Quantitation of Carbohydrate Monomers and Dimers by Liquid Chromatography Coupled with High-Resolution Mass Spectrometry T

Carbohydrate Research 468 (2018) 30–35

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Carbohydrate Research

journal homepage: www.elsevier.com/locate/carres

Quantitation of carbohydrate monomers and dimers by liquid chromatography coupled with high-resolution mass spectrometry T

∗ Krista A. Barzen-Hanson1,2, Rebecca A. Wilkes2, Ludmilla Aristilde

Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA

ARTICLE INFO ABSTRACT

Keywords: As remnants of plant wastes or plant secretions, carbohydrates are widely found in various environmental Liquid chromatography matrices. Carbohydrate-containing feedstocks represent important carbon sources for engineered bioproduction Mass spectrometry of commodity compounds. Routine monitoring and quantitation of heterogenous carbohydrate mixtures requires Glucose fast, accurate, and precise analytical methods. Here we present two methods to quantify carbohydrates mixtures Monosaccharides by coupling hydrophilic interaction liquid chromatography with electrospray ionization high-resolution mass Disaccharides spectrometry. Method 1 was optimized for eleven different carbohydrates: three pentoses (ribose, arabinose, xylose), three hexoses (glucose, fructose, mannose), and five dimers (sucrose, cellobiose, maltose, trehalose, lactose). Method 1 can monitor these carbohydrates simultaneously, except in the case of co-elution of xylose/ arabinose and lactose/maltose/cellobiose peaks. Using the same stationary and mobile phases as in Method 1, Method 2 was developed to separate glucose and galactose, which were indistinguishable in Method 1. Both methods have low limits of detection (0.019–0.40 μM) and quantification (0.090–1.3 μM), good precision (2.4–13%) except sucrose (18%), and low mass error (0.0–2.4 ppm). Method 1 was robust at analyzing high ionic strength solutions, but a moderate matrix effect was observed. Finally, we apply Method 1 to track concurrently the extracellular depletion of five carbohydrates (xylose, glucose, fructose, mannose, and maltose) by Pseudomonas protegens Pf-5, a biotechnologically-important soil bacterial species.

1. Introduction chromatography (HPLC) [6,7], ligand exchange chromatography [8], or high-performance anion-exchange chromatography (HPAEC) [9]. Carbohydrates, which serve as important carbon sources for cellular Common detection methods, which can be coupled with these chro- metabolism, are essential feedstocks for engineered bioproduction. In matographic methods, include UV–Vis, fluorescence, or refractive index the environment, complex cellulosic materials from plant biomass are (RI) detectors. These chromatographic and detection methods are not broken down by microorganisms into bioavailable carbohydrate desirable for high-throughput sample analysis because they require monomers [1]. Taking advantage of the energy-rich renewable resource chemical pre-processing involving either derivatization that makes of cellulosic biomass, engineered bioproduction employs microbial cell sample preparation tedious or metal ion additives that complicate factories to produce value-added products sustainably [2–4]. Therefore, analysis [10–12]. Furthermore, RI detectors require isocratic elution, there is special interest in obtaining accurate and precise quantification which decreases selectivity and sensitivity, and can have high inter- of the heterogeneous composition of carbohydrates in environmental ference from sample matrices [10,11,13]. Capillary zone electrophor- samples, during chemical hydrolysis of cellulosic polymers, and in esis, which has been used to obtain high-resolution separation of sugar biological reactors during bioproduction of value-added products. alcohols and carbohydrates, has improved detection limits compared to A variety of analytical methods have been reported for quantifying HPLC coupled with RI or UV–Vis detectors, but is not preferred for mixtures of carbohydrates despite the challenge of separating and de- mixture analysis due to common co-elution of compounds and difficulty tecting analytes that are isomeric and lack chromophores or fluor- in detecting sucrose (a common dimer) [10]. To overcome the draw- ophores. Previous studies with chromatographic separations have em- backs of these detection methods, evaporative light-scattering detection ployed gas chromatography [5], reverse phase high performance liquid (ELSD) has become a popular alternative method to couple with

∗ Corresponding author. 214 Riley-Robb Hall, Cornell University, Ithaca, NY, 14850, USA. E-mail address: [email protected] (L. Aristilde). 1 Present Address: Division of Mathematics and Natural Sciences, Elmira College, Elmira, NY, 14901, USA. 2 Co-ﬁrst authors. https://doi.org/10.1016/j.carres.2018.08.007 Received 8 June 2018; Received in revised form 8 August 2018; Accepted 11 August 2018 Available online 12 August 2018 0008-6215/ © 2018 Published by Elsevier Ltd. K.A. Barzen-Hanson et al. Carbohydrate Research 468 (2018) 30–35

Table 1 Analytical performancea of Method 1 and Method 2 in MiliQ water.

Carbohydrate Retention Time Theoretical m/z Measured m/z Mass Errorb LODc LOQc Precisiond (%RSD at Precision (%RSD at LOD Comparison (μM) (min) (ppm) (μM) (μM) 2.5 μM) 10 μM)

Method 1

Ribose 3.25 149.0455 149.0458 2.0 0.30 0.92 10 10 NDe Xylose 4.68 149.0455 149.0455 0.0 0.12 0.37 10 7.5 0.18 (LC-MS) [12] Arabinose 4.33 149.0455 149.0457 1.3 0.11 0.32 6.9 7.8 NDe Fructose 6.15 179.0561 179.0563 1.1 0.14 0.37 12 12 0.090 (LC-MS) [12] 0.73 (LC-MS) [23] 0.95 (GC-MS) [23] 340 (LC-ELSD) [11] 3.4 (LC-ELSD) [23] Mannose 8.46 179.0561 179.0563 1.1 0.15 0.44 14 12 NDe Glucose 9.91 179.0561 179.0563 1.1 0.14 0.42 11 13 0.30 (LC-MS) [12] 0.056 (LC-MS) [23] 3.4 (GC-MS) [23] 170 (LC-ELSD) [11] 5.6 (LC-ELSD) [23] Sucrose 14.88 341.1089 341.1097 2.4 0.24 0.84 18 18 0.18 (LC-MS) [12] 0.85 (GC-MS) [23] 27 (LC-ELSD) [11] 7.0 (LC-ELSD) [23] Maltose 15.47 341.1089 341.1094 1.5 0.28 0.76 11 8.1 0.018 (LC-MS) [12] 1.7 (GC-MS) [23] 7.5 (LC-ELSD) [23] Cellobiose 15.38 341.1089 341.1097 2.3 0.36 1.1 13 14 NDe Trehalose 16.26 341.1089 341.1083 1.8 0.019 0.090 7f 9f 0.028 (LC-MS) [24] 0.022 (LC-MS) [25] 600 (HPLC-RID) [25] Lactose 15.46 341.1089 341.1083 1.8 1.2 4.7 7f 11f 2.63–5.84 [14]

Method 2

Glucose 13.15 179.0561 179.0561 0.0 0.19 0.61 2.4 2.5 See above Galactose 11.70 179.0561 179.0561 0.0 0.40 1.3 3.7 3.6 750 (LC-ELSD) [11]

a The values in the table were obtained from six replicates (n = 6). b Mass error was calculated in parts-per-million (ppm). c Limit of detection (LOD) and limit of quantification (LOQ). d Relative standard deviation (RSD). e Not Determined. f These values were obtained from five replicates (n = 5). chromatography for detecting carbohydrates [10,11,14,15]. While still interfere with the sensitivity [19]. ELSD enhances the analyte intensity over RI detectors, the sensitivity Application of MS with electrospray ionization (ESI) has been suc- can still be poor, and quantification can be challenging due to nonlinear cessful at distinguishing between carbohydrate homologues of different detector responses [10,11]. The combination of HPAEC with pulsed masses and, when coupled with HPLC, can differentiate carbohydrate amperometric detection (PAD) has been used as a high-resolution, isomers [12]. Coupling HPLC with ESI-MS can achieve low detection sensitive, and selective technique to analyze carbohydrates at low limits, high selectivity, and robust analysis of different carbohydrates in concentrations [10]. However, the application of HPAEC-PAD requires a mixture [6,12,13]. Recently, amide columns perform reproducible highly alkaline conditions and suffers from non-linear detector response and robust separations of carbohydrates based on hydrophilic interac- and baseline shifting [10,11,16]. tions [12,13,20–22 ]. At high temperatures and alkaline conditions, Besides chromatography-based methods, both Fourier transform amide columns minimize salt interferences, anomer mutarotation, and infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) Schiff base formation [20]. By combining hydrophilic-interaction-based spectroscopy have been used to analyze carbohydrates without prior HPLC with MS/MS analysis, sixteen carbohydrates were quantified in a separation. In FTIR application, which does not require extensive mammalian plasma matrix [12]. sample preparations or expensive pre-processing reagents, the sensi- In this Research Note, we employ ultra-high performance LC tivity and accuracy are typically poor [10,17]. On the other hand, NMR (UHPLC) with an amide column and high-resolution MS (HRMS) with presents an effective non-derivatization method for monitoring single ESI to separate and quantify carbohydrate mixtures relevant to en- carbohydrates from mixtures including monosaccharides, dis- vironmental matrices and bioproduction, yielding good sensitivity, se- accharides, and trisaccharides [18,19]. To overcome the poor the sen- lectivity, a large linear dynamic range, and good separation of the sitivity of NMR, recent studies have reported the use of high fields, majority of analytes. Specifically, we achieve robust and precise cryogenic probes, dynamic nuclear polarization, chemical shift selec- quantification of pentoses (xylose, arabinose, and ribose), hexoses tive filtration, and total correlation spectroscopy [18,19]. Without these (glucose, galactose, mannose, and fructose), and dimers (maltose, cel- enhancing approaches, the sensitivity of NMR-based detection (milli- lobiose, sucrose, trehalose, and lactose). These carbohydrates are re- molar to molar concentrations) is several orders of magnitude higher presentative of both reducing and nonreducing sugars that are im- than the sensitivity of mass spectrometry (MS)-based detection (nano- portant for monitoring environmental matrices and for sustainable molar to micromolar concentrations) [19]. However, these additional conversion of biomass to natural products. Although eight of the tar- methods can complicate analysis and high ionic strength solutions can geted carbohydrates (xylose, glucose, fructose, galactose, sucrose,

31 K.A. Barzen-Hanson et al. Carbohydrate Research 468 (2018) 30–35

(LOD) and quantification (LOQ), which are included in method validation, are typically determined using the signal-to-noise ratio of chromatographic peaks at or near the detection limit. However, the signal-to-noise method is subjective and not very robust [26]. Vial and Jardy [26] presented a more robust and reproducible method to determine LOD and LOQ of analytical methods, which involves weighted least squares linear regression, and this method of determining LOD and LOQ was used. The chromatographic and MS conditions were optimized to differentiate compounds based on both chromatographic retention times and mass-to-charge ratios (m/z) (see section 2.2 and Table 1). The final optimized conditions in our first method (Method 1) isolated ribose, arabinose, fructose, mannose, glucose, sucrose, maltose, and trehalose with clear baseline separation (Fig. 1A; Table 1). However, in Method 1, there was partial co-elution of arabinose and xylose and co-elution of cellobiose, maltose, and lactose (Fig. 1A). A second method (Method 2) was used to separate the glucose and galactose peaks, which could not be distinguished using Method 1 (Fig. 1C). The limit of detection (LOD) and limit of quantification (LOQ) were determined according to the weighted least squares regression method as previously outlined [26] (see Section 2.3). The LOD for the targeted carbohydrates ranged from 0.019 μM to 0.40 μM(Table 1). Overall, our LODs are comparable to other LC-MS methods (Table 1)[12,23]. However, our method is at least 24 times more sensitive than LC-ELSD methods and at least 4 times more sensitive than GC-MS methods (Table 1)[11,23]. For each compound, the relative standard deviation (RSD) values were determined at low- (2.5 μM) and mid- (10 μM) range concentrations in the calibration curve. The %RSD values ranged from 6.9% to 18% in Method 1, but were below 4% for Method 2 (Table 1). Similar interday variability was seen in other LC-MS [12] and LC-ELSD [11] methods. The mass error for all the compounds analyzed remained below 2.4 ppm (i.e., there was less than 0.00024% difference between the measured m/z and the theoretical m/z). The applicability of Method 1 was tested for carbohydrate quantification in a high ionic strength minimal-nutrient growth medium (MNGM), which is used for bacterial growth experiments (see section 2.4). For standards prepared with the MNGM solution, baseline separation of the chromatographic peaks was still achieved with minimal to no shift in retention time, and the %RSD remained comparable to standards prepared in MiliQ water (Table 2). The high ionic strength matrix increased the LOD and LOQ by at least 0.2 μM for all the carbohydrates. Maltose was impacted most by the high ionic strength matrix, as the LOD and LOQ increased by over 3 μM(Table 2). The mass Fig. 1. Extracted ion chromatograms following UHPLC-HRMS analysis of the error remained below 3.5 ppm for analytes tested in the MNGM solution targeted carbohydrates analyzed in MiliQ water with (A) Method 1 or (B) (Table 2). Both the standards prepared in MilliQ and MNGM main- Method 2. tained a strong linear fit(R2 = 1.00) at a range of 0.1 μM–25 μM, except maltose prepared in MNGM that had a linear fit from 2.5 μMto25μM maltose, trehalose, and lactose) were analyzed by previous methods (Fig. 2). However, the slopes of the calibration curves indicated a clear [5,7,11–14,20,23–25], the remaining four carbohydrates (ribose, ara- matrix effect (Fig. 2). There was a small matrix effect in the calibration binose, mannose, and cellobiose) were absent. Method validation re- curve slopes for glucose and fructose, but almost no effect for mannose ported in previous studies was limited or less robust. Limits of detection (Fig. 2). However, the MNGM solution had a substantial effect on

Table 2 Analytical performance of select carbohydrates in minimal-nutrient growth medium (MNGM) used for bacterial growth experiments.

Compound Retention Time (min) Theoretical m/z Measured m/z Mass Errorc (ppm) LODd (μM) MNGM LOQd (μM) MNGM Precisione (%RSD) at 15 μM

Xylosea 4.61 149.0455 149.0445 3.4 0.33 1.2 8 Fructoseb 6.18 179.0561 179.0555 3.4 0.36 1.2 13 Mannoseb 8.48 179.0561 179.0555 3.4 0.43 1.5 10 Glucoseb 9.84 179.0561 179.0556 2.8 0.66 2.2 15 Maltosea 16.07 341.1089 341.1080 2.6 3.3 7.5 7

a The values associated with the compound were obtained from three replicates (n = 3). b The values associated with the compound were obtained from ﬁve replicates (n = 5). c Mass error was calculated in parts-per-million (ppm). d Limit of detection (LOD) and limit of quantiﬁcation (LOQ). e Relative standard deviation (RSD).

32 K.A. Barzen-Hanson et al. Carbohydrate Research 468 (2018) 30–35

and P. saccharophila) may also be present in P. protegens Pf-5 [31,32]. Only the concentration of the disaccharide maltose remained un- changed throughout the growth of P. protegens Pf-5 (Fig. 3). The lack of maltose depletion is consistent with previous findings which showed that P. protegens Pf-5 cannot utilize maltose as a sole carbon source [30]. Both carbohydrate transformation and hierarchical carbohydrate depletion from heterogenous feedstocks during microbially-mediated bioconversion can be observed and quantified using Methods 1 and 2. In conclusion, the present work provides a new UHPLC-HRMS method to analyze multiple carbohydrates in mixtures with high mass accuracy, sample throughput, and resolution. We employed UHPLC with an amide column to provide selective hydrophilic separation of each carbohydrate, even when present in high ionic strength solutions. The HRMS operated with ESI in negative mode led to sensitive quantitation of 12 different carbohydrates: 3 pentoses (ribose, xylose, arabinose); 4 hexoses (glucose, mannose, galactose, fructose); 5 dimers (sucrose, maltose, cellobiose, trehalose, lactose). While previous analytical methods for quantification have encompassed a wide-range of carbohydrates, methods that provide sensitive and precise analysis of carbohydrates such as cellobiose, arabinose, ribose, or mannose in complex mixtures of carbohydrates are lacking. Therefore, the present method can be instrumental in tracking mixtures of carbohydrates to understand mass balances in carbohydrate depletion or formation, carbon usage during metabolism, or sustainable substrate bioconversion to valuable products.

2. Experimental

2.1. Materials

For UHPLC-HRMS analysis, high-purity HPLC-grade 2-propanol, acetonitrile (ACN), and water were purchased from Fisher Scientific (Pittsburgh, PA). Triethylamine was bought from Sigma-Aldrich (St. Louis, MO). The carbohydrates (xylose, arabinose, ribose, glucose, galactose, mannose, fructose, cellobiose, sucrose, maltose, trehalose, and lactose) and chemicals for growth media were obtained from either Sigma-Aldrich (St. Louis, MO), MP Biomedicals (Santa Ana, CA), Alfa Aesar (Tewksbury, MA), Cayman Chemical (Ann Arbor, MI), or Fisher Scientific (Pittsburgh, PA). The P. protegens Pf-5 cells were acquired Fig. 2. Comparison of linear standard curves of maltose, glucose, mannose, from the American Type Culture Collection (Manassas, VA). All bac- fructose, and xylose when prepared in MiliQ water versus minimal-nutrient terial culture solutions were prepared with Millipore water growth medium (MNGM) with high ionic strength. (18.2 MΩ cm, Millipore; Billerica, MA, USA). Solutions were filter- sterilized using 0.22-μm nylon filters (Waters Corporation, MA). maltose and xylose; MNGM increased the xylose response and de- creased the maltose response (Fig. 2). Therefore, calibration standards 2.2. UPLC-HRMS conditions need to be prepared in the appropriate background solution to com- fi pensate for the ionization suppression or enhancement of the analytes The analysis was conducted on an UHPLC (ThermoFisher Scienti c in samples during quantification. DionexUltiMate 3000, Waltham, MA, USA) coupled to a high-resolu- fi We applied our method to determine the depletion of five carbo- tion/accurate-mass MS (ThermoFisher Scienti c Q Exactive quadru- hydrates (xylose, glucose, mannose, fructose, and maltose) in the ex- pole-Orbitrap hybrid MS) with ESI operating in negative mode. tracellular matrix during the growth of the soil bacterium Pseudomonas Chromatographic separation was performed using a μ ™ protegens Pf-5, which has important relevance to biotechnological ap- 4.6 × 100 mm × 3.5 m XBridge Amide column (Waters, Milford, plications [27–30](Fig. 3). A previous study reported that P. protegens MA). A column temperature of 85 °C was maintained throughout the μ Pf-5 can grow on glucose, mannose, or fructose as a single carbon run. The volume injected was 10 L of samples prepared in 50% v/v source, but not on xylose or maltose [30]. However, the preferential ACN. For Method 1, which separated all carbohydrates except glucose fl −1 carbon usage amongst the different carbohydrates remains unknown. In and galactose, the ow rate was maintained at 0.8 mL min . Mobile the following order, P. protegens Pf-5 depleted glucose, mannose, xylose, phases for Method 1 consisted of 100% ACN with 0.05% v/v triethy- and fructose in the extracellular matrix during 12 h of growth (Fig. 3). lamine (Solvent A) and 50:50 v/v isopropanol:LC-MS water with 0.05% Glucose was rapidly consumed and completely depleted by 4.5 h of v/v triethylamine (Solvent B). A multi-step gradient was used as follows growth (Fig. 3). Once glucose was depleted, both mannose and xylose for Solvent A: 0 min, 90%; 12 min, 90%; 12.05 min, 60%; 17 min, 60%; started to be depleted (Fig. 3). By 12 h of growth, mannose was fully 19.05 min, 40%; 25 min, 20%; 26.05 min, 90%; 30.5 min, 90%. depleted, but half of the xylose concentration remained (Fig. 3). In- Separation and elution of the carbohydrates analyzed occurred within fi fl terestingly, the consumption of mannose was accompanied by an initial the rst 18 min. The additional ush steps with a higher percentage of increase in the fructose level, which implied that the mannose iso- Solvent B added before column re-equilibration were required in order merase previously reported in other Pseudomonas species (P. cepacia to reduce the baseline shifting and prevent precipitation of salts in the amide column. Method 2 for the separation of glucose and galactose

33 K.A. Barzen-Hanson et al. Carbohydrate Research 468 (2018) 30–35

Fig. 3. Extracellular depletion of carbohydrates during 12 h feeding of P. protegens Pf-5 on a mixture of carbohydrates. A) Extracted ion chromatograms of targeted compounds diluted 1:50 during four representative times. B) Quantified depletion curves of the five carbohydrates (in carbon-equivalence concentration, mM) C) during the growth stage and stationary stage of P. protegens Pf-5. The values represent means ± standard deviation obtained from biological replicates (n = 3). used the same mobile phases as Method 1 but maintained an isocratic regression residual standard deviation was multiplied by 3, then sub- − elution of 90% Solvent A for 19.5 min at a flow rate of 0.5 mL min 1. tracted by the y-intercept, and finally divided by the slope. The LOQ The MS was operated in full scan negative mode with a scan range was calculated the same way as LOD, except that the residual standard of m/z 100 to 500 and resolution of 70,000. The spray voltage was 4 kV deviation was multiplied by 10 [26]. with a capillary temperature of 390 °C. Sheath, auxiliary, and sweep gas fl ow rates were 60, 15, and 0 arbitrary units, respectively. The HRMS 2.3.2. Precision, mass error, and matrix effects ™ was mass calibrated weekly using Pierce LTQ ESI Negative Calibration Precision was determined at two different concentrations, 2.5 and fi fi solution (ThermoFisher Scienti c). Quanti cation of carbohydrates was 10 μM for solutions prepared in MiliQ water. For solutions prepared in ™ fi conducted with Xcalibur 3.0 Quan Browser (ThermoFisher Scienti c). MNGM, 15 μM was used for precision calculations. The area counts from the 2.5 μM, 10 μM, and 15 μM standards in the LOD and LOQ 2.3. Method validation experiment were used to calculate the percent relative standard deviation. Mass error in ppm was calculated by subtracting the measured 2.3.1. LOD and LOQ mass from the theoretical exact mass, dividing by the theoretical exact 6 ff Eight standards (0.05, 0.1, 0.5, 2.5, 5, 10, 15, and 25 μM) were mass, then multiplying by 10 . Matrix e ects were determined by prepared in 50% v/v ACN and 50% v/v MiliQ or MNGM from stock comparing the slopes of calibration curves made in both 1:1 v/v solutions (250 μM) that contained the carbohydrates from Method 1 ACN:MiliQ and 1:1 v/v ACN:MNGM. separated into four stock mixes (mix 1 contained xylose, fructose, mannose, glucose, and maltose; mix 2 contained ribose and sucrose; 2.4. Method demonstration mix 3 contained arabinose and cellobiose; mix 4 contained trehalose and lactose) and a stock solution (250 μM) that contained glucose and Pseudomonas protegens Pf-5 was grown (three biological replicates) galactose (Method 2). Trehalose required three additional standards on a mixture of maltose, glucose, fructose, mannose, and xylose in (0.005, 0.01, and 0.025 μM) to capture the LOD. With the exception of MNGM at an equimolar concentration (20 mM C or 0.6 g/L of each trehalose (62 injections), the series of eight standards were injected six carbohydrate adding up to 100 mM C total). Growth experiments were times, for a total of 48 injections for each mix. Area counts from the 48 conducted in an incubator (model I24; New Brunswick Scientific, standards and weighted least squares regression analysis were used to Edison, NJ) maintained at 30 °C and shaken at 220 rpm. Serial transfers calculate LOD and LOQ, as described previously [26]. Briefly, the LOD to acclimate cells in the growth condition was done as previously de- was calculated using a 1/x-weighted linear regression of the expected scribed [33]. Experiments to determine extracellular carbohydrate de- concentration and corresponding area counts. The 1/x-weighted pletion were conducted in 125 mL baffled flasks with pH-adjusted (7.0)

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