Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

doi:10.1038/s41422-019-0216-x

. 2019 Oct;29(10):787-803.

doi: 10.1038/s41422-019-0216-x. Epub 2019 Sep 6.

Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

Xinyi Wang¹, Guangqiang Sun¹, Teng Feng¹, Jing Zhang¹, Xun Huang², Tao Wang³, Zuoquan Xie², Xingkun Chu¹, Jun Yang¹, Huan Wang², Shuaishuai Chang¹, Yanxue Gong¹, Lingfei Ruan¹, Guanqun Zhang¹, Siyuan Yan¹, Wen Lian¹, Chen Du¹, Dabing Yang¹, Qingli Zhang⁴, Feifei Lin⁴, Jia Liu⁴, Haiyan Zhang², Changrong Ge¹, Shifu Xiao³, Jian Ding², Meiyu Geng⁵

Affiliations

¹ Shanghai Green Valley Pharmaceutical Co., Ltd, Shanghai, 201203, China.
² State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
³ Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, 200025, China.
⁴ Institutional Technology Service Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
⁵ State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. mygeng@simm.ac.cn.

PMID: 31488882
PMCID: PMC6796854
DOI: 10.1038/s41422-019-0216-x

Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

Xinyi Wang et al. Cell Res. 2019 Oct.

. 2019 Oct;29(10):787-803.

doi: 10.1038/s41422-019-0216-x. Epub 2019 Sep 6.

Authors

Affiliations

¹ Shanghai Green Valley Pharmaceutical Co., Ltd, Shanghai, 201203, China.
² State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
³ Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, 200025, China.
⁴ Institutional Technology Service Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
⁵ State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. mygeng@simm.ac.cn.

PMID: 31488882
PMCID: PMC6796854
DOI: 10.1038/s41422-019-0216-x

Abstract

Recently, increasing evidence has suggested the association between gut dysbiosis and Alzheimer's disease (AD) progression, yet the role of gut microbiota in AD pathogenesis remains obscure. Herein, we provide a potential mechanistic link between gut microbiota dysbiosis and neuroinflammation in AD progression. Using AD mouse models, we discovered that, during AD progression, the alteration of gut microbiota composition leads to the peripheral accumulation of phenylalanine and isoleucine, which stimulates the differentiation and proliferation of pro-inflammatory T helper 1 (Th1) cells. The brain-infiltrated peripheral Th1 immune cells are associated with the M1 microglia activation, contributing to AD-associated neuroinflammation. Importantly, the elevation of phenylalanine and isoleucine concentrations and the increase of Th1 cell frequency in the blood were also observed in two small independent cohorts of patients with mild cognitive impairment (MCI) due to AD. Furthermore, GV-971, a sodium oligomannate that has demonstrated solid and consistent cognition improvement in a phase 3 clinical trial in China, suppresses gut dysbiosis and the associated phenylalanine/isoleucine accumulation, harnesses neuroinflammation and reverses the cognition impairment. Together, our findings highlight the role of gut dysbiosis-promoted neuroinflammation in AD progression and suggest a novel strategy for AD therapy by remodelling the gut microbiota.

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Conflict of interest statement

XYW, GQS, TF, JZ, XKC, JY, SSC, YXG, LFR, GQZ, SYY, WL, CD, DBY and CRG are full-time employees of Shanghai Green Valley Pharmaceutical Co., Ltd. The other authors declare no competing interests.

Figures

**Fig. 1**
Gut dysbiosis and immune cell changes during disease progression in 5XFAD transgenic (Tg) mice. a Changes in the relative RNA expression levels of synaptophysin in the hippocampus of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7 and 9 months and in wild-type (WT) mice at 2 months (n = 5–12). The data are presented as mean ± standard error of the mean (mean ± SEM) relative to the expression level of actin. *P < 0.05, **P < 0.01 by one-way ANOVA (F (5, 43) = 2.952). b Changes in the time out of 10⁴ s taken to achieve 80% success (see “Materials and methods”) in a test to evaluate the discrimination learning abilities of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7, and 9 months and wild-type (WT) mice at 2 months (n = 4–8). Data are presented as mean ± standard error of the mean (mean ± SEM). *P < 0.05 by Student’s t-test. s seconds. c Enterotype analysis at the genus level of the gut microbiomes of 5XFAD transgenic (Tg) mice and wild-type (WT) mice at 2-, 3-, 5-, 7- and 9-month old (n = 4–10). The separation of gut microbial taxa into a norank genus under the family of *Muribaculaceae* of Tg mice and the genus *Lactobacillus* enterotypes of WT mice is achieved by calculating Bray-Curtis distance based on the relative abundance at the genus level and clustered using PAM (Partitioning Around Medoids). The data are most naturally separated into two clusters, as determined by the Calinski-Harabasz (CH) index and represented using principal coordinate analysis (PCoA). The shapes and colours of the points indicate samples from each individual from various months. The coloured ellipses indicate the 0.95 confidence interval (CI) ranges within each enterotype group. M months. d Principal component analysis (PCA) of the gut microbiome composition of WT and 5XFAD transgenic (Tg) mice on the operational taxonomic unit (OTU) level at different time points (n = 4–10). The shapes and colours of the points indicate samples from each individual from various months. The coloured ellipses indicate 0.95 confidence interval (CI) ranges within each tested group. M months. e Abundance changes of operational taxonomic units (OTUs) in the overall population in the gut microbiome of 5XFAD transgenic (Tg) mice at various months, coloured at the phylum level on a stream graph (n = 4–10). The two most abundant phyla, *Bacteroidetes* and *Firmicutes*, are labelled on the graph. Colours indicate different phyla of the gut microbiota. f Changes in the positive densities of IBA1 immune-fluorescent staining, reflecting activation of microglial cells in the hippocampus of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7, and 9 months relative to the values of 2-month-old wild-type (WT) mice (n = 2–7). The data are presented as mean ± standard error of the mean (mean ± SEM); lines are fitted with a cubic spline. g Changes in activated M1 and M2 type microglia detected in the whole-brain homogenates of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7 and 9 months (n = 4–8). M1-type microglia (CD45^lowCD11b⁺CX3CR1⁺Siglec-H⁺CD86⁺) and M2-type (CD45^lowCD11b⁺CX3CR1⁺Siglec-H⁺ CD206⁺) microglia were detected by flow cytometry, and their cell counts are presented relative to the frequency of CD45^low CD11b⁺ cells. Red points and lines: M1 microglia. Green points and lines: M2 microglia. The data are presented as mean ± standard error of the mean (mean ± SEM); lines are fitted with a cubic spline algorithm. h Changes in infiltrating cells (CD45^high) detected in the whole-brain homogenates of 5XFAD transgenic (Tg) mice (red points and lines) and WT mice (black points and lines) at different time points as detected by flow cytometry (n = 4–8). Cell counts are presented relative to the frequency of CD45⁺ cells and formatted as mean ± standard error of the mean (mean ± SEM). Lines are fitted with a cubic spline algorithm. i Changes in CD45^high cells in 5XFAD transgenic (Tg) mice at different time points (n = 4–8). On the barplot, cell counts are presented relative to the frequency of CD45^high cells. Colours indicate different subtypes of CD45^high cells: Neu, neutrophils; DC, dendritic cells; NK, natural killer cells; Mo/Mϕ ː monocytes and macrophages; B, B cells; Others, unclassified cells. j Changes in infiltrating CD4 T cells (CD45^highCD4⁺) detected in the whole-brain homogenates of 5XFAD transgenic (Tg) mice (red points and lines) at 2, 3, 5, 7 and 9 months as detected by flow cytometry (n = 4–8). Cell counts are presented relative to the frequency of CD45^high cells and formatted as mean ± standard error of the mean (mean ± SEM). Lines are fitted with a cubic spline. k Changes in infiltrating peripheral Th1 and Th2 cells detected in the whole-brain homogenates of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7 and 9 months (n = 4–8). Th1 cells (CD45^highCD4⁺CXCR3⁺) and Th2 cells (CD45^highCD4⁺CCR4⁺) were detected by flow cytometry, and presented relative to the frequency of CD45^highCD4⁺ T cells. Red points and lines: Th1 cells. Green points and lines: Th2 cells. The data are presented as mean ± standard error of the mean (mean ± SEM). Lines are fitted with a cubic spline. l Correlation between the trend changes of the frequency of brain lymphocytes and the abundance of gut microbiota represented at genus level during the Th2/M2-related stage and Th1/M1-related stage in the early and late phase (2–3 months and 7–9 months), respectively (left panels, n = 4–8). Bacteria that are significantly correlated with Th2/M2 or Th1/M1 in 5XFAD mice are listed in the right-hand panels. Squares in red (positive correlation) or blue (negative correlation) with a yellow asterisk (*) indicate significant correlations with P-values < 0.05 measured by the Pearson parametric correlation test (see “Materials and methods”). f family, M months, T regulatory cells Treg, Mo monocytes, B B cells, DC dendritic cells, NK natural killer cells

**Fig. 2**
The gut microbiota is required for immune cell infiltration and microglial activation. a The effects of five-month oral gavage of antibiotics on the relative abundance of gut microbes in 7-month-old 5XFAD transgenic (Tg) mice (n = 5–7). ABX, a cocktail of mixed antibiotics composed of ampicillin (0.1 mg/mL), streptomycin (0.5 mg/mL) and colistin (0.1 mg/mL). Different genera of gut microbes are coloured differently, and their changes in relative abundance are presented on the barplot. The abundance of *Bacteroides* in Tg mice is below 0.01 and was thus not shown in both groups. **b-c** The effects of five-month oral gavage of antibiotics on the frequency of Th1 cells (b) and M1-type microglia (c) in the brain homogenate of 7-month-old 5XFAD transgenic (Tg) mice. Cell counts of Th1 cells (CD45^highCD4⁺CXCR3⁺) are presented relative to the frequency of CD45^high cells (b), while those of M1-type microglia (CD45^lowCD11b⁺CX3CR1⁺Siglec-H⁺CD86⁺) are presented relative to the frequency of CD45^low cells (c). Both are detected by flow cytometry, n = 7, the data are presented as mean ± standard error of the mean (mean ± SEM). d The relative abundance of gut microbes at the genus level in WT, co-housed WT and 5XFAD transgenic (Tg) mice (n = 6–7). All three groups of mice were at 7-month old. Different colours represent different genera. Co-housed WT: WT mice that were housed with Tg mice. **e-f** Changes in the frequency of Th1 cells (e) and M1 type microglia (f) in the brain homogenates of 7-month-old WT, co-housed WT and 5XFAD transgenic (Tg) mice (n = 4–7). Th1 cells (CD45^highCD4⁺CXCR3⁺) are presented relative to the frequency of CD45^high cells (e), while the frequency of M1-type microglia (CD45^lowCD11b⁺CX3CR1⁺Siglec-H⁺CD86⁺) are presented relative to the frequency of CD45^lowCD11b⁺ cells (f). Both are detected by flow cytometry. The data are presented as mean ± standard error of the mean (mean ± SEM). *P < 0.05, **P < 0.01 by Student’s t-test. g Levels of cytokine proteins in the brain homogenates of WT, co-housed WT and 5XFAD transgenic (Tg) mice at 7-month old as detected by a cytokine antibody array (n = 5–6). Colours in the heatmap indicate relative cytokine levels; red indicates cytokines that are upregulated, and blue indicates cytokines that are downregulated

**Fig. 3**
The effects of GV-971 on behaviour changes in APP/PS1 mice models. a Structure of GV-971. GV-971 is a mixture of acidic linear oligosaccharides with degrees of polymerization ranging from dimers to decamers with an average molecular weight of approximately 1 kDa. b The escape latency time results of the Morris Water Maze (MWM) test as a measurement of spatial learning and memory in APP/PS1 mice. Nine-month-old APP/PS1 mice were treated with 50 mpk and 100 mpk of GV-971 for 3 months until 13-month old. Then, the MWM test for spatial learning and memory abilities were conducted for 6 additional days. During the test, GV-971 was continuously administrated. The escape latency time starting (seconds) was measured as one of the final readouts of the test (see Materials and methods). Higher escape latency time shows that these mice will spend more time to reach the target, which indicates a more severely impaired spatial learning and memory ability (n = 11–14). The data are presented as mean ± standard error of the mean (mean ± SEM). Black asterisk indicates the comparison between WT and APP/PS1 group. Blue asterisk indicates the comparison between GV-971(100 mpk) treatment and APP/PS1 group. *P < 0.05, ***P < 0.001 by two-way ANOVA. c The number of platform-site crossovers in MWM test as a measurement of spatial learning and memory in APP/PS1 mice. Nine-month-old APP/PS1 mice were treated with 50 mpk and 100 mpk of GV-971 for 3 months until 13-month old. Then, the MWM test for spatial learning and memory abilities were conducted for 6 additional days. During the test, GV-971 was continuously administrated. The number of platform-site crossovers was measured as the other readout of the test (see Materials and methods). Larger numbers of platform-site crossovers indicate less severely impaired spatial learning and memory ability (n = 11–17). *P < 0.05, ***P < 0.001 by one-way ANOVA (F (3, 55) = 6.542). d The accuracy of spatial working memory as tested using the Y maze in APP/PS1 mice. Nine-month-old APP/PS1 mice were treated with 50 mpk and 100 mpk of GV-971 for 3 months until 12-month old. Then the Y maze test was conducted. During the test, GV-971 was continuously administrated. The accuracy of the Y maze was the ratio between the correct alternation and the total alternation (see “Materials and methods”). Higher accuracy indicates less severely inpaired working memory abilities. (n = 17–20). The data are presented as mean ± standard error of the mean (mean ± SEM). **P < 0.01, ***P < 0.001 by one-way ANOVA (F (3, 71) = 12.39)

**Fig. 4**
GV-971 alleviates neuroinflammation by reconditioning the gut microbiota. a Principal coordinate analysis (PCoA) of the gut microbiome composition on the operational taxonomic unit (OTU) level based on the Bray-Curtis distance for 5XFAD (Tg) mice and GV-971-treated Tg mice at 7-month old (n = 7). The shapes and colours of the points indicate samples from each individual. Coloured ellipses indicate 0.95 confidence interval (CI) ranges within each tested group. PC principal component. b Heatmap of significant gut microbiota changes represented at the genus level between 5XFAD (Tg) mice and GV-971-treated Tg mice at 7-month old (n = 7). Colours on the heatmap indicate the relative abundance of gut microbiota; red indicates bacteria that are upregulated, and blue indicates bacteria that are downregulated. Gut microbiota with significant changes were chosen using a Wilcoxon rank-sum two-tailed test with P-value that is less than 0.05 between Tg and GV-971-treated groups. f family, o order. c Changes in correlational links between the gut microbiome at the genus level (designated with numbers near the purple circles) and brain lymphocytes (other coloured circles) before (left) and after (right) oral gavage of GV-971 in 7-month-old 5XFAD (Tg) mice (n = 5–7). Lines represent either significant (P-value < 0.05) positive or negative correlation (Pearson coefficient). The size of each circle of immune cells are positively related to the links connected to this circle. The right side lists the name of each gut microbiome, also see “Materials and methods”. f family, o order, DC dendritic cells, NK natural killer cells, Neu neutrophils, B B cells, Mo/Mϕ monocytes and macrophages. d Effect of GV-971 treatment on the frequency of brain Th1 cells in 5XFAD (Tg) mice at 7 months old (n = 5–7). Th1 cell counts (CD45^highCD4⁺CXCR3⁺) are presented relative to CD45^highCD4⁺ T cell counts detected by flow cytometry. The data are presented as mean ± standard error of the mean (mean ± SEM). *P < 0.05, ***P < 0.001, by Student’s t-test. e Effect of GV-971 treatment on the positive signal density of IBA1 immunofluorescent staining detected in hippocampal slices from 5XFAD (Tg) mice at 7-month old, reflecting activation of microglial cells (n = 4–6). The data are presented as mean ± standard error of the mean (mean ± SEM). **P < 0.01, ***P < 0.001, by one-way ANOVA (F (2, 15) = 21.94). f Effect of GV-971 treatment on levels of cytokine proteins in the brain homogenates of 5XFAD (Tg) mice at 7-month old as detected by a cytokine antibody array (n = 5–6). Colours on the heatmap indicate relative cytokine levels; red indicates cytokines that are upregulated, and blue indicates cytokines that are downregulated. **g, h** Effect of GV-971 on Aβ-positive area (g) and tau-positive area (h) in the hippocampus of 5XFAD (Tg) mice at 7-month old, evaluated in brain slices (n = 4–7). The data are presented as mean ± standard error of the mean (mean ± SEM). For Aβ analysis: *P < 0.05, ***P < 0.001 (F (2, 14) = 22.78). For tau analysis: *P < 0.05, ***P < 0.001 (F (2, 15) = 13.06) by one-way ANOVA. i Effects of GV-971 on the time out of 10⁴ sec (s) taken to achieve 80% success (see “Materials and methods”) in a test to evaluate the discrimination learning abilities of 5XFAD (Tg) mice at 7-month old (n = 10–13). Time means the time to reach the 80% performance level (seconds); the longer it takes, the severer the cognitive impairment is (see “Materials and methods”). *P < 0.05, ***P < 0.001 by One-way ANOVA (F(2,31) = 9.751). The concentration of GV-971 was 100 mpk for all of the above results

**Fig. 5**
GV-971 inhibits neuroinflammation by harnessing amino acid metabolism. a Pathway enrichment analysis of faecal metabolites in 7-month-old 5XFAD (Tg) mice with or without GV-971 treatment (100 mpk) using MBROLE (n = 6–8). A partial ist of the enrichment results is presented with KEGG modules and KEGG enzyme interactions which have been screened using a criterion of FDR-adjusted P-value < 0.05. b Lists of the most important blood amino acids of the random forest model ranked from most to least important between WT (2 m-9 m) and Tg (2 m-9 m) group from a ROC curve analysis. Red indicated high concentration, green indicated low concentration. (n = 30 for WT, n = 26 for Tg). c Changes in histidine, phenylalanine and isoleucine levels in the feces of WT, 5XFAD mice (Tg), and GV-971-treated Tg mice (100 mpk) (n = 6–11) at 7-month old. Colours in the heatmap indicate relative metabolite levels; red indicates metabolites that are upregulated, and blue indicates metabolites that are downregulated. d Changes in histidine, phenylalanine and isoleucine levels in the blood of WT, 5XFAD mice (Tg), and GV-971-treated Tg mice (100 mpk) (n = 6–7) at 7-month old. Colours in the heatmap indicate relative metabolite levels. Red indicates metabolites that are upregulated, and blue indicates metabolites that are downregulated. e The effects of GV-971 on the differentiation of naïve CD4⁺ T cells (Th0 cells) to Th1 cells induced by phenylalanine and isoleucine in vitro. Naïve CD4⁺ T cells were cultured for 5 days with/without GV-971 in the presence of phenylalanine (1 mM) or isoleucine (1 mM). The frequency of Th1 (CD4⁺IFN-γ⁺) cells was tested by flow cytometry (see Materials and methods). GV-971 was used at a final concentration of 100 µg/mL. The data are presented as mean ± standard error of the mean (mean ± SEM); n = 3 replicates per group, one of three replicated results was represented. Left, *P < 0.05, **P < 0.01 by one-way ANOVA (F (2, 6) = 15.64). Right, *P < 0.05, **P < 0.01 by one-way ANOVA (F (2, 6) = 10.35). f The effects of GV-971 on the proliferation of Th1 cells induced by phenylalanine and isoleucine. Th1 cells were stained with CellTrace and cultured for 4 days with/without GV-971 in the presence of phenylalanine (1 mM) and isoleucine (1 mM). The density of CellTrace fluorescence in Th1 (CD4⁺IFN-γ⁺) cells was tested by flow cytometry (see Materials and methods). GV-971 was used at a final concentration of 100 µg/mL. The data are presented as mean ± standard error of the mean (mean ± SEM), n = 3 replicates per group, one of three replicated results was represented. *P < 0.05, ***P < 0.001 by one-way ANOVA (F (4, 9) = 28.34). Phe, phenylalanine; Ile, isoleucine. g Frequency of blood Th1 cell changes in C57 mice after 4-day intraperitoneal (i.p.) injection of phenylalanine and isoleucine (n = 8). ***P < 0.001 by one-way ANOVA (F (2, 21) = 101.8). h Random forest classification of amino acid changes in healthy controls (HC) and mild cognitive impairment (MCI) due to AD patients. The amino acids are ranked by mean decrease in classification accuracy (first cohort, n = 9 for MCI due to AD, n = 18 for HC). Red indicated high concentration, green indicated low concentration. i Frequency of Th1 cells in the blood of healthy controls (HC) and mild cognitive impairment (MCI) due to AD patients (first cohort, n = 8 for MCI due to AD, n = 9 for HC). *P < 0.05 by Student’s t-test. j Levels of phenylalanine and isoleucine in the blood of healthy controls (HC) and mild cognitive impairment (MCI) due to AD patients (second cohort, n = 22 for both groups). *P < 0.05 by Student’s t-test

**Fig. 6**
Schematic diagram of gut-brain axis in AD progression and the intervention strategy. Along with Aβ deposition and tau phosphorylation, the alteration of the gut microbiota during AD progression causes metabolic disorder. The abnormal metabolites production provoke peripheral inflammation, increases the brain infiltration of immune cells which crosstalk with M1 microglial cells in the brain, resulting in pathological neuroinflammation and cognitive impairment (left panel). Oral administration of GV-971 reconditions the gut microbiota, normalizes disordered metabolites, reduces the peripheral immune cell infiltration to the brain, resolves neuroinflammation, and reduces Aβ deposition and tau phosphorylation, leading to ultimate improvement of cognitive functions (right panel)

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The microbiome: A target for Alzheimer disease?
Seo DO, Boros BD, Holtzman DM. Seo DO, et al. Cell Res. 2019 Oct;29(10):779-780. doi: 10.1038/s41422-019-0227-7. Cell Res. 2019. PMID: 31488883 Free PMC article. No abstract available.
Omission of previous publications by an author should be corrected.
Rao Y. Rao Y. Cell Res. 2020 Sep;30(9):819. doi: 10.1038/s41422-020-0344-3. Cell Res. 2020. PMID: 32632254 Free PMC article. No abstract available.
Geng et al. reply.
Wang X, Sun G, Feng T, Zhang J, Huang X, Wang T, Xie Z, Chu X, Yang J, Wang H, Chang S, Gong Y, Ruan L, Zhang G, Yan S, Lian W, Du C, Yang D, Zhang Q, Lin F, Liu J, Zhang H, Ge C, Xiao S, Ding J, Geng M. Wang X, et al. Cell Res. 2020 Sep;30(9):820. doi: 10.1038/s41422-020-0377-7. Cell Res. 2020. PMID: 32661309 Free PMC article. No abstract available.

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References

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1. Kodamullil AT, et al. Trial watch: tracing investment in drug development for Alzheimer disease. Nat. Rev. Drug Discov. 2017;16:819. doi: 10.1038/nrd.2017.169. - DOI - PubMed
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1. Blander JM, Longman RS, Iliev ID, Sonnenberg GF, Artis D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 2017;18:851–860. doi: 10.1038/ni.3780. - DOI - PMC - PubMed

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[1] Cummings J, Lee G, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2018. Alzheimer’s Dement. 2018;4:195–214. - PMC - PubMed

[2] Cummings J, Lee G, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2018. Alzheimer’s Dement. 2018;4:195–214. - PMC - PubMed

[3] Kodamullil AT, et al. Trial watch: tracing investment in drug development for Alzheimer disease. Nat. Rev. Drug Discov. 2017;16:819. doi: 10.1038/nrd.2017.169. - DOI - PubMed

[4] Kodamullil AT, et al. Trial watch: tracing investment in drug development for Alzheimer disease. Nat. Rev. Drug Discov. 2017;16:819. doi: 10.1038/nrd.2017.169. - DOI - PubMed

[5] Honda K, Littman DR. The microbiota in adaptive immune homeostasis and disease. Nature. 2016;535:75–84. doi: 10.1038/nature18848. - DOI - PubMed

[6] Honda K, Littman DR. The microbiota in adaptive immune homeostasis and disease. Nature. 2016;535:75–84. doi: 10.1038/nature18848. - DOI - PubMed

[7] Thaiss CA, Zmora N, Levy M, Elinav E. The microbiome and innate immunity. Nature. 2016;535:65–74. doi: 10.1038/nature18847. - DOI - PubMed

[8] Thaiss CA, Zmora N, Levy M, Elinav E. The microbiome and innate immunity. Nature. 2016;535:65–74. doi: 10.1038/nature18847. - DOI - PubMed

[9] Blander JM, Longman RS, Iliev ID, Sonnenberg GF, Artis D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 2017;18:851–860. doi: 10.1038/ni.3780. - DOI - PMC - PubMed

[10] Blander JM, Longman RS, Iliev ID, Sonnenberg GF, Artis D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 2017;18:851–860. doi: 10.1038/ni.3780. - DOI - PMC - PubMed

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Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

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Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

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