Application of biomarker HMDB0254633 in macaque HBB gene mutation detection

Abstract

The invention discloses application of a biomarker HMDB0254633 in detection of HBB gene mutation of macaque, relates to the technical field of biology, and solves the technical problem of lack of a specific biomarker for early detection of thalassemia intestinal complications caused by HBB mutation. The key points of the technical scheme are as follows: the invention provides application of the biomarker HMDB0254633 in preparation of a quantitative detection product for macaque HBB gene mutation, the chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, the molecular formula of the biomarker HMDB0254633 is C18H34O3, the quantitative detection product is a reagent or a kit, and the invention further provides a method for quantitatively detecting HBB gene mutation. Comprising the following steps: 1) extracting metabolites in excrement; 2) detecting metabolites by using a quantitative detection product; the effects of providing a high-specificity non-invasive marker and a detection tool and realizing early-stage rapid and non-invasive detection are achieved.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and more particularly to application of a biomarker HMDB0254633 in detecting HBB gene mutations in macaques. Background Art

[0002] Thalassemia is a hemoglobin disorder, a hereditary disease caused by autosomal gene defects. Due to deletions or mutations in genes regulating globin synthesis, the synthesis ratio of the α- and β-chain globins that make up hemoglobin is unbalanced, shortening the lifespan of red blood cells and causing hemolytic anemia.

[0003] The gut microbiome plays a vital role in the host's metabolism, immune response, and overall health. In recent years, it has been recognized as a key regulator of the onset and progression of various diseases. The gut microbiome influences the host immune response, maintains intestinal barrier function, and participates in energy metabolism through bacterial metabolites (such as short-chain fatty acids). Dysbiosis of the gut microbiome is closely associated with a variety of conditions, including metabolic syndrome, diabetes, inflammatory bowel disease, obesity, and neurological diseases. Therefore, a deep understanding of the mechanisms of action of the gut microbiome under different physiological states is crucial for revealing disease mechanisms and developing new therapies.

[0004] HBB gene mutations in patients with thalassemia can lead to increased intestinal iron absorption. In addition, patients with thalassemia also have intestinal calcium absorption disorders, and the disease's regulatory mechanism of intestinal calcium transport further highlights the interaction between calcium and iron metabolism. Patients with β-thalassemia are often accompanied by iron overload, chronic inflammation and oxidative stress, all of which may affect the intestinal environment. Many studies have shown that the intestinal flora composition of patients with thalassemia is significantly different from that of healthy people, manifested by an increase in the number of pathogenic bacteria and a decrease in beneficial bacteria.

[0005] Therefore, the technical problem we need to solve is to deeply explore the intrinsic mechanism of changes in the intestinal microbiome of patients with thalassemia and identify potential biomarkers that can be used for early non-invasive disease diagnosis, prevention and treatment. Summary of the Invention

[0006] To solve the problems in the prior art, the application provides an application of a biomarker HMDB0254633 in detection of a HBB gene mutation of a macaque, simulates a human by constructing an animal model of non-primate beta-thalassemia, collects fecal samples of juvenile cynomolgus monkey HBB mutants and wild-type cynomolgus monkeys, comprehensively analyzes microbial diversity and functions in the fecal samples by using metagenomics and metabolomics technologies, compares the HBB-deficient monkey with a control monkey, evaluates the influence of HBB gene editing on the intestinal flora of the juvenile cynomolgus monkey and the metabolic functions thereof, determines that the differential metabolite 3-oxooctadecanoic acid HMDB0254633 can be used as a biomarker, and verifies that 3-oxooctadecanoic acid can play a rescue treatment role in effectively relieving diarrhea and improving the composition of the intestinal flora of the mouse with diarrhea, reveals that the HBB mutation affects the composition and metabolic functions of the microbial community through a "blood-gut axis", and determines that the HBB mutation leads to a decrease in lactobacillus and an increase in bacteroides, and affects amino acid (such as phenylalanine) and lipid metabolic pathways, provides a basis for a targeted therapy, achieves the provision of a high-specificity non-invasive marker and a detection tool, and provides a basis for the development of a drug for treating diarrhea by targeting the intestinal flora, and achieves the effect of early, rapid and non-invasive detection.

[0007] The above technical purposes of the application are achieved by the following technical solutions.

[0008] In a first aspect, the application provides an application of a biomarker HMDB0254633 in preparation of a product for quantitatively detecting a HBB gene mutation of a macaque, wherein the chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, the molecular formula is C 18 H 34 O3.

[0009] Further, the quantitative detection product is a reagent.

[0010] Further, the quantitative detection product is a kit.

[0011] In a second aspect, the application provides an application of a reagent for detecting an expression level of a biomarker HMDB0254633 in a biological sample in preparation of a detection product for intestinal complications of thalassemia caused by a HBB gene mutation of a macaque, wherein the chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, the molecular formula is C 18 H 34 O3.

[0012] In a third aspect, the application provides a method for quantitatively detecting a HBB gene mutation, comprising the following steps:

[0013] 1) extracting metabolites in feces;

[0014] 2) Detecting the metabolite using the quantitative detection product described in the first aspect.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The present invention provides 3-oxooctadecanoic acid HMDB0254633 as a highly specific biomarker for HBB gene mutations with high discriminatory power (AUC = 0.933), providing an effective and non-invasive means of detecting HBB gene mutations.

[0017] 2. The present invention provides a detection product such as a reagent or kit based on the biomarker HMDB0254633, which can quickly screen the expression level of the biomarker HMDB0254633 and is non-invasive to individual animals;

[0018] 3. Through combined metabolomics and metagenomics analysis, this study confirmed that HBB mutations affect microbial community composition and metabolic function through the "blood-gut axis." It also confirmed that HBB mutations lead to a decrease in Lactobacillus and an increase in Bacteroides, and affect amino acid (such as phenylalanine) and lipid metabolic pathways, providing a basis for targeted therapies.

[0019] 4. The present invention verifies the cross-species effectiveness by validating biomarkers in the crab-eating macaque model and the effectiveness of diarrhea treatment in the mouse model, which is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0021] Figure 1 This is the experimental process of the metagenome and metabolome and the generation diagram of the gene-edited cynomolgus monkey in Example 4 of the present invention; wherein:

[0022] Figure 1 A is a diagram of the metagenomic and metabolomics experimental procedures in Example 4 of the present invention;

[0023] Figure 1 B is a map of target sites for HBB gene knockout constructed in Example 4 of the present invention. The HBB gene of cynomolgus monkeys is located on chromosome 14. The target to be knocked out is selected based on its expression and transcription mechanism;

[0024] Figure 1 C is a graph showing the T7E1 digestion results of blastocysts in Example 4 of the present invention, T7E1- is the gel electrophoresis result without the addition of T7E1 enzyme, T7E1+ is the gel electrophoresis result after the addition of T7E1 enzyme, and 1 to 10 are embryo numbers;

[0025] Figure 1 D is the Sanger sequencing result diagram in Example 4 of the present invention; the black peak in the peak diagram is base G, the blue peak is base C, the green peak is base A, and the red peak is base T;

[0026] Figure 1 E is a diagram of the 4-month-old HBB mutant group and the control group in Example 4 of the present invention; the red arrow on the far right points to the HBB mutant, and the one on the left is the infant monkey of the control group;

[0027] Figure 1 F is an agarose gel electrophoresis image of the KO monkey ear tissue and blood in Example 4 of the present invention;

[0028] Figure 2 This is a differential metabolite screening diagram in Example 4 of the present invention; wherein:

[0029] Figure 2 A is a KEGG pathway classification diagram of metabolites in Example 4 of the present invention, where the Y-axis represents the metabolic pathway category, the X-axis shows the number of metabolites, different colors represent different metabolic pathway categories, and the percentage represents the proportion of metabolites in each category relative to the total number of metabolites with pathway information;

[0030] Figure 2 B is a principal component analysis cluster diagram in Example 4 of the present invention; the horizontal axis represents the first principal component (PC1), the vertical axis represents the second principal component (PC2), the numbers in brackets represent the scores of each principal component, each point in the figure represents a sample, the green mark represents the wild type group, the yellow mark represents the HBB mutant, and the elliptical line represents the 95% confidence interval;

[0031] Figure 2 C is a bar graph of significantly differential metabolites in Example 4 of the present invention, where the x-axis represents inter-group comparison information, the y-axis shows the number of differential metabolites, blue columns represent significantly upregulated metabolites, and orange-yellow columns represent significantly downregulated metabolites;

[0032] Figure 2 D is a volcano plot of differential metabolites in Example 4 of the present invention, where the horizontal axis represents the logarithmic fold change with base 2, and the vertical axis represents the logarithmic q value (p value) with base 10. Blue dots represent significantly downregulated metabolites, orange dots represent significantly upregulated metabolites, and gray dots represent metabolites with no significant difference.

[0033] Figure 2 E is a heat map of differential metabolite abundance in Example 4 of the present invention. Each row in the heat map represents a differential metabolite, each column represents a sample, and the color represents the expression level, with green to red representing low to high expression levels;

[0034] Figure 3This is a differential metabolite analysis diagram in Example 4 of the present invention; wherein:

[0035] Figure 3 A is a chord diagram of metabolite correlations in Example 4 of the present invention. The inner lines represent significantly different metabolites, and the outer arcs represent the categories to which these metabolites belong. The colored lines show the correlations within each metabolite category, with green lines indicating positive correlations and red lines indicating negative correlations.

[0036] Figure 3 B is an enrichment analysis histogram in Example 4 of the present invention. In this figure, the metabolic pathways with -log (p value) values ​​higher than the red dashed line have p values ​​< 0.01 in the significance test, while the metabolic pathways with -log (p value) values ​​higher than the blue dashed line have p values ​​< 0.05. The numbers on the histogram represent the number of differential metabolites annotated for each metabolic pathway.

[0037] Figure 3 C is an enrichment analysis pathway score graph in Example 4 of the present invention. The vertical axis represents the metabolic pathway name, and the horizontal axis represents the differential abundance score (DAscore). A score of 1 indicates that the expression levels of all annotated differential metabolites in the pathway are upregulated, and a score of -1 indicates downregulation. The length of the line represents the absolute value of the DAscore, and the size of the dot at the end of the line segment reflects the number of metabolites in the pathway. The larger the dot, the more metabolite types the pathway contains.

[0038] Figure 3 D is a ROC curve diagram in Example 4 of the present invention, where the x-axis represents specificity, the y-axis represents sensitivity, and the area under the curve (AUC) is represented by the area under the line;

[0039] Figure 4 This is a graph of intestinal microbial diversity in Example 4 of the present invention; wherein:

[0040] Figure 4 A is a box plot of the species dilution curve in Example 4 of the present invention, where the x-axis represents the number of samples, the y-axis represents the number of species (detected species counts), and the color of the box represents the grouping;

[0041] Figure 4 B is a Chao1 boxplot of species α diversity in Example 4 of the present invention, where the horizontal axis represents the group and the vertical axis represents the index value. When the p value is <0.05, it indicates that there is a significant difference in the α diversity index between the groups;

[0042] Figure 4 C is a scatter plot of the PLS-DA analysis in Example 4 of the present invention, where each dot represents a sample, blue dots represent the wild-type group, and yellow dots represent the HBB mutant. The x-axis and y-axis represent the PLS dimensions used to distinguish samples, respectively. The values ​​in brackets in the axis titles represent the variance explained by the samples after dimensionality reduction.

[0043] Figure 5 This is a graph showing the difference in intestinal microbial communities in Example 4 of the present invention; wherein:

[0044] Figure 5 A is a species-level Venn diagram in Example 4 of the present invention, where the numbers in the overlapping areas represent the number of species shared between multiple groups, and the numbers in the non-overlapping areas represent the number of species unique to each group;

[0045] Figure 5 B is a stacked bar chart of phylum-level species abundance in Example 4 of the present invention, where the horizontal axis represents the taxonomic group, the vertical axis represents the relative abundance of the species, and the color of the bar indicates the species classification;

[0046] Figure 5 C is a stacked bar chart of species abundance at the genus level in Example 4 of the present invention;

[0047] Figure 5 D is a boxplot of species abundance at the genus level in Example 4 of the present invention, where the x-axis represents species with statistically significant differences, the y-axis represents the relative abundance of each species, and an asterisk "*" indicates p < 0.05;

[0048] Figure 5 E is the LEfSe phylogenetic tree diagram in Example 4 of the present invention, where each dot represents a specific species classification, the size of the dot corresponds to its relative abundance, species with significant differences are colored by group, and fan-shaped colors represent higher-level species classifications;

[0049] Figure 6 This is a functional difference analysis diagram in Example 4 of the present invention; wherein:

[0050] Figure 6 A is a bar graph of functional gene statistics in Example 4 of the present invention, where the x-axis represents the number of genes, the y-axis represents the functional category, and the color of the bar represents the group or functional category;

[0051] Figure 6 B is a ring diagram of the difference function in Example 4 of the present invention. The left side of the ring displays grouping information, the right side displays functional categories, and the outermost dial displays the proportion of each function in the sample or the ratio between different functions. The color of the inner arc segment represents the different groups, and the line between the arcs indicates the presence of the function in the sample. The width of the arc segment reflects its proportion.

[0052] Figure 6 C is a KEGG pathway enrichment difference diagram in Example 4 of the present invention, where the horizontal axis represents the Reporter score, the vertical axis represents the pathway, and the dotted line represents the threshold at which the Reporter score reaches a significant level;

[0053] Figure 7 This is a comprehensive analysis diagram of metabolomics and metagenomics in Example 4 of the present invention; wherein:

[0054] Figure 7 A is a PCA cluster diagram of the species and metabolite data combination in Example 4 of the present invention, which combines species and metabolite data for PCA clustering;

[0055] Figure 7 B is a canonical correlation analysis (CCA) diagram in Example 4 of the present invention. The inner small circle represents a correlation coefficient of 0.5, and the outer large circle represents a correlation coefficient of 1. Species are represented by green dots, and metabolites are represented by orange triangles.

[0056] Figure 7 C is the comprehensive analysis of the unsupervised model of species and metabolites and the Sankey diagram in Example 4 of the present invention, where the orange line represents the negative correlation between species and metabolites, and the blue line represents the positive correlation;

[0057] Figure 7 D is the univariate correlation analysis between differentially expressed genes and metabolite functions in the Sankey diagram in Example 4 of the present invention. The green line indicates a negative correlation between differentially expressed genes and metabolite functions, and the purple line indicates a positive correlation.

[0058] Figure 7 E is the network analysis of differentially expressed genes and metabolite functions in the unsupervised model in Example 4 of the present invention. Each point represents a metabolite or related omics indicator. Green points represent omics indicators, yellow points represent metabolites, and red lines connecting points indicate positive correlations, while blue lines indicate negative correlations.

[0059] Figure 7 F is an enrichment analysis diagram of functional genes and metabolite pathways in Example 4 of the present invention, where the x-axis represents the enrichment factor (Rich Factor), the triangle represents the functional gene pathway, and the circle represents the metabolic pathway;

[0060] Figure 8 This is a diagram showing the effects of 3-Oxooctadecanoic Acid on intestinal health in mice;

[0061] Figure 8 A is the timetable for the mouse intestinal flora modeling experiment in Example 4 of the present invention.

[0062] Figure 8 B is a graph showing the effects of different treatments on the severity of diarrhea in mice in Example 4 of the present invention. This graph shows a comparison of the severity of diarrhea in mice in different treatment groups, including three indicators: loose stool incidence, loose stool severity, and diarrhea index. Each indicator has six treatment groups: control group, CO group, 3-AOMP group, 3-AOL group, and 3-AOH group.

[0063] Figure 8 C is a comparative graph of weight changes of mice in different treatment groups in Example 4 of the present invention, which shows the weight changes of mice in different treatment groups on day 0 and day 10. The graph includes five treatment groups: control group, CO, 3-AOMP, 3-AOL, and 3-AOH group;

[0064] Figure 9 This is the metabolite analysis and final classification diagram performed by LC-MS in Example 4 of the present invention; wherein:

[0065] Figure 9 A is the base peak chromatogram in Example 4 of the present invention, with the left side in negative ion mode and the right side in positive ion mode. The horizontal axis is the retention time, and the vertical axis is the most abundant ion response intensity detected at each time point.

[0066] Figure 9 B is a bar chart of the final classification of metabolites in Example 4 of the present invention, where the vertical axis represents the metabolite category and the horizontal axis shows the number of metabolites in each category;

[0067] Figure 9 C is the final classification pie chart of metabolites in Example 4 of the present invention. Different colors in the chart represent different types of metabolites, and the percentage shows the proportion of each type of metabolite in the total metabolites with annotated classification information;

[0068] Figure 10 This is a correlation analysis diagram of differential metabolites and metabolic pathway enrichment in Example 4 of the present invention; wherein:

[0069] Figure 10 A is a heat map of the correlation analysis in Example 4 of the present invention. Red indicates a strong positive correlation, and green indicates a strong negative correlation. The darker the color, the larger the absolute value of the correlation coefficient between samples. An asterisk "*" indicates a statistically significant correlation with a p-value < 0.05.

[0070] Figure 10 B is the enrichment analysis bubble chart in Example 4 of the present invention. The horizontal axis represents the enrichment factor (RichFactor), which is calculated by calculating the ratio of the number of differentially abundant metabolites in a specific metabolic pathway to the total number of metabolites in the pathway. A larger value indicates a higher proportion of differentially abundant metabolites in the pathway. The size of the dot represents the specific number of differentially abundant metabolites in the pathway.

[0071] Figure 11 This is a biodiversity and species difference analysis diagram in Example 4 of the present invention; wherein:

[0072] Figure 11A is a distribution diagram of gene fragment lengths in Example 4 of the present invention, where the x-axis represents the gene length interval and the y-axis shows the number of genes in each length interval;

[0073] Figure 11 B is a boxplot of gene alpha diversity in Example 4 of the present invention, where each boxplot represents a diversity index, the x-axis represents the group, and the y-axis represents the index value;

[0074] Figure 11 C is a boxplot of gene beta diversity in Example 4 of the present invention, where the x-axis and the colors of the boxes represent different groups, and the y-axis shows the distance between samples;

[0075] Figure 11 D is a boxplot of species alpha diversity in Example 4 of the present invention, where the horizontal axis represents the group and the vertical axis represents the index value;

[0076] Figure 11 E is the PCA cluster diagram in Example 4 of the present invention, where each point represents a sample and different colors represent different groups;

[0077] Figure 11 F is a stacked bar chart of species abundance at the phylum level in Example 4 of the present invention;

[0078] Figure 11 G is a boxplot of species-level differential abundance in Example 4 of the present invention. DETAILED DESCRIPTION

[0079] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0080] Example 1:

[0081] This embodiment provides an application of a biomarker HMDB0254633 in the preparation of a quantitative detection product for HBB gene mutation in macaques. The chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, and its molecular formula is C 18 H 34 O3.

[0082] The 3-oxooctadecanoic acid provided in this example has high discrimination ability as a biomarker for HBB gene mutation, with an AUC of 0.933, providing an effective and non-invasive means for detecting HBB gene mutation.

[0083] Quantitative detection products are reagents; quantitative detection products are test kits.

[0084] The above-mentioned detection products can quickly screen the expression level of the biomarker HMDB0254633 to determine whether there is an HBB gene mutation.

[0085] Example 2:

[0086] Provided is a method for detecting the expression level of a biomarker HMDB0254633 in a biological sample for use in preparing a detection product for intestinal complications of thalassemia caused by HBB gene mutation in macaques. The chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, and its molecular formula is C 18 H 34 O3.

[0087] Example 3:

[0088] A method for quantitatively detecting HBB gene mutations is provided, comprising the following steps:

[0089] 1) Extraction of metabolites from feces;

[0090] 2) Metabolites were detected using the quantitative detection product described in Example 1.

[0091] Example 4: This example provides an experimental research process and experimental analysis, which are as follows:

[0092] 1. Experimental Animals and Experimental Analysis

[0093] 1.1 Experimental Animals

[0094] All cynomolgus macaques used in this study were provided and maintained by Guangdong Blue Island Biotechnology Co., Ltd. (GBIB, China), a certified member of the Association for Assessment and Accreditation of Laboratory Animal Care (AAAC). GBIB's Institutional Animal Care and Use Committee approved the use of these animals (No. LD2017-003). All animal experiments adhered to the Guide for the Care and Use of Laboratory Animals. Eight six-month-old macaques (three males and five females) were used in this study. The HBB mutant cynomolgus macaque model was constructed using CRISPR / Cas9 gene editing technology to target and abrogate the HBB gene coding region, resulting in abnormal HBB gene expression and defective β-globin peptide chain production. This model was constructed in accordance with the ARRIVE guidelines. The health status of the macaques was confirmed through comprehensive health records and pre-experimental veterinary examinations. The macaques were kept in an environment with a temperature of approximately 26°C and a humidity of 30%-70%. They strictly adhered to a daily routine with lights on and off at 7:00 am and 7:00 pm to simulate a circadian rhythm. They had free access to feed (formulated feed and fresh vegetables / fruits) and drinking water.

[0095] Healthy C57BL / 6 wild-type mice (aged 6–8 weeks, weighing 20–24 g, half male and half female) were purchased from Weitonglihua Laboratory Animal Co., Ltd. (Beijing, China) and housed at 22°C under a 12-h light / dark cycle. Mice had free access to standard laboratory chow and drinking water.

[0096] Castor oil-induced diarrhea mouse model

[0097] Prior art documents that castor oil is commonly used to induce diarrhea in experiments due to its reproducibility and stability. It stimulates intestinal mucosal cells, reducing the active absorption of sodium (Na) and potassium (K), thereby triggering intestinal inflammation and diarrhea. Forty mice (ten mice per group) were individually housed and randomly divided into four groups. To induce diarrhea, the experimental group received a dose of castor oil equivalent to 20 mL / kg of body weight via gastric tube at 2:00 PM daily for six consecutive days; the control group received normal saline instead of castor oil. Starting on the third day of treatment, the cage bedding was replaced with appropriately sized filter paper. Defecation was monitored continuously for five hours, and diarrhea symptoms were recorded.

[0098] The control group received 10 mL / kg of normal saline. The castor oil group received 20 mL / kg of CO via a gastric tube to induce diarrhea. 3-AO (3-oxooctadecanoic acid) and 3-AOL (3-oxooctadecanoic acid low-dose group) received 1.0 mL / kg of 3-oxooctadecanoic acid. 3-AOH (3-oxooctadecanoic acid high-dose group) received 2.0 mL / kg of 3-oxooctadecanoic acid. 3-AOMP served as the positive control group and was administered montmorillonite powder at doses of 3.0 g / kg and 0.3 g / mL. All test substances were administered via gastric tube daily at 9:00 AM for four consecutive days.

[0099] Stool analysis included counting the total number of bowel movements and the number of loose stools, and determining the loose stool rate, stool consistency grade, and diarrhea index according to established methods. The loose stool rate was calculated as the proportion of unformed stool to the total number of bowel movements. Stool consistency was classified according to the diameter of the stool spot left on the filter paper: (1) <1 cm, (2) ≥1 cm but <1.9 cm, (3) ≥2 cm but ≤3 cm, and (4) >3 cm. For irregularly shaped stool spots, the average of the longest and shortest diameters was used. The diarrhea index was calculated by multiplying the loose stool rate by the consistency grade.

[0100] 1.2 Sample collection and storage

[0101] Fresh fecal samples were collected from wild-type and HBB gene-edited macaques, immediately frozen in liquid nitrogen, and stored at -80°C. The samples were then transported on dry ice to BGI Genomics for processing and analysis.

[0102] 1.3 Experimental Analysis

[0103] Metabolite extraction

[0104] The sample was slowly thawed at 4°C, and 25 mg of sample was weighed into a 1.5 mL centrifuge tube. Extraction buffer (800 µL of methanol:acetonitrile:water = 2:2:1, volume ratio, pre-chilled at -20°C) and 10 µL of internal standard were added. Two small steel beads were then added, and the tube was placed in a tissue grinder for homogenization (50 Hz, 5 minutes). The sample was then sonicated in an ice-water bath at 4°C for 10 minutes. The sample was then centrifuged at 25,000 g for 15 minutes at 4°C. The supernatant (600 µL) was collected and concentrated using a vacuum concentrator. The sample was then reconstituted with 600 µL of resuspension buffer (methanol:water = 1:9, volume ratio), vortexed for 1 minute, sonicated at 4°C for 10 minutes, and centrifuged again at 25,000 g for 15 minutes. The supernatant was transferred to a sample vial. For quality control analysis, 50 µL of the supernatant from each sample was mixed to prepare the quality control sample.

[0105] UPLC-MS analysis

[0106] Metabolite separation and detection were performed using a Waters UPLC I-Class Plus system (Waters, USA) coupled to a Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, USA).

[0107] Chromatographic conditions: Separation was performed on a Waters BEH C18 column (1.7µm, 2.1×100mm). The mobile phases for positive ion mode consisted of 0.1% formic acid in water (A) and 0.1% formic acid in methanol (B); the mobile phase for negative ion mode consisted of 10mM ammonium formate in water (A) and 95% methanol (B). The gradient elution program was as follows: 0-1 minute, 2% B; 1-9 minutes, 2% to 98% B; 9-12 minutes, 98% B; 12-12.1 minutes, 98% B to 2% B; 12.1-15 minutes, 2% B. The flow rate was set at 0.35mL / min, the column temperature was maintained at 45°C, and the injection volume was 5µL.

[0108] Mass spectrometry conditions: Data were acquired on a Q Exactive mass spectrometer from Thermo Fisher Scientific (USA) in both full-scan and tandem mass spectrometry (MS / MS) modes. The mass-to-charge ratio (m / z) scan range was set to 70–1050, with a resolution of 70,000 for MS and 17,500 for tandem MS. The automatic gain control (AGC) target was set to 3 × 10⁶ for MS and 1 × 10⁵ for tandem MS. The maximum injection time was 100 milliseconds for MS and 50 milliseconds for tandem MS. The fragmentation energy was set to 20, 40, and 60 electron volts, respectively, by selecting the top three ions with the highest ion intensity.

[0109] 2. Software and parameters

[0110] The raw data were imported into Compound Discoverer version 3.3 software (Thermo Fisher Scientific, USA) for processing. Ions with mass deviation less than 5 ppm, fragment ion deviation less than 10 ppm, and retention time deviation less than 0.2 min were screened out. The generated data matrix contained metabolite peak areas and identification results. Subsequently, in-depth analysis was performed using databases such as the BGI Metabolome Database (BMDB), mzC, and ChemSpider to conduct detailed metabolomics research.

[0111] 2.1 Metagenomic sequences

[0112] Metagenomic sequences were generated using the BGI DNBSEQ-T7 platform in PE150 mode to obtain high-quality clean reads, which were assembled into contigs and further processed for gene prediction and functional annotation.

[0113] 2.2 Library construction and sequencing

[0114] The researchers first extracted DNA from the sample and then used a Corvallis ultrasonic processor to fragment some of the metagenomic DNA to generate DNA fragments of appropriate size. They then used magnetic bead screening to control the fragment length to within the 200-400bp range. After screening, the DNA fragments were end-repaired, A-tailed, and adapter-ligated. PCR amplification was used to circularize the product, and then enzyme digestion was performed on the uncircularized linear DNA molecules to obtain the target library.

[0115] 2.3 Data Processing

[0116] Data filtering and processing were performed using SOAPnuke (v1.5.0) and Samtools (v1.2). Raw sequencing data containing more than 10% undetermined bases (N bases), reads with 15 or more sequencing adapter sequences, or reads with a low-quality base ratio exceeding 50% (Q value < 20) were removed. Bowtie2 (v2.2.5) was then used to remove host genome sequences to obtain clean data.

[0117] 2.4 Metagenome assembly

[0118] The clean data passed the quality control sequence and were assembled de novo using MEGHIT assembler, and contigs shorter than 200 bp were excluded from subsequent analysis.

[0119] 2.5 Gene prediction and abundance information

[0120] The present invention uses MetaGeneMark software to predict genes from metagenomic assembly data. After removing redundant sequences using CD-HIT software, similarity clustering is used for analysis, with a sequence identity threshold of 95% and a coverage threshold of 90%. This clustering process generates representative sequences for each group. Gene abundance is then quantitatively analyzed using Salmon software, and gene expression levels are estimated by calculating TPM values.

[0121] 2.6 Gene function prediction

[0122] The BLASTP function of Diamond software was used to perform functional annotation of non-redundant genes.

[0123] 2.7 Species annotation and abundance calculation

[0124] Species were annotated using Kraken2 software with default parameters and the Nt (202011) database. Based on the Kraken classification results, the species-level abundance in the metagenomic samples was estimated using the Bracken platform's Bayesian algorithm.

[0125] 2.8 Population diversity analysis

[0126] The present invention uses the R language software package to analyze species diversity and calculates alpha diversity indicators (including Chao1, Shannon, and Simpson indices). Beta diversity indicators are used to reflect differences between samples or groups. Bray-Curtis distance and Jensen-Shannon divergence are calculated to indicate significant differences in microbial community composition between different samples or groups.

[0127] 3. Results and Analysis

[0128] In the present application, fecal samples of 6-month-old HBB mutants and age-matched wild-type controls were collected, as shown in Figure 1 A, and subjected to metabolomic and metagenomic sequencing analysis. By integrating the data from the two omics methods, significant differences in HBB mutant post-microbial communities and metabolites were found. Rich ion peaks were detected in both positive and negative ion modes, indicating that the number of metabolites obtained was sufficient for analysis (as shown in Figure 9 A). The identified metabolites were subjected to quantitative analysis according to the final classification (as shown in Figure 9 B and Figure 9 C), and classified and statistically analyzed according to their roles in KEGG metabolic pathways in positive and negative ion modes. The results showed that the number of metabolites obtained was sufficient to support subsequent research (as shown in Figure 9 A). These metabolites were not only subjected to quantitative analysis based on the final classification ( Figure 9 B and Figure 9 C), but also further classified and statistically analyzed according to their roles in KEGG metabolic pathways (SuperPathways).

[0129] In terms of lipid metabolism, the number of metabolites was classified according to sub-pathways, and statistical analysis showed that in the fecal metabolites of 6-month-old crab-eating macaques, as shown in Figure 2 A, the HBB-deficient group had the most abundant metabolites involved in amino acid metabolism, a total of 72, accounting for 28% of the total metabolites; there were 37 lipid metabolism-related metabolites, accounting for 15%; and there were 34 other secondary metabolite biosynthesis-related metabolites, accounting for 13%. The results showed that amino acid metabolism and lipid metabolism were the most significant metabolic pathway categories in the fecal metabolites of crab-eating macaques.

[0130] The present application established a principal component analysis (PCA) model for analyzing the principal component characteristics of wild-type (WT) and HBB mutant fecal samples. The results showed that, as shown in Figure 2 B, the two groups of samples showed significant clustering in the principal component space, indicating that there was a clear differentiation between wild-type and HBB mutants. Subsequently, the present application continued to use fold change (FC≥1.2) and p-value (p<0.05) as screening criteria for single variable analysis of the differential metabolites in the two groups of feces. The results showed that, as shown in Figure 2 C and Figure 2As shown in Figure D, compared with the biotype group, 177 metabolites were significantly upregulated and 233 metabolites were significantly downregulated in the HBB mutant. To further explore the expression patterns of these differential metabolites, cluster analysis was performed on their expression levels. The data were first log2 transformed and processed using z-score standardization (zero mean normalization). Hierarchical cluster analysis was performed using Euclidean distance calculation. The results showed that 410 differential metabolites had significant differences in expression levels between groups, and their expression patterns were highly consistent. The test was performed in samples of wild type (WT) and HBB mutants, as shown in Figure 4. Figure 2 E. These results indicate that there are significant differences in the metabolite composition between the wild type and the HBB mutant.

[0131] 3.1 Significant abnormal metabolite patterns in HBB mutants

[0132] To more intuitively illustrate the synergistic or antagonistic effects between different metabolites, the present invention calculated the Spearman correlation coefficients (Spearman r value>0.8 and p<0.05) between metabolites and selected metabolites, and drew a correlation chord diagram. The analysis showed that metabolites in the "amino acids, peptides and analogs" category were negatively correlated with the "organic acids" category, while other categories showed positive correlations, such as Figure 3 As shown in A. In addition, we also generated a correlation heat map to show the correlation strength between the top 20 differential metabolites with the smallest p-value. The results showed that except for the HMDB006217 metabolite, which was negatively correlated, the remaining 19 metabolites were positively correlated. Figure 10 As shown in A.

[0133] Next, we performed pathway enrichment analysis on the differential metabolites based on the KEGG database and displayed the top 10 significantly enriched pathways (p value < 0.05). The results showed that the ABC transporter, phenylalanine metabolism, linoleic acid metabolism, and amino acid biosynthesis pathways had the largest number of differential metabolites, and the enrichment levels of these pathways were highly significant (p value < 0.01). Figure 3 B and Figure 10 As shown in Figure B. To further analyze the overall changes of these differential metabolites in the pathways, we performed differential abundance score analysis on the top 10 significantly enriched pathways in the two groups. The results showed that in the "Intestinal immune network regulates IgA production", "Small cell lung cancer" and "Th17 cell differentiation" pathways, one metabolite in each pathway of the HBB mutant was significantly upregulated; while in the "Alanine-Aspartate-Glutamate Metabolism" and "Pentose Phosphate Pathway", two metabolites in each pathway of the HBB mutant were significantly downregulated. In addition, in the "Linoleic acid metabolism" pathway, the expression levels of all three metabolites were significantly downregulated in the HBB mutant. In addition, the expression of metabolites in the "ABC transporter" and "phenylalanine metabolism" pathways showed an overall upregulation trend, as shown in Figure 3. Figure 3 C and Table 1. Finally, we performed ROC curve analysis on the differential metabolites, and the results showed that the area under the curve value of HMDB0254633 metabolite was 0.933, indicating that this metabolite has a high discriminatory ability and can be used as a potential biomarker for distinguishing wild type and HBB mutants, such as Figure 3 D. The amount of HMDB025463 marker contained in the metabolites of normal individuals is small, while the amount of HMDB025463 marker contained in individuals with HBB mutation is large. Based on this, the present invention determines whether a macaque individual has HBB mutation by quantitative analysis of metabolites.

[0134] Table 1 Scoring table of differential metabolite enrichment pathways

[0135] path On top down Metabolite number DA score state ABC transporters 4 3 7 0.1428 On top The intestinal immune network produces IgA 1 0 1 1 On top Small cell lung cancer 1 0 1 1 On top Th17 cell differentiation 1 0 1 1 On top Phenylalanine metabolism 3 2 5 0.2 On top Amino acid biosynthesis 2 2 4 0 On top Central carbon metabolism in cancer 1 1 2 0 On top Alanine, aspartate, and glutamate metabolism 0 2 2 -1 down Linolenic acid metabolism 0 3 3 -1 down pentose phosphate pathway 0 2 2 -1 down

[0136] 3.2 Abnormal intestinal microbiota in HBB-deficient cynomolgus monkeys

[0137] The present invention uses MEGAHIT software to assemble the quality control clean data of each sample, and removes fragments less than 300bp in length from the assembly results. The evaluation criteria for the assembly results include continuity (assembly length, maximum length) and completeness (N50, N90, minimum length). The assembly results are detailed in Table 2. After quality control, assembly and gene prediction, the present invention obtains the gene information of the metagenome, such as Figure 11 As shown in A, the gene fragment lengths are mainly distributed between 200 and 1499 bp. α diversity reflects the richness and uniformity of genes. Figure 11 As shown in B, there was no significant difference in Chao1, Shannon and Simpson index between the HBB group and the WT group, indicating that both groups performed well in terms of gene richness, uniformity and diversity. β diversity reflects the degree of dispersion within the sample group. Figure 11 As shown in C, there is no significant difference between WT and HBB mutants, and the dispersion within each group is within an acceptable range. Through species accumulation curve analysis, we can accurately measure and predict the changing trend of community species richness as the number of samples increases. The data show that Figure 4 As shown in A, the species accumulation curves of the wild type (WT) and the HBB gene mutant eventually tended to be stable, which indicated that the sampling scale was sufficient to reveal the diversity characteristics of the community. When the alpha diversity index was used to evaluate the species richness and evenness of the community, it was found that Figure 4 As shown in B, the Chao1 index of the HBB mutant was significantly higher than that of the wild-type group (p=0.036), indicating that the number of microbial species in the fecal microbial community of the HBB gene-edited macaques was less than that of the wild-type macaques. Figure 11As shown in Figure D, there was no significant difference between the two groups in terms of the Shannon index and Simpson index. The median of the wild-type group was higher than that of the HBB mutant group, indicating that the species uniformity and microbial community diversity in the feces of macaques edited by the HBB gene were reduced to a certain extent. Through β-diversity analysis, we studied the shared diversity of the microbial community and the differences between groups. Partial least squares discriminant analysis (PLS-DA) showed that there were differences in the microbial community composition between the HBB group and the wild-type group, but these differences did not reach statistical significance. Figure 4 C and Figure 11 E. Therefore, although HBB gene editing had a certain impact on the species richness of the fecal microbial community of crab-eating macaques, its overall evenness and diversity differences were still at a low level.

[0138] Table 2 Genome assembly statistics

[0139] Sample name software Continuous serial number Assembly length (bp) N50(bp) N90(bp) Maximum length (bp) Minimum length (bp) Average length (bp) wild type 1 Super Hot 233896 279443907 1774 466 268135 300 1194 WT2 Super Hot 224707 295608964 1786 564 324693 300 1315 WT3 Super Hot 209853 254344378 1715 493 214246 300 1212 HBB1 Super Hot 99817 175508977 3747 629 369896 300 1758 HBB2 Super Hot 128396 215565465 3079 623 739507 300 1678 HBB3 Super Hot 154921 243490757 2521 622 265151 300 1571 HBB4 Super Hot 185582 267353569 2528 514 289539 300 1440 HBB5 Super Hot 178893 199734930 1694 420 199910 300 1116

[0140] Abnormal patterns of the gut microbiota in HBB-deficient macaques

[0141] By drawing a species-level Venn diagram of the intestinal microorganisms in the feces of wild-type and HBB mutants, it was found that the two groups contained a total of 3653 species, of which 185 were unique to HBB mutants and 996 were unique to wild-type macaques, such as Figure 5 As shown in A. Further analysis of the relative abundance at the species, genus, and phylum levels showed that at the phylum level, Firmicutes was the dominant phylum in both the wild type and the HBB mutant, as shown in Figure 5 As shown in Figure 2B; at the species level, Faecalibacterium prausnitzii was the most abundant species in both groups, and the abundance of HBB mutants was significantly higher than that of wild-type macaques. In contrast, the abundance of the other two highly abundant species, Lactobacillus reuteri and Lactobacillus johnsonii, was lower in HBB mutants than in wild-type macaques, as shown in Figure 2C. Figure 5 As shown in C; at the genus level, the abundance of Prevotella and Faecalibacterium in HBB mutants was higher than that in wild-type macaques, while the abundance of Lactobacillus was the highest in the wild-type group and significantly higher than that in HBB mutants, as shown in Figure 11 As shown in F. In the differential microbiome analysis, at the species level, Lactobacillus johnsonii and Lactobacillus amyloliquefaciens were the most abundant species, with significant differences between the two groups (p < 0.05). The abundance of wild-type monkeys was significantly higher than that of HBB mutants, as shown in Figure 5 As shown in D; at the genus level, Bacteroides and Lactobacillus are the most significantly different genera in abundance. Bacteroides is significantly more abundant in HBB mutants, while Lactobacillus is significantly more abundant in wild-type monkeys, as shown in Figure 11 As shown in G.

[0142] Subsequently, the present invention conducted a linear discriminant analysis effect (LEfSe) study on the top eight species with the highest relative abundance in the fecal microbiota of wild-type WT and HBB mutants, setting the LAD threshold to 2. The results showed that the genus Bacteroides was more abundant in HBB mutants, while the genus Lactobacillus was more dominant in wild-type monkeys. In addition, there was a significant difference in the abundance of Lactobacillus johnsonii and Lactobacillus amyloliquefaciens between the two groups, with the abundance of wild-type monkeys significantly higher than that of HBB mutants, such as Figure 5 E. Thus, the fecal microbiota of wild-type and HBB mutants showed significant differences at the species, genus, and phylum levels, particularly in Lactobacillus and its related species. These findings suggest that HBB deficiency may be associated with regulatory mechanisms of the intestinal microbiota.

[0143] 3.4 Functional analysis of gut microbial differences in HBB-deficient canine macaques

[0144] To explore the effect of HBB deficiency on the functional distribution of intestinal flora, the present invention functionally annotated the non-redundant genes predicted in the fecal flora of wild-type and HBB-deficient macaques. KEGG functional enrichment analysis showed that the two groups of metabolic pathways were mainly enriched in carbohydrate metabolism, lipid metabolism, cofactor and vitamin metabolism, and energy metabolism-related pathways, such as Figure 6 As shown in A. Based on the abundance changes of KEGG enriched metabolic pathways, we drew a circular diagram, as shown in Figure 6 As shown in Figure B, the abundance differences of the most abundant KEGG metabolic pathways between HBB and wild-type macaques are intuitively presented. The results showed that the K07133 and K21572 pathways were significantly enriched in HBB mutants, while the abundance of the K07729 and K07497 pathways showed a downward trend. In order to further explore the functional differences between the two groups, the present invention performed a KEGG pathway enrichment analysis using a module database. The results showed that wild-type macaques were mainly enriched in carbohydrate metabolism, amino acid metabolism, and translation pathways, and these pathways were significantly different (|Reporter score|>1.65). In contrast, HBB mutants were mainly enriched in methane metabolism, pyruvate metabolism, and atrazine degradation pathways, which also showed significant differences (|Reporter score|>1.65), as shown in Figure 1. Figure 6 C. This shows that HBB deficiency significantly affects the metabolic function of the macaque intestinal flora, especially in some key metabolic pathways, which show significant differences in abundance.

[0145] HBB deficiency affects the function and metabolic pathways of the intestinal microbial community in macaques. To explore the effects of HBB deficiency on the function of the microbial community, metabolic pathways and their interactions with the host, the present invention conducted a combined metabolomics and metagenomics analysis. First, principal component analysis (PCA) was performed on the species and metabolite data of wild-type monkeys and HBB mutants. The results showed that there were significant differences between the two groups of samples, such as Figure 7 As shown in A, the samples in each group were closely clustered, indicating that there were essential differences in the microbial community structure and metabolite composition between the two groups; Figure 7 As shown in Figure B, canonical correlation analysis (CCA) further revealed significant differences between the two groups in terms of microbial communities and metabolites; Figure 7 As shown in Figure C, the unsupervised model analysis of species and metabolite abundance revealed that the expression of metabolite HMDB0082176 was negatively correlated with the abundance of multiple species, while the expression of other differentially abundant metabolites was positively correlated with species abundance. Subsequently, the present invention conducted a comprehensive analysis of the functional differences of genes and metabolites and found through univariate correlation analysis that Figure 7 As shown in D, metabolite HMDB0082176 was negatively correlated with pathways such as K11212, K07244, and K14124, while other metabolites and species were positively correlated with multiple KEGG pathways; Figure 7 As shown in Figure E, the results of the unsupervised model joint analysis are consistent with these findings. Finally, the present invention conducted a pathway enrichment analysis on functional genes and metabolites. The results showed that the "fructose and mannose metabolism" pathway had a high RichFactor value. This pathway involved a large number of metabolites and species genes, indicating that it may play a key role in metabolic function and species distribution. In addition, pathways such as "fructose and mannose metabolism", "folate biosynthesis", "citric acid cycle (tricarboxylic acid cycle)", "aminoacyl-tRNA biosynthesis" and "ABC transporter" also showed significant enrichment in the RichFactor and Count indicators, suggesting that these pathways may have an important regulatory role in metabolic function and species distribution. Figure 7 F. Thus, HBB deficiency significantly impacts the function and metabolic pathways of the macaque gut microbiome, particularly in several key metabolic pathways. These changes in microbial community structure may regulate host metabolic function.

[0146] 3.5 Effect of 3_OAH on the body weight of mice with castor oil-induced diarrhea

[0147] Figure 8 B shows a comparison of the severity of diarrhea in mice treated with different treatment groups, including three indicators: the incidence of loose stools, the degree of loose stools, and the diarrhea index. Each indicator has six treatment groups: control group, CO group, 3-AOMP group, 3-AOL group, and 3-AOH group. In the present invention, it was observed that the defecation of mice after castor oil treatment was significantly changed, especially the stools were loose and soft, suggesting that a diarrhea model was successfully established in mice. The control group excreted dry, hard, granular, and soft stools throughout the experiment. The experimental mice developed soft, loose, or watery stools after taking castor oil. In addition, these mice showed a listless state, loose hair, and minimal activity. Figure 8C shows the changes in body weight of mice in different treatment groups on day 0 and day 10. The figure includes five treatment groups: control group, CO, 3-AOMP, 3-AOL and 3-AOH group. Compared with the control group, the incidence of loose stools, looseness and diarrhea index of the 3-AO group were significantly increased. By day 10, the emotional state of the mice in the 3-AOH group improved, and their body weight was slightly higher than that of the other treatment groups, almost the same as that of the control group. This shows that a high dose of 3-oxooctadecanoic acid can effectively inhibit diarrhea caused by castor oil. Therefore, the present invention prepares 3-oxooctadecanoic acid into a drug for treating diarrhea based on this. The drug contains the 3-oxooctadecanoic acid for relieving diarrhea, repairing intestinal barrier function or regulating intestinal microbial flora. The drug dosage forms include liquid, capsule or enteric-coated tablet, which can be delivered orally.

[0148] 3.6 Summary and Discussion

[0149] A metagenomic study conducted in an HBB gene-knockout cynomolgus macaque model aimed to investigate how HBB mutations affect gut microbiota composition and function and elucidate their role in the pathogenesis of β-thalassemia. HBB gene mutations may lead to anemia and gastrointestinal dysfunction, negatively impacting intestinal homeostasis. The study found that amino acid metabolism and lipid metabolism were the major metabolic pathways in macaque feces, suggesting that the metabolic signature of HBB gene-edited macaques may be related to metabolic interactions between the gut microbiota and the host. Significant alterations in amino acid and lipid metabolism may reflect the regulatory role of the gut microbiota in host metabolic processes. The gut microbiota can influence host metabolic status through its metabolites, such as short-chain fatty acids. Furthermore, HBB gene editing may indirectly affect the metabolic function of the gut microbiota by altering host metabolic demands or immune responses. Principal component analysis (PCA) modeling and differential metabolite analysis revealed significant differences in metabolite expression between the HBB mutant and wild-type groups. In the HBB mutant, 177 metabolites were significantly upregulated, while 233 were significantly downregulated. These differential metabolites provide key clues for a deeper understanding of the impact of HBB gene editing on the metabolome. Microbiome analysis revealed significant gut microbial differences between HBB-edited and wild-type macaques at the species, genus, and phylum levels. Notably, Lactobacillus and Bacteroides genera showed significant differences in abundance between the HBB and wild-type groups, potentially reflecting the regulatory effects of gene editing on the gut microbiota. Lactobacillus is known to be closely associated with the host immune system, and its decreased abundance may be associated with reduced host immune tolerance or enhanced inflammatory responses. On the other hand, Bacteroides participates in multiple metabolic processes, and its changes in abundance may reflect the long-term regulatory effects of gene editing on the gut microbiota. The present invention suggests that HBB gene editing significantly altered the gut microbial community in macaques, with particularly pronounced changes in the abundance of Lactobacillus and Bacteroides genera. These microbial community changes may further influence the host's immune response, metabolic function, and disease susceptibility. Subsequent species richness analysis revealed that the Chao1 index of HBB mutants was significantly lower than that of the wild-type group, suggesting that gene editing may lead to a decrease in gut microbial community diversity. Based on this, it is speculated that the disease caused by gene editing may also lead to a decrease in the number of microbial communities and their related genes. However, the overall difference in the uniformity and diversity of the microbial communities between the two groups was small.

[0150] Functional studies using KEGG pathway enrichment analysis revealed significant changes in amino acid metabolism, lipid metabolism, and energy metabolism in HBB mutants. It is speculated that alterations in these metabolic pathways may be closely related to changes in microbial communities and host metabolism caused by HBB mutants. The present invention also discovered that HBB gene editing may affect the host's metabolic state by regulating specific metabolic pathways. These pathways play a key role in host metabolism and may serve as an important bridge for the interaction between the intestinal microbiome and the host.

[0151] The overall results of the KEGG enrichment analysis indicated that the majority of metabolites in the metabolomics data and redundant genes in the metagenomic data were closely related to metabolic pathways such as amino acid metabolism, lipid metabolism, secondary metabolite biosynthesis, cofactor and vitamin metabolism, xenobiotic biodegradation and metabolism, and carbohydrate metabolism, demonstrating high consistency across the different omics data sets. Particularly noteworthy was the overall upregulation of phenylalanine metabolism in the HBB mutants in the metabolomics analysis. Metagenomic analysis revealed significant enrichment of genes related to phenylalanine biosynthesis in the wild-type group, but not in the HBB mutants. This finding reveals an imbalance between phenylalanine biosynthesis and metabolism in the HBB mutants, suggesting that this metabolic dysregulation may be an early pathological marker of β-thalassemia caused by HBB gene mutations. Furthermore, prior art has documented that levels of the phenylalanine metabolites o-tyrosine and m-tyrosine are significantly elevated in β-thalassemia patients compared with controls. These metabolites are considered useful biomarkers of protein oxidative damage in patients with thalassemia intermedia. Therefore, dysregulation of phenylalanine metabolism may be closely related to the pathophysiology of β-thalassemia.

[0152] The HBB mutant cynomolgus macaque model exhibits high consistency with human β-thalassemia. At the phylum level, the Proteobacteria / Firmicutes ratio is elevated, while the Bacteroidetes is relatively depleted. Furthermore, Lactobacilli (particularly L. johnsonii and L. amylovorus) are significantly reduced, while Bacteroidetes are enriched. These characteristics are consistent with the reduction of short-chain fatty acid (SCFA)-producing bacteria and the expansion of potential pathogens in human fecal samples. Metabolomic analysis further revealed decreased levels of short-chain fatty acids (butyrate and propionate) and increased levels of primary bile acids (such as glycocholic acid and taurocholic acid; HMDB0082176), consistent with published human data. Taken together, these parallels between the microbiome, metabolites, and host phenotype suggest that this model can serve as a reliable non-human primate platform for studying the mechanisms and interventions of the blood-gut axis in β-thalassemia.

[0153] Rescue experiments in mice demonstrated that 3-oxooctadecanoic acid (HMDB0254633) effectively inhibited castor oil-induced diarrhea in mice. Experimental data indicated that the high-dose group (3_AOH) not only significantly reduced the frequency, looseness, and diarrhea index of diarrheal stools, but also achieved weight restoration. The 3_AOH group showed improved intestinal microbial composition, a key mechanism for alleviating diarrheal symptoms. Specifically, 3_AOH reduces inflammation and diarrhea by regulating intestinal microbial balance and enhancing intestinal barrier function. Weight restoration in this group of mice was similar to that in the control group, demonstrating that high-dose 3-oxooctadecanoic acid not only effectively alleviates diarrheal symptoms but also promotes overall health and weight restoration in mice.

[0154] 3.7 Conclusion

[0155] The present invention integrates data from metabolomics and metagenomic sequencing analysis and finds significant differences in microbial communities and metabolites after HBB mutants. Significant differences exist in amino acid and lipid metabolic pathways between HBB mutants and wild-type groups. Metagenomic analysis shows that the abundance of Lactobacillus and Bacteroides is significantly different between the two groups, with the HBB mutant group having lower microbial diversity. Functional analysis indicates that gene knockout significantly alters multiple key metabolic pathways, including amino acid metabolism and energy metabolism.

[0156] ROC curve analysis of the differential metabolites showed that the area under the curve value of the HMDB0254633 metabolite was 0.933, indicating that the metabolite had a high discriminatory ability, and the biomarker 3-oxooctadecanoic acid HMDB0254633 for distinguishing wild type and HBB mutants was obtained.

[0157] By using 3-oxooctadecanoic acid to treat castor oil-induced diarrhea in C57BL / 6 mice, it was found that high doses of 3-oxooctadecanoic acid could effectively alleviate diarrhea and improve the composition of intestinal microbiota.

[0158] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Use of a biomarker HMDB0254633 in the preparation of a quantitative detection product for rhesus monkey HBB gene mutation, characterized in that: The chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, and its molecular formula is C 18 H 34 O3.

2. The use according to claim 1, characterized in that The quantitative detection product is a reagent.

3. The use according to claim 1, characterized in that The quantitative detection product is a kit.

4. Use of a reagent for detecting the expression level of the biomarker HMDB0254633 in a biological sample in the preparation of a detection product for intestinal complications of thalassemia caused by HBB gene mutation in macaques, characterized in that: The chemical name of the biomarker HMDB0254633 is 3-oxooctadecanoic acid, and its molecular formula is C 18 H 34 O3.

5. A method for quantitatively detecting HBB gene mutations, characterized in that: The following steps are involved: 1) Extraction of metabolites from feces; 2) Detecting the metabolite using the quantitative detection product described in claim 1.

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