Abstract
Cerebral ischemia, resulting from compromised blood flow, is one of the
leading causes of death worldwide with limited therapeutic options.
Potential deleterious injuries resulting from reperfusion therapies
remain a clinical challenge for physicians. This study aimed to explore
the metabolomic alterations during ischemia-reperfusion injury by
employing metabolomic analysis coupled with gas chromatography
time-of-flight mass spectrometry (GC-TOF-MS) and ultraperformance
liquid chromatography quadrupole (UPLC/Q)-TOF-MS. Metabolomic data from
mice subjected to middle cerebral artery occlusion (MCAO) followed by
reperfusion (MCAO/R) were compared to those of the sham and MCAO
groups. A total of 82 simultaneously differentially expressed
metabolites were identified among each group. The top three major
classifications of these differentially expressed metabolites were
organic acids, lipids, and organooxygen compounds. Metabolomics pathway
analysis was conducted to identify the underlying pathways implicated
in MCAO/R. Based on impactor scores, the most significant pathways
involved in the response to the reperfusion after cerebral ischemia
were glycerophospholipid metabolism, linoleic acid metabolism,
pyrimidine metabolism, and galactose metabolism. 17 of those 82
metabolites were greatly elevated in the MCAO/Reperfusion group, when
compared to those in the sham and MCAO groups. Among those metabolites,
glucose-6-phosphate 1, fructose-6-phosphate, cellobiose 2,
o-phosphonothreonine 1, and salicin were the top five elevated
metabolites in MCAO/R group, compared with the MCAO group. Glycolysis,
the pentose phosphate pathway, starch and sucrose metabolism, and
fructose and mannose degradation were the top four ranked pathways
according to metabolite set enrichment analysis (MSEA). The present
study not only advances our understanding of metabolomic changes among
animals in the sham and cerebral ischemia groups with or without
reperfusion via metabolomic profiling, but also paves the way to
explore potential molecular mechanisms underlying metabolic alteration
induced by cerebral ischemia-reperfusion.
Keywords: ischemic-reperfusion, metabolites, non-targeted metabolomics,
UPLC/Q-TOF-MS, GC-TOF-MS
1 Introduction
Cerebral ischemia, resulting from compromised blood flow, is one of the
leading causes of death worldwide with limited therapeutic options
([46]DeSai and Hays Shapshak, 2022). Acute reperfusion therapies,
including intravenous thrombolytics and mechanical thrombectomy, which
can help with the restoration of cerebral blood flow and energy supply,
have been used for the treatment of acute ischemic stroke ([47]Imran et
al., 2021). However, the clinical outcomes after reperfusion to
ischemic brain are not optimal, as expected. Researchers have
demonstrated that the restoration of oxygen and glucose supply
resulting from reperfusion could also cause subsequent deleterious
injury aside from ischemia alone, eventually leading to profound
inflammatory and neuronal death in the brain ([48]Wu et al., 2018).
Advanced understanding of ischemia-reperfusion (IR) injury revealed
that several underlying pathophysiological mechanisms are involved in
reperfusion-induced injuries, such as oxidative stress ([49]de Vries et
al., 2013; [50]Wu et al., 2020; [51]2016), excitotoxicity,
mitochondrial dysfunction, activation of the complement system,
blood-brain-barrier disruption, and neuroinflammation ([52]2016). Owing
to the above-mentioned complexity of IR injury, no effective
therapeutic targets have been developed to date. Therefore, further
investigations of potential molecular pathways and neuroprotective
interventions to minimize reperfusion injury are urgently warranted to
improve the clinical outcomes of patients with ischemic stroke.
The brain requires 20%–25% of the energy provided by basal metabolism,
which is a massive demand for energy considering its relatively small
size and weight ([53]Camandola and Mattson, 2017). Energy metabolism in
the brain plays a critical role in the maintenance of functionality of
the central nervous system, which is highly depended on the blood flow
and supply of glucose and oxygens to neurons and glial cells ([54]Watts
et al., 2018). Cerebral ischemia-induced disruption of oxygen
consumption and glucose utilization contributes to metabolic
perturbations of the brain, while reperfusion injury leads to further
metabolic alterations and increased brain infarct volume compared to
permanent occlusion ([55]Zhang et al., 1994). However, metabolic
profiles after reperfusion are poorly characterized.
Advances in omics have allowed for comprehensive and systemic profiling
of small molecular substances alterations, which provided scientists
with comprehensive insight into mechanisms underlying the pathogenesis
of diseases. Metabolomic analysis can be used to identify potential
pathways and understand pathological mechanisms ([56]Wang et al., 2014;
[57]Shin et al., 2020). In this study, we employed metabolomic analysis
coupled with gas chromatography time-of-flight mass spectrometry
(GC-TOF-MS) and ultraperformance liquid chromatography quadrupole
(UPLC/Q)-TOF-MS to determine the alterations in the brain metabolome of
mice after 90 min of middle cerebral artery occlusion (MCAO) with or
without reperfusion for 24 h. This study aimed to investigate the
metabolic characteristics and pathogenesis of cerebral
ischemia-reperfusion injury to shed a light on therapeutic
interventions for cerebral ischemia-reperfusion injury.
2 Materials and methods
2.1 Animals
Adult male C57BL/6 mice (specific-pathogen-free, eight-week-old,
20–25 g) were obtained from the Animal Center of the Army Medical
University (Chongqing, China; Certificate No. SCXK 2019–0004). Colonies
were maintained in a specific pathogen-free grade environment with a
12 h light/12 h dark cycle. The use of mice was performed in accordance
with the Guide of Care and Use of Experimental Animals of the Animal
Ethics Committee of the Army Medical University.
2.2 MCAO and MCAO/R models
As shown in the flow chart in [58]Figure 1A, the mice from the original
mouse cages were randomly allocated to the different experimental
groups (sham, MCAO, and MCAO/R; n = 10 in each group). The cerebral
ischemia was induced using an intraluminal filament as previously
described ([59]Longa et al., 1989). Briefly, the left middle cerebral
artery was occluded with a blunt-tip 6–0 nylon monofilament, 16–17 mm
past the carotid bifurcation until a slight resistance was felt. Body
temperature was maintained at 37°C ± 0.5°C. The MCAO mice were
sacrificed immediately after 1.5 h of ischemia, while the mice in the
MCAO/R group were subsequently reperfused for 24 h by the careful
withdrawal of the filament. The MCAO/R mice were euthanized after 24 h
of reperfusion. Animals in the sham group were subjected to sham
surgery, which underwent the same procedure but without insertion of
filament to occlude the carotid bifurcation. Animals were anesthetized
and transcardially perfused with 20 mL of phosphate-buffered saline
(PBS). Brain tissues were collected for the fsubsequent analyses.
FIGURE 1.
[60]FIGURE 1
[61]Open in a new tab
(A) Experimental design diagram. Mice were maintained in the animal
center for 1 week after arrival for adaptation. Middle cerebral artery
occlusion was performed on the eighth day. Reperfusion (for 24 h) was
conducted after ischemia (for 1.5 h). Brain tissue was collected after
euthanizing the animals. (B) Representative of triphenyl tetrazolium
chloride (TTC)-stained brain slices and infarct volume measured using
the ImageJ software. (C) Neurological scores were measured by using a
four-point system. N = 10 biological replicates for each group. ****p
[MATH: < :MATH]
0.0001.
2.3 Infarct volume measurement and neuroscore assessment
Infarct volume measurements and neuroscore assessments were performed
according to a previously developed method ([62]Longa et al., 1989).
Brain tissues were rapidly removed, frozen at −20°C for 15 mins, and
coronally sectioned into 1–2 mm slices from the frontal tips. Sections
were stained with 2% 2,3,5-tripenyltetrazolium chloride (TTC, Sangon
biotech, Shanghai) at 37°C for 30 mins, and then stored in 4%
paraformaldehyde. The infarct volume (white or pale pink areas) was
measured as a percentage of the total brain volume using the ImageJ
software.
Neuroscore assessment was performed by an experimenter blinded to the
experimental groups (Rating scale: 0 = no deficit, 1 = failure to
extent the left forepaw, 2 = decreased grip strength of the left
forepaw, 3 = circling to the left by pulling the tail, and 4 =
spontaneous circling).
2.4 Metabolite extraction
For LC with tandem mass spectrometry (LC-MS/MS) detection, metabolite
extraction was performed according to a previously developed method
([63]Rashad et al., 2020). Fifty milligrams of sample were weighted in
an EP tube, and 1,000 μL extract solution (acetonitrile: methanol:
water = 2:2:1) with 1 μg/mL internal standard was added. After 30 s
vortex, the samples were homogenized at 35 Hz for 4 min and sonicated
for 5 min on ice. Homogenization and sonication cycles were repeated
for three times. Then the samples were incubated for 1 h at −40°C and
centrifuged at 10000 rpm for 15 min at 4°C. The resulting supernatant
was transferred to a fresh glass vial for analysis. The quality control
(QC) sample was prepared by mixing an equal aliquot of the supernatants
from all the samples.
For GC/TOF-MS analysis, metabolite extraction was performed according
to a previously developed method ([64]Wang et al., 2017). A 50 ± 1 mg
sample was transferred into a 2 mL tube, and 1,000 μL pre-cooled
extraction mixture (methanol/chloroform (v:v) = 3:1) with 0.5 μg/mL
internal standard was added. Each sample prepared with the same
procedure as in LC-MC/MC was evaporated in a vacuum concentrator. Then,
40 μL of methoxyamination hydrochloride (20 mg/mL in pyridine) was
added and the sample was subsequently incubated at 80°C for 30 mins,
then derivatized with 60 μL of BSTFA regent (1% TMCS, v/v) at 70°C for
1.5 h. Next, samples were gradually cooled to room temperature, and
5 μL of FAMEs (in chloroform) was added to the QC sample. All samples
were then analyzed using GC-TOF-MS.
2.5 LC-MS/MS analysis
LC-MS/MS analyses were performed using an UHPLC system (1,290, Agilent
Technologies) with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm)
coupled to a Q Exactive mass spectrometer (Orbitrap MS, Thermo)
according to a previously developed method ([65]Rashad et al., 2020).
Mobile phase A consisted of 0.1% formic acid in water for the positive
mode, and 5 mmol/L ammonium acetate in water for the negative mode.
Mobile phase B consisted of acetonitrile. The elution gradient was set
as follows: 0–1.0 min, 1% B; 1.0–8.0 mins, 1%–99% B; 8.0–10.0 mins, 99%
B; 10.0–10.1 mins, 99%–1% B; and 10.1–12 mins, 1% B. The flow rate was
0.5 mL/min. The injected volume was 3 μL. The QE mass spectrometer was
used for its ability to acquire MS/MS spectra on information-dependent
acquisition (IDA) mode in the sham of the acquisition software
(Xcalibur 4.0.27, Thermo). In this mode, the acquisition software
continuously evaluated the full-scan MS spectrum. The electrospray
ionization source conditions were set as follows: sheath gas flow rate
of 45 Arb, Aux gas flow rate of 15Arb, capillary temperature of 400°C,
full MS resolution of 70000, MS/MS resolution of 17500, collision
energy of 20/40/60 eV in normalized collision energy mode, and spray
Voltage of 4.0 kV (positive) or −3.6 kV (negative), respectively.
2.6 GC-TOF-MS analysis
GC-TOF-MS analysis was performed using an Agilent 7,890 gas
chromatograph coupled with a TOF mass spectrometer according to a
previously developed method ([66]Fiehn, 2016). The system utilized a
DB-5MS capillary column. A 1 μL aliquot of the sample was injected in
splitless mode. Helium was used as the carrier gas, the front inlet
purge flow rate was 3 mL/min, and the gas flow rate through the column
was 1 mL/min. The initial temperature was maintained at 50°C for 1 min,
then raised to 310°C at a rate of 10°C/min, and then maintained for
8 min at 310°C. The injection, transfer line, and ion source
temperatures were 280, 280°Cand 250°C, respectively. The energy was
−70 eV in the electron impact mode. Mass spectrometry data were
acquired in full-scan mode with the m/z range of 50–500 at a rate of
12.5 spectra per second after a solvent delay of 6.25 min.
2.7 Metabolome data processing
Raw data from the LC-MS/MS analysis were converted to the mzXML format
using ProteoWizard and processed using an in-house program, which was
developed using R and based on XCMS, for peak detection, extraction,
alignment, and integration according to a previously described method
([67]Smith et al., 2006). An in-house MS2 database (BiotreeDB) was
usded for metabolite annotation. The cutoff for annotation was set at
0.3.
Raw data from the GC-TOF-MS analysis, including peak extraction,
baseline adjustment, deconvolution, alignment and integration, were
completed using the Chroma TOF (V 4.3x, LECO) software. The LECO-Fiehn
Rtx5 database was used for metabolite identification by matching the
mass spectrum and retention index according to a previously described
study ([68]Dunn et al., 2011). Finally, the peaks detected in less than
half of the QC samples or those with RSD>30% in the QC samples were
removed.
2.8 Statistical analysis
Differences among multiple groups were determined using one-way ANOVA,
followed by Bonferroni’s multiple comparisons test. Statistical
analyses were performed using the GraphPad software (GraphPad Prism,
United States). Data are presented as the mean ± SD. A p-value of <0.05
was considered statistically significant.
3 Results
3.1 Reperfusion increased the infarct volume following focal ischemia
To examine and compare the ischemic brain damage induced by MCAO with
or without reperfusion, TTC (2,3,4-triphenyltetrazolium chloride)
staining was applied to analyze the infarct size in the sham, MCAO, and
MCAO/R groups. Transient MCAO for 90 min followed by 24 h-reperfusion
induced a significant increase in the infarct volume and neurological
deficit compared with those in the sham and MCAO groups (p
[MATH: < :MATH]
0.05) ([69]Figures 1B, C). Notably, the infarct volume induced by
permanent MCAO was greatly smaller than that in MCAO/R group
([70]Figure 1B).
3.2 Ischemia and ischemia/reperfusion induced prominent metabolic alterations
in the brain
Multivariate statistical analysis methods, including principal
component analysis (PCA) and partial least squares-discriminant
analysis (PLS-DA), were employed to investigate the separation of the
gas-liquid chromatography data of the sham, MCAO, and MCAO/R groups. As
shown in [71]Figure 2A, the PCA plot revealed a clear separation and a
close association in the metabolic profiles among the sham group
(purple triangle), MCAO (red cycle), and MCAO/R (blue rectangle)
groups. Consistent with the PCA plot, the PLS-DA plots of the MCAO
versus sham, MCAO/R versus sham, and MCAO/R versus MCAO groups were
cross-validated, demonstrating the metabolic differences among these
three groups ([72]Figures 2B–D). In addition, the permutation plots
indicated that the original PLS-DA model was valid and had no
overfitting ([73]Figures 2E–G).
FIGURE 2.
[74]FIGURE 2
[75]Open in a new tab
(A) Principal component analysis (PCA) plot of samples in the sham
group (purple triangle), middle cerebral artery occlusion (MCAO) (red
cycle), and MCAO/reperfusion (R) (blue rectangle) groups. N = 10
biological replicates for each group. (B–G) Score scatter plot of the
orthogonal projections to latent structures-discriminant analysis
(OPLS-DA) model for groups of the (B) MCAO versus sham, (C) MCAO/R
versus sham, and (D) MCAO/R versus MCAO groups. Permutation test of the
OPLS-DA model for the (E) MCAO versus sham, (F) MCAO/R versus sham, and
(G) MCAO/R versus MCAO groups.
3.3 Differential metabolomic profiling among the sham, MCAO, and MCAO/R
samples
Differential metabolomic profiling analysis was performed to identify
the correlated metabolites involved in the reperfusion-induced
alterations. A total of 188 differentially expressed metabolites were
identified in the sham versus MCAO group, while 190 metabolites were
significantly altered in the MCAO/R versus sham group. A total of 82
metabolites were simultaneously differentially expressed in these three
groups, according to the Venn diagram ([76]Figure 3A). The category
showed that 19.27%, 12.851%, and 11.647% of the metabolites belonged to
organic acids, lipids, and organooxygen compounds, respectively
([77]Figure 3B). In addition, the changes in the levels of the 82
identified metabolites were plotted using a hierarchical clustering
heatmap ([78]Figure 3C). Specifically, the levels of 28 metabolites
were significantly upregulated after MCAO and returned to near baseline
levels after reperfusion (green box, [79]Figure 3C), while the levels
of 26 metabolites in both the MCAO and MCAO/R groups were elevated
relative to those in the sham group (blue box, [80]Figure 3C). In
addition, the levels of 11 metabolites in both the MCAO and MCAO/R
groups were reduced when compared with those in the sham group (orange
box, [81]Figure 3C). Notably, the levels of 17 out of these 82
differential metabolites in the MCAO/R group were significantly
elevated when compared with those in the sham and MCAO groups (red box,
[82]Figure 3C).
FIGURE 3.
[83]FIGURE 3
[84]Open in a new tab
(A) Venn diagram of differential metabolites between groups the middle
cerebral artery occlusion (MCAO) versus sham and MCAO/reperfusion (R)
versus sham groups. (B) Metabolic categories of the 82 identified
metabolites. (C) Heatmap profile of the 82 metabolites that are
simultaneously differentially expressed in the sham, MCAO, and MCAO/R
groups. Red and blue indicate upregulation and downregulation relative
to the median level, respectively (see color scale).
3.4 Metabolic pathway analysis (MetPA) of differential metabolites in the
response to MCAO or MCAO/R
Differential metabolites among these groups were selected and subjected
to MetPA to identify the metabolic pathways involved. “The metabolome
view” showed that multiple pathways, including histidine metabolism,
butanoate metabolism, valine, leucine and isoleucine biosynthesis,
glycerophospholipid metabolism, and D-glutamine and D-glutamate
metabolism pathways were the most significantly altered in the MCAO
group, compared to the sham group, according to their impact value
(x-axis) or -ln p) value (y-axis) ([85]Figure 4A). In addition, the
following pathways: glycerophospholipid metabolism, histidine
metabolism, citrate cycle, aminoacyl-tRNA biosynthesis, alanine,
aspartate and glutamate metabolism, and linoleic acid metabolism were
significantly altered in the MCAO/R group, compared to the sham group
([86]Figure 4B). Relative to MCAO, reperfusion induced alterations in
pathways associated with glycerophospholipid metabolism, linoleic acid
metabolism, pyrimidine metabolism, and galactose metabolism ([87]Figure
4C).
FIGURE 4.
[88]FIGURE 4
[89]Open in a new tab
Metabolic pathway analysis (MetPA) analysis of differential metabolites
for the (A) middle cerebral artery occlusion (MCAO) versus sham, (B)
MCAO/reperfusion (R) versus sham, and (C) MCAO/R versus MCAO groups.
The -ln p-value on the y-axis and the size of each circle were obtained
from the pathway enrichment analysis, while the impact values on the
x-axis were from the pathway topology analysis.
3.5 Identified metabolites and involved pathways involved in reperfusion
response
We focused on the reperfusion-induced alterations in the levels of
metabolites and found that concentrations of the 17 identified
metabolites (red box in [90]Figure 3C) in the MCAO/R group were higher
than those in both the sham and MCAO groups. As shown in [91]Table 1,
among these metabolites, glucose-6-phosphate 1 (G6P, PubChem CID
439958), fructose-6-phosphate (F6P, PubChem CID 69507), cellobiose 2
(PubChem CID 294), o-phosphonothreonine 1 (PubChem CID 3246323), and
salicin (PubChem CID 439503) were the top five elevated metabolites in
the group of MCAO/R versus MCAO. In addition, the level of G6P with
50.75 of fold change showed the greatest change in the MCAO/R versus
MCAO groups ([92]Table 1A). Moreover, amino acids, including
N-acetylhistidine (PubChem CID 75619) and N-methyl-dl-alanine (PubChem
CID 4377), and the organooxygen compounds cellobiose 2 (PubChem CID
294) were identified as the top three up-regulated metabolites in the
MCAO/R group when compared with those in the sham group ([93]Table 1B).
TABLE 1.
List of upregulated metabolites (red box in [94]Figure 3C) in mouse
brain samples in the (A) middle cerebral artery occlusion/reperfusion
(MCAO/R) versus MCAO group and (B) MCAO/R versus sham group. VIP,
variable importance in the projection. ***p < 0.001, **p < 0.01, *p <
0.05.
(A)
No. Peak PubChem CID VIP p-value Fold change LOG[2]-FOLD change Symbol
1 Glucose-6-phosphate 1 439958 2.864225757 0.000804747 50.74652331
5.66523708 ***
2 Fructose-6-phosphate 69507 2.765175119 0.000806093 46.90199453
5.55157737 ***
3 Cellobiose 2 294 2.804271305 0.000987392 19.73237667 4.302492827 ***
4 O-phosphonothreonine 1 3246323 2.721198038 1.00348E-05 14.21486503
3.829328495 ***
5 Salicin 439503 2.548609014 0.008142101 7.884287086 2.978980309 **
6 N-methyl-dl-alanine 4377 2.676090698 0.002328932 6.995360732
2.806398455 **
7 Fructose 1 2723872 2.599885584 7.17725E-05 3.894194913 1.961325096
***
8 Alpha-aminoadipic acid 469 2.365301057 0.002195341 3.171746923
1.665277662 **
9 2-deoxy-d-galactose 2 439804 2.845734446 6.11E-08 2.276842157
1.187034279 ***
10 2-hydroxyvaleric acid 98009 2.671278079 4.87E-06 1.957959376
0.969350832 ***
11 N-acetylhistidine 75619 0.438649611 0.271502312 1.683982608
0.751877239
12 Pentanenitrile 8061 0.883952465 0.245370607 1.475431114 0.561136565
13 Histamine 774 0.835540146 0.171526856 1.453719767 0.539749189
14 Trans-hexadec-2-enoyl carnitine 53477817 1.586571196 0.055386681
1.452087929 0.538128816
15 4-(diaminomethylideneamino)butanoic acid 500 1.203379571 0.115068444
1.316114929 0.396285477
16 (2s)-pyrrolidine-2-carboxylic acid 145742 1.003407193 0.217092683
1.230730605 0.299515005
17 2,4-dioxo-1h-pyrimidine-6-carboxylic acid 967 0.92441045 0.181361639
1.172030427 0.229010024
(B)
No. Peak PubChem CID VIP p-value Fold change LOG[2]-FOLD change Symbol
1 N-Acetylhistidine 75619 1.425608156 0.043606643 4.546159384
2.184648265 *
2 Cellobiose 2 294 1.567909166 0.006986065 3.227477798 1.690407171 **
3 N-methyl-dl-alanine 4377 1.560412669 0.008813379 3.186375681
1.671916374 **
4 Histamine 774 1.793338316 0.01072478 2.66211923 1.412575188 *
5 Pentanenitrile 8061 1.601302512 0.029806448 2.618295492 1.388627924 *
6 Salicin 439503 1.587012182 0.044418362 2.549591691 1.350266222 *
7 Glucose-6-phosphate 1/D-Glucose 6-Phosphate 439958 1.349921938
0.020958135 2.447203193 1.291133894 *
8 Fructose 1/D-Fructose 2723872 1.8930859 0.000899086 2.196254683
1.135045363 ***
9 Trans-hexadec-2-enoyl carnitine 53477817 1.833941894 0.003975182
2.108838044 1.076448302 **
10 Fructose-6-phosphate 69507 1.2351927 0.048094572 2.079969744
1.056562542 *
11 O-phosphonothreonine 1 3246323 1.19286643 0.013213685 1.943043504
0.958318203 *
12 Alpha-aminoadipic acid 469 1.245659705 0.01876246 1.888682345
0.917380077 *
13 4-(diaminomethylideneamino) butanoic acid 500 1.937622772
0.004980972 1.871241168 0.903995507 **
14 (2s)-pyrrolidine-2-carboxylic acid 145742 1.543836617 0.011032748
1.658322057 0.729724215 *
15 2-hydroxyvaleric acid 98009 1.879766387 4.33983E-05 1.57365224
0.654116756 ***
16 2,4-dioxo-1H-pyrimidine-6-carboxylic acid 967 1.540557642
0.005099663 1.465252377 0.551149178 **
17 2-deoxy-d-galactose 2 439804 1.573420229 0.003235868 1.326628475
0.407764397 **
[95]Open in a new tab
To better visualize the changes in the metabolites concentration, we
illustrated the results using a horizontal lollipop plot ([96]Figures
5A, B). The web-based platform Metaboanalyst 5.0
([97]https://www.metaboanalyst.ca/) was used to perform metabolite set
enrichment analysis (MSEA) to identify pathways and biological
functions significantly enriched in these 17 differential metabolites.
MSEA results are presented graphically; the horizontal bar summarizes
the most significant metabolites sets identified during this analysis.
The bars are colored based on their p-values and the bar length is
based on the fold enrichment. The top four ranked pathways of MSEA were
glycolysis, the pentose phosphate pathway, starch and sucrose
metabolism, and fructose and mannose degradation (p-values ∼0.05)
([98]Figure 5C).
FIGURE 5.
[99]FIGURE 5
[100]Open in a new tab
(A) Differential expression of metabolites in the middle cerebral
artery occlusion/reperfusion (MCAO/R) versus MCAO group. (B)
Differential expression of metabolites in the MCAO/R versus sham group.
(C) Metabolite set enrichment analysis (MSEA) pathway enrichment of
ANOVA significant metabolites using metabolic datasets.
4 Discussion
In the current study, TTC staining showed that reperfusion caused an
increased brain infarct volume, when compared with that in the sham
group and permanent occlusion ([101]Figure 1B). Therefore, although
restoration of blood flow following cerebral ischemia is considered as
a beneficial therapy for reducing the infarct size, reperfusion injury
remains a clinical challenge for physicians. Emerging evidence
indicates that ischemia-reperfusion injury-induced metabolites
perturbations may be considered as an underlying molecular mechanism by
which reperfusion causes greater injury, thus a potential therapeutic
target for cerebral ischemia-reperfusion injury ([102]Chen et al.,
2019; [103]Kula-Alwar et al., 2019; [104]Ma et al., 2022). Therefore,
this study aimed to characterize the metabolic basis of cerebral
ischemia-reperfusion injury by using an metabolomic analysis coupled
with the combined GC-TOF-MS and UPLC/Q-TOF-MS, which are complementary
techniques for screening a wide range of metabolites ([105]Portoles et
al., 2009). In the present study, a total of 188 and 190 differentially
expressed metabolites were identified in mouse brain samples in the
sham versus MCAO and sham versus MCAO/R groups, respectively. Notably,
82 differentially expressed metabolites and several enriched pathways
were significantly altered following ischemia-reperfusion. Of those
remarkably altered metabolites, the top three major classifications
were organic acids, lipids, and organooxygen compounds.
The primary goal of the current study was to investigate
reperfusion-induced metabolic alterations. Therefore, we compared the
metabolic changes in the MCAO/R group with those in the MCAO group. The
results indicated that only the metabolites in the green and red boxes
in [106]Figure 3C showed a significant change after reperfusion,
relative to MCAO alone. Notably, the levels of 28 metabolites in the
green box decreased back to near the baseline levels after reperfusion,
suggesting a potential protective role of reperfusion. However, the
level of the 17 metabolites in the red box were higher relative to
those in both the sham and MCAO groups. These differences may be
associated with reperfusion injury. Thus, we mainly focused on these 17
elevated metabolites after reperfusion. Among these, G6P, F6P,
cellobiose 2, o-phosphonothreonine 1, and salicin were the top five
elevated metabolites in the MCAO/R group compared with those in the
MCAO group. Interestingly, G6P, F6P, cellobiose2, and salicin are
organooxygen compounds. In general, it can be postulated that
organooxygen compounds and their related pathways may be essential for
the pathogenesis of ischemia-reperfusion injury.
Impaired glucose metabolism has been implicated in patients with stroke
and related reperfusion injury ([107]Vancheri et al., 2005; [108]Matz
et al., 2006; [109]Robbins and Swanson, 2014). The MSEA results of this
study showed that the significantly affected pathways were glycolysis,
the pentose phosphate pathway, starch and sucrose metabolism, and
fructose and mannose degradation. G6P is a shared intermediator of two
major metabolic pathways: glycolysis and the pentose phosphate pathway
([110]Rajas et al., 2019). Of note, glycolysis is a metabolic pathway
that produces ATP by converting glucose into G6P and subsequently to
F6P, while the pentose phosphate pathway, a parallel pathway to
glycolysis, converts G6P to nicotinamide-adenine dinucleotide phosphate
(NADPH), which is paramount for fatty acid synthesis and redox state
maintenance ([111]Jin and Zhou, 2019; [112]Yen et al., 2020;
[113]Chaudhry and Varacallo, 2022). Our data revealed that the
activation of glycolysis and the pentose phosphate pathway was involved
in the cerebral ischemia-reperfusion injury. Other studies have also
shown that inhibition of “hyperglycolysis” is capable of attenuating
the brain damage induced by ischemic stroke ([114]Guan et al., 2022). A
recent study indicated that the enhanced glycolysis pathway is
responsible for microglia-mediated neuroinflammation via a hexokinase
2-dependent mechanism, which might account for the reperfusion injury
([115]Wolf et al., 2016; [116]Li et al., 2018). In addition, hexokinase
is substantially upregulated in aged and post-stroke rat brains
([117]UmadeviVJB et al., 2017). This result is consistent with our
finding of elevated G6P levels after reperfusion, as hexokinase is a
rate-limiting enzyme converting glucose to G6P in glycolysis. In
addition to the upregulation of hexokinase, glucose-6-phosphatase
complex (G6PC)-induced gluconeogenesis may also be involved in the
dysregulation of glucose metabolism. The discovery of G6PC in
astrocytes in the mouse brains suggests that the dephosphorylation of
G6P to glucose by G6PC could be an alternative reservoir of endogenous
brain glucose in physiological conditions ([118]Ghosh et al., 2005).
However, there is still a need to investigate the changes in G6PC
levels in the setting of cerebral ischemia-reperfusion. Another recent
study, which was also consistent with our finding, demonstrated that
neural function and ischemic damage can be exacerbated by the altered
glycose metabolism with decreased G6P and F6P ([119]Diaz and Raval,
2021). Therefore, our data raised the possibility that targeting the
glycolysis pathway, such as by hexokinase inhibition and G6PC
manipulation, could be considered as a therapeutic strategy for
reperfusion injury.
The pentose phosphate pathway is critical for neuroprotection during
cerebral ischemia-reperfusion. Glucose-6-phosphate dehydrogenase
(G6PD), a rate-limiting enzyme in this pathway, can alleviate the
reactive oxygen species-induced damage through the elevation of NADPH
([120]Cao et al., 2017). Consistent with our results, it has been
demonstrated that ischemia-reperfusion can induce the elevation of the
pentose phosphate pathway and associated G6PD activation, which may
exhibit a neuroprotective effect via the phosphorylation of heat shock
protein 27 ([121]Yamamoto et al., 2018). Furthermore, a recent study
suggested that G6PD deficiency leads to poor prognosis and relatively
high death rate in patients with cerebral ischemia ([122]Ou et al.,
2020; [123]Li et al., 2022). Moreover, accumulation of G6P caused by
metabolic stress could be redirected from glycolysis into the pentose
phosphate pathway to generate NADPH, thus leading to the activation of
mTOR, which is greatly involved in neurogenesis ([124]Lipton and Sahin,
2014; [125]Takei and Nawa, 2014; [126]LiCausi and Hartman, 2018;
[127]Karlstaedt et al., 2020).
Salicin and cellobiose two are another two major differentially
expressed metabolites identified in this study. Although their
metabolisms in the brain is still under investigation, a recent study
revealed the neuroprotective potential of salicin against cerebral
ischemia-reperfusion through the activation of the PI3K/AKT pathway and
its antioxidant efficacy ([128]Kim et al., 2015; [129]Tawfeek et al.,
2019; [130]Park et al., 2021). Moreover, a single-arm, dose-escalation
study has confirmed the safety and tolerability of cellobiose in
healthy subjects. However, its role as a disaccharide in cerebral
ischemia-reperfusion remains unclear ([131]More et al., 2019). Taken
together, glycolysis, the pentose phosphate pathway, and their related
organooxygen metabolites after reperfusion can be considered as
potential targets for the therapeutic strategies.
In addition to organooxygen metabolites, amino acids, including
N-acetylhistidine and N-methyl-dl-alanine, were also significantly
upregulated among the metabolites in the MCAO/R group when compared
with those in the sham group. Our findings are consistent with a
previous metabolomic analysis of the cerebrospinal fluid of patients
with Alzheimer’s disease ([132]Nagata et al., 2018). Interestingly,
N-acetylhistidine was considered a false positive result as there was
no evidence showing that this compound was a metabolite in the brain
([133]Wishart et al., 2007; [134]Nagata et al., 2018). On the contrary,
several studies, including this one, identified N-acetylhistidine as an
important metabolite in the mouse and human brain ([135]Lewitt et al.,
2013; [136]Ding et al., 2021; [137]Hammond et al., 2021). L-histidine
is known for its potential neuroprotective effects ([138]Kim and Kim,
2020). Interestingly, histidine was normally used in preservation
solutions and perfusates in medicine, which has been replaced by
N-acetyl-L-histidine nowadays due to its ability to protect cells from
ROS ([139]Datta et al., 2021). A study published this year found that
serum levels of N-acetylhistidine was significantly increased during
ischemia phase in the patients with acute myocardial infarction, which
can be considered as a potential biomarker ([140]Goetzman et al.,
2022); however, further studies are needed to explore the role of
N-acetylhistidine in neuroscience, as a derivative of L-histidine. The
increased level of N-methyl-dl-alanine in the MACO/R group is an
alanine derivative, which is also associated with amino acid
metabolism. Different stressors can induce various alterations in the
level of N-methyl-dl-alanine. The level of N-methyl-dl-alanine was
increased by heat stress, an environmental factor, in finishing pigs
([141]Cui et al., 2019), while it was decreased by venlafaxine in the
mouse hippocampus ([142]Shen et al., 2017). Notably, the downregulation
of N-methyl-dl-alanine was considered as a more specific biomarker for
migraine other than serotonin in a metabolomic study ([143]Ren et al.,
2018). Another amino acid derivative, o-phosphonothreonine 1, was
proved its interaction with serum amyloid p component, which was
neurocytotoxic and present in cerebrovascular diseases ([144]Kolstoe et
al., 2009). In addition to the above-mentioned metabolites,
organonitrogen compounds, including pentanenitrile and histamine, were
also identified to be upregulated after reperfusion.
Besides glucose metabolism and the pentose phosphate pathway, we also
found that other important metabolic pathways, such as starch and
sucrose metabolism, fructose and mannose degradation, and linoleic acid
metabolism are also implicated in cerebral ischemia-reperfusion injury.
A GO enrichment analysis suggested the involvement of starch and
sucrose metabolism in an estrogen neuroprotection study of cerebral
ischemia ([145]He et al., 2018). Ischemia-induced elevation of fructose
and mannose showed a negative effect on the neural activity in the
hippocampal slices ([146]Yamane et al., 2000). A Danish cohort study
proved the intake of linoleic acid revealed a detrimental association
with the risk of ischemic stroke ([147]Veno et al., 2018). The
protective role of isosteviol sodium in cerebral ischemia was studied
by metabolomics. Their results also demonstrated the association of
several key pathways, including glycerophospholipid metabolism,
arachidonic acid metabolism and linoleic acid metabolism ([148]Yang et
al., 2018). Cerebral ischemia-reperfusion injury contributed to the
alterations of these metabolic pathways. Further studies still await to
investigate their guiding significance for clinical therapy against
cerebral ischemia.
Although reperfusion could a reason for the differentially expressed
metabolites in the current study, a study limitation is that, despite
the importance of penumbral tissue, metabolic differences between the
infarct core and penumbra were not analyzed. Targeting the ischemic
penumbra, the potentially salvageable tissue, is the cornerstone for
the development of novel therapeutic strategies for ischemic stroke
([149]Liu et al., 2010). Follow-up work is urgently required to explore
the dynamic interactions among the metabolic state of the tissue, the
availability of blood flow, and the duration of ischemia.
In summary, the current study demonstrated that significant changes in
metabolites and pathways are involved in cerebral ischemia-reperfusion.
This work not only advances our understanding of metabolomic changes in
response to cerebral ischemia-reperfusion via metabolomic profiling,
but also provides the basis for exploring potential molecular
mechanisms associated with the pathogenesis of reperfusion injury.
Therefore, these findings contributed to the exploration of novel
therapeutic strategies against injury induced by the re-establishment
of blood flow.
Funding Statement
This work was supported by grants from the National Nature Science
Foundation of China (82001265, 82001264, 81901236 and 82090041).
Data availability statement
The original contributions presented in the study are included in the
article/supplementary materials, further inquiries can be directed to
the corresponding authors.
Ethics statement
The animal study was reviewed and approved by the Animal Ethics
Committee of the Army Medical University.
Author contributions
CL and QY are responsible for the whole research conception, manuscript
writing, and revision. QC, LH, HL, and QH performed animal surgery and
other experiments. TZ wrote the paper and analyzed the data. JY and XX
helped review the study design and data analysis. XL and ZQ assisted in
writing the paper. All authors reviewed the results and approved the
final version of the manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
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and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
References