Abstract
Blood circulation mainly aims at distributing the nutrients required
for tissue metabolism and collecting safely the by-products of all
tissues to be further metabolized or eliminated. The simultaneous study
of arterial (A) and venous (V) specific metabolites therefore has
appeared to be a more relevant approach to understand and study the
metabolism of a given organ. We propose to implement this approach by
applying a metabolomics (NMR) strategy on paired AV blood across the
intestine and liver on high fat/high sugar (HFHS)-fed minipigs. Our
objective was to unravel kinetically and sequentially the metabolic
adaptations to early obesity/insulin resistance onset specifically on
these two tissues. After two months of HFHS feeding our study of AV
ratios of the metabolome highlighted three major features. First, the
hepatic metabolism switched from carbohydrate to lipid utilization.
Second, the energy demand of the intestine increased, resulting in an
enhanced uptake of glutamine, glutamate, and the recruitment of novel
energy substrates (choline and creatine). Third, the uptake of
methionine and threonine was considered to be driven by an increased
intestine turnover to cope with the new high-density diet. Finally, the
unique combination of experimental data and modelling predictions
suggested that HFHS feeding was associated with changes in tryptophan
metabolism and fatty acid β-oxidation, which may play an important role
in lipid hepatic accumulation and insulin sensitivity.
Subject terms: Metabolomics, Obesity, Homeostasis
Introduction
The main function of blood circulation is to provide and distribute the
nutrients required for tissue metabolism and to collect the by-products
that need to be removed from that organ and excreted safely. The
modifications of the metabolites in the arterial and venous circulation
allow to integrate the whole-body metabolism and therefore to maintain
homeostasis. Many molecules are transported in the blood, including
nutrients (amino acids, lipids, and carbohydrates), electrolytes and
messengers (hormones, peptides, miRNA…). The composition of the blood
passing through a tissue is therefore able to reflect its metabolic
function: substances taken up by the tissue on a net basis will be in
higher concentration in the arterial inflow than in the venous outflow,
and vice et versa for released products. Then, for many of these
substances, the circulatory system is not just a way to go in and out
of an organ, but is also a complex system of cross-talk allowing a
whole body integrative metabolism^[46]1.
Metabolomics has proved to be a valuable tool to further understand
metabolic processes^[47]2. For instance, we and other have shown that
untargeted metabolomics was particularly adapted to study the
time-course adaptation of metabolism^[48]3–[49]7. However, so far, the
majority of the untargeted studies has focused on biomarkers discovery
by profiling blood or urine samples^[50]8, which provides an
interesting static snapshot of the whole body metabolism but offers
limited mechanistic information. Indeed, little can be drawn from urine
metabolomics about the metabolic activity of a particular organ.
Concerning blood, venous samples, which are easily accessible and
widely used in clinical diagnosis, likely reflect the metabolism of a
particularly-drained organ. On the contrary, arterial blood
(exceptionally used in clinical diagnosis) is able to provide a picture
of available molecules for the whole body metabolism as it represents
the phenotype of the fluid prior to being processed by any particular
organ^[51]9. The simultaneous study of both venous and arterial samples
seems therefore a particularly relevant approach to better understand
the metabolism a given organ. Assessment of respiratory gases, glucose
and amino acids (AA) have been traditionally explored using the
arterio-venous (AV) approach since 1930’s in order to perform
fundamental metabolic studies at different levels (brain, splanchnic
area, kidney, forearm…). However, such studies remained focused on a
very limited and targeted set of metabolites or nutrients. Therefore,
the global profile of metabolic utilization and exchange of metabolites
and nutrients by tissues remains poorly known. In order to obtain an
extended view of the organ metabolism beyond biomarker discovery by
metabolomics, the assessment of a large number of metabolites in
arterial and venous blood is needed. We therefore performed a global
profiling of paired arterial and venous (portal and hepatic veins)
blood, focusing on the splanchnic area (portal vein-drained viscera and
the liver), as the central metabolic crossroad and link between
absorption and intermediary metabolism. Samples were collected using a
time-course protocol in a minipig model of obesity and insulin
resistance (overnutrition with a high fat-high sucrose diet,
HFHS)^[52]6,[53]10 in order to challenge the metabolism with an
overload of energy and nutrients. Blood was simultaneously drained at
the fasting state before and after 7, 14, 30 and 60 days of a HFHS diet
and plasma samples were analysed by NMR-based metabolomics platform.
The metabolites showing significant changes between venous and arterial
samples were discussed in the context of the biochemical knowledge of
these organs and based on the concept of arteriovenous metabostasis, as
recently developed by Ivanisevic et al. for the study of the skeletal
muscle metabolism^[54]11.
One challenge when interpreting metabolomics data is to explore the
direct physiological roles of metabolites and their involvement in
organs’ metabolic networks. Metabolic networks can provide a
mechanistic context for understanding metabolic profiles and explore
the metabolic pathways altered during the onset of obesity. In this
study, we therefore integrated the AV metabolic profiles with
Genome-Scale Metabolic Network Models (GSMNM) to model the global
metabolism of liver and intestine. GSMNMs gather, in an organized and
mathematical format, all the biochemical reactions (and associated
metabolites) that can occur in a given organism^[55]12. We used one of
the most consolidated reconstruction of the human metabolic network,
Recon2.04^[56]13. It includes more than 7700 reactions and 2600
metabolites, capturing all the reactions that can possibly occur in any
human tissue or cell. Constraint-based modelling (CBM) methods then
allow to predict the metabolic state of a tissue, by setting bounds
(lower and upper constraints) to some specific reactions based on
available experimental data^[57]14. In this study, constraints were set
on exchange reaction fluxes in the models to enforce uptake or release
of metabolites according to the consumption and production profiles.
This modelling strategy allows contextualizing the generic model and
generating time- and tissue-specific constrained models, which can
specifically account for the functional metabolic network in these
conditions.
Materials and Methods
Animals and experimental procedure
The study involved five female adult Yucatan mini-pigs (31.5 ± 1.4 kg).
Three weeks before the experimentation, mini-pigs were surgically
fitted with a catheter in the abdominal artery aorta (Art), the portal
vein (PV) and the hepatic vein (HV). They were housed in subject pens
(1 × 1.5 m) in a ventilated room with controlled temperature (21 °C)
and regular light cycle (L12:D12). They were fed once daily with
400 g/d of a concentrate feed containing 17.5% protein, 3.2% fat, 4.3%
cellulose, and 5.2% ash (Porcyprima; Sanders Centre Auvergne,
Aigueperse, France) and had free access to tap water. All procedures
were in accordance with the guidelines formulated by the European
Community for the use of experimental animals (L358-86/609/EEC, Council
Directive, 1986). The protocol was approved by the Ethical Committee
for Animal Experimentation Auvergne, authorization 02090.01.
After the recovery period, minipigs were fed a HFHS diet consisting in
a regular pig diet enriched with fat (13% palm oil) and sugar (10%
sucrose) (1 kg/day, 15.5 kJ/day) for two months. Animals ingested the
whole mixture in no more than 10 minutes. After an overnight fasting,
blood was sampled through the three catheters simultaneously on
heparinized tubes, before (d0), and 7, 14, 30, and 60 days after HFHS
feeding. Blood was centrifuged at 4,500 × g for 10 min, plasma rapidly
collected and stored at −80 °C until further analyses. Biochemical and
phenotype measures were also performed at the different time points:
the related procedures and data have been previously published^[58]10.
Plasma metabolomics
200 µL of plasma samples were mixed with 500 µL of phosphate buffer (pH
7.0) prepared in deuterated water, and then centrifuged at 5500 g at
4 °C for 15 minutes, and 600 µL of supernatant were transferred to 5 mm
NMR tubes. All ^1H NMR spectra were obtained on a Bruker DRX-600-Avance
NMR spectrometer operating at 600.13 MHz for ^1H resonance frequency
using an inverse detection 5 mm ^1H-^13C-^15N cryoprobe attached to a
Cryoplatform (the preamplifier unit). The ^1H NMR spectra were acquired
at 300 K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse
sequence with presaturation, with a total spin-echo delay (2nτ) of
240 ms to attenuate broad signals from proteins and lipoproteins. A
total of 256 transients were collected into 32k data points using a
spectral width of 20 ppm, a relaxation delay of 2 s and an acquisition
time of 1.36 s. Prior to Fourier Transformation, an exponential line
broadening function of 0.3 Hz was applied to the FID. All NMR spectra
were phased and baseline corrected, then data were reduced using AMIX
(version 3.9 Bruker, Rheinstetten, Germany) to integrate 0.01 ppm wide
regions corresponding to the δ 8.6–0.7 ppm region. The δ 5.1–4.5 ppm
region, which includes the residual water resonance, was excluded. A
total of 581 NMR buckets were included in the data matrix. To account
for differences in sample concentration, each integrated region was
normalized to the total spectral area. For the exchange of metabolites
across the organs we calculated the ratios between the artery and the
veins: Art/PV for the intestine, and [(Art × 0.2) + (PV × 0.8)]/HV for
the liver. An averaged relative contribution of 20% of the hepatic
arterial flow (Art) to the total hepatic flow was used as an
approximation based on previously reported results^[59]15. We then
expressed these ratios as percentage of metabolisation from steady
state, considering steady state as inflow/outflow = 1.
[MATH: %metabolisation(metm,indivi,dayd)=(Artmetm,indivi,daydPVmetm,indivi,dayd−1
)×100,forintestine; :MATH]
[MATH: %metabolisation(metm,indivi,dayd)=(Artmetm,indivi,dayd×0.2+
PVmetm,indivi,dayd×0.8<
mrow>PVmetm,indivi,dayd−<
/mo>1)×100,forliver; :MATH]
where Art, PV and HV are the NMR integration area of each metabolite
measured in plasma samples from Art, PV and HV respectively. For each
metabolite, a signal without overlapping has been chosen for
integration ratio calculation. %metabolisation for all measured
metabolites are presented in Suppl. Fig. [60]1.
Statistical analyses
Multivariate analyses were used to study the effect of high fat diet
along time course on the metabolome. Principal components analysis
(PCA) was first performed to reveal intrinsic clusters and detect
eventual outliers. Partial least squares-discriminant analysis (PLS-DA)
was then used to model the relationship between group and spectral
data. PLS-DA is similar to PCA but uses discriminant variables that
correlate to class membership. Before analysis, orthogonal signal
correction (OSC) filtering was used to remove variability not linked to
the studied conditions (physiological, experimental or instrumental
variation). Filtered data were mean-centered and Pareto scaled. For all
the figures, Hotelling’s T2 statistics were used to construct 95%
confidence ellipses. The R2Y parameter represents the explained
variance. Seven-fold cross validation was used to determine the number
of latent variables to include in the PLS-DA model and to estimate the
predictive ability (Q^2 parameter) of the adjusted model. In addition,
the robustness and validity of the PLS-DA models were calculated using
a permutation test (number of permutations = 200). Significant NMR
variables were identified using 1D and 2D NMR spectra. Discriminated
time points (i.e. with different metabolic profiles) d0, d7 and d60
were selected based on the multivariate statistical analyses. For these
three time points, comparisons between venous and arterial blood across
the organs were performed using t-paired Student tests and the time
effect was assessed using a repeated-measures one-way ANOVA test.
SIMCA-P software (V13, Umetrics AB, Umea, Sweden) was used to perform
the multivariate analyses. SigmaPlot v12.3 (Systat Software, San Jose,
CA) was used to perform the univariate analyses.
Metabolic network modelling
For each time point (d0, d7 and d60) and for each of the metabolites
measured experimentally, paired t-tests between Art and PV and between
HV and (Art × 0.2 + PV × 0.8) were performed, to identify metabolites
significantly released or taken up by the gut or the liver. Metabolites
with p-value < 0.05 and with a metabolisation % higher than 5% were
considered as taken up by the tissue, whereas metabolites with
p-value < 0.05 and with a metabolisation % lower than −5% were
considered as released. For the metabolisation % between −5 and +5% the
metabolites were considered as not exchanged or used by the tissue. The
remaining metabolites, with a % metabolisation lower than −5% or higher
than +5% but with a p-value > 0.05, were considered as not
significantly exchanged and therefore were not constrained in the
modelling.
We integrated these extratissular exchange data into the GSMNM
Recon2^[61]16 (version 2.04, downloaded from
[62]http://vmh.uni.lu/#downloadview)^[63]17 to simulate metabolic
fluxes through intratissular reactions and predict the changes in gut
and liver metabolic state during HFHS feeding. The Recon2 model
contains 7440 reactions and 2626 metabolites, including 642 exchange
reactions and associated metabolites which can potentially be taken up
or secreted from the model. All these exchange reactions are initially
unbounded by default (with lower and upper bounds set to −500 and +500
respectively), allowing uptake and secretion of all these metabolites.
By definition of the exchange reactions in the model, a negative flux
through an exchange reaction corresponds to an uptake of metabolite
(incoming flux into the model), whereas conversely a positive flux
corresponds to a release of metabolite (outgoing flux out of the
model). Acquired metabolomics data were used to set specific
constraints on these exchange reactions considering the metabolites
evidenced as taken up, secreted and not exchanged from metabolomics
data at each time. For released metabolites, the corresponding exchange
reactions were constrained to enforce minimal release (i.e., minimal
positive flux through exchange reaction), by setting the upper bound
(ub) to the flux equivalent to the limit of detection (LOD) value
(Suppl. Fig. [64]2). Similarly, for metabolites taken up, corresponding
exchange reactions were constrained to enforce minimal negative flux
through exchange reaction (lower bound (lb) set flux equivalent to
–LOD). For metabolites considered as not exchanged (with %
metabolisation within −5% and +5%), the corresponding exchange
reactions were constrained to have a limited absolute flux value, lower
than the LOD. Of note, contrary to not exchanged metabolites, no
constraints were set for metabolites whose changes in level between
inflow and outflow were not found to be significant (both uptake and
release were a priori allowed). The concentration was defined as LOD in
^1H NMR spectroscopy when the signal-to-noise ratio (S/N) of signals
reached 3:1. LOD was estimated at 25 μM in NMR tube for signals
corresponding to 3 protons, i.e. 87.5 uM in plasma samples. LOD was
estimated at 75 μM in NMR tube for signals corresponding to one proton,
i.e. 260 μM in plasma samples. LOD, were converted to equivalent fluxes
using an estimated blood flow value of 100 ml/g liver/h^[65]18.
Constraints were set with a 20% margin from the LOD. Three of the
experimentally exchanged metabolites (betaine, glycerophosphocholine
and ethanolamine) could theoretically not be exchanged in the Recon2
model as they were not associated to any exchange reactions: exchange
reactions and extracellular transport reactions were added for these
metabolites to allow their import and export. All exchange reactions
not constrained from experimental data were set to secretion only
(lb = 0), except for a list of metabolites which are considered as
being always present in plasma and for which uptake from plasma was
allowed in the model. A constrained model was generated for each time
point and each tissue, therefore leading to 6 time- and tissue-specific
models: SI-d0, SI-d7, SI-d60, Liver-d0, Liver-d7 and Liver-d60. The
list of constraints defined for each model in given in Suppl.
Table [66]1.
The coefficients of the biomass reactions were adapted to better fit
with the composition of an hepatic cell using data from the literature
(Suppl. Table [67]2)^[68]13,[69]19 and to account only for tissue
maintenance (deoxyribonucleotides were excluded). Coefficients were
also rescaled to get fluxes in µmol/g tissue/h instead of mmol/gDW/h
(using an estimate of 3.8 for the liver Wet Weight (WW)/Dry Weight (DW)
ratio)^[70]20. The lower bound for the biomass reaction was constrained
in order to get a minimal protein fractional synthesis rate of
5%.h^−1 ^[71]21 and the albumin production and release was allowed in
the 3 liver-specific models.
The possible range of flux values through each reaction (minimal and
maximal possible fluxes) was computed using Flux Variability Analysis
(FVA)^[72]22,[73]23. FVA results are provided in Suppl. Table [74]3. To
compare the simulated flux ranges between pairs of time points, we
calculated the Jaccard distance of each reaction as the ratio of the
intersection to the union of the flux ranges at 2 different time
points. For instance, for reaction i, the Jaccard distance between
simulated flux ranges at d0 ([min[d0]; max[d0]]) and d7 ([min[d7];
max[d7]]) is computed as follow:
[MATH: Ji,d0−d7=intersection([min;max]d0,[min;max]d7)union([min;max]d0,[min;max]d7)=min(maxd0;maxd7)−max(mind0;mind7)max(maxd0;maxd7)−min(mind0;mind7) :MATH]
This represents the overlap between the possible flux values at d0 and
d7: J distance of 0 indicates that the 2 flux intervals do not overlap
at all and therefore that the reaction i has distinct flux values at d0
and d7, whereas a J distance of 1 (100%) indicates that there is a
complete overlap between possible flux values at d0 and d7.
For each pairwise time comparison and each tissue, we performed a
pathway enrichment analysis on the reactions with J distance lower than
1. This aims to assess whether the reactions with modulated flux values
between 2 time points are significantly over-represented in a metabolic
pathway. Pathway enrichment statistics were performed using one-tailed
exact Fisher test, with a Bonferroni correction for multiple
tests^[75]24, using the metabolic pathways defined in Recon2.
Results
After two months of HFHS feeding minipigs developed an obesity-like
phenotype, with a significant increase in body weight (from
31.5 ± 1.4 kg to 44.7 ± 1.7 kg), most likely as the consequence of fat
deposition at the visceral and subcutaneous adipose tissue^[76]6. More
details about the biochemical and clinical phenotyping have been
previously published^[77]10,[78]25.
The trajectory of the metabolome obtained from different sampling
sites, i.e., PV, Art and HV is shown in Fig. [79]1. Clustering of
observations is very similar between the vessels. Three clusters
depending on the duration of HFHS diet feeding were observed: d0, d7,
d14 and d60. After d14, the phenotype was no longer discriminated from
those of d30 or d60. In all cases, the model was valid and robust,
including three latent variables, R² = 64.4% Q² = 0.51 for the Art; two
latent variables, R² = 41.4% and Q² = 0.31 for the PV; and three latent
variables, R² = 64.3% and Q² = 0.47 for the HV. Because d14 and d30
were not metabolically distinct from d60, only d0, d7 and d60 were
selected for further analyses.
Figure 1.
[80]Figure 1
[81]Open in a new tab
(A) Scores plot of partial least squares discriminant analysis model
(n = 5) after OSC filter for five classes: d0, d7, d14, d30 and d60 of
HFHS feeding. For the artery, R² = 64.6%, Q² = 0.51; for the portal
vein, R² = 44.4%, Q² = 0.31; for the hepatic vein, R² = 64.3%,
Q² = 0.47. (B) Scores plot of partial least squares discriminant
analysis model (n = 5) after OSC filter for six classes: artery (Art),
portal vein (PV) and hepatic vein (HV) and d0 and d7 of HFHS feeding.
R² = 37.4%, Q² = 0.32, two latent variables.
Based on the results presented in the Fig. [82]1A, we decided to
analyse separately the metabolic profiles of all vessels between d0 and
d7, where the major shift occurred. We obtained a valid and robust
model (R² = 37.4% and Q² = 0.32) that did not discriminate the
metabolomes from the different vessel at d0 (Fig. [83]1B). However, at
d7 the metabolome of the PV was clearly discriminated from that of the
HV and the Art. All the metabolomes at d0 were also discriminated from
those at d7.
The global profiling of inflow and outflow blood samples measured
simultaneously using NMR metabolomics approach highlighted the
significant changes in arterial vs. venous plasma during circulation
across the gut and liver of HFHS minipigs. Based on the PLS-DA results
those metabolites with significant different levels in venous vs.
arterial plasma at d0, d7 or d60 are represented in Figs [84]2 and
[85]3 (as % of metabolisation from the steady state). Direction of the
% of metabolisation change implies either a positive AV balance,
reflecting metabolite uptake by the organ, or a negative arteriovenous
balance, reflecting metabolite release by the organ^[86]11. Among the
significant changing metabolites, several AA were identified displaying
consistently shifted ratios in time across the gut: higher arterial vs.
venous levels were observed for glutamine (20–60% of metabolisation),
glutamate (5–40% of metabolisation), threonine (20–30% of
metabolisation, except at d0), and methionine (20–40% of
metabolisation, except at d0), whereas increased venous levels were
found for glycine (no more than −20% of metabolisation at d0 and 60).
Several of these AA showed also modified levels between the inflow (Art
and PV) and outflow (HV) across the liver. Opposite trend compared to
gut was observed for glutamate and glycine, with a release profile for
glutamate (−35% metabolisation) and an uptake profile for glycine
(20–40% metabolisation). Higher levels of alanine (20–45% of
metabolisation), methionine (18% of metabolisation at d0, but no
metabolisation at d7 and d60), lysine (10% of metabolisation at d7 and
60), and phenylalanine (8% of metabolisation at d0) were found in the
inflow plasma when compared to the outflow levels across liver.
Figure 2.
[87]Figure 2
[88]Open in a new tab
Arteriovenous fold-change (A/V) across the splanchnic area (liver and
portal drained viscera) of amino acids on five Yucatan minipigs
submitted to a HFHS diet during two months. Data is presented in % of
metabolisation (mean + sem): positive values = taken up by the organ;
negative values = released by the organ. Based on the results obtained
in the Fig. [89]1, only day 0, 7 and 60 are shown. Data were analysed
using a repeated-measures one-way ANOVA test (p-value for time is shown
when < 0.05). *Significantly different from 0 (equilibrium) (P < 0.05).
Different letter indicate significant differences among the days,
p < 0.05.
Figure 3.
[90]Figure 3
[91]Open in a new tab
Arteriovenous fold-change (A/V) across the splanchnic area (liver and
intestine) of energy-related metabolites on five Yucatan minipigs
submitted to a HFHS diet during two months. Data is presented in % of
metabolisation (mean + sem): positive values = taken up by the organ;
negative values = released by the organ. Based on the results obtained
in the Fig. [92]1, only day 0, 7 and 60 are shown. Data were analysed
using a repeated-measures one-way ANOVA test (p-value for time is shown
when < 0.05). *Significantly different from 0 (equilibrium) (P < 0.05).
Different letter indicate significant differences among the days,
p < 0.05.
Several metabolites directly involved in the energy flow through
metabolism were also found to significantly differ in arterial vs.
venous plasma across liver, whereas no significant differences were
evidenced between inflow and outflow across the gut (Fig. [93]3). Among
them, several metabolites participating to the hepatic glucose
metabolism were identified, including glucose, lactate, pyruvate and
succinate. Glucose displayed higher concentration in outflow (hepatic
vein) blood than in the inflow (arterial and portal) blood at d0
(averaging −6% of metabolisation), whereas this differential release
was no longer observed on d7 and d60. Inversely, lactate and pyruvate
appeared to be released (with a −20 to −40% metabolisation) at later
times only (d7 and d60). Succinate also showed a differential
utilisation across the liver, but with a clear uptake pattern,
increasing from a 20% metabolisation at d0, up to 60% at d60. Two
metabolites produced by the gut microbiota, namely acetate and
propionate, were also identified. While for propionate the profile
clearly shows an inverse pattern between gut and liver, with a positive
balance (up to 100% metabolisation between d7-60) at the liver level
and a negative balance (−50%) for the gut, the acetate results are less
clear, with a significant negative balance evidenced only across the
gut (averaging −50% of metabolisation) irrespectively of the day.
Other small metabolites showing significant differences between the
venous and arterial blood samples were further identified, including
betaine, ethanolamine, creatine, phosphocholine and choline
(Fig. [94]4). While creatine and choline showed a clear pattern of
uptake by the gut particularly significant at d60 (about 30% of
metabolisation), betaine showed a positive metabolisation across the
liver at d7 and 60 (averaging 30%), and across the gut (about 20% of
metabolisation) at d0. Finally, we recorded a positive metabolisation
across the liver at d0 for phosphocholine (20%) and ethanolamine (5%),
along with a negative metabolisation of the latter across the gut.
Figure 4.
[95]Figure 4
[96]Open in a new tab
Arteriovenous fold-change (A/V) across the splanchnic area (liver and
intestine) of metabolites on five Yucatan minipigs submitted to a HFHS
diet during two months. Data is presented in % of metabolisation
(mean + sem): positive values = taken up by the organ; negative
values = released by the organ. Based on the results obtained in the
Fig. [97]1, only day 0, 7 and 60 are shown. Data were analysed using a
repeated-measures one-way ANOVA test (p-value for time is shown
when < 0.05). *Significantly different from 0 (equilibrium) (P < 0.05).
Different letter indicate significant differences among the days,
p < 0.05.
Significant observed changes in metabolites levels between inflow and
outflow across gut and liver were used to model the changes in gut and
liver metabolism at d0, d7 and d60. Based on the uptake and release of
metabolites at each time, possible ranges for intratissular fluxes were
simulated for all the reactions defined in the human metabolic network
Recon2 (Suppl. Table [98]3). Changes in fluxes were compared between d0
and d7 and between d7 and d60 for gut and liver separately. We
predicted that about 100 reactions would have potentially different
flux values between d0 and d7, but no more than 20 reactions between d7
and d60 (Suppl. Table [99]4). These predicted altered reactions between
d0 and d7 mostly belong to the fatty acid oxidation pathway
(Fig. [100]5 & Suppl. Table [101]5). Reactions involved in the
tryptophan degradation pathway are also significantly present among the
predicted changed reactions between d0 and d7 for liver, but between d7
and d60 for intestine. Also, the purine synthesis pathway included a
significant number of predicted modulated reactions for liver (8
reactions), with a trend to increased possible fluxes from d0 to d7 and
reduced possible fluxes from d7 to d60.
Figure 5.
[102]Figure 5
[103]Open in a new tab
Predicted modulated metabolic pathways. Pathway enrichment analysis
showing Recon 2 pathways that are contain a significantly high number
of reactions which predicted flux ranges differ between time points (as
assessed by a Jaccard distance lower than 1). The size of the circles
is proportional to the number of differing reactions. The colour of the
circles depends on the p-value (one-tailed exact Fisher test, with
Bonferroni correction for multiple tests). Pathways for which reactions
tend to have a larger (resp., tighter) flux range at d7 than d0 and at
d60 than d7 are coloured in green (resp. red).
Discussion
The paired arteriovenous metabolomics data-driven study presented here
provides a comprehensive overview of the metabolite exchange and
utilization across the splanchnic area in normal and obese minipigs.
While in previous studies single organs have been explored using this
approach (muscle in healthy humans^[104]11 and mammary gland in dairy
cows^[105]26), this is the first time that such strategy is
simultaneously applied to two organs (liver and intestine) in a
pathophysiological model (obesity). Applying this AV measurement
strategy for in vivo exploration using the obese minipig model offers
two main advantages: first, to solve the problem of the PV access in
humans^[106]27; and second, to be able to perform a follow up on the
onset of obesity on the same individuals. We evidenced a similar
metabolic trajectory in each blood vessel metabolic profile, with a
clear discrimination of three steps: d0, d7 and d14 to d60, which is
consistent with our previous study of the urine metabolome
trajectory^[107]6. We therefore focused on the comparison of the
metabolites exchanges in healthy state (d0) and in early (d7) and late
(d60) obesity stages. The metabolomics data obtained across the
intestine and the liver were also analysed by constraint-based
mathematical modelling, which allowed to formulate further explanations
about the fate of the exchanged metabolites.
Characterization of the splanchnic metabolism based on the AV differences in
healthy minipigs
At day 0 the liver metabolism shows a classical postabsorptive profile.
The major role of the liver during this period is to provide glucose to
the peripheral tissues through the de novo glucose production
(gluconeogenesis)^[108]28 (Fig. [109]6A). In our study, we indeed
observed a negative A/V balance of glucose across the liver,
demonstrating its hepatic production. We also observed that different
substrates like lactate, pyruvate or AA^[110]29 could be delivered from
other organs to the liver in order to produce glucose. Several
gluconeogenic AA were actively taken up by the liver, including
glycine, alanine, phenylalanine and methionine. While glycine seemed to
have an intestinal origin, the rest of AA were most likely released
from the skeletal muscle. In the particular case of alanine, its
release from the muscle participates in the glucose-alanine
cycle^[111]30 and can also explain the glutamate liver release, as the
result of alanine transamination. Lactate and pyruvate, also tend to be
taken up by the liver, which could contribute to the concomitant
glucose output. Given that lactate was not released by the intestine,
its origin would be likely the muscle, as previously shown in fasted
pigs^[112]30 and as part of the Cori cycle.
Figure 6.
[113]Figure 6
[114]Open in a new tab
Schematic representation of the main biochemical reactions observed
across the splanchnic area (intestine and liver) in HFHS-fed minipigs
for 3 different stages in the onset of obesity. Exchange fluxes
represented in the figure are based on the inflow to outflow ratios
measured by NMR and the modelling results. Acet, acetate; Ala, alanine;
Bet, betaine; Gln, glutamine; Gluc, glucose; Glu, glutamate; Gly,
glycine; Kynu, kynurenine; Lac, lactate; OAA, oxaloacetate; Phe,
phenylalanine; Pyr, pyruvate; sarco, sarcosine; Thr, threonine; Trp,
tryptophan; Xanth ac, xanthurenic acid.
The intestine is a metabolically active organ^[115]31: during the
postabsorptive period, its AA supply comes mainly from the artery and
is especially fueled by glutamine^[116]32 and glutamate^[117]31, which
are used for energy purposes^[118]33. In our study, these two AA were
actively taken up by the intestine, which is in agreement with the
extensive glutamine utilization by the pig intestine^[119]31. The
precise origin of glutamine could not be determined in the present
study, but it would be likely released from the skeletal
muscle^[120]30, while according to our data glutamate was rather
released by the liver.
Finally, both, acetate and propionate showed a positive A/V ratio
across the liver and a negative ratio across the intestine, reflecting
their intestinal production and their hepatic uptake from the portal
circulation. Acetate has been shown to be used as an energy substrate
by the liver through its oxidation by the tricarboxylic acid cycle
(TCA), but also as a substrate for the synthesis of cholesterol and
long-chain fatty acids^[121]34. Concerning propionate, it is also known
that the liver clears the major part of this metabolite from the portal
circulation^[122]35, and that it is mostly used for gluconeogenesis
purposes^[123]36, which is further consistent with our results.
Effects of HFHS feeding: focus on the first week
From the metabolome trajectory, we noticed that the phenotype of all
vessels shifted a d7 (Fig. [124]1A). We therefore performed at this
particular time a blood vessel comparison of the phenotype, evidencing
that the metabolome of the PV is clearly discriminated from those of
the Art and HV (Fig. [125]1B). Interestingly the modelling results also
support this idea of a larger metabolic shift during this first week of
HFHS feeding, with a larger number of reactions having potentially
modulated flux ranges between d0 and d7 than between d7 and d60
(Fig. [126]5).
In contrast to the situation observed at d0, where several metabolites
involved in phospholipids metabolism (methionine, choline,
phosphocholine and ethanolamine) were actively taken up by the liver,
their uptake was blunted after only one week of HFHS feeding
(Fig. [127]6B). A reduced synthesis of phospholipids could be
deleterious for the liver, as insufficient phosphocholine supplies will
inevitably cause triglyceride accumulation and eventually fatty
liver^[128]37.
After one week of HFHS feeding, we observed a reduced glucose release
from the liver, most likely due to a reduced utilization of
gluconeogenic substrates, like lactate and AA (methionine,
phenylalanine). During the postabsorptive period, gluconeogenesis is
predominantly fuelled by lactate^[129]38, which at d7 was no longer
taken up by the liver, but rather released. This is also consistent
with a reduction in glucose catabolism, which could further explain the
pyruvate accumulation and release^[130]39,[131]40. Thus, the increased
availability of lipids could displace the use of pyruvate as a
substrate for oxidative metabolism. Pyruvate will then cumulate and be
subsequently diverted into lactate, resulting in both pyruvate and
lactate release from the liver. In the same line, we observed that
succinate was importantly taken up by the liver. In high substrate
availability contexts, such as HFHS feeding, succinate could be
released from tissues^[132]41. Under these conditions, the TCA may
increase the H^+ gradient over the mitochondrial membrane, leading to
inhibition of enzymatic steps mediated by complexes within the electron
transport chain and eventually, release of succinate into the blood
stream^[133]42. In mice fed on a high fat diet and supplemented with
sodium succinate, a reduced lipid accumulation in the liver was
observed^[134]43. We may therefore consider that the early (d7)
increased succinate uptake by the liver of the HFHS-fed minipigs could
be part of a strategy aiming at reducing hepatic steatosis as the
consequence of the deleterious fat and sugar intake. Despite a
maintained intestinal release of acetate under HFHS feeding, its uptake
by the liver was blunted after d7, which is consistent with the
strategy to reduce lipid production from this substrate, by redirecting
the intestinal acetate to peripheral organs for further
oxidation^[135]44.
As a whole, in the first week of HFHS feeding, we observed several
features suggesting that the minipig metabolism rapidly reprogrammed
and that there might be a metabolic shift at the hepatic level toward
utilizing the most abundant nutrients (i.e. lipids) while alleviating
the potentially associated deleterious effects.
Longer-term effects of HFHS feeding
Globally the shifts observed at d7 tend to persist at d60 as suggested
by the fewer changes in metabolome observed between d7 and d60
(Fig. [136]6C). The major impact of two months of HFHS feeding on the
intestine metabolism can be summarized by an increased energy demand
coped by an enhanced metabolism of several metabolites. Glutamine and
glutamate are the preferred substrates to fuel the physiological
processes stimulated by the HFHS feeding, like epithelium renewal and
expansion and cellular turnover^[137]45. The positive A/V balance of
glutamine and glutamate is significantly increased from d0 to d60.
Interestingly, the energy demand seems to be complemented by other
metabolites poorly used at d0, like creatine, a major metabolite
involved in energy metabolism^[138]46. Creatine could be then
preferentially used to rapidly yield energy^[139]47, which would be
consistent with the positive correlation found between the adiposity
and creatine levels in HF-fed pigs^[140]48.
HFHS consumption may induce cellular stress and inflammation at the
intestinal level^[141]49. Some metabolic features observed in this
study are consistent with this hypothesis and could be related to
biological processes aiming at developing the intestinal functionality.
For instance, methionine and threonine were more highly taken up after
two months of HFHS consumption. This is consistent with the fact that
methionine participates in the epithelial cell turnover as well as in
the maintenance of the antioxidant status (by keeping cysteine
available) and the cell level of reduced glutathione^[142]50, while
threonine is largely used for mucins synthesis in pig
mucosa^[143]51,[144]52, which is of outmost importance in epithelium
protection and innate immune defence.
In parallel, we also reported that glutamate A/V across the liver was
further increased at d60, suggesting its larger exportation into the
peripheral circulation. Barber et al. reported that in cafeteria-fed
animals glutamate was released from the liver while urea production was
reduced^[145]53, which is consistent with our previous
observations^[146]6 and could reflect a nitrogen sparing mechanism
already observed in other situations^[147]54,[148]55. We may therefore
suggest that these changes would result from a metabolic switch in
which AA catabolism would be reduced in detriment of lipids
utilization, highly abundant in the HFHS diet, and able to induced a
reduction in the urea cycle in both rodents^[149]53,[150]56 and
pigs^[151]10.
Splanchnic metabolism in obese HFHS-fed minipigs: insights from mathematical
modelling
In order to go deeper into the exploration of the changes in splanchnic
metabolism under HFHS feeding, we have submitted the AV metabolomics
data to constraint-based mathematical modelling. Our results allowed
gaining new insights into the intrahepatic routing of exchanged
metabolites experimentally assessed by NMR and mainly predicted changes
in three metabolic pathways: fatty acid oxidation, tryptophan
metabolism and purine synthesis (Fig. [152]5).
Our in silico simulations predicted that the metabolic fluxes through
the tryptophan pathway reactions tend to be increased towards the
production of kynurenine and its downstream metabolites, some of them
(like 3-hydroxy-kynurenine, 2-Amino-3-carboxymuconate semialdehyde)
being major crossroads for the production of kynurenate, xanthunerate
or quinolenate^[153]57. These metabolites were shown to be elevated in
obese and insulin resistant subjects, and some of the metabolites
participating at these particular pathways are known to reduce insulin
production and sensitivity in laboratory animals^[154]58,[155]59.
Further, changes in tryptophan and its related metabolites have been
associated with altered indoleamine 2,3-dioxygenase-1 activities, one
of the rate-limiting enzyme of tryptophan catabolism and a possible
indirect indicator of immune mediated inflammation in obese and
overweight conditions^[156]60,[157]61.
We also predicted that the activity of the purine synthesis pathway
would be transitory increased in liver after 7 days of HFHS diet, with
a potentially larger flux through a set of reactions belonging to this
pathway at d7. This temporary shift might be linked to a possible
higher hepatic uptake of glutamine, which is used as a co-substrate for
some reactions in the pathway, at d7.
Simulations based on the metabolomics data also predicted modulations
in the fatty acid oxidation pathway, with smaller fluxes through about
60 fatty acid mitochondrial β-oxidation reactions, which is in line
with our previous study showing a reduced expression level of limiting
enzymes of this pathway^[158]25. The reactions predicted to be
modulated are specifically those relying on FAD as co-factor. The fact
that betaine became significantly taken up at d7 and d60 and that its
metabolism also consumes FAD could therefore be linked with the
reduction of the fatty acid β-oxidation pathway as a result of FAD
competition between reactions.
It should be noted, that one limit of the modelling approach is that
the predictions highly depends on the metabolic network model used as a
basis: in this study, because there is currently no manually curated,
and therefore reliable, reconstruction for the metabolic network of the
minipig, or even the pig, we chose to use one of the most up-to-date
and consolidated reconstruction of the human metabolic network,
Recon2.04. Also, we use the global human metabolic model rather than
tissue-specific models since we wanted to be able to compare the
analyses made from the metabolomics data obtained both for the liver
and intestine. Despite this, it is interesting to notice that the
pathways pointed out by the modelling are different from those
identified when looking directly at the metabolomics data. By
considering the metabolic network and all the interconnections between
the metabolic reactions, we are able to predict changes in metabolic
pathways that could be the indirect consequence of a differential use
of external metabolites. Therefore, we provided strong cues that this
modelling approach based on the metabolic network is useful to suggest
new and complementary pathways that could not be evidenced by looking
directly at the data and would be a helpful tool when digging further
into the mechanisms of the observed changes.
Conclusions and perspectives
Overall, our time-course study of the paired arterial and venous
metabolomes across the intestine and liver of HFHS-fed minipigs
provided original information about the subtle metabolic regulation
taking place during the onset of obesity and insulin
resistance^[159]10,[160]25. Further, our high-throughput approach
allowed reflecting the metabolic activity of these organs by providing
unique information that could not be obtained by a single venous
sampling.
Our approach allowed characterizing the splanchnic area metabolism on
healthy minipigs (Fig. [161]6), including the major role of the liver
at the postabsorptive state as supplier of glucose to the peripheral
organs, such as the intestine. This glucose output is thought to be fed
by different substrates, including muscle-derived lactate and alanine,
but also intestinal AA. Finally, we reported that in the absence of
luminal nutrients, the intestine was largely taking up glutamine and
glutamate, its preferred energy substrates, most likely from muscular
and hepatic sources respectively. After two months of HFHS feeding and
obesity onset, our study of A/V ratios of the metabolome highlighted
three major features: first, the hepatic metabolism shifted from
carbohydrate to lipid utilization; second, the energy demand of the
intestine increased, resulting in an enhanced uptake of already (d0)
used metabolites (glutamine, glutamate), and the recruitment of novel
(d7) energy substrates, like choline and creatine; third, other
metabolic changes, like the uptake of methionine and threonine were
considered to be driven by an increased intestine turnover and
epithelium development to cope with a new diet rich in energy and
nutrients. Eventually, the unique combination of experimental and
modelling-derived data suggested that HFHS feeding would be indirectly
associated with modulations of original metabolic pathways, such
tryptophan metabolism and fatty acid mitochondrial beta-oxidation,
which may play an important role on lipid hepatic accumulation and
whole-body insulin sensitivity.
Supplementary information
[162]Supplementary Figures^ (392KB, pdf)
[163]Supplementary Dataset^ (340.9KB, xlsx)
Acknowledgements