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Gut Microbes
Gut Microbes
Gut Microbes
1949-0976
1949-0984
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39262376
10.1080/19490976.2024.2395907
2395907
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Research Article
Research Paper
Fecal microbiota transplantation derived from mild cognitive impairment individuals impairs cerebral glucose uptake and cognitive function in wild-type mice: Bacteroidetes and TXNIP-GLUT signaling pathway
T. WANG ET AL.
GUT MICROBES
Wang Tao a *
Hao Ling a b *
Yang Kexin a
Feng Wenjing a
Guo Zhiting a
Liu Miao a
https://orcid.org/0000-0002-0603-9518
Xiao Rong a
a School of Public Health, Capital Medical University , Beijing, China
b Institute for Nutrition and Food Hygiene, Beijing Center for Disease Prevention and Control , Beijing, China
CONTACT Rong Xiao xiaor22@ccmu.edu.cn School of Public Health, Capital Medical University, No. 10 Xitoutiao, You an Men Wai, Beijing 100069, China
* These authors contributed equally to this work.

12 9 2024
2024
12 9 2024
16 1 2395907Integra10 9 2024
Integra10 9 2024
22 5 2024
23 7 2024
16 8 2024
© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Gut microbiome dysbiosis has been widely implicated in cognitive impairment, but the identity of the specific bacterial taxa and mechanisms are not fully elucidated. Brain glucose hypometabolism coincides with the cognitive decline. This study explored the link among cognition, gut microbiota and glucose uptake based on the fecal microbiota transplantation from mild cognitive impairment individuals (MCI-FMT) and investigated whether similar mechanisms were involved in 27-hydroxycholesterol (27-OHC)-induced cognitive decline. Our results showed that the MCI-FMT mice exhibited learning and memory decline and morphological lesions in the brain and colon tissues. There were reduced 18F-fluorodeoxyglucose uptake, downregulated expression of glucose transporters (GLUT1,3,4) and upregulated negative regulator of glucose uptake (TXNIP) in the brain. MCI-FMT altered the bacterial composition and diversity of the recipient mice, and the microbial signatures highlighted by the increased abundance of Bacteroides recapitulated the negative effects of MCI bacterial colonization. However, inhibiting Bacteroidetes or TXNIP increased the expression of GLUT1 and GLUT4, significantly improving brain glucose uptake and cognitive performance in 27-OHC-treated mice. Our study verified that cognitive decline and abnormal cerebral glucose uptake were associated with gut microbiota dysbiosis; we also revealed the involvement of Bacteroidetes and molecular mechanisms of TXNIP-related glucose uptake in cognitive deficits caused by 27-OHC.

GRAPHICAL ABSTRACT

KEYWORDS

Fecal microbiota transplantation
Bacteroidetes
cognitive decline
27-hydroxycholesterol
cerebral glucose uptake
TXNIP
National Natural Science Foundation of China 10.13039/501100001809 81330065 National Natural Science Foundation of China 10.13039/501100001809 82373557 This work was supported by the State Key Program of the National Natural Science Foundation of China [Grant No. 81330065] and the National Natural Science Foundation of China [Grant No. 82373557; 82173501].
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pmcIntroduction

Mild cognitive impairment (MCI) is considered as a critical transitional stage between normal aging and dementia caused by Alzheimer’s disease (AD), approximately 50% of MCI individuals will progress to AD in the following 4–5 y.1 In the pathophysiologic continuum of AD, MCI represents a crucial opportunity for early detection and intervention to slow down or even prevent AD.

The gut microbiome has been regarded as a significant mediator in AD due to its susceptibility to lifestyle and environmental factors, which plays a vital role in linking gut health and cognitive function via microbiota-gut-brain axis.2 A growing number of studies have established the causal links between gut microbiota and cognitive decline by performing fecal microbiota transplantation (FMT) in germ-free (GF) and microbiome depletion (antibiotic cocktails (Abx)-treated) mice.3 However, GF mice might show inherent neurobehavioral abnormalities due to the alterations in brain structure and neurochemistry, as well as leakage of the blood–brain barrier (BBB).4 Instead, antibiotic treatment has been considered to be a less intrusive pharmacological tool and has been widely recognized and applied.5 In addition, benefited by the powerful tool of metagenomics, recent studies have emphasized the importance of microbial community structure, especially the phylum-through-genus-wide differences of microbiota in AD pathology, to screen strains with altered abundance as target biomarkers.6 Compelling evidence shows that the Bacteroides strains are commonly enriched in the feces of AD and MCI subjects.7,8 Nonetheless, the extent to which and through what pathways Bacteroidetes affect brain function remains largely underexplored.

Dysfunctional glucose metabolism is an early feature in AD brains. Reduction of cerebral glucose uptake in the aged brain, mainly caused by decreased glucose transporters (GLUTs) and increased central regulator of glucose metabolism (thioredoxin-interacting protein, TXNIP), has been validated in AD mouse models.9,10 Recent evidence shows that the balance of gut microbiota is important to maintain glucose metabolic homeostasis and protect cognitive function.11 A clinical study found gut bacterial disturbance in diabetic cognitive dysfunction patients.11 In animal models, probiotic oral administration upregulated the expression of GLUT1 and GLUT3, improving the brain glucose uptake and memory performance in 3 × Tg-AD mice. However, the mechanism underlying these phenomena has not been elucidated.

27-hydroxycholesterol (27-OHC), the most abundant cholesterol derivatives in the periphery is proven to be responsible for the pathogenesis of neurodegenerative and gastrointestinal diseases.12 Previous research demonstrated that excess 27-OHC reduced the glucose uptake and GLUT4 expression in the brain, showed negative effects on spatial learning and memory performance, and revealed a potential link between 27-OHC and cerebral glucose metabolism.13 Our recent study further emphasized that 27-OHC treatment altered the microbiota composition, and the mice also exhibited the phenotypes of decreased glucose uptake and cognitive deficits, while these results were not observed in mice treated with 27-OHC as well as antibiotics to deplete the gut microbiota, suggesting that the 27-OHC-induced cognitive impairment and glucose hypometabolism might be mediated by gut microbiota.14

The present study aimed to show the effects and therapeutic mechanisms of gut microbiota and brain glucose metabolism in mouse models of cognitive impairment. First, we transplanted the MCI fecal microbiome to Abx-treated recipient mice to establish the causal link between gut microbiota and AD-like pathology. Further, 27-OHC combined with inhibition of Bacteroidetes and TXNIP was used to provide mechanistic insights into the impact of 27-OHC on the brain glucose uptake by regulating the microbiota-gut-brain axis, and the potential contribution of Bacteroidetes.

Materials and methods

Animals

Male C57BL/6J mice aged 6-months old were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed under SPF condition (12 h light/dark cycle; humidity, 50–55%; temperature, 20–23°C) with standard diet and water ad libitum. The study was approved by the Animal Ethics Committee of Capital Medical University (AEEI-2014-047) and performed according to the relevant ethical standards.

Experimental design

After 1 week of acclimatization, mice were randomly assigned to perform the following three parts of experiments as visualized in Figure 1. Figure 1. Flow diagram of treatment and protocol design.

Part I. Fecal microbiota transplantation experiment

To elucidate the causal link between gut microbiota and AD-like pathology, we established the humanized gut microbiota through FMT in mice pretreated with Abx. First, gut microbiota-depletion mice were developed by giving broad-spectrum antibiotic cocktails for 4 weeks before transplantation, the components of which included metronidazole (1 g/L), neomycin (1 g/L), ampicillin (1 g/L), and vancomycin (500 mg/L).15 The Abx solution was freshly prepared every 2 d to promise its activity. Then, 40 mice were randomly divided into four groups according to the cognition of the donors, including early MCI (EMCI), late MCI (LMCI), healthy control subjects (HC) and Control (Con, mice receiving phosphate-buffered saline (PBS)) groups. During the transplantation, Abx water was replaced with sterilized regular drinking water. Fresh feces samples of the recipient mice were collected immediately for 16S ribosomal DNA (rDNA) gene sequencing pre- and post-FMT. Then, mice were then fasted for 12–14 h, and the blood glucose level was measured by testing tail blood with an Accu-Chek Aviva device (Roche, Mannheim, Germany). The18F-fluorodeoxyglucose (18F-FDG) PET imaging and neurobehavioral tests were performed, and then the tissue samples were collected and stored at −80°C until use.

Part II. Bacteroidetes inhibition experiment

To explore the involvement of Bacteroidetes in 27-OHC-induced cognitive decline, a total of 20 mice were randomly divided into four groups, named Control (saline, 0.2 mL/d, s.c.), 27-OHC (5.5 mg/kg, s.c.), bacteroidetocins (Bd) (15 mg/kg, i.p.), and 27-OHC+bacteroidetocins (27-OHC+Bd) group. The bacteroidetocins was inhibitor of Bacteroidetes and was synthesized by GL Biochem Ltd. (Shanghai, China). The dose and duration of bacteroidetocins were determined based on our preliminary results according to previous literature16 (see details in Supplementary materials 1). The 27-OHC treatment lasted for 21 d, and bacteroidetocins was administrated the first 5 d before the 27-OHC treatment and then from the 17th to the 21st d during the whole treatment. Gut microbiota profiling, neurobehavioral tests, and tissue samples collection were performed after the treatment.

Part III. Glucose uptake inhibition experiment

To explore the molecular mechanism of TXNIP in 27-OHC-induced reduction of cerebral glucose uptake, 40 mice were randomly divided into four groups, named Control, 27-OHC (5.5 mg/kg, s.c.), verapamil (Ver), and 27-OHC+verapamil (27-OHC+Ver) group. Verapamil was selected as TXNIP inhibitor and administered in drinking water (1 mg/mL) for three consecutive weeks.17 Similarly, mice were subjected to tail blood glucose tests and 18F-FDG PET imaging. Neurobehavioral tests were conducted, and tissue samples were collected for use.

Fecal samples collection, processing and transplantation

Feces were collected from human donors aged 50–70 y old from a prospective cohort study of our group. The baseline examinations of our human participants took place from 2014 to 2015, and follow-up examinations were conducted every 2 y (see details of population donor selection in Supplementary material 2). MCI participants were assessed through Montreal Cognitive Assessment (MoCA) scores and secondarily examined by neurologists as previously described.18 Those with normal cognition at baseline but were diagnosed with MCI at follow-up were defined as EMCI, those with a diagnosis of MCI at both baseline and follow-up were LMCI, and healthy control subjects were required to be cognitively normal at baseline and follow-up. The population study was approved by the Ethics Committee of Capital Medical University (2013SY35) and all subjects signed the informed consent before inclusion.

Fresh fecal pellets were collected from donors (5–6 subjects/group) and frozen at −80°C immediately. The fecal samples were then homogenized in sterile PBS (1 g fecal pellet/10 ml)19 and vortexed for 5 min and centrifuged at 1000 × g for 3 min. The resulting slurry was aliquoted into individual cryotubes and administered immediately to recipient mice to minimize the changes in microbial contents. All mice were orally gavaged with 200 μL slurry weekly for 8 weeks.20

Neurobehavioral tests

Morris Water Maze (MWM) test: The MWM test is used to evaluate spatial learning and memory ability. The test was conducted in a white circular pool (diameter 120 cm, 21 ± 1°C) with a submerged platform (10 cm diameter) 1.0 cm below the surface. Briefly, each mouse was subjected to a total of 15 trials (3 trials per day for 5 consecutive days). The time taken to reach the platform (escape latency), the average swimming speed, and the mean distance were recorded. On day 6, animals were given a 90-s probe trial with the platform removed. The time spent in the target quadrant and the number of platform-site crossovers of each mouse were recorded using a tracking system (JX business, Shanghai, China).

Novel object recognition test: The novel object recognition test was performed as previously described.21 Briefly, mice were habituated in an empty white chamber for 15 min. After 24 h, each mouse was returned to the same chamber with two identical objects placed in the corners and allowed to freely explore for 10 min. After another 24 h, replacing one of the familiar objects with a novel one, mice were released into the chamber from the same position to explore for another 10 min. The box was cleaned with 75% alcohol after each operation to avoid odor-guided navigation. The time and frequency of exploring the familiar and novel objects were recorded. The time discrimination index (TDI), frequency discrimination index (FDI), and novel object recognition index (NORI) were calculated as follows: TDI = (tnovel − tfamiliar)/(tnovel + tfamiliar), FDI = (fnovel − ffamiliar)/(fnovel + ffamiliar), NORI = fnovel/ tnovel ×100% (f: frequency; t: time).

16S rDNA gene sequencing

Microbial diversity and community composition were determined by 16S rDNA gene sequencing as previously described.12 Metagenomic DNA was extracted from fecal samples with the TIANamp Stool DNA Kit (TIANGEN, Beijing, China) according to the manufacturer’s instructions. The V4 region of the 16S rDNA gene was amplified with universal primers. α-diversity metrics including Observed_otus, Chao1, Shannon, and Simpson index were calculated by QIIME2 software. β-diversity was calculated using weighted and unweighted UniFrac distance and visualized by Principal Coordinates Analysis (PCoA) or non-metric multidimensional scaling (NMDS) analysis. Similarity percentage (SIMPER) analysis was performed to identify the major bacterial amplicon sequence variants (ASVs) responsible for the differences observed between the groups. Linear discriminatory analysis effect size (LEfSe) was used to identify the differences in the relative abundance of bacterial taxa, and the differential taxa were selected with default criteria (p < 0.05 by Kruskal–Wallis test; linear discriminant analysis score >4).

18F-FDG PET imaging

Mice underwent prior fasting for 12–14 h, and their blood glucose level was measured. The animals were then intraperitoneally injected with 18.5 MBq of 18F-FDG (China Institute of Atomic Energy, Beijing, China) and maintained for an uptake period of 40 min. Less than 5 min before PET acquisition, anesthesia was conducted with 5% isoflurane gas and 95% oxygen, and mice were gently fixed on the imaging bed of a small animal PET-SPECT-CT scanner (Siemens Medical Solutions, Knoxville, TN, USA) in the prone position for 15 min. Brain PET images were coregistered to the corresponding CT images, and then the CT images were fused into an MRI weighted template. Regions of interest (ROI) of the brain were manually drawn based on CT images, and the mean of standardized uptake values (SUVmean) was calculated to reflect the metabolic burden of different brain regions. SUVmean was calculated as ROI activity/injected 18F-FDG activity (kBq) per gram of body weight.22 Seven brain regions were analyzed, including the cortex, hippocampus, amygdala, caudate putamen, olfactory bulb, hypothalamus, and cerebellum. Since the blood glucose level has an inverse connection with brain glucose uptake, SUVmean was further corrected for the blood glucose level, defined as SUVglc (SUVglc = SUVmean × blood glucose level (mg/dl)).23

Histopathological analysis

Hematoxylin–eosin (HE) staining: HE staining was conducted according to a conventional method. Briefly, the whole brain and distal colon tissues (~2 cm) of mice were embedded and sectioned and then stained with hematoxylin solution for 5 min after deparaffinization and rehydration, followed by soaking in 1% acid ethanol (1% HCl in 75% ethanol) for 5 min. The sections were then stained with eosin solution for 2 min and washed in tap water followed by dehydration with graded alcohol and cleaning in xylene. The prepared slides were photographed using a Pannoramic scanner (3DHISTECH Ltd., Budapest, Hungary).

Transmission Electron Micrograph: The hippocampal CA1 region of mice (1 × 1 × 1 mm3) was fixed in 2.5% glutaraldehyde for 2 h, then washed with 0.1 M phosphate buffer three times followed by fixation in 1% osmic acid at 4°C for 2 h. After dehydration in graded ethanol, the samples were embedded in epoxy resin. Ultrathin sections (50 nm) were sequentially stained with uranyl acetate and lead citrate. Sections were observed under a transmission electron microscope (HT7700, Hitachi, Japan).

Immunofluorescence staining: Immunofluorescence staining was performed as previously described.24 Briefly, the brain tissues were fixed with 4% paraformaldehyde for 24 h at room temperature and embedded in paraffin. The tissues were cut into slides with 4-μm thickness, followed by deparaffinization and rehydration. The sections were incubated with anti-GLUT1 (1:200, ab115730, Abcam), anti-GLUT3 (1:200, bs-1207 R, Bioss), anti-TXNIP (1:200, sc -271,238, Santa Cruz), anti-GFAP (1:200, ab4648, Abcam), and anti-NeuN (1:200, ab104224, Abcam) antibodies overnight at 4°C. The next day, secondary antibodies were incubated for 1 h in the dark at room temperature. The nuclei were counterstained with DAPI (1 μg/mL). Images were acquired with a fluorescent microscope (Ti2 E; Nikon; Japan).

Reverse transcription and quantitative real-time polymerase chain reaction (qPCR)

Total RNA was isolated from the cerebral cortex using the SV Total RNA Isolation system (Promega Corporation, Madison, WI, USA). Reverse transcription and qPCR experiments were conducted with the First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA, USA) and KAPA SYBR FAST qPCR kit (Kapa Biosystems, Woburn, MA, USA), respectively. The primers used are listed in Table 1. The abundance of Bacteroides was also detected by qPCR, and the primers were shown in Table 1 described as Mouse Intestinal Bacteroides (MIB). 27F/1492 R universal bacterial primers were used as an internal reference for normalization.Table 1. Primers used in this study.

Sample	Primer	Forward sequence (5′-3′)	Reverse sequence (5′-3′)	
Brain	GLUT1	TCAACACGGCCTTCACTG	CACGATGCTCAGATAGGACATC	
GLUT3	ACCTCCAACTTTCTGGTCGG	CTTTGGTCTCCGGGACTTTG	
GLUT4	GTAACTTCATTGTCGGCATGG	AGCTGAGATCTGGTCAAACG	
TXNIP	TCTTTTGAGGTGGTCTTCAACG	GCTTTGACTCGGGTAACTTCACA	
GAPDH	GGTTGTCTCCTGCGACTTCA	TGGTCCAGGGTTTCTTACTCC	
Feces	MIB	CCAGCAGCCGCGGTAATA	CGCATTCCGCATACTTCTC	
27F/1492 R	AGAGTTTGATCMTGGCTCAG	TACGGYTACCTTGTTACGACTT	

Western blotting

Forty-milligram flash-frozen cerebral cortex was homogenized in RIPA buffer with phosphatase and protease inhibitors. The concentration of protein was quantified using the BCA Protein Assay Kit (Keygen Biotech Corp, Jiangsu, China). The protein samples were supplemented with 5 × loading buffer and then subjected to boiling, separating by 10% SDS-PAGE and transferring to PVDF membrane. The anti-GLUT1 (1:50000, ab115730, Abcam), anti-GLUT3 (1:1000, ab191071, Abcam), anti-GLUT4 (1:1000, 2213S, CST), and anti-TXNIP (1:1000, K0205–3, Medical & Biological Laboratories) primary antibodies were incubated overnight, followed by species-matched secondary antibodies. Membranes were imaged using Image System Fusion FX (Vilber Lourmat Co., Ltd., Paris, France).

Statistical analysis

Statistical analysis was carried out using SPSS 23.0 (SPSS, Inc., Chicago, USA) and GraphPad Prism 7.0.0 software. Data are presented as mean ± SEM or median (quartiles). Parametric data were compared by one-way analysis of variance (ANOVA) with LSD-t post-hoc test. Non-parametric data were assessed using the Kruskal–Wallis test. Escape latency of the MWM test was analyzed using repeated-measures ANOVA. A two-sided p < 0.05 was considered significant.

Results

FMT affected the composition and diversity of gut microbiota and intestinal structure

Fecal samples of the recipient mice were collected to analyze the microbial composition and diversity of mice pre- and post-FMT. As shown in the flower diagram, a total of 4218 ASVs were displayed at 100% sequence similarity and shared among all samples (Figure 2a). NMDS analyses based on weighted UniFrac distance demonstrated that bacterial characteristics in recipient mice were evidently separated from those before the transplantation but were similar to their donors (stress = 0.082) (Figure 2b), indicating that the differences in gut microbiota composition were successfully established in recipient mice through FMT. PCoA plots based on the weighted and unweighted UniFrac distance supported that the bacterial characteristics were clearly different among the four groups (Figure 2c). Figure 2. FMT affected the composition and diversity of gut microbiota (n = 6 mice/group). (a) Flower diagram based on ASVs among donors and recipient (pre‐ and post‐transplant) mice. Abbreviations: pre, pre transplant mice; D.HC, healthy control donor; D.EMCI, EMCI donor; D.LMCI, LMCI donor; con, pbs-treated mice; HC, healthy control recipient; EMCI, EMCI recipient; LMCI, LMCI recipient. (b) The non-metric multidimensional scaling (NMDS) analysis of bacterial β-diversity of each group. (c) PCoA data of bacterial communities based on the weighted and unweighted UniFrac distance of each group. (d) α-diversity of gut microbiota among groups. (Observed_otus; Chao1; Simpson; Shannon index). (e) β-diversity of gut microbiota among groups based on weighted and unweighted UniFrac distance. Each box plot represents the median, interquartile range, minimum, and maximum values. Relative abundance of microbial species at phylum (f) and genus (g) levels. (h) Linear discriminant analysis (LDA) scores by LEfSe analysis > 4.0 and p<0.05 are listed in the histogram. (i) Taxonomic cladogram by LEfSe analysis. Abbreviations: p, phylum; c, class; o, order; f, family; g, genus; s, species. All data were showed as mean ± SEM. *p < 0.05, **p < 0.01.

We measured the microbial diversity using several methodologies and found that MCI recipients exhibited higher α-diversity. The Simpson and Shannon indices were significantly higher in the EMCI and LMCI groups than those in the Con group (p < 0.05), and a higher Simpson index was also observed in the LMCI group when compared with the HC group (p < 0.05) (Figure 2d). Meanwhile, we analyzed the β-diversity based on the unweighted UniFrac distance metric, which revealed an evident decrease in β-diversity in the gut microbe of EMCI and LMCI recipient mice (p < 0.05) (Figure 2e).

We then analyzed the intestinal flora composition and found that at the phylum level, Bacteroidota and Firmicutes were the predominant phyla after FMT (Figure 2f). At the genus level, the microbiota was dominated by Bacteroides, Muribaculaceae, Parabacteroides, Lactobacillus, and Akkermansia (Figure 2g). LEfSe analysis further reflected differentially abundant bacterial taxa among the recipient groups. The results showed that four bacterial taxa including f_Muribaculaceae, g_Muribaculaceae, g_Faecalibaculum, and s_Parabacteroides_merdae were significantly enriched in the EMCI group, and c_Clostridium, f_Lachnospiraceae, o_Lachnospirales, and g_Turicibacter were more abundant in the LMCI group. In contrast, a higher proportion of o_Burkholderiales and f_Enterobacteriaceae were found in the feces of HC and Con mice (Figure 2h,i).

In addition, HE staining of colon tissue was also conducted to intuitively assess the effects of FMT on intestinal structure. The results showed that mice in the Con and HC group displayed a complete villous structure with no epithelial disruption. Moreover, smooth mucous membranes and a large number of goblet cells were seen microscopically. On the contrary, transplantation of microbiota from EMCI donors induced loss of epithelial integrity, little colonic villi shedding, and increased crypt depth. In addition, mice in the LMCI group exhibited seriously damaged colonic villi shedding and obvious inflammatory cell infiltration (Supplementary material 3).

Inoculation with microbiota from MCI individuals induced brain pathological lesions and cognitive deficits

HE staining was also conducted to evaluate the effects of FMT on brain histopathology. As shown in Figure 3a, the neurons in the Con and HC groups were tightly and neatly arranged, and the cell membrane and nucleus were clear, while neurons in the EMCI group were sparse and disorderly arranged with increased nucleus pyknosis. The ultrastructure of the hippocampal CA1 region (Figure 3b) showed that the nuclei in the Con and HC groups were large in size, round or oval in shape, with an obvious nucleolus located in the center and with even distribution of nuclear chromatin. In comparison, the nuclei in EMCI and LMCI groups were loose with swollen endoplasmic reticulum. In the LMCI group, the nuclear chromatin was unevenly distributed and fused into small pieces, and the number of organelles was reduced with obvious damage. Figure 3. Inoculation with microbiota from MCI individuals induced brain pathological lesions and cognitive deficits. (a) HE staining of the whole brain (n = 3 mice/group, 10×: scale bar = 200 μm, 40×: scale bar = 20 μm). Black arrows: nuclei pyknosis. (b) Ultrastructure of hippocampal CA1 region under transmission electron microscope (n = 3 mice/group, 2K×: scale bar = 5 μm, 4K×: scale bar = 2 μm, 8K×: scale bar = 1 μm). Red letter N: nucleus; green arrows: endoplasmic reticulum. (c-e) novel object recognition test (n = 9–10 mice/group). (c) Novel object recognition index (NORI). (d) Time discrimination index (TDI). (e) Frequency discrimination index (FDI). (f-k) Morris water maze test (n = 8–10 mice/group). (f) Escape latency (^**con group, Day 3 Vs Day 1, p < 0.01; ^* con group, Day 4 Vs Day 1, p < 0.05; #**LMCI group Vs con group, p < 0.01). (g) Top view of the swimming path. (h) Average speed. (i) Mean distance. (j) Time in the target quadrant (%). (k) The number of platform-site crossovers. All data were showed as mean ± SEM. *p < 0.05, **p < 0.01.

Further, the novel object recognition was conducted to evaluate the short‐term working memory of mice. As shown in Figure 3c, decreased NORI was observed in the EMCI and LMCI groups when compared with the Con (EMCI: p = 0.016; LMCI: p = 0.016) and HC group (EMCI: p = 0.019; LMCI: p = 0.019). The TDI value in the EMCI group was also lower than that in the Con (p = 0.017) and HC group (p = 0.016) (Figure 3d). No statistical difference was found in FDI (H = 4.972, p = 0.174) (Figure 3e).

The MWM test was followed to determine the spatial learning and memory ability of mice. As shown in Figure 3f, the escape latency of the Con group was significantly shorter on Day 3 (F = 21.704, p = 0.009) and Day 4 (F = 19.667, p = 0.019) than Day 1, suggesting that better spatial learning performance could be obtained by repeated trials in the control mice. Comparing the differences in escape latency among groups on the same day, we found that on Day 3, the escape latency of the LMCI group was much longer than that of the Con group (F = −19.569, p = 0.003). During the probe trails on Day 6, when the escape platform was removed, mice in EMCI and LMCI groups showed an aimless searching strategy with reduced number of platform-site crossovers (EMCI: p = 0.014; LMCI: p = 0.004), but similar time spent in the target quadrant (H = 2.613, p = 0.455), mean distance (H = 1.165, p = 0.761) and average speed (H = 2.255, p = 0.521) were observed compared to the Con group (Figure 3g–k).

Inoculation with microbiota from MCI individuals decreased glucose uptake in the brain

18F-FDG PET was performed to determine whether FMT changed the glucose uptake in the brain. Initially, no statistical difference in SUVmean was observed in the seven brain regions (p > 0.05) (Figure 4a). But when correcting the SUVmean for the blood glucose level, the LMCI group exhibited a lower SUVglc compared to the Con and HC group in the cerebral cortex (Con: p = 0.003; HC: p < 0.001), hippocampus (Con: p = 0.005; HC: p < 0.001), and caudate putamen (Con: p = 0.016; HC: p < 0.001). Similarly, the SUVglc in the cortex (p = 0.016) and hippocampus (p = 0.014) were lower in the EMCI group than in the HC group. Moreover, SUVglc in the cortex (p = 0.038) and caudate putamen (p = 0.014) were considerably lower in the LMCI group than in the EMCI group (Figure 4c), suggesting that decreased glucose uptake in the brain might be due to altered gut microbiota and might be associated with the progression of cognitive decline. Figure 4a. Quantification of 18F-FDG uptake in different brain regions (n = 3 mice/group). (a) SUVmean. (b) Representative18F-FDG PET three-plane images with coronal, transaxial, and sagittal in the brain. (c) SUVglc. All data were showed as mean ± SEM. *p < 0.05, **p < 0.01. Abbreviations: SUVmean, mean of standardized uptake values; SUVglc, the SUV corrected for the blood glucose level.

Figure 4c. The mRNA and protein expression of glucose uptake-related factors in the cerebral cortex (n = 6 mice/group). (a) GLUT1 mRNA. (b) GLUT3 mRNA. (c) GLUT4 mRNA. (d) TXNIP mRNA. (e) GLUT1 protein (f) GLUT3 protein. (g) GLUT4 protein. (h) TXNIP protein. (i) Western blot results. All data were shown as mean ± SEM. *p < 0.05, **p < 0.01.

The fluorescence intensity of glucose uptake-related proteins in the brain was detected in our study. Compared with the HC group, mice in the LMCI group showed a lower GLUT1 fluorescence intensity in both cortex (p = 0.015) and hippocampus (p = 0.002), and GLUT3 fluorescence intensity was significantly decreased in the cortex (p = 0.034); while in the EMCI group, reduced GLUT3 was observed in both cortex (p < 0.001) and hippocampus (p < 0.001), lower GLUT1 was observed only in the hippocampus (p = 0.001). In contrast, the EMCI and LMCI groups exhibited a significantly increased TXNIP fluorescence intensity in the cortex when compared to the two control groups (p < 0.01), and the highest level of TXNIP was observed in the LMCI group than the other three groups (p < 0.01) (Figure 4b). Figure 4b. Immunofluorescent double staining of TXNIP and GLUTs in the cortex and hippocampus. Representative images and statistical results of TXNIP and GLUT1 in the (a) cortex and (b) hippocampus. Representative images and statistical results of TXNIP and GLUT3 in the (c) cortex and (d) hippocampus. The nucleus, TXNIP and GLUTs were represented in blue, green and red, respectively (n = 3 mice/group, scale bar = 50 μm). All data were presented as the mean ± SEM. *p < 0.05; **p < 0.01.

In addition, we examined the gene and protein expression of glucose uptake-related factors in the cerebral cortex. As shown in Figure 4c, compared with the Con group, the expression levels of GLUT1 in the EMCI and LMCI groups were significantly downregulated at both mRNA (EMCI: p = 0.002; LMCI: p < 0.001) and protein (EMCI: p = 0.018; LMCI: p = 0.002) levels, and there were also decreased expression levels of GLUT1 in MCI recipient mice when compared with the HC group (EMCI: mRNA p < 0.001; LMCI: mRNA p < 0.001, protein p = 0.023). Lower GLUT3 and GLUT4 in EMCI and LMCI groups were found when compared to the HC group at both mRNA (GLUT3: EMCI: p = 0.001, LMCI: p = 0.001; GLUT4: EMCI: p < 0.001, LMCI: p < 0.001) and protein (GLUT3: LMCI: p = 0.014; GLUT4: EMCI: p = 0.010) levels and the expression level of GLUT3 protein in the LMCI group was lower than that of the EMCI group (p = 0.038). Similarly, lower GLUT3 (LMCI: p = 0.001) and GLUT4 (EMCI: p = 0.003, LMCI: p = 0.018) protein levels were found in the two MCI group when compared to the Con group. As for TXNIP, the expression level of which in MCI group was clearly upregulated compared to that of Con group (EMCI: mRNA p < 0.001; LMCI: mRNA p < 0.001, protein p = 0.004) and HC group (EMCI: mRNA p < 0.001; LMCI: mRNA p < 0.001, protein p = 0.003). Moreover, the protein expression levels of TXNIP in the LMCI group were significantly higher than that of EMCI group (p = 0.014).

27-OHC treatment increased the abundance of Bacteroides

Given that a higher proportion of Bacteroidetes has been found in the feces of MCI-FMT mice and the recognized roles of 27-OHC in cognitive decline, we performed the treatment with 27-OHC and bacteroidetocins to explore whether Bacteroidetes is the major contributor of gut microbiota in 27-OHC-induced cognitive decline. The SIMPER analysis was performed with R software (Version 3.2.4) to reveal the differential bacterial taxa between the 27-OHC and Control groups. The results suggested that Bacteroidetes was the largest contributor to the variability at the phylum level between the two groups, the relative abundance of which in the 27-OHC group was significantly higher than that of the Control group. Muribaculaceae, Bacteroides, Lactobacillus, and Prevotella were the first four contributing genera across the two groups, and three of them were the members of the Bacteroidetes phylum except for Lactobacillus (Figure 5a,b). Figure 5. 27-OHC treatment increased the abundances of Bacteroides. similarity percentage (SIMPER) analysis of the control and 27-OHC groups at (a) phylum and (b) genus levels (n = 5 mice/group). A11.1–A11.5: control group; A22.1–A22.5: 27-OHC group. (c) The relative abundance of Bacteroides. Abbreviations: bd, bacteroidetocins. All data were shown as mean ± SEM. *p < 0.05, **p < 0.01.

Further, the relative abundance of Bacteroides was detected using qPCR (Figure 5c). Treatment with bacteroidetocins reduced the relative abundance of Bacteroides by about 74% than the Control mice, indicating its effective inhibition (p = 0.011). Importantly, compared to the Control group, 27-OHC treatment drastically increased the relative abundance of Bacteroides (p = 0.043); while the level of Bacteroides in the 27-OHC+Bd group was significantly decreased than 27-OHC group (p < 0.001).

Bacteroidetes inhibition improved 27-OHC-induced cognitive decline and cerebral glucose uptake

As shown in Figure 6a, in the hidden platform test, the escape latency of the control group was progressively decreased over the 5 d of training (F = 6.021, p = 0.018). Compared to the Control group, the 27-OHC-treated mice exhibited longer escape latency from Day 2 to Day 5 (p = 0.036; p = 0.002; p < 0.001; p < 0.001) and the average speed (p = 0.004), the number of platform-site crossovers (p = 0.003), and time in the target quadrant (p < 0.001) were significantly decreased. However, when compared to the 27-OHC group, mice in 27-OHC+Bd group showed shorter escape latency on Day 2 (p = 0.014) and Day 3 (p = 0.005), and the average speed (p = 0.025) and the number of platform-site crossovers (p=0.045) were significantly increased (Figure 6b–f). Figure 6. Bacteroidetes inhibition improved 27-OHC-induced cognitive decline and cerebral glucose uptake. (a-f) Morris water maze test (n = 5 mice/group). (a) Escape latency (^27-OHC group Vs control group, *p < 0.05, **p < 0.01. #27-OHC group Vs 27-OHC+Bd group, **p < 0.01). (b) Top view of the swimming path. (c) Average speed. (d) Mean distance. (e) Time in the target quadrant (%). (f) The number of platform-site crossovers. All data were showed as mean ± SEM. *p < 0.05, **p < 0.01. (g-k) glucose uptake-related factors in the cerebral cortex. (g) GLUT1 protein. (h) GLUT3 protein. (i) GLUT4 protein. (j) TXNIP protein. (k) Western blot results. Abbreviations: bd, bacteroidetocins. All data were shown as mean ± SEM. *p < 0.05, **p < 0.01.

In addition, compared to the Control group, 27-OHC down-regulated the protein expression level of GLUT1 (p = 0.009), GLUT3 (p = 0.012) and GLUT4 (p = 0.043), whereas up-regulated the expression of TXNIP (p = 0.026). In contrast, compared to the 27-OHC group, the expression of GLUT1 (p = 0.017) and GLUT4 (p = 0.047) protein were significantly upregulated in 27-OHC+Bd group; while TXNIP protein showed the opposite results (p = 0.014), suggesting that Bacteroidetes might mitigate the glucose uptake decrease induced by 27-OHC (Figure 6g–k).

27-OHC reduced glucose uptake via regulating TXNIP in the brain

18F-FDG uptake measured as SUVglc revealed that there were significant differences in the cerebral cortex (F = 12.940, p < 0.001), hippocampus (F = 10.558, p < 0.001), amygdala (F = 9.304, p = 0.001), cerebellum (F = 8.683, p = 0.020), olfactory bulb (F = 5.174, p = 0.013), and caudate putamen (F = 4.617, p = 0.019) among the groups. Compared with the Control group, the SUVglc in the cerebral cortex (p < 0.001), hippocampus (p < 0.001), amygdala (p < 0.001), cerebellum (p = 0.017), and olfactory bulb (p = 0.007) were lower in the 27-OHC group. In contrast, the SUVglc in 27-OHC+Ver group in the cerebral cortex (p < 0.001), hippocampus (p = 0.001), amygdala (p = 0.001), olfactory bulb (p = 0.005), and caudate putamen (p = 0.003) were higher than that of 27-OHC group (Figure 7a–c). Figure 7. 27-OHC reduced glucose uptake via regulating TXNIP in the brain. Quantification of 18F-FDG uptake (n = 3 mice/group) in different brain regions with (a) SUVmean and (b) SUVglc. (c) Representative18F-FDG PET three-plane images with coronal, transaxial, and sagittal in the brain. Representative images (d) and statistical results (e-f) of TXNIP and GLUT1 in the hippocampus. The nucleus, TXNIP and GLUT1 were represented in blue, green and red, respectively (n = 3 mice/group, scale bar = 50 μm). The expression levels of (g) GLUT1, (h) GLUT3, (i) GLUT4, and (j) TXNIP protein in the cerebral cortex (n = 6 mice/group). (k) Western blot results. Abbreviations: SUVmean, mean of standardized uptake values; SUVglc, the SUV corrected for the blood glucose level. All data were shown as mean ± SEM. *p < 0.05, **p < 0.01.

Similarly, immunofluorescence analysis of GLUT1 and TXNIP in the brain was performed, and the results demonstrated significant differences in the two factors (Figure 7d–f). The 27-OHC group showed a lower GLUT1 fluorescence intensity than the Control (p < 0.001) group, while the value in 27-OHC+Ver group was higher than 27-OHC (p = 0.004) but lower than in the Control group (p = 0.019). By contrast, the fluorescence intensity of TXNIP showed opposite results. Specifically, the 27-OHC group showed higher fluorescence intensity of TXNIP compared to the Control (p = 0.003) and 27-OHC+Ver group (p = 0.001).

Results from the western blot showed lower GLUT1 protein in 27-OHC treated group (p = 0.015) compared with the Control group, but there was only a non-significant similar trend in GLUT3 and GLUT4 protein (p >0.05). Meanwhile, an increased level of TXNIP protein was observed in 27-OHC treated mice when compared to the Control group (p = 0.011). Then, verapamil was used to effectively downregulate the expression of TXNIP and acquire direct evidence of TXNIP regulating glucose uptake in the brain. Our results showed that Verapamil-treated mice had lower TXNIP protein levels in the 27-OHC+Ver group compared with the 27-OHC group (p = 0.05). By inhibiting TXNIP in 27-OHC-loaded mice, the expression level of GLUT1 (p = 0.014) and GLUT4 (p = 0.006) protein in 27-OHC+Ver group was significantly increased than that of the single 27-OHC-treated group. These results suggested that 27-OHC-induced cerebral glucose uptake decrease could be mediated by TXNIP (Figure 7g–k).

Improved glucose uptake ameliorated 27-OHC-induced cognitive impairment

Cognitive impairment was reconfirmed by the poor neurobehavioral performance in 27-OHC-treated mice, as shown in Figure 8, mice in the two groups (27-OHC: p < 0.001, 27-OHC+Ver: p = 0.021) displayed longer escape latency on Day 5 compared to the Control group, and 27-OHC group spent less time in target quadrant (p < 0.001) and showed fewer times crossed the platform location in probe tests (p = 0.001). Notably, the deficits in spatial learning and memory exhibited in 27-OHC mice were not evident in 27-OHC+Ver mice, as indicated by reduced escape latency on Day 2 (p = 0.028) and increased target quadrant occupation (p = 0.023). Figure 8. Improved glucose uptake ameliorated 27-OHC-induced cognitive impairment (n = 6 mice/group). (a) Escape latency (^27-OHC group Vs control group, #27-OHC+Ver group Vs control group, &27-OHC+Ver group Vs 27-OHC group, *p < 0.05, **p < 0.01). (b) Top view of the swimming path. (c) Average speed. (d) Mean distance. (e) Time in the target quadrant (%). (f) The number of platform-site crossovers. All data were showed as mean ± SEM. *p < 0.05, **p < 0.01.

Discussion

In this study, we demonstrated that cognitive decline and cerebral glucose uptake impairment were associated with alterations in gut microbiota. The main findings of this work are summarized as follows: (i) FMT from MCI individuals altered the composition and diversity of gut microbiota of wild-type recipients, among which the members of Bacteroidetes were the major fraction of gut bacteriome. (ii) FMT from MCI individuals reduced the glucose uptake in the brain, inducing brain pathological lesions and cognitive deficits in recipient mice. (iii) 27-OHC could also increase the relative abundance of Bacteroides and reduce the brain glucose uptake; while administration with bacteroidetocins (Bacteroidetes inhibitor) or verapamil (TXNIP inhibitor) ameliorated the glucose uptake impairment and cognitive decline induced by 27-OHC. All these suggested a possible modulatory role of the gut microbiome in cerebral glucose uptake impairment and cognitive decline and revealed that Bacteroidetes inhibition or glucose uptake improvement in the brain might be promising strategies for 27-OHC-induced cognitive dysfunction.

Growing research has reported that microbiota dysbiosis acted as a contributing factor in neurodegenerative diseases including AD via the microbiota-gut-brain axis.25 Our previous study has found reduced diversity and different community composition of gut microbiota in both MCI individuals and 27-OHC-treated mice, and microbiota features of 27-OHC-treated wild-type mice were closer to the APP/PS1 mice.12,26 However, the specific bacterial communities that affect microbiota-gut-brain crosstalk are still missing. Our results showed that the perturbation of gut microbiota caused by MCI-derived fecal transplantation worsened the spatial and short-term/working memory in recipient mice, which confirmed the causal relationship between gut microbiota disturbance and cognitive decline. However, MCI-FMT recipient mice showed higher α-diversity than the Con or HC mice, which was not completely consistent with the current studies. A systematic review by Heravi et al. reported that the α-diversity in neurodegenerative disorders showed variable results across different studies. They found higher α-diversity in six studies of Parkinson’s disease, while no significant difference was observed in eight AD studies. The inconsistency might be partly due to the race of the population we included, which could evidently affect the interpretation of gut microbiota results. In the future research, limiting factors such as sample size, host–microbiome interaction, and covariates (diet, exercise, etc.) should be considered and addressed to enhance the roles of gut microbiota in cognitive decline and provide robust insights into its potential mechanisms.

We also found that Bacteroidetes was the leading phyla across all bacterial taxa. Multiple literatures pointed out that Bacteroidetes has been identified as predictors of AD, influencing neuroinflammatory signaling.27,28 There were studies indicated that AD elders showed a higher abundance of Bacteroidetes, which was in line with several findings in Chinese and US populations, as well as our present study based on FMT from MCI individuals.6,27 Meanwhile, the relative abundance of Bacteroides notably increased in 27-OHC treated mice, and such alteration was reversed after adding bacteroidetocins in 27-OHC-loaded mice. Interestingly, the 27-OHC+Bd group preferentially exhibited a selective search for the platform as well as an increase in swimming speed and number of platform-site crossovers when compared with the single 27-OHC-treated group. A high level of 27-OHC in plasma is recognized as an independent risk factor of MCI according to our population study.29 In the present study, we further demonstrated that the Bacteroidetes made a major contribution to microbiota dysbiosis, mediating cognitive decline caused by 27-OHC.

Certainly, the systemic or even central influence of antibiotics per se is a possible drawback that cannot be ignored. Of the antibiotics chosen in our study, ampicillin shows low oral bioavailability and can be absorbed by the intestine to some extent, while it is not detected in the brain.3 Metronidazole is reported can penetrate into BBB, where it might exert undesirable effects, such as limb dyscoordination, gait instability, and dysarthria, but the dose and time of antibiotics, and the state of mice matter.30,31 Notably, Wu et al. reported that neither intracerebroventricular nor chronic systemic injection of antibiotics (ampicillin and metronidazole) in wild-type C57BL/6J mice showed social behavior changes, indicating that antibiotics do not appear to be directly neurotoxic.32 When comparing GF and Abx-treated mice, the researchers report similar effects on intestinal physiology, indicating that the changes in intestinal function are more likely to be associated with depletion or absence of gut microbiota, rather than the side effects of antibiotics.33 Nevertheless, future studies are warranted to specifically detect and analyze the neurobehavioral and neurochemical factors in the brain at baseline after antibiotic treatment to better explain and minimize the antibiotic-associated phenotypic differences.

Abnormal glucose metabolism in the brain has been considered a potential biomarker to predict the progression of cognitively unimpaired AD.34 Cross-cohort experiments by Zhang Q et al. indicated that the brain SUV showed a steady downward trend across the whole continuum of AD.34 Some evidence supported the hypothesis that gut microbiota played a role in modulating glucose metabolism in AD and finally affecting the disease progression.35 Indeed, our previous research has established the possible involvement of gut microbiota in cerebral glucose uptake decrease and learning and memory decline caused by 27-OHC.14 Here, by transferring MCI-derived microbiota to recipient mice, we directly proved that altered gut microbiota impaired brain glucose uptake and led to cognitive deficits. Our data showed that MCI-FMT mice exhibited progressively reduced SUVglc in several brain regions, which was also supported by decreased fluorescence intensity of GLUT1 and GLUT3 in cortex and hippocampus, as well as downregulated expression levels of glucose transporters in the cerebral cortex. Liu et al. reported that dietary intervention improved diabetes-induced cognitive impairment via the microbiota-gut-brain axis.36 Consistently, probiotic implementation showed positive impacts on glucose metabolism and cognitive performance in AD patients.37 These studies reinforce our results and further demonstrate the regulation of gut microbiota in glucose hypometabolism-related cognitive deficits.

To further test our hypothesis, we also evaluated the brain glucose uptake in bacteroidetocins-treated mice to clarify the potential modulation of Bacteroidetes. Our results showed higher GLUT1 and GLUT4 protein but lower TXNIP protein in 27-OHC+Bd group than that of the single 27-OHC-treated group, suggesting that Bacteroidetes was indeed the main contributing factor in gut microbiota modulating 27-OHC-induced glucose uptake impairment.

Bacteroidetes is one of the dominant phyla in AD, T2DM and the mixed models.38 A recent study reported that recolonization of mice with AD patient fecal samples (AD-FMT) elicited AD pathologies, and an increased abundance of proinflammatory Bacteroides fragilis was detected in AD-FMT mice.39 A study by Sun et al. found that the hyperglycemia-lowering action of antidiabetic agent could be the result of altering the gut microbiota community, among which the Bacteroides exhibited the largest decrease in abundance.40 Taken together, these results supported the critical roles of microbiota dysbiosis due to altered Bacteroidetes abundance in regulating glucose uptake and cognitive function. The molecules and signaling pathways that transmit the Bacteroidetes and gut microbial signatures into the brain deserve further investigation.

Notably, TXNIP functions as a key node of glucose uptake by negatively regulating GLUT1 and GLUT4.41 Increased levels of TXNIP mRNA and protein have been found in the hippocampus and cortex of postmortem human AD patients.42,43 A growing quantity of evidence shows that treatment approaches, such as antidiabetic drugs and herbal medicines, could reduce the level of TXNIP, decrease the fasting glucose level, ameliorate glucose tolerance in diabetic mice, and improve their cognitive performance in the MWM and Y-maze test.44,45 In this study, by using verapamil in 27-OHC-loaded mice, decreased brain TXNIP protein was accompanied by increased GLUT1/4 protein and higher SUVglc. Further, although 27-OHC mice performed worse in MWM test, the 27-OHC+Ver mice exhibited reduced escape latency but increased time in the target quadrant, confirming that the mice in this group had better memory retention on the platform through continuous training. Our findings indicated the important roles of TXNIP in mediating 27-OHC-induced glucose uptake impairment and the consequent cognitive decline.

Several limitations in this study need to be the focus of future research. Firstly, we only used Abx-treated mice to deplete the gut microbiota. The possible neurotoxic effects of antibiotics per se, as well as its potential and limitations as a model to probe the causality between gut microbiome and cognition, need further exploration. Second, the development and progression of cognitive decline have been proven to be clearly associated with abnormal glucose uptake in specific brain areas. As the brain is highly heterogeneous, we did not analyze the regional gene and protein differences and the cell-specific characteristics of cerebral glucose uptake, as well as the underlying mechanisms. In the current study, although 18F-FDG PET imaging of several brain regions, and immunofluorescence analysis of GLUTs in the cortex and hippocampus were performed, the expression of GLUTs at the mRNA and protein levels in specific brain areas and cell types was not assessed. In future studies, we would prefer to conduct in vitro study to confidently identify the roles of multiple GLUT isoforms, in particular brain cells in cognitive impairment.

Conclusion

Overall, this study shed light on the roles of gut microbiota, especially Bacteroidetes, in cerebral glucose uptake and cognitive decline, highlighting the involvement of Bacteroidetes and establishing TXNIP as a critical molecular target of brain glucose uptake in 27-OHC-induced cognitive impairment. Our study reveals a novel therapeutic approach to AD, but further studies are needed to explore which Bacteroidetes species or their metabolites trigger AD pathology and investigate their cellular and molecular mechanisms.

Supplementary Material

Supplemental Material

Author contributions

Rong Xiao conceived and designed the study. Tao Wang and Ling Hao conducted the experiments, performed the analyses and wrote the manuscript. Kexin Yang, Wenjing Feng, Zhiting Guo and Miao Liu helped with the experiments and collected the data. All authors read and approved the final manuscript.

Data availability statement

The 16S rDNA sequencing datasets have been deposited in the NCBI BioProject database with the accession number PRJNA1138413 and PRJNA1138520 (https://www.ncbi.nlm.nih.gov/).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

The animal experiment protocols were approved by the Ethics Committee of Capital Medical University (Ethics: AEEI-2014-047) and performed according to the guide for the care and use of laboratory animals. The population study was approved by the Ethics Committee of Capital Medical University (2013SY35), and all participants have signed the informed consent before inclusion.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2395907
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