Environ. Sci. Technol. 2003, 37, 4982-4989
NMR-Based Metabolomics: A Powerful Approach for Characterizing the Effects of Environmental Stressors on Organism Health MARK R. VIANT,* ERIC S. ROSENBLUM, AND RONALD S. TJEERDEMA Department of Environmental Toxicology, University of California, One Shields Avenue, Davis, California 95616
It is important to assess the chronic effects of chemical, physical, and biological stressors on organisms in the environment. Appropriate methods must enable rapid, inexpensive, and multibiomarker analyses of organism health. Here we investigate withering syndrome in red abalone (Haliotis rufescens), an important wild and farmed shellfish species along the Pacific coast, using a metabolomic approach that combines the metabolic profiling capabilities of nuclear magnetic resonance spectroscopy (NMR) with pattern recognition methods. Foot muscle, digestive gland, and hemolymph samples were collected from healthy, stunted, and diseased abalone, and the extracts were analyzed by NMR. Following spectral preprocessing, principal components analyses of the metabolite profiles were conducted. Our results confirm that NMR-based metabolomics can successfully distinguish the biochemical profiles of the three groups of animals, in every type of tissue or biofluid studied. Furthermore, this discovery-based approach successfully identified novel metabolic biomarker profiles associated with withering syndrome. The application of these methods for investigating other environmental stressors is discussed, as are the advantages of NMR-based metabolomics for biomonitoring, particularly in conjunction with gene and protein expression profiling.
Introduction Withering syndrome (WS) is a fatal disease in abalone that results from infection of digestive epithelial cells by a Rickettsiales-like procaryote (RLP (1)). It has decimated black abalone (H. cracherodii) populations in California (2) and has been observed in wild and farmed red abalone (3). Although RLP-infected red abalone have exhibited some resilience to the disease under optimal conditions, recent studies suggest that the pathogen in combination with an additional environmental stressor can synergistically stimulate WS pathogenesis (3). This is consistent with the increased WS-like mortalities at abalone farms that occurred during the elevated seawater temperatures of the 1997-1998 El Nin ˜o event. The potential spread of WS into red abalone stocks throughout California is of great concern, necessitating an * Corresponding author phone: (530)752-2473; fax: (530)752-3394; e-mail:
[email protected]. Current address (after Nov 1, 2003): School of Biosciences, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K. 4982
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increased understanding of the effects of stressors (e.g., changes in seawater temperature and food resources) on WS development. Such studies would benefit greatly from highthroughput methods that can measure multiple biomarkers associated with various stages of the disease. Currently, no such methods exist. Although the exact mechanism of WS is not understood, RLP-infected abalone experience degenerative changes in their digestive glands, hampering their ability to digest food (4). The ensuing starvation induces the clinical symptoms of the disease, a withered foot muscle, and eventual death. Current methods for assessing both RLP and WS status include a somewhat subjective “condition index” based upon overt clinical signs, histological analyses, and a polymerase chain reaction (PCR) protocol for the RLP (5). Although histology of the digestive gland and foot can provide an earlier indication of WS, it is time-consuming and necessitates sacrificing the animal. The PCR protocol offers a highly sensitive tool for detecting RLP infections. However, RLP infections alone do not necessarily lead to the development of WS, and thus evidence of infection is not evidence of disease. Since WS impacts abalone metabolism, we hypothesized that the postgenomic technique of metabolomics (also termed metabonomics) would provide a rapid screening method for the disease, via identification of multiple, sensitive, metabolic biomarkers. Metabolomic methods combine the metabolic profiling capabilities of a wide range of technologies, including gas chromatography-mass spectrometry (GC-MS) (6), liquid chromatography-mass spectrometry (LC-MS) (7), and 1H NMR spectroscopy (8-10), with pattern recognition techniques (11). This approach measures the molecular phenotype of an organism directly and provides an integrated “snapshot” of the low molecular weight metabolites that can change throughout disease, exposure to xenobiotics, or during organism development (12, 13). Metabolic profiles can then be “mined” by pattern recognition algorithms to reveal the subset of metabolites that change most significantly, potentially identifying diagnostic biomarker profiles. Previous metabolomic studies on environmentally relevant species have identified novel biomarker patterns in stressed terrestrial invertebrates, particularly earthworm species. These include NMR-based investigations into the toxicity of fluorinated phenols (13) and anilines (12) to Eisenia veneta and the effects of short-term starvation in E. veneta and Lumbricus terrestris (14). Here we present the first application of NMR-based metabolomics to the aquatic environment, by investigating a bacterial disease in an important aquaculture species. Specifically, we describe the metabolic effects of WS by comparing healthy, stunted, and diseased red abalone from an aquaculture farm. Our initial goal was to classify these animals into three distinct groups, based upon changes in their metabolite profiles associated with progression of the disease. Furthermore, we aimed to identify novel biomarker profiles that are characteristic of diseased abalone. Such biomarkers would not only provide insight into the disease process but would also be used in future studies to assess the efficacy of antibiotic treatments as well as to rapidly assess the disease status of abalone. Our final goal was to employ these metabolic biomarkers in a comparative study of WS and starvation in red abalone.
Materials and Methods Animals and Culture Conditions at Aquaculture Farm. Red abalone were spawned on April 17th, 1998 at The Abalone Farm (Cayucos, CA). After reaching 2.5 cm in length, they 10.1021/es034281x CCC: $25.00
2003 American Chemical Society Published on Web 09/19/2003
were transferred to and maintained in either an upstream tank (received fresh seawater at ambient ocean temperature) or an adjacent downstream tank (received poorer quality seawater that had passed through several upstream tanks). The abalone in the upstream tank were administered oxytetracycline (OTC) in October 2001, to rid them of a potential RLP infection; the downstream tank received no antibiotic treatment. In July 2002, abalone were selected from the upstream tank (“healthy”; N ) 10) and downstream tank (“stunted” and “diseased”; each N ) 5). The stunted abalone (unknown disease status) had significantly reduced shell length compared to the healthy abalone, but otherwise appeared in fair health. Diseased animals had both a significantly reduced shell length and visible atrophy of the foot muscle, indicative of clinical WS. All abalone were shipped to the Pathogen Containment Facility at the Bodega Marine Laboratory (BML) and maintained in flow-through aerated seawater at ambient temperature (14 °C). During this 2-week acclimation period they were fed giant kelp (Macrocystis pyrifera) ad libitum and then fasted for 3 days prior to dissection. Sample Collection. Abalone shell length and whole body wet mass were recorded. Up to 4 mL of hemolymph was collected from the foot muscle via sinus puncture and centrifuged, and the cell-free supernatant was flash-frozen and stored at -80 °C. The foot muscle (specifically the center of the pedal sole) and digestive gland (hepatopancreas) of each abalone were rapidly dissected, separated into duplicate samples, freeze-clamped, and stored at -80 °C. Animals and Controlled Laboratory Exposures. An independent group of healthy red abalone (approximately 4 cm in length) were shipped from The Abalone Farm to BML, where they received OTC-medicated feed for 21 days and then fed kelp ad libitum for 16 days. The RLP-uninfected status of the animals was subsequently confirmed in a subgroup of abalone using a PCR assay (5). Next, animals were randomly divided into three groups, “control”, “fooddeprived”, and “RLP-exposed”. The RLP-exposed group was infected with RLP via a 1-month cohabitation with withered abalone, which was confirmed in a subgroup of animals by PCR. Then all three groups were cultured for 447 days at 19 °C (six 2-L tanks per treatment group), with the control and RLP-exposed groups each receiving full food ration, and the food-deprived group receiving 1/4-food ration. Finally, hemolymph samples were obtained from N ) 12 control, N ) 12 RLP-exposed, and N ) 12 food-deprived abalone (fooddeprived animals yielded less hemolymph and so the original 12 samples were pooled into 6 analyzable volumes). Preparation of Hemolymph. Centrifugal filter devices (Millipore Amicon Ultra-4; 30 000 MWCO) were sequentially washed with Nanopure water, 0.1 M NaOH, water, and phosphate buffer. The hemolymph samples were deproteinized by centrifugal filtering, lyophilized, resuspended in 650 µL of 0.02 M sodium phosphate buffer (in D2O; pH 7.0), centrifuged, and analyzed by NMR. The buffer contained 1 mM sodium 3-(trimethylsilyl)proprionate-2,2,3,3-d4 (TMSP; Cambridge Isotope Laboratories, Andover, MA) as an internal NMR chemical shift standard. To assess experimental precision, hemolymph samples from two healthy abalone (from the aquaculture farm study) were mixed and then split into two replicates, which were prepared and analyzed individually. Preparation of Tissue Extracts. Tissue samples were ground and then extracted using 5 mL/g (wet mass) of icecold 6% perchloric acid. Following centrifugation, supernatants were neutralized with 2 M K2CO3 to pH 7.0 and recentrifuged. Next, 600 µL of each supernatant was lyophilized, resuspended in 600 µL of 0.2 M sodium phosphate buffer (in D2O; pH 7.0; 1 mM TMSP), centrifuged again, and analyzed by NMR. To assess experimental precision, tissue
samples from two healthy abalone were pooled (to generate sufficient tissue), and then split into five and six replicates for digestive gland and muscle, respectively, which were prepared and analyzed individually. Finally, each of the duplicate muscle and digestive gland samples obtained at dissection were weighed, lyophilized, and reweighed, to determine their dry/wet mass ratios. NMR Spectroscopy. NMR spectra of abalone biofluids and tissue extracts were measured at 500.11 MHz using an Avance DRX-500 spectrometer (Bruker, Fremont, CA) at 295 K. Specifically, one-dimensional (1D) 1H NMR spectra of muscle extracts were obtained using a 9-µs (60°) pulse, 7-kHz spectral width, and 2.5-s relaxation delay, with 200 transients collected into 32k data points, requiring a 16-min total acquisition time. The residual water resonance was presaturated during the relaxation period. To facilitate the removal of broad resonances from high molecular weight compounds, 1D 1H NMR spectra of digestive gland extract and hemolymph were obtained using a Carr-PurcellMeiboom-Gill (CPMG) spin-echo sequence ([relaxation period-90°-(τ-180°-τ)n-acquisition] with n ) 100 and a total spin-spin relaxation delay 2τn ) 80 ms). These T2edited spectra were obtained using a 13-µs (90°) pulse, 3.5-s relaxation delay, and other acquisition parameters as described above, requiring a 19-min acquisition time. All 1D data sets were zero-filled to 64k points, and exponential linebroadenings of 0.5 Hz were applied before Fourier transformation. The resulting spectra were phase and baseline corrected and then calibrated (TMSP peak at 0.0 ppm), all using XWINNMR software (Version 3.1; Bruker). Peaks were assigned by comparison to tabulated chemical shifts (1517) and confirmed by 2D NMR methods, including 1H-1H homonuclear correlation spectroscopy (COSY) and 1H-13C heteronuclear single quantum coherence (HSQC). Preprocessing of NMR Data. The 1D NMR spectra were converted to an appropriate format for multivariate analysis using custom-written MATLAB code (Version 6.1; The MathWorks, Natick, MA). Each spectrum was segmented into 1960 chemical shift bins between 0.2 and 10.0 ppm, corresponding to bin widths of 0.005 ppm (2.5 Hz). The spectral area within each bin was integrated to yield a 1 × 1960 vector containing intensity-based descriptors of the original spectrum. Bins between 4.7 and 5.0 ppm containing the residual water peak were removed, and the total spectral area of the remaining 1902 bins was normalized to the TMSP internal standard. Each muscle and digestive gland spectrum was further normalized to the dry/wet mass ratio, enabling interpretation of relative metabolite levels on a dry mass basis. Hemolymph spectra received no further normalization and were interpreted on a wet volume basis. Vectors describing each muscle, digestive gland, and hemolymph spectra were compiled into three tissue-specific n × 1902 matrices, with each row representing an individual sample. For each matrix a constant was added to all elements, such that the smallest element assumed a value of one, thus enabling log transformation. The columns were meancentered before multivariate analysis. Statistical Analyses of NMR Data. Principal components analyses (PCA) of the preprocessed NMR data were conducted using the PLS_Toolbox (Version 2.1; Eigenvector Research, Manson, WA) within MATLAB. PCA is an unsupervised method of analysis (and thus is blind to the disease status of each sample) and serves to reduce the dimensionality of the data and summarize the similarities and differences between multiple NMR spectra. This requires calculation of new variables (the PCs) that are linear combinations of the original intensity-based descriptors (chemical shift bins), such that all PCs are orthogonal, and the first PC captures the most variance between the spectra (11). Statistical tests were performed using Number Cruncher Statistical System (2001 VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Physical and Spectroscopic Parameters Describing the Healthy (N ) 10), Stunted (N ) 5), and Diseased (N ) 5) Groups of Abalone from the Aquaculture Farm Experimenta parameter
healthy
stunted
diseased
shell length (mm) total tissue wet mass (g) tissue dry/wet mass ratio (%) muscle digestive gland NMR total spectral area (%)d muscle digestive gland hemolymph
99.3 ( 4.7 140.4 ( 28.6
4.9b
68.3 ( 34.6 ( 7.0b
71.0 ( 5.4b 22.6 ( 5.1b
28.6 ( 1.0c 35.8 ( 1.1c
24.4 ( 2.2c 31.5 ( 1.3c
17.3 ( 0.8c 26.7 ( 0.8c
100.0 ( 7.9 100.0 ( 5.5 100.0 ( 36.6
100.8 ( 6.7 120.8 ( 6.5 84.4 ( 39.9
56.5 ( 26.5 104.2 ( 3.5 29.8 ( 8.2
a All values represent mean ( SD. b Overall differences between all groups (p < 0.001), with post-hoc tests revealing a difference from the healthy group (p < 0.05). c Overall differences between all groups (p < 0.001), with pairwise tests revealing differences between all groups (p < 0.05).d Total spectral areas are normalized to the healthy samples.
Edition; NCSS Statistical Software, Kaysville, UT). Specifically, following the addition of group identifiers (i.e., healthy, stunted, and diseased) to the PCA scores, the classification of these groups along PC1 and PC2 were analyzed using oneway ANOVAs followed by Tukey-Kramer post-hoc tests. These same tests were used to analyze shell lengths, total tissue wet masses, and tissue dry/wet mass ratios. Relative metabolite concentrations (and metabolite ratios) were obtained by integrating the chemical shift bins in which the metabolite resonances occurred. These were subsequently evaluated using one-way ANOVAs and Tukey-Kramer post-hoc tests for both the aquaculture farm and laboratory studies.
Results NMR Spectroscopy of Healthy Abalone Samples. The 1D 1H NMR spectra of tissue extracts and hemolymph comprise many hundreds of peaks (Figure 1), corresponding to low molecular weight endogenous metabolites. See Table 1, Supporting Information, for all metabolites assigned and confirmed (Figure 2) in the muscle spectra. Although several metabolite classes were observable, all spectra were dominated by the organic osmolytes betaine-glycine and taurine. Other observed metabolite classes included amino acids (e.g., alanine and valine), organic acids (e.g., acetate and formate), carbohydrates (e.g., glucose), nucleotides (e.g., ATP), and phosphagens (e.g., phosphoarginine) as well as glycolytic products (e.g., lactate) and Kreb cycle intermediates (e.g., succinate). Classification of WS using Physical Parameters. There were overall differences between the healthy, stunted, and diseased groups for the total tissue wet masses (i.e., excluding shell) and shell lengths (p < 0.001; see Table 1). Post-hoc tests revealed differences between both healthy and stunted and healthy and diseased groups (p < 0.05). There were no significant differences between the wet masses of the stunted and diseased abalone. The muscle and digestive gland dry/ wet mass ratios provided more robust classification, with overall significant differences within each tissue (p < 0.001), and post-hoc tests revealing differences between healthy and stunted, healthy and diseased, and stunted and diseased (p < 0.05). This provides another physiological parameter for the classification of the disease status, in addition to the visual scoring system that can be somewhat subjective. Classification of WS by Metabolomics. Scores plots from the individual PCA of the muscle, digestive gland, and hemolymph spectra (from the aquaculture farm experiment) are shown in Figure 3(a-c), respectively. These plots of PC1 versus PC2 describe the majority of variance between spectra (88.1, 69.1, and 90.5% in the muscle, digestive gland, and hemolymph data sets, respectively) and group together samples that have similar metabolite profiles. The metabolic information encoded in PC1 (that effectively differentiates 4984
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FIGURE 1. Representative one-dimensional 1H NMR spectra of a healthy abalone: (a) foot muscle extract and (b) hemolymph. Key to spectrum: 1. isoleucine, 2. leucine, 3. valine, 4. lactate, 5. threonine, 6. alanine, 7. phosphoarginine/arginine, 8. acetate, 9. proline, 10. glutamate, 11. glutamine, 12. acetylcholine, 13. succinate, 14. carnitine, 15. r-ketoglutarate, 16. hypotaurine, 17. aspartate, 18. N-methyltaurine, 19. dimethylglycine, 20. glycine-betaine, 21. taurine, 22. glycine, 23. homarine, 24. β-glucose, 25. residual HOD, 26. r-glucose, and 27. glycogen. diseased vs healthy abalone) and PC2 (that differentiates stunted vs healthy abalone) is summarized in Table 2 and discussed in detail below. The replicate measurements were tightly clustered for all tissues and biofluids, confirming high precision of the entire analytical procedure (see Figure 3); e.g., the six muscle replicates occurred at -2.21 ( 0.507 (mean ( SD on PC1) and -0.922 ( 0.181 (PC2), compared to the eight individual healthy muscle samples at -2.48 ( 0.974 (PC1) and -0.869 ( 0.709 (PC2). Note that two digestive gland samples are missing from Figure 3(b) since no tissue was available to determine the dry/wet mass ratio, thus precluding normalization of the corresponding spectra.
The plots in Figure 3 appear remarkably similar, with clear separation of the healthy, stunted, and diseased groups of animals. Specifically, for the muscle data (Figure 3(a)), overall differences between groups are evident along PC1 (p < 0.001) and PC2 (p < 0.01). Post-hoc comparisons reveal differences between healthy and both stunted and diseased along PC1 (p < 0.05) and between stunted and both healthy and diseased along PC2 (p < 0.05). Similar results occur for the digestive gland data (Figure 3(b)), with overall differences between groups along PC1 and PC2 (p < 0.001). Post-hoc tests reveal differences between diseased and both healthy and stunted along PC1 (p < 0.05) and between stunted and both healthy and diseased along PC2 (p < 0.05). For the hemolymph data (Figure 3(c)), overall differences occur along both axes (p < 0.01), with healthy and diseased different along PC1 (p < 0.05) and healthy and stunted different along PC2 (p < 0.05). Figure 3(d) presents the scores plot from the combined analysis of the muscle, digestive gland, and hemolymph data. This was achieved by concatenating the three tissue-specific n × 1902 matrices, yielding a data set with 5706 spectral bins. FIGURE 2. Two-dimensional 1H-1H homonuclear correlation spectroscopy (COSY) NMR spectrum of foot muscle extract used to confirm metabolite identification. Key to spectrum as described in Figure 1. Furthermore, we were unable to collect hemolymph from two abalone (one healthy, one diseased), and a further two hemolymph samples thawed and subsequently bubbled-over while being lyophilized and hence are missing from Figure 3(c); no NMR spectra were selectively excluded from the analyses.
Metabolite Profiles of Healthy, Stunted, and Diseased Abalone. The NMR spectra in Figure 4(a,b) illustrate differences in adenylate, aromatic amino acid, and homarine levels between typical samples of healthy and diseased muscle. Figure 4(c) shows the corresponding loads plot (for PC1, which separates healthy and diseased) for the analysis of all muscle spectra. This plot summarizes the contribution of the original variables (chemical shift bins) to each PC, i.e., it describes which peaks in the NMR spectra differ most between the healthy and diseased abalone and has successfully identified the increase in homarine in diseased muscle (positive peaks) and the decrease of adenylates and
FIGURE 3. Scores plots from the PCA of (a) muscle spectra, (b) digestive gland spectra, (c) hemolymph spectra, and (d) a concatenation of all three data sets, showing clear separation of the healthy (2), stunted (b), and diseased (9) farm-raised abalone. Replicates obtained from two pooled healthy abalone samples (that produced 6 muscle, 5 digestive gland, and 2 hemolymph replicates) are also presented (∆), illustrating the high precision of the metabolomic analysis. The ellipses represent the mean ( SD (along PC1 and PC2) of each of the three groups. VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Relative Changes in Metabolite Concentrations between Both Stunted and Healthy Abalone and between Diseased and Healthy Abalonea stunted vs healthy abalone (changes along PC2) metabolite
dg
hml
muscle
dg
hml
46% Vb
25% v 27% V
43% V
61% Vb
86% Vc
72% Vb 94% Vb 86% Vb 84% Vb 94% Vb 79% Vb
23% V 42% V
318% vb
Amino Acids alanine β-alanine glutamine glycine phenylalanine tryptophan tyrosine valine
43% Vb 64% Vb 47% Vb 44% Vb 44% Vb 54% Vb
no change
62% Vc 58% V 53% Vc 71% Vc 46% Vb 34% V
Organic Acids acetate formate
109% v
41% vb 33% v
53% v
Nucleotides ATP (and ADP)
diseased vs healthy abalone (changes along PC1)
muscle
13% V
no change
58% Vb
Phosphagens phosphoarginine (and arginine)
14% v
54% Vb
Carbohydrates glucose glycogen
36% Vb 84% Vb
Lipid-Containing resonance at 1.2 ppm Organic Osmolytes glycine-betaine homarine hypotaurine N-methyltaurine e Miscellaneous Metabolites carnitine acetylcholinee dimethylglycine e unknown at 2.93 ppm unknown at 1.10 ppm
37% Vb
23% vb 24% v
78% vb
13% v 206% v no change
52% v
69% Vb
34% V 20% V 45% v
38% Vc 328% vb 63% Vb
146% v 33% v 248% vb 27% v
165% vb 81% v
66% Vb 99% Vb
16% v no change 139% vb no change
29% V
92% Vc 89% Vd 81% Vc 93% Vc 95% Vb 80% Vd
44% V
61% V
53% Vc no change 76% Vb 55% V
12% v 214% vb
77% Vd 6% V
492% vb
62% Vc 1570% vb 57% V
64% V
a
Representative metabolites have been selected to illustrate the wide range of metabolite classes accessible by NMR spectroscopy. Relative metabolite levels in muscle and digestive gland (dg) tissues were calculated on a dry-mass basis, while levels in hemolymph (hml) were based upon wet volume. b-d Overall differences between all groups (p < 0.001,b p < 0.01,c p < 0.05d), with post-hoc tests revealing a difference from the healthy group (p < 0.05). e Unconfirmed assignment.
aromatic amino acids (negative peaks). Thus, loads plots are extremely powerful for identifying potential biomarkers. Furthermore, they facilitate studying the comparative biochemistry of different tissue types as illustrated in Figure 4(d), which shows the equivalent loads plot from the PCA of digestive gland tissue. While homarine is also increased in the diseased digestive gland, no changes in the levels of aromatic amino acids occur. Loads plots for both PC1 (effectively comparing diseased vs healthy) and PC2 (comparing stunted vs healthy) have been analyzed for all three types of tissues and biofluid. Representative metabolites from several different classes that are involved in the disease process have been identified, along with the percent changes of those metabolite levels from the healthy to both stunted and diseased states (Table 2). Specific metabolite ratios, discussed further below, are shown in Tables 3 and 4 for the aquaculture farm and laboratory experiments, respectively.
Discussion Diagnostic Metabolic Biomarker Profiles for WS. Our results confirm that NMR-based metabolomics can successfully distinguish the metabolic profiles of healthy, stunted, and diseased abalone, in foot muscle, digestive gland, or hemolymph samples. Furthermore, this approach has successfully identified several metabolic biomarkers associated with WS. Comparing changes in tissues and in hemolymph is hindered by the data being normalized to dry mass and 4986
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wet volume, respectively. Normalizing the hemolymph to wet volume fails to remove the apparent dilution of metabolites from healthy to stunted to diseased abalone. Consequently, the PCA of hemolymph spectra tends to separate these groups according to this large variance in total spectra area (Table 1), an effect that was largely removed in the tissue samples by normalizing to dry mass. Calculating the ratio of two metabolites in a given spectrum, however, removes all sources of variation associated with normalization. Table 3 lists key tissue-specific metabolite concentration ratios that provide the greatest discrimination between the healthy and diseased states and thus can serve as sensitive biomarkers of WS. Each ratio employs a numerator and denominator that decreases and increases, respectively, as the severity of withering increases. Thus, the metabolite concentrations in Table 3 were determined relative to homarine, which reliably and significantly increased in diseased abalone. Our NMR preprocessing methods included important changes to previously published protocols (10, 11, 14). First, a bin width of only 0.005 ppm was employed, almost an order of magnitude smaller than the more typical 0.04 ppm. The smaller bin size corresponded to approximately two peak widths and yielded far greater resolution in the PCA loads plots (Figure 4). This is crucial for identifying when two closely spaced peaks behave oppositely in the diseased state (i.e., one increases, the other decreases), an effect that would be
FIGURE 4. A section of the NMR spectra of (a) diseased and (b) healthy foot muscle extract, illustrating differences in several metabolites including adenylates, homarine, and amino acids. (c) Corresponding loads plot from the PCA of all muscle spectra, clearly indicating the differences in the metabolite profiles between healthy and diseased muscle. (d) Loads plot from a similar analysis of all the digestive gland spectra, facilitating comparison of the biochemical changes between different tissues. missed with a wide bin. Second, we log transformed the binned data before multivariate analysis. This reduced the dominance of the organic osmolytes peaks relative to the other metabolites and proved superior to the more usual methods of mean centering and autoscaling (11). Finally, the spectra were normalized to tissue dry mass (as opposed to total spectra areas), thus enabling an evaluation of relative metabolite levels on a traditional dry mass basis. Two separate screening methods for WS can be envisioned based on the results reported here. The first is to employ the same NMR-based techniques to obtain metabolite profiles, which can then be classified as healthy, stunted, or diseased, using the existing PCA models. This approach offers excellent specificity as the entire metabolic signature is employed in the classification. Alternatively, more commonly available methods such as high-performance liquid chromatography could be used to determine specific biomarker ratios such as glycine to homarine, which has been shown to decrease significantly in the muscle, digestive gland, and hemolymph of diseased animals (Table 3). This option relies upon an initial discovery-based metabolomic approach to identify sensitive metabolic biomarkers. Experiments must be designed to ensure that changes identified in loads plots correspond to endogenous bio-
chemical changes in the organism. For example, injection of abalone with oxytetracycline immediately before dissection could have generated significant chemical differences between that group and controls, based solely on the NMR profile of the antibiotic. In the current study, the oxytetracycline treatment occurred almost a year before dissection. Furthermore the RLP could, in principle, contribute weakly to the NMR signal, although this would require an extremely heavy infection. Fortunately, the RLP associated with WS is localized to the digestive gland and therefore does not contribute to either the hemolymph or muscle spectra. Biochemistry of WS. In addition to identifying metabolic biomarkers associated with WS, metabolomics can provide insightful mechanistic information on the effects of a stressor. Many of the biochemical changes observed here support the clinical suggestion that WS involves starvation. Organisms attempt to continue cellular energy production during starvation via the mitochondrial oxidation of glucose, fatty acids, amino acids, and ketone bodies. This catabolic period is characterized by degradation of lipids, glycogen, and protein. Previous studies have suggested, however, that marine molluscs have a decreased reliance on fatty acid and ketone body metabolism compared with mammals and freshwater molluscs (18). The physiological basis for this difference may be related to the strategy of osmoconformity in marine molluscs, which employ high intracellular concentrations of free amino acids to balance their intracellular osmolarity with the environment. Evidence suggests that these large pools of oxidizable amino acids are used extensively in cellular energy metabolism (19). This is supported by our results, which show significant decreases in many amino acids in muscle, digestive gland, and hemolymph of both stunted and diseased abalone (Table 2). These decreases are consistent with previous studies; e.g., the ketogenic amino acids phenylalanine, tryptophan, and tyrosine decreased by 38%, 46%, and 58% in disk abalone muscle (H. discus) following 46 days of starvation (20), compared to 47%, 44%, and 44% in stunted red abalone. Similarly, the oxidizable amino acid glycine fell by 68% in the starved disk abalone and by 64% in the stunted animals here. Examination of glucose levels lends further credence to the NMR-based approach, with almost identical decreases occurring in the muscle and hemolymph in stunted (36% and 37%, respectively) and diseased abalone (66% and 69%, respectively). Note that the muscle and hemolymph samples were collected, prepared, and then NMR-analyzed using different methods, before being individually preprocessed and subjected to PCA. The glucose peaks in the digestive gland spectra could not be accurately measured due to spectral congestion. In Northern abalone (H. kamtschatkana) blood glucose levels dropped by 50% after 6 days of starvation and then held steady for 3 weeks, which was thought to result from continued gluconeogenesis (21). As was expected, glycogen levels decreased dramatically in both stunted (84%) and diseased (99%) red abalone foot muscle, which is again consistent with earlier studies in disk abalone (79% decrease) (20). The CPMG NMR pulse sequence that was used to minimize broad lipid and lipoprotein resonances in the digestive gland spectra unfortunately precluded the observation of glycogen in this organ, which is functionally equivalent to the mammalian liver. The residual lipid-like resonances were observed to decrease in the digestive glands of diseased abalone (10). Other noteworthy changes include significant decreases in both adenylate nucleotides and a phosphagen in diseased muscle and the increase in muscle carnitine in stunted abalone, an effect previously associated with increased protein turnover (22). The significantly elevated acetate levels in the digestive glands of stunted and diseased abalone is potentially the end product of a metabolic VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Concentration Ratios of Selected Metabolic Biomarkers in Muscle (mus), Digestive Gland (dg), and Hemolymph (hml) Samples from Healthy, Stunted, and Diseased Abalone Raised at an Aquaculture Farma metabolite ratio glycogen/homarine glucose/homarine β-alanine/homarine glycine/homarine phenylalanine/homarine tryptophan/homarine tyrosine/homarine valine/homarine
healthy
stunted
diseased
mus: 22.6 ( 5.5 mus: 1.60 ( 0.30 hml: 4.75 ( 3.11 dg: 0.820 ( 0.394 mus: 51.7 ( 16.8 dg: 4.42 ( 2.21 hml: 27.9 ( 15.2 mus: 0.612 ( 0.220 hml: 0.708 ( 0.337 mus: 0.293 ( 0.068 hml: 0.366 ( 0.183 mus: 0.825 ( 0.216 hml: 0.724 ( 0.309 mus: 2.14 ( 0.60 dg: 0.220 ( 0.144 hml: 51.4 ( 20.0
mus: 1.70 ( mus: 0.502 ( 0.247b hml: 1.78 ( 0.72 dg: 0.521 ( 0.306 mus: 8.90 ( 7.23b dg: 3.20 ( 1.37 hml: 7.13 ( 3.96d mus: 0.170 ( 0.112b hml: 0.220 ( 0.080c mus: 0.092 ( 0.073b hml: 0.069 ( 0.038c mus: 0.262 ( 0.205b hml: 0.254 ( 0.119c mus: 0.524 ( 0.339b dg: 0.180 ( 0.118 hml: 22.2 ( 7.5c 1.44b
mus: 0.072 ( 0.016b mus: 0.126 ( 0.013b hml: 1.27 ( 0.41 dg: 0.156 ( 0.056d mus: 0.699 ( 0.165b dg: 0.822 ( 0.348d hml: 3.28 ( 0.81d mus: 0.019 ( 0.005b mus: 0.011 ( 0.007b hml: 0.034 ( 0.044c mus: 0.011 ( 0.009b hml: 0.034 ( 0.030c mus: 0.099 ( 0.024b dg: 0.049 ( 0.020 hml: 10.3 ( 2.1c
a All ratios represent mean ( SD. b-d Overall differences between all groups (p < 0.001,b p < 0.01,c p < 0.05d), with post-hoc tests revealing a difference from the healthy group (p < 0.05).
TABLE 4. Glucose/Homarine Concentration Ratios in Abalone Hemolymph Following a 447-Day Laboratory Exposure to the Following Conditions: RLP-Uninfected Animals Received Either Full-Feed (“Control”; N ) 12) or 1/4-Feed (“Food-Deprived”; N ) 6), While RLP-Infected Abalone Received Full-Feed (“RLP-Infected”; N ) 12)a metabolite ratio
control
glucose/homarine
1.98 ( 0.90
food-deprived
RLP-infected
0.33 (
0.99 ( 0.76b
0.24b
a All ratios represent mean ( SD. b Overall difference between all groups (p < 0.001) with post-hoc tests revealing a difference from the healthy group (p < 0.05).
adaptation to increase ATP yield compared to glycolysis alone. A similar increase has been reported in the foot muscle of the limpet Patella caerulea during anoxia (23). The most unexpected metabolic changes concerned the significant increase in homarine (1-methyl-2-pyridinecarboxylic acid) in both foot muscle (328%) and digestive gland (214%) of diseased abalone. Homarine, an endogenously synthesized heteroaromatic quaternary ammonium compound found almost exclusively in the marine environment, is believed to have roles in osmoregulation and as a transmethylating agent (24). The increase in homarine might therefore help to balance the loss of oxidizable amino acid osmolytes from the intracellular medium during WS. Previously, starved disk abalone were reported to exhibit 3% and 47% decreases in homarine in foot muscle and digestive gland, respectively (20). Collectively, these results suggested that homarine could differentiate starved from diseased abalone, a hypothesis that we have subsequently tested in the chronic laboratory-based study. The glucose/homarine ratio, which we previously identified as a discriminatory biomarker from the aquaculture farm study, was used to compare fooddeprived and RLP-infected abalone. In both treatment groups homarine levels increased and glucose decreased, producing hemolymph glucose/homarine ratios that are statistically equivalent (see Table 4). However, both food-deprivation and RLP infection induced changes in this biomarker ratio that are significantly different from those in control red abalone (p < 0.001). Thus, although homarine appears incapable of differentiating starvation from withering syndrome, these results further support the clinical suggestion that WS is biochemically equivalent to starvation. Application of Metabolomics in Environmental Studies. NMR-based metabolomics has several characteristics that are well suited to biomonitoring, particularly the ability to 4988
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rapidly and inexpensively assess the functional metabolic status of an organism. With appropriate hardware, over 200 samples can be analyzed per day in an automated manner, providing robust and semiquantitative data. This approach is not limited to monitoring sentinel species for which gene sequences are known (as is typically required for gene expression profiling) or for which antibodies are available (often needed for protein expression studies). Furthermore, metabolomics can provide valuable mechanistic insight into the effects of chemical and other environmental stressors. Such data complement high throughout gene and protein expression profiling, both of which are emerging as valuable tools for assessing the effects of environmental stressors (2527). In particular, combined gene expression and metabolomic profiles promise a highly specific and functional assessment of organism status. Here we have demonstrated an application of this technique for studying the impact of a biological stressor. Other studies in our laboratory have successfully characterized the effects of subtle thermal stress on migratory steelhead trout (Oncorhynchus mykiss (28)). In addition to biological and physical stressors, other researchers have employed metabolomics to investigate the effects of chemical pollutants on terrestrial species (12, 13). One of the greatest challenges ahead for implementing metabolomics into biomonitoring studies is in dealing with biological variability. Fortunately, due to the high throughput nature of this approach, analysis of large numbers of samples is feasible, thus increasing statistical power. Furthermore, several computational tools are available to minimize the impact of unwanted variance, such as “supervised” multivariate methods (e.g., partial least squares regression (11)) and data filtering techniques (e.g., orthogonal signal correction (29)). Tools such as these are continually being developed in response to the massive quantities of data produced by the “-omic” technologies. Metabolomics promises to provide a powerful new technology for measuring the effects of environmental stressors on organism health. Initially, this would include identifying novel stress-induced biomarker profiles in laboratory studies, thus exploiting the discovery-based nature of metabolomics. These metabolic profiles could then be used for environmental monitoring to assess organism health and potentially to identify the class of stressor. Finally, metabolomics could directly assess remediation efforts, by following the recovery of the metabolic profiles of impacted organisms back to those found in healthy populations.
Acknowledgments This research was supported in part by the National Sea Grant College Program of the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration under NOAA Grant #NA06RG0142, project #R/A-117, through the California Sea Grant College Program, and in part by the California State Resources Agency. Further support was obtained from the National Science Foundation (NSF OSTI 97-24412) and the University of California Toxic Substances Research and Teaching Program. The views expressed herein do not necessarily reflect the views of those organizations. We thank B. A. Braid, J. D. Moore (BML, UC Davis), and C. S. Friedman (University of Washington) for help with the animal studies, J. S. de Ropp and J. G. Bundy for NMR technical assistance, and D. M. Rocke, D. L. Woodruff, and P. V. Purohit for several discussions on the data processing.
Supporting Information Available List of major metabolites and their NMR assignments identified in abalone muscle tissue (Table 1). This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review March 28, 2003. Revised manuscript received August 14, 2003. Accepted August 19, 2003. ES034281X
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