Microbial marker for reducing uric acid level, screening method and application

Abstract

The invention relates to the technical field of biology, in particular to a microbial marker for reducing the uric acid level, a screening method and application. The invention relates to a microbial marker, which is characterized in that the microbial marker comprises any one or a combination of any more of the following components: Christsenelace R-7group, Anerostipes, Collinsella, Phil1sp001940855, Anerobutyric acid hallii, and Collinsella aerofaciens, and the microbial marker comprises any one or a combination of any more of the following components: the Christsenelace R-7group, the Anerostipes, the Collinsella, the Phil1sp001940855, the Anerobutyric acid hallii, the Collinsella aerofaciens, the Collinsella, the Collinsella, the Phil1sp001940855 and the Collinsella. According to the method in the scheme, by integrating intestinal flora data of a plurality of research queues and combining the steps of high-quality sequencing data quality control, species annotation, batch effect removal, differential species analysis, specific metabolic gene cluster screening and the like, the accuracy and effectiveness of the microbial markers are ensured. The microbial markers are significantly reduced in intestinal flora of patients with hyperuricemia and gout, carry specific metabolic gene clusters, have a potential function of reducing the uric acid level, and can be applied to development of novel drugs for treating or preventing hyperuricemia and gout.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a microbial marker for reducing uric acid levels, a screening method and an application thereof. Background Art

[0002] In the related art, hyperuricemia, as a common metabolic disease, is characterized by a persistently higher than normal uric acid concentration in the blood, which is an important risk factor for various complications such as gout, kidney stones, cardiovascular disease and renal impairment. According to statistics, the incidence of hyperuricemia in my country has reached 14%, and it is showing a trend of younger age. Uric acid is the final product of purine metabolism in the human body, and its elevated level is mainly caused by purine metabolism disorders, excessive uric acid synthesis or excretion disorders. Long-term high uric acid state can easily lead to sodium urate crystals deposited in the joints, which in turn causes gout, seriously affecting the patient's quality of life. At present, drug interventions for hyperuricemia mainly include xanthine oxidase inhibitors and drugs that promote uric acid excretion, such as allopurinol, febuxostat and benzbromarone. However, related drugs still have obvious limitations in terms of safety, effectiveness and long-term patient compliance, and long-term use of uric acid-lowering drugs may increase the risk of cardiovascular disease. Therefore, exploring and developing safer and more efficient new uric acid-lowering therapies has become an urgent need for patients with hyperuricemia and gout.

[0003] Previous studies have emphasized the importance of the stability of the intestinal microbial community in maintaining blood uric acid levels. Although some studies have attempted to establish predictive models using microbial markers to distinguish healthy people from patients with hyperuricemia or gout, most of these models rely on a single cohort, limiting their general applicability. In addition, there is currently insufficient understanding of other intestinal bacterial species that may regulate uric acid metabolism and their underlying mechanisms. Therefore, there is an urgent need to develop a method that uses multi-cohort comprehensive analysis to screen stable and effective uric acid-lowering microbial markers. This is crucial for accurately locating microbial targets for the treatment of hyperuricemia and gout, and provides a scientific basis for the development of new uric acid-lowering therapeutic strategies.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present invention is to provide a microbial marker for lowering uric acid levels, the use of a microbial marker in the preparation of a product for treating or preventing hyperuricemia and / or gout-related diseases, a method for screening a microbial marker, a device for screening a microbial marker, and a computer program product, which can effectively overcome the defects existing in the prior art.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] According to a first aspect of the present invention, a microbial marker for lowering uric acid levels is provided, wherein the microbial marker comprises any one of the following or any combination thereof: Christensenellaceae R-7 group, Anaerostipes, Collinsella, Phil1sp001940855, Anaerobutyricum hallii, Collinsellaaerofaciens.

[0008] In some exemplary embodiments, the microbial markers include: Phil1sp001940855, Anaerobutyricum hallii, and Collinsella aerofaciens.

[0009] According to a second aspect of the present invention, there is provided an application of a microbial marker in the preparation of a product for treating or preventing hyperuricemia and / or gout-related diseases, comprising the microbial marker composition as described in the first aspect.

[0010] In some exemplary embodiments, the product comprises a pharmaceutical composition.

[0011] In some exemplary embodiments, the dosage form of the pharmaceutical composition is selected from one or more of capsules, tablets, suspensions, emulsions, and granules.

[0012] According to a third aspect of the present invention, a method for screening microbial markers is provided, the method comprising:

[0013] Constructing a sample data set; wherein the sample data set includes 16S rRNA data and metagenomic data corresponding to multiple healthy people, hyperuricemia patients, and gout patients;

[0014] Annotating the sample data set with species to obtain species classification results; and calculating first abundance information corresponding to the sample data set;

[0015] Performing microbiome data batch effect removal processing on the sample data set after species annotation, and determining second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data;

[0016] Conduct differential species analysis based on species classification results, and determine candidate microbial markers based on the differential species analysis results;

[0017] The candidate microbial markers are screened based on a specific metabolic gene cluster to obtain a target microbial marker; wherein the target microbial marker is used to reduce uric acid levels.

[0018] In some exemplary embodiments, the method further comprises: performing data quality control on each sample in the sample data set, removing low-quality sequences and host contamination, and obtaining a high-quality sequencing data set for species annotation of the quality sequencing data set.

[0019] In some exemplary embodiments, performing data quality control on each sample in the sample data set includes:

[0020] Removing at least one of the primer sequence, the adapter sequence, the sequence containing N or the sequence containing low-quality bases greater than a preset threshold, and the chimera sequence in the 16S rRNA data to obtain amplicon sequence variation;

[0021] At least one of the following is removed from the metagenomic data: a linker sequence, a host sequence, and a sequence with low-quality bases greater than a preset threshold.

[0022] In some exemplary embodiments, the differential species analysis based on the species classification results is performed to determine species that have a reduced abundance in healthy samples relative to samples of patients with hyperuricemia and samples of patients with gout and meet preset requirements as candidate microbial markers, including:

[0023] Perform linear discriminant analysis effect size analysis on the species classification results to obtain effect size scores;

[0024] Based on the effect size score, species whose abundance reduction in healthy human samples relative to that in hyperuricemia patient samples and gout patient samples met the preset threshold were identified as candidate microbial markers.

[0025] In some exemplary embodiments, the screening of the candidate microbial markers based on a specific metabolic gene cluster to obtain a target microbial marker comprises:

[0026] Bin the metagenomic data to obtain the corresponding species sequence information;

[0027] The key marker genes involved in uric acid degradation function were used as specific metabolic gene clusters;

[0028] The specific metabolic gene cluster is detected for the candidate microbial marker, and the species having the specific metabolic gene cluster is used as the target microbial marker.

[0029] In some exemplary embodiments, the specific metabolic gene cluster includes one or more of the ygeX gene, the hyuA gene, the ygfK gene, the ssnA gene, the ygeY gene, and the xdhAC gene.

[0030] According to a fourth aspect of the present invention, there is provided a screening device for microbial markers, comprising:

[0031] A sample data construction module is used to construct a sample data set; wherein the sample data set includes 16S rRNA data and metagenome data corresponding to multiple healthy people, hyperuricemia patients, and gout patients;

[0032] A species annotation module, used to perform species annotation on the sample data set to obtain species classification results; and calculate first abundance information corresponding to the sample data set;

[0033] A batch effect processing module, used to perform microbiome data batch effect removal processing on the sample data set after species annotation, and determine second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data;

[0034] A differential species analysis module is used to perform differential species analysis based on species classification results and determine candidate microbial markers based on the differential species analysis results;

[0035] A microbial marker screening module is used to screen the candidate microbial markers based on a specific metabolic gene cluster to obtain a target microbial marker; wherein the target microbial marker is used to reduce uric acid levels.

[0036] According to a fifth aspect of the present invention, there is provided a computer program product, comprising a computer program / instruction, which implements the above-mentioned method for screening microbial markers when executed by a processor.

[0037] The microbial markers and corresponding products provided in the embodiments of the present invention have the ability to reduce uric acid levels due to carrying specific metabolic gene clusters, and have a certain positive effect on the development of live bacterial drugs for the treatment and prevention of hyperuricemia and gout.

[0038] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0040] Figure 1 A schematic diagram schematically illustrates a method for screening a microbial marker in an exemplary embodiment of the present invention;

[0041] Figure 2 A PCoA schematic diagram of a microbial community structure before and after removing the batch effect is schematically shown in an exemplary embodiment of the present invention;

[0042] Figure 3 A schematic diagram schematically showing the difference in microorganisms between a healthy person, a hyperuricemia patient and a gout patient at the genus level according to an exemplary embodiment of the present invention;

[0043] Figure 4 A schematic diagram schematically illustrates the screening results of species carrying a specific metabolic gene cluster in an exemplary embodiment of the present invention;

[0044] Figure 5 A schematic diagram schematically illustrates a process flow diagram of a method for screening a microbial marker in an exemplary embodiment of the present invention;

[0045] Figure 6 A schematic diagram schematically shows the composition of a screening device for microbial markers in an exemplary embodiment of the present invention; DETAILED DESCRIPTION

[0046] The embodiments of the present invention will be described in detail below in conjunction with the examples, but those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present invention. In view of the shortcomings and deficiencies of the prior art, a microbial marker is provided in this example embodiment, which can be used to reduce uric acid levels.

[0047] In one embodiment of the present invention, the above-mentioned microbial markers include any one of the following or any combination of multiple thereof: Christensenellaceae R-7 group (Christensenella R-7), Anaerostipes (anaerobic rod-shaped bacteria), Collinsella (Collinsella), Phil1sp001940855, Anaerobutyricum hallii (anaerobic butyric acid bacteria), Collinsella aerofaciens (aerofaciens).

[0048] Among them, Phil1 sp001940855 belongs to the order Christensenellales and is numbered MGYG000000730_00045 in the dbCAN_seq database.

[0049] In one embodiment of the present invention, the above-mentioned microbial markers for reducing uric acid levels can be a combination of microbial markers. In a preferred embodiment, the combination of microbial markers can include: Phil1sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens. These microorganisms are accurate to the species level and carry specific metabolic gene clusters, with potential uric acid-lowering capabilities.

[0050] Preferably, the microbial marker combination may also include: Christensenellaceae R-7group, Anaerostipes, Collinsella, Phil1 sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens. This combination comprehensively covers microorganisms at the genus and species levels, and these microorganisms have reduced abundance in patients with hyperuricemia and gout, and all have potential uric acid-lowering capabilities.

[0051] In one embodiment of the present invention, there is provided a use of a microbial marker in the preparation of a product for treating or preventing hyperuricemia and / or gout-related diseases, and the product may include the above-mentioned microbial marker.

[0052] In one embodiment of the present invention, the above-mentioned product may be a pharmaceutical composition, which may include the above-mentioned microbial marker, or an active ingredient of the microbial marker.

[0053] In one embodiment of the present invention, the pharmaceutical composition may further include: a pharmaceutical carrier, and an excipient or a stabilizer.

[0054] Exemplarily, the above-mentioned pharmaceutical preparations may be capsules, tablets, suspensions, emulsions, granules or any other form suitable for oral, enteral or topical administration, and may contain appropriate carriers, excipients or stabilizers.

[0055] In one embodiment of the present invention, a method for screening microbial markers is provided, referring to Figure 1 As shown, the method includes:

[0056] Step S11, constructing a sample data set; wherein the sample data set includes 16S rRNA data and metagenomic data corresponding to a plurality of healthy people, hyperuricemia patients, and gout patients;

[0057] Step S12, performing species annotation on the sample data set to obtain species classification results; and calculating first abundance information corresponding to the sample data set;

[0058] Step S13, performing microbiome data batch effect removal processing on the sample data set after species annotation, and determining second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data;

[0059] Step S14, performing differential species analysis based on the species classification results, and determining candidate microbial markers according to the differential species analysis results;

[0060] Step S15, screening the candidate microbial markers based on the specific metabolic gene cluster to obtain target microbial markers; wherein the target microbial markers are used to reduce uric acid levels.

[0061] Exemplarily, in the above-mentioned step S11, the user can create a screening task for microbial markers on the smart terminal device, and the task can be executed locally on the smart terminal device or on the server. When executing the task, sample data can be first collected from a preset database, and a sample data set can be constructed. Among them, data can be collected from a public database. For example, a total of 4 16S rRNA data sets containing healthy people, hyperuricemia patients or gout patients can be retrieved and downloaded from the database: PRJNA1131142, PRJNA754261, PRJNA869365 and PRJNA550142. In addition, a metagenomic data set containing healthy people and gout patients can be downloaded: CNP0000284.

[0062] In one embodiment of the present invention, the above method also includes: performing data quality control on each sample in the sample data set, removing low-quality sequences and host contamination, and obtaining a high-quality sequencing data set for species annotation of the quality sequencing data set.

[0063] Exemplarily, the performing data quality control on each sample in the sample data set includes:

[0064] Removing at least one of the primer sequence, the adapter sequence, the sequence containing N or the sequence containing low-quality bases greater than a preset threshold, and the chimera sequence in the 16S rRNA data to obtain amplicon sequence variation;

[0065] At least one of the following is removed from the metagenomic data: a linker sequence, a host sequence, and a sequence with low-quality bases greater than a preset threshold.

[0066] Specifically, considering that the original sequencing data may contain low-quality sequences, host contamination sequences, adapters, primers, etc. In order to ensure the reliability and accuracy of the analysis results and improve the efficiency of data processing, these sequences need to be removed before they can be used for the next step of analysis.

[0067] Specifically, the 16S rRNA raw sequencing data can use the q2-demux plug-in in the QIIME2 v2023.5.1 software to remove primer sequences, adapter sequences, and sequences with more N or more low-quality bases in the raw sequencing data; and use the DADA2 algorithm to denoise the sequence, filter out chimeric sequences, and obtain amplicon sequence variations (ASVs). In addition, for metagenomic raw sequencing data, Trimmomatic v0.33 software can be used to remove sequences with more low-quality bases and adapter sequences, and Bowtie2 v2.4.1 software can be used to align the sequencing data to the GRCh38 reference database to remove host sequences.

[0068] Illustratively, in the above-mentioned step S12, when performing species annotation, the species composition of samples in each research cohort can be obtained; and the corresponding abundance information can be calculated.

[0069] Specifically, the q2-feature-classifier plug-in can be used to classify ASVs into species based on the naive Bayes classifier for the 16S rRNA data after data quality control, and the reference database is SILVA 138. The corresponding abundance information is calculated as the first abundance information of the 16S rRNA data.

[0070] In addition, the metagenomic data after data quality control can be assembled using the Megahit tool for single sample sequences. The assembled metagenomic data were binned in MetaWRAP v1.3.2 software to obtain metagenomic assembly draft genomes (MAGs). This process can use tools such as CONCOCT v1.0.0, MaxBin2 v2.2.6, and metaBAT2v2.12.1. The quality of MAGs was evaluated using CheckM v1.0.12, which estimates genome integrity and contamination levels, and MAGs with integrity scores above 70% and contamination levels below 10% were selected for further analysis. High-quality MAGs were clustered using dRepv3.4.3 to identify the most representative genomes, thereby obtaining species-level genome bins (SGBs). The parameter database GTDB-Tk v2.3.2 was used to classify each SGB into species. The corresponding abundance information was calculated as the first abundance information corresponding to the metagenomic data. The calculation of abundance information can be achieved using existing technologies, and this application does not specifically limit the calculation method of abundance information.

[0071] For example, in the above step S13, after species annotation is completed, the batch effects between studies in the microbiome data can be removed to obtain species abundance information after integrating multiple research cohorts, and use it as the second abundance information.

[0072] Specifically, in order to address batch effects in microbiome data across different cohorts, which may complicate comprehensive analysis and may introduce false positives, hindering the development of biomarkers. The present invention uses the R package ConQuR v2.0 to treat each project ID as a different batch to adjust the batch effect. Conditional quantile regression (ConQuR) is a comprehensive method that adapts to the complex distribution of microbial read counts through non-parametric modeling and generates batch-removed zero-inflated read counts, which can be used for common subsequent analysis and facilitate analysis. In addition, based on the Bray-Curtis dissimilarity metric derived from the microbiome data, PCoA was performed to graphically represent the impact of batch effects on microbial community structure, such as Figure 2 As shown. The r-value and p-value of the similarity analysis (ANOSIM) test were used to quantify the extent of the batch effect. The p-value exceeded 0.05, and the r-value was minimal, indicating that the contribution of batch effects in the model was reduced, reducing their influence on model building. After removing the batch effect, the relative abundance data of species from the integrated cohort were further analyzed to ensure a more accurate and reliable interpretation of the microbiome data in the context of the research of this application.

[0073] For example, in the above step S14, differential species analysis may be performed to obtain species whose abundance is significantly reduced in hyperuricemia patients and gout patients relative to healthy persons.

[0074] Specifically, the R package microeco v1.5.0 can be used to perform Linear Discriminant Analysis Effect Size (LEfSe) analysis, aiming to pinpoint taxa associated with disease states. The output of the LEfSe analysis provides an effect size score, which represents the linear discriminant analysis (LDA) score, reflecting the degree of difference between healthy people, hyperuricemia, and gout patients. The higher the score, the more significant the difference. Species with p < 0.05, LDA score > 2, and significantly reduced abundance in both hyperuricemia and gout patients were selected as candidate microbial markers, such as Figure 3 As shown in Table 1, the abundance expression of candidate microbial markers in healthy people, hyperuricemia patients and gout patients is shown. Among them, ↑ represents an increase in abundance, and ↓ represents a decrease in abundance.

[0075] Table 1

[0076] Candidate microbial markers Healthy people hyperuricemia gout Christensenellaceae R-7group ↑ ↓ ↓ Anaerostipes ↑ ↓ ↓ Odoribacter ↑ ↓ ↓ UCG-002 ↑ ↓ ↓ Collinsella ↑ ↓ ↓

[0077] Exemplarily, in step S15, the screening of the candidate microbial markers based on the specific metabolic gene cluster to obtain target microbial markers includes:

[0078] Bin the metagenomic data to obtain the corresponding species sequence information;

[0079] The key marker genes involved in uric acid degradation function were used as specific metabolic gene clusters;

[0080] The specific metabolic gene cluster is detected for the candidate microbial marker, and the species having the specific metabolic gene cluster is used as the target microbial marker.

[0081] Specifically, according to the species sequence information obtained by binning the metagenomic data, six key marker genes (ygeX, hyuA, ygfK, ssnA, ygeY, and xdhAC) involved in the uric acid degradation function can be used as specific metabolic gene clusters, and gutSMASH software can be used to detect species with specific metabolic gene clusters. Among them, Phil1 sp001940855 (belonging to the order Christensenellales), Anaerobutyricumhallii, and Collinsella aerofaciens, which are related to candidate microbial markers, carry specific metabolic gene clusters, such as Figure 4This indicates that the six species of Christensenellaceae R-7group, Anaerostipes, Collinsella, Phil1sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens can be used as microbial markers for lowering uric acid levels.

[0082] Particularly, Phil1 sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens are preferred.

[0083] In this exemplary embodiment, the microbial marker screening method for reducing uric acid levels is provided, referring to Figure 5 As shown, it may include:

[0084] Step 1: Collection of original data on intestinal flora of healthy people, hyperuricemia and gout patients, including collection of 16S rRNA and metagenomic datasets containing healthy people, hyperuricemia and gout patients from multiple research cohorts.

[0085] Step 2: Use QIIME2, Trimmomatic, Bowtie2 and other software to perform quality control on the raw sequencing data to remove low-quality sequences and host contamination and obtain high-quality sequencing data.

[0086] In step 3, naive Bayes classifier, MetaWRAP, GTDB-Tk and other tools were used for species annotation to obtain the species composition and abundance information of samples in each research cohort.

[0087] Step 4: Use R packages such as ConQuR to remove batch effects between studies in the microbiome data to obtain species abundance information after integrating multiple study cohorts.

[0088] Step 5: Differential species analysis was performed using LEfSe analysis to identify species with significantly reduced abundance in patients with hyperuricemia and gout.

[0089] Step 6: Based on the binning results of the metagenomic data, gutSMASH software was used to detect species carrying specific metabolic gene clusters and determine the final microbial markers.

[0090] Compared with the prior art, the method provided by the embodiment of the present invention has the following technical effects:

[0091] (1) Comprehensiveness: By integrating multiple research cohorts, heterogeneity between studies was eliminated and widely representative microbial markers were obtained.

[0092] (2) Accuracy: A variety of analytical methods, including quality control, species annotation, and removal of batch effects, are used to ensure the accuracy of microbial markers.

[0093] (3) Innovation: It was discovered for the first time that Phil1 sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens carry specific metabolic gene clusters, providing new targets for the development of new uric acid-lowering drugs.

[0094] (4) Application prospects: The screened microbial markers have significant potential for lowering uric acid and can be used to develop drugs for the treatment or prevention of hyperuricemia and gout, and have broad application prospects.

[0095] The method of the present invention uses multiple cohorts including patients with hyperuricemia and gout for a more comprehensive comprehensive analysis, eliminating the heterogeneity between studies to obtain common intestinal microbial characteristics, and discovers stable and effective microbial markers enriched in healthy people compared to patients with hyperuricemia and gout. In particular, Phil1sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens were confirmed for the first time to carry specific metabolic gene clusters, have the potential to reduce uric acid levels, and have a certain guiding role in the development of live bacterial drugs for the treatment and prevention of hyperuricemia and gout. The screening method of microbial markers provided by the present invention can greatly improve the accuracy and effectiveness of the obtained microbial markers.

[0096] For further reference, Figure 6 As shown, in the embodiment of this example, a screening device 60 for microbial markers is also provided, and the device comprises:

[0097] The sample data construction module 601 is used to construct a sample data set; wherein the sample data set includes 16S rRNA data and metagenome data corresponding to a plurality of healthy people, hyperuricemia patients, and gout patients;

[0098] The species annotation module 602 is used to perform species annotation on the sample data set to obtain species classification results; and calculate the first abundance information corresponding to the sample data set;

[0099] The batch effect processing module 603 is used to perform microbiome data batch effect removal processing on the sample data set after species annotation, and determine the second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data;

[0100] The differential species analysis module 604 is used to perform differential species analysis based on the species classification results, and determine candidate microbial markers according to the differential species analysis results;

[0101] The microbial marker screening module 605 is used to screen the candidate microbial markers based on a specific metabolic gene cluster to obtain a target microbial marker; wherein the target microbial marker is used to reduce uric acid levels.

[0102] In some exemplary embodiments, the apparatus 60 further comprises:

[0103] The data quality control module is used to perform data quality control on each sample in the sample data set, remove low-quality sequences and host contamination, and obtain a high-quality sequencing data set for species annotation of the quality sequencing data set.

[0104] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0105] In one embodiment, the present application provides a computer program product, including a computer program / instruction, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0106] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0107] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0108] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A microbial marker for reducing uric acid levels, characterized in that: The microbial markers include any one of the following or any combination thereof: Christensenellaceae R-7 group, Anaerostipes, Collinsella, Phil1 sp001940855, Anaerobutyricum hallii, Collinsella aerofaciens.

2. The microbial marker for reducing uric acid levels according to claim 1, characterized in that The microbial markers include: Phil1 sp001940855, Anaerobutyricum hallii and Collinsella aerofaciens.

3. Use of microbial markers in the preparation of products for treating or preventing hyperuricemia and / or gout-related diseases, characterized in that: Comprising the microbial marker as described in claim 1 or 2.

4. The use according to claim 3, characterized in that: The products include pharmaceutical compositions.

5. The use according to claim 4, characterized in that: The dosage form of the pharmaceutical composition is selected from one or more of capsules, tablets, suspensions, emulsions and granules.

6. A method for screening microbial markers according to any one of claims 1 to 2, characterized in that: The method comprises: Constructing a sample data set; wherein the sample data set includes 16S rRNA data and metagenomic data corresponding to multiple healthy people, hyperuricemia patients, and gout patients; Annotating the sample data set with species to obtain species classification results; and calculating first abundance information corresponding to the sample data set; Performing microbiome data batch effect removal processing on the sample data set after species annotation, and determining second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data; Conduct differential species analysis based on species classification results, and determine candidate microbial markers based on the differential species analysis results; The candidate microbial markers are screened based on a specific metabolic gene cluster to obtain a target microbial marker; wherein the target microbial marker is used to reduce uric acid levels.

7. The method according to claim 6, characterized in that The method also includes: performing data quality control on each sample in the sample data set, removing low-quality sequences and host contamination, and obtaining a high-quality sequencing data set for species annotation of the quality sequencing data set.

8. The method according to claim 7, characterized in that The performing data quality control on each sample in the sample data set includes: Removing at least one of the primer sequence, the adapter sequence, the sequence containing N or the sequence containing low-quality bases greater than a preset threshold, and the chimera sequence in the 16S rRNA data to obtain amplicon sequence variation; At least one of the following is removed from the metagenomic data: a linker sequence, a host sequence, and a sequence with low-quality bases greater than a preset threshold.

9. The method according to claim 6, characterized in that The differential species analysis based on the species classification results is performed to determine species whose abundance in healthy samples is reduced compared with that in samples of patients with hyperuricemia and gout and that meet preset requirements as candidate microbial markers, including: Perform linear discriminant analysis effect size analysis on the species classification results to obtain effect size scores; Based on the effect size score, species whose abundance reduction in healthy human samples relative to that in hyperuricemia patient samples and gout patient samples met the preset threshold were identified as candidate microbial markers.

10. The method according to claim 6, characterized in that The method of screening the candidate microbial markers based on a specific metabolic gene cluster to obtain target microbial markers includes: Bin the metagenomic data to obtain the corresponding species sequence information; The key marker genes involved in uric acid degradation function were used as specific metabolic gene clusters; The specific metabolic gene cluster is detected for the candidate microbial marker, and the species having the specific metabolic gene cluster is used as the target microbial marker.

11. The method according to claim 6, characterized in that The specific metabolic gene cluster includes one or more of ygeX gene, hyuA gene, ygfK gene, ssnA gene, ygeY gene and xdhAC gene.

12. A screening device for microbial markers, characterized in that: The device comprises: A sample data construction module is used to construct a sample data set; wherein the sample data set includes 16S rRNA data and metagenome data corresponding to multiple healthy people, hyperuricemia patients, and gout patients; A species annotation module, used to perform species annotation on the sample data set to obtain species classification results; and calculate first abundance information corresponding to the sample data set; A batch effect processing module, used to perform microbiome data batch effect removal processing on the sample data set after species annotation, and determine second abundance information based on the first abundance information; wherein the second abundance information includes species relative abundance data; A differential species analysis module is used to perform differential species analysis based on species classification results and determine candidate microbial markers based on the differential species analysis results; A microbial marker screening module is used to screen the candidate microbial markers based on a specific metabolic gene cluster to obtain a target microbial marker; wherein the target microbial marker is used to reduce uric acid levels.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method for screening microbial markers according to any one of claims 6 to 11 is implemented.

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