Abstract
Priming and activating immune stimuli have profound effects on
macrophages, however, studies generally evaluate stimuli in isolation
rather than in combination. In this study we have investigated the
effects of pro-inflammatory and anti-inflammatory stimuli either alone
or in combination on macrophage metabolism. These stimuli include host
factors such as IFNγ and ovalbumin-immunoglobulin immune complexes, or
pathogen factors such as LPS. Untargeted LC-MS based metabolomics
provided an in-depth profile of the macrophage metabolome, and revealed
specific changes in metabolite abundance upon either individual stimuli
or combined stimuli. Here, by factoring in an interaction term in the
linear model, we define the metabolome interactome. This approach
allowed us to determine whether stimuli interact in a synergistic or
antagonistic manner. In conclusion this study demonstrates a robust
approach to interrogate immune-metabolism, especially systems that
model host-pathogen interactions.
Introduction
Immuno-metabolism is a rapidly growing area of research. Recent studies
have shown how metabolites such as succinate and itaconate modulate the
function of key innate immune cells such as macrophages [[36]1–[37]4].
Normally, the exposure of macrophages to various stimuli such as
cytokines or pathogenic antigens results in the initiation of various
signalling cascades through their specific receptors (i.e., cytokine
receptors or pattern recognition receptors (PRRs)). Importantly,
metabolites act not only as precursors for anabolic and catabolic
processes but engage with these intracellular and extracellular
signalling pathways to alter the cell phenotype drastically.
Perturbations in pathways such as glycolysis and the pentose phosphate
pathway (PPP) are required to meet the inflammatory cells’ demands for
ATP, NADPH, and ribonucleotide precursors.
Inflammatory macrophage upregulate the production of metabolites such
as succinate and itaconate, which have key effector functions. In
macrophages, itaconate is produced from citrate that accumulates as a
result of the downregulation of isocitrate dehydrogenase [[38]2].
Itaconate has recently been proposed to limit inflammation by
inhibiting succinate dehydrogenase, leading to an accumulation of
succinate [[39]3,[40]4] although other possible mechanisms, including
itaconate acting as a trap for coA [[41]5] and thus affecting
macrophage metabolism directly have also been suggested. Moreover, this
metabolite can also act as an inhibitor of microbial isocitrate lyase,
which was proposed to offer a direct bacteriocidal effect [[42]6].
In contrast to inflammatory macrophages, anti-inflammatory macrophages
rely on mitochondrial oxidative phosphorylation with an intact TCA
cycle that is supplemented by glutamine. Certain key metabolic
processes are differentially regulated in anti-inflammatory
macrophages. For example, they increase uptake of triglycerides via the
CD36 receptor for use in fatty acid oxidation [[43]7]. Recently it has
been found that pathogen generated substrates such as butyrate or
indolepyruvate promote an anti-inflammatory phenotype [[44]8,[45]9]. In
the context of immune-tumour interactions, tumour associated
macrophages reduce glucose availability and promote the formation of an
organized tumor vasculature [[46]10].
To generate inflammatory macrophages for metabolic profiling, different
groups have used different stimuli. For example, Tannahill et al used
LPS [[47]1] while Jha et al used IFNγ + LPS [[48]2]. These studies have
not, however, distinguished between what these stimuli do on their own
and what they do together. An obvious example is the argino-succinate
shunt, which Jha et al demonstrated was upregulated in an attempt to
replenish a fragmented TCA cycle. An essential part of this shunt is
L-citrulline generated by iNOS (inducible Nitric Oxide Synthase), an
enzyme that is maximally upregulated in macrophages treated with both
IFNγ + LPS. In the context of anti-inflammatory macrophages, IL-4 is
commonly used. While these cells are primed towards an
anti-inflammatory phenotype, the effects of pathogen-related stimuli
e.g. LPS, have not been explored at a metabolic level.
To address this gap in knowledge, with regard to the impact of
inflammatory- and anti-inflammatory-driving stimuli on macrophage
metabolism, studies were undertaken to investigate mono- vs
combination-stimuli. To model inflammatory macrophages, naive cells
(M0) were stimulated with either IFNγ (primed cells), LPS or both IFNγ
(primed) and LPS. The rationale behind this was to systematically
determine the immuno-metabolic processes that were driven by the immune
system (IFNγ), a pathogen (LPS) or the interaction of both (IFNγ +
LPS). Similarly, to model anti-inflammatory macrophages,
ovalbumin-immunoglobulin immune complexes (OIC), LPS, and OIC plus LPS
were compared with M0 macrophage.
Materials and methods
Reagents
All cell culture media, serum, and supplements were purchased from
Gibco^®. The stimuli used were murine IFNγ (Peprotech, 315–05), LPS
(Sigma: E. coli 0111:B4, Ref L2630) and murine IL-4 (Ebioscience:
14–8041). IFNγ (100 U/mL: 0.02 μG/mL) was typically added where
indicated for overnight stimulation, after which either LPS (100 nG/mL)
or OIC were added for indicated times. To make OIC, 136.4 μg anti ova
(Creative diagnostics: DPAB26522 polyclonal rabbit serpinb14)) and
13.63 μg albumin (Sigma: A3912) were dissolved in dPBS (Gibco,
Magnesium and Calcium free), mixed and incubated at 37°C for 30 minutes
prior to use. If different concentrations were used, these are
indicated. Note that LPS and OIC were added to wells without replacing
medium (i.e. IFNγ was not removed).
Animal procedures: Macrophage generation
C57BL/6 mice (8–12 weeks old) were bred and housed under standard
laboratory conditions at the University of Glasgow (Glasgow, Scotland).
All experiments were performed under UK Home Office License. Mice were
culled by a Schedule One method (exposure to carbon dioxide gas in a
rising concentration) that is authorized by the Animals (Scientific
Procedures) Act 1986. To generate bone marrow derived macrophages
(BMDM), bones were harvested and cut at each end, and a 23-gauge needle
(Kenke Sass Wolf Fine-Ject^®) was used to flush out bone marrow with
complete RPMI (RPMI (Thermo Fisher) supplemented with 2 mM L-glutamine,
1% (v/v) penicillin streptomycin solution (Sigma), 10% FBS (Thermo
Fisher) :cRPMI) into a 9 cm Petri dish (Thermo Fisher). To obtain a
single cell suspension, cells were passed through a 70 μM cell strainer
(Easy strainer^™ Greiner bio-one), pelleted by centrifugation (300 RCF,
5 min) and resuspended in cRPMI to obtain a density of 6–7 x 10^6
cells/mL. Cell number was calculated using Trypan blue exclusion (1:1
cells: Trypan blue (Sigma)) to ensure that >95% were viable. The BMDM
were matured using 20% L929 supernatant over 7 days (5% CO2, 37°C). In
order to quantify M-CSF levels, a Mouse M-CSF DuoSet (R&D Systems) was
used with L929 supernatant, according to manufacturer’s instructions.
The amount of M-CSF in the L929 supernatant ranged between 165 pg/mL to
285 pg/mL ([49]S1 Fig). Thus, depending on the batch used the cultures
will be receiving 33–57 pg/mL; less than a half log difference.
On day three, 5 mL cRPMI and 2 mL L929 supernatant were added. On day
six, BMDM were harvested. Medium was removed by aspiration and plates
were washed once with warm dPBS to remove remaining non-adherent cells.
Note that this wash was only done for experiments involving overnight
IFNγ treatments. To each plate 6 mL of ice-cold dPBS was added for 1–2
minutes. Cells were detached by gentle scraping, transferred to a 50 mL
falcon tube. Plates rinsed once more with 6 mL of ice-cold dPBS,
transferring the same 6 mL between plates to collect remaining cells.
Cells were pelleted by centrifugation (300 RCF, 5 min), resuspended in
cRPMI and viable cell number was calculated using Trypan blue
exclusion. Cell density was then adjusted by dilution to the desired
density (10^6 cells/mL in this study). Next, cells were left to
re-adhere overnight in tissue culture plates. Typically 6, and 96 flat
well plates were used (Costar).
Characterisation of BMDM
Flow cytometry analysis was used to verify that BMDM had expected
phenotypic surface markers ([50]S2 Fig). For cell surface staining,
10^6 cells were detached from plates as described above, transferred to
5 ml FACS tubes (BD Falcon), washed twice with 1 mL of dPBS (Mg^2+ and
Cl^- free, 1 mM EDTA). Viability staining was executed using APC-eFluor
670 (Ebioscience) according to manufacturer’s instructions. This
protocol ends with cells in FACs buffer (PBS, 1 mM EDTA, 2% FBS). For
these experiments, alongside fluorescent–1 (FLO-1) controls,
compensation beads (eBioscience: OneComp eBeads) were used with each
antibody (Biolegend: Cd11b [Brilliant violet 510, Rat IgG2a, κ] and
F4/80 [Brilliant violet 421, Rat IgG2a, κ]), according to
manufacturer’s instructions. For nitrite determination, supernatants
were transferred to a 96 well plate, centrifuged (300 RCF, 5 minutes)
and 50 μL transferred to a new 96 well plate. Standard curves were made
using a serial dilution from 0.1 M Nitrite (Sigma). The concentrations
used were 100, 50, 25, 12.5, 6.25, 3.125 and 0 μM. To the samples and
standards, 100 μL of Greiss reagent (Sigma: 03553) was added and plates
read at 570 nm using a FLUOstar OPTIMA micro-plate reader (BMG
Labtech). Medium incubated in empty wells were used as matrix blanks.
Sample preparation for LCMS
3 million macrophages/well/replicate were seeded in a 6 well plate at a
density of 10^6 cells/mL and extracted in a volume of 400 μL
chloroform/methanol/water, 1:3:1 (CMW). Medium was aspirated and cells
were washed once with dPBS (Magnesium and Calcium free, no EDTA),
aspirating immediately. Ice-cold extraction solvent
(chloroform/methanol/water, 1:3:1) was added to each well, including an
empty well (for solvent blank), and cells were left shaking (1 hour,
4°C). Samples/replicates were not pooled. Solvent was then transferred
to 1.5 mL micro-tubes, and centrifuged (18,000 RCF, 15 minutes, 4°C).
Supernatant was immediately transferred to 2 mL screw-cap tubes. Sample
from each tube was transferred to a new screw-cap tube for a pooled
sample used in mass spectrometry quality control. At this point,
samples were capped with Argon, and then stored at -80°C until LC-MS
analysis. To calculate protein levels post extraction, NaOH (0.1 M, 400
μL/10^6 cells) was added to each well. The plates were left shaking (15
minutes 4°C). Plates were next centrifuged (300 RCF, 5 min) and 10 μL
of supernatant transferred to a 96 well plate. Note that for each
replicate well (n = 4) 3 ([51]Fig 1a) -4 ([52]Fig 1b) technical
replicates were taken for protein concentration measurement. To the
same plate, a 10 μL of a BSA (Sigma) dilution series (2 fold: 0.5–0
mg/mL in 0.1 M NaOH) was added to allow for determining of protein
concentration. Finally, 190 μL of Bradford reagent (BioRad) was added
to each plate on top of samples and standards. Absorbance was measured
at 595 nm using a FLUOstar OPTIMA micro-plate reader. In [53]Fig 1a and
1b the average of the technical replicates is shown.
Fig 1. Method of quantifying protein content (mg/mL) post extraction.
[54]Fig 1
[55]Open in a new tab
(A): Macrophages were seeded at densities indicated (n = 4) and protein
content quantified post extraction. Results are representative of two
independent experiments. (B): The same protocol was employed post
extraction on cells cultured using a standard protocol (n = 4). Two
sample groups were primed with IFNγ (100 U/mL) overnight, then LPS (100
ng/mL) for 4h (IFN LPS) while the other sample did not get LPS (IFN).
One group of samples was treated with LPS without prior IFNγ
stimulation (LPS) while M0 is non-treated. The cells that did not
receive a given stimulus received an equal volume of cRPMI. (C) As in
B, protein quantification was carried out post extraction on samples (n
= 4) that were prepared using the modified culturing protocol. Stimuli
use and duration were as used in B. (D) PCA on anti-inflammatory
(Dataset 1, n = 4) and (E) inflammatory (Dataset 2, n = 4) macrophage
data. For D, either LPS (100 nG/mL), ovalbumin-immunoglobulin immune
complexes (OIC) (136.4 μg anti ova:13.63 μg albumin), or both were
added for six hours. For E, culture conditions and stimuli treatment
were as used in C. Note that C is the actual protein measurements of
the samples in E. Data was log transformed prior to analysis.
MetaboAnalyst 3.0, which utilises the pcaMethods Bioconducter package
[[56]17] was used to construct PCA plots. 95% confidence intervals are
highlighted by the respective background colour.
LCMS
All samples were separated with high performance liquid chromatography
(HPLC) on a Dionex Ultimate 3000 RSLC system (Thermo) using ZIC-pHILIC
(Merck) column. The mass spectrometry platform used was a qExactive
Orbitrap mass spectrometer (Thermo). Analysis was performed in positive
and negative mode; using 10 μL injection volume and samples were
maintained at 4°C during analysis. A linear biphasic LC gradient was
conducted from 80% B to 20% B over 26 minutes, where solvent B was
acetonitrile and solvent A was 20 mM ammonium carbonate in water. The
flow rate was 300 μL/min, and column temperature maintained at 25°C.
For longer LC protocol the gradient was identical except that it was
over 46 minutes. Each sample was run in a randomized order, with a
pooled sample run between every 4 samples.
The MS set up was calibrated [Thermo calmix (Pierce^™ calibration
solutions from Thermo Scientific) with masses at lower m/z; 74.0393 m/z
(C2H6NO2: +) and 89.0244 (C3H5NO3: −)] in both ionization modes before
analysis and a tune file targeted towards the lower m/z range was used.
Full scan (MS1) data was acquired in both ionization modes in profile
mode at 50,000 resolution (at m/z range 70–1400), an automatic gain
control (AGC) target of 106 cts, with spray voltages +4.5 kV (capillary
+50 V, tube: +70 kV, skimmer: +20 V) and −3.5 kV (capillary -50 V,
tube: -70 kV, skimmer: -20 V), capillary temperature 275°C, probe
temperature 150°C, sheath gas flow rate 40, auxiliary gas flow rate 5
a.u., and a sweep gas flow rate of 1 a.u. For fragmentation, the
settings are in [57]S1 File and the analysis was conducted using
mZCloud.
Data processing and analysis
Data analysis was performed using the XCMS [[58]11] mzMatch [[59]12]
and IDEOM [[60]13] software for untargeted analysis. Xcalibur (Thermo)
was used for targeted peak picking and exporting fragmentation spectra,
which were used as queries with which to search mzCloud [[61]14]. A
mixture of 240 standards (in three separate mixes), covering a range of
metabolic pathways, was run alongside each sample batch to allow
metabolite identifications (MSI level 1). According to the metabolomics
standards initiative (MSI), metabolite identifications (MSI level 1)
are given when more than one feature matches an authentic standard
(i.e., mass and retention time) while annotations are made when
matching to a metabolite is made by mass only (MSI level 2) [[62]15].
Peaks were visually interrogated on the identification tab of the IDEOM
spreadsheet, resulting in some annotated features being removed at this
stage due to poor peak quality. To avoid the lipid bolus, a 4.2-minute
retention time cut off was applied. As lipids and peptides are not
reproducibly detected using this platform, they were removed from
statistical analysis. If available, fragmentation data was used to
strengthen confidence in identifications. PLSDA statistical analysis
was conducted using MetaboAnalyst on log-transformed data. To avoid
over-fitting, 1,000 permutations were run using prediction accuracy
during training as well as separation distance (the ratio of the
between sum of the squares and the within sum of squares: B/W).
Graphpad Prism (One way ANOVA with Tukey’s correction for pairwise
comparisons) was used to test variance between batches of MCSF levels
in L929 supernatant and significance denoted as adjusted p less than or
equal to 0.05). For the GLM analysis conducted using R [[63]16] with
the Benjamini-Hochberg procedure (false discovery rate (FDR) less than
0.05). Finally, β-alanine and L-alanine could be separated based on
retention time but the gap was too small for standard settings within
the IDEOM pipeline [[64]13]. For these metabolites, a custom method was
written in Xcalibur (Thermo) where an appropriate retention time window
(±30 seconds of authentic standard) was used to obtain an accurate peak
area measurement.
Results & discussion
Standardising sample preparation and global metabolic profiling
Overnight priming with IFNγ led to increased cell density with fewer
non-adherent cells. Therefore, to ensure that samples were comparable,
we quantified the levels of protein that was precipitated during the
extraction protocol ([65]Fig 1A). This revealed that IFNγ treated cells
had higher protein levels ([66]Fig 1B) so our culturing technique was
modified to account for this ([67]Fig 1C). Here we added a washing
stage with warm (37°C) PBS to ensure that macrophage subsequently
removed via scraping for use in experiments were firmly attached. In
both experiments, using different types of inflammatory or
anti-inflammatory macrophage, clear separation was evident when the
data was subjected to principal component analysis (PCA) with the
inflammatory macrophages being the most distinct ([68]Fig 1D and 1E).
For the inflammatory macrophage dataset, filtering in IDEOM and the
Xcalibur software resulted in a list of 233 metabolites. The identity
of 74 of these was confirmed using authentic standards (MSI level 1).
For the anti-inflammatory macrophage dataset, 372 metabolites were
annotated, with 98 of these confirmed using authentic standards (MSI
level 1).
We conducted Partial Least Squares Discriminant Analysis (PLSDA) to
obtain VIP scores (a measure of a variable's importance) for
metabolites in both data sets. Similar to the results from PCA, there
was separation between sample classes in both datasets ([69]S3 Fig).
PLSDA is a supervised method (not blind to class types) and it can
over-fit data. To decrease the possibility of this occurring, 1,000
permutations were run using prediction accuracy during training as well
as separation distance (the ratio of the between sum of the squares and
the within sum of squares: B/W). For both datasets, results of both
permutation tests each gave a p value <0.02, hence the method was
applied herein.
There was some overlap between both datasets in the top 80 results (VIP
score). Metabolites of interest included IMP, inosine, guanine,
D-ribose 5-phosphate, adenosine and itaconate. The complete results
from this analysis are located in [70]S2 File. While these metabolites
belong to pathways that are known to be important for macrophage
function, this approach did not take into account the extent to which
each immune stimulus contributed to perturbations in these pathways.
For inflammatory macrophages, iNOS activity is a key part of their
microbicidal machinery. The activity of this enzyme is maximised in the
presence of both IFNγ and LPS (nitrite levels shown in [71]Fig 2) so
this would be an example of a metabolite that could be subject to an
interaction effect. Note that [72]Fig 2 is a separate experiment. As
L-citrulline is a by-product of this enzyme and given that it has
recently been demonstrated that this metabolite is required to
supplement the anaplerotic TCA cycle present in inflammatory
macrophage, we chose a method of statistical analysis that allows
testing for interactions between the various immune stimuli used.
Fig 2. Response in nitrite levels to varying stimuli levels.
[73]Fig 2
[74]Open in a new tab
Cells were primed with IFNγ (indicated concentrations) overnight, and
subsequently with LPS (indicated concentrations) for 24h. Nitrite was
quantified using the Griess assay (n = 3).
A generalised linear model, categorising interactions
For analysing both datasets a generalised linear model (GLM), as
implemented in the R coding language (GLM) ([75]S3 File), was used. GLM
is a generalised instance of the linear model LM (e.g., ANOVA
procedure) [[76]16]. Here the permutations were performed using a least
squares regression approach to describe the statistical relationship
between one or more predictors (in this case the stimuli) and a
continuous response variable (in this instance a given metabolite
intensity).
Since we were interested in assessing whether one immune stimulus could
affect another we incorporated an interaction term to determine whether
two predictor variables affect the outcome variable in a way that is
non-additive. This is particularly appropriate for this experiment as
it is widely accepted that the combined effect of IFNγ and LPS can
increase inflammation (e.g., [77]Fig 2). In general terms as
implemented here:
[MATH:
model=g<
mi>lm(Y~X1<
/mn>+X2+X1:X1) :MATH]
(1)
In R, this function regresses Y on X1 (IFNγ or OIC), X2 (LPS), and the
X1-by-X2 (IFNγ or OIC-by-LPS) interaction term.
The input file required for this analysis in text format and the
results of this analysis are in [78]S4 and [79]S5 Files. A list of all
metabolites detected in both experiments, confirmation of
identification using accurate Mass (MS1), authentic standards or
fragmentation can be found in [80]S6 File.
Stimuli specific signatures induced by OIC/IFNγ and/or LPS ([81]Fig 3)
denotes effects that are additive, antagonistic or synergistic; with
increases or decreases in metabolite levels noted in comparison to the
non-treated control (M0). An example of this can be seen in Dataset 2
(inflammatory set) where IFNγ and LPS each perturb the levels of
L-citrulline on their own but in combination are highly synergistic.
Fig 3. Heatmaps showing metabolites with significantly (GLM FDR < 0.05)
altered levels.
[82]Fig 3
[83]Open in a new tab
Treatment groups are as indicated (n = 4 for both 3a and 3b). For the
“Significance” grey refers to GLM corrected p-value (FDR< 0.05).
Dataset 1 (anti-inflammatory, [84]Fig 1D) is shown in the first heatmap
and Dataset 2 (inflammatory set, [85]Fig 1E) in the second.
IFNγ, a primer and active agent
In Dataset 2 (inflammatory set), IFNγ causes a clear depletion of
purine and pyrimidine related metabolites such as xanthine and uridine,
and in some cases, this is enhanced by the presence of LPS (e.g.
hypoxanthine). In purine metabolism, xanthine oxidase (XO) catalyses
the conversion of hypoxanthine to xanthine, producing H[2]O[2] as a
by-product. The H[2]O[2] produced by XO has been shown to activate the
p38-MAPK-NFAT5 pathway and inhibiting XO with allopurinol, limited
inflammation in a mouse model of arthritis (14). Perturbations to
levels of both the substrate and product of XO in stimulated cells were
also observed. It is notable that increases in many of the hallmark
metabolites of immune-metabolism can be explained by only one stimulus
or a slight additive effect. For example, metabolites involved in
glycolysis (fructose 1,6-bisphosphate and 3-phospho D-glycerate), the
TCA cycle (2-oxoglutarate), and the PPP (ribose 5-phosphate and
erythrose 4-phosphate), are all primarily driven by LPS.
Both LPS and IFNγ induce several classical inflammatory metabolites
such as succinate, L-glutamate and (S)-malate. Both these stimuli also
increased itaconate levels, although this metabolite has recently been
proposed to limit inflammation [[86]3,[87]4], suggesting that its
elevation may act as a constraint to macrophage activation.
Perturbations were also detected in NAD^+ and NADH levels, which have
roles in the cells’ redox state ([88]Fig 4). Note that the LC-MS
platform employed in these studies is not optimised to detect
metabolites in their native redox state. Glutathione is critical to
cellular redox chemistry. Significant differences in either reduced
(GSH), or oxidised (GSSG) were not apparent. It is important that the
roles of the single treatments and the combinations used here be
investigated in the context of cell redox state using biochemical
assays designed specifically for these metabolites.
Fig 4. Our results on (A) inflammatory macrophages and (B) anti-inflammatory
macrophages in the context of macrophage immune-metabolism.
[89]Fig 4
[90]Open in a new tab
Note that location of metabolites does not refer to cellular location.
1: & 2: Stimuli (LPS and IFNγ) were used as described in [91]Fig 1. 3:
This causes increase in metabolites in glycolysis ((R))-Lactate) and
the PPP ([92]S4 File). 4: Previously reported perturbations in the TCA
cycle are indicated including Itaconate production and Succinate
accumulation. 5: L-Arginine is metabolised to L-Citrulline and effector
molecule NO*. 6: Arginine and Glutamate metabolism are connected with
N-L- (Arginino)-Succinate and aspartate (from glutamate) allow for
replenishment of the TCA cycle at the point of Fumarate. 7:
Dihydrobiopterin is used to form the iNOS cofactor tetrahydrobiopterin
by Dihydrobiopterin reductase. 8: Levels of select carnitine related
metabolites are modulated by LPS and/or IFNγ. 9: Taurine metabolism is
modulated by IFNγ. 10: Purine and pyrimidine metabolism displays as
stimuli specific profile as did some modified analogues. 11: Levels of
β-alanine, which can originate from arginine, pyrimidine fatty acid or
glutamate metabolism, were increased in inflammatory macrophage. 12:
alterations in nucleotide analogues were present in both datasets. 13:
C-ADP-ribose in increased in an IFNγ dependent manner. For colour
schemes, blue refers to nucleotide analogues, purple to purine and
pyrimidine metabolism; orange to carnitine related metabolites; red
refers to central carbon and arginine metabolism; and pink refers to
taurine metabolism. Differences in metabolites present in each A and B
is due to a lack of significant changes or metabolite not being
detected. Direct reactions are in bold lines; dashed lines show
multistep reactions.
The increase of several acylcarnitines by LPS e.g.
trans-hexadec-2-enoylcarnitine, elaidiccarnitine and
linoelaidylcarnitine is striking. Notably, elaidiccarnitine and
linoelaidylcarnitine had similar retention times and thus one may be an
adduct of the other. Adding IFNγ with LPS, however, reverses the
stimulatory effect of LPS on these metabolites. L-carnitine and other
O-acetyl-L-carnitine are, however, increased in the presence of IFNγ
with the presence of LPS having little effect. Importantly, an increase
in L-carnitine levels has previously been observed in anti-inflammatory
macrophage [[93]2]. Further work will be needed to elucidate whether
these metabolites have a function in a pro-/anti-inflammatory (or both)
responses in the context of macrophage metabolism. This is especially
relevant when considering that macrophage activation has been shown to
be a spectrum rather than a binary pro/anti-inflammatory system at a
transcriptional level [[94]18].
Inosine monophosphate (IMP) displays a similar trend to
trans-hexadec-2-enoylcarnitine, elaidiccarnitine and
linoelaidylcarnitine in that it was increased in LPS treated samples
but not in samples treated with both IFNγ and LPS ([95]S4 Fig).
Similarly, inosine was increased in LPS treated samples but while it
was lower in the combination treatment, this did not reach statistical
significance.
Possible role of OIC in counteracting effects of LPS
While there are some similarities to the role of OIC and IFNγ, it is
clear that LPS is the dominant stimulus in Dataset 1 (anti-inflammatory
set), although the variability within groups visible in [96]Fig 3 may
contribute to this. Pyrimidines, purines, L-carnitine,
hydroxybutyrylcarnitine and O-butanolycarnitine (the latter two are
acyl-carnitines) are mostly increased when compared to the M0 group.
Two acylcarnitines are increased in an OIC dependent manner. The
acylcarnitines measured here reveal complex patterns with regard to
increases and decreases with various stimuli. Ascertaining specific
roles for these metabolites will be an intriguing subject of future
studies.
Both OIC and LPS cause an increase in itaconate, which has been shown
to limit inflammation. As this is the first study to use OIC to
interrogate immune metabolism, further studies will be required to
elucidate the mechanisms and consequences of this increase in
itaconate. Itaconate appears to have complex roles in inflammation. For
example, it has been proposed to have direct anti-bacterial properties
[[97]6], but also dampen inflammatory response [[98]3,[99]4]. The
results reported here conform to this complex role of itaconate in the
inflammatory response. L−citrulline, N−(L−arginino)succinate, and
D−fructose 6−phosphate and are all increased by LPS and these
inflammatory signature metabolites remain increased even when OIC are
present. Note that these increases driven by LPS match that of previous
studies that examined metabolism [[100]1] or metabolism and
transcriptomics [[101]2] of inflammatory macrophages but here the
effect of LPS and IFNγ can be separated. (S)-malate and guanine are
both increased by OIC, an effect which is abrogated by the presence of
LPS. Interestingly, in both datasets, the priming stimuli on its own
caused a slight increase in guanine, which was further increased in the
presence of LPS. The converse of this was true for hypoxanthine and
IMP, which decrease when both stimuli are present ([102]S4A and
[103]S6A Figs).
This may be a consequence of increased IMPDH activity (which catalyses
the conversion of IMP to xanthosine monophosphate (XMP, not detected),
as the first committed step towards the de novo biosynthesis of guanine
nucleotides ([104]S4A and [105]S6B Figs). Increases in the abundance of
transcripts from genes encoding the guanosine metabolising enzymes pnp
and pnp2, which also catalyse inosine-hypoxanthine interconversions has
also been noted [[106]2]. A key immune-modulatory role for IMPDH has
been proposed [[107]19]. In that study, the authors found that the
IMPDH inhibitor mycophenolate mofetil suppressed production of
pro-inflammatory cytokines, nitric oxide, and lactate dehydrogenase in
a macrophage cell line. It is important to note that in these
experiments the intensity of IMP and inosine was near the lower
detection limit of our LCMS system. Interestingly, adenosine, which is
known to down-regulate classical macrophage activation [[108]20], is
increased in an IFNγ-dependent manner. In Dataset 1 (anti- inflammatory
set), the increase in adenosine induced by LPS is repressed by the
presence of OIC. Whether this is indicative of a regulatory mechanism
remains to be determined. Finally, there were two modified nucleotide
analogues that had altered levels; 5’-methyl-2’deoxycytidine (marker of
de-novo DNA methylation: decreased by all stimuli) and 3-methyguanine
(altered in leukaemia, tumours and immunodeficiency: increased by all
stimuli).
Thus, perturbations in these pathways are not explained by just one
stimulus simply increasing or decreasing overall levels. Nucleotides
clearly have important roles in cellular signalling pathways as well as
nucleic acid biosynthesis and untangling the varied contributions of
purine metabolism to macrophage differentiation will be a key challenge
in understanding immunometabolism more generally. Metabolic responses
to different stimuli can be different, contradictory and certainly
complex. This implies that studies involving single or even a limited
set of stimuli will not create a true reflection on the complicated
picture in vivo.
Pathway analysis
To formally categorise the contribution of LPS and OIC or IFNγ to
biological pathways, pathway analysis was performed on log-transformed
data using the pathway analysis module in MetaboAnalyst. This allowed
us to use the murine pathway library and upload a relevant background
(all detected metabolites). The use of a background is important as it
allows technical bias specific to the instrument used to be taken into
account.
Herein, a list of all detected metabolites was used as the background.
The Pathway Enrichment Analysis used is based on the GlobalTest
algorithm [[109]21]. To estimate node importance, the
Relative-betweeness Centrality algorithm was selected. This measures
the number of shortest paths going through the node, focusing more on
global network topology. Thus changes in metabolites at central nodes
within or between pathways are given more importance than those at the
extremities as they are more likely to effect the pathway (s)
[[110]21].
First, metabolites either effected by LPS or IFNγ (OIC in the case of
Dataset 1), or the interaction of the two: IFN:LPS (OIC:LPS in the case
of Dataset 1) were analysed separately and together ([111]Table 1). An
impact score of ≥0.1 was used as a threshold to select a shortlist of
pathways for further investigation.
Table 1. Pathway analysis of inflammatory (Dataset 2) and anti-inflammatory
(Dataset 1) macrophage datasets.
Dataset 2
Total Compounds IFN list LPS list Interaction list Entire list
FDR Impact Hits FDR Impact Hits FDR Impact Hits FDR Impact Hits
Pyrimidine metabolism 12 0.003 0.021 2 0.002 0.021 2
Butanoate metabolism 5 0.005 0.029 1 0.000 0.029 2
Citrate cycle (TCA cycle) 8 0.002 0.068 1 0.002 0.068 1
beta-Alanine metabolism 4 0.003 0.444 1 0.002 0.444 1 0.002 0.444 1
Glycerophospholipid metabolism 7 0.005 0.044 1 0.003 0.068 2 0.004
0.068 2
Glycine, serine and threonine metabolism 7 0.007 0.031 2 0.019 0.031 2
0.011 0.031 3
Purine metabolism 15 0.000 0.127 2
Arginine and proline metabolism 18 0.005 0.047 4 0.002 0.023 1 0.000
0.054 2 0.004 0.047 4
Alanine, aspartate and glutamate metabolism 13 0.005 0.114 1 0.002
0.063 1 0.001 0.022 1 0.000 0.177 2
Taurine and hypotaurine metabolism 4 0.001 0.714 2
Primary bile acid biosynthesis 1 0.001 0.030 1
Dataset 1
Total Compounds OIC list LPS list Interaction list Entire list
FDR Impact Hits FDR Impact Hits FDR Impact Hits FDR Impact Hits
Glycerophospholipid metabolism 6 0.010 0.044 1 0.010 0.044 1
Glutathione metabolism 8 0.010 0.003 1 0.011 0.003 1
Histidine metabolism 11 0.016 0.108 1 0.016 0.108 1
Tyrosine metabolism 5 0.021 0.001 1 0.021 0.001 1
Glycine, serine and threonine metabolism 10 0.002 0.031 1 0.002 0.031 1
0.003 0.031 2
Arginine and proline metabolism 20 0.002 0.023 1 0.006 0.012 2 0.002
0.023 1 0.003 0.035 3
Citrate cycle (TCA cycle) 9 0.002 0.068 1 0.003 0.068 1 0.002 0.068 1
0.003 0.068 1
Alanine, aspartate and glutamate metabolism 12 0.002 0.063 1 0.003
0.063 1 0.002 0.063 1 0.003 0.063 1
Pyrimidine metabolism 20 0.002 0.140 5 0.003 0.147 6 0.002 0.133 4
0.003 0.147 6
beta-Alanine metabolism 5 0.002 0.444 1 0.003 0.444 1 0.002 0.444 1
0.003 0.444 1
Purine metabolism 21 0.009 0.005 1 0.011 0.005 1
Phenylalanine metabolism 5 0.018 0.130 1 0.017 0.130 1 0.018 0.130 1
[112]Open in a new tab
Analysis was conducted on log-transformed data (Kegg IDS) using
MetaboAnalyst. Here the GlobalTest was used in conjunction with the
Relative-betweeness Centrality algorithm. For each dataset, a list of
all detected metabolites (Kegg IDs) was used as a background. Dataset 1
refers to the anti-inflammatory dataset and Dataset 2 refers to the
inflammatory dataset. The maximum importance of each pathway is 1, and
the pathway impact is the cumulative proportion from the matched
metabolite nodes.
As mentioned above, LPS induced accumulation of inosine and inosine
monophosphate (IMP) and an IFNγ mediated increased guanine were among
the most significant changes detected in our study (note that OIC had a
similar effect on guanine). Pathway analysis confirms the perturbation
of pyrimidine metabolism by the stimuli used in this study. Whether
these changes in nucleotide metabolism represent a predisposition to
the transcriptional changes associated with macrophage activation, or
else other nucleotide signalling events is unknown. Purine metabolism
is also significantly altered in both datasets.
β-alanine metabolism was significantly altered according to pathway
analysis in both datasets. There are only 5 metabolites in this KEGG
pathway and while it is part of several other KEGG pathways, it has
been reported that β-alanine is a by-product of increased flux through
arginine metabolism via carnosine synthase 1 (Carns1) in
anti-inflammatory macrophages [[113]2]. Carns1 catalyses the conversion
of carnosine (no difference detected) and ADP (not detected) to
L-histidine (no difference detected), ATP (no difference detected) as
well as β-alanine. An alternative source of β-alanine is pyrimidine
metabolism that was subject to stimuli specific perturbations ([114]S5
and [115]S6C Figs). Tracing the fate of arginine in polarised
macrophage should help to determine if this is the case,
Several studies have investigated the immune-modulating capability of
β-alanine [[116]22]. Prabha et al, for example, reported that β-alanine
caused down-regulation of lipoprotein lipase (LPL) activity and altered
cholesterol metabolism. Harris et al found that IFNγ inhibits LPL
transcription in macrophages [[117]23]. As the increases presented here
were present in all combinations of IFNγ treatment, it would be
interesting to determine if β-alanine has a role modulating LPL
activity. We also detected IFNγ-mediated increases in taurine and its
related metabolites hypotaurine and taurocyamine ([118]S4B and [119]S6C
Figs). These metabolites have been shown to modulate cholesterol
metabolism and LPL activity [[120]22].
Conclusion
This study has revealed hitherto unexpected complexity in macrophage
response to different immune-stimuli. Further targeted analysis (e.g.
through isotope labelling studies) and validation studies (medium
deprivation and inhibitors) will enable further dissection of roles
played by individual metabolites and their pathways in regulating the
behaviour of macrophage. By combining single and combinatorial
treatment of macrophage with a linear model that incorporates an
interaction term, we are able to denote which key pathways are
perturbed by each stimulus and whether they interact in a synergistic
or antagonistic manner or not, findings what are likely to be relevant
when considering the multiple stimuli present in vivo ([121]Fig 4).
This will be especially important for future studies when taking in to
consideration that the diverse range of different TLR signalling
pathways, i.e. TLR2, can and do result in different immune-metabolic
programme [[122]24] while use of bacteria, virus or co-infections can
model in vivo situations where multiple signalling cascades are
engaged. Other factors to consider are the tissue of origin of either
monocytes or mature macrophage or if the tissue is diseased. For
example, mature peritoneal macrophages are distinct metabolically from
BM derived macrophage [[123]25]. Additionally the differentiation of BM
derived macrophage can be achieved by MCSF or GMCSF, the latter of
which generates inflammatory-primed macrophage and it would not be
unexpected that this could have an effect on the mature macrophage
metabolic phenotype. This knowledge will be essential if deciding to
target inflammation via the pathogen, immune system, or both.
Supporting information
S1 Fig. MCS-F of 4 batches of L929 supernatant was quantified using
ELISA.
A one-way ANOVA with a Tukeys multiple comparison test was used to test
for significance (p<0.05).
(TIF)
[124]Click here for additional data file.^ (98KB, tif)
S2 Fig. Flow cytometry characterisation of cultured macrophage.
(A) Selecting population based on forward scatter area (FSC-Area)
against side scatter area (SSC-Area). (B) Gating on single cells using
SSC-Area against side scatter area (SSC-Height). (C) Gating on live
cells using gating on cells negative for the viability dye Ef780. (D)
Gating on Cd11B+ cells then, (E) characterising presence of F4 80. (F)
Fluorescence -1 (FLO-1) control for Cd11b and, (G) F4-80. (H) Histogram
overlay of D (blue) and F (red). (I) Histogram overlay of (E) (blue)
and (I) (red). Results are representative of 3 biological replicates.
(TIF)
[125]Click here for additional data file.^ (1.6MB, tif)
S3 Fig
(A) PLS-DA conducted on dataset 1 and 2 (log transformed data). The
given conditions are denoted in the inset box. (B) 1000 permutations
were run using prediction accuracy during training as well as (C)
separation distance (B/W).
(TIF)
[126]Click here for additional data file.^ (1MB, tif)
S4 Fig. (A) Purine and (B) taurine metabolism related metabolites from
Dataset 2.
Significance as determined by GLM is denoted by asterisk. Broken lines
denote multi-step reactions.
(TIF)
[127]Click here for additional data file.^ (822.4KB, tif)
S5 Fig. Pyrimidine metabolism related metabolites from Dataset 2.
Significance as determined by GLM is denoted by asterisk. Broken lines
denote multi-step reactions.
(TIF)
[128]Click here for additional data file.^ (502.6KB, tif)
S6 Fig. (A): Purine, (B): pyrimidine and C: Taurine metabolism related
metabolites from Dataset 1.
Significance as determined by GLM is denoted by asterisk. Broken lines
denote multi-step reactions.
(TIF)
[129]Click here for additional data file.^ (548.2KB, tif)
S1 File. Fragmentation settings used in creating Dataset 2.
(TXT)
[130]Click here for additional data file.^ (3.4KB, txt)
S2 File. Results of PLDA that was conducted on Datasets 1 and 2.
(XLSX)
[131]Click here for additional data file.^ (40.8KB, xlsx)
S3 File. R-code used to run GLM.
(R)
[132]Click here for additional data file.^ (2.8KB, R)
S4 File. Input, summary and supplemental information about results of
GLM on Dataset 2.
For GLM, p values and Benjamini-Hochberg corrected values are shown. In
the ANOVA tab, the given pairwise comparisons that are significantly
different at the stated false discovery rate are shown. If a metabolite
matched an authentic standard in retention time or its fragmentation on
mZCLOUD, this is denoted in the final two columns on the GLM
significant tab.
(XLSX)
[133]Click here for additional data file.^ (174.4KB, xlsx)
S5 File. Input, summary and supplemental information about results of
GLM on Dataset 1.
For GLM, p values and Benjamini-Hochberg corrected values are shown. In
the ANOVA tab, the given pairwise comparisons that are significantly
different at the stated false discovery rate are shown. If a metabolite
matched an authentic standard in retention time this is denoted in the
final column on the GLM significant tab.
(XLSX)
[134]Click here for additional data file.^ (213.6KB, xlsx)
S6 File. Confidence of metabolite identification in two datasets is
denoted.
Authentic standards were run along side samples. MS2 was carried out on
pooled sample (Dataset 2 only).
(XLSX)
[135]Click here for additional data file.^ (13.9KB, xlsx)
Data Availability
Raw and processed data in this study has been successfully processed
and is now identified in MetaboLights as MTBLS570. All other relevant
data are within the paper and its Supporting Information files.
Funding Statement
This study was supported by funding from the Wellcome Trust (Wellcome
Centre for Molecular Parasitology grant number 107046/Z/15/Z.
[136]https://wellcome.ac.uk/) (MB). KR was funded by the Wellcome Trust
PHD programme and this funding covered stipend, bench fees. FA is a
Leverhulme Trust fellow (ECF-2015-392). The funders had no role in
study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
References