Liquid Chromatography-Mass Spectrometry (LC/MS)-based parallel metabolic profiling of
human and mouse model serum reveals putative biomarkers associated with the
progression of non-alcoholic fatty liver disease.
Jonathan Barr, Mercedes Vázquez-Chantada, Cristina Alonso, Miriam Pérez-Cormenzana, Rebeca Mayo, Asier
Galán, Juan Caballería, Antonio Martín-Duce, Albert Tran, Conrad Wagner, Zigmund Luka, Shelly C. Lu, Azucena
Castro, Yannick Le Marchand-Brustel, M. Luz Martínez-Chantar, Nicolas Veyrie, Karine Clément, Joan Tordjman,
Philippe Gual, José M. Mato.
-40 -30-20 -10010
% Deviation (GNMTKO-WT)
% Deviation (NAFLD-Healthy)
Synopsis: This article describes a parallel animal model / human NAFLD exploratory metabolomics study, using
ultra performance liquid chromatography-mass spectrometry (UPLC®-MS) to analyze 42 serum samples collected
from non-diabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-
methyltransferase knockout (GNMT-KO) NAFLD mouse model. Many of the altered metabolites observed could be methyltransferase knockout (GNMT KO) NAFLD mouse model. Many of the altered metabolites observed could be
associated with biochemical perturbations associated with liver dysfunction (e.g. reduced Creatine) and inflammation
(e.g. eicosanoid signaling).
Liquid Chromatography-Mass Spectrometry (LC/MS)-based parallel
metabolic profiling of human and mouse model serum reveals putative
biomarkers associated with the progression of non-alcoholic fatty liver
Jonathan Barr1, Cristina Alonso1, Mercedes Vázquez-Chantada1, Miriam Pérez-
Cormenzana1, Rebeca Mayo1, Asier Galán1, Juan Caballería2, Antonio Martín-Duce3,
Albert Tran 4,5,6, Conrad Wagner7,8, Zigmund Luka7, Shelly C. Lu9, Azucena Castro1,
Yannick Le Marchand-Brustel4,5,6 , M. Luz Martínez-Chantar10, Nicolas Veyrie11, Karine
Clément11, Joan Tordjman11, Philippe Gual4,5,6, José M. Mato*.
1OWL Genomics, Bizkaia Technology Park, 48160-Derio, Bizkaia, Spain. 2Liver Unit,
Hospital Clínic, Centro de Investigación Biomédica en Red de Enfermedades
Hepáticas y Digestivas (Ciberehd) and Institut d’Investigacions Biomediques August
Pi Sunyer (IDIBAPS), Barcelona, Catalonia, Spain. 3Departamento de Enfermería,
Alcalá de Henares University, Madrid, Spain. 4Institut National de la Santé et de la
Recherche Médicale (INSERM), U895, Team 8 «Hepatic complications in obesity »,
Nice, France. 5University of Nice-Sophia-Antipolis, Faculty of Medicine, Nice, France.
6Centre Hospitalier Universitaire of Nice, Digestive Center, Nice, France.
7Department of Biochemistry, Vanderbilt University, Nashville, TN.
Valley Department of Medical Affairs Medical Center, Nashville, TN. 9Division 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, CA 90033. 10CIC bioGUNE, Centro de Investigación Biomédica en Red de
Enfermedades Hepáticas y Digestivas (Ciberehd), Bizkaia Technology Park, 48160-
Derio, Bizkaia, Spain. 11Institut National de la Santé et de la Recherche Médicale
(INSERM), U872, Team 7, Paris, France; University Pierre et Marie Curie-Paris;
Assistance Publique Hôpitaux de Paris, Pitié Salpêtrière and Hôtel-Dieu hospital,
NAFLD, steatosis, NASH, metabolomics, biomarkers.
José M. Mato, CIC bioGUNE, Technology Park of Bizkaia, 48160 Derio, Bizkaia,
Spain. firstname.lastname@example.org; Tel: +34-944-061300; Fax: +34-944-061301.
ALT, Alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass
index; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis;
HCC, hepatocellular carcinoma; GNMT, glycine N-methyltransferase; SAMe, S-
adenosylmethionine; SAH, S-adenosylhomocysteine; FFAs, free fatty acids; LPC,
lysophosphatidylcholine; BAs, bile acids; SM, sphingomyelin; PC,
phosphatidylcholine; MUFA, monounsaturated fatty acid derivatives; SFA, saturated
fatty acids; PCA, principal component analysis; OPLS-DA, orthogonal partial least-
squares to latent structures discriminant analysis; UPLC®-MS, ultra performance
liquid chromatography-mass spectrometry.
Non-alcoholic 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 non-diabetic, 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.
According to the World Health Organization, there are more than 1 billion overweight
adults [body mass index (BMI) > 25 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
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 non-alcoholic
steatohepatitis (NASH), and affecting up to 24% of the US population2-8. 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 reticulum and oxidative stress,
and altered methionine metabolism11-13. The contribution of molecules secreted by
visceral adipose tissue depots, including inflammatory mediators, has also been
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 cases15-17. 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 specimen21, 22. However, imaging
techniques are expensive and non-specific (they are unable to distinguish NASH from
simple steatosis, or detect hepatic fibrosis), whilst liver biopsy is an expensive, invasive
and subjective procedure, associated with potential complications and prone to
sampling error23. Transient elastography or FibroScan has been proposed for the non-
invasive diagnosis of liver fibrosis24. 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 non-invasive 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, non-invasive platform, the emergent field of
metabolomics has the potential to provide new NAFLD diagnostic tools33, 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 NAFLD35, 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 development13, 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 practice38. Although the NAFLD condition is typically associated
with key metabolic syndrome factors such as obesity, insulin 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 stages39. 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
inflammation39, 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,
amongst which transmethylation reactions involve the donation of a methyl group to a
variety of acceptor molecules, catalyzed by methyltransferases43-45. 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 events46. 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 mice41.
Further evidence supporting the suitability of the GNMT-KO 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 cancer47, 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), non-diabetic 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. 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
steatosis - grades S0 (normal liver), S1, S2, S3 (in increasing order of steatosis
severity) - and NASH (grade 1) was established histologically in liver biopsy samples,
in the absence of other (viral-, alcohol-, metabolic-, or drug-induced) causes of
NAFLD22. The study was approved by the human research review committee of the two
participating hospitals (Nice and Pitié-Salpêtrière 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 previously49. 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), 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 mice41. The WT
mice histologies were normal at both the 4- and 6.5-month-old time points41. Serum
samples were collected from the animals at the time of death, for determination of ALT,
AST levels, and metabolic profiling experiments.
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 samples50-53.
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, whilst offering relatively high-throughput
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 minutes, 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, USA) using an ACQUITY
UPLC® system (Waters Corp., Milford, USA). The column was maintained at 40 ºC and
eluted with a 10 minute linear gradient. The mobile phase, at a flow rate of 140 µL/min,
consisted of 100% solvent A (0.05% formic acid) for 1 minute 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 minutes before returning to
the initial composition in readiness for the subsequent injection which proceeded a 45 s
system re-cycle time. The volume of sample injected onto the column was 1 μL.
Mass spectrometry. The eluent was introduced into the mass spectrometer (LCT
PremierTM, Waters Corp., Milford, USA) by electrospray ionisation, with capillary and
cone voltages set in the positive and negative ion modes to 3200 V and 30 V, and 2800
V and 50 V respectively. The nebulisation gas was set to 600 L/h at a temperature of
350 ºC. The cone gas was set to 50 L/h and the source temperature set to 150 ºC.
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 Erythromicyn – all 5nM in water) was analyzed before and after the
entire set of randomized, duplicated sample injections in order to examine the retention
time stability (generally < 6 s variation, injection-to-injection), mass accuracy (generally
< 3 ppm for m/z 400-1000, and < 1.2 mDa for m/z 50-400) and sensitivity of the system
throughout the course of the run which lasted a maximum of 26 h per batch of samples
injected. For each injection batch, the overall quality of the analysis procedure was
monitored using five repeat extracts of a pooled serum sample. For all biomarker
metabolites reported in this work, coefficients of variation, CV, between the repeat
extracts were less than 25%.
Online tandem mass spectrometry (MS/MS) experiments for metabolite identification
were performed on a Waters QTOF PremierTM (Waters Corp., Milford, USA) instrument
operating in both the positive and negative ion electrospray modes; source parameters
were identical to those employed in the profiling experiments, except for the cone
voltage which was increased (30-70 V) when pseudo MS/MS/MS data was required.
During retention time windows corresponding to the elution of the compounds under
investigation the quadrupole was set to resolve and transmit ions with appropriate
mass-to-charge values. The selected ions then traversed an argon-pressurized cell,
with a collision energy voltage (typically between 5 and 50 V) applied in accordance
with the extent of ion fragmentation required. Subsequent TOF analysis of the fragment
ions generated accurate mass (generally < 3 ppm for m/z 400-1000, and < 1.2 mDa for
m/z 50-400) MS/MS or pseudo MS/MS/MS spectra corrected in real time by reference
to leucine enkephalin, infused at 50 µL/min through an independent reference
electrospray, sampled every 10 s. Centroid data were acquired between m/z 50-1000
using an accumulation time of 0.2 s per spectrum.
Data processing. All data were processed using the MarkerLynx application manager
for MassLynx 4.1 software (Waters Corp., Milford, USA). The LC/MS data are peak-
detected and noise-reduced in both the LC and MS domains such that only true
analytical peaks are further processed by the software (e.g. noise spikes are rejected).
A list of intensities (chromatographic peak areas) of the peaks detected is then
generated for the first chromatogram, using the Rt-m/z data pairs as identifiers. This
process is repeated for each LC-MS analysis and the data sorted such that the correct
peak intensity data for each Rt-m/z pair are aligned in the final data table. The ion
intensities for each peak detected are then normalized, within each sample, to the sum
of the peak intensities in that sample. There was no significant correlation (F < Fcrit)
between the total intensities used for normalization and the sample groups being
compared in the study. The resulting normalized peak intensities form a single matrix
with Rt-m/z pairs for each file in the dataset. All processed data were mean centered
and pareto scaled during multivariate data analysis54.
Multivariate data analysis. The first objective in the data analysis process is to reduce
the dimensionality of the complex data set to enable easy visualization of any
metabolic clustering of the different groups of samples. This has been achieved (Figure
1) by principal components analysis (PCA)55 where the data matrix is reduced to a
series of principal components (PCs), each a linear combination of the original Rt-m/z
pair peak areas. Each successive PC explains the maximum amount of variance
possible, not accounted for by the previous PCs. Hence the scores plots shown in the
figures – where the first two principal components, t and t, are plotted – represent
the most important metabolic variation in the samples captured by the analysis.
The second stage of the data analysis process concerns the identification of
metabolites contributing to the clustering observed in the PCA plots. This information
was obtained either by studying the corresponding PCA loadings plot, where the
projections of the model variables onto the principal components (p, p etc.) are
represented, or more specifically using orthogonal partial least-squares to latent
structures discriminant analysis (OPLS-DA)56, 57. The latter method is a supervised
approach, allowing pre-defined inter-class variance to be captured within a single
predictive component. The performance of the OPLS-DA models were evaluated using
the Q2 parameter (a Q2 score between 0.7 – 1.0 is indicative of a reliable classifier),
calculated by iteratively leaving out samples from the model and predicting their group
classification. Paired sample injections were randomly distributed over the cross-
validation groups (7-fold), minimizing the risk of calculating falsely predictive
components. Appropriate filtration of the loading profile associated with the OPLS-DA
predictive components resulted in a set of candidate biomarkers that were further
evaluated by calculating group percentage changes and unpaired Student’s t-test p-
Metabolite biomarker identification. Exact molecular mass data from redundant m/z
peaks corresponding to the formation of different parent (e.g. cations in the positive ion
mode, anions in the negative ion mode, adducts, multiple charges) and product
(formed by spontaneous “in-source” CID) ions were first used to help confirm the
metabolite molecular mass. This information was then submitted for database
searching, either in-house or using the online ChemSpider database
(www.chemspider.com) where the Kegg, Human Metabolome Database and Lipid
Maps data source options were selected. MS/MS data analysis highlights neutral
losses or product ions, which are characteristic of metabolite groups and can serve to
discriminate between database hits. Specific metabolite groups were characterized as
follows: (1) Glycerophospholipids: All species showed clear, diagnostic headgroup ions
corresponding to the different lipid sub-classes – m/z = 184.0739 (phosphoryl choline,
observed in the MS/MS spectra of [M+H]⁺ monoacyl, monoalkenyl, monoalkyl, diacyl,
and 1-alkenyl,2-acyl glycerophosphocholine) in the positive ion mode, and m/z =
168.0426 (demethylated phosphoryl choline observed in MS/MS spectra of [M-CH3]⁻
monoacyl, monoalkenyl, monoalkyl, diacyl, and 1-alkenyl,2-acyl
glycerophosphocholine), 196.0380 (dilyso phosphoryl ethanolamine, observed in
MS/MS spectra of [M-H]⁻ monoalkenylglycerophosphoethanolamine) in the negative
ion mode58. The two ether subclasses of lysoglycerophosphocholine could be readily
distinguished from the monoacyl species by analysis of the MS/MS behaviour of their
[M+H]⁺ and [M-CH3]⁻ ions in the positive and negative modes respectively59. Positive
ion mode MS/MS spectra of the [M+H]⁺ ions showed strong water loss for the acyl
species, which was barely observed in the ether subclass spectra. This was consistent
with previous reports that rationalized this specificity in fragmentation behavior in terms
of different protonation sites for the ether molecular ions that did not eliminate water59.
Negative ion mode MS/MS spectra of the [M-CH3]⁻ ions corresponding to these species
also showed highly specific fragmentation behavior of the acyl species, which display
clear elimination of a highly stable carboxylate ion, whilst the spectra corresponding to
the ether species showed strong loss of dimethylaminoethylene followed by
dehydration. Monoacylglycerophosphocholine regiochemistry was determined by the
fragmentation behavior of the corresponding [M+Na]⁺ ions, as observed in their positive
ion mode MS/MS spectra. This followed previous reports that have shown that spectra
of sn-1 monoacyl species show a high m/z = 104.1070 (choline) / 146.9818 (sodiated
cyclic ethylene phosphate) product ion ratio from the [M+Na]⁺ ion60. The converse
situation is observed in the case of the sn-2 monoacyl species60. The vinyl ether
subclass was distinguished from the alkyl ether species by observation of the m/z =
224.0688 ion in the MS/MS spectra of the [M-CH3]⁻ ions, formed by a charge remote
fragmentation mechanism facilitated by the vinyl ether double bond, resulting in the
loss of the vinyl ether moiety – no similar ion was observed in the corresponding alkyl
ether subclass spectra59. The only corresponding glycerophosphoethanolamine
species putative biomarker [PE(P-16:0/0:0)] described in this work showed cleavage of
the glycerol backbone in both the positive (m/z = 198.0531) and negative (m/z =
239.2375) ion mode MS/MS spectra of the [M+H]⁺ and [M-H]⁻ ions respectively.
Diacylglycerophosphocholine species were readily identified by the facile loss of the
sn-1 and sn-2 carboxylate ions (the sn-2 ion was taken to be the most intense
carboxylate moiety – taking into account the formation of second generation fragments,
in accordance with previous reports58). Composite nomenclature, referred to as the
sum of fatty acid pairs was used where evidence was found for the contribution of
multiple species to a single chromatographic peak [e.g. PC 36:5 = PC(16:0/20:5) +
PC(16:1/20:4)]. The only corresponding ether subclass described as a putative
biomarker in this work [PC(P-18:0/20:4)] showed strong agreement with published
(www.lipidmaps.org) MS/MS spectra of the compound in both the positive [[M+H]⁺;
showing loss of trimethylammonia (m/z = 735.5329) and cleavage of the vinyl ether
backbone (m/z = 526.3298)] and negative [[M-CH3]⁻; showing loss of the arachidonic
acid moiety as a neutral ketene (m/z = 492.3454) and as a carboxylate ion (m/z =
303.2324)] ion modes. (2) Phosphosphingolipids: The putative biomarkers discussed in
the current work also showed clear, diagnostic headgroup ions – m/z = 184.0739
(phosphoryl choline, observed in the MS/MS spectra of [M+H]⁺ ions) in the positive ion
mode, and m/z = 168.0426 (demethylated phosphoryl choline observed in MS/MS
spectra of [M-CH3]⁻ ions) in the negative ion mode. These compounds could be easily
distinguished from glycerophospholipids by their odd m/z values (nitrogen rule) and
highly stable [M-CH3]⁻ fragment ions61. Specific phosphosphingolipids were further
identified by high collision energy CID of the [M-CH3]⁻ moiety in the negative ion mode.
Loss of the N-fatty acyl chain in the corresponding MS/MS spectra allowed
identification of the sphingoid bases d18:0 (m/z = 451.3301), d18:1 (m/z = 449.3144) or
d18:2 (m/z = 447.2988)61. Composite nomenclature, referred to as the sum of
sphingoid base and N-linked fatty acid was used where evidence was found for the
contribution of multiple sphingoid base lipids to a single chromatographic peak [e.g. SM
34:2 = SM(d18:1/16:1) + SM(d18:2/16:0)].
The MassFragment™ application manager (Waters MassLynx v4.1, Waters corp.,
Milford, USA) was used to facilitate the MS/MS fragment ion analysis process by way
of chemically intelligent peak-matching algorithms. The identities of free fatty acids
(Figure 2A), bile acids (Figure 2D), and other selected metabolites (all indicated by † in
the tables and figures) were confirmed by comparison of their mass spectra and
chromatographic retention times with those obtained using commercially available
reference standards (Sigma Aldrich, Avanti Polar Lipids Inc.). A full spectral library,
containing MS/MS data obtained in the positive and negative ion modes, for all
metabolites reported in this work is available on request from the authors.
GNMT WT/KO Metabolic Profiles. In total, sera taken from 10 WT and 7 GNMT-KO
mice were analyzed by UPLC®-MS. PCA was used to produce a two-dimensional
visual summary of the observed variation in the serum metabolic profiles of these
samples (Figure 1). The results reveal clear metabolic differentiation between the WT
and KO animals, with KO samples having a more negative score in the first principal
component, t. Additional separation, between the KO mice at 4 (fibrosis + steatosis)
and 6.5 (fibrosis + NASH) months is also apparent in the second principal component,
t, in the negative ion mode – KO mice at 6.5 months of age have a more negative
score. The top three variable loadings – increased (negative loading, p) and
decreased (positive loading, p) in the GNMT-KO mice with respect to their WT
littermates - corresponding to the PCA model in Figure 1 are displayed in Table 2.
Common biomarkers – GNMT-KO / Human NAFLD. Having established that the
animal model metabolic profiles recorded by UPLC®-MS were correlated with NAFLD
progression, the proceeding analysis was focused towards the identification of
biomarkers with similar trends in the human NAFLD samples. OPLS-DA models of the
animal data were created, comparing the WT animals with their KO littermates. The
model diagnostics, R2(Y) and Q2(Y) were 0.99 and 0.93, and 0.99 and 0.91, in the
negative and positive modes respectively, indicating a robust trend, as was suggested
by the PCA result. The loadings profiles associated with the OPLS-DA predictive
component were filtered according to the ratio of the loadings p to the standard error
of the loadings, pcvSE, as obtained from the cross-validation rounds. Variables
having this ratio less than 2 were eliminated from further investigation, and those
remaining ordered by their p value. These data were then compared with percentage
changes and unpaired Student’s t-test p-values obtained from the human sample
UPLC®-MS data, comparing subjects with a normal liver biopsy to those with NAFLD.
Significant overlap was found between the two data sets, with common perturbed
groups of compounds including free fatty acids (FFAs), lysophosphatidylcholine (LPC),
bile acids (BAs), and sphingomyelin (SM). Detected compounds belonging to these
metabolite classes, showing percentage changes of the metabolites in NAFLD (S1, S2,
S3, S3+NASH for humans; GNMT-KO for animals) compared to normal liver (S0 for
humans, WT for animals) samples, and unpaired Student’s t-test p-values are
represented in Figure 2. Two further common biomarkers were also observed: Creatine
[-28% (NAFLD-normal liver), p-value 0.063; -24% (GNMT-KO/WT), p-value 0.011] and
PE(P-16:0/0:0) [+28% (NAFLD-normal liver), p-value 0.063; +17% (GNMT-KO/WT), p-
value 0.13]. In all cases, negative ion biomarker data are shown, for the most intense
ionic species observed. Percentage changes for other Rt-m/z pairs, corresponding to
the formation of different parent (e.g. cations in the positive ion mode, adducts, multiple
charges) and product (formed by spontaneous “in-source” CID) ions were broadly
consistent with these data, further validating the structural assignment of the
Metabolite biomarkers discriminating between human steatosis and NASH.
Since NASH is considered to be a significant NAFLD development, corresponding to a
more aggressive condition that may progress to cirrhosis2, the human samples were
further subjected to univariate statistical analyses, focusing on metabolite biomarkers
differentiating between the S3 (severe steatosis) and S3 + NASH (severe steatosis +
NASH) sample groups. No corresponding supervised analysis was performed for the
animal model samples, since the number of samples in the corresponding groups (4
month-old GNMT-KO, n = 4; 6.5 month old GNMT-KO, n = 3) was deemed to be too
low to provide statistically significant results. A list of significant (p < 0.1) biomarkers,
discriminating between the human S3 and S3 + NASH sample groups is provided in
Table 3. Amongst compound classes found significantly altered between the S3 +
NASH and S3 sample groups were antioxidative ether glycerophospholipids, sn-2
arachidonly diacylglycerophosphocholine, and free arachidonic acid, the latter two
species being involved in eicosanoid signaling pathways.
Targeted metabolite / lipid profiling studies have already indicated that alterations of
critical hepatic metabolic pathways, such as methionine and phospholipid metabolism
are strongly associated with NAFLD progression; this information has been collected in
independent animal and human studies13, 62-65. To the best of our knowledge, this is the
first serum global metabolite profiling study correlating with biopsy proven NAFLD
histology in a BMI matched, non-diabetic subject population. Highly controlled subject
populations are of key importance in the study of metabolic syndrome related disease
where associated factors such as obesity or diabetes can have a large effect on the
interpretation of results.
The results from parallel metabolic profiling experiments in the current work, in human
NAFLD and in the GNMT-KO NAFLD mouse model, show evidence for strong
metabolic correlation with progression of the disease. A series of common, putative
biomarker metabolites were observed in the GNMT-WT/KO animal model and human
NAFLD patients. Since the pathological characteristics of the GNMT-KO animals
closely resemble those observed in human patients across the whole NAFLD
spectrum, it is reasonable to presume that similar mechanisms may be responsible for
the common metabolic alterations observed. Additionally, the fact that the metabolites
are found in both species provides extra confidence for their selection as candidate
biomarkers for targeted validation studies. Metabolite markers forwarded from global
profiling experiments to such validation studies are often discarded at an early stage,
mainly due to the large natural variation that is found in human metabolism, as well as
sample handling effects.
Whether the metabolite biomarkers play a role in promoting NAFLD progression or are
due to secondary phenomena will require further investigation. However, the results
demonstrate the potential of the mass spectrometric based metabolomics approach to
facilitate physiologically holistic inter-species studies via the rapid identification and
quantification of common metabolites.
The groups of metabolites altered in NAFLD with respect to normal liver subjects
include organic acids, free fatty acids, phosphatidylcholine (PC),
lysophosphatidylcholine (LPC), bile acids (BAs) and sphingomyelin (SM). All these
compound classes are involved in key hepatic metabolic pathways. Creatine, found to
be decreased in both the animal (-20%, p-value 0.09) and human (-24%, p-value 0.01)
NAFLD samples with respect to their normal liver counterparts, has long been used to
assess possible liver dysfunction - lower levels indicating that metabolic reactions in
the liver are not occurring to their full capacity66-68. The potential role of fatty acid
composition in NAFLD progression has been studied previously37, 69-72, and most
recently in the work of Puri et.al. where fatty acid compositions of plasma lipids were
studied in lean controls versus obese NAFLD and NASH patients37. In the current work,
the overall fatty acid profile observed in the human NAFLD patients bares a strong
resemblance to that found in the GNMT-KO/WT animals (Figure 2a). Data collected in
various experimental models have suggested that lipid-induced cell toxicity and
apoptosis are specific to or made more severe by saturated fatty acids (SFA) when
compared to their monounsaturated fatty acid derivatives (MUFA)69. Consistent with
these reports, in this work SFA were generally increased in the NAFLD animals and
human patients whilst MUFA were either decreased or remained constant, when
compared with their normal liver counterparts. Increased Δ6-desaturase activity upon
essential fatty acids has recently been associated with NAFLD progression71. The
current study also shows evidence for Δ6-desaturase activity modulation, in both the
animal model GNMT-KO samples and human NAFLD patients, where decreased levels
of circulating linoleic (18:2n-6) and α-linolenic (18:3n-3) building blocks are observed.
One of the downstream products along this pathway, arachidonic acid, is observed in
higher quantities in the NAFLD samples. Arachidonic acid may be converted to
prostaglandins, leukotrienes and lipoxins, of which prostaglandins and leukotrienes are
potent pro-inflammatory lipid mediators. Endocannabinoids are also metabolites of
arachidonic acid, which have been recently linked with the development of hepatic
steatosis73. The lipoxygenase (LOX) pro-inflammatory metabolites of arachidonic acid,
also very recently described as correlating with human NAFLD37, were found to be
positively expressed in the GNMT-KO mice [12-HETE (+96.0%, p-value 3.4E-05), 15-
HETE (+151.7%, p-value 4.2E-06)] with respect to their WT littermates; unfortunately
the levels of these compounds were too low in the human NAFLD patients to be
measured using the current global metabolite profiling methodology.
Interestingly, two of the three top variable loadings found in the animal model PCA
model (Table 2), correlating positively in GNMT-KO with respect to WT, correspond to
phospholipids which are involved in key arachidonic acid metabolic pathways. Indeed,
all six sn-2 arachidonoyl diacylglycerophosphocholine compounds [PC(18:2/20:4),
PC(18:1/20:4), PC(18:0/20:4), PC(16:0/20:4), PC(14:0/20:4), and PC(36:5) –
containing PC(16:1/20:4)] detected were significantly (p<0.05) increased in GNMT-KO
with respect to WT (Figure 3). Although this finding was not repeated in the human
NAFLD patients as compared to normal liver controls, similar lipids [PC(14:0/20:4) and
PC(P-18:0/20:4)] were found significantly increased in the S3+NASH group with
respect to the S3 patients (Table 3). Phospholipases A1 (PLA1) and A2 (PLA2) (much
more abundant) constitute a large family of enzymes that catalyze the hydrolysis of
phospholipids at the sn-1 and sn-2 positions respectively, producing free fatty acid and
lysophospholipid74, 75. The ability of PLA2s to selectively mobilize arachidonic acid from
endogenous phospholipid storage depots has served as an important and defining
characteristic in identifying enzymes contributing to eicosanoid-mediated signaling
processes. It is well established that cPLA2α possesses high hydrolytic selectivity
towards lipids containing arachidonic acid at the sn-2 position76-79, making it an
important enzymatic candidate for intracellular eicosanoid signalling studies80, 81.
Modulation of PLA2 activity has been associated with NAFLD in the past70; the
observation that GNMT-KO and human NASH patients have elevated sn-2
arachidonoyl diacylglycerophosphocholine may also reflect phospholipase enzymatic
activity changes, perturbing key phospholipid deacylation/reacylation reactions involved
in eicosanoid signalling.
Strong overlap of the animal model samples and human NAFLD patients is also
observed in the
sn-1 monoacylglycerophosphocholine profile (Figure 2b).
Lysophospholipids are biologically active lipids that are involved in a variety of
important processes, including cell proliferation, cell migration, angiogenesis, and
inflammation82. Inflammatory effects promoted by LPC include expression of
endothelial cell adhesion molecules, growth factors, chemotaxis, and activation of
monocytes / macrophages83.
A significant increase of bile acids was also found (Figure 2c); deoxycholic acid was
found significantly higher in both the animal model samples and in the human NAFLD
patients. Elevated serum bile acids have been strongly related to liver disease in a
number of recent studies84-88.
Seven sphingomyelin type lipids: (SM 36:3), (d18:2/16:0), (d18:2/14:0), (d18:1/18:0),
(d18:1/16:0), (d18:1/12:0), and (d18:0/16:0) were found to be significantly altered in the
human NAFLD patients compared to normal liver subjects; similar tendencies were
also found in the animal model samples (Figure 2C). Sphingolipids have been
previously associated with stress and death ligand-induced hepatocellular death, which
contributes to the progression of several liver diseases including steatohepatitis,
ischaemia-reperfusion liver injury or hepatocarcinogenesis89-91.
A further series of metabolites were found to significantly discriminate between the
human S3 and S3+NASH sample groups (Table 3). The potential role of the sn-2
arachidonoyl phospholipids [PC(14:0/20:4) and PC(P-18:1/20:4)] has already been
discussed in terms of arachidonic acid storage / mobilization. Also associated with
these pathways, free arachidonic acid (20:4n-6) was decreased in the S3+NASH
group, with respect to S3 patients. This finding may reflect the increased utilization of
arachidonic acid by the NASH patients in eicosanoid synthesis and / or its reduced
mobilization from phospholipids, as suggested by the increased sn-2 arachidonoyl
species. Two lyso plasmalogen species [PC(P-24:0/0:0) and PC(P-22:0/0:0)] were
significantly decreased in the S3+NASH patients, as compared to the S3 group.
Previous evidence has shown that plasmalogens, characterized by a vinyl-ether bond
at the sn-1 position, have anti-oxidant properties92, 93. Glutamic acid was also found to
be reduced in the NASH patients, as has been previously found in other non-alcoholic
liver diseases94. A similar decrease of glutamic acid has also been reported in high-fat
diet rat livers95.
Collectively, the results from this study identify a series of putative biomarkers that
correlate with NAFLD progression. The biological implications of the changes that have
been observed are likely to be complex and difficult to predict based on global
metabolite profiling data alone. Nonetheless, the current data have allowed the
identification of a number of altered metabolic pathways potentially involved in NAFLD,
using comparisons with previously published information and drawing confidence from
the fact that very similar changes have been observed in human patients and animal
From a practical point of view, one of the most significant obstacles facing the
introduction of metabolomics technology into routine clinical practice is the inability to
produce simple-to-use kits of the type that are the product of sister omics technologies
such as transcriptomics or proteomics. Since it is highly unlikely that sufficiently specific
antibodies, or other chemical detection technologies, will be developed for metabolites
in the near future, clinical applications will have to rely on scaled-down versions of
existing technology such as that used in the current work. In this regard, mass
spectrometric metabolomics analysis has the potential to offer a number of significant
practical advantages over rival technologies. The analytical turn-around time is short (~
10 min / sample in this study) and specific, with little sample pre-treatment necessary,
allowing for the possibility of high-throughput studies that are required, for example, in
clinical trials used in drug development. Reduction of the wide-coverage analytical
methodology presented here to a targeted, quantitative or semi-quantitative platform is
the next step in the validation procedure for the putative biomarkers described.
Longitudinal studies will need to be performed, where the levels of the discriminating
metabolites are studied during NAFLD progression / regression and in response to
treatment. Moreover, it will be necessary to study much larger patient cohorts, in
particular those belonging to different age and ethnic groups. If successful, such a
procedure may be optimized to provide a robust, fast-turnaround analytical platform
that could be operated in a clinical setting for efficient, non-invasive NAFLD
In conclusion, UPLC®/MS metabolic profiling was found to be a suitable platform for the
study of NAFLD. The metabolite profiles obtained revealed NAFLD perturbations that
may be further exploited for future research in disease pathogenesis and development,
or harnessed for use in diagnosis, monitoring, and treatment development applications.
This work is supported by grants from SAF 2008-04800 and ETORTEK-2008 (J.M.M.
and M.L.M.-C.), NIH AT-1576 (S.C.L., M.L.M-C. and J.M.M.), INTEK 06-20, 07-29 and
FIT-06-101 (JB), FIS PI060085 (J.C.), HEPADIP-EULSHM-CT-205 (J.M.M., M.L.M.-C.,
J.B., Y.L.M.B., P.G., K.C., J.T. and N.V.), the FLIP UP consortium (K.C., J.T., N.V.) the
Institut National de la Santé et de la Recherche Médicale (France), the University of
Nice, the Programme Hospitalier de Recherche Clinique (CHU of Nice), Assistance-
Publique Hôpitaux de Paris, Hospitalier de Recherche Clinique, Paris region lle de
France, and charities (ALFEDIAM and AFEF/Schering-Plough to P.G.). Y.L.M.B. and
P.G. are the recipients of an Interface Grant from CHU of Nice. N.V. is supported by
Fondation pour la Recherche Médicale (FRM). Ciberehd is funded by the Instituto de
Salud Carlos III.
The contribution to this work from the technicians Ziortza Ispizua, Jessica Arribas,
Mónica Martínez and Stephanie Bounnafous is gratefully acknowledged.
Supporting Information Available
Raw data mean values and standard deviations within the different subgroups shown in
Figures 2 and 3 are included in supplementary Tables 1 and 2. This information is
available free of charge via the Internet at http://pubs.acs.org/.
No. 3011. .
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-1,4-1,2 -1,0-0,8 -0,6-0,4 -0,2 0,0
0,2 0,4 0,6 0,81,0 1,2 1,4
-2,1-1,8 -1,5-1,2 -0,9-0,6-0,3 -0,0
0,30,60,9 1,2 1,51,82,1
Figure 1. PCA scores plots discriminating GNMT-KO mice from
their WT littermates [Upper plot was obtained from negative ion
UPLC™-MS data (t: R2X = 0.28, Q2= 0.20; t: R2X = 0.09,
Q2= 0.03), lower plot from positive ion data (t: R2X = 0.23, Q2
= 0.12; t: R2X = 0.10, Q2= 0.007) ]: 4 month old WT (n = 6),
open squares; 6 5 month old WT (n = 4) open triangles; 4open squares; 6.5 month old WT (n
month old GNMT-KO (n = 4), squares; 6.5 month old GNMT-KO
(n = 3), triangles. Duplicate sample injection data are shown in
4), open triangles; 4
20:3n-3†+ n-6†+ n-9†
% Deviation (GNMTKO-WT)
-20 -100 1020
% Deviation (NAFLD-Healthy)
-100 -500 50
% Deviation (GNMTKO-WT)
-30 -20-100 1020
% Deviation (NAFLD-Healthy))
d18 1/18 0†
phosphosphingolipids, (d) bile acids in human NAFLD (S0 vs. S1, S2, S3, S3+NASH - right) and GNMT mice
(GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higher levels of metabolites in (GNMT WT vs. GNMT KO left) sera. Positive and negative percentages indicate higher levels of metabolites in
NAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-values are indicated where
appropriate: *p < 0.15, **p < 0.1, ***p < 0.05.†Metabolite identifications performed by comparison of mass spectra and
chromatographic retention times with those obtained using commercially available standards. All other identifications
were performed by accurate mass database searching with fragment ion analysis. Lipid nomenclature follows the LIPID
MAPS convention (www.lipidmaps.org). Raw data mean values and standard deviations within the different subgroups
are detailed in supplementary tables 1 and 2.
Meanpercentchangesof (a)freefatty acids,(b) sn-1 monoacylglycerophosphocholine,(c)
-40 -30-20-100 10
% Deviation (GNMTKO-WT)
% Deviation (NAFLD-Healthy)
-1000 100200 300
% Deviation (NAFLD-Healthy)
-100 -500 50 100150
% Deviation (GNMTKO-WT)
% Deviation (GNMTKO-WT)% Deviation (GNMTKO WT)
-30 -20 -100 1020 30
% Deviation (NAFLD-Healthy)% Deviation (NAFLD Healthy)
Figure 3. Mean percent changes of diacylglycerophosphocholine in human NAFLD (S0 vs. S1, S2, S3, S3+NASH -
right) and GNMT mice (GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higher
levels of metabolites in NAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-
values are indicated where appropriate: *p < 0.15, **p < 0.1, ***p < 0.05.†Metabolite identifications performed by
comparison of mass spectra and chromatographic retention times with those obtained using commercially available
standards. All other identifications were performed by accurate mass database searching with fragment ion analysis.
Lipid nomenclature follows the LIPID MAPS convention (www.lipidmaps.org). Raw data mean values and standard
deviations within the different subgroups are detailed in supplementary tables 1 and 2.
GroupN (males) Age (years)
(kg/m2) ( g/
(IU) ( U)
(IU) ( U)
9 (0) 35.0 ± 3.5 47.0 ± 1.9 23.3 ± 2.3 25.1 ± 3.2 5.0 ± 0.2 4.8 ± 0.2 1.2 ± 0.2
8 (0) 43.8 ± 3.8 45.4 ± 1.7 21.8 ± 3.5 24.8 ± 3.1 5.0 ± 0.2 6.2 ± 0.6 1.9 ± 0.4
7 (0) 41.2 ± 5.1 43.5 ± 2.0 24.9 ± 2.3 34.9 ± 3.2 5.2 ± 0.2 5.6 ± 0.7 1.6 ± 0.2
9 (0) 39.9 ± 4.7 45.5 ± 2.7 27.8 ± 2.5 40.8 ± 7.2 5.3 ± 0.2 4.8 ± 0.4 1.4 ± 0.2
S3 + NASH
9 (1) 44.6 ± 3.5 43.2 ± 1.5 32.8 ± 3.2 44.6 ± 5.6 5.5 ± 0.4 5.1 ± 0.3 1.4 ± 0.2
Table 1: Clinicopathological characteristics of the human patients included in the study. NAFLD
diagnoses were established histologically21. 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.
PC (20 4/0 0)PC (20:4/0:0)
0 25 (0 03)-0.25 (0.03)
GNMTWT/KO PCA model (negative ion data). The
standard error of the loading pcvSE generated from
the cross-validation rounds is shown in parenthesis.
†M t b lit idtifiti
mass spectra and chromatographic retention times with
those obtained using commercially available standards.
All other identifications were performed by accurate
mass database searching with fragment ion analysis (all
Principal variableloadings p inthe
f d bif
†Metabolite identifications performed by comparison of
LIPID MAPS convention LIPID MAPS convention
Table 3 Download full-text
(NASH – S3)(NASH S3)
(NASH – S3)(NASH S3)
Table 3: Biomarker metabolites found in human sera.
Mean percentage changes are provided, comparing the
S3 + NASH and S3 sample groups. Positive and
metabolites in S3 + NASH and S3 sera, respectively.
Student’s t-test.†Metabolite identifications performed by
comparison of mass spectra and chromatographic
retention times with those obtained using commercially
performed by accurate mass database searching with
f t il i ( llfragment ion analysis (all mass spectra are available on
request). Lipid nomenclature follows the LIPID MAPS
t il bl