What this is
- This research investigates the impact of gut microbiota on cognitive impairment due to () in mice.
- It explores how Akkermansia muciniphila supplementation can prevent cognitive decline by modulating microglial activity.
- The study highlights the connection between gut health, , and brain function, suggesting potential therapeutic strategies.
Essence
- Akkermansia muciniphila supplementation prevents cognitive impairment in sleep-deprived mice by reducing microglial engulfment of synapses and restoring SCFA levels.
Key takeaways
- Akkermansia muciniphila supplementation alleviated cognitive dysfunction in sleep-deprived mice, demonstrating its potential as a preventive strategy.
- Microglial activation and synaptic engulfment were inhibited in the hippocampus of sleep-deprived mice receiving the supplementation, indicating a protective mechanism.
- Restoration of serum levels of acetate and butanoic acid was observed following supplementation, suggesting a link between gut microbiota and cognitive function.
Caveats
- The study is limited to mouse models, which may not fully translate to human conditions.
- Further research is needed to explore the specific mechanisms by which Akkermansia muciniphila affects cognitive function.
Definitions
- sleep deprivation (SD): A condition resulting from insufficient sleep, leading to cognitive deterioration and other health issues.
- short-chain fatty acids (SCFAs): Fatty acids with fewer than six carbon atoms, produced by gut bacteria, that influence gut and brain health.
AI simplified
Introduction
Sleep disorders are common among adults in modern society. Lacking restorative sleep introduces detrimental effects on the brain and bodily function.1 The animal model for sleep insufficiency is defined as sleep deprivation (SD). Accumulated evidence suggests that SD may induce cognitive deterioration, such as loss of attention, memory impairment, cognitive decline,1–3 even an increased risk of dementia.4 However, the underlying mechanism for SD-induced cognitive dysfunction as well as effective, clinically available therapy is still lacking.
Recent studies have shown that sleep insufficiency/deprivation alters the composition of gut microbiota in both humans and rodents.5,6 Growing evidence suggests that the gut microbiota – brain axis plays a crucial role in acute and chronic brain diseases,7,8 which affect host behavior, such as cognitive dysfunction,9,10 depression, and anxiety-related responses.11,12 Better sleep quality might be associated with better cognitive performance in healthy older adults with higher proportions of Verrucomicrobia and Lentisphaerae in the gut microbial.13 Gut dysbiosis contributes to both peripheral and central inflammation and impaired cognition induced by SD.14 However, the mechanisms underlying these phenomena remain underexplored.
SD may result in deregulated immune responses with increased pro-inflammatory signaling.15 Microglia, a major cellular component in the innate immune system of the central nervous system (CNS), actively respond to injury, infection and neuroinflammation.16 Microglia engulf synapses and mediate forgetting in the hippocampus of healthy adult mice.17 Chronic sleep restriction but not acute sleep loss activates microglia phagocytosis of synaptic elements without obvious signs of inflammation in the cerebrospinal fluid.18 Furthermore, inhibition of the microglia activation improves the hippocampus-dependent spatial memory in rats during 48-h SD.19 However, the triggers for microglia activation, which could be therapeutic targets in sleep-deprived mice, are still unknown. Recent studies have pointed out that gut microbiota mediates microglial activation under multiple pathological conditions, including Alzheimer’s Disease.20 Despite the connection between gut microbiota change and microglia activation in SD mice has been established.14 However, the underlying mechanistic underpinnings mediating these changes remain elusive.
This study aimed to investigate the role of gut microbiota alterations and cognitive dysfunction in sleep-deprived mice, as well as the role of microglia in this process. We assessed the effect of antibiotic treatment on SD-induced cognitive impairment and the cognitive function in normal mice that received fecal microbiota transplantation (FMT) from SD mice. We then performed 16S rDNA gene sequencing and qPCR to assess the composition of the gut microbiota and to confirm the quantity change in Akkermansia muciniphila (A. muciniphila, AKK) in SD mice. Next, we examined the effects of A. muciniphila on cognitive impairment, microglial phagocytic activity, and hippocampal synaptic reduction. Furthermore, gut microbiome-associated metabolites were measured after A. muciniphila supplementation in mice to identify the targets responsible for microglial activation. Finally, we investigated whether short-chain fatty acids (SCFAs) inhibit synaptic engulfment of microglia and prevent synapse loss in vivo and in vitro.
Materials and methods
Animals and treatments
The animal experiments were approved by the Institutional Animal Care and Use Committee of Xi’an Jiaotong University (Xi’an, China). Male C57BL/6J mice were purchased from Charles River (Beijing, China) and maintained in a specific pathogen-free environment with a strict 24-h reverse light-dark cycle (lights turned on from 8:00 to 20:00). The mice were fed sterile water and commercial standard feed (GB14924.3, China) ad libitum.
Antibiotic treatment started when mice were five weeks old. An antibiotic cocktail21 was administered by oral gavage for 14 consecutive days (50 mg/kg vancomycin, 100 mg/kg neomycin, 100 mg/kg metronidazole, and 1 mg/kg amphotericin B). Ampicillin was provided in drinking water (1 g/L).
The SD mouse model was developed using a propeller-based automatic SD system (XR-XS108, Shanghai Xin Ruan) (Supplementary Figure S1a). The system contained a cage in which mice could feed freely while a clockwise-counterclockwise rotating bar was set rotating at the bottom of the cage, 20:00 until 16:00 the next day at 5 rpm, preventing the mice from sleeping. There are 3 turns clockwise and counterclockwise and 18-s intervals every 6 rotations for mice to achieve food and water when needed. The subjects were sleep-deprived for 20 h every day for seven consecutive days (Supplementary Figure S1b). Control (Con) animals were placed in the same cage without a rotating bar to disturb their sleep.
Fecal microbiota transplantation
Before transplantation, the mice were treated with the antibiotic cocktail for 14 consecutive days, as previously described. Thereafter, mice were given 200 µL of the microbiota suspension from either the sleep-deprived or control mice. Three times a week (FMT was administered on days 1st, 3rd and 5th) for 3 weeks, starting 48 h after the last gavage of the antibiotics. The microbiota suspension preparation was based on the method described in previous studies.22,23 Mice in the empty transplant group received the same antibiotic treatment and were transplanted only with reduced PBS.
Behavioral tests
Behavioral tests were conducted 7 days after SD or 24 h after the last session of FMT. Performance was tracked and evaluated using a video tracking system (SMART 3.0; Panlab Harvard apparatus). The open-field test (OFT) and elevated plus maze (EPM) tests were used to assess the anxiolytic properties of mice (Supplementary Figure S2a-b), and the novel object recognition (NOR) and Y-maze tests were performed to examine recognition memory and spontaneous rodent behaviors (Supplementary Figure S2c-d). Detailed information is provided in the supplementary material.
Quantification of bacterial DNA in mice feces
Fecal samples were collected at four key times to identify the effectiveness of antibiotic treatment, including the first day without any treatment, 14 days after antibiotic treatment, 7 days after SD, and the day after all behavioral tests were completed. The samples were immediately frozen at −80°C until DNA extraction. After stool samples were weighed, total genomic DNA was extracted from feces using the Stool DNA Kit (Omega, D4015, China) following the manufacturer’s instructions. DNA quantity was determined fluorometrically using a dsDNA Broad Range-Fluorescence Quantification Assay with a T20 Fluorometer (Life Real, China).
16S rDNA sequencing
Fecal samples for the 16S sequencing were collected from mice, either with normal sleep or 7 days after SD. 16S rDNA gene sequencing was performed on the Illumina sequencing platform in a Biotechnology company (Beijing, China). Briefly, PCR amplification was performed spanning the V3-V4 region of the 16S rDNA using primers 341F/806 R (forward 5′-ACTCCTACGGGAGGCAGCAG-3′ and reverse 5′-GGACTACHVGGGTWTCTAAT-3′). Subsequently, sequencing was performed on a MiSeq platform (Illumina) using a 2 × 300 bp-end protocol.
Quantification of A. muciniphila in cecal content
Cecal contents were collected on the day after all behavioral tests were completed. Total genomic DNA was extracted from cecal contents using the Stool DNA Kit (Omega, D4015, China) as described above. After diluting the DNA templates to 1 ng/µL using RNase-Free Distilled Water, each sample was prepared in triplicate using 2 µL (2 ng of the total DNA template). After diluting the DNA templates to 1 ng/µL with RNase-Free Distilled Water, 2 µL (2 ng of total DNA template) of each sample was loaded in triplicates. The forward primer 5′-CAGCACGTGAAGGTGGGGAC-3′ and reverse primer 5′ -CCTTGCGGTTGGCTTCAGAT-3′24 were premixed with SYBR Green Nucleic Acid Gel Stains (SYBR Green) Premix (AG11701, Accurate Biology, China) according to the manufacturer’s protocol. Real-time PCR (qPCR) was performed using a Bio-Rad CFX96 thermal cycler (Bio-Rad, Hercules, California, USA). Reaction mixtures (20 µL total volume) were held at 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. Melting curve analysis was conducted to confirm the specificity of amplification. Plasmid DNA (gene sequence shown in the supplementary materials), including the corresponding conservative sequence of A. muciniphila was prepared in a dilution series to create a standard curve. The absolute copy number of the 16S rDNA gene of each sample was then calculated.
culture and oral supplementation A. muciniphila
A. muciniphila culture and oral supplementation were performed as previously described22 with a few modifications. A. muciniphila (ATCC BAA-835) was grown in fresh pre-reduced Brain Heart Infusion (BHI) broth (Sigma-Aldrich, BD 237,500, USA) under anaerobic conditions (75% N2, 20% CO2, and 5% H2) at 37°C and incubated for 3–4 days. Afterward, cultures were centrifuged and condensed in anaerobic PBS containing 20% (vol/vol) glycerol to a concentration of ~ 1 × 1010 c.f.u/mL under strictly anaerobic conditions and stored at −80°C until use. The bacteria were incubated in a pre-reduced Tryptic Soy Agar medium (Sigma-Aldrich, BD 236,950, USA) for 4–5 days to determine the A. muciniphila counts (c.f.u./mL). A. muciniphila glycerol stocks were diluted with anaerobic PBS to a final concentration of 2 × 108 viable c.f.u. per 0.2 mL. Mice were treated by oral gavage with 200 μL of either A. muciniphila suspension or anaerobic PBS three days a week (on days 1st, 3rd and 5th) for four weeks, starting 48 h after the last gavage of antibiotics.
SCFAs treatment
SCFA mix (67.5 mM acetate, 40 mM Butyrate) (Sigma-Aldrich, China) was added to drinking water as described previously.25 Mice were treated with drinking water with/without SCFAs for 4 weeks before SD until the behavior tests were completed.
Microglia culture and SCFAs treatment
Primary microglial cultures were prepared from the cortical tissue of mouse pups on postnatal day 3 as previously described.26 Microglia were plated at a density of 2 × 104 cells/cm2 and maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum, penicillin (100 IU/mL), and streptomycin (100 μg/mL). SCFAs treatment was performed as previously described with a few modifications.27 SCFAs mixtures containing sodium acetate (Sigma-Aldrich, S2889, China) (236 μmol/L) and sodium butyrate (117 μmol/L) (Sigma-Aldrich, 303410, China) or vehicle (V) solution (H2O) were added to the cultured microglia for 15 min. The cells were then stimulated with LPS (100 ng/mL) for 24 h before all cultures were fixed for immunostaining.
SCFAs treatment of neuron and neuron/microglia co-cultures
For mouse cortical neuron cultures, cortices from embryonic days (e) 14–17 embryos were dissected in a cold dissection buffer (1×HBSS).28 The meninges, blood vessels and choroid plexus were carefully separated; the tissue was finely minced into approximately 1-mm3 pieces with iris scissors for digestion. The suspension cells were obtained according to the manufacturer’s protocol.28 The cells were plated on poly l-lysine/laminin-coated coverslips at a density of 2.4 × 104 cells/cm2 and cultured for 7 days in vitro. At DIV 7, microglia were plated onto primary neurons in a 1:3 microglia-to-neuron ratio. As described above, the SCFA mixture (sodium acetate 236 μmol/L and sodium butyrate 117 μmol/L) was added to the co-culture system or neuron cultures for 15 min, and LPS (100 ng/mL) was added for 24 h before all cultures were fixed using 4% paraformaldehyde (PFA) for immunostaining.
Western blot assay
Western blotting was performed as previously described.29 Protein samples were extracted from hippocampal tissue in RIPA buffer supplemented with a protease and phosphatase inhibitor cocktail. After assessing the protein concentration with a BCA Protein Assay kit (Thermo Fisher Scientific, 23227, USA), they were degenerated by heating at 95°C for 10 min with loading buffer before cooling to room temperature and loaded onto 10% SDS-PAGE separation gels. The protein bands were then transferred to polyvinylidene fluoride (PVDF) membranes. Appropriate primary antibodies and HRP-conjugated secondary antibodies were used to detect the proteins of interest (details are shown in Supplementary Table S1). Immunoreactive bands were visualized using a chemiluminescent substrate. β-actin was used as the internal standard. Antibody information can be found in Supplementary Table S1. Immunoblots were quantified using ImageJ analysis software (Version 2.0, NIH, USA).
Immunofluorescent staining
Mice were deeply anesthetized using isoflurane and transcardially perfused with 20–30 mL of ice-cold normal saline, followed by 15–20 mL of 4% PFA. The brains were removed and post-fixed in 4% PFA at 4°C for 6–8 h. After dehydration in 30% sucrose in 0.1 M Phosphate buffer at 4°C for 3–4 days, 30-µm thick coronal brain sections were obtained using a cryostat (Thermo, CryoStar N×50OPD). Free-floating sections were blocked with 5% donkey serum/0.3% Triton X-100 in PBS for 2 h at room temperature (RT), followed by incubation with primary antibodies (antibody information is shown in Supplementary Table S1) diluted at 4°C overnight. The sections were rinsed three times in PBS the morning before incubation with the corresponding secondary antibodies for 2 h at RT.
Imaging and analyses
Immunolabeled proteins were imaged using a Nikon A1 confocal microscope (Nikon, Japan). The images for dentate gyrus synaptic marker analysis were captured using a 63× oil immersion objective lens and 3× zooms with an interval of 5 μm along the Z-axis. Iba1 and CD68 were captured using a 40× objective lens. Iba1 and synaptophysin (SYP) were captured using a 63× objective. VGLUT1 and PSD95 of primary neurons were captured using 60× objectives and 4× zooms using a Leica TCS SP8 STED 3X confocal microscope (Leica, Germany). Images were acquired from three sections representing the rostral, middle, and caudal regions of the hippocampal dentate gyrus. For imaging, we used three brain slices per mouse from three mice per experimental group, and three images were taken for each slice at random locations in the dentate gyrus, as defined by DAPI staining. For in vitro experiment, images from five fields per coverslip distributed in each quadrant or center with three replicates were taken for analysis. The acquired Z-stacks were then individually projected onto a two- dimensional plane. For the synaptic marker count per unit, the area was analyzed using the ImageJ program with a threshold of normalized fluorescence intensity. For synapses inside microglia, synaptic puncta labeled with SYP and Iba1 were imaged, as described above. Subtracted background and smooth images were obtained using ImageJ software. Microglia engulfment was quantitatively measured using the Imaris software 9.0.1. All experimental analyses were performed by a researcher who was blinded to the treatment groups.
Metabolome analyses
Untargeted LC-MS/MS metabolomics
Approximately 500 µL blood samples were collected from the four groups of mice (Con/V, AKK/V, SD/V, and AKK/SD) after anesthetizing the mice with isoflurane. For serum extraction, blood samples were centrifuged at 3000×g for 10 min at 4°C and stored at −80°C. Metabolites were extracted from mouse serum using the MLPEx method.30 Briefly, 40 μL of serum was extracted using 160 μL of methanol/acetonitrile (1:1, v/v). After vortexing for 2 min, the mixture was incubated at −20°C for 1 h to precipitate proteins and centrifuged at 13,000 rpm for 15 min. The supernatant was collected and dried using a nitrogen concentrator. The dried residue was reconstituted in 100 µL of 50% acetonitrile before analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS), as reported previously.31
Untargeted metabolites data analysis was performed by dedicated Compound Discoverer 3.2 software. The Principal Component Analysis view was used to display the results of the principal component analysis. The principal component analysis reduces the dimensionality of the data set to a set of principal components, PC1 and PC2, where PC1 is the principal component with the most variance. Welch t-test was used for the comparison of Con/v vs. SD/V, SD/AKK vs. SD/V, and SD/AKK vs. Con/V. For the significant difference analysis, the P-value was set to 0.05 and the Log2 Fold Change parameter was set to 1. The volcano plot is a plot of the P-value, the result of a significance test, on the y-axis versus the Log2 fold change between two sample groups on the x-axis. The y-axis scale is the – Log10 of the P-value.
Targeted metabolomics
Blood sample preparation was performed as described in the previous Section. Serum samples (50 µL in 50% methanol) were dried in a speed vac to remove methanol before drying was performed in a lyophilizer. All the samples were re-dissolved in 100 µL of 0.1% formic acid. Then, these were analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) as reported previously.32 The Peak area of each isotope-labeled internal standard was used to normalize that of the 7 SCFAs (Acetic acid, propanoic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid and hexanoic acid) with the same number of carbons.
Bioinformatic analysis
The microbiomes were analyzed at the Illumina sequencing platform by the Beijing Genomics Institute (Beijing, China). High-quality reads for bioinformatics analysis were selected, and all of the valid reads from all samples were clustered into operational taxonomic units (OTUs) based on 97% sequence similarity using the Usearch (v7.0.1090_i86linux32). Then, OTUs were annotated by comparing representative sequences to the Greengene (V201305) and Ribosomal Database Project (RDP) (Release16, 20160930) using the RDP classifier (1.9.1). The difference comparisons among different groups were displayed by the principal coordinate analysis (PCoA) based on the UniFrac distance matrix. Alpha-diversity (Shannon index and Simpson index) indices were calculated by Wilcoxon Rank-Sum Test. Beta-diversity analysis was performed using weighted UniFrac distances. LEfSe (Linear discriminant analysis effect size) analysis was performed under the following conditions: the P-value for the factorial Wilcoxon tests among classes was 0.05 and the threshold on the logarithmic LDA score for discriminative features was 2.0 (the code from https://github.com/SegataLab/lefse↗). The analysis was conducted using R (V3.5.1).
Statistical analysis
Results are presented as the mean ± SEM, and differences were considered significant when P < .05. Statistical analyses were performed using Prism 7.0 (GraphPad, USA). Student’s t-test was used to compare two groups with a normal distribution, while One-way ANOVA with Tukey’s multiple comparisons was used for comparisons of three or more groups. Correlations were analyzed using linear regression.
Results
Altered gut microbiome exacerbates cognitive impairment in SD mice
The effects of antibiotic pretreatment on the cognitive performance in SD mice.
Inoculation with microbiota from SD mice induces cognition impairment in healthy mice
The effects of fecal microbiota transplant on cognitive performance in mice.
SD induces dysbiosis of gut microbiota and reduction inin mice A. muciniphila
SD alters gut microbiome composition in mice.
colonization alleviates SD-induced cognitive impairment in mice A. muciniphila
supplement restores SD-induced cognitive deficits in mice. A. muciniphila
colonization ameliorates SD-induced synapse loss in mice A. muciniphila
supplement attenuates synaptic loss in SD mice. A. muciniphila
reduces extensive microglia activation and synapse engulfment in the hippocampus of SD mice A. muciniphila
supplement inhibited extensive microglial activation and synaptic engulfment in the hippocampus of SD mice. A. muciniphila
colonization reversed serum SCFAs reduction in SD mice A. muciniphila
Furthermore, to better understand the connection between SCFAs levels and cognitive function, a correlation analysis was performed between the levels of several SCFAs and percentage of exploration time. The results demonstrated that the serum levels of acetate and butanoic acid were positively correlated with the percentage of exploration time (Figure 7j,k). Additionally, the results of SCFA-targeted metabolomics showed that the serum concentrations of acetate and butanoic acid were decreased in SD mice compared with those in control mice, which were significantly restored by oral administration of AKK (Figure 7l,m). We then tested the effects of SCFAs (acetate and butanoic acid) pre-supplements in SD mice and conducted behavioral analysis (Supplementary Figure S9a). The NOR test showed that the reduction in exploration time for the novel object in SD mice was prevented by SCFAs pretreatment (Supplementary Figure S9b, SD/SCFAs 69.16 ± 3.658%, vs. SD/V 55.81 ± 3.70%, P = .0178). The Y-maze results indicated that the spontaneous alternation index drop observed in SD mice was also blocked by SCFAs pretreatment. (Supplementary Figure S9d, SD/SCFAs 56.87 ± 2.97%, vs. SD/V 46.19 ± 2.03%, P = .0165), while the total distance traveled in the NOR and Y-maze tests was similar among groups (Supplementary Figure S9c and e). The above results indicate that SCFAs pretreatment alleviated SD-induced cognitive impairment. These results suggest that AKK treatment restored acetate and butanoic acid levels, which may be the basis for its protective effect against SD-induced cognitive dysfunction.
The effects ofcolonization on metabolic dysfunction-associated SD in mice. A. muciniphila
SCFAs prevent synapse loss via reducing synapse engulfment of microglia
VGLUT1 and PSD-95 protein levels were significantly increased in SD mice pretreated with SCFAs compared with the SD/V group (Supplementary Figure S10g-i). Therefore, to investigate whether SCFAs prevent synapse loss via reducing synapse engulfment of microglia. We used a co-culture system in which microglia were added to primary neurons cultured for 7 days in vitro. SCFAs were pre-administered to the co-culture system 15 min before LPS treatment. After 24 h, cells were fixed for immunostaining (Figure 8b). Quantitative analyses of neuron-microglia co-cultures revealed decreased synaptophysin density in neuron/microglia treated with LPS compared to that in control neurons (Figure 8f,g). Moreover, a significant increase in synaptophysin-positive puncta inside microglia was found in co-cultures treated with LPS compared to the control cells. However, pretreatment with SCFAs blocked this change in co-culture (Figures 8f–h). In primary cultured neurons, LPS treatment did not alter VGLUT1 or PSD95 expression (Supplementary Figure S11), indicating that other necessary factors outside the neurons are responsible for synaptic loss. In contrast, VGLUT1 and PSD95 were significantly reduced in neuron/microglia co-cultured with LPS compared to control neurons. SCFAs administration significantly rescued VGLUT1 and PSD95 expression in the neuron/microglia co-culture after LPS stimulation (Figures 8i and k). Together, these results suggest that SCFAs protect against synapse loss by inhibiting synaptic engulfment of microglia.
Short-chain fatty acids (SCFAs) alleviated synapse loss by inhibiting synaptic engulfment of microglia. in vitro
Discussion
In this study, we demonstrated that alterations in gut microbiota were associated with SD-induced cognitive impairment. Transplanting the “SD-derived microbiota” into specific pathogen-free mice impaired the cognitive function of the recipient mice. We analyzed the gut microbiome and found a significant reduction in A. muciniphila in SD mice. Notably, the administration of A. muciniphila alleviated cognitive dysfunction and prevented synaptic reduction in the hippocampi of SD mice. The extensive microglial activation and synapse engulfment observed in the hippocampus of SD mice can be inhibited by A. muciniphila pre-treatment. Moreover, A. muciniphila pretreatment elevated the serum levels of acetate and butanoic acid in SD mice. The levels of acetate and butanoic acid in the serum were positively correlated with the performance of mice in the NOR test. SCFAs pretreatment improved SD-induced cognitive impairment in mice and reduced microglial activation and synaptic engulfment in vivo. Furthermore, SCFAs may inhibit synaptic loss by reducing synapse engulfment of microglia in vitro. Therefore, our results suggest a potential modulatory role of the gut microbiome in SD-induced cognitive impairment and reveal that A. muciniphila supplementation or SCFAs delivery may be a novel therapeutic strategy for preventing SD-induced cognitive dysfunction.
It was reported that brain dysfunction was accompanied by alterations in the gut microbiota in rats that experienced a 7-day paradoxical SD.33 There was also a possible correlation among sleep quality, the composition of the gut microbiome, and cognitive flexibility in healthy older adults.13 However, the causal relationship between gut dysbiosis and brain dysfunction in sleep-deprived subjects is still missing. Our results showed that antibiotic perturbation of gut microbiota worsened spatial and object memory in SD mice with no significant change in anxiety levels, which indicated that a few protective microbes remained in the gut of SD mice, which could be erased by antibiotic pretreatment. In addition, we confirmed the causal relationship between gut microbiota alteration and cognitive impairment by transferring “SD-derived microbiota” to recipient mice, which induced a decline in cognitive function. This was following a previous study that indicated that germ-free (GF) mice that received FMT from human participants after 48-h SD exhibited impaired cognitive behavior.14 The same study also revealed that sleep deprivation in GF mice exhibited less cognitive disturbance than control mice, indicating a possibility that the “bad microbiota,” which may cause or worsen the cognitive impairment, was increased after SD in non-GF mice. These results highlight the importance of deciphering microbiota alterations in the gut following SD.
Previous studies have shown that the abundance of A. muciniphila was distinctly reduced after SD in rats who experienced 7 days chronic paradoxical SD.33 Mice subjected to 3 days SD also exhibited downregulation in commensal bacteria, including Akkermansia, Bacteroides and Faecalibacterium, and upregulation in the pathogen Aeromonas.34 Moreover, it was reported that A. muciniphila improves cognitive function in high-fat diet-induced obesity mice36 and a rat model of Nonalcoholic steatohepatitis (NASH).37 In the current study, A. muciniphila supplementation inhibited the development of cognitive impairment after SD, suggesting that A. muciniphila may be a probiotic that may prevent SD-induced cognitive dysfunction.
Emerging studies suggest that the interaction of microglia with synapses contributes to synaptic remodeling during neural development, sleep and memory formation.38–40 SD could promote extensive microglia activation.18 Inhibiting microglial activation could reduce spatial memory decline in rats that underwent 48-h SD.19 It was also shown that SD induced extensive microglia activation and promoted phagocytic activity.18 We speculated that the status of microglia may be involved in preserved cognitive function provided by A. muciniphila in SD mice. Indeed, our data are consistent with these results, showing increased microglial activation in the hippocampi of SD mice. Further exploration showed that A. muciniphila pre-supplementation significantly inhibited microglial activation and reduced CD68/Iba1, C1q/Iba-1, and SYP/Iba-1 co-immunofluorescence staining. These results indicated that microglial phagocytosis of synapses was blocked by A. muciniphila pretreatment in SD mice.
However, exactly how does A. muciniphila pre-supplementation inhibit microglial activation and synaptic loss? We analyzed the gut metabolite changes induced by SD, and further compared the metabolites of mice that suffered from SD with or without AKK supplementation. The analysis showed that acetate and butanoic acid levels were significantly decreased in the SD/V group compared to those in the controls. However, these changes were inhibited by AKK supplementation. Consistent with our results, one study displayed that SCFAs levels were decreased in fecal samples from humans who experienced 24 h or 48 h SD, and in the fecal samples and serum of mice that received SD human-derived fecal microbiota transplantation.14 The study on the growth, metabolism, and morphology of A. muciniphila in vitro has shown that the main metabolites produced were SCFAs (acetic acid and butyric acid) under dynamic culture using porcine mucin or human mucin.41 In our study, the administration of AKK recovered the dropped SCFAs levels in the serum of SD mice. Meanwhile, acetate and butanoic acid levels in gut metabolites were significantly positively correlated with the percentage of exploration time in the NOR test. SCFAs pretreatment alleviated cognitive impairment in sleep-deprived mice. These results suggest that the effect of AKK pre-treatment on cognitive impairment is associated with SCFAs production in mice after SD.
Acetate and butanoic acid belong to SCFAs that have been shown to readily cross the blood-brain barrier42,43 and affect brain function in development, health, and disease.44 SCFAs may modulate the growth, survival and differentiation of neurons and synapses in the CNS by altering the levels of neurotransmitters and neurotrophic factors.45–47 In addition, SCFAs may affect the microglia maturation in neurodevelopmental and neurodegenerative disorders.25,48 Multiple literatures pointed out that sodium butyrate decreased microglia activation and inhibited pro-inflammatory cytokines secretion in pathologic conditions.49–51 In consistence with the previous research,18 our results showed that chronic SD activates microglia and promotes their phagocytic activity toward synaptic elements. AKK pretreatment inhibited the decrease in serum SCFAs levels in SD mice. Microglial activation and synapse loss were also alleviated by SCFAs pretreatment in the hippocampi of SD mice. To further corroborate the causal relationship between SCFAs levels and microglial phagocytic activation, we tested the effects of acetate and butanoic acid on LPS-induced microglial phagocytosis of neuronal synapses in a microglia-neuron co-culture system. Microglial phagocytosis was activated by exposure to LPS in vitro as previous reports.52 SCFAs treatment inhibited microglia engulfment and significantly reduced synaptic loss. Thus, SCFAs produced by AKK pretreatment may be the key components that translocate to the CNS and inhibit microglial activation, thus preventing synaptic loss and preserving cognitive function after SD.
A. muciniphila is a second-generation probiotic that has been tested in humans with insulin resistance. It is worth exploring whether AKK supplementation provides cognitive benefits to patients with sleep disorders. Other gut metabolite changes in SD mice were rescued by AKK supplementation, as shown in Figure 7d–g. Exploration of the specific effects of these metabolites will provide a better understanding and strategy for the composition of potential nutritional supplements for patients with sleep disorders.
Conclusion
In conclusion, we identified that dysbiosis of the gut microbiota contributes to hippocampal synapse loss in SD mice with cognitive impairment. More importantly, A. muciniphila supplementation alleviates cognitive dysfunction and prevents synaptic phagocytosis by microglia in the hippocampus. This was accompanied by the restoration of serum levels of the microbiome-associated metabolites, acetate, and butanoic acid. On the other hand, SCFAs pretreatment was also found to improve cognitive impairment and reduce microglial activation and synapse engulfment in SD mice. Furthermore, our findings suggest that SCFAs prevent synapse loss via reducing synapse engulfment of microglia in microglia-neuron co-culture. We believe that further research on the potential benefits of A. muciniphila in preventing cognitive impairment caused by human sleep disorders is necessary. Finally, our findings highlight the essential role of SCFAs in maintaining microglial homeostasis against neural inflammatory stimulation. Disturbance of SCFAs level in the brain may be a key pathological process underlying multiple neurodegenerative disorders.