An LCâMS-Based Chemical and Analytical Method for Targeted

ARTICLE pubs.acs.org/ac

An LCMS-Based Chemical and Analytical Method for Targeted Metabolite Quantification in the Model Cyanobacterium Synechococcus sp. PCC 7002 Nicholas B. Bennette,†,‡ John F. Eng,‡ and G. Charles Dismukes*,† †

Waksman Institute and Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 190 Frelinghuysen Road, Piscataway, New Jersey 08854, United States ‡ Department of Chemistry, Princeton University, Princeton, New Jersey 08540, United States

bS Supporting Information ABSTRACT: Herein, we detail the development of a method for the chemical isolation and tandem LCMS/MS quantification of a targeted subset of internal metabolites from cyanobacteria. We illustrate the selection of target compounds; requirements for and optimization of mass spectral detection channels, screening, and optimization of chromatography; and development of a sampling protocol that seeks to acheieve complete, representative, and stable metabolite extraction on the seconds time scale. Several key factors influencing the separation by reversed-phase ion pairing chromatography, specifically the hydrophobicity of the sample matrix and sensitivity to mobile phase acidity, are identified and resolved. We illustrate this methodology with an example from the autofermentative metabolism in the model cyanobacterium Synechococcus sp. PCC 7002, for which intracellular levels of 25 metabolites were monitored over 48 h, including intermediates in central carbon metabolism together with those involved in the cellular energy charge and redox poise. Upon removal of alternative reductant sinks (nitrate), anoxia induces autofermentation of carbohydrates with a parallel rise in the intracellular pyridine nucleotide redox poise that is specific to NAD(H) and alongside a gradual decline in the adenylate energy charge. This LCMS/MS-based method provides for accurate time-resolved quantification of multiple metabolites in parallel, thus enabling experimental verification of the active metabolic pathways.

O

ver the past decade, metabolomics—the measurement and study of the small-molecule metabolites that constitute biochemical networks—has rapidly expanded in its contribution to systems biology research; a simple key word search reveals the per annum usage of “metabolomics” in publications has steadily increased from a few dozen in 2001 to nearly 1000 in 2010. In particular, the evaluation of these intracellular components has or can aide in identification of novel biochemical pathways,13 evaluating consequences of and targets for metabolic engineering,46 and improving the quantitative understanding of metabolism from an organismal (systems biology) perspective.710 Although both nuclear magnetic resonance11,12 and mass spectrometric techniques1315 have been employed in metabolite measurements, GC/MS and LCMS methods typically offer limits of detection 23 orders of magnitude lower than those involving NMR16 and have rapidly emerged as the dominant approach to the quantification of intracellular metabolites. Although GC/MS has proven extremely adept at broad metabolite fingerprinting and untargeted metabolomics due to high chromatographic resolution, reproducibility, and sensitivity,17,18 many key metabolites of interest prove incompatible with the required volatilization or ionization processes. Conversely, the use of soft ionization techniques (ESI and APCI) typically r 2011 American Chemical Society

employed in LCMS tend to be more broadly applicable, particularly toward chemically unstable metabolites. For example, quantification is possible of both the redox active nucleotides (NADPH, NADH) and the hydrolytically unstable nucleotides (ATP, GTP, cAMP, PEP) that are crucial for the characterization of essentially all metabolic pathways.19,20 LCMS and GC/MS metabolomics approaches may be classified as either an unbiased profiling of all detectable compounds in a biological fluid/extract using a full-scan mass spectrometer, or a targeted measurement of a selected subset of metabolites using pre-established compound-specific decomposition channels with a tandem (MS/MS) instrument. Although the former approach can prove extremely powerful in identifying unknowns, the latter affords extremely low measurement noise and is suitable for examining metabolic networks in which the pathways and interstitial chemical components are known or suspected.1315,21,22 Targeted metabolite profiling has been successfully employed in several well-studied model organisms, most notably Escherichia coli8,9,2326 and Saccharomyces cerevisiae.9,2730 Often, Received: February 3, 2011 Accepted: April 5, 2011 Published: April 05, 2011 3808

dx.doi.org/10.1021/ac200108a | Anal. Chem. 2011, 83, 3808–3816

Analytical Chemistry however, the specifics of these approaches—extraction methodology, sample preparation, separation chemistry, and targeted compounds—are species- and hypothesis-dependent, though optimization of these elements may follow the common “roadmap”, described herein. In contrast, efforts toward complete metabolomics (the entire metabolome) have been developed for large, sometimes undefined, groups of metabolites approaching the diversity of chemical constituents of the complete cell.18,28,31 The resulting protocols are typically far less than ideal for analyzing selected subsets of metabolites, the characterization of which might be critical to the testing of specific hypotheses or models. Defining the critical intracellular species essential to understanding and evaluating subgroups of metabolism and pursuing their optimal quantification in method development enhances data quality when applied to the biological system of interest. Alhough the literature is full of methodological efforts in the individual elements of targeted LCMS/MS metabolomics (extraction, sample preparation, chromatographic separation, and mass spectrometric analysis), little has been reported concerning a holistic approach that optimizes all four steps for an atypical target species or for fermentative metabolism. Aquatic microbial oxygenic phototrophs, or AMOPs, are a diverse group consisting primarily of prokaryotic cyanobacteria and eukaryotic algae, for which detailed metabolomics investigations are needed owing to their growing prominence as potential biomass and biofuel precursors. With over 500 genera and tens of thousands of metabolically distinct strains, LCMS/MS studies of AMOPs have only begun to explore the enormous range of metabolic diversity these organisms utilize for nutrient management under varying trophic states;18,32,33 the response to and production of toxins;3436 and synthesis of biofuel precursors, mainly lipids.37,38 Herein, we describe the development of a complete chemical and analytical protocol for the isolation and LCMS/MS-based characterization of the fermentative metabolome of the hydrogen-producing cyanobacterium Synechococcus sp. 7002. Among the well-characterized cyanobacteria, Synechococcus sp. strain PCC 7002 is a fully sequenced, unicellular, marine cyanobacterium that is both tolerant of broad solar intensities and capable of surviving long periods of dark anaerobiosis,39 during which catabolism of photosynthetically stored glycogen may lead to hydrogen production via a nickeliron hydrogenase.6

’ MATERIAL AND METHODS For the LCMS/MS metabolomics process described here, we defined and pursued the following four-step cycle: (1) A target subset of metabolites was identified on the basis of the genome-derived biochemical network and the specific subset of metabolism to be studied. (2) Instrument-specific mass spectral detection channels were tested and optimized on the basis of fragmentation products and interferences for each target compound. (3) Chromatographic conditions affording optimal separation and yield of recovery of the selected metabolome were established, including necessary sample preparation steps to ensure chromatographic amenability. (4) The iterated LCMS/MS analytical method was used to optimize the chemical method of metabolite isolation and sample workup that produces the best “snapshot”; namely, the highest recovery of the target metabolome over the shortest period of time. The resulting metabolite concentrations were measured following the onset of dark anoxia and verified against previous observations under fermentative conditions, where available.

ARTICLE

2.1. Chemicals and Reagents. HPLC grade water (Optima, Fisher Chemical) and methanol (HPLC, Fisher Chemical) were used. Individual metabolite standards in the highest grade available, and tributylamine (puriss. plus, g99.5%), acetic acid (puriss. p.a., for LCMS), and all media components, were obtained through Sigma-Aldrich (St. Louis, MO). 2.2. Optimization of Mass Spectral Parameters. Experiments were conducted on an Agilent 1200 series binary HPLC system (Agilent Technologies, Waldbronn, Germany) coupled to an Agilent 6410 triple quadrupole mass analyzer equipped with an ion-spray source. Data were acquired and analyzed using Agilent Mass Hunter proprietary software (Build 1.02). The MS was operated in negative mode for selected reaction monitoring (SRM) development, method optimization, and sample analysis. Individual analyte standards (2 μL) dissolved in 50:50 MeOH/ H2O at a concentration of 1 μg/mL were injected at a flow rate of 500 μL/min for determining compound-dependent MS parameters. The parent and product ions of each compound were identified and employed to optimize the fragmentor voltage and collision voltage (CV). Entrance potential (Delta EMV) was set at 400 V for all SRM experiments. Nitrogen nebulizer gas temperature was generated from a Microbulk system (Airgas, Radnor, PA) and maintained at an inlet temperature of 350 °C. Collision gas was obtained from an Ultra High Purity pressurized nitrogen tank (Airgas, Radnor, PA). 2.3. Chromatographic Selection and Optimization. Previously reported hydrophilic interaction (HILIC),25 ion-exchange (IEX),40 and reversed-phase ion pairing (RIP)26 chromatographic parameters were taken from the literature and individually optimized toward maximal separation and collective peak efficiency for the target metabolite subset. Optimized separation conditions for each approach are described in Section 1.1 of the Supporting Information (SI 1.1) and summarized in Table 1. Injected sample volume for all cases was 10 μL; capillary voltage was 4000 V; and nebulizer gas flow rate and pressure were 11 L/min and 50 psi, respectively. MS1 and MS2 heaters were set to 100 °C. The mass spectrometer was set to “wide” resolution in Q1 and “unit” resolution in Q3. During chromatographic comparison, a single MRM segment was used with a 50 ms dwell time for each transition. 2.4. Quantification and Validation Procedure. Primary stock solutions were prepared in 50:50 MeOH/H2O at a concentration of 1000 μg/mL for each metabolite, with the exception of 6PG, GAP, GLU, and GLN, which were prepared at 250, 510, 500, and 100 μg/mL, respectively. From these solutions, one stock 50 μM standard mixture was prepared in 50:50 MeOH/H2O. Aliquots of this standard mixture were used for calibration, quality control (QC), and spiking into cell extracts for absolute quantification via standard addition. Calibration curves were obtained by analyzing standard solutions with the optimized LCMS/MS method at 10 concentrations, ranging from 1 nM to 50 μM (1, 5, 10, 50, 100, 500, 1000, 5000, 10 000, 50 000 nM) and plotting the height of the peak signal against the concentration of each compound (x). Weighted linear regression (weighted to 1/x, with x in nanomolar) was used to establish linearity for each compound. Measurement precision was determined by measuring the metabolites in one cellular extract (3:1 resuspension in water) and three standard mix samples at 100 nM, 500 nM and 5 μM (n = 5 replicates), where precision is indicated by the coefficient of variation, expressed in percent. The limit of quantification (LOQ) and the limit of detection 3809

dx.doi.org/10.1021/ac200108a |Anal. Chem. 2011, 83, 3808–3816

Analytical Chemistry

ARTICLE

Table 1. Individual Chromatographic Parameters for Each of the Three Developed Separation Methods, Optimized for Maximal Separation and Signal of the Compounds Listed in Table S-1 flow rate

time (min):

method

abbrev.

column

MPA

MPB

(mL/min)

MPB (%)

T (°C)

hydrophilic interaction

HILIC

Luna NH2; 150 2.0 mm, 3 μM

95% H2O, 5% ACN, 7 mM ammonium acetate

90% ACN, 10% MPA

0.15

0:50%, 3:0%, 14:0%, 19:90%, 20:90%, 20.1:50%

25

ion exchange

IEX

Rezex RHM Hþ,

H2O, 0.05%

N/A

0.4

isocratic (20 min)

45

100% MeOH

0.3

0:0%, 8:35%, 10.5:35%,

40

300 7.8 mm reversed-phase ion paring

RIP

Synergi Hydro RP, 150 2,1 mm, 3 μM

formic acid H2O, 10 mM tributylamine, 11 mM acetic acid

(LOD) were determined as 3- and 10-times the standard deviation of the blank, according to DIN 32645 (German Institute for Standardization) guidelines. To account for matrix effects, absolute concentrations in Synechococcus extracts were determined by the method of standard addition, whereupon a stock standard mixture was spiked into samples at 5% v/v at eight final spiked concentration levels (0, 10, 25, 50, 75, 100, 500, 1000 nM), from which linear calibration curves were created. 2.5. Optimizaton of Chemical Metabolome Isolation. The methodology for metabolite extraction optimization is described in Section 1.2 of the Supporting Information (SI 1.2). The optimized protocal is summarized below in Section 2.7. 2.6. Growth and Autofermentative Conditions. Cultures of Synechococcus sp. strain PCC 7002 were grown photoautotrophically in medium Aþ41 supplemented with 2 μM NiCl2 (for optimal hydrogenase activity) and continuously sparged with 2% (v/v) CO2 in air. Cells were grown for 3 days to late exponential phase (OD 550 nm ≈2.5) at 35 °C in 500 mL flasks using fluorescent lighting at an intensity of 200 μmol photons m2 s1 incident on one side. Fermentative conditions were induced as previously described.6 Briefly, cells were concentrated by centrifugation and resuspended in nitrate-free medium A at similar cell densities. Three milliliter aliquots of cell suspension were transferred to opaque, sealed, 10 mL vials and anoxic conditions induced by purging with argon. At each time point, metabolite quenching and extraction was performed as described below. 2.7. Sample Quenching, Extraction, And Preparation for LCMS/MS Analysis. At each time point, 3 108 cells were removed from the dark, anaerobic vials via syringe under argon and immediately injected into the vacuum filtration system described above, under argon and in darkness with a transfer time of less than 5 s. Following 1015 s of dark anoxic aspiration to remove media, filters were directly inverted into BD Falcon standard Petri dishes (Fisher Scientific 08-757-100A) loaded with 1.8 mL of 80:20 MeOH/H2O extraction solvent, precooled to 20 °C. The time for quenching to 20 °C was presumed to be near instantaneous for practical purposes, because the ratio of masses of cold solvent to sample was typically 1000:1. The total time for transfer, aspiration, and quenching (cooling) was tightly controlled. Following a 20 min incubation at 20 °C, filters were scraped and rinsed to ensure all cell material was in the liquid solvent, the solvent was transferred to microfuge tubes, and the dishes were rinsed with an additional 250 μL of fresh solvent that was subsequently combined with the initial solvent fraction. This combined volume was centrifuged at 14000g at 4 °C for 5 min, and the supernatant was removed and stored at 20 °C. The remaining pellet was resuspended in 100 μL of fresh extraction solvent, incubated at 20 °C for 15 min to ensure maximal metabolite extraction, and centrifuged at 14000g and 4 °C for

13:90%, 18:90%. 18.1:0%

5 min. The supernatant was then combined with that from the first extraction, which was not sufficient to isolate most metabolites (see Section 3.5). Total extraction time was ∼75 min, with samples continually cooled at 20 °C to maintain metabolic quenching, with the exception of the two 5 min centrifugation steps, performed at 4 °C. This combined fraction was vacuumcentrifuged (Conco Centri-Vap Concentrator) to remove all solvent. The resulting pellet was resuspended and fully soluble in HPLC grade H2O at one-third the volume of the vacuum centrifuged volume (optimization and reasoning described in Section 3.5) and either transferred to LCMS vials for analysis or stored at 80 °C for up to 24 h. 2.8. LCMS/MS Analysis. As detailed in Section 3.3, the RIP chromatographic approach was determined to be most effective for separating and resolving the suite of target metabolites. For time-dependent analyses of Synechococcus, samples were prepared as detailed above, separated using the optimized RIP method, and injected directly into the mass spectrometer via the electrospray ionization (ESI) source. To maximize sensitivity, the 18-min chromatographic run was divided into three MRM windows on the basis of retention time, with a 50 ms dwell time for each SRM. Chromatographic peaks were subsequently manually integrated via Agilent Qualitative Analysis software and entered into Microsoft Excel for further analysis.

’ RESULTS AND DISCUSSION 3.1. Target Metabolome Determination. Figure 1 shows an in silico map of selected reactions of central carbon metabolism under dark anoxia, as predicted using the fully sequenced genome of Synechococcus sp. PCC 7002 (http://www.genome. jp/kegg-bin/show_organism?org=syp) and biochemical observations.6 This network contains 70 metabolites, providing a list of potential targets for the quantitative characterization of autofermentation in Synechococcus. Requirements for inclusion into the suite of detected metabolites additionally included availability of a suitably pure standard, amenability to ESI, and stability and retention under the applied chromatographic conditions. The resulting 32 compounds employed for LCMS method development based on these parameters are listed in Table S-1 of the Supporting Information. 3.2. Mass Spectral Parameters. Table S-1 lists the abbreviations, molecular weights, and SRM values determined and optimized for each of the 32 detected metabolites as well as the product ion formulas, where possible. With the exception of CoA, for which the [M 2H]2- species proved to be dominant, [M H] parent ions were detected as the primary negative mode electrospray product. Individual optimization of the precollision fragmentation voltage for maximal parent ion signal 3810



ARTICLE

Figure 1. Hypothetical anoxic central metabolism for Synechococcus sp. PCC 7002, reconstructed from genetic and biochemical data. Dashed arrows represent reactions for which a mediating enzyme was not detected in the genome. Metabolites in bold indicate ultimate inclusion in the described LCMS method.

indicates a correlation with molecular ion size (r = 0.804), as anticipated. Employing these parameters, product ion spectra were collected for each species under increasing collision cell voltage up to the absence of any detectable parent ion. From these data, the strongest product ion signals were selected, and the CV locally tuned in SRM mode. Although the mean optimal CV across all metabolites was 24 V, the median value of 10 V indicates the majority of target compounds optimally decomposed in the 420 V range, with a few select species (AcCoA, ADP, ATP, CoA, NADH, and NADPH) requiring >40 V for efficient product ion formation.

Dihydrogen phosphate (H2PO4, m/z = 97) and metaphosphate (empirical formula PO3, m/z = 79) comprised the majority of predominant sugar phosphate collision products, as seen previously in other quadrupole mass analyzers,26 with the remainder of product ions typically in the 70150 m/z range. All species produced strong SRM signals, with the exception of PYR, which produced a very small 43 m/z product ion, and E4P, which has shown prior disposition to poor electrospray performance.26,42 3.3. Chromatographic Optimization and Comparison. Figure 2 shows the total ion chromatographs (TICs) of three 3811



Figure 2. Combined SRM chromatograms for a standard mixture of 30 metabolites under three optimized chromtographic conditions: (a) RIP; (b) HILIC; (c) IEX. Abscissa: retention time in minutes; ordinate; total ion count, in thousands. A pure standard mix dissolved in 50:50 MeOH/ H2O containing 100 ng of each the components listed in Table S-1 was injected on-column. Chromatographic parameters listed in Table 1.

distinct and previously applied separation methods selected from the literature and locally optimized for maximal peak efficiency and retention of the selected suite of target compounds, the parameters of which are described in Table 1. The intent of this comparison was to evaluate which of the available chromatography approaches was most suitable for the specific group of metabolites in question. As Figure 2 illustrates, the RIP separation, employing tributylamine (TBA) as a pairing reagent, afforded the best retention and separation of the targeted compounds, as compared with IEX and HILIC methodologies. In addition, the somewhat more stable operating conditions of the RIP approach, such as the less basic pH (4.95 vs 9.4) than HILIC methods and the lower temperature than IEX, made it particularly practical for the evaluation of biological extracts and thermally labile metabolites. Although ion-pairing approaches and the requisite millimolar concentrations of pairing agents in the aqueous mobile phase can cause significant interference with full-scan mass spectrometric analyses, the SRM-based detection employed here effectively eliminates such concerns. Having determined the RIP chromatography as most suitable for the target subset of the metabolome of Synechococcus, a thorough optimization of its parameters was undertaken toward balancing and maximizing peak efficiency and separation. Luo et al.26 thoroughly examined many of these variables as they influenced the separation of glycolytic intermediates, ultimately settling on TBA as the most suitable alkylamine in balancing

ARTICLE

retention with peak quality. Although increasing TBA in the aqueous mobile phase improves overall retention, particularly for complex samples, concerns regarding peak suppression at high pairing reagent concentrations led them to employ 10 mM TBA as most suitable. Luo et al. additionally examined the effect of aqueous phase pH, controlled via the addition of acetic acid (AA), and determined that although increasing MPA acidity from pH 6.8 to 4.95 had little effect on phosphate-containing molecules and nucleotides, the pKa of which tend to be around 1, the separation of significantly less acidic carboxylic acids was significantly improved due to pH dependent-dissociation. On the basis of these conclusions, we conducted a more detailed analysis of pH dependence between 10 mM AA (pH 6.8) and 15 mM AA (pH 4.6), a 2-order of magnitude range in proton concentration. As expected and observed for similar metabolite subclasses, decreasing MPA pH resulted in decreased retention time and sharper peaks and an overall view of the separation, as evidenced by the TIC supported the use of 15 mM AA. Once individual SRMs were analyzed at 1015 mM AA in 1 mM increments, however, it was apparent that lower pH significantly diminished peak height and shape for a number of critical metabolites, as shown in Figure 3. In addition, an increase in the AA concentration led to actual deterioration in peak shape for several key components (ADP, ATP, and NADPH in Figure 3), attributed to increased protonation of the anionic forms detected in the negative mode and indicating a significant effect of even slight alterations in aqueous mobile phase pH on the effectiveness of the RIP method as applied to the selected metabolome. On the basis of these results, 11 mM AA (pH 5.6) was selected as offering the best overall separation efficiency and maximal overall peak quality for the individual metabolite targets. In addition, the effect of column temperature was examined because a higher temperature in ion-pairing separation tends to reduce backpressure and permit higher flow rates without exceeding permissible column pressure. It was determined that a column oven setting of 40 °C afforded no loss in total peak area of the targeted metabolites while reducing back pressure roughly 30% (data not shown). Flow rate was subsequently increased, leading to improved chromatographic efficiency. Last, numerous chromatographic timetable parameters, including the delay in and rate of the initial increase of the organic mobile phase (methanol), length, and magnitude of the intermediate plateau in MPB and the rate of the second, postplateau increase in percent MPB were examined and compared, the optimized values of which are reported in Section 2.3. 3.4. Method Validation and Performance. Table S-2 (Supporting Information) shows performance parameters for the optimized 30-min RIP chromatographic method and multiple reaction monitoring (MRM) mass spectral detection. Overall performance was in line with previously reported values for similar groups of metabolites,26,42 with the majority of components detected into the nanomolar range. Limits of detection were similar to those reported by Luo et al.,26 wherein a 50% longer column was employed, requiring 3-fold longer run times. Calibration curves generally showed excellent linearity across 34 orders of magnitude, with the majority of R2 coefficients 0.99 and above. The quantitative detection of two target compounds, PYR and E4P, proved difficult on the basis of their poor mass spectral ionization and SRM signal strength. Sensitivity performance was reported according to DIN32645, with LOD and LOQ values in line with those previously reported for similar methodological approaches. Last, repeatability was measured 3812



Figure 3. pH effect on overall RIP separation and peak shape for a selected subset of metabolites. Left column, 10 mM acetic acid (pH 6.8); middle column, 11 mM acetic acid (pH 5.6); right column, 12 mM acetic acid (pH 4.95). All other RIP chromatographic parameters as listed in Table 1.

at three standard mixture concentrations (100, 1000, and 10 000 nM) as well as for metabolite extract spiked to a final added standard concentration of 10 μM. Although coefficients of variation for many species are high (>20%), at the lowest concentration, they generally fall below 20% at 1 μM and are mostly within 10% for the 10 μM standard and spiked cell extract. Critically, the optimized method affords clear retention time differentiation of similar chemical species, such as G6P (8.25 min) and F6P (8.7 min), DHAP (10.75 min) and GAP (9.3 min), and ADP (13.8 min) and ATP (14.8 min). As seen in Figure 3, insource fragmentation of the later-eluting ATP peak produces ions seen in the ADP SRM spectrum and does not affect peak quantification, given the distinctly offset retention time. We presume this retention time specificity and the use of spiked samples to measure elution largely eliminates concerns for metabolites with identical mass spectral parameters that are

ARTICLE

not directly measured, such as convolution of 3PG by intracellular 2PG. 3.5. Extraction Method Optimization. Using the optimized LCMS/MS method, evaluation of the most suitable extraction methodology for isolating the targeted metabolome from Synechococcus was conducted. Given the labile nature of many of these central energy metabolism compounds, rapid cessation of metabolism and preservation of metabolite pools was established as a key feature of any method selected for further optimization. Although methods for the quenching of microbial metabolism without cell rupture via subzero iso-osmotic solvents have been reported in the literature,26,42,43 other reports indicate such procedures induce irreducible cold-shock phenomenon.44 Consequently, combined quench/extraction methodologies were pursued in which cells are rapidly separated from aqueous media and then subjected to chemical solvents aimed at arresting metabolism and rupturing cells simultaneously. Such methodologies additionally permitted examination and optimization of a single quench/extraction step, albeit one with numerous variables. In particular, we employed the rapid filtration methodology first reported by Brauer et al.,9 in which suspended cells are rapidly injected onto nylon filters under vacuum and subsequently transferred into the combined quench/extraction solvent. This approach allows for complete sample transfer to quenching conditions within a few seconds, is amenable to use with extremely cold solvents (less than 20 °C) shown to be more effective in arresting metabolism,45,46 and is compatible with maintaining the dark anaerobic conditions requisite for autofermentation up to the point of metabolic cessation. A complete discussion of the optimization process is contained in section 1.3 of the Supporting Information (SI 1.3), along with results as displayed in heat-map format (Table S-3). Despite claims in the literature that 10 mM TBA in the aqueous mobile phase is sufficient to retain the same separation efficiency evidenced in analysis of pure standards, comparison of the TICs from a 10 μM standard mixture and 10 μM spiked Synechococcus extract (95% sample, 5% standard mixture) revealed significantly poorer peak shapes and loss of retention in the latter. Although it was possible that the markedly more complex biological matrix was responsible for an effective saturation of the ion-pairing ability, the forward shifting elution times suggested an effect similar to the introduction of organic mobile phase. Given the 80% MeOH composition of the biological extract, compared with 50% in the pure standard, it was presumed that the net introduction of 8 μL MeOH on column (10 μL injection volume) was perturbing the established gradient profile. To investigate this effect, 10 μM spiked Synechococcus extract was vacuum-concentrated in triplicate and resuspended in an equivalent amount of HPLC H2O. Figure 4 shows the comparative TICs of the original and matrix-substituted spiked biological extracts, in which the latter displays substantially sharper peak shapes and separation efficiency, particularly in the 810 min elution region, where the majority of monophosphorylated sugars elute, and clearly indicates the detrimental influence of a large organic fraction in 10 μL sample injections. The effect of matrix-substitution on total peak area was evaluated using both argon aspiration and vacuum centrifugation for removal of the 80% MeOH matrix. Following sample resuspension in H2O, neither method showed significant metabolite loss and produced equivalent and overlapping results (data not shown). Given the adoption of a matrix removal step in the 3813



Figure 4. Effect of matrix replacement on chromatographic efficiency: (a) Combined SRMs of all metabolites for a Synechococcus metabolite extract spiked with a standard mix of target compounds to a final concentration of 10 μM. Analysis of triplicate biological extracts shown. (b) Combined SRMs for the same 10 μM spiked Synechococcus extract following removal of the extraction matrix via argon aspiration and resuspension in 100% HPLC grade H2O.

sample preparation process, the possibility of replacing the 80% organic matrix with a lesser amount of H2O was considered, multiplying the concentration of all constituents by the fold reduction in volume and potentially boosting signal heights of critical near-LOQ metabolites to levels more amenable to quantification. As shown in Figure S-1 (Supporting Information), up to a 5-fold increase in total sample concentration was possible with a correspondingly linear increase in signal-tonoise, above which deviation from predicted behavior and high variance was observed. Correspondingly, and to ensure no illeffects for samples with particularly high metabolite concentrations, matrix replacement at a 3-fold reduction in volume was pursued for all reported biological sample metabolite quantification. In summary, final sample preparation protocol included two sequential rounds of 80:20 M extraction on 3.0 1011 cells, yielding a final extraction solvent volume of ∼2.0 mL, which was subsequently vacuum-concentrated and resuspended in 3-fold less H2O (by volume). Although matrix replacement was demonstrated not to alter metabolite yields and profile, calibration curves for matrix-substituted samples were prepared by the method of standard addition (spiking with 5% (by volume) standard mix at varying final spiked concentration values) prior to matrix removal and subjected to the identical concentration procedure to ensure accurate metabolite quantification from cell extracts. Metabolite-specific calibration curves were subsequently constructed, from which absolute metabolite concentrations were obtained.

ARTICLE

3.6. Application to Autofermentation in Synechococcus. An overlay of three biological replicate TICs for a Synechococcus metabolite extract prepared under the above developed protocol and analyzed using the optimized RIP LCMS/MS method (Table 1) is shown in Figure S-2 (Supporting Information), demonstrating both high reproducibility and resolution in biological samples. As expected, the most prominent peak is that due to GLU, one of the largest absolute intracellular pool sizes in E. coli,10 and many of the central carbon intermediates and energy carriers display strong signal-to-noise, a fidelity even more evident in their individual SRM detection channels. In addition to the 16 metabolites identified in Figure S-2, 10 additional species (PYR, R5P, 6PG, RuBP, FBP, NADH, AcCoA, SUC, NADP, NADPH) were routinely detectable in the spike-free extract of Synechococcus, although routine quantification is often condition-specific. NADH, for example, is readily observable only following a period of autofermentation, whereas pyruvate (PYR) is measurable under photoautotrophy and not dark anoxia. These detection thresholds are established by the method as well as the instrument. To confirm the efficacy of the developed methodology toward deciphering biological phenomena, it was employed to measure the pool sizes of the above detected metabolites in a timedependent analysis of autofermentation in Synechococcus, the analysis of which is the primary motivation for this work. Figure 5 shows the behavior of two key intracellular markers, the cellular energy charge (CEC), (ATP þ 1/2ADP)/(ATP þ ADP þ AMP), and the pyridine nucleotide redox poise, here shown as the separate contributions from NADH/NADþ and NADPH/ NADPþ. The data provided were taken during the course of autofermentation over 48 h in the presence of varying amounts of extracellular nitrate, a terminal electron sink, following growth under conventional (nitrate replete) conditions. In a wide variety of aerobic microbial and animal cells, maintenance of a CEC near 0.85 under metabolically sustainable states has been observed.19 The minimal value for viability, however, is somewhat species-specific; following carbon starvation in E. coli, a steady drop to 0.5 is observed,47 after which cell death and a more rapid drop occurs, whereas the equivalent experiment in S. cerevisiae48 produces a value closer to 0.1. As shown in Figure 5, an analogous drop in CEC following the onset of autofermentation is evidenced in Synechococcus, from a maximal initial value of 0.70.8 at the beginning of dark anoxic incubation to a value of 0.30.4, which remains somewhat constant for up to 4 days of autofermentation, after which cells lose viability and the onset of lysis is observed. Inactivation of nitrate reductase activity in Chlorella fusca resulted in no major change to the cellular energy charge during autotrophic conditions, and similar effects were seen in Cichorium intybus.49 Likewise in Synechococcus, Figure 5 indicates the observed drop in CEC is unaffected by the presence of nitrate, corroborating internal observations that net glycogen catabolism and cell viability during autofermentation bear no dependence on extracellular nitrate in this organism. Although nitrate shows no effect on cellular energy charge in Synechococcus, consistent with previous observations, the presence of this potent terminal electron acceptor has a significant impact on the redox poise during autofermentation in Synechococcus. Specifically, the addition of nitrate during autofermentation delays the increase in the ratio of reduced-to-oxidized NAD(H), which builds from the onset of dark catabolism in nitrate-free cultures, shown in Figure 5. Notably, no such 3814



ARTICLE

which corresponds to the amount of nitrate present at the onset of dark anoxia. Because the reduction potential of protons is roughly 800 mV more negative than that of nitrate, the latter must be completely reduced to nitrite before appreciable hydrogen evolution is observed,51 presuming chemical equilibrium conditions prevail in the cell. Last, the mass-spectrometric quantification of NAD(P)(H) reported here corroborates in vivo-reduced pyridine nucleotide fluorescence measurements in A. maxima, in which the combined NAD(P)H signal is strongly correlated with the hydrogen production rate (Ananyev and Dismukes, unpublished). Notably, the method described here permits separate measurement of NADH and NADPH, currently not possible using fluorescence-based techniques.

Figure 5. Synechococcus intracellular metabolite data following dark anaerobic induction under varying levels of extracellular sodium nitrate (0, 0.5, or 1 mM): (A) CEC: (ATP þ 1/2ADP)/(ATP þ ADP þ AMP); (B) NAD(H) redox poise, NADH/NADþ; (C) NADP(H) redox poise, NADPH/NADPþ.

increase in the ratio of NADPH/NADPþ occurs, indicating both the selective catabolism of glycogen via the EmbdenMeyerhof over the oxidative pentose phosphate pathway, as well as a disequilibrium between the two redox carriers. The observed delay in internal redox poise measured here supports previous observations, both elsewhere for Synechocystis sp. PCC 680350 and in our laboratory, wherein the presence of nitrate significantly diminishes autofermentative hydrogen production in Synechococcus as well as in the cyanobacterium Arthrospira maxima.51 In the case of A. maxima, a more robust hydrogen producer, H2 evolution occurs following a temporal delay, the magnitude of

4. CONCLUSION Herein, we have described the development of a complete hypothesis-to-data LCMS metabolomics approach for the analysis of autofermentative hydrogen production in the model cyanobacterium Synechococcus 7002, including identification of target metabolites, development of mass spectral detection channels, selection of chromatographic parameters, and optimization of the chemical method for target metabolome isolation. To analyze dark anoxic metabolism in a time-dependent fashion across differing environmental and genetic perturbations with biological replicates, a suitably rapid analysis methodology is preferred to analyze the potentially large number of samples with minimal delay. The method described herein includes a total LCMS analysis time of 30 min, approximately a 3-fold reduction as compared with the chromatographic approach on which the optimized separation is based.26 Although MS techniques are transferable across organisms, with alterations depending only on instrumentation differences, chemical extraction and separation techniques tend to be organism- and hypothesis-specific. This is evidenced by the selection of extraction solvent composition, which was markedly different from that shown to be most effective for E. coli.52 Similarly, the mobile phase requirements differ, depending on the classes of metabolites most critical for quantification, as demonstrated in the varying effect of aqueous phase pH on the separation and peak quality on several key compounds (Figure 3), and suggests that local optimization of such variables is important for adaptation to the target set of metabolites. Additional parameters that were adjusted here for overall optimal separation and detection of the selected metabolome include column temperature; flow rate; delay and rate of organic phase introduction; and elements of the mobile phase gradient profile, such as length and composition of the timetable “plateau”. Last, our results indicate that sample solvent composition has a dramatic effect on the overall quality of RIP chromatographic separation and that matrix replacement with H2O resulted in significant improvements in retention time repeatability, peak shape, and overall method performance. Overall, in addition to outlining the complete development of a species-specific LCMS/MS methodology and identifying several critical factors influencing analytical performance, the results of an initial analysis of the influence of nitrate on autofermentation in Synechococcus that both corroborate and enhance the understanding of previous phenomenological observations and measurements are reported. We anticipate the subsequent application of the described approach toward detailed characterization of the pathways leading to and competing with hydrogen production in this model cyanobacterium. 3815



’ ASSOCIATED CONTENT

bS

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

’ AUTHOR INFORMATION Corresponding Author

*Phone: (732) 445-6786. Fax: (732) 445-5735. E-mail: dismukes@ rci.rutgers.edu.

’ ACKNOWLEDGMENT This work was supported by the Air Force Office of Scientific Research (MURI Grant FA9550-05-1-0365), which is gratefully acknowledged, as well as fellowship support from the NSF IGERT program (Award No. 0903675). The authors thank Agilent Technologies for its partnership in obtaining instrumentation, developing the described methodology, and continued technical support. ’ REFERENCES (1) Amador-Noguez, D.; Feng, X.-J.; Fan, J.; Roquet, N.; Rabitz, H.; Rabinowitz, J. D. J. Bacteriol. 2010, 192, 4452–4461. (2) Farag, M. A.; Huhman, D. V.; Dixon, R. A.; Sumner, L. W. Plant Physiol. 2008, 146, 387–402. (3) Baran, R.; Bowen, B. P.; Bouskill, N. J.; Brodie, E. L.; Yannone, S. M.; Northen, T. R. Anal. Chem. 2010, 82, 9034–9042. (4) Shastri, A. A.; Morgan, J. A. Biotechnol. Prog. 2005, 21, 1617–1626. (5) Chong, W. P. K.; Reddy, S. G.; Yusufi, F. N. K.; Lee, D.-Y.; Wong, N. S. C.; Heng, C. K.; Yap, M. G. S.; Ho, Y. S. J. Biotechnol. 2010, 147, 116–121. (6) McNeely, K.; Xu, Y.; Bennette, N.; Bryant, D. A.; Dismukes, G. C. Appl. Environ. Microbiol. 2010, 76, 5032–5038. (7) Kell, D. B. Drug Discovery Today 2006, 11, 1085–1092. (8) Yuan, J.; Fowler, W. U.; Kimball, E.; Lu, W. Y.; Rabinowitz, J. D. Nat. Chem. Biol. 2006, 2, 529–530. (9) Brauer, M. J.; Yuan, J.; Bennett, B. D.; Lu, W.; Kimball, E.; Botstein, D.; Rabinowitz, J. D. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 19302–19307. (10) Bennett, B. D.; Kimball, E. H.; Gao, M.; Osterhout, R.; Van Dien, S. J.; Rabinowitz, J. D. Nat. Chem. Biol. 2009, 5, 593–599. (11) Wishart, D. S. TrAC, Trends Anal. Chem. 2008, 27, 228– 237. (12) Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181–1189. (13) Lu, W.; Bennett, B. D.; Rabinowitz, J. D. J. Chromatogr. B 2008, 871, 236–242. (14) Wilson, I. D.; Plumb, R.; Granger, J.; Major, H.; Williams, R.; Lenz, E. M. J. Chromatogr. B 2005, 817, 67–76. (15) Villas-Boas, S. G.; Mas, S.; Akesson, M.; Smedsgaard, J.; Nielsen, J. Mass Spectrom. Rev. 2005, 24, 613–646. (16) Carrieri, D.; McNeely, K.; De Roo, A. C.; Bennette, N.; Pelczer, I.; Dismukes, G. C. Magn. Reson. Chem. 2009, 47, S138–S146. (17) Pasikanti, K. K.; Ho, P. C.; Chan, E. C. Y. J. Chromatogr. B 2008, 871, 202–211. (18) Bolling, C.; Fiehn, O. Plant Physiol. 2005, 139, 1995–2005. (19) Pradet, A.; Raymond, P. Annu. Rev. Plant Physiol. 1983, 34, 199–224. (20) Pollak, N.; Dolle, C.; Ziegler, M. Biochem. J. 2007, 402, 205– 218. (21) Fiehn, O. Plant Mol. Biol. 2002, 48, 155–171. (22) Bino, R. J.; Hall, R. D.; Fiehn, O.; Kopka, J.; Saito, K.; Draper, J.; Nikolau, B. J.; Mendes, P.; Roessner-Tunali, U.; Beale, M. H.;

ARTICLE

Trethewey, R. N.; Lange, B. M.; Wurtele, E. S.; Sumner, L. W. Trends Plant Sci. 2004, 9, 418–425. (23) Edwards, J. S.; Palsson, B. O. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 5528–5533. (24) Rabinowitz, J. D. Expert Rev. Proteomics 2007, 4, 187–198. (25) Bajad, S. U.; Lu, W. Y.; Kimball, E. H.; Yuan, J.; Peterson, C.; Rabinowitz, J. D. J. Chromatogr. A 2006, 1125, 76–88. (26) Luo, B.; Groenke, K.; Takors, R.; Wandrey, C.; Oldiges, M. J. Chromatogr. A 2007, 1147, 153–164. (27) Cipollina, C.; ten Pierick, A.; Canelas, A. B.; Seifar, R. M.; van Maris, A. J. A.; van Dam, J. C.; Heijnen, J. J. J. Chromatogr. B 2009, 877, 3231–3236. (28) Lu, W.; Clasquin, M. F.; Melamud, E.; Amador-Noguez, D.; Caudy, A. A.; Rabinowitz, J. D. Anal. Chem. 2010, 82, 3212–3221. (29) Martins, A. M.; Sha, W.; Evans, C.; Martino-Catt, S.; Mendes, P.; Shulaev, V. Yeast 2007, 24, 181–188. (30) Wu, L.; Mashego, M. R.; van Dam, J. C.; Proell, A. M.; Vinke, J. L.; Ras, C.; van Winden, W. A.; van Gulik, W. M.; Heijnen, J. J. Anal. Biochem. 2005, 336, 164–171. (31) Lee, D.; Fiehn, O. Plant Methods 2008, 4, 7. (32) Renberg, L.; Johansson, A. I.; Shutova, T.; Stenlund, H.; Aksmann, A.; Raven, J. A.; Gardestrom, P.; Moritz, T.; Samuelsson, G. Plant Physiol. 2010, 154, 187–196. (33) Eisenhut, M.; Huege, J.; Schwarz, D.; Bauwe, H.; Kopka, J.; Hagemann, M. Plant Physiol. 2008, 148, 2109–2120. (34) Sans-Piche, F.; Kluender, C.; Altenburger, R.; Schmitt-Jansen, M. Mar. Environ. Res. 2010, 69, S28–S30. (35) Kr€uger, T.; M€onch, B.; Oppenh€auser, S.; Luckas, B. Toxicon 2010, 55, 547–557. (36) Diehnelt, C. W.; Peterman, S. M.; Budde, W. L. TrAC. Trends Anal. Chem. 2005, 24, 622–634. (37) Barupal, D.; Kind, T.; Kothari, S.; Lee, D.; Fiehn, O. BMC Biotechnol. 2010, 10, 40. (38) Vieler, A.; Wilhelm, C.; Goss, R.; S€uss, R.; Schiller, J. Chem. Phys. Lipids 2007, 150, 143–155. (39) Carrieri, D.; Kolling, D.; Ananyev, G.; Dismukes, G. C. Ind. Biotechnol. 2006, 2, 133–137. (40) Eisenreich, W.; Ettenhuber, C.; Laupitz, R.; Theus, C.; Bacher, A. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 6764–6769. (41) Stevens, S. E.; Patterson, C. O.; Myers, J. J. Phycol. 1973, 9, 427–430. (42) Huck, J. H. J.; Struys, E. A.; Verhoeven, N. M.; Jakobs, C.; van der Knaap, M. S. Clin. Chem. 2003, 49, 1375–1380. (43) Lu, W. Y.; Kimball, E.; Rabinowitz, J. D. J. Am. Soc. Mass Spectrom. 2006, 17, 37–50. (44) Wittmann, C.; Kromer, J. O.; Kiefer, P.; Binz, T.; Heinzle, E. Anal. Biochem. 2004, 327, 135–139. (45) Faijes, M.; Mars, A. E.; Smid, E. J. Microb. Cell Fact. 2007, 6, 27. (46) Maharjan, R. P.; Ferenci, T. Anal. Biochem. 2003, 313, 145–154. (47) Chapman, A. G.; Fall, L.; Atkinson, D. E. J. Bacteriol. 1971, 108, 1072–1086. (48) Ball, W. J., Jr; Atkinson, D. E. J. Bacteriol. 1975, 121, 975–982. (49) Chraibi, A.; Paîms, B.; Druart, N.; Goupil, P.; Gojon, A.; Rambour, S. J. Exp. Bot. 1995, 46, 1525–1533. (50) Gutthann, F.; Egert, M.; Marques, A.; Appel, J. Biochim. Biophys. Acta, Bioenerg. 2007, 1767, 161–169. (51) Ananyev, G.; Carrieri, D.; Dismukes, G. C. Appl. Environ. Microbiol. 2008, 74, 6102–6113. (52) Rabinowitz, J. D.; Kimball, E. Anal. Chem. 2007, 79, 6167– 6173.

3816


An LCâMS-Based Chemical and Analytical Method for Targeted

Recommend Documents

An LCâMS-Based Chemical and Analytical Method for Targeted