Metabonomic Study on the Biochemical Profiles of A Hydrocortisone

This paper describes the metabonomic study of a biochemical modification in vivo induced by hydrocortisone, which led to a unique pathologic condition...
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Metabonomic Study on the Biochemical Profiles of A Hydrocortisone-Induced Animal Model Minjun Chen,†,‡ Liping Zhao,‡ and Wei Jia†,‡,* School of Pharmacy, Shanghai Jiaotong University, Shanghai 200030, China, Shanghai Institute for Systems Biology, Shanghai 200030, China Received June 1, 2005

Abstract: This work describes the metabonomic study of a biochemical modification in vivo induced by high dose of hydrocortisone, which led to a unique pathologic condition similar to the ‘kidney deficiency syndromes’, an early stage of obesity and diabetes in traditional Chinese medicine. The methodology of the metabonomic approach consisted of GC/MS and multivariate statistical technique for the establishment of urine metabolic patterns of the treatment rats. In the study, 24-h urine was collected pre-dose and at days 1, 3, 7, and 10 post-dose after rats were injected with hydrocortisone at 1.5 mg/ 100 g. The acquired data were transferred into Matlab to be processed using principal components analysis (PCA). The results indicated that clear and consistent biochemical changes following hydrocortisone intervention under controlled conditions could be identified using chemometric analysis. The work suggests that this metabonomic approach could be used as a potentially powerful tool to investigate the biochemical changes of certain physiopathologic conditions such as metabolic syndrome, as an early diagnostic means. Keywords: metabonomics • hydrocortisone • GC/MS • kidneydeficiency • metabolic syndrome

Introduction Many drug- or chemical-induced effects involve disturbed endogenous metabolite concentration fluxes or ratios that result from direct chemical reactions, modified control mechanism, and induction or inhibition of enzymes, etc.1 As metabolite concentrations in several key body fluids related closely to cell and tissue process, toxin-, or disease-induced disequilibria are reflected in those fluids. Metabonomics, as one of the ‘-omics’ technologies involving modern chemical instrumental analysis and chemometrics analysis, is used to characterize the biochemical pattern of endogenous metabolic composition in biological samples, which is ideal for detecting the physiopathologic response to the toxin- or disease-induced disturbance or disequilibria in endogenous metabolic network. Therefore, metabonomics is becoming an increasingly impor* To whom correspondence should be addressed. Tel: 86-21-6293-2292. Fax: 86-21-6294-5529. E-mail: [email protected]. † School of Pharmacy, Shanghai Jiaotong University. ‡ Shanghai Institute for Systems Biology. 10.1021/pr050158o CCC: $30.25

 2005 American Chemical Society

tant tool and has been successfully used in drug toxicity evaluation,2-6 disease diagnosis,7-9 and so on. In pharmaceutical research, practical modeling of metabolism and physiological processes following pathophysiological stimuli can be important in connecting molecular events at the gene and protein level to those occurring at the macrosystem level, including pathological end-points.10,11 Although nuclear magnetic resonance (NMR) is fast and requires no complicated sample preprocessing to generate the chemical profiles, a combination of mass spectroscopy and chromatographic techniques such as GC/MS is being used by many investigators to detect more “delicate” fluctuations of the endogenous metabolite compositions due to its high sensitivity and simplicity.12,13 Currently, GC/MS based metabolic profiling analysis is having profound applications in discovering the mode of action of drugs or herbicides, identifying the marker compounds of disease, identifying new protein function in functional proteomics, and helping unravel the effect of altered gene expression on metabolism and organism performance in biotechnological applications.14-20 Hydrocortisone is one of steroid hormones produced by the adrenal gland (adrenal cortex), which play a complex role in regulating body functions. A unique physiopathologic state can be established by injecting rats with a high dose of hydrocortisone, in which animals will show signs of exhaustion, such as weight loss, decreased activity, slowed reaction, tendency to cluster and dropped appetite. These symptoms greatly resemble those described in traditional Chinese medicine (TCM) as ‘kidney deficiency syndromes’.21 Therefore, this animal model has been widely accepted and used to mimic the ‘kidney deficiency’ related conditions in TCM. However, the underlying metabolic pathways in this “hydrocortisone perturbed” pathological status are not known. In this paper, we applied the GC/MS and pattern recognition (PR) methods to investigate the biochemical abnormalities in animal due to hydrocortisone induction. The study indicates that this approach was able to elucidate the pathological status as well as the underlying metabolic pathways caused by chemical stimuli and the adaptive response in vivo.

Experimental Section Dosing and Sample Collection. Male wistar rats weighing 200-250 g (n ) 12) were adapted to a simisynthetic diet. These animals were divided into a treatment group (n ) 7) and a control group (n ) 5). After an initial acclimation period of 2 weeks in cages, the animals were transferred to individual Journal of Proteome Research 2005, 4, 2391-2396

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Profiles of A Hydrocortisone Induced Animal Model

technical notes

Figure 1. Comparison of GC/MS total ion current (TIC) chromatograms of urine from rats treated with hydrocortisone (A) or control group (B).

metabolism cages and allowed to acclimatize for a further 24 h. Following this period, 24-h urine of the rats was collected. Free access to food and drinking water was provided throughout the study period. The light cycle consisted of 12 h light and 12 h dark, the temperature was maintained at 20∼22 °C, and the humidity between 45% and 65% throughout the study. Following established protocol,21 hydrocortisone at dose of 1.5 mg/100 g (0.5% hydrocortisone injection, Shanghai Xinyi pharmaceutical company, Shanghai, China) was administered intravenously to the treatment group animals, while 0.9% NaCl solution was administered to the control group. Samples of 24-h urine were collected pre-dose and at days 1, 3, 7, and 10 post-dose after hydrocortisone injection. Sodium azide was added to the collection vessels as an antibacterial agent. All the collected urine samples were immediately stored at £-20 °C pending GC/MS analysis. After the final collection time point, all animals were sacrificed by decapitation after halothane anesthesia and subjected to autopsy. Sample Preparation and GC/MS Analysis. To each urine sample (1 mL), 2 mL of methanol and 0.5 mL of 37% HCl were added. The mixture was placed in a 95 °C water bath to hydrolyze. After 1-h hydrolization, 0.5 mL of 0.1 mol/mL KOH was added into the tube to neutralize the acidity, and 100 mg buffer salt (Na2CO3: NaHCO3 ) 1:8) was added to the solution to minimize pH variation. The solution was then evaporated to dryness under vacuum in a thermostatic waterbath (70 °C) for about 30 min in a concentrator (Bu ¨ chi Labortechnik AG, Switzerland), and the residue was dissolved with 5 mL ethyl ether. After shaken for 10 min with subsequent centrifugation (5 min at 3000 rpm), the ethyl ether of supernatant was separated and evaporated under a stream of N2 gas to dryness. Metabolytes in collected urine samples were derivatized prior to GC/MS analysis following the same procedure recently proposed by Gullberg,22 in which a derivatization agent consisting of TMS (trimethylsilyl) group and MSTFA (N-methyl-NtrimethylsilyltriXuoroacetamide) was used for the analysis of endogenous metabolites including nonvolatile polar compounds containing functional groups, such as -OH, -SH, or -NH groups, in a single GC/MS analysis. Fifty microliters of derivatization agent (MSTFA (Sigma Inc.): TMSI (AldriChemical Inc.) at 1000:1 ratio) was added to the dried residue, and the 2392

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solution was sucked into a capillary tube. The capillary tube was capped and placed in a constant temperature box at 70 °C for 30 min before 0.5 µL aliquot of each sample was injected into GC/MS for analysis. A PerkinElmer GC/MS system consisting of a Model gas chromatograph and a Model TURBOMASS mass spectrometer was used. Samples were injected into a fused-silica capillary column coated with cross-linked methyl-polyoxysiliane (HP1, 17 m × 0.2 mm I. D., 0.11 µm film thickness) in the split injection mode (1:3). The oven temperatures were as follows: the initial temperature was 180 °C. It was raised to 231 °C at a rate of 3.3 °C/min, then to 310 °C at rate of 30 °C/min and was maintained there for 2 min. Other instrumental parameters were following: the electron energy at 70 ev, the ion source temperature at 300 °C and the injector temperature at 280 °C. Helium, as a carrier gas, was set to a column flow rate of 1 mL/min. All data were collected in the full scan mode (30∼550 m/z). The dwell time for each scan was set at 100 ms. AMDIS software,23,24 the automated mass spectral deconvolution and identification system (National Institute of Standards and Technology, Gaithersburg, MD), is first employed to support peak finding and for a mated deconvolution of reference mass spectra. Identification of structures/compounds of the interested peaks was supported by a commercial mass spectral library in NIST98 format and mass spectral interpretation. In addition, comparison of relative retention time and mass spectra of commercially available standards was performed when available. Standards of amino acids and fatty acids mixture were purchased from Sigma-Aldrich. Data Processing and Pattern Recognition. All the collected urine samples with the above method were analyzed. Each sample was represented by a GC/MS total ion current (TIC) chromatogram. The chromatogram of control group was selected as a contrast one, and the Turbomass software (PerkinElmer Inc.) was used to process the chromatograms. Among the detected maximal area peaks, 23 peaks were confirmed as endogenous metabolites (shown in Figure 1) based on the NIST library, which were then used to construct a 23-dimensional vector to characterize the urine biochemical pattern. Each vector was normalized to the total sum of the

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Table 1. Endogenous Urinary Metabolites of Rats Perturbed after Treatment of the Rats with Hydrocortisone as Measured by GC/MSa peak no.

retention time

metabolites identification

control group (day 10)

treatment group (day 10)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

3.10 3.50 4.09 4.95 5.35 6.07 6.79 7.20 8.19 8.50 8.59 10.03 11.20 11.88 12.37 12.55 12.78 12.87 13.23 13.54 13.68 13.86 14.22

Tyramine Dopamine Tetradecanoic acid Glycine Galacturonic acid Hexadecanoic acid Acetate Uridine Oleic acid 3-Hydroxyproline Octadecanoic acid Noradrenaline Alanine Gulonate 4-(3-Hydroxy-phenyl)- butyric acid 2-Monopalmitin 1-Monoeleoylglycerol Cholesterol Butanoic acid D-galactose Stearin Eicosanoic acid Tyrosine

2.427e5 ( 1.671e5 3.390e5 ( 1.548e5 0.512e6 ( 0.327e6 3.722e5 ( 1.199e5 0.811e5 ( 0.505e5 6.145e5 ( 2.866e5 2.151e5 ( 0.786e5 2.530e4 ( 1.292e4 1.788e4 ( 1.329e4 1.518e5 ( 0.782e5 3.059e5 ( 1.844e5 1.439e4 ( 0.462e4 3.731e4 ( 2.719e4 0.581e5 ( 0.373e5 0.389e6 ( 0.308e6 0.649e5 ( 0.431e5 1.343e6 ( 0.334e6 0.532e5 ( 0.415e5 0.845e5 ( 0.568e5 9.848e4 ( 4.378e4 1.314e5 ( 0.708e5 8.971e5 ( 4.147e5 2.681e5 ( 0.301e5

6.225e5 ( 3.401e5* 4.309e5 ( 2.287e5* 1.756e6 ( 0.605e6** 4.198e5 ( 2.865e5 2.206e5 ( 1.581e5* 8.647e5 ( 2.369e5* 1.574e5 ( 0.625e5 6.008e4 ( 4.082e4* 2.071e4 ( 1.676e4 3.694e5 ( 1.449e5** 3.638e5 ( 0.882e5 3.694e4 ( 1.512e4** 1.712e5 ( 1.534e5* 3.479e5 ( 1.805e5** 1.073e6 ( 0.906e6 1.311e5 ( 0.587e5 1.179e6 ( 0.572e6 1.325e5 ( 0.601e5* 1.593e5 ( 0.441e5* 6.732e4 ( 2.432e4 0.850e5 ( 0.509e5 9.714e5 ( 4.689e5 4.007e5 ( 2.803e5*

a The relative intensity of metabolites in the control and treatment group is expressed with their peak area. Values are represented as mean(SD, significant difference between the treatment group (n ) 7) and control group (n ) 5) is based on a two-tailed student’s T-test. (* p < 0.05, ** p < 0.01). The identification of metabolites is based on NIST mass spectral database using MASS searching v1.7.

vector intensity, thereby partially accounting for concentration difference due to the volume of urine excreted. Principal component analysis (PCA), the most-commonly used algorithm in metabonomics studies,25,26 was employed in the study to process the GC/MS data using the program contained in Matlab (version 6.5, Mathwork Inc.). The simultaneous comparison of a large number of complex objects was facilitated by reducing the dimensionality of the data set via two-dimensional mapping procedures. The resulting data was displayed as “score plots”, which represent the distribution of samples in multivariate space. The score plots of the first two or three principal components allowed visualization of the data and to establish whether there was any intrinsic drug-induced difference in the metabolic composition of urine. Furthermore, mean values of the peaks descriptors were calculated for urine samples of drug-induced rats at each time point and plots of PC1 vs PC2 with the mean data were constructed.

Result and Discussion GC/MS Analysis of Urine Samples. Figure 1 (A,B) is the GC/ MS total ion current (TIC) chromatogram of urine sample of the control group and the treatment group. On the basis of NIST mass spectral database, the chromatograms are found to contain endogenous metabolites such as amino acids and fatty acids. Since these substances are involved in multiple biochemical processes, typically, energy metabolism, lipid metabolism and amino acid metabolism, the chromatograms are considered the representations of endogenous metabolites of rats as chemical fingerprint to describe the metabolic perturbation induced by hydrocortisone. Significant difference between the TIC profile of control and treatment group were observed, in Figure 1, indicating that the endogenous metabolite levels fluctuated after hydrocortisone interference. From Table 1, a number of marked changes were observed on the GC/MS chromatograms of urine following hydrocorti-

Figure 2. Biochemical pathway of the biosynthetic of catecholamines.

sone treatment. It is noted that the raised tyrosine, tyramine, dopamine, and noradrenaline levels may be a representation of drug-induced acceleration of the biosynthetic pathway of catecholamines as illustrated in Figure 2. This is consistent with the function of glucocorticoids in increasing catecholamine synthesis and secretion.27 Other references reported that the weight of three glands, pituitary, adrenal, and thymus, decreased in experimental rats after the intervention of exogenous glucocorticoid,28 presumably as a result of the drug-induced ‘over-consumption’ of the immune system of the animal. The accumulation of alanine may be a result of hydrocortisone’s effect on gluconeogenesis by a stimulation on the synthesis of certain key enzymes of the pathway of the glucose and alanine cycle as suggested in the literature.29 Glucocorticoid-induced maturation of glycoprotein galactosylation has also been reported,30 which may explain the perturbation of galacturonic acid. Elevated cholesterol and some free fatty acids observed post-dose are in agreement with the general opinion that glucocorticoids promote lipolysis. The significantly increased Journal of Proteome Research • Vol. 4, No. 6, 2005 2393

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Profiles of A Hydrocortisone Induced Animal Model

Figure 3. PCA map of rats from the control group (]) and the treatment group (×).

amount of 3-hydroxyproline may be due to the hydrocortisonemediated metabolism of arginine and glutamine, as glucocorticoids play an important role in mediating the accelerated arginine and gluamine metabolism, leading to an increased accumulation of hydroproline.31,32 Additionally, the elevated uridine level observed in the study is presumably caused by the enhanced activity of uridine diphosphate-GT, as a result of hydrocortisone induced hepatic glucuronyltransferase (GT) expression.33 The analysis of the endogenous urinary metabolites from these experimental rats indicated a general and significant ‘increase’ in metabolic activities. That is, after the high dose of hydrocortisone intervention the animals entered into a state of ‘hyperfunction’, involving a series of changes in metabolic 2394

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network, such as activated hypothalamic monoamine transmitters and accelerated energy metabolism. The ‘over-consumption’ of the energy and immune systems led to a state of ‘exhaustion’ as evidence by the animals showing the signs of exhaustion, decreased activity, and weight loss, a physiopathologic state used to mimic ‘kidney deficiency syndromes’ in TCM. It can be further anticipated that the prolonged intervention of hydrocortisone is likely to result in a worsened state, involving physical changes in immuno-glands and organs in experimental animals, as other investigators reported,28 although our hydrocortisone-induced experiment was too short to demonstrate such changes. This may be the state where subsequent conditions such as diabetes and cardiovascular diseases gradually develop.

technical notes

Figure 4. Score plot of principal components derived from the GC/MS profiles from urine sample obtained from rats injected with hydrocortisone, showing a time-related trajectory of metabolite patterns at different time points.

Identification of Biochemical Effects. Figure 3 A-E provides an indication of the progression of the disturbance through time and were used to identify the time points of maximum biochemical effect for hydrocortisone. Figure 3A represents the pre-dosed situation that the control and treatment group showed no tendency of separation, no difference in their metabolic patterns. In Figure 3B, however, the spots representing the treatment group shifted away from the spots, suggesting that the different metabolic patterns have been established due to the hydrocortisone interference. Figure 3B-E provides indication of the progression of the disturbance through time and the maximum biochemical effect achieved by hydrocortisone, in this case, at day 3 as shown in Figure 3C. Time-Dependent Metabolic Effect Following Hydrocortisone Treatment. Figure 4 showes a time-dependent trajectory of metabolite patterns at different time points after hydrocortisone treatment. In the PCA map, each spot represented a sample, and each assembly of samples indicated a particular metabolic pattern at different time points. The locus marked by arrows represented the mean metabolite pattern changing by the time. The starting point was the principal component’s mean value of the sample group collected prior to administration of hydrocortisone. From Figure 4, biochemical metabolic pattern changed with time, all spot at different time points showed distinct difference from the sample collected pre-dose. Furthermore, metabolic pattern of days 1 and 3 appeared different from days 7 and 10, suggesting the metabolic pattern at days 1 and 3 might be undergoing a transition period with high fluctuations, and that the metabolic network were being restored at days 7 and 10, leading to a stable pattern approaching the pre-dose group. Kidney Deficiency Syndrome and Metabolic Disorders. Steroids such as hydrocortisone interfere with the chemical balance of the body and therefore, produce metabolic side effect. They alter salt, potassium, and calcium balance, and increase blood sugar levels (diabetes may be unmasked in susceptible individuals). From TCM experience, the fundamental causes of obesity and subsequent conditions are ‘kidney deficiency syndromes’, which manifest as “an overflow of body fluids, accumulation of dampness and phlegm evils and stagnation in blood flow”.34 The TCM clinical status of a person

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with kidney deficiency may change leading to metabolic syndrome and a complex diagnosis including Xiao-ke (diabetes) and chest pain, etc.34 In metabonomic approach, involving multiple endogenous metabolites information, biochemical perturbation induced by hydrocortisone was detected through the comparison of the metabolic patterns between the control and the treatment group. The significance of studying a certain physiopathologic state using metabonomics approach lies in the great potential of establishing a new methodology of early disease diagnosis. Although no sufficient evidence is established with regard to linking this hydrocortisone induced model with specific pathological conditions such as the early stage of type 2 diabetes, we believe that ‘kidney deficiency syndrome’ may serve as a good precursor or a starting point for the study of the complex conditions related to metabolic disorders, especially at their early stages.

Conclusion In this paper, a metabonomic method based on GC/MS and multivariate statistical technique has been used to study a specific physiopathologic state named ‘kidney deficiency syndromes’ in Chinese traditional medicine, induced with a high dose of hydrocortisone in rats. Using PR coupled with GC/MS, we were able to characterize the biochemical fingerprints of physiopathologic status in the animal model. Despite a high degree of intersubject variability in the urinary composition, clear and consistent biochemical changes following hydrocortisone modification can be identified using chemometric analysis. The work demonstrates the metabonomic approach is a potentially powerful tool to investigate the biochemical nature of physiopathologic symptoms of complex conditions, especially metabolic syndrome.

Acknowledgment. The authors wish to thank Yumin Liu, Jingjing Gu and Yuan He for assistance with the GC/MS analysis and Wenjuan Zhao, Congyi Guo with the animal experiment section. This study was financially supported with a Young Faculty Grant No. A2816A by Shanghai Jiao Tong University. References (1) Nicholson, J. K.; Lindon, J. C.; Holmes, C. E. Xenobiotica 1999, 29, 1181-1189. (2) Mortishire-Smith, R. J.; Skiles, G. L.; Lawrence, J. W.; Spence, S.; Nicholls, A. W.; Johnson, B. A.; Nicholson, J. K. Chem. Res. Toxicol. 2004, 17, 165-173. (3) Waters, N. J.; Holmes, E.; Williams, A.; Waterfield, C. J.; Farrant, R. D.; Nicholson, J. K. Chem. Res. Toxicol. 2001, 14, 1401-1412. (4) Coen, M.; Lenz, E. M.; Nicholson, J. K.; Wilson, I. D.; Pognan, F.; Lindon, J. C. Chem. Res. Toxicol. 2003, 16 (3), 295-303. (5) Bollard, M. E.; Keun, H. C.; Beckonert, O.; Ebbels, T. M. D.; Antti, H.; Nicholls, A. W.; Shockcor, J. P.; Cantor, G. H.; Stevens, G.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Toxicol. Appl. Pharmacol. 2005, 204, 135-151. (6) Waters, N. J.; Waterfield, C. J.; Farrant, R. D.; Holmes, E.; Nicholson, J. K. Chem. Res. Toxicol. 2005, 18, 639-654. (7) Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W. L.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E.; Grainger, D. J. Nat. Med. 2002, 8 (12), 14391444. (8) Constantinou, M. A.; Papakonstantinou, E.; Benaki, D.; Spraul, M.; Shulpis, K.; Koupparis, M. A.; Mikros, E. Anal. Chim. Acta 2004, 511, 303-312. (9) Beckonert, O.; Monnerjahn, J.; Bonk, U.; Leibfritz, D. NMR Biomed. 2003, 16, 1-11. (10) Nicholson, J. K.; Lindon, J. C.; Homles, E. Xenobiotica 1999, 29, 1181-1189.

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