Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with
variability in response to galactosamine hepatotoxicity
Coen, Muireann1*, Goldfain-Blanc, Françoise
2a
, Rolland-Valognes, Gaëlle2b, Walther,
Bernard2c, Robertson, Donald3, Holmes, Elaine 1, Lindon, John 1, Nicholson, Jeremy 1*
1. Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial
College London, United Kingdom.
2. a) Biologie Servier, Toxicology, 905 Route de Saran, 45520 Gidy, France; b) Institut de
Recherches Servier, Molecular Pharmacology and Pathophysiology, 125 Chemin de Ronde,
78290 Croissy Sur Seine; c) DMPK, Technologie Servier, 2527 Rue Eugène Vignat, Orleans
45000, France.
3. Research and Development, Bristol-Myers Squibb, Princeton, New Jersey, USA
* Authors for correspondence: m.coen@imperial.ac.uk / j.nicholson@imperial.ac.uk
1
Abstract
Galactosamine (galN) is widely used as an in vivo model of acute liver injury. We have
applied an integrative approach, combining histopathology, clinical chemistry, cytokine
analysis and nuclear magnetic resonance (NMR) spectroscopic metabolic profiling of
biofluids and tissues, to study variability in response to galactosamine following successive
dosing. On re-challenge with galN, primary non-responders displayed galN-induced
hepatotoxicity (induced response) whilst primary responders exhibited a less marked
response (adaptive response). A systems level metabonomic approach enabled
simultaneous characterization of the xenobiotic and endogenous metabolic perturbations
associated with the different response phenotypes. Elevated serum cytokines were
identified and correlated with hepatic metabolic profiles to further investigate the
inflammatory response to galN. The presence of urinary N-acetylglucosamine (glcNAc)
correlated with toxicological outcome and reflected the dynamic shift from a resistant to a
sensitive phenotype (induced response). In addition, the urinary level of glcNAc and hepatic
level of UDP-N-acetylhexosamines reflected an adaptive response to galN. The unique
observation of galN-pyrazines and altered gut microbial metabolites in fecal profiles of nonresponders suggested that gut microfloral metabolism was associated with toxic outcome.
Pharmacometabonomic modeling of pre-dose urinary and fecal NMR spectroscopic profiles
revealed a diverse panel of metabolites that classified the dynamic shift between a resistant
and sensitive phenotype. This integrative pharmacometabonomic approach has been
demonstrated for a model toxin, however it is equally applicable to xenobiotic interventions
that are associated with wide variation in efficacy or toxicity and in particular, for prediction
of susceptibility to toxicity.
2
Introduction
Drug-induced toxicity represents a serious clinical challenge which is associated with high
morbidity and mortality rates1. The combination of pre-clinical toxicity (animals) and clinical
adverse drug reactions (humans) accounts for up to a third of the cases of drug attrition2. A
meta-analysis of a cohort of US hospital data reported the incidence of serious and fatal
adverse drug reactions in hospitalized patients as 6.7% and 0.32% respectively3. Fatal
adverse drug reactions were found to be the sixth leading cause of death in this cohort of
hospitalized patients, highlighting the importance of this clinical challenge3. It is clear that
novel methods for understanding the mechanistic bases for pre-clinical and clinical
toxicological outcomes are still urgently required. An enhanced understanding of the
mechanisms underlying differential and idiosyncratic toxic response, together with a means
of prediction of susceptibility to drug-induced toxicity would significantly drive the
development of personalized healthcare.
The aminosugar galactosamine (galN) is not found in isolation in mammalian systems and
has widely been used as a model of acute liver injury. The key biochemical understanding of
galN-induced hepatotoxicity relates to perturbation of the hepatic uridylate pool as a
consequence of UDP-glucose depletion4-6. In addition to the primary metabolic insult, an
important role for inflammatory processes in the pathogenesis of galN-induced injury has
become clear. Inflammation is believed to result from increased gut permeability, bacterial
translocation to portal blood, arterial blood and intra-abdominal organs, which results in
endotoxemia7-9. Indeed, the extent of liver injury is greatly attenuated following colectomy7,
9
or exacerbated on co-treatment with both galN and endotoxin10. Mice are relatively
resistant to galN, which is believed to be due to a higher capacity for de novo uridylate
synthesis. However, co-administration of galN and endotoxin (lipopolysaccharide) to mice
represents a model of synergistic toxicity as sensitivity to galN is increased by several
thousand fold11.
Metabonomic approaches, utilizing advanced spectroscopic platforms coupled with
multivariate statistical modeling, have been successfully applied to study the systems level
metabolic response to a diverse range of disease and toxin-induced alterations in both preclinical and clinical studies12-16. The development of pharmacometabonomics has enabled
prediction of variability in drug metabolism and toxic outcome from baseline urinary
metabolic profiles17,
18
. This predictive approach has been applied to the study of the
3
metabolism and toxicity of acetaminophen in humans and rats19-21, and streptozotocininduced toxicity in a rat model of diabetes22. NMR-based pharmacometabonomics has also
been successfully applied in the clinic to predict toxicological outcomes to capecitabine in
patients with inoperable colorectal cancer23 and to predict response to breast cancer
treatment24.
The value of NMR-based metabolic profiling in pre-clinical toxicological assessment has
been assessed in the COMET consortium project, which carried out a large number of
toxicological metabonomic studies (approx. 150 wide-ranging toxins and treatments,25-27).
The present work on variable response to galN-induced hepatotoxicity has been carried out
as part of the second phase of the COMET consortium project (COMET-2). The COMET-2
project applied both NMR spectroscopic and mass spectrometric metabonomic platforms to
elucidate the mechanistic bases for toxicity induced by selected model hepatic and renal
toxins28-33.
We have previously identified extreme inter-individual variability in the rat in response to
galN from numerous studies which incorporated metabonomic, clinical chemistry and
histopathological assessment
34-37
. More recently we have determined distinct metabolic
phenotypes based on NMR spectroscopic profiles of liver, feces, urine and serum, which
provided mechanistic insight into susceptible (responders) or resistant (non-responders)
phenotypes to galN-induced toxicity 28, 30.
In the present study we have applied an integrative, multi-compartmental, metabonomic
and pharmacometabonomic approach38-41 to further our understanding of galN-induced
variable response phenotypes through a study of successive dosing of galN. Importantly, as
will be shown herein, we have investigated whether a second dose of galN, after an
appropriate time-period, has an effect on the primary responder/non-responder
phenotype. This systems level study of dynamic metabolic phenotypes (metabotypes) has
involved the acquisition of NMR spectroscopic profiles of urine, feces (pre- and posttreatment), liver and sera (post-treatment) from rats treated with successive doses of galN.
Multivariate
statistical
modeling
of
pre-treatment
metabolic
profiles
enabled
characterization of pre-dose urinary and fecal metabolites that discriminated with respect
to post-treatment response to galN. We also present analysis of circulating serum cytokines
and correlation of multi-compartmental metabolic profile data with cytokine profiles to
4
further understand the relationship between the primary galN-induced metabolic insult and
secondary inflammatory response. This metabonomic and cytokine study was anchored
with traditional histopathological and clinical chemistry assessment, which together
facilitated progress towards a complete understanding of differential response phenotypes.
This integrative approach has provided much mechanistic insight into variability in response
for a model toxin, however it is equally applicable to xenobiotic interventions that are
associated with wide variation in efficacy or toxicity and in particular, for prediction of
susceptibility to toxicity.
5
Experimental Procedures
Animals. Male Sprague-Dawley (SPF Crl:CD(SD)IGS BR) rats (8 wks old, 275-325 g) were
obtained from Charles River Laboratories, France (Domaine des Oncins, 69210 SaintGermain sur l'Arbresle, France) and acclimatized to the facility for 7 days. Animals were
housed in temperature (20-24 C) and humidity (40 – 70 % RH) controlled rooms with a 12hour light/dark cycle throughout the study, and handled and maintained in accordance with
the requirements of the EEC guideline (1986). During sample collection periods, rats were
housed in metabolism cages. When samples were not collected, animals were housed in
individual stainless steel wire hanging cages. Animals were provided with food (sterilized
A04C-10 feed pellets purchased from SAFE, Villemoisson-sur-Orge, France) and filtered
drinking water ad libitum throughout the study.
GalN Administration. On Day -1, animals were selected and assigned to 2 groups (control
and galN-treatment), using a stratified randomization procedure based on bodyweight.
Galactosamine hydrochloride was purchased from Sigma-Aldrich (St. Louis, MO) and
dissolved in 0.9% saline to give a free base concentration of 41.5 mg/mL and filter sterilized
using a 0.2 micron filter. On Day 1, all rats were divided into two groups and given a single
intra-peritoneal (I.P.) injection of 0 (n=8) or 415 (n=30) mg/kg galactosamine in a dose
volume of 10 mL/kg (freshly prepared dosing solution). This dose level was selected to
complement our previous in vivo metabonomic studies of galN hepatotoxicity 28-30 and was
expected to induce serum clinical chemistry and histopathological responses indicative of
acute but reversible liver injury.
After the first administration (dose 1), the animals were separated according to their clinical
pathology results into subgroups (responders and non-responders) intended for a second
galactosamine administration (dose 2) which was given after an 11-day wash-out period. All
rats were euthanized, by exsanguinations under isoflurane anesthesia, 24 hrs after dose 2.
Body weights of all rats were obtained once during the acclimation phase and on each day
of galN administration.
Sample collection.
All animals were placed in metabolism cages 24 hours before each administration, and urine
was collected over the following time periods: -24 to 0; 0 to 24h after each dosing and 24 to
48 and 48h to 72h after the first dosing. Animals were not fasted during these collection
periods. Urine samples were collected into chilled tubes containing 1 mL of 1% sodium
6
azide. All samples were centrifuged (3000 rpm, 10 min) to remove particulate matter,
divided into aliquots and stored at -70C.
Blood was collected from all animals 24 hrs post-dose (dose 1 and 2) into tubes containing a
serum separator, for clinical chemistry analysis and metabonomic analysis. Serum was
collected by rapid centrifugation after clotting and kept at 4C for clinical chemistry
measurements, while that used for NMR spectroscopic analysis was stored at -70C.
Fecal pellets were collected from each rat for 24 hours pre-dose and from 0-24 hrs postdose (dose 1 and 2) and stored at -70C until analyzed by NMR.
Necropsy was performed immediately after euthanasia (24h post-dose 2) with a section of
the left lateral lobe of the liver being immediately frozen in liquid nitrogen and stored at 70C until analyzed by NMR spectroscopy.
Clinical Chemistry Serum was analyzed for albumin, total protein, glucose, creatinine,
triglycerides, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline
phosphatase (ALP), glutamate dehydrogenase (GLDH), bile acid and total bilirubin levels
using a Hitachi 917 analyzer (Roche Diagnostics).
Serum Cytokine Profiling
Cytokines were profiled using the Meso-Scale Discovery (MSD, Gaithersburg, MD20877)
multiplex sandwich immunoassay kit for rat serum which simultaneously measures 7
cytokines (Interferon gamma, IFN-; Interleukin-1 beta, IL-1; Interleukin-4, IL-4; Interleukin5, IL-5; Interleukin-13, IL-13; Chemokine (C-X-C motif) ligand 1 / Rat cytokine-induced
neutrophil chemoattractant (rodent IL-8 like), KC/GRO/CINC; Tumor necrosis factor-alpha,
TNF-; 7-plex kit; MSD K15014C). This panel of cytokines was measured in duplicate aliquots
of serum (20 μl) and was calibrated using a 12-point calibration curve from authentic
standards of the cytokines. The data were analyzed using the MSD Workbench software
which enabled cytokine concentration (pg/ml,  standard error of the mean) for each
duplicate pair to be determined. The cytokines IL-5 and KC/GRO were reliably detected in
the sera samples; the remainder of the cytokine panel was found to be below the limit of
detection or could not be fitted to the calibration curve. Statistically significant differences
were determined between groups by calculation of a p-value from a two-tailed, non-paired
Mann Whitney t-test (Prism 5, Graphpad Software).
Histopathology All tissues selected for microscopic examination were embedded in paraffin
wax, cut at approximately 4 µm in thickness and stained with hemalun eosin saffron (HES).
Microscopic examination was performed on liver sections that were assigned a histological
score (HS) relative to control livers according to the following severity scale: HS0 = no
7
lesions, HS1 = minimal lesions, HS2 = mild lesions, HS3 = moderate lesions, and HS4 =
marked lesions30.
1
H NMR Spectroscopy of Urine
Urine samples were thawed, vortexed and allowed to stand for 10 minutes prior to mixing
aliquots (400 L) with phosphate buffer (200 L, 0.2 M containing 10 % deuterium oxide
(D2O), 3 mM 3-(trimethylsilyl)-[2,2,3,3-2H4]-propionic acid sodium salt (TSP) and 3 mM
sodium azide) and centrifuged at 13,000 rpm for 10 min. Supernatants (550 L) were
transferred into 96 well plates (1 mL, Lablinks, U.K.). The D2O provided a field frequency lock
and TSP provided a chemical shift reference (1H,  0). 1H NMR spectra were acquired on a
Bruker Avance-600 spectrometer, operating at 600.13 MHz 1H frequency and a temperature
of 300 K, using a Bruker flow injection probe (Bruker Biospin, Rheinstetten, Germany) with
an active volume of 120 μL and an automated sample-handling unit (BEST, Bruker). Samples
were transferred from the 96-well plate (with a cooling rack at 277 K) to the NMR flow
probe using a Gilson 215 automatic sample handling system (Gilson, Middleton, Wisconsin,
USA). For each sample, 500 L of urine was injected at a rate of 3 mL/min from the well into
the transfer line which was maintained at a temperature of 303 K. Urine samples were
separated from subsequent samples by approx. 500 L of push solvent (1 % sodium azide in
H2O) and each solution was separated by an air bubble. Blank samples were run after every
tenth sample to confirm that there was no cross contamination between samples. NMR
spectra were acquired using the standard one-dimensional solvent suppression pulse
sequence (relaxation delay - 90° pulse- 4 μs delay - 90° pulse - mixing time - 90° pulse acquire FID 42). For each sample, 128 transients were collected into 32 K data points using a
spectral width of 12 000 Hz with a relaxation delay of 2 s, an acquisition time of 1.32 s and a
mixing time of 100 ms. The water resonance was selectively irradiated during the relaxation
delay and the mixing time. A line-broadening function of 0.3 Hz was applied to all spectra
prior to Fourier transformation (FT).
1
H NMR Spectroscopy of Serum
Serum samples were thawed, vortexed and allowed to stand for 10 minutes prior to mixing
aliquots (200 L) with saline containing 20 % D2O (400 L). Samples were spun at 10,000
rpm for 10 minutes. Samples (500 L) were placed in NMR tubes and NMR spectra were
acquired at a 1H observation frequency of 600.13 MHz and temperature of 300 K. Chemical
shifts were referenced to that of α-glucose (H-1,  5.23) and D2O provided a field-frequency
lock. The Carr–Purcell–Meiboom–Gill (CPMG) spin-echo pulse sequence with a fixed spinspin relaxation delay, 2n of 200 ms (n = 250,  = 400 s), was applied to acquire 1H NMR
8
spectra of all sera samples. For each sample, 128 transients were collected into 32 K data
points using a spectral width of 12 000 Hz with a relaxation delay of 2 s and an acquisition
time of 1.36 s. A line-broadening function of 0.3 Hz was applied to all spectra prior to FT.
1
H NMR Spectroscopy of Water-soluble Liver Tissue Extracts
Liver tissue samples (median sample weight of 80 mg) were added to 1.5 mL cold
acetonitrile/water (50:50) and homogenized for 8 minutes using a ball-bearing tissue lyser
(QiagenTissueLyser, Retsch GmBH, Haan Germany). The homogenized samples were spun at
13,000 rpm for 10 minutes and the supernatant removed and lyophilized prior to
reconstitution in 600 L of D2O/H2O (90:10) containing TSP (1.3 mM) and sodium azide (1.4
mM). NMR spectra were acquired using the standard one-dimensional solvent suppression
pulse sequence (relaxation delay - 90° pulse- 4 μs delay - 90° pulse - mixing time - 90° pulse acquire FID). For each sample, 128 transients were collected into 64 K data points using a
spectral width of 12 000 Hz with a relaxation delay of 4 s, an acquisition time of 2.7 s and a
mixing time of 100 ms. The water resonance was selectively irradiated during the relaxation
delay and the mixing time. An exponential function corresponding to a line-broadening of
0.3 Hz was applied to all spectra prior to FT.
1
H NMR Spectroscopy of Aqueous Fecal Extracts
Fecal samples (median sample weight of 100 mg) were suspended in 3 ml D 2O,
homogenized and the resultant samples spun at 13,000 rpm for 10 mins. The supernatant
was removed and placed in 5 mm NMR tubes (500 l). TSP in D2O (50 μL, 1 mg/mL) was
added as a chemical shift reference. NMR spectra were acquired using the same protocol as
for the aqueous liver extract analyses.
Statistical Analysis of NMR Spectral Data
Full-resolution NMR data were imported into MATLAB (R2006a, Mathworks Inc., 2006). The
regions corresponding to water/HDO (δ 4.7 – 4.9) and TSP (δ -0.2 – 0.2) were removed from
all spectra. In addition, the urea –NH resonance was removed from all spectra (δ 5.6 – 6).
The spectral data (urine, feces, liver) were then normalized using the probabilistic quotient
normalization method 43 and scaled to unit variance. The serum metabolic profiles were not
normalized. Data were used to compute models using principal component analysis (PCA),
partial least squares (PLS) regression and pair-wise orthogonal-projection on latent
structures-discriminant analysis (O-PLS-DA). O-PLS-DA is a supervised pattern recognition
algorithm that pre-filters classification-irrelevant variation from data and improves
interpretability of spectral variation between classes
44-46
. O-PLS-DA extends the traditional
supervised algorithm of projection on latent structures and enables maximal information to
9
be extracted from complex spectral data. The pre-filtered, structured noise in a data set is
modeled separately from the class variation and can also be further interpreted via the
loading matrices. The loadings coefficients are constructed from a ‘back-scaled’ model: the
data are auto-scaled by division of each variable by its standard deviation and the variable
weights are also incorporated. The loadings coefficient plot of the full resolution spectral
data is represented with the weight of the variables color-coded; hence the highly
discriminatory variables are clearly identified in the plot as those with high correlation value
colors, typically red/orange. As full-resolution NMR data are used in the models, the
spectral structure is retained and the plot can be visually interpreted as a pseudo-NMR
spectrum to determine the significant metabolites that vary in response to treatment. To
prevent over-fitting of spectral data, the 7-fold cross validation method was used, from
which the cross-validation parameter Q2 was calculated 45, 46. In addition, permutation tests
were carried out to test the validity of models, where the Y vector was permuted randomly
1000 times and the p-value for Q2Y calculated.
10
Results:
Primary responders, non-responders and induced responders to galactosamine – clinical
chemistry and histopathology
Administration of galactosamine to a cohort of animals induced differential response and
enabled classification of animals as responders or non-responders to galactosamine-induced
hepatotoxicity. Phenotypic differentiation was initially achieved using clinical chemistry and
subsequently by histopathology and metabolic profiling. Response to the first dose of
galactosamine enabled discrimination of primary responders and non-responders. After a
wash-out period (11 days), the animals were re-dosed at the same level of galN and the
response to this secondary galactosamine challenge in a sub-group of primary nonresponders/responders was determined. It was found that all animals now responded to the
secondary challenge of galactosamine, i.e. all dosed non-responders had become
responders and all responders remained responders.
Sera ALT values for controls and treated animals are presented in Figure 1 for both galN
administrations. Following dose 1 (Dose 1, Figure 1, n=30 treated animals, n=8 controls), the
sera ALT levels, enabled classification of 14 animals (47%) as non-responders to galN
(denoted the NR1 group) and 16 animals (53 %) as responders to galN (denoted the R1
group, p<0.001 relative to controls). The administration of a second dose of galactosamine
resulted in significantly raised ALT levels for all animals (Dose 2, Figure 1, n=13). The animals
selected for administration of dose 2 represented 9 primary non-responders (NR1) and 4
primary responders (R1). The animals selected for the second dose were reduced in number
due to the death of 8 primary responders from day 3 post-dose following dose 1 (1 rat was
found dead on day 3, 6 rats on day 4 and 1 rat on day 5). Such mortality was not expected
and not seen in our earlier, 24 hour time-course studies of galN toxicity
28-30, 34-37
and may
reflect the longer time-course of this study. Keppler and colleagues have shown that
histological analysis of galN-induced hepatotoxicity revealed a more severe lesion 2 days
post-dose with recovery visible from day 7 to 12 47. The longer duration and scale of this
study has revealed a cohort of responders who cannot recover from the toxic effects of
galN, and this finding may warrant further investigation.
On administration of the second dose of galN, the cohort of animals that were primary nonresponders to galN (NR1) were found to respond to galN, and will be termed induced
responders (denoted the iR2 group, p<0.01 relative to controls). This discriminates the iR2
11
population from the cohort of responders who responded to galN following both dose
administrations (R1 and R2). The ALT response of R2 was seen to be significantly less than
that of iR2 (p<0.1), which is suggestive of an adaptive response to galactosamine between
primary responders (R1) and secondary responders (R2).
Further serum clinical pathology measures of liver function were assessed (Supplementary
Figure 1), which included aspartate aminotransferase (AST), alkaline phosphatase (ALP)
glutamate dehydrogenase (GLDH), total bilirubin and bile acids. The sera levels of the
transaminases, which reflect the degree of cellular necrosis, were significantly elevated for
all responders to galactosamine and non-responders were seen to have levels
indistinguishable from controls. Serum GLDH levels were also significantly elevated for all
responders to galactosamine, suggestive of mitochondrial damage. The levels of total serum
bilirubin and bile acids were also significantly increased in responders to galactosamine,
which suggests impaired hepatic biliary function as a result of galactosamine-induced
necrosis. The serum transaminases, GLDH, bilirubin and bile acid levels also reflected an
adaptive response in secondary responders (R2) as a less dramatic increase was seen
following the secondary challenge with galactosamine, in comparison to primary responders
or induced responders.
Histopathological analysis was carried out on terminal liver sections, 24 hours after the
second dose of galN, on completion of the study. GalN induced a minimal to marked
hepatocellular necrosis, with a multifocal mixed inflammatory cell infiltrate for all treated
animals. No histopathological lesions were identified in the control population. The scores
representing the severity of galN-induced necrosis are given in Supplementary Figure 1
(Table inset).
The iR2 group was found to have the most marked necrotic response, with histopathological
necrosis severity scores of 3 (HS3, n=4, moderate) and 4 (HS4, n=5, marked). In contrast, the
cohort of secondary responders (R2) had necrosis scores of 2 (HS2, n=2, slight) and 3 (HS3,
n=2, moderate). This complements the observation of a less marked increase in the serum
clinical pathology parameters for secondary responders to galN and suggests a role for an
adaptive mechanism for the primary responder phenotype upon re-challenge.
Metabonomic differentiation of primary responders and non-responders to galactosamine
hepatotoxicity
12
Multivariate modeling (PCA/PLS/O-PLS-DA) of 1H NMR spectroscopic profiles enabled
metabolic differences between non-responders, responders and controls to be determined
in urine, serum, fecal and liver extracts. The discriminatory metabolites identified from such
a multivariate modeling approach of systemic response to galN exposure are discussed
below and summarized in Table 1.
1
H NMR spectroscopic-based profiling of urine enabled exploration of the different response
phenotypes to galN exposure, namely controls, non-responders (NR1) and primary
responders (R1) following dose 1 and controls, induced responders (iR2) and secondary
responders (R2) following dose 2. The control and non-responder metabolic profiles
following dose 1 were very similar and could not be discriminated. Importantly, no galN or
galN metabolites were detected by 1H NMR spectroscopy in the non-responder urinary
spectral profile. In contrast, the primary responders to galN showed increased levels of
galN, N-acetylglucosamine (glcNAc), galN-pyrazines, urocanate and depleted levels of Nmethylnicotinamide (NMND), N-methylnicotinic acid (NMNA), citrate and 2-oxoglutarate
relative to controls/non-responders. The median spectral profiles for urine collected 24h
post-administration of galN for each of the response phenotypes are given in Figure 2a,
showing the galN and glcNAc anomeric proton spectral resonances. The urinary level of
glcNAc, assessed from integration of the NMR spectral resonance (Figure 2b), was positively
correlated with galN-induced toxicological outcome, as it was observed in the urine of
responders, whereas it was absent in the urine of non-responders.
The time-course of metabolic changes induced by galN was also explored in both 48 h (urine
collection from 24-48 h) and 72 h (urine collection from 48-72h) urinary profiles following
the first administration of galN. The non-responders (NR1) to galN were indistinguishable
from controls, whereas marked metabolic changes were identified across the time-course in
responders. At 48 h, galN and its metabolites were visible, but at much lower levels than the
24 h urine collection profile. In addition, increased levels of NMND, betaine, urocanate and
bile acids were seen together with a reduction in the levels of NMNA, citrate, 2-oxoglutarate
and creatinine in responders. At 72 h post galN administration, responders were seen to
have continued elevation of NMND, betaine, urocanate and depletion of NMNA. Urinary
bile acid levels were significantly raised, albeit at lesser levels than seen at 48 h post-dose.
Metabolic changes that were unique to the later 72 h time-point included decreased urinary
levels of lactate, alanine, tyrosine and creatine in primary responders to galN (Table 1b).
13
Fecal samples collected 24 h post-administration of galN enabled discrimination of fecal
extract profiles representing the diverse galN-related phenotypes (Figure 3). The NMR fecal
extract spectral profiles of primary non-responders to galN revealed the presence of galNpyrazine metabolites, which were absent for all responders. An O-PLS-DA model
discriminating the fecal extract NMR spectral profiles of primary non-responders and
responders to galN is presented in Supplementary Figure 2. In addition to the presence of
galN pyrazines in non-responders, higher levels of acetoin (3-hydroxy-2-butanone) and
lower levels of N-butyrate were also seen in comparison to responders. The profiles of
responders largely resembled control profiles and could not be significantly differentiated
from controls using multivariate modeling approaches or on visual inspection of the NMR
spectroscopic profiles. Hence, the metabolic differences identified from fecal extract NMR
spectroscopic profiles were unique to primary non-responders to galN-induced toxicity.
1
H NMR spectroscopic profiles of sera collected 24 h after the initial administration of galN
enabled discrimination of responder and non-responder phenotypes. Responders to galN
(primary responders, R1) were found to have increased serum levels of betaine, tyrosine,
and decreased levels of glucose, lipid moieties, histidine, choline/phosphocholine and
alanine. The sera metabolic profiles of non-responders could not be distinguished from
controls.
Induced response to galactosamine in primary non-responders following a secondary
challenge
Following a secondary administration of galN, the 24 h urinary metabolic profiles for
induced responders (iR2) were comparable to the profiles seen for responders following
dose 1, reflecting a phenotypic switch and differential toxic outcome. The iR2 urinary
profiles show the presence of galN, glcNAc, galN-pyrazines and urocanate and a reduction in
NMND, NMNA, citrate and 2-oxoglutarate relative to control urinary profiles. The median
urinary spectra showing galN and glcNAc anomeric proton resonances present in the iR2
group are given in Figure 2a. Figure 2b presents the urinary NMR integral for the glcNAc
resonances, the dramatic increase for iR2 compared to control/non-responder levels
supports the phenotypic switch from non-responders to responders. In summary, the
presence of glcNAc in the urine of responders and its absence in the urine of non-
14
responders correlated well with clinical chemistry and histopathological measures of acute
liver injury.
The fecal extract metabolic profiles also reflected this phenotypic switch as the induced
responders were now seen to have a metabolic profile which largely resembled the primary
responders, in that galN-pyrazines were absent from the feces (Figure 3) and acetoin and Nbutyrate were present at control levels. The sera metabolic profiles also reflected the
phenotypic switch from non-responders to induced responders in that increased levels of
tyrosine and betaine and reduced levels of glucose, lipid moieties, choline/phosphocholine
and histidine were seen relative to controls (or primary non-responders).
1
H NMR spectroscopic profiles of aqueous extracts of terminal liver tissue collected 24 hours
post-dose were analyzed to identify metabolic discrimination of controls from responders.
NMR spectroscopic profiles of all responders to galN (both iR2 and R2 groups) revealed a
marked depletion of hepatic glycogen and glucose together with considerable depletion of
glutathione, lactate, inosine and adenosine relative to controls. In addition, the presence of
the galN-derived metabolites, UDP-galN/UDP-glcN, UDP-galNAc, UDP-glcNAc, glcNAc-6-P
and glcNAc-1-P was apparent for all responders relative to controls. In addition, increased
levels of hepatic uridine, uracil, tyrosine and bile acids were seen for all responders relative
to controls.
Adaptive response to galactosamine in primary responders following a secondary
challenge
Clinical chemistry and histopathological measures (Figure 1 and Supplementary Figure 1)
showed a less marked response in responders to the second galN challenge (R2) compared
to induced responders (iR2) or primary responders (R1). This suggests an adaptive response
to galactosamine following a secondary challenge in responders.
Visual inspection of the urinary spectra or multivariate statistical modeling revealed a lower
level of glcNAc, an absence of urocanate and higher level of NMND, NMNA, 2-oxoglutarate
and citrate in R2 compared to R1 or iR2. The urinary level of glcNAc (Figure 2b) supports the
adaptive response to galN in secondary responders in that lower levels correlate with less
marked toxicological outcome. The sera NMR spectral profiles also reflected the lesser,
15
adaptive response in R2 as the profiles were closer to control profiles, in that there was a
less marked increase in tyrosine, and a less marked decrease in glucose.
The adaptive response to galactosamine was also investigated through analysis of NMR
spectroscopic profiles of aqueous liver tissue extracts. The metabolic profiles could be
discriminated based on the severity of response/degree of histopathological necrosis, in
that animals with HS4 (iR2) could be distinguished from those with lower histopathological
severity scores (HS2 and HS3, R2). Those R2 with an end-stage necrotic score of HS2 and
HS3 were found to have higher levels of UDP-galNAc/UDP-glcNAc than those with a necrotic
score of HS4 (iR2). Additionally, the secondary responders were found to have lower levels
of tyrosine and bile acids present in the hepatic profiles compared to those induced
responders with more marked histopathological necrosis scores (HS4, iR2).
Serum cytokine profiles of primary responders, non-responders and induced responders
to galactosamine
Cytokine profiling of rat serum, collected 24 hours post-dose, found statistically significant
differential levels of two cytokines, interleukin-5 (IL-5) and KC/GRO (IL-8 related protein in
rodents), which correlated with the variability in response induced by galN. The mean sera
concentration (pg/mL  standard error of mean) for these two cytokines are shown in
Figure 4. IL-5 is increased in primary responders to galactosamine (R1) and in induced
responders (iR2), whereas is not increased in non-responders (NR1) or secondary
responders to galN (R2). Hence, the serum concentration of IL-5 not only reflects toxic
response to galN but is also reflective of the adaptive response in secondary responders.
The concentration of KC/GRO is increased in all responders to galN; namely primary,
secondary responders and induced responders. The systemic cytokine levels mirror the
clinical chemistry measures of liver function and hepatic necrosis in each of the distinct
response phenotypes.
The systemic cytokine concentrations were correlated with the hepatic metabolic profiles to
assess the link between inflammatory and hepatic metabolic markers related to toxic
outcome. The correlation of KC/GRO levels with the liver aqueous extract spectra via a PLS
regression model revealed clear separation of the cross-validated scores of controls from
responders (iR2, R2, Supplementary Fig 3). The loadings plot from this model revealed
hepatic metabolites that inversely correlated with KC/GRO serum concentration included
16
inosine, glucose and glycogen, which are markedly depleted in the NMR spectroscopic
profiles of liver in responders to galN. The correlation of IL-5 levels with the liver aqueous
extract spectra revealed a strong correlation between serum IL-5 concentration and hepatic
tyrosine and bile acids, both of which are markedly elevated in response to galN
administration.
Pharmacometabonomic study of variability in galactosamine-induced hepatotoxicity
Multivariate statistical modeling of pre-treatment urine (collected for 24 h prior to
administration of galN dose 1 and 2), representing non-responder and induced responder
outcomes, enabled the metabolic alterations associated with this phenotypic shift to be
deduced. Statistical modeling of the urinary metabolic profiles enabled determination of the
basal metabolites that altered in response to an initial galN challenge. An O-PLS regression
model computed from pre-dose urinary spectra representing non-responders (NR1) and
induced responders (iR2) and the histopathological necrosis severity score is given in Figure
5. The scores plot, showing the cross-validated predictive component scores (Tcv) and the
orthogonal scores (TYosc) show significant separation of pre-dose non-responders (red) and
induced responders (blue) urinary profiles (in Tcv). The accompanying model statistics
display a high predictive power (Q2Y 0.52) and ‘goodness of fit’ (R2Y 0.89) with a
permutation test (500 times, p-value < 0.002) further confirming a robust statistical model.
The loadings coefficient plot (Figure 5b) shows the baseline metabolites relevant to
differential toxic outcome; which included resonances in the N-acetyl region of the
spectrum which represent the N-acetyl moieties of most probably 1-acid glycoproteins and
resonances which have been tentatively assigned to the short chain fatty acid; hexanoic
acid. The N-acetyl moiety resonances were decreased in pre-dose urine of induced
responders and hexanoic acid was increased in intensity in the pre-dose urine of induced
responders (Table 1c).
The collection of pre-dose feces also enabled investigation of baseline fecal metabolic
alterations related to the phenotypic switch from non-responders to induced responders.
The induced responders with the most marked histopathological severity score (HS4) were
clearly discriminated based on an O-PLS-DA model of pre-dose fecal profiles (Figure 6) and
on visual inspection of the spectra. However, discrimination of the entire cohort of induced
responders including those with histopathological severity scores of 3 was not possible due
17
to a degree of variation in the fecal metabolic profiles from animals with the less severe HS3
outcome. The O-PLS-DA model scores plot, showing the cross-validated predictive
component scores (Tcv) and the orthogonal scores (TYosc) show significant separation of
pre-dose NR1 (red) and iR2 (blue) fecal extract profiles relevant to the marked HS4 outcome
(Figure 6a). The corresponding O-PLS-DA loadings coefficient plot (Figure 6b) revealed
decreased levels of -aminobutyrate (GABA), -ketoisovalerate and lactate in pre-dose fecal
profiles of induced responders (Table 1c). These metabolic changes were also identified in
PLS regression models of the pre-dose data modeled against the HS outcome or serum ALT
value.
Discussion
The metabolic consequences of multiple challenge with the hepatotoxin, galactosamine and
the metabolic discrimination of variable response phenotypes were elucidated in this study.
The application of an advanced NMR-based metabonomic platform in combination with
serum cytokine measurements and conventional clinical chemistry and histopathology
metrics have provided a systems-wide window into toxic outcome. Metabolic profiles that
simultaneously reflect both the endogenous and xenobiotic metabolome have been
identified for non-responders and responders to galactosamine. A primary finding was that
all animals that failed to show a toxic response to galN, when challenged with a subsequent
identical dose then showed the expected toxic response.
Primary responders and non-responders to galN-induced toxicity – Xenobiotic profile
This work supported earlier findings of unique metabotypes that reflected marked interanimal variability in susceptibility to galactosamine-induced hepatotoxicity
28-30, 34-37
. The
discrimination of response phenotypes was based on the urinary presence of galN
metabolites in responders and the fecal presence of galN metabolites in non-responders.
This has been confirmed in this study where non-responders and responders were identified
following the first dose of galactosamine, with galN metabolites present in the urine of
responders and the feces of non-responders. A secondary challenge with galactosamine
resulted in a phenotypic switch of non-responders to responders and showed that galN
metabolites were now present in the urine and absent from the feces of induced
responders. The presence of urinary glcNAc was shown to correlate strongly with
histopathological and clinical chemistry assessment of galN-induced toxic outcome, which
18
corroborates earlier findings
28, 30
. GlcNAc was identifiable in the urinary profiles of all
responders to galN and is present at highest levels in the urine of primary responders and
induced responders who display the most marked toxic response to galN with respect to
clinical chemistry and histopathological measures. Hence, the correlation of glcNAc with
toxic outcome was shown for differential and dynamic galN-induced hepatotoxicity
phenotypes. Importantly, urinary glcNAc represents a continuous measure for assessing the
extent of galN-induced hepatic uridylate pool depletion.
Responders to galN-induced hepatotoxicity
The metabolic alterations associated with primary responders to galN in urine, serum and
fecal extracts have been summarized in Table 1 for time-points of 24, 48 and 72 h postdose. GalN and its metabolites, glcNAc and galN-pyrazines, were present in the urine of
responders at both 24 and 48 hour post-dose time-points. Urinary levels of Nmethylnicotinamide (NMND) were initially depleted in responders 24 h post-dose but then
increased at 48 and 72 h post-dose suggesting early depletion may be related to demands
on energy metabolism, which was temporal with a compensatory shift towards cellular
recovery. Depletion of urinary levels of citrate and 2-oxoglutarate was also observed in
responders to galactosamine, which suggested a general disturbance of TCA cycle
bioenergetics. The galN-induced elevation of urinary urocanic acid was previously identified
37
and attributed to the inhibition of histidine breakdown at the level of urocanic acid
hydratase. This metabolic alteration is unique to galN-induced hepatotoxicity and has not
been reported with respect to metabonomic analyses of other treatments/toxins. Urocanic
acid was not present in control urine, but was visible at 24 h and was dramatically increased
at both 48 and 72 h post-dose for primary responders. GalN is also known to inhibit tyrosine
aminotransferase (TAT) activity
48, 49
and this was supported in this work through the
identification of increased serum and hepatic levels of tyrosine in responders 24 h postadministration of galN (dose 1 and 2). This was complemented by the observation of
increased urinary levels of tyrosine in responders at 72 h post-dose, which suggested the
early inhibition of TAT and subsequent increased hepatic and serum pool of tyrosine could
be identified at a later stage in the urinary profile. The complementary information
obtained from metabolic profiling of multiple biofluids, was further highlighted in the
observation of increased levels of serum betaine at 24h post-dose and the urine at later
time-points of 48 and 72h. The serum increase in betaine was accompanied by a decrease in
choline/phosphocholine, which suggested upregulated hepatic oxidation of choline (choline
19
dehydrogenase and betaine aldehyde dehydrogenase) to produce betaine. In responders to
galN, decreased levels of serum lipid moieties were also observed, which complemented
earlier findings of increased levels of hepatic lipid triglycerides 30. GalN is known to induce
hepatic steatosis which is believed to result from impaired formation and secretion of
lipoproteins 50, 51 as a result of depleted UTP.
NMR spectroscopic profiles of aqueous extracts of hepatic tissue enabled determination of
both the xenobiotic and endogenous consequences of galN administration. The galN
metabolites, UDP-galN / UDP-glcN, UDP-galNAc, UDP-glcNAc, glcNAc-1-P, glcNAc-6-P, were
identified in the metabolic profiles of hepatic aqueous extracts for all responders to galN.
Severe depletion of hepatic glycogen and glucose were also identified in response to galN
toxicity, attributable to the well-established galN-induced depletion of UDP-glucose,
corroborating previous findings 30. The depleted hepatic level of glucose and glycogen was
supported by a reduced serum glucose level, which together suggested a significant
disturbance in energy metabolism. This disturbance of energy homeostasis, would result in
a depletion of ATP which was supported by the observation of reduced hepatic adenosine
levels. A reduction in ATP was also suggested through observation of decreased hepatic
lactate, which suggested upregulation of gluconeogenesis had occurred in an attempt to
compensate for impaired energy reserves. Decreased levels of hepatic glutathione were also
apparent, which corroborated findings from the published literature
52
. However, GalN-
induced depletion of glutathione is not believed to be critical with respect to toxic outcome
and is seen as reversible, given the lack of electrophilic galN metabolites and associated
oxidative stress insult. The primary metabolic driver in galN-induced hepatotoxicity is as a
result of depletion of UDP-glucose and the overall uridine nucleotide pool. NMR
spectroscopic profiling of hepatic extracts also revealed increased bile acids 24 h post-dose
and increased urinary bile acids at 48 and 72 h post-dose in responders to galN, which
supports serum clinical chemistry measures. This is expected as a result of galN-induced bile
duct proliferation, depleted UDP-glucose and concomitant depletion of UDP-glucuronic
acid. This finding also complements our earlier work which involved sensitive profiling of
serum bile acids using an ultra-performance liquid-chromatography-mass spectrometry
platform (UPLC-MS), where significant elevations of glycine- and taurine-conjugated bile
acids were found in serum 24 h post-dose 33.
Non-responders to galN-induced hepatotoxicity
20
The fecal extract metabolic profiles proved informative for identification of metabolic
differences in non-responders to galN, which highlighted the value of profiling multiple
biological matrices to obtain a systems level overview of toxic insult. Fecal extract profiles
from controls and responders did not show significant discriminatory metabolic differences.
However, the fecal extract metabolic profiles were unique for non-responders to galN in
that galN-pyrazines were present together with endogenous metabolic perturbations. The
post-dose fecal extract profiles of primary non-responders to galN showed an elevation in
levels of acetoin and reduction in levels of N-butyrate. The fecal presence of N-butyrate, a
short chain fatty acid, is believed to predominantly originate from colonic microfloral
metabolism of dietary fibre
53, 54
. The reduced fecal levels of N-butyrate in non-responders
suggested an alteration in gut bacterial metabolic capacity in response to galN challenge,
relative to responders. This was further supported by the identification of altered fecal
levels of acetoin in primary non-responders to galN. Acetoin (3-hydroxy-2-butanone) is a
microbial carbohydrate fermentation product produced via the butanediol pathway in
gram-positive microorganisms. The presence of acetoin is used in the Voges-Proskauer test
55
to identify bacteria such as Enterobacter and Klebsiella 56. Acetoin has not previously been
identified in NMR-spectroscopic metabolic profiles of rat fecal extracts and here, potentially
represents a metabolic marker of gut-microbial function reflecting resistance to galN
toxicity. It was notable that the galN-induced fecal metabolic disturbances are only seen for
non-responders which strongly implicates the gut microflora in determination of the nonresponder phenotype. One possibility is that the microflora of the non-responders were
able to utilize galN as a carbon substrate, the end-products of which may enter normal
endogenous metabolism 57-59.
Adaptive response to a secondary galN challenge in primary responders
The confirmation of galN-induced responder and non-responder metabotypes has been
further extended through identification of an adaptive response to a secondary
galactosamine challenge in primary responders. The serum pathology parameters and
histopathological necrosis severity scores showed that the secondary galN challenge
resulted in a less marked toxicological response in responders (R2, relative to iR2 or R1),
suggestive of an adaptive response to galactosamine. The clinical chemistry and
histopathological evidence was supported by metabolic profile changes in urine, serum,
hepatic and fecal extracts that reflected this compensatory, adaptive ability. The urinary
profiles of secondary responders (R2) revealed lesser levels of glcNAc in comparison to
21
primary responders or induced responders, which correlated with the less marked
toxicological outcome. Additional urinary markers that reflected an adaptive response
included the absence of urocanate in secondary responders, which was visible in induced
responders 24h post-dose. In addition, a lesser depletion of urinary NMND, NMNA, citrate
and 2-oxoglutarate were seen for the secondary responders in comparison to induced
responders. The sera profiles also reflected the lesser, adaptive response in R2 as the
tyrosine increase and the glucose decrease in secondary responders were less marked than
for induced responders. The hepatic metabolic profiles also provided mechanistic insight
into this adaptive response with the presence of lesser levels of tyrosine and bile acids and
greater levels of UDP-galNAc/glcNAc in secondary responders relative to induced
responders. The observation of greater levels of UDP-galNAc/glcNAc in the liver of
secondary responders highlights a greater capacity to metabolize galN, indicative of a
compensatory increase in the basal uridylate pool. It has previously been shown that lower
hepatic levels of UDP-galNAc/glcNAc correlated with the most marked histopathological
necrosis in responders to galN hepatotoxicity30. A compensatory increase in synthesis of
uridine phosphates following galN challenge was reported to approximate to a rise to 150%
of control levels at 30h following a single dose of galN (400 mg/kg i.p. 60). Adaptation to galN
has been reported following repeated dosing of galN over a time-course of three months,
with a minimal inflammatory response, an insignificant rise in plasma enzymes and
enhanced uridylate biosynthesis and hepatic uridine nucleotide pool levels 4. Here, the
application of metabolic profiling to elucidate an adaptive response to galN with a short
time frame (11 days) between successive dosing of galN has been clearly shown.
Systemic inflammatory response to galN-induced hepatotoxicity
Previous research has shown that inflammatory processes plays a crucial role in galNinduced injury (following the primary metabolic challenge), which is believed to be
associated with bacterial translocation from the gut as a result of increased gut permeability
and decreased motility7-9. Indeed, gram-negative, enteric bacteria are often implicated in
the infectious and septic complications associated with clinical and experimental models of
acute liver failure61. Kasravi and colleagues have shown a significant rate of enteric bacterial
translocation from the gut to the systemic circulation and intra-abdominal organs (liver,
spleen and mesenteric lymph nodes), in a galN model (1.1 g/kg, i.p.) of acute liver injury 8.
The rate of bacterial translocation and the associated degree of galN-induced toxicity can be
attenuated through supplementation with probiotics such as lactobacilli 62-65. GalN is known
22
to activate liver macrophages (Kupffer cells), which release inflammatory mediators such as
IL-1, IL-6 and TNF-, that are believed to enhance susceptibility to galN-induced injury10, 66,
67
. Indeed, macrophages are believed to play a crucial role in the translocation of intestinal
bacteria to extra intestinal sites67. In order to gain an enhanced understanding of the role of
inflammation in differential response to galN multiplex cytokine profiling was utilized, and
correlated with traditional clinical chemistry, histopathology and hepatic metabolic profiles.
This enabled a systems-wide assessment of the pathological, morphological, metabolic and
inflammatory characteristics of galN-induced hepatotoxic insult. The levels of the serum
cytokines, IL-5 and KC/GRO (IL-8 like in rodents), correlated with the degree of toxic
response, in that they were elevated in responders to galactosamine.
KC/GRO is a chemoattractant for neutrophils released by hepatic Kupffer cells and is
stimulated by lipopolysaccharide, IL-1 or TNF- administration 68. KC-GRO has been shown
to be elevated in the serum of patients with sepsis 69. In this study, KC/GRO serum levels
were significantly increased in primary, induced and secondary responders to galN and were
present at control levels in non-responders. Hence, it is likely that the serum levels of
KC/GRO reflect galN-induced activation of Kupffer cells and translocation of bacteria. IL-5 is
a Th2 cytokine, which attracts and activates eosinophils. The level of serum IL-5 was
significantly increased for primary and induced responders to galN and also reflected the
adaptive ability of secondary responders, as concentrations were significantly lower in
secondary responders than in induced responders. The lower IL-5 level in secondary
responders suggests a less marked inflammatory response which supports the lesser extent
of hepatic necrosis (determined from clinical chemistry and histopathology) and lesser
hepatic UDP-glucose depletion (determined from the urinary level of glcNAc and the hepatic
levels of UDP-hexosamines).
In addition, a PLS regression model of the sera cytokine concentrations with the hepatic
metabolic profiles, provided a novel means of linking the hepatic metabolic insult with the
resultant inflammatory response. The level of serum KC/GRO was found to negatively
correlate with hepatic glucose, glycogen and inosine, which are markers of galN-induced
hepatotoxicity. The level of serum IL-5 was found to positively correlate with hepatic
tyrosine and bile acid levels which are markers of galN-induced inhibition of TAT and
impaired biliary function respectively. Indeed, the galN-induced alteration in microfloral
population balance has previously been postulated to result from factors which include
impaired biliary secretion 70. The successful correlation of NMR-based spectral profiles and
23
plasma cytokine levels has previously been shown in a rodent model of parasitic infection 71
to highlight covariance in perturbed metabolic pathways and immune response.
Application of pharmacometabonomics to classify susceptibility to galN hepatotoxicity
Application of an NMR-based metabonomic approach to differentiate marked and subtle
variability in post-treatment toxic outcome phenotypes has been extended to encompass
modeling of pre-treatment profiles. This was achieved through the multivariate modeling of
pre-dose NMR metabolic data, prior to administration of an initial dose of galN and prior to
administration
of
a
secondary
challenge.
This
represents
the
application
of
pharmacometabonomics to assess the disturbance in baseline metabolic status associated
with a shift from a resistant to a sensitive phenotype. This approach enabled discrimination
of the urinary and fecal pre-dose metabolic profiles of non-responders to galN and induced
responders to galN. The urinary metabolites that classified sensitivity to galN included Nacetylated moieties and the short-chain fatty acid, hexanoic acid. Increased urinary levels of
N-acetylglycoproteins, have been identified in the post-dose urine of responders to galN at
time-points of 72 to 144h37. These characteristic, N-acetyl resonances have been identified
in human serum using NMR spectroscopy (and in unpublished data on rat urine) and
attributed to the N-acetyl moieties of 1-acid glycoproteins (1-AGP,72, 73). In this study, we
observed a decrease in these moieties in the pre-dose urinary metabolic profiles of induced
responders and hence, they represent a predictive marker of the phenotypic switch from
non-responders to induced responders. These acute phase proteins, which are synthesized
and excreted by hepatocytes, are thought to have a protective effect on lethality in the
LPS/galN-sensitized murine model, which is believed to be linked to prevention of
hepatocyte apoptosis74. The reduced baseline urinary levels in pre-treatment induced
responders may represent a lower protective capacity and hence may represent a marker
for enhanced susceptibility to a second dose of galN.
The application of pharmacometabonomics to model multivariate fecal profiles revealed
that pre-dose levels of -aminobutyrate (GABA) classified susceptibility to galN in the shift
from non-responders to susceptible, induced responders. The ubiquitous, non-protein
amino acid, GABA, is synthesized from glutamate and is known to be produced by
microorganisms in the gut, for example by Escherichia Coli75. GalN-induced acute liver
failure has also been utilized as a model of hepatic encephalopathy and hepatic coma and
has been linked with associated changes in serum and brain GABA levels, however, the
mechanistic bases for these alterations remain unclear 76, 77. It is probable that alterations in
24
fecal GABA content reflect differential gut microfloral metabolism in non-responders
compared to responders, as has recently been identified in a rat model of bariatric
surgery78. Lower levels of fecal lactate were also observed, which suggests a difference in
the fermentation of carbohydrates between pre-dose non-responders and pre-dose induced
responders. The perturbed microfloral population of induced-responders may have lost the
ability to utilize galN as a carbon source, which may explain the lesser production of lactate.
A lower fecal level of -ketoisovalerate was also observed pre-dose for induced responders
which suggests a disturbance in valine, isoleucine and leucine metabolism, which may also
be linked to differential gut microfloral populations and associated co-metabolism. The
urinary and fecal pre-dose data suggestive of altered gut microbiome function was
supported by the post-dose differentiation of primary non-responders based on the
microbial products, acetoin and N-butyrate. Taken together, this panel of metabolic
markers, in pre- and post-treatment urine and feces, classified both differential response to
galN and the translation from a resistant to a sensitive phenotype.
Conclusion
The successful application of metabonomics for temporal modeling of differential toxic
outcome with respect to a model hepatotoxin has been shown. The systems-level metabolic
data generated is complementary to traditional clinical chemistry and histopathological
approaches. We have integrated traditional pathological approaches with metabolic profile
and cytokine analysis to understand primary metabolic injury and secondary inflammatory
responses in this model of acute liver injury. This multi-disciplinary approach has generated
novel mechanistic insight into variable response phenotypes and toxic outcome.
Furthermore, a pharmacometabonomic approach, in the context of a repeat dosing
regimen, enabled classification of pre-treatment metabolic profiles with respect to variation
in the susceptibility to galN-induced toxic insult. The simultaneous identification of both
exogenous (toxin) and endogenous metabolites in multiple biological matrices has enabled
both variation in the metabolic fate of a drug and its associated endogenous consequences
to be elucidated. The identification of both pre-and post-dose discriminatory metabolites
enabled hypotheses for the underlying mechanistic bases related to variability in response
to be generated. Importantly, this approach is translatable to the clinical setting and serves
as an exemplar for future studies of clinical compounds associated with variability in toxic
response and efficacy. The predictive power of this approach, together with the ability to
25
simultaneously monitor the metabolic fate of a drug and its endogenous consequences, will
prove highly relevant with respect to the future development of personalized healthcare.
Acknowledgements
The MRC Integrative Toxicology Training Partnership (ITTP) is acknowledged for financial
support to M.C. The galactosamine research work was carried out as part of the COMET 2
consortium project (directed by Professor JK Nicholson) and received financial support from
Pfizer, Bristol-Myers-Squibb, Sanofi-Aventis, Servier, and Waters Corporation. The COMET
Steering Committee are gratefully acknowledged and include the authors and the following:
L.D. Lehman-McKeeman, N. Aranibar, G.H. Cantor, M.D. Reily, C.M. Rohde, E.S. Harpur, L.
Boyling, A. Amberg, R.S. Plumb, T.D. Ebbels, H.C. Keun, E.J. Want. Mr Michael Kyriakides,
Biomolecular Medicine, is acknowledged for assistance with ‘spike-in’ NMR experiments to
confirm the identity of certain metabolites. The authors thank Dr Johann Trygg for use of
the O-PLS-DA algorithm.
Abbreviations:
O-PLS-DA, orthogonal-projection on latent structures discriminant analysis; GlcNAc, Nacetylglucosamine; GlcNAc-1-P, N-acetylglucosamine-1-phosphate; GlcNAc-6-P, Nacetylglucosamine-6-phosphate; GalNAc, N-acetylgalactosamine; GalN, galactosamine;
GalN-1-P, galactosamine-1-phosphate; GABA, -aminobutyrate; UDP, uridine 5′diphosphate;
UDP-GlcNAc,
UDP-N-acetylglucosamine;
UDP-GalNAc,
UDP-Nacetylgalactosamine; UDP-Glc, UDP-glucose; UDP-gal, UDP-galactose; UPLC-MS, ultra
performance liquid chromatography coupled to mass spectrometry; UTP, Uridine 5'triphosphate; UMP, Uridine 5'-monophosphate; LPS, lipopolysaccharide; NMR, nuclear
magnetic resonance, 2-OG, 2-oxoglutarate; NMND, N-methylnicotinamide; NMND, Nmethylnicotinic acid; HS1, Histopathological severity score grade 1 (minimal); HS2,
histopathological severity score grade 2 (mild); HS3, histopathological severity score grade 3
(moderate); HS4, histopathological severity score grade 4 (marked); ALT, alanine
aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GLDH,
Glutamate dehydrogenase; NR1, primary non-responders to galN dose 1; iR2, Induced
responders to galN dose 2; R1, primary responders to galN dose 1; R2, responders to galN
dose 2.
26
Figures
Figure 1. Mean ALT following a first galN administration (dose 1) for controls, responders
(R1) and non-responders (NR1) and a second galN administration (dose 2) for controls,
induced responders (iR2) and responders (R2). Error bars relate to the standard error of the
mean for each cohort. (*) p<0.1, (**) p<0.01, (***) p<0.001 (****) p<0.0001 calculated from
a Mann–Whitney unpaired t-test. See Supplementary Figure 1 for further serum clinical
pathology measures that complement the ALT profile.
27
Figure 2a. Median partial 600 MHz 1H NMR urinary spectral profiles 24h post galN dosing for
administration one controls (Ctrl), non-responders (NR1), responders (R1) and
administration two induced responders (iR2) and responders (R2). Expansion of the spectral
region showing the (i) galN-H1 and (ii) glcNAc-H1 anomeric 1H NMR spectral resonances.
Figure 2b. The integral (arbitrary units) for the anomeric glcNAc proton resonance at 5.22
ppm in urinary NMR spectra. The data displayed represent the average value ( standard
error of the mean). (*) p<0.1, (**) p<0.01, (***) p<0.001 (****) p<0.0001 calculated from a
28
Mann–Whitney unpaired t-test. The control average which represents spectral
noise/background (as no glcNAc was present) was subtracted from each group.
29
Figure 3. Median partial 600 MHz 1H NMR fecal extract spectral profiles 24 h post galN
dosing for controls (Ctrl), non-responders (NR1), responders (R1) and administration two
induced responders (iR2) and responders (R2). Expansion of the spectral region showing (A)
galN-pyrazine (B) N-butyrate, lactate, alanine and acetoin resonances.
30
Figure 4. Serum cytokine levels (pg/ml) of KC/GRO and IL-5, 24 hours post-dose, for the
galN-induced differential toxicity phenotypes. The data displayed represent the average
concentration ( standard error of the mean). (*) p<0.1, (**) p<0.01, (***) p<0.001 (****)
p<0.0001 calculated from a Mann–Whitney unpaired t-test.
31
Figure 5. (A) O-PLS scores plot (Tcv and TYosc) derived from a regression model of pre-dose
urinary profiles with post-dose histopathological outcome (histopathological necrosis
severity score). The individual spectra are color-coded with respect to the post-dose
outcome, non-responders to galN dose 1 (NR1, HS0, red) and induced responders to galN
dose 2 (iR2, HS3 and 4, blue). (B) loadings coefficient plot highlighting the discriminatory
metabolites that correlate with histopathological necrosis. Model statistics; Q2Y 0.52; R2Y
0.89, permutation test p < 0.002.
32
Figure 6. (A) O-PLS scores plot (Tcv and TYosc) derived from a regression model of pre-dose
fecal extract profiles with a post-dose histopathological outcome of marked necrosis (HS4).
The individual spectra are color-coded according to the post-dose class, non-responders to
galN dose 1 (HS0, red) and induced responders to galN dose 2 (HS4, blue), (B) Loadings
coefficient plot highlighting the discriminatory metabolites that correlate with
histopathological necrosis. Model statistics; Q2Y 0.44; R2Y 0.99, permutation test p < 0.1.
33
Table 1: Metabolic changes identified in urine, serum, liver and fecal extracts following
administration of galactosamine for the variable response phenotypes. (A) 24h post dose
(B) 48 and 72 h post dose for the variable response phenotypes and (C) Pre-dose fecal and
urinary metabolic differences between NR1 and iR2.
34
A
DOSE ONE
NR1 - 24H

Urine

DOSE TWO
R1 - 24H
iR2 - 24H
R2 - 24H






GalN
NMND
GalN
NMND
GalN
NMND
GlcNAc
NMNA
GlcNAc
NMNA
GlcNAc
NMNA
GalN-
Citrate
GalN-pyrazines
Citrate
GalN-
Citrate
pyrazines
2-OG
Urocanate
2-OG
pyrazines
2-OG
Betaine
Glucose
Betaine
Glucose
Betaine
Glucose
Tyrosine
Lipids
Tyrosine
Lipids
Tyrosine
Lipids
Serum
Urocanate
Histidine
Histidine
Histidine
Choline/phosph
Choline/
Choline/
ocholine
phosphocholine
phosphocholine
GalN-
N-
pyrazines
butyrate
Acetoin
Liver Extract
Feces
Alanine
UDP-galN/UDP-glcN
Glycogen
UDP-
Glycogen
UDP-galNAc
Glucose
galN/UDP-
Glucose
UDP-glcNAc
Glutathione
glcN
Glutathione
glcNAc-6-P
Lactate
UDP-galNAc
Lactate
glcNAc-1-P
Inosine
UDP-glcNAc
Adenosine
Uridine
Adenosine
glcNAc-6-P
Uracil
glcNAc-1-P
Tyrosine
Uridine
Bile acids
Uracil
Tyrosine
Bile acids
Urine
B
R1 - 48H
R1 - 72H




GalN
NMNA
Urocanate
NMNA
GlcNAc
Citrate
NMND
GalN-
2-OG
Betaine
Lactate
pyrazines
Creatinine
Bile Acids
Alanine
Tyrosine
Urocanate
Creatine
NMND
Betaine
Bile Acids
C
PRE-DOSE TWO – Induced Responders

Hexanoic acid
N-acetyl resonance of a1-acid glycoprotein
Urine

Feces
Lactate
GABA
-ketoisovalerate
35
Supplementary Figure 1: Serum clinical pathology measures collected 24 hours post
administration of galactosamine (mean  standard error of mean). The table shows
histopathological necrosis severity scores of terminal liver sections (representing the
number of animals with a given score), following the secondary administration of
galactosamine (24h post-dose). (*) p<0.1, (**) p<0.01, (***) p<0.001 (****) p<0.0001
calculated from a Mann–Whitney unpaired t-test.
36
Supplementary Figure 2. (A) O-PLS scores plot (Tcv and TYosc) derived from a discriminant
analysis model of post-dose fecal extract profiles of primary non-responders (NR1, blue) and
responders (R1, red), (B) Loadings coefficient plot highlighting the discriminatory
metabolite, acetoin, increased in non-responders and (C) aromatic region showing the galNpyrazine resonances. Note: The two NR1 samples overlapped with R1 are those with
minimal or no galN pyrazines present in the fecal extract spectra, with a non-responder
clinical chemistry profile and subsequent classification. They may represent a slower
responder phenotype. Model statistics; Q2Y 0.46; R2Y 0.91, permutation test p < 0.001
37
Supplementary Figure 3. (A) PLS scores plot (Tcv and serum KC/GRO concentration) derived
from a regression model of post-treatment (24 h) liver extract profiles with serum KC/GRO
concentration. The individual spectra are color-coded according to the post-dose class,
controls dose 2 (red), induced responders to galN dose 2 (iR2, blue) and secondary
responders to galN dose 2 (R2, green), (B) Loadings coefficient plot highlighting the hepatic
metabolites that correlate (r2) with serum KC/GRO concentrations. Model statistics; Q2Y
0.33; R2Y 0.52, permutation test p < 0. 1
38
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