fed on days three and six with Nannochloropsis sp. (1.4 × 107 algae cells per clam). After eight days, one small and one large clam from each aquarium were dissected, the soft tissue frozen immediately in liquid nitrogen and kept at −20 ◦ C prior to metabolomic analysis. Another pair of clams from each aquarium was removed, kept in tap water for a 48 h

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Table 1 Actual sediment concentrations at the start of the experiment (day 0) in the four treatments in mg/kg dw (mean ± standard deviation, n = 10 or n = 9 for zinc + cadmium). Cu, Cr, Ni and Pb were only determined for the control (unspiked) sediment. Treatment

Cd

Control Zinc Cadmium Zinc + cadmium

3.0 3.0 4.3 4.5

Zn ± ± ± ±

0.1 0.1 0.1 0.3

169 507 182 518

± ± ± ±

11 19 11 32

depuration time and then frozen at −20 ◦ C. After thawing, these clams were used to measure the condition index of the individuals; determined as the ratio of soft tissue dry weight (dried to constant mass for 48 h at 75 ◦ C) to available volume inside the shell valves (determined by filling shell valves with salt).

2.2. Tissue metabolite extraction The whole soft tissue of an animal was crushed to a fine powder using pestle, mortar and dry ice. Metabolites were extracted from a 100 mg subsample by methanol–chloroform-extraction (Le Belle et al., 2002). This technique has been used on a variety of tissue samples from a large range of species. It gives clear, reproducible results on a wide range of metabolites and as such is preferred over perchloric acid and acetonitrile extractions and indeed is by far the most widely used extraction method in metabolic studies (Le Belle et al., 2002; Lin et al., 2007). Briefly, 600 ␮l of a methanol–chloroform mixture (2:1, v/v) were added to the frozen powder and the samples were mixed and sonicated for 15 min. An aliquot of 300 ␮l each of ultrapure water and chloroform were added and the samples were then centrifuged for 15 min at 13 200 rpm (12 662 × g) resulting in separate organic and aqueous layers. The aqueous upper layer was then utilised for NMR and GC–MS analysis.

Cu

Cr

Ni

Pb

31 ± 6 – – –

35 ± 1 – – –

21 ± 1 – – –

47 ± 2 – – –

2.4. GC–MS analysis A 150 ␮l subsample of the aqueous extract was evaporated to dryness in an evacuated centrifuge, derivatised (using 30 ␮l of 20 mg/ml methoxyamine hydrochloride in pyridine), vortex mixed for 30 s and incubated at room temperature for 17 h. The samples were then silylated using 30 ␮l N-methyl-Ntrimethylsilyltrifluoroacetamide (MSTFA), vortex mixed, left to react for 1 h at room temperature (Gullberg et al., 2004), and diluted with 500 ␮l hexane. GC–MS analyses were performed using a Thermo Finnegan Trace GC ultra (Thermo Fisher Scientific, UK) coupled to a Thermo Finnegan Trace DSQ mass spectrometer with helium as the carrier gas and a ZB-5MS column (Phenomenex, UK – 30 m × 0.25 mm ID × 0.25 ␮m). The column eluate was introduced into the mass spectrometer (transfer line temperature 340 ◦ C, ion source temperature 250 ◦ C, electron beam 70 eV, source current 100 ␮A) and full scan spectra were collected using 3 scans per second across a mass range of 50–650 m/z after a solvent delay of 4 min. GC–MS peaks were integrated individually, each integrated peak normalised to the total peak area and peaks assigned with reference to the National Institute of Standards and Technology (NIST) database (2002 edition; Xcalibur 2.0, Thermo Scientific, UK).

2.5. Statistical analysis 2.3.

1H

NMR analysis

A detailed description of the NMR and GC–MS analysis can be found in Vandenbrouck et al. (2010) and is thus only briefly described here. The aqueous layer was evaporated to dryness, reconstituted in 500 ␮l deuterised water (D2 O) and 100 ␮l of 240 mM sodium phosphate buffer also containing 0.5 mM (3-trimethylsilyl)-2,2,3,3-tetradeuteriopropionate (TSP). Samples were analysed on a AVANCE II NMR spectrometer (Bruker, Germany) operating at 500.13 MHz for the 1 H frequency using a 5 mm TXI probe. Spectra were obtained using a 1-dimensional NOESY pulse sequence (relaxation delay = 2 s, t1 = 3 ␮s, mixing time = 150 ms), and using 128 transients into 65 K data points over a spectral width of 16 ppm at 27 ◦ C. Individual free induction decays (FIDs) were Fourier transformed, after multiplication by exponential line broadening of 1 Hz, referenced to the TSP peak at 0 ppm, phase and baseline corrected manually, and split into buckets of 0.01 ppm width using ACD Spectrum Manager 8.11 (Advanced Chemistry Development Inc., Canada). The water region from 4.23 to 5.05 ppm, the TSP peak at 0 ppm and the region above 10 ppm were excluded and each spectral region was normalised to a total value of one. Individual metabolites were identified with reference to previous literature and chemical shift tables (Fan, 1996; Jones et al., 2008b; Simpson and McKelvie, 2009; Tuffnail et al., 2009; Viant et al., 2003). The spectra integration resulted in 937 buckets and to reduce background noise and improve the fit of the multivariate models only buckets with a normalised intensity higher than 0.001 in at least one sample spectrum were retained for further analysis (362 variables).

The NMR and GC–MS datasets were imported into SIMCA-P version 12.0.1 (Umetrics, Umeå, Sweden). GC–MS data were mean centred and scaled to unit variance. NMR data were mean centred and Pareto-scaled since they contained more noise (Jones et al., 2008a). Both datasets were analysed using principal components analysis (PCA) and partial least squares-discriminate analysis (PLSDA). Outliers were identified from Hotellings T2 range plots (95% confidence level) for a PCA model of all samples for each dataset, and excluded from further analysis (9% of NMR samples and 19% of GC–MS samples). For a detailed introduction to the statistical techniques see Jones et al. (2008a). For multivariate models with a sample size >20 a seven-fold cross-validation was used, whereas for samples ≤20 a leave-one-out cross-validation was performed (Umetrics, 2011). PLS-DA models were calculated comparing pairwise the three metal treatments (zinc, cadmium and mixture) to the control group and comparing small and large control clams. If the software did not provide significant components (three out of four models, comparing zinc-exposed clams to the control), models were adjusted to include the first two components to allow model validation as described below. Pairwise models were validated by iteratively omitting each sample once, rebuilding the model and predicting the class membership (0 or 1) of the omitted sample. The left out sample was assumed to be correctly classified if the predicted value for class membership was larger than 0.5 for the correct class. This procedure resulted in a 2 × 2 matrix (rows = original class membership, columns = predicted class membership) and the statistical significance was tested by a one-sided Fisher’s exact test. In addition, all PLS-DA models were validated by random permutation of the Y-matrix. PLS-DA models incorporating all four treatments for a size class were also run and validated only

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condition index

0.20

ab

ab

a

ab

ab

ab

b

3.1. Metabolite composition of C. fluminea tissue

ab

With 1 H NMR, more than 30 peaks were detected in the spectra (Fig. 2), of which up to 17 metabolites could be identified from the literature. Prominent resonances were those of malonate, betaine, and lysine. The represented metabolite categories included amino acids (e.g. valine, alanine, isoleucine), Krebs cycle intermediates (succinate) and nucleotides (ATP and ADP). For GC–MS, 32 peaks (Fig. 3) were identified for the pattern recognition analysis, with 24 of them assigned a specific metabolite. Assigned metabolites encompassed amino acids (e.g. glutamine, adenosine) and energy related compounds (e.g. glucose, turanose).

0.15

0.10

0.05

0 c

Zn

Cd

Zn+Cd

c

Zn

Cd

Zn+Cd

treatment

3.2. Metabolic differences for clam size classes

Fig. 1. Condition index of C. fluminea exposed to sediments spiked with zinc (Zn), cadmium (Cd), zinc and cadmium mixture (Zn + Cd), and controls (c). White boxes: small clams; grey boxes: large clams. Whiskers extend to the complete ranges of values found. Different letters (a and b) represent significant differences.

Small and large clams could be separated by their metabolic composition for NMR as well as GC–MS (Fig. 4 and Table 2). In the PCA plot for the NMR data the second principal component (PC) is responsible for the size class differentiation, in the GC–MS PCA plot it is the first PC. Metabolites important for the separation in the NMR data were malonate, glycogen and glucose (higher in large clams than in small ones) and ATP, ADP and amino acids (lower in large clams with the exception of threonine; Fig. 6A and Table 3). PLS-DA coefficients for the GC–MS dataset revealed the same pattern. Sugar molecules (glucose and turanose) and ␣methyl-glycoside, were increased in large clams compared to small ones, whereas the levels of many amino acids, e.g. lysine, proline and asparagine, ␣-glycerophosphoric acid, benzoic acid and putrescine were increased in small clams (Fig. 8A).

by random permutation of the Y-matrix. For models that passed validation (p < 0.05 in Fisher’s exact test), metabolites were identified that contributed significantly to the pairwise comparisons. Clam survival (as a percentage) between treatments was assessed with the Kruskal–Wallis test. The influence of the independent factors clam size class and treatment on condition index (untransformed) was tested with a full factorial two-way ANOVA with Tukey HSD post hoc tests. The software package R version 2.11 (R Foundation for Statistical Computing, Vienna, Austria) was used for univariate statistics and figures.

3.3. Effects of metal exposure on the metabolome PLS-DA models of all four treatments together for each dataset and size class had very low predictive capability (low Q2 ) and did not pass validation by random permutation of the Y-matrix (Table 2). For showing group differences we therefore used corresponding PCA plots and pairwise comparisons of each metal exposure to the control. Pairwise PLS-DA models for the NMR data gave a clear separation only for the control clams to the metal mixture (both size classes) and for small control C. fluminea to the cadmium exposed ones (Table 2). This classification result was also visible in PCA plots of all four treatments together (Fig. 5). The small control clams were separated from the cadmium and mixture ones along the

3. Results The survival of C. fluminea in the aquaria over the one week experimental duration was 80% or higher for all four treatments (control, zinc, cadmium, zinc + cadmium) with no differences between treatments (Kruskal–Wallis test: H = 6.98, df = 3, p = 0.073). For the condition index there was a significant interaction for the factors clam size class and metal treatment (Fig. 1; two-way ANOVA: F3,72 = 4.41, p = 0.007), which was due to a significantly higher condition index in the larger clams compared to the small ones in cadmium-spiked treatments (Tukey post hoc test: p = 0.027).

excluded water region

TSP

9

10 4

3 8 14

10 2 13

14

9

8

7

6

4+5 6

4+5 2

7

1

7

12 11

5

4

3

2

1

0

Chemical Shift [ppm] Fig. 2. Representative 500 MHz 1 H NMR spectrum of a control clam. Key to suggested assignments: 1: isoleucine, leucine and valine, 2: threonine, 3: alanine; 4: lysine, 5: arginine, 6: glutamate, 7: glutamine, 8: succinate, 9: malonate, 10: betaine, 11: glucose, 12: glycogen, 13: guanine, 14: ATP and ADP.

N. Spann et al. / Aquatic Toxicology 105 (2011) 589–599

593

14

20 12 26

22 9

4 1 2 3

5 6

10 7

8

23 18 16

11

6

7

8

9

29 30 31

24

19

13 17

5

32

27 28

15

25

21

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Retention time [min] Fig. 3. Representative GC–MS chromatogram of a control clam with all analysed peaks labelled. Key to metabolite assignments: 1: phosphoric acid, 2: valine, 3: benzoic acid, 4: isoleucine, 5: serine, 6: threonine, 7: alanine, 8: 3-aminopiperidine-2-one, 9: proline, 10: unknown 1, 11: unknown 2, 12: glutamine, 13: unknown 3, 14: putrescine, 15: ␣-glycerophosphoric acid, 16: ornithine, 17: 1,2,3-propanetricarboxylic acid, 18: asparagine, 19: unknown 4, 20: ␣-methyl-glycoside, 21: glycylglycine, 22: glucose, 23: lysine, 24: tyrosine, 25: myo-inositol, 26: adenosine, 27: turanose, 28: unknown 5, 29: AMP, 30: unknown 6, 31: unknown 7, 32: unknown 8.

A

B

0.3

4 0.2

PC 2 (17%)

PC 2 (22%)

2 0.1

0

0

-0.1 -2 -0.2 -4 -0.3 -0.4

-0.2

0

0.2

0.4

-4

-2

0

PC 1 (34%)

2

4

PC 1 (26%)

Fig. 4. PCA plots for the comparison of the metabolite composition of small () and large clams (䊉) from the unspiked sediment (control), (A) NMR dataset and (B) GC–MS dataset. Values in parentheses denote variance of the X-matrix explained by each PC. Uneven sample sizes are present due to exclusion of metabolic outliers (identified from Hotellings T2 range plots, 95% confidence level).

Table 2 Summary table for the validation results of the different PLS-DA models. Models that passed validation with Fisher’s exact test (p < 0.05, bold font) were used to extract the coefficients (metabolites) that contribute significantly to treatment separation. For performance comparison the results for the Y-matrix permutation test are also given (yes: passed validation, i.e. all permuted Q2 were lower than the original one). c: control treatment, Zn: zinc spiked sediment, Cd: cadmium spiked sediment, Zn + Cd: sediment spiked with both zinc and cadmium. Model

NMR

GC–MS

2

R X

R Y

Q

Y-matrix permutation

Fisher’s p

R2 X

R2 Y

Q2

Y-matrix permutation

Fisher’s p

Small c – Zn c – Cd c – Zn + Cd All

0.45 0.63 0.23 0.17

0.72 0.99 0.78 0.21

−0.17 0.76 0.60 0.07

No Yes Yes No

0.867 0.001 0.009 –

0.38 0.24 0.20 0.23

0.84 0.65 0.77 0.17

−0.15 0.35 0.54 0.03

No No Yes No

0.249 0.157 0.020 –

Large c – Zn c – Cd c – Zn + Cd All

0.44 0.18 0.26 0.27

0.50 0.59 0.53 0.12

−0.20 0.18 0.21 0.03

No No No No

0.930 0.185 0.031 –

0.23 0.22 0.42 0.25

0.52 0.69 0.94 0.19

0.08 0.46 0.75 0.11

No No Yes No

0.215 0.088 0.001 –

Small – large

0.23

0.85

0.63

Yes

0.004

0.29

0.89

0.82

Yes

0.001

2

2

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Table 3 Summary table for the metabolites that were changed between classes for the PLS-DA models that passed validation (Table 2 and Figs. 6 and 8); comparing small and large C. fluminea from the control, and the cadmium (Cd) or mixture (Zn + Cd) exposed ones to the control. −: metabolites were lower in the second class; +: metabolites were higher in the second class. Metabolite

Model Small–large

Amino acids Alanine Glutamine Asparagine Isoleucine Lysine Proline Threonine Valine Leucine Arginine Glutamate Energy related Succinate Malonate ADP and ATP Adenosine Glucose Glycogen Turanose ␣-Glycerophosphoric acid Miscellaneous Putrescine Betaine 3-Aminopiperidine-2-one Benzoic acid ␣-Methyl-glycoside Unidentified in NMR spectrum (chemical shift 3.4–4.2 ppm)

Small: control – Cd

− − − − − − + − − − −

Small: control – Zn + Cd

Large: control – Zn + Cd

− −

+ + + +

− −

+ − + +

− − − − −

+ −

+

+ + + −

+

second PC. The clams only exposed to zinc displayed a variable metabolite composition and could not be distinguished from the other three treatments. The samples from the cadmium only and zinc + cadmium spiked sediment also overlapped. A similar picture was evident for the large clams. Interestingly, the metabolites liable for the separation of control and mixture exposed clams were the same for large and small C. fluminea, whereas the mixture caused opposing effects in the two size classes (Fig. 6 and Table 3). In the small clams subjected to the cadmium + zinc containing sediment the levels of ATP and ADP, glutamate, alanine, isoleucine, leucine and valine decreased. On the contrary, these metabolites increased in large C. fluminea kept in sediment spiked with cadmium + zinc,

A

− − −

− + −

+

− + +

+ + +

+ − + + + +

+

− −

+

− + − −

+

and only threonine decreased. For the large-sized clams the levels of glycogen, glucose and betaine as well as many unidentifiable metabolites (sugar and amino acid resonances) from the region 3.4–4.2 ppm declined but were raised in small individuals. The cadmium spiked sediment alone had fewer effects on the small clams than the mixture. Malonate was elevated in small and large clams compared to the control, whereas succinate was only higher in large clams exposed to the mixture. The same pattern of effects emerged from the GC–MS dataset. Small and large control clams were well separated from mixture exposed clams by PLS-DA (Table 2). In a PCA plot with all four treatments separation of these two classes occurred mainly along

B

0.4

0.3

0.2

0.1

PC 2 (19%)

PC 2 (15%)

0.2

0

0

-0.1 -0.2

-0.2 -0.3

-0.4 -0.4

-0.2

0

PC 1 (31%)

0.2

0.4

-0.4

-0.2

0

0.2

0.4

PC 1 (30%)

Fig. 5. PCA plots of metabolic composition identified by NMR of C. fluminea exposed to sediment containing no additional metal ions (control 䊉), zinc (*), cadmium () and a zinc + cadmium mixture ( ). (A) Small clams and (B) large clams. Values in parentheses denote variance of the X-matrix explained by each PC. Uneven sample sizes are present due to exclusion of metabolic outliers (identified from Hotellings T2 range plots, 95% confidence level).

A

PLSDA coefficients [large]

N. Spann et al. / Aquatic Toxicology 105 (2011) 589–599

595

1.0 2

12 11

9

1

0.5

0

4

-0.5 14

PLSDA coefficients [cadmium] PLSDA coefficients [zinc+cadmium]

C

9

0

-2

-4

1.0

10 12

10 11

9

0.5

1

0

6

-0.5

14

3

4 9

14

PLSDA coefficients [zinc+cadmium]

6

10

12

2

14

D

4+5

7

14 10

B

3

7

10

4+5 8

2

7 6

0.5

1 3

14

0

-0.5

2

10

11 12

-1.0 9

8

7

6

5

4

3

2

1

chemical shift [ppm] Fig. 6. Coefficients for the positively validated PLS-DA models of the NMR data, significant coefficients are coloured black, non-significant ones grey. Buckets for which all samples had a normalised intensity ≤0.001 were not included in the PLS-DA models. (A): small control clams – large control clams; (B): small clams control – cadmium; (C): small clams control – zinc + cadmium; (D): large clams control – zinc + cadmium. Positive/negative values denote upregulated/downregulated levels for the class named in the y-axis label in square brackets. Key to suggested metabolite assignments: 1: isoleucine, leucine and valine, 2: threonine, 3: alanine; 4: lysine, 5: arginine, 6: glutamate, 7: glutamine, 8: succinate, 9: malonate, 10: betaine, 11: glucose, 12: glycogen, 13: guanine, 14: ATP and ADP.

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B 4

4

2

2

PC 2 (13%)

PC 2 (14%)

A

0

0

-2

-2

-4

-4

-6

-4

-2

0

2

4

6

PC 1 (26%)

-6

-4

-2

0

2

4

6

PC 1 (27%)

Fig. 7. PCA plots of metabolic composition identified by GC–MS of C. fluminea exposed to sediment containing no additional metal ions (control 䊉), zinc (*), cadmium () and a zinc + cadmium mixture ( ). (A) Small clams and (B) large clams. Values in parentheses denote variance of the X-matrix explained by each PC. Uneven sample sizes are present due to exclusion of metabolic outliers (identified from Hotellings T2 range plots, 95% confidence level).

the first PC (Fig. 7). The models for zinc and cadmium separately, each compared to control C. fluminea, did not pass validation and could not be considered reliable. Here as well metabolic perturbations of the metal mixture could be assigned to increased levels of glucose and turanose as well as adenosine in the small sized clams; putrescine, 3-aminopiperidine-2-one and the amino acids glutamine, isoleucine and lysine decreased. The opposite was true for large clams: glucose and turanose as well as ␣-methyl-glycoside declined whereas benzoic acid, asparagine, isoleucine and proline were increased (Fig. 8). 4. Discussion Our results for the first time show that the metabolomic composition of small and large sized freshwater clams is different in relation to energy related metabolites, amino acids and nucleotides. Metal stress caused by artificially spiked sediment with a mixture of zinc and cadmium to the clams manifested in alterations of the amino acid and energy metabolism, showing contrasting changes in small and large clams. 4.1. Effects of zinc and cadmium exposure on C. fluminea No effects of the zinc spiked sediment could be detected in the metabolome of the clams. For cadmium only an effect on the small clams was discernible in the NMR dataset, though this involved fewer significantly changed metabolites than the mixture treatment. C. fluminea is able to survive within sediments of relatively elevated levels of zinc and cadmium and thus must be able to modify its metabolism to deal with them. In the River Yare, Norfolk UK, Müller (2003) reported recruiting populations within sediments of 350 mg/kg zinc and 1.4 mg/kg cadmium (measured in 2004; Broads Authority, 2007). Angelo et al. (2007) reported live C. fluminea from mining-impacted rivers (USA) with sediment concentrations up to 14.7 mg/kg cadmium and 2233 mg/kg zinc, although MacDonald et al. (2000) suggested toxic contamination thresholds for sediments (probable effects concentration, PEC) of 4.98 mg/kg cadmium and 459 mg/kg zinc. Background metal levels (Table 1) in the natural sediment used for our experiment were below (Cr, Cu, Ni), slightly above (Pb, Zn) or three times higher

(Cd) than sediment quality guidelines (threshold effect concentration, TEC) proposed by MacDonald et al. (2000), but they were all well below the limits that these authors suggested as posing a significant threat to aquatic communities (PEC: Cr: 111 mg/kg, Cu: 149 mg/kg, Ni: 48.6 mg/kg and Pb: 128 mg/kg). It is therefore possible that our spiked concentrations were too low and/or too short in duration to induce a discernible response in the metabolome, especially if the clams were already adapted to slightly elevated metal levels from their natural habitat (clams and sediment for the experiment were collected from the same location). This is supported by the similar condition index and mortality values between all treatments and controls in our experiment. Almost exclusively, only the mixture of zinc and cadmium exhibited discernible effects on the metabolic composition of the clams. We cannot distinguish if the effects of zinc and cadmium were additive or synergistic as nearly no effects were seen in the exposure to each metal separately. But for cadmium and zinc mixtures antagonistic and additive toxicity have been reported previously for freshwater bivalves (Hemelraad et al., 1987; Keller and Zam, 1991). For four species of cladocera it was found that the metal interaction occurring, additive or antagonistic, depends on the species and metal concentrations tested (Shaw et al., 2006).

4.2. Size effects on the normal state metabolome of C. fluminea Large C. fluminea were characterised by higher levels of energy related metabolites such as glucose, glycogen and turanose, but lower levels of nucleotides and amino acids than small clams. Phenotypic anchoring, i.e. characterising and accounting for metabolic variability introduced by e.g. organism age, size and gender, is considered a major requirement for studying the response of organisms to toxicants (Hines et al., 2007). To the best of our knowledge, in environmental metabolomics no studies exist to date looking at the impact of age or size, but it is known from studies on mice and other mammals, that metabolomes change with age (Atherton et al., 2009; Pears et al., 2005). The two chosen size classes in our study also represent clams of different ages. C. fluminea from our sampling site the River Chet grow to a size of 4.1 mm until the end of their first winter and then

597

0.10 0.05 0 -0.05 -0.10

0.1

0

-0.1

0.3 0.2 0.1 0 -0.1

unknown 8

unknown 6

unknown 7

unknown 4

unknown 5

unknown 2

unknown 3

putrescine

unknown 1

phosphoric acid

valine

myo-inositol

tyrosine

serine

threonine

proline

lysine

ornithine

isoleucine

asparagine

isocitric acid

glycylglycine

α-methyl-glycoside

turanose

glutamine

alanine

glucose

benzoic acid

α-glycerophosphoric acid

AMP

adenosine

-0.2 3-aminopiperidine-2-one

C

PLSDA coefficients [zinc+cadmium]

B

PLSDA coefficients [zinc+cadmium]

A

PLSDA coefficients [large clams]

N. Spann et al. / Aquatic Toxicology 105 (2011) 589–599

Fig. 8. Coefficients for the positively validated PLS-DA models of the GC–MS data (error bars are 95% confidence intervals), significant coefficients are coloured black, nonsignificant ones grey. (A) small control clams – large control clams; (B) small clams control – zinc + cadmium; (C) large clams control – zinc + cadmium. Positive/negative values denote upregulated/downregulated levels for the class named in the y-axis label in square brackets.

to 16 mm at the end of their second winter (Müller, 2003). They can reach maturity within 3–6 months and at a shell length of 6–10 mm (Füreder and Pöckl, 2007). Thus our small clams were likely to have been released as pediveligers in the summer 2007, probably having reproduced for the first time in the summer 2008. The large clams were likely to be from the cohort of the summer 2006, making them two and a half years old. 4.3. Biochemical mechanisms of metal toxicity The two different C. fluminea size classes not only had distinct compositions in the control treatment, but their reactions to metal stress were also different, with the main metabolic perturbations relating to amino acid and energy metabolism. In the small clams, levels of amino acids decreased under the mixture stress whereas for large clams the opposite effect was observed. The amount of free amino acids in tissues has for a long time been used as a biomarker for general stress, including pollution with toxic chemicals, in aquatic organisms. The results for freshwater bivalves show that, in contrast to marine bivalves, free amino acid concentrations increase in response to stress (Day et al., 1990; Gardner et al., 1981; Graney and Giesy, 1988), and, in concurrence with our results,

Gardner et al. (1981) also stated a negative relationship between size (measured as shell length) and concentration of free amino acids in Amblema plicata. Adenylate energy charge (AEC = (ATP + 1/2 ADP)/(ATP + ADP + AMP)), as a measure for instantaneously available energy is another biomarker employed as a general stress response, assuming that physiological defence mechanisms induce an increase in energy utilisation (Giesy et al., 1983). Cadmium lowered the AEC in the freshwater bivalves C. fluminea and Anodonta cygnea (Giesy et al., 1983; Hemelraad et al., 1990), although in our study only the small clams responded to zinc and cadmium stress with lower levels of ADP and ATP. In contrast, the large unstressed clams in our study had higher levels of carbohydrates like glycogen, glucose and turanose than the small ones, and these declined in response to the zinc and cadmium treatment. A change in these energy molecules is also considered to be a general stress response, but the reported results from the literature so far are variable. For example, glycogen was found to decrease in bivalves subjected to polyaromatic hydrocarbon, cadmium or surfactant exposure (Bidwell et al., 1995; Hemelraad et al., 1990; Hyötyläinen et al., 2002), but glucose or total sugars increased (Gagné et al., 2001; Hemelraad et al., 1990).

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It is possible that large clams had accumulated bigger reserves of carbohydrates during their longer life-time which were then utilised under toxic stress to meet the increased energy demands for detoxification. The increased amounts of amino acids caused by exposure of clams to sediment spiked with zinc and cadmium might be related to a breakdown of muscle and other tissues because a similar increase was described for earthworms exposed to the toxicant pyrene (Jones et al., 2008a). The free amino acids can then be used to metabolise proteins or peptides, e.g. metallothioneins (low molecular weight proteins, cysteine rich) and glutathione (tripeptide: cysteine, glutamic acid and glycine), which bind and therefore detoxify metals (Luoma and Rainbow, 2008). The elevated levels of ATP and ADP might then reflect the momentarily highly available energy for the processes mentioned above. Levels of succinate were higher in large clams exposed to sediment spiked with zinc and cadmium, suggesting that anaerobiosis took place (De Zwaan and Zandee, 1972) or that there was an impairment of the oxidative metabolism as suggested by Hemelraad et al. (1990) who hypothesised that cadmium might inhibit the enzyme succinate dehydrogenase in the citric acid cycle. The small clams instead used the instantaneously available energy in ATP and ADP and possibly utilised free amino acids as well for the increased energy demand induced by the zinc and cadmium spiked sediment owing to their lower glycogen, glucose and turanose levels compared to large clams (in controls). Utilisation of amino acids for energy production was also seen in M. galloprovincialis in response to nickel and chlorpyrifos exposure (Jones et al., 2008b), but a decrease in amino acids can also be attributed to the induction of defence (stress proteins) and repair mechanisms (DNA repair enzymes; Taylor et al., 2009). The concentration of carbohydrates in small clams was higher when exposed to the mixture spiked sediment, possibly implying a mechanism to conserve energy. Elevated levels of glucose and maltose were found in earthworms from a conventional land management regime compared to a biologically managed one (Rochfort et al., 2009). Hemelraad et al. (1990) stated that glucose levels were elevated and glycogen decreased in the freshwater bivalve Anodonta cygnea subjected to cadmium and provided as one possible explanation that the hormonal regulation of glucose levels was disrupted and led to hyperglycemia. Consequently, the differential response of small and large clams to the mixture could have been caused by the fact that larger clams had higher levels of carbohydrates which could be used for the increased energy demand under toxic stress whereas smaller clams had to draw immediately on amino acids and ATP/ADP as an energy resource. Other metabolites which were important for the separation between classes in the PLS-DA models were in the chemical shift region of 3.4–4.2 ppm in the NMR dataset. This area of the spectra contains overlapping resonances from sugars and amino acids which are hard to differentiate. These compounds show a similar pattern to the carbohydrates (higher in large clams compared to small ones, decreased in large clams and increased in small clams exposed to the mixture stress) and are thus likely to mainly reflect sugars. Betaine was increased in cadmium and mixture exposed small clams, but not in large ones. It is a derivative of glycine and an osmolyte in marine invertebrates. It was also found in NMR metabolomic studies of terrestrial organisms (Gibb et al., 1997) and, as a methyl donor, is involved in the regeneration of methionine and cysteine and thus can be important for detoxification reactions. The metabolite malonate was higher in large clams and it was present at higher levels in cadmium or mixture subjected small and large clams compared to the control. Malonate inhibits the enzyme succinate dehydrogenase (Long et al., 1984) and therefore interferes with the citric acid cycle. A similar result was previously reported in M. galloprovincialis (Jones et al., 2008b).

Metabolomic studies looking at different stressors in a variety of species have often found perturbations in amino acid and energy metabolism (Jones et al., 2008a,b; Vandenbrouck et al., 2010; Wu and Wang, 2010) as a general stress response. Thus the documented changes in C. fluminea for the amino acid and energy pathways most likely constitute a general stress response which is not restricted to heavy metals, or indeed clams. Although the influence of organism size on metal toxicity has been acknowledged for a long time, studies with bivalves focused on the relation of size to bioaccumulation and less on the varying toxic effects of metals. The studies available (acute tests based on mortality) vary and report both decreasing (Harrison et al., 1984; Ringwood, 1993; Wang et al., 2010) as well as increasing (Hoare and Davenport, 1994) sensitivity with age or size. However, toxicity estimates based on chronic, low-level exposures indicate similar sensitivities of adults, juveniles and larvae (Cope et al., 2008). In fact, the only study that looked at sublethal toxicity (valve movement behaviour) in a freshwater bivalve, Velesunio angasi, found adults to be less sensitive than juveniles (Markich, 2003). It is possible that small and large C. fluminea accumulated different amounts of cadmium and zinc in their tissues. However, metal ions can be stored by the organism in a detoxified form and thus total metal body concentrations often do not reflect toxicity (Rainbow, 2007). In addition, the regulation processes (uptake, storage, excretion) in the clams would have also affected the metabolome.

5. Conclusions Our results show that short term exposure to environmentally relevant concentrations of a cadmium and zinc mixture in the sediment induced metabolic changes in C. fluminea detectable by NMR and GC–MS, demonstrating the great potential of metabolomic techniques to detect toxic effects of anthropogenic pollution even at low levels and over short time spans. The main metabolic changes in the clams were related to a perturbed amino acid and energy metabolism. Differences in the more traditional endpoints, mortality and condition index, between the single and mixed treatments and the control were not seen. The results also revealed that the metabolic composition of small and large sized clams was different which, if not controlled for in experiments, can increase the metabolic variability and thus hide metabolic perturbations related to stress. In addition, small and large C. fluminea reacted in different ways to the metal stress. This is very important when extrapolating ecotoxicological results from the laboratory environment to the natural environment as standard laboratory tests often only consider one life stage and results cannot necessarily be extrapolated to older or younger individuals.

Acknowledgements We would like to thank Dr. Alexandra Zieritz, Dr. Rebecca Mant and Holly Barclay for their help with collecting the clams. Kai Ristau provided constructive comments on the manuscript and helped with the sampling at the end of the experiment. Dr. Dan Hoare (Broads Authority) kindly provided data on sediment metal levels in the Norfolk Broads. Steven Murfitt and Dr. Steve Boreham gave advice and help with the metabolomic sample procedures and metal analysis, respectively. Dr. Denis Rubstov helped with suggestions on statistical data analysis. The comments of two anonymous reviewers greatly improved this manuscript. NS received Ph.D. grants from the Biotechnology and Biological Sciences Research Council, the Cambridge European Trust and the Department of Zoology, University of Cambridge. OAHJ was supported by the

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