Abstract
Gut microbiota dysbiosis has been reported as a risk factor in the
development of type 2 diabetes mellitus (T2DM). Polysaccharides from
Phellinus igniarius (P. igniarius) possess various properties that help
manage metabolic diseases; however, their underlying mechanism of
action remains unclear. Therefore, in this study, we aimed to evaluate
the effect of P. igniarius polysaccharides (SH-P) on improving
hyperglycemia in mice with T2DM and clarified its association with the
modulation of gut microbiota and their metabolites using 16S rDNA
sequencing and liquid chromatography–mass spectrometry. Fecal
microbiota transplantation (FMT) was used to verify the therapeutic
effects of microbial remodeling. SH-P supplementation alleviated
hyperglycemia symptoms in T2DM mice, ameliorated gut dysbiosis, and
significantly increased the abundance of Lactobacillus in the gut.
Pathway enrichment analysis indicated that SH-P treatment altered
metabolic pathways associated with the occurrence and development of
diabetes. Spearman’s correlation analysis revealed that changes in the
dominant bacterial genera were significantly correlated with metabolite
levels closely associated with hyperglycemia. Additionally, FMT
significantly improved insulin sensitivity and antioxidative capacity
and reduced inflammation and tissue injuries, indicating improved
glucose homeostasis. These results indicate that the ameliorative
effects of SH-P on hyperglycemia are associated with the modulation of
gut microbiota composition and its metabolites.
Keywords: Phellinus igniarius, polysaccharide, hypoglycemia, gut
microbiota, metabolites, fetal microbiota transplantation
1. Introduction
Type 2 diabetes mellitus (T2DM), which accounts for 90–95% of all
diabetes, is a chronic metabolic disease characterized by insulin
resistance and relative insulin insufficiency and has become one of the
most prevalent public health challenges worldwide [[44]1,[45]2].
Different types of oral hypoglycemic agents, including insulin,
thiazolidinediones, sulfonylureas, glucagon-like peptide-1 (GLP-1)
agonists, dipeptidyl peptidase-4 inhibitors, metformin, meglitinides,
sodium-glucose cotransporter 2 inhibitors, and pramlintide are used to
treat T2DM [[46]3]. However, the targets and pathways associated with
these drugs are relatively simple and long-term use can produce many
adverse effects. Therefore, developing safer and effective drugs for
the treatment of T2DM is necessary.
Natural compounds have become a new prevention and treatment approach
for T2DM and can attenuate the complications of hyperglycemia owing to
their non-toxicity, cost-effectiveness, and easy absorption
[[47]4,[48]5]. P. igniarius is a rare, large, perennial, edible, and
medicinal fungus that belongs to Basidiomycotina, Hymenomycetes,
Aphyllophorales, and Hymenochaetaceae and is commonly referred to as
“forest gold” [[49]4,[50]6]. According to the literature, P. igniarius
is used to treat diarrhea, promote blood circulation, improve
gastroenteric dysfunction, and treat cancer [[51]7]. P. igniarius
contains various bioactive substances, including polysaccharides,
flavonoids, styrylpyrones, and phenolic compounds [[52]8]. Emerging
evidence has demonstrated that polysaccharides isolated from P.
igniarius exhibit significant hypoglycemic activity, as oral
administration of these polysaccharides lowered blood glucose levels
and stimulated insulin excretion in streptozocin (STZ)-induced diabetic
rats, consequently restoring pancreas, liver, and kidney functions
[[53]9]. These polysaccharides also exhibit a considerable hypoglycemic
effect and improve insulin sensitivity, possibly through regulating
peroxisome proliferator-activated receptor (PPAR)-γ-mediated lipid
metabolism [[54]10]. However, the mechanism by which these
polysaccharides improve hyperglycemia symptoms requires further
exploration.
Emerging evidence indicates that disturbances in the gut microbiota are
associated with the development of diabetes and related metabolic
disorders [[55]11]. For example, butyrate-producing bacteria are
negatively correlated with hyperglycemic parameters, Lactobacilli are
more prevalent in T2DM patients, and the class Clostridia and phylum
Firmicutes are significantly positively correlated with inflammation
[[56]12]. A metabolite-based genome-wide association study analysis
showed that the gut microbiota was dysbiotic in patients with T2DM, the
abundance of many butyrate-producing bacteria decreased, and the
abundance of various opportunistic pathogens increased [[57]13].
Gram-negative bacteria, which are relatively enriched in the gut, are
closely related to low-grade inflammation and insulin resistance in
patients with T2DM. Prevotella copri and Bacteroides vulgatus can be
used as characteristic bacteria to predict the occurrence and
development of diabetes [[58]14]. Therefore, gut microbiota may serve
as a potential new target for diabetes therapy. However, it is still
unclear whether pathogenic bacteria result in the occurrence of T2DM or
the changes in gut microbiota caused by T2DM lead to an increase in
pathogenic bacteria, promoting the development of T2DM. Therefore,
analyzing the characteristics of the gut microbiota in patients with
T2DM and elucidating the microbiome changes of T2DM before and after
treatment will help delineate the role of gut bacteria in the
development of T2DM.
Fecal microbiota transplantation (FMT) is an effective method for
reestablishing the gut microbiota by transplanting the intestinal
microbial community of a healthy donor into the gastrointestinal tract
of the recipient, thereby rapidly changing the recipient’s gut
microbiota composition [[59]15]. An oral glucose tolerance test (OGTT)
showed that FMT significantly decreased blood glucose and glycated
hemoglobin (HbA1c) levels, improved insulin resistance, and increased
insulin sensitivity in T2DM mice [[60]16]. FMT treatment also
significantly decreased the kidney to body weight ratios, urinary
albumin to creatinine, and urinary N-acetyl-β glucosaminase to
creatinine and relieved desquamation and necrosis of renal tubular
epithelial cells in diabetic rats [[61]17]. In addition,
transplantation of the intestinal microbiota of normal rats into obese
diabetic rats significantly decreases body weight and improves insulin
and leptin resistance via the Janus kinase 2 (JAK2)/insulin receptor
substrate (IRS)/protein kinase B (Akt) pathway [[62]18]. However, the
specific pathophysiological role of the microbial community and its
underlying mechanism of action, and the efficacy of fecal or intestinal
microbiota trans-plantation as a method to treat T2DM remains unclear.
Elucidating these processes will provide a theoretical basis for
further optimizing the feasibility of microbiota transplantation
methods and developing appropriate microbiota regulation techniques.
Recent studies have demonstrated that active polysaccharides can
improve T2DM by regulating the composition of intestinal microbiota and
the production of bacteria-derived metabolites [[63]19]. Cordyceps
militaris polysaccharides alleviate diabetic symptoms by regulating gut
microbiota against the toll-like receptor 4 (TLR4)/nuclear factor kappa
B (NF-κB) pathway [[64]20]. Polysaccharides extracted from small black
soybeans alleviate T2DM by modulating the gut microbiota and serum
metabolism [[65]21]. Brasenia schreberi polysaccharides improve
hyperglycemia symptoms and reverse gut microbiota dysbiosis by
enhancing the abundance of Lactobacillus to activate
phosphatidylinositol 3-kinase (PI3K)/Akt-mediated signaling pathways in
T2DM mice [[66]22]. Although polysaccharides isolated from P. igniarius
have hypoglycemic functions, their effects on the gut microbiota and
their metabolites remain unclear.
In this study, the hypoglycemic effect of polysaccharides isolated from
P. igniarius and their regulatory effects on the structure and
characteristics of the gut microbiota and their metabolites in high-fat
diet/STZ-induced diabetic mice was evaluated. In addition, FMT was used
to investigate the therapeutic effects of feces from diabetic mice
treated with polysaccharides from P. igniarius. This study provides a
reference for the development of new natural polysaccharide drugs to
treat diabetes mellitus and its related complications.
2. Results
2.1. SH-P Treatment Alleviated Hyperglycemia Symptoms in T2DM Mice
The hypoglycemic effects of polysaccharides isolated from P. igniarius
were evaluated in high-fat diet/STZ-induced diabetic mice. Although no
significant difference in body weight was observed, SH-P treatment
significantly decreased the food and water intake of diabetic mice in a
dose-dependent manner after four weeks of treatment compared to that in
the T2DM group ([67]Figure 1a–c). The HbA1c level was significantly
lower in SH-P-treated mice, decreasing by 43.80% (p < 0.001) compared
with the diabetic control mice at 800 mg/kg SH-P ([68]Figure 1d).
Moreover, SH-P treatment significantly reduced blood glucose and
insulin levels, an effect that was positively correlated with SH-P
concentration; these levels decreased by 33.60% (p < 0.001) and 32.56%
(p < 0.001), respectively, at a dosage of 800 mg/kg ([69]Figure 1e,f).
Additionally, SH-P treatment significantly improved insulin sensitivity
compared with the diabetic control group (0.0057 ± 0.00066 vs. 0.0026 ±
0.00031, p < 0.001; [70]Figure 1g). The OGTT indicated that SH-P
administration significantly improved glucose tolerance ([71]Figure
1h), and the AUC was reduced by 16.23% compared with that of diabetic
mice at 800 mg/kg SH-P ([72]Figure 1i). Moreover, SH-P administration
reversed the abnormally enlarged and irregularly arranged adipose cells
([73]Figure 1j).
Figure 1.
[74]Figure 1
[75]Open in a new tab
The effects of SH-P treatment on improving hyperglycemia symptoms in
type 2 diabetes mellitus (T2DM) mice. (a) Body weight; (b) Food intake;
(c) Water intake; (d) HbA1c; (e) Blood glucose; (f) Insulin; (g)
Homeostasis model assessment of insulin sensitivity (HOMA-IS); (h) Oral
glucose tolerance test (OGTT); (i) Area under the curve (AUC); (j)
Representative images of H&E-stained adipose tissue. *, p < 0.05 vs.
Con; ***, p < 0.001 vs. Con; #, p < 0.05 vs. Dia; ##, p < 0.01 vs. Dia;
###, p < 0.001 vs. Dia. Scale bars = 50 μm.
2.2. SH-P Treatment Changed the Microbiota Composition in T2DM Mice
Next, 16S rDNA sequencing was employed to detect the composition and
changes in the gut microbiota of mice following SH-P treatment. The
fecal samples used for analyzing the gut microbiota were selected from
the SH-P-800 treatment group because SH-P treatment alleviated
hyperglycemia symptoms in a dose-dependent manner in T2DM mice. No
significant differences were observed in the Simpson index between the
diabetic and SH-P-treated groups. The Chao1 index results showed a
significant increase after SH-P treatment, indicating an increase in
species abundance, richness, and diversity ([76]Figure 2a). The PCoA
based on Jaccard and Bray–Curtis distances showed that microbial
communities between the diabetic and SH-P-treated groups were clearly
separated and clustered into distinct groups, indicating a significant
change in gut microbiota composition in response to SH-P treatment
([77]Figure 2b). The gut microbiota was then analyzed at different
taxonomic levels. At the phylum level, although the dominant bacterial
communities in the fecal samples from both the diabetic and
SH-P-treated groups were Bacteroidetes, Firmicutes, Proteobacteria, and
Desulfobacterota, the relative proportions of the phyla in the
different groups varied considerably ([78]Figure 2c). An increased
ratio of Firmicutes/Bacteroidetes (F/B ratio) is a key indicator of
microbiota imbalance and can be used to assess the degree of obesity
and hyperglycemia [[79]23]; SH-P treatment decreased the F/B ratio
compared to the diabetic group, although not significantly ([80]Figure
2d). The relative abundances of other taxonomic levels, such as class,
order, family, and species, are shown in [81]Figure S1. At the genus
level, compared with the diabetic group, SH-P treatment significantly
increased the proportions of Bacteroides, Lactobacillus,
Alloprevotella, Alistipes, and Parabacteroides and significantly
decreased the abundances of Helicobacter, Desulfovibrio, Odoribacter,
Mucispirillum, and Ruminiclostridium ([82]Figure 2e).
Figure 2.
[83]Figure 2
[84]Open in a new tab
Changes in the diversity and relative abundance in the gut microbiota
between diabetic and SH-P-treated mice. (a) The alpha diversity of gut
microbiota based on Chao1 and Simpson indices; (b) Principal
coordinates analysis (PCoA) of the gut microbial communities based on
Jaccard and Bray–Curtis distances; (c) The relative abundance of the
gut microbiota at the phylum level; (d) Firmicutes/Bacteroidetes (F/B)
ratio; (e) Relative abundance of the gut microbiota at the genus level;
(f) Linear discriminant analysis Effect Size (LEfSe) analysis of the
taxon at significantly different levels between the gut microbiota of
diabetic and SH-P-treated mice. The cladogram showed the microbial
species with significant differences (left) and the differences in the
abundances of the gut microbiota. The LDA scores were (log10) > 3 and p
< 0.05 (right). * p < 0.05 indicated significant differences.
LEfSe analysis is a comparative method used to investigate specific
groups of bacteria that may explain the differences in phenotypes. The
LefSe results indicated significant differences at various taxon levels
between the diabetic and SH-P-treated groups, including 19 species
enriched in the SH-P-treated mice and 28 species enriched in diabetic
mice ([85]Figure 2f). The increased abundance was primarily related to
an increase in Lactobacillales, Lactobacillaceae,_Paraprevotella,
Lachnospiraceae NK4A136 group, Lactobacillus, Prevotellaceae, Bacilli,
uncultured Bacteroidales bacterium, and other unclassified or
uncultured bacterium, whereas decreased abundance was mainly associated
with Deltaproteobacteria, Blautia, Lachnospiraceae NK4A136 group,
Eubacterium coprostanoligenes group, Anaerotruncus sp. G3, Clostridium
sp. ASF356, Desulfovibrionaceae, Anaerotruncus, Desulfovibrio,
Desulfovibrionales, and some unclassified or uncultured bacterium in
the SH-P-treated mice compared with that in the diabetic mice
([86]Figure 2f). Notably, SH-P treatment significantly increased the
abundance of Lactobacillus from the phylum to the genus level.
2.3. SH-P Treatment Improved the Fecal Metabolomic Profile in T2DM Mice
To explore the molecular mechanisms underlying the beneficial effects
of the gut microbiota and analyze the effect of SH-P treatment on fecal
metabolic profiles, untargeted metabolomics analysis by LC-MS was
performed. The QC samples clustered in the center of the PCA score
plots demonstrate the repeatability of the acquisition method
([87]Figure 3a and [88]Figure S2a). The PCA plots showed a complete
distinction, and PLS-DA indicated a clear separation trend between the
diabetic and SH-P-treated groups ([89]Figure 3a,b and [90]Figure
S3a,b), indicating that the SH-P-treated group formed a distinct
metabolic cluster separate from that of the diabetic group. A
permutation test of the PLSDA model confirmed that the model did not
overfit ([91]Figure 3b and [92]Figure S2b). Considering the screening
criteria of variable importance in projection is >1 and a p < 0.05,
differentially expressed metabolites were identified between the
diabetic and SH-P-treated groups ([93]Figure 3c and [94]Figure S2c).
Differential metabolites (16 and 22 in the POS and NEG modes,
respectively) between the diabetic and SH-P-treated groups were
visualized using heatmaps ([95]Figure 3d and [96]Figure S2d). Notably,
SH-P treatment greatly increased metabolite levels closely associated
with hyperglycemia, including those of 3-Ketocholanic acid (3.33 ×
10^−4 ± 2.27 × 10^−4 vs. 8.19 × 10^−5 ± 7.54 × 10^−5, p < 0.05), indole
(2.01 × 10^−3 ± 3.52 × 10^−4 vs. 9.25 × 10^−4 ± 5.37 × 10^−4, p <
0.01), oleoylethanolamide (2.37 × 10^−4 ± 7.75 × 10^−5 vs. 1.08 × 10^−4
± 6.17 × 10^−5, p < 0.05), and nicotinamide (7.43 × 10^−4 ± 3.14 ×
10^−4 vs. 2.97 × 10^−4 ± 7.19 × 10^−5, p < 0.05) in the POS mode
([97]Figure 3e–h) and hexanoylglycine, dodecanedioic acid,
12-oxo-phytodienoic acid, 3,4-dihydroxyphenylacetic acid, and
polyunsaturated fatty acids (PUFAs), such as linoleic acid and
linolenic acid, in the NEG mode ([98]Figure S2f–n).
Figure 3.
[99]Figure 3
[100]Open in a new tab
Analysis of differential metabolites by liquid chromatography–mass
spectrometry (LC-MS) between diabetic and SH-P-treated mice in
ESI-positive (POS) detection mode. (a) Score plot of principal
component analysis (PCA) showing comparisons of the metabolomic
profiles; (b) Partial least squares discriminant analysis (PLS-DA)
score plot of metabolomic features and validation of the PLS-DA model
by permutation testing; (c) The deferentially expressed metabolite
analysis by volcano plot. The up-regulated metabolites are indicated in
red, the down-regulated metabolites are indicated in blue, and the gray
dots denote metabolites with insignificant changes in expression; (d) A
hierarchical clustering heatmap exhibiting the metabolites that
significantly differ in abundance; (e) Relative abundance of
3-Ketocholanic acid; (f) Relative abundance of indole; (g) Relative
abundance of oleoylethanolamide; (h) Relative abundance of
nicotinamide. *, p < 0.05 vs. diabetic group (Dia); **, p < 0.01 vs.
Dia.
To comprehensively investigate the metabolic pathways involved in the
different metabolites, a KEGG pathway enrichment analysis of the
differentially expressed metabolic pathways between the control and
SH-P-treated groups was performed. This analysis indicated that SH-P
treatment resulted in major alterations in metabolic pathways
associated with amino acid biosynthesis and metabolic pathways,
including arginine biosynthesis and histidine, tyrosine, alanine,
aspartate, glutamate, and tryptophan metabolism ([101]Figure 4a). In
addition, correlations were observed between the differential
metabolites 3-Ketocholanic acid, indole, oleoylethanolamide, and
nicotinamide ([102]Figure 4b), as well as 3,4-dihydroxyphenylacetic
acid, 12-oxo-phytodienoic acid, hexanoylglycine, dodecanedioic acid,
linoleic acid, and linolenic acid ([103]Figure S2e).
Figure 4.
[104]Figure 4
[105]Open in a new tab
Metabolic pathways involving the differential metabolites, and
Spearman’s correlation analysis of the metabolites, and the association
between gut microbiota and differential metabolites. (a) The enriched
signaling of differential metabolites between diabetic and SH-P-treated
mice by KEGG enrichment analyses; (b) Network illustrating the
interactions of the differential metabolites using the POS mode; (c) A
hierarchical clustering heatmap showed the correlation between the
dominant gut microbiota genera and differential metabolites. *, p <
0.05; **, p < 0.01; (d) A network illustrating the interactions between
the dominant gut microbiota genera and differential metabolites.
2.4. Correlation Analysis between the Gut Microbiota and Metabolites in Mice
Correlations between the gut microbiota and metabolites were assessed
using Spearman’s correlation analysis. Notably, in the positive
detection mode, 3-Ketocholanic acid, indole, oleoylethanolamide, and
nicotinamide were all positively correlated with Lactobacillus
([106]Figure 4c,d). In the negative detection mode,
3,4-dihydroxyphenylacetic acid and PUFAs, such as linoleic acid and
linolenic acid, which are involved in tyrosine metabolism, linoleic
acid metabolism, and the biosynthesis of unsaturated fatty acids, were
positively correlated with Bacteroides, Lactobacillus, and
Parabacteroides ([107]Figure S2o,p).
2.5. Effects of FMT on Alleviating Symptoms of T2DM in Mice
Because our findings are consistent with the supposition that SH-P
treatment can improve diabetes by changing the composition of the
intestinal microbiota and regulating metabolic function, we further
investigated the potential effects of FMT in improving hyperglycemia.
Although no significant differences in body weight and food intake were
observed, the water intake of diabetic mice significantly decreased
after FMT from SH-P-treated mice compared with that of the diabetic
control mice ([108]Figure S3a–c). FMT significantly reduced blood
glucose and insulin levels ([109]Figure 5a,b). Additionally, although
the difference was not significant, insulin sensitivity improved in FMT
mice, and the urinary output was significantly alleviated after FTM
from SH-P-treated mice compared to that in the diabetic control mice
([110]Figure 5c,d). Glucose tolerance was also ameliorated based on the
OGTT and AUC in FMT mice compared with that in diabetic mice
([111]Figure S3d,e). Moreover, FMT relieved tissue damage and
dysfunction in diabetic mice. The pancreas produces insulin, which
plays a major role in regulating blood sugar levels; compared to the
control group, the number of islets and islet cells was significantly
reduced, islet volume was significantly decreased, and the boundary was
blurred in diabetic mice. FMT significantly recovered the abnormal
shape and size of islets and islet cells and clearly distinguished the
boundaries of the islets ([112]Figure 5e). In addition, the liver and
kidney of the diabetic control mice showed obvious swelling, and the
surface of the kidney was covered with fat ([113]Figure 5f). FMT
significantly improved the swelling of the liver and kidney and reduced
the fatty tissue surrounding the kidney. Histopathological analysis
showed that FMT significantly improved the vacuolization and swelling
of hepatocytes and alleviated glomerular volume hypertrophy and renal
mesangial hyperplasia in comparison to that in the diabetic control
mice ([114]Figure 5g).
Figure 5.
[115]Figure 5
[116]Open in a new tab
The effects of fecal microbiota transplantation (FMT) on alleviating
hyperglycemia, antioxidant, and anti-inflammatory activities in
diabetic mice. (a) Blood glucose; (b) Insulin; (c) HOMA-IS; (d) Urinary
output; (e) Representative images of hematoxylin and eosin
(H&E)-stained pancreas tissue; (f) Effects of FMT on the organ indexes
of the liver and kidney; (g) Representative images of H&E-stained liver
and kidney tissues, scale bars = 50 μm; (h) Malondialdehyde (MDA)
content; (i) Total superoxide dismutase (T-SOD) activity; (j) IL-6
levels; (k) IL-1β levels; (l) TNF-α levels; (m) Representative images
of the H&E-stained aorta, scale bars = 20 μm. **, p < 0.01 vs. normal
control group (F-NG); ***, p < 0.001 vs. F-NG; #, p < 0.05 vs. diabetic
control group (F-DG); ##, p < 0.01 vs. F-DG; ###, p < 0.001 vs. F-DG.
Oxidative stress and inflammation are important triggers of
hyperglycemia and insulin resistance. It has been shown that
polysaccharides from P. igniarius exhibit good anti-inflammatory and
antioxidant activities [[117]24,[118]25]. FMT significantly decreased
the MDA content (13.41 ± 1.03 vs. 16.40 ± 1.66, p < 0.05) and increased
the T-SOD activity (102.14 ± 9.26 vs. 81.38 ± 8.30, p < 0.05) in the
serum of FMT-treated mice compared to that in diabetic mice
([119]Figure 5h,i). In addition, FMT also markedly reduced the levels
of the pro-inflammatory cytokines IL-6 (114.05 ± 12.0 vs. 141.17 ±
12.58, p < 0.01), IL-1β (103.95 ± 8.55 vs. 121.99 ± 10.51, p < 0.05),
and TNF-α (137.85 ± 11.60 vs. 164.12 ± 15.77, p < 0.05) compared to
that in the diabetic control group ([120]Figure 5j–l). Moreover, the
arrangement of endothelial cells in the aorta was disordered,
endothelial cells were swollen, the subcutaneous structure was loose,
the basement membrane was discontinuous, and inflammatory cells were
infiltrated in diabetic mice. However, FMT significantly alleviated
aortic endothelial cell lesions and inflammatory cell infiltration,
similar to that observed in normal endothelial cell structure
([121]Figure 5m).
3. Discussion
Polysaccharides are basic substances that maintain the activity of
living organisms and are involved in various metabolic processes. The
results of this study showed that SH-P treatment significantly improved
hyperglycemia symptoms and alleviated hyperglycemia-induced tissue
damage. Additionally, SH-P increased insulin sensitivity and glucose
tolerance in a dose-dependent manner. STZ induces selective pancreatic
islet β-cell cytotoxicity and has been extensively used to induce
diabetes mellitus in animals. Thus, whether the hypoglycemic mechanism
of SH-P is related to regenerating the pancreatic β-cell must be
further elucidated. The hypoglycemic effect of Dendrobium huoshanense
polysaccharide was related to the improvement of pancreatic β-cell
quantity and function and the regulation of hepatic glucose metabolism
[[122]26]. A polysaccharide purified from Hovenia dulcis was found to
ameliorate type 1 diabetes mellitus (T1DM) by up-regulating PDX-1,
activating and up-regulating IRS2 expression, and regulating apoptosis
and regeneration of islet β-cells to recover islet β-cell function
injury in T1DM rats [[123]27]. Tinospora cordifolia polysaccharide
possesses hypoglycemic, glucose oxidizing, and hypolipidemic abilities.
In addition, it exhibits β-cell regenerative properties in the
pancreatic sections [[124]28]. A water-soluble polysaccharide obtained
from pumpkin could promote the regeneration of damaged pancreatic
islets by stimulating β-cell proliferation, which was accompanied by a
decrease in plasma glucose levels [[125]29]. Consequently, stimulating
β-cell proliferation or inhibiting β-cell apoptosis may be one of the
main reasons for the hypoglycemic effect of SH-P.
The gut microbiota is responsible for controlling energy metabolism,
body weight, pro-inflammatory activity, bile acid metabolism, insulin
resistance, and modulation of gut hormones [[126]30]. In northern
China, the diversity of the gut microbiota in patients with diabetes is
significantly reduced compared with that in healthy individuals
[[127]31]. The decrease in gut microbiota diversity in patients with
T2DM results in the malnutrition of intestinal bacteria to a certain
degree, which interferes with the interaction between the gut
microbiota and the host. Individuals with a lower diversity of gut
microbiota composition show greater weight gain, reduced insulin
sensitivity, dyslipidemia, and increased markers of inflammation
[[128]32]. Although a consensus regarding which bacteria are
significantly altered is lacking, it is generally considered that the
number of gut bacteria is reduced in patients with T2DM. In this study,
we evaluated the effects of P. igniarius polysaccharides on the
characteristics of gut microbiota in T2DM mice. The alpha
diversity-based Chao1 and Simpson indices suggested that the P.
igniarius polysaccharide treatment improved the species richness and
evenness of the gut microbiota in diabetic mice. Moreover, PCoA based
on Jaccard and Bray–Curtis distances showed that the microbial
communities between the treatment and T2DM groups were clearly
separated and clustered, indicating that P. igniarius polysaccharide
treatment significantly influenced the composition of the gut
microbiota. These results provide a new direction for studying the
mechanisms of action of P. igniarius polysaccharides in the treatment
of diabetes.
A significant difference in the gut microbiota between patients with
T2DM and healthy individuals was observed at the phylum level
[[129]33]. The present study found that the dominant phyla in the SH-P
and T2DM groups were Firmicutes and Bacteroidetes. Although the F/B
ratio was not significantly different between the two groups, the
proportion of Bacteroidetes in the SH-P treatment group was
significantly lower than that in the T2DM group, and the proportion of
Firmicutes in the T2DM group was higher than that in the SH-P treatment
group. As Bacteroidetes mainly provide energy by producing acetic and
propionic acids, a decrease in Bacteroidetes abundance not only reduces
microbiota diversity but also disrupts the balance of energy and
glucose homeostasis [[130]34,[131]35]. Therefore, a decrease in the
abundance of Bacteroidetes may be related to the development of
diabetes. At the genus level, SH-P treatment significantly increased
the proportions of Bacteroides, Lactobacillus, Alloprevotella,
Alistipes, and Parabacteroides. Interestingly, SH-P treatment also
significantly increased the abundance of Lactobacillus from the phylum
to the genus level. Lactobacillus is a commonly used probiotic,
accounting for 6% of the total bacteria in the human duodenum and 0.3%
of the total bacteria in the human colon [[132]36,[133]37].
Lactobacillus abundance appears to be related to weight gain or loss
and has been shown to improve metabolic disorders caused by dietary and
genetic factors, particularly impaired glucose metabolism, by enhancing
intestinal barrier function and promoting the secretion of GLP-1
[[134]38]. The development of metabolic syndrome induced by a high-fat
diet is associated with decreased Arl hydrocarbon receptor (AhR) ligand
levels in mice. Lactobacillus supplementation can efficiently activate
AhR, subsequently reducing hepatic lipid accumulation and serum
triglyceride levels, thereby improving lipid metabolism disorders
[[135]39]. The abundance of Lactobacillus is lower in adults and
children with type 1 diabetes than in healthy individuals [[136]40]. In
addition to Lactobacillus, Bacteroides, Alloprevotella, Alistipes, and
Parabacteroides are also closely related to the occurrence and
development of diabetes. Bacteroides were considered the effective
degraders for polysaccharides, which play a positive role in diabetes
by up-regulating glucagon-like peptide-1 and serum insulin levels
[[137]41]. High abundances of Alistipes decreased the serum LDL-C, GSP,
and IL-6 levels and reduced serum lipid, glucose, and inflammation
marker levels, ultimately improving T2DM symptoms [[138]42]. Hyaluronic
acid increases the abundance of Bacteroides and Alistipes and may
contribute to the decrease in fasting blood glucose [[139]43].
Alloprevotella exhibits the capacity to produce short-chain fatty acids
(SCFAs) and plays a positive role in alleviating inflammation
[[140]44]. Moreover, it has been elucidated that Alloprevotella
contributes to the favorable outcomes observed in nutritional
interventions targeting metabolic parameters associated with obesity
[[141]45]. Parabacteroides were also positively correlated with the
production of SCFAs and have beneficial effects in reducing weight
gain, hyperglycemia, and inflammation risk, maintaining intestinal
barrier integrity, and improving insulin resistance and antioxidant
enzyme activity [[142]46]. These results indicate that SH-P treatment
alleviates diabetic symptoms by improving the structure and composition
of the gut microbiota and selectively restoring the abundance of
probiotics while possibly also suppressing the growth of potential
pathogens. However, the mechanism by which gut microbes, such as
Lactobacillus, improve diabetes requires further study.
The gut microbiota influences human health by producing bioactive
metabolites [[143]47]. Amino acid-related metabolites, short-chain
fatty acids, bile acids, trimethylamine N-oxide, and other microbial
metabolites have potential effects on T2DM [[144]11]. In this study,
SH-P treatment greatly increased the levels of metabolites closely
associated with hyperglycemia, including 3-Ketocholanic acid, indole,
oleoylethanolamide, and nicotinamide in the POS mode, and
3,4-dihydroxyphenylacetic acid, linoleic acid, and linolenic acid in
the NEG mode, demonstrating that the protective effects of SH-P
treatment against diabetes are related to the regulation of microbial
metabolites. KEGG pathway enrichment analysis showed that the
differentially expressed metabolic pathways following SH-P treatment
involved phenylalanine, tyrosine, arginine, and tryptophan
biosynthesis; tryptophan, linoleic acid, nicotinate, and nicotinamide
metabolism; and the biosynthesis of unsaturated fatty acids. Amino
acid-derived metabolites such as these alleviate T2DM in several ways.
For example, as a biosynthetic precursor of many microbial metabolites,
tryptophan can be converted into indole and its derivatives by the gut
microbiota. A reduction in arginine fermentation products was
associated with a significant increase in fasting blood glucose and
HbA1C levels in STZ-induced diabetic rats [[145]48]. Tyrosine is
metabolized to tyramine by gut microbes and is negatively correlated
with inflammation biomarkers and cardiometabolic risk factors
[[146]49]. Linoleic acid, nicotinate, and nicotinamide metabolism and
unsaturated fatty acid biosynthesis are all closely associated with the
occurrence and development of diabetes
[[147]21,[148]50,[149]51,[150]52,[151]53]. Notably, Spearman’s
correlation analysis indicated that Lactobacillus abundance was
positively correlated with 3-Ketocholanic acid, indole,
oleoylethanolamide, and nicotinamide levels; 3-Ketocholanic acid is a
derivative of cholanic acid involved in the pathogenesis of T2DM and
insulin resistance [[152]54]. Indole, an interspecies signaling
molecule involved in tryptophan metabolism and phenylalanine, tyrosine,
and tryptophan biosynthesis, modulates the secretion of GLP-1 from
intestinal enteroendocrine L-cells [[153]55]. GLP-1 decreases blood
glucose levels by stimulating insulin secretion, inhibiting glucagon
secretion, and slowing gastric emptying in a glucose-dependent manner
[[154]56]. Oleoylethanolamide is an endogenous PPAR-α agonist with
antihyperlipidemic, anti-inflammatory, and neuroprotective activities
[[155]33]. Nicotinamide is involved in the nicotinate and nicotinamide
metabolic pathways; treatment with nicotinamide prevents or ameliorates
STZ-induced diabetes and the progression of diabetes in non-obese
diabetic mice [[156]57,[157]58]. Our results suggest that the
beneficial effects of SH-P on hyperglycemia are at least partially
achieved by regulating the gut microbiota, particularly the composition
and metabolic functions of Lactobacillus. Additionally, Bacteroides,
Lactobacillus, and Parabacteroides abundance were positively correlated
with the metabolite levels of 3,4-dihydroxyphenylacetic acid, linoleic
acid, and linolenic acid, which are involved in the pathways of
tyrosine metabolism, linoleic acid metabolism, and the biosynthesis of
unsaturated fatty acids and are all closely related to the occurrence
and development of diabetes.
FMT or probiotics can improve glucose tolerance and insulin resistance
by regulating gut microbiota. Clinical data have shown that probiotics
can adjust the intestinal microbiota composition in a patient, which
has great applicability in the treatment of diabetes [[158]59]. In this
study, FMT significantly improved hyperglycemia symptoms in T2DM mice,
including decreased water intake, reduced blood glucose and insulin
levels, and alleviated urinary output. Moreover, FMT ameliorated the
tissue damage and dysfunction caused by diabetes. Administration of a
polysaccharide isolated from P. linteus mycelia decreased the
production of lipopolysaccharide-stimulated inflammatory cytokines,
such as TNF-α, IL-1, and IL-6, in RAW264.7 mouse macrophages by
regulating the PPAR-γ and mitogen-activated protein kinase (MAPK)
signaling pathways [[159]60]. P. linteus polysaccharides also show
strong 2,2-Diphenyl-1-picrylhydrazyl free radical scavenging activity
in a dose-dependent manner [[160]61]. The activities of peroxidase
dismutase, catalase, and glutathione peroxidase and MDA content in the
serum and liver of senile mice significantly increased after treatment
with different doses of a polysaccharide from P. linteus for 40 days
[[161]62]. In this study, FMT significantly decreased MDA content,
increased T-SOD activity, and markedly reduced the levels of the
pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 in the serum of
diabetic mice. Therefore, SH-P may exert its anti-inflammatory and
antioxidant activities, at least in part, by regulating changes in the
gut microbiota and its metabolites.
This study provides a new theoretical basis for the prevention and
improvement of T2DM by regulating the gut microbiota and its
metabolites, which is conducive to developing personalized
interventions based on the microbiota to establish new methods for
treating human metabolic diseases. However, as a special method of
organ transplantation, the mechanism of action, method of use, clinical
efficacy, and safety of FMT require further study. In the future,
microbiota transplantation may no longer be limited to FMT but may be
more focused on the specific use of different microbiota in different
organs (i.e., selective microbiota transplantation), which has greater
development potential in precision medicine. Additionally, the complex
interplay among factors such as ethnicity, host genetics, dietary
habits, and drug use plays an important role in shaping microbial
communities, making it an interesting and challenging research topic.
4. Materials and Methods
4.1. Polysaccharides Isolated from P. igniarius
P. igniarius was dried, pulverized, and soaked in distilled water
according to a solid-liquid ratio of 1:30 and reflux extracted at 90 °C
for 3 h. The extraction solution was filtered using a vacuum filter
pump (SHZ-DIII, Shanghai Dongxi Refrigeration Instrument Equipment Co.,
Ltd., Shanghai, China) and concentrated using a rotary evaporator
(R205B, Shanghai SENCO Technology Co., Ltd., Shanghai, China) at 65 °C
in a vacuum. The concentrated solution was mixed with anhydrous ethanol
at a ratio of 1:4 (v/v) and stored overnight at 4 °C to precipitate
polysaccharides. The precipitate was obtained by centrifugation at
5000× g for 10 min at 4 °C and dissolved in distilled water. Then, the
solution was dialyzed in a dialysis bag (3500 Da mw cut-off) for 48 h
at 4 °C, and the protein impurities were removed using the Sevag
method. Polysaccharides (SH-P) were obtained by freeze-drying.
4.2. Animal Treatment
Male-specific pathogen-free grade C57/BL6J mice (20.0 ± 2.0 g) were
purchased from Pengyue Experimental Animal Breeding Co., Ltd. (Jinan,
China). Diabetes was induced in mice using a high-fat diet and
treatment with STZ (Sigma-Aldrich, Sigma-Aldrich Co. LLC, St. Louis,
MO, USA). After acclimatization for one week under a 12 h light/dark
cycle at 23 ± 2 °C with a relative humidity level of 50 ± 5%, the mice
were randomly divided into a normal control group (n = 5) and a
diabetes group (n = 20). The control group was fed a maintenance diet,
and the diabetes group was fed a high-fat diet (66.5% maintenance diet,
10% lard, 20% sucrose, 2.5% cholesterol, and 1% sodium cholate). After
four weeks of feeding, the diabetes group received an intraperitoneal
injection of 100 mg/kg STZ dissolved in citrate buffer (0.1 mol/L, pH
4.4) after fasting overnight. Mice with fasting blood glucose levels
higher than 11.1 mmol/L after one week of STZ injection were selected
as diabetic mice. Diabetic mice were further divided into five groups:
a diabetes control group (n = 5), treated with 0.9% NaCl; a metformin
treatment group, which received 300 mg/kg metformin; and three SH-P
treatment groups orally administered SH-P at doses of 200, 400, and 800
mg/kg according to body weight once daily for four weeks. Blood glucose
levels were measured weekly throughout the study. The OGTT was
performed one day before euthanasia [[162]63]. The area under the curve
(AUC) and homeostasis model assessment of insulin sensitivity (HOMA-IS)
were calculated using the following formulas:
[MATH: AUCmin·mmolL=12×[BG0 min+BG120 min]+BG90 min+BG60 min−BG30 min×
30 min :MATH]
[MATH: HOMA−IS=1/(FBG ∗ FINS) :MATH]
At the end of the experiment, the mice were euthanized, and blood and
tissue samples were collected for further analysis. Tissues were fixed
with 4% paraformaldehyde (Biosharp, Beijing Labgic Technology Co.,
Ltd., Beijing, China), dehydrated in a series of graded ethanol (70,
80, 90, 95, and 100%), and embedded in paraffin. Paraffin-embedded
tissues were cut into 5 μm thick slices using a microtome (RM2235,
Leica Microsystems Nussloch GmbH, Nussloch, German) and stained with
hematoxylin–eosin (H&E).
4.3. Gut Microbiota Analysis
Genomic DNA was extracted from fecal samples using DNA recovery kit
(AxyPrep, Axygen Scientific Inc., San Francisco, CA, USA) according to
the manufacturer’s instructions. Primers 357F
(5′-ACTCCTACGGRAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were
used to amplify the V3-V4 variable region of the 16S rDNA by polymerase
chain reaction, followed by purification and quantification. The
amplicons were used to construct sequencing libraries, and sequencing
was performed using the NGS Illumina sequencing platform (Illumina,
Illumina, Inc., San Diego, CA, USA). All sequences were divided into
operational taxonomic units (OTUs) with a 97% similarity cutoff using
FLASH (version 1.2.11). The representative OTU sequence was compared
with the database for species annotation using Mothur (classify.seqs)
software (version 1.39.5). Alpha diversity was estimated using the
Chao1 and Simpson indices to evaluate the species richness and
diversity of the samples. Beta diversity was evaluated using principal
coordinates analysis (PCoA) based on Jaccard and Bray–Curtis distances
to reveal the aggregation and dispersion of samples. Linear
discriminant analysis Effect Size (LEfSe) was performed to generate a
cladogram for identifying different biomarkers in the gut microbiota
(linear discriminant analysis (LDA) > 3, p < 0.05).
4.4. Untargeted Metabolomics Analysis by Liquid Chromatography–Mass
Spectrometry
Fecal samples (50 g) were fully vortexed with 800 μL of 80% methanol.
The mixtures were ground using a high-throughput tissue grinder
(SCIENTZ-48, Ningbo Scientz Biotechnology Co., Ltd., Ningbo, China) at
65 Hz for 3 min and ultrasonicated (PS-80A, Shanghai Ledon Industrial
Co., Ltd., Shanghai, China) in an ice-water bath at 4 °C for 30 min.
Subsequently, the mixtures were stored at −40 °C for 1 h, vortexed for
30 s, centrifuged at 12,000× g and 4 °C for 15 min, and the supernatant
was collected. Then, 200 μL of supernatant was mixed with 5 μL of
dichlorophenylalanine (0.14 mg/mL, Aladdin, Shanghai Aladdin
Biochemical Technology Co., Ltd., Shanghai, China) as an internal
standard for further analysis. A liquid chromatography–mass
spectrometry (LC-MS) platform (Waters, UPLC; Thermo Fisher Scientific,
Q Exactive) and an ACQUITY HSS T3 column (2.1 × 100 mm, 1.8 μm, Waters)
were used to analyze the untargeted metabolomics profiling in both
ESI-positive (POS) and ESI-negative (NEG) ion modes. The gradient
elution system comprised water (mobile phase A, containing 0.05% formic
acid; Aladdin, China) and acetonitrile (mobile phase B; Sigma-Aldrich,
USA). The column temperature was 40 °C, and the flow rate was 0.300
mL/min. The temperature of the automatic injector was 4 °C, and the
injection volume was 5 μL. The mobile phase gradients are listed in
[163]Table S1. The raw data were analyzed using Compound Discoverer
software (version 3.1) to obtain qualitative and quantitative results
for the metabolites. Quality control (QC) was used to ensure the
accuracy and reliability of the results. Principal component analysis
(PCA) and partial least squares discriminant analysis (PLS-DA) were
performed to evaluate the variation in metabolites in different groups.
The differential metabolites were selected based on p-values < 0.05
(Student’s t-test) and the variable importance in the projection values
of orthogonal projections to latent structures discriminant analysis >
1.0. Heatmaps and cluster plots were used to assess changes in the
expression of the differential metabolites. Metabolic pathway
enrichment of differentially expressed metabolites was determined using
the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
analysis. Metabolite correlation analyses were performed to reveal the
relationships between samples and metabolites. The biological
significance of the metabolites was explained through a functional
analysis of the metabolic pathways. Hierarchical clustering analysis
was used to interpret the correlation between gut microorganisms and
metabolites.
4.5. Fecal Microbiota Transplantation (FMT)
Fecal samples from the SH-P treatment group were collected in an empty,
sterile, frozen tube using a sterile toothpick. The tube was quickly
frozen in liquid nitrogen and stored at −80 °C until preparation of the
fecal suspension. Fecal samples (300 mg) were dissolved in 2 mL of
normal saline and homogenized by vortexing for 5 min until pellets were
no longer visible. Homogenized fecal mixtures were centrifuged at 2000×
g for 1 min at 25 °C, and the supernatants were sub-packaged into new
tubes and stored at −80 °C until the day of administration. Diabetic
mice were randomly divided into a diabetic control group (F-DG, n = 5)
and an FMT group (F-TG, n = 5). Healthy C57/BL6J mice were selected as
the normal control group (F-NG, n = 5). Mice in the FMT group were
supplemented with 300 μL of the fecal suspensions by oral gavage daily
for four weeks. The normal control and diabetic control mice received
equivalent volumes of normal saline. Blood samples were collected to
measure antioxidant parameters (malondialdehyde (MDA) and total
superoxide dismutase (T-SOD) activity) and inflammatory factors (tumor
necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1β
(IL-1β)) according to the manufacturer’s instructions of commercially
available kits (Jiancheng, China). Tissue samples (pancreas, liver,
kidney, and aorta) were collected for H&E staining.
4.6. Statistical Analysis
Data are expressed as the mean ± standard deviation. GraphPad Prism
(version 8.0.2) was used to perform statistical analyses. One-way
analysis of variance (ANOVA), followed by least significant difference
analysis, was used to evaluate differences between the two groups.
Differences were considered statistically significant at p < 0.05.
5. Conclusions
In conclusion, this study demonstrates that oral SH-P at a reasonable
dose can alleviate hyperglycemia symptoms in mice with high-fat
diet/STZ-induced diabetes. SH-P treatment significantly decreased the
food and water intake, HbA1c level, and blood glucose and insulin
levels and significantly improved insulin sensitivity and glucose
tolerance in diabetic mice. Additionally, P. igniarius polysaccharide
treatment decreased the F/B ratio compared to the diabetic group at the
phylum level and significantly increased the proportions of
Bacteroides, Lactobacillus, Alloprevotella, Alistipes, and
Parabacteroides at the genus level. In particular, SH-P treatment
significantly increased the abundance of Lactobacillus from the phylum
to the genus level. SH-P treatment greatly increased the levels of
metabolites closely associated with hyperglycemia, demonstrating that
the protective effects of SH-P treatment against diabetes are related
to the regulation of microbial metabolites. KEGG pathway enrichment
analysis showed that the differentially expressed metabolic pathways
following SH-P treatment involved phenylalanine, tyrosine, arginine,
and tryptophan biosynthesis; tryptophan, linoleic acid, nicotinate, and
nicotinamide metabolism; and the biosynthesis of unsaturated fatty
acids. Moreover, FMT significantly relieved hyperglycemia symptoms,
ameliorated tissue damage and dysfunction, and significantly improved
anti-inflammatory and antioxidant activities of diabetic mice. The
beneficial effects of SH-P were achieved, at least in part, by
regulating the composition and metabolic functions of the gut
microbiota, although future studies are required to identify the
molecular and metabolic mechanisms underlying the role of SH-P in
improving diabetes.
Supplementary Materials
The following supporting information can be downloaded at:
[164]https://www.mdpi.com/article/10.3390/molecules28207136/s1, Table
S1: The gradient of mobile phase; Figure S1: Gut microbiome diversity
and composition analysis. (a) Relative abundance of gut microbiota at
the class level; (b) Relative abundance of gut microbiota at the order
level; (c) Relative abundance of gut microbiota at the family level;
(d) Relative abundance of gut microbiota at the species level; Figure
S2: Analysis of the differential metabolites by LC- MS between diabetic
and SH-P treatment groups, the correlation of the metabolites, and the
association between gut microbiota and the differential metabolites in
NEG mode. (a) Score plot of PCA showing comparisons of the metabolomic
profiles; (b) PLS-DA score plot of metabolomic features and validation
of the PLS-DA model by permutation testing; (c) Deferentially expressed
metabolites analysis by volcano plot. The up-regulated metabolites are
indicated in red, the down-regulated ones are in blue, and the gray
dots denote the insignificant metabolites; (d) Hierarchical clustering
heatmap exhibiting the metabolites that differ significantly in
abundance; (e) Network illustrating the interactions of the
differential metabolites; (f) Relative abundance of linoleic acid; (g)
Relative abundance of 5-hydroxyindole-3-acetic acid; (h) Relative
abundance of gamma-glutamylleucine; (i) Relative abundance of
dodecanedioic acid; (j) Relative abundance of 3-methylglutaric acid;
(k) Relative abundance of 3,4-dihydroxyphenylacetic acid; (l) Relative
abundance of 12-oxo phytodienoic acid; (m) Relative abundance of
linolenic acid; (n) Relative abundance of hexanoylglycine. *, p < 0.05,
vs. Dia; **, p < 0.01, vs. Dia. (o) Hierarchical clustering heatmap
showed the correlation between the dominant gut microbiota genera and
differential metabolites; (p) Network illustrating the interactions
between the dominant gut microbiota genera and differential
metabolites. Significant correlations are indicated as * p < 0.05 and
** p < 0.01; Figure S3: Effect of FMT on relieving hyperglycemic
symptoms. (a) Body weight; (b) Food intake; (c) Water intake; (d) OGTT;
(e) AUC. **, p < 0.01, vs. F-NG; ***, p < 0.001, vs. F-NG; #, p < 0.05,
vs. F-DG; ##, p < 0.01, vs. F-DG.
[165]Click here for additional data file.^ (2.1MB, zip)
Author Contributions
Conceptualization, Z.N., Y.S. and A.C.; Data curation, Z.N. and Y.W.;
Methodology, J.L., X.Q., Y.Y., M.W., W.L., S.Z., Y.Z. and A.C.;
Software, J.L., X.Q., Y.Y., M.W. and W.L.; Supervision, Y.W.;
Writing—original draft, Z.N.; Writing—review and editing, A.C. All
authors have read and agreed to the published version of the
manuscript.
Institutional Review Board Statement
The animal study protocol was approved by the Experimental Animal
Ethics Committee of Xuzhou Medical University (approval number:
L20210226457).
Informed Consent Statement
Not applicable.
Data Availability Statement
The data that support the findings of this study are available from the
corresponding authors upon reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This work was supported by Jiangsu Province’s industry university
research cooperation project [Grant No. BY2022773, BY2022777]; Xuzhou
Science and Technology Program [Grant No. KC22477].
Footnotes
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References