Urinary Metabolite Variation Is Associated with Pathological

Article pubs.acs.org/jpr

Urinary Metabolite Variation Is Associated with Pathological Progression of the Post-Hepatitis B Cirrhosis Patients Xiaoning Wang,†,∥ Xiaoyan Wang,‡ Guoxiang Xie,§ Mingmei Zhou,∥,⊥ Huan Yu,#,¶ Yan Lin,†,∥ Guangli Du,† Guoan Luo,# Wei Jia,*,†,⊥,§ and Ping Liu*,†,∥ †

E-institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai 201204, China ‡ Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China § Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States ⊥ Center for Chinese Medical Therapy and Systems Biology, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China # Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100083, China ¶ School of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi 440004, China ∥

S Supporting Information *

ABSTRACT: Cirrhosis is a common and terminal outcome of many chronic liver conditions. A urinary metabonomic study using gas chromatography−mass spectrometry (GC−MS) and ultra performance liquid chromatography time-of-flight mass spectrometry (UPLC−TOFMS) was carried out to elucidate the pathophysiological basis of posthepatitis B cirrhosis in 63 posthepatitis B cirrhosis patients and 31 health controls. Urinary metabolic profile and corresponding differential metabolites associated with Child-Pugh (CP) grading of liver function were characterized, in addition to the blood routine, liver, and renal function tests. Multivariate statistical tools including principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were employed in the metabolite analysis along with a univariate statistical method, Wilcoxon−Mann−Whitney test. The alterations of differential metabolites contributing to the intergroup variation between healthy controls and cirrhotic patients, and among cirrhosis of CP grade A, B and C were also investigated. Six metabolites, α-hydroxyhippurate, tyrosine-betaxanthin, 3-hydroxyisovalerate, canavaninosuccinate, estrone, and glycoursodeoxycholate, were significantly altered among cirrhotic patients with CP A, B, and C, reflecting abnormal metabolism of amino acid, bile acids, hormones, and intestinal microbial metabolism. The results show that dynamic alteration of urinary metabolome, characterized by the changes of a panel of the differential metabolite markers, is indicative of an exacerbated liver function, highlighting their diagnostic and prognostic potential for the liver cirrhosis development. KEYWORDS: cirrhosis, hepatitis B, urine, urinary metabolites, metabonomics, Child-Pugh classification

Received: April 5, 2012 Published: May 24, 2012 © 2012 American Chemical Society

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dx.doi.org/10.1021/pr300337s | J. Proteome Res. 2012, 11, 3838−3847

Journal of Proteome Research

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Article

INTRODUCTION Cirrhosis is the final stage of chronic liver damage characterized by replacement of liver tissue by fibrosis, scar tissue, and regenerative nodules, leading to portal hypertension and end-stage liver disease.1 Among patients with different liver conditions, the ability to identify those most likely to have cirrhosis noninvasively and to determine the stages of cirrhosis is challenging. Over the past several decades, many prognostic models have been developed for liver cirrhosis, including the most widely used Child-Pugh (CP) classification2,3 and the model for end-stage liver disease (MELD).4,5 Patients with liver cirrhosis can be classified into three stages, A (least), B (moderate), and C (worst) according to the CP score,2,3 with one-year survival rates of 100%, 81%, and 45%, respectively. The CP score employs a set of clinical measures including ascites and hepatic encephalopathy, along with laboratory assessment including serum albumin, total bilirubin, and prothrombin time, where each measure is scored 1−3, with 3 indicating most severe derangement. To date, the CP classification is still considered the cornerstone in prognostic evaluation of cirrhosis, since it reasonably predicts survival in many chronic liver conditions and the likelihood of major complications such as bleeding from varices and spontaneous bacterial peritonitis.6,7 However, the CP score does not provide direct evidence of the pathological stage or state of cirrhosis.8 Moreover, it has some drawbacks such as the limited discriminatory ability as well as the fact that it depends greatly on the clinician’s experience.9,10 Understanding the severity of cirrhosis as well as the range of potential outcomes is essential to predict treatment outcomes and individualize therapy. With the increasing use of effective antiviral treatments and the emergence of effective antifibrotic agents, there is a pressing necessity for establishing more refined cirrhosis staging method for the management of liver cirrhosis. The clinical distinction between compensated (the liver is heavily scarred but can still perform many important bodily functions) and decompensated cirrhosis (the liver is extensively scarred and unable to function properly) has been associated to the clinical entities of quantitative variables such as portal pressure measurements and emerging noninvasive diagnostics.11 Additionally, mounting evidence suggests that cirrhosis encompasses a pathological spectrum which is dynamic and bidirectional in many patients at different stages.11 Thus, there is a strong need to redefine cirrhosis in a manner that better recognizes its underlying relationship to portal hypertension and related circulatory changes, and more accurately reflects its progression, reversibility, and prognosis, ultimately linking these parameters to clinically relevant outcomes and therapeutic strategies.8 The liver is the most important metabolic organ of the human body, responsible for metabolism of a large array of substrates, such as sugar, protein, fat, and phytochemical compounds.12 The liver cirrhosis has been linked closely to metabolic disorders.13 Metabonomics, the quantitative measurement of the multiparametric metabolic response of living systems,14,15 has been widely accepted as an effective approach for the study of the pathophysiological changes associated with or resulting from disease or injury.16 The metabonomic approaches, using highfield nuclear magnetic resonance (NMR), gas chromatography− mass spectrometry (GC−MS), and liquid chromatography− mass spectrometry (LC−MS), provide mechanistic information to allow the diagnosis and prognosis of diseases17,18 with a variety of endogenous metabolites differentially expressed in biofluids such as blood and urine.19−21 In this study, we report a urinary

metabonomic study on a cohort of liver cirrhosis patients (n = 63) and healthy subjects (n = 31) using a combined GC-MS and ultraperformance liquid chromatography time-of-flight mass spectrometry (UPLC−TOFMS).

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MATERIALS AND METHODS

Participants

A total of 63 male patients diagnosed with liver cirrhosis, aged 33−58, were recruited at Shuguang Hospital, Longhua Hospital and Putuo District Center Hospital affiliated with Shanghai University of Traditional Chinese Medicine (Shanghai, China), and Shanghai Public Health Center affiliated with Fudan University (Shanghai, China) from January 2007 to December 2008. Patients were clinically diagnosed with liver cirrhosis and infection with chronic hepatitis B according to the “Guideline on prevention and treatment of chronic hepatitis B in China (2005)”. The guideline (2005 version) was jointly revised in 2007 by Chinese Society of Hepatology, Chinese Medical Association and Chinese Society of Infectious Diseases, and Chinese Medical Association.22 All patients were clinically stable at the time of assessment. Patients with hepatitis C infection, alcohol consumption, neoplastic liver diseases, and hepatotoxic medication in the past 6 months were ruled out before entering the study. Patients were divided into three subgroups, class A (n = 15), class B (n = 31), and class C (n = 17) according to CP scores. A cohort of 31 male participants was recruited as healthy controls from the Physical Examination Center of Shuguang Hospital. There was no significant difference in age, height, body weight, and BMI between healthy controls and liver cirrhosis patients (Tables 1 and 2). Table 1. Clinical Information of Human Subjects Number Age (y, mean ± SD) Body height (cm, mean ± SD) Body weight (kg, mean ± SD) BMI (kg/m2)

control

liver cirrhosis

p

31 49.70 ± 5.46 170.8 ± 3.05 67.48 ± 6.37 23.14 ± 2.14

63 52.95 ± 8.86 171.5 ± 5.07 65.91 ± 9.26 22.73 ± 2.78

0.09 0.46 0.76 0.18

Ethical approval for these studies was obtained from the ethics committee of the above four hospitals and all participants signed the informed consent prior to the study. Urine Sample Collection and Preparation

Clean voided midstream urine samples were obtained in the morning from cirrhosis patients and control subjects before breakfast and stored at −80 °C until GC−MS or UPLC− TOFMS analysis. Biochemistry Tests

Serum biochemical assay was performed with an automatic biochemistry analyzer (Hitachi Ltd., Tokyo, Japan) for the analysis of blood routine, liver, and renal function markers. Ascites was examined by ultrasonography. GC−MS Profiling and Data Analysis

The urine sample preparation for GC−MS analysis was performed according to our previously published report with minor modification.23 Each 500 μL urine sample was supplemented with 100 μL of L-2-chlorophenylalanine (100 μg/mL, used as internal standard), 100 μL of pyridine, and 400 μL of anhydrous ethanol. The mixture was added to 50 μL of ethyl chloroformate (ECF) and sonicated (100 kHz, 60 s) at room 3839



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Table 2. Clinical Information and Characteristics of Liver Cirrhosis Subjects Child-Pugh score variable

class A (score 5−6)

class B (score 7−9)

class C (score 10−13)

pa

Patients (n) Age (mean ± SD) BMI (kg/m2) RBC (1012/L) WBC (109/L) HB (g/L) NEUT# (109/L) LYM# (109/L) PLT (109/L) Alb (g/L) Glb (g/L) A/G (100%) ALT (IU/L) AST (IU/L) GGT (IU/L)) ALP (IU/L) CHE (IU/L) TBiL (μmol/L) DBiL (μmol/L) PT (sec) INR (%) BUN (mmol/L) Cr (μmol/L) TCH (mmol/L) TG (mmol/L) APOA-1 (g/L) FPG (mmol/L) AFP (ng/mL)

15 55.85 ± 7.99 23.63 ± 3.05 4.05 ± 0.52 4.29 ± 1.25 132.2 ± 14.91 2.48 ± 0.88 1.48 ± 0.75 101.2 ± 48.99 37.95 ± 5.21 37.06 ± 8.62 1.08 ± 0.28 55.31 ± 29.00 57.77 ± 37.92 104.3 ± 63.68 187.50 ± 116.40 4126 ± 1205 24.35 ± 8.98 8.38 ± 5.52 15.30 ± 1.28 1.31 ± 0.15 5.24 ± 1.17 76.93 ± 13.17 3.60 ± 0.68 0.89 ± 0.19 0.96 ± 0.17 5.87 ± 1.13 50.84 ± 71.87

31 53.55 ± 8.77 22.50 ± 2.43 3.44 ± 0.67 4.51 ± 1.99 113.7 ± 23.48 2.42 ± 1.01 1.19 ± 0.90 84.32 ± 56.15 30.64 ± 4.61 32.49 ± 7.81 0.99 ± 0.27 85.16 ± 74.10 99.81 ± 73.10 87.61 ± 76.31 114.10 ± 63.51 3549 ± 1336 61.60 ± 95.87 31.46 ± 70.27 15.91 ± 1.75 1.45 ± 0.23 6.07 ± 2.70 86.24 ± 36.86 3.42 ± 0.88 0.91 ± 0.32 0.84 ± 0.17 6.23 ± 2.41 60.53 ± 114.60

17 49.47 ± 8.97 21.75 ± 2.41 3.04 ± 0.55 5.24 ± 3.23 105.8 ± 16.49 2.78 ± 1.4 1.23 ± 0.71 80.87 ± 53.82 25.28 ± 4.17 35.21 ± 8.82 0.77 ± 0.25 46.94 ± 21.54 72.71 ± 31.45 46.88 ± 36.26 90.88 ± 34.48 2135 ± 616.10 88.32 ± 69.96 37.09 ± 42.66 19.80 ± 2.27 1.90 ± 0.29 6.14 ± 3.49 88.23 ± 33.43 2.82 ± 0.89 0.73 ± 0.29 0.70 ± 0.20 4.78 ± 0.71 59.29 ± 111.90

ns ns *†‡ ns *‡ ns ns ns *†‡ ns *‡ ns ns †‡ *‡ †‡ †‡ *‡ †‡ †‡ ns ns †‡ ‡ †‡ † ns

a Asterisk (*) indicates P < 0.05, Class A vs Class B. Dagger (†) indicates P < 0.05, Class B vs Class C. Double dagger (‡) indicates P < 0.05, Class A vs Class C, ns, nonsignificant.

temperature for the first derivatization; subsequently, 300 μL of chloroform was added with strong vortex for 30 s prior to centrifugation at 3000 rpm for 5 min. After aqueous layer was further adjusted using 100 μL of NaOH (7 mol/L), the derivatization procedure was repeated with the addition of 50 μL of ECF into the aqueous phase (upper layer). The chloroform layer was isolated and dried with anhydrous sodium sulfate prior to GC/MS analysis. Each 1 μL aliquot of derivatives was injected in splitless mode into an Agilent 6890N gas chromatography coupled with an Agilent 5975B inert MSD mass spectrometry (Agilent Technologies, Santa Clara, CA). A HP-5 ms capillary column (30 m × 250 μm i.d., 0.25 μm film thickness, 5%-phenylmethylpolysiloxane bonded and cross-linked. Agilent J&W Scientific, Folsom, CA) was applied to separate the derivatives with helium as carrier gas at a constant flow rate of 1.0 mL/min. The injector temperature was 280 °C. The analysis was performed under the following temperature program: 2 min isothermal heating at 80 °C, followed by 10 °C/min oven temperature ramps to 140 °C, 4 °C/min to 240 °C, and 10 °C/ min to 280 °C, and hold for 3 min. The temperatures of transfer line and ion source were 260 and 200 °C, respectively. The measurements were obtained using MS detection in electron impact ionization (70 eV) and full scan mode (m/z 30−550). Raw GC−MS data were converted into AIA format (NetCDF) files by Agilent GC−MS 5975 Data Analysis software, and subsequently the data information was extracted by the XCMS toolbox using the parameters as previously

described.24 The XCMS output (TSV file) was introduced to Matlab software version 7.0 (The MathWorks, Inc.), where internal standard (IS) peaks, and impurity peaks from column bleeds and derivatization procedure were excluded. The remaining ion features with high correlation of abundance within the same retention time group were combined into a single compound so as to obtain the total numbers of compounds and simplify data matrix for multivariate statistical analysis. The intensities of ion features (area) were further normalized to total area for each sample to eliminate the variations caused by the different volume of individual urine sample and arranged on a three-dimensional matrix consisting of arbitrary peak index (RT−m/z pair), sample names (observations), and peak area (variables). The resulting three-dimensional matrix data was imported to SIMCA-P 11.0 software (Umetrics, Umeå, Sweden). Principle component analysis (PCA) was performed on the meancentered and UV-scaled data to visualize general clustering, trends, and outliers among all samples on the scores plot. These differential metabolites selected from the orthogonal partial leastsquares discriminant analysis (OPLS-DA) model with VIP value (VIP > 1) are validated at a univariate level with Wilcoxon− Mann−Whitney test with a critical p-value usually set to 0.05. UPLC−TOFMS Profiling and Data Analysis

The urinary samples preparation for UPLC−TOFMS was performed according to our previous works.25 A 100 μL of each urinary sample was mixed with 400 μL of methanol and 3840



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Table 3. List of Urinary Differential Metabolites in Cirrhotic Patients and among Child-Pugh A, B, and C Classes Relative to Controls liver cirrhosis vs control compoundsa

VIPb

FCc

CP A vs control

Pd

4-Pyridinecarboxylate Threonine* Proline* Citrate* Aconitate* 2-Pentendioate Hippurate* 2-Aminobutyrate* Acetyl citrate 3,4-Dihydroxyphenylacetate* 4-Hydroxy-benzenepropanedioate

1.9 1.5 1.5 1.3 1.4 1.7 1.9 2.0 1.5 2.1 1.7

0.46 0.64 1.30 1.50 1.36 1.58 0.47 0.38 2.75 1.88 4.29

1.53 × 10−2 5.96 × 10−2 1.79 × 10−1 8.28 × 10−3 1.97 × 10−3 1.95 × 10−2 2.34 × 10−2 2.04 × 10−2 2.44 × 10−3 2.08 × 10−4 6.84 × 10−4

cis-Aconitate* Pyroglutamate* O-Phosphotyrosine 3-Methoxy-4-Hydroxyphenylglycol sulfate Alpha-Hydroxyisobutyrate* 3-Hydroxyisovalerate* Dopaxanthin Alpha-Hydroxyhippurate* Canavaninosuccinate L-Aspartyl-4-phosphate Isoxanthopterin Tyrosine-betaxanthin Estrone* Glycocholic acid 3-glucuronide Taurohyocholate* Cortolone-3-glucuronide Tetrahydroaldosterone-3-glucuronide 11-beta-Hydroxyandrosterone-3glucuronide N-Acetyl-leukotriene E4 11-Oxo-androsterone glucuronide Glycocholate* Dehydroepiandrosterone 3glucuronide Androsterone sulfate Testosterone sulfate Glycoursodeoxycholate* Androsterone glucuronide 17-hydroxyandrostane-3-glucuronide Glycolithocholate 3-sulfate

2 2.1 2 2.1

0.75 0.69 0.70 1.70

6.30 × 10−5 7.74 × 10−7 3.71 × 10−6 3.15 × 10−9

2.4 2.4 1.8 2.1 3.1 1.6 1.6 2.6 1.4 1.7 1.5 2.5 2.6 2.4

0.42 0.55 0.23 0.35 25.57 0.71 0.74 0.38 0.78 5.18 119.52 0.36 0.31 0.38

1.28 × 10−6 1.84 × 10−10 7.15 × 10−4 8.93 × 10−6 9.19 × 10−21 1.41 × 10−4 2.66 × 10−3 2.92 × 10−8 1.70 × 10−3 7.02 × 10−7 2.29 × 10−7 2.32 × 10−9 3.97 × 10−9 2.13 × 10−7

2.6 2.3 1.9 2.4

0.12 0.25 12.72 0.29

2.5 2.3 1.3 3.1 2.9 2.9

0.01 0.21 16.41 0.27 0.27 0.04

FCe

Pd

GC−MS 0.43 0.67 1.06 1.53 1.50 1.34 0.71 0.97 1.79 1.73 1.48 UPLC−MS 0.86 0.74 0.83 1.44

CP B vs control FCf

Pd

CP C vs control FCg

Pd

1.60 × 10−2 1.18 × 10−1 8.02 × 10−1 7.51 × 10−2 1.17 × 10−2 2.58 × 10−1 3.96 × 10−1 9.52 × 10−1 2.69 × 10−1 2.53 × 10−2 4.42 × 10−1

0.47 0.70 1.36 1.37 1.32 1.85 0.47 0.22 2.99 2.03 4.07

1.87 × 10−2 1.72 × 10−1 2.00 × 10−1 1.11 × 10−1 4.77 × 10−2 7.09 × 10−3 3.19 × 10−2 3.23 × 10−3 3.89 × 10−2 7.60 × 10−3 9.14 × 10−3

0.50 0.47 1.47 1.78 1.31 1.23 0.18 0.04 3.32 1.72 8.19

2.02 × 10−1 1.10 × 10−2 3.17 × 10−1 1.22 × 10−1 3.09 × 10−2 4.32 × 10−1 1.42 × 10−3 2.83 × 10−4 1.13 × 10−2 1.05 × 10−2 2.11 × 10−2

2.74 × 10−2 3.29 × 10−4 1.66 × 10−2 6.49 × 10−3

0.74 0.72 0.70 1.69

1.04 × 10−4 1.03 × 10−4 1.11 × 10−5 1.13 × 10−5

0.66 0.59 0.59 1.94

1.49 × 10−6 7.32 × 10−9 3.76 × 10−6 1.81 × 10−6

0.66 0.79 0.42 0.56 20.56 0.81 0.81 0.61 0.88 6.04 84.43 0.53 0.59 0.64

9.03 × 10−3 6.43 × 10−3 2.95 × 10−2 1.73 × 10−2 1.50 × 10−4 3.43 × 10−2 3.21 × 10−2 1.80 × 10−3 1.21 × 10−1 1.17 × 10−2 1.93 × 10−2 4.27 × 10−4 3.00 × 10−3 9.66 × 10−3

0.39 0.56 0.26 0.40 24.94 0.81 0.78 0.38 0.81 5.62 132.41 0.38 0.28 0.37

8.06 × 10−7 5.09 × 10−9 1.53 × 10−3 8.68 × 10−5 3.44 × 10−10 5.03 × 10−2 2.72 × 10−2 5.63 × 10−8 1.22 × 10−2 1.89 × 10−4 2.35 × 10−4 1.42 × 10−7 8.01 × 10−9 3.85 × 10−7

0.26 0.33 0.01 0.08 31.18 0.41 0.62 0.20 0.63 3.57 125.26 0.16 0.12 0.18

1.05 × 10−8 9.99 × 10−12 2.30 × 10−5 5.99 × 10−9 2.42 × 10−9 1.96 × 10−6 2.38 × 10−3 5.06 × 10−11 4.69 × 10−6 5.07 × 10−2 7.38 × 10−3 7.93 × 10−12 8.13 × 10−12 3.13 × 10−10

2.82 × 10−6 3.44 × 10−5 3.61 × 10−10 1.45 × 10−7

0.22 0.45 12.31 0.58

4.48 × 10−5 2.06 × 10−3 4.32 × 10−3 2.61 × 10−3

0.12 0.25 13.79 0.25

3.31 × 10−6 3.65 × 10−5 6.55 × 10−6 2.35 × 10−7

0.02 0.09 11.01 0.10

4.59 × 10−7 1.46 × 10−6 2.21 × 10−3 7.60 × 10−10

1.28 × 10−5 4.09 × 10−5 1.30 × 10−4 1.16 × 10−13 2.05 × 10−10 3.09 × 10−7

0.00 0.19 8.22 0.51 0.49 0.04

1.11 × 10−5 3.52 × 10−5 1.69 × 10−1 6.18 × 10−6 1.88 × 10−4 3.82 × 10−7

0.01 0.23 13.17 0.24 0.24 0.05

1.27 × 10−5 6.27 × 10−5 1.92 × 10−2 6.96 × 10−14 1.04 × 10−10 4.50 × 10−7

0.02 0.18 29.85 0.11 0.13 0.00

1.51 × 10−5 3.15 × 10−5 7.23 × 10−3 7.54 × 10−16 2.92 × 10−12 1.53 × 10−7

a Asteriks (*) indicates metabolites are verified by reference standards. bVariable importance in the projection (VIP) was obtained from OPLS-DA model with a threshold of 1.0. cFold change (FC) was obtained by comparing those metabolites in liver cirrhosis patients to controls. dP values were calculated from Wilcoxon−Mann−Whitney test. eFC was obtained by comparing those metabolites in CP A liver cirrhosis patients to controls. fFC was obtained by comparing those metabolites in CP B liver cirrhosis patients to controls. gFC was obtained by comparing those metabolites in CP C liver cirrhosis patients to controls. FC with a value >1 indicates a relatively higher concentration present in liver cirrhosis patients or CP A, B, C liver cirrhosis patients while a value 1) from a typical 7-fold cross-validated OPLS-DA model. These differential metabolites selected from the OPLS-DA model were validated at a univariate level with Wilcoxon−Mann−Whitney test with a critical p value usually set to 0.05.

Compound Annotation

Compound annotation from UPLC−TOFMS data was performed by comparing the accurate mass (m/z) and retention time (Rt) of reference standards in our in-house library and the accurate mass of compounds obtained from the web-based resources such as the Human Metabolome Database (www. hmdb.ca). For GC−TOFMS data, compound annotation was carried out by comparing the mass fragments and Rt with our in-house library or mass fragments with NIST 05 Standard mass spectral databases in NIST MS search 2.0 (NIST, Gaithersburg, MD) software with a similarity of greater than 70%.

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RESULTS

Clinical Characteristics of Liver Cirrhosis Patients

Patients’ clinical characteristics of the three stage subgroups were summarized in Table 2. As liver function gradually aggravated with increased CP scores, serum levels of red blood cell (RBC), hemoglobin (HB), albumin (Alb), γ-glutamyl transferase (GGT), alkaline phosphatase (ALP), cholinesterase (CHE), total cholesterol (TCH), and apolipoprotein A-1 (APOA-1) were decreased progressively, while serum levels of total bilirubin (TBiL) and prothrombin time (PT) were significantly increased. GC−MS Analysis

A total of 165 ion features were obtained and 11 identified urine metabolites were differentially expressed in cirrhotic patients relative to healthy controls. Peak intensity comparison of the differentially expressed metabolite levels between control and CP A, B, C liver cirrhosis patients was summarized in Table 3. To distinguish healthy subjects from cirrhosis patients as well as cirrhosis patients of 3 stages, PCA and OPLS-DA analysis were performed in this study. With the 165 features generated from GC−TOFMS, a PCA scores plot (figure not shown) using 4 components (R2Xcum = 0.486, Q2cum = 0.32) and a crossvalidated OPLS-DA model using 1 predictive component and 3842



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Figure 2. OPLS-DA scores plot of urinary metabolites in Child-Pugh class A, B, and C of hepatic cirrhosis patients using GC−MS spectral data.

Figure 3. OPLS-DA scores plots of the urinary metabolites in healthy controls and hepatic cirrhosis patients with Child-Pugh class A, B, and C using UPLC−TOFMS negative ion spectral data.

UPLC−TOFMS Analysis

2 orthogonal components (R2Xcum = 0.265, R2Ycum = 0.688, Q2Ycum = 0.446) were constructed as shown in Figure 1. There appears to be a separation between healthy controls and cirrhosis patients, reflecting the pathophysiological variations of liver cirrhosis. Analogously, distinct separation was seen among the metabolite profiles of the 3-staged cirrhosis patients (A, B, and C), indicative of the progressive aggravation of liver function (Figure 2). OPLS-DA scores plot of the urinary metabolites in healthy controls and hepatic cirrhosis patients with Child-Pugh class A, B, and C using GC−MS spectral data were provided as Supporting Information Figure S1. The OPLS-DA scores plot constructed with all the GC−MS spectral features and identified metabolites from urine of control group and liver cirrhosis group were provided in Supporting Information Figure S2.

The ESI W negative ion mode was more efficient with a significantly greater number of urinary metabolites detected than the ESI W+ mode and, therefore, was selected for the full scan detection mode in our analytical procedure. A PCA scores plot using 5 components (R2Xcum = 0.472, Q2cum = 0.058) and a cross-validated OPLS-DA model using one predictive component and three orthogonal components (R2Xcum = 0.121, R2Ycum = 0.742, Q2Ycum = 0.237) were constructed with 8,163 ion features detected on the UPLC−QTOFMS spectra. A clear separation among healthy controls and cirrhotic patients of class A, B, and C (Figure 3) was obtained, suggesting that systemic metabolic variations in urine are associated with the different stages of liver function. The OPLS-DA scores plot constructed with all the UPLC−TOFMS spectral features and identified metabolites from urine of control group and 3843



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Figure 4. Bar charts of six representative metabolite markers (mean ± SE) that are differentially expressed among Child-Pugh A, B, and C cirrhotic patients, with *p < 0.05, **p < 0.01 from a Student’s t test.

altered in cirrhosis subjects (Table 3). The combination of the two analytical platforms broadened the window of metabolite detection in this study. The OPLS-DA models derived from our current GC−MS and UPLC−TOFMS analysis showed good and similar separations between cirrhosis patients and healthy controls, highlighting the diagnostic potential of this noninvasive analytical approach. A group of glucuronidated products was significantly altered, including cortolone-3-glucuronide, tetrahydroaldosterone 3-glucuronide, 11-beta-hydroxyandrosterone 3-glucuronide,11oxo-androsterone glucuronide, dehydroepiandrosterone 3-glucuronide, androsterone glucuronide, 17-hydroxyandrostane 3-glucuronide, and glycocholate 3-glucuronide, most of which are found depleted in urine. This suggests an altered metabolic pathway involving glucuronidation which occurs primarily in the liver by means of UDP-glucuronyltransferase for the phase II metabolism (conjugation reactions with glucuronate, etc) and removal of toxic substances, drugs, or other xenobiotics.26 It was reported that a consistent down regulation of UGT1A4

liver cirrhosis group were provided in Supporting Information Figure S2. A total of 28 characteristic urinary metabolites were identified from UPLC−TOFMS negative ion mode for liver cirrhosis, as summarized in Table 3. These metabolites represent key metabolic pathways involving tricarboxylic acid (TCA) cycle, steroids, and bile acid metabolism.

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DISCUSSION

Cirrhosis is a consequence of chronic liver injury characterized by replacement of liver tissue by fibrosis, scar tissue, and regenerative nodules, contributing to an altered expression of a large number of metabolites at systemic level which can be measured by metabonomics approach. The present study is designed to characterize the alteration of urinary metabolite markers associated with the pathophysiology of cirrhosis complementary to the conventional biochemical indices. Two panels of markers, 11 and 28 urinary metabolites identified by GC−MS and UPLC−TOFMS, respectively, were significantly 3844



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representative differential metabolites was constructed to visualize the relationship between the differential metabolites and the cirrhosis progression from CP A to B and C. The resulting scatter plot of PC1 versus PC2 was shown in Supporting Information Figure S3. The first two components explained 44.2% of the total variance (PC1 33.6%, PC2 10.6%). It was shown that CP C was clearly separated from CP A and CP B, but CP A and B were not separated well from each other (Supporting Information Figure S3), which is consistent with the results from Figure 4.

(UDP glucuronosyltransferase 1 family, polypeptide A4), UGT2B4 (UDP glucuronosyltransferase 2 family, polypeptide B4), and UGT2B7 (UDP-glucuronosyltransferase-2B7) in patients with high inflammation scores was negatively related to the degree of fibrosis.27 Additionally, our results indicate that the decreased levels of glucuronidated metabolite markers in urine were consistent with the progression of cirrhosis from CP scores A to C. Another evidence for the lowered glucuronidation is the elevation of a sulfation product, 3-methoxy-4-hydroxyphenylglycol (MHPG) sulfate. Because the level of MHPG derived from dietary components remains steady in the blood pool, the impaired glucuronidation results in an increased level of MHPG sulfate.28 Therefore, the depleted glucuronidation metabolites may serve as biomarkers indicative of progressive development of cirrhosis or the progressive loss of physiological function of liver. The significantly elevated level of citrate and aconitate indicates an altered TCA cycle. The changes of TCA cycle, gluconeogenesis pathway and amino acids metabolism, may play key roles in the collagen synthesis and consequently the fibrosis.29 Elevated levels of proline observed in cirrhotic patients has been regarded as a consequence, rather than a cause, of collagen accumulation in the liver30 due to the altered liver prolidase activity during the fibrotic process.31,3231, 32 Several amino acids, including threonine, pyroglutamate, tyrosine-betaxanthin, O-phosphotyrosine, and 2-aminobutyrate, were found decreased in the urine of cirrhotic patients. 2-Aminobutyrate is reported as a precursor in the biosynthesis of ophthalmate through consecutive reactions with γ-glutamylcysteine synthetase and glutathione synthetase.33 Pyroglutamate is associated with glutamine or glutathione metabolism.34 The decreased urinary 2-aminobutyrate and pyroglutamate can be regarded as biomarker for increased oxidative stress where the depletion of glutathione takes place. O-Phosphotyrosine is a phosphorylated amino acid in a number of proteins. Tyrosine phosphorylation and dephosphorylation play a role in cellular signal transduction and possibly in cell growth control and carcinogenesis.35,36 Tyrosine-betaxanthin is the production of nonenzymatic condensation from the amino acid, tyrosine. Both of the two tyrosine conjugates were gradually depleted along with the progression of cirrhosis grade A to C. As shown in Table 3, glycocholate, taurohyocholate, and glycoursodeoxycholate, the conjugated bile acids, were found significantly increased in the urine of cirrhotic patients. It was believed that the conjugated bile acids could be the indicators of liver dysfunction in cirrhosis or chronic hepatitis.37 The higher level of bile acids in cirrhotic patients was probably due to bile duct obstruction common in liver impairments, with reduced transformation of bile acid to intestine and the feedback from intestine.38 A decreased level of glycolithocholate 3-sulfate, a bile acid sulfation product, in the urine may be due to the impaired bile acid sulfation associated with liver dysfunction.39 Significantly decreased α-hydroxyhippurate and hippurate, metabolites produced via gut microbial-human co-metabolism, were observed in cirrhotic patients. The change in α-hydroxyhippurate among A, B, and C grades is significant. Additionally, as illustrated in Figure 4, 6 urinary metabolites, including tyrosine-betaxanthin, canavaninosuccinate, 3-hydroxyisovalerate, α-hydroxyisobutyrate, glycoursodeoxycholate, and estrone, were significantly altered among the three CP grades, A, B, and C, suggesting that these metabolites could be potential biomarkers for patients stratification at different pathological stages of cirrhosis. Further, the PCA model based on the 6

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CONCLUSION The results show that metabonomic profiling as a powerful approach identifies a unique urinary metabolic profile characterized by a panel of metabolite markers that are of clinical potential for the disease diagnosis and patient stratification for liver cirrhosis. These metabolite markers are involved in several key metabolic pathways such as bile acids, glucuronidation, TCA cycle, and intestinal microbial metabolism. Metabolites listed in Table 3, including bile acids, glucuronidated products, α-hydroxyhippurate, and hippurate, are of great statistical significance and, therefore, warrant further validation as biomarkers for diagnosis and prognosis of posthepatitis B cirrhosis. Six metabolites, α-hydroxyhippurate, tyrosine-betaxanthin, 3-hydroxyisovalerate, canavaninosuccinate, estrone, and glycoursodeoxycholate, were significantly altered among cirrhotic patients with CP A, B, and C, reflecting abnormal metabolism of amino acid, bile acids, hormones, and intestinal microbial metabolism and showing a mechanistic association between urinary metabolite alteration and pathological progression of the posthepatitis B cirrhosis patients.

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary figures as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Ping Liu, Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 528 Zhangheng Road, Shanghai 201203, China. Phone: 86-2151322059. Fax: 86-21-51322059. E-mail: [email protected]. Wei Jia, Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA. Phone: 704-250-5803. Fax: 704-250-5809. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was financially supported in part by Program for Outstanding Medical Academic Leader of Shanghai Municipal Health Bureau, China (LJ06023) and E-institutes of Shanghai Municipal Education Commission, China (E03008).

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ABBREVIATIONS BMI, body-mass index; RBC, red blood cell; WBC, white blood cell; HB, hemoglobin; NEUT#, absolute neutrophil count; LYM#, absolute lymphocyte count; PLT, platelet; Alb, albumin; Glb, globulin; A/G, albumin/globulin; ALT, 3845



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alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma glutamyl transferase; ALP, alkaline phosphatase; CHE, cholinesterase; TBiL, total bilirubin; DBiL, direct bilirubin; PT, prothrombin time; INR, international normalized ratio; BUN, blood urea nitrogen; Cr, creatinine; TCH, total cholesterol; TG, triglycerides; APOA-1, apolipoprotein A-1; FPG, fasting plasma glucose; AFP, alpha-fetoprotein

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Urinary Metabolite Variation Is Associated with Pathological

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