RU2806017C2 - Method of identifying patient sensitive to medicinal product based on mannuronic acid oligosaccharides among patients with alzheimer's disease - Google Patents

Abstract

FIELD: medicine; pharmaceuticals.

SUBSTANCE: use of a means for the production of a kit for identifying a drug-sensitive patient based on mannuronic acid oligosaccharides with dysbiosis of the intestinal microbiota and Alzheimer's disease, and a method and a corresponding kit are proposed. The use of the means for identifying an individual with dysbiosis of the intestinal microbiota, who has Alzheimer's disease, and who is sensitive to a drug based on mannuronic acid oligosaccharides is proposed. A method of treating this patient is proposed.

EFFECT: proposed group of inventions ensures restoration of the intestinal microbiota by introducing a pharmaceutical composition containing a drug based on mannuronic acid oligosaccharides to a patient who has a decreased level of Bacteroidetes, Firmicutes and Verrucomicrobia, or an increased level of phenylalanine and/or isoleucine, or an increased level of Th1-cells in a sample compared to those in healthy individuals.

16 cl, 23 dwg, 15 tbl, 4 ex

Description

The present invention relates to the identification of a carbohydrate drug-sensitive patient among patients with Alzheimer's disease (AD). More specifically, the present invention relates to the use of brain-gut association for sensitive identification.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with gradual progression. Clinically, it is characterized by general dementia such as memory impairment, aphasia, apraxia, agnosia, impaired visuospatial skills, impaired ability to perform goal-directed activities, and changes in personality and behavior. Two pathological hallmarks of Alzheimer's disease are extracellular deposits of β-amyloid (senile plaques) and intracellular neurofibrillary tangles (paired coiled-coil filaments). β-Amyloid deposits and neurofibrillary tangles lead to loss of synapses and neurons, which results in severe atrophy of the affected brain regions, typically beginning in the temporal lobe. The mechanism of this damage, caused by β-amyloid peptides and neurofibrillary tangles, is not fully understood.

Mannuronic acid oligosaccharides, developed by a research team led by Geng Meiyu, a researcher at the Institute of Pharmaceutical Innovation of the Chinese Academy of Sciences/Shanghai Institute of Materia Medica, is a new oral innovative drug against Alzheimer's disease (AD) with independent intellectual property rights . On July 17, 2018, the results of a phase III clinical trial with disclosures demonstrated that mannuronic acid oligosaccharides achieved expected results in terms of key efficacy indicators for improving cognitive function, which is of extremely significant statistical and clinical significance. In addition, the incidence of adverse events is comparable to the incidence of adverse events in the placebo group, and they have good safety and are suitable for long-term use. Mannuronic acid oligosaccharides became the first drug in the global AD treatment field to be successful in a phase III clinical trial over 16 years.

Regarding mannuronic acid oligosaccharides, many Chinese patents have been issued related to them. CN2017114675966 describes a mannuronic diacid composition. CN2016100697039 describes a method for producing oligomannuronic diacid. CN2018107134113 describes the use of a mannuronic diacid composition for the treatment of Parkinson's disease. CN2015104243401 describes the use of mannuronic acid oligosaccharides with a carboxyl group at position 1 from the reducing end and their derivatives for the treatment of Parkinson's disease. These patents are incorporated herein by reference in their entirety.

(mannuronic acid oligosaccharides)

What is needed in the art are methods for identifying a carbohydrate drug sensitive patient among patients with Alzheimer's disease.

SUMMARY OF THE INVENTION

Herein, we describe a causal relationship between gut microbiota dysbiosis and neuroinflammation during the progression of AD. In particular, changes in the composition of the gut microbiota may lead to the peripheral accumulation of flora metabolites such as phenylalanine and isoleucine. They promote proliferation and differentiation of peripheral proinflammatory T helper type 1 (Th1) cells during AD progression. Peripheral immune cells infiltrate the brain and increase neuroinflammation. Importantly, increased levels of metabolites such as phenylalanine and isoleucine in peripheral blood were confirmed in two independent samples of patients with mild cognitive impairment (MCI) caused by AD. We also demonstrated that mannuronic acid oligosaccharides, which demonstrated consistent and sustained improvements in cognitive function in a phase III clinical trial in China, alter gut flora balance, reduce the accumulation of flora amino acid metabolites in the blood, and inhibit neuroinflammation. Essentially, our data highlight the role of gut dysbiosis-induced neuroinflammation in AD progression and propose a new strategy for identifying carbohydrate drug-sensitive patients among patients with Alzheimer's disease using the brain-gut axis.

In one aspect, the present invention relates to the use of an agent for inhibiting amino acid uptake by naïve T cells in the peripheral blood of a mammal for the production of a medicament for the treatment of Alzheimer's disease in an individual.

In another aspect, the present invention provides a pharmaceutical composition for treating Alzheimer's disease in an individual, comprising an effective amount of an agent for inhibiting amino acid uptake by naïve T cells in the peripheral blood of a mammal.

In another aspect, the present invention relates to a method for screening a drug candidate that can be used to treat Alzheimer's disease, comprising:

a) administering the test agent to in vivo or in vivo SLC7A5 transporter models, and

b) selecting a test agent that inhibits the SLC7A5 transporter as a drug candidate that can be used to treat Alzheimer's disease.

In another aspect, the present invention provides a method for treating a patient having Alzheimer's disease, comprising:

administering to a patient an agent for inhibiting amino acid uptake by naïve T cells in the peripheral blood of a mammal such that the proportion of pro-inflammatory Th1 cells in CD4+ T cells in the patient is regulated to approximate or achieve a corresponding proportion of pro-inflammatory Th1 cells in CD4+ T cells -cells in the corresponding normal sample.

In another aspect, the present invention provides a method for treating a patient having Alzheimer's disease, comprising:

administering an agent for inhibiting amino acid uptake by naïve T cells in the peripheral blood of a mammal to a patient such that the relative level of amino acid uptake by naïve T cells in the patient is regulated so that it is close to or achieves a corresponding relative level of amino acid uptake by naïve T cells in the corresponding normal sample.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1. Gut dysbiosis and immune cell changes during disease progression in 5XFAD transgenic (Tg) mice.

(a) Changes in relative synaptophysin RNA expression levels 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). Data are presented as mean ± standard error of the mean (mean ± s.e.m.) relative to actin expression level. *P < 0.05, **P < 0.01 by one-way ANOVA (F(5, 43) = 2.952).

(b) Changes over time relative to the 10 ⁴ s taken to achieve 80% success (see Methods) in the Discrimination Learning Test in 5XFAD Transgenic (Tg) mice at 2, 3, 5, 7, and 9 months and in wild-type (WT) mice after 2 months (n = 4–8). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). *P < 0.05 according to Student's t test. S, seconds.

(c) Principal component analysis (PCA) for gut microbiome composition in WT and 5XFAD transgenic (Tg) mice at the operational taxonomic unit (OTU) level at different time points (n = 4–10). The shapes and colors of the dots indicate patterns from each individual and in different months. Colored ovals indicate confidence interval (CI) ranges corresponding to 0.95 in each test group. M, months.

(d) Relative changes in operational taxonomic unit (OTU) abundance in the total gut microbiome population of 5XFAD transgenic (Tg) mice across months, shaded at the phylum level in the flow plot (n = 4–10). The two most common phyla, Bacteroidetes and Firmicutes , are labeled in the graph. The colors indicate different types of gut microbiota.

(e) Changes in IBA1 immunofluorescence staining positive density reflecting microglial cell activation in the hippocampus of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7, and 9 months relative to values in wild-type (WT) mice at 2 months of age (n = 2 -7). Data are presented as mean ± standard error of the mean (mean ± s.e.m.); the lines are approximated using a cubic spline. The IBA1 value on the Y axis represents the relative amount of fluorescent staining in the transgenic animal at each time point relative to the amount of fluorescent staining in the wild type animal at the same time point.

(f) Changes in activated M1 and M2 microglia detected in whole brain homogenates of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7, and 9 months (n = 4–8). Microglia type M1 (CD45 ^low CD11b ⁺ CX3CR1 ⁺ Siglec-H ⁺ F4/80 ⁺ CD86 ⁺ ) and microglia type M2 (CD45 ^low CD11b ⁺ CX3CR1 ⁺ Siglec-H ⁺ F4/80 ⁺ CD206 ⁺ ) were detected by flow cytometry, and the numbers of these cells are presented relative to the frequency of CD45 ^low CD11b ⁺ cells. Red dots and lines: M1 microglia. Green dots and lines: M2 microglia. Data are presented as mean ± standard error of the mean (mean ± sem); the lines are approximated using a cubic spline algorithm.

(g) Changes in infiltrating cells (CD45 ^high ) detected in whole brain homogenates of 5XFAD transgenic (Tg) mice (red dots and lines) and WT mice (black dots and lines) at different time points as determined by flow cytometry (Tg mice: 2 months, n=5; 3 months, n=4; 5 months, n=4; 7 months, n=7; 9 months, n=3. WT mice: n=6). Cell numbers are presented relative to the frequency of CD45 ⁺ cells, and they are in the format of mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using a cubic spline algorithm.

(h) Changes in CD45 ^high cells in 5XFAD transgenic (Tg) mice at different time points (n = 4–8). The bar graph plots cell numbers relative to the frequency of CD45 ^high cells. Colors indicate different CD45 ^high cell subtypes: Neu, neutrophils; DC, dendritic cells; NK, natural killer cells; Mo/M monocytes and macrophages; B, B cells; other, unclassified cells.

(i) Changes in infiltrating CD4 T cells (CD45 ^high CD4 ⁺ ) detected in whole brain homogenates of 5XFAD transgenic (Tg) mice (red dots and lines) at 2, 3, 5, 7 and 9 months as determined by flow cytometry ( n = 4-8). Cell numbers are presented relative to the frequency of CD45 ^high cells and are in the format of mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using cubic splines.

(j) Changes in infiltrating peripheral Th1 and Th2 cells detected in whole brain homogenates of 5XFAD transgenic (Tg) mice at 2, 3, 5, 7, and 9 months ( n = 4–8). Detection of Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) and Th2 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- CCR4 ⁺ ) was performed by flow cytometry, and the results are presented relative to the frequency of CD45 ^high CD4 ⁺ T cells. Red dots and lines: Th1 cells. Green dots and lines: Th2 cells. Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using cubic splines.

(k) Correlation of brain lymphocytes and gut microbiota reflected at the genus level during the Th2/M2 associated stage and the Th1/M1 associated stage in the early (2-3 months) and late phase (7-9 months), respectively (left panels, n = 4-8). All bacteria that were significantly correlated with lymphocyte counts in the brains of 5XFAD mice are shown in the right panel. Squares in red (positive correlation) or blue (negative correlation) with a yellow asterisk (*) indicate significant correlations with P values < 0.05 determined by parametric Pearson correlation test.

Figure 2. Gut microbiota is required for immune cell infiltration and microglial activation.

(a) Effects of three months of antibiotic gavage on the relative abundance of microorganisms in the intestine in 5XFAD transgenic (Tg) mice at 7 months of age (n = 6-7). ABX, a mixed antibiotic cocktail consisting of ampicillin (0.1 mg/ml), streptomycin (0.5 mg/ml) and colistin (0.1 mg/ml). Different genera of intestinal microorganisms are colored differently, and their changes in relative abundance are presented in a bar graph.

(bc) Effects of three months of antibiotic gavage on the frequency of Th1 cells (b) and M1-type microglial cells (c) in the brain homogenate of 5XFAD transgenic (Tg) mice at 7 months of age. The numbers of Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) are presented relative to the frequency of CD45 ^high CD4 ⁺ cells ( b ), while the numbers of M1-type microglial cells (CD45 ^low CD11b ⁺ CX3CR1 ⁺ Siglec-H ⁺ F4/80 ⁺ CD86 ⁺ ) are presented relative to the frequency of CD45 ^low CD11b ⁺ cells ( c ). In both cases, detection was performed by flow cytometry. Data are presented as mean ± standard error of the mean (mean ± s.e.m.).

(d) Relative abundance of gut microorganisms at the genus level in WT mice, cohoused WT mice, and transgenic (Tg) 5XFAD mice ( n = 6–7). All three groups of mice were 7 months old. Different colors correspond to different genera. WT cohoused: WT mice housed with Tg mice.

(ef) Changes in the frequency of Th1 cells ( e ) and M1 type microglia ( f ) in brain homogenates from cohoused WT, WT, and transgenic (Tg) 5XFAD mice at 7 months of age ( n =6-7). Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) are presented relative to the frequency of CD45 ^high CD4 ⁺ cells ( e ), while the frequency of M1-type microglial cells (CD45 ^low CD11b ⁺ CX3CR1 ⁺ Siglec-H ⁺ F4/80 ⁺ CD86 ⁺ ) is presented relative to the frequency of CD45 ^low CD11b ⁺ cells ( f ). In both cases, detection was performed by flow cytometry. Data are presented as mean ± standard error of the mean (mean ± s.e.m.). * P < 0.05, ** P < 0.01 according to Student's t test.

(g) Cytokine protein levels in brain homogenates from WT mice, cohoused WT mice, and transgenic (Tg) 5XFAD mice at 7 months of age as detected using a cytokine antibody chip (n = 6–7). In all three groups, the mice were 7 months old. Colors in the heat map indicate relative cytokine levels normalized by crude Z-score; red indicates cytokines whose levels increase, and blue indicates cytokines whose levels decrease.

Figure 3. Effects of OM1 on behavioral changes in APP/PS1 mouse models.

(a) Structure of OM1. OM1 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) Results for escape latency in the Morris water maze (MWM) test as an indicator of spatial learning and memory in APP/PS1 mice. Nine-month-old APP/PS1 mice were treated with 50 mg/kg and 100 mg/kg OM1 for 3 months until 12 months of age. The MWM test of spatial learning and memory abilities was then administered for 6 additional days. During the test, OM1 was administered continuously. Rescue onset latency (seconds) was determined as one of the test endpoints (see Methods). Higher latencies indicate that these mice take longer to reach the goal, indicating more severely impaired spatial learning and memory (n = 11–14). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). Black asterisks indicate comparisons between the WT and APP/PS1 groups. The blue asterisk indicates the comparison between the OM1 (100 mg/kg) administration group and the APP/PS1 administration group. *P < 0.05, ***P < 0.001 by two-way ANOVA.

(c) Number of crossings of platform areas in the MWM test as an indicator of spatial learning and memory in APP/PS1 mice. Nine-month-old APP/PS1 mice were treated with 50 mg/kg and 100 mg/kg OM1 for 3 months until 12 months of age. The MWM test of spatial learning and memory was then administered for 6 additional days. OM1 was continuously administered throughout the test. The number of intersections of platform areas was determined as another test result (see Methods). Higher numbers of platform region intersections indicate less severely impaired spatial learning and memory abilities (n = 11–17). *P < 0.05, ***P < 0.001 according to one-way ANOVA (F (3, 55) = 6.542).

(d) Spatial working memory accuracy when tested using the Y-maze in APP/PS1 mice. Nine month old APP/PS1 mice were treated with 50 mg/kg and 100 mg/kg OM1 for 3 months at 12 month old doses. Then the test was carried out in the Y-maze. OM1 was continuously administered throughout the test. Y-maze accuracy was the ratio between correct alternation and overall alternation (see “Materials and methods”). Higher accuracy indicates less impaired working memory ability (n = 17–20). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). **P < 0.01, ***P < 0.001 by one-way ANOVA (F(3, 71) = 12.39).

Figure 4. OM1 mitigates neuroinflammation by reorganizing the gut microbiota.

(a) Principal coordinate analysis (PCoA) for gut microbiome composition at the operational taxonomic unit (OTU) level based on Bray-Curtis distance for 5XFAD (Tg) mice and OM1-injected Tg mice at 7 months of age (n = 7) . The shapes and colors of the dots indicate samples from each individual. The colored ovals indicate the confidence interval (CI) ranges corresponding to 0.95 in each group tested. PC, main component.

(b) Heat map of significant gut microbiota changes represented at the genus level between 5XFAD (Tg) mice and OM1-injected Tg mice at 7 months of age ( n =17). The colors on the color map indicate the relative abundance of the gut microbiota; red indicates bacteria whose levels are increasing, and blue indicates bacteria whose levels are decreasing.

(c) Changes in correlations between the gut microbiome (indicated by numbers near blue circles) and brain lymphocytes (filled circles) before (left) and after (right) gavage of OM1 in 5XFAD (Tg) mice at 7 months of age. On the right side is the name of each gut microbiome.

(d) Effect of OM1 administration on the frequency of Th1 cells in the brain of 5XFAD (Tg) mice at 7 months of age ( n = 5–7). The numbers of Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) are presented relative to the numbers of CD45 ^high CD4 ⁺ T cells as determined by flow cytometry. Data are presented as mean ± standard error of the mean (mean ± s.e.m.). * P < 0.05, *** P < 0.001, according to Student's t test.

(e) Effect of OM1 administration on positive signal density in IBA1 immunofluorescence staining performed on hippocampal sections from 7-month-old 5XFAD (Tg) mice, reflecting microglial cell activation ( n = 4–6). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). * P < 0.05, according to one-way ANOVA (F (2, 15) = 21.94).

(f) Effect of OM1 administration on cytokine protein levels in brain homogenates from 7-month-old 5XFAD (Tg) mice as detected by the cytokine antibody chip ( n = 5–6). Colors in the heat map indicate relative cytokine levels normalized by crude Z-score; red indicates cytokines whose levels increase, and blue indicates cytokines whose levels decrease.

(gh) Effect of OM1 on the Aβ-positive region (g) and tau-positive region ( h ) in the hippocampus of 5XFAD (Tg) mice at 7 months of age, assessed in brain slices ( n =4-7). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). For Aβ analysis: * P < 0.05, ** P < 0.01 (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 OM1 on time relative to the 10 ⁴ seconds (s) taken to achieve 80% success (see Methods) in the discrimination learning test in 5XFAD mice at 7 months of age (n = 10-13). Time means the time to reach the 80% efficiency level (seconds); the longer it takes, the more severe the violation (see methods). * P < 0.05, *** P < 0.001 according to one-way ANOVA (F(2,31) = 9.751).

Figure 5. OM1 inhibits neuroinflammation by taking control of amino acid metabolism.

(a) Signaling pathway enrichment analysis for 31 types of fecal metabolites from 7-month-old 5XFAD (Tg) mice with or without OM1 administration and using MBROLE ( n = 6–8). Enrichment results are listed with KEGG modules and KEGG enzyme interactions screened using the P < 0.05 value criterion.

(b) Lists of blood amino acids between WT (n=30) and Tg (n=26) that could be distinguished during disease progression using a random forest model (number of trees: 500; number of predictors: 7; randomness: included ; see method).

(c) Changes in histidine, phenylalanine, and isoleucine levels in feces of WT, Tg, and OM1-treated Tg mice (n = 6–11). Red, increased level; blue, reduced level.

(d) Changes in blood levels of histidine, phenylalanine, and isoleucine in WT, Tg, and OM1-treated Tg mice (n = 6–7). Red, increased level; blue, reduced level.

(e) Effects of OM1 on the differentiation of naive CD4 ⁺ T cells (Th0 cells) into Th1 cells induced by phenylalanine and isoleucine. Naive CD4 ⁺ T cells were cultured for 3 days with/without OM1 in the presence of phenylalanine (1 mM) or isoleucine (1 mM). The frequency of Th1 cells (CD4 ⁺ IFN-γ ⁺ ) was tested by flow cytometry. Data are presented as mean ± standard error of the mean (mean ± s.e.m.); n = 3 repetitions per group. 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) Effects of OM1 on Th1 cell proliferation induced by phenylalanine and isoleucine. Naïve CD4 ⁺ T cells were stained with CellTrace and cultured for 3 days with/without OM1 in the presence of phenylalanine (1 mM) and isoleucine (1 mM). CellTrace fluorescence density in Th1 (CD4 ⁺ IFN-γ ⁺ ) cells was tested by flow cytometry (see methods). Data are presented as mean ± standard error of the mean (mean ± s.e.m.), n = 3. * P < 0.05, *** P < 0.001 by one-way ANOVA (F(4, 9) = 28, 34).

(g) Changes in Th1 cells in the blood after intraperitoneal (IP) injection of phenylalanine and isoleucine for 4 days ( n = 6). *** P < 0.001 according to one-way ANOVA (F (2, 21) = 101.8).

(h) Classification based on random trees of amino acid changes in healthy controls (HC) and patients with mild cognitive impairment (MCI) due to AD (first group, n = 9 for MCI due to AD, n = 18 for HC).

(i) Frequency of Th1 cells in the blood of healthy controls (HC) and patients with mild cognitive impairment (MCI) due to AD (first group, n = 8 for MCI due to AD, n = 9 for HC). * P < 0.05 according to Student's t test. The vertical axis shows the percentage of Th1 cells/CD4 ⁺ T cells.

(j) Phenylalanine and isoleucine levels in the blood of healthy controls (HC) and patients with mild cognitive impairment (MCI) due to AD (second group, n = 22 for both groups). * P < 0.05 according to Student's t test.

Figure 6. Schematic diagram of neuroinflammation during AD progression and intervention strategy

Changes in the intestinal microbiota during the progression of AD cause disruption of amino acid metabolism. It promotes the differentiation of naïve CD4 T cells into Th1 cells in the blood. Meanwhile, amino acids and Th1 cells can infiltrate the brain through the blood circulation. Infiltration of peripheral immune cells and activation of microglia lead to pathological neuroinflammation in the brain, leading to cognitive decline. Oral administration of OM1 can restore the intestinal microbiota, inhibit abnormal amino acid production and reduce the infiltration of peripheral immune cells in the brain, and ultimately eliminate neuroinflammation.

Figure 7

(a-b) Changes in Aβ-positive regions (a) and phosphorylated tau positive (p-tau) regions (b) in hippocampal slices from 5XFAD transgenic (Tg) mice (2, 3, 5, 7, and 9 months of age) vs. mice wild type at 2 months of age (WT) (n=2-7). Representative fluorescence images are shown; the scale mark corresponds to 100 µm. Line graphs summarize the results for all individual points relative to WT mice and are presented as mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using a cubic spline algorithm. M, months.

(c) Principal component (PC) 1 from principal component analysis (PCA) for gut microbiome composition at the operational taxonomic unit (OTU) level in WT and 5XFAD transgenic (Tg) mice (aged 2, 3, 5, 7, and 9 months) (n=4-10). Red dots and lines, Tg mice; blue dots and lines, WT mice. Natural connection into a line without approximation. Using a two-tailed Wilcoxon rank sum test (rank sum); * indicates a significant difference compared to 2 months of age in the same group. *P<0.05, **P<0.01. # indicates a significant difference compared to age-matched WT mice. #P<0.05; ##P<0.01; ###P<0.001.

(d) Changes in the relative abundance of intestinal microorganisms at the family level in 5XFAD transgenic (Tg) mice (2, 3, 5, 7 and 9 months of age) (n=4-10). Different colors correspond to different families.

(e) Cladogram of linear discriminant analysis effect size (LEfSe) analysis of gut microbiome composition in 5XFAD transgenic (Tg) mice (2, 3, 5, 7, and 9 months of age) (n=4-10). Bacteria with the highest discriminatory power are indicated on the graph for each month. The colors indicate bacterial taxa that were enriched each month. Circles from inner to outer indicate different taxonomic levels (inner to outer: phylum, class, order, family and genus). M, months. Verrucomicrobia ; AlphaProteobacteria ; Bacteroidetes ; Prevotellaceae ; Erysipelotrichia ; Firmicutes ; Lachnoclostridium .

(f) PCA analysis of the gut microbiome at the OTU level in APP/PS1 transgenic mice at 3, 6, 8, 9, 12 and 14 months of age (n=4-12). Colors and shapes indicate data for different months. Colored ovals indicate confidence interval (CI) ranges corresponding to 0.95 in each test group. PC, main component.

(g) Changes in the relative abundance of gut microbes at the phylum level in APP/PS1 transgenic mice at 3, 6, 8, 9, 12, and 14 months of age (n=4-12). The colors correspond to the different types. M, months.

(h) Representative fluorescence images of changes in IBA1-positive regions in hippocampal slices from 5XFAD transgenic (Tg) mice (2, 3, 5, 7, and 9 months of age) versus 2-month-old wild-type (WT) mice (n=2- 7). The scale mark corresponds to 500 µm in the small frame (top), 250 µm in the large frame (bottom). M, months.

(i) Changes in infiltrating cell frequency (CD45 ^high ) detected in whole brain homogenates of APP/PS1 mice at 3, 6, 9 and 14 months of age as detected by flow cytometry (n=5-8). Cell counts are presented relative to the frequency of CD45 ⁺ cells and are in the format of mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using a cubic spline algorithm.

(j) Changes in the frequency of infiltrating peripheral Th1 cells in whole brain homogenates of APP/PS1 mice at 3, 6, 9, and 14 months of age as detected by flow cytometry (n=3-8). Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) are presented relative to the frequency of CD45 ^high CD4 ⁺ T cells. Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The lines are approximated using a cubic spline algorithm.

Figure 8. Changes in AD hallmarks in five animal models and wild-type (WT) mice at 2, 4, 8, and 18 months of age from the Mouseac database.

(a) Change in relative density of Aβ plaques or neurofibrillary tangles (n=4 for each group). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The colors indicate the different models; the lines are approximated using a polynomial algorithm.

(b) Changes in log ₂ -normalized synaptophysin expression levels (n=4 for each group). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The colors indicate different models; the lines are approximated using a polynomial algorithm.

(cd) Changes in _log2 normalized expression levels of CD86 and ARG1 corresponding to changes in M1 and M2 cell numbers (n=4 for each group). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The colors indicate different models; the lines are approximated using a polynomial algorithm.

(eh) Changes in log ₂ -normalized expression levels of TIPM, CCL3, GATA-3 and MIF corresponding to changes in Th1 and Th2 amounts (n=4 for each group). Data are presented as mean ± standard error of the mean (mean ± s.e.m.). The colors indicate different models; the lines are approximated using a polynomial algorithm.

Figure 9

(a) Effects of co-housing on the time of 10 ⁴ seconds taken to achieve 80% success in a test to assess pattern recognition learning ability in WT mice, co-housed WT and transgenic (Tg) 5XFAD mice at 7 months of age (n= 5-8). Time taken means the time to reach the 80% efficiency level (seconds); the longer it takes, the more severe the cognitive impairment (see methods).

(b) Effects of fecal microbiota transplantation (FMT) on immune cell numbers in the brains (Th1 and Th2) of recipient WT mice injected with Aβ (see Methods) using feces from either WT mice or transgenic (Tg) 5XFAD mice. Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) and Th2 cells (CD45 ^high CD4 ⁺ CXCR3 ^- CCR6 ^- CCR4 ⁺ ) are represented relative to CD45 ^high CD4 ⁺ T cells (n=6-8); data are presented as mean ± standard error of the mean (mean ± s.e.m.). *P<0.05, Student's t test.

(c) Effects of FMT from WT mice at 2 months of age on Th1 cells in the brain of 5XFAD transgenic (Tg) mice (n=6-7). Data are presented as mean ± standard error of the mean (mean ± sem). *P<0.05, Student's t test.

Figure 10

(a) Cladogram of linear discriminant analysis effect size (LEfSe) analysis of the gut microbiome composition of 7-month-old 5XFAD transgenic (Tg) mice orally administered OM1 (n=5-7). Bacteria with the highest discriminatory power at the phylum level are indicated in the graph. Blue, Bacteria are enriched in Tg mice at 7 months of age. Red, Bacteria are enriched in OM1-treated 7-month-old Tg mice. Circles from inner to outer indicate different taxonomic levels (inner to outer: phylum, class, order, family and genus). M, months

(b-c) Correlation between OM1-induced increased and decreased microbiota levels and the frequency of immune cell subtypes in the brains of 5XFAD transgenic (Tg) mice at 7 months of age. Squares in red (positive correlation) or blue (negative correlation) with a yellow asterisk (*) have a P value < 0.05 determined by the parametric Pearson correlation test, the net in each square represents the correlation coefficient.

(d) Representative images of IBA1 staining, Aβ deposition, and tau phosphorylation in the hippocampal brains of WT, Tg, and OM1-injected Tg mice. The scale mark corresponds to 250 µm. A positive signal was visualized using the substrate 3,3'-diaminobenzidine (DAB), shown in dark brown.

(e) Effects of FMT from WT, Tg, and OM1-injected Tg mice on brain Th1 cells in hippocampal Aβ-injected C57 recipient mice (n=4-5). Data are presented as mean ± standard error of the mean (mean ± sem).

(f) Effects of antibiotic administration (ampicillin (0.1 mg/ml), streptomycin (0.5 mg/ml) and colistin (0.1 mg/ml) on the relative abundance of the gut microbiota at the genus level in the APP transgenic mouse model /PS1 at 6 months of age treated orally with OM1 (n=6-8) Colors indicate different genera.

(g) Effects of OM1 on Th1 cell frequency in the brains of 6-month-old APP/PS1 mice treated with antibiotics (see Methods). Th1 cells (CD45 ^high CD4 ⁺ CXCR3 ⁺ CCR6 ^- ) are presented relative to CD45 ^high CD4 ⁺ T cells (n=6-8), and data are presented as mean ± standard error of the mean (mean ± s.e.m.). From left to right: *P<0.05, Student's t test. NS, no significant differences.

(h) Effects of OM1 on the relative density of IBA1-positive immunofluorescence staining detected in hippocampal sections from 6-month-old APP/PS1 mice treated with antibiotics (n=4-6, see methods). The IBA1-positive region reflects the activation of microglial cells. Data are presented as mean ± standard error of the mean (mean ± sem). ***P < 0.0001 according to Student's t test. NS, no significant differences.

(i) Representative immunofluorescence staining of IBA1 in the brains of APP/PS1 mice and OM1-treated APP/PS1 mice with or without antibiotics. IBA1 was visualized using a FITC-conjugated secondary antibody as shown in green. The nucleus was stained with DAPI as shown in blue. The scale mark corresponds to 250 µm.

(j) Effects of OM1 on cytokine levels in brain homogenates from 7-month-old APP/PS1 mice as determined by anti-cytokine chip (n=5-6). Colors in the heat map indicate relative cytokine levels normalized by crude Z-score; red indicates cytokines whose levels increase, and blue indicates cytokines whose levels decrease.

Figure 11

(a) Effect of OM1 on the differentiation of naïve CD4 ⁺ T cells treated with bacterial supernatant into Th1 and Th2 cells. Naive CD4 T cells were cultured and supernatant from the microbiota of 5XFAD mice was added in the presence/absence of OM1 for 3 days. Gating of Th1 (CD4 ⁺ IFN-γ ⁺ ) and Th2 (CD4 ⁺ IL-4 ⁺ ) cells was performed by flow cytometry. Data are presented as mean ± standard error of the mean (mean ± s.e.m.); n=3 repetitions per group.

(bc) Volcano plot represents the distribution of metabolites from raw fecal metabolomics data from WT and 5XFAD (Tg) mice aged 7 months (n=6-8) (b) and Tg mice aged 7 months treated or not treated with OM1 (n=6) (c). Red dots indicate significant changes in metabolites. Significance was defined as P value < 0.05 according to Student's t test and fold change (FC) < 0.83 or > 1.2 between (T) and wild type (WT). The x-axis represents log ₂ FC, and the y-axis represents -log ₁₀ P.

(d-e) Heat map for seven hundred and eighty-six metabolites that were differentially regulated between Tg and WT mice (d), as well as 149 metabolites between OM1-treated and non-OM1-treated Tg mice (e) (n=6-8). These metabolites were identified and annotated by matching molecular weight (m/z) data of significant peaks with the online METLIN database.

(f) Venn diagram shows intestinal fecal metabolites whose levels are often reduced when comparing Tg and WT mice (T_W) and Tg mice treated with or without OM1 (Ttreat_T) (n=6-8). One hundred and twenty-four metabolites had reversal patterns among the two comparisons, i.e. metabolites that are either both high for T_W and low for Ttreat_T, or both low for T_W and high for Ttreat_T.

(g) Heat map shows 31 identified differentially regulated metabolites among WT, Tg and Tg with OM1 injection groups (n=6-8), which can be compared with all three databases (Human Metabolites Database (HMDB), METLIN, Kyoto Encyclopedia of Genes and Genomes (KEGG)). Red, level up; blue, level decrease.

(h) Receiver operating characteristic (ROC) curve and area under the curve (AUC) value for all amino acids during disease progression are calculated using a random forest algorithm.

(i) Effects of fecal microbiome transplantation (FMT) on blood phenylalanine and isoleucine levels. Feces from 2-month-old WT mice were transplanted into 7-month-old Tg mice (n=6-7). For phenylalanine ***P < 0.001((F (2, 17)=26.59)). For isoleucine, **P < 0.01 (Tg vs. WT), **P < 0.01 (Tg+FMT vs. Tg) by one-way ANOVA (F(2, 17)=8.181).

(j) Amino acid levels in blood samples of WT and Tg mice cohoused at different months (M) of age. Red, increased level; blue, reduced level.

(k) Phenylalanine uptake by naive CD4 T cells. Naïve CD4 T cells were cultured with/without ¹³ C-labeled phenylalanine for 0.5 h. Mass spectrometry of phenylalanine-related compounds was performed. ¹³ C-Phenylalanine was administered at a concentration of 5 mmol/L and is taken up by the L-type amino acid transporter.

Figure 12. JPH203, 50 mg/kg, effectively inhibits a subset of Th1 cells in the brain.

Figure 13. Trend in OTU levels in Enterobacteriaceae after OM1 administration compared to control - PCoA analysis of the effect of phenylalanine-degrading bacteria on fecal phenylalanine content 1 week after transplantation.

Figure 14. Trend in changes in the relative abundance of Enterobacteriaceae after administration of OM1 compared to control. Effect of phenylalanine-degrading bacteria on the proportion of Th1 cells in the blood one week after transplantation.

Figure 15. Change in the trend of cytokine content in the brain compared to control after administration of OM1.

Figure 16. Change in the distribution of intestinal microbes before and after the use of agents used to regulate the relative abundance of microorganisms in the intestine: OM1 or fecal bacteria (intestinal microbial complex).

Figure 17. Differences between phylum level and genus level for AD and HC, partially detailed. AD: patients with AD; HC: healthy controls.

Figure 18. Part of the significantly changed flora in AD and HC (at the genus and species level). AD: patients with AD; HC: healthy controls.

Figure 19. List of amino acids associated with AD. ROC curve-based multivariate exploratory analysis (Explorer) was used to analyze amino acids in the blood of wild-type mice and 5XFAD mice at different months of age, and it searched for potential amino acid combinations as markers to distinguish wild-type mice from 5XFAD mice. Below is a list of the top 15 amino acids, sorted by selection frequency and all types.

Figure 20. 5XFAD mice at 6.5 months of age following administration of 100 mg/kg OM1 for 1 month have blood amino acid levels that tend to recover to amino acid levels in wild-type mice.

Figure 21. 5XFAD mice at 6.5 months of age following administration of 100 mg/kg OM1 for 1 month have fecal amino acid levels that tend to recover to amino acid levels in wild-type mice.

Figure 22. List of cytokines whose levels were restored after administration of OM1. 5XFAD mice aged 6.5 were treated with 100 mg/kg OM1 for 1 month and had cytokines in the brain that tended to recover to cytokine levels in wild-type mice.

Figure 23. Trend of brain M1 cells in APP/PS1 mice at different ages corresponding to different numbers of months.

DETAILED DESCRIPTION OF THE INVENTION

Certain illustrative embodiments are described below to provide a thorough understanding of the structure, function, preparation and use of the products, methods and uses described herein. One or more examples of these implementations are illustrated below. Those skilled in the art will appreciate that the products, methods and applications specifically described herein and specifically illustrated in the accompanying drawings are non-limiting exemplary embodiments and the scope of the present invention is limited only by the claims. Features illustrated or described together in one exemplary embodiment may be combined with features of other embodiments. Such modifications and changes are intended to be included within the scope of the present invention. All cited publications, patents and patent applications are incorporated herein by reference in their entirety.

As used herein (especially in the context of the claims appended below), unless otherwise indicated in the present description or unless the context clearly contradicts it, terms in the singular form should be interpreted to include both the singular and the plural. Unless otherwise indicated, the terms “comprising,” “having,” “including,” and “encompassing” are to be interpreted as open terms (i.e., “including, but not limited to”), but they also include partially closed or closed terms "essentially consisting of" and "consisting of". Unless otherwise indicated herein, the description of a numerical range herein is intended to be used only as a shorthand for independently referring to each individual value included within the range, and each individual value is included herein as if it were independently given in the present description. Unless otherwise indicated herein or unless the context clearly contradicts it, all methods described herein may be performed in any suitable order. Unless otherwise indicated, the use of any and all examples or illustrative language (eg, "such as") provided herein is intended to better illustrate the present invention only and does not limit the scope of the present invention. Nothing in this specification should be construed as indicating that any element not claimed is necessary for the practice of the present invention. All percentages are percentages by weight, and all percentages by weight are based on the total weight of the composition (with any optional concentration and/or dilution). The expression "one or more" includes "two or more" and "three or more" and the like.

Preferred embodiments of the present invention are described in the present specification. Upon reading the foregoing description, variations in the preferred embodiments may become apparent to those skilled in the art. The inventor believes that those skilled in the art will make appropriate use of such modifications, and the inventor anticipates that the present invention will be practiced in a manner other than that specifically set forth herein. Thus, the present invention includes all modifications and equivalents to the subject matter described in the appended claims as permitted by applicable law. Moreover, unless otherwise indicated in the present description or unless the context clearly contradicts it, the present invention covers any combination of all possible variations of the above elements.

Whenever a sequence of values is given in this application, it should be understood that any given value may represent the upper or lower limit of the numerical range. It should also be understood that the present invention covers all such numerical ranges, i.e. a range having a combination of an upper limit and a lower limit, where the corresponding values of the upper limit and lower limit may be any of the numerical values given herein. It should be understood that the range given herein. includes all values within the range. For example, it will be understood that 1-10 includes all of the values 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10, and includes fractional values where appropriate. A range expressed by "up to" a specified value (for example, up to 5) should be understood to include all values (including the upper limit of the range) such as 0, 1, 2, 3, 4 and 5, and it includes fractional values in the appropriate cases. It should be understood that “not more than one week” or “within a week” includes 0.5, 1, 2, 3, 4, 5, 6 or 7 days. Likewise, it should be understood that the range defined by “at least” includes the lower values given and all higher values.

Unless otherwise noted, all percentages are weight/weight percentages.

As used herein, "about" is meant to be within three standard deviations of the mean or within the standard range of tolerance in a particular field. In certain embodiments, by “about” is meant a deviation of no more than 0.5. "About/Approximately" modifies all listed quantities after them. For example, "about 1, 2, 3" means "about 1", "about 2", "about 3".

Unless the context clearly indicates otherwise, the term “or” is used in the present specification in an inclusive sense to mean the term “and/or,” and they may be used interchangeably herein.

The term "such as/for example/for example" is used herein to refer to the expression "such as/for example/for example, but not limited to", and they may be used interchangeably herein.

Those skilled in the art will appreciate that the technical features described in the various embodiments above can be used alone or in combination with technical solutions of various aspects of the present invention.

We used the 5XFAD transgenic (Tg) mouse model, widely used in AD research due to its severe and accelerated cognitive decline. Through enterotype analysis of Tg mice and WT mice at different stages of AD progression, the gut microbiota of Tg mice was found to be highly dynamic (Figure 1d). At 2–3 months of age, Bacteroidetes , Firmicutes , and Verrucomicrobia are the three most common bacterial phyla (46.8%, 32.3%, and 12.6%, respectively), while at 7–9 months of age, Firmicutes become the predominant bacteria , while the content of Bacteroidetes and Verrucomicrobia is significantly reduced. This is in stark contrast to the gut microbiota of WT mice.

We also analyzed peripheral Th1 and Th1 immune cells and M1 and M2 microglial cells and found that the two major CD4+ cell subtypes (infiltrating Th1 and Th2 cells) showed similar dynamics to the two major microglial cell subtypes (M1 and M2 microglial cells) (Fig. 1f and Fig. 1j). In the early stage, they are mainly Th2 cells and neuroprotective M2 microglia cells, and in the late stage, they are mainly Th1 cells and pro-inflammatory M1 microglia cells. We believe that as gut microbiota patterns change, the immune cell population tends to achieve a Th1 and M1 predominant status.

We also analyzed the correlation between gut microbiota content and brain cells in Tg mice, and also noted that bacterial composition at an early stage (2-3 months) was highly correlated with the numbers of M2 and Th2 cells in the brain (Figure 1k , top), while at the late stage (7-9 months), changes in bacterial pattern are highly correlated with M1 and Th1 cells (Fig. 1k, bottom). Overall, these results indicate that during AD progression, gut bacteria are associated with peripheral immune cell infiltration and neuroinflammation.

Moreover, we discovered that dysbiosis of the gut microbiota is required for the infiltration of various peripheral immune cells (including CD4+ and CD8+ T cells, B cells, natural killer (NK) cells, neutrophils, dendritic cells (DC), and monocytes) into the brain . Among them, Th1 cells are particularly noteworthy as they are closely associated with the activation of M1 microglial cells during AD progression. In view of the well-established functional relationship between Th1 and M1 microglia cells in the brain, we hypothesized that gut dysbiosis promotes Th1 cell infiltration by mediating local interaction with M1 microglia, thereby triggering microglial differentiation into a pro-inflammatory state. Through a series of discoveries from this study, understanding of this mechanism has increased. First, dynamic changes in the composition of the intestinal microbiota during the progression of AD are significantly associated with increased infiltration of Th1 cells. Second, reduction of gut microbiota through antibiotic administration blocked Th1 cell infiltration and subsequent activation of M1 microglia in AD mice (Fig. 2a–c). Third, long-term exposure to fecal bacteria (co-housing experiment) and fecal microbiota transplantation from AD mice significantly enhanced both Th1 cell infiltration (Fig. 2e) and M1 microglial activation in WT mice, whereas fecal microbiota transplantation WT mice to Tg mice decreased Th1 cell levels in recipient Tg mice (Fig. 9c). The results of the inventor's study overall indicate that the gut microbiota is a triggering factor contributing to Th1/M1 microglia-driven neuroinflammation in the progression of AD.

The cause-and-effect relationship between gut microbiota dysbiosis and neuroinflammation in AD is still unclear. In this study, the inventors found that more than 100 metabolites were altered at significant levels in AD mice compared to WT mice. Among them, the most pronounced changes occurred among amino acids, especially amino acids in the phenylalanine-related cascade. We were able to confirm that phenylalanine and isoleucine were elevated in the feces and blood of AD mice relative to WT mice. Functional evaluation, both in vitro and in vivo, demonstrated the role of phenylalanine and isoleucine in promoting the differentiation and proliferation of peripheral inflammatory Th1 cells. The use of antibiotics to reduce the gut microbiota resulted in a concomitant decrease in blood phenylalanine and isoleucine levels, Th1 cell infiltration, and M1 cell activation. These data highlight the role of abnormal phenylalanine and isoleucine production in the gut microbiota in promoting Th1 cell-driven neuroinflammation. Consistent with this view, the inventors found that the concentration of phenylalanine/isoleucine and the number of Th1 cells in the blood of patients with MCI caused by AD are higher than in a group of healthy controls.

Newly emerging evidence suggests that polysaccharides or oligosaccharides have benefits in regulating the gut microbiota. Mannuronic acid oligosaccharides are carbohydrate-based anti-AD drugs. In a phase III clinical trial recently completed in China, they were shown to improve cognitive performance in patients with mild to moderate AD. Mannuronic acid oligosaccharides are well tolerated and have safety similar to placebo controls. In this trial, we found that OM1 effectively restores the gut microbiota (Fig. 4a-b; Fig. 10a), reduces phenylalanine and isoleucine in feces and blood (Fig. 5c-d), and reduces Th1-mediated neuroinflammation (Fig. 4g-i). It should be noted that OM1-treated Tg feces can largely mimic the therapeutic effect of OM1 treatment itself, and antibiotic treatment eliminates its therapeutic effect. These data provide important evidence that the therapeutic effect of OM1 is primarily mediated by restoration of the gut microbiota. Thus, OM1 may provide an attractive approach for microbiota-focused treatment strategies for AD that merits further investigation.

All these data allow the inventors to suggest a conceptual breakthrough in understanding the pathogenesis of AD. AD is not only a localized Aβ-mediated disease of the brain, but its development also requires systemic interactions between the gut, brain, and inflammatory intermediates (Figure 6). In the case of Aβ deposition, the altered composition of the gut microbiota during AD progression causes abnormal increases in amino acid levels (especially phenylalanine and isoleucine). These amino acids promote the infiltration of peripheral Th1 cells into the brain through the bloodstream. Infiltrating peripheral Th1 cells may locally interact with M1 microglia in the brain, leading to pathological neuroinflammation and cognitive decline. These insights into AD pathogenesis can be used to restore the gut microbiota to promote anti-neuroinflammation responses and provide novel therapeutic solutions.

The results of the inventor's research may have revolutionary implications for the diagnosis and treatment of AD. A composition of specific bacteria (eg, Th1/M1-associated bacteria), amino acids (eg, phenylalanine and isoleucine), and brain-infiltrating immune cells (eg, Th1 cell predominance), or a combination of one or more of these, can be used as early diagnostic biomarkers for patients with MCI caused by AD, and this deserves further confirmation in a larger sample of AD patients.

More importantly, the established microbiota-focused anti-AD effect of OM1 will open up new therapeutic approaches to treat AD by reorganizing the gut microbiota, and will guide the development of effective therapies in the future by exploring highly unexplored sugar chemistry.

Each of the inventors' identification of numerous representative gut microorganisms, amino acids, immune cells, and cytokines associated with the brain-gut axis constitutes a gut microbial profile, amino acid profile, immune cell profile, and cytokine profile. One or more of these profiles (such as the gut microbial profile) demonstrate differences between normal and diseased individuals. The present invention aims to detect or regulate the condition of an individual by detecting or regulating one or more of these profiles (such as the gut microbial profile). In some embodiments, for diagnostic purposes, a profile (eg, gut microbial profile) of an individual may be determined for comparison with a profile (eg, gut microbial profile) with normal characteristics of the corresponding normal individual and/or a profile (eg, gut microbial profile) with disease characteristics the corresponding diseased individual to determine whether the individual's condition is normal or diseased, thereby diagnosing the individual. In some embodiments, for therapeutic purposes, the profile of an individual with disease characteristics may be adjusted to that of a corresponding normal individual, such that the disease state of the individual may be adjusted to the normal state, thereby treating the individual.

Thus, in one aspect, the present invention relates to a method for identifying a carbohydrate drug-sensitive patient among patients having Alzheimer's disease, comprising:

determining one or more of the following in a patient sample and comparing them with appropriate reference values:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels; And

selecting patients whose findings are substantially comparable to the reference values as the carbohydrate drug-sensitive sample.

In another aspect, the present invention relates to a kit for identifying a carbohydrate drug-sensitive patient among patients having Alzheimer's disease, comprising

means for determining one or more of the following in a sample from patients having Alzheimer's disease:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels.

In another aspect, the present invention relates to the use of a means for determining one or more of the following parameters in a sample from patients having Alzheimer's disease, for the production of a kit for identifying a carbohydrate drug-sensitive patient among these patients:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels.

In another aspect, the present invention provides a method for treating a patient having Alzheimer's disease, comprising:

Determine one of the following values in a patient sample and compare them with appropriate reference values:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels; And

administering a pharmaceutical composition containing a carbohydrate drug to a patient whose test result is substantially comparable to the reference value.

In another aspect, the present invention provides a method for identifying an individual who may have Alzheimer's disease, comprising:

determining one or more of the following in a patient sample and comparing them with appropriate reference values:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels; And

identifying an individual whose detected score is substantially comparable to the reference score as an individual who may have Alzheimer's disease.

In another aspect, the present invention relates to the use of a means for determining one or more of the following in a sample from an individual for the production of a kit for identifying an individual who may have Alzheimer's disease:

(1) relative abundance of intestinal microbes;

(2) amino acid levels;

(3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And

(4) cytokine levels.

In some embodiments, the carbohydrate drug is selected from monosaccharides, disaccharides, oligosaccharides, polysaccharides, or derivatives thereof, or combinations thereof and/or derivatives thereof; preferably oligosaccharides and polysaccharides; more preferably mannuronic acid oligosaccharides or compositions containing mannuronic acid oligosaccharides.

In some embodiments, the sample is feces or peripheral blood.

In some embodiments, the gut microbes are one or more selected from Firmicutes , Bacteroidetes , Proteobacteria , Actinomycetes , Fusobacteria , Cyanobacteria , Verrucomicrobia , or a combination thereof.

In some embodiments, the amino acid is one or more selected from the following: 4-OH proline, acetylornithine, alanine, alpha-aminoadipic acid, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glutamine, glycine, histidine, hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamic acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine ; preferably selected from phenylalanine, isoleucine, serotonin, histidine and acetylornithine; more preferably phenylalanine and/or isoleucine; most preferably phenylalanine.

In some embodiments, the level of one or more amino acids selected from the following is higher than the level of the corresponding amino acid in a corresponding normal individual: 4-OH proline, acetylornithine, alanine, alpha-aminoadipic acid, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glutamine, glycine, histidine, hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamic acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine; preferably the level of one or more of phenylalanine, isoleucine, serotonin, histidine and acetylornithine is higher than the corresponding level of these amino acids in a corresponding normal individual; more preferably, the level of phenylalanine and/or isoleucine is higher than the corresponding level of these amino acids in a corresponding normal individual; most preferably the level of phenylalanine is higher than the corresponding level of this amino acid in a corresponding normal individual.

In some embodiments, the cytokine is one or more selected from the following: OPG, resistin, TARC, VEGF, chemerin, IL-9, MMP-2, VEGF-D, TCA-3, gp130, MMP-10, 6Ckine, VEGF -B, IL-22, IL-1a, IFNg R1, granzyme B, LIX, CT-1, CD27L, endoglin, TRANCE, MCSF, 4-1BB, leptin R, CD36, TremL 1, VEGF R2, TGFb1, IL- 3 Rb, H60.

In some embodiments, the kit further includes a user guide for instructing the consumer to compare the result of the metric in the sample with a corresponding reference metric.

In some embodiments, the user manual includes appropriate benchmarks.

In some embodiments, the user guide is used to instruct the consumer to identify a patient as a carbohydrate drug-sensitive patient when the measurement result in the sample is substantially comparable to the corresponding reference value.

In some embodiments, the carbohydrate drug is OM1.

In some embodiments, the carbohydrate drug is not OM1.

In some embodiments, the mannuronic acid oligosaccharide is OM1.

In some embodiments, the mannuronic acid oligosaccharide is not OM1.

The structure and method of preparation of mannuronic acid oligosaccharides are described in many prior art documents. The prior art patent CN2016100697039 describes a method for the preparation of oligomannuronic acid, the prior art patent CN2017107964853 describes a method for determining the weight average molecular weight and amount of mannuronic acids, the prior art patent CN2017114675966 describes the composition of mannuronic acid and its preparation, all of which are incorporated herein by reference in full. OM1 used herein is composition A according to CN2018107213276 (“alginic acid oligosaccharide composition”), which is incorporated herein by reference.

Microbiota

Described herein are methods and compositions that involve altering the microbiota of an individual's intestinal tract. The term "microbiota" is used to refer to one or more bacterial communities that can be found or exist (colonize) in the intestinal tract of an organism. It may be used interchangeably with "microbes/microorganisms" or "gut microbes/microorganisms" herein. When referring to more than one microbiota, microbiotas may be of a single type (strain) or may be a mixture of groups such as Bacteroidetes, Proteobacteria and/or Firmicutes , or subgroups thereof (class, order, family, genus, species). The microbiota may be a mixture of microorganisms at one level, such as Bacteroidetes , Proteobacteria and/or Firmicutes ; it may also be a mixture of different levels of microorganisms such as Bacteroidetes and Proteobacteria .

In some embodiments, the gut microbes are selected from one or more selected from the type, class, order, family, genus or species shown in Table 1, or a combination thereof.

In some embodiments, the gut microbes are selected from one or more selected from the type, class, order, family, genus or species shown in Table 2, or a combination thereof.

In some embodiments, the gut microbes are selected from one or more selected from Firmicutes , Bacteroidetes , Proteobacteria , Actinobacteria , Fusobacteria , Cyanobacteria , Verrucomicrobia , or a combination thereof.

In some embodiments, the gut microbes are one or more selected from the type shown in the table below, or a combination thereof:

type Firmicutes type Bacteroidetes type Proteobacteria type Actinobacteria type Fusobacteria type Cyanobacteria type Verrucomicrobia

In some embodiments, the gut microbes are one or more selected from the classes shown in the following table, or combinations thereof:

Class Bacterodia Class Clostridia Class Gamma Proteobacteria Class Bacilli Class Negativicutes Class Actinobacteria Class Fusobacteria Class Beta Proteobacteria Class Alpha Proteobacteria

In some embodiments, the gut microbes are one or more selected from the orders shown in the table below, or combinations thereof:

order Bacteroidales order Clostridiales order Enterobacterials order Selenomonadales order Lactobacillales order Bifidobacterials order Fusobacterials order Burkholderiales order Bacillales

In some embodiments, the enteric microorganism is one or more selected from the families listed in the table below, or combinations thereof:

family Rikenellaceae family Ktedonobacteraceae family Nannocystaceae

In some embodiments, the gut microbes are one or more selected from the genera listed in the table below, or combinations thereof:

genus Lachnospiraceae_NK4A136_group genus Alistipes genus Ruminococcus_1 genus Ruminococcaceae_UCG-002 genus Ruminococcaceae_UCG-005 genus Coprococcus_2 genus Tyzzerella_4 genus Lachnospiraceae_UCG-001 genus Anaerotruncus genus Cloacibacterium genus norank_f__Ktedonobacteraceae genus Nannocystis genus norank_f__Hydrogenophilaceae

In some embodiments, the gut microbes are one or more selected from the following types or combinations thereof:

view unclassified_g__Subdoligranulum View unclassified_g__Alistipes View uncultured_organism_g__Parasutterella View unclassified_g__Tyzzerella_4 View uncultured_organism_g__Ruminococcaceae_UCG-005 View uncultured_organism_g__Anaerotruncus View uncultured_Alistipes_sp._g__Alistipes View Lachnospiraceae_bacterium_TF01-11 View unclassified_g__Anaerotruncus View unclassified_g__norank_o__Mollicutes_RF9 View uncultured_bacterium_g__Family_XIII_AD3011_group View uncultured_bacterium_g__norank_f__Christensenellaceae View uncultured_bacterium_g__Cloacibacterium View uncultured_organism_g__Peptococcus View Mycobacterium_celatum_g__Mycobacterium View unclassified_g__Oceanobacillus View Alistipes_putredinis_DSM_17216 View Prevotella_loescheii View uncultured_bacterium_g__norank_o__MBA03 View Eubacterium_brachy View uncultured_bacterium_adhufec108 View uncultured_Clostridiales_bacterium_g__norank_f__Ktedonobacteraceae View Nannocystis_pusilla View bacterium_2013Ark19i View Auxenochlorella_protothecoides_g__norank View uncultured_Bacteroidetes_bacterium_g__Dinghuibacter

The inventors have discovered that a variety of gut microbes are involved in the brain-gut axis in accordance with the present invention. As shown in the examples, between WT mice and TG mice the levels of certain gut microbes were altered and after administration of, for example, OM1, the levels of these gut microbes were restored to the levels in WT mice. Such gut microbes make up the gut microbial profile. As mentioned earlier, such a profile can be used for diagnostic and/or therapeutic purposes.

In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 , 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 , 66 or 67) of gut microbes selected from the table above in an individual relative to the level of corresponding gut microbes in a corresponding normal individual indicates that the individual is at risk for AD or has AD. In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 , 95, 96, 97, 98, 99 or 100% of gut microbes in a gut microbial profile consisting of gut microbes of the type, class, order, family, genus and species shown in the table above in an individual relative to the level of corresponding gut microbes in the corresponding normal individual indicates that the individual is at risk for AD or has AD.

In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 , 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 , 66 or 67) gut microbes selected from the type, class, order, family, genus and species from the table above in an individual with AD to the level of corresponding gut microbes in a corresponding normal individual relative to the level of corresponding gut microbes in a corresponding normal individual indicates that that the individual is receiving appropriate treatment. In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 , 95, 96, 97, 98, 99, or 100% of gut microbes in a gut microbe profile consisting of gut microbes of the type, class, order, family, genus, and species listed in the table above, in an individual with AD to the level of the corresponding gut microbes in a corresponding normal individual relative to the level of corresponding intestinal microbes in a corresponding normal individual indicates that the individual is receiving appropriate treatment.

The relative abundance of intestinal microbes can be altered using the composition or method of the present invention as follows: administration of a composition containing the appropriate microbiota or administration of a composition containing one or more compounds that significantly increase and/or decrease the relative abundance of the appropriate intestinal microbes.

Bacteroidetes include three main classes of bacteria: Bacteroidia , Flavobacteria , and Sphingobacteria . They are common in the environment, including soil, mud, seawater and animal intestines and skin.

Proteobacteria are the largest phylum of bacteria. These organisms exhibit extremely high metabolic diversity and reflect the medical, industrial and agricultural importance of most known bacteria. This is important from an evolutionary, geological and ecological point of view. All Proteobacteria bacteria are gram-negative and their outer wall is composed of lipopolysaccharide. Many of them have gas vesicles, flagella, or can move by sliding; they may have stalks, other processes, or have the ability to form multicellular fruiting bodies. Most are facultative or obligate anaerobic, autotrophic and heterotrophic, but there are exceptions. Some species are capable of photosynthesis, others store sulfur inside or outside the cell.

Firmicutes are a type of primarily gram-positive bacteria. However, some have porous pseudoouter membranes that make them Gram-negative. Scientists once classified the Firmicutes as including gram-positive bacteria, but have recently identified them as a major group with a related form called the low-G+C form. They are mostly round cells called cocci (single cocci) or are rod-shaped (bacilli). Many Firmicutes produce endospores that are resistant to desiccation and can survive in extreme conditions, allowing them to survive in a variety of environmental conditions.

The method of modifying the microbiota may also include measuring the relative abundance of one or more gut microorganisms in a sample from an individual. In last-generation sequencing, multiple sequences are determined for each sample. After preprocessing, sequence clustering algorithms are used to combine sequences with greater than 97% similarity to obtain OTUs. The OTU_table can then be obtained, which is a matrix indicating how many reads each OTU contains in each sample, i.e. each sample corresponds to the number of sequence reads in each OTU. Relative abundance is set to 100% for each sample (each row), and the percentage of the number of reads in each OTU relative to the number of all reads in the sample is calculated. Thus, relative abundance refers to the relative percentage of the number of sequences of each microorganism in a sample. The relative abundance can be detected by the method described herein or by a method known in the art that can be used to detect it, such as 16S rRNA gene detection.

The relative abundance of gut microbes can be determined by obtaining samples from an individual. The sample may be saliva, feces, and stomach, intestinal, and/or rectal contents; digestive tract tissue samples (such as tissue from the oral cavity, esophagus, stomach, small intestine, ileum, cecum, colon, and/or rectum); ascites fluid in gastrointestinal tissues; and any other samples that may be used by individuals familiar with microbiota analysis.

The relative abundance of one or more species of intestinal microorganisms can be compared with the normal relative abundance of the corresponding intestinal microbes. Normal relative abundance may be that of one or more individuals of similar age, gender, race, or the like. Normal relative content may be that of healthy individuals of similar age, sex, race, etc., who have responded to or demonstrated favorable results from a treatment or therapeutic intervention. In a specific embodiment, the normal relative abundance is the relative abundance of intestinal microbes in healthy individuals.

The methods and compositions of the invention may involve changing the relative abundance of one or more gut microbes. Exemplary embodiments may involve methods or compositions for changing the relative abundance of gut microbes by administering gut microbes to an individual. Depending on the desired outcome (eg, reduced metabolite levels, reduced lymphocytic infiltration in the brain, reduced microglial activation, reduced neuroinflammation, improved cognitive function, and alleviation of Alzheimer's disease symptoms) and the specific individual, the relative abundance of gut microbes can be adjusted by 1, 2. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99-100% or more, or to a range consisting of the end points of the above values or any values in between, such as from about 7% to about 28%, etc., or by about 7%, 14%, 21%, 28%, and the like. .; and/or the relative abundance of gut microbes in an individual can be provided to be similar to or equal to the relative abundance of the corresponding gut microbes in a corresponding normal individual.

As described herein, the present invention is directed to regulating a score that differs from a corresponding score in a corresponding normal individual toward a corresponding score in a corresponding normal individual such that the score after regulation is similar to or reaches the corresponding score in a corresponding normal individual. This regulation is applicable to various indicators subject to regulation or controllability, as described herein. Obviously, achieving the corresponding score in a suitable normal individual is ideal, but approximating the corresponding score in a corresponding normal individual is also desirable. Thus, the present invention is directed to obtaining one or more indicators from an individual in whom one or more indicators are intended to be adjusted to approximate or achieve the corresponding indicators in corresponding normal individuals. As used herein, the term "close to or reaches" means that the difference between the pre-adjustment score and the corresponding score in a corresponding normal individual is reduced by an amount greater than or equal to approximately 0, 1, 2, 3, 4, 5, 6. 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 100%, or the range consisting of the endpoints of the above values or any values in between, such as from about 7% to about 28% and the like. regarding differences after regulation. When the difference between the pre-regulation score and the corresponding score in the corresponding normal individual is reduced by approximately 100% relative to the difference after regulation, the regulated score reaches the corresponding score in the corresponding normal individual. Those skilled in the art understand that the specific magnitude of the difference depends, for example, on the object of regulation, the type of indicator, the method of measurement, etc.

An agent capable of adjusting one or more parameters in an individual to match or achieve those of a corresponding normal individual is therapeutically desirable. The selection of such agents may be based, for example, on agents (such as OM1) that serve as positive controls. Preferably, a test substance is selected that controls the relevant parameter substantially comparable to the agent used as a positive control (eg, OM1).

On the other hand, one or more indicators of an individual in whom regulation of one or more indicators is required is substantially comparable to a reference indicator (for example, a reference indicator for diagnosing Alzheimer's disease or identifying carbohydrate-drug-sensitive patients in patients with Alzheimer's disease) that may indicate that an individual is at risk for Alzheimer's disease or has Alzheimer's disease.

As used herein, the term "substantially the same" refers to the standardization of the test score relative to the reference score (i.e., the reference score is taken as 100%), the difference between the test score and the reference score is less than or equal to approximately 0.1. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100% or range , consisting of the end points of the above values or any values in between, for example, from about 7% to about 28%, etc., or about 7%, 14%, 21%, 28%, and the like. Those skilled in the art will understand that the specific magnitude of the difference will depend, for example, on the object being regulated, the type of indicator, the method of measurement, and the like.

In some embodiments, the agent for regulating the relative abundance of gut microbes comprises a complex of gut microbes. In some embodiments, the gut microbial complex includes microorganisms derived from fecal material or a bacterial library. In some embodiments, the fecal material comes from a normal individual or a patient with Alzheimer's disease.

Bacterial library identification: To obtain species classification information corresponding to each OTU, the Bayesian RDP classification algorithm is used to perform taxonomic analysis at the 97% similarity level of representative OTU sequences and the community composition for each sample is calculated at each classification level (domain, kingdom, phylum, class, order, family, genus, species). The taxonomic comparative species database used for the analysis of 16S bacterial flora is silva132/16S:silva132/16S (http://www.arb-silva.de).

Involved metabolites

The inventor has discovered that certain gut microbial metabolites, such as amino acids, are involved in the brain-gut axis in AD investigated by the present invention. These amino acids may be, for example, one or more selected from the following table; preferably one or more selected from phenylalanine, isoleucine, serotonin, histidine and acetylornithine; more preferably phenylalanine and/or isoleucine; most preferably phenylalanine.

4-OH proline Acetylornithine Alanin Alpha-aminoadipic acid Asparagine Aspartic acid Asymmetric dimethylarginine Beta-alanine Carnosine Citrulline Creatinine GABA Glutamic acid Glutamine Glycine Histidine Hypotaurine Isoleucine Kynurenin Leucine Lysine Methionine Methionine sulfoxide Ornithine Phenylalanine Pipecolic acid Proline Putrescine Pyroglutamic acid Serin Serotonin Taurine Threonine Tryptophan Tyrosine Valin

The inventors have discovered that various amino acids are involved in the brain-gut axis in accordance with the present invention. As shown in the examples, between WT mice and TG mice the levels of certain amino acids were altered and after administration of, for example, OM1, the levels of these amino acids were restored towards the levels in WT mice. Such amino acids make up the amino acid profile. As mentioned earlier, such a profile can be used for diagnostic and/or therapeutic purposes. In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 or 36) amino acids selected from the table above , in an individual relative to the level of corresponding amino acids in a corresponding normal individual, indicates that the individual is at risk for AD or has AD. In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 95, 96, 97, 98, 99, or 100% of the amino acids in the amino acid profile of the amino acids in the table above in an individual relative to the level of the corresponding amino acid in a corresponding normal individual indicates that the individual is at risk of having AD or has AD.

In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 or 36) amino acids selected from the table above , in an individual having AD, relative to the level of the corresponding amino acid in the corresponding normal individual in the direction of the level of the corresponding amino acid in the corresponding normal individual, indicates that the individual is receiving appropriate treatment. In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 , 95, 96, 97, 98, 99 or 100% of the amino acids in the amino acid profile consisting of the amino acids in the table above in an individual having AD, relative to the level of the corresponding amino acids in the corresponding normal individual, in the direction of the level of the corresponding amino acids in the corresponding normal individual , indicates that the individual is receiving appropriate treatment.

The inventors have discovered that the level of one or more amino acids in the above table in an individual does not correspond to the corresponding amino acid level in a corresponding normal individual, which may be due to the disease state of the individual. When the amino acid level in an individual is lower than the corresponding amino acid level in a corresponding normal individual, upregulation of the amino acid level is considered to have occurred. When an individual's amino acid level is higher than the corresponding amino acid level in a corresponding normal individual, downregulation of the amino acid level is considered to have occurred.

In some embodiments, the level of one or more amino acids in the table above is lower than the corresponding amino acid level in a corresponding normal individual. In some embodiments, the level of one or more amino acids in the table above is higher than the corresponding amino acid level in a corresponding normal individual. In some embodiments, the level of one or more amino acids in the table above is lower than the corresponding amino acid level in a corresponding normal individual, while the level of another one or more amino acids is higher than the corresponding amino acid level in a corresponding normal individual. Increased regulation or decreased regulation may depend, for example, on the object being regulated, the type of indicator, the method of measurement, etc.

In some embodiments, the level of glutamine is lower than the corresponding level of this amino acid in a corresponding normal individual. In some embodiments, the level of methionine is lower than the corresponding level of this amino acid in a corresponding normal individual. In some embodiments, the levels of both glutamine and methionine are lower than the corresponding levels of these amino acids in a corresponding normal individual.

In some embodiments, the level of glutamine is higher than the corresponding level of this amino acid in a corresponding normal individual. In some embodiments, the level of methionine is higher than the corresponding level of this amino acid in a corresponding normal individual. In some embodiments, the levels of both glutamine and methionine are higher than the corresponding levels of these amino acids in a corresponding normal individual.

In some embodiments, the level of one, two, three, four, or five of phenylalanine, isoleucine, serotonin, histidine, and acetylornithine is higher than the corresponding level of these amino acids in a corresponding normal individual. In some embodiments, the level of phenylalanine and/or isoleucine is higher than the corresponding level of these amino acids in a corresponding normal individual. In some embodiments, the level of phenylalanine is higher than the corresponding level of this amino acid in a corresponding normal individual.

In some embodiments, the levels of both glutamine and methionine are lower than the corresponding levels of these amino acids in a corresponding normal individual, while the level of one or more of 4-OH proline, acetylornithine, alanine, alpha-amino adipate, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glycine, histidine, hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamine ova acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine is higher than the corresponding level of these amino acids in a corresponding normal individual.

In some embodiments, the level of one or more of 4-OH proline, acetylornithine, alanine, alpha-amino adipate, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glutamine, glycine, histidine , hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamic acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine is higher than the corresponding level of these amino acids in a corresponding normal individual.

In some embodiments, the level of one or more amino acids from the table above is increased. In some embodiments, the level of one or more amino acids from the table above is reduced. In some embodiments, the level of one or more amino acids from the table above is increased while the level of another one or more amino acids is decreased. In some cases, an increase or decrease in the level may depend, for example, on the object being regulated, the type of indicator, the method of measurement, etc.

In some embodiments, glutamine levels are increased. In some embodiments, the methionine level is increased. In some embodiments, both glutamine and methionine levels are increased.

In some embodiments, glutamine levels are reduced. In some embodiments, the methionine level is reduced. In some embodiments, both glutamine and methionine levels are reduced.

In some embodiments, the level of one, two, three, four, or five of phenylalanine, isoleucine, serotonin, histidine, and acetylornithine is reduced. In some embodiments, the level of phenylalanine and/or isoleucine is reduced. In some embodiments, the level of phenylalanine is reduced.

In some embodiments, the level of both glutamine and methionine is increased, while the level of one or more of 4-OH proline, acetylornithine, alanine, alpha-amino adipate, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glycine, histidine, hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamic acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine decreases.

In some embodiments, the level of one or more of 4-OH proline, acetylornithine, alanine, alpha-amino adipate, asparagine, aspartic acid, asymmetric dimethylarginine, beta-alanine, carnosine, citrulline, creatinine, GABA, glutamic acid, glutamine, glycine, histidine , hypotaurine, isoleucine, kynurenine, leucine, lysine, methionine, methionine sulfoxide, ornithine, phenylalanine, pipecolic acid, proline, putrescine, pyroglutamic acid, serine, serotonin, taurine, threonine, tryptophan, tyrosine and valine are reduced. The inventor has discovered that immune cells, such as naïve T cells or undifferentiated T cells, take up amino acids (as exemplified by phenylalanine and isoleucine) in some cases, and, for example, differentiate into certain types of T cells, such as Th1 cells , through certain transporters such as SLC7A5 as an example. The inventors have discovered that preventing differentiation into Th1 cells, which leads to a state of Th1 dominance, can treat Th1 dominance diseases in various ways. Such measures include, but are not limited to, reducing the level of the corresponding amino acids, preventing the uptake of the corresponding amino acids by immune cells, and the like.

SLC7A5 is called L-type amino acid transporter 1 (LAT1) and belongs to the APC superfamily. It forms a heterodimeric amino acid transporter that interacts with the CD98 glycoprotein (SLC3A2) through conserved disulfide bonds. CD98 (4F2hc, SLC3A2) is a type II glycoprotein that acts as a chaperone protein for LAT1, stabilizing and promoting its translocation to the plasma membrane. This complex is responsible for capturing essential amino acids in key areas of the body such as the placenta and blood-brain barrier. The substrate includes a series of large neutral amino acids such as tyrosine, leucine, isoleucine, valine and phenylalanine, as well as drugs including L-DOPA and gabapentin.

Various agents (such as enzymes) can be used to degrade amino acids to reduce the levels of the corresponding amino acids, thereby reducing the differentiation of naïve T cells into Th1 cells. The enzymes presented in the table below can be used to degrade corresponding amino acids such as phenylalanine and isoleucine. The present enzymes can be delivered to appropriate areas in an individual by various means, such as the intestine or the peripheral circulation (such as peripheral blood), to degrade amino acids. For example, the enzyme can be delivered to the appropriate area by delivering a microorganism (eg, Escherichia coli ) expressing the enzyme to the appropriate area to express the enzyme in the appropriate area. The microorganism may express one or more of these enzymes. Those skilled in the art will understand that any microorganism can be used to deliver the enzyme and can be delivered to the appropriate area by various routes (eg, oral) without losing the ability to express the enzyme in the appropriate area. For example, Escherichia coli engineered to express an enzyme to degrade phenylalanine was used as a phenylalanine-degrading bacterium that was orally administered to mice to reduce phenylalanine levels in mice (as demonstrated by fecal phenylalanine testing) and reduce the proportion of Th1 cells (as indicated by detecting the proportion of Th1 cells in peripheral blood).

Enzymes that can be used to degrade phenylalanine

Convertible Products Metabolic enzymes 1 Tyrosine Phenylalanine 4-hydroxylase, PAH, preferred 2 2-phenylacetamide Phenylalanine 2-monooxygenase, PAO Catalase-peroxidase, katG 3 Phenylacetaldehyde Phenylacetaldehyde synthase, PAAS 4 Phenylethylamine Aromatic L-Amino Acid/L-Tryptophan Decarboxylase, DDC, Preferred Phenylalanine decarboxylase, AADC 5 N-acetyl-L-phenylalanine Phenylalanine N-acetyltransferase 6 Phenylpyruvate Aspartate aminotransferase, AST, preferred Tyrosine aminotransferase, TAT, preferred L-amino acid oxidase, IL4I1, preferred Phenylalanine dehydrogenase Aromatic amino acid transaminase Histidinol phosphate aminotransferase Aromatic amino acid aminotransferase II, AR09 7 D-phenylalanine Phenylalanine racemase 8 Trans-cinnamate Phenylalanine ammonia lyase, PAL Phenylalanine/tyrosine ammonia lyase, PTAL

Enzymes that can be used to degrade isoleucine

Convertible Products Metabolic enzymes 1 (S)-3-methyl-2-oxopentanoate Branched chain amino acid aminotransferase L-amino acid oxidase, IL4I1

Transporter inhibitors can be used to prevent immune cell uptake of the corresponding amino acids by inhibiting the uptake of naïve T cells by inhibiting the transporter (eg, the common transporter SLC7A5 for phenylalanine and isoleucine), thereby reducing the differentiation of naïve T cells into Th1 cells. Inhibitors that can be used to inhibit the SLC7A5 transporter include, but are not limited to, JPH 203, BCH and KMH-233, known in the art. For example, as shown in Example 4, administration of the SLC7A5 inhibitor JPH 203 to mice significantly reduced the proportion of Th1 cells in the mouse brain.

JPH203 is a chemically synthesized small molecule compound available from J-Pharma of Japan (http://www.j-pharma.com/b3_e.html) that selectively inhibits LAT1, with CAS number 1037592-40-7 (https:/ /www.medkoo.com/products/9544).

BCH is a known LAT1 inhibitor with CAS number 20448-79-7 (https://www.tocris.com/cn/products/bch_5027).

KMH-233 is a known LAT1 inhibitor with CAS number 1941174-13-5 (https://www.medkoo.com/products/9545).

Those skilled in the art will appreciate that the methods that can be used to prevent naïve T cells from differentiating into Th1 cells are not limited to this. Any methods that can be used to prevent the influence of gut microbes and/or their metabolites on the differentiation of naive T cells into Th1 cells can be used to reduce the proportion of Th1, and alleviate or reverse the Th1 dominant state, as described herein.

The inventors have discovered that various cytokines are involved in the brain-gut axis in accordance with the present invention. As shown in the examples, between WT mice and TG mice the levels of some cytokines changed and after administration, for example, OM1, the levels of these cytokines were restored to the levels in WT mice. Such cytokines constitute the cytokine profile. As mentioned earlier, such a profile can be used for diagnostic and/or therapeutic purposes.

In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31) cytokines selected from the following: OPG, resistin, TARC, VEGF, chemerin, IL -9, MMP-2, VEGF-D, TCA-3, gp130, MMP-10, 6Ckine, VEGF-B, IL-22, IL-1a, IFNg R1, granzyme B, LIX, CT-1, CD27L, endoglin , TRANCE, MCSF, 4-1BB, leptin R, CD36, TremL 1, VEGF R2, TGFb1, IL-3 Rb, H60 in an individual relative to the level of the corresponding cytokine in a corresponding normal individual indicates that the individual is at risk for AD or has AD . In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 , 95, 96, 97, 98, 99 or 100% cytokines in a cytokine profile consisting of cytokines selected from the following: OPG, resistin, TARC, VEGF, chemerin, IL-9, MMP-2, VEGF-D, TCA- 3, gp130, MMP-10, 6Ckine, VEGF-B, IL-22, IL-1a, IFNg R1, granzyme B, LIX, CT-1, CD27L, endoglin, TRANCE, MCSF, 4-1BB, leptin R, CD36 , TremL 1, VEGF R2, TGFb1, IL-3 Rb, H60 in an individual relative to the level of the corresponding cytokine in a corresponding normal individual indicates that the individual is at risk for AD or has AD.

In some embodiments, changing (e.g., increasing or decreasing) the level of one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31) cytokines selected from the following: OPG, resistin, TARC, VEGF, chemerin, IL -9, MMP-2, VEGF-D, TCA-3, gp130, MMP-10, 6Ckine, VEGF-B, IL-22, IL-1a, IFNg R1, granzyme B, LIX, CT-1, CD27L, endoglin , TRANCE, MCSF, 4-1BB, leptin R, CD36, TremL 1, VEGF R2, TGFb1, IL-3 Rb, H60 in an individual with AD to the level of corresponding cytokines in a corresponding normal individual relative to the level of corresponding cytokines in a corresponding normal individual indicates that the individual is receiving appropriate treatment. In one embodiment, changing (e.g., increasing or decreasing) level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 , 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 , 95, 96, 97, 98, 99 or 100% cytokines in a gut microbial profile consisting of cytokines selected from the following: OPG, resistin, TARC, VEGF, chemerin, IL-9, MMP-2, VEGF-D, TCA -3, gp130, MMP-10, 6Ckine, VEGF-B, IL-22, IL-1a, IFNg R1, granzyme B, LIX, CT-1, CD27L, endoglin, TRANCE, MCSF, 4-1BB, leptin R, CD36, TremL 1, VEGF R2, TGFb1, IL-3 Rb, H60 in an individual with AD to the level of corresponding gut microbes in a corresponding normal individual indicates that the individual is receiving appropriate treatment.

The present invention provides a composition that can directly or indirectly change the relative abundance of intestinal microbes to a predetermined level (such as a therapeutic level) for a predetermined period of time (eg, until the next dose). The target level can be obtained by measuring the relative abundance of the microbiota that resulted in a therapeutic response (eg, reduction in metabolite levels, reduction in lymphocyte infiltration into the brain, reduction in microglial activation, reduction in neuroinflammation, improvement in cognition, improvement in Alzheimer's disease symptoms).

In some embodiments of the method, the agent or composition of the present invention may contain sufficiently purified or enriched intestinal microbes such that the agent or composition may contain at least about 5 wt.%, 10 wt.%, 20 wt.%, 30 wt. %, 40 wt%, 50 wt%, 60 wt%, 70 wt%, 80 wt%, 85 wt%, 90 wt%, 95 wt%, 99 wt% or more of desired intestinal microbes based on the weight of the product or composition, and/or less than about 40 wt.%, 30 wt.%, 20 wt.%, 15 wt.%, 14 wt.%, 13 wt.%, 12 wt.%, 11 wt.%, 10 wt.%, 9 wt.%, 8 wt.%, 7 wt.%, 6 wt.%, 5 wt.%, 4 wt.%, 3 wt.%, 2 wt.%, 1 wt.% or less of unwanted intestinal microbes based on the weight of the product or composition.

Agents and compositions that regulate the relative content of intestinal microbes in accordance with the present invention may lead to changes in metabolic functions. For example, altered metabolic function may involve regulation of amino acid levels among microbial metabolites.

In some embodiments, when performing detection in a peripheral blood sample of an individual, the methods, tools and compositions of the present invention can adjust the amino acid level by approximately 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 , 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185 , 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300 µM or more , or by the range defined by the end points of the above values, or by any value in between; and/or make the amino acid level close to or achieve the corresponding amino acid level in a suitable normal individual.

In some embodiments, when performing detection in a fecal sample of an individual, the methods, tools and compositions of the present invention can adjust the amino acid level by approximately 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000 µmol/g µM or more, or per range, defined by endpoints from the above values, or any amount in between; and/or make the amino acid level close to or achieve the corresponding amino acid level in a suitable normal individual.

In some embodiments, the methods, agents, and compositions of the present invention can cause altered immune cell infiltration in the brain. For example, the proportion of proinflammatory Th1 cells among CD4+ T cells decreases. In some embodiments, the agents and compositions of the present invention can reduce the proportion of pro-inflammatory Th1 cells relative to CD4+ T cells in an individual's sample by approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37 , 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 , 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87 , 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100% or more; and/or make the proportion of proinflammatory Th1 cells relative to CD4+ T cells similar to or equal to the proportion of the corresponding proinflammatory Th1 cells relative to CD4+ T cells in the corresponding normal individual.

In some embodiments, the methods, agents, and compositions of the present invention can reduce the relative uptake of amino acids by naïve T cells. In some embodiments, the agents and compositions of the present invention can reduce the relative amino acid uptake of naïve T cells by approximately 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200 , 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300 or more; and/or making the relative amino acid uptake level of naïve T cells similar to or equal to the relative amino acid uptake level of the corresponding naïve T cells in the corresponding normal individual.

In some embodiments, the methods, agents, and compositions of the present invention may result in altered activation of microglia in the brain. For example, altered microglial activation in the brain may involve increased or decreased microglial activation in the brain. Microglial activation in the brain may be increased or decreased by approximately 1-100%, e.g., approximately 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or 99% or 100%, or any range, defined by endpoints of the above values, or any amount in between, for example, by about 7% to about 28%, etc., or by about 7%, 14%, 21%, 28%, and the like.

In some embodiments, the methods, agents, and compositions of the invention may result in altered levels of IBA1. For example, a change in the level of IBA1 may involve increasing or decreasing the level of IBA1. The level of IBA1 may be increased or decreased by about 0 to about 3000 relative levels, for example, by a relative level of about 0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600 , 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1650, 1700, 1750, 1800, 1850 , 1900, 1950, 2000, 2050, 2100, 2150, 2200, 2250, 2300, 2350, 2400, 2450, 2500, 2550, 2600, 2650, 2700, 2750, 2800, 2850, 2900, 2950, 3000 relative levels, or by the range defined by the endpoints of the above values, or by any amount between them, for example, by about 500 to about 3000, or by about 500, 1000, 1500, and the like.

Compositions

The agent or pharmaceutical composition of the present invention may contain intestinal microbes, which can regulate the relative abundance of intestinal microbes in an individual. Gut microbes can be viable (living), dormant, inactive, or dead bacteria. The agent or pharmaceutical composition of the present invention may contain a compound or agent that can regulate the relative abundance of intestinal microbes in an individual. One or more compounds may be used to alter the microbiota of an individual. Such compounds or agents may include, but are not limited to, antibiotics and/or antibacterial agents, prebiotics such as bacterial cell wall components, bacterial nucleic acids (such as DNA and RNA), bacterial membrane components, and bacterial structural components (such as proteins , carbohydrates, lipids and their combinations, such as lipoproteins, sugar esters and glycoproteins), organic acids, inorganic acids, alkalis, proteins and peptides, enzymes and coenzymes, amino acids and nucleic acids, sugars, lipids, glycoproteins, lipoproteins, esters sugars, vitamins, biologically active compounds, metabolites containing inorganic components, low molecular weight compounds such as nitrogen-containing molecules or sulfurous acid-containing molecules, resistant starch, potato starch or high-amylose starch, modified starch (including carboxylated starch, acetylated starch , propionated starch and butylated starch), non-digestible oligosaccharides such as fructooligosaccharide, oligodextrose, xylooligosaccharide, galactooligosaccharide, arabinoxylan, arabinogalactan, galactomannan polysaccharides, polydextrose, oligofructose, inulin and their derivatives, but other oligosaccharides, other soluble fibers and their combination, which may play a prebiotic role, cannot be excluded. The sugar may be selected from monosaccharides, disaccharides, oligosaccharides, polysaccharides or derivatives thereof, combinations thereof and/or derivatives thereof; preferably oligosaccharides and polysaccharides; more preferably mannuronic acid oligosaccharides.

The agent or pharmaceutical composition of the present invention may also include, for example, amino acids, amino sugars, sugar alcohols, proteins, carbohydrates, monosaccharides, disaccharides, oligosaccharides, polysaccharides, nucleic acids, buffers, surfactants, lipids, liposomes, other excipients and their mixtures. Other suitable ingredients may include steroids, anti-inflammatory agents, non-steroidal anti-inflammatory drugs, analgesics, cells, anti-inflammatory agents, growth factors, growth factor fragments, small molecule wound healing promoters, hormones, cytokines, peptides, antibodies, enzymes, isolated cells, platelets, immunosuppressants , nucleic acids, cell types, viruses, viral particles, essential nutrients, minerals, metals or vitamins, and a combination thereof. In addition, the agents and compositions of the present invention may include diluents such as water, saline or buffer.

The agent or pharmaceutical composition of the present invention may be formulated as a pharmaceutical composition comprising a pharmaceutically acceptable carrier. As used herein, a "pharmaceutically acceptable carrier" includes any and all physiologically compatible solvents, dispersants, coatings, antibacterial and antifungal agents, isotonicity and absorption retardants, and the like. In one embodiment, an agent or pharmaceutical composition of the invention may be included in a pharmaceutical composition suitable for delivery to an individual. The pharmaceutical composition may also include a pharmaceutically acceptable carrier. As used herein, a "pharmaceutically acceptable carrier" includes any and all physiologically compatible solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonicity and absorption retardants, and the like. Examples of pharmaceutically acceptable carriers include one or more of the following: water, saline, phosphate-buffered saline, glucose, glycerol, ethanol, etc., and combinations thereof. In many cases, it is preferable to include isotonic agents such as sugars, polyhydric alcohols such as mannitol, sorbitol or sodium chloride in the compositions.

The pharmaceutical composition can be formulated in various forms. These include, for example, liquid, semi-solid and solid forms of the drug, such as liquid solutions (such as injections and infusions), dispersions or suspensions, tablets, pills, powders, liposomes, suppositories and other formulations. The pharmaceutical composition can be formulated to provide a high concentration of drug. The pharmaceutical composition may further be sterile and stable under conditions of manufacture and storage. Sterile injection solutions can be prepared by incorporating one of the ingredients listed above, or a combination thereof (if necessary), in a suitable solvent in the required quantity, followed by filtration and sterilization.

The exemplary form of the pharmaceutical composition may depend on the intended mode of delivery and therapeutic use. In one embodiment, the pharmaceutical composition is formulated for oral delivery. Some compositions may be in the form of pill-based delivery (as described in US patent application No. 12/976648 entitled "pill catchers", filed October 22, 2010) and have a sustained release. In one embodiment, pill-based delivery may be part of a system that allows delivery to a specific location in the intestinal tract. In another embodiment, the pharmaceutical composition may be formulated as a sustained release formulation. In another embodiment, the pharmaceutical composition may be enclosed in a coating that does not begin to degrade until it leaves the stomach. In another embodiment, the pharmaceutical composition may be formulated with a carrier that protects the composition from immediate release, such as in the case of sustained or controlled release formulations, including implants, transdermal patches, and microencapsulated delivery systems. Biodegradable, biocompatible polymers such as ethylene vinyl acetate, polyanhydrides, polyglycolic acid, collagen, polyorthoesters and polylactic acid can be used. Many methods for preparing such compositions have been patented or are well known to those skilled in the art. See, for example, Sustained and Controlled Release Drug Delivery Systems, J.R. Robinson, ed., Marcel Dekker, Inc., New York, 1978. "Sustained release" refers to the release of a composition or active compound thereof over a prolonged period of time relative to the release achieved by delivery of a conventional composition formulation.

Another type of pharmaceutical composition includes an activated form, such as formulation with microbiota in a dormant or inactive state (eg, in a lyophilized state). In the combination composition, the microbiota may be present in a dormant or inactive state, or the compound or agent in which the microbiota is cultured may be inactive. In one illustrative embodiment, the pharmaceutical composition may be formulated to include at least one type of dormant or inactive microbiota and an inactive compound or substance that cultivates the microbiota.

The described pharmaceutical compositions and combination pharmaceutical compositions may also be formulated in the form of foods, beverages, dietary supplements and/or additional substances. Such compositions are compositions suitable for human and/or animal consumption. Those skilled in the art may be well aware of specific microbiota compositions that can be used in oral or ingestible formulations and are considered suitable for administration to humans and/or animals. Many compositions are used for the production of food or food additives/additional substances; Thus, another important aspect is to provide food or nutritional supplements/supplements including microbiota to a person or animal to regulate the gut microbes of the individual.

The usable composition may be formulated to include sweeteners, stabilizers or binders, humectants, and/or natural and/or artificial flavors. The composition may also include natural and/or artificial colors and preservatives. In one embodiment, the composition may include monosaccharides, disaccharides and polysaccharides such as, but not limited to, sucrose (sugar), dextrose, maltose, dextrin, xylose, ribose, glucose, mannose, galactose, sucromalt, fructose (levose), invert sugar, corn syrup, maltodextrin, oligofructose syrup, partially hydrolyzed starch, corn syrup powder, polydextrose, soluble fiber, insoluble fiber, natural sugar cane juice, gelatin, citric acid, lactic acid, natural colors, natural flavors, fractionated coconut oil, carnauba wax or a combination of both.

Dosage

The agent or composition of the present invention may contain a "therapeutically effective amount" or "effective amount" of ingredients. "Therapeutically effective amount" refers to an amount that is effective to achieve the desired therapeutic result at the required dose and over a period of time. The therapeutically effective amount of the agent or pharmaceutical composition may vary depending on various factors, such as the disease state of the individual, age, sex and brain condition, and the ability of the composition to produce the desired response in the individual. A therapeutically effective amount is also an amount in which the therapeutically beneficial effects of the agent or pharmaceutical composition exceed the toxic or harmful effects of the agent or pharmaceutical composition. In an illustrative embodiment, a therapeutically effective amount of an agent or pharmaceutical composition is an amount in which the relative abundance of one or more gut microbes increases. For example, a therapeutically effective amount of an agent for regulating the relative abundance of intestinal microbes increases the relative abundance of one or more intestinal microbes in an individual.

As used herein, the term "treatment" generally refers to achieving the desired pharmacological and/or physiological effect. The effect may be prophylactic in terms of complete or partial prevention of the disease or its symptoms; and/or it may be therapeutic in terms of partial or complete stabilization or treatment of the disease and/or side effects of the disease. "Treatment" as used herein includes any treatment of a patient's disease, including: (a) prevention of diseases or symptoms that occur in patients who are predisposed to the diseases or symptoms but who have not yet been diagnosed with the disease; (b) inhibiting symptoms of a disease, eg, inhibiting the progression of a disease and preventing the development of a disease; or (c) relief of symptoms of the disease, i.e. ensuring the extinction of the disease or symptoms.

Indications

Through research within the scope of the present invention, the inventors discovered that the abnormal intestinal microbial pattern caused naïve T cells to overdifferentiate into Th1 cells, and the level of Th1 cells in the peripheral blood increased in an abnormal manner. Increased levels of peripheral Th1 cells cause more Th1 cells to infiltrate into the brain, causing diseases such as Alzheimer's disease. By restoring the intestinal microbiota, the differentiation of naive T cells into Th1 cells can be reduced, and the abnormally elevated level of Th1 cells in the peripheral blood can be reduced, thereby treating the disease. Thus, the present invention provides a method for treating diseases associated with high levels of Th1 cells in the peripheral blood of an individual, the method comprising regulating the relative abundance of gut microbes in the individual, as described herein, reducing the level of amino acids in the peripheral blood, as described herein, or inhibiting the uptake of naïve T cells of an amino acid in peripheral blood, as described herein.

The terms “Th1 dominance” and “Th1 cell dominance” are used interchangeably herein. In some cases, Th1 dominance may be represented by high levels of Th1 cells in the peripheral blood. High level of Th1 cells in peripheral blood may refer to the level of Th1 cells in an individual's peripheral blood, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30% , 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 250 %, 300%, 350%, 400%, 450%, 500%, 550%, 600%, 650%, 700%, 750%, 800%, 850%, 900%, 950%, 1000% or higher a suitable control (for example, a normal control, such as a sample of healthy people), or by a range defined by the end points of the above values, or by any value in between, for example, from about 7% to about 28%, etc. or by approximately 7%, 14%, 21%, 28%, etc. Such diseases include, but are not limited to, multiple sclerosis, Crohn's disease, type 1 diabetes, rheumatoid arthritis, Hashimoto's thyroiditis, vitiligo, Sjogren's syndrome, polycystic ovarian disease (PCOS), celiac disease and Graves' disease.

As used herein, the term "individual" refers to any living organism, including, but not limited to, humans, non-human primates such as chimpanzees and other apes and monkeys, farm animals such as cows, sheep, pigs , goats and horses, domestic mammals such as dogs and cats, laboratory animals including rodents such as mice, rats, rabbits, guinea pigs, etc. The term does not indicate a specific age or gender. In one embodiment, the individual is a human. In the context of the present invention, the terms "subject", "individual" and "patient" are used interchangeably.

In one embodiment, the relative abundance of one or more intestinal microorganisms in an individual exceeds their relative abundance in a healthy individual. In one embodiment, the relative abundance of one or more intestinal microorganisms in an individual is lower than that of a healthy individual. In one embodiment, the relative abundance of one or more intestinal microorganisms in an individual is greater than that of a healthy individual, and the relative abundance of the other one or more microorganisms is lower than that of a healthy individual.

The dosage may depend on the type of microbiota present in the agent or pharmaceutical composition of the invention. Dosage may also be determined based on the relative abundance of one or more types of microbiota present in an individual.

In one embodiment, the agent or pharmaceutical composition of the present invention can effectively change the relative abundance of one or more types of microbiota. In another embodiment, the agent or pharmaceutical composition can effectively increase or decrease the relative abundance of the microbiota in an individual. In one embodiment, the agent or pharmaceutical composition may increase or decrease the relative abundance of specific microbiota microorganisms in an individual and decrease the relative abundance of other specific microbiota microorganisms in an individual.

In another embodiment, the agent or pharmaceutical composition of the present invention can also effectively alter the microbiota in an individual such that, upon administration of the agent or pharmaceutical composition, the microbiota in the individual mimics the microbiota present in the individual responding to treatment for Alzheimer's disease. The agent or pharmaceutical composition can effectively modify the microbiota to mimic the microbiota of normal and healthy individuals with similar brain conditions, age, gender, race, and the like.

The dosage regimen may be adjusted to provide the most appropriate desired response (such as a therapeutic response or a prophylactic response). For example, a single bolus may be delivered, multiple divided doses may be delivered over time, or the dose may be decreased or increased in proportion to the need of the treatment situation. It is particularly advantageous to formulate parenteral compositions in unit dosage form for ease of delivery and uniformity of dosage. Unit dosage form, as used herein, refers to a physically discrete unit suitable as a single dose for a mammal being treated; each unit contains a predetermined amount of active compound, calculated to achieve the desired therapeutic effect, in combination with the desired pharmaceutical carrier. The characteristics of the unit dosage form used in the present invention are determined by or directly dependent on the following: (a) the unique properties of the active compound and the particular therapeutic or prophylactic effect to be achieved; and (b) the limitations inherent in the field of combining this active compound for individual therapy.

When used for the methods provided by the present invention, an exemplary dosage range for the agent or pharmaceutical composition of the present invention may be from about 0.001 to about 100 mg/kg body weight per day, from about 0.01 to about 50 mg/kg body weight body weight per day, such as about 0.05 to about 10 mg/kg body weight per day, delivered in one or more doses, such as 1-3 doses. The exact dosage will depend on the frequency and route of delivery, sex, age, body weight and general condition of the individual being treated, the nature and severity of the condition being treated, any concomitant diseases to be treated, and other factors known to those skilled in the art.

In one embodiment, the agent or pharmaceutical composition of the present invention contains microbiota, such as Verrucomicrobia , having a total concentration ranging from about 0.001 mg/kg to about 100 mg/kg. In another embodiment, the agent or pharmaceutical composition contains microbiota, such as Verrucomicrobia , having a total concentration ranging from about 0.1 mg/kg to about 50 mg/kg. In a further embodiment, the agent or pharmaceutical composition contains a microbiota, such as Verrucomicrobia , having a total concentration ranging from about 1 mg/kg to about 10 mg/kg.

In some aspects, the agents, compositions, or kits described herein are administered in dosages based on the body weight of the individual. In some embodiments, the agents, compositions, or kits described herein contain one or more of the agents mentioned herein in an amount based on the body weight of the individual.

In some embodiments, the amount of one or more agents mentioned herein in the agents, compositions or kits described herein ranges from about 1.0 to about 50.0 mg/kg or more, preferably from about 5 .0 to 40.0 mg/kg, more preferably from about 10.0 to 30.0 mg/kg, based on the body weight of the individual. In some embodiments, the amount of one or more agents mentioned herein in the agents, compositions, or kits described herein is about 1.0, 1.5, 2.0, 2.5, 3.0, 3 ,5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5 , 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16 ,0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0 , 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28 .5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5 , 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 40.5, 41 ,0, 41.5, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0, 46.5, 47.0 , 47.5, 48.0, 48.5, 49.0, 49.5, 50.0 mg/kg or more, or any range defined by the endpoints of the above values, or any value in between, such as, about 1.1 to 1.4 mg/kg and the like, or about 1.1, 1.2, 1.3, 1.4 mg/kg and the like. based on the body weight of the individual.

In other aspects, the agents, compositions or kits described herein are administered in fixed doses. In some embodiments, the agents, compositions, or kits described herein contain a fixed amount of one or more of the agents mentioned herein.

In some embodiments, the amount of one or more agents referred to herein in the agents, compositions, or kits described herein is in each instance independently 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 , 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 , 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200 mg or more, or range as determined by endpoints of the above values, or any value between them, for example, from about 1.1 to 1.4 mg, and the like. or approximately 1.1, 1.2, 1.3, 1.4, etc.

In other aspects, some of the components of the agents, compositions or kits described herein are administered in dosages based on the individual's weight, as described above, while other components are administered in fixed doses, as described above. In some embodiments, the amounts of some of the components in the agents, compositions, or kits described herein. are amounts based on the weight of the individual, as described above, and the other components are fixed amounts, as described above.

Delivery

The agent or pharmaceutical composition of the present invention can be delivered or administered by various methods known in the art. The terms "delivery", "deliver", "administer" and "apply" are used interchangeably herein. As will be appreciated by those skilled in the art, the route and/or method of delivery varies depending on the desired result. In one embodiment, the agent or pharmaceutical composition of the invention is delivered orally. In another embodiment, the agent or pharmaceutical composition of the invention is delivered orally. In another embodiment, the agent or pharmaceutical composition of the invention is delivered by the nasal route. Another delivery method may include methods and combinations for delivery to the intestine.

The agent or pharmaceutical composition of the present invention can be delivered to a predetermined area and/or structure in an individual. The area that can be targeted in the intestine may include, but is not limited to, the stomach, biliary-pancreatic branch (segment), Roux branch, common branch, ileum, cecum or colon. They can target a structure that constitutes an area with a customized environment with a specific pH range, temperature, humidity, and metabolite content. Diseases or conditions associated with altered microbiota lineages may demonstrate the presence of new microorganisms, the absence of normal microorganisms, or a change in the ratio of microorganisms.

Delivery of an agent or pharmaceutical composition of the present invention may be targeted to one or more areas in an individual. The area may include, but is not limited to, the area in the intestine. In an illustrative embodiment, delivery is targeted to the oral cavity, stomach, biliary-pancreatic branch, Roux branch, common branch, small intestine, ileum, cecum, colon, or colon in the intestine. Delivery may also be targeted to one or more tissues of an individual. The tissue may include any tissue in the intestine, such as stomach, biliary-pancreatic branch, Roux branch, common branch, small intestine, ileum, cecum, colon or colon.

The components of the agent or pharmaceutical composition of the present invention may be formulated separately, or a portion of them may be formulated together. In one embodiment, the agent or pharmaceutical composition of the present invention may be formulated as an agent or pharmaceutical composition suitable for single or multiple administrations.

The components of the agent or pharmaceutical composition of the present invention may be administered separately, or some or all of them may be administered together. The agents or components of the pharmaceutical composition of the present invention may be administered at substantially different times, or some or all of them may be administered substantially simultaneously.

Each of the agents or components of the pharmaceutical composition of the present invention can be independently administered by various suitable routes, including, but not limited to, oral or parenteral (intravenous, intramuscular, topical or subcutaneous routes). In some embodiments, each of the components of the agents or pharmaceutical composition of the present invention may be independently administered orally or by injection, for example, by intravenous injection or intraperitoneal injection.

Each of the components of the agent or pharmaceutical composition of the present invention may independently be in a suitable dosage form, including, but not limited to, dosage forms of tablets, lozenges, pills, capsules (e.g., hard capsules, soft capsules, enteric-coated capsules, microcapsules), elixirs, granules, syrups, injection forms (intramuscular, intravenous, intraperitoneal), granules, emulsions, suspensions, solutions, dispersions and slow-release preparations for oral or parenteral administration.

Each of the agents or components of the pharmaceutical composition of the present invention may independently contain a pharmaceutically acceptable carrier and/or excipient.

The agents of the present invention or the components of the pharmaceutical composition can be administered independently every 1 day, every 2 days, every 3 days, every 4 days, every 5 days, every 6 days, every week, every 2 weeks, every 3 weeks or every month or from lower frequency.

The agents of the present invention or components in the pharmaceutical composition can be administered independently 1 time, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times or 10 times or more per day.

The agents of the present invention or the components of the pharmaceutical composition can be administered independently within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 consecutive days or more.

One of the components of the agent or pharmaceutical composition of the present invention may be administered 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 days or more before or after the administration of the other component. For example, in one embodiment, agent or component 1 of the pharmaceutical composition of the present invention is administered on day 1, and agent or component 2 of the pharmaceutical composition of the present invention is administered after 2 days (i.e., day 3), and agent or component 1 of the pharmaceutical composition the compositions of the present invention are administered after another three days (ie on day 6).

Delivery of the agent or pharmaceutical composition of the present invention can also be repeated one or more times. Repeated delivery of the agent or pharmaceutical composition of the present invention may be performed one or more times before and/or after treatment of the disease. Re-delivery may be made in the same manner as the original delivery.

The agent or pharmaceutical composition of the present invention can also be administered together with an agent, which may include a therapeutic, prophylactic or diagnostic agent. The therapeutic, prophylactic or diagnostic agent is selected from small molecule compounds, nucleic acids, proteins such as polypeptide prebiotics, including bacterial components (such as bacterial cell wall components (such as peptidoglycan), bacterial nucleic acid (such as DNA and RNA), bacterial components membranes and structural components of bacteria (such as proteins, carbohydrates, lipids and their combinations such as lipoproteins, sugar esters and glycoprotein)), bacterial metabolites, organic acids, inorganic acids, bases, proteins and peptides, enzymes and coenzymes, amino acids and nucleic acids, carbohydrates, lipids, glycoproteins, lipoproteins, sugar esters, vitamins, biologically active compounds, metabolites containing inorganic components, low molecular weight compounds (for example, nitrogen-containing molecules or sulfurous acid-containing molecules), resistant starch, potato starch or high amylose content, modified starch (including carboxylated starch, acetylated starch, propionated starch and butylated starch), digestible oligosaccharides such as fructooligosaccharides, dextrose, xylooligosaccharide, galactooligosaccharide, arabinoxylan, arabinogalactan, galactomannan polysaccharides, polydextran, oligofructose, inulin and their derivatives (however other oligosaccharides that may play a role as prebiotics cannot be excluded), other soluble fibers and combinations thereof. In one embodiment, the delivered agent is a delivered small molecule compound that has low oral bioavailability and acts on the microbial region of the host intestine. Low bioavailability is generally undesirable in medicine, since intestinal absorption is the goal of most oral treatments.

The agent or pharmaceutical composition of the present invention can also be administered in the same composition as the above-mentioned agent, or it can be administered separately from the agent administered before, simultaneously and/or after the agent or pharmaceutical composition of the present invention. The agent or pharmaceutical composition of the present invention can be administered in at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 or more days before administration of the drug. The agent or pharmaceutical composition of the present invention can also be administered at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 , 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 or more days after administration of the drug.

The agent or pharmaceutical composition of the invention can also be delivered via an at least partially implantable system. The implantable system may be any system known and used in the art. The system may include programmable pumps (such as the pumps often used to deliver insulin to diabetic patients). One or more of these components may be modular or coupled to a transdermal delivery system, which may include orifices, needles, patches, or the like. In an illustrative embodiment, the implantable system includes a reservoir and an orifice. The reservoir may include a refillable or refillable container for housing the composition. In another embodiment, the system may include a catheter. In another embodiment, the implantable system is a transluminal catheter. The system may also be arranged to deliver the composition at a prescribed dose and/or at a prescribed interval. The prescribed dose and/or the prescribed interval can be determined by those skilled in the art.

Some embodiments of the invention are illustrated in the following non-limiting examples.

EXAMPLE

Materials And ways

Animals. Tg mice and cohoused WT mice (corresponding to WT mice obtained by crossing Tg mice and C57 mice) were housed in the same cage after birth. WT (C57) mice were housed in separate cages to avoid cross-transfer of microbiota. All mice were housed at 23°C on a 12-hour (h) light/dark cycle. Mice were randomly assigned to different groups before treatment. Throughout the analysis, Tg mice, male and female Tg mice were sacrificed at 2, 3, 5, 7, and 9 months of age. Brains were removed and stained for immune cell analysis and cytokine analysis. Before killing the mice, feces were collected for microbiota analysis. To administer OM1 at 6.5 months of age on a 450 mg bid (bid) basis in a phase III trial, Tg mice were administered 100 mg/kg OM1 via oral administration once daily for one month. To monitor cognitive activity, behavioral tests were performed after the last administration. The brains and feces of the mice were then used for various analyses. For behavioral testing, Tg mice and WT mice at different months and treated mice were tested through discrimination training as previously described. All mouse movements that occurred in the Phenotyper (HomeCage) were recorded through a computerized tracking system that calculates the time and number of entries required to achieve an 80% efficiency standard (Noldus, Ethovision). For intraperitoneal injection of phenylalanine and isoleucine, 4-month-old C57 mice were given 50 mg/kg phenylalanine or isoleucine for 4 days. For hippocampal injection of phenylalanine and isoleucine, 4-month-old C57 mice were injected with 3 μl of phenylalanine or isoleucine at a dose of 120 ml/l. After 10 days, these mice underwent blood sample analysis and behavioral testing using FACS.

For analysis of APP/PS1 mice at different time points, mice and age-matched wild-type mice were sacrificed at 3, 9, and 14 months of age. The brains and blood of the mice were collected for various analyses. Before sacrifice, mouse feces were collected for microbiota analysis. To administer APP/PS1 to mice, 6-month-old mice were administered with or without antibiotics for 3 months with 50 mg/kg OM1 by gavage once daily. Behavioral tests were performed to monitor cognitive activity after the last administration. The animal experiment was approved by the ethical committee of Shanghai SIPPR-Bk Lab Animal Co., Ltd (No.: 2016002).

DNA extraction from a fecal sample and amplification by PCR . All fecal samples were frozen at −80°C prior to DNA extraction and analysis. Microbial DNA was extracted from fecal samples using the EZNA® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, US) according to the manufacturer's protocols. The final DNA concentration and purity were determined using a NanoDrop 2000 UV–Vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was checked by 1% agarose gel electrophoresis. Hypervariable regions V3-V4 of the bacterial 16S rRNA gene were amplified using primers 338 F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) through a PCR thermal cycler system (GeneAmp 9700, ABI, USA). PCR reactions were performed using the following program: 3 minutes (min) of denaturation at 95°C, 27 cycles of 30 seconds (s) at 95°C, annealing for 30 s at 55°C, and elongation for 45 s at 72 °C, and final extension at 72°C for 10 min. PCR reactions were performed in triplicate in 20 μl of a mixture containing 4 μl of 5× FastPfu buffer, 2 μl of 2.5 mmol/l dNTPs, 0.8 μl of each primer (5 μmol/l), 0.4 μl of FastPfu polymerase and 10 ng DNA template. The resulting PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA) according to the manufacturer's protocol . A degenerate primer refers to a mixture of different sequences that reflect all the different base possibilities for coding a single amino acid. To increase specificity, a table of codon usage can be consulted to reduce degeneracy according to the base usage preferences of different organisms. In addition to the normal four base symbols A, T, G and C, other letters such as R, Y, M, K, etc. are present in the nucleotide chain, where R=A/G, Y=C/T , M=A/C, K=G/T, S=C/G, W= A/T, H= A/C/T, B= C/G /T, V=A/C/G, D =A/G/T, N=A/C/G/T.

Sequencing using Illumina MiSeq . In accordance with the standard protocol of Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China), purified amplicons were pooled in equimolar amounts and subjected to paired-end sequencing (2×300) on the Illumina MiSeq platform (Illumina, San Diego, USA).

Processing of sequencing data . Raw fastq files were demultiplexed, quality filtered by Trimmomatic, and merged by FLASH based on the following criteria: (i) Reads were trimmed at any region that received an average quality score < 20 over a 50-bp sliding window. (ii) Precision primer selection was performed, allowing for 2 nucleotide mismatches, and reads containing ambiguous bases were removed. (iii) Sequences with overlap greater than 10 bp. were combined according to their overlap sequence. Operational taxonomic units (OTUs) were clustered at a 97% similarity threshold using UPARSE (version 7.1 http://drive5.com/uparse/ ), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP Classifier algorithm ( http://rdp.cme.msu.edu/ ) against the Silva 16S rRNA database (SSU123) using a confidence threshold of 70%.

Obtaining metabolite samples . Samples stored at -80°C were collected and thawed at room temperature. The experiment used 50 mg samples and also added 400 μL of methanol-water (4:1, v/v) to homogenize the sample using a homogenizer for 10 seconds. The solution was subjected to ultrasonic extraction on ice for 10 min and stored at -20°C for 30 min, then centrifuged for 15 min at 13,000 rpm at 4°C. 200 μL of supernatant was used for LC-MS analysis.

LC/MS Analysis Parameters . LC-MS was carried out on an AB Sciex TripleTOF 5600TM mass spectrometric system (AB SCIEX, USA). LC conditions: Column: Acquity BEH C18 (100 mm × 2.1 mm id, 1.7 µm; Waters, Milford, USA). Solvent: The column was maintained at 40°C and separations were achieved using the following gradient: 5% B-30% B over 0-3 min, 30% B-95% B over 3-9 min, 95% B-95% B for 9-13.0 minutes; 95% B-5% B for 13.0-13.1 min, and hold for 13.1-16 min at 5% B; the flow rate is 0.40 ml/min, where B is acetonitrile:isopropanol 1:1 (0.1% (v/v) formic acid) and A is an aqueous solution of formic acid (0.1% (v/v) ./vol.) formic acid). The injection volume was 20 μl. Mass spectrometry data were acquired using an AB Sciex TripleTOF 5600TM mass spectrometry system equipped with an electrospray ionization (ESI) source operating in either positive ion mode or negative ion mode, 1.0 kV capillary voltage, 40 V sampling cone, collision energy 6 eV. The source temperature was set to 120°C, with a desolvation gas flow of 45 L/h. Centroid data were acquired at 50 to 1000 m/z with a resolution of 30,000.

Obtaining a sample of QC metabolites . The QC sample was prepared by mixing aliquots of all samples to create a pooled sample and then analyzed using the same method with the analytical samples. QC was injected at regular intervals (every 10 samples) throughout the analytical run to obtain a data set from which repeatability could be assessed.

In vitro differentiation of naive CD4 into Th1 and Th2 induced by mouse fecal supernatant . Naive CD4 ⁺ T cells were separated from splenocytes of female C57BL/6 mice at 8 months of age using a naïve CD4 ⁺ T cell isolation kit (Stem Cell, #19765). The fecal supernatant of 7-month-old mice was obtained as described above. A total of 0.5 x 10 ⁶ cells/well in 0.5 ml RPMI-1640 medium was seeded in 48-well plates coated with anti-CD3 antibody and anti-CD28 antibody, and the culture medium was supplemented with cytokines and blocking antibodies. Th0: 20 ng/ml mIL-2; Th1: 20 ng/ml mIL-2, 10 µl supernatant, 5000 ng/ml 11B11; Th2: 20 ng/ml mIL-2, 10 µl supernatant, 5000 ng/ml XMG1.2. OM1 was added to the indicated wells to a final concentration of 100 μg/ml. After incubation at 37°C in 5% CO ₂ for 5 days, cells were acquired on a BD Aria III cytometer and data were analyzed using FlowJo software.

Immunohistochemistry . For 3,30-diaminobenzidine (DAB) development staining, sections were immunostained using a Leica BOND-RX Autostainer (Leica Microsystems) and Coverslipper CV5030 (Leica Microsystems) according to the manufacturer's IHC protocol. Briefly, sections were subjected to heat-induced epitope unmasking with using Epitope Retrieval solution 2 (ER2, AR9640, Leica Biosystems) for 20 min. Endogenous peroxidase activity was blocked with 3%-4% (v/v) hydrogen peroxide (part DS9800, Leica Biosystems) for 10 min. Then sections incubated with blocking buffer (10% NGS in PBS with 0.3% Triton x-100) for 60 min. Finally, staining was performed using the Bond Polymer Refine Detection System (DS9800, Leica Biosystems) according to the manufacturer's protocol. incubation with primary antibody was 30 min. Sections were stained for activated microglia using rabbit anti-IBA1 antibody (1:1000, catalog no. 019-19741, Wako), amyloid deposits using mouse anti-Aβ42 antibody (1:1000; cat. no. 803003, Biolegend) and tau phosphorylation using mouse anti-PHF-Tau & tangles-Thr231 antibody (1:300, cat. no. MN1040, Thermo Fisher). Stained sections were automatically scanned using a high-performance bright-field scanner (NanoZoomer 2.0HT, Hamamatsu), and images were acquired using NDP.scan 3.2 software (Hamamatsu). For fluorescent staining, slides were blocked with blocking buffer (10% NGS in PBS with 0.3% Triton x-100) at RT. for 1 h and then incubated in a primary antibody solution (Iba-1 1:1000, Aβ42 1:1000, Tau 1:300) overnight at 4°C. After washing, the slides were incubated with fluorescent anti-rabbit or anti-mouse secondary antibody (1:1000, Invitrogen) for 60 min at RT. and then washed 3 times in PBS. Finally, slides were counterstained with DAPI (1:10,000 in PBS, Sigma) for 5 min at RT, washed, sealed, and stored at 4°C for imaging. Representative fluorescence images were obtained using a Zmager-m2 upright fluorescence microscope (Zeiss, Germany) under a 10× objective using Zen software (Zeiss).

Neurotoxicity test . SH-SY5Y cells (ATCC, USA) were seeded on a white polystyrene 96-well plate (Corning Inc., USA) at a density of 5000 cells/well and cultured in a humidified incubator at 37 and 5% CO ₂ in Dulbecco's modified Eagle's medium ( DMEM) (Corning Inc., USA) supplemented with 10% fetal bovine serum (FBS), 100 units/ml penicillin and 100 μg/ml streptomycin. After 24 h, cells were treated with L-phenylalanine or L-isoleucine (Sigma) at final concentrations of 10 mM, 1 mM, and 0.1 mM, respectively. CellTiter-Glo (Promega Inc., USA) was used to detect cell viability after 24 hours. A Cytation5 device (BioTek) was used to record the luminescent signal.

Detection of amino acids . A set of solutions of standard mixtures of amino acids was prepared in the concentration range of 100-2000 µmol/L. A 10 μL portion of each standard mixture or plasma sample solution was pipetted into the bottom of the tube, followed by 70 μL of sodium borate buffer (200 mmol/L at pH 8.8). 20 μl of 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) (4 mg/ml in acetonitrile) was added, the tube was capped and heated for 10 min at 55°C to obtain AQC-amino acid. The solution was then cooled to room temperature and a 2 μL portion of each solution was injected into the UPLC-ESI-MS system for amino acid analysis without further purification.

People

Blood was collected from patients with MCI due to AD and healthy controls in a pilot trial. MCI due to AD was defined according to the 2011 NIA-AA clinical and scientific diagnostic criteria for MCI due to AD. Patients with MCI due to AD in this trial must meet the following criteria. The first factor is changes in cognitive function. The second is impairment in one or more cognitive domains. The third is maintaining independence of functional abilities. The fourth is the absence of dementia (NIA-AA 2011 clinical criteria for the diagnosis of MCI due to AD). All participants were subjected to a screening process that included a review of their medical history, physical and neurological examination, laboratory tests, and MRI images. Clinical assessment of mild cognitive impairment or dementia included neuropsychological tests and behavioral and psychiatric interviews administered by treating physicians. For patients with AD, scores <4 on the Hachinsky Ischemia Scale were recorded and they had no history of significant systemic or psychiatric conditions or traumatic brain injury that could impair brain function. The Clinical Dementia Rating (CDR) and the Montreal Cognitive Assessment (MoCA) were administered to all participants. Based on this assessment, the inventors retained individuals with MCI due to AD and excluded others, such as individuals who had single nonmemory domain impairment (single nonmemory domain MCI subtype) and individuals who had impairment two or more domains (multiple domains, mildly impaired MCI subtype). Normal control individuals had no history of cognitive decline, neurological impairment, or uncontrolled systemic medical conditions. All individuals included in this trial had ischemia of no more than two lacunae (<1 cm in diameter) as determined by a fluid-attenuated inversion recovery (FLAIR) MRI sequence.

The sample size for the first group (Fig. 5h, i) is 9 patients with MCI due to AD and 18 healthy individuals. The sample size for the second group (Fig. 5j) is 22 patients with MCI due to AD and 22 healthy individuals. The diagnosis of MCI was based on the following criteria, which were adapted from Petersen's diagnostic criteria for MCI: (1) memory complaints, preferably confirmed by a spouse or relative; (2) objective memory impairment; (3) normal general cognitive function; (4) intact activities of daily living and (5) absence of dementia. The ethics committee of the Shanghai Mental Health Center Institutional Review Board approved the trial (number: 2016-22R1). Informed consent was obtained from all individuals and individuals' guardians. Information about age and gender, etc. is given below.

First group:

Type Sample ID Age Floor Healthy HC-01 75 AND Healthy HC-02 71 AND Healthy HC-04 69 AND Healthy HC-05 73 AND Healthy HC-08 63 M Healthy HC-09 62 M Healthy HC-10 68 M Healthy HC-11 61 M Healthy HC-12 68 M Healthy HC-13 75 AND Healthy HC-14 70 AND Healthy HC-15 68 M Healthy HC-16 62 M Healthy HC-17 75 M Healthy HC-18 65 AND Healthy HC-19 64 AND Healthy HC-20 64 AND Healthy HC-22 63 AND MCI due to AD MCI-02 61 AND MCI due to AD MCI-03 79 M MCI due to AD MCI-04 71 AND MCI due to AD MCI-05 72 AND MCI due to AD MCI-06 80 AND MCI due to AD MCI-07 65 M MCI due to AD MCI-09 61 M MCI due to AD MCI-10 69 M MCI due to AD MCI-14 77 M

Second group:

Type Sample ID Age Floor Healthy HC-0149 73 M Healthy HC-0150 70 AND Healthy HC-0153 63 AND Healthy HC-0156 67 M Healthy HC-0158 71 AND Healthy HC-0165 61 AND Healthy HC-0168 62 AND Healthy HC-0172 70 AND Healthy HC-0176 71 AND Healthy HC-0187 73 AND Healthy HC-0191 65 M Healthy HC-0013 70 AND Healthy HC-0043 81 M Healthy HC-0050 64 AND Healthy HC-0051 66 M Healthy HC-0056 75 M Healthy HC-0068 67 AND Healthy HC-0074 57 AND Healthy HC-0076 60 AND Healthy HC-0081 71 AND Healthy HC-0089 57 AND Healthy HC-0090 76 AND MCI due to AD MCI-0169 65 AND MCI due to AD MCI-0175 61 AND MCI due to AD MCI-0211 69 M MCI due to AD MCI-0229 65 M MCI due to AD MCI-0240 59 AND MCI due to AD MCI-0244 64 AND MCI due to AD MCI-0257 61 AND MCI due to AD MCI-0260 60 AND MCI due to AD MCI-0282 66 AND MCI due to AD MCI-0284 64 AND MCI due to AD MCI-0287 75 AND MCI due to AD MCI-0303 62 AND MCI due to AD MCI-0319 63 AND MCI due to AD MCI-0332 67 AND MCI due to AD MCI-0374 61 M MCI due to AD MCI-0385 68 AND MCI due to AD MCI-0401 57 AND MCI due to AD MCI-0414 64 AND MCI due to AD MCI-0001 75 M MCI due to AD MCI-0043 55 AND MCI due to AD MCI-0061 63 AND MCI due to AD MCI-0091 78 AND

Differentiation And proliferation in vitro, induced phenylalanine And isoleucine. Naive CD4⁺ T cells were separated from splenocytes of female C57BL/6 mice at 8 months of age using a naïve CD4 isolation kit⁺ T cells (Stem Cell, #19765), and cells were stained using CellTrace (Thermo Fisher, #C34557). Total 1x10⁵ cells/well in 0.2 ml of RPMI-1640 medium were seeded in 96-well plates and the culture medium was supplemented with cytokines or blocking antibodies. Th0: 10 ng/ml mIL-2; Th1: 10 ng/ml mIL-2, 5000 ng/ml 11B11. Phenylalanine (final concentration, 2 mmol/L), isoleucine (final concentration, 2 mmol/L), and OM1 (final concentration, 100 μg/ml) were added to the indicated wells, respectively. After incubation at 37°C in 5% CO₂ within 5 days, cells were obtained on a BD Fortessa cytometer and data were analyzed using FlowJo software.

Phenylalanine uptake . Naive CD4 ⁺ T cells were separated from splenocytes of female C57BL/6 mice at 8 months of age using a naïve CD4 ⁺ T cell isolation kit (Stem Cell, catalog no. 19765) and induced to differentiate into Th1 by 20 ng/ml IL- 12. After 3 days, a total of 5×10 ⁵ Th1 cells and Th0 cells/well in 0.5 ml of RPMI-1640 medium were seeded into 48-well plates. ¹³ C-labeled phenylalanine and 5 mM amino acid transporter inhibitor BCH were added to the indicated wells. After 0.5 h, cells were harvested and washed twice with ice-cold D-PBS. 50 μl of deionized water was added and cells were lysed by freezing and thawing twice at -80°C. The cell lysate was centrifuged at 12,000×g for 10 min and ¹³ C-labeled phenylalanine in the supernatant was analyzed by mass spectrometry.

Antibiotic treatment . Mice were administered by adding to sterile drinking water an antibiotic solution (ATB) containing ampicillin (0.1 mg/ml, final concentration in drinking water), streptomycin (0.5 mg/ml, final concentration in drinking water) and colistin ( 0.1 mg/ml final concentration in drinking water) (Sigma-Aldrich). Solutions and bottles were replaced 3 times and once a week, respectively. The activity of the antibiotic was confirmed by sequencing of the 16S rRNA gene. Bacterial types with relative abundances less than 0.01 were removed for the Tg group. The duration of ABX administration varied slightly based on experimental conditions. In the context of fecal microbiota transplantation experiments, mice were administered ATB for 3 days before undergoing fecal microbiota transplantation the following day via gavage using animal feeding needles.

Fecal microbiota transplantation ( FMT) experiments . FMT was performed by thawing fecal material. 200 μl of the suspension was then transferred by gavage to each of the ATB recipients. FMT was performed 3 times over 5 days. Twelve-month-old C57 mice were first administered a cocktail of antibiotics in drinking water for 3 days, and then 40 mg of mixed stool suspended in PBS was gavaged into each mouse 3 times with an interval of 2 days between administrations. After 3 days, 4.2 μg of Aβ 1-40 oligomer was injected into both sides of the hippocampus. After 3 days, mice were sacrificed and used for various analyses.

Bioinformatics analysis . Signaling pathway analysis and biological function enrichment analysis were performed using Kyoto Encyclopedia of Gene Genotype (KEGG). Data enrichment was performed using the R package “DOSE”, “GO.db”, “GSEABase” and “ClusterProfiler”. Only cascades with a false-positive rate (FDR)-corrected p value of <0.05 were displayed. The R package “ggcorrplot” was used to analyze correlations. The R package “igraph” was used to generate pie charts of correlations. The R packages “ggalluvial” and “networkD3” were used to produce flowcharts for bacteria and Sankey diagrams. The R package “Mfuzz” was used for cluster trend analysis. Other bioinformatics analysis methods were performed using the Majorbio I-Sanger Cloud online platform (www.i-sanger.com). ROC biomarker analysis and random forest classification were performed using MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/).

Sample acquisition and analysis for flow cytometry . Mice were anesthetized, blood samples were collected in EDTA tubes, and red blood cells were removed using 1× red cell lysis buffer. Before tissue collection, the brain was perfused with ice-cold PBS to avoid ingress of circulating immune cells from the blood, and the brain was removed, sliced, and dissected according to the instructions for the Adult Brain Dissociation Kit (Miltenyi, catalog no. 130-107-677 ) using gentleMACS dissociator (Miltenyi Biotec). The brain homogenate was filtered through a 70-μm cell sieve and centrifuged at 300 × g for 5 min at 4°C. The cell pellet was resuspended in cold PBS buffer and centrifuged again at 300 × g for 5 min at 4°C. All samples were counted and adjusted to a density of 2-3 x 10 ⁶ /100 µl, labeled using the Live/Dead Kit for 30 min, and then centrifuged at 500 x g for 30 min at 4°C. Cells were resuspended in 100 μl PBS buffer, blocked with anti-CD16/32 antibody (Biolegend, catalog no. 101320) for 10 min, and incubated with antibody according to the manufacturer's protocols at 4°C for 30 min. The following antibodies were used for FACS analysis: CD45(30-F11)-APC-Cy7 (103116, Biolegend), CD11b (M1/70)-FITC (101205, Biolegend), CX3CR1 (SA011F11)-PE-Dazzle 594 (149014, Biolegend), F4/80 (BM8)-BV421 (123132, Biolegend), CD86 (IT2,2)-PE (305438, Biolegend), CD206 (15-2)-APC (321110, Biolegend), CD206 (15-2 )-BV785 (321142, Biolegend), CD11c (N418)-PE-Cy7 (117318, Biolegend), CD8 (53-6,7)-Percp-Cy5.5 (100734, Biolegend), Ly-6C (HK1.4 )-PE-Dazzle 594 (128044, Biolegend), Gr-1 (RB6-8C5)-Percp-Cy5.5 (108428, Biolegend), B220 (RA3-6B2)-BV421 (103240, Biolegend), CD19 (6D5) -PE (115508, Biolegend), CD49b (DX5)-PE-Cy7 (108922, Biolegend), CD4 (GK1,5)-PE-Cy7 (100422, Biolegend), CD4 (GK1,5)-FITC (100406, Biolegend ), CXCR3 (CXCR3-173)-BV421 (126522, Biolegend), CCR4 (L291H4)-PE-Cy7 (359410, Biolegend), CCR6 (29-2L17)-APC (129814, Biolegend), CD127 (A019D5)-PE (351304, Biolegend), CD25 (3C7)-Percp-Cy5.5 (101912, Biolegend), Live/Dead (423104, Biolegend). Cells were added to 500 μl of PBS buffer, centrifuged at 500 × g for 3 min at 4°C, and resuspended in 200 μl of running buffer. The corresponding negative control, Fluorescence Minus One (FMO) control, and each fluorescence compensation sample were used to correct for fluorescence compensation and identify populations of interest. Cells were processed on a BD Aria III cytometer and data were analyzed using FlowJo software.

Antibody chip . Brain homogenates (from 20 mg tissue) were analyzed using a 200-protein glass slide-based antibody cytokine chip (RayBiotech, GSM-CAA-4000-1). 100 μL of sample diluent was added to each well and incubated at room temperature for 30 min. The buffer was then replaced with another 100 μl of sample and incubated at room temperature for 2 hours. Samples were removed and washed 5 times (5 min each time) with 150 μl of 1× wash buffer I and 2 times (5 min each time) with using 150 µl of 1× wash buffer II at room temperature with gentle shaking. 80 μl of detection antibody cocktail was added to each well and incubated at room temperature for 2 hours. The slide was washed 5 times (5 min each time) with 150 μl of 1× wash buffer I, and then 2 times (5 min each). each time) with 150 μl of 1× wash buffer II at room temperature with gentle shaking. Eighty microliters of streptavidin conjugated to an equivalent Cy3 dye was added to each well and incubated at room temperature for 1 hour. After 5 washes (5 minutes each time), the signal was visualized using a laser scanner. The data was then visualized using a heat map diagram (www.metaboanalyst.ca).

Obtaining brain slices . Mice were transcardially perfused with 0.9% NaCl after deep anesthesia with pentobarbital (100 mg/kg, i.p.). Brain tissues were quickly removed, frozen and stored at -70°C. Serial coronal brain sections (12 μm thick) were prepared using a freezing microtome slicer (Leica Microsystems), mounted on glass slides, and air-dried overnight at room temperature. Tissue sections were stored at -70°C and used immediately.

Laser microdissection and q -PCR analysis . Frozen mouse brain samples were sectioned and collected on PEN membrane slides (Leica, 11600288). The hippocampus was removed by laser dissection microscopy (Leica Microsystems, LMD6). RNA was extracted using the RNeasy Micro Kit (Qiagen, 74004) and reverse transcribed into cDNA (Takara, PrimeScript RT Master Mix, RR036A). Q-PCR was performed using the ABI 7500 system using the SYBR method (Takara, SYBR Premix Ex Taq II). The following primers were used: synaptophysin-forward: CAGTTCCGGGTGGTCAAGG; synaptophysin-reverse: ACTCTCCGTCTTGTTGGCAC; actin-direct: GCTCTTTTCCAGCCTTCCTT; actin-reverse: AGGTCTTTACGGAT GTCAACG.

Statistical analysis . In the behavioral test, animals were randomly assigned to different groups. An unpaired two-tailed Student's t test was used for comparisons between two groups. To compare more than two groups, one-way ANOVA or two-way ANOVA followed by Dunnett's test was performed. All data with error bars are presented as mean ± ± s.e.m. P < 0.05 was considered statistically significant. Most data were analyzed in GraphPad Prism. For image quantification, Iba-1-positive, Aβ42-positive, and phosphorylated tau-positive cells were analyzed using ImageJ v1.8.0 with “area” recording.

Behavioral tests . The following behavioral tests were administered: Morris water maze (Morris water maze, MWM), Y-maze, and elevated plus maze (EPM). All movements were recorded using a camera, and software (Jiliang) was used to calculate the corresponding parameters.

The MWM test is used to measure spatial learning and memory according to a previously published protocol. Briefly, mice were subjected to data acquisition experiments for 6 days, and 3 trials were performed each day for each mouse. Animals were released into the water at the desired starting position and the latency to find the platform was determined. On day 7, the platform was removed and the mice were allowed to swim for 60 seconds. The trajectory and number of intersections of platform areas were recorded using a video camera.

Working memory was assessed using the Y-maze according to the literature with some modifications. Y-consisted of three identical links (A, B, C) with different keys. Mice were placed in the starting link (A) and the sequence of the tested links (such as ABCBA) was recorded. The total number of unit entries and behavioral changes were recorded using a video camera. Accuracy in the Y-maze was the ratio between correct alternation and overall alternation.

The EPM test is widely used to assess anxiety in animals and consists of two open sections (30 cm × 6 cm), two closed sections (30 cm × 6 cm × 22 cm) and a central region (6 cm × 6 cm). Each mouse was placed in the center of the maze, facing the closed arm, and allowed to explore the maze for 5 min. The trajectory was recorded and the frequency of visits to the open link was calculated, which negatively correlated with anxiety in mice.

Examples

Example 1: Progression AD associated With change intestinal microbiota And infiltration immune cells

To evaluate the role of altered gut microbiota in the pathogenesis of AD, we used the 5XFAD transgenic (Tg) mouse model, which is widely used in AD research due to its rapid onset and plausible replication of multiple AD-associated pathological features and clinical phenotypes. She demonstrates memory impairment at 4 months postnatal, behavioral changes at 7 months, and neuronal loss at 9 months. Age-matched wild-type (WT) mice from the same strain raised under the same conditions were used as controls. Consistent with previous reports, we identified changes in Aβ plaque deposition, tau phosphorylation, synaptophysin expression, behavior, and the like. In particular, Aβ plaque deposition in the cortex and hippocampus accumulates rapidly starting at 3 months postnatally (Fig. 7a). Tau phosphorylation in the brain of Tg mice was first detected at 5 months and gradually increased with increasing age (Fig. 7b); the expression level of synaptophysin in the hippocampus decreased significantly from months 7 to 9, indicating synaptic degeneration (Fig. 1a). A behavioral test in Tg mice demonstrated significant impairment in discrimination learning at 9 months of age (Fig. 1b). It should be noted that the early (2-3 months) and late (7-9 months) stages in Tg mice of the present invention are not the same as the late stages of clinical AD in humans. The late stage in Tg mice is symptomatically comparable to mild cognitive impairment (MCI) due to AD in humans. Thus, within the scope of the present invention, patients with MCI due to AD are used as subjects for human studies.

A marked shift in gut microbiota composition during AD progression in Tg mice was revealed by further principal component analysis (PCA) across a series of time points, while virtually no change was observed in WT mice over time (Figure 1c; Figure 7c ).

Using OTUs to track the dynamics of different bacterial phyla in Tg mice, we discovered two characteristic changes in gut microbiota profiles in Tg mice during disease progression. At 2–3 months of age, Bacteroidetes, Firmicutes , and Verrucomicrobia were three of the most common bacterial phyla (46.8%, 32.3%, and 12.6%, respectively). At 7–9 months of age, Firmicutes became the predominant bacteria, while Bacteroidetes and Verrucomicrobia decreased significantly, indicating a change in bacterial types (Fig. 1d). We further examined representative bacteria from Tg mice at each time point (Fig. 7d, Fig. 7e). These results indicated that the gut microbiota of Tg mice was highly dynamic, which is largely different from WT mice. Similar results were also obtained in the APP/PS1 double transgenic mouse model, another widely used model for AD research (Fig. 7f, Fig. 7g).

We hypothesized that the observed change in gut microbiota during AD progression may be associated with neuroinflammation. To test this hypothesis, we assessed the inflammatory status in Tg mice. Immunostaining for IBA1, a hallmark of microglial activation, in brain sections from AD mice demonstrated two apparent stages of microglial activation, at 3 months and 7–9 months, respectively (Fig. 1e). Given that microglial activation can be classified into two opposing types, the pro-inflammatory M1 subtype and the neuroprotective M2 subtype, we also carefully characterized the M1 and M2 phenotypes. At an early stage at 2–3 months of age, there was an increase in both M1 and M2 microglial cells. In the following months, the level of M1 subtype cells continued to increase and peaked at 7–9 months, while the level of M2 microglial cells decreased from months 3 to 5 and remained low thereafter (Fig. 1f). It can be seen that, as AD Tg mice progressed over time, the pro-inflammatory subtype of M1 microglia in the brain changed from comparable to the neuroprotective subtype of M2 microglia (Fig. 1f, 2-3 months) to predominant in microglia (Fig. 1f, 5- 9 months) and there was an increase in overall microglial activation (Fig. 1e, 5–9 months).

In addition to microglial activation, AD-associated neuroinflammation is known to involve the infiltration of peripheral immune cells. Thus, we also analyzed the infiltration of peripheral immune cells into the brain during AD progression. It was found that the level of CD45 ^high cells in the brain was significantly higher in Tg mice than in WT mice, similar to the result of IBA1 staining (Fig. 1g). CD45 ^high cell subtypes over a series of time points were further analyzed during AD progression, demonstrating CD4 ⁺ T cell changes as the bulk of CD45 ^high cells showed a similar pattern of change to IBA1 (Figure 1h-i). Over the time period studied by us, infiltrating Th1 and Th2 cells, the two major subtypes of CD4 ⁺ cells, showed similar dynamics to M1 and M2 microglial cells (Figure 1j). It can be seen that as AD progressed in Tg mice over time, Th1 changed from comparable to Th2 (Fig. 1j, 2-3 months) to predominant in CD4 ⁺ T cells (Fig. 1j, 5-9 months) and there was a general increased levels of CD4 ⁺ T cells (Fig. 1i, 5–9 months).

Thus, we realized that as the gut microbe pattern changed, the immune cell population tended to change to a Th1- and M1-dominant state, particularly striking is the significance of these changes over time (Figure 1d-j). Similar results were also found in the APP/PS1 mouse model (Fig. 8, Fig. 7i, Fig. 7j, Fig. 23). It should be noted that the APP/PS1 mouse model and the 5XFAD transgenic (Tg) mouse model differ with respect to AD progression over time. Tg mice have obvious Aβ deposits at 5 months of age (Fig. 7a), while APP/PS1 mice have no obvious Aβ deposits at 5 months of age (Fig. 8a). Changes in gut microbes in these two models over time (Fig. 1d, Fig. 7g) are not entirely consistent, reflecting intra- and inter-individual progression of AD and differences in gut microbes, but they all show a correlation between changes in gut microbe patterns and changes in immune cells over time.

We also analyzed the correlation between gut microbiota content and the frequency of immune cells in the brain, and noted that early (2-3 months) bacterial composition was highly correlated with the numbers of M2 and Th2 cells in the brain (Figure 1k, top). , however, bacterial pattern changes were highly correlated with M1 and Th1 cells (Fig. 1k, bottom). Bacteria that were significantly associated with immune cells are shown in the right panel of Fig. 1k, again confirming the correlation between gut microbiota pattern and immune cells, especially Th1 and M1.

Overall, these results indicated that gut bacteria are associated with peripheral immune cell infiltration and neuroinflammation during the progression of AD, studies of which are described below.

Example 2: Changes intestinal microbiota lead To excessive infiltration Th1 cells And excessive activation cells microglia M1 V head brain

To determine whether changes in the gut microbiota are required to trigger peripheral immune cell infiltration and in turn neuroinflammation during AD progression, we used an antibiotic cocktail containing ampicillin (0.1 mg/ml), streptomycin (0.5 mg/ml) and colistin (0.1 mg/ml) in late-stage Tg mice (7 months of age) to reduce gut microbiota. Administration of the antibiotic to Tg mice at a late stage (at 7 months of age) resulted in a marked reduction in both microbial diversity and abundance in the gut (Fig. 2a).

Concomitant with this change, we detected a decrease in the levels of both infiltrating pro-inflammatory Th1 cells (Figure 2b) and M1 cells (Figure 2c) in the brain. This tentatively shows that by interfering with the distribution of gut microbes in AD mice, the predominant Th1 and M1 state in late-stage AD mice can be reversed, indicating a causal relationship between changes in gut microbes and changes in immune cells.

Additionally, these findings were confirmed through a co-content experiment. WT mice were cohoused with age-matched Tg mice for 7 months from birth. Cohoused WT mice showed a reduction in discrimination learning comparable to Tg mice (Fig. 9a). To determine the reasons for this, analysis of changes in the gut microbiota in these mice at 7 months of age showed that the composition of the gut microbiota was quite similar between housed WT mice and late stage Tg mice (at 7 months of age), but was significantly different from that of WT mice. same age housed alone (Figure 2d), indicating that long-term exposure of WT mice to Tg bacteria (i.e., a Tg mouse model of AD gut microbes) caused a shift in gut microbiota composition in WT mice towards Tg mice , and at the same time caused a decrease in cognitive functions. This allows us to propose a strategy for creating an AD model that differs from the conventional one, i.e. by regulating the gut microbiota pattern of the WT model to change to that of the AD model.

Moreover, the levels of infiltrating Th1 cells between cohoused WT and Tg mice were also comparable, in both cases significantly exceeding the levels in single housed WT mice (Fig. 2e). Meanwhile, M1 cell levels increased in cohoused WT mice (Fig. 2f). Concomitant with the change in immune cells, cytokine expression in the brain showed marked similarity between cohoused WT and Tg mice, but was different from their expression in WT mice (Fig. 2g).

Thus, the above description confirmed that by changing the gut microbiota of WT mice to the gut microbiota of Tg mice, the Th1 and M1 cells of WT mice were transformed into a state similar to that of Tg mice, resulting in a similar decline in cognitive abilities, and also confirming the cause -consecutive relationship between changes in intestinal microbiota and changes in immune cells, as well as cognitive function.

Moreover, the inventors used C57BL/6WT mice to create a model to demonstrate the therapeutic value of the method described above. We performed fecal microbiota transplantation (FMT) experiments using C57BL/6 WT mice and the results are presented in columns 6-10 of FIG. 16.

C57BL/6WT mice were treated with antibiotics to provide an initial environment for subsequent fecal microbiota transplantation. Aggregated Aβ was injected into their hippocampus, followed by oral administration of fecal bacteria from 7 to 9 month old Tg mice (“Receptor_C57_ Receiving TG Fecal Bacteria”) or fecal bacteria from WT mice (“Receptor_C57_Receive WT Fecal Bacteria”) as a control . This resulted in a significant increase in Th1 cell levels and a significant decrease in Th2 levels in the brain compared to controls (Fig. 9b), which was consistent with the changes in Tg mice described above. This proved that Tg fecal transplantation could lead to a change in the pattern of intestinal microorganisms in WT mice, which also leads to a change in the pattern of immune cells.

On the other hand, in contrast, fecal transplantation from WT mice with a normal gut microbial distribution pattern reduced the level of Th1 cells in the brains of recipient Tg mice (Fig. 9c). This showed that the gut microbe pattern of Tg mice changes relative to the gut microbe pattern of WT mice, and the pattern of immune cells also changes.

These results reaffirmed the causal relationship between changes in gut microbiota and changes in brain immune cells (especially Th1/M1) and cognitive function during AD progression. The strategy of using the brain-gut axis has been proposed and validated for the treatment of AD.

Taken together, these data indicate that changes in the gut microbiota trigger peripheral immune cell infiltration and neuroinflammatory activation during AD progression. By administering agents (eg, WT feces with normal gut microbial distribution patterns, or OM1, illustrated below) that regulate the relative abundance of gut microbes to Tg mice whose gut microbial distribution exhibits a disease-related pattern, the gut microbial distribution of Tg mice was reorganized toward early and normal distribution of intestinal microorganisms. This effectively reversed the Th1/M1 dominant immune cell pattern in Tg mice, thereby improving cognitive function and treating AD. This provides a strong basis and compelling evidence to further improve cognitive function and treat AD by reorganizing the distribution of intestinal microorganisms in human patients with AD (eg, the strategy of using agents to regulate the relative abundance of intestinal microorganisms) to reverse the pattern of immune cells; and to use gut microbial distribution and/or immune cell status (eg, Th1 status (eg, population proportion), M1 status (eg, population proportion)) as markers of AD progression.

Example 3 OM1 makes it easier neuroinflammation through regulation intestinal microbiota

The essential role of the gut microbiota in AD progression demonstrated herein may indicate the therapeutic implications of interfering with the gut microbiota. To reaffirm this, we used OM1, which is a clinically approved anti-AD drug extracted from brown algae. OM1 is a mixture of acidic linear oligosaccharides with degrees of polymerization ranging from dimer to decamer with an average molecular weight of approximately 1 kDa (Fig. 3a).

In APP/PS1 late-stage AD mouse model aged 9 months to 12 months that were treated with OM1 for 3 months, OM1 exhibited a significant effect of alleviating cognitive impairment, as demonstrated by enhanced spatial learning, and memory performance in APP/PS1 mice. in both the training trial (Fig. 3b) and the test trial (Fig. 3c) in the Morris water maze (MWM) task. OM1 also significantly improved mice's performance in the Y-maze (Fig. 3d). Recently, OM1 showed a therapeutic effect in reversing impairment in AD patients in a 36-week, multicenter, randomized, double-blind, placebo-controlled, phase 3 clinical trial (clinical trial code: CTR20140274) in China. We were interested in understanding whether OM1 exhibits cognitive enhancement effects by influencing the microbiota of AD patients.

Interestingly, oral administration of OM1 for one month to late-stage Tg mice starting at 7 months of age altered the composition of the gut microbiota (Fig. 4a-b; Fig. 10a), restoring it towards the WT pattern (Fig. 13-14; Fig. 16).

Along with altering gut microbiota, administration of OM1 to Tg mice disrupted the correlation between brain lymphocyte counts and altered gut bacteria (Fig. 4c; Fig. 10b and c), reduced Th1 cell levels in the brain (Fig. 4d), and significantly reduced activated microglia to levels similar to WT mice (Figure 4e) and reduced cytokine levels in the brain (Figure 4f), restoring them towards the WT pattern (Figure 15). In parallel, OM1 administration significantly reduced Aβ plaque deposition, tau phosphorylation, and reduced discrimination learning observed in Tg mice (Fig. 4g-i). This again supported the strategy of restoring the distribution of gut microbes, for example, using agents to regulate the relative abundance of gut microbes to reverse the pattern of immune cells, thereby improving conditions and treating AD.

We next performed fecal microbiome transplantation (FMT) by transplanting feces from OM1-treated Tg mice into recipient C57BL/6 WT mice that received an intraventricular injection of aggregated Aβ. Feces from Tg mice administered OM1 resulted in decreased levels of brain Th1 cells in recipient mice compared to Tg mice without OM1 administration (Fig. 10e). It can be seen that the feces of Tg mice administered OM1 and the feces of WT mice tested in Example 2 have a similar effect on restoring gut microbe distribution and reducing Th1, which confirms the effect of restoring OM1 on the gut microbes of Tg mice. Consistent with this, antibiotic administration reduced the effect of OM1 on Th1 cells, IBA1 levels and cytokine expression in the APP/PS1 mouse model (Fig. 10f-j). All these data suggested that OM1 may alleviate neuroinflammation and cognitive decline through modulating gut dysbiosis.

Generally, the above in vivo mouse experiments have fully confirmed the changes in gut microbes and their tendency to regulate Th1/M1 immune cell predominance and its reversal in cognitive impairments such as AD and their ameliorations for brain-gut axis regulatory cascades for AD and treatment methods with intervention in them. In addition, such regulatory cascades and interventions are consistently demonstrated in various mouse models (transgenic (Tg) 5XFAD mouse model, APP/PS1 mouse model, C57BL/6 mouse model), although there are some intraspecific and interindividual differences in the initial state and transition process for gut microbes among these mice. Thus, the commonality of such regulatory cascades and interventions across different individuals is fully confirmed. Thus, a strategy is proposed to improve cognitive function and treat AD by regulating gut microbes to restore gut microbes to a normal and healthy state to reverse the immune cell-mediated disease state.

We used data from AD patients and healthy controls to compare and analyze AD brain-gut axis-associated microorganisms that differ between the two models, as listed in Tables 1-2 and as illustrated in FIG. 17-18, as a target for regulation. When applying the above-mentioned treatment strategy targeting the brain-gut axis in AD to humans, in addition to the interspecies differences in gut microbes between mice as rodents tested and humans as primates tested in the present invention, intraspecific and interindividual differences in gut microbes between AD patients must also be fully taken into account. Thus, the types of mouse intestinal microorganisms specifically shown in the above experiments are for guidance only, and should not be rigidly and mechanically applied to human AD patients.

Example 4: OM1 inhibits neuroinflammation through regulation metabolism amino acids

The present invention also explores the intermediary relationship between alteration of the intestinal microbiota and alteration or intervention of immune cells.

Causal connection between metabolites intestinal microbes And differentiation naive T cells V Th1/Th2, And intervention V them

To test the possible involvement of metabolites in immune modulation, the supernatant of in vitro cultured feces from Tg mice at 7 months of age was added to a culture of naive CD4 ⁺ T cells, which stimulated the differentiation of naive CD4 ⁺ T cells into Th1 and Th2 cells. In contrast, fecal supernatant from OM1-treated Tg mice promoted Th2 differentiation and inhibited Th1 differentiation (Fig. 11a), indicating that gut microbial metabolites influence the differentiation of naïve T cells into Th1 and Th2 cells.

Causal connection between amino acids V metabolites intestinal microbes And differentiation naive T cells V Th1/Th2, And intervention V them

We further used an untargeted metabolomics approach to characterize the fecal metabolome. A total of 11,289 metabolites were identified in fecal samples from WT, Tg, and OM1-treated Tg mice (Fig. 11b-c). Among these metabolites, 124 metabolites annotated in the METLIN database were significantly altered, decreased, or increased in Tg mice relative to WT mice, indicating that they are associated with AD. Strikingly, all these metabolite changes could be largely reversed by OM1 treatment (Fig. 11d-f), indicating that AD treatment restored the abnormal state of these AD-related metabolites. These altered metabolites were further annotated using the Human Metabolome Database (HMDB) and the Kyoto Encyclopaedia of Genes and Genomes (KEGG), yielding a total of 31 metabolites that were differentially regulated in WT, Tg mice, and OM1-treated Tg mice that could be compared with all three databases (HMDB, METLIN, KEGG). Cascade enrichment analysis for these metabolites using MBROLE or MetaboAnalyst further demonstrated significant changes in amino acid-related metabolic cascades and enzymes, especially phenylalanine-related cascades (Figure 5a).

Thus, the inventors decided to focus on amino acids for further testing. Plasma concentrations of a total of 36 amino acids were screened (Table 3) in both WT and Tg mice. A random forest algorithm was used to classify these amino acids, and some of them were sorted as shown in Fig. 19 and presented in Table 4. Phenylalanine was rated as the highest result, followed by isoleucine, serotonin, histidine and acetylornithine. Multivariate exploratory analysis of these five amino acids revealed significant differences between WT and Tg mice according to the receiver operating characteristic (ROC) curve (Fig. 5b; Fig. 11h), indicating that they are closely associated with disease progression. We then examined the concentrations of selected amino acids in fecal and blood samples in OM1-treated or untreated Tg mice and compared them to WT mice. Referring to FIG. 20 and fig. 21, the inventors found that OM1 affected various amino acids and they were reduced from the Tg pattern to the wild type pattern. Specifically, phenylalanine and isoleucine concentrations were significantly higher in the feces of Tg mice than in the feces of WT mice, and administration of OM1 significantly reduced their concentrations to levels comparable to those in WT mice (Fig. 5c). Similar changes in phenylalanine and isoleucine levels were found in the blood (Fig. 5d). It should be noted that phenylalanine and isoleucine are typical representatives of gut microbial metabolites that change during disease progression and are reversed by OM1. In addition, we also found that after administration of OM1, mouse cytokines tended to recover to the wild-type pattern (FIG. 22). This is consistent with the cytokine status in WT mice tending to change to the pattern in Tg mice, as shown in FIG. 2g, when housed together with Tg mice.

To test whether the increase in the levels of these amino acids was the result of changes in the gut microbiota, thereby interfering with amino acids by interfering with the gut microbiota, we examined amino acid concentrations in the FMT study. Feces from WT mice at 2 months of age could significantly reduce phenylalanine and isoleucine levels in Tg mice (Fig. 11i). Similarly, in the cohoused WT mice described in FIG. 2, blood phenylalanine and isoleucine concentrations were also increased, which was comparable to Tg mice (Fig. 11j). Thus, it could be determined that when WT mice were cohoused with Tg mice to convert their own WT gut microbial pattern into the Tg pattern, abnormal production of phenylalanine and isoleucine occurred and the levels of phenylalanine and isoleucine increased. In contrast, transplantation of feces from WT mice containing normal WT gut microbiota into Tg mice caused a shift in the gut microbiota of Tg mice toward the WT pattern, resulting in the restoration of abnormal phenylalanine and isoleucine production, and the levels of phenylalanine and isoleucine were reduced. This confirmed that levels of the gut microbial metabolites phenylalanine and isoleucine could be restored by restoring the gut microbiota of Tg mice.

Confirmation Togo, What phenylalanine And isoleucine influence on differentiation naive T cells V Th1/Th2, And intervention V them

To evaluate the effects of phenylalanine and isoleucine on cells, these two compounds were added directly to a culture of naïve CD4 ⁺ T cells. There was an increase in both the differentiation of Th0 cells into Th1 cells (Fig. 5e) and the proliferation of Th1 cells (Fig. 5f). In contrast, administration of OM1 can inhibit Th1 differentiation induced by phenylalanine and isoleucine and stimulation of Th1 cell proliferation, indicating that OM1 not only affects gut microbes but also directly affects their amino acid metabolite itself. In addition, we gave WT mice an intraperitoneal injection of phenylalanine and isoleucine and found that the number of Th1 cells in the blood increased significantly (Figure 5g), confirming that amino acids promote Th0 to Th1 differentiation in vivo . These results indicate that the accumulation of phenylalanine and isoleucine may increase the number of Th1 cells in the blood.

Intervention V differentiation naive T cells by obstruction capture amino acids naive T cells

Amino acids can be taken up by immune cells through specialized transporters. We further examined the expression levels of SLC7A5, a phenylalanine and isoleucine transporter, in naive CD4 ⁺ T cells and found that SLC7A5 was expressed in CD4 ⁺ T cells. Incubation of CD4 ⁺ T cells with ¹³ C-labeled phenylalanine demonstrated phenylalanine uptake by CD4 ⁺ T cells, which could be blocked by the pharmacological SLC7A5 inhibitor, JPH 203 (Fig. 11k), indicating that it is capable of inhibiting amino acid uptake by immune cells through inhibition amino acid transporters. The inventors further tested the resulting change in Th1 cell ratio. 5XFAD mice at 6 months of age were treated with 50 mg/kg JPH 203 or vehicle control via intraperitoneal injection every 24 hours. One month after administration, mice were sacrificed, and the proportion of pro-inflammatory Th1 cells in the brain of mice in the control and JPH 203 administration groups was analyzed by flow cytometry. This was representative, reflecting neuroinflammation in the brain. The results are presented in Fig. 13. Compared with the vehicle control group, JPH203 administration for one month significantly reduced the proportion of pro-inflammatory Th1 cells in the mouse brain, demonstrating that by inhibiting the amino acid transporter, amino acid uptake by immune cells is inhibited, thereby preventing amino acids from promoting immune cell differentiation and reducing neuroinflammation in the brain.

Confirmation abnormal levels phenylalanine And isoleucine at of people With AD

Finally, the inventor confirms this in humans by examining whether the above findings can be replicated in patients with MCI due to AD (see the method used in this study to determine MCI used in this study). Indeed, the concentrations of phenylalanine and isoleucine, as well as the number of Th1 cells in the blood of individuals with MCI due to AD (n=9) were significantly higher than those of age-matched healthy individuals (n=18) (Fig. 5h, i). Elevated levels of both phenylalanine and isoleucine in the blood were also confirmed in another small group with MCI due to AD (Fig. 5j), thus allowing diagnostic and treatment strategies to be planned in humans based on this.

In summary, we found that several gut microbial metabolites are involved in the subsequent transition of immune cells to a Th1 predominant state. We specifically examined amino acid metabolites, particularly phenylalanine and isoleucine, and confirmed a causal relationship between changes in gut microbiota composition, abnormal production of phenylalanine and isoleucine, and overdifferentiation into Th1 naïve T cells. By administering fecal bacteria WT and OM1, which were confirmed above to restore the abnormal microbiota of Tg mice towards the normal intestinal microbiota of WT, the abnormally high levels of phenylalanine and isoleucine were reduced and restored to normal levels. Moreover, the differentiation of Th0 cells into Th1 cells was inhibited, reversing the Th1-dominant disease state, suggesting that gut microbial metabolites bridge the gap between abnormal gut microbiota composition and abnormal immune cell health, and they are part of the crosstalk in the brain-gut axis with AD. The inventors have also tested other methods of interfering with metabolites, such as reducing the level of metabolites or interfering with the uptake of metabolites by immune cells, to interfere with the subsequent abnormal state of immune cells through metabolites of gut microbes, and thus the cognitive state.

Although the principle of the present invention has been described above in relation to preferred embodiments, it should be clearly understood that the description is given by way of example only, and not to limit the scope of the present invention.

Table 1. List of altered flora in AD patients or mice Classification Name in English Appropriate name Type Actinobacteria p__Actinobacteria Type Bacteroidetes p__Bacteroidetes Type Firmicutes p__Firmicutes Type Proteobacteria p__Proteobacteria Type Tenericutes p__Tenericutes Class BetaProteobacteria c__BetaProteobacteria Order Burkholderiales o__Burkholderiales Family Bacteroidaceae f__Bacteroidaceae Family Bifidobacteriaceae f__Bifidobacteriaceae Family Burkholderiaceae f__Burkholderiaceae Family Clostridiaceae f__Clostridiaceae Family Desulfovibrionaceae f__Desulfovibrionaceae Family Erysipelotrichaceae f__Erysipelotrichaceae Family Fusobacteriaceae f__Fusobacteriaceae Family Gemellaceae f__Gemellaceae Family Helicobacteriaceae f__Helicobacteraceae Family Lachnospiraceae f__Lachnospiraceae Family Mogibacteriaceae f__Mogibacteriaceae Family norank_Bacteroidales_S24-7_group f__Bacteroidales_S24-7_group Family Peptostreptococcaceae f__Peptostreptococcaceae Family Prevotellaceae f__Prevotellaceae Family Rikenellaceae f__Rikenellaceae Family Ruminococcaceae f__Ruminococcaceae Family Staphylococcaceae f__Staphylococcaceae Family Turicibacteraceae f__Turicibacteraceae Family Turicibacteriaceae f__Turicibacteriaceae Genus Actinobacillus_actinomycetemcomitans g__Actinobacillus_actinomycetemcomitans Genus Actinobacteria g__Actinobacteria Genus Adlercreutzia g__Adlercreutzia Genus Alistipes g__Alistipes Genus Alternaria g__Alternaria Genus Bacteroides g__Bacteroides Genus Bifidobacteria g__Bifidobacteria Genus Bifidobacterium g__Bifidobacterium Genus Bilophila g__Bilophila Genus Blautia g__Blautia Genus Botrytis g__Botrytis Genus Candida g__Candida Genus cc115 g__cc115 Genus Clostridium g__Clostridium Genus Dialister g__Dialister Genus E_coli_K99 g__E_coli_K99 Genus Escherichia g__Escherichia Genus Eubacterium_rectale g__[Eubacterium]_rectale Genus Fusarium g__Fusarium Genus Gemella g__Gemella Genus Lactobacilli g__Lactobacilli Genus Malassezia g__Malassezia Genus Phascolarctobacterium g__Phascolarctobacterium Genus Shigella g__Shigella Genus SMB53 g__SMB53 Genus Sutterella g__Sutterella Genus Tannerella_forsythia g__Tannerella_forsythia Genus Turicibacter g__Turicibacter Genus g__Escherichia-Shigella

Table 2. List of flora with significant differences in the relative abundance of intestinal bacteria between patients with AD and healthy controls (HC) Classification Generic name AD - average value (%) HC - average value (%) Fold change in AD compared to HC Up- or down-regulation in AD compared to HC Type p__Firmicutes 39.41 44.07 0.8942591 Downregulation Type p__Bacteroidetes 38.03 39.9 0.9531328 Downregulation Type p__Proteobacteria 12.85 8,086 1.5891665 Upregulation Type p__Actinobacteria 5.065 3.522 1.4381034 Upregulation Type p__Fusobacteria 2,028 1.001 2.025974 Upregulation Type p__Cyanobacteria 0.7771 1.227 0.6333333 Downregulation Type p__Verrucomicrobia 0.2726 0.7585 0.3593935 Downregulation Class c__Bacteroidia 37.38 39.51 0.9460896 Downregulation Class c__Clostridia 25.5 32.33 0.7887411 Downregulation Class c__GammaProteobacteria 10.19 4,734 2.1525137 Upregulation Class c__Bacilli 5.575 6,606 0.8439298 Downregulation Class c__Negativicutes 6,319 4,507 1.4020413 Upregulation Class c__Actinobacteria 5,791 3.522 1.6442362 Upregulation Class c__Fusobacteria 2,379 1.001 2.3766234 Upregulation Class c__BetaProteobacteria 1,298 1.438 0.9026426 Downregulation Class c__AlphaProteobacteria 1.104 1,349 0.818384 Downregulation Order o__Bacteroidales 37.38 39.51 0.9460896 Downregulation Order o__Clostridiales 25.43 32.24 0.7887717 Downregulation Order o__Enterobacteriales 8,983 3.453 2.6015059 Upregulation Order o__Selenomonadales 6,319 4,507 1.4020413 Upregulation Order o__Lactobacillales 4,803 5,774 0.8318324 Downregulation Order o__Bifidobacteriales 3.887 1,304 2.9808282 Upregulation Order o__Fusobacteriales 2,379 1.001 2.3766234 Upregulation Order o__Burkholderiales 1.012 1.158 0.8739206 Downregulation Order o__Bacillales 0.7707 0.8287 0.9300109 Downregulation Family f__Rikenellaceae 0.5028 1.018 0.4939096 Downregulation Family f__Ktedonobacteraceae 0.000187 0.001769 0.1057094 Downregulation Family f__Nannocystaceae 0 0.001947 0 Downregulation Genus g__Lachnospiraceae_NK4A136_group 0.5582 1.478 0.3776725 Downregulation Genus g__Alistipes 0.4237 0.9265 0.4573125 Downregulation Genus g__Ruminococcus_1 0.2783 0.9167 0.303589 Downregulation Genus g__Ruminococcaceae_UCG-002 0.4232 0.6621 0.6391784 Downregulation Genus g__Ruminococcaceae_UCG-005 0.2549 0.5889 0.4328409 Downregulation Genus g__Coprococcus_2 0.1136 0.6677 0.1701363 Downregulation Genus g__Tyzzerella_4 0.4165 0.08026 5.1893845 Upregulation Genus g__Lachnospiraceae_UCG-001 0.04241 0.1747 0.242759 Downregulation Genus g__Anaerotruncus 0.08045 0.1342 0.5994784 Downregulation Genus g__Cloacibacterium 0.005284 0.001851 2.8546731 Upregulation Genus g__norank_f__Ktedonobacteraceae 0.000187 0.001769 0.1057094 Downregulation Genus g__Nannocystis 0 0.001947 0 Downregulation Genus g__norank_f__Hydrogenophilaceae 0.001435 0.0002894 4.9585349 Upregulation View s__unclassified_g__Subdoligranulum 0.1108 0.7963 0.1391435 Downregulation View s__unclassified_g__Alistipes 0.2412 0.6416 0.3759352 Downregulation View s__uncultured_organism_g__Parasutterella 0.09518 0.4442 0.2142729 Downregulation View s__unclassified_g__Tyzzerella_4 0.4165 0.08026 5.1893845 Upregulation View s__uncultured_organism_g__Ruminococcaceae_UCG-005 0.1649 0.1728 0.9542824 Downregulation View s__uncultured_organism_g__Anaerotruncus 0.02364 0.05794 0.4080083 Downregulation View s__uncultured_Alistipes_sp._g__Alistipes 0.02727 0.04977 0.5479204 Downregulation View s__Lachnospiraceae_bacterium_TF01-11 0.009628 0.05391 0.178594 Downregulation View s__unclassified_g__Anaerotruncus 0.01064 0.01785 0.5960784 Downregulation View s__unclassified_g__norank_o__Mollicutes_RF9 0.003794 0.01637 0.2317654 Downregulation View s__uncultured_bacterium_g__Family_XIII_AD3011_group 0.008094 0.007951 1.0179852 Upregulation View s__uncultured_bacterium_g__norank_f__Christensenellaceae 0.007214 0.007841 0.9200357 Downregulation View s__uncultured_bacterium_g__Cloacibacterium 0.005284 0.001851 2.8546731 Upregulation View s__uncultured_organism_g__Peptococcus 0 0.006374 0 Downregulation View s__Mycobacterium_celatum_g__Mycobacterium 0.00115 0.005163 0.2227387 Downregulation View s__unclassified_g__Oceanobacillus 0.004827 0.001018 4.7416503 Upregulation View s__Alistipes_putredinis_DSM_17216 0.000187 0.003568 0.0524103 Downregulation View s__Prevotella_loescheii 0.002635 0.0004672 5.6399829 Upregulation View s__uncultured_bacterium_g__norank_o__MBA03 0.002428 0.0004532 5.3574581 Upregulation View s__Eubacterium_brachy 0.002443 0.0001614 15.136307 Upregulation View s__uncultured_bacterium_adhufec108 0 0.002173 0 Downregulation View s__uncultured_Clostridiales_bacterium_g__norank_f__Ktedonobacteraceae 0.000187 0.001769 0.1057094 Downregulation View s__Nannocystis_pusilla 0 0.001947 0 Downregulation View s__bacterium_2013Ark19i 0.001435 0.0002894 4.9585349 Upregulation View s__Auxenochlorella_protothecoides_g__norank 0.001463 0.0001447 10.110574 Upregulation View s__uncultured_Bacteroidetes_bacterium_g__Dinghuibacter 0 0.0009268 0 Downregulation

Table 3. The list below provides the names of amino acids identified in the blood of WT (2-9 months) and Tg (2-9 months) mice. Related to Fig. 5b. [01] Name of amino acids glycine alanine serine valine 4-OH proline asparagine glutamine tyrosine hypotaurine methionine sulfoxide beta-alanine threonine leucine ornithine aspartic acid methionine histidine phenylalanine arginine proline taurine isoleucine glutamic acid citrulline tryptophan kynurenine GABA lysine pyroglutamic acid acetylornithine asymmetric dimethylarginine alpha-aminoadipic acid carnosine creatinine putrescine serotonin

Table 4. ROC analysis of all amino acid markers in the blood of WT mice and Tg mice in order of frequency of selection Amino acid Rank Frequency W.T. Tg phenylalanine 0.42 short high isoleucine 0.4 short high Serotonin 0.36 short high histidine 0.34 short high acetylornithine 0.32 high short Kynurenin 0.32 short high tryptophan 0.28 short high leucine 0.28 short high glutamic acid 0.26 short high pyroglutamic acid 0.22 short high valine 0.2 short high glutamine 0.2 short high beta-alanine 0.2 short high hypotaurine 0.18 short high tyrosine 0.12 short high asymmetric dimethylarginine 0.12 high short glycine 0.1 short high Carnosine 0.08 short high 4-OH proline 0.08 short high aspartic acid 0.06 high short threonine 0.06 short high alpha-aminoadipic acid 0.04 short high ornithine 0.04 short high Creatinine 0.04 short high alanine 0.04 short high GABA 0.02 short high methionine sulfoxide 0.02 short high proline 0.02 short high

Claims (37)

1. A method for identifying a patient with intestinal microbiota dysbiosis and Alzheimer's disease who is sensitive to a drug based on mannuronic acid oligosaccharides, including: determining one or more of the following in a patient sample and comparing them with corresponding values in healthy individuals: (1) the relative abundance of the gut microbes Bacteroidetes, Firmicutes, and Verrucomicrobia; (2) levels of the amino acids phenylalanine and/or isoleucine; (3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And selecting a patient with a decreased level of Bacteroidetes, Firmicutes and Verrucomicrobia, or an increased level of phenylalanine and/or isoleucine, or an increased level of Th1 cells in the sample compared to those of healthy individuals as a patient sensitive to a mannuronic acid oligosaccharide drug. 2. The method according to claim 1, wherein the mannuronic acid oligosaccharide-based drug is selected from mannuronic acid oligosaccharides or a composition containing mannuronic acid oligosaccharides. 3. The method according to claim 1, where the sample is feces or peripheral blood. 4. A kit for identifying a drug-sensitive patient based on mannuronic acid oligosaccharides with intestinal microbiota dysbiosis and Alzheimer's disease, containing a user manual for instructing the consumer to compare the test result in the sample with a corresponding reference test in healthy individuals, and means for determining one or more of the following parameters in a sample from patients with gut microbiota dysbiosis and Alzheimer's disease: (1) the relative abundance of the gut microbes Bacteroidetes, Firmicutes, and Verrucomicrobia; (2) levels of the amino acids phenylalanine and/or isoleucine; (3) the proportion of proinflammatory Th1 cells among CD4+ T cells. 5. The set according to claim 4, where the user manual includes the corresponding benchmarks. 6. The kit of claim 5, wherein the user manual is used to instruct the consumer to identify the patient as a mannuronic acid oligosaccharide drug-sensitive patient who has decreased levels of Bacteroidetes, Firmicutes, and Verrucomicrobia, or increased levels of phenylalanine and/or isoleucine, or the level of Th1 cells in the blood is increased compared to the corresponding indicators in healthy individuals. 7. The kit according to claim 4, wherein the mannuronic acid oligosaccharide-based drug is selected from mannuronic acid oligosaccharides or a composition containing mannuronic acid oligosaccharides. 8. The kit according to claim 4, where the sample is feces or peripheral blood. 9. Use of an agent for determining one or more of the following parameters in a sample from patients with gut microbiota dysbiosis and Alzheimer's disease for the production of a kit for identifying a drug-sensitive mannuronic acid oligosaccharide based patient with gut microbiota dysbiosis and Alzheimer's disease: (1) the relative abundance of the gut microbes Bacteroidetes, Firmicutes, and Verrucomicrobia; (2) levels of the amino acids phenylalanine and/or isoleucine; (3) the proportion of proinflammatory Th1 cells among CD4+ T cells. 10. Use according to claim 9, wherein the mannuronic acid oligosaccharide-based drug is selected from mannuronic acid oligosaccharides or a composition containing mannuronic acid oligosaccharides. 11. Use according to claim 9, wherein the sample is feces or peripheral blood. 12. A method for treating a patient with intestinal microbiota dysbiosis and Alzheimer’s disease, including: determining one or more of the following in a patient sample and comparing them with corresponding reference values in healthy individuals: (1) the relative abundance of the gut microbes Bacteroidetes, Firmicutes, and Verrucomicrobia; (2) levels of the amino acids phenylalanine and/or isoleucine; (3) the proportion of proinflammatory Th1 cells among CD4+ T cells; And administering a pharmaceutical composition containing a drug based on mannuronic acid oligosaccharides to a patient who has a decreased level of Bacteroidetes, Firmicutes and Verrucomicrobia, or an increased level of phenylalanine and/or isoleucine, or an increased level of Th1 cells in the sample compared to those of healthy individuals. 13. The method according to claim 12, wherein the mannuronic acid oligosaccharide-based drug is selected from mannuronic acid oligosaccharides or a composition containing mannuronic acid oligosaccharides. 14. The method according to claim 12, where the sample is feces or peripheral blood. 15. Use of an agent for determining one or more of the following in a sample from an individual for the production of a kit for identifying an individual with gut microbiota dysbiosis who has Alzheimer's disease and is sensitive to a mannuronic acid oligosaccharide drug: (1) the relative abundance of the gut microbes Bacteroidetes, Firmicutes, and Verrucomicrobia; (2) levels of the amino acids phenylalanine and/or isoleucine; (3) the proportion of proinflammatory Th1 cells among CD4+ T cells. 16. Use according to claim 15, where the sample is feces or peripheral blood.

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