Comprehensive Profiling and Quantitation of Amine Group Containing

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Comprehensive Profiling and Quantitation of Amine Group Containing Metabolites Berin A. Boughton,*,† Damien L. Callahan,† Claudio Silva,‡ Jairus Bowne,†,‡ Amsha Nahid,‡ Thusita Rupasinghe,‡ Dedreja L. Tull,‡ Malcolm J. McConville,‡,§ Antony Bacic,†,‡,|| and Ute Roessner†,^ †

Metabolomics Australia, School of Botany, The University of Melbourne, Parkville, Victoria, Australia, 3010 Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia, 3010 § Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Victoria, Australia, 3010 ARC Centre of Excellence in Plant Cell Walls, School of Botany, The University of Melbourne, Parkville, Victoria, Australia, 3010 ^ ACPFG, School of Botany, The University of Melbourne, Parkville, Victoria, Australia, 3010

)

‡

bS Supporting Information ABSTRACT: Primary and secondary amines, including amino acids, biogenic amines, hormones, neurotransmitters, and plant siderophores, are readily derivatized with 6-aminoquinolyl-Nhydroxysuccinimidyl carbamate using easily performed experimental methodology. Complex mixtures of these amine derivatives can be fractionated and quantified using liquid chromatography
electrospray ionization-mass spectrometry (LC
ESI-MS). Upon collision induced dissociation (CID) in a quadrupole collision cell, all derivatized compounds lose the aminoquinoline tag. With the use of untargeted fragmentation scan functions, such as precursor ion scanning, the loss of the aminoquinoline tag (Amq) can be monitored to identify derivatized species; and the use of targeted fragmentation scans, such as multiple reaction monitoring, can be exploited to quantitate amine-containing molecules. Further, with the use of accurate mass, charge state, and retention time, identification of unknown amines is facilitated. The stability of derivatized amines was found to be variable with oxidatively labile derivatives rapidly degrading. With the inclusion of antioxidant and reducing agents, tris(2-carboxyethyl)-phosphine (TCEP) and ascorbic acid, into both extraction solvents and reaction buffers, degradation was significantly decreased, allowing reproducible identification and quantification of amine compounds in large sample sets.

T

he metabolome encompasses a rich diversity of small molecules which provide the source of energy, are the building blocks of life, and can be key cellular regulatory (e.g., hormones) and signaling molecules. Metabolomics refers to the chemical analysis of multiple metabolite classes in biological systems.1 Untargeted profiling methods can detect thousands of metabolites; however, in most cases only a small proportion of metabolite signals are unambiguously identified. Also, most untargeted methods provide relative metabolite levels rather than absolute concentrations. An alternative strategy that is increasingly employed in metabolomics is to target either particular molecules or classes of molecules using highly specific methodology that enhances chromatographic resolution and is typically much more sensitive (for a review, see Beckles and Roessner2). Targeted approaches allow absolute quantification of a subset of the metabolome, resulting in a more clearly defined and robust data set for subsequent biological interpretation. At present, the differential levels of extraction and stability of metabolites once extracted have been poorly studied. Various groups have identified these problems and tested different classes of metabolites or biological samples under various storage and r 2011 American Chemical Society

handling conditions.3
6 However, reactive metabolites can quickly oxidize or break down once removed from a biological matrix. Long-term storage of samples can lead to loss of sample integrity by degradation of metabolites leading to poor or variable analytical results. There is therefore a need to further develop robust quantitative methods for known compounds and classes found within metabolomics experiments that account for the inherent instability of many metabolites. Biological amines are a large and important subset of organic molecules including amino acids, other biogenic amines, and important signaling molecules, such as the catecholamine and tryptamine neurotransmitters. The chemical reactivity of nucleophilic primary and secondary biological amines can be exploited by derivatization with a labeling reagent. The derivatization of amino acids and other biogenic amines with a fluorescent tag has become a common procedure used for quantification.7
10 Typically, these methods rely upon liquid chromatography Received: July 14, 2011 Accepted: August 31, 2011 Published: August 31, 2011 7523

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Figure 1. Derivatization reaction of Aqc (1) with a nucleophilic amine (2) followed by ESI ionization and collision induced dissociation (CID) Amq (3) fragmentation products.

(HPLC or UPLC) or capilliary electrophoresis to separate derivatized molecules and either fluorescence or UV absorption for detection and quantification. However, identification using these methods relies solely on retention time and coeluting compounds are not revealed. Modification of nucleophilic amines has been successfully achieved using a variety of reagents. Both primary and secondary amines are readily modified by sulfonyl chlorides dansyl and dapoxyl 4-(chloro or fluoro)-7-nitrobenzofurazan’s (NBD-Cl, NBD-F) and other reagents such as succinimidyl-esters including 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (Aqc).11
15 The use of Aqc as a labeling reagent results in the attachment of a fluorescent- and UV-active amino-quinoline moiety to amines via a carbamide linkage (Figure 1). The Aqc tag confers hydrophobicity to the target metabolite, allowing separation of hydrophilic amines under hydrophobic reversed phase conditions, and is an excellent identifier for amine containing molecules when using MS. Aqc modified amines fragment upon collision induced dissociation (CID) into discrete product ions. Methods developed for a LC
ESI-ion trap-MS observed fragment ions corresponding to each amino acid and an amino-quinoline moiety (Amq) with a m/z of 171.05.16,17 Further, as the proton affinity of the amino acid increased, the abundance of the Amq fragment decreased and vice versa. With the use of nontargeted fragmentation methods on an LC
quadrupole-quadrupole time-of-flight-mass spectrometer (QqTOF-MS) or triple quadrupole-mass spectrometer (QQQMS), such as data dependent tandem mass spectrometry (MS/ MS) or precursor ion scanning, both known and unknown amine derivatives can be profiled. This provides a new method to separate and putatively identify unknown and novel amines by monitoring for the presence of the characteristic Aqc tag. From these results, a detailed targeted list of amines can be derived and used to generate a targeted multiple reaction monitoring (MRM) list for accurate quantification. The mass selective capabilities of a triple-quadrupole mass spectrometer (LC
QqQ-MS) enables the resolution of coeluting peaks and the concentrations of a variety of amines to be determined by comparison against a standard calibration curve. Here we describe an MS-based technique using the Aqc tag for profiling amine group containing metabolites and providing suitable methods of stabilizing oxidatively labile compounds during extraction, storage, and derivatization prior to analysis.

’ EXPERIMENTAL SECTION Reagents. All chemicals and solvents were purchased from Sigma Aldrich (Australia) and were either of analytical or mass

spectrometric grades. The Aqc reagent was synthesized according to Cohen et al.7 Deionized water (18.2 MΩ) was produced using a Synergy UV Millipore System (Millipore) and was used throughout. Standards. Concentrated individual solutions of each amine were prepared by dissolving each amine in either 0.1% formic acid
water or 50% acetonitrile 0.1% formic acid
water for more hydrophobic amines such as phenethylamine. The concentrated amine solutions were then combined to form standard stock solutions containing between 21 and 40 amines and diluted to a final concentration of 2.5 mM using volumetric glassware to give a final solvent mix of 10% acetonitrile and 0.1% formic acid
water. Calibration standards were generated by diluting the stock solutions to 100, 50, 25, 10, 5, 1, 0.5, 0.1, 0.05, and 0.01 μM in water using volumetric glassware. Buffers. For stability studies, borate buffers containing various antioxidant and reducing reagents were prepared by dissolving boric acid and the respective additive in water. The pH was then adjusted to 8.8 with the addition of a concentrated sodium hydroxide solution. The final concentration of borate was 200 mM, with all other additives present at a concentration of 1
10 mM, a minimum 10-fold excess relative to the highest concentration of standards used for quantification purposes (100 μM; see the Supporting Information Table S2). Amine Derivatization Procedure. Derivatization was performed by following the procedure of Cohen et al.18,19 (see the Supporting Information.) Instrumentation. An Agilent 1200 LC-system coupled to an Agilent 6420 ESI-QqQ-MS (Santa Clara, CA) was used for quantification experiments. An Agilent 1200 LC system coupled to an Agilent 6520 ESI-QqTOF-MS was used for profiling and accurate mass experiments. For UPLC experiments, an Agilent 1290 LC-system coupled to an Agilent 6460 ESI-QqQ-MS was used. (See the Supporting Information.)

’ RESULTS AND DISCUSSION Derivatization of Amine Containing Metabolites. The use of the Aqc reagent for the quantification of amino acids has been shown to be accurate and robust with many groups targeting amine specific metabolites.7
10,20
24 The methodology described here expands on its utility by allowing accurate analysis and identification of both unstable and novel amine containing metabolites. Both primary and secondary amines are readily modified using Aqc as a derivatization reagent. For monoaminecontaining metabolites, provided the amine is sufficiently nucleophilic, derivatization with Aqc produces a derivative containing a 1:1 ratio of Aqc/amine which upon ionization provides the 7524

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Analytical Chemistry [M + Aqc + H]+ ion (Figure 2). In contrast to previous ion-trap experiments,16,17 the fragmentation of the [M + Aqc + H]+ ion using ESI quadrupole collision cell (ESI-QqQ or ESI-QqTOF) with optimized fragmentation voltages resulted in the Amq moiety (171.05+) as the highest abundant fragment ion in all cases. Further, polyamino containing compounds are multiply derivatized, with the predominant ion corresponding to the maximum number of additions and charges possible with a molecular ion of [M + Aqcn + Hn](n)+, where n = total number of 1 and 2 amines. A smaller proportion of ions corresponding to [M + Aqcn + Hn
1](n
1)+ were also seen, typically with a 10
1000-fold reduced ion intensity. For example, lysine and cadaverine, both diamines, displayed the addition of two Aqc units with a doubly derivatized and double charged molecular ion ([M + (2  Aqc) + (2  H)]2+) present at m/z 244 and 222, respectively. A singularly charged ion ([M + (2  Aqc) + H]+) is also observed at m/z 487 and 443. For the majority of other polyamines containing two or more reactive amine moieties, a similar pattern of multiple derivatizations and charges are observed. The use of 13C/15N heavy isotope labeled standards can easily be incorporated into the method with the calculated m/z of the parent ion shifted by the number of heavy isotopes present. For example, fully 13C and 15N labeled valine led to a singly charged species with a mass shift from the observed m/z of 288 to 294 [M + Aqc + H]+. Care must be taken to ensure that the total concentration of labeled and unlabeled amino acid present does not exceed the upper limits of accurate quantification Thiol or sulfur-containing amino acids and biogenic amines such as cysteine, methionine, and glutathione are also readily derivatized. The inherent reactive nature of the thiol moiety with molecular oxygen leads to oxidative production of a disulfide bond between two thiols.25 Although other oxidation products such as the sulfinic and sulfonic acids are possible, with the pH at e8.8 the disulfide is the dominant product. Oxidation of cysteine to cystine and subsequent derivatization led to observation of a dominant ion at m/z 291 corresponding to the doubly derivatized, double charged derivative [M + (2  Aqc) + (2  H)]2+. Other thiol-containing amines display similar oxidation profiles to cysteine. The sulfur-containing amines methionine and Sadenosyl homocysteine are oxidized to form sulfones and sulfoxides with mass shifts observed for the parent ion m/z by 16 and 32 amu, respectively.26 Neurotransmitters and their metabolites from the catecholamine and tryptamine classes are attractive targets to develop new and accurate techniques for quantification as they are currently separated by HPLC and quantified using either an electrochemical assay or UV spectroscopic techniques.27
33 Derivatization and quantification of a series of catecholamines (Figure 2), epinephrine, norepinephrine, octopamine, normetanephrine, dopamine, and 5-methoxytyramine, were performed. The corresponding tryptamines such as serotonin, its precursors, tryptophan and 5-hydroxytryptophan, and simple tryptamine derivatives are all readily modified (Figure 2). The secondary amine of the indole moiety of N-acetyl-5-hydroxytryptamine and serotonin, containing a 5-hydroxy substituent, were also derivatized. Under these conditions, we did not observe any derivatization of the indole N
H in tryptamines with either an unsubstituted indole or one containing a 5-methoxy substituent (see the Supporting Information for further discussion). Quantification of Amines. Quantification was achieved using an external calibration curve method with an internal standard,

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Figure 2. Biogenic amines and polyamines displaying the number of derivatizations with an overall charge state equal to the number of additions. R, R1 = any alkyl group. Catecholamines (1
6) (1) dopamine, R2 = OH, R3, R4 = H; (2) 5-methoxytyramine, R2 = OMe, R3, R4 = H; (3) norepinephrine, R2, R4 = OH, R3 = H; (4) normetanephrine, R2 = OMe, R3 = H, R4 = OH; (5) epinephrine, R2, R4 = OH, R3 = Me; (6) octopamine, R2, R3 = H, R4 = OH. Tryptamine derivatives (7
11) using Aqc; (7) tryptamine, R5 = H; (8) tryptophan, R5 = CO2H; (9) serotonin; (10) N-acetyl-5-hydroxytryptamine; (11) melatonin.

2-aminobutyric acid (25 μM), for instrument/analyst error correction. Relative responses were calculated by dividing the area of each analyte by the area of the internal standard. Calibration curves within the concentration range of 100 nM to 100 μM were prepared from a combined solution of standards (see Table S6 in the Supporting Information for the full calibration parameters). Further, complex mixtures containing amino acid and biogenic amine standards can be readily generated with each standard identified by its respective MRM transition, thereby allowing the dwell time for each analyte to be maximized by use of dynamic MRM. A second internal standard such as nor-leucine or an isotopically labeled amino acid can be employed prior to extraction to improve accuracy or account for extraction efficiency. Under the standard conditions, poor derivatization is observed if the relative molar concentration of reactive amines (including ammonia) is too high (g10 mM relative concentration). Furthermore, unstable standards were observed to degrade rapidly postderivatization and that cross-reactivity with breakdown products within 7525

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the derivatized solution increased the rates of degradation. The photosensitivity and reactivity of aromatic amino acids, many of the neurotransmitters, and biological sulfur-containing molecules under an oxygen atmosphere are a well accepted phenomenon.25,34,35 Many of the aromatic and phenolic amino acids and biogenic amines are observed to degrade more rapidly under basic conditions. Accurate measurement of these reactive species requires the inclusion of stabilizing agents in the form of antioxidants and reductants. Selection of Stabilizing Agents. A number of chelating agents, antioxidants, and reductants were tested for their capacity to improve the stability of labile metabolites. Care was taken to choose reagents that would not be retained on the reversed phase LC column. Tested reagents included sodium thiosulphate (Na2S2O3), ethylenediamine-tetraacetic acid (EDTA), ascorbic acid, propyl-gallate, and tris(2-carboxyethyl)-phosphine (TCEP). The contemporary reducing agent, TCEP, was chosen over traditional thiols as the reduction potential is reported to be greater and TCEP is commonly used to reduce disulfides.36
39 The presence of transition metals from contaminants derived from glassware or biological matrixes accelerates the oxidation process. EDTA was included to chelate any metal ions that may be present, particularly Cu2+, Mn2+, and Fe2+/3+ in solution derived from biological matrixes.25 To determine the effect of each stabilizing solution and define an optimal buffer solution, a single point experiment was conducted to measure the response for each metabolite at first injection (T0). The response was corrected using an internal standard then expressed as a relative response to the highest response recorded for that metabolite, eq 1. To derive an overall comparison, the relative response for each of the 40 amines was averaged across the whole data set for each buffer containing the different stabilizing agents (eq 2, Table 1). Results indicated that solutions of thiosulphate and ascorbate provided the overall highest relative response with thiosulphate, ascorbate, and ascorbate/TCEP providing a higher response factor than the standard borate buffer. The presence of EDTA significantly reduced the observed signal relative to standard borate buffer and other solutions. relative response for amineðamineA, buffer ¼

observed respðamineA, buffer AÞ max respðamineA, anybufferÞ

∑rel resp all aminesðbuffer AÞ number of amines

buffer additive

mean relative response (%)

thiosulphate

97

ascorbate

94

ascorbate + TCEP

66

borate

63

thiosulphate + TCEP thiosulphate + TCEP + propyl gallate

57 52

thiosuphate + EDTA

46

ascorbate + EDTA

45

a

The mean relative response for each buffer is the average response measured across 40 amines. Individual metabolite response ratios were generated using eq 1. Mean relative response was generated using eq 2.

AÞ

ð1Þ

mean relative responseðbuffer AÞ ¼

Table 1. Mean Relative Response Factors of Various Buffers with Different Additives Measured Using a Single Point Experimenta

ð2Þ

The top five stabilizing additives providing the best responses were then chosen to test the longer term stability of each derivatized amine over 44 h (Figure 3A), where repeat aliquots were taken from the same vial. In each case, the measured response of the initial time point was compared to the observed response at 22 (T22) and 44 (T44) h. The relative signal (expressed as a percentage of T0) was determined (see Table S4 in the Supporting Information for the full results). Ascorbate and thiosulphate additives or the standard borate buffer did not provide stable solutions over time. This has significant implications for large or longitudinal experiments where samples are stored for long periods of time. In contrast, a combination of ascorbate and TCEP provided maximum stability of the five

Figure 3. Single point measurements taken from the same vial at T = 0, 22, and 44 h, in five borate buffers with the following stabilizing additives, ascorbate (blue, diamond), ascorbate and TCEP (red, square), borate only (green, triangle), thiosulphate (purple, cross), thiosulphate and TCEP (light blue, crossed-cross), displaying a drop in concentration of (A) average relative concentration of all amines, (B) methionine, (C) Nacetyl-5-hydroxytryptamine, and (D) dopamine.

stabilizing additives tested (Figure 3A). Less than a 3% difference between T0 and T44 measurements, averaged across all amines, was observed. Thiosulphate additive, which provided the highest signal, was not significantly better than the standard borate buffer over time, dropping over 7% of the averaged signal after 22 h compared with over 10% for the standard borate buffer within the same time period. Many of the amines are stable and use of the overall average masks the subset of reactive amines that degrade over time. Analysis of each amine present and its degradation profile identified a collection of typically reactive species. These included sulfur containing amines, neurotransmitters, and their 7526

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Table 2. Summary Table of Linear Fit Dataa sample

slope (m)

intercept (b)

R2

Standards initial (T0)

1.00

0.01

week 1

1.01

0.00

0.95 0.95

week 2

1.07


0.04

0.96

week 4

1.04

0.00

0.95

week 12

0.97

0.02

0.95

week 16

0.88

0.04

0.85

FT 1 (week 1) FT 2 (week 1)

1.06 1.04


0.05 0.01

0.95 0.98

initial (T0)

0.95


0.06

0.99

week 1

0.91


0.27

0.97

week 2

1.13

0.25

0.99

week 4

0.82


0.55

0.91

week 16

1.19

0.62

0.89

Freeze Thaw

Plasma

a

Comparing the mean and standard deviation of each standard repeat (T0
week 16) against the mean and standard deviation for all standards; comparison of freeze thaw cycles to week 1 standard; comparison of the mean and standard deviation of the plasma samples (T0
week 16) with the mean and standard deviation of all plasma samples. Linear fit of the data in the form of y = mx + b.

Figure 4. (A) Boxplot of relative concentration for each repeat standard demonstrating sample reproducibility over a 16 week time course and comparable freeze thaw cycles. (B) Boxplot of relative concentration for stored human plasma samples doped with TCEP and ascorbate over a 16 week time course demonstrating comparable variability across samples 0
4. (C) Boxplot of relative concentration of each metabolite generated from standards measured over a 16 week period. (D) Boxplot of relative concentration of each metabolite generated from plasma samples measured over a 16 week period. Boxplots describe the upper quartile (Q3), median, and the lower quartile (Q1) values. Upper and lower limit whiskers describe values in the data set that are within 1.5 times the interquartile range (IQR), with any values outside of these defined as outliers, represented by open circles.

metabolites. In all cases, reactive biogenic amines and amino acids were observed to rapidly decompose under the standard borate buffer conditions. Oxidation of cysteine to cystine over time leads to lowered concentrations of cysteine in solution. Inclusion of TCEP into the standards dilution buffer and also the derivatization reaction buffer reduced formation of cystine but did not completely halt oxidation. Methionine was observed to decrease rapidly under standard conditions when only ascorbate was contained in the sample buffer (Figure 3B). Inclusion of a reductant (thiosulphate

or TCEP) significantly reduced oxidation, with the ascorbate and TCEP solution providing the best stability. N-Acetyl-5-hydroxytryptamine was observed to rapidly decrease by 50% over 2 days. Inclusion of both TCEP and ascorbate limited the degradation of this amine (Figure 3C). The catecholamines are notoriously labile and degrade rapidly under basic and oxidative conditions through several different mechanisms to semiquinone and quinone byproducts.40,41 Autooxidation of the catecholamines via a radical pathway leads to rapid degradation which is also accelerated in the presence of transition metals (such as Fe2+/3+ or Cu2+). Previous studies have noted the limited antioxidant effects of both ascorbate and metabisulfite protection against degradation of dopamine.35 In our experiments, inclusion of TCEP with ascorbate in the borate buffer significantly reduced the degradation profile demonstrating a drop of 5% over 44 h, providing a greater degree of protection (Figure 3D). Similar results were recorded for normetanephrine, epinephrine, and norepinephrine. With an increase in the concentration of TCEP and ascorbic acid from 1 to 10 mM, longer periods of protection could be achieved (data not shown). Further, preparation of fresh buffers with stabilizers was required to avoid degradation of ascorbate and following from that the loss of TCEP. Long-Term Stability of Standards and Extracted Metabolites. The stability of standards containing the amino acids and neurotransmitters was tested by derivatizing stored aliquots of the original set of standards over a period of time in a standard buffer solution containing a known amount of internal standard. Aliquots were tested at times 0, 1, 2, 4, 12, and 16 week intervals alongside a standard (week 1 standard) that was monitored through three repeat freeze thaw cycles. Relative internal standard response ratios for each amine were calculated to derive a relative concentration. Because of the extended period of time 7527

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Analytical Chemistry over which the experiment was conducted, differing states of instrumental cleanliness, and signal variability, the data required preprocessing prior to analysis. In general, transformations are applied to correct for heteroscedasticity found in data when the standard deviation of signal responses are dependent on the mean of the signals.42 Power transformations were applied to correct the heteroscedasticity found in the data for both standards and plasma samples. Results indicated excellent reproducibility between measurements and excellent stability for the majority of metabolites present (Figure 4A,C and Figure S6 in the Supporting Information). The average obtained from the transformed samples (for different weeks) was plotted against each of the individual week samples. Ideally, if there is no difference between the average and the respective week sample, then a straight line relationship exists between them with a slope of 1 and an intercept of zero. However, such an ideal occurrence is highly unlikely. Table 2 shows the slopes, intercepts, and R2 fitted to different weeks for standards, freeze thaw cycles, and plasma samples. Results indicate that for standards, there is very good reproducibility up to 12 weeks for all of the metabolites. At week 16, the slope deviates from unity, due to a drop in response from epinephrine. Cysteine, methionine, and dopamine, which had previously been shown to rapidly degrade, demonstrated good stability under these storage conditions for a period of 16 weeks (Figure 4C). The week 1 standard that had been cycled through two extra freeze thaw cycles was also found to be comparable to the original standard. A sample of human plasma that had been treated with ascorbate and TCEP and then stored under the same conditions also displayed good stability across the initial 3 weeks but displayed greater variability at later time points (Figure 4B,D). Amine Profiling in Biological Matrixes. Recent work by Cooper et al.43 has demonstrated the power and effectiveness of targeted profiling of small molecules, in this case amino acids, in very large sample sizes (∼5000 samples). Their method detected 13 amino acids using a fluorescent derivatization reagent NBD-F. To demonstrate the effectiveness of the Aqc method for detecting and separating biogenic amines, a selection of liquid samples, including white wine, urine, and serum, were extracted and then derivatized under standard conditions. Examples of solid (multicellular) samples including plant tissues (leaf and root) and mammalian muscle tissue were first homogenized and extracted using a biphasic chloroform/methanol/water extraction procedure before derivatization. After removal of protein by either filtration or organic denaturation and precipitation, aliquots of the aqueous fractions were derivatized. The samples were then profiled using accurate mass RP-LC
ESI-QqTOF. Auto MS/MS spectra (see the Supporting Information) were collected, and the fragment ion corresponding to the Amq fragment (171.0550+) was extracted from the data. Analysis of the acquired spectrum demonstrated up to 42 compounds were present for mouse muscle that contained the Amq fragment, with many corresponding to known amino acids and biogenic amines. In other matrixes, more than 24 compounds were identified, human urine (24) and serum (28), Leishmania mexicana (28), root (23), leaf (33), and white wine (31). The benefits of using MS-based detection include an increase in sensitivity and that coeluting compounds may be extracted and separated within the acquired data. This demonstrates a significant advantage over other spectroscopy based detectors which rely upon absorbance or fluorescence of the Aqc tag where coeluting metabolites cannot be identified. Three separate and

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Figure 5. Mass spectrum of derivatized and oxidized trypanothione from L. mexicana displaying three separate molecular ions, with the inset structure showing derivatized amines (see Figure S6 in the Supporting Information for a magnified view of each ion).

important features can be gleaned from the acquired data: the charge state corresponding to the number of reactive amine groups, the accurate mass from the QqTOF which provides the molecular formula, and retention time giving a relative polarity. With the combination of this information, identification of unknown amines becomes much easier. As an example, we sought to identify trypanothione, a conjugate of two glutathione molecules linked via a spermidine and a major thiol in Leishmania and Trypanosoma protozoan parasites.44,45 Polar metabolite extracts of L. mexicana were derivatized then profiled using LC
ESI-MS/MS under standard conditions. We then searched for the predicted mass of derivatized oxidized trypanothione. The oxidized form of trypanothione possesses three reactive amines and is readily observed as the [M + (3  Aqc) + (3  H)]3+ with m/z 411.4862, (calculated [M]3+ m/z 411.4849, 3.24 ppm difference) (Figure 5). Two other molecular ions [M + (3  Aqc) + (2  H)]2+ m/z 616.7247 (calculated m/z 616.7237,
1.68 ppm difference) and [M + (2  Aqc) + (2  H)]2+ m/z 531.6999 (calculated m/z 531.6997,
2.34 ppm difference) were also observed in the peak. Although an authentic standard of trypanothione was not available, the presence of these two complementary ions and mass accuracy within 3.24 ppm of the calculated mass provided a high confidence identification of trypanothione. Quantification of Amines in Biological Matrixes. Initial quantification data demonstrated significant variation in the determined concentrations of metabolites when protein was present in the sample (data not shown). Thus, it was also necessary to remove protein from samples prior to derivatization by incorporating either a protein precipitation step or filtering the extract through a 3.5 kDa molecular weight cut off filter. Recoveries of urine spiked with a standard mixture of 31 amino acids and biogenic amines were between 70 and 146% with an average of 101% and % RSD between 1.54 and 5.43 and an average of 3.1% (See Table S6 in the Supporting Information). Notable outliers skewing the results are early eluting arginine and glutamine which demonstrated magnified responses. Further, the magnified response is not limited to early eluting compounds. Lysine which is found in the middle of the chromatogram demonstrates an enhanced recovery of 123%. Recoveries from human muscle and plasma samples spiked with only amino acids either during homogenization of the tissue or prior to extraction provided overall lower recoveries and higher variability with % recoveries ranging between 70 and 114%. For muscle, an average of 80.1% recovery with % RSD between 1.4 and 16 and an average of 8.0%; for plasma, 88.6% recovery with % RSD between 3.8 and 25.6% and average of 13.6%. In plasma, a similar pattern of enhanced recovery was observed for glutamine and lysine. 7528

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’ CONCLUDING REMARKS With the use of the unique Amq fragment ion (171.0550+ amu) of derivatized amines, the method described can be used for amine-targeted metabolite profiling of biological extracts. Untargeted measurement on a high-resolution QqTOF-MS using MS/MS acquisition allows accurate mass identification of any amine derivative and determination of the molecular formula. The charge state of any amine derivative is observed to be proportional to the number of derivatized amine functional groups with retention time providing further information on the chemical properties of unknown metabolites. The combination of information allows unknown amines to be putatively identified and then targeted for quantitative MRM based methods. This method can be readily adapted to many analytical platforms such as ion traps, QqTOF, QqQ, UPLC, normal LC, and nano LC if necessary offering a powerful technique to identify and quantify primary and secondary amine-containing compounds within biological matrixes. In general, when the method is applied to a different biological sample, there is a need to perform extraction and stabilization optimization to ensure correct quantification. The loss of oxidatively labile metabolites can be addressed by addition of stabilizing additives (EDTA, TCEP, and ascorbate) into extraction solvents and derivatization buffers, providing a greater window of time in which to measure these important metabolites. ’ ASSOCIATED CONTENT

bS

Supporting Information. Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: +61-3-8344 4261. E-mail: [email protected].

’ ACKNOWLEDGMENT B.A.B. and D.L.C. contributed equally to this work. The authors are grateful to the Victorian Node of Metabolomics Australia, which is funded through Bioplatforms Australia Pty Ltd, a National Collaborative Research Infrastructure Strategy (NCRIS), 5.1 biomolecular platforms and informatics investment, and coinvestment from the Victorian State government and The University of Melbourne. U.R. thanks the Australian Centre for Plant Functional Genomics (ACPFG), which is funded by grants from the Australian Research Council (ARC) and the Grains Research and Development Corporation (GRDC), the South Australian Government, and the University of Adelaide, the University of Queensland, and The University of Melbourne. A.B. acknowledges the support of the ARC Centre of Excellence in Plant Cell Walls. M.J.M. is a NHMRC Principal Research Fellow. We thank Mr. David de Souza and Dr. Eleanor Saunders for providing the Leishmania mexicana cell samples. ’ REFERENCES (1) Roessner, U.; Bowne, J. BioTechniques 2009, 46, 363. (2) Roessner, U.; Beckles, D. M. In Plant Metabolic Networks; Schwender, J., Ed.; Springer Science + Business Media, LLC: Dordrecht, The Netherlands, 2009; p 71.

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(3) Gika, H. G.; Theodoridis, G. A.; Wilson, I. D. J. Chromatogr., A 2008, 1189, 314. (4) Zivkovic, A. M.; Wiest, M. M.; Nguyen, U. T.; Davis, R.; Watkins, S. M.; German, J. B. Metabolomics 2009, 5, 507. (5) Saude, E. J.; Sykes, B. D. Metabolomics 2007, 3, 19. (6) Lauridsen, M.; Hansen, S. H.; Jaroszewski, J. W.; Cornett, C. Anal. Chem. 2007, 79, 1181. (7) Liu, H.; Sanuda-Pena, M. C.; Harvey-White, J. D.; Kalra, S.; Cohen, S. A. J. Chromatogr., A 1998, 828, 383. (8) Mayer, H. K.; Fiechter, G.; Fischer, E. J. Chromatogr., A 2010, 1217, 3251. (9) Wang, X.; Chen, X.; Chen, L.; Wang, B.; Peng, C.; He, C.; Tang, M.; Zhang, F.; Hu, J.; Li, R.; Zhao, X.; Wei, Y. Biomed. Chromatogr. 2008, 22, 1265. (10) Weiss, T. S. J. Chromatogr. Libr. 2005, 70, 502. (11) Cai, H.-L.; Zhu, R.-H.; Li, H.-D. Anal. Biochem. 2010, 396, 103. (12) Guo, K.; Li, L. Anal. Chem. 2009, 81, 3919. (13) Cha, S.; Zhang, H.; Ilarslan, H. I.; Wurtele, E. S.; Brachova, L.; Nikolau, B. J.; Yeung, E. S. Plant J. 2008, 55, 348. (14) Li, Z.-J.; Xue, C.-H.; Feng, J.-F.; Zhou, R.; Zhou, X.-F.; Wu, Y.-N.; Zhao, Y.-F. Zhongguo Haiyang Daxue Xuebao, Ziran Kexueban 2007, 37, 427. (15) Tang, Z.; Guengerich, F. P. Anal. Chem. 2010, 82, 7706. (16) Callahan, D. L.; Kolev, S. D.; O’Hair, R. A. J.; Salt, D. E.; Baker, A. J. M. New Phytol. 2007, 176, 836. (17) Callahan, D. L.; Roessner, U.; Dumontet, V.; Perrier, N.; Wedd, A. G.; O’Hair, R. A. J.; Baker, A. J. M.; Kolev, S. D. Phytochemistry 2008, 69, 12. (18) Cohen, S. A. J. Chromatogr. Libr. 2005, 70, 242. (19) Cohen, S. A.; Michaud, D. P. Anal. Biochem. 1993, 211, 279. (20) Hou, S.; He, H.; Zhang, W.; Xie, H.; Zhang, X. Talanta 2009, 80, 440. (21) Jaworska, M.; Szulinska, Z.; Wilk, M.; Anuszewska, E. Talanta 2010, 83, 513. (22) Martinez-Giron, A. B.; Dominguez-Vega, E.; Garcia-Ruiz, C.; Crego, A. L.; Marina, M. L. J. Chromatogr., B 2008, 875, 254. (23) Spacil, Z.; Eriksson, J.; Jonasson, S.; Rasmussen, U.; Ilag, L. L.; Bergman, B. Analyst 2010, 135, 127. (24) Yang, Y.; Xie, H.; Wu, Z.; Wu, F. Shipin Kexue 2009, 30, 214. (25) Bagiyan, G. A.; Koroleva, I. K.; Soroka, N. V.; Ufimtsev, A. V. Russ. Chem. Bull. 2003, 52, 1135. (26) Wise, C.; Fullerton, F. J. Liq. Chromatogr. 1995, 18, 2005. (27) Hiroshima, O.; Ikenoya, S.; Naitoh, T.; Kusube, K.; Ohmae, M.; Kawabe, K.; Ishikawa, S.; Hoshida, H.; Kurahashi, T. Chem. Pharm. Bull. 1983, 31, 3571. (28) Jeon, H. K.; Nohta, H.; Ohkura, Y. Anal. Biochem. 1992, 200, 332. (29) Jeon, H. K.; Nohta, H.; Ohkura, Y. Anal. Sci. 1990, 6, 677. (30) Nohta, H.; Mitsui, A.; Ohkura, Y. Anal. Chim. Acta 1984, 165, 171. (31) Plotsky, P. M. Brain Res. 1982, 235, 179. (32) Ramos, B. L.; Mike, J. H. Microchem. J. 1993, 47, 33. (33) Yakabe, T.; Yoshida, H.; Nohta, H.; Yamaguchi, M. Anal. Sci. 2002, 18, 1375. (34) Hugh, D.; Grennan, A.; Abugila, M. A.; Weinkove, C. Clin. Chem. 1987, 33, 569. (35) Sutor, B.; Ten, B. G. Neurosci. Lett. 1990, 116, 287. (36) Cline, D. J.; Redding, S. E.; Brohawn, S. G.; Psathas, J. N.; Schneider, J. P.; Thorpe, C. Biochemistry 2004, 43, 15195. (37) Getz, E. B.; Xiao, M.; Chakrabarty, T.; Cooke, R.; Selvin, P. R. Anal. Biochem. 1999, 273, 73. (38) Krezel, A.; Latajka, R.; Bujacz, G. D.; Bal, W. Inorg. Chem. 2003, 42, 1994. (39) Lykkesfeldt, J. Anal. Biochem. 2000, 282, 89. (40) Chavdarian, C. G.; Karashima, D.; Castagnoli, N., Jr.; Hundley, H. K. J. Med. Chem. 1978, 21, 548. (41) Hawley, M. D.; Tatawawadi, S. V.; Piekarski, S.; Adams, R. N. J. Am. Chem. Soc. 1967, 89, 447. 7529

dx.doi.org/10.1021/ac201610x |Anal. Chem. 2011, 83, 7523–7530

Analytical Chemistry

ARTICLE

(42) Kvalheim, O. M.; Brakstad, F.; Liang, Y. Anal. Chem. 1994, 66, 43. (43) Cooper, S. J.; Finney, G. L.; Brown, S. L.; Nelson, S. K.; Hesselberth, J.; MacCoss, M. J.; Fields, S. Genome Res. 2010, 20, 1288. (44) Krauth-Siegel, R. L.; Meiering, S. K.; Schmidt, H. Biol. Chem. 2003, 384, 539. (45) Fairlamb, A. H.; Blackburn, P.; Ulrich, P.; Chait, B. T.; Cerami, A. Science 1985, 227, 1485.

7530

dx.doi.org/10.1021/ac201610x |Anal. Chem. 2011, 83, 7523–7530