Jia et al. Journal of Neuroinflammation(2024) 21:201
https://doi.org/10.1186/s12974-024-03195-4

Journal of Neuroinflammation

Open Access

RESEARCH

Secoisolariciresinol diglucoside attenuates
neuroinflammation and cognitive impairment
in female Alzheimer’s disease mice via
modulating gut microbiota metabolism
and GPER/CREB/BDNF pathway
Mengzhen Jia1†, Fangjie Ning1†, Junqing Wen1, Xiaorui Wang1, Jiao Chen2, Jun Hu2, Xuhui Chen2* and Zhigang Liu1,3*
Abstract
Background Gender is a significant risk factor for late-onset Alzheimer’s disease (AD), often attributed to the declineof estrogen. The plant estrogen secoisolariciresinol diglucoside (SDG) has demonstrated anti-inflammatory andneuroprotective effects. However, the protective effects and mechanisms of SDG in female AD remain unclear.
Methods Ten-month-old female APPswe/PSEN1dE9 (APP/PS1) transgenic mice were treated with SDG to assess
its potential ameliorative effects on cognitive impairments in a female AD model through a series of behavioral
and biochemical experiments. Serum levels of gut microbial metabolites enterodiol (END) and enterolactone (ENL)
were quantified using HPLC-MS. Correlation analysis and broad-spectrum antibiotic cocktail (ABx) treatment were
employed to demonstrate the involvement of END and ENL in SDG’s cognitive improvement effects in female APP/
PS1 mice. Additionally, an acute neuroinflammation model was constructed in three-month-old C57BL/6J micetreated with lipopolysaccharide (LPS) and subjected to i.c.v. injection of G15, an inhibitor of G protein-coupled
estrogen receptor (GPER), to investigate the mediating role of the estrogen receptor GPER in the cognitive benefits
conferred by SDG.
Results SDG administration resulted in significant improvements in spatial, recognition, and working memoryin female APP/PS1 mice. Neuroprotective effects were observed, including enhanced expression of CREB/BDNF
and PSD-95, reduced β-amyloid (Aβ) deposition, and decreased levels of TNF-α, IL-6, and IL-10. SDG also altered
gut microbiota composition, increasing serum levels of END and ENL. Correlation analysis indicated significant
associations between END, ENL, cognitive performance, hippocampal Aβ-related protein mRNA expression, andcortical neuroinflammatory cytokine levels. The removal of gut microbiota inhibited END and ENL production and†Mengzhen Jia and Fangjie Ning contributed equally to this work.

*Correspondence:
Xuhui Chenxuhuichen@pkuszh.com
Zhigang Liuzhigangliu@nwsuaf.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
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Jia et al. Journal of Neuroinflammation(2024) 21:201

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eliminated the neuroprotective effects of SDG. Furthermore, GPER was found to mediate the inhibitory effects of SDG
on neuroinflammatory responses.
Conclusion These findings suggest that SDG promotes the production of gut microbial metabolites END and
ENL, which inhibit cerebral β-amyloid deposition, activate GPER to enhance CREB/BDNF signaling pathways, andsuppress neuroinflammatory responses. Consequently, SDG exerts neuroprotective effects and ameliorates cognitive
impairments associated with AD in female mice.
Keywords Secoisolariciresinol diglucoside, Alzheimer’s disease, Cognitive impairment, GPER, Gut microbiota,
Neuroinflammation

Introduction
With the intensification of the global aging population
trend, the prevalence of Alzheimer’s disease (AD) is
showing a trend of accelerated growth. AD is the mostcommon form of dementia, characterized by the excessive deposition of β-amyloid (Aβ) and the neurofibrillarytangle. Additionally, core features such as neuroinflammation [1] and synaptic dysfunction [2] play crucial rolesin the occurrence and development of physiologicalimpairments associated with AD. Over the years, epidemiological studies have indicated that, regardless of age
and ethnicity, female patients with AD account for twothirds of the total patient population [3, 4]. Women inpremenopausal, perimenopausal, or postmenopausal
stages are more susceptible to developing AD, indicatinga correlation between gender factors and the attenuationof estrogen signaling in the pathogenesis of AD [5]. Asa neuroactive steroid hormone, estrogen exerts neuroprotective effects by reducing Aβ and glutamate toxicity[6], enhancing synaptic plasticity, maintaining neuronal
nutritional components, facilitating transcription factor initiation, reducing brain inflammation [7, 8], anddecreasing tau protein hyperphosphorylation [9–11],
thereby ameliorating cognitive impairments associatedwith brain aging [5, 12]. In recent years, estrogen replacement therapy (HRT) has demonstrated significant potential as an intervention approach for AD [13]. However,considering the side effects of HRT, such as an increased
risk of stroke and venous thromboembolism [13, 14],
phytoestrogens appear to be a promising alternative [15].
Secoisolariciresinol diglucoside (SDG) is the principal component of lignans in flaxseed, belonging to thephytoestrogen family [16]. SDG content is remarkably
high in flaxseeds, ranging from 1 to 4% (w/w) [17, 18].
The study indicates that SDG alleviates the pathological features of postmenopausal osteoporosis model miceinduced by ovariectomy, and its mechanism of action
involves phytoestrogenic effects and anti-inflammatory
properties [19]. Moreover, SDG exerts protective effects
by inhibiting inflammation in various diseases, including
peripheral local inflammatory diseases such as osteoarthritis [20], colitis [21, 22], postmenopausal osteoporosis [19], atopic dermatitis [23], as well as systemic andneural inflammation induced by LPS [24, 25]. Additionally, dietary supplementation of SDG effectively alleviates
depressive-like behavior in ovariectomized mice exposed
to chronic stress. This may be partially attributed to its
inhibitory effects on neuroinflammatory responses andits promotion of brain-derived neurotrophic factor(BDNF) expression [26]. SDG has the potential to extend
the lifespan of C. elegans and protect them from thetoxic effects induced by Aβ1−42 [27]. However, it remains
unclear whether SDG can alleviate cognitive impairments and pathological features associated with AD in
females.
Flaxseed lignan SDG is metabolized by bacteria in thecolon to the biologically active lignans, enterodiol (END)
and enterolactone (ENL) [28, 29]. The structures of END
and ENL are analogous to the endogenous estrogen
estradiol. This structural similarity elucidates their ability
to bind to estrogen receptors and exhibit weak estrogenic
effects [16]. The study found that the biological effects of
SDG partially depend on the production of endogenous
estrogen-like compounds, such as END and ENL, as wellas the activation of the GPER [30, 31]. GPER is a 7-transmembrane protein that recognizes and transduces estrogen signals, structurally unrelated to estrogen receptor(ER) α/β [32]. GPER is widely expressed in systemic tissues, with high expression in the central nervous system
including the hippocampus, cortex, and hypothalamus[33–37]. In addition, ERα requires GPER to exert neuroprotective effects, while GPER can exert beneficial effects
on Parkinson’s disease independently of ERα/β [38]. Similarly, activation of GPER significantly enhances recognition memory in female 5XFAD mice [39]. In the brain,
microglial cells exhibit high levels of GPER expression,and their activation can downregulate Toll-like receptor 4
expression and reduce the release of inflammatory cytokines [40, 41]. Furthermore, GPER activation is involved
in hippocampal-dependent spatial memory [42], synaptic
plasticity [43] and the expression of neurotrophic factors.
Therefore, here it was hypothesized that SDG may exert
neuroprotective effects by activating GPER through its
gut microbial metabolites END and ENL, thereby rescuing cognitive impairments associated with AD in females.

Jia et al. Journal of Neuroinflammation(2024) 21:201

Herein, female APPswe/PSEN1dE9 (APP/PS1) mice
were treated with SDG to preliminarily investigate thepotential of SDG in ameliorating cognitive impairment
and neuropathology, including the expression of postsynaptic density protein-95 (PSD-95) and BDNF, Aβ deposition, and neuroinflammation. The regulatory effects of
SDG on gut microbiota composition and its metabolites
END/ENL were also explored. Subsequently, to investigate the key role of END and ENL in SDG attenuating
cognitive impairment in female APP/PS1 mice, broad
spectrum antibiotic cocktail (ABx) treatment was performed to remove the gut microbiota and inhibit the
production of END and ENL. Finally, the potential
involvement mechanisms of GPER in the SDG-mediatedamelioration of neuroinflammation associated with AD
was examined by using G15, a GPER antagonist, in an
LPS-induced neuroinflammation animal model.

Materials and methods
Animals

Ten-month-old female APP/PS1 mice, wild-type (WT)
mice and the three-month-old female C57BL/6J mice
were procured from the model animal research centerof Beijing HFK Biotechnology Co., LTD. DNA samples
were extracted from mouse ears and genotyped using
polymerase chain reaction (PCR) methodology (DNAextraction kit, centrifugal column method; Beijing Betek
Biotechnology Co. LTD, Beijing, China). All mice werehoused individually in cages under standard laboratory
conditions with a 12/12-h light-dark cycle, at a temperature of 22 ± 2 °C, and a relative humidity of 55 ± 5%. Additionally, the mice utilized in the experiment were bredin-house. The Animal Ethics Committee of Northwest
A&F University (authorization number: XN2023-0716)
approved the experimental protocol.
Experimental design
Experiment 1 (SDG intervention)

Ten-month-old female APP/PS1 mice and their littercontrol WT mice were randomly assigned to the following groups: (A) WT + Vehicle group (n = 10); (B)WT + SDG group (n = 9); (C) APP/PS1 + Vehicle group(n = 6); (D) APP/PS1 + SDG group (n = 7). SDG (70 mg/kg) was administered orally once daily at the same time,
while the control group received saline [44, 45]. The bodyweight of mice was measured weekly, along with monitoring of food intake and water consumption. Behavioral
tests were conducted after the 8th week. Following anesthesia, serum samples were collected from retro-orbital
bleeding. Brains were then extracted to separate the cortex and hippocampus, with collection of other tissues asnecessary.

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Experiment 2 (ABx treatment)

Ten-month-old female APP/PS1 mice were randomly
allocated to the following groups: (A) APP/PS1 group(n = 5); (B) APP/PS1 + SDG group (n = 5); (C) APP/
PS1 + ABx + SDG group (n = 5).
In brief, mice were treated with an ABx containing
Penicillin G sodium (1 g/L), metronidazole (1 g/L), neomycin sulfate (1 g/L), streptomycin sulfate (1 g/L), andgentamicin hydrochloride (0.5 g/L). Solutions and bottles
were changed twice weekly. Mice received 2 weeks ofABx before the SDG intervention. Microbiota depletion
was confirmed by the fecal genome DNA extraction kit(DP328-02) and quantitative real-time PCR (qRT-PCR).
qRT-PCR was performed with the universal bacteriaspecific primer: (8 F: ​A​G​A​G​T​T​T​G​A​T​C​C​T​G​G​C​T​C​A​G;
338R: ​C​T​G​C​T​G​C​C​T​C​C​C​G​T​A​G​G​A​G​T).
SDG (70 mg/kg) was administered orally once daily at
the same time, while the control group received saline.
Behavioral tests were conducted after the 8th week ofintervention.
Experiment 3 (GPER inhibition)

Three-month-old female C57BL/6J mice were randomly
allocated to the following groups: (A) Control (CON)group (n = 7); (B) LPS group (n = 7); (C) LPS + SDG group(n = 7); (D) LPS + SDG + G15 group (n = 7).
G15 was dissolved in 0.9% saline containing 10%
dimethyl sulfoxide (DMSO). 1 µL of this solution at aconcentration of 0.5 µg/µL was stereotaxically injected
into the lateral ventricle at a rate of 1 µL/min [46–48].
The control group of rats receives stereotactic injectionof 1 µL of saline into the lateral ventricle. Following a
2-day recovery period, mice are orally administered SDG(70 mg/kg) or saline for 7 days. In the last 2 days, mice
are injected with LPS (0.5 mg/kg; Solarbio) intraperitoneally, according to the previously described method [49].
Injections are spaced 24 h apart, with the CON group
receiving injections of saline.
Behavioral experiments
Morris water maze (MWM) test

The water maze experiment consists of two phases: a
3-day spatial acquisition phase followed by a 1-day spatial
probe trial. A circular pool, maintained at a temperatureof 25 °C, is partitioned into four quadrants. Within one
randomly chosen quadrant, a visible platform is positioned 1.5 cm beneath the water surface. Throughout the
spatial acquisition phase, mice are introduced sequentially into the pool, facing the wall, from each quadrant.
The duration taken by a mouse to successfully ascend
onto the platform, termed as escape latency, is monitored using a photobeam tracking system. In instanceswhere a mouse fails to locate the platform within 1 min,it is gently guided to the platform using a probe. Once on

Jia et al. Journal of Neuroinflammation(2024) 21:201

the platform, the mouse remains for 30 s. This sequenceis repeated for every quadrant over the span of 3 days.
Subsequently, on the spatial probe trial, the platform isremoved, and the photobeam tracking system records thenumber of crossings over the former platform location
within 1 min, alongside the time spent and distance traveled in the quadrant where the platform was previously
positioned. The movement trajectory of the mice within
1 min is analyzed to evaluate spatial cognitive abilities.
Following each trial, mice are dried with a hairdryer andreturned to their respective cages.
Barnes maze (BM) test

The BM test was utilized to evaluate the learning andmemory capabilities of mice. The BM apparatus comprises a circular platform with 20 equidistant holes along
its circumference. One hole contains an escape box serving as a refuge for the experimental animals, while theremaining holes act as visual cues. A bright light source
encourages the experimental animals to seek out theescape hole. Adaptation training commenced on day 0,
during which mice were given 60 s to familiarize themselves with the escape chamber. Learning trials were conducted from day 1 to 4, each lasting 180 s. Irrespectiveof whether the mice located the target escape site, they
were afforded an additional 60 s of acclimation within theescape chamber. Upon completion of the learning trials,
the escape chamber was removed, and the experimental animals were positioned in the center of the elevatedplatform for a 180 s period of free exploration. The time
taken by the experimental animals to locate the correct
hole and the frequency of entries into incorrect holes
were documented to evaluate spatial reference memory.
Moreover, working memory was assessed by recording
the number of occasions the animals re-entered incorrect holes. Following each training session, the apparatus
underwent cleaning with 75% alcohol, and the correct
hole was randomly altered, while maintaining the spatialconfiguration of the escape chamber constant, to prevent
animals from relying on olfactory cues.
Novel object recognition (NOR) test

The NOR test serves as a method to evaluate the shortterm non-spatial recognition memory of experimental
animals. The experimental setup comprises a box measuring 60 cm × 60 cm × 46 cm and an automated datacollection and processing system. The test encompasses
three stages: the adaptation stage, the familiarization
stage, and the testing stage. During the adaptation stage(day 1), mice are allowed to freely explore the empty
apparatus for 5 min on the training day to minimize theirapprehension of the new environment. In the familiarization stage on the second day, two identical objects are
positioned diagonally across from each other within the

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apparatus, and mice are positioned in the center of thetesting box. Subsequently, the mice are placed equidistant from the objects and permitted to freely explore for5 min, with the exploration time on each object recorded.
During the testing stage on the third day, one of theobjects is substituted with a novel object while retaining its original location, and the exploration time spenton the novel and familiar objects is documented. Uponcompletion of the experiment, the Preference Index (PI)are computed to evaluate the mice’s short-term non-spatial recognition memory. It is noteworthy that following
each mouse’s completion of the test, the objects undergo
disinfection with alcohol and are left to evaporate before
reintroducing the mice to eliminate any odor cues left by
the mice on the objects.
PI = time to recognize new objects / (time to recognize
new objects + time to recognize familiar objects).
Y-maze test

The Y-maze serves as a primary tool for evaluating theworking memory abilities, specifically short-term memory, of experimental animals. The experimental apparatus
comprises three arms, each forming angles of 120°, withdimensions of 35 cm × 5 cm × 15 cm (length × width ×
height) per arm. At the initiation of the experiment, mice
are introduced into the center of the Y-maze apparatus
and permitted to freely explore the maze for a duration of
5 min. Real-time software tracks the total number of arm
entries by the mice and records the frequency of consecutive entries into the three different arms. Following eachtrial, the maze undergoes cleaning with alcohol to eliminate odors, and the alternation percentage is computed
using a predefined formula.
Spontaneous alternation = actual number of alternations/ possible number of alternations (defined as thetotal number of arm entries − 2) × 100%.
Thioflavin S staining

Thioflavin S staining was employed for the detection ofamyloid-like protein deposits. Upon euthanasia of themice, brain tissues were isolated, and one-half of thebrain was dissected and placed in vials containing 4%(v/v) paraformaldehyde (pH 7.4) for 24 h. Subsequently,
the specimens were embedded in paraffin and sectioned
into 5 μm slices. Sections underwent deparaffinization inxylene, followed by washes in 100%, 90%, 80%, and 70%
ethanol for 5 min each, and three washes with PBS (pH7.4) for 5 min each. Slices were then permeabilized for
15 min, boiled for 20 min, and washed with PBS for 5 min
× 3 times. A 0.5% Thioflavin S solution (CAS#1326-12-1;
Shanghai Yuanye Biotechnology Co., Ltd.) was applied
to the slices for 8 min. Subsequently, the slices underwent sequential rinses with PBS, 50% ethanol, and PBS
for 5 min × 3 times. Finally, the sections were mounted

Jia et al. Journal of Neuroinflammation(2024) 21:201

with DAPI for fluorescence imaging. Slice observationand image acquisition were conducted using an inverted
fluorescence microscope (DM5000 B, Germany). Image
analysis was performed using Image J software to quantify the area ratio of amyloid-like plaques in the region ofhippocampus and cortex.
Quantitative real-time PCR (qRT-PCR)

An appropriate amount of tissue was weighed into 2.0
mL grinding tubes, and BIOZOL (from Hangzhou Bioer
Technology Co., China) was incorporated to assess
mRNA levels within mouse tissues. NanoDropOne(Thermo Fisher Scientific) was used for quantification.
The ueIris II qRT-PCR System First-Strand cDNA Synthesis kit (from US Everbright Inc., Suzhou, China) was
utilized to reverse transcribe total RNA into cDNA, andthe 2× SYBR Green qPCR Master Mix kit (from US Everbright Inc., China) was used for qRT-PCR to measuremRNA levels. Primer sequences employed are delineatedin Table S1. Normalizing Ct values to GADPH and computing relative gene expression levels utilizing the 2−ΔΔCt
method.
Western blot (WB) analyses

Brain tissue protein extraction was performed using
protein extraction reagents. Tissue total protein was
subsequently separated via SDS-polyacrylamide gel electrophoresis and transferred onto polyvinylidene difluoride membranes using a wet transfer apparatus. Following
transfer, appropriate antibodies were utilized, and immunoreactive bands were visualized using enhanced chemiluminescence reagents. Information regarding antibodies
can be found in Table S2. Immunoreactive bands were
captured using enhanced chemiluminescence reagentsand scanned utilizing the ChemiDoc XRS imaging system (ChemiDoc™ XRS+, Bio-Rad). Quantitative analysisof the protein blot results was performed using Image J
analysis software.
Immunofluorescence staining

The tissue sections were subjected to dewaxing and dehydration using xylene and a gradient of ethanol. Subsequently, the slices were incubated overnight at 4 °C withprimary antibodies targeting PSD-95, BDNF, β-amyloid(6E10), and IBA-1. Following the incubation period, theslides were washed with PBS and then incubated with asecondary antibody for 2 h. Information regarding antibodies can be found in Table S2. DAPI was utilized fornuclear staining to seal the sections. Immunofluorescence staining images were acquired using an inverted
fluorescent microscope (DM5000 B, Germany). The positive regions were quantified using Image J.

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Enzyme-linked immunosorbent assay (ELISA)

Cortex samples were processed for the detection of
inflammatory cytokines, such as tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), interleukin-6 (IL-6), interleukin-10 (IL-10). All procedures
were performed according to the instructions provided
with the ELISA kit (xl-Em0359, xl-Em0187, xl-Em0196,
xl-Em0201, Xinle Biotechnology, Shanghai, China). A
standard curve was generated to calculate the content,
which was expressed as ng/g protein.
High-performance liquid chromatography-mass
spectrometry (HPLC-MS)

Sample preparation and calibration standards: Blood
samples were collected in 1.5 mL plastic microcentrifuge tubes, maintained in a 37 °C water bath, and centrifuged at 3000 rpm for 15 min within 30 min of collection.
Serum was transferred into 1.5 mL plastic microcentrifuge tubes and stored at -80 °C. Subsequently, 40 µL ofmouse serum was added to 5 mL plastic centrifuge tubes.
To extract lignans, 3 mL of ether was added to all samples
and stirred for 10 min. The samples were then centrifuged
at 1000 g for 5 min and placed at -80 °C for 5–10 min to
freeze the serum layer. The upper layer was transferredto 2 mL centrifuge tubes and vacuum dried for approximately 20 min using a centrifugal concentrator (RVC2-18
CDplus). The dried samples were reconstituted in 200µL of water-acetonitrile mixture (9:1), vortexed for 15 s,
and then transferred to Whatman Mini-UniPrep syringeless filter vials (PTFE membrane, 0.2 μm pore size, Fisher
Scientific Canada, Ottawa, ON) for HPLC-MS analysis.
We followed the detection method previously establishedfor END and ENL in mouse serum [50] and entrusted
the detection to the Life Science Research Core Services(Northwest A&F University, Yangling, China).
16 S rRNA gene sequencing

Fecal samples were collected prior to mouse sacrifice.
Total DNA from feces was extracted using the E.Z.N.A.
fecal DNA extraction kit (Omega Bio-Tek, Norcross,GA). The primers utilized for amplifying the v3 − v4
region of 16 S rDNA were as follows: 341 F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT. Sequencing was performed on the HiSeq
platform. Reads underwent quality filtering and wereassembled into Tags based on their overlapping relationships. Operational taxonomic units (OTUs) wereclustered from the assembled Tags using USEARCH(V7.0.1090). Qiime2 (2018.11), in conjunction with the
R Package, was employed to generate a Venn diagram,conduct α-diversity analysis, and create a heatmap andhistogram of species abundance. Partial least squares discrimination analysis (PLS-DA) data were produced using
the mixOmics package in R (V3.2.1). The 16 S rRNA gene

Jia et al. Journal of Neuroinflammation(2024) 21:201

Page 6 of 20

sequencing analysis detected a total of 875 operationaltaxonomic units (OTUs). We calculated the LOG2 values
for the APP/PS1 + Vehicle and APP/PS1 + SDG groups,
and performed t-tests to calculate the p-values for thedifferential OTUs between the two groups. Subsequently,
the bioinformatics analysis platform in the Microbiome
Online tool was utilized to generate volcano plot datavisualization(https://www.bioinformatics.com.cn/).
Furthermore, we specifically analyzed the OTUs annotated at the genus level, which amounted to a total of 397
species.

Software, USA) for comparisons among multiple groups.
For the analysis of 16 S rRNA sequencing data, nonparametric tests were applied, specifically a Kruskal-Wallis
analysis of variance (ANOVA) test, to identify significant
differences among the various groups. The bioinformatics
analysis platform in Microbiome Online was employed
for correlation data analysis (https://www.bioinformatics.
com.cn/). p < 0.05 was considered statistically significant.

Statistical analysis

Ten-month-old female APP/PS1 mice were subjected to
SDG (70 mg/kg b.w.) gavage for 8 weeks (Fig. 1A). During
the experiment, genotype and SDG intervention did not

The data were expressed as the mean ± SEM. Statisticalanalyses were conducted using One-way and Two-way
ANOVA followed by Tukey’s test (GraphPad Prism 8.0

Results
SDG alleviated cognitive impairment in female APP/PS1
mice

Fig. 1 SDG alleviated cognitive impairment in female APP/PS1 mice. (A) Experimental schedule of the SDG intervention in female APP/PS1 mice(n = 6–10). (B) Body weight gain during the 8-week intervention (n = 6–10). (C) The average weekly food intake and (D) water intake of each mouse(n = 6–10). (E) Escape latency change during the MWM test training days (n = 6–10, *p < 0.05, **p < 0.01, compared to the WT + Vehicle group, #p < 0.05,##p < 0.01, compared to the APP/PS1 + Vehicle group). (F) Escape latency and (G) average speed on the testing day (n = 6–10). (H) Preference index in the
NOR test (n = 6–10). (I) Spontaneous alternation (%) in the Y-maze test (n = 6–10). Data are presented as mean ± SEM and were analyzed using two-way
ANOVA with Tukey’s test. *p < 0.05, **p < 0.01, n.s., no significanceJia et al. Journal of Neuroinflammation(2024) 21:201

significantly affect the body weight gain, food intake, andwater intake of each group of mice (Fig. 1B-D).
To investigate the alleviating effect of SDG on cognitive impairment in female APP/PS1 mice, MWM tests,
NOR tests, and Y-maze tests were conducted. During thetraining period of the MWM test on the 1st, 3rd, and 5thday, compared to the WT + Vehicle group mice, the APP/
PS1 + Vehicle group mice showed a significant increasein escape latency (Fig. 1E, p < 0.01), indicating significant
spatial memory impairment in the APP/PS1 + Vehicle
group mice. However, the SDG intervention significantly
decreased the escape latency in female APP/PS1 micebut not in the WT mice (Fig. 1E) (p < 0.01). On the 6thday, the platform was removed for the probe trial. Similarly, in the probe trail, the APP/PS1 + Vehicle groupmice showed a longer escape latency (Fig. 1F) (p < 0.01).
However, SDG intervention significantly reversed thisspatial memory impairment in female APP/PS1 mice
(Fig. 1F) (p < 0.01). Notably, alleviation of this phenotype
upon SDG intervention was not affected by the swimming speed of mice (Fig. 1G). In addition, in the NOR
test, the APP/PS1 group mice also showed a lower preference index, indicating their recognition memory wereimpaired (Fig. 1H) (p < 0.05). SDG intervention significantly increased the preference index of female APP/PS1
mice (Fig. 1H) (p < 0.05). The result of the Y-maze test
used to evaluate spatial working memory showed thatcompared to the WT + Vehicle group mice, female APP/
PS1 mice had significantly reduced spontaneous alternation (Fig. 1I) (p < 0.01), while SDG intervention significantly increased spontaneous alternation in female APP/
PS1 mice (Fig. 1I) (p < 0.01). These results illustrated that
the SDG intervention significantly rescued the spatial
memory, recognition memory, and the working memoryin female APP/PS1 mice.
The common pathological features of AD also include
synaptic abnormalities and functional impairments,
which occur in the early stages of the disease [51]. To
examine whether SDG intervention affected synaptic
function in the female APP/PS1 mice, synaptic plasticity-related proteins, including PSD-95 was assessed byimmunofluorescence staining. Representative images andquantitative results show that the positive area of PSD95 protein in the hippocampal dentate gyrus (DG) andcortex of female APP/PS1 mice is significantly lower thanthat of WT mice (Fig. 2A-C) (p < 0.01). However, SDG
intervention significantly increased the expression levelsof PSD-95 protein in these two regions of female APP/
PS1 mice (Fig. 2A-C) (p < 0.01).
BDNF plays a crucial role in maintaining synaptic plasticity in learning and memory [52]. In the pathologicalprocess of AD, BDNF and CREB are key downstream
mediators of Aβ toxicity [53]. And Aβ decreases BDNF
primarily by reducing phosphorylated CREB protein

Page 7 of 20[53]. Therefore, to clarify the effect of SDG intervention
on CREB-BDNF signaling pathway, the expression levelsof BDNF in the hippocampal DG and cortex were analyzed through immunofluorescence staining, and analyzed the expression levels of p-CREB and CREB proteinsthrough WB. The results showed that SDG intervention
promoted BDNF expression in female WT and APP/
PS1 mice (Fig. 2D-F) (p < 0.01). Consistently, the mRNA
expression levels of PSD-95 and BDNF in the hippocampus were significantly upregulated by SDG intervention(Fig. 2G-H) (p < 0.05). Moreover, SDG intervention significantly activated p-CREB (Fig. 2I & J) (p < 0.05). These
results indicate that SDG intervention may increase PSD95 protein expression by activating the CREB-BDNF signaling pathway, thereby alleviating cognitive impairment
in female APP/PS1 mice.
SDG reduced Aβ deposition and neuroinflammatory response

Aβ deposition is one of the main pathological featuresof AD. To investigate the influence of SDG on Aβ deposition in female APP/PS1 mice, the brain sections were
stained by Thioflavin S staining and immunofluorescence staining with an anti-6E10 antibody (Fig. 3A and
Figure S1A). Compared to the APP/PS1 + Vehicle groupmice, the mice treated with SDG showed a remarkabledecrease of Aβ deposition in the DG of the hippocampus and the cortex (Fig. 3B-C and Figure S1B-C). Consistently, the mRNA expressions of amyloid precursor
protein (APP), Presenilin-1 (PS1), in the hippocampus
of female APP/PS1 mice were higher than the WT mice
(Fig. 3D-E) (p < 0.01), while SDG remarkably downregulated the expression of these genes (Fig. 3D-E) (p < 0.01).
In addition, SDG significantly downregulated the mRNA
expression of β-secretase (BACE1) (Fig. 3F) (p < 0.05).
Therefore, SDG intervention reduced the accumulationof Aβ in female APP/PS1 mice brain.
Neuroinflammation has demonstrated to be crucial tothe pathogenesis of AD, and among the innate immune
cells, microglia are the primary players in neuroinflammation [54]. Here, whether SDG can inhibit the neuroinflammatory response in female APP/PS1 mice wasfurther investigated. It has been found that the numberof ionized calcium binding adapter molecule 1 (IBA-1)
positive cell was significantly increased in the DG of the
hippocampus and the cortex in female APP/PS1 micecompared to the WT mice (Fig. 3G-I) (p < 0.01). Obviously, compared to the vehicle-treated APP/PS1 mice,
SDG significantly reduced the number of IBA-1 positive
cells in these two regions (Fig. 3G-I) (p < 0.01). Although
the levels of pro-inflammatory cytokines such as TNF-α
and IL-6 did not show statistically significant differencesin the cortex of WT + Vehicle and APP/PS1 + Vehicle
groups mice, SDG intervention significantly reduced
their expression in the cortex of female APP/PS1 miceJia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 2 SDG promoted the expression of synapse-related protein and BDNF. (A) Representative images of PSD-95 immunofluorescence staining in the
hippocampal DG and cortex.( B) and (C) Quantification of PSD-95 positive area based on immunofluorescence staining sections by ImageJ software(n = 3). (D) Representative images of BDNF immunofluorescence staining in the hippocampal DG and cortex. (E) and (F) Quantification of BDNF positive
area based on immunofluorescence staining sections by ImageJ software (n = 3). (G) and (H) The mRNA expression levels of PSD-95 and BDNF in the hippocampus region (n = 6–10). (I) and (J) Protein levels of CREB and p-CREB in the cortex (n = 3). Data are presented as mean ± SEM and were analyzed using
two-way ANOVA with Tukey’s test. *p < 0.05, **p < 0.01, n.s., no significance(Fig. 3J-K) (p < 0.05). In addition, SDG intervention significantly increased the levels of anti-inflammatory cytokine such as IL-10 in the cortex of female APP/PS1 mice
(Fig. 3L) (p < 0.05). Interestingly, compared to WT mice,the levels of IL-10 were significantly correlated in thecortex of female APP/PS1 mice (Fig. 3L) (p < 0.05). These
results indicate that central immune homeostasis is disrupted in female APP/PS1 mice, possibly associated with
an imbalance in pro-inflammatory and anti-inflammatory cytokine levels. The SDG intervention contributes toJia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 3 SDG reduced Aβ deposition and neuroinflammatory response. (A) Representative images of Aβ plaques immunofluorescence staining in the hippocampal DG and cortex. (B) and (C) Quantification of Aβ deposition area based on immunofluorescence staining sections by ImageJ software (n = 3). (D)– (F) The mRNA expression levels of APP, PS1, and BACE1 in the hippocampus region (n = 6–10). (G) Representative images of IBA-1 immunofluorescencestaining in the hippocampal DG and cortex.( H) and( I) Quantification of the number of IBA-1 positive cells based on immunofluorescence staining sections by ImageJ software (n = 3). (J) – (L) The levels of TNF-α, IL-6, and IL-10 in the cortex (n = 6–10). Data are presented as mean ± SEM and were analyzed
using two-way ANOVA with Tukey’s test. *p < 0.05, **p < 0.01, n.s., no significanceJia et al. Journal of Neuroinflammation(2024) 21:201

regulating central immune homeostasis in female APP/
PS1 transgenic mice, thereby suppressing neuroinflammatory responses.
SDG reshaped the composition of gut microbiota

SDG reaches the colon, where it is catalyzed by the gut
microbiota for metabolism [55]. Therefore, it was speculated that the potential cognitive improvement effectof SDG on AD may be attributed to its influence on thecomposition of the gut microbiota or its metabolites produced by the gut microbiota. To validate this hypothesis,
the gut microbiota structure alteration after SDG treatment was firstly investigated by conducting 16 S rRNA
gene sequencing on fecal samples from mice.
The species accumulation curve results indicatethat the detected sample size is sufficient to reflect the

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species composition of the community (Figure S2A). In
the four groups of mice, WT + Vehicle, WT + SDG, APP/
PS1 + Vehicle, and APP/PS1 + SDG, 796, 801, 754, and764 OTUs were detected, respectively (Fig. 4A). There
were 45 and 37 specific OTUs in the WT + Vehicle groupand APP/PS1 + Vehicle group, respectively. Besides, compared to the APP/PS1 + Vehicle group, there were 63
specific OTUs in the APP/PS1 + SDG group (Fig. 4A).
The α-diversity analysis including Ace, Chao, coverage, Shannon, Simpson, and Sobs indices indicate thatthe abundance of gut microbiota in each group of miceis relatively high, with good evenness, and no significant differences are observed among the groups (Figure
S2B). Both unweighted and weighted UniFrac analyses of β-diversity indicate significant differences in thegut microbiota community structure between the APP/

Fig. 4 The effect of SDG on the gut microbiota composition. (A) Venn diagrams illustrating the discrepancy of OTUs. (B) – (C) Beta diversity index box(unweighted unifrac and weighted unifrac). The five lines from bottom to top are minimum, first quartile, median, third quartile, and maximum. (D) The
result of Partial least squares discrimination analysis (PLS-DA) of each group. (E) Volcano plot of differential Relative abundance of OTUs in APP/PS1 + Vehicle and APP/PS1 + SDG group (grey represents not significant; green represents a significant Log2FC value; blue represents a significant p value; and red
represents significant Log2FC value and p value). On the right side of the volcano plot, the top 3 OTUs (in blue font) with significantly increased and the
top 10 OTUs (in red font) with significantly decreased were listed. The default p-value threshold is 0.05, and the default FC threshold is 2.0

Jia et al. Journal of Neuroinflammation(2024) 21:201

PS1 + Vehicle and APP/PS1 + SDG groups (Fig. 4B & C)
(p < 0.05). In addition, the partial least squares discrimination analysis (PLS-DA) plot illustrated a clear and separate clustering of the samples among different groups(Fig. 4D). We calculated the Log2FC values and p-values(using a T-test) for the relative abundance of OTUs in
the APP/PS1 + Vehicle and APP/PS1 + SDG groups, andgenerated the volcano plot in Fig. 4E. And the volcano
plot displays 396 OTUs with taxonomic annotations at
the species and genus levels. We focused on the OTUs
with significant Log2FC values and p-values (highlighted
with pink circular markers). Three OTUs (Butyricicoccus pullicaecorum, Desulfovibrio, and Megamonas funiformis) with significantly increased relative abundancein the APP/PS1 + SDG group compared to the APP/
PS1 + Vehicle group are listed (in blue font). Additionally, the top 10 OTUs with significantly decreased relative abundance in the APP/PS1 + SDG group are listed
as Intestinimonas butyriciproducens, Dialister invisus,
Acetatifactor muris, Helicobacter mastomyrinus, Terasakiella pusilla, Clostridium XlVa, Barnesiella intestinihominis, Draconibacterium orientale, Bifidobacterium
longum, and Enterococcus saccharolyticus (in red font).
The comprehensive results indicate that SDG has effects
on the composition of the gut microbiota in female APP/
PS1 mice.

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The cognitive improvement effect of SDG depended on theexistence of gut microbiota

In general, whether in humans or other mammals,
SDG undergoes a series of biotransformation processes
through colonic microbiota, ultimately metabolizing
into END and ENL [50]. To assess the metabolic levels of
SDG by the gut microbiota, the levels of END and ENL
in serum were detected by using HPLC-MS. The results
showed that SDG intervention increased the content
of END and ENL in the serum of female WT and APP/
PS1 mice (Fig. 5A & B) (p < 0.01). To further analyze therelationship between these two metabolites and SDG
in enhancing cognitive function, inhibiting Aβ deposition, and neuroinflammatory responses, the correlation
analysis on the relevant data from the APP/PS1 + Vehicle
and APP/PS1 + SDG groups was conducted (Fig. 5C).
The correlation analysis results indicate that the levels of
END and ENL in serum are significantly negatively correlated with the escape latency in the MWM test (Fig. 5C)(p < 0.05). Besides, END and ENL are significantly positively correlated with the preference index in the NOR
test, spontaneous alternation in the Y-maze (Fig. 5C)(p < 0.05).
To demonstrate the crucial role of gut microbiota andits metabolism in mediating SDG intervention to attenuate AD related cognitive deficits, it sought to investigate whether the beneficial effects of SDG intervention
observed after removing gut microbiota in female APP/

Fig. 5 The gut microbiota-dependent SDG metabolism. (A) and (B) The levels of END and ENL in the serum. (C) Correlation Analysis between the levels of
END and ENL in the serum and other biochemical index. In the matrix, the size and color of the circles indicate the degree of correlation (blue represents
a positive correlation, and red represents a negative correlation). The calculation method for correlation coefficient is Pearson. *p < 0.05, **p < 0.01, andnon-significance is not indicatedJia et al. Journal of Neuroinflammation(2024) 21:201

PS1 mice would be affected (Fig. 6A). 2-week of ABx
treatment can effectively remove the gut microbiota ofmice and significantly inhibit the in vivo conversion of
SDG to END and ENL [56, 57]. After a 2-week treatmentof ABx, the copy numbers of 16 S rDNA of the microbiota in the feces of mice in the APP/PS1 + ABx + SDG
group were significantly lower compared to the other
two ABx untreated groups (Fig. 6B) (p < 0.01). During theexperiment, it did not observe any statistically significant
changes in body weight gain, food intake, or water intake(Fig. 6C-E). However, the cognitive improvement effectof SDG on AD-related cognition, as observed in the BM
test, NOR test, and Y-maze test, was counteracted upon
removal of the microbiota (Fig. 6F-J) (p < 0.05). Furthermore, following SDG intervention, the content of END
and ENL in mouse serum significantly decreased with theremoval of the microbiota (Fig. 6K-L) (p < 0.01).
Studies indicate that CREB is downstream of G protein-coupled estrogen receptor 1 (GPER), which serves
as the receptor for END and ENL [31, 36]. To elucidate
the mediating roles of gut microbiota, END and ENL
in the activation of the CREB-BDNF signaling pathway and the increase in PSD-95 protein expression by
SDG, the expression of relevant proteins was evaluatedthrough immunofluorescence staining and WB analysis.
ABx treatment abolished the promoting effect of SDG
on PSD-95 protein expression (Fig. 7A-C) (p < 0.05) andthe activation of the CREB-BDNF signaling pathway(Fig. 7D-H) (p < 0.05). In conclusion, these findings suggest that the neuroprotective effect of SDG on female
APP/PS1 mice relies on an intact gut microbiota. Overall, these results showed that the improvement effect of
SDG intervention on AD related cognitive impairment
depends on the gut microbiota and may be related to its
metabolites END and ENL.
The inhibitory effect of SDG on Aβ deposition andneuroinflammation depended on gut microbiota existence

The correlation analysis results also indicate that the levels of END and ENL in serum are significantly negatively
correlated with the PS1 mRNA expression levels (Fig. 5C)(p < 0.05). Furthermore, ENL, but not END, is significantly negatively correlated with APP mRNA expression
levels (Fig. 5C) (p < 0.05). Consistent with this, the reduction of Aβ deposition in the hippocampal DG and cortex
of female APP/PS1 mice by SDG was eliminated by ABx
treatment (Fig. 8A-C) (p < 0.05).
The correlation analysis results also indicate that thelevels of END and ENL in serum are significantly negatively correlated with the cortical pro-inflammatory
cytokines TNF-α and IL-6 levels (Fig. 5C) (p < 0.05).
Besides, END and ENL are significantly positively correlated with the cortical anti-inflammatory cytokine IL-10
level (Fig. 5C) (p < 0.05). Interestingly, it was observed

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in female APP/PS1 mice that the escape latency is significantly negatively correlated with cortical TNF-α andIL-6 levels, and positively correlated with IL-10 levels. In
contrast, the preference index and spontaneous alternation show an opposite correlation with these inflammatory-related cytokines. Similarly, compared to the APP/
PS1 + SDG group mice, the APP/PS1 + ABx + SDG groupmice showed a significant increase in the number of
IBA-1 positive cells in the hippocampal DG and cortex(Fig. 8D-F) (p < 0.05). ABx treatment also disrupted therebalancing between pro-inflammatory and anti-inflammatory cytokines in the cortex of female APP/PS1 mice
established by SDG intervention (Fig. 8G-J) (p < 0.05).
These results indicate that the inhibitory effect of SDG
on Aβ deposition and neuroinflammation in female APP/
PS1 mice cannot be separated from the gut microbiota
and two metabolites, namely END and ENL.
The activation of GPER was involved in the preventive effectof SDG on LPS-induced neuroinflammation

To further demonstrate the mediating mechanism of
GPER in the neuroprotective effect of SDG, G15, an
inhibitor of GPER, was injected into the bilateral lateral ventricles of C57BL/6 mice through i.c.v. injection.
A 4-day intervention based on SDG was implemented,
followed by 3-day of intraperitoneal LPS injection treatment (Fig. 9A). LPS treatment resulted in a significant
increase in the mRNA expression levels of hippocampal
inflammatory cytokines TNF-α, IL-6, and IL-1β (Fig. 9BD) (p < 0.05). However, compared to the control group,
there was no significant decrease in hippocampal IL-10mRNA expression induced by LPS (Fig. 9E). SDG intervention effectively downregulated the mRNA expressionof TNF-α, IL-6, and IL-1β, while upregulating the mRNA
expression of IL-10 (Fig. 9B-E) (p < 0.05). Similarly, theinhibitory effect of SDG on neuroinflammation is dependent on the activation of GPER (Fig. 9B-E) (p < 0.05).
To verify the hypothesis that SDG increases the expression of PSD-95 and BDNF through END and ENL,
thereby exerting neuroprotective effects and improving
cognitive impairment associated with AD, the mRNA
expression levels of PSD-95 and BDNF were assessed.
The results indicate that treatment with G15 significantly
inhibited the promoting effect of SDG on the corticalmRNA expression of PSD-95 and BDNF in mice with
LPS-induced neuroinflammation (Fig. 9F & G) (p < 0.05).
Overall, these results suggest that GPER plays an
indispensable mediating role in the suppression of neuroinflammatory response, and neuroprotective effects
exerted by SDG in mice with neuroinflammation models.

Jia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 6 The cognitive improvement effect of SDG depended on the existence of gut microbiota. (A) Experimental schedule of the SDG intervention after
the ABx treatment in female APP/PS1 mice (n = 5). (B) The 16 S rDNA copies in the feces (n = 5). (C) Body weight gain during the 8-week intervention(n = 5). (D) The average weekly food intake and (E) water intake of each mouse (n = 5). (F) Escape latency change during the BM test training days (n = 5,
#p < 0.05, ##p < 0.01, compared to the APP/PS1 group, &p < 0.05, &&p < 0.01, compared to the APP/PS1 + SDG group). (G) Head exploration latency and (H)Number of head explorations on the testing day (n = 5). (I) Preference index in the Novel object recognition test (n = 5). (J) Spontaneous alternation (%) in
the Y-maze test (n = 5). (K) and (L) The levels of END and ENL in the serum. Data are presented as mean ± SEM and were analyzed using one-way ANOVA
with Tukey’s test. *p < 0.05, **p < 0.01, n.s., no significanceJia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 7 The promoting effect of SDG on PSD-95 and BDNF expression depended on gut microbiota existence. (A) Representative images of PSD-95 immunofluorescence staining in the hippocampal DG and cortex. (B) and (C) Quantification of PSD-95 positive area based on immunofluorescence staining
sections by ImageJ software (n = 3). (D) Representative images of BDNF immunofluorescence staining in the hippocampal DG and cortex. (E) and (F)Quantification of BDNF positive area based on immunofluorescence staining sections by ImageJ software (n = 3). G) and H) Protein levels of CREB andp-CREB in the cortex (n = 4). Data are presented as mean ± SEM and were analyzed using one-way ANOVA with Tukey’s test. *p < 0.05, **p < 0.01, n.s., nosignificanceDiscussion
In this study, it was found that oral administration of
SDG (70 mg/kg) to female APP/PS1 mice increased theexpression of BDNF and PSD-95, reduced Aβ depositionin the hippocampal DG and cortex, and suppressed central nervous system inflammation exerting central nervous system protective effects, thereby rescuing cognitive
impairments associated with AD in females. Additionally,
SDG influenced the composition of gut microbiota andpromoted the production of END and ENL. Correlation

analysis revealed significant associations between serum
levels of END and ENL and cognitive behavioral indices,Aβ expression, neuroinflammation, and other biochemical markers. Furthermore, the neuroprotective effects of
SDG were abolished by the inhibition of the productionof END/ENL and the GPER activation. These results collectively suggested that the preventive effects of SDG on
cognitive impairments and neuroinflammation in female
APP/PS1 mice depended on the existence of gut microbiota and microbial metabolites END and ENL, which

Jia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 8 The inhibitory effect of SDG on Aβ deposition and neuroinflammation depended on gut microbiota existence. (A) Representative images of Aβplaques immunofluorescence staining in the hippocampal DG and cortex. (B) and (C) Quantification of Aβ deposition area based on immunofluorescence staining sections by ImageJ software (n = 3). (D) Representative images of IBA-1 immunofluorescence staining in the hippocampal DG and cortex.
(E) and (F) Quantification of the number of IBA-1 positive cells based on immunofluorescence staining sections by ImageJ software (n = 3). (G) – (J) The
levels of TNF-α, IL-6, IL-1β, and IL-10 in the cortex (n = 5). Data are presented as mean ± SEM and were analyzed using one-way ANOVA with Tukey’s test.
*p < 0.05, **p < 0.01, n.s., no significanceconsequently activating GPER and down-stream CREB/
BDNF pathway.
Numbers of human studies suggest that dietary supplementation with foods rich in SDG may be associatedwith better cognitive abilities. A study incorporating 394
postmenopausal Dutch women (mean age: 66.3 years)
from the PROSPECT study revealed that an increased
intake of dietary lignans was associated with better performance on the MMSE [OR and (95% CI): 1.49 (0.94–
2.38)] [58]. Another cross-sectional study involving 301

Dutch women aged 60–75 years demonstrated that ahigh intake of lignans was associated with better performance in processing speed, executive function, and cognitive abilities [59]. Similarly, an observational study onelderly individuals (aged 50 years and above) in Southern
Italy found a negative correlation between all individualcompounds (excluding SDG) and cognitive impairment[60]. The association between dietary plant estrogens andcognition is multifactorial and may depend on factors
such as the cognitive domain assessed and ethnic groups

Jia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 9 The activation of GPER was involved in the preventive effect of SDG on LPS-induced neuroinflammation. (A) Experimental schedule of the of SDG
intervention on LPS induced neuroinflammation mouse model after G15 treatment (n = 7). (B-E) The mRNA expression levels of TNF-α, IL-6, IL-1β, and IL-10
in the hippocampus (n = 7). (F) and (G) The mRNA expression levels of PSD-95 and BDNF in the hippocampus (n = 7). Data are presented as mean ± SEM
and were analyzed using one-way ANOVA with Tukey’s test. *p < 0.05, **p < 0.01, n.s., no significance[61]. Although our study has shown that SDG can alleviate cognitive impairment in female AD mice, further
exploration is needed in clinical intervention trials targeting female AD patients (Fig. 1E-I).
Research has found that SDG remains stable during
the transport process through the upper gastrointestinal tract [55]. Therefore, ingested SDG reaches the distal
intestine, where various gut microbes catalyze its metabolism to END and ENL. Culturing flaxseed with human
fecal bacteria in vitro can generate END and ENL, whilesterile fecal cultures do not produce these metabolites[62]. Moreover, ABx treatment reduces the production of
END and ENL in the colon, indicating that the gut microbiota can metabolize plant lignans into gut lignans [62].
A pharmacokinetic study of oral SDG in rats revealed
that SDG was undetectable in serum within 48 h [50].
END and ENL reached peak concentrations in serum at
11 and 12 h after oral administration of SDG [50]. Consistent with these results, in the current study, following
oral gavage of SDG, there was a significant increase in
the levels of END and ENL in the serum of female APP/
PS1 mice (Fig. 5A & B). Correlation analysis results indicated a significant correlation between the levels of END
and ENL in the serum and cognitive behavioral indices inmice (Fig. 5C). Furthermore, when using ABx treatment

to deplete the mouse microbiota to inhibit the production of END and ENL, the beneficial effects of SDG on
cognitive improvement in female APP/PS1 mice wereabolished (Fig. 6F-L). Our study, for the first time, investigated the effect of SDG on cognitive function in an
animal model and found that the alleviative effect in ADrelated cognitive dysfunction by SDG is dependent on
the gut microbial metabolites END and ENL. In addition,
SDG also influenced the β-diversity and the compositionof the gut microbiota (Fig. 4A-E). However, the specificrole of SDG in shaping the gut microbiota and its effecton the metabolism of SDG to END and ENL remains
unclear. Future studies should further investigate the specific mechanisms of microbial transformation of SDG
in vivo. In addition, APP/PS1 mice also suffer from gut
pathological damage, including impaired gut morphology
and barrier function, as well as inflammatory response[63]. Research has shown that SDG has a significant
inhibitory effect on high-fat diet induced gut inflammation or Dextran sulfate sodium salt induced colitis [21,22]. Therefore, it remains to be explored whether SDG
can affect gut pathological damage and related mechanisms in AD model mice.
END and ENL share structural similarities with estradiol, one of the most prevalent and potent estrogens [57].

Jia et al. Journal of Neuroinflammation(2024) 21:201

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Fig. 10 SDG attenuated cognitive impairment in female APP/PS1 mice SDG is metabolized by gut microbiota to produce END and ENL, which in turnenhance the expression levels of PSD-95 and BDNF, and inhibit neuroinflammatory responses through GPER receptors, and reduce Aβ deposition toenhance the cognitive ability, such as spatial memory, recognition memory, and working memory in the female APP/PS1 mice

GPER, a membrane estrogen receptor [64], is expressedin almost all regions of the brain [37], particularly significantly in the forebrain areas such as the hippocampus and frontal cortex [65]. Additionally, GPER-deficient
rats exhibit increased anxiety-like behavior and learningand memory impairments [66]. Therefore, GPER may be
involved in memory formation or consolidation. In addition, activation of GPER can inhibit neuroinflammatory
responses [67–70], enhance synaptic transmission andplasticity in the hippocampal CA3-CA1 synapse [43,71], and increase neuronal survival [72]. Furthermore,consistent with our findings, G1, a GPER agonist, effectively alleviated recognition memory impairment infemale 5xFAD mice through GPER activation, but not
in male mice [39]. Study has found that ENL and END.
can effectively activate GPER [30, 31]. Similarly, the activation of GPER depends on the presence of END and
ENL (Figs. 2 and 7). However, the neuroprotective effectsof GPER may exhibit sexual dimorphism, although no

gender-specific differences were observed in GPER
expression in 5xFAD mice [39]. Future research on GPER
should take gender differences into consideration.
Although the pathogenesis of AD is not completely
understood, Aβ deposition remains a major pathological feature of AD. It was found that SDG significantly
reduces Aβ deposition in the hippocampal DG region
and cortex (Fig. 3A-C, Supplementary Fig. 1). This may
be due to the downregulation of mRNA expression of key
proteins involved in Aβ production process, including
APP, PS1, and BACE1 by SDG (Fig. 3D-F). Correlation
analysis results indicated a significant negative correlation between serum END levels and hippocampal PS1
mRNA expression, but no significant correlation with
APP and BACE1 mRNA expression (Fig. 5C). On theother hand, ENL showed a significant negative correlation with APP and PS1 mRNA expression, but no significant correlation with BACE1 mRNA expression (Fig. 5C).
When the production of END and ENL was inhibited, theJia et al. Journal of Neuroinflammation(2024) 21:201

inhibitory effect of SDG on Aβ deposition was also abolished (Fig. 8A-C). Previous study has shown that GPER
may be involved in the effects of SDG on Aβ production
and deposition [31]. In summary, the preventive effectof SDG on Aβ deposition depends on the activation of
GPER by END/ENL.
Dietary supplementation of flaxseed, rather thanflaxseed oil, can partially attenuate systemic and neuroinflammation induced by LPS [24]. The absence of
SDG in flaxseed oil may be a reason for the differential
effects observed between the two interventions [57]. Our
research results also indicate that SDG intervention significantly inhibited the neuroinflammatory responsein female APP/PS1 mice (Fig. 3G-L). Furthermore, theinhibitory effect of SDG on neuroinflammatory response
depends on the production of END and ENL (Fig. 8DJ). Additionally, in an LPS-induced neuroinflammation
model in mice, it was observed that SDG downregulates
the mRNA expression of TNF-α, IL-6, and IL-1β in the
hippocampus, and increases the mRNA expression of
IL-10 (Fig. 9B-E). When GPER activation was inhibitedby inhibitor G15, the effect of SDG on the expression ofhippocampal inflammation-related cytokines mRNA wasabolished (Fig. 9B-E). Consistent with our findings, in
1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced
animal model of parkinsonism and an in vitro model in
BV2 microglial cells, the anti-inflammatory effect including reduced activation of microglia and the abatementof proinflammatory cytokines of G1 was abolished by
G15 [68]. These results suggest that SDG may exert neuroinflammatory inhibitory effects by activating GPER
through the production of END and ENL, thereby participating in the pathogenesis of AD.
In conclusion, our results provide a solid theoreticalbasis for the improvement of AD-related cognitive ability
by SDG intervention, and the potential mediating mechanisms involving END, ENL, and GPER activation wererevealed through ABx treatment removal and intracerebral injection of GPER inhibitor.

Conclusion
In summary, our results indicate that SDG intervention
influences the composition of gut microbiota, increases
the production of END and ENL, and then inhibits neuroinflammatory response, enhances BDNF and PSD-95
protein expression, and reduces Aβ deposition through
GPER receptor activation, thereby rescuing cognitive
dysfunction related to the female AD (Fig. 10). Importantly, SDG shows significant potential in delaying thepathological progression of AD in females, providing
essential theoretical basis for SDG-related interventions
and targeting GPER receptor modulation in the development of AD. Additionally, to avoid wastage of resources,

Page 18 of 20

fully exploiting the use of flaxseed meal to extract SDG
holds practical and economic value.
Abbreviations
AD	Alzheimer’s diseaseAβ	β-amyloid
HRT	Estrogen replacement therapy
SDG	Secoisolariciresinol diglucoside
END	Enterodiol
ENL	Enterolactone
LPS	Lipopolysaccharide
GPER	G protein-coupled estrogen receptor
ER	Rstrogen receptor
BDNF	Brain-derived neurotrophic factor
ABx	Broad spectrum antibiotic cocktail
MWM	Morris water mazeBM	Barnes maze
NOR	New object recognition
qRT-PCR	Quantitative real-time PCR
WB	Western blot
ELISA	Enzyme-linked immunosorbent assay
HPLC-MS	High-performance liquid chromatography–mass spectrometry
DG	Dentate gyrus
APP	Amyloid precursor protein
PS1	Presenilin-1
BACE1	Beta-secretase
IBA-1	Ionized calcium binding adapter molecule 1
TNF-α	Tumor necrosis factor alphaIL-6	Interleukin-6
IL-1β	Interleukin 1 beta
IL-10	Interleukin-10
PSD-95	Postsynaptic density protein-95

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03195-4.
Supplementary Material 1: SDG reduces Aβ pathology (Figure S1)； SDG
reshapes the composition and structure of gut microbiota (Figure S2)
Supplementary Material 2： Primer sequences used for qRT-PCR (Table S1)
Supplementary Material 3： List of antibodies for immunofluorescencestaining and WB (Table S2)
Supplementary Material 4：Original data
Acknowledgements
The authors thanks Yao Liu (Life Science Research Core Services, Northwest A& F University, Yangling, China) for her assistance and detection with HPLC-MS
experiments. The author also thanks Dr. Tai An (COFCO Nutrition and HealthResearch Institute) for providing valuable editing suggestions for the article.
Author contributions
M.J. and F.N. performed the experiments; F.N. analyzed the data; J.W. and X.W.
helped with animal experiments. M.J., F.N., X.C., and Z.L. wrote the manuscript;
Z.L., M.J. J.H. J.C. and X.C designed the experiments; Z.L. and X.C. gave finalcontent approval. All authors read and approved the final manuscript.
Funding
This work was supported by the Sci-Tech Innovation 2030 Brain Scienceand Brain-Like Intelligence Technology Project (2022ZD0208100 to ZL),
Shenzhen Science and Technology Program (JCYJ20220818102810022,
JH), Basic Research Project of Shenzhen Natural Science Foundation(JCYJ20230807111402005 to ZL), The Key R&D Plan of Shaanxi Provincial
Department of Science and Technology (2023-YBNY-184 to ZL).
Data availability
All data generated or analyzed during this study are included in this publishedarticle and its Additional file 4. Raw data that support the findings of this study

Jia et al. Journal of Neuroinflammation(2024) 21:201

are available from the corresponding author, upon reasonable request. The
accession number for the entire 16 S rRNA sequencing dataset reported in thismanuscript is NCBI BioProject: PRJNA1100227 https://www.ncbi.nlm.nih.gov/
bioproject/.

Declarations
Ethics approval and consent to participate
Human samples were not used in this study. All animal procedures were
performed in accordance with the local welfare legislation and approved by
the Animal Ethics Committee of Northwest A&F University (No. XN2023-0716).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
College of Food Science and Engineering, Northwest A&F University,
Yangling 712100, Shaanxi, China
2
Department of Neurology, Peking University Shenzhen Hospital,
Shenzhen 518000, Guangdong, China
3
Northwest A&F University Shenzhen Research Institute,Shenzhen 518000, Guangdong, China
Received: 27 May 2024 / Accepted: 5 August 2024

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