Metabolomic Profiling of Autoimmune Hepatitis - American Chemical

Article pubs.acs.org/jpr

Metabolomic Profiling of Autoimmune Hepatitis: The Diagnostic Utility of Nuclear Magnetic Resonance Spectroscopy Jia-bo Wang,†,¶ Shi-biao Pu,†,§,¶ Ying Sun,‡,¶ Zhong-feng Li,∥ Ming Niu,† Xian-zhong Yan,⊥ Yan-ling Zhao,† Li-feng Wang,# Xue-mei Qin,∇ Zhi-jie Ma,○ Ya-ming Zhang,† Bao-sen Li,‡ Sheng-qiang Luo,◆ Man Gong,◆ Yong-qiang Sun,◆ Zheng-sheng Zou,*,‡ and Xiao-he Xiao*,◆ †

China Military Institute of Chinese Medicine, 302 Military Hospital, Beijing 100039, PR China Diagnosis and Treatment Center for Non-infectious Diseases, 302 Military Hospital, Beijing 100039, PR China § Yunnan University of Traditional Chinese Medicine, Kunming 650500, PR China ∥ Capital Normal University, Beijing 100089, PR China ⊥ National Center of Biomedical Analysis, Academy of Military Medical Sciences, Beijing 100850, PR China # The Institute of Translational Hepatology, The Research Center for Biological Therapy, 302 Military Hospital, Beijing 100039, PR China ∇ Shanxi University, Taiyuan 030006, PR China ○ Beijing Friendship Hospital, Capital Medical University, Beijing 100050, PR China ◆ Integrative Medical Center, 302 Military Hospital, Beijing 100039, PR China ‡

S Supporting Information *

ABSTRACT: Autoimmune hepatitis (AIH) is often confused with other liver diseases because of their shared nonspecific symptoms and serological and histological overlap. This study compared the plasma metabolomic profiles of patients with AIH, primary biliary cirrhosis (PBC), PBC/AIH overlap syndrome (OS), and drug-induced liver injury (DILI) with those of healthy subjects to identify potential biomarkers of AIH. Metabolomic profiling and biomarker screening were performed using proton nuclear magnetic resonance spectroscopy (1H NMR) coupled with a partial least-squares discriminant analysis. Compared with the levels in healthy volunteers and other liver disease patients, AIH patients exhibited relatively high levels of plasma pyruvate, lactate, acetate, acetoacetate, and glucose. Such metabolites are typically related to energy metabolism alterations and may be a sign of metabolic conversion to the aerobic glycolysis phenotype of excessive immune activation. Increased aromatic amino acids and decreased branched-chain amino acids were found in the plasma of AIH patients. The whole NMR profiles were stepwisereduced, and nine metabolomic biomarkers having the greatest significance in the discriminant analysis were obtained. The diagnostic utility of the selected metabolites was assessed, and these biomarkers achieved good sensitivity, specificity, and accuracy (all above 93%) in distinguishing AIH from PBC, DILI, and OS. This report is the first to present the metabolic phenotype of AIH and the potential utility of 1H NMR metabolomics in the diagnosis of AIH. KEYWORDS: Autoimmune hepatitis, metabolomics, NMR, biomarker, diagnosis

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INTRODUCTION

together, these criteria comprise a codified diagnostic scoring system.2 A clinical diagnosis of AIH may be confounded because the clinical features of this condition are similar to those of other autoimmune liver diseases, such as primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), PBC/AIH and PBC/PSC overlap diseases, and drug-induced liver injury (DILI).3

Autoimmune hepatitis (AIH) is considered to be an immunerelated disease and is characterized by a progressive necroinflammatory and fibrotic process in the liver. AIH can be successfully treated when the diagnosis is timely, accurate, and definitive. However, a diagnosis of AIH is difficult because its pathogenesis is unknown. Currently, the diagnosis of AIH is based on histological abnormalities, characteristic clinical and laboratory findings, abnormal levels of serum globulins, and the presence of one or more characteristic autoantibodies.1 Taken © 2014 American Chemical Society

Received: May 13, 2014 Published: June 18, 2014 3792

dx.doi.org/10.1021/pr500462f | J. Proteome Res. 2014, 13, 3792−3801

Journal of Proteome Research

Article

Figure 1. Flow diagram of the study.

cinoma. These biomarkers have been systematically reviewed.8 Additionally, metabolomics has been well-studied in immune and allergy diseases, such as asthma and multiple sclerosis.9−11 However, metabolomics has been rarely addressed in studies of autoimmune liver diseases. Only a pilot metabolomic study of two types of autoimmune liver diseases, PBC and PSC, has been reported,12 whereas no reports are available on AIH. In this study, we aimed to characterize the metabolomic profile of AIH and to identify potential diagnostic biomarkers for AIH using proton nuclear magnetic resonance spectroscopy (1H NMR). A total of 135 participants (39 AIH, 41 PBC, 18 PBC/ AIH overlap, and 14 DILI patients as well as 23 healthy volunteers) were enrolled in the study (Figure 1). To the best of our knowledge, this is the first report on the metabolomic profile of AIH and the first study that has assessed the utility of metabolomics in the diagnosis of the disease.

Many studies have attempted to identify autoantibodies that are specific to AIH patients; however, serological overlap can confound a diagnosis of AIH with other chronic liver diseases, such as PBC, acute and chronic viral hepatitis, and druginduced hepatitis, or with concurrent autoimmune diseases, such as inflammatory bowel disease (IBD).4,5 Recently, the metabolic liver disease non-alcoholic steatohepatitis (NASH) was found to share autoimmune features with AIH and PBC, including anti-nuclear antibodies (ANA) and anti-mitochondrial antibodies (AMA), which were more frequently detected in women of advanced age.3 The overall sensitivity of the codified scoring system for establishing a definite or probable AIH diagnosis is 89.8%;6 however, the specificity for discriminating AIH from overlapping syndromes, such as PBC and/or PSC, is only 60.8%.7 In addition to the relatively low sensitivity and specificity of this scoring system, this diagnostic approach is complex, time-consuming, and expensive. Additionally, laboratory or histological tests are required. However, histological tests cannot be performed in patients with acute and severe liver lesions because of the lack of permissive conditions for a liver biopsy. In these patients, the diagnostic accuracy of the scoring system is limited. Recently, scoring systems have been proposed to simplify clinical testing and to accelerate diagnosis; however, the reliability of these new scoring systems needs to be further validated. Therefore, a novel rapid and specific diagnostic method for AIH is greatly needed for clinical practice. Metabolomics is defined as the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modifications, which is often used in toxicity assessment, disease diagnosis, and functional genomics. By measuring changes in metabolite concentrations, the range of biochemical effects that are induced by a disease can be determined. Because the liver has a primary role in metabolism, metabolomics has been successfully applied to identify the biomarkers in most types of liver diseases, including non-alcoholic fatty liver disease (NAFLD), alcoholic liver diseases, viral hepatitis, fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and cholangiocar-

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EXPERIMENTAL PROCEDURES

Patients

Patients (n = 39) who were diagnosed with AIH and who were not on drug treatment were enrolled in the study. Age- and sexmatched PBC (n = 41), OS (n = 18), DILI (n = 14), and healthy (n = 23) subjects were enrolled in the study as control groups. The diagnoses of the enrolled patients with PBC, AIH, OS, and DILI were made according to international codified criteria. The study protocol was approved by the ethics committee of our hospital, and written informed consent was obtained from each subject. The study flow is presented in Figure 1. Sampling and Sample Preparation

Venous blood samples were preprandially collected in the morning using lithium heparin tubes (BD Vacutainer; 6 mL; Becton, Dickinson and Company, Franklin Lakes, NJ, USA), and plasma was collected by centrifugation at 1000g at 4 °C for 15 min. The plasma samples were immediately stored at −80 °C until they were used for the metabolomics analysis. Before the NMR analysis, the plasma samples were thawed at room 3793


clinical parameters

3794

27 (19−38) 31 (22−41) 9.7 (7.6−11.9) 1.2 (0.6−3.1)

137 (51−560)

213 (86−473)

27.5 (16.1−38.9)

2.3 (0.9−4.3)

yes no

yes

no

1:320 (1:80−1:880), 5

1:160 (1:80−1:1040), 16

77 (39−128)

153 (83−801)

(n = 23) 1/22 51 (35−67) 8 (5−19)

(n = 21)

1/20 53 (35−67) 39 (24−144)

(n = 21)

PBC

no

yes

1:160 (1:40−1:440), 21

1:320 (1:160−1:1000), 14

4.2 (1.8−9.2)

15.3 (9.8−28.4)

165 (77−1011)

115 (69−958)

461 (271−918)

1/20 52 (37−65) 35 (17−126)

controls

no

yes

1:320 (1:80−1:880), 3

1:160 (1:80−1:1040), 15

1.8 (0.9−4.4)

28.2 (17.3−39.7)

231 (79−574)

149 (63−591)

155 (96−824)

1/17 54 (34−67) 36 (19−137)

(n = 18)

AIH

no

yes

1:160 (1:40−1:480), 20

1:320 (1:160−1:1000), 15

3.7 (1.5−10.0)

17.3 (10.7−36.5)

159 (67−964)

123 (81−941)

467 (269−906)

2/18 53 (36−67) 34 (16−129)

(n = 20)

PBC

no

yes

1:160 (1:40−1:520), 16

1:160 (1:80−1:560), 8

1:320 (1:80−1:1120), 13

2.8 (1.6−8.5)

21.1 (11.4−42.3)

179 (84−986)

151 (71−814)

487 (252−919)

1/17 54 (38−68) 41 (17−151)

(n = 18)

OS

controls

step 2: diagnostic performance test and prediction

no

yes

2.9 (1.5−8.7)

16.2 (9.7−29.3)

159 (78−976)

132 (59−1002)

351 (196−519)

1/13 51 (34−62) 51 (31−172)

(n = 14)

DILI

Abbreviations: ALP, alkaline phosphatase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; IgG, immunoglobulin G; IgM, immunoglobulin M; ANA, anti-nuclear antibody; SMA, smooth muscle antibody; ASMA, anti-smooth muscle antibody.

a

gender (M/F), n age, median, range (y) bilirubin, median, range (μmol/L) ALP, median, range (IU/L) AST, median, range (IU/L) ALT, median, range (IU/L) IgG, median, range (g/L) IgM, median, range (g/L) ANA, median titers (ranges), n ASMA, median titers (ranges), n AMA, median titers (ranges), n absence of viral hepatitis pretreatment

healthy

AIH

step 1: screening metabolic markers

Table 1. Clinical Characteristics of the Populations Enrolled for Screening Metabolic Markers, Diagnostic Performance Test, and Prediction in the Studya

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Figure 2. Discrimination of AIH patients from PBC and healthy controls according to the projection of the latent structure discriminant analysis (PLS-DA) model. The discrimination ability of the models decreased when the data set was reduced. (a−c) Discriminations using the entire NMR chemical shift data set. (d−f) Discriminations using the screened integral segments with a |Pcorr| value > 0.58 and a VIP value > 1. (g−i) Discriminations using the screened integral segments with a |Pcorr| value > 0.81 and a VIP value > 2.38. (j−l) Discriminations using the screened integral segments with a |Pcorr| value > 0.82 and a VIP value > 2.40. Indicators: red triangle, AIH; black circle, a combination of PBC and healthy individuals; black square, healthy volunteers; asterisk, PBC patients. R2, the model fitness parameter; Q2, the predictive ability parameter.

temperature, and 200 μL aliquots were mixed with 400 μL of D2O and centrifuged at 12 000g for 10 min at 4 °C. Thereafter, 500 μL of the supernatant was added into a 5 mm NMR tube, and NMR acquisition was immediately performed.

width of 8000 Hz and 64 scans. The FIDs were zero-filled to double size and multiplied by an exponential line-broadening factor of 1.0 Hz before Fourier transform. All of the plasma 1H NMR spectra were manually phased and baseline-corrected using MestReNova software (Mestrelab, Inc., Santiago de Compostela, Spain). The NMR spectra were calibrated by the endogenous lactate (1.33 ppm) as reference. For the CPMG spectra, each spectrum over the range of δ 0.4−10.0 was data-reduced into integrated regions of equal width (0.005 ppm). The regions that contained the resonance from residual water (δ 4.6−5.1) were excluded. The integral values of each spectrum were normalized to a constant

NMR Measurement 1

H NMR spectra were obtained using an NMR spectrometer (Varian Medical Systems, Inc., Palo Alto, CA, USA) at a proton frequency of 600 MHz at 25 °C with a Carr−Purcell− Meiboom−Gill (CPMG) spin−echo pulse sequence and a total spin−spin relaxation delay (2nτ) of 320 ms to attenuate the broad signals from proteins and lipoproteins because of their short transverse relaxation time. The free induction decays (FIDs) were collected into 64 K data points with a spectral 3795



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Table 2. Nine Identified Metabolic Biomarkers of AIH and Their Changing Trend versus the Other Diseases changing trenda no. 1 2 3 4 5 6 7 8 9 a

metabolite

AIH vs healthy

AIH vs PBC

AIH vs OS

AIH vs DILI

acetoacetate acetone β-hydroxyisobutyrate citrate creatine dimethylamine glutamine histidine pyruvate

↑ ↑b ↑b ↑b ↑b ↑b ↑b ↑b ↑b

↑ ↑b ↑b ↑b ↑b ↑b ↑b ↑b ↑b

↑ ↑b ↑b ↑b ↑b ↑b ↑b ↑b ↑b

↑b ↑b ↑b ↑b ↑b ↑b ↑b ↑b ↑b

b

b

b

↑ denotes significantly higher amounts of the metabolite in the AIH patients compared to that in the healthy controls or other diseases. bp < 0.000.

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sum of all the integrals in a spectrum to reduce any significant concentration differences between the samples.

RESULTS

Patient Clinical Baseline Characteristics

Metabolomics Data Analysis and Modeling

The clinical baseline characteristics of the patients are summarized in Table 1. Previous epidemiological studies have demonstrated that women are affected by these diseases more frequently than men. Consistent with those results, the incidence of PBC, AIH, and OS was higher in the females than in males in our data set. Furthermore, to avoid the impact of the treatment drugs on the metabolomics analysis, none of the enrolled patients had received any pretreatment, including traditional Chinese medicine therapy.

The resulting integral data were imported into the SIMCA-P program (version 11.00; Umetrics, Umeå, Sweden) for a multivariate analysis. All of the NMR data variables were meancentered and Pareto-scaled before the analysis. A partial leastsquares discriminant analysis (PLS-DA) was used to identify the differential metabolites between the groups. The variable importance in the projection (VIP) value and the correlation coefficient (Pcorr) were used to reflect the importance of the metabolites.13 A |Pcorr| value > 0.58 and a VIP value > 1 were a priori considered to be statistically significant on the basis of the literature.14 Then, the proper cutoff values of the VIP and | Pcorr| values were stepwise-screened to find a small set of metabolite biomarkers with the greatest significance to discriminate AIH from the other liver diseases.

PLS-DA Discrimination and Metabolic Marker Screening

The metabolomic profiles of the AIH patients were compared with those of the PBC and healthy volunteers to preliminarily screen potential biomarkers and to test their diagnostic utility. When using the entire NMR chemical shift data set (1802 integral segments of a chemical shift), the established PLS-DA model exhibited good ability to discriminate the AIH individuals from PBC and healthy individuals (Figure 2a−c). To reduce the data set in the model, we used an a priori cutoff VIP value > 1 and a |Pcorr| value > 0.58, based on the literature.14 Using this reduced data set, the established model still exhibited a good ability to discriminate the AIH individuals from the PBC and healthy individuals (Figure 2d−f). To further reduce the data set, the cutoffs of the VIP and |Pcorr| values were screened stepwise with incremental steps of 0.02 and 0.01, respectively. In the screening process, the evaluation parameters for model fitness (R2) and predictive ability (Q2) were continuously reduced. The established model still had good diagnostic ability for correctly discriminating the AIH subjects from the PBC and healthy subjects when the cutoffs for the VIP and |Pcorr| values were set at 2.38 and 0.81, respectively (Figure 2g−i). However, the model could not efficiently discriminate the AIH individuals from the PBC and healthy individuals when the cutoffs for the VIP and |Pcorr| values were set at 2.40 and 0.82, respectively (Figure 2j−l). Therefore, the selected data comprised the minimal data set to establish a diagnostic model with VIP and |Pcorr| values of 2.38 and 0.81, respectively. Such integral segments corresponded to nine metabolomic biomarkers, including citrate, glutamine, acetone, pyruvate, β-hydroxyisobutyrate, acetoacetate, histidine, dimethylamine, and creatinine (Table 2). In addition to the nine metabolites, other significant metabolites were identified in the AIH patients that were present in different levels in the patients with other liver diseases (Table S1). The correlation between the nine diagnostic metabolic

Model Validation

The established model was subjected to a Y-scrambling statistical validation to test the possibility of a chance correlation, where the class membership was randomly shuffled 200 times, and the parameters for model fitness (R2) and predictive ability (Q2) were calculated. The Q2 value was expected to be low (less than 0); a high Q2 value would suggest that the predictive ability of the model was due to a chance correlation.15 In addition, the leave-one-out cross-validation method was employed to estimate the actual performance of the established PLS-DA model in diagnosing AIH, PBC, DILI, and AIH/PBC overlap patients. Using this method, one patient sample was left out at a time, and the PLS-DA prediction model was constructed with the remaining data (a training set). The prediction model was constructed with the same number of original components as the previous PLS-DA classification model. An a priori cutoff value of 0.5 was used to predict the class membership of the left-out sample. This process was repeated until every sample had been tested once.16 Additionally, receiver operating characteristic (ROC) testing was conducted to further evaluate the performance of the established models in diagnosing the different patients. The diagnostic values were assessed using the area under the receiver operating characteristic (ROC) curve. 3796



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Figure 3. Discrimination of AIH patients from PBC, OS, and DILI patients using the nine screened metabolic markers. (a) Discrimination between AIH individuals and PBC, OS, and DILI individuals (R2 = 84.5%, Q2 = 78.6%). (b) Discrimination between AIH patients and DILI patients (R2 = 79.7%, Q2 = 78.3%). (c) Discrimination between AIH patients and PBC patients (R2 = 85.2%, Q2 = 78.9%). (d) Discrimination between AIH patients and OS patients (R2 = 88.7%, Q2 = 81.2%). (e−h) Results of the leave-one-out predictive tests that correspond to panels a−d. For panel e, the sensitivity, specificity, and accuracy were 94.7, 94.5, and 94.6%, respectively. For panel f, the sensitivity, specificity, and accuracy were 100, 93.3, and 97.0%, respectively. For panel g, the sensitivity, specificity, and accuracy were all 100%. For panel h, the sensitivity, specificity, and accuracy were 94.7, 100, and 97.3%, respectively. (i−l) Results of the receiver operating characteristic (ROC) tests that correspond to panels a−d; the areas under the ROC curves (AUCs) of the discrimination results for the different groups were 0.953, 0.954, 1.000, and 0.952, respectively. Indicators: red triangle, AIH; black circle, a combination of PBC, OS, and DILI; black triangle, DILI; asterisk, PBC; black diamond, OS.

markers and the clinical indices of AIH and other diseases are shown in Tables S2−S5. The intercept of the Q2 curves (Figure S3) indicated that the diagnostic models were statistically reliable and that the high predictability was not due to overfitting of the data.

Q2 curve for the diagnostic models were −0.15, −0.22, −0.19, and −0.19 (Figure S4), which indicates that the separation models were statistically reliable and that the high predictability was not due to overfitting of the data.

Diagnostic Performance Test and Prediction

DISCUSSION Because the liver has a primary role in metabolism, metabolomics has been successfully applied to find biomarkers in most types of liver diseases, excluding AIH. The current diagnostic procedures for AIH require integrative information from both clinical and laboratory tests and the exclusion of other liver diseases.17 The specificity for discriminating AIH from overlapping syndromes with PBC and/or PSC has been limited; therefore, a new diagnostic method is greatly needed. In this study, the established NMR-based metabolomic model achieved good diagnostic performance for AIH in distinguishing this disease from PBC, AIH/PBC overlap syndrome (OS), and DILI, with a sensitivity and specificity of over 93% (Figure 3). Despite the importance of histopathology in accurately diagnosing AIH, liver biopsy is not applicable in all patients. The proposed NMR method uses 200 μL of plasma, which can be obtained during routine clinical biochemistry tests, and this method can be performed within a half day, thereby providing a rapid reference for physicians who are diagnosing AIH. From an economic perspective, the current diagnostic procedures for

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On the basis of these nine metabolite biomarkers of AIH, we further investigated the diagnostic utility of the biomarkers in discriminating between AIH patients and patients with PBC, OS, and DILI. As shown in Figure 3a−d, the established PLSDA model with the nine metabolic biomarkers achieved good discrimination between AIH and the other selected liver diseases. To estimate the actual performance of the PLS-DA model in diagnosing AIH, the leave-one-out predictive test (Figure 3e−h) and a receiver operating characteristic (ROC) curve analysis (Figure 3i−l) were performed. According to the leave-one-out predictive test, the wrong predictions were visually displayed when the AIH sample (red triangle) was not clustered in the region under an a priori cutoff value of 0.5. The sensitivity, specificity, and accuracy of the established PLSDA models in the differential diagnosis between AIH and the other three diseases (PBC, OS, and DILI) or between each disease alone were all above 93%. Therefore, the selected nine biomarkers had a high diagnostic accuracy for the discrimination of AIH from PBC, OS, and DILI. The intercepts of the 3797



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Figure 4. Metabolites changed significantly, and the metabolic pathways that were involved in the AIH patients versus the healthy control groups are shown. Indicators: upward-pointing red triangle, an elevation of the metabolite levels in the AIH subjects compared with the healthy subjects; downward-pointing red triangle, a decrease in the metabolite levels in the AIH subjects compared with the healthy subjects.

immune activation in the liver. In addition, glucose metabolism in lymphocytes is a regulated process with significant effects on T cell activation and function.20 Compared with the levels in healthy volunteers, elevated plasma citrate levels were observed in the AIH patients but not in the PBC and DILI patients. The higher citrate content is interesting because citrate is the starting molecule of the tricarboxylic acid (TCA) cycle, which is the hallmark of oxidative phosphorylation (OXPHOS) in aerobic energy metabolism. Higher levels of plasma citrate have been observed in biliary tract cancer patients.22 The higher citrate levels in the AIH patients may result from a low dependence on OXPHOS energy metabolism. Higher citrate and acetate levels would affect the concentration of acetyl CoA, which in turn can affect acetoacetate, acetone, and β-hydroxyisobutyrate formation and alter ketone metabolism.23 Increased levels of plasma ketone bodies, such as pyruvate, acetate, acetoacetate, acetone, and lactate, were observed in the AIH patients. In contrast, acute hepatitis B patients have exhibited decreased plasma levels of βhydroxyisobutyrate, acetone, and lactate compared with those in healthy controls.24 Lactate, which is involved in glycolysis, gluconeogenesis, and pyruvate metabolism, and two other metabolites, acetone and β-hydroxyisobutyrate, which are associated with ketoacidosis, were significantly elevated in the AIH patients but not in acute hepatitis B patients.24 This finding represents a major difference between AIH and hepatitis B. Interestingly, these three metabolites are elevated in acute hepatitis E patients.24 However, a metabolomic study of patients with liver failure due to the hepatitis B virus did not find any changes in the metabolites that are involved in ketone metabolism when these patients were compared with healthy volunteers.25 In addition,

AIH, which include a set of autoimmune antibodies and clinical biochemistry indices, cost over 1000 RMB (160 USD); however, the NMR-based metabolomics test costs approximately 50 RMB (8 USD). Therefore, the 1H NMR-based metabolomics diagnostic approach may offer useful reference values for clinicians in practice. AIH is an immune-related disease with progressive liver immune activation and necroinflammation of unknown etiology. Immune activation and immune responses are energy-dependent processes that require abundant adenosine triphosphate (ATP) and are thus closely related to metabolism. Currently, an increasing number of studies in the literature have emphasized the significant connection between immunology and metabolism.18−20 However, little is known about the metabolic adaptions and profiles in immune-related diseases, such as AIH. In this study, we found that a set of metabolites that were involved in energy metabolism pathways and cycles composed the characteristic metabolomic profile of AIH (Figure 4). Interestingly, the relatively high levels of plasma glucose, pyruvate, lactate, acetate, and acetoacetate in the AIH patients were typically related to energy metabolism alterations, which may be a sign of metabolic conversion to the aerobic glycolysis phenotype of immune overactivation. The elevated level of plasma glucose in AIH does not indicate a glycometabolism disorder, such as metabolic disease; however, the elevated glucose level and the high concentrations of pyruvate and lactate may have physiological importance according to a metabolic perspective. Activated T cells have a higher metabolic demand to support the energy and biosynthetic needs for proliferation and effector function.21 It would be interesting to investigate whether the elevated glucose levels and aerobic glycolysis are associated with excessive 3798



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decreased BCAAs, including leucine/isoleucine and valine, were observed in the AIH patients compared with those in the healthy controls. According to this amino acid metabolism feature, AIH patients may progress to hepatic encephalopathy, especially cirrhosis and late-stage patients. An acute severe presentation has been observed in AIH patients, which is characterized by hepatic encephalopathy within 8 weeks of clinical symptoms.5,39 In summary, we presented the metabolomic profile and metabolic phenotype of AIH for the first time. Our results emphasize the relationship and interaction between the autoimmune response and metabolism. Despite the importance of altering cell metabolism using current treatments (prednisone and azathioprine) and despite the immunosuppressive effects of these drugs, little is known about the metabolic adaptations that occur in vivo, which interfere with the autoimmune response and inflammation. Further investigation into this interaction could offer a new perspective on the pathogenesis of autoimmune liver diseases and novel therapeutic targets. Moreover, we provided proof-of-concept evidence that the 1H NMR analysis of plasma metabolites can be used to differentiate AIH patients from PBC, DILI, and PBC/AIH overlap patients. Magnetic resonance imaging technology has become commonplace in clinical diagnosis; therefore, NMR technology could be easily incorporated into a standard laboratory test approach. There were two main limitations to this study. First, the sample size of this study was limited due to the small number of overlap patients and the absence of PSC patients. In addition, blood samples from asymptomatic patients in the early stage of AIH were not collected. It is challenging to make an accurate diagnosis in asymptomatic patients who account for 34−45% of all AIH patients.40 Therefore, the ability to make an early diagnosis of AIH is valuable. In future studies, we will use this metabolomics method to screen asymptomatic patients with suspected AIH in the clinic. Those patients will be followed, and the progression of their disease will be monitored to evaluate the early diagnostic capability of the metabolomics method.

high levels of plasma lactate and pyruvate have been found in patients with non-alcoholic steatohepatitis (NASH)26 and in a mouse model of non-alcoholic fatty liver disease (NAFLD);27 however, the most altered metabolic pathways in fatty liverrelated diseases were lipids28 and bile salts.29 In the AIH patients, we did not observe any evident changes in plasma bile salts compared with that in the healthy controls. However, significant alterations in bile acids have been found in other autoimmune liver diseases, primary biliary cirrhosis (PBC), and primary sclerosing cholangitis (PSC) in a pilot study.12 Therefore, bile salts may be important biomarkers in discriminating AIH from PBC and PSC. In a recent review, cholestatic phenotypes have an estimated prevalence of 14− 18% in patients with AIH.30 A diagnosis of AIH with cholestatic phenotypes is important for optimizing clinical treatments because the severity of cholestasis in AIH patients has been associated with diminished responsiveness of corticosteroid treatment.31 Metabolomics may be useful in investigating the pathogenic mechanisms of overlap between AIH and other autoimmune liver diseases. Limited by the resolving ability of NMR, the detailed profiles of the bile acid spectrum were inaccessible in this study. In other metabolomic studies that utilized gas or liquid chromatography−tandem mass spectrometry, the ratios of conjugated versus unconjugated bile acids and glyco- versus tauro-conjugated bile acids were important biomarkers in chronic cholestatic liver diseases.12,32 However, the differences in bile acid metabolism between AIH, PBC, PSC, and other cholestatic liver diseases that are induced by viruses are unclear. Elevated creatine levels were observed in the AIH group compared with that in the healthy controls and in the PBC, OS, and DILI patients. Creatine is phosphorylated to phosphocreatine in muscle, and an elevated concentration may indicate the physiological state of an energy metabolism disorder.9,33 Because most creatine (>95%) is stored in muscles as phosphocreatine, which should not diffuse across the membrane of muscle cells, elevated plasma creatine levels may indicate low blood uptake by muscles.32 This reduced uptake would lead to a low energy supply and may be associated with clinical symptoms, such as fatigue, which frequently occur in AIH patients. Detecting muscle phosphocreatine levels in AIH patients may further confirm this postulation. In addition, the codevelopment of AIH and polymyositis, dermatomyositis, and other inflammatory myopathies that are closely related to creatine metabolism may occur.34,35 The observed elevation of plasma creatine levels in the AIH patients needs further clarification in relationship to coexisting myopathic diseases. Elevated serum histidine, tyrosine, and phenylalanine levels were observed in the AIH patients compared with those in the healthy controls, indicating that abnormal amino acid metabolism occurs in AIH. In contrast, these three amino acids were found to decrease in patients with acute hepatitis B and E, and these decreases were attributed to low levels of biosynthesis precursors.24 Elevated serum tyrosine and phenylalanine levels have been observed in NAFLF/NASH26 and HCC patients.36 Therefore, AIH, HCC, and NASH, but not acute hepatitis B and E, may trigger similar molecular events that are involved in tyrosine and phenylalanine metabolism. Tyrosine, phenylalanine, and histidine are aromatic amino acids (AAAs) that are typically elevated in patients with liver failure and fulminant hepatitis37 and are accompanied by decreased branched-chain amino acids (BCAAs).38 Elevated AAAs and

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ASSOCIATED CONTENT

S Supporting Information *

Representative plasma 1H NMR spectra; representative 600 MHz one-dimensional CPMG 1H NMR spectra of healthy human plasma sample measured at 64 K; PLS-DA model validation of AIH and controls (healthy and PBC individuals); PLS-DA model validation of AIH and controls (PBC, OS, and DILI individuals); identification for the screened integral segments with the specified |Pcorr| cutoffs and VIP values; correlation analysis between the nine diagnostic metabolic biomarkers and clinical indices in AIH; correlation analysis between the nine diagnostic metabolic biomarkers and clinical indices in PBC; correlation analysis between the nine diagnostic metabolic biomarkers and clinical indices in OS; and correlation analysis between the nine diagnostic metabolic biomarkers and clinical indices in DILI. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*(Z.-s.Z.) E-mail: [email protected]; Tel/Fax: +86 10 6693 3424. 3799



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*(X.-h.X.) E-mail: [email protected], pharmacy302xxh@ 126.com; Tel/Fax: +86 10 6693 3322.

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Author Contributions ¶

J.-b.W., S.-b.P., and Y.S. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (nos. 81373984 and 81330090) and the National Key Technology R&D Program (no. 2012ZX10005 010-002-002). The authors acknowledge Fu-sheng Wang for his suggestions regarding the study design and comments on the manuscript.

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ABBREVIATIONS AIH, autoimmune hepatitis; AAAs, aromatic amino acids; AMA, antimitochondrial antibodies; ANA, antinuclear antibodies; BCAAs, branched chain amino acids; DILI, drug-induced liver injury; OS, PBC/AIH overlap syndrome; PBC, primary biliary cirrhosis; Pcorr, correlation coefficient; PLS-DA, partial least-squares discriminant analysis; Q2, parameter for the predictive ability of the model; R2, parameter for the fitness of the model; ROC, receiver operating characteristic; VIP, variable importance in the projection; 1H NMR, proton nuclear magnetic resonance spectroscopy

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Metabolomic Profiling of Autoimmune Hepatitis - American Chemical

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