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,‡,§ andIan 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 beenapplied to the analysis of untreated bile obtained from bile-cannulated rats and dogs. The use of UPLCprovided a high-resolution system that enabled global metabolite profiles of bile from the two speciesto be obtained that were suitable for metabolomic and metabonomic applications. When thesemetabolite profiles were analyzed using unsupervised multivariate statistical methods, based onprinciple components analysis (PCA), they were correctly classified by species of origin. Conventionalapproaches to characterizing sample components via, for example, mass and retention time comparedto authentic standards resulted in the identification of a range of bile acids. In addition, the value ofusing an “MSE” approach to simplify the problem of classifying and identifying the metabolites presentin the sample (as e.g., sulfates or taurine conjugates) was demonstrated.

Keywords: bile • metabolism • metabolism • liquid chromatography • mass spectrometry • biofluidprofiling • metabolomics • metabonomics

IntroductionBile is a complex biofluid with a wide range of important

physiological functions including maintaining cholesterol ho-meostasis, emulsifying fats and lipid absorption and in theexcretion (and often recirculation) of drugs and endogenousand exogenous toxins. Major constituents of the bile are thebile acids, which comprise a complex and structurally diversefamily of molecules, but the bile also contains a range oforganic acids, bases, amino acids and lipids including choles-terol and phosphotidylcholine, etc. However, because of thewide range of physiological functions that they perform, thebile acids are of particular interest. The primary bile acids,cholic and chendeoxycholic acid, are synthesized in the liverfrom cholesterol, and this is followed by further biotransfor-mations via conjugation to various polar endogenous com-pounds such as glycine, taurine, sulfate, glucuronic acid etc.and then, after secretion into the bile, by a wide range of gut-microfloral-mediated deconjugations and further metabolismvia, 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 thesesecondary bile acids and then their conjugation to glycine,taurine, sulfate, glucuronic acid etc., with secretion once againinto the bile, thereby generating very complex mixtures of

primary, secondary and tertiary bile acids. Normally only lowconcentrations of bile acids are found in plasma or urinehowever, in the case of disease or toxicity affecting the liver orGI tract (e.g., colon cancer), there may be significant distur-bance to processes involved in the synthesis, metabolism, andexcretion of these compounds.2-6 These changes may affectnot only the concentration of the bile acids and their composi-tion in bile but also in other biofluids such as plasma and urine.The bile acids therefore present a useful metabolic window forthe study of liver/intestinal/gut microfloral functions or activity.However, the complexity of the metabolic profile in samplessuch as bile, which contain many isobaric and isomeric bileacids, presents a significant technical challenge. Both gaschromatography-mass spectrometry (GC-MS) and HPLC-MShave been used for many years for the analysis and profilingof bile acids (recently reviewed by Wang and Griffiths7). In thecase of GC-MS-based methods extensive, and time-consum-ing, sample pretreatment is required (extraction, hydrolysis ofconjugates and derivatization) (see ref 7). For example oneapproach employed for the analysis of bile samples usedaminosulfonic acids and sodium hydroxide for derivatization,7

and required prepreparation by solid phase extraction. Theadvent of LC-MS-based methods thus offered the potentialfor significant simplification of the methodology, with muchreduced sample preparation, and a number of methods havebeen reported (e.g., 5,7-11). Indeed LC-MS has recently beenapplied to the quantitative analysis of 17 bile acids in plasma,urine and liver.11 That said, despite its ease of use, traditionalliquid chromatography does not possess the resolving powerof capillary GC-MS and therefore has been a less attractive

* 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 Journal of Proteome Research 2009, 8, 2495–2500 2495Published on Web 03/02/2009

means for the separation of complex mixtures such as bile.Thus, despite the undoubted analytical power offered by GCand LC-MS the complexity of the sample, combined with theneed to separate many close structural analogues still presentsa severe analytical problem. The commercial availability ofliquid chromatography systems that allow chromatography onsub 2 µm particles,12 at relatively high flow rates (ultraperformance liquid chromatography (UPLC)) has significantlyreduced the performance differential between capillary GC andLC. These higher resolution LC separations have been exploitedby many researchers for the analysis of complex mixtures infields such as sports medicine,13 global endogenous metaboliteprofiling,14 pharmaceutical analysis and drug metaboliteidentification.15,16 When operated at higher column tempera-tures, in excess of 90 °C, the reduction in mobile phase viscosityallows the use of longer columns and higher mobile phasevelocities.17-19 The use of these longer columns allows thegeneration of very high-resolution separations, with peakcapacities in the order of 1000 in one hour.17 The advent ofthe high-resolution separations offered by UPLC has encour-aged us to reinvestigate the use of LC-MS for bile analysis witha view to applications in metabonomics (the “quantitativemeasurement of time-related multiparametric metabolic re-sponses of multicellular systems to pathophysiological stimulior genetic modification”20). The application of sub 2 µm particleLC to metabonomics has already had an impact on the analysisof urine and plasma samples via increased throughput and thenumber of peaks detected,14,21 and would seem ideally suitedto enhancing the LC analysis of bile.

Experimental Section

Chemicals. Optima grade acetonitrile (HPLC grade) waspurchased from Fisher Scientific (Hampton, NH), formic acid(spectroscopic grade) and 0.1 M sodium hydroxide solutionwere purchased from Sigma/Aldrich (MO). Distilled water waspurified “in-house” using a Milli-Q system Millipore (MA).Leucine-enkephalin and bile acid standards, Taurocholic Acid,Glycocholic acid, Chenodeoxycholic Acid, Taurochenodeoxy-cholic Acid, Glycochenodeoxycholic Acid, Deoxycholic Acid,Taurodeoxycholic Acid, Glycodeoxycholic Acid, Lithocholicacid, Taurolithocholic acid, Glycolithocholic acid, Ursodeoxy-cholic Acid, Tauroursodeoxycholic Acid and Glycoursodeoxy-cholic Acid, were obtained from Sigma-Aldrich (MO).

Samples. Control bile samples were obtained via bile ductcannulation 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 analysisby UPLC-MS.

Chromatography. The separations were performed on a2.1 × 150 mm ACQUITY UPLC 1.7 µm BEH C18 column usingan ACQUITY Ultra Performance LC chromatography System(Waters Corporation, MA, USA). The column was maintainedat 90 °C and eluted with a linear acetonitrile-aqueous am-monium acetate pH 5 gradient over 20 min at 900 µL/min,starting at 20% acetonitrile and rising to 80% over the courseof the gradient. The column eluent was directed to the massspectrometer for analysis.

Mass Spectrometry. Mass spectrometry was performed ona Waters Micromass Q-Tof Premier mass spectrometer (WatersMicromass, Manchester, UK) operated in both positive andnegative ion mode with “V-Optics”, with the resolving quad-rupole set to a wide pass mode, the collision cell was set toalternate between a collision energy of 5 and 25 eV every 60

ms with a 20 ms inter scan delay. The instrument was operatedwith Lockspray frequency of 11 with a scans to average set to5, Luecine enkephalin was employed as the lockspray solutionat a concentration of 50 fmol/µL.

Identification of Bile Acids. The bile acids were identifiedby comparison of their retention time to that of authenticstandards, their accurate mass and fragmentation patterns.

Statistical Analysis. Data were analyzed using the MicromassMarkerLynx applications manager Version 1.4 (Waters, UK); thisapplication manager integrates peaks in the LC-MS data byusing ApexTrack peak detection. The LC-MS data were peak-detected and noise-reduced in both the LC and MS domainssuch that only true analytical peaks were further processed bythe software (e.g., noise spikes are rejected). A list of theintensities of the peaks detected was then generated for thefirst sample, using the retention time (RT) and m/z data pairsas the identifier for each peak. An arbitrary number is thenassigned 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 thiswas completed the data from each LC-MS analysis in the batchwere then sorted such that the correct peak intensity data foreach RT- m/z pair was aligned in the final data table. The ionintensities for each peak detected are then normalized, withineach sample, to the sum of the peak intensities in that samplewith these normalized peak intensities then multiplied by10 000. The resulting 3-dimensional data, peak number (RT -m/z pair), sample name, and ion intensity were analyzed byprincipal components analysis (PCA) using mean centering andPareto scaling.

Results and Discussion

As described in the introduction the high chromatographicefficiency of the sub 2 µm stationary phase offers improvedresolution over conventional LC and, operated at the highertemperatures employed here (90 °C), provided excellent resolu-tion of the complex mixture of bile acids encountered in thesesamples, as shown below. Examples of the results obtained fortypical rat and dog bile samples are illustrated in Figures 1 and2 for positive and negative ESI, respectively. Clearly the negativeion TIC reveals a more complex profile than the positive ESIdata. In negative ESI TIC major contributions to the bilemetabolite profile can be expected to result from the presenceof the bile acids and examination of the results from thenegative ESI data afforded the data provided in Table 1where the retention times and negative ion accurate massvalues for some of the bile acids detected are listed. The twolarge peaks detected in the dog bile -ve ESI chromatogramwere identified as taurocholic acid (8.9 min) and taurodeoxy-cholic acid (10.8 min). These bile acids, and those given in Table1, were identified by a mixture of comparison with authenticstandards (where available), accurate mass-derived atomiccompositions and fragmentation data. The need for a high-resolution separation system for the analysis of bile is amplyillustrated by the fact that analytes such as chenodeoxycholicacid (9.13 min), deoxycholic acid (10.46 min) and ursodeoxy-cholic acid (8.46 min) in rat bile all share the same mass of391.2848, 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), butwere also well separated from each other as shown in Figure3b with retention times of 12.37 and 12.05 min respectively.

In addition to bile acids the untargeted nature of the profilingperformed here allowed a range of other compounds to be

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identified in the profile including, for example, compoundssuch as arginine (RT.0.77 min), asparagine (RT.0.33 min),

creatine (RT.0.46 min), leucine (RT.1.4 min), lysine (RT.0.30min), ornithine (RT.0.33 min), and phosphocholine (RT.2.4min).

However, one of the obvious features of these profiles is theircomplexity, especially when minor components are taken intoaccount, with up to 2400 ions detected even in +ve ESI and12 500 features in -ve ESI for a typical rat bile sample alone.An obvious difficulty presented by mixtures of this sort is how

Figure 1. Total ion current mass chromatograms in -ve ESI fortypical rat (upper trace) and dog bile ESI (lower trace) samples.

Figure 2. Total ion current mass chromatograms in +ve ESI fortypical rat (upper trace) and dog bile (lower trace) samples.

Table 1. Retention Times and Negative Ion Accurate MassValues for Some of the Bile Acids Detected

bile acidelemental

compositionm/z -ve

ESIretention

time observed

Cholic Acid C24H40O5 407.2797 11.81 Rat/DogTaurocholic Acid C26H45NO7S 514.2828 9.14 Rat/DogGlycocholic acid C26H43NO6 464.3012 8.46 Rat/DogChenodeoxycholic Acid C24H40NO4 391.2848 9.13 Rat/DogTaurochenodeoxycholic

AcidC26H44NO6S 497.2811 10.5 Rat

GlycochenodeoxycholicAcid

C26H43NO5 448.3063 7.00 Rat

Deoxycholic Acid C24H40NO4 391.2848 10.46 Rat/DogTaurodeoxycholic Acid C26H45NO6S 498.2889 10.8 Rat/DogGlycodeoxycholic Acid C26H43NO5 448.3063 12.37 DogLithocholic acid C24H40NO3 375.2899 5.38 DogTaurolithocholic acid C26H44NO5S 481.2862 8.53 Rat/DogGlycolithocholic acid C28H47NO4 460.3427 7.43 Rat/DogUrsodeoxycholic Acid C24H40O4 391.2848 8.46 Rat/DogTauroursodeoxycholic

AcidC26H45NO6S 498.2968 8.95 Rat/Dog

GlycoursodeoxycholicAcid

C26H43NO5 448.3141 12.05 Rat

Figure 3. Selected ion current mass chromatogram for m/z391.2848 (upper trace) corresponding to ursodeoxycholic acid(8.46 min), chenodeoxycholic acid (9.13 min), and deoxycholicacid (10.46 min) and m/z 448.3063 (lower trace) correspondingto glycodeoxycholic acid (12.37 min) and glycoursodeoxycholicacid (12.05 min).

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to go about identifying the various components present in orderto better define the bile metabolome. Clearly, for specificclasses of compounds expected to be in bile, such as, forexample, the bile acids, where standards for the major com-pounds may be available, the most powerful approach is bystandard addition and spectral comparison. However, not allthe compounds that are needed for such a rigorous approachare readily available. An alternative, that enables the reductionof the “search space” for an unknown component, is to usethe power of mass spectrometry to subdivide the analytes intoclasses, and then work through those classes individually. Thus,given that much of the diversity found in the bile acids is dueto their modification with a range of polar substituents viaconjugation in the liver (“combinatorial” metabolism) theLC-MS data can be used to specifically target, for example,taurine, sulfate, glycine, glucuronide and glutathione conjugateswith benefits to simplifying subsequent identification. In orderto identify the particular classes of metabolites the fragmenta-tion pattern and mass accuracy of the Q-Tof Premier operatingin MSE mode was exploited.22 In MSE experiments the elutingpeaks are subjected to both high and low collision energies inthe collision cell of the mass spectrometer such that bothmolecular ion and fragment data are obtained. Here, asdescribed in the experimental section, the collision cell wasset to alternate between a collision energy of 5 and 25 eV every60 ms, providing a cycle of high and low fragmentationenergies. In this instance, where specific conjugations weresought, the high collision energy data was processed such thatonly those chromatographic peaks that contained the two ionsof interest, separated by the accurate mass value of theparticular conjugate, were displayed. For example if theconjugate of interest was a glucuronide, only those LC peakswhich contained two MS ions separated by the mass 176.0321((10 mDa) were selected. This is illustrated in Figure 4 forlosses of 79.9568, 176.0321, and 129.0426 ((10 mDa) charac-teristic of sulfate, glucuronide and glutathione conjugates

respectively present in rat bile. As can be seen from this figure,examining the mass chromatograms for the various classes ofconjugate shows them to be rather simpler than the originalTIC, with various degrees of complexity depending on whethersulfate, glucuronide or glutathione-containing compounds weresought. Examination of the data derived from these experi-ments suggests that over 20 sulfated, 32 glucuronidated and46-glutathione containing compounds were present in the ratbile sample exemplified here.

The data displayed in Figure 5a-d shows the profiles for thesulfate and taurine conjugates for the rat and dog, and fromthese conjugate-specific profiles it can easily be seen that dogbile appears to contain relatively more taurine and fewer sulfateconjugates than the rat bile. This approach of targetingcompound classes enabled the rapid identification of bile acids,for example, the following sulfated bile acids were detected andidentified by their MS/MS spectra, taurodeoxycholic sulfate,glycodeoxycholic sulfate, and lithocolic sulfate with retentiontimes of 6.65, 10.22, and 14.4 min.

This strategy of selecting compound classes out of the wholemixture as a means of simplifying the identification of me-tabolites in complex mixtures is, of course, by no means newin this area. Early examples for LC-MS investigations for, forexample, urinary metabolite classes include studies on the toxiceffects of heavy metals23 and antibiotics24 and more recentlyurinary glucuronides22 and glutathione conjugates25 have beensought (the latter following drug administration). However, asfar as we are aware the deliberate and systematic use of thisapproach for deconvoluting particular metabolomes via com-pound classes has not previously been advocated. In additionto simply breaking up the metabolic profile into more manage-able groups of compounds this reductionist approach can alsobe used for making comparisons between experimental classesor, in this case, species. The differences between rat and dogwith respect to sulfates have already been alluded to, butsimilar differences were also seen for other classes such as the

Figure 4. Typical total ion current for rat bile with mass chro-matograms showing losses of 80, 176, and 129 characteristic ofsulfate, glucuronide and glutathione conjugates respectivelypresent in the sample.

Figure 5. Typical total ion current for rat and dog bile sampleswith mass chromatograms showing losses characteristic ofsulfate and taurine conjugates respectively, showing speciesdifferences in conjugate patterns.

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glucuronide conjugates etc., in rat and dog bile samples (datanot shown). The simultaneous collection of high and lowcollision energy data allows the facile searching of data, as seenabove the detection of compounds that contain a specificconjugate, for example, sulfate. The spectra displayed in Figure6 also illustrates how it is possible to use the MSE approach toidentify peaks with common fragments resulting from, forexample, a number of conjugates to the same aglycone wherethis is conjugated to a number of different polar molecules.This is illustrated for taurocholic acid linked to give both asulfate (peak eluting at 7.39 min) defined by the transition ofm/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 experimentalsection, this UPLC-MS separation system was used to profilebile obtained from 24 control male rats and 2 dogs (from whichmultiple samples had been obtained). Here we have analyzedthe data obtained from the UPLC-MS of these samples usingthe unsupervised approach of principle components analysis(PCA). The result of this type of analysis for the positive ionUPLC/MS bile data analyzed using PCA approach employingmean centering and pareto scaling is shown in the scores plotdisplayed in Figure 7 wherein the rat samples (squares) areobserved 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 dogsamples 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) (allelevated in the rat) and taurodeoxycholic acid (m/z ) 498.2889at 10.8 min), taurolithocholic acid (m/z ) 481.2862 at 10.9 min)

(both elevated in the dog). These bile acids are particularlyinteresting as tauroursodeoxycholic acid is a tertiary bile acid,synthesized in the liver processed by the gut microflora andthen remetabolized in the liver through enterohepatic recir-culation. These ions were significantly higher in the dog thanthe rat.

If just the rat bile samples are considered, the scores plotshown in Figure 8 is obtained. The simplified data set showsthat there is one main group of samples with 6 outliers in asatellite group. Examination of the corresponding loadings plot(data not shown) reveals that the ions contributing mostsignificantly 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 minand m/z 498 at 10.8 min. These ions corresponded to tauro-cholic acid, tauroursodeoxycholic acid glycodeoxycholic acidand taurodeoxycholic acid respectively. The bile samplesexamined in this study were derived from a number of differentstudies (all of which were conducted in the same facility) andthe 6 members of the second group were derived from a singlestudy. As all of the samples were acquired from undosedanimals there is no obvious reason why such differences shouldhave occurred however, this type of finding highlights thepotential for batch-to-batch (or rather experiment-to-experi-ment) biological variability in biofluid analysis.

Conclusions

UPLC-MS provides a high-resolution system for obtainingglobal metabolite profiles for complex biological fluids andextracts, including bile, for metabolomic and metabonomicapplications. Here we have demonstrated that PCA can be usedto correctly classify rat and dog bile samples on the basis oftheir UPLC-MS-derived metabolite profiles. The data obtainedin this study also show the value of using the MSE approach asa method for simplifying the problem of classifying andidentifying the metabolites present in the sample thereby aidingin the determination of the biliary metabolome.

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Figure 6. MSE spectra for taurocholic acid conjugated to sulfate(peak eluting at 7.39 min, m/z ) 594 to 514) and glucuronidemoieties (peak eluting at 7.50 min, m/z ) 690 to 514).

Figure 7. Principle components analysis scores plot for rat anddog bile samples, using principle components 2 and 3. Rat bilesamples are denotes by 0 and the dog bile samples by ) and ∆.

Figure 8. Principle Components Analysis scores plot for rat bilesamples using principle components 1 and 2 showing thepresence of the main group together with a “satellite” groupfrom another experiment.

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