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Selective Amino Acid-Only in Vivo NMR: A Powerful Tool To Follow Stress Processes

In vivo NMR of small 13C-enriched aquatic organisms is developing as a powerful tool to detect and explain toxic stress at the biochemical level. Amino acids are a very important category of metabolites for stress detection as they are involved in the vast majority of stress response pathways. As such, they are a useful proxy for stress detection in general, which could then be a trigger for more in-depth analysis of the metabolome. 1H–13C heteronuclear single quantum coherence (HSQC) is commonly used to provide additional spectral dispersion in vivo and permit metabolite assignment. While some amino acids can be assigned from HSQC, spectral overlap makes monitoring them in vivo challenging. Here, an experiment typically used to study protein structures is adapted for the selective detection of amino acids inside living Daphnia magna (water fleas). All 20 common amino acids can be selectively detected in both extracts and in vivo. By monitoring bisphenol-A exposure, the in vivo amino acid-only approach identified larger fluxes in a greater number of amino acids when compared to published works using extracts from whole organism homogenates. This suggests that amino acid-only NMR of living organisms may be a very sensitive tool in the detection of stress in vivo and is highly complementary to more traditional metabolomics-based methods. The ability of selective NMR experiments to help researchers to “look inside” living organisms and only detect specific molecules of interest is quite profound and paves the way for the future development of additional targeted experiments for in vivo research and monitoring.


■ INTRODUCTION
Metabolomics has rapidly evolved as a powerful tool for assessing sublethal stress in organisms. 1 Nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite screening 2−4 due to its unique benefits including rapid sample analysis, 5 minimal sample perturbation, 6 high reproducibility, 7 and accurate quantification. 8,9 In the medical field, NMR-based metabolomics has served a crucial role in early disease screening, 10 while in an environmental context, it is commonly applied to understand and explain environmental stress. 7,11 Daphnia magna (water flea) is well recognized as a keystone species as it consumes algae and becomes a key prey for invertebrates and fishes, thus linking key trophic levels in the food web. 12 Due to its environmental significance, D. magna is one of the most commonly used organisms for aquatic toxicity testing. 13 Traditional toxicity studies generally emphasize physiological end points such as reproduction and mortality rate, but such studies provide little information of sublethal toxicity or the toxic mode of action (i.e., the biochemical pathways impacted). 2 A review conducted by the U.S. Environmental Protection Agency entitled Toxicity Testing in the 21 st Century: A Vision and a Strategy stated "a paradigm shift must be established moving forward to aid in our understanding of toxicity towards the underlying cellular processes associated with generating a toxic response". 14 To obtain the most in-depth understanding of metabolic flux in real time requires an organism to be living. Solution-state in vivo NMR-based metabolomics is the only modern analytical technique that can provide detailed molecular profiles in a noninvasive manner and allows for the assessment of metabolic changes inside a living organism under minimal stress conditions. 2−4,15,16 D. magna is an excellent test species as its small size allows it to fit inside standard high-field NMR systems. 4 For in vivo studies, a specially designed flow system that circulates oxygenated water to D. magna was recently developed by Tabatabaei Anaraki et al. 3 and is utilized in this study. Amino acids represent a major class of metabolites and have shown to be intimately linked to the vast majority of biochemical pathways related to stress. 17−20 As such, if amino acid fluxes can be selectively detected in vivo, they represent a key proxy for stress detection in general, which could then be a trigger for more in-depth analysis of the metabolome. However, when D. magna is 13 C isotopically enriched and studied using nonselective experiments such as 1 H-13 C HSQC, while amino acids can be detected, overlap from a wide range of other metabolites makes spectral assignment challenging. [2][3][4]16 Interestingly, a range of multidimensional protein NMR experiments have previously been used to extract information related to protein structure 21,22 and protein dynamics. 23 Many of these sequences selectively transfer magnetization between structural units specific to amino acids (for example, carbonyl group in combination with α protons and side-chain protons). 24−26 While such approaches were developed to highlight structural features within proteins, they can be adapted to similarly select amino acids within mixtures or even within a living organism. Here, a 2D projection of a 3D NMR experiment, 24 termed here as a (H)CbCa(COCa)Ha experiment, was applied to extracts and living organisms. Both proteins and free amino acids are selectively detected, but with the application of simple apodization functions, the proteins can be easily suppressed during processing to leave signals from only amino acids. For each amino acid, proton and carbon chemical shifts are correlated for the α−β fragment, providing enough information for easy assignment of all 20 amino acids, both in extracts and inside the living organisms. Furthermore, nonuniform sampling (NUS) is explored to evaluate its potential to speed up data acquisition. 27−29 Finally, the approach is tested in vivo by exposing D. magna to a sublethal concentration of bisphenol-A (BPA) for 12 h. The technique performs extremely well and suggests that in vivo NMR will likely develop into a very powerful tool for detecting stress inside living systems.
■ RESULTS AND DISCUSSION 1D 1 H NMR Spectrum of D. magna. Figure 1 depicts a standard 1D 1 H NMR spectrum of D. magna in vivo acquired with SPR-W5-WATERGATE. 30 While the water suppression is quite effective at inhibiting the large broad water signal resulting from both the organism and the surrounding water, the 1 H signals from the organism itself are relatively broad due to the magnetic susceptibility variations arising from the intact sample. 31 Different components such as cell walls, cell contents, and physical structures (for example egg sack, stomach) interact with the external magnetic field slightly differently, giving rise to field distortions across the sample. As such, metabolite signals at varying locations within the sample experience slightly different magnetic fields, which leads to broadening in the final spectrum. The result is that general regions including lipids, sugars, and some amino acids in the aromatic region can be discerned but in large part, exact assignment of individual metabolites is not possible from the 1 H NMR alone. While approaches such as lipid suppression techniques 32−34 and newly developed 1 H NMR sequences to reduce susceptibility broadening 31 hold great potential, combining isotopic labeling of the living organisms with 2D 1 H− 13 C NMR detection has proven to be the most robust approach. 3,4,35−37 Selecting Amino Acids in 13 C-Enriched D. magna. Figure 2A displays a standard in vivo 2D HSQC NMR spectrum, which contains considerable overlap in the regions dominated by lipids and carbohydrates. 2,4 Both lipid and carbohydrate signatures are important as they are both involved in energy metabolism, which is impacted by various contaminant stressors. 3,38,39 Lipids in D. magna are of great scientific interest as daphnids are unable to synthesize lipids de novo, leading to a reliance on food sources for these molecules. 4 As such, there is still a need to better understand the molecular processes behind the transfer of carbon between trophic levels and the assimilation of lipids by D. magna, which can be accessed using in vivo NMR approaches.
Unfortunately, due to their abundance in vivo, lipid and carbohydrate signals tend to dominate in vivo HSQC data, masking signals from lower-concentration components such as amino acids. 2,4 Amino acids are of particular importance as they are involved in the vast majority of biological responses, 17,19,40,41 making them a critically important indicator for both detecting and monitoring environmental stress. Interestingly, as the field of protein NMR is so well developed, many different NMR pulse programs exist that exploit specific coherence pathways within proteins and amino acids. 42−44 These often involve selectively exciting specific structural units and then passing the magnetization through bonds to select a particular structural motif. Although they were developed to highlight structural features within proteins, they can at least, in theory, be applied to select the same structural features within mixtures or even within a living organism. To our knowledge, such an approach has not been adapted to target specific subgroups within mixtures.
The goal here is both to assign and to selectively detect amino acid signatures in vivo. As such, the ideal sequence would be proton-detected (rather than being carbon-detected) for sensitivity, would select all amino acids while rejecting the majority of other structures, and would provide an additional secondary correlation to permit the amino acids to be easily assigned. Indeed, such a 3D experiment exists and is commonly referred to as a (H)CbCaCO(Ca)Ha correlation. 24 However, as the third dimension is not required for selecting amino acids, it can be simply collected as a 2D experiment, termed (H)CbCa(COCa)Ha. The experiment involves five transfer steps as outlined in Figure 2B. These steps act as a spectral filter only selecting the structural units that contain β HC α HCCO fragments that are labeled in all carbon positions. Note that α HC and CO are selected by using shaped RF pulses targeted on specific chemical shift regions where the α and carbonyl groups in amino acids specifically resonate. The result is a relatively strict filter that specifically targets amino acids in mixtures. When applied in vivo, the sequence performs remarkably well and cleanly selects amino acids (see Figure 2C).
Separating Protein and Amino Acid Signals. For each amino acid (with the exception of glycine, which only has an α resonance), two resonances appear, one describing the 1 H− 13 C chemical shifts of the α position and the second describing the relayed 13 C chemical shifts of the β (i.e., adjacent) position. However, as proteins and amino acids all contain the same structural motif, both are selected by the sequence. Fortunately, it is relatively easy to select just the free amino acids by applying a post-processing filter that biases the components with longer relaxation times. This is easiest to demonstrate in an extract from the whole organisms, as the decreased spectral overlap provides better separation of the protein and amino acids signals, in turn making the action of the post-processing filter easier to discern. Figure 3A inset shows a free induction decay (FID) processed using a 90°-shifted sine-squared window function, commonly applied for the processing of 2D NMR data. 45 At the start of the FID, both protein and amino acids resonate; however, proteins have a rapid T 2 * (time during which a signal contributes to the FID). 46 As such, later in the FID, the proteins relax, while the signals from the more dynamic free amino acids persist. 47 As the decay of the 90°-shifted sinesquared window function matches the decay of a standard FID (see Figure 3A inset), all components in the mixture are well represented when this function is used. As such, Figure 3A contains signals from both the proteins and amino acids. However, when an unshifted sine-squared function is used, the start of the FID (where proteins signals are the strongest) is suppressed, and the tail of the FID (where signals from free amino acids persist) is exaggerated. The result is a filter that strongly suppresses the protein signals while emphasizing the free amino acids. When applied to the extract data ( Figure 3B), the protein signals are strongly suppressed, while signals from the free amino acids remain. Theoretically, such a filter may also allow very dynamic species such as disordered proteins or peptides to be detected. However, as shown in the next section, all the major peaks can be assigned to free amino acids due to their relatively high concentration. As such, the low abundance of these other species with respect to free amino acids is enough to make them invisible to the amino acid only experiment. However, one major exception existsthat being glutathione (a tripeptide of glutamic acid−cysteine−glycine). Although they are slightly broader, the glycine peak and cysteine peaks from glutathione are clearly visible with the glutamic acid peak overlapping with the free amino acid. The   detection of glutathione is interesting as it is closely linked to oxidative stress, 48,49 and thus represents an important biomarker for in vivo research.
Amino Acid Assignment. Once the proteins are removed, it becomes relatively easy to assign all 20 amino acids in the D. magna extract. Figure 4 shows an expansion of the amino acids with the α H−C unit and corresponding relay to the β carbon assigned. Having both the α and β carbon chemical shifts makes assignment easier (see experimental section) and unambiguous. When both carbons are considered, every amino acid is well resolved with the exception of glutamic acid and glutamine, which show some overlap due to their very similar structures. The only non-amino acid concentrated enough to appear is lactic acid, which has a similar β HC α HCCO motif to an amino acid. However, the lactic acid resonances are well separated from the amino acid and do not lead to an overlap (see Figure 4A). Lactic acid is an indicator of anoxic stress (anaerobic glycolysis), and its presence could be advantageous in the detection of anaerobic responses. 50,51 Interestingly, when applied in vivo, both the protein suppression and selective amino acid detection are highly effective (see Figure 4B). With resolution nearly on par with the extract, as such, all 20 amino acids can be detected and assigned. Amino acids have a direct correlation with multiple biochemically important pathways in D. magna. Lysine is among one of the amino acids that is stored as energy reserves during the molting cycle for crustaceans. 52 The branched amino acids (leucine, isoleucine, and valine) are essential in muscular protein and neurotransmitter synthesis by governing the degree of mRNA translation. 53,54 Phenylalanine is the precursor to tyrosine, which is in turn used in the production of neurotransmitters such as octopamine and dopamine 55 as well as pigment compounds such as melanin. Poynton et al. 18 deduced that when exposed to cadmium, D. magna generated L-DOPA and dopamine as a function of metal stress. Furthermore, in crustaceans, the amines octopamine, dopamine, and serotonin are generally perceived as neuromodulators 56 as well as aggression regulators. 56 Another notable biomolecule is melanin, which is also a byproduct of L-DOPA (a tyrosine derivative). For D. magna in the environment, their melanin abundance is highly important as it acts a shield against UV radiation, while also potentially providing visibility to predators. 57 Other amino acids such as glutamic acid and alanine are readily found in the axoplasm of the nervous system of marine organisms constituting more than 20% of the dry weight of the nerves. 58 Amino acids including glycine, glutamic acid, and alanine have been connected to other important chemicals for osmoregulation in marine crustaceans such as D. magna. 59 Reducing Experiment Time via Nonuniform Sampling. The experiment time of NMR can be appreciably reduced by employing nonuniform sampling (NUS) strategies. 60−62 Unlike standard methods that acquire data in a linear fashion in the indirect dimension, the NUS approach samples in a randomized fashion that can permit a net reduction in the total experiment time of the experiment. 63 The data sets obtained via NUS can later be reconstructed in the time domain, for example, with the multidimensional decomposition method (MDD), 64,65 or taken from the frequency domain using maximum entropy (MER)-based approaches. 66,67 While higher-ordered NMR experiments (i.e., 2D or 3D) provide additional spectral dispersion, their application is often limited due to their longer experimental times. 68 With advancements in fast NMR data acquisition techniques, such as NUS, metabolomic analysis of complex biological samples using multidimensional NMR becomes more feasible. 69−72 In order to monitor and delineate processes that are taking place inside a living system via NMR spectroscopy, it is important to optimize data acquisition in the shortest time possible. 19,20,40,41,73 Here, the applicability of NUS is tested to see if 2D metabolite profiles can be captured in an accelerated fashion while still retaining spectral information.
There is a correlation between the number of sampling points required and the number of cross peaks in the NMR spectra. 74 Based on the selective (H)CbCa(COCa)Ha NMR sequence applied here, at most 39 cross peaks are expected (2 per amino acid except for glycine that only has one) as such NUS has considerable potential to speed up data acquisition. Figure 5 shows a series of in vivo (H)CbCa(COCa)Ha NMR spectra of D. magna acquired under NUS conditions with varying sampling densities relative to the conventional spectrum acquired with 128 points. Unfortunately, due to the peak from histidine being extremely weak in the original data (see red circle in Figure 5C), this resonance is lost even with a relatively conservative sampling of 50%. However, both 40 and 50% sampling methods ( Figure 5B,C) retain signals from all the remaining 19 amino acids. Even when the sampling is dropped to just 20% (26 real points), the remaining 19 amino acids are still detected, and the experimental time is lowered from 12 to 2.4 h. Given that in some studies, it may not always be essential to see all the amino acids, NUS holds considerable potential for many applications.
It is well established in the literature that as a byproduct of NUS reconstruction, spectral artifacts can appear, which can complicate NMR data interpretation. 75−78 To examine this, a 3D oblique view of NMR data is shown in Figure S2. In a conventional spectrum without NUS ( Figure S2A), few artifacts are present, and the amino acids signals predominate even close to the noise level. With the most aggressive implementation of NUS (only 20% sampling points, Figure  S2B), artifacts appear but fortunately largely appear as negative lobes, which do not interfere with the detection of the positive amino acid only signals.
It should also be noted that D. magna represents one of the most challenging scenarios for NMR spectroscopy as they contain >90% water. D. magna has a weight ranging from ∼10 (24 h old) to 170 μg (largest adults) in terms of dry weight. 79 The organisms in this study were ∼4 weeks old with a dry weight of ∼100 μg per organism. In this study, all of the NMR spectra were recorded using 20 fully 13 C-enriched adult D. magna samples leading to only ∼2 mg of total biomass. Many other species have much higher biomass, which will reduce the number of scans required, in turn shortening the time needed to collect in vivo (H)CbCa(COCa)Ha NMR spectra, especially when NUS is employed. For example, another typical species used routinely in aquatic toxicity testing is Hyallela azteca (water shrimp), which has an even greater biomass than D. magna at a dry weight of 0.62 mg per organism. 80 As the signal-to-noise ratio (SNR) increases with the square root of the number of scans, it can be extrapolated that 6 times more biomass would allow the amino acid-only experiment to be performed in ∼5 min using 20% of the 128 real points under NUS sampling. This can potentially open up the possibility of in vivo monitoring in close to "realtime" in other species with the current technology or possibly in D. magna with further improvements in NMR sensitivity, or with larger-diameter (for example, 10 mm) cryoprobes, which can hold 4−6 times more amount of sample compared to a 5 mm probe. Furthermore, it may be possible in the future to apply the (H)CbCa(COCa)Ha experiment using a "moving window"-type approach as previously described. 81 In this case, instead of 2D spectra being collected as separate experiments (for example, 128 increments for each 2D spectrum), the data can be continuously sampled in a nonuniform fashion after which any time window (with a temporal resolution equal to the time required for one increment) can be reconstructed using NUS. The advantage is that such an approach allows faster kinetics changes to be detected that may be missed if the data are collected in linear blocks required for conventional Fourier transform.
Example Application: Monitoring Metabolic Stress Due to BPA Exposure. Understanding toxic impacts of contaminants in aquatic model organisms such as D. magna is critical for setting appropriate environmental policies. 4,82 A wide range of compounds entering the aquatic environment are known to have negative impacts on D. magna even at sublethal concentrations. 3,16,31,83 As a proof of principle and to demonstrate that the amino acid-only NMR experiment can be used to monitor stress responses in vivo, bisphenol A (BPA) was selected as an example contaminant. BPA used widely in the manufacturing of plastics and epoxy resins is ubiquitous in many water bodies and exhibits adverse toxic effects at sublethal concentrations toward D. magna. 20 Amino acid concentrations are of particular significance as their levels are perturbed in the vast majority of stress responses, and as such, they act as an excellent proxy to gauge first-order stress. [2][3][4]35,36 As the same organisms are used for the control and exposed studies and are run back-to-back to reduce natural variation, 35 it is very easy to gauge relative changes in metabolite quantities upon exposure to BPA. However, it should be noted that the amino acid-only approach does not provide absolute quantification in its current form. While it has been recently shown that quantitative 2D NMR in vivo is possible for basic 1 H-13 C HSQC, 84 the situation with the amino acid-only approach is more complex. First, the sequence contains multiple INEPT and COSY-type transfers, the efficiency of which depends on the exact coupling constants involved and will vary for each amino acid residue. Second, the approach employs apodization, which acts as a relaxation filter to suppress proteins; due to different relaxation times for each of the free amino acids, this will also impact absolute quantification.
Generally speaking, for NMR metabolomics research, samples are usually quantified in a relative manner by representing the change between a control group and exposed group as a percentage change. 19,40,41 As such, the amino acid experiment in its present form is well suited for studying metabolite changes in response to external stressors. In the future, if absolute quantification is required, the most feasible solution may be to apply the approach of Lewis et al. 85 In this approach, a quantification "correction factor" is calculated based on the behavior of standards, which can then be used to correct data collected under nonquantitative conditions for complex mixtures. Such an approach should also hold with the addition of NUS. While artifacts from reconstruction and sampling have, in the past, impacted the accuracy of peak integrals, 72,86 modern NUS reconstruction methods such as compression sensing (as used here) have been able to yield high peak accuracy across a high dynamic range. 87,88 As such, highly quantitative measurements on breast cell extracts using NUS of 2D NMR data have been recently reported. 89 Finally, the use of 2D ERETIC-based approaches, 90 already proven to give excellent quantitation of metabolites under in vivo conditions, 84 would remove the need for internal standards, which is especially important for in vivo stress response studies where chemical additives would be highly problematic. Figure 6 displays the percentage change in amino acid concentrations that were statistically significant (paired t test, p < 0.05) with BPA exposure relative to the control. In this case, the NUS was not used such that all 20 amino acids were detected. BPA was added at the start of the exposure experiment and data collection performed over 12 h. As such, the amino acids detected represent the averaged flux occurring during the first 12 h post-exposure. The changes in amino acid concentrations are in strong agreement with a previously published study conducted in vitro with D. magna 20 where free amino acids were also seen to increase upon exposure. An increase in a wide range of amino acids could result from toxicant-induced protein damage 91 or protein degradation in response to increased energy demands. 92,93 In D. magna, an increase in concentration in free amino acids is a general indicator for oxidative stress. 3,19,40 Furthermore, the implications of oxidative stress can also be confirmed not only through protein degradation, but also by DNA methylation as illustrated in a genomics study by Asselman et al. 94 and confirmed by genomic analysis. 95,96 Nagato et al. 20 performed in vitro NMR spectroscopy of D. magna exposed to BPA. In their study, seven amino acids were reported to increase significantly on the order of ∼8 to 20%. Interestingly, here, 14 amino acids are seen to flux, with an approximately 1.5-fold increase in amino acid concentrations relative to the non-exposed controls. The difference can be explained by considering how the in vitro analysis is performed. For the in vitro analysis, the organisms are flashfrozen, homogenized, and then extracted with buffer. 20 The homogenization and freezing will cause cells to break and potentially undergo macromolecular degradation; as such, the extract represents a snap shot averaged across all components inside the organisms. As such, a small flux in response to BPA may be "diluted" by the large background of amino acids that are extracted from all parts of the organism not in flux. Conversely, for in vivo studies, the (H)CbCa(COCa)Ha NMR experiment introduced here strongly biases the dissolved components, with protein and bound amino acid contributions filtered out (see Figure 3); as such, it targets the most meaningful metabolite pool, that is, the specific flux released during a stress response. Indeed, the relative increase of 150% detected in vivo compared to ∼8 to 20% in vitro suggests that in vivo NMR holds great potential for the sensitive detection of stress responses directly and represents an important and complimentary approach to in vitro metabolomics for understanding biological processes and responses.

■ CONCLUSIONS
In this study, we have demonstrated that it is plausible to selectively target free amino acids in vivo using 13 C-enriched D. magna in combination with the (H)CbCa(COCa)Ha NMR pulse sequence. Amino acid concentrations are of particular significance as their concentration levels are perturbed in the vast majority of stress responses, and as such, they act as an excellent proxy to gauge stress. All 20 amino acids could be detected and assigned in living 13 C-enriched D. magna. Considering that D. magna contains ∼90% water and very low dry mass (∼100 μg per organism), the detection is highly encouraging and bodes well for the study of other small aquatic species important for toxicity testing such as H. azteca, which contains ∼6 times more biomass per organism. Nonuniform sampling shows great potential in combination with the (H)CbCa(COCa)Ha experiment given the well-defined number of peaks expected in the amino acids metabolite pool. Furthermore, a preliminary application used to monitor the metabolic changes of D. magna in response to BPA exposure detected 14 amino acids that increased consistently with protein breakdown related to altered energy dynamics as previously observed. 20 Interestingly, the amount of amino acids was seen to increase by ∼150%, much higher than that seen in in vitro homogenization/extraction-based metabolomics that detected only ∼8 to 20% increases and fewer amino acids. This suggests that specifically targeting the amino acid flux in vivo Figure 6. Plot of relative concentration changes in amino acids that experienced a statistically significant change after 12 h of BPA exposure vs non-exposed controls using 13 C− 1 H in vivo NMR spectroscopy. The amino acid content in the controls is normalized to 100%, and the change in the exposed group is shown relative to this. All bars represent the average ± SD (replicates = 3, number of organisms per replicate = 20). Note that a change in an amino acid concentration was regarded significant when a p value of less than 0.05 was obtained in a paired t test between the exposed vs control populations. Each amino acid in the plot is designated with a threeletter code. provides a sensitive tool to detect stress responses and represents a key complimentary approach to more traditional nonselective in vivo NMR approaches as well as in vitro metabolomics. To extend this application further, other biological systems could be monitored in vivo such as living cells. 97 Some of the work in this area has utilized 1D NMR analysis, but due to spectral overlap in such complex NMR data sets, metabolite identification can be challenging. 98 As such, utilizing heteronuclear 2D NMR experiments to increase spectral dispersion, thereby leading to a more in-depth analysis of metabolic flux under in vivo conditions, is beneficial. 99 The (H)CbCa(COCa)Ha NMR experiment discussed here could be highly complementary due to its high resolution and specificity to amino acids, which are central to in vivo cell processes.
■ EXPERIMENTAL SECTION 13 C-Enriched D. magna. D. magna samples were fed 99% 13 C algae (Chlamydomonas reinhardtii ) as their sole carbon source for their entire life cycle. The algae were grown in a small-scale closed loop system photobioreactor designed and built by Silantes GmbH. Each fermentation was carried out autotrophically with 13 CO 2 (99% 13 C). The environmental conditions for growth such as media, temperature, light intensity, and pH were optimized for maximum growth of C. reinhardtii. Whole algae cells were then collected and freezedried for storage at −20°C prior to use. A more detailed description of the isotopic labeling protocol can be found elsewhere. 36 Daphnia Culturing. D. magna samples (original colony purchased from Ward's Scientific and maintained in-house since 2008) were cultured at 20 ± 1°C in 4 L glass vessels filled with hard reconstituted water at pH 7.5−8.5 (tap water that had been dechlorinated using an API tap water conditioning agent for a period of 2 weeks). The cultures were kept under a 16:8 light to dark photoperiod and were fed 13 C enriched C. reinhardtii (∼20 mg per 100 daphnids per day) as their only food source. The population density was 20 daphnids per L of culture medium. On a weekly basis, the culture medium was changed. The culture was fed unlabeled C. reinhardtii as a method to purge gut contents of 13 C media for a 24 h period prior to use in NMR experiments. 2,4 Extraction Procedure. A subset of Daphna samples were extracted using buffer to compliment the in vivo NMR assignments. For the extraction procedure, D. magna was flashfrozen in liquid nitrogen to halt enzymatic activity. 20 Metabolites were extracted using an aqueous buffer. 19 In Vivo NMR Flow System. For each analysis, 20 4 weekold D. magna samples were maintained inside a flow system, which was previously described. 3 Briefly, the flow system consisted of a 5 mm NMR tube with bottom and top plugs to maintain the organisms in the coil region. A narrower inlet supplied food and oxygenated water, while a wider sump pump that can handle air/water/suspensions removes the excess liquid and maintains the water height at 4 cm, which leads to improved shimming and water suppression. 3 The use of the sump pump removes the need for filters and allows food to be introduced into the system. During in vivo exposure studies, it is essential that the organisms be fed 35 so that the response from a toxin can be easily discerned and not be complicated by additional stress from starvation. 35 Aerated water (20 ± 1°C) was circulated from a water reservoir at a rate of 0.5 mL per min.
Exposure to BPA. Based on the past literature, it was determined that the concentration required for mortality of 50% of the population (LC 50 value) was approximately 14.4 mg/L for BPA. 20 Therefore, to establish a sublethal concentration, D. magna samples were exposed to 15% of the LC 50 value (2.1 mg/L). 20 The compound was added to the recirculating oxygenated water reservoir. To eliminate the possibility of natural variation in the D. magna metabolite profile due to differences in age, only fully grown adults (∼4 weeks old) were used. 4 The D. magna samples were fed 12 C unlabeled C. reinhardtii (1 mg per 20 mL of water) for 12 h before analysis (to purge gut contents of 13 C algae) 4 inside the flow system during the control and exposed period 3,35 to eliminate the effects of starvation. The 12 C unlabeled C. reinhardtii is not detectable in 2D HSQC NMR experiments as verified in a previous study, 4 and thus changes observed during the in vivo experiments arise from the organism utilizing its own 13 C-enriched biomass. The organisms were allowed to equilibrate inside the NMR for 3 h before starting data collection. Water containing BPA was purged and replaced between exposures to ensure that D. magna in each subsequent control group was maintained in clean water. The unexposed group was identical with the exception that BPA was not introduced into the NMR flow system. The unexposed control groups (60 Daphnia in total, 20 × 3 replicates) functioned as a reference (60 Daphnia in total, 20 × 3 replicates) to which each exposed population was then compared against. 19,20,40 The exposed group was the same organisms as the control group, and the control and exposure studies were performed back-to-back without removing the organisms from the NMR. This procedure was recently introduced and significantly reduces natural variation in in vivo NMR versus using separate control populations. 35 NMR Spectroscopy. All NMR experiments were performed on a Bruker Avance III-HD 500 MHz NMR spectrometer equipped with a triple resonance TCI ( 1 H, 13 C, 15 N) Prodigy cryoprobe. All samples were controlled at a temperature of 20°C during NMR data acquisition. A sealed D 2 O lock capillary (5 μL) was used for all in vivo experiments in order to separate the solvent from the organisms. 2−4 For all NMR experiments used in this study, a repetition time of 1 s was used between successive scans. The 90°pulse was calibrated for each sample in the study. The 1 H 1D NMR spectra were acquired with 256 scans and used shaped presaturation water suppression by gradient-tailored excitation with W5 pulse trains (SPR-W5 WATERGATE) 101 using a binomial delay of 125 μs in combination with a train of 1000 2 ms rectangle-shaped pulses centered on the water during the recycle delay. 2−4 Both HSQC and 2D (H)CbCa-(COCa)Ha NMR experiments were collected in phasesensitive mode using Echo/Antiecho-TPPI gradient selection. 3,35,36 The pulse sequence code for the (H)CbCa(COCa)-Ha experiment is provided in Supporting Information, Section 3. HSQC spectra were collected with 2112 time domain points (F2), a 1 J CH coupling of 145 Hz, and 1030 scans for each of the 128 increments in the F1 dimension. F2 was processed using an exponential function corresponding to line broadening of 8 Hz and F1 using sine-squared functions with a π/2 phase shift. (H)CbCa(COCa)Ha spectra were collected as 2D experiments. F3 ( 1 H) was collected using 276 scans and 1926 data points for each of the 128 increments in the F1 ( 13 C). The F2 dimension was not incremented during data collection such that the result was a 2D experiment. To suppress the protein signals, an unshifted sine-squared function was applied to the F3 dimension during processing. F1 was zero-filled to 512 points, and a sine-squared functions shifted by 90°was applied during processing. The HSQC experiment employed GARP-4 decoupling, and the (H)CbCa(COCa)Ha experiment incorporated both GARP-4 ( 13 C) and WALTZ-65 ( 1 H) decoupling. All other parameters for the (H)CbCa(COCa)Ha experiments were set in accordance with the original NMR pulse sequence. 24 The NUS spectra were acquired with 276 scans, and sampling densities of 20, 40, 50, and 100% of the 128 points in F1 were tested. Reconstruction of the NUS spectra was executed with the compressed sensing (CS) algorithm 88 available within the Bruker software TopSpin 3.5.
All of the 2D NMR spectra from the exposure analysis were acquired with the (H)CbCa(COCa)Ha pulse sequence and manually calibrated to the glycine peak (δ = 3.56 ( 1 H ppm), 43.95 ( 13 C ppm)). Data analysis was performed with Analysis of Mixtures (AMIX, version 3.9.14, Bruker BioSpin). The peak volumes were measured using the multi-integration function. The integration regions were centered on the peak of interest, and their size was optimized in a manner that allowed the entire peak to be integrated while minimizing the overlap with neighboring peaks. Identical integration regions were used for all spectra collected. To discern metabolic changes between exposed individuals and the control, the change in peak volumes in the exposed population is expressed as a relative percentage change in comparison to the control. 102 To assess whether the changes in amino acid concentrations were statistically important, a paired t test 103−106 was applied on the control and exposed BPA NMR data. A p value below 0.05 was used as the cutoff for statistical importance and is a widely approved criterion in metabolite analysis for aquatic toxicity testing. 19,20,40,107 A custom R-script (R Core Development Team, 2019) was written in-house for computing the paired t test on the BPA exposure data. The statistically important p values generated from the R-script are provided in Table S1.
Metabolite Identification. Spectra were calibrated against amino acid chemical shifts in the Bruker biofluid reference compound database (v.2.0.0-2.0.5) using a protocol developed earlier for complex mixtures. 108 For each amino acid, both the HSQC data and COSY data from the biofluid reference compound database were used. First, the α H-C unit was matched in HSQC. Afterward, the COSY correlation from the α proton was used to identify the 1 H chemical shift of the neighboring β proton. The β carbon chemical shift was then obtained via back reference to the HSQC data. Due to the significant reduction of overlap afforded by the amino acid selective pulse sequence, it was relatively easy to assign all 20 common amino acids both in the extract and in the living organisms. The chemical shifts of the identified compounds were compared with the database values (r 2 = 0.99, σ = 0.01) to gauge the quality of spectral assignments (see Figure S1).

* S Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.9b00931.
Correlation graphs for the assignments, oblique views of the data, R 2 values, and pulse sequence code (PDF)