ARTICLE pubs.acs.org/jpr
A Proton Nuclear Magnetic Resonance Metabonomics Approach for Biomarker Discovery in Nonalcoholic Fatty Liver Disease Hao Li,†,‡ Lan Wang,§ Xianzhong Yan,|| Qijun Liu,†,^ Chaohui Yu,§ Handong Wei,† Youming Li,§ Xuemin Zhang,|| Fuchu He,*,†,# and Ying Jiang*,† †
)
State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China ‡ Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China § Digestive Department, the First Affiliated Hospital, College of medicine, Zhejiang University, Hangzhou 310003, P. R. China National Center of Biomedical Analysis, Beijing 100850, P. R. China ^ National Laboratory for Parallel & Distributed Processing, National University of Deference and Technology, Changsha 410073, P. R. China # Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, P. R. China
bS Supporting Information ABSTRACT:
This study was undertaken to discover novel biomarkers for the noninvasive early diagnosis of nonalcoholic fatty liver disease (NAFLD). A methionine and choline deficient (MCD) diet was used to represent different stages of NAFLD in male C57BL/6 mice. 1H NMR spectroscopy and principal components analysis (PCA) were used to investigate the time-related biochemical changes in mice sera induced by the MCD diet. Many serum metabolites’ concentrations changed between control and MCD-fed mice. Hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) were used to select the least number of metabolites to be used for the noninvasive diagnosis of various stages of NAFLD; four potential biomarkers, serum glucose, lactate, glutamate/ glutamine, and taurine were selected. To verify the diagnostic accuracy of these selected metabolites, their serum concentrations were measured in healthy controls (n = 28), NAFLD patients with steatosis (n = 15), steatosis patients with necro-inflammatory disease (n = 11), and NASH patients (n = 6). On the basis of results from MCD-fed mice model, clinical tests, and previous reports, we propose using the levels of the four metabolites for diagnosing NAFLD at various stages. Furthermore, the probability of developing NAFLD at a particular stage was assessed by multinomial logistic regression (MLR) based on the clinical results of the four serum metabolites. KEYWORDS: nonalcoholic fatty liver disease, metabonomics, proton nuclear magnetic resonance, artificial neural networks, serum, multinomial logistic regression, noninvasive diagnosis biomarkers
’ INTRODUCTION Nonalcoholic fatty liver disease (NAFLD) represents a wide spectrum of conditions ranging from a simple fatty liver to nonalcoholic steatohepatitis (NASH), which is a more serious form of NAFLD that may progress to cirrhosis and end-stage liver disease.14 The mechanisms by which these conditions r 2011 American Chemical Society
manifest are largely unknown. The prevalence of NAFLD, one of the most common, chronic liver diseases in developed countries,5 is increasing worldwide610 and currently affects approximately Received: January 16, 2011 Published: May 12, 2011 2797
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Journal of Proteome Research 1224% of adults in Asian countries like China and Japan.11,12 In China, the rate of NAFLD has nearly doubled in the last 1015 years12 and has surpassed chronic hepatitis B as the most common chronic liver disease in certain developed provinces. The diagnosis and risk assessment of NAFLD are currently determined using biochemical and clinical variables, but none are conclusive on their own. At present, as currently available noninvasive tests lack both sensitivity and specificity, liver biopsy remains the only reliable approach for the diagnosis of NASH and the detection of fibrosis.4 However, as an invasive procedure, liver biopsy is unsuitable as a clinical screening test. Therefore, the discovery of novel biomarkers to allow the reliable, noninvasive diagnosis of the various stages of NAFLD is needed. In the postgenomic era, systems biology is central to the biological and medical sciences.13,14 Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or in pathological situations. However, these changes can not be coupled directly to changes in either biological function or phenotype. Compared with genomics or proteomics approaches, metabonomics analysis, as the last step in a series of changes following external stimuli insult15 or in pathological states, can directly reveal phenotypic changes of metabolites in living systems. A significant portion of metabonomics research focuses on both generating small-molecule portraits that distinguish healthy from diseased states and discovering new metabolite biomarkers under certain pathophysiological conditions.1619 Nuclear magnetic resonance (NMR)-based metabonomics approaches can rapidly and noninvasively detect metabolic changes both in vivo20 and in vitro21 for the diagnoses of the presence and severity of disease. No study to date has used proton NMR (1H NMR) to discover novel metabolite biomarkers for the early diagnosis of NAFLD. Animal models of human diseases are useful tools in the study of pathogenic processes;22 a reliable animal model that parallels a disease that is clinically relevant to humans could be used for elucidation of the mechanism of disease pathogenesis23 and diagnostic biomarker discovery. Methionine and choline deficient (MCD) feeding has recently become popular as a model of hepatic steatosis and steatohepatitis.24 Following the administration of an MCD diet, mice rapidly and consistently develop a severe form of steatohepatitis.25 An MCD diet induces not only steatosis but also hepatic inflammation and fibrosis,24,26,27 and the histologic changes that occur in the MCD model have been thoroughly characterized and determined to be remarkably similar to those seen in human cases of NAFLD. In the present study, we attempted to discover novel biomarkers for the reliable, noninvasive diagnosis of various stages of NAFLD. To verify the diagnostic accuracy of selected metabolites, their serum concentrations were measured in both healthy controls and NAFLD patients at different stages of disease. Based on results from the MCD-fed mice model, clinical tests, and previous reports, we propose using the levels of the four metabolites for diagnosing NAFLD at various stages. Multinomial logistic regression (MLR) was used to construct a best-fit model to predict the probability of developing NAFLD at a particular stage.
’ MATERIALS AND METHODS Animals, Treatments, and Sample Collection
Eight-week-old male C57BL/6 mice used in this study were purchased from Shanghai Slac Laboratory Animal Co. LTD
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[animal license number: SCXK (Shanghai) 20030003], and raised in the Laboratory Animal Center, Zhejiang Academy of Medical Sciences, Hangzhou [experimental animal use permits: SYXK (Zhejiang) 20030001]. Animal breeding, care and all experiments were performed in adherence to the Zhejiang animal experiment center guidelines and approved by the Animal Ethics Committee. The mice were randomly divided into two groups, and each group was then randomly divided into three subgroups according to the expected model establishment time; that is, 2, 5, and 8 weeks, with six mice in each subgroup. Both MCD and normal diets were purchased from MP Co., U.S.A., and the Zhejiang animal experiment center, respectively. The MCD diet contains 40% sucrose and 10% fat but lacks methionine and choline; while the normal diet contains adequate levels of methionine and choline and can provide adequate nutrition to mice. Experimental group mice were fed an MCD diet; control group mice were fed an isocaloric normal diet. Water was supplied ad libitum throughout the study. Hematoxylin and eosin (HE) staining, ultra structure observations, and serum enzyme activity measurements were performed to evaluate the established NAFLD model induced by the MCD diet. After fasting overnight, mice were killed by cervical dislocation at weeks 2, 5, and 8; serum samples were isolated from blood samples collected via the vena orbitalis, immediately prior to killing the mice. Serum samples were stored at 80 C for 1H NMR spectroscopic analysis. 1
H NMR Spectroscopic Analysis of Serum
Serum samples (100 μL) were thawed, vortexed, and allowed to stand for 10 min prior to mixing with 500 μL D2O and 50 μL 3-trimethylsilyl-[2,2,3,3-2H4]-propionate (TSP,). Samples were centrifuged at 10 000 rpm for 10 min to remove visible particles and were then placed into NMR tubes. 1H NMR spectra were measured at 600.13 MHz on a Bruker DPX-600 spectrometer (Bruker Biospin GmbH, Rheinstetten, Germany) at a probe temperature of 300 K with the water resonance suppressed using a NOESY pulse sequence and Carr-Purcell-Meiboom-Gill (CPMG) spinecho pulse sequence for serum. TSP served as a chemical shift reference (δ 0.0) and D2O provided a fieldfrequency lock signal for the NMR spectrometer. Data Processing and Pattern Recognition Analysis 1
H NMR serum spectra were subdivided into 0.04 ppm designated regions, and each region was integrated. Then principal component analysis (PCA) with mean centering was carried out on the 1H NMR data using the SIMCA package (Umetrics, Umea, Sweden) to discern control from MCD-fed mice. Differences between samples could be detected in the PCA score plots, whereas spectral regions or chemical shifts responsible for the differences could be viewed in the corresponding PCA loading plots. One-way analysis of variance (ANOVA) t tests were carried out on specific metabolites and their ratios to assess the statistical significance of the metabolic changes. To select the lowest numbers of serum metabolites to be combined for easy and inexpensive diagnosis of NAFLD, metabolites at all three time points (i.e., 2, 5, and 8 weeks) extracted by PCA were subjected for hierarchical cluster analysis (HCA). HCA was performed using Euclidian distances and complete linkage grouping. Based on HCA results, different combinations of metabolites were used as inputs for artificial neural networks (ANNs). Three layers of feed-forward neural networks were used, with N neurons (i.e., N is the number of combinations determined from the HCA) in the input layer, M neurons [M = (N þ 1)/2] in 2798
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Table 1. Baseline Characteristics of Study Groupsa steatosis with necro-inflammatory healthy controls (n = 28)
steatosis alone (n = 15)
disease (n = 11)
NASH (n = 6)
Age (years)
43.6 ( 1.9
49.0 ( 2.2
47.2 ( 2.0
Gender (M/F)
12/16
9/6
4/7
41.0 ( 6.8 5/1
Albumin (g/dL)
4.66 ( 0.11
4.54 ( 0.14
4.64 ( 0.14
5.04 ( 0.20
Globulin (g/dL)
2.57 ( 0.08
2.69 ( 0.08
2.68 ( 0.16
2.78 ( 0.14
ALT (U/L)
21.7 ( 2.0
25.5 ( 2.9
69.2 ( 2.6c
133.3 ( 23.0c
AST (U/L)
21.1 ( 0.9
22.4 ( 1.3
42.1 ( 3.0
GTP (U/L) Triglycerides (mg/dL)
15.9 ( 1.4 87.8 ( 7.6
Total C (mg/dL)
165.1 ( 6.4
62.5 ( 9.0c
c
31.7 ( 4.9 128.2 ( 14.9b
c
76.9 ( 13.1 198.3 ( 33.1c
94.8 ( 7.9c 294.4 ( 67.9b
168.9 ( 8.2
183.4 ( 4.4b
216.7 ( 20.3c
c
HDL-C (mg/dL)
47.8 ( 1.8
42.8 ( 1.7
41.5 ( 4.2
43.1 ( 4.7
LDL-C (mg/dL)
93.0 ( 5.7
95.5 ( 3.2
102.5 ( 8.1
127.4 ( 7.5b
Data are presented as mean ( SEM. Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; F, female; GTP, glutamyl transpeptidase; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; M, male; NASH, nonalcoholic steatohepatitis; Total C total cholesterol. b P < 0.05 compared with healthy controls. c P < 0.01 compared with healthy controls.
a
one hidden layer, and one neuron in the output layer. One serum sample was randomly selected for testing, while the remaining 35 serum samples were first used for training and then for testing. The training was repeated four times, and four sets of weights were obtained. The final prediction score was the average of the four predictions that were calculated using the four sets of weights and was checked by Jackknife method. HCA was then used to assess the predictability of the potential biomarkers. Pearson correlation analysis was done using SPSS13.0 software to determine the connection between two random metabolites of the selected biomarkers.
with steatosis only, y = 2 represents steatosis patients with necroinflammatory disease, and y = 3 represents NASH patients. P0, P1, P2 and P3 refer to the probabilities for health, NAFLD with steatosis alone, steatosis with necrto-inflammatory disease, and NASH, respectively.
Patient Selection and Measurement of Selected Serum Metabolites
logitP1/0, logitP2/0 and logitP3/0 are the logarithms of P1/P0, P2/P0 and P3/P0, respectively; R1, R2 and R3 are constants; β1p, β2p and β3p refer to the MLR coefficients for the models; Xp represents the selected serum metabolite. The probabilities of developing NAFLD at each of the four stages can be calculated as follows:
All patients were hepatitis B virus-negative as determined by lack of reactivity to antibodies to hepatitis B virus, and patients with evidence of alcohol consumption (>10 g/d) or other causes of liver disease were excluded. Twenty-eight healthy controls, fifteen NAFLD patients with steatosis alone, eleven steatosis patients with necro-inflammatory disease, and six NASH patients diagnosed by elevated liver enzymes, ultrasonographic presence of bright liver, and liver biopsy were selected for the measurements of serum glucose, lactate, glutamate and taurine levels (Table 1). Patient blood samples were collected after fasting overnight. Serum glucose levels were measured using the glucose oxidase method on a Hitachi 7170S automatic analyzer (Tokyo, Japan). Serum lactate levels were analyzed by an enzymatic colorimetric method by reading the absorbance at 546 nm with a spectrometer (SmartSpec 3000, BIO-RAD, Hercules, CA). Assay of amino acid concentrations was performed using an automatic amino acid analyzer (Hitachi 83550, Tokyo, Japan). One-way ANOVA t tests were carried out on these four metabolites and their ratios to assess the statistical significance of the changes. Multinomial Logistic Regression (MLR)
Based on the clinical results of these four serum metabolites, MLR was used to construct the best-fit model for the use of changes in these metabolites as a potential screening tool to diagnose the different stages of NAFLD. MLR was performed using SPSS 13.0 software. Before running the regression, healthy controls were regarded as the reference group; thus, y = 0 represents healthy controls, y = 1 represents NAFLD patients
logitP1=0 ¼ R1 þ β11 X1 þ β12 X2þ 3 3 3 3 3 3 β1p Xp ¼ g1ðXÞ logitP2=0 ¼ R2 þ β21 X1 þ β22 X2þ 3 3 3 3 3 3 β2p Xp ¼ g2ðXÞ logitP3=0 ¼ R3 þ β31 X1 þ β32 X2þ 3 3 3 3 3 3 β3p Xp ¼ g3ðXÞ
P0 ¼
1 1 þ eg1ðXÞ þ eg2ðXÞ þ eg3ðXÞ
P1 ¼
eg1ðXÞ 1 þ eg1ðXÞ þ eg2ðXÞ þ eg3ðXÞ
P2 ¼
eg2ðXÞ 1 þ eg1ðXÞ þ eg2ðXÞ þ eg3ðXÞ
P3 ¼
eg3ðXÞ 1 þ eg1ðXÞ þ eg2ðXÞ þ eg3ðXÞ
Statistical Analysis
Data were analyzed using SPSS13.0 software. Differences with P-values less than 0.05 were considered statistically significant.
’ RESULTS Establishment of an NAFLD Model in Mice
HE staining (Supplementary Figure 1, Supporting Information), ultra structure observations (Supplementary Figure 2, Supporting Information), and serum enzyme activity 2799
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Figure 1. Proton NMR and PCA-derived metabolite profiles. (A) Series of 600.13 MHz single pulse 1H NMR spectra of serum CPMG sequences at various time points following the administration of MCD chow. (B) Scores plots for control mice serum and MCD-fed mice sera at all time points from the PC1 and PC2. Two groups were separated along PC2. In the scores plot, the confidence interval is defined by the Hotelling’s T2 ellipse (95% confidence interval), and observations outside the confidence ellipse are considered outliers. (C) Loading plot for control mice and MCD-fed mice sera at all time points from the PC1 and PC2. The metabolites with the largest intensities contributed to the clustering. c2, c5 and c8: control mice at 2, 5, and 8 weeks, respectively. m2, m5 and m8: MCD-fed mice at 2, 5, and 8 weeks, respectively. Ace, acetate; Ala, alanine; Arg/Lys, arginine/lysine; Cho/PCho, choline/phosphorylcholine; Cre, creatine; Glc, glucose; Glu/Gln, glutamate/glutamine; Ile, isoleucine; Lac, lactate; Leu, leucine; Met, methionine; NAG, N-acetyl glycoproteins; Pyr, pyruvate; Tau, taurine; TMAO/Bet, trimethylamine N-oxide/betaine; Val, valine; VLDL/LDL, very low density lipoprotein/low density lipoprotein.
measurements (Supplementary Figure 3, Supporting Information) showed that the NAFLD model was established successfully in mice after 8 weeks. Characterization of MCD Diet Feeding on Serum Metabolite Profiles
Typical 1H NMR spectra from control and MCD-fed mice sera were shown in Figure 1A, with metabolites indicated based on their chemical shifts. A PCA scores plot was used to represent the sample distribution in the new multivariate space, on which the distinct clustering of serum CPMG sequences was observed between control and MCD-fed mice (Figure 1B). The separation
between MCD-fed and control mice was caused by decreased relative concentrations of glucose, choline, trimethylamine N-oxide (TMAO), betaine, and very low density lipoprotein (VLDL), and an increased concentration of lactate in the spectrum from the latter group, as confirmed by the PCA loading plot of the data (Figure 1C). Changes in Serum Metabolites at Each Time Point
Furthermore, after applying PCA as a data reduction tool, serum CPMG sequences of MCD-fed and control mice at each time point were readily separated across PC1 (Figure 2A, C, and E). The major metabolic perturbations causing these clusters 2800
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Figure 2. PCA to compare the metabonome of control mice and MCD-fed mice sera based on the 1H NMR CPMG spectra. (A and B) Scores and loading plots for control mice serum and MCD-fed mice serum at two weeks; two groups were separated along PC1. (C and D) Scores and loading plots for control mice serum and MCD-fed mice serum at 5 weeks; two groups were separated along PC1. (E and F) Scores and loading plots for control mice and MCD-fed mice sera at 8 weeks; two groups were separated along PC1. In the scores plot, the confidence interval is defined by the Hotelling’s T2 ellipse (95% confidence interval), and observations outside the confidence ellipse are considered outliers. Red square in A, C and E indicated the control group; black circle in A, C and E indicated the MCD-fed group.
Figure 3. Biomarker selection using HCA and ANNs. (A) Comparison of metabolites in mice with NAFLD at different stages with normal mice yields 15 differential serum metabolites. (B) ANNs result. The highest predictive value was 32 of 36 mice; that is, the highest predictive accuracy was 88.9%. The selected metabolites were serum glucose, glutamate/glutamine, lactate and taurine. The right color bar indicates the identified sample numbers. (C) Comparison of metabolites in mice with NAFLD at different stages with normal mice yields four serum metabolites. Ace, acetate; Ala, alanine; Arg/Lys, arginine/lysine; Cho/PCho, choline/phosphorylcholine; Cre, creatine; Glc, glucose; Lac, lactate; Leu, leucine; Met, methionine; NAG, N-acetyl glycoproteins; Pyr, pyruvate; Tau, taurine; TMAO/Bet, trimethylamine N-oxide/betaine.
were identified from the loadings plots associated with the PCA (Figure 2B, D, and F). Inspection of the loadings plots suggested that, compared to control mice, sera of MCD-fed mice at all three
time points (i.e., 2, 5, and 8 weeks) had higher levels of alanine (δ 1.32), arginine/lysine (δ 1.86), glutamate/glutamine (δ 2.42, 2.46), and creatine (δ 3.02) but lower levels of VLDL/LDL 2801
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Figure 4. Pair-wise metabolite correlations in scatter plots. (A) r = 0.403, P < 0.05. (B) r = 0.727, P < 0.05. (C) P > 0.05. (D) P > 0.05. (E) r = 0.554, P < 0.05. (F) r = 0.624, P < 0.05.
Table 2. Clinical Biochemical Results for Healthy Controls, NAFLD Patients with Steatosis, Steatosis Patients with Necro-inflammatory Disease, and NASHa healthy controls (n = 28)
steatosis alone (n = 15)
steatosis with necro-inflammatory disease (n = 11)
NASH (n = 6)
Serum glucose (mg/dL)
79.70 ( 2.79
86.27 ( 4.03
90.90 ( 4.66b
105.76 ( 10.25c
Serum lactate (mg/dL)
28.88 ( 1.37
33.44 ( 1.68
27.51 ( 1.09
27.32 ( 1.82
parameters
b
Serum glutamate (mg/dL)
1.55 ( 0.12
1.98 ( 0.15b
Serum taurine (mg/dL)
1.59 ( 0.14
2.03 ( 0.16
2.07 ( 0.19b
2.30 ( 0.24b
1.68 ( 0.08
2.22 ( 0.13b
Data are presented as mean ( SEM. Abbreviations: NASH, nonalcoholic steatohepatitis. P < 0.05 compared with healthy controls. c P < 0.01 compared with healthy controls. a
(δ 0.86, 1.26), leucine (δ 0.90, 0.94), N-acetyl glycoproteins (δ 2.02, 2.06), TMAO/betaine (δ 3.26), and glucose (δ 3.404.00). Furthermore, concentrations of lactate (δ1.34), acetate (δ 1.90), and methionine (δ 2.14) increased and concentrations of pyruvate (δ 2.38) decreased at 2 weeks; concentrations of choline/ phosphocholine (δ 3.18, 3.22) decreased at 5 and 8 weeks and concentrations of taurine (δ 3.38) increased at 8 weeks. Selection of Potential Biomarkers for Diagnosis of NAFLD Stages
Fifteen differential serum metabolites extracted by PCA were analyzed by HCA and ANNs to select the lowest number of metabolites to be combined for diagnosing of NAFLD stages. After HCA and ANNs analyses, four potential biomarkers for diagnosis of NAFLD stages were selected (Figure 3A and B): serum glucose, lactate, glutamate/glutamine, and taurine (Figure 3C). Correlative Analysis
Correlations between two random metabolites were observed (Figure 4). Glutamate/glutamine showed a positive correlation with taurine (r = 0.554, P < 0.05), while negative correlations were observed between glucose and glutamate/glutamine (r = 0.403,
b
P < 0.05), glucose and lactate (r = 0.727, P < 0.05), and taurine and lactate (r = 0.624, P < 0.05). However, no correlation was found between glucose and taurine, glutamate/glutamine and lactate (P > 0.05). Clinical Test of the Four Potential Biomarkers
Serum concentrations of the selected metabolites were measured in healthy controls, NAFLD patients with steatosis alone, steatosis patients with necro-inflammatory disease, and NASH patients (Table 2). Compared with healthy controls, serum lactate and glutamate levels both significantly increased in NAFLD patients with steatosis alone (P < 0.05), while glucose and taurine levels had little change (P > 0.05); serum glucose and glutamate levels both significantly increased (P < 0.05) in steatosis patients with necro-inflammatory disease, while lactate and taurine levels had little change in these patients (P > 0.05); serum glucose, glutamate, and taurine levels all significantly increased (P < 0.05) in NASH patients, while lactate content had little change in these patients (P > 0.05). To predict the risk of developing NAFLD at a particular stage, MLR was used to construct a best-fit model based on the clinical results of the four serum metabolites. The probabilities of 2802
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Chart 1
developing NAFLD at each of the four stages are shown in Chart 1. P0, P1, P2 and P3 refer to the probabilities for health, steatosis alone, steatosis with necro-inflammatory disease, and NASH, respectively; X1, X2, X3 and X4 refer to the serum concentrations (mg/dL) of glucose, glutamate, lactate and taurine, respectively.
’ DISCUSSION Distinguishing different NAFLD stages with high sensitivity and specificity is necessary for the successful treatment of this disease. At present, hepatic imaging, liver biopsy and serologic testing are the main clinical diagnostics methods for NAFLD. However, these clinically available, noninvasive tests lack sensitivity and specificity.4,28,29 Although liver biopsy remains the only reliable approach to diagnose NAFLD and establish the presence of fibrosis,4,6,28 an invasive liver biopsy is unsuitable as a feasible clinical screening test. Therefore, there is a need for novel biomarkers to allow the reliable and noninvasive diagnosis of NAFLD at various stages. The liver is a metabolically active organ. Changes in metabolite levels induced by NAFLD could be directly reflected in the blood that flows through the damaged liver and is noninvasive detected by NMR for evaluation of the degree of liver injury. In the present study, 1H NMR spectroscopy of serum combined with PCA were used to investigate metabolite changes induced in C57BL/6 mice by MCD diet over time. Results showed that there were clearly many differences in levels of serum metabolites between MCD-fed and control mice (Figure 1), and 15 differential metabolites were extracted by PCA (Figure 2). All 15 metabolites could thoroughly discriminate between the control and MCD-fed groups at different time points (Figure 2A, C and E). However, using too many metabolites will make clinical diagnoses difficult and expensive. Discriminatory capacity is a key target to be optimized in any predictive model.30 To select the lowest number of metabolites for effective diagnosis of NAFLD, the above-mentioned 15 metabolites were further analyzed by HCA and ANNs. After analysis, four potential biomarkers (i.e., serum glucose, lactate, glutamate/glutamine, and taurine) for diagnosis of NAFLD at various stages were selected (Figure 3A and B). These four potential biomarkers, identified by ANNs, showed high discriminatory capacity in that they correctly distinguished mice with NAFLD at different stages from normal mice (Figure 3C). Metabolites often vary concertedly with other metabolites,31 especially functionally or metabolic pathway-related metabolites. To some degree, the correlations between any two of the four serum metabolites reflected a function- or pathway-related connection between metabolites, such as the negative correlation
observed between glucose and glutamate/glutamine. Glutamate and glutamine are glucogenic amino acids that can be converted to R-ketoglutarate and pyruvate which follow the gluconeogenic pathway to glucose. However, as the tricarboxylic acid (TCA) cycle and gluconeogenic pathway are inhibited in MCD-fed mice, glutamate and glutamine can not effectively be converted to glucose. Thus, reduced glucose levels were negatively correlated with increased glutamate/glutamine levels (r = 0.403, P < 0.05; Figure 4A). To verify the ability of these selected metabolites to be used for accurate diagnoses, concentrations of serum glucose, lactate, glutamate and taurine were measured in healthy controls and NAFLD patients at different stages. The clinical measurement results of glutamate, lactate, and taurine were consistent with levels measured in the mice model, which also indicated their diagnostic accuracy. Glutamate and glutamine are usually reported as glutamate/ glutamine, because their NMR spectra partially overlap.32 They are both precursors of the body’s most important antioxidants, glutathione33 and the amino acid taurine. Their blood concentrations may significantly decrease or increase in states of critical illness,34 suggesting that they may become conditionally essential amino acids in patients with catabolic disease.33 For example, low plasma glutamate concentrations and glutamate/glutamine ratios are used to predict patients at risk of septic shock with acute liver dysfunction.35 In our study, a significant increase in serum glutamate/glutamine levels in MCD-fed mice was consistent with an increase of glutamate/glutamine levels in liver (Supplementary Table 1, Supporting Information). Down-regulation of the key enzymes carbamoyl phosphate synthetase I (CPS-I) and argininosuccinate synthetase (ASSY) in the urea cycle identified by proteomics approaches in our study (Supplementary Table 1) indicated that the urea cycle is blocked in MCD-fed mice and the complete degradation of amino acids is inhibited. Furthermore, compared with control mice, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations increased remarkably (P < 0.05) in MCD-fed mice (Supplementary Figure 3, Supporting Information), suggesting increased transamination. Both increased transamination and inhibition of the complete degradation of amino acids result in a relative higher concentration of amino acids in the liver, which probably leads to an increase in glutamate/glutamine concentrations in serum. Furthermore, concentrations of glutathione also increased significantly (P < 0.05) in MCD-fed mice at 5 and 8 weeks (Supplementary Table 1). As the precursors of glutathione, increases in glutamate and glutamine levels in both liver and serum are likely related to adaptive cellular responses in states of NAFLD. 2803
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Table 3. Changes in the Four Serum Metabolites for Diagnosis of NAFLD at Various Stagesa
a
parameters
healthy controls
steatosis alone
steatosis with necro-inflammatory disease
NASH
Serum glucose
Normal
Normal
v
v
Serum lactate
Normal
v
Normal
Normal
Serum Glu/Gln
Normal
v
v
v
Serum taurine
Normal
Normal
Normal
v
Abbreviations: Glu, glutamate; Gln, glutamine; NASH, nonalcoholic steatohepatitis.
Taurine, a sulfur-containing β-amino acid, is a major free intracellular amino acid present in many tissues of human and animals36 it is suggested to have a number of protective properties including protection against hepatic damage.37 Blood taurine concentrations may change under external stimuli or pathological situations. For example, plasma taurine concentrations may decrease in response to surgical injury, trauma, sepsis, and other critical illnesses.38 Its production is regulated by glutamine, and an enteral glutamine-enriched diet was shown to promote the uptake of taurine by the kidney in a rat model.39 Our results also showed that concentrations of serum taurine from MCD fed mice were positively correlated with those of glutamate/glutamine (r = 0.554, P < 0.05; Figure 4E). Similar to glutamate and glutamine, increases of serum taurine levels were also likely related to adaptive cellular responses in mice at various stages of NAFLD. Blood lactate is considered a reflection of multiorgan and hepatocellular failure.40 Serum or bile lactate concentrations in cholangiocarcinoma and hepatocellular carcinoma patients have been observed to be significantly higher than levels in benign hepatobiliary diseases patients and healthy controls.41 During anaerobic metabolism, glucose is converted to lactate, resulting in increased lactate levels, which are measurable in secretions. Large increases in lactate concentrations in serum measured by 1H NMR in the present study likely reflect a significant increase in liver anaerobic metabolism. Such an increase is consistent with the down-regulation of the key enzyme R-oxoglutarate dehydrogenase complex (ODO) in the TCA cycle (Supplementary Table 1, Supporting Information), which indicates a decrease in aerobic metabolism. Furthermore, increased serum lactate levels could provide energy for other organs while the TCA cycle was inhibited. What is worth noting is that serum glucose levels did not differ between healthy controls and NAFLD patients with steatosis alone (P > 0.05); these levels increased significantly in both steatosis patients with necro-inflammatory disease and NASH patients (P < 0.05). At first glance, this result contrasts with serum glucose results in mice model. MCD diets lessen systemic insulin resistance (IR) and affect markers of systemic insulin sensitivity, such that, for example, serum glucose and insulin levels significantly decrease.10,23,24 Our results showed that the MCD diet also decreased serum glucose concentrations significantly (P < 0.05) in mice, which was consistent with reported results in the MCD induced mice model, indicating that IR is unlikely to be essential for NAFLD progression. However, IR is a common finding in NAFLD patients and is traditionally thought to play a pivotal role in NAFLD progression.6,8,4244 Furthermore, prevalence of NAFLD parallels the frequency of central obesity, age, IR, hyperglycemia, metabolic syndrome, and type 2 diabetes mellitus.8,23,45 The development of NAFLD is related to increased fasting plasma glucose levels; in fasting, hyperglycemic individuals, the prevalence of NAFLD rose from 27% among those with normal fasting blood glucose levels to 43%.8 During NAFLD progression, fasting blood glucose gradually increases
according to the stage of disease. Compared with healthy controls, fasting blood glucose levels in NAFLD patients with steatosis alone showed no change,1,5,46 while levels in steatosis patients with necro-inflammatory disease and NASH increased significantly.47,48 Clinical test result in this study were also consistent with previous reports about changes in fasting blood glucose levels in NAFLD patients with steatosis alone, steatosis patients with necro-inflammatory disease and NASH patients. Based on results from MCD-fed mice, clinical test and previous reports, we propose using the levels of the four metabolites for diagnosing NAFLD at various stages (Table 3). For example, if the changes in serum glucose, lactate, glutamate/ glutamine, and taurine show, respectively, no change, increase, increase, and no change, steatosis would be the diagnosis; while changes that show increase, no change, increase, and no change would suggest steatosis with necro-inflammatory disease and changes of increase, no change, increase, and increase would be indicative of NASH. Furthermore, the different NAFLD stage probabilities (P0, P1, P2, and P3) were deduced through MLR based on the clinical results of the four serum metabolites. For example, if an individual’s serum levels (mg/dL) of glucose, glutamate, lactate and taurine were 92.42, 0.80, 28.53, and 0.66, respectively, then the corresponding P0, P1, P2, and P3 for this individual would be 0.82, 0.10, 0.06, and 0.02, respectively, which would mean that he or she is healthy. In summary, a strategy for the optimization of diagnostic biomarkers for NAFLD at various stages was proposed. Significant changes in certain serum metabolites were observed using a metabonomics approach in control and MCD-fed mice. Alternatively, a specific combination of spectroscopic changes in glucose, lactate, glutamate/glutamine, and taurine levels may prove to be an accurate means of noninvasively diagnosing various stages of NAFLD. Although a combination of these serum metabolites has not been used for this purpose previously, these potential biomarkers will assist clinicians in predicting the risk for progression of NAFLD and in deciding whether liver biopsy is needed. These results also reveal that this metabonomics method is a powerful tool for biomarker discovery.
’ ASSOCIATED CONTENT
bS
Supporting Information
Supplementary Tables S1 and Supplementary Figures S1S3. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Ying Jiang, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. Phone: 86-010-80705299. Fax: 86-1080705002. E-mail:
[email protected]. Fuchu He, State 2804
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Journal of Proteome Research Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. Phone: 86-010-68177417. Fax: 86-010-68177417. E-mail: hefc@ nic.bmi.ac.cn.
’ ACKNOWLEDGMENT All authors express sincere thanks to all NAFLD patients and healthy participants in this study. This work was partially supported by Chinese State Key Projects for Basic Research (Nos. 2011CB910601, 2011CB910700, 2010CB912700 and 2011CB505304), Chinese State High-tech Program (863) (2006AA02A308), National Natural Science Foundation of China (30700356, 30700988, 30972909, 81000192, 81001470 and 81010064), Chinese State Key Project Specialized for Infectious Diseases (2008ZX10002-016 and 2009ZX10004-103), the National Key Technologies R&D Program for New Drugs (2009ZX09301002), International Scientific Collaboration Program (2009DFB33070, 2010DFA31260 and 2011DFB30370) and State Key Laboratory of Proteomics (SKLP-Y200901 and SKLP-O200901). ’ ABBREVIATIONS: ALT, alanine aminotransferase; AST, aspartate aminotransferase; ANNs, artificial neural networks; AUC, area under the ROC curve; CC, cholangiocarcinoma; CPMG, Carr-PurcellMeiboom-Gill; CPS-I, carbamoyl phosphate synthetase I; CT, computer tomography; 1H NMR, proton nuclear magnetic resonance; HCA, hierarchical cluster analysis; HCC, hepatocellular carcinomas; IR, insulin resistance; LCMS, liquid chromatographymass spectrometry; MCD, methionine and choline deficient; MLR, multinomial logistic regression; MOF, multiorgan failure; MRI, magnetic resonance imaging; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PCA, principal components analysis; T2DM, type 2 diabetes mellitus; TCA, tricarboxylic acid; TMAO, trimethylamine N-oxide; TSP, 3-trimethylsilyl-[2,2,3,3-2H4]-propionate; US, ultrasonography; VLDL, very low density lipoprotein. ’ REFERENCES (1) Bacon, B. R.; Farahvash, M. J.; Janney, C. G.; NeuschwanderTetri, B. A. Nonalcoholic steatohepatitis: an expanded clinical entity. Gastroenterology 1994, 107 (4), 1103–9. (2) Matteoni, C. A.; Younossi, Z. M.; Gramlich, T.; Boparal, N.; Liu, Y. C.; McCullough, A. J. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology 1999, 116 (6), 1413–9. (3) Nelson, J. E.; Bhattacharya, R.; Lindor, K. D.; Chalasani, N.; Raaka, S.; Heathcote., E. J.; Miskovsky, E.; Shaffer, E.; Rulyak, S. J.; Kowdley, K. V. HFE C282Y mutations are associated with advanced hepatic fibrosis in Caucasians with nonalcoholic steatohepatitis. Hepatology 2007, 46 (5), 723–9. (4) Wieckowska, A.; McCullough, A. J.; Feldstein, A. E. Noninvasive diagnosis and monitoring of nonalcoholic steatohepatitis: present and future. Hepatology 2007, 46 (2), 582–9. (5) Gambino, R.; Gassader, M.; Pagano, G.; Durazzo, M.; Musso, G. Polymorphism in microsomal triglyceride transfer protein: a link between liver disease and atherogenic postprandial lipid profile in NASH? Hepatology 2007, 45 (5), 1097–107. (6) Angulo, P. Nonalcoholic fatty liver disease. N. Engl. J. Med. 2002, 346 (16), 1221–31. (7) Browning, J. D.; Szczepaniak, L. S.; Dobbins, R.; Nuremberg, P.; Horton, J. D.; Cohen, J. C.; Grundy, S. M.; Hobbs, H. H. Prevalence of
ARTICLE
hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 2004, 40 (6), 1387–95. (8) Farrell, G. C.; Larter, C. Z. Nonalcoholic fatty liver disease: from steatosis to cirrhosis. Hepatology 2006, 43 (2 Suppl 1), S99–S112. (9) Sanyal, A. J. American Gastroenterological Association. AGA technical review on nonalcoholic fatty liver disease. Gastroenterology 2002, 123 (5), 1705–1725. (10) Yamaguchi, K.; Yang, L.; McCall, S.; Huang, J.; Xu, X. X.; Pandey, S. K.; Bhanot, S.; Monia, B. P.; Li, Y. X.; Diehl, A. M. Inhibiting triglyceride synthesis improves hepatic steatosis but exacerbates liver damage and fibrosis in obese mice with nonalcoholic steatohepatitis. Hepatology 2007, 45 (6), 1366–74. (11) Amarapurkar, D.; Kamani, P.; Patel, N.; Gupte, P.; Kumar, P.; Agal, S.; Baijal, R.; Lala, S.; Chaudhary, D.; Deshpande, A. Prevalence of non-alcoholic fatty liver disease: population based study. Ann. Hepatol. 2007, 6 (3), 161–3. (12) Fan, J. G.; Saibara, T.; Chitturi, S.; Kim, B. I.; Sung, J. J.; Chutaputti, A. Asia-Pacific Working Party for NAFLD. What are the risk factors and settings for non-alcoholic fatty liver disease in Asia-Pacific? J. Gastroenterol. Hepatol. 2007, 22 (6), 794–800. (13) Hood, L.; Heath, J. R.; Phelps, M. E.; Lin, B. Systems biology and new technologies enable predictive and preventative. Science 2004, 306 (5696), 640–3. (14) Hwang, D.; Rust, A. G.; Ramsey, S.; Smith, J. J.; Leslie, D. M.; Weston, A. D.; de Atauri, P.; Aitchison, J. D.; Hood, L.; Siegel, A. F.; Bolouri, H. A data integration methodology for systems biology. Proc. Natl. Acad. Sci. U.S.A. 2005, 102 (48), 17296–301. (15) van Ravenzwaay, B.; Cunha, G. C.; Leibold, E.; Looser, R.; Mellert, W.; Prokoudine, A.; Walk, T.; Wilemer, J. The use of metabolomics for the discovery of new biomarkers of effect. Toxicol. Lett. 2007, 172 (12), 21–8. (16) Fernie, A. R.; Trethewey, R. N.; Krotzky, A. J.; Willmitzer, L. Metabolite profiling: from diagnostics to systems biology. Nat. Rev. Mol. Cell Biol. 2004, 5 (9), 763–9. (17) Griffin, J. L. Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr. Opin. Chem. Biol. 2003, 7 (5), 648–54. (18) Griffin, J. L.; Shockcor, J. P. Metabolic profiles of cancer cells. Nat. Rev. Cancer 2004, 4 (7), 551–61. (19) Shockcor, J. P.; Holmes, E. Metabonomic applications in toxicity screening and disease diagnosis. Curr. Top Med. Chem. 2002, 2 (1), 35–51. (20) Griffiths, J. R.; McSheehy, P. M.; Robinson, S. P.; Troy, H.; Chung, Y. L.; Leek, R. D.; Williams, K. J.; Stratford, I. J.; Harris, A. L.; Stubbs, M. Metabolic changes detected by in vivo magnetic resonance studies of HEPA-1 wild-type tumors and tumors deficient in hypoxiainducible factor-1beta (HIF-1beta): evidence of an anabolic role for the HIF-1 pathway. Cancer Res. 2002, 62 (3), 688–95. (21) Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E.; Grainger, D. J. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMRbased metabonomics. Nat. Med. 2002, 8 (12), 1439–44. (22) McCuskey, R. S.; Ito, Y.; Robertson, G. R.; McCuskey, M. K.; Perry, M.; Farrell, G. C. Hepatic microvascular dysfunction during evolution of dietary steatohepatitis in mice. Hepatology 2004, 40 (2), 386–93. (23) Rinella, M. E.; Green, R. M. The methionine-choline deficient dietary model of steatohepatitis does not exhibit insulin resistance. J. Hepatol. 2004, 40 (1), 47–51. (24) Rizki, G.; Arnaboldi, L.; Gabrielli, B.; Yan, J.; Lee, G. S.; Ng, R. K.; Turner, S. M.; Badger, T. M.; Pitase, R. E.; Maher, J. J. Mice fed a lipogenic methionine-choline-deficient diet develop hypermetabolism coincident with hepatic suppression of SCD-1. J. Lipid. Res. 2006, 47 (10), 2280–90. (25) Tomita, K.; Tamiya, G.; Ando, S.; Ohsumi, K.; Chiyo, T.; Mizutani, A.; Kitamura, N.; Toda, K.; Kaneko, T.; Horie, Y.; Han, J. Y.; 2805
dx.doi.org/10.1021/pr200047c |J. Proteome Res. 2011, 10, 2797–2806
Journal of Proteome Research Kato, S.; Shimoda, M.; Oike, Y.; Tomizawa, M.; Makino, S.; Ohkura, T.; Saito, H.; Kumagai, N.; Nagata, H.; Ishii, H.; Hibi, T. Tumour necrosis factor R signaling through activation of Kupffer cells plays an essential role in liver fibrosis of non-alcoholic steatohepatitis in mice. Gut 2006, 55 (3), 415–24. (26) Ip, E.; Farrell, G.; Hall, P.; Robertson, G.; Leclercq, I. Administration of the potent PPARalpha agonist, Wy-14,643, reverses nutritional fibrosis and steatohepatitis in mice. Hepatology 2004, 39 (5), 1286–96. (27) Rahman, S. M.; Schroeder-Gloeckler, J. M.; Janssen, R. C.; Jiang, H.; Qadri, I.; Maclean, K. N.; Friedman, J. E. CCAAT/Enhancing binding protein β deletion in mice attenuates inflammation, endoplasmic reticulum stress, and lipid accumulation in diet-induced nonalcoholic steatohepatits. Hepatology 2007, 45 (5), 1108–17. (28) Adams, L. A.; Angulo, P.; Lindor, K. D. Nonalcoholic fatty liver disease. Can. Med. Assoc. J. 2005, 172 (7), 899–905. (29) Mofrad, P.; Contos, M. J.; Haque, M.; Sargeant, C.; Fisher, R. A.; Luketic, V. A.; Sterling, R. K.; Shiffman, M. L.; Stravitz, R. T.; Sanyal, A. J. Clinical and histologic spectrum of nonalcoholic fatty liver disease associated with normal ALT values. Hepatology 2003, 37 (6), 1286–92. (30) Barbini, E.; Cevenini, G.; Scolletta, S.; Biagioli, B.; Giomarelli, P.; Barbini, P. A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery-Part I: model planning. BMC Med. Inform. Decis. Mak. 2007, 7, 35. (31) Morgenthal, K.; Weckwerth, W.; Steuer, R. Metabolomic networks in plants: transitions from pattern recognition to biological interpretation. BioSystems 2006, 83 (23), 108–17. (32) Danielsen, E. R.; Elberling, T. V.; Rasmussen, A. K.; Dock, J.; Hording, M.; Perrild, H.; Waldemar, G.; Feldt-Rasmussen, U.; Thomsen, C. Reduced parieto-occipital white matter glutamine measured by proton magnetic resonance spectroscopy in treated Graves’ disease patients. J. Clin. Endocrin. Metab. 2008, 93 (8), 3192–8. (33) Melis, G. C.; ter Wengel, N.; Boelens, P. G.; van Leeuwen, P. A. Glutamine: recent developments in research on the clinical significance of glutamine. Curr. Opin. Clin. Nutr. Metab. Care 2004, 7 (1), 59–70. (34) Houdijk, A. P.; Rijnsburger, E. R.; Jansen, J.; Wesdorp, R. I.; Weiss, J. K.; McCamish, M. A.; Teerlink, T.; Meuwissen, H. J.; Thijs, L. G.; van Leeuwen, P. A. Randomised trial of glutamine-enriched enteral nutrition on infectious morbidity in patients with multiple trauma. Lancet 1998, 352 (9130), 772–6. (35) Poeze, M.; Luiking, Y. C.; Breedveld, P.; Manders, S.; Deutz, N. E. Decreased plasma glutamate in early phases of septic shock with acute liver dysfunction is an independent predictor of survival. Clin. Nutr. 2008, 27 (4), 523–30. (36) Kerai, M. D.; Waterfield, C. J.; Kenyon, S. H.; Asker, D. S.; Timbrell, J. A. Reversal of ethanol-induced hepatic steatosis and lipid peroxidation by taurine: a study in rats. Alcohol Alcohol 1999, 34 (4), 529–41. (37) Chen, S. W.; Chen, Y. X.; Shi, J.; Lin, Y.; Xie, W. F. The restorative effect of taurine on experimental nonalcoholic steatohepatitis. Dig. Dis. Sci. 2006, 51 (12), 2225–34. (38) Chiarla, C.; Giovannini, I.; Siegel, J. H.; Boldrini, G.; Castagneto, M. The relationship between plasma taurine and other amino acid levels in human sepsis. J. Nutr. 2000, 130 (9), 2222–7. (39) Boelens, P. G.; Houdijk, A. P.; de Thouars, H. N.; Teerlink, T.; van Engeland, M. I.; Haarman, H. J.; van Leeuwen, P. A. Plasma taurine concentrations increase after enteral glutamine supplementation in trauma patients and stressed rats. Am. J. Clin. Nutr. 2003, 77 (1), 250–6. (40) Blei, A. T. Selection for acute liver failure: have we got it right? Liver Transpl. 2005, 11 (Suppl 2), S30–4. (41) Nishijima, T.; Nishina, M.; Fujiwara, K. Measurement of lactate levels in serum and bile using proton nuclear magnetic resonance in patients with hepatobiliary diseases: its utility in detection of malignancies. Jpn. J. Clin. Oncol. 1997, 27 (1), 13–7. (42) Adams, L. A.; Lymp, J. F.; Sauver, J. S. T.; Sanderson, S. O.; Lindor, K. D.; Feldstein, A.; Angulo, P. The natural of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology 2005, 129 (1), 113–21.
ARTICLE
(43) Bugianesi, E.; McCullough, A. J.; Marchesini, G. Insulin resistance: a metabolic pathway to chronic liver disease. Hepatology 2005, 42 (5), 987–1000. (44) Utzschneider, K. M.; Kahn, S. E. Review: the role of insulin resistance in nonalcoholic fatty liver disease. J. Clin. Endocrinol. Metab. 2006, 91 (12), 4753–61. (45) Nocito, A.; Dahm, F.; Jochum, W.; Jang, J. H.; Georgiev, P.; Bader, M.; Renner, E. L.; Clavien, P. A. Serotonin mediates oxidative stress and mitochondrial toxicity in a murine model of nonalcoholic steatohepatitis. Gastroenterology 2007, 133 (2), 608–18. (46) Sreekumar, R.; Rosado, B.; Rasmussen, D.; Charlton, M. Hepatic gene expression in histologically progressive nonalcoholic steatohepatitis. Hepatology 2003, 38 (1), 244–51. (47) Chitturi, S.; Farrell, G.; Frost, L.; Kriketos, A.; Lin, R.; Liddle, C.; Samarasinghe, D.; George, J. Serum leptin in NASH correlates with hepatic steatosis but not fibrosis: a manifestation of lipotoxicity? Hepatology 2002, 36 (2), 403–9. (48) Younossi, Z. M.; Baranova, A.; Ziegler, K.; Del Giacco, L.; Schlauch, K.; Born, T. L.; Elariny, H.; Gorreta, F.; VanMeter, A.; Younoszai, A.; Ong, J. P.; Goodman, Z.; Chandhoke, V. A genomic and proteomic study of the spectrum of nonalcoholic fatty liver disease. Hepatology 2005, 42 (3), 665–74.
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