Prediction and application of post-stroke intestinal butyrate production microbiota and intestinal butyrate level on post-stroke cognitive function

Abstract

The invention relates to prediction of post-stroke intestinal butyrate production microbiota and intestinal butyrate level on a post-stroke cognitive function and application of the post-stroke intestinal butyrate production microbiota and the intestinal butyrate level. Experimental research shows that the butyrate and / or the butyrate-producing flora can be used as a marker for predicting the cognitive function after the cerebral apoplexy, and the cognitive function after the cerebral apoplexy can be predicted by detecting the intestinal butyrate level after the cerebral apoplexy and / or the abundance of the butyrate-producing flora.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to the prediction of post-stroke cognitive function by gut butyrate-producing microbiota and gut butyrate levels after stroke. Background Art

[0002] The Global Burden of Disease Study 2021 showed that neurological diseases caused 443 million healthy life years lost, and stroke had a particularly high proportion in the ranking of age-standardized Disability-Adjusted Life Year (DALY). Nearly one-third to half of stroke patients may develop cognitive impairment, but the mechanisms associated with cognitive impairment are still poorly understood. In addition to preventing stroke by controlling vascular risk factors, active treatment is also essential for reducing post-stroke cognitive impairment (PSCI). Currently, there are no clear targets and effective drugs for treating PSCI, and using drugs for treating Alzheimer's disease (AD) alone cannot significantly improve the cognitive function of stroke patients.

[0003] Research on AD and cognitive impairment has shifted from ineffective single treatments to combination therapies, including symptomatic and disease-modifying treatments. Among them, neuroinflammation-modifying therapy has received a great deal of attention. Studies have shown that neuroinflammatory responses are involved in the pathogenesis of PSCI, and both animal models and patient studies have linked stroke-induced cognitive impairment to neuroinflammation. However, there is still a lack of sufficient animal studies and clinical validation for neuroinflammation-modifying therapy of PSCI.

[0004] In recent years, the involvement of the gut microbiota in the progression of neuroinflammation has attracted great attention. Previous studies by the inventor's team have shown that stroke can lead to dysbiosis of the gut microbiota, characterized by the expansion of Enterobacteriaceae, which promotes neuroinflammation and exacerbates brain injury. It was also found in the team's latest study that the upregulation of lipopolysaccharide (LPS) caused by persistent gut microbiota dysbiosis after stroke can lead to post-stroke cognitive dysfunction through neuroinflammation, while butyrate can mitigate this effect. Therefore, treatment methods targeting post-stroke gut butyrate metabolism disorders and suppressing related neuroinflammation have considerable clinical value. However, the changes in gut short-chain fatty acids (SCFAs) in PSCI patients and the relationship between butyrate and cognitive impairment have not been fully confirmed. Summary of the Invention

[0005] Based on the above problems, this application established a clinical cohort to study the relationship between the decline of intestinal butyrate and PSCI, and found that the intestinal butyrate-producing microbiota and intestinal butyrate levels after stroke can be used to predict cognitive function after stroke, providing a new target for the prediction of cognitive function after stroke.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a biomarker for predicting cognitive function after stroke, and the biomarker is butyrate and / or butyrate-producing microbiota.

[0008] The present invention provides the application of the biomarker in screening preparation products for treating and / or assisting in treating cognitive dysfunction after stroke.

[0009] The present invention provides the application of the biomarker in preparing preparation products for treating and / or assisting in treating cognitive dysfunction after stroke.

[0010] The present invention provides the application of a reagent for detecting the level of the biomarker in preparing products for prognostic evaluation, diagnosis or monitoring of cognitive dysfunction after stroke. Among them, the reagent for detecting the level of the biomarker can reflect the biomarker level by detecting the butyrate content in the sample or the abundance of butyrate-producing microbiota, and the sample can be feces.

[0011] The present invention provides a product for prognostic evaluation, diagnosis or monitoring of cognitive dysfunction after stroke, and the product includes a reagent for detecting the level of the biomarker, and the reagent is used to detect the butyrate content or the abundance of butyrate-producing microbiota in the sample, and the sample can be feces.

[0012] The present invention provides a system for prognostic evaluation, diagnosis or monitoring of cognitive dysfunction after stroke, including:

[0013] A detection result collection module for collecting the test results of the biomarker level in the biological sample to be tested, and the biomarker level can be the butyrate content or the abundance of butyrate-producing microbiota;

[0014] An analysis module for comparing the obtained test results of the biomarker level with the corresponding indicators of the group without cognitive dysfunction after stroke, obtaining an analysis result, and judging the risk of cognitive dysfunction after stroke in the biological sample to be tested.

[0015] Advantages and beneficial effects of the present invention:

[0016] In the present invention, by establishing a clinical team, analyzing and comparing the gut microbiota results and SCFAs metabolism results between different experimental groups, it is observed that the abundance of butyrate-producing bacteria in PSCI patients is significantly decreased, and the fecal butyrate level is significantly positively correlated with the cognitive score. Further, by constructing a mouse tMCAO model, the mechanism of PSCI is explored, the relationship between the gut microbiota and butyrate level and cognitive impairment is evaluated and verified, and the predictive value of gut butyrate-producing microbiota and gut butyrate level in post-stroke cognitive function is proposed. Compared with previous studies, the focus is on metabolites, providing a basis for subsequent stable production and application. Description of the Drawings

[0017] Figure 1 Correlation between gut butyrate content and PSCI: (A) Example of the composition of cohort 1; (B) Results of microbiota α-diversity, with the Shannon index analyzed by two-tailed Student's t-test for two independent samples and the Chao1 index analyzed by Mann-Whitney U test; (C) Results of microbiota β-diversity: PCoA results based on Bray-Curtis; (D) VIP value represents the importance of the first principal component in OPLS-DA, and a VIP value greater than 1 represents metabolite differences; (E) OPLS-DA S-plot, with the horizontal axis representing the co-correlation coefficient between the principal component and the metabolite and the vertical axis representing the correlation coefficient between the principal component and the metabolite; (F) OPLS-DA score plot, with the horizontal axis representing the variance explained by the first principal component and the vertical axis representing the variance explained by the second principal component; each point represents a sample, and different groups are represented by colors; (G) Results of targeted GC analysis of fecal SCFAs in the non-PSCI group and the PSCI group, both analyzed by Mann-Whitney U test; (H) Comparison of the relative abundances of butyrate-producing bacterial genera, analyzed by Mann-Whitney U test; (I) Correlation between the MoCA score and the butyrate level, analyzed by Spearman rank correlation analysis; (J) Example of the composition of cohort 2; (K) Changes in the MoCA score from stroke onset to 3 months after stroke; (L) Changes in fecal butyrate levels from stroke onset to 3 months after stroke; (M) Correlation between the MoCA score and fecal butyrate levels at 3-month follow-up, analyzed by Spearman rank correlation analysis; (N) Results of multivariate logistic regression analysis after adjusting for confounding factors.

[0018] Figure 2Modeling experiment for male mice with tMCAO: (A) Example of experimental design: Fecal samples of mice were collected at 7, 14, and 28 days after surgery respectively. Cognitive tests were performed on the Sham group and the tMCAO group on the 29th day after surgery; (B) Representative trajectories of the two groups in NORT, with familiar objects represented in white and new objects represented in orange; (C) The discrimination rate was calculated as the time spent exploring the new object divided by the total time spent exploring both objects. Statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 10 mice in each group; (D) Representative images of the movement trajectories of the two groups; (E) Escape latency during the learning period (days 1-5), analyzed using repeated measures ANOVA, with 8 mice in each group; (F) Number of platform crossings during the exploration test (day 6). Statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 8 mice in each group; (G) Distance in the target quadrant (day 6). Statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 8 mice in each group; (H) Microbial community α-diversity, analyzed using one-way ANOVA, with 5 mice in each group; (I) Results of microbial community β-diversity: PCoA results based on Bray Curtis; (J) Bacterial taxa with significantly different abundances between the Sham group and the tMCAO group at 28 d obtained by LEfSe analysis; (K) Average relative abundances of the phylum-level microbiota in Sham, tMCAO 7d, 14d, and 28d; (L) Average relative abundances of butyrate-producing bacteria at the family level in Sham, tMCAO 7d, 14d, and 28d; (M) Comparison of the relative abundances of butyrate-producing bacteria at the genus level, analyzed using one-way ANOVA, with 5 mice in each group; (N) Targeted GC analysis of cecal SCFAs in the Sham group and the tMCAO group at 28d, with 5-7 mice in each group; (O) Levels of peripheral blood LPS and LBP at 28d. Statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 12 mice in each group.

[0019] Figure 3 On the 28th day after surgery, microglia and astrocytes in the hippocampus of male mice with tMCAO modeling were significantly infiltrated: (A) Number of Iba-1+ cells (microglial marker) in the hippocampus and percentage of Iba-1+ area, analyzed using one-way ANOVA, with n = 8 mice in each group; (B) Number of GFAP+ cells (astrocyte marker) in the hippocampus and percentage of GFAP+ area, analyzed using one-way ANOVA, with n = 8 mice in each group; Scale bar = 20 μm, tMCAO-I: ipsilateral to the ischemic lesion, tMCAO-C: contralateral to the ischemic lesion.

[0020] Figure 4Modeling experiment for female mice with tMCAO: (A) Experimental design: Fecal samples of mice were collected on the 7th, 14th, and 28th days after surgery respectively. Cognitive tests were performed on the 29th day after surgery in the Sham group and the tMCAO group; (B) Representative trajectories of the two groups in the NORT. The familiar objects are shown in white, and the new objects are shown in orange; (C) The discrimination rate was calculated as the time spent exploring the new object divided by the total time spent exploring the two objects. The statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 10 mice in each group; (D) Representative images of the movement trajectories of the two groups; (E) Escape latency during the learning period (days 1 - 5), analyzed by repeated measures ANOVA, with 8 mice in each group; (F) Number of platform crossings during the exploration test (day 6), the statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 8 mice in each group; (G) Distance to the target quadrant (day 6), the statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 8 mice in each group; (H) Microbial community α-diversity: Shannon index, Chao1 index, the statistical comparison was performed using one-way ANOVA, with 5 mice in each group; (I) Results of microbial community β-diversity: PCoA results based on BrayCurtis; (J) Bacterial taxa with significantly different abundances between the Sham group and the tMCAO group at 28d obtained by LEfSe analysis; (K) Average relative abundances of the phylum-level microbiota in Sham, tMCAO 7d, 14d, and 28d; (L) Average relative abundances of butyrate-producing bacteria at the family level in Sham, tMCAO 7d, 14d, and 28d; (M) Comparison of the relative abundances of butyrate-producing bacteria at the genus level, analyzed by one-way ANOVA, with 5 mice in each group; (N) Targeted GC analysis of cecal SCFAs in the Sham group and the tMCAO group at 28d; 5 - 7 mice in each group; (O) Levels of peripheral blood LPS and LBP at 28d, the statistical comparison was performed using two independent sample two-tailed Student's t-tests, with 12 mice in each group.

[0021] Figure 5 On the 28th day after surgery, microglia and astrocytes in the hippocampus of female mice with tMCAO modeling were significantly infiltrated. (A) Number of Iba-1+ cells (microglia marker) and percentage of Iba-1+ area in the hippocampus, analyzed by one-way ANOVA, with n = 8 mice in each group; (B) Number of GFAP+ cells (astrocyte marker) and percentage of GFAP+ area in the hippocampus, analyzed by one-way ANOVA, with n = 8 mice in each group; Scale bar = 20μm, tMCAO-I: ipsilateral to the ischemic lesion, tMCAO-C: contralateral to the ischemic lesion.

[0022] Figure 6Hippocampal neuron loss in male and female mice after tMCAO modeling: (A) Nissl staining of hippocampal sections of male Sham and tMCAO groups at 28 days, one-way ANOVA, 6 mice per group; (B) Nissl staining of hippocampal sections of female Sham and tMCAO groups at 28 days, one-way ANOVA, 6 mice per group, scale bar = 20 μm, tMCAO-I: ipsilateral to ischemic lesion, tMCAO-C: contralateral to ischemic lesion.

[0023] Figure 7 Experiment for antibiotic intervention: (A) Experimental design: Mice in the tMCAO group were given sterile water after surgery. Mice in the Abx group were continuously given antibiotics from 14 days before surgery to 28 days after surgery. Cognitive tests were performed on both groups on the 29th day after surgery; (B) Representative trajectories of the two groups in the NORT. Familiar objects are shown in white and new objects are shown in orange; (C) The discrimination rate was calculated as the time spent exploring the new object divided by the total time spent exploring both objects. Statistical comparison was performed using two independent samples two-tailed Student's t-test, 10 mice per group; (D) Representative images of the movement trajectories of the two groups; (E) Escape latency during the learning period (days 1-5), analyzed using repeated measures ANOVA, 8 mice per group; (F) Number of platform crossings during the exploration test (day 6), statistical comparison was performed using two independent samples two-tailed Student's t-test, 8 mice per group; (G) Dilution curves of the Abx group and the tMCAO group at day 0 before surgery. The Chao 1 index represents species richness. Statistical comparison was performed using the Mann-Whitney U test, 5 mice per group; (H) Dilution curves of the Abx group at 28 days after surgery and the tMCAO group at 28 days after surgery. The Chao 1 index represents species richness. Statistical comparison was performed using two independent samples two-tailed Student's t-test, 5 mice per group; (I) Targeted GC analysis of cecal SCFAs in the Abx group and the tMCAO group 28 days after modeling. 5-7 mice per group; (J) Comparison of the relative abundances of butyrate-producing bacteria at the genus level in the Abx group and the tMCAO group 28 days after modeling. Statistical comparison was performed using the Mann-Whitney U test, 5 mice per group.

[0024] Figure 8Observation experiment of male mice intervened with Abx on the 28th day after surgery: (A) Nissl staining in the ipsilateral hippocampal region of ischemic injury, quantification of Nissl staining + cells, statistical comparison using two independent sample two-tailed Student's t-test, 6 mice in each group, scale bar = 20 μm; (B-C) Number of Iba-1+ cells (microglial marker) and percentage of Iba-1+ area in the ipsilateral hippocampal region of ischemic injury; (D-E) Number of GFAP+ cells (astrocyte marker) and percentage of GFAP+ area in the ipsilateral hippocampal region of ischemic injury; statistical comparison using Mann-Whitney U test, 6-8 mice in each group, scale bar = 20 μm. Detailed implementation manner

[0025] In the embodiments of the present invention, by establishing a clinical team, analyzing and comparing the results of intestinal flora and the results of short-chain fatty acids (SCFAs) metabolism between different experimental groups, it is observed that the abundance of butyrate-producing bacteria in PSCI patients is significantly decreased, and the fecal butyrate level is significantly positively correlated with the cognitive score (MoCA).

[0026] Furthermore, by constructing a mouse tMCAO model, the mechanism of PSCI is explored, and the relationship between the gut microbiota and cognitive impairment is evaluated and verified.

[0027] The specific experimental methods are as follows:

[0028] The specific experimental methods used in the above embodiments are as follows:

[0029] 1. Animal feeding and treatment

[0030] Adult male and female C57BL / 6J mice (8-10 weeks old, 22-25 g) were purchased from BestBio Technology Co., Ltd., Zhuhai, Guangdong, China. The mice were housed in a specific pathogen-free animal room, with a breeding temperature of 20-22 °C, a humidity of 50%, and a light-dark cycle of 12 hours. All animals had access to sufficient water and food, and 5 mice were placed in each cage. The formal experiment began after one week of environmental adaptation.

[0031] Male and female mice were randomly divided into two groups: sham operation group (sham) and operation group (tMCAO). To explore the connection between the gut microbiota and cognitive impairment, we also added an antibiotic intervention group (Abx). The treatment for the antibiotic group was as follows: 3 broad-spectrum antibiotics (including 1 g ampicillin, 1 g neomycin sulfate, and 1 g metronidazole) were dissolved in 1 L of sterile water, and the antibiotic solution was changed every three days to prevent drug deterioration in water. Mice in the Abx group continued to drink the antibiotic solution 14 days before the tMCAO surgery and 28 days after the surgery.

[0032] 2. Cerebral ischemia-reperfusion model

[0033] Transient (30 - minute) middle cerebral artery occlusion (tMCAO) was used to induce focal cerebral ischemia. The surgical anesthesia was induced by intraperitoneal injection of 1.25% tribromoethanol (0.02 mL / g body weight). The body temperature was maintained using a self - controlled temperature - sensing heating blanket throughout the anesthesia process. A suture was inserted through the external carotid artery into the internal carotid artery and finally reached the middle cerebral artery. After 30 minutes of cerebral ischemia, the suture was removed to establish reperfusion. After the operation, the modified neurological severity score (mNSS) was used to evaluate the mice within 12 hours after they regained consciousness. Mice with mNSS < 1 were excluded because mNSS < 1 was considered to have minimal brain injury and no infarction.

[0034] 3. Cognitive assessment

[0035] All mice started cognitive assessment on the 29th day after the operation. The Morris water maze was used to evaluate the spatial learning and memory ability of the mice. The mice were subjected to 4 water maze experiments per day for 5 consecutive days. In each experiment, the mice were placed in a pool with a diameter of 120 cm and a height of 50 cm. There was a platform with a diameter of 10 cm at a depth of 1 cm below the water surface in the fourth quadrant of the pool. During the task, the mice used the visual cues around the pool to find the hidden platform. Each mouse was placed in the water against the pool wall and had 60 seconds to find the hidden platform. If the mouse did not find the platform within 60 seconds, it was guided to the platform and stayed on the platform for 20 seconds. The time required to reach the platform (escape latency) was recorded. On the 6th day of the experiment, a 60 - second exploration test was conducted to evaluate memory retention. A digital camera recorded the number of times the mouse crossed the position of the platform and the proportion of time spent in the quadrant where the platform was located (the fourth quadrant).

[0036] The Novel Object Recognition Test (NORT) was used to evaluate the short - term memory of the mice. In the adaptation experiment, the mice were placed in a box without any objects for 15 minutes for three consecutive days. The training experiment started 24 hours after the last adaptation training. In the training experiment, two identical objects were placed in the box, and the mice were allowed to freely explore the box for 10 minutes. One hour later, a new object was randomly replaced with a familiar object, and the mice were given an additional 5 minutes to freely explore the box. After each experiment, the odor traces were removed with 70% alcohol. In this experiment, only sniffing with the nose or touching with the front paws was defined as exploratory behavior, climbing on an object or sitting on an object was not defined as exploration. The discrimination rate was calculated as follows: the time of exploring the new object divided by the total time of exploring the two objects.

[0037] 4. Enzyme-linked immunosorbent assay (ELISA)

[0038] Serum LPS (MM-0634M1), LPS-binding protein (LBP, MM-44515M1), TNF-α (MM-0132M1) and IL-1β (MM-0040M1) levels were detected using an ELISA kit (enzyme immunoassay, Jiangsu, China).

[0039] 5. Nissl staining

[0040] After anesthetizing the mice, the heart was perfused with cold normal saline and fixed with paraformaldehyde (PFA). The brain and intestinal tissues were carefully isolated and fixed in 4% PFA for 24 hours. After fixation, the tissues were dehydrated and embedded in paraffin. 4-μm thick sections were taken and stored for further staining. Nissl staining: The sections were dewaxed and stained with 1% toluidine blue for 5 minutes. The sections were washed three times with distilled water (3 minutes each time), placed in 70% ethanol for 2 minutes, washed twice with 95% ethanol (2 minutes each time), and finally washed with xylene for 5 minutes. All sections were visualized using a 3D digital slide scanner (3DHISTECH Pannoramic MIDI).

[0041] 6. Immunofluorescence staining

[0042] Tissue sections were prepared as described in 5. Nissl staining. The following primary antibodies were used: Anti-Iba1 (1:1000; Abcam, ab178846), Anti-GFAP (1:1000; Abcam, ab68428). These antibodies were incubated overnight at 4°C. Then, secondary antibodies conjugated with AlexaFluor (AF) 488, 594 or 647 (donkey anti-rabbit IgG (H+L) highly cross-adsorbed secondary antibody, AlexaFluor TM 488, 1:400, Thermo Fisher Scientific, A21206; donkey anti-rabbit IgG (H+L) highly cross-adsorbed secondary antibody, Alexa Fluor TM 594, 1:600, Thermo Fisher Scientific, A32754; donkey anti-rabbit IgG (H+L) highly cross-adsorbed secondary antibody, Alexa Fluor TMStored at room temperature for 1 hour. Then counterstained with 4′,6-diamidino-2-phenylindole (DAPI, 1:1000, Solarbio, C0060). To evaluate apoptosis, brain sections were stained with TUNEL (Thermo Fisher Scientific) according to the manufacturer's instructions and counterstained with DAPI. Images were recorded using a fluorescence microscope (Olympus IX73, Japan), and all sections were visualized using a 3D digital slide scanner (3DHISTECH Pannoramic MIDI). Image analysis was performed using ImageJ (version 1.45, National Institutes of Health, Bethesda, MD, USA). The positive staining threshold was set to exclude background staining, and the average percentage of immunostaining-positive cells and immunostaining-positive area was determined.

[0043] 7.16S rRNA Sequencing and Analysis

[0044] DNA was extracted from fecal samples using the QIAamp Power-Fecal Pro DNA Kit (QIAGEN, USA). The primers 515F (5′-gtgtgycagcmgccggtaa-3′) and 806R

[0045] (5'ccggactacnvgggtwtctaat3') The V4 hypervariable region of the 16S rRNA gene was amplified by PCR. Subsequently, all PCR amplicons were sequenced using the Illumina Nova 6000 platform of Guangdong Magigene Biotechnology Co., Ltd. (Guangzhou, China). All microbial data were imported into QIIME 2 in FASTQ format for analysis, including demultiplexing, primer removal, quality control, and taxonomic annotation. The length- and quality-filtered reads were classified into amplicon sequence variants using DADA2. Taxonomic annotation was performed using the Silva 138 16S rRNA database and the q2-feature-classifier plugin. Microbial α-diversity was calculated, including the Shannon index and the Chao1 index. Beta-diversity was estimated by calculating the Bray-Curtis distance and then applied to principal coordinate analysis (PCoA). Linear discriminant analysis (LDA) effect size (LEfSe) was used to calculate the LDA results of different taxonomic groups. In addition, functional prediction of the 16S rRNA sequencing data was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). The PICRUSt2 results were analyzed using ReporterScore to obtain the enrichment of pathways and modules in each group.

[0046] 8. Extraction and quantification of short-chain fatty acids (SCFAs)

[0047] SCFAs, including acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, and caproic acid, were quantified using gas chromatography (GC). Fecal samples (0.2 g) were homogenized in physiological saline, centrifuged, acidified, and SCFAs were extracted with ether. SCFAs were determined using GC Smart (GC-2018). The SCFAs results were reported as μg / mL of concentration normalized to the internal standard.

[0048] 9. Data processing

[0049] All experimental data were statistically analyzed using SPSS 26.0 software (IBM, USA) and GraphPad Prism 8.0 software (GraphPad software, San Diego, USA). The data were expressed as the mean ± standard error of the mean (SEM). Before any parametric tests were performed, the normality and homogeneity of variance were evaluated. According to the data distribution, the Mann-Whitney U test or Student's t test was used to determine the statistical significance between two groups. One-way analysis of variance and the least significant difference (LSD) post hoc test or Kruskal-Wallis test were used to compare the statistical significance among three or more groups. Repeated measures analysis of variance was used for the comparison of escape latency. Spearman rank correlation was used to evaluate the correlation. A P value < 0.05 was considered statistically significant. *P < 0.05, **P < 0.01, ***P < 0.001.

[0050] Study on the changes of intestinal SCFAs in patients with PSCI and the relationship between butyrate and cognitive impairment

[0051] Two clinical cohorts were established to study the changes of intestinal SCFAs in patients with PSCI and the relationship between butyrate and cognitive impairment

[0052] All participants provided written informed consent in accordance with the Declaration of Helsinki. This study has been approved by the Ethics Committee of Nanfang Hospital (NFEC-2020-169).

[0053] Cohort 1: Subjects were recruited from the Department of Neurology, Nanfang Hospital, Southern Medical University from January 2022 to December 2023. Inclusion criteria for PSCI: (1) Age > 18 years; (2) History of stroke within the past 3 - 6 months, with clinical or imaging evidence; (3) Post-stroke cognitive impairment, with neuropsychological evidence of deficits in multiple cognitive domains or symptoms of cognitive decompensation compared to pre-stroke. An MoCA score < 22 was defined as PSCI. Exclusion criteria: (1) Significant neurological deficits (somnolence, delirium, aphasia, limb weakness), unable to cooperate with cognitive assessment; (2) History of seizure before stroke, severe cognitive impairment (AD8 ≥ 2), mental disorder or emotional problem; (3) Active infectious diseases (such as pneumonia, urinary tract infection); (4) Use of antibiotics or probiotics; (5) Unable to provide fecal samples. Inclusion criteria for Non-PSCI: (1) Age > 18 years; (2) History of stroke within the past 3 - 6 months, with clinical or imaging evidence; (3) No significant decline in the patient's cognitive function, MoCA score ≥ 22. Exclusion criteria: (1) Significant neurological deficits (somnolence, delirium, aphasia, limb weakness), unable to cooperate with cognitive assessment; (2) History of seizure before stroke, severe cognitive impairment (AD8 ≥ 2), mental disorder or emotional problem; (3) Post-stroke cognitive impairment, with neuropsychological evidence of deficits in multiple cognitive domains or symptoms of cognitive decompensation compared to pre-stroke; (4) Active infectious diseases (such as pneumonia, urinary tract infection); (5) Use of antibiotics or probiotics; (6) Unable to provide fecal samples.

[0054] Cohort 2: Subjects were recruited from the Department of Neurology, Nanfang Hospital, Southern Medical University from January 2023 to December 2023. Inclusion criteria: (1) Age > 18 years; (2) Acute ischemic stroke (AIS) was diagnosed according to the criteria defined in the 11th edition of the International Classification of Diseases (ICD-11), including the presence of a responsible ischemic lesion on neuroimaging (such as CT or MRI), or symptoms / signs lasting more than 24 hours in the absence of neuroimaging evidence; (3) NIHSS score ≤ 8. Exclusion criteria: (1) Significant neurological deficits (somnolence, delirium, aphasia, limb weakness), unable to cooperate with cognitive assessment; (2) History of seizure before stroke, severe cognitive impairment (AD8 ≥ 2), mental disorder or emotional problem; (3) Active infectious diseases (such as pneumonia, urinary tract infection); (4) Use of antibiotics or probiotics during follow-up; (5) Unable to provide fecal samples within 4 days of admission or at 3-month follow-up. Cognitive test results and fecal specimens were collected during the acute phase and follow-up.

[0055] Cognitive level was evaluated using the Montreal Cognitive Assessment (MoCA). MoCA scores range from 0 to 30, and the final score is adjusted according to the years of education. If the subject has ≤ 12 years of education (high school level), 1 point can be added, but the total score cannot exceed 30 points. Early cognitive impairment was defined as a MoCA score < 22 within two weeks of onset, and PSCI was defined as a MoCA score < 22 at 90-day follow-up.

[0056] In cohort 1, 170 patients were included, of whom 89 had no post-stroke cognitive impairment (Non-PSCI) and 81 had PSCI( Figure 1 A). Patients in the PSCI group were older and had a higher proportion of females (Table 1).

[0057] Table 1 Baseline of the Non-PSCI group and the PSCI group

[0058]

[0059]

[0060] NIHSS: National Institutes of Health Stroke Scale, the National Institutes of Health Stroke Scale

[0061] Table 2 Univariate and multivariate Logistic regression analysis of risk factors in the Non-PSCI group and the PSCI group

[0062]

[0063] OR (Odds ratio): Odds ratio; 95% CI (95% confidence interval): 95% confidence interval

[0064] The results of gut microbiota analysis showed that the species richness (Chao1 index) in the PSCI group was significantly lower, and there were significant differences in the microbial composition between the two groups( Figure 1 B-C). Targeted GC analysis showed that SCFA metabolism was altered in PSCI patients, and the levels of acetic acid, butyric acid, propionic acid, and valeric acid were significantly decreased( Figure 1 G). OPLS-DA analysis found that butyrate was the key differential metabolite( Figure 1 D-F). In addition, a significant decrease in the abundance of butyrate-producing bacteria was observed in PSCI patients( Figure 1 H). Fecal butyrate levels were significantly positively correlated with cognitive scores (MoCA) Figure 1I). After adjusting for confounding factors, an elevated level of butyrate in feces remained an independent protective factor against PSCI (Table 2).

[0065] Cohort 2 was a prospective cohort that included 65 AIS patients and was followed up for 3 months. The MoCA score was used to evaluate cognition at 3 months after stroke, and 23 patients developed PSCI, while the other 42 patients were non-PSCI ( Figure 1 J). Patients with PSCI were older and had higher initial NIHSS scores (Table 3). After follow-up, it was found that the intestinal butyrate content in non-PSCI patients was significantly higher than that in PSCI patients. Figure 1 L). The butyrate level at 3 months was significantly positively correlated with the MoCA score. Figure 1 M). Multivariate logistic regression analysis showed that after adjusting for age, gender, years of education, initial NIHSS, and erythrocyte sedimentation rate (ESR), a higher butyrate level at 3 months was an independent protective factor against the occurrence of PSCI in AIS patients. Figure 1 N, Table 4).

[0066] Table 3 Baseline of non-PSCI group and PSCI group in Cohort 2

[0067]

[0068]

[0069] NIHSS: National Institutes of Health Stroke Scale

[0070] Table 4 Univariate and multivariate Logistic regression analysis of risk factors in non-PSCI group and PSCI group

[0071]

[0072] Example 2 Mouse model validation experiment

[0073] 2.1 Transient middle cerebral artery occlusion leads to long-term cognitive impairment, neuronal loss, and hippocampal microglial aggregation

[0074] In this study, the tMCAO model was used to further explore the mechanism of PSCI in mice. Female and male C57BL / 6 mice were divided into the Sham group and the tMCAO group for modeling and observation. Figure 2 A, 4A).

[0075] Previous studies have shown that MCAO-induced cortical ischemic injury leads to long-term contextual memory impairment in mice, with gradually worsening cognitive impairment, which is most obvious 1 month after surgery. In this study, the Morris water maze was used to evaluate spatial learning and memory ability 1 month after modeling. Compared with the Sham group, the number of platform crossings of male and female mice in the tMCAO group was significantly reduced, and the distance traveled to the target quadrant was shorter, indicating impaired spatial learning and memory ability after stroke ( Figure 2 D-G, 4D-G). In the NORT test to evaluate short-term memory, the discrimination rate in the tMCAO group was significantly decreased, further indicating a defect in short-term memory ( Figure 2 B-C, 4B-C). Nissl staining showed that 28 days after surgery, there was significant neuronal loss in the ischemic hippocampal regions (CA1, CA2, and CA3) of the ipsilateral hemisphere in male and female tMCAO mice ( Figure 6 ). In addition, immunofluorescence staining showed a significant increase in the number of microglia and astrocytes in the same region, suggesting obvious neuroinflammation in this region ( Figure 3 , 5).

[0076] 2.2 Persistent changes in the gut microbiota and reduced butyrate levels in tMCAO mice

[0077] To evaluate the changes in the gut microbiota after tMCAO modeling, 16S rRNA sequencing was performed on mouse fecal samples at 7, 14, and 28 days after surgery.

[0078] The results showed that there was no significant difference in α-diversity between the Sham group and the tMCAO group in male mice ( Figure 2 H). Using Bray-Curtis distance to evaluate β-diversity and presenting the results through PCoA plots, there was a significant separation between the gut microbiota of the Sham group and the tMCAO group, indicating significant changes in the microbiota composition after tMCAO modeling ( Figure 2 I). LEfSe analysis showed that the abundance of Lachnospiraceae in the Sham group was higher than that in the tMCAO group on the 28th day ( Figure 2 J). A continuous decrease in the abundance of butyrate-producing bacteria was observed in tMCAO mice, and the number of butyrate-producing bacteria was significantly reduced on the 28th day compared with the Sham group ( Figure 2 K-M). Targeted GC analysis showed that SCFAs metabolism changed 28 days after modeling, and the levels of acetate, butyrate, and propionate were significantly reduced ( Figure 2 N). In addition, the levels of LPS and LBP in peripheral blood in the tMCAO group were significantly increased ( Figure 2 O, 4O).

[0079] Similarly, compared with the Sham group, the composition of the microbiota in female tMCAO mice changed significantly, the abundance of butyrate-producing bacteria continued to decrease, and the levels of acetate, butyrate, and propionate in the feces also decreased significantly ( Figure 4 ).

[0080] In summary, tMCAO mice showed persistent cognitive deficits, a significant reduction in the abundance of butyrate-producing bacteria in the gut, and a decrease in butyrate levels, and this phenomenon did not differ by gender.

[0081] 2.2 Disruption of the gut microbiota and reduction of SCFA levels induced by broad-spectrum antibiotics promote hippocampal microglial aggregation

[0082] To investigate whether the gut microbiota is associated with cognitive impairment, tMCAO mice were intervened with an antibiotic cocktail (Abx) composed of ampicillin, neomycin sulfate, and metronidazole. From 14 days before surgery until 28 days after surgery, mice were provided with water containing the antibiotic mixture daily ( Figure 7 A). Abx treatment led to a significant decrease in the richness of the microbiota in the feces of mice ( Figure 7 H), resulting in a significant decrease in the abundance of butyrate-producing microbiota in the gut ( Figure 7 J), and a significant decrease in SCFA concentrations, especially almost undetectable butyrate ( Figure 7 I). However, compared with the tMCAO group, Abx intervention had no significant effect on the results of cognitive assessment ( Figure 7 A-F). At the same time, the loss of ipsilateral hippocampal neurons in Abx-treated mice was significantly aggravated, and the number of infiltrating microglia increased ( Figure 8 ).

[0083] In the above embodiments, the relationship between butyrate levels, the abundance of intestinal butyrate-producing bacteria flora and PSCI was verified through experiments, indicating that the detection of intestinal butyrate levels or the abundance of butyrate-producing bacteria flora in PSCI patients can be used to predict cognitive function after stroke. Although the change in the abundance of Lachnospira was observed in Example 2, due to different samples, the specific types of butyrate-producing bacteria genera in the intestine are not completely consistent. Therefore, the butyrate-producing bacteria genera (Butyrate Producing genera) mentioned in the present invention are not limited to a single Lachnospira, etc., and may include various known intestinal butyrate-producing bacteria, including but not limited to: Acetivibrio, Acetoanaerobium, Acetonema, Acidaminococcus, Actinocatenispora, Actinokineospora, Actinomadura, Actinomyces, Actinoplanes, Actinoplanes I, Actinopolymorpha, Agathobacter, Albidiferax, Alcaligenes, Alicyclobacillus, Alistipes, Alkaliphilus, Alloactinosynnema, Allobaculum, Allokutzneria, Anaeroarcus, Anaerobacter, Anaerobutyricum, Anaerococcus, Anaerofustis, Anaeroglobus, Anaeromyxobacter, Anaerosalibacter, Anaerosphaera, Anaerostipes, Anaerotruncus, Anaerovorax, Angustibacter, Aquabacterium, Arsenicicoccus, Azoarcus, Bacillus, Blastococcus, Blautia, Brachyspira, Butyricicoccus,Butyricimonas, Butyrivibrio, Caldanaerobacter, Caldithrix, Caloramator, Caloranaerobacter, Caminicella, Carboxydothermus, Catelliglobosispora, Catenibacterium, Catenuloplanes, Cetobacterium, Chondromyces, Christensenella, Christensenellaceae, Chryseobacterium, Chrysiogenes, Clostridiisalibacter, Clostridium, Clostridium sensu stricto, Clostridiumsensu stricto, ClostridiumIV, ClostridiumXI, ClostridiumXII, ClostridiumXlVa, Coprobacillus, Coprococcus, Corallococcus, Cryptanaerobacter, Cystobacter, Dactylosporangium, Deferribacter, Defluviitaleaceae, Dehalobacter, Desulfarculus, Desulfatibacillum, Desulfitibacter, Desulfitobacterium, Desulfobacter, Desulfobacterium, Desulfobacula, Desulfobulbus, Desulfococcus, Desulfomonile, Desulforegula, Desulfosarcina, Desulfospira,Desulfosporosinus, Desulfotignum, Desulfotomaculum, Desulfovirgula, Desulfurispirillum, Desulfurispora, Desulfuromonas, Dethiobacter, Dorea, Eggerthella, Eisenbergiella, Elizabethkingia, Erysipelotrichaceae_incertae_sedis, Eubacterium, Faecalibacterium, Fastidiosipila, Fervidicella, Fervidobacterium, Filifactor, Flavonifractor, Fusobacterium, Gardnerella, Geoalkalibacter, Geobacter, Geopsychrobacter, Geothrix, Glycomyces, Goodfellowiella, Guggenheimella, Halanaerobium, Haliangium, Hallella, Haloglycomyces, Haloplasma, Hamadaea, Heliobacterium, Herbidospora, Hyalangium, Ignavibacterium, Intestinimonas, Intrasporangium, Johnsonella, Kibdelosporangium, Kineosporia, Knoellia, Kosmotoga, Kribbella, Kroppenstedtia, Kutzneria, LabilithrixLaceyella, Lachnoanaerobaculum, Lachnobacterium, Lachnospira, Lachnospiracea_incertae_sedis, Lechevalieria, Lechevalieria_Nocardia, Lentzea, Longispora, Marinifilum, Marinitoga, Marmoricola, Megasphaera, Mesotoga, Micromonospora, Modestobacter, Mogibacteriaceae, Moorella, Moryella, Murdochiella, Myxococcus, Myxococcus_Cystobacter, Natranaerobius, Nocardioides, Odoribacter, Olsenella, Ornithinimicrobium, Oscillibacter, Oscillospira, Oxobacter, Paludibacter, Papillibacter, Pelobacter, Pelosinus, Peptococcus, Peptoniphilus, Peptostreptococcaceae_incertae_sedis, Peptostreptococcus, Petrotoga, Phycicoccus, Porphyromonas, Proteocatella, Pseudobutyrivibrio, Pseudoflavonifractor, Pseudoramibacter, Pseudothermotoga, Psychrilyobacter, Rikenella, RoseburiaRuminococcus, Saccharofermentans, Saccharothrix, Salinispora, Sarcina, Sedimentibacter, Shimazuella, Shuttleworthia, Smithella, Smithella, Solobacterium, Sorangium, Sphaerobacter, Spirillospora, Sporichthya, Sporolituus, Stackebrandtia, Stigmatella, Streptomyces, Subdoligranulum, Symbiobacterium, Syntrophomonas, Syntrophothermus, Tannerella, Tepidimicrobium, Terrabacter, Tetrasphaera, Thermicanus, Thermoactinomyces, Thermoanaerobacter, Thermoanaerobacterium, Thermohydrogenium, Thermomonospora, Thermosipho, Thermosulfidibacter, Thermotalea, Thermotoga, Treponema, Tumebacillus, Turicibacter, Verrucosispora, etc.

[0084] Example 3

[0085] This example provides a product for the prognostic evaluation, diagnosis or monitoring of post-stroke cognitive impairment, which includes a reagent for detecting the level of the said biomarker, and this reagent is used to detect the butyrate content or the abundance of butyrate-producing flora in a sample, and usually the sample can be feces. This product can be a kit, etc.

[0086] Example 4

[0087] This embodiment provides a prognosis evaluation, diagnosis or monitoring system for post-stroke cognitive impairment, and the system includes:

[0088] A detection result collection module, which is used to collect the test results of the biomarker levels in the biological sample to be tested, and the biomarker levels can be the butyrate content or the abundance of butyrate-producing flora;

[0089] An analysis module, which is used to compare the test results of the butyrate content or the abundance of butyrate-producing flora in the sample with the corresponding indicators of the group without post-stroke cognitive impairment, obtain the analysis results, and judge the risk of post-stroke cognitive impairment of the biological sample to be tested.

[0090] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0091] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A marker for predicting cognitive function after stroke, wherein the marker is butyrate and / or butyrate-producing bacteria.

2. Use of the marker as claimed in claim 1 in screening preparation products for treating and / or assisting in treating post-stroke cognitive dysfunction.

3. Use of the marker as claimed in claim 1 in the preparation of a preparation product for the treatment and / or auxiliary treatment of post-stroke cognitive dysfunction.

4. Use of a reagent for detecting the marker level according to claim 1 in the preparation of a product for prognosis assessment, diagnosis or monitoring of cognitive dysfunction after stroke, wherein the reagent is used to detect the butyrate content or the abundance of butyrate-producing bacteria in a sample.

5. The use according to claim 4, characterized in that: The sample is feces.

6. A product for prognosis assessment, diagnosis or monitoring of cognitive dysfunction after stroke, the product comprising a reagent for detecting the level of the marker as claimed in claim 1, wherein the reagent is used to detect the butyrate content or the abundance of butyrate-producing bacteria in the sample.

7. The use according to claim 6, characterized in that: The sample is feces.

8. A prognostic assessment, diagnosis or monitoring system for post-stroke cognitive dysfunction, characterized in that: include: A test result collection module, used to collect the test results of the marker level as claimed in claim 1 in the biological sample to be tested; The analysis module is used to compare the obtained biomarker level test results with the corresponding indicators of the group without stroke cognitive dysfunction, obtain analysis results, and judge the risk of post-stroke cognitive dysfunction of the biological sample to be tested.

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