Abstract

Background

   Tripterygium glycoside (TG) has been reported to have the effect of
   ameliorating Alzheimer’s disease (AD)-like symptoms in mice model.
   However, the underlying mechanism is largely unknown. This study aimed
   to investigate the potential mechanism of TG against AD by integrating
   metabolomics, 16s rRNA sequencing, network pharmacology, molecular
   docking, and molecular dynamics simulation.

Methods

   Memory and cognitive functions were assessed in mice via the Morris
   water maze. The pathological changes were assessed using hematoxylin
   and Nissl’s straining. Pathological changes in p-Tau and Aβ[1-42] were
   assessed using immunohistochemistry, immunofluorescence, ELISA, and
   Western blotting. 16S rRNA sequencing and metabolomics were performed
   to analyze alterations in the structure of gut microbiota and
   hippocampus metabolites. Network pharmacology, molecular docking, and
   molecular dynamics simulation were performed to determine the putative
   molecular regulatory mechanism of TG in treating AD.

Results

   TG significantly could inhibit neuron loss, improved cognitive and
   memory functions, and significantly reduce the expression of p-Tau and
   Aβ[1-42.] In addition, 16s rRNA analysis revealed that TG could reverse
   AD-induced gut microbiota dysbiosis in AD model mice by reducing the
   abundance of Alistipes. Furthermore, metabolomic analysis revealed that
   TG may reverse AD-induced metabolic disorders by regulating
   glycerophospholipid metabolism. And spearman analysis revealed that
   glycerophospholipids metabolism might closely related to Alistipes.
   Moreover, network pharmacology, molecular docking, and molecular
   dynamics simulation analyses indicated that TG might regulate lipid
   metabolism-related pathways via SRC for the treatment of AD.

Conclusion

   TG may serve as a potential therapeutic drug for preventing AD via the
   microbiota-gut-brain axis.

   Keywords: tripterygium glycoside, Alzheimer’s disease, gut microbiota,
   metabolomic, glycerophospholipids metabolism

Graphical Abstract

   [38]graphic file with name NDT-21-1911-g0001.jpg
   [39]Open in a new tab

Introduction

   Alzheimer’s disease (AD), a complex degenerative disease in the central
   nervous system, was characterized by memory loss, cognitive
   dysfunction, and mental behavioral abnormalities.[40]^1^,[41]^2
   Epidemiology has shown that the prevalence of AD is increasing among
   people over 65 years of age, and the number of patients will reach 152
   million by 2050.[42]^3 The pathogenesis of AD is complex. Cholinergic
   deficiency,[43]^4 Aβ toxicity,[44]^5 tau protein
   hyperphosphorylation,[45]^6 synaptic dysfunction,[46]^7 oxidative
   stress,[47]^8 and neuroinflammation[48]^9 were causative factors of AD.
   The drugs used for the treatment of AD mainly target cholinergic
   neurotransmitters, Aβ, and p-tau yet,[49]^10 while the clinical effects
   were not significant.

   Tripterygium glycoside (TG), an extract of Tripterygium wilfordii Hook.
   C,[50]^11 has the effects of anti-inflammatory, antiviral, and
   immunomodulatory,[51]^12 which was mainly used in treating rheumatoid
   arthritis (RA),[52]^13 systemic lupus erythematosus (SLE),[53]^14
   diabetic nephropathy (DN),[54]^15 lupus nephritis (LN),[55]^16
   hyperthyroidism,[56]^17 and ovarian cancer (OC).[57]^18 Our previous
   study revealed that TG could significantly ameliorate neuroinflammation
   in an Aβ[25-35]-induced AD mouse model by inhibiting IκBα and p38
   phosphorylation.[58]^19 Moreover, TG could regulate the expression of
   noncoding RNAs in the hippocampus of AD mice.[59]^20^,[60]^21 However,
   the in-depth mechanism of TG treatment for AD is not clear.

   The gut microbiota is a complex and dynamic ecosystem. Changes in the
   diversity and abundance of the gut microbiota in AD patients and a link
   between dysbiosis of the gut microbiota and the pathological process of
   AD have been reported.[61]^22–24 In addition, the regulatory effect of
   traditional Chinese medicine on the gut microbiota has become a
   hotpot.[62]^25–29 Zhang et al[63]^30 reported that Xiao Yao San could
   improve the structure of the gut microbiota in AD model mice, increase
   the richness and diversity of the gut microbiota, and subsequently
   alleviated the dysfunction of bacterial metabolism and improve AD
   symptoms. Liu et al[64]^31 reported that icariin can regulate the
   abundance of Akkermansia and Alistipes in APP/PS1 mice. Furthermore, TG
   was shown to change the structure of gut microbiota in RA rats[65]^32
   and ulcerative colitis (UC) mice.[66]^33 The above studies suggested
   that TG may have a regulatory effect on the gut microbiota.

   Researches have shown that imbalance of gut microbiota may lead to
   increased permeability of the intestinal barrier and the blood–brain
   barrier through bidirectional communication within the gut-brain
   axis.[67]^34^,[68]^35 In addition, the metabolites of the gut
   microbiota may lead to changes in neuroreactive metabolites such as the
   secretion of γ-aminobutyric acid, serotonin, β
   -n-methylamino-L-alanine, and the expression of brain-derived
   neurotrophic factor.[69]^36^,[70]^37 Therefore, it is reliable that
   alteration in the structure of the gut microbiota could cause
   differences in the expression of metabolites in the brain and
   subsequently induce AD. However, whether TG could change the structure
   of gut microbiota, and then alter the expression of metabolites in the
   AD mice model is not clear yet.

   The 16S rRNA sequencing technique has provided insights into the gut
   microbiota’ s role in AD, revealing significant differences in
   microbial composition between AD and control.[71]^26 Moreover,
   metabolomics, a rapidly advancing field, offers insights into
   disease-induced metabolic disturbances.[72]^38 Increasing evidence has
   revealed that multiomics including 16S rRNA sequencing and
   metabolomics, play important roles in investigating the pathogenesis or
   mechanism of drug intervention in neurodegenerative diseases including
   AD and vascular dementia (VD).[73]^39^,[74]^40 In the present study, we
   aimed to treat an Aβ[25-35]-AD mouse model with TG and investigate the
   potential mechanism integrating metabolomics, 16S rRNA sequencing, and
   network pharmacology analysis.

Materials and Methods

AD Animal Modeling and Grouping

   A total of 54 SPF KM mice (male, 8 months, 30±5 g) were purchased from
   Hunan Slake Jinda Laboratory Animal Co. (Animal Production License No.
   SYXK (Xiang) 2019–004). The animal experimental procedures followed the
   ARRIVE Guidelines ([75]https://arriveguidelines.org), the American
   Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of
   Animals (2020), and were approved by the Ethics Committee of the Tibet
   University (Ethics Approval No. 2023SQ005). The mice were randomly
   divided into a control group (N=6) and an AD model group (N=36) and
   were then acclimatized and reared for 1 week. The temperature ranged
   from 18 to 22 °C, and the relative humidity ranged from 50% to 60%. AD
   mice model were conducted as described by Tang et al[76]^21 and Gao et
   al.[77]^41 The mice in the AD model group and control group were
   injected with D-gal (1.2 mg/10 g) and 0.9% saline subcutaneously in the
   dorsum of the neck for 28 days (once a day). The mice in the AD model
   group and control group were subsequently injected with 1 μL of
   Aβ[25-35] (1 μg/μL) (Sigma, USA) and 1 μL of 0.9% saline into the
   lateral ventricle using stereotaxic localization (bregma: −2.3 mm,
   lambda: ±1.8 mm, depth: −2.0 mm, unilateral, once) separately. The mice
   in the AD model group were then randomly divided into AD (N=12), TG-L
   (N=12), and TG-H (N=12) groups. Another control group (N=12) was also
   established. The mice in the control group were gavaged with 0.1 mL of
   0.9% saline. The mice in the TG-L and TG-H groups were gavaged with 9.1
   mg/kg and 13.65 mg/kg TG, respectively (purchased from Zhejiang Duende
   Co. Ltd., China) according to Yan et al[78]^42 (once a day for 28
   consecutive days). The procedure of the animal experiment is shown in
   [79]Figure 1A.

Figure 1.

   [80]Figure 1
   [81]Open in a new tab

   The changes of memory and cognitive function in AD mice detected by
   Morris water maze. (A) The animal experimental procedure. (B)
   Comparison of escape latency of mice in the control, AD, TG-L, and TG-H
   groups. (C) Comparison of the frequency of crossing the virtual
   platform in the control, AD, TG-L, and TG-H groups. (D) The swimming
   trajectory of mice in the control, AD, TG-L, and TG-H groups. Data was
   expressed as the means ± standard deviation (SD). (n =6/group in each
   group). “**” means P < 0.05. All experimental results were performed in
   triplicates.

Morris Water Maze

   The Morris water maze (MWM) was used to detect learning and memory
   functions in the mice. The MWM test was performed according to Vorhees
   et al.[82]^43 Data from the place navigation test and spatial probe
   test were collected following the methods of our previous
   studies.[83]^44^,[84]^45

Sample Collection

   The mice were anesthetized via intraperitoneal injection of 0.4% sodium
   pentobarbital. Hippocampal tissues from the control, AD, and TG-H
   groups (N=6) were collected, rapidly frozen in liquid nitrogen, and
   then placed in a −80 °C freezer for metabolomic analysis after the mice
   were sacrificed by cervical dislocation. In addition, feces from the
   control, AD, and TG-H groups (N=6) were randomly collected from the
   colon and stored at −80 °C for 16S rRNA sequencing. The left hemibrain
   of the remaining mice from the control, AD, TG-L, and TG-H groups (N=6)
   was preserved in 4% paraformaldehyde solution for morphological
   examination. The hippocampus from the remaining right hemibrain (N=6)
   were isolated and stored at −80 °C for biochemical assays.

Enzyme Linked Immunosorbent Assay (ELISA)

   PBS buffer was added to make hippocampus tissue homogeneity, and the
   supernatant was extracted after centrifugation. The levels of Aβ[1-42]
   were detected according to the instructions of mouse Aβ[1-42] ELISA kit
   (Cat: ML59211, R&D). The absorbance at 450 nm was measured by
   enzyme-labeled instrument (Cmax plus, Molecular Ltd).

HE Staining

   Hemibrain tissue was immersed in 4% paraformaldehyde at room
   temperature for 36 h. Hematoxylin–eosin (HE) staining was performed
   after fixation, and the samples were dehydrated, cleared,
   paraffin-embedded, and sectioned at a thickness of 4 μm. Morphological
   changes in the hippocampal tissue were observed under a light
   microscope.

Nissl’s Staining

   The post-fixed hemibrains were cut into approximately 0.5 cm tissue
   blocks, which were then immersed in 0.1 M PBS configured in 20–40%
   sucrose with a gradient of sinking sugar in 4°C. The hemibrain tissue
   was dehydrated with alcohol, embedded in paraffin and sliced at a
   thickness of 4 mm. Following dewaxing, Nissl dye (ASPEN) was applied
   for 40 min (60 °C). The slices were washed with distilled water three
   times and differentiated with 95% ethanol for 5 min, followed by
   dehydration with alcohol-xylene until they were transparent. The
   sections were observed and photographed under a Ckx53 inverted
   microscope (Olympus, Japan).

Immunohistochemistry

   After the paraffin sections were deparaffinized, 3% H[2]O[2] was added
   to inactivate endogenous peroxidase. After microwave antigen retrieval
   and horse serum blocking, anti-Aβ[1-42] antibody (Abcam, #: ab201060)
   and anti-p-Tau (p-T181) (Abcam, #: ab254409) were added dropwise, and
   the samples were incubated at 37 °C for 1 h. After rinsing with PBS,
   the samples were incubated with SABC mouse IgG (Solarbio, #: SA0011,
   China) at 37 °C for 30 min. After sequential DAB color development,
   hematoxylin restaining, dehydration, and clearing, the slices were
   sealed. The optical density of positive cells were photographed under a
   Ckx53 inverted microscope (Olympus, Japan) and analyzed using Image J
   6.0.

Immunofluorescence

   The dewaxed sections were placed in 0.01M citrate buffer (PH6.0) and
   microwave heated at 65°C for antigen repair. Next, 50 μL anti-NeuN
   (1:400) (PTG, #: 66,836-1-Ig) and anti-p-Tau (1:200)(Abcam, #:
   ab254409) were added to each section, followed by incubation at 4°C
   overnight. Subsequently, 50 μL FITC labeled goats against rats IgG
   (Sanying, #: SA00003-11)(1:100) and Cy3-labeled sheep anti-mouse IgG
   (Aspen, #: AS1111) (1:100) were added, followed by incubation at 37°C
   for 40 min without light. Next, 50 μL DAPI (Sigma, #: D8417-1MG) was
   added to each section, followed by incubation for 30 min at room
   temperature away from light. Finally, 50 μL anti-fluorescence quencher
   dropwise (Aladdin, #: T105635-50g) was added, and the sections were
   observed and photographed under a confocal laser microscope (Leica
   CTS-SP5). The light source used a laser wavelength of 488 nm and a
   laser wavelength of 561 nm to excite green and red fluorescence,
   respectively. 20 hippocampal neurons were randomly selected for each
   high-power field of view, and the fluorescence intensity was analyzed
   by Leica confocal software. The experiment was repeated for 3 times,
   and the average fluorescence intensity was used as the relative
   expression of the protein.

Western Blotting

   Tissue protein extraction kit (Invitrogen, USA) was used to extract
   total protein from brain tissues of each group. The protein
   concentration was determined by BCA method. Equal amounts of protein
   extracts were separated by SDS-PAGE and then transferred to PVDF
   membranes. After blocking with 5% skim milk, they were incubated
   overnight at 4°C with anti-p-tau (p-Tau181) (1:500) (Abcam, #:
   ab254409, Abcam), anti-tau (1:500)(Abcam, #: ab254256, Abcam),
   anti-P-src (Y416)(1:800)(Abcam, #: ab278693, Abcam) and anti-src
   (1:800)(Abcam, #: ab109381, Abcam) primary antibodies, respectively.
   Washed with PBS-Tween-20 for 5 minutes and incubated with goat
   anti-mouse IgG antibody (1:6,000)(Abcam, #: ab150113, Abcam) at room
   temperature for 1 h. Then, the impression is developed and fixed.
   ImageJ 6.0 software was used to scan and calculate the gray values of
   each protein band. The relative protein expression levels of p-Tau,
   Tau, p-SRC, and SRC were normalized with the relative protein
   expression levels of GAPDH.

Metabolomic Analysis

   Metabolomic analysis of the hippocampus in the control, AD and TG-H
   groups was performed via LC-MS/GC-MS via a Waters ACQUITY UPLC I-Class
   plus/Thermo QE ultrahigh-performance liquid tandem high-resolution mass
   spectrometer and an Agilent 7890B‒5977A gas chromatograph mass
   spectrometer platform system (OE Biotech, Shanghai).

   The chromatographic conditions were as follows: LC: column: ACQUITY
   UPLC HSS T3 (100 mm×2.1 mm, 1.8 µm); column temperature: 45 °C; mobile
   phase: A-water (containing 0.1% formic acid), B-acetonitrile; flow
   rate: 0.35 mL/min; inlet volume: 2 μL; GC: DB-5MS capillary column (30
   m × 0.25 mm × 0.25 μm, Agilent J&W Scientific, Folsom, CA, USA);
   carrier gas: high-purity helium; flow rate: 1.0 mL/min; temperature at
   the injection port: 1.0 mL/min, 0.25 μm (Agilent J&W Scientific,
   Folsom, CA, USA); carrier gas: high purity helium at a flow rate of 1.0
   mL/min, inlet temperature: 260 °C; inlet volume: 1 μL; programmed
   temperature: the initial temperature of the column oven was 60 °C and
   held for 0.5 min; the temperature was programmed to increase to 125 °C
   with a programmed increase of 8 °C/min; the initial temperature of the
   column oven was 60 °C and held for 0.5 min; the temperature was
   programmed to increase to 125 °C with an increase of 8 °C/min, 8 °C/min
   to 210 °C, 15 °C/min to 270 °C, and 20 °C/min to 305 °C for 5 min.

   The LC-MS/MS mass spectrometry conditions were as follows: ion source:
   ESI; sample mass spectrometry signal acquisition was performed with
   separate scans for positive and negative ions, and the acquisition mode
   was DDA (data-dependent acquisition) data-dependent scanning mode.

   The GC-MS mass spectrometry conditions were as follows: electron
   bombardment ion source (EI), ion source temperature of 230 °C,
   quadrupole temperature of 150 °C, and electron energy of 70 eV. The
   scanning mode used was full scan mode (SCAN), and the mass scanning
   range was m/z 50–500.

   The raw data were obtained after being baseline filtered, peak
   identified, integrated, retention time corrected, peak aligned, and
   normalized via Progenesis QI v3.0 software (Nonlinear Dynamics,
   Newcastle, UK). The Human Metabolome Database (HMDB)
   ([85]https://hmdb.ca/), Lipidmaps (v2.3)
   ([86]https://www.lipidmaps.org/), METLIN database
   ([87]https://ngdc.cncb.ac.cn/databasecommons/database/id/5907) and
   LuMet-Animal database were used for identification analysis.
   Unsupervised principal component analysis (PCA) was used to detect the
   overall distribution among samples and the stability of the process.
   Partial least squares discriminant analysis (PLS-DA) and orthogonal
   partial least squares discriminant analysis (OPLS-DA) were used to
   distinguish the overall differences in metabolism between groups. The
   parameters of R2X (cum), interpretation rate R2Y (cum), and prediction
   rate Q2 (cum) were used to evaluate the validity of the PLS-DA model.
   The OPLS-DA model was established by using 7-fold cross validation and
   200-response permutation testing (RPT). The R2 and Q2 values of the
   random model were obtained to examine the quality of the model.
   Differentially expressed metabolites (DEMs) were screened on the basis
   of the VIP and P values (variable importance in the projection (VIP)>1,
   P<0.05). The DEMs with the top 50 VIPs were subjected to hierarchical
   clustering. Correlations of DEMs with the top 20 VIPs were analyzed
   using the Pearson correlation coefficient. Metabolic pathway enrichment
   analysis was performed for the DEMs via the KEGG database
   ([88]https://www.genome.jp/kegg/).

16S rRNA Amplicon Sequencing

   Total DNA from feces in the control, AD, and TG-H groups was extracted
   using a Genomic DNA Extraction Kit (Thermo Fisher Scientific, USA). The
   purity and concentration of the DNA were tested using a NanoDrop 2000
   (Thermo Fisher Scientific, USA). PCR amplification of the V3-V4
   variable region of the 16S rRNA gene was performed with the primers
   343F (5’-TACGGRAGGCAGCAG-3’) and 798R (5’-AGGGTATCTAATCCT-3’).[89]^46
   16s rRNA sequencing was performed out using the Illumina NovaSeq 6000
   sequencing platform (Shanghai OE Biotechnology Co). The abundance of
   amplicon sequence variant (ASV) was analysis by using the DADA2,[90]^47
   and QIIME 2.[91]^48 The α diversity was assessed by using the ACE,
   Chao1, Simpson, coverage, and Shannon indices. Unweighted UniFrac
   principal coordinate analysis (PCoA) was used to assess the β diversity
   via R package. The Wilcoxon statistical algorithm was used in variance
   analysis. Species differences between groups were analyzed using Linear
   discriminant analysis Effect Size (LEfSe). KEGG pathway enrichment was
   analyzed via the KEGG database ([92]https://www.genome.jp/kegg/).

Gut Microbiota–Metabolite Correlation Analysis

   Data with significant differences in microbial abundance (P<0.05) and
   metabolite abundance (P<0.05, |VIP| > 1.0) were selected for joint
   analysis. The Spearman correlation coefficients were calculated between
   all the differentials. Correlation heatmaps were plotted using
   matplotlib (version 3.3.4).

Network Pharmacology Analysis

   The active compounds of TG were then obtained by screening previous
   studies.[93]^49^,[94]^50 Information of the active compounds of TG were
   retrieved from the Traditional Chinese Medicine Systems Pharmacology
   Database and Analysis Platform (TCMSP) (OB ≥ 30% and DL ≥ 0.18)
   ([95]https://tcmsp-e.com/) and HERB ([96]http://herb.ac.cn/). The
   chemical structures were retrieved from the PubChem database
   ([97]https://pubchem.ncbi.nlm.nih.gov/). The target of the TG active
   compounds were retrieved using the SwissTarget prediction target
   prediction ([98]http://swisstargetprediction.ch/). The GeneCards
   ([99]https://www.genecards.org/), DrugBank
   ([100]https://www.drugbank.ca/), and DisGeNET
   ([101]https://www.disgenet.org/) databases were used to retrieve the
   potential targets of AD by using the keyword “Alzheimer’s disease”. AD
   targets were obtained after standardizing via UniProt database and
   removing duplicate data. The target intersection of TG and AD were
   screened by using InteractiVenn software. Protein‒protein interaction
   (PPI) network was constructed using the String database
   ([102]https://www.string-db.org). Gene Ontology (GO) functional and
   KEGG pathway enrichment analyses were performed via DAVID software
   ([103]https://david.ncifcff.gov/). The “drug-compound-target” and
   “compound-target-pathway” networks were constructed using Cytoscape
   3.9.0 software.

Molecular Docking

   The 3D structures of the active compounds were obtained from the
   PubChem database ([104]https://pubchem.ncbi.nlm.nih.gov/). The AD
   target protein structure was downloaded from the PDB database
   ([105]https://www.rcsb.org/). Molecular docking was performed out using
   Discovery Studio (2024) software ([106]http://www.discoverystudio.net/)
   with the CDOCKER module. A binding energy of less than 5 kcal/mol
   indicates a relatively stable docking between the receptor and the
   ligand.[107]^51^,[108]^52

Molecular Dynamics Simulation (MDs)

   Gromacs 2020.6 software, Charmm36 force field, and TIP3P model were
   selected to simulate the system, force field, water model. After
   setting the ion concentration at 0.145M, the system was heated to 310 K
   and 1bar, respectively. The MDs were performed at 100 ns. The cut-off
   value for the non-bond interaction was set to 1.2 nm, and the
   interaction of long distance electrostatic is calculated. The root mean
   square deviation (RMSD) and the root mean square fluctuation (RMSF) of
   atomic position were analyzed.

Statistical Analysis

   The GraphPad Prism (Version 8) software
   ([109]https://www.graphpad.com/) was used for statistical analysis.
   Measurement data were expressed as means ± standard deviation (SD) and
   analyzed by one-way ANOVA with Tukey multiple comparison test. A P<0.05
   indicated statistically significant difference.

Results

TG May Significantly Reduce Neuronal Damage, Decrease p-Tau and Aβ[1-42]
Production, and Improve Learning and Memory Functions in AD Mice

   The escape latency in the AD group was significantly higher than that
   in the control group (P<0.05). The escape latency in the TG-H group was
   significantly lower than that in the AD group (P< 0.05). There was no
   difference in escape latency between the TG-L group and the AD group
   (P>0.05) ([110]Figure 1B–D). In addition, the number of platform
   crossings was significantly lower in the AD group than in the control
   group (P<0.05). The number of platform crossings was significantly
   higher in the TG-H group than that in the AD group (P<0.05). The
   difference in the number of platform crossings between the TG-L group
   and the AD group was not statistically significant (P>0.05)
   ([111]Figure 1B–D). These data suggested that high-dose of TG may
   effectively improve memory and cognitive function in AD mice.

   HE staining revealed that the hippocampal neurons in the control group
   were regularly arranged, with clear edges and clear nuclei and nucleoli
   ([112]Figure 2A–D). While, the hippocampal neurons in the AD group were
   irregularly arranged, with blurred structures, shrunken nuclei, and
   deep staining. Compared to the AD group, the TG-H group had more cell
   layers, a larger cytosol, and a clearer structure. The hippocampal
   neurons in the TG-L group were irregularly arranged, with a blurred
   structure, shrunken nuclei, and dark staining ([113]Figure 2A–D). These
   results indicated that a high dose of TG may reduce the damage of
   hippocampal neuron in AD mice.

Figure 2.

   [114]Figure 2
   [115]Open in a new tab

   The morphologic changes of neurons in the hippocampus of AD mice
   detected by HE and Nissl’s staining. A-D. The results of HE staining
   (Scale bars: 20 µm, 200µm). (A) control; (B) AD; (C) TG-L; (D) TG-H.
   E-H. Nissl’s stained brain sections from each group (Scale bars: 20 µm,
   200µm). (E) control; (F) AD; (G) TG-L; (H) TG-H. (I) The comparison of
   the number of hippocampus neuron among groups. Data was expressed as
   the means ± standard deviation (SD) (n =6). “**” means P < 0.05. All
   experimental results were performed in triplicates.

   The Nissl’s staining results showed that the number of neuron was
   observed to be significantly lower in the AD group when compared with
   that in the control group (P < 0.05) ([116]Figure 2E–I). Compared with
   the AD group, the number of neuron in the TG-H group, but not the TG-L
   group, was significantly higher (P < 0.05) ([117]Figure 2E–I). Thus,
   high-dose of TG could significantly reduce the neuronal loss.

   The expressions of p-Tau and Aβ[1-42] in the mice hippocampus in the AD
   group were significantly higher than those in the control group
   (P<0.05). Compared to the AD group, the expressions of p-Tau and
   Aβ[1-42] in the mice hippocampus in the TG-H group (P<0.05), but not
   the TG-L group (P>0.05) were significantly lower ([118]Figure 3A–J).
   Moreover, the expressions of Aβ[1-42] and p-Tau proteins were
   identified via ELISA and western-blotting separately. The results
   revealed that the expressions of Aβ[1-42] and p-Tau proteins in the AD
   group were significantly higher than those in the control group, while
   they were significantly lower than those in the TG-H group, which
   indicated that TG could reduce the production of Aβ[1-42] and p-Tau
   proteins ([119]Figure 3K–M).

Figure 3.

   [120]Figure 3
   [121]Open in a new tab

   The effect of TG on AD mice model by immunohistochemistry (IHC). (A–D)
   The IHC results of Aβ[1-42] (Scale bars: 20 µm, 200µm). (E–H) The IHC
   results of p-tau (Scale bars: 20 µm, 200µm). (I and J) The comparisons
   of expression of Aβ[1-42] and p-tau between groups. (I) Aβ[1-42;] (J)
   p-tau. (K) The comparison of Aβ[1-42] levels detected by ELISA. (L) The
   Western blotting images of p-Tau and Tau proteins. (M) The comparison
   of expression of p-tau protein between groups. Data was expressed as
   the means ± standard deviation (SD). (n =6). “**” means P < 0.05. All
   experimental results were performed in triplicates.

   Furthermore, the accumulation of p-Tau in neurons was detected by
   NeuN/p-Tau immunodouble labeling fluorescence staining. The NeuN
   protein localized in the nucleus and cytoplasm. The p-tau protein
   localized in cytoplasm and cell membrane. And NeuN and p-Tau proteins
   were co-expressed in hippocampal neurons ([122]Figure 4A–Q). The
   average fluorescence intensities of NeuN and p-Tau proteins showed a
   significantly decreasing expression of p-Tau in the TG-H group compared
   to the AD group (P<0.05) ([123]Figure 4R–S). These results further
   confirmed that TG may effectively reduce the production of p-Tau.

Figure 4.

   [124]Figure 4
   [125]Open in a new tab

   The co-expression of NeuN and p-Tau proteins in hippocampus neuron.
   (A), (E), (I), (M) Immunofluorescence images of DAPI. (B), (F), (J),
   (O) Immunofluorescence images of NeuN. (C), (G), (K), (P)
   Immunofluorescence images of p-Tau. (D), (H), (L), (Q) Merged
   immunofluorescence images between groups (Scale bars: 20 µm). (R)
   Comparison of the fluorescence intensities of NeuN and p-Tau between
   groups. (R) NeuN; (S) p-Tau. “**” means P < 0.05. All experimental
   results were performed in triplicates.

TG May Reverse Hippocampal Metabolites Disorder in AD Mice

   The chromatographic mass spectral peaks of the mouse hippocampal tissue
   samples were clearly separated and evenly distributed
   ([126]Supplemental file Figure 1). The results of PCA, PLS-DA, and
   OPLS-DA analysis showed that the metabolites in the control, AD, and TG
   groups were in high intragroup clustering and significant among groups
   both in GC/MS and LC/MS ([127]Figure 5A–H). The parameters including
   R2X(cum), R2Y(cum), Q2(cum), R2, and Q2 used in measuring the PLS-DA
   and OPLS-DA models were listed in the [128]Supplemental file Table 1.
   The volcano plots and clustering diagrams of DEMs are shown in
   [129]Figure 6A–6D. There were 103 DEMs (42 upregulated and 61
   downregulated) between the AD group and the control group
   ([130]Supplemental file Table 2) and 96 DEMs (62 upregulated and 34
   downregulated) between the TG group and the AD group ([131]Figure 6E)
   ([132]Supplemental file Table 3). A total of 49 common DEMs among the
   control, AD, and TG groups were found ([133]Figure 6F)
   ([134]Supplemental file Table 4).

Figure 5.

   [135]Figure 5
   [136]Open in a new tab

   The PCA, PLS-DA, and OPLS-DA in GC-MS and LC-MS/MS. (A and B) PCA
   analysis of the three groups. (A) GC-MS. (B) LC-MS/MS. (C and D) (C)
   PLS-DA analysis between groups. (C) GC-MS; (D) LC-MS/MS. (E and F)
   OPLS-DA score plot between the AD and control groups. (E) GC-MS. (F)
   LC-MS/MS. (G and H) OPLS-DA score plot between AD and TG groups. (G)
   GC-MS. (H) LC-MS/MS.

Figure 6.

   [137]Figure 6
   [138]Open in a new tab

   Hippocampus metabolomics of the control, AD, and TG groups. (A and B)
   Volcano plot of metabolomics between groups. (A) AD group vs Control
   group; (B) AD group vs TG group. (C and D) Heatmap of the DEMs between
   groups. (C) AD group vs Control group; (D) AD group vs TG group. (E)
   Number of DEMs between groups. (F) Venn diagram of differential
   metabolites.

   The top 10 DEMs with the highest VIP values were used to plot a
   LolipopMap. Compared with those in the control group, the levels of
   4-methyltridecanoylcarnitine (fatty acyls) (P<0.05), PC
   (20:0/18:3(9Z,12Z,15Z)) (glycerophospholipids) (P<0.05),
   histidylproline (carboxylic acids and derivatives) (P<0.05), and
   tetraethylene glycol monododecyl ether (organooxygen compounds)
   (P<0.05) were significantly lower in the AD group. While, PGP (6
   keto-PGF1alpha/20:1(11Z)) (fatty acyls) (P<0.001), PG (19:1(9Z)/15:0)
   (glycerophospholipids) (P<0.01), PC (P-18:0/20:3(6,8,11)-OH(5))
   (glycerophospholipids) (P<0.05), and thiabendazole (benzimidazoles)
   (P<0.01) significantly elevated in the AD group ([139]Figure 7A). The
   levels of the above metabolites significantly reversed after TG
   intervention ([140]Figure 7B). These results indicates that TG may
   modulates various metabolites, especially glycerophospholipids, and
   fatty acyls, in the hippocampus of AD mice.

Figure 7.

   [141]Figure 7
   [142]Open in a new tab

   Analysis of hippocampus DEMs. (A and B) Lolipopmap of differential
   metabolites between groups. (A) AD group vs Control group; (B) AD group
   vs TG group. Red indicates upward adjustment, blue indicates downward
   adjustment, and asterisks indicate significance of DEMs (* means 0.01<
   P <0.05; ** means 0.001< P <0.01; *** means 0.0001< P <0.001), and the
   size of the dots is determined by VIP values. (C and D) KEGG pathway
   terms enriched by DEMs of hippocampus between groups. (C) AD group vs
   Control group; (D) AD group vs TG group.

   KEGG pathway enrichment analysis shown 137 and 97 metabolic pathways
   were enriched in the AD vs control and TG vs AD comparisons,
   respectively. The glycerophospholipid metabolism, choline metabolism in
   cancer, and the sphingolipid signaling pathway were the top 3 enriched
   pathways in the AD vs control comparison. The choline metabolism in
   cancer, Kaposi sarcoma-associated herpesvirus infection, and
   glycerophospholipid metabolism were the top 3 enriched pathways in the
   TG vs AD comparison ([143]Figure 7C–D). 93 common metabolic pathways
   were found between the AD vs control comparison and TG vs AD comparison
   ([144]Supplemental file Table 5). Taken together, these findings
   suggest that TG may modulate hippocampus metabolism and the consequent
   synthesis of metabolites, particularly choline and
   glycerophospholipids.

TG Might Alter the composition of the Gut Microbiota in AD Mice

   The α diversity results indicated that the Ace and Chao1 indices
   (P<0.01), but not the Simpson and Shannon indices (P>0.05), were
   significantly lower in the AD group than those in the control group
   ([145]Supplemental file Table 6). Compared with those in the AD group,
   the Ace and Chao1 indices, but not the Simpson and Shannon indices
   (P>0.05), were significantly greater in the TG group (P<0.01)
   ([146]Supplemental file Table 6). PCoA analysis indicated that there
   were significant separations between the groups. The contribution of
   PCoA difference were 16.44% (AD vs control) and 15.79% (TG vs AD)
   separately ([147]Figure 8A and [148]B). The degree of taxonomic
   similarity between the bacteria revealed that Bacteroidetes (control:
   57.9%; AD: 60.5%; TG: 53.9%) and Firmicutes (control: 34.9%; AD: 30.6%;
   TG: 32.6%) were the major phyla among groups ([149]Figure 8C and
   [150]D). The Firmicutes/Bacteroidetes (F/B) ratio in the AD group
   (0.51) was lower than that in the control group (0.60) and higher than
   that in the TG group (0.60). The above results suggested that TG might
   increase the abundance of gut microbiota in AD mice.

Figure 8.

   [151]Figure 8
   [152]Open in a new tab

   16S rRNA analysis in the control, AD, and TG groups. (A and B) PCoA
   analyses between groups. (A) AD group vs control group. (B) AD group vs
   TG group. (C and D) Percent of community abundance at the phylum level
   between groups. (C) AD group vs control group; (D) AD group vs TG
   group. (E and F) Boxplot of differential species at the genus level
   between groups. (E) AD group vs control group; (F) AD group vs TG
   group. (G and H) Histogram of the LDA scores of differential abundant
   gut microbiota between the groups. (G) AD group vs control group; (H)
   AD group vs TG group. (I and J) Heatmap of the KEGG pathway analysis
   between groups. (I) AD group vs control group; (J) AD group vs TG
   group.

   The presence of certain lipid and glucose metabolism-associated
   bacterial species, such as Alistipes and Aeromonas, is considered a
   health concern. In our study, the abundances of Alistipes and Aeromonas
   in the control group were significantly greater than those in the AD
   group (P<0.05). However, the abundance of Alistipes but not Aeromonas
   significantly decreased after TG intervention ([153]Figure 8E and
   [154]F). The LEfSe results revealed that the TG-treated mice presented
   a decreased abundance of Alistipes but not Aeromonas ([155]Figure 8G
   and [156]H). The KEGG pathway results revealed that the enriched
   pathways in the TG group were associated primarily with choline and
   lipid metabolism, especially sphingolipid and glycosphingolipid
   metabolism ([157]Figure 8I and [158]J).

Interaction Between the Gut Microbiota and Metabolites

   At the genus level, Aeromonas and Alistipes were significantly
   correlated with DEMs in both the AD vs control and TG vs AD
   comparisons. For the AD vs control comparison, 2-amino-5-nitrophenol,
   allopurinol, PGP (6 keto-PGF1alpha/20:1(11Z)), PS(18:1(9Z)/0:0), and
   thiabendazole were negatively correlated with Aeromonas and Alistipes.
   While, creatinine, Glc-GP(18:0/20:4(5Z,8Z,11Z,14Z)),
   PG(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), and
   PGP(i-19:0/18:1(12Z)-O(9S,10R)), were positively correlated with
   Aeromonas and Alistipes ([159]Figure 9A). After treatment with TG,
   2-amino-5-nitrophenol, allopurinol, PGP (6 keto-PGF1alpha/20:1(11Z)),
   PS (18:1(9Z)/0:0), and thiabendazole were positively correlated with
   Alistipes but not Aeromonas. In addition, creatinine,
   Glc-GP(18:0/20:4(5Z,8Z,11Z,14Z)), PG(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)),
   and PGP(i-19:0/18:1(12Z)-O(9S,10R)) were negatively correlated with
   Alistipes but not Aeromonas ([160]Figure 9B). Taken together, these
   results indicated that the therapeutic effect of TG may be associated
   with the metabolism of glycerophospholipids, and Alistipes may play a
   role in glycerophospholipids metabolism.

Figure 9.

   [161]Figure 9
   [162]Open in a new tab

   Spearman analysis heatmap between the control, AD, and TG groups (A) AD
   group vs Control group. (B) AD group vs TG group.“*” means P < 0.05,
   “**” means P < 0.01, and “***” means P < 0.001. Orange-red indicates
   positive correlation, blue indicates negative correlation.

Prediction of potential Drug-Target Pathways Associated with the Anti-AD
Activity of TG

   A total of 29 TG active compounds were obtained through the TCMSP (OB ≥
   30% and DL ≥ 0.18), HERB databases, and existing references ([163]Table