Application of Ultra Performance Liquid Chromatography-Mass Spectrometry to Profiling Rat and Dog Bile Robert S. Plumb,† Paul D. Rainville,† Warren B. Potts III,† Kelly A. Johnson,† Eleni Gika,‡,§ and Ian D. Wilson*,‡ Pharmaceutical Business Operations, Waters Corporation, Milford, Massachusettes, and AstraZeneca, Department of Clinical Pharmacology, Drug Metabolism and Pharmacokinetics, Alderley Park, Macclesfield, Cheshire SK10 4TG, United Kingdom Received December 17, 2008
Reversed-phase gradient UPLC-ESI-MS, in both positive and negative ionization modes, has been applied to the analysis of untreated bile obtained from bile-cannulated rats and dogs. The use of UPLC provided a high-resolution system that enabled global metabolite profiles of bile from the two species to be obtained that were suitable for metabolomic and metabonomic applications. When these metabolite profiles were analyzed using unsupervised multivariate statistical methods, based on principle components analysis (PCA), they were correctly classified by species of origin. Conventional approaches to characterizing sample components via, for example, mass and retention time compared to authentic standards resulted in the identification of a range of bile acids. In addition, the value of using an “MSE” approach to simplify the problem of classifying and identifying the metabolites present in the sample (as e.g., sulfates or taurine conjugates) was demonstrated. Keywords: bile • metabolism • metabolism • liquid chromatography • mass spectrometry • biofluid profiling • metabolomics • metabonomics
Introduction Bile is a complex biofluid with a wide range of important physiological functions including maintaining cholesterol homeostasis, emulsifying fats and lipid absorption and in the excretion (and often recirculation) of drugs and endogenous and exogenous toxins. Major constituents of the bile are the bile acids, which comprise a complex and structurally diverse family of molecules, but the bile also contains a range of organic acids, bases, amino acids and lipids including cholesterol and phosphotidylcholine, etc. However, because of the wide range of physiological functions that they perform, the bile acids are of particular interest. The primary bile acids, cholic and chendeoxycholic acid, are synthesized in the liver from cholesterol, and this is followed by further biotransformations via conjugation to various polar endogenous compounds such as glycine, taurine, sulfate, glucuronic acid etc. and then, after secretion into the bile, by a wide range of gutmicrofloral-mediated deconjugations and further metabolism via, for example, dehydroxylation to give secondary bile acids (e.g., deoxycholic acid (DCA) and lithocholic acid (LCA)).1 Further diversity results from subsequent absorption of these secondary bile acids and then their conjugation to glycine, taurine, sulfate, glucuronic acid etc., with secretion once again into the bile, thereby generating very complex mixtures of * To whom correspondence should be addressed. E-mail: Ian.Wilson@ AstraZeneca.com. † Waters Corporation. ‡ AstraZeneca. § Present address: Laboratory of Analytical Chemistry, Aristotle University of Thessaloniki, 541 24 Greece. 10.1021/pr801078a CCC: $40.75
2009 American Chemical Society
primary, secondary and tertiary bile acids. Normally only low concentrations of bile acids are found in plasma or urine however, in the case of disease or toxicity affecting the liver or GI tract (e.g., colon cancer), there may be significant disturbance to processes involved in the synthesis, metabolism, and excretion of these compounds.2-6 These changes may affect not only the concentration of the bile acids and their composition in bile but also in other biofluids such as plasma and urine. The bile acids therefore present a useful metabolic window for the study of liver/intestinal/gut microfloral functions or activity. However, the complexity of the metabolic profile in samples such as bile, which contain many isobaric and isomeric bile acids, presents a significant technical challenge. Both gas chromatography-mass spectrometry (GC-MS) and HPLC-MS have been used for many years for the analysis and profiling of bile acids (recently reviewed by Wang and Griffiths7). In the case of GC-MS-based methods extensive, and time-consuming, sample pretreatment is required (extraction, hydrolysis of conjugates and derivatization) (see ref 7). For example one approach employed for the analysis of bile samples used aminosulfonic acids and sodium hydroxide for derivatization,7 and required prepreparation by solid phase extraction. The advent of LC-MS-based methods thus offered the potential for significant simplification of the methodology, with much reduced sample preparation, and a number of methods have been reported (e.g., 5,7-11). Indeed LC-MS has recently been applied to the quantitative analysis of 17 bile acids in plasma, urine and liver.11 That said, despite its ease of use, traditional liquid chromatography does not possess the resolving power of capillary GC-MS and therefore has been a less attractive Journal of Proteome Research 2009, 8, 2495–2500 2495 Published on Web 03/02/2009
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means for the separation of complex mixtures such as bile. Thus, despite the undoubted analytical power offered by GC and LC-MS the complexity of the sample, combined with the need to separate many close structural analogues still presents a severe analytical problem. The commercial availability of liquid chromatography systems that allow chromatography on sub 2 µm particles,12 at relatively high flow rates (ultra performance liquid chromatography (UPLC)) has significantly reduced the performance differential between capillary GC and LC. These higher resolution LC separations have been exploited by many researchers for the analysis of complex mixtures in fields such as sports medicine,13 global endogenous metabolite profiling,14 pharmaceutical analysis and drug metabolite identification.15,16 When operated at higher column temperatures, in excess of 90 °C, the reduction in mobile phase viscosity allows the use of longer columns and higher mobile phase velocities.17-19 The use of these longer columns allows the generation of very high-resolution separations, with peak capacities in the order of 1000 in one hour.17 The advent of the high-resolution separations offered by UPLC has encouraged us to reinvestigate the use of LC-MS for bile analysis with a view to applications in metabonomics (the “quantitative measurement of time-related multiparametric metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification”20). The application of sub 2 µm particle LC to metabonomics has already had an impact on the analysis of urine and plasma samples via increased throughput and the number of peaks detected,14,21 and would seem ideally suited to enhancing the LC analysis of bile.
ms with a 20 ms inter scan delay. The instrument was operated with Lockspray frequency of 11 with a scans to average set to 5, Luecine enkephalin was employed as the lockspray solution at a concentration of 50 fmol/µL. Identification of Bile Acids. The bile acids were identified by comparison of their retention time to that of authentic standards, their accurate mass and fragmentation patterns. Statistical Analysis. Data were analyzed using the Micromass MarkerLynx applications manager Version 1.4 (Waters, UK); this application manager integrates peaks in the LC-MS data by using ApexTrack peak detection. The LC-MS data were peakdetected and noise-reduced in both the LC and MS domains such that only true analytical peaks were further processed by the software (e.g., noise spikes are rejected). A list of the intensities of the peaks detected was then generated for the first sample, using the retention time (RT) and m/z data pairs as the identifier for each peak. An arbitrary number is then assigned to each of these RT- m/z pairs in order of elution, (1, 2, 3, 4,...etc.). This process was repeated for each run; once this was completed the data from each LC-MS analysis in the batch were then sorted such that the correct peak intensity data for each RT- m/z pair was aligned in the final data table. The ion intensities for each peak detected are then normalized, within each sample, to the sum of the peak intensities in that sample with these normalized peak intensities then multiplied by 10 000. The resulting 3-dimensional data, peak number (RT m/z pair), sample name, and ion intensity were analyzed by principal components analysis (PCA) using mean centering and Pareto scaling.
Experimental Section
Results and Discussion
Chemicals. Optima grade acetonitrile (HPLC grade) was purchased from Fisher Scientific (Hampton, NH), formic acid (spectroscopic grade) and 0.1 M sodium hydroxide solution were purchased from Sigma/Aldrich (MO). Distilled water was purified “in-house” using a Milli-Q system Millipore (MA). Leucine-enkephalin and bile acid standards, Taurocholic Acid, Glycocholic acid, Chenodeoxycholic Acid, Taurochenodeoxycholic Acid, Glycochenodeoxycholic Acid, Deoxycholic Acid, Taurodeoxycholic Acid, Glycodeoxycholic Acid, Lithocholic acid, Taurolithocholic acid, Glycolithocholic acid, Ursodeoxycholic Acid, Tauroursodeoxycholic Acid and Glycoursodeoxycholic Acid, were obtained from Sigma-Aldrich (MO). Samples. Control bile samples were obtained via bile duct cannulation from 24 Wistar-derived rats and 2 male beagle dogs (3 samples per animal). The samples were stored frozen at -20 °C and were diluted 1:5 with distilled water prior to analysis by UPLC-MS. Chromatography. The separations were performed on a 2.1 × 150 mm ACQUITY UPLC 1.7 µm BEH C18 column using an ACQUITY Ultra Performance LC chromatography System (Waters Corporation, MA, USA). The column was maintained at 90 °C and eluted with a linear acetonitrile-aqueous ammonium acetate pH 5 gradient over 20 min at 900 µL/min, starting at 20% acetonitrile and rising to 80% over the course of the gradient. The column eluent was directed to the mass spectrometer for analysis. Mass Spectrometry. Mass spectrometry was performed on a Waters Micromass Q-Tof Premier mass spectrometer (Waters Micromass, Manchester, UK) operated in both positive and negative ion mode with “V-Optics”, with the resolving quadrupole set to a wide pass mode, the collision cell was set to alternate between a collision energy of 5 and 25 eV every 60
As described in the introduction the high chromatographic efficiency of the sub 2 µm stationary phase offers improved resolution over conventional LC and, operated at the higher temperatures employed here (90 °C), provided excellent resolution of the complex mixture of bile acids encountered in these samples, as shown below. Examples of the results obtained for typical rat and dog bile samples are illustrated in Figures 1 and 2 for positive and negative ESI, respectively. Clearly the negative ion TIC reveals a more complex profile than the positive ESI data. In negative ESI TIC major contributions to the bile metabolite profile can be expected to result from the presence of the bile acids and examination of the results from the negative ESI data afforded the data provided in Table 1 where the retention times and negative ion accurate mass values for some of the bile acids detected are listed. The two large peaks detected in the dog bile -ve ESI chromatogram were identified as taurocholic acid (8.9 min) and taurodeoxycholic acid (10.8 min). These bile acids, and those given in Table 1, were identified by a mixture of comparison with authentic standards (where available), accurate mass-derived atomic compositions and fragmentation data. The need for a highresolution separation system for the analysis of bile is amply illustrated by the fact that analytes such as chenodeoxycholic acid (9.13 min), deoxycholic acid (10.46 min) and ursodeoxycholic acid (8.46 min) in rat bile all share the same mass of 391.2848, but were well separated here, as shown in Figure 3a. Similarly glycodeoxycholic acid and glycoursodeoxycholic acid, present in rat bile, also share the same mass (448.3063), but were also well separated from each other as shown in Figure 3b with retention times of 12.37 and 12.05 min respectively. In addition to bile acids the untargeted nature of the profiling performed here allowed a range of other compounds to be
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Table 1. Retention Times and Negative Ion Accurate Mass Values for Some of the Bile Acids Detected bile acid
Cholic Acid Taurocholic Acid Glycocholic acid Chenodeoxycholic Acid Taurochenodeoxycholic Acid Glycochenodeoxycholic Acid Deoxycholic Acid Taurodeoxycholic Acid Glycodeoxycholic Acid Lithocholic acid Taurolithocholic acid Glycolithocholic acid Ursodeoxycholic Acid Tauroursodeoxycholic Acid Glycoursodeoxycholic Acid
Figure 1. Total ion current mass chromatograms in -ve ESI for typical rat (upper trace) and dog bile ESI (lower trace) samples.
Figure 2. Total ion current mass chromatograms in +ve ESI for typical rat (upper trace) and dog bile (lower trace) samples.
identified in the profile including, for example, compounds such as arginine (RT.0.77 min), asparagine (RT.0.33 min),
elemental composition
m/z -ve ESI
C24H40O5 C26H45NO7S C26H43NO6 C24H40NO4 C26H44NO6S
407.2797 514.2828 464.3012 391.2848 497.2811
11.81 9.14 8.46 9.13 10.5
C26H43NO5
448.3063
7.00
C24H40NO4 C26H45NO6S C26H43NO5 C24H40NO3 C26H44NO5S C28H47NO4 C24H40O4 C26H45NO6S
391.2848 498.2889 448.3063 375.2899 481.2862 460.3427 391.2848 498.2968
10.46 10.8 12.37 5.38 8.53 7.43 8.46 8.95
Rat/Dog Rat/Dog Dog Dog Rat/Dog Rat/Dog Rat/Dog Rat/Dog
C26H43NO5
448.3141
12.05
Rat
retention time observed
Rat/Dog Rat/Dog Rat/Dog Rat/Dog Rat Rat
creatine (RT.0.46 min), leucine (RT.1.4 min), lysine (RT.0.30 min), ornithine (RT.0.33 min), and phosphocholine (RT.2.4 min). However, one of the obvious features of these profiles is their complexity, especially when minor components are taken into account, with up to 2400 ions detected even in +ve ESI and 12 500 features in -ve ESI for a typical rat bile sample alone. An obvious difficulty presented by mixtures of this sort is how
Figure 3. Selected ion current mass chromatogram for m/z 391.2848 (upper trace) corresponding to ursodeoxycholic acid (8.46 min), chenodeoxycholic acid (9.13 min), and deoxycholic acid (10.46 min) and m/z 448.3063 (lower trace) corresponding to glycodeoxycholic acid (12.37 min) and glycoursodeoxycholic acid (12.05 min). Journal of Proteome Research • Vol. 8, No. 5, 2009 2497
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Figure 4. Typical total ion current for rat bile with mass chromatograms showing losses of 80, 176, and 129 characteristic of sulfate, glucuronide and glutathione conjugates respectively present in the sample.
to go about identifying the various components present in order to better define the bile metabolome. Clearly, for specific classes of compounds expected to be in bile, such as, for example, the bile acids, where standards for the major compounds may be available, the most powerful approach is by standard addition and spectral comparison. However, not all the compounds that are needed for such a rigorous approach are readily available. An alternative, that enables the reduction of the “search space” for an unknown component, is to use the power of mass spectrometry to subdivide the analytes into classes, and then work through those classes individually. Thus, given that much of the diversity found in the bile acids is due to their modification with a range of polar substituents via conjugation in the liver (“combinatorial” metabolism) the LC-MS data can be used to specifically target, for example, taurine, sulfate, glycine, glucuronide and glutathione conjugates with benefits to simplifying subsequent identification. In order to identify the particular classes of metabolites the fragmentation pattern and mass accuracy of the Q-Tof Premier operating in MSE mode was exploited.22 In MSE experiments the eluting peaks are subjected to both high and low collision energies in the collision cell of the mass spectrometer such that both molecular ion and fragment data are obtained. Here, as described in the experimental section, the collision cell was set to alternate between a collision energy of 5 and 25 eV every 60 ms, providing a cycle of high and low fragmentation energies. In this instance, where specific conjugations were sought, the high collision energy data was processed such that only those chromatographic peaks that contained the two ions of interest, separated by the accurate mass value of the particular conjugate, were displayed. For example if the conjugate of interest was a glucuronide, only those LC peaks which contained two MS ions separated by the mass 176.0321 ((10 mDa) were selected. This is illustrated in Figure 4 for losses of 79.9568, 176.0321, and 129.0426 ((10 mDa) characteristic of sulfate, glucuronide and glutathione conjugates 2498
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Figure 5. Typical total ion current for rat and dog bile samples with mass chromatograms showing losses characteristic of sulfate and taurine conjugates respectively, showing species differences in conjugate patterns.
respectively present in rat bile. As can be seen from this figure, examining the mass chromatograms for the various classes of conjugate shows them to be rather simpler than the original TIC, with various degrees of complexity depending on whether sulfate, glucuronide or glutathione-containing compounds were sought. Examination of the data derived from these experiments suggests that over 20 sulfated, 32 glucuronidated and 46-glutathione containing compounds were present in the rat bile sample exemplified here. The data displayed in Figure 5a-d shows the profiles for the sulfate and taurine conjugates for the rat and dog, and from these conjugate-specific profiles it can easily be seen that dog bile appears to contain relatively more taurine and fewer sulfate conjugates than the rat bile. This approach of targeting compound classes enabled the rapid identification of bile acids, for example, the following sulfated bile acids were detected and identified by their MS/MS spectra, taurodeoxycholic sulfate, glycodeoxycholic sulfate, and lithocolic sulfate with retention times of 6.65, 10.22, and 14.4 min. This strategy of selecting compound classes out of the whole mixture as a means of simplifying the identification of metabolites in complex mixtures is, of course, by no means new in this area. Early examples for LC-MS investigations for, for example, urinary metabolite classes include studies on the toxic effects of heavy metals23 and antibiotics24 and more recently urinary glucuronides22 and glutathione conjugates25 have been sought (the latter following drug administration). However, as far as we are aware the deliberate and systematic use of this approach for deconvoluting particular metabolomes via compound classes has not previously been advocated. In addition to simply breaking up the metabolic profile into more manageable groups of compounds this reductionist approach can also be used for making comparisons between experimental classes or, in this case, species. The differences between rat and dog with respect to sulfates have already been alluded to, but similar differences were also seen for other classes such as the
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Application of UPLC-MS to Profiling Rat and Dog Bile
Figure 6. MSE spectra for taurocholic acid conjugated to sulfate (peak eluting at 7.39 min, m/z ) 594 to 514) and glucuronide moieties (peak eluting at 7.50 min, m/z ) 690 to 514).
Figure 7. Principle components analysis scores plot for rat and dog bile samples, using principle components 2 and 3. Rat bile samples are denotes by 0 and the dog bile samples by ) and ∆.
glucuronide conjugates etc., in rat and dog bile samples (data not shown). The simultaneous collection of high and low collision energy data allows the facile searching of data, as seen above the detection of compounds that contain a specific conjugate, for example, sulfate. The spectra displayed in Figure 6 also illustrates how it is possible to use the MSE approach to identify peaks with common fragments resulting from, for example, a number of conjugates to the same aglycone where this is conjugated to a number of different polar molecules. This is illustrated for taurocholic acid linked to give both a sulfate (peak eluting at 7.39 min) defined by the transition of m/z ) 594 to 514, and a glucuronide (peak eluting at 7.50 min) defined by the transition of m/z ) 690 to 514. Metabonomic Analysis. As indicated in the experimental section, this UPLC-MS separation system was used to profile bile obtained from 24 control male rats and 2 dogs (from which multiple samples had been obtained). Here we have analyzed the data obtained from the UPLC-MS of these samples using the unsupervised approach of principle components analysis (PCA). The result of this type of analysis for the positive ion UPLC/MS bile data analyzed using PCA approach employing mean centering and pareto scaling is shown in the scores plot displayed in Figure 7 wherein the rat samples (squares) are observed in one major cluster and 2 smaller satellite groups, with the dog samples clearly separated from the rat samples. The major ions responsible for separating the rat from the dog samples were glycholic acid (m/z ) 464.3012 at 8.46 min), taurochenodeoxycholic acids (m/z ) 497.2811at 10.5 min), glycodeoxycholic acid (m/z ) 448.3063 at 12.37 min) (all elevated in the rat) and taurodeoxycholic acid (m/z ) 498.2889 at 10.8 min), taurolithocholic acid (m/z ) 481.2862 at 10.9 min)
Figure 8. Principle Components Analysis scores plot for rat bile samples using principle components 1 and 2 showing the presence of the main group together with a “satellite” group from another experiment.
(both elevated in the dog). These bile acids are particularly interesting as tauroursodeoxycholic acid is a tertiary bile acid, synthesized in the liver processed by the gut microflora and then remetabolized in the liver through enterohepatic recirculation. These ions were significantly higher in the dog than the rat. If just the rat bile samples are considered, the scores plot shown in Figure 8 is obtained. The simplified data set shows that there is one main group of samples with 6 outliers in a satellite group. Examination of the corresponding loadings plot (data not shown) reveals that the ions contributing most significantly to the variance observed in the rat were m/z ) 514 at 9.14 min, m/z )498 at 8.95 min, m/z 448 at 12.37 min and m/z 498 at 10.8 min. These ions corresponded to taurocholic acid, tauroursodeoxycholic acid glycodeoxycholic acid and taurodeoxycholic acid respectively. The bile samples examined in this study were derived from a number of different studies (all of which were conducted in the same facility) and the 6 members of the second group were derived from a single study. As all of the samples were acquired from undosed animals there is no obvious reason why such differences should have occurred however, this type of finding highlights the potential for batch-to-batch (or rather experiment-to-experiment) biological variability in biofluid analysis.
Conclusions UPLC-MS provides a high-resolution system for obtaining global metabolite profiles for complex biological fluids and extracts, including bile, for metabolomic and metabonomic applications. Here we have demonstrated that PCA can be used to correctly classify rat and dog bile samples on the basis of their UPLC-MS-derived metabolite profiles. The data obtained in this study also show the value of using the MSE approach as a method for simplifying the problem of classifying and identifying the metabolites present in the sample thereby aiding in the determination of the biliary metabolome.
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Plumb et al. (16) Castro-Perez, J.; Plumb, R.; Granger, J. H.; Beattie, I.; Joncour, K.; Wright, A. Rapid Commun. Mass Spectrom. 2005, 19, 843–848. (17) Plumb, R.; Rainville, P.; Smith, B. W.; Johnson, K. A.; Castro-Perez, J.; Wilson, I. D.; Nicholson, J. Anal. Chem. 2006, 78, 7278–7283. (18) Edge, A. M.; Shillingford, S.; Payne, R.; Smith, C. J.; Wilson, I. D. J. Chromatogr. A 2006, 1132, 206–210. (19) Gika, H. G.; Theodoridis, G.; Extance, J.; Edge, A. M.; Wilson, I. D. J. Chromatogr. B 2008, 871, 279–287. (20) Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181–1189. (21) Plumb, R. S.; Granger, J. H.; Stumpf, C. L.; Johnson, K.; Smith, B. W.; Gaulitz, S.; Wilson, I. D.; Castro-Perez, J. Analyst 2005, 130, 844. (22) Plumb, R. S.; Johnson, K. A.; Rainville, P.; Smith, B. W.; Wilson, I. D.; Castro-Perez, J.; Nicholson, J. K. Rapid Commun. Mass Spectrom. 2006, 20, 1989–1994. (23) Lafaye, A.; Junot, C.; Ramounet-le Gall, B.; Fritsch, P.; Tabet, J.-C.; Ezan, E. Rapid Commun. Mass Spectrom. 2003, 17, 2541–2549. (24) Lenz, E. M.; Bright, J.; Knight, R.; Westwood, F. R.; Davies, D.; Major, H.; Wilson, I. D. Biomarkers 2005, 10, 173–187. (25) Lutz, U.; Lutz, R. W.; Lutz, W. K. Anal. Chem. 2006, 78, 4564–4571.
PR801078A
