Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography–mass spectrometry

Journal of Chromatography A, 1147 (2007) 153–164

Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography–mass spectrometry Bing Luo, Karsten Groenke, Ralf Takors 1 , Christian Wandrey, Marco Oldiges ∗ Institute of Biotechnology, Research Centre Juelich GmbH, D-52425 J¨ulich, Germany Received 10 October 2006; received in revised form 2 February 2007; accepted 6 February 2007 Available online 16 February 2007

Abstract A highly selective and sensitive method for identification and quantification of intracellular metabolites involved in central carbon metabolism (including glycolysis, pentose phosphate pathway and tricarboxylic acid cycle) by means of liquid chromatography–tandem quadrupole mass spectrometry (LC–MS/MS) was developed. The volatile ion pair modifier tributylammonium acetate (TBAA) was employed in the mobile phase for simultaneously separation of 29 negatively charged compounds including sugar phosphates, nucleotides, and carboxylic acids on a common C18 reversed-phase column. Method validation results displayed that limits of detection (LODs) calculated according to DIN (German Institute for Standardization) 32645 are mostly below 60 nM, only with the exception of pyruvate and malate. The calibration curves showed excellent linearity mainly over three orders of magnitude with correlation coefficients R2 > 0.9982. This LC–MS/MS method was successfully applied to determine these metabolites in cell extracts of Escherichia coli. Most of the intracellular metabolites were found within the detection range and the relative standard deviations of the measurements were smaller than 5.65% (n = 5) for a cell extract sample. © 2007 Elsevier B.V. All rights reserved. Keywords: Liquid chromatography–mass spectrometry (LC–MS); Ion pair chromatography; Sugar phosphate; Phosphorylated sugar; Nucleotide; Carboxylic acid

1. Introduction Metabolomics, a new “omics,” joining genomics, transcriptomics, and proteomics as a tool employed toward the understanding of global systems biology, has become widespread since 2002 [1]. Metabolomics focuses on the comprehensive and quantitative study of metabolites in a biological system [2]. In contrast to genomics, transcriptomics and proteomics which address macromolecules with similar chemical properties, such as DNA, RNA and proteins, metabolomics analysis deals with diverse properties of low molecular weight bio-compounds. Metabolomics offers a means

∗ Corresponding author at: Institute of Biotechnology 2, Research Centre Juelich GmbH, D-52425 J¨ulich, Germany. Tel.: +49 2461 613951; fax: +49 2461 613870. E-mail addresses: [email protected] (B. Luo), [email protected] (K. Groenke), [email protected] (R. Takors), [email protected] (C. Wandrey), [email protected] (M. Oldiges). 1 Present address: Degussa AG, Feed Additives, 33790 Halle/Westf., Germany.

0021-9673/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2007.02.034

of deciphering cellular metabolism and metabolic regulation. As metabolomics is the downstream product of genomics and proteomics, metabolomics is also complement of other “omics” for interpretation of gene function (functional genomics) [3,4]. Due to a wide range of metabolites in the metabolic network, e.g., approximately 600 metabolites in Saccharomyces cerevisiae [4], 1692 metabolites in Bacillus subtilis [5] and up to 200 000 metabolites in plant kingdom [2], it is a very challenging task to establish analytical tools for identifying and quantifying all of them. Instead of extensive metabolomics research on plant by gas chromatography–mass spectrometry (GC–MS) [3] or Bacillus subtilis by capillary electrophoresis–mass spectrometry (CE–MS) [5], investigations of metabolic regulation and in vivo kinetic studies in central carbon metabolism are also major interests for basic research and metabolic engineering which demands absolute quantitative metabolite data [6–8]. In all biological systems central carbon metabolism, which consists of glycolysis, pentose–phosphate-pathway, tricarboxylic acid cycle (TCA) and the corresponding cofactors, plays a key function in the substrate degradation, energy and cofactor regeneration, and biosynthetical precursor supply. In the biotechnological

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production of fine chemicals (e.g., amino acids, vitamins, and antibiotics) the metabolites of central carbon metabolism serve as important precursors for the anabolic biosynthetic pathways. Hence, their concentrations and the concentration dynamics induced by environmental or genetic modifications are of high interest for the rational design of recombinant microbial production organisms by metabolic engineering [9–11] and systems biology [12–14]. For example, LC–MS/MS measurements of the intracellular metabolites of the l-phenylalanine and l-valine biosynthesis together with some metabolites from central carbon metabolism already led to the identification of significant metabolic engineering targets to improve the production of these two amino acids [15,16], although all information of metabolites in central carbon metabolism would have been highly desirable with respect to mathematical modeling of the underlying metabolic networks [17]. The relevant metabolites involved in central carbon metabolism can be classified into carboxylic acids (e.g., pyruvate), phosphorylated compounds including sugar phosphates (e.g., glucose 6-phosphate), phospho-carboxylic acids (e.g., phosphoenolpyruvate) and nucleotides such as ATP, NAD and NADP. To simultaneously determine a large number of metabolites with high polarity, low in vivo concentration and complex matrix, Mass Spectrometry (MS) with electrospray ionization (ESI) is specially preferred in terms of universality, high throughput, resolution, and sensitivity [18]. In addition, MS provides sufficient resolution to discriminate and quantify stable-isotope labeled (e.g., 13 C, 2 H, 15 N) and unlabelled low molecular weight metabolites [19,20] and hence it paves the way for 13 C metabolic flux analysis [21,22]. When chromatography is coupled with MS detection, an even higher resolution can be achieved owing to its two dimensional separation. This is particularly advantageous for the detection of isomers like glucose-6-phosphate/fructose-6-phosphate or glyceraldehyde-3-phosphate/dihydroxy-acetone-phosphate, which usually cannot be distinguished by MS/MS detection due to their similar or equivalent MS/MS patterns. For determination of these thermo-labile and non-volatile metabolic compounds, precolumn derivatization of samples is usually required with GC. Although another technique CE makes derivatization step unnecessary and it is general applicable for ionic metabolites [23], this technique suffers from poor sensitivity and migration time shift [24]. The application of CE is limited especially when low detection limits are often required because metabolites are generally present at submicromolar levels in the cells. In the past, liquid chromatography (LC) was predominantly used for studies of these metabolites. For instances, adenosine phosphates were studied with ion pair chromatography detected by diode array detector [25] or by mass spectrometry [26,27]. Identifications of sugar phosphates were based on anion exchange chromatography detected by pulsed amperometric detection [28] or by mass spectrometry [29,30]. Huck et al. attempted to separate sugar phosphates with ion pair chromatography–mass spectrometry [31]. Nucleotides and sugar phosphates were studied together with anion exchange chromatography by conductimetric detector [32], by fluorometric and ultraviolet detection [33]. Ion pairing chromatography–

mass spectrometry was also used for investigation of nucleotides and sugar bisphosphates [34]. Carboxylic acids in TCA cycle were separated by cation exchange chromatography with ultraviolet detector or two detectors in series (ultraviolet and refractive index detector) [35,36]. Ion suppression chromatography was also been applied to separate them and they are detected by UV [37] or by potentiometric detector [38]. Derivatized carboxylic acids were measured by liquid chromatography–mass spectrometry (LC–MS) as well [39]. Moreover, these multiple classes compounds were studied together by anion exchange chromatography-conductivity and ultraviolet detector [40,41], or by potentiometric detector [42]. Recently a method using hydrophilic interaction chromatography–mass spectrometry was used to investigate these metabolites and other water soluble metabolites [43]. Among these multiple metabolites, separation of sugar phosphates by LC–MS is the most challenging task due to their extremely high polarity and similar structure including isomers. The past approaches were achieved dominantly with anion exchange chromatography (AEC). However, when electrospray mass spectrometry is coupled to AEC, the high ionic strength required to elute the analytes from anion exchange column causes deposition of non-volatile salts at the ion source inlet, so that reduction of salts concentration with a membrane suppressor before coupling of AEC with MS is necessary. In order to avoid the utilization of high salts concentration in the eluents other authors also attempted to apply different methods (e.g., employment of beta-cyclodextrin column in reverse phase mode and normal phase mode) to separate sugar phosphates, however, most of the analytes could not be well separated or they eluted at nearly dead volume [43–45]. Taking anionic nature of the analytes into account, ion pair liquid chromatography is a promising alternative to ion exchange chromatography. Ion pair chromatography is such a type of chromatography that utilizes hydrophobic stationary phases and MS compatible solvent to separate ionic compounds by addition of ion pair reagents into mobile phase. Through formation of ion pairs with opposite charged ion pair reagent, the retention and selectivity of charged analytes could be dramatically improved on a reverse phase column. The retention mechanism of ion pair chromatography has been extensively reviewed by Stahlberg [46]. In the past, tetraalkylammonium salts were frequently used as ion pair reagents to separate anionic compounds [47]. However, tetrabutylammonium salts are inherently nonvolatile and therefore incompatible with electrospray mass spectrometer. Recently, the use of volatile ion pair reagent in the LC–MS separation of nucleotides were depicted in a few articles [26,27,34] and of some sugar phosphates was mentioned once by Huck et al. [31], but the separation of investigated sugar phosphates clearly needs to be improved. Moreover, to our knowledge, ion pair chromatography with volatile ion pair reagent still has not been applied for separation of carboxylic acids in TCA cycle and simultaneous determination of multiple classes of compounds in central carbon metabolism. Here we describe a versatile, convenient and highly selective and sensitive LC–MS/MS method using tributylamine as volatile ion pair reagent. The method allows the combined separation

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and quantification of 29 metabolites from the most interesting biological classes in central carbon metabolism (sugar phosphates, organic acids and nucleotides). This method has been successfully applied to identification and quantification of these intracellular metabolites in various cell extract samples. In this paper determination of intracellular metabolites in Escherichia coli was given as an example.

25% ethanol) were added and the cells were then stored at −80 ◦ C after vortexing. After thawing, the mixture was neutralized by adding 40 ␮L of glacial acetic acid. Cell debris was removed by centrifugation for 5 min at 10,000 × g and −20 ◦ C. The supernatant was filtered through a 0.2 ␮m filter (FP30/0.2 CA, Schleicher & Schuell, Dassel, Germany) and stored at −80 ◦ C.

2. Experimental

2.3. LC–MS/MS measurement

2.1. Chemicals

All experiments were carried out on an Agilent 1100 series binary HPLC system (Agilent Technologies, Waldbronn, Germany) coupled with an Applied Biosystems/MDS Sciex Q 4000 TRAPTM linear ion trap mass Spectrometer (AB/MDS Sciex, Concord, Canada) equipped with a TurboIon spray source. Data were acquired and evaluated via Analyst software (version 1.4, AB /MDS Sciex, Concord, Canada). The MS was operated in the negative ion and selected reaction monitoring (SRM) mode. Single analyte standard dissolved in a mixture of water/methanol 50:50 (v/v) were infused at a flow rate of 5 ␮L/min for tuning compound dependent MS parameters. Infusion experiments were performed using a model 11 PLUS syringe pump (Harvard Apparatus, Holliston, USA) directly connected to the interface. The major MS/MS fragment patterns of each analyte were determined. Declustering potential (DP), collision energy (CE) and collision cell exit potential (CXP) of each transition were optimized. Entrance potential (EP) was fixed to −10 V for all of transitions. To finely tune the source dependent parameters 10 ␮L standard mixture solution was injected by the autosampler into mobile phase flow (80% A:20% B; A: 10 mM tributylamine adjusted with 15 mM acetic acid; B: methanol) at a flow rate of 200 ␮L/min. These optimized parameters were as following: ionspray voltage −4500 V, nebulizer gas (GS1), auxiliary gas (GS2), curtain gas (CUR) and collision gas (CAD) were 60, 60, 30, 5 (arbitrary units), respectively. The auxiliary gas temperature was maintained at 550 ◦ C. The curtain and collision gas was nitrogen generated from pressurized air in a NM20ZA nitrogen generator (Peak Scientific, Bedford, MA, USA). Zerograde air was used for nebulizing and auxiliary gas. To obtain adequate selectivity and sensitivity, the mass spectrometer was set to unit resolution. To improve the sensitivity the run has been divided into five segments and the dwell time for each transition was 150 ms. The chromatographic separation was achieved on ˚ a Synergi Hydro-RP (C18) 150 mm × 2.1 mm I.D., 4 ␮m 80 A particles column (Phenomenex, Aschaffenburg, Germany) at room temperature with eluent A (10 mM tributylamine aqueous solution adjusted pH to 4.95 with 15 mM acetic acid) and eluent B (methanol). The gradient profile is displayed in Table 1. Before each run, the column was equilibrated for 10 min. The injected volume is 10 ␮L. Mobile phase at a flow rate of 0.2 mL/min was directly introduced into the mass spectrometer via the Turbo ionspray source.

Except S7P that was purchased from GLYCOTEAM GmbH (Hamburg, Germany), all other metabolite standards were purchased from Sigma or Fluka (Steinheim, Germany). High purity solvents and reagents were purchased in order to avoid appearances of interfering MS peaks and high background. Tributylamine was obtained from Aldrich GmbH (Steinheim, Germany) and acetic acid (GC standard grade) was purchased from Fluka (Steinheim, Germany). Solvents used for chromatography were of HPLC–MS grade, which were purchased from Sigma-Aldrich (Steinheim, Germany). Water was deionized and filtered through a 0.22 ␮m filter by using a Millipore water generation system (Millipore, Schwalbach, Germany). 2.2. Fermentation, sampling, quenching and extraction The wildtype E. coli K12 W3110 strain was used for the experiments. Cryocultures were stored at −80 ◦ C in Luria-Bertani (LB) medium containing 30% (v/v) glycerol. Precultivation was done overnight (12 h, 150 rpm, 37 ◦ C) in a shake flask (500 mL) containing 70 mL precultivation medium inoculated with 20 ␮L cryoculture. The preculture solution was used to inoculate the bioreactor containing 630 mL fermentation medium: 8 g/L glucose, 5 g/L (NH4 )2 SO4 , 3 g/L KH2 PO4 , 1 g/L NaCl, 0.3 g/L MgSO4 × 2H2 O, 0.112 g/L FeSO4 × 7H2 O, 0.75 g/L Thiamin, 0.15 g/L 3,4-Dihydroxybenzoat, 0.015 g/L CaCl2 × 2H2 O and trace elements: 0.75 mg/L AlCl3 × 6H2 O, 0.6 mg/L CoCl2 × 6H2 O, 2.5 mg/L CuSO4 × 5H2 O, 0.5 mg/L H3 BO3 , 17.1 mg/L MnSO4 × 1H2 O, 3 mg/L NaMoO4 × 2H2 O, 1.7 mg/L NiCl2 × 6H2 O, 15 mg/L ZnSO4 × 7H2 O. The same medium was used for the precultures with the following change: 6 g/L glucose. Fermentation was performed at 37 ◦ C, pH 7, dissolved oxygen (DO) 30% and a mixture of NH4 /KOH was used for pH control. After Glucose dropped to 1 g/L a glucose feed (60%, w/w) was started to maintain glucose concentration between 1 and 2 g/L. Samples were taken at a cell density of 12 g/L CDW in exponential growth phase. 5 mL of cell suspension was rapidly sprayed into a sample tube with 15 mL cold (−50 ◦ C) aqueous methanol solution (60%, v/v) to stop cell metabolism quickly. Directly after sampling the samples were centrifuged for 5 min at 10,000 × g and −20 ◦ C (AvantiTM 30 centrifuge, Beckmann Coulter, Krefeld, Germany) and the supernatant was removed. Addition of 500 ␮L of −20 ◦ C methanol-water (60%, v/v) mixture was used to resuspend the solid cell pellet by vortexing. As the extraction solution, 2 mL of 0.3 M KOH (dissolved in

2.4. Quantification and validation procedure Primary stock solutions were prepared in water at a concentration of 5 mM for each metabolite. From this solution one

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Table 1 Gradient profile applied in the developed LC–MS/MS method Step

Total time (min)

Eluent A (vol.%)

Eluent B (vol.%)

1 2 3 4 5 6 7 8 9

15.00 25.00 55.00 60.00 65.00 70.00 75.00 75.10 80.00

100.0 80.0 80.0 65.0 65.0 40.0 40.0 10.0 10.0

0.0 20.0 20.0 35.0 35.0 60.0 60.0 90.0 90.0

Eluent A: 10 mM tributylamine aqueous solution adjusted pH to 4.95 with 15 mM acetic acid, Eluent B: methanol.

stock solution of 100 ␮M standard mixture was prepared in water. Aliquots of this standard mixture were used for calibration standards, quality control (QC) standards and spiking into cell extracts for the standard addition method. The calibration curve was obtained by analyzing standard solutions at nineteen concentrations, ranging from 1 nM to 50 ␮M (1, 2.5, 5, 7.5, 10, 25, 50, 75, 100, 250, 500, 750, 1000, 2500, 5000, 7500, 10000, 25000, 50000 nM). Calibration curves were constructed by plotting the area of the compound against the concentration of the compound (x). Weighted linear regression (weighted to 1/x, where x is the concentration in nM) was used to fit the calibration curve and the linearity for each compound was evaluated from it. Intra-assay precision were evaluated by determining the metabolites in one real sample (1:3 dilution with water) and three QC samples at 100, 500 nM and 5 ␮M (n = 5) in one analytical batch. The precision is expressed as coefficient of variation (CV) in percent. The limit of quantification (LOQ) and the limit of detection (LOD) were calculated according to the calibration method in DIN (German Institute for Standardization) 32645 guidelines. The absolute concentration in the E. coli cell extract sample was determined by the standard addition method to correct for matrix effects. Stock standard mixture was spiked into the samples to obtain six concentration levels (0, 500 nM, 1, 2, 5, 10 ␮M) and a linear calibration curve was constructed for concentration calculation. 3. Results and discussion 3.1. Mass spectrometry In Table 2 the mass spectrometric parameters (i.e., precursor and product ions used in SRM and optimized voltages) of the metabolites under investigation were shown. In all cases, [M − H]− is the dominant molecular ion. The main product ions of metabolites containing phosphate moieties are 97 and 79, which result from [H2 PO4 ]− and [PO3 ]− , respectively. For most of the organic acids, decarboxylation and/or water elimination are the most intense fragmentation patterns. Similar fragmentation pattern of analogous compounds owing to their structure similarity implies that these non-specific transitions could produce a false positive signal if weak collision induced dissociation (CID) takes place in the ionization source or interface, in particular when energy required for initiation of

Table 2 Specific compound dependent MS parameters used in selected reaction monitoring (SRM) Compound

[M − H]−

G6P/F6P FBP DHAP/GAP 3PG/2PG PEP PYR AcCoA 6PG R5P/Ribu5P/ Xylu5P E4P S7P OXA AKG SUC FUM MAL ACT CIT/ISOCIT ISOCIT CIT NAD NADH NADP NADPH ATP ADP AMP cGMP

259 339 169 185 167 87 808.3 275 229

97 97 97 79 79 43 79 79 97

199 289 131 145 117 115 133 173 191 191 191 662.3 664.3 742.2 744.3 506.1 426.1 346.2 344

97 97 87 101 73 71 115 85 111 73 87 540.1 79 620.1 79 79 79 79 150

Main product ion

DP (V)

CE (V)

CXP (V)

−50 −60 −35 −35 −35 −30 −120 −60 −50

−22 −28 −14 −44 −20 −12 −130 −66 −22

−5 −5 −7 −3 −5 −1 −1 −5 −5

−55 [H2 PO4 ]− [H2 PO4 ]− −50 −25 –CO2 –CO2 −35 −45 –CO2 –CO2 −35 –H2 O −30 –2CO2 −35 −30 –CO2 –2H2 O −40 –a –a −35 –Nicotinamide −50 −110 [PO3 ]− –Nicotinamide −60 [PO3 ]− −110 −90 [PO3 ]− [PO3 ]− −80 −70 [PO3 ]− [Guanine-H]− −70

−14 −22 −10 −12 −16 −10 −14 −18 −18 −28 −26 −22 −120 −22 −118 −106 −88 −62 −34

−5 −5 −5 −7 −5 −3 −9 −5 −7 −3 −5 −9 −3 −11 −3 −1 −3 −5 −11

[H2 PO4 ]− [H2 PO4 ]− [H2 PO4 ]− [PO3 ]− [PO3 ]− –CO2 [PO3 ]− [PO3 − [H2 PO4 ]−

Abbreviations: G6P: glucose-6-phosphate; F6P: fructose-6-phosphate; FBP: fructose-1,6-diphosphate; DHAP: dihydroxy-acetone-phosphate; GAP: glyceraldehyde-3-phosphate; 3PG: 3-phosphoglycerate; 2PG: 2phosphoglycerate; PEP: phosphoenolpyruvate; PYR: pyruvate; AcCoA: acetyl-coenzyme A; CIT: citrate; ISOCIT: isocitrate; AKG: 2-oxoglutarate; SUC: succinate; FUM: fumarate; MAL: malate; OXA: oxaloacetate; ACT: cis-aconitate; 6PG: 6-phosphogluconate; R5P: ribose-5-phosphate; RIBU5P: ribulose-5-phosphate; XYLU5P: xylulose-5-phosphate; S7P: sedoheptulose-7-phosphate; E4P: erythrose-4-phosphate; NAD: nicotinamide adenine dinucleotide; NADH: nicotinamide adenine dinucleotide reduced; NADP: nicotinamide adenine dinucleotide phosphate; NADPH: nicotinamide adenine dinucleotide phosphate reduced; ATP: adenosine-5-triphosphate; ADP: adenosine-5-diphosphate; AMP: adenosine-5-monophosphate; cGMP: guanosine 3 ,5 -cyclic-monophosphate. a The fragmentation pattern is unknown.

this fragmentation is low. For example, in-source fragmentation of molecule ion m/z = 133 ([M − H]− ) of malate gave rise to a main product ion m/z = 115 ([M − H − 18]− ) resulting from loss of a water. Then it decomposed further in the collision cell to generate a product ion m/z = 71 ([M − H − 18–44]− ) due to loss of CO2. However, the same transition 115 → 71 was also observed with fumarate. Therefore in the selective ion chromatogram of FUM (Fig. 1a) it exhibited a noticeable interference peak at around 42 min, which is identical to the retention time of MAL. Similarly, this problem also occurs in the determination of other analogous compounds. Fig. 1b illustrates that the analyte FBP, which is chemically a sugar phosphate, causes an additional peak at around 61 min in the selective ion chromatogram

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Fig. 1. (a) Selective ion chromatogram of MAL (solid line) and FUM standard (dashed line). (b) Selective ion chromatogram of FBP (solid line), GAP and DHAP standard (dashed line). X axis is retention time in minutes.

of DHAP or GAP which are also sugar phosphates. Besides the in-source fragmentation, the crosstalk could attribute to reactions occurred in the gas phase as well. As displayed in Fig. 1b, transition 339 → 97 for FBP (m/z = 339) gave two additional signals at the retention time of DHAP or GAP (m/z = 169), which might be due to the temperature induced aldol-formation of FBP from DHAP and GAP during the ionization process. Although these interfering reactions might be reduced by changes of the ion source or interface parameters (e.g., temperature, solvent or flow), it will be almost impossible to eliminate them and simultaneously maintain highest performance of the MS system. In order to avoid false positive signal due to these interfering effects of contaminants and in-source reactions, it is strongly recommended to separate these analogous compounds by chromatography, especially when their concentrations are expected to be over a broad range as it is true in cell extract sample of biological origin. Since the investigated metabolites are presented as preformed anions in the mobile phase due to the existence of phosphate and carboxylic moieties, all reference compounds (except E4P and PYR) showed high signal response after optimization of the MS parameters with infusion studies. Attributed to the lower molecular weight of PYR (Mw = 88), the intensity of its fragment ion (m/z = 43) is very low. When 10 ␮M E4P standard was infused into MS, in the full scan MS spectrum signals at m/z = 399, 421 and 199 are observed (Fig. 2a). The identity of m/z = 339 is proved to be a dimer of E4P by a subsequent fragmentation of m/z = 399, which produced product ions of 199 and 97 (Fig. 2b) (In the past, some authors already attempted to confirm the dimeric form of E4P by NMR and GC–MS [48–50], which is now further attested by electrospray mass spectrometry). The intensity of E4P is much lower compared to other sugar phosphates, which is in agreement with observation from

Fig. 2. (a) Full MS scan spectrum of E4P standard, (b) product ion MS spectrum of 399.1 in E4P standard. Both spectra were acquired in the negative ion mode with m/z range from 50 to 500.

Huck et al. [31]. The formation of dimer and sodium adduct of dimer (m/z = 421) might be one of reasonable explanation. In addition, in the selective ion chromatogram (using transition 199 → 97) E4P standard was observed as two peaks (one minor peak and one major broad peak, data not shown) in our experiments and as four peaks by Sekiguchi et al. [29]. Since we as well as Feurle et al. observed the fragment ion of 199 was presented in most of hexose phosphates [44], in-source fragmentation of contaminating sugar phosphates might be one of the reasons for these unexpected peaks when taking into account the biological origin and limited purity of a commercial E4P standard. Instead, a single peak in E4P elective ion chromatogram was obtained by using transition 399 → 199, but with even lower intensity. 3.2. LC–MS/MS method development and optimization In the past, tetra-alkylammonium salts were frequently used as ion pair reagents to separate anionic compounds. However, the application of these nonvolatile ion pair reagents in LC–ESI-MS has been excluded due to their drawbacks in high background contribution, source pollution and ion suppression. In order to circumvent these problems but maintain sufficient retention and resolution, a promising approach is to use alternative volatile alkylamines as ion pair reagents. The use of volatile alkylamines in the eluent may cause interference with full scan in the ion trap function due to memory effects in the ion source, but this is

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not observed with the selective reaction monitoring in the triple quadrupole function. We investigated several volatile alkylamines with diverse alkyl chains for separation of metabolites including sugar phosphates, organic acids and nucleotides. 5 mM of different volatile amines, e.g., triethylamine (TEA), tripropylamine (TPrA), tributylamine (TBA), tripentylamine (TPeA), and trihexylamine (THA), adjusted to pH 6.8 with 5 mM acetic acid were used as aqueous eluent A. At such neutral pH the acidic groups of the analytes and the amine groups of the alkylamines are charged to assist the formation of ion pairs. The results for the different amine alkyl chains showed that the systematic increase of retention (TEA < TPrA < TBA < TPeA < THA) is clearly correlated with the increase of hydrophobicity by an increase of alkyl chain lengths. It was found that amines with shorter alkyl chain (e.g., TEA, TPrA) could not provide sufficient resolution for the separation of the isomers such as G6P/F6P and R5P/RIBU5P/XYLU5P. In contrast, the long alkyl chains in TPeA and THA led to redundant retention and lower sensitivity due to the broadened peaks. Higher proportions of organic solvent improved peak shapes and shortened elution time, but coincided with some loss of resolution. As in the article of Huck et al. showed, coeluted G6P/F6P and overlapping of Ribose5P with Ribulose5P/xylulose5P could be results of inappropriate selection of alkyl chain length and organic solvent content [31]. Among the investigated amines, TBA provided a reasonable compromise between elution time, resolution and sensitivity and was therefore exclusively used for the further investigation. The effect of pH on the resolution was studied for two aqueous eluents at pH 4.95 and 6.8 (10 mM TBA mixed with 10 mM (pH 6.8) and 15 mM (pH 4.95) acetic acid, respectively) with methanol as organic eluent and gradient profile described in Table 1. Not surprisingly, the influence of eluent pH on the separation of sugar phosphates and nucleotides was insignificant, which can be explained with the almost unchanged ionization ratio of the acidic phosphate groups (pKa around 1) of these metabolites. However, a remarkable change in selectivity was observed for the compounds containing carboxylic group. Fig. 3a and b illustrate the separation behavior of several carboxylic acids at pH 4.95 and 6.8. It clearly showed that at pH 4.95 most of organic acids were better resolved, except that 2PG and 3PG (containing both of phosphate and carboxylic moieties) coelute in contrast to their base-line resolution at pH 6.8. As can be expected from their pKa values, carboxylic acids are only partially ionized at pH 4.95 and almost fully ionized at pH 6.8. Obviously, this pH dependent dissociation resulted in significant variation of selectivity for organic acids. Interestingly, this phenomenon is also presenting for 2PG and 3PG, although their chromatographic properties should be dominated by the highly charged phosphate group. Although the carboxylic acid metabolites could be already resolved at pH 6.8 by the assistance of MS, the use of the enhanced resolution of pH 4.95 was highly desirable because of the occurrence of impurities from the solvent and chemicals used. The contaminants typically showed decarboxylation as their favorite MS/MS transition which interfered with most

Fig. 3. Combined selective ion chromatograms for several metabolites containing carboxylic group in a standard mixture. (a) pH 4.95 (b) pH 6.8. Peak identification: (1) PYR; (2) SUC; (3) MAL; (4) FUM; (5) AKG; (6) 2PG; (7) 3PG. X axis is retention time in minutes.

of the carboxylic acid metabolites and therefore resulted in significantly reduced sensitivities for these carboxylic acids due to high background. Moreover, when numerous endogenous organic acids are expected to be presenting in biological cell extracts, the high resolution at pH 4.95 is essential. Equally important, more stable retention time achieved at pH 4.95 due to the higher capacity of the acetate/acetic acid buffer makes the LC–MS method more robust. The influence of pH and TBA concentration has been further investigated. Aqueous eluents with various concentrations of tributylamine (TBA) and acetic acid (AA) were prepared as following: (a): 5 mM TBA mixed with 7.5 mM AA (pH 4.95); (b): 5 mM TBA mixed with 15 mM AA (pH 4.38); (c): 7.5 mM TBA mixed with 15 mM AA (pH 4.68); (d): 10 mM TBA mixed with 15 mM AA (pH 4.95). Other LC–MS/MS conditions were kept equal. Fig. 4 shows the chromatograms under four conditions. While the concentrations of TBA and acetic acid were doubled but their concentration ratio remains fixed to 1:1.5 so that pH was identical in the solutions (a) and (d), the sugar phosphate peaks from G6P to DHAP eluted much earlier in Fig. 4d than in Fig. 4a. When TBA concentration was maintained at 5 mM, an increase in AA concentration from 7.5 to 15 mM led to an apparently shorter retention for almost all analytes and a slight change in selectivity (see Fig. 4a and b). The first sugar phosphate peak (i.e., G6P) eluted at around 23.96 min in Fig. 4a instead of 15.66 min in Fig. 4b. Therefore, we can rationally assume that the competitive effect from the increased concentration of the acetate anion has more influence on the total retention than pH variation in that range. Due to decreasing peak symmetry at lower pH values than 4.38, the pH effect was not further investigated (data not shown). When the concentration of acetic acid was kept at 15 mM and TBA concentrations were increased from 5 to 10 mM (see

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Fig. 4. Total ion chromatogram (TIC) of investigated compounds in a standard mixture. LC–MS condition: Eluent A: (a) 5 mM TBA mixed with 7.5 mM AA, pH 4.95; (b) 5 mM TBA mixed with 15 mM AA, pH 4.38; (c) 7.5 mM TBA mixed with 15 mM AA, pH 4.68; (d) 10 mM TBA mixed with 15 mM AA, pH 4.95. Other LC–MS/MS conditions were kept equal.

Fig. 4b–d), the retention time of analytes extended more or less. For example, the retention time of G6P slightly changed from 15.66 min in Fig. 4b to 16.35 min in Fig. 4d, but the retention time of DHAP changed from 25.41 min in Fig. 4b to 30.97 min in Fig. 4d. Stronger retention with increasing concentration of ion pair reagent is a well-known phenomenon in ion pair chromatography. When TBA concentration increased to 10 mM, the selectivity was also slightly improved for separation of doubly charged analytes such as 6PG, 2/3PG, FBP and PEP. On the other hand, a reduction of the MS response by an increased TBA concentration from 5 to 10 mM was not observed in the experiments. In contrast, the intensity of sugar phosphates even slightly increased. Tuytten et al. [27] also reported an intensity increase of adenosine nucleotides with increased dimethylhexylamine (DMHA) concentration up to 10 mM. The increased response of strongly acidic compounds like sulphonates and phosphates with a slight increase in alkylamine concentration has been explained by a displacement mechanism [51]. This mechanism assumes that the addition of small amount of volatile alkylamine lead to formation of alkylamine adducts, which displace the common potassium and sodium adducts. The reduction of potassium and sodium adducts or multiple charged anions thereby increase the signal of singly charged molecular anion [M − H]− . In practice, the addition of 5 mM TBA in the mobile phase was capable of achieving good peak shape and sufficient resolution for most of analytes only when standard solution was injected. If a real cell extract sample was introduced under this condition, the peak shape for weakly retained compounds

such as G6P was deteriorated. This behavior could be due to the presence of high amounts of endogenous substances in real samples, which compete with analytes in the formation of TBA ion pairs. In order to ensure the amount of TBA is sufficient to form ion pair with all analytes, a higher concentration of TBA such as 10 mM was necessary. When 10 mM TBA adjusted with 15 mM acetic acid (pH 4.95) was used as aqueous eluent, it made the best compromise to assure the sufficient resolution, reasonable retention time, better peak shape and high sensitivity. Finally, optimization of organic eluents has been preceded by evaluating acetonitrile and methanol. Because of the extremely similar properties of the analytes, an adequate elution time should be ensured and the gradient has to be carefully adjusted in order to achieve a high performance separation. Due to the weaker elution strength of methanol, higher resolution is relatively easily achieved by adjusting methanol content in the gradient comparing to acetonitrile. A 5% difference in methanol content (for example, methanol content in the gradient increases from 20 to 25%) is available for finely tuning the separation between “high resolution” and “low resolution,” in contrast to 2 % difference in acetonitrile content. When methanol was applied it also showed slightly higher resolution for the separation of FBP, 2/3PG, 6PG and PEP. As it was discussed earlier, the separation of these metabolites should be as good as possible to prevent potential crosstalk due to their structural similarity (Fig. 1). A better resolution is also advantageous for clearly define the time for five segments in the LC–MS run, by which more scan time is available for each transition, resulting in

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higher sensitivity. However, a concession to high resolution with methanol is the prolonged elution time. Based on the previous optimization results, ultimately an LC–MS/MS method using 10 mM TBA mixed with 15 mM acetic acid as aqueous eluent and methanol as organic eluent was adopted. This resulted in a run time of 90 min, if the maximum resolution and sensitivity was required for the difficult chromatographic separation of mass and structural isomers. It was not possible to speed up the gradient elution profile, but maintaining the resolution. If less chromatographic resolution is sufficient to separate a single metabolite or a set of metabolites of interest (e.g., set of nucleotides), the gradient profile can be fastened and the whole run time can be significantly reduced. In Fig. 5 the combined selective ion chromatogram shows the simultaneous separation of 29 analytes. E4P, CIT and OXA were left out because of the low signal response, tailing peak and standard instability, respectively. The method provides high resolution and symmetric peak shapes. The elution order of the metabolites is closely related to their hydrophobicity, i.e., the very polar sugar phosphates elute much earlier than the carboxylic acids and nucleotides. In addition, the number of ionic groups (e.g., retention time: FBP > F6P; ATP > ADP > AMP) as well as the strength of ionic groups (e.g., retention time: FBP > 6PG) in the compounds also play an important role on the separation. This new LC–MS/MS method also separates the important redox cofactors such as NADH, NAD, NADPH and NADP and energy cofactors such as ATP, ADP, AMP (see Fig. 5). Moreover, the employed experimental pH condition is so mild that degradation of NADH, NAD, NADPH and NADP under acidic or basic pH condition is not observed. The structural isomers (e.g., G6P/F6P; R5P/Ribu5P/Xylu5P; GAP/DHAP), which MS could not distinguish due to their similar MS/MS transitions, were also resolved by running 100% aqueous eluent for 15 min at the beginning of gradient. The separation of sugar phosphate isomers is primarily based on the aldehyde or ketone groups in the sugar phospates and all phosphorylated aldoses

Fig. 5. Combined selective ion chromatogram of 29 metabolites in a standard mixture. The LC–MS/MS method is described in experimental part. Peak identification: (1) G6P; (2) GAP; (3) R5P; (4) S7P; (5) F6P; (6) Xylu5P; (7) Ribu5P; (8) DHAP; (9) PYR; (10) NAD; (11) SUC; (12) AMP; (13) MAL; (14) cGMP; (15) AKG; (16) 6PG; (17–18) 2PG and 3PG; (19) FUM; (20) FBP; (21) ISOCIT; (22) PEP; (23) NADP; (24) ACT; (25) ADP; (26) NADH; (27) NADPH; (28) ATP; (29) AcCoA. X axis is retention time in minutes.

were eluted earlier than phosphorylated ketoses. For another pair of isomers such as CIT and ISOCIT, the specific transitions of m/z = 191 → 87 and m/z = 191 → 73 were utilized to identify partially coeluted CIT and ISOCIT, respectively, although the sensitivities of these transitions were considerably lower compared to those acquired with the more sensitive common transition m/z = 191 → 111. In contrast to ISOCIT, CIT showed peak tailing, which make the quantification almost unfeasible. As an alternative method, ion suppression chromatography could be employed to separate CIT and ISOCIT. By running 100%, 0.1% formic acid on the same Synergi Hydro-RP column, they could be well resolved in less than 8 min with flow rate of 0.2 mL/min (data not shown). 3.3. Method validation and application Validation results for the established method are reported in Table 3. In general, the calibration graphs showed excellent linearity mainly over three orders of magnitude with correlation coefficients (R2 ) higher than 0.9982. The LOD and the LOQ were calculated according to DIN 32645. As shown in Table 3 the LODs vary from 1.17 to 364.25 nM and the LOQs are from 4.18 nM to 1.26 ␮M. For sugar phosphates their LODs are mostly below 10 nM and LODs of other metabolites are generally below 60 nM. Only pyruvate and malate exhibited low sensitivities which could attribute to the lower intensity of the product ion (m/z = 43) of PYR and high baseline level of MAL. On the whole, the LOD and LOQ values acquired with the new LC–MS/MS method are 1 or 2 orders of magnitude lower than the values described by other related articles [23,30,31]. With such low LODs and LOQs, determination of these metabolites by direct injection of cell extract samples without any previous enrichment step is readily achieved. To check the method repeatability, three concentration levels of standard mixture were injected five times and the relative standard deviations (RSD) are better than 9% for 100 nM standard and 4% for the 500 nM and 5 ␮M standards (Table 3). The validated method was successfully applied to routine analysis of the intracellular metabolites in cell extracts. After sample preparation the E. coli cell extract was diluted 1:3 (v/v) to reduce potassium acetate salts left during cell extraction, which may result in peak shape deterioration. Fig. 6 shows the comparison of the selective ion chromatograms of 23 metabolites in the cell extract samples of E. coli with a standard mixture. It was shown that most of the metabolites in the cell extracts were detected as single peak due to excellent selectivity of SRM, although for a few transitions additional peaks were still observed in the selective ion chromatograms. For example, an additional peak was found between G6P and F6P, which might originate from endogenous galactose-1-phosphate or mannose-6-phosphate, since both standards elute at this retention time. Moreover, Fig. 6 shows that the retention times of the metabolites in the real samples are consistent with those in the standard mixture. Hence, the retention times are not affected by high salt concentration left from the extraction solution (at least 100 mM potassium acetate after 1:3 dilution) or

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161

Table 3 Retention factors(k , retention time of uracil was set as dead time marker), linearity of calibration, relative standard deviation of three concentration levels of standards and one sample from E. coli cell extracts (n = 5), and limits of detection and quantification, determined by the established LC–MS/MS method (10 ␮L injection) Compound

G6P F6P GAP R5P XYLU5P + RIBU5P E4P S7P FBP PEP DHAP 2PG/3PG 6PG ATP ADP AMP PYR SUC MAL AKG ACT FUM NADH NAD NADPH NADP ISOCIT cGMP AcCoA

Retention factor, k

Linearity

9.74 11.99 10.37 11.57 14.17 11.43 11.48 35.83 39.03 15.40 34.55 31.91 44.41 42.71 22.37 16.09 20.93 24.97 30.29 41.50 34.33 43.24 18.04 44.36 39.53 38.20 26.29 44.87

Repeatability (n = 5, CV %)

Range

R2

100 nM

500 nM

5 ␮M

Sample (1:3 dilution)

5 nM–10 ␮M 5 nM–10 ␮M 7.5 nM–10 ␮M 5 nM–10 ␮M 2.5 nM–10 ␮M 25 nM–10 ␮M 5 nM–10 ␮M 10 nM–50 ␮M 5 nM–10 ␮M 2.5 nM–10 ␮M 7.5 nM–50 ␮M 50 nM–50 ␮M 100 nM–10 ␮M 50 nM–50 ␮M 10 nM–25 ␮M 500 nM–25 ␮M 50 nM–10 ␮M 250 nM–50 ␮M 50 nM–50 ␮M 50 nM–10 ␮M 25 nM–10 ␮M 5 nM–10 ␮M 2.5 nM–10 ␮M 5 nM–10 ␮M 5 nM–10 ␮M 100 nM–10 ␮M 5 nM–50 ␮M 7.5 nM–10 ␮M

0.9999 0.9999 0.9998 0.9999 0.9993 0.9997 0.9999 0.9991 0.9996 0.9998 0.9991 0.9996 0.9998 0.9994 0.9998 0.9994 0.9997 0.9993 0.9998 0.9997 0.9991 0.9994 0.9995 0.9999 0.9999 0.9987 0.9995 0.9982

1.86 0.51 2.71 1.32 2.00 2.80 1.38 5.76 0.86 1.53 3.82 3.97 6.16 3.67 – 6.21 – 8.95 5.11 6.80 2.69 3.62 6.18 2.75 6.30 2.53 2.49

0.66 0.41 1.31 0.68 2.32 0.82 0.77 2.63 1.33 0.57 0.35 1.27 3.86 1.35 0.37 3.60 3.98 3.30 2.15 3.25 1.41 2.51 1.88 1.93 1.97 3.18 1.64 2.30

0.26 0.67 0.88 0.73 0.96 1.64 0.83 1.30 1.00 1.58 0.67 0.55 0.92 0.70 0.36 3.02 0.91 0.98 0.49 1.28 0.56 1.87 1.94 0.65 1.58 1.40 1.03 2.23

0.81 0.93 2.56 2.10 0.83 5.32 1.15 1.93 0.48 1.26 2.41 5.30 1.22 2.11 4.95 2.73 2.22 3.68 0.38 3.51 2.40 5.65 1.70 2.06 3.08 2.73 1.25 1.76

from intracellular salts. The reliability of retention times were additionally confirmed by spiking the standards into the cell extracts. The absolute metabolite concentrations were determined by the standard addition method. In the cell extracts of E. coli wild type, most of metabolites could be well quantified and their concentrations range from several hundred nM to several ␮M. Only E4P, 6PG, NADH and NADPH, ISOCIT and AcCoA were present in a very low concentration and SUC in a quite high concentration (Table 3). The thermal and chemical instability of NADH, NADPH and AcCoA might contribute to such low concentrations found in the cell extracts. A general aspect regarding all of the biological results presented here is, that there is strong indication that E. coli leak many of their metabolic contents quickly after exposure to cold conditions. This is evident from the significant metabolite concentrations found in the methanolic supernatant after cold metabolic quenching. Such a cold leakage effect has been described for proteinogenic free amino acids in C. glutamicum [52]. The sample preparation procedure used in this study is well suited to provide the immediate stop of metabolic activity (by use of a −50 ◦ C methanol solution) which is an undoubtful prerequisite for valid and biologically relevant metabolome studies. This originates from the very high turn-over rates in

LOD (nM)

LOQ (nM)

C sample (1:3 dilution, ␮M), standard addition

3.3 3.1 6.9 3.4 2.0 21.7 4.3 10.2 3.8 1.2 7.5 32.7 58.9 31.5 10.1 364.3 28.3 189.3 34.1 30.8 10.7 3.9 2.6 3.4 5.4 49.5 3.5 6.6

11.5 10.8 23.4 11.7 6.9 74.9 15.2 37.5 13.1 4.2 26.7 113.6 217.2 109.4 36.5 1260.2 102.1 617.0 122.0 112.3 38.8 14.0 9.2 12.1 18.7 179.8 12.2 23.0

1.20 0.21 0.18 0.30 0.14 0.62 2.44 0.45 0.29 1.40 – 1.63 1.43 1.75 1.89 75.45 1.30 1.83 0.53 1.15 0.02 1.95 0.10 1.68 – 1.07 –

the primary metabolic network of prokaryotes and eukaryotes [6,53–55]. Hence, the determined metabolite concentrations from the extracts of the cell pellets are most likely lower than the in vivo situation. One possible solution to correct for this effect could be the balancing of the metabolite concentration in the cell extract and the quenching supernatant. The developed LC–MS/MS method is applicable to both samples types, especially the higher diluted quenching supernatants can profit from its sensitivity. The sample from the E. coli cell extracts was injected five times (n = 5) and the measurement also showed high reproducibility with CV < 5.65% (Table 3). If internal standards such as uniformly 13 C labeled cell extracts can be obtained, an even higher precision can be expected [56]. In combination with a setup to generate representative metabolome samples [57], this LC–MS/MS method can be used to investigate the intracellular concentration levels of biological systems stimulated by environmental changes or of different genotypes. This will facilitate the elucidation of the metabolic network operation in vivo and regulatory structures. Besides E. coli, this method has been applied to mammalian cell extracts [58] and showed sufficient sensitivity and selectivity for the metabolite analysis as well. Therefore this LC–MS/MS method seems to be robust and general applicable to samples from various biological sources.

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Fig. 6. Selective ion chromatograms of 23 metabolites in glycolysis, pentose phosphate pathway and TCA cycle. X axis is retention time in minutes. The solid lines are selective ion chromatograms of the metabolites in a standard mixture and the dashed lines are selective ion chromatograms of the corresponding metabolites in E. coli cell extracts.

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4. Conclusion and outlook A powerful LC–MS/MS method using TBA as volatile ion pair reagent has been developed for identification and quantification of intracellular metabolites involved in glycolysis, pentose phosphate pathway and TCA cycle. This optimized method achieved a compromise on chromatographic resolution (especially for structural isomers) and mass spectrometric performance. The validation results demonstrated high sensitivity, selectivity and reliability of this method. The successful application of the presented method to analyze the intracellular metabolites in cell extracts from E. coli as well as those from mammalian cells gives rise to the conclusion that it is a general approach to quantify intracellular metabolites in samples from various biological sources. In the chromatogram there is still a wide space left for the analysis of other anionic intracellular metabolites, such as the huge variety of carboxylic acids (e.g., shikimate, quinate, glyoxylate, aspartate, glutamate, etc.), phosphorylated compounds (e.g., UDP-glucose, glycerol-phosphate, deoxy-ribose-phosphate, etc.), and nucleosides and nucleotides (e.g., FMN, GDP, GTP, etc.). Moreover, the possibility of simultaneous separation of unlabelled and labeled metabolites make this LC–MS/MS method applicable for 13 C metabolome. Current research activities are aiming at the measurement of the 13 C-labelling enrichment in the intracellular metabolites after application of 13 C-labelled substrate (e.g., 13 C6 -glucose). This will open up the possibility to perform 13 C-metabolic flux analysis based on the 13 C-metabolome information. Acknowledgments The author Bing Luo would like to thank Prof. Dr. rer. nat. Wolfgang Rotard in the Institute of Environmental Technology (ITU) at the Technical University of Berlin for supervision of her Ph.D. work and his valuable discussions. This project was funded by the German Research Foundation (DFG Project No. TA 241/2, GR1711/1). References [1] S. Rochfort, J. Nat. Prod. 68 (2005) 1813. [2] O. Fiehn, Compar. Funct. Genom. 2 (2001) 155. [3] O. Fiehn, J. Kopka, P. Dormann, T. Altmann, R.N. Trethewey, L. Willmitzer, Nat. Biotechnol. 18 (2000) 1157. [4] L.M. Raamsdonk, B. Teusink, D. Broadhurst, N.S. Zhang, A. Hayes, M.C. Walsh, J.A. Berden, K.M. Brindle, D.B. Kell, J.J. Rowland, H.V. Westerhoff, K. van Dam, S.G. Oliver, Nat. Biotechnol. 19 (2001) 45. [5] T. Soga, Y. Ohashi, Y. Ueno, H. Naraoka, M. Tomita, T. Nishioka, J. Proteome Res. 2 (2003) 488. [6] C. Chassagnole, N. Noisommit-Rizzi, J.W. Schmid, K. Mauch, M. Reuss, Biotechnol. Bioeng. 79 (2002) 53. [7] A.K. Gombert, M.M. dos Santos, B. Christensen, J. Nielsen, J. Bacteriol. 183 (2001) 1441. [8] U. Sauer, D.R. Lasko, J. Fiaux, M. Hochuli, R. Glaser, T. Szyperski, K. Wuthrich, J.E. Bailey, J. Bacteriol. 181 (1999) 6679. [9] J. Bongaerts, M. Kramer, U. Muller, L. Raeven, M. Wubbolts, Metab. Eng. 3 (2001) 289. [10] R.M. Raab, K. Tyo, G. Stephanopoulos, Biotechnology for the Future, Springer-Verlag Berlin, Berlin, 2005, p. 1. [11] J. Nielsen, Biotechnol. Bioeng. 58 (1998) 125.

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