Development and Application of a Low-Volume Flow System for

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Development and application of a low volume flow system for solution-state in vivo NMR Maryam Tabatabaei Anaraki, Rudraksha Dutta Majumdar, Nicole Wagner, Ronald Soong, Vera Kovacevic, Eric J Reiner, Satyendra P Bhavsar, Xavier Ortiz Almirall, Daniel Lane, Myrna J Simpson, Hermann Heumann, Sebastian Schmidt, and Andre J Simpson Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00370 • Publication Date (Web): 04 Jun 2018 Downloaded from http://pubs.acs.org on June 4, 2018

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Analytical Chemistry

Development and application of a low volume flow system for solution-state in vivo NMR Maryam Tabatabaei Anaraki,† Rudraksha Dutta Majumdar,† Nicole Wagner,† Ronald Soong,† Vera Kovacevic,† Eric Reiner,‡ Satyendra Bhavsar,‡ Xavier Ortiz Almirall, ‡ Daniel Lane,† Myrna J. Simpson,†,⊥ Hermann Heumann,§ Sebastian Schmidt, § André J. Simpson,* †,⊥ †Department of Physical and Environment Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, Canada, M1C 1A4 ‡ Ministry

of Environment and Climate Change, Toronto, Ontario, M9P 3V6, Canada

⊥ Department §

of Chemistry, University of Toronto, 80 St. George St., Toronto, ON, Canada, M5S 3H6

Silantes GmbH, München, Germany

KEYWORDS: in vivo NMR, flow system, 1H-13C HSQC, 13C enrichment, D. magna, environmental stress, metabolomics ABSTRACT: In vivo nuclear magnetic resonance (NMR) spectroscopy is a particularly powerful technique, since it allows samples to be analyzed in their natural, unaltered state, criteria paramount for living organisms. In this study, a novel continuous low-volume flow system, suitable for in vivo NMR metabolomics studies, is demonstrated. The system allows for improved locking, shimming and water suppression, as well as allowing the use of trace amounts of expensive toxic contaminants or low volume of precious natural environmental samples as stressors. The use of double pump design with a sump slurry pump return, allows algal food suspensions to be continually supplied without the need for filters, eliminating the possibility of clogging and leaks. Using the flow-system, the living organism can be kept alive without stress indefinitely. To evaluate the feasibility and applicability of the flow system, changes in the metabolite profile of 13C enriched Daphnia magna over a 24-hour period are compared when feeding laboratory food vs. exposing them to a natural algal bloom sample. Clear metabolic changes are observed over a range of metabolites including, carbohydrates, lipids, amino acids and a nucleotide demonstrating in vivo NMR as a powerful tool to monitor environmental stress. The particular bloom used here was low in microcystins and the metabolic stress impacts are consistent with the bloom being a poor food source forcing the Daphnia to utilize their own energy reserves.

NMR spectroscopy is a powerful tool for studying molecular structure and interactions. Traditional applications focused on structural elucidation of isolated compounds, but over the years have evolved to include more complex matrices including biomolecules,1 biofluids,2,3 foods,4 and various intact natural samples.5–12 Recently, it has been shown that when combined with isotopic enrichment, multidimensional NMR can identify a wide range of metabolites in-vivo.13 This is particularly important as it provides a direct “molecular window” into a living system ideal for understanding natural processes such as growth and metabolism as-well evaluating the impacts of environmental change (temperature, contaminants, salts, oxygen etc.) on these processes. Metabolomics has emerged as an important field in environmental research aimed at understanding the biochemical response of organisms to environmental stressors such as toxic contaminants.14 Specifically, NMR-based environmental metabolomics has evolved into an versatile tool, having been successfully applied to understand, for instance, the toxicity of trichloroethylene to fish embryos during embryogenesis,15 poor diet quality,16 and the impact of sub-lethal contaminant on Daphnia magna.15–21 As

primary consumers, D. magna are essential for trophic transfer of nutrients and contaminants, and thereby sustaining a healthy freshwater ecosystem.22 Therefore, they are routinely used for monitoring the impact of poor nutrition and contaminant exposure both at an organism and ecosystem level.16,18,21,23 However, studies are commonly based around in vitro metabolomics that, in the environmental context, often involves homogenizing the organisms and analysis of the homogenate by NMR or MS.24 In this case, a large number of control and exposed replicates are required for subsequent analysis using chemometric methods. The approaches are very powerful and provide metabolic information in a high throughput platform with minimal sample preparation, but can lack temporal resolution required to monitor stress and decipher complex interconnected response pathways. On the other hand, in vivo NMR has the potential to provide metabolomics flux monitoring in close to real time which is essential for better understanding the mode-of-action of a toxic contaminant and mechanistic impacts on the organism. In vivo NMR-based metabolomics9,13,25 is relatively new that holds great promise as a complementary tool to current in vitro approaches. In addition, NMR spectroscopy is particularly

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suited for in vivo studies, since it is non-invasive while providing an information-rich non-targeted overview of the in vivo metabolites present. Previously, high resolution-magic angle spinning (HR-MAS) NMR spectroscopy has been employed for the metabolite profiling of D. magna25 and Aporrectodea caliginosa (grey worms).26 By spinning the organism at the magic angle, magnetic susceptibility broadening is reduced providing high resolution data for the soluble and swellable components. A novel technique called comprehensive multiphase (CMP) NMR11 has also been used recently for in vivo characterization of Hyalella azteca.27 Unlike HR-MAS probes, CMP-NMR probes can handle high power, permitting the true solids (shell) as well as the semi-solid or gel-like (membranes) and dissolved phases (metabolites), to be studied and differentiated in vivo. However, both HR-MAS and CMP-NMR, require spinning at the magic angle (often around 2.5KHz) which exerts additional stress on the organisms. Moreover, neither of the above techniques is amenable to coupling with a flow system for the purpose of constant supply of water, along with food and the contaminants being studied. Solution-state in vivo NMR-based metabolomics, on the other hand, offers a comparatively low-stress environment for in vivo studies on living organisms since they are not spun, and a flow system can be optimized to provide ideal living conditions. However, in static samples, magnetic susceptibility distortions lead to broad 1D spectral profiles, resulting in little metabolic information especially in 1D 1H NMR.28 Furthermore, the intense and broad water signal in vivo makes water suppression very challenging.29 As such, applications thus far have been restricted to heteronuclear NMR with 31P being the most common, with example including Japanese medaka fish,30,31 molluscs32,33 spider crabs, cuttlefish and marine worms.34,35 Owing to the larger size of the organisms, these studies used either 10 mm NMR probes,30,31 or MRI systems32–35 that are not common in all NMR facilities. The advantage of using D. magna or any other small living organisms is that they can be easily fit inside a standard 5 mm NMR tube, and therefore, can be potentially studied in any NMR facility, drastically extending the scope of such research. To overcome the low spectral resolution observed in 1H 1D NMR, signals can be dispersed into 2D dimensions using 1H-13C HSQC which permits a rich metabolic profile to be recovered in vivo especially when combined with 13C isotopic enrichment.9,13 Flow-based NMR probes with custom-made flow cells such as flow injection analysis (FIA)36, direct injection (DI)37–40, and microflow NMR41,42 have been introduced commercially for various purposes. To our knowledge, commercial flow probes are based around flow cells which are most commonly blown glass cells that cannot be opened to place organisms inside. Based on our knowledge, there is not any flow system commercially available in the market for small aquatic living organisms allowing studying in vivo metabolomics. In vivo metabolomics studies of intact aquatic living organisms dates back to 1981.43 In the early 2000’s a flow system was designed, using 10-mm NMR tube, to deliver stressors to the living organisms in order to study in vivo 31P NMR.30–33 To date, only two studies using routine NMR instrumentation, i.e. a 5 –mm probe, have been reported.9,13 In both studies, D. magna were the mod-

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el organism and the flow systems (as with the earlier systems) all used a fairly rudimentary approach having a single pump to push the flow in a loop. The water was recycled from a large vessel or tank and combined with the very wide diameter tubing required on the outlet to ensure no backpressure or clogging, meant a large volume of water was required. As a result, this design is not ideal for the study of expensive contaminants, such as microcystins (algal toxins, which cost up to $4000/mg, as a pure standard), as even at sub-lethal concentrations the large volume of the flow system requires dissolving mg quantities for a single exposure. Furthermore, in the previous design the NMR tube itself was completely full of water, which can reduce the effectiveness of automated shimming and water-signal suppression. In this study, a new flow design is introduced which addresses all the aforementioned problems. To demonstrate an application of the system, D. magna are monitored overtime and their metabolic trajectories compared while being provided a nutrient-rich alga (Chlamydomonas reinhardtii) vs blue-green algal bloom collected from a eutrophic canal. Natural phenomena algal blooms are dramatic perturbation to food web that may cause a severe impact to the whole aquatic ecosystem and human health.44 Algal blooms are attributed to the eutrophication from anthropogenic loadings, as one of the main factors.45,46 Celadocera species (include D. magna) are keystone species and are known to be negatively influenced by algal blooms.47 However, the factors causing D. magna to decline are not well understood with the toxicity from microcystins (common toxins in blue-green algae) as well as the poor food quality of blue-green algae (low in lipids with Daphnia unable to synthesize lipids)48 often being suggested as the 2 key factors. The aim of this paper is to demonstrate a low-volume flow system that allows for faster and easier lock, shim and water suppression, while at the same time enabling the exposure of the living organism to low volumes of natural environmental samples or trace amounts of expensive toxic contaminants for in vivo sub-lethal toxicity studies in the future.

EXPERIMENTAL SECTION Please see supporting information for details regarding Daphnia culturing, exposure and statistical analysis. The additional information section towards the end of the supporting information further discusses stability of the flow system, the need for 2D NMR with isotopic labelling and the choice of bucket width for the statistical analysis. Low-volume flow-system. The continuous low volume flow system was built in-house and developed specifically for the in vivo NMR studies. Two capillary tubes, one for injection (PEEK tubing 0.76 mm ID) (Agilent Technologies Canada Inc., Mississauga, Canada), and one for suction (PTFE 1.5 mm ID) (Sigma-Aldrich, Canada) were used. The NMR tube was a high-precision, thin-walled 5-mm (Wilmad- LabGlass, NJ, USA). A Knauer HPLC pump model K120, was used as the injection pump, and a FMI Q pump model QG 6 was used as the suction pump. The flow system is discussed in more detail in the results and discussion.


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NMR spectroscopy and data processing. All the experiments were performed using a Bruker Avance III HD 500 MHz (1H) NMR spectrometer using a 1H-13C-15N TCI Prodigy cryoprobe fitted with an actively shielded z-gradient. The external D2O capillary lock (~5µL) integrated into the flow system was used for locking all experiments. The HSQC experiment was performed via double INEPT transfer using sensitivity improvement, States-TPPI phase cycling and Garp-4 decoupling. The W5-WATERGATE sequence was incorporated into the pulse-sequence prior to acquisition to further suppress the water signal. A total of 128 increments were collected, each with 36 scans and 1024 time domain points, and recycle delay of 1 s. The INEPT transfer was based on 1JHC of 145 Hz. Data were processed with a sine-squared function phase shifted by 90° in both dimensions and a zero filling factor of 2.

RESULTS AND DISCUSSION Development of low volume flow system A flow system was designed to allow the use of a low volume of natural environmental samples or trace amounts of expensive contaminants for in vivo sub-lethal toxicity studies in future. Various improvements were implemented (which will be discussed later) that when compared to our previously published system,9 provided numerous advantages including the ability to feed the organism during the experiment, easier and more consistent lock, as well as improved shimming and water suppression. Figure 1 is a schematic of the low volume flow system. Two capillary tubes, one for injection of fluid to NMR tube and one for suction of fluid from NMR tube, were inserted into a 5 –mm NMR tube. The injection capillary tube was attached to a glass tip, made from a hand-blown disposable glass pipette tip. To maximize the space for Daphnia the injection glass tip was bent using the torch to place the tip adjacent to the NMR tube wall. A Teflon plug (machined inhouse) was inserted into the bottom of the NMR tube to

prevent the daphnids from swimming to the bottom (outside the coil region). The top plug was a Teflon plug shaped to hold both injection capillary glass and D2O capillary. The top plug provides spacing for flow and excess algae (i.e. food) to return to the slurry pump but prevents daphnids passing, keeping them in the coil region (Figure 1). The capillary tubes were attached to two pumps (Figure 1), which in turn were connected to the reservoir (a glass scintillation vial) via another pair of capillary tubes to complete the looped flow-system. A Knauer HPLC pump was used to transfer the fluid, containing water and food, through the capillary tube from the reservoir to Daphnia inside the NMR tube. The flow rate of injection pump was kept at 0.5 mL/min for the entire experiment. An FMI Q pump model QG 6, that sucks water, slurries and air, was used for the suction of fluid from the NMR tube back to the reservoir. The flow rate of suction pump was kept at a much higher, 20 mL/min, during the experiments, which means when the water level inside the NMR tube rises above 4 cm, it is immediately siphoned off by the suction tip, maintaining the desired height. The other end of the suction loop, transferring the siphoned water back to the reservoir, was secured above the water to prevent bubbling. With two pumps, injection capillary tubes, and suction tubes (mostly filled with air), the total volume of fluid in the whole looped system can be as low as 5 mL. However, still using a large volume of fluid with this flow system, such as a tank, is possible if required. The air was bubbled to water in the reservoir continuously with the lowest flow rate possible to aerate water while preventing vigorous bubbling. Care must be taken to provide a low flow rate of air bubbling and positioning the suction tip properly inside the reservoir furthest from the air source to prevented bubbles entering the NMR system, which otherwise can lead to artefacts in the spectra. The sealed reservoir was placed in a cooling system to keep its temperature same as the NMR. For this study, both NMR and cooling system

ACSinParagon Plus Figure 1. Low volume flow system built in-house for vivo NMR. TheEnvironment system allows for improved locking, shimming and water suppression. The low volume of liquid enables the exposure of the living Daphnia to smaller amounts of natural environmental samples or requires less expensive toxic contaminants for future in vivo sub-lethal toxicity studies.


were kept at 10 °C during the entire experiment but can be controlled as required for future studies. One of the concerns regarding any flow systems is leakage inside the NMR spectrometer. While the authors have found the Qpump extremely reliable and have not encountered issues, if the Q-pump acting as a sump does stop working, the water level inside the NMR would rise. To prevent this being an issue, the NMR tube was not sealed and instead an outer plastic tube was attached to the outside of the NMR tube with the capillary tubes passing through it (Figure 1). Therefore, in case the fluid rises inside the NMR tube, fluid can be transferred (through the outer plastic tube) to outside the NMR instrument to a collection vessel. This is simply a “fail-safe” considering the cost of cryogenically cooled NMR probe used in this study. Water suppression. Water suppression is critical in order to optimize receiver gain and detect trace components at low concentration.49,50 Water which is located beyond the RF coil extremities experiences a weaker RF field than the coil center and as such doesn’t experience the desired flip angles which can lead to poor cancellation and large residual water signals. Due to their additional sensitivity, this is especially problematic in cryoprobes which pick up signals from far-water very efficiently. As such it is recommended in practice to restrict the water to just above the coil height in cryoprobes which is often ~4cm total height in the NMR to reduce the signal from water beyond the coil extremities. In the previous in vivo NMR flow system,9 where the NMR tube was completely full of water, water suppression was difficult. In contrast, in the current low volume flow system, the top water surface is maintained at 4 cm from the bottom of the NMR tube to allow for optimal water suppression (see Figure 2). Spectrometer locking. The previous flow design utilized a round lock bulb at the bottom of the NMR tube. In many Residual water A

B

1H

NMR chemical shift (ppm)

Figure 2. 1D 1H NMR spectrum of Daphnia magna alive in the NMR flow system. If the level of water exceeds the NMR detection coil, large residual water signal arises from a poor cancellation of far water. A) Collected with a 5cm water height from the base such that considerable water beyond the coil is present, and B) collected with a 4cm water height from the base, optimal for the cryoprobe used here.

ways, this is ideal as it does not take up room in the active coil region. However, in practice, the solution is challeng-

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ing to work with. If the bulb is slightly too low, the signal is A

Second day

First day

Time Zero

1H


B Lactic Acid

1H


Figure 3. A series of 1H projections from 2D 1H-13C NMR to investigate proper oxygen delivery to D. magna using low volume of fluid. The results demonstrate that low volume of fluid doesn’t cause anoxic stress as long as aerated water (fluid) is constantly transferred to the flow cell through close looped circulation (A). For contrast, in (B) the flow is stopped for 30 mins and lactic acid signals quickly appear.

too low for the spectrometer lock, and if it is too high, the tube will fail automated shimming (topshim 3D on a Bruker system), due to susceptibility distortions from the lock bulb surface running perpendicular to the magnetic field lines. As such, in the past, it has taken students hours to adjust the height exactly, which is not realistic when the goal is to keep organisms between runs in the NMR for equal periods of time. Therefore, in the low volume flow system introduced here, a simple capillary is used instead (see Figure 1). A sealed Teflon capillary tube filled with D2O was placed inside the NMR tube, preventing the D2O from coming in contact with the organisms while permitting a stable lock signal. The capillary-D2O lock also facilitates significantly easier and more consistent automated shimming without the need for tube height adjustment, and consequently better water suppression, in comparison to the glass lock-bulb used previously.9,13 Moreover, by moving the capillary to the side wall of the NMR tube obstruction to the Daphnia is minimal. Improved oxygen delivery. L-lactate is the main end product of anaerobic metabolic pathways in D. magna.51–53 A previous study showed that aerated water circulation was vital to maintain D. magna inside the NMR tube for an extended period of time.9 A large volume water reservoir, as used previously,9 ensures sufficient oxygen for a long


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period. However, water circulation using a low-volume reservoir (the scintillation vial used herein) needs continuous aeration to transfer sufficient oxygen to the organism. Therefore, the air was continuously bubbled inside the water reservoir at a relatively low rate (~5mL per min) to minimize bubbling. Moreover, to ensure all Daphnia receive aerated water, the glass injection tip was positioned to the bottom of the NMR tube (above the bottom-plug) (Figure 1) to have the flow from the bottom to the top. To investigate proper oxygen delivery, 1H projection from 2D 1H-13C NMR were obtained (Figure 3A) in three different time points representing time zero, first day and second day. With aeration of water, no signs of anoxic stress were observed with the flow system. However, if the flow is stopped even for a relatively short time (30 mins) lactate quickly builds up and dominates the 1H profile of the organisms (Figure 3B). Feeding the organisms. Feeding the organism is one of the critical factors in toxicity studies. As yet, none of the in vivo NMR approaches reported have met this significantly important challenge.9,27 Feeding the living organisms during the experiment becomes possible using the flow system design introduced here. The FMI Q pump is central to this and is designed to transfer slurries and is self-priming, meaning that it runs with any combination of air, water, and suspensions. As such, excess liquid and food is continually recirculated, but done so in a manner that filters are not required. In our experience, we have found filters, no matter how well designed, cannot be used as they eventually become clogged causing back pressure which can potentially lead to catastrophic failure of the NMR cell and damage to NMR probe components. Along with the proper pumps and proper inner diameter of suction and injection capillary tubes, the optimized top-plug’s size not only helps maintain the organisms in the coil region but permit fluid, contained food, to pass through and reach the suction tip (Figure 1). Example application: laboratory food compared to an algal bloom To demonstrate the flow system’s capability and feasibility for in vivo studies, Daphnia were exposed to an algal bloom sample and the response compared to control groups which were continued to be fed laboratory grown algae. Algae blooms often arise from eutrophication and are known to have detrimental impacts on Daphnia. Two main reasons are cited for this. The first being presence of naturally occurring toxic compounds such as microcystins within the bloom, the second being that the blooms have a high contribution from cyanobacteria (blue green algae) which can be lower in key nutrients such as fatty acids which Daphnia cannot synthesize and must get from their diet. 54–56 In this study the analysis of the algae bloom was performed by the Ministry of the Environment for microcystins (RR, LA, LR, LF, LY, HilR, LW, YR) using a prepublished method57 and the results showed that microcystins’ concentrations were all below the detection limit (0.01 µg L-1). Considering this, any negative impacts of the algal bloom on the Daphnia are more likely due to the nutritional quality of the bloom (or other factors) rather than direct toxicity from microcystins themselves.58,59 Overall, 120 Daphnia were compared in three sets of control-

experiments (20 Daphnia per set) to three sets of exposedexperiments to the natural bloom sample (20 Daphnia per set) and monitored continuously over 24 hours inside the NMR. Prior to the experiments, all the daphnids were cultured from birth using 13C enriched food, as such the 1H-13C HSQC experiments detect changes in the fully labelled organisms and their energy reserves. 12C laboratory grown algae is fed to both the control population and the natural bloom fed populations for 4 hours before the experiment begins. This helps clear enriched algae from the digestive systems of the Daphnia. The natural abundance 13C in the laboratory algae is below the detection limit of the HSQC experiment as previously demonstrated.13 As such changes observed during the exposure in this study reflect the changes to the 13C pools stored within the organism’s biomass. Manual spectral analysis and metabolite time trajectories. A manual screening approach was performed using MestReNova (v. 12.0.1). First, all 2D 1H-13C datasets were overlaid in “superimposed mode” and full spectra were subject to full peak peaking and fitting using the quantitative Global Spectral Deconvolution (qGSD) module.60 The data analysis module was used to create interactive graphs where the integral for each peak was plotted automatically across all spectra on peak selection. The result was a series of concatenated graphs displaying changes in the three controls and three exposed groups, successively, as each peak in the 2D HSQC is selected. This approach allowed a fast visualization of how the metabolite trajectories change over time between the groups. For peaks of interest, integral graphs of responsive chemical shifts were created and plots of relative signal intensity changes over time (each 1.5 h) were created. The peaks identified as significant were triacylglyceride (TAG), glucose, glycine, tyrosine, histidine, uridine, and serine (see Figure 4). A t-test (twotailed, equal variances) was used to compare the relative signal intensity from control versus exposed groups to examine statistical significance (p < 0.05) of the separations (Figure 4). Note choline was not identified as important in the initial manual screening but was identified later by the statistical analysis as significant (see later). Multivariate statistical analysis In addition to the manual analysis, a completely separate statistical analysis of the data was also performed and both loading and scores plots were supplied in each case. These analyses can be summarized as 1) PCA of whole spectra region, 2) PCA of whole spectra region with lipids supressed, 3) PCA of carbohydrates-only, and 4) PCA of aromatics-only. 1) PCA of whole spectra region. As indicated in Figure S1, lipids dominate the NMR 1H-13C HSQC spectra of Daphnia magna. The PCA analysis of the entire datasets including all signals in the data (Figure S2) demonstrated that the lipids dominated the principle components (due to their significant intensity contribution to the data), but failed to cause separation in the scores plot. This reveals while lipids vary between the datasets they are not a discriminating factor separating the exposed and control groups.



demonstrated carbohydrate, TAG, glycine, serine, choline, betaine, and alanine as the most important discriminators. However, the PCA didn’t identify contribution of aromatic signals. Therefore, based on a published approach which shows that the analysis of sub spectral regions can help identify less dominant signals that could otherwise be missed,61 the carbohydrate and aromatic regions were analyzed separately. 3) PCA of carbohydrates-only. A PCA analysis of the car-

2) PCA of whole spectra region with lipids supressed. Based on the information from point one, a PCA analysis was performed where all signals were left in the analysis with the exception of the intense lipids signals. The purpose was to see if when the very intense lipids signals were supressed, smaller variations previously hidden would become apparent (Figure 5A and 5B). The idea here is to retain the relationships between the carbohydrates>aliphatics->aromatics. Conducting PCA of whole spectra

Glucose

1.4 1.2 Control^

1

Control^^ 0.8

Exposed^

0.6

Exposed^^

0.4

p-value= 4.6023E-25

0.2 0 0

10

20

30

Normalized relative signal intensity


Triacylglyceride (TAG) 2.5

Control*

2

Control** Control***

1.5

Exposed* Exposed**

1

Exposed*** p-value= 2.84118E-57

0.5 0 0

10

Time (hrs.)

0.8

Control

0.6

Exposed

0.4

p-value= 1.09908E-14

0.2 0 10

15 20 Time (hrs.)

25

30


1


1.2

1.4

1.2



1.4

5

2.5

1.2 1 0.8 Control

0.6

Exposed p-value= 1.00557E-07

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5


Histidine (7.05, 119.50 ppm)

1 0.8 0.6

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20

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0.60

Exposed p-value=0.0127

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Serine (3.95, 63.5 ppm)

1.40

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Uridine (7.95, 144.50 ppm)

1.2

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30

Tyrosine (6.85, 118.50 ppm)

1.6

0

20 Time (hrs.)

Glycine (3.75,46.45 ppm)


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30

2 1.5 Control

1

Exposed p-value=3.04405E-14

0.5 0 0

5

10

Time (hrs.)

15 20 Time (hrs.)

25

30

Figure 4. Change in selected metabolites (showing statistically significant changes) during 21 hours (14 time points) of natural bloom sample exposed to D. magna (n=20) compared with the control D. magna (n=20).* Chemical shift at 3.23, 77.16 ppm** Chemical shift at 3.46, 78.60 ppm*** Chemical shift at 3.37, 72.85 ppm^ Chemical shift at 4.26, 64.79 ppm^^ Chemical shift at 4.09, 64.67 ppm. Note choline was not identified as important in the initial manual screening but was identified later by the statistical analysis as significant.

region with lipids suppressed, separation was seen between the control and exposed groups and the loading plot

bohydrate-only was performed separately. The glycerol unit from TAG resonates in the carbohydrate region and


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since TAG represents important energy reserve, it was included in this PCA analysis. Interestingly, near identical loadings plots with the same identified metabolites were obtained for the carbohydrate-only (Figure S3) when compared to the same region within the full datasets (lipids suppressed) (Figure 5A and 5B). In this case the selective analysis of the carbohydrate region provided no new information. 4) PCA of aromatics-only. Due to the relatively weak signals from aromatics, PCAs can often underestimate their contribution. As such as with the carbohydrates region above, the aromatic region was also analyzed separately. By performing PCA exclusively for the aromatic region, separation is seen between the exposed and control groups in the scores plot (Figure 5C) with tyrosine, histi-

5D). Interestingly if the separate PCA analysis of the aromatic region had not been performed these discriminating metabolites may have been missed (i.e. see supporting, Figure S2, and in the main paper, Figure 5B, where aromatics are not identified as important when all signals or all signals excluding lipids are considered). Comparison of manual and statistical approaches. Both the manual and statistical analysis identified the same metabolites as statistically significant. Namely, tyrosine, histidine, uridine, glucose, glycine, serine and TAG with the exception of choline, betaine and alanine which were only highlighted by the statistical approaches. However, when plotted (see supporting information Figure S4 and Figure 4), there is clearly an increase in betaine and choline with time along with a decrease in alanine but it is less clear

A

C

04 02 0

Con o

-0 2

Exposed

-0 4

R = 0 993 Q = 0 936

PC2 19% var ance exp a ned

06 PC2 24% var ance exp a ned

1 2 3 4 5 6 7 8 9 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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Ana yt ca Chem stry

-0 6 -1

-0 5

0

05

1

04 03 02 01 0 -0 1 -0 2 -0 3 -0 4 -0 5 -0 6 -0 7 -1 5


Con o Exposed R = 0 942 Q = 0 896

-0 5

05


D 0.6

B 0.3

Histidine 7.05, 119.50

0.5

0.2 3 75 63 50

A an ne

Ca bohyd a e 0.1

3 85 62 50 62 50 3 453 75 78 50 3 35 72 50

0.4

1 45 18 50 1 45 19 50

Ove apped a pha p o on

3 75 533 50 75 50 57 50 3 75 46 50 253 42 7.05, 119.50 7.05, 120.50 4.55, 7.05, 118.50 3 75 47 50 4.45, 4.55, 7.75, 7.75, 148.50 129.50 149.50 139.50 138.50 7.35, 4.55, 4.55, 4.55, 4.55, 4.45, 7.05, 100.50 92.50 138.50 137.50 136.50 134.50 133.50 115.50 148.50 147.50 146.50 145.50 121.50 3.75, 3.65, 3.55, 3.45, 3.35, 3.25, 3.15, 3.05, 2.95, 8.45, 8.35, 8.25, 8.15, 8.05, 7.95, 7.85, 7.75, 7.65, 7.55, 7.45, 7.35, 7.25, 7.15, 7.05, 6.95, 6.85, 6.75, 6.65, 6.55, 6.45, 6.35, 6.25, 6.15, 6.05, 5.95, 5.85, 4.45, 4.35, 4.25, 4.15, 4.05, 3.95, 3.85, 110.50 109.50 108.50 107.50 106.50 105.50 104.50 103.50 102.50 101.50 100.50 99.50 98.50 97.50 96.50 95.50 94.50 93.50 92.50 91.50 90.50 89.50 88.50 87.50 8 104.50 5.85, 6.55, 5.85, 7.25, 5.95, 6.45, 6.65, 3.85, 3.75, 3.65, 3.55, 3.45, 3.35, 3.25, 3.15, 3.05, 2.95, 8.45, 8.35, 8.25, 8.15, 8.05, 7.95, 7.85, 7.75, 7.65, 7.55, 7.45, 7.35, 7.25, 7.15, 7.05, 6.95, 6.85, 6.75, 6.65, 6.55, 6.45, 6.35, 6.25, 6.15, 6.05, 5.95, 5.85, 4.55, 4.45, 4.35, 4.25, 4.15, 4.05, 3.95, 7.75, 7.95, 130.50 132.50 131.50 130.50 126.50 118.50 117.50 149.50 148.50 147.50 146.50 145.50 144.50 143.50 142.50 141.50 140.50 139.50 138.50 137.50 136.50 135.50 134.50 133.50 132.50 131.50 130.50 129.50 128.50 127.50 126.50 125.50 124.50 123.50 122.50 121.50 120.50 119.50 118.50 117.50 116.50 115.50 114.50 113.50 112.50 111.50 110.50 140.50 138.50 136.50 7.75, 4.55, 4.55, 7.65, 7.85, 6.95, 5.95, 4.55, 7.95, 131.50 92.50 88.50 87.50 143.50 144.50 143.50 142.50 138.50 137.50 136.50 121.50 137.50 5.95, 104.50 91.50 105.50 7.05, 7.95, 6.85, 7.85, 7.65, 134.50 120.50 118.50 143.50 137.50 139.50 138.50 3 75 45 50 6.75, 6.75, 5.85, 5.85, 5.95, 90.50 105.50 117.50 119.50 90.50 144.50 144.50 91.50 7.05, 6.95, 132.50 120.50 6.75, 6.95, 118.50 133.50 118.50 7.15, 134.50 7.15, 132.50 6.95, 119.50 6.85, 117.50 6.85, 119.50 7.15, 133.50 G yc ne 6.85, 118.50 3 25 56 50 4 25 65 50 3 65 56 50 Be a ne 3 05 56 50 Se ne 3 95 63 50 4 15 63 50 3 15 57 50 4 25 63 50 3 95 64 50 4 254 15 64 50 Cho ne 64 50 3 15 55 50

0 -0.1 -0.2 -0.3

7.75, 138.50 7.05, 118.50 7.75, 139.50 6.95, 120.50 7.65, 138.50 7.65, 139.50 6.95, 118.50

0.2 0.1

7.95, 137.50 7.75, 140.50 7.05, 121.50 7.95,7.95, 138.50 136.50

4 05 64 50

0

-0.5

7.15, 7.15,134.50 132.50 Tyrosine 6.85, 117.50

5.95, 105.50 5.95, 104.50 7.05, 133.50 6.75, 117.50 6.75, 118.50 7.95, 143.50 5.85, 131.50 7.95, 144.50

-0.2

3 15 56 50

7.15, 133.50 6.85, 119.50 6.85, 118.50

7.85, 143.50 7.75, 7.65, 137.50 137.50 6.95, 121.50 7.65, 140.50 6.05, 129.50 7.95, 7.05, 8.35, 7.55, 7.55, 139.50 117.50 141.50 141.50 140.50 7.85, 7.85, 138.50 8.05, 136.50 142.50 7.85, 137.50 7.25, 6.75, 6.05, 6.85, 6.45, 5.95, 141.50 135.50 130.50 125.50 113.50 110.50 143.50 126.50 107.50 7.85, 7.05, 8.05, 7.35, 8.45, 8.25, 5.85, 7.75, 7.25, 6.65, 6.55, 144.50 5.85, 6.15, 122.50 143.50 124.50 115.50 112.50 106.50 103.50 148.50 147.50 144.50 123.50 113.50 112.50 111.50 108.50 102.50 146.50 129.50 116.50 132.50 132.50 5.85, 8.15, 7.15, 8.35, 6.85, 5.95, 7.05, 6.35, 7.85, 6.55, 6.75, 7.75, 7.15, 6.05, 5.75, 8.35, 6.95, 6.15, 7.45, 6.85, 7.85, 7.55, 7.05, 7.35, 8.25, 6.75, 8.45, 8.15, 7.95, 6.35, 7.85, 6.15, 7.95, 6.05, 6.95, 8.35, 7.25, 6.85, 7.55, 8.45, 6.25, 6.25, 6.15, 144.50 142.50 141.50 140.50 138.50 137.50 136.50 134.50 133.50 132.50 128.50 126.50 125.50 123.50 121.50 120.50 114.50 113.50 107.50 105.50 104.50 101.50 100.50 146.50 145.50 142.50 141.50 140.50 139.50 138.50 135.50 134.50 133.50 132.50 131.50 129.50 128.50 126.50 124.50 122.50 121.50 115.50 106.50 105.50 103.50 101.50 100.50 147.50 145.50 137.50 135.50 133.50 131.50 128.50 127.50 125.50 112.50 106.50 105.50 104.50 103.50 149.50 133.50 131.50 130.50 127.50 7.95, 7.05, 6.05, 7.65, 6.15, 8.05, 8.25, 8.45, 7.95, 6.95, 6.45, 5.95, 5.85, 6.55, 7.25, 7.75, 6.35, 8.05, 7.65, 6.45, 6.25, 5.85, 7.45, 6.45, 6.65, 8.05, 6.75, 5.95, 7.35, 8.25, 7.15, 7.05, 7.95, 7.85, 6.75, 7.05, 6.05, 5.85, 6.45, 146.50 116.50 104.50 149.50 147.50 146.50 143.50 135.50 130.50 129.50 127.50 124.50 119.50 116.50 112.50 111.50 110.50 106.50 102.50 149.50 148.50 147.50 144.50 143.50 137.50 136.50 130.50 127.50 125.50 123.50 120.50 119.50 116.50 114.50 113.50 112.50 111.50 110.50 109.50 108.50 107.50 104.50 102.50 148.50 146.50 144.50 142.50 141.50 134.50 130.50 123.50 122.50 114.50 113.50 110.50 108.50 102.50 100.50 135.50 134.50 129.50 126.50 117.50 7.85, 7.85, 6.35, 8.05, 6.85, 7.25, 7.75, 7.85, 6.55, 5.85, 7.35, 7.15, 8.15, 6.35, 6.25, 8.35, 6.85, 7.45, 6.65, 7.55, 6.75, 5.95, 8.45, 8.25, 6.75, 7.55, 6.05, 8.15, 7.95, 7.15, 7.05, 6.95, 8.35, 7.85, 7.45, 7.35, 6.85, 6.65, 6.15, 5.75, 7.75, 7.85, 6.85, 8.45, 5.75, 6.35, 7.95, 7.65, 7.55, 6.15, 8.35, 6.05, 6.25, 5.95, 7.55, 7.55, 139.50 145.50 131.50 115.50 148.50 145.50 142.50 140.50 139.50 138.50 131.50 128.50 126.50 125.50 123.50 122.50 121.50 120.50 115.50 113.50 109.50 105.50 104.50 101.50 100.50 146.50 145.50 142.50 141.50 140.50 139.50 138.50 135.50 134.50 133.50 131.50 129.50 128.50 126.50 124.50 122.50 121.50 118.50 117.50 115.50 106.50 103.50 101.50 100.50 149.50 147.50 143.50 140.50 138.50 137.50 132.50 131.50 129.50 128.50 127.50 126.50 125.50 124.50 121.50 120.50 119.50 118.50 117.50 115.50 109.50 107.50 106.50 104.50 103.50 101.50 132.50 123.50 119.50 118.50 116.50 138.50 139.50 6.55, 6.25, 5.85, 6.05, 8.25, 5.75, 8.05, 6.45, 6.45, 7.65, 7.75, 6.25, 8.05, 5.95, 7.25, 6.55, 6.35, 5.85, 8.25, 6.65, 8.15, 8.05, 7.45, 7.35, 6.55, 5.95, 5.85, 6.75, 6.25, 7.05, 7.25, 6.95, 6.95, 6.85, 6.35, 7.15, 8.25, 6.75, 5.85, 7.15, 6.85, 6.65, 5.85, 135.50 129.50 122.50 103.50 146.50 141.50 134.50 133.50 132.50 130.50 129.50 103.50 149.50 148.50 147.50 144.50 143.50 136.50 132.50 130.50 127.50 125.50 123.50 116.50 114.50 112.50 111.50 110.50 105.50 104.50 102.50 148.50 146.50 145.50 141.50 139.50 136.50 135.50 134.50 133.50 130.50 123.50 122.50 116.50 114.50 113.50 111.50 110.50 108.50 105.50 102.50 100.50 134.50 122.50 121.50 117.50 115.50 109.50 107.50 129.50 134.50 119.50 106.50 6.85, 6.65, 7.45, 8.25, 8.15, 7.55, 7.35, 5.75, 7.05, 6.05, 6.85, 6.05, 7.85, 7.95, 6.15, 8.35, 7.75, 7.55, 6.35, 142.50 137.50 142.50 141.50 139.50 138.50 137.50 135.50 133.50 131.50 128.50 124.50 121.50 119.50 108.50 103.50 100.50 126.50 119.50 118.50 117.50 106.50 103.50 120.50 119.50 6.55, 6.55, 6.65, 5.95, 7.65, 7.55, 7.25, 7.45, 7.85, 7.65, 5.85, 8.05, 7.95, 6.95, 8.05, 6.75, 7.25, 8.25, 6.05, 7.75, 6.85, 7.15, 7.15, 136.50 139.50 131.50 129.50 145.50 144.50 143.50 128.50 104.50 147.50 144.50 134.50 130.50 120.50 115.50 145.50 121.50 134.50 115.50 106.50 142.50 132.50 118.50 135.50 7.35, 6.25, 6.45, 6.65, 5.95, 7.45, 5.85, 7.35, 6.95, 133.50 127.50 125.50 132.50 107.50 105.50 6.85, 135.50 128.50 116.50 145.50 120.50 5.95, 6.25, 6.45, 5.75, 7.85, 7.85, 7.75, 6.95, 6.95, 106.50 6.85, 128.50 139.50 132.50 140.50 141.50 136.50 132.50 133.50 133.50 6.55, 6.85, 6.85, 118.50 117.50 121.50 116.50 5.75, 5.95, 6.45, 7.35, 6.95, 130.50 133.50 127.50 129.50 117.50 7.95, 5.95, 6.45, 6.05, 6.75, 142.50 7.25, 7.05, 103.50 136.50 105.50 116.50 133.50 131.50 6.05, 5.75, 104.50 131.50 6.55, 5.85, 5.85, 7.25, 7.15, 5.85, 138.50 137.50 133.50 129.50 129.50 130.50 103.50 5.85, 6.65, 105.50 7.05, 117.50 134.50 6.45, 6.65, 126.50 118.50 7.15, 131.50 7.75, 7.75, 144.50 143.50 6.75, 119.50 7.35, 7.35, 132.50 130.50 7.25, 132.50 6.45, 5.95, 138.50 130.50 5.95, 132.50 7.25, 6.45, 130.50 137.50 7.35, 7.25, 131.50 5.85,131.50 132.50 7.05, 132.50 7.95, 145.50 5.95, 5.85, 131.50 104.50 5.85, 130.50

-0.1

TAG 4 05 63 50 -0.4

6.95, 119.50 7.05, 120.50

0.3

Uridine

-0.3 -0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-0.4

-0.2

0

0.2

0.4

0.6

Figure 5. Principal Component Analysis (PCA) scores plots and loading plots of the whole spectra with lipids suppressed (A and B) and aromatic-only (C and D). The most dominant signals from lipids highlighted with stars in Figure S1 were excluded from the bucketing (A and B), however, all other signals including the remaining aliphatic signals were left in the analysis. The idea here is to suppress the lipid peaks which otherwise dominate the PC’s due to their abundance, but leave all other peaks such that any relationships between carbohydrate->aliphatics->aromatics remain. The PCA of all components as well as all components with carbohydrate-only are given in supporting section (Figures S2 and S3). Scores plots (A and C) show separation between control and exposed D. magna. Loadings plots (Band D) indicate buckets responsible for the separation within PCA.

dine, and uridine accounting for the discrimination (Figure

whether there is discrimination between the control and

ACS Paragon P us Env ronment


exposed groups. It is important to remember PCA is an unsupervised method. Therefore, it attempts to identify any important trends and differentiators within the datasets, but not necessarily discriminates between the control and exposed groups. For these 3 metabolites, it is likely the clear trend with time is a major factor that contributes to their influence in the PCA analysis. More detailed analysis shows for alanine the statistical separation between the groups over time is not significant (p-value 0.f, Figure S4). However, in the case of betaine (p-value 0.0015, Figure S4) and choline (p-value 0.013, Figure 4) there appears to be some statically significant separation albeit much less than for the other metabolites (Figure 4). This reemphasizes the importance of using statistical methods alongside manual analysis to avoid misinterpretation. If PCA had been used alone, alanine may have been incorrectly identified as influential discriminator between the groups. Conversely, if manual analysis alone had been used, more subtle but potentially important differentiators, such as choline and betaine could have been missed. Furthermore, the trajectory plots demonstrate the importance of repeat sampling over time which becomes possible with living organisms. For example, in case of choline and alanine where intergroup discrimination is less pronounced, a single sampling point (for example after 12 hours) may have not identified either metabolite as significant whereas with a trajectory overtime, the detection of more subtle trends becomes more feasible. Metabolite changes with bloom exposure From the combined statistical and manual analysis tyrosine, histidine, uridine, glucose, glycine, serine, TAG, choline, and betaine were identified as statically important discriminators between the control and exposed groups. The decrease in TAG tends to suggest that the Daphnia are using up their TAG resources possibly as an energy response. The increase in glucose could be produced from the utilized TAG or could be from the breakdown of other reserves. Note as the bloom (exposed group) and 12C lab algae (control) are both non-enriched carbon sources (99% 12C), these responses are coming from the organisms’ stored pools of 13C carbon. Utilization of reserved energy is the basic stress response in D. magna.62 Carbohydrates, proteins, and lipids are energy reserves and fluxes in their concentrations in D. magna can be in response to exposure to stressful conditions.63–67 Carbohydrates provide the energy and building blocks for anabolic pathways.68 Interestingly, signals from glucose showed an upward trend. Studies show that different kinds of pollutants in the aquatic environment induce hyperglycemia in crustaceans.69 Crustacean hyperglycemic hormone (CHH) is known as “crustacean stress hormone” since it is involved in raising glycemnia70 in response to various stimuli such as pesticides, heavy metals and even starvation.71 Therefore, an increase in glucose can be an indicator of CHH release and hence hyperglycemia in Daphnia.20 Moreover, an increase in glucose correlated with a decrease in glycogenic amino acids, tyrosine and glycine, during first five time points that may indicate the use of amino acids during the process of gluconeogenesis.72,73 Finally, catabolism of glycogen reserves is attributed to the increased energy de-

Page 8 of 12

mand.62,68,74 The physiological state and energy budget of living organisms is also reflected by protein content.64,75 General depletion of free amino acids (such as histidine and tyrosine) may be attributed to increased synthesis of proteins with antioxidant defensibility against induced stressors.76,77 Free amino acids can be also used to produce energy through the tricarboxylic acid cycle (TCA) in stressful situations.62,65 The decrease in glycine and increase in serine on expose in this study can be attributed to glycine metabolism to serine which is used as energy in the TCA cycle.78 Many amino acids in this study show a downward trend which can be due to the catabolism of amino acids for energy generation which would lead to the utilization of many amino acids levels at the same time.21 Tyrosine is the precursor of biogenic amines such as dopamine.79 Dopamine has many physiological roles in cladocerans such as release of neurohormones.80 Interestingly, tyrosine appears to increase from point six (9.5 hours) in the time series which coincides with the leveling off of the glucose increase.81 One possible hypothesis could be that Daphnia find the bloom a poor food source and compensate by producing glucose for energy from its own reserves. However, overtime, tyrosine is produced to make signaling molecules which trigger starvation and slow down the utilization of the remaining energy reserves. A slight decrease in betaine is observed in the exposed group. Betaine uptake is known response to various forms of environmental stress and the increased utilization may explain the lower concentration of the free metabolite in the exposed population.82 Finally, choline shows a slight increase in the exposed group. Choline is considered as an important bioindicator involved in lipid and fatty acid metabolism83,84, and osmoregulatory maintenance and is a key precursor to phosphatidylcholine which is essential to membrane integrity.85,86 Uridine shows a decline in the exposed population that may be the indication of the organisms focusing on survival vs reproduction.87,88 From an energetic point of view, lipid reserves are strongly influenced by stressful conditions.65 Lipids reserves along with TAG depletion in this study may be attributed to the increased energy demand resulting from stress 74 or a decline in the feeding rate. The feeding rate of Daphnia has been investigated and the results highlight that feeding rate declines after exposure to stressors.65,89–92 Studies show that reduced food consumption results in a reduced calorie intake93 and stimulates an increase in energy-related substrates. Studies have shown that the lipid fraction of D. magna is the most affected energetic parameter65,94, and the loss of TAG overtime, may indicate that D. magna may be feeding less on the bloom sample. It is not clear whether the bloom itself is a poor source of nutrition for the organism (note food in the control and bloom-exposed were at the same concentration) or that the algae in the bloom were physically different (for example the cells were the wrong size or shape for the daphnids to consume), both of which could explain the energetic impacts detected by NMR. However, what the study clearly demonstrates is that in vivo NMR has great potential to monitor metabolism over time in living organisms and detect responses to environmental change. The flow system introduced here, including the ability to better lock, shim and suppress water, while


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feeding the organisms being an important step towards routine in vivo environmental stress screening.

Anal. Bioanal. Chem. 2007, 389 (5), 1311–1327. (7)

Hertkorn, N.; Benner, R.; Frommberger, M.; Schmitt-Kopplin, P.; Witt, M.; Kaiser, K.; Kettrup, A.; Hedges, J. I. Geochim. Cosmochim. Acta 2006, 70 (12), 2990–3010.

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Simpson, A. J.; McNally, D. J.; Simpson, M. J. Prog. Nucl. Magn. Reson. Spectrosc. 2011, 58 (3–4), 97–175.

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Soong, R.; Nagato, E.; Sutrisno, A.; Fortier-Mcgill, B.; Akhter, M.; Schmidt, S.; Heumann, H.; Simpson, A. J. Magn. Reson. Chem. 2015, 53 (9), 774–779.

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Farooq, H.; Courtier-Murias, D.; Soong, R.; Bermel, W.; Kingery, W.; Simpson, A. Curr. Org. Chem. 2013, 17 (24), 3013–3031.

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Courtier-Murias, D.; Farooq, H.; Masoom, H.; Botana, A.; Soong, R.; Longstaffe, J. G.; Simpson, M. J.; Maas, W. E.; Fey, M.; Andrew, B.; Struppe, J.; Hutchins, H.; Krishnamurthy, S.; Kumar, R.; Monette, M.; Stronks, H. J.; Hume, A.; Simpson, A. J. J. Magn. Reson. 2012, 217, 61–76.

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Lam, L.; Soong, R.; Sutrisno, A.; De Visser, R.; Simpson, M. J.; Wheeler, H. L.; Campbell, M.; Maas, W. E.; Fey, M.; Gorissen, A.; Hutchins, H.; Andrew, B.; Struppe, J.; Krishnamurthy, S.; Kumar, R.; Monette, M.; Stronks, H. J.; Hume, A.; Simpson, A. J. J. Agric. Food Chem. 2014, 62 (1), 107–115.

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Majumdar, R. D.; Akhter, M.; Fortier-McGill, B.; Soong, R.; Liaghati-Mobarhan, Y.; Simpson, A. J.; Spraul, M.; Schmidt, S.; Heumann, H. eMagRes 2017, 6, 133–148.

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Bundy, J. G.; Davey, M. P.; Viant, M. R. Metabolomics 2009, 5 (1), 3–21.

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Viant, M. R.; Bundy, J. G.; Pincetich, C. A.; de Ropp, J. S.; Tjeerdema, R. S. Metabolomics 2005, 1 (2), 149–158.

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Wagner, N. D.; Lankadurai, B. P.; Simpson, M. J.; Simpson, A. J.; Frost, P. C. Physiol. Biochem. Zool. 2015, 88 (1), 43–52.

AUTHOR INFORMATION

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Samuelsson, L. M.; Förlin, L.; Karlsson, G.; Adolfsson-Erici, M.; Larsson, D. G. J. Aquat. Toxicol. 2006, 78 (4), 341–349.

Corresponding Author

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Wagner, N. D.; Simpson, M. J.; Simpson, A. J. Environ. Toxicol. Chem. 2016, 9999, 1–9.

(19)

Kariuki, M. N.; Nagato, E. G.; Lankadurai, B. P.; Simpson, A. J.; Simpson, M. J. Metabolites 2017, 7 (2), 1–13.

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

(20)

Nagato, E.; Simpson, M. J.; Simpson, A. J. Aquat. Toxicol. 2016, 170, 175–186.

(21)

Kovacevic, V.; Simpson, A. J.; Simpson, M. J. Comp. Biochem. Physiol. - Part D Genomics Proteomics 2016, 19, 199–210.

ACKNOWLEDGMENT

(22)

Martin-Creuzburg, D.; Wacker, A.; von Elert, E. Oecologia 2005, 144 (3), 362–372.

(23)

Sotero-Santos, R. B.; Rocha, O.; Povinelli, J. Water Res. 2005, 39 (16), 3909–3917.

(24)

Hollywood, K.; Brison, D. R.; Goodacre, R. Proteomics. 2006, pp 4716–4723.

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Bunescu, A.; Garric, J.; Vollat, B.; Canet-Soulas, E.; GraveronDemilly, D.; Fauvelle, F. Mol. Biosyst. 2010, 6, 121–125.

(26)

Bon, D.; Gilard, V.; Massou, S.; Pérès, G.; Malet-Martino, M.; Martino, R.; Desmoulin, F. Biol. Fertil. Soils 2006, 43 (2), 191–198.

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Liaghati Mobarhan, Yalda , Fortier-McGill, Blythe, Soong, Ronald; E. Maas, W.; Fey, M.; Monette, M.; J. Stronks, Henrry, Schmidt, Sebastian, Heumann, Hermann, Norwoode, W.; J. Simpson, A. Chem. Sci. 2016, 7, 4856–4866.

CONCLUSION The low volume flow NMR system introduced here permits a way to monitor living organisms with higher temporal resolution than more conventional metabolomics approaches focusing on homogenates or extracts. The new flow system, not only allows in vivo NMR studies when only a small volume of natural sample or toxin is available, but its design also permits feeding the living organisms, along with improved water suppression, and more stable locking and shimming. By utilizing 13C enriched organisms a range of metabolites can be monitored in vivo and changes with stress can be easily observed, demonstrating in vivo NMR as a powerful tool to monitor environmental stress. The ability to track metabolites over time as trajectories should help discern interconnected metabolic pathways. Future improvements could include taking advantage of 15N and 13C enriched organisms as well as 31P NMR spectroscopy, to provide additional metabolic discrimination and assignment. Moreover, assuming adequate signal-to-noise, it may be possible to use ultra-fast methods, such as single scan 2D NMR95 to provide changes for concentrated components on the millisecond time scale opening up real-time studies of biomolecular dynamic processes and metabolites fluxes.

*[email protected]

Author Contributions

Andre Simpson would like to thank the Strategic (STPGP 494273-16) and Discovery Programs (RGPIN-201405423), the Canada Foundation for Innovation (CFI), the Ontario Ministry of Research and Innovation (MRI), the Krembil Foundation for providing funding and the Government of Ontario for an Early Researcher Award.

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Analytical Chemistry For TOC only

Intensity

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Time (hrs.)

12 ACS Paragon Plus Environment

Development and Application of a Low-Volume Flow System for

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