Liquid Chromatography-Mass Spectrometry-Based Parallel Metabolic Profiling of Human and Mouse Model Serum Reveals Putative Biomarkers Associated with the Progression of Nonalcoholic Fatty Liver Disease Jonathan Barr,† Mercedes Va´zquez-Chantada,‡ Cristina Alonso,† Miriam Pe´rez-Cormenzana,‡ Rebeca Mayo,† Asier Gala´n,† Juan Caballerı´a,§ Antonio Martı´n-Duce,| Albert Tran,⊥,#,∇ Conrad Wagner,O,[ Zigmund Luka,O Shelly C. Lu,¶ Azucena Castro,† Yannick Le Marchand-Brustel,⊥,#,∇ M. Luz Martı´nez-Chantar,‡ Nicolas Veyrie,+ Karine Cle´ment,+ Joan Tordjman,+ Philippe Gual,⊥,#,∇ and Jose´ M. Mato*,‡ OWL Genomics, Bizkaia Technology Park, 48160-Derio, Bizkaia, Spain, CIC bioGUNE, Centro de Investigacio´n Biome´dica en Red de Enfermedades Hepa´ticas y Digestivas (Ciberehd), Bizkaia Technology Park, 48160-Derio, Bizkaia, Spain, Liver Unit, Hospital Clı´nic, Centro de Investigacio´n Biome´dica en Red de Enfermedades Hepa´ticas y Digestivas (Ciberehd) and Institut d’Investigacions Biomediques August Pi Sunyer (IDIBAPS), Barcelona, Catalonia, Spain, Departamento de Enfermerı´a, Alcala´ de Henares University, Madrid, Spain, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM), U895, Team 8 “Hepatic complications in obesity”, Nice, France, University of Nice-Sophia-Antipolis, Faculty of Medicine, Nice, France, Centre Hospitalier Universitaire of Nice, Digestive Center, Nice, France, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, Tennessee Valley Department of Medical Affairs Medical Center, Nashville, Tennessee, Division of Gastrointestinal and Liver Diseases, USC Research Center for Liver Diseases, Southern California Research Center for Alcoholic Liver and Pancreatic Diseases and Cirrhosis, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM), U872, Team 7, Paris, France, University Pierre et Marie Curie-Paris, and Assistance Publique Hoˆpitaux de Paris, Pitie´ Salpeˆtrie`re and Hoˆtel-Dieu hospital, Paris, France Received March 22, 2010
Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols. Keywords: NAFLD • steatosis • NASH • metabolomics • biomarkers
Introduction According to the World Health Organization, there are more than 1 billion overweight adults [body mass index (BMI) > 25 * To whom correspondence should be addressed. Jose´ M. Mato, CIC bioGUNE, Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain. E-mail:
[email protected]. Tel: +34-944-061300. Fax: +34-944-061301. † OWL Genomics. ‡ CIC bioGUNE, Centro de Investigacio´n Biome´dica en Red de Enfermedades Hepa´ticas y Digestivas. § Liver Unit, Hospital Clı´nic, Centro de Investigacio´n Biome´dica en Red de Enfermedades Hepa´ticas y Digestivas and Institut d’Investigacions Biomediques August Pi Sunyer. | Alcala´ de Henares University. ⊥ Institut National de la Sante´ et de la Recherche Me´dicale, Team 8. # University of Nice-Sophia-Antipolis. ∇ Centre Hospitalier Universitaire of Nice. O Vanderbilt University. [ Tennessee Valley Department of Medical Affairs Medical Center. ¶ University of Southern California. 10.1021/pr1002593
2010 American Chemical Society
kg/m2], of which at least 300 million are obese (BMI > 30 kg/ m2).1 Morbid obesity (BMI > 40 kg/m2) prevalence is also increasing rapidly worldwide. Obesity poses a major risk factor for nonalcoholic fatty liver disease (NAFLD).2-7 NAFLD is a progressive disease, ranging from the simple accumulation of fat in the liver (steatosis) to the more severe necroinflammatory complication nonalcoholic steatohepatitis (NASH), and affecting up to 24% of the US population.2-8 Fortunately, only a small fraction of NAFLD patients develop cirrhosis and hepatocellular carcinoma (HCC),9,10 although rising obesity prevalence may result in a corresponding increase in these more severe diseases, representing a major health risk. Several molecular mechanisms have been proposed to explain how steatosis progresses to NASH, including free fatty acid-induced apoptosis, endoplasmic reticulumandoxidativestress,andalteredmethioninemetabolism.11-13 The contribution of molecules secreted by visceral adipose Journal of Proteome Research 2010, 9, 4501–4512 4501 Published on Web 07/15/2010
research articles tissue depots, including inflammatory mediators, has also been underlined.14 Although there is currently no generally accepted medical therapy for NAFLD, weight loss, induced by caloric restriction diets, bariatric surgery, or drug-induced fat mal-absorption, improves the condition in some cases.15-17 Efficient diagnosis methods are needed for the facile identification of NAFLD patients, disease progression risk assessment, and monitoring the response to potential new treatment strategies. NAFLD may be suspected in subjects with one or more components of the metabolic syndrome, especially obesity and type 2 diabetes, and elevated serum aminotransferase levels [alanine aminotransferase (ALT) and aspartate aminotransferase (AST)].18-20 Currently, the most reliable methods for NAFLD diagnosis include imaging techniques such as ultrasound and magnetic resonance imaging and the histological examination of a liver biopsy specimen.21,22 However, imaging techniques are expensive and nonspecific (they are unable to distinguish NASH from simple steatosis or detect hepatic fibrosis), whereas liver biopsy is an expensive, invasive and subjective procedure, associated with potential complications and prone to sampling error.23 Transient elastography or FibroScan has been proposed for the noninvasive diagnosis of liver fibrosis.24 Its main application is to avoid liver biopsy in assessing disease progression in patients with chronic hepatitis C. Several predictive panels, based on the multivariate analysis of well-established clinical and laboratory variables (such as age, BMI, ALT, AST, glucose, insulin resistance, albumin)25 have been proposed as noninvasive markers for the quantitative assessment of fibrosis (FibroTest,),26,27 steatosis (SteatoTest),28 NASH (NashTest),29 and fibrosis in patients with NAFLD (ELF test, NAFLD fibrosis score).30-32 However, these methods are not all validated in obese or morbidly obese patients. Offering a physiologically holistic, noninvasive platform, the emergent field of metabolomics has the potential to provide new NAFLD diagnostic tools.33,34 Recent technological breakthroughs have provided researchers with the capacity to measure hundreds or even thousands of small-molecule metabolites in as little as a few minutes per sample, paving the way for hypothesis generation studies ideally suited to complex diseases such as NAFLD.35,36 Metabolomics is particularly suited to liver injury assessment applications, where serum or urine are the most common samples made available for laboratory tests, as opposed to other disease scenarios, such as cancer, where tissue is readily available for transcriptomic and proteomic analysis. Targeted metabolite analysis studies have already shown that alterations of critical hepatic metabolic pathways, such as methionine and phospholipid metabolism, are strongly associated with NAFLD development.13,37 Such changes are expected to be reflected in wider coverage metabolic profiles, which may in turn be explored as potential biomarkers for NAFLD assessment and treatment stratification. One of the great promises of the metabolomics approach is the fact that groups of metabolite biomarkers are expected to be less species dependent than gene or protein markers, facilitating the direct comparison of animal models with human studies, which in turn improves the potential of the technique to rapidly convert laboratory based research into clinical practice.38 Although the NAFLD condition is typically associated with key metabolic syndrome factors such as obesity, insulin + Institut National de la Sante´ et de la Recherche Me´dicale (INSERM), Team 7; University Pierre et Marie Curie-Paris; Assistance Publique Hoˆpitaux de Paris.
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Barr et al. resistance, diabetes, and hypertriglyceridemia, the mechanisms of disease pathogenesis and progression remain unclear. This has brought about the need for the development of animal models, which have provided further insight into the many complex processes which may occur as the liver progresses through different NAFLD stages.39 Ideal animal models should resemble as closely as possible the disease pathological characteristics observed in humans. For the study of NAFLD, they should show, together with biochemical alterations, liver fat accumulation, progressing through hepatocyte degeneration and inflammation.39,40 All of these features are observed in the glycine N-methyltransferase knockout (GNMT-KO) mouse model, based on methionine metabolism perturbations, where animals have elevated serum ALT, AST and S-adenosylmethionine (SAMe) levels and develop liver steatosis, fibrosis, and hepatocellular carcinoma (HCC).41 Mammalians catabolize up to half of their daily methionine intake in the liver, via conversion to SAMe in a reaction catalyzed by methionine adenosyltransferase I/III (MATI/III).42 SAMe is involved in a number of different key metabolic pathways, among which transmethylation reactions involve the donation of a methyl group to a variety of acceptor molecules, catalyzed by methyltransferases.43-45 In quantitative terms, the most important methlytransferase acting upon hepatic SAMe is GNMT, catalyzing its conversion to S-adenosylhomocysteine (SAH), a potent inhibitor of methylation reactions. The importance of the GNMT enzyme is therefore to maintain a constant SAMe/ SAH ratio, thus avoiding aberrant methylation events.46 This mechanism was recently exemplified by the finding that as well as provoking NAFLD, loss of GNMT induces aberrant methylation of DNA and histones, resulting in epigenetic modulation of critical carcinogenic pathways in mice.41 Further evidence supporting the suitability of the GNMTKO model for comparison with human NAFLD includes the finding that several children with GNMT mutations had mild to moderate liver disease, and the report of a loss of heterozygosity of GNMT in around 40% of HCC patients, with GNMT being proposed as a tumor-susceptibility gene for liver cancer.47,48 In this study we aimed to use the differential global serum metabolite profile of GNMT-KO animals, as compared to their wild type (WT) littermates to help define a NAFLD metabolic signature for comparison with that found in humans. Common biomarkers may provide further mechanistic insights, and have great potential for practical use in NAFLD management applications. A liquid chromatography-mass spectrometry (UPLC-MS) platform-based metabolomics approach was used to explore the serum metabolic profiles of the GNMT-WT/KO animals and biopsy-proven22 human NAFLD patients. Multivariate statistical analysis of the UPLC-MS data revealed some similarities in the GNMT-KO and human NAFLD patients’ relative serum metabolite levels, as compared to normal liver subjects. The results illustrate the potential of metabolite profiling to provide biomarkers for staging, prognosis and therapy selection in NAFLD management.
Materials and Methods Clinical Population and Animal Experiments. Human NAFLD Patients. Serum samples were collected from a total of 42 morbidly obese (BMI > 40 kg/m2), nondiabetic subjects. The individuals were bariatric surgery candidates, all being weight stable before intervention. Oral glucose tolerance tests (OGGT) were performed to confirm the absence of diabetes.
research articles
Biomarkers Associated with Nonalcoholic Fatty Liver Disease a
Table 1. Clinicopathological Characteristics of the Human Patients Included in the Study group
N (males)
age (years)
BMI (kg/m2)
AST(IU)
ALT(IU)
glucose (mM)
cholesterol (mM)
triglycerides (mM)
S0 S1 S2 S3 S3 + NASH
9 (0) 8 (0) 7 (0) 9 (0) 9 (1)
35.0 ( 3.5 43.8 ( 3.8 41.2 ( 5.1 39.9 ( 4.7 44.6 ( 3.5
47.0 ( 1.9 45.4 ( 1.7 43.5 ( 2.0 45.5 ( 2.7 43.2 ( 1.5
23.3 ( 2.3 21.8 ( 3.5 24.9 ( 2.3 27.8 ( 2.5 32.8 ( 3.2
25.1 ( 3.2 24.8 ( 3.1 34.9 ( 3.2 40.8 ( 7.2 44.6 ( 5.6
5.0 ( 0.2 5.0 ( 0.2 5.2 ( 0.2 5.3 ( 0.2 5.5 ( 0.4
4.8 ( 0.2 6.2 ( 0.6 5.6 ( 0.7 4.8 ( 0.4 5.1 ( 0.3
1.2 ( 0.2 1.9 ( 0.4 1.6 ( 0.2 1.4 ( 0.2 1.4 ( 0.2
a NAFLD diagnoses were established histologically.21 Values are given as mean (1 standard error of the mean. ALT, a known biomarker of liver damage, is the only parameter found significantly altered (p < 0.05) between the groups of patients under comparison.
The clinicopathological characteristics of the patients are summarized in Table 1. All of the patients were of Caucasian origin; there were 41 females (98%) and 1 male (2%), with a mean age of 41 ( 2 years at the time of NAFLD diagnosis. For all patients the diagnosis of hepatic steatosissgrades S0 (normal liver), S1, S2, S3 (in increasing order of steatosis severity)sand NASH (grade 1) was established histologically in liver biopsy samples, in the absence of other (viral-, alcohol-, metabolic-, or drug-induced) causes of NAFLD.22 The study was approved by the human research review committee of the two participating hospitals (Nice and Pitie´-Salpeˆtrie`re Paris). No clinical differences were observed between patients recruited at the two sites. Animal Handling and Sample Collection. All animal experimentation was conducted in accordance with Spanish guidelines for the care and use of laboratory animals, and protocols approved by the CIC bioGUNE ethical review committee. The generation of GNMT-KO mice has been described previously.49 All animals were supplied with a standard laboratory diet and water ad libitum. Male homozygous GNMT-KO mice were killed at 4 (n ) 4) and 6.5 (n ) 3) months of age and their WT littermates at 4 (n ) 6) and 6.5 (n ) 4) months of age. Histological examination of the 4-month-old mutant mice showed steatosis and fibrosis, which was more prominent in the 6.5-month-old mice.41 The WT mice histologies were normal at both the 4- and 6.5-month-old time points.41 Serum samples were collected from the animals at the time of death, for determination of ALT, AST levels, and metabolic profiling experiments. Metabolic Profiling. A global metabolite profiling UPLC-MS methodology was employed where all endogenous metabolite related features, characterized by their mass-to-charge ratio m/z and retention time Rt, are included in a subsequent multivariate analysis procedure used to study metabolic differences between the different groups of samples.50-53 Where possible, Rt-m/z features corresponding to putative biomarkers were identified. The analytical methodology was designed to provide maximum coverage over classes of compounds involved in key hepatic metabolic pathways, such as major phospholipids, fatty acids, and organic acids, while offering relatively highthroughput with minimal injection-to-injection carryover effects. Sample Preparation. Proteins were precipitated from the defrosted serum samples (50 µL) by adding four volumes of methanol in 1.5 mL microtubes at room temperature. After brief vortex mixing the samples were incubated overnight at -20 °C. Supernatants were collected after centrifugation at 13 000 rpm for 10 min and transferred to vials for UPLC-MS analysis. Chromatography. Chromatography was performed on a 1 mm i.d. × 100 mm ACQUITY 1.7 µm C8 BEH column (Waters Corp., Milford, MA) using an ACQUITY UPLC system (Waters Corp., Milford, MA). The column was maintained at 40 °C and eluted with a 10 min linear gradient. The mobile phase, at a flow rate of 140 µL/min, consisted of 100% solvent A (0.05%
formic acid) for 1 min followed by an incremental increase of solvent B (acetonitrile containing 0.05% formic acid) up to 50% over a further minute, increasing to 100% B over the next 6 min before returning to the initial composition in readiness for the subsequent injection, which proceeded a 45 s system recycle time. The volume of sample injected onto the column was 1 µL. Mass Spectrometry. The eluent was introduced into the mass spectrometer (LCT Premier, Waters Corp., Milford, MA) by electrospray ionization, with capillary and cone voltages set in the positive and negative ion modes to 3200 and 30 V, and 2800 and 50 V respectively. The nebulization gas was set to 600 L/h at a temperature of 350 °C. The cone gas was set to 50 L/h, and the source temperature was set to 150 °C. Centroid data were acquired from m/z 50-1000 using an accumulation time of 0.2 s per spectrum. All spectra were mass corrected in real time by reference to leucine enkephalin, infused at 50 µL/ min through an independent reference electrospray, sampled every 10 s. A test mixture of standard compounds (Acetaminophen, Sulfaguanidine, Sulfadimethoxine, Val-Tyr-Val, Terfenadine, Leucine-Enkephaline, Reserpine and Erythromicynsall 5nM in water) was analyzed before and after the entire set of randomized, duplicated sample injections to examine the retention time stability (generally
