Microbial marker for treating irritable bowel based on coprophilous fungus transplantation and screening method

Abstract

The invention discloses a microbial marker for treating irritable bowel on the basis of coprophilous fungus transplantation and a screening method of the microbial marker. The microbial marker is prepared from the following components: Butyrimonas faecalis, Odoribacter laneus, Parabacteroides verdea, Bacteroides eggerii, Phocaeia massiformis, Phocaeia dorrei, Phocaeia vulgaris, Bacteroides nordii, Phocaeia viscera, Muriaceae bacteria, Clostridium fesum, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces Through metagenome sequencing and a machine learning algorithm, the key microbial markers significantly related to intestinal stress symptom improvement in the coprophilous fungus transplantation treatment process are screened out, and a scientific basis and a standardized scheme are provided for coprophilous fungus transplantation treatment of irritable bowel.

Description

Technical Field

[0001] The present invention relates to the fields of biomedicine and microbiome, and in particular to a microbial marker and screening method for treating irritable bowel syndrome (IBS) based on fecal microbiota transplantation (FMT). Background Art

[0002] Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by abdominal pain, bloating, and irregular bowel habits. Its global prevalence is as high as 10%-15%. Its pathological mechanisms are closely related to intestinal microecological imbalance, disrupted gut-brain axis signaling, and impaired mucosal barrier function. While traditional therapies (such as antispasmodics and a low-FODMAP diet) can partially alleviate symptoms, they are limited by short-lived efficacy, significant individual variability, and inability to cure the disease. Therefore, breakthrough treatment strategies are urgently needed.

[0003] In recent years, the use of fecal microbiota transplantation (FMT) to treat irritable bowel syndrome by restoring the patient's gut microbiota has faced significant challenges. Clinical data show that the overall efficacy of FMT for irritable bowel syndrome is less than 60%, and the efficacy fluctuates significantly (some patients experience worsening symptoms). The core issue is the lack of scientific evidence for the functional adaptability of the donor microbiota. Breakthroughs in multi-omics technologies have revealed the absence of some commensal bacteria in the gut of IBS patients, as well as a strong correlation between the overgrowth of harmful bacteria and bloating. However, these markers are fragmented and have yet to be integrated into a multidimensional evaluation system through machine learning modeling to guide donor selection. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the current application of fecal microbiota transplantation in the treatment of irritable bowel syndrome by reconstructing the patient's intestinal flora, the present invention provides a microbial marker and screening method for the treatment of irritable bowel syndrome based on fecal microbiota transplantation, which can provide a scientific basis and standardized scheme for the treatment of irritable bowel syndrome with fecal microbiota transplantation.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: Microbial markers for treating irritable bowel based on fecal microbiota transplantation, wherein the microbial markers for treating irritable bowel based on fecal microbiota transplantation include one or any combination of Butyricimonas faecalis, Odoribacter laneus, Parabacteroides merdae, Bacteroides eggerthii, Phocaeicola massiliensis, Phocaeicola dorei, Phocaeicolavulgatus, Bacteroides nordii, Phocaeicola coprocola, Barnesiella viscericola, Muribaculaceae bacterium, Clostridium fessum, Sutterella wadsworthensis, and Slackiaisoflavoniconvertens.

[0006] According to one aspect of the present invention, the microbial markers for treating irritable bowel based on fecal microbiota transplantation further include: one or a combination of Bacteroides faecis, Barnesiella intestinihominis, Eubacterium ventriosum, and Collinsella aerofaciens.

[0007] A method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation, the method comprising the following steps: Primary data collection; Quality control data; Sequence assembly; Assemble the sequence Binning to obtain MAG; MAG clustering resulted in SGB; Microbial markers were screened.

[0008] According to one aspect of the present invention, the quality control data includes: 1) using FastQC to remove adapter primers and perform quality trimming; 2) using bowtie2 to compare the human host genome and perform host sequence filtering.

[0009] According to one aspect of the present invention, the sequence assembly includes: 1) assembling the filtered sequencing data into contigs using metaSPAdes software; 2) merging the sequencing sequences of each sample that were not used for assembly and performing secondary assembly using metaSPAdes software.

[0010] According to one aspect of the present invention, the MAG clustering to obtain SGB includes: 1) using Mash software to calculate the sequence distance between each pair of MAGs of all samples; 2) based on the calculated sequence distance and 5% distance, using fcluster software to cluster all sample MAGs to obtain SGBs.

[0011] According to one aspect of the present invention, the screening for microbial markers includes 1) performing differential analysis: analyzing the differences in SGB abundance before and after treatment for all samples, treatment response groups, and non-response groups, respectively, and extracting microorganisms with significant differences before and after treatment in all samples and response groups, and no significant differences before and after treatment in the non-response group as candidate marker set I; 2) screening microbial markers: performing species annotation on the microorganisms in candidate microbial marker set I, and using SGB that can be annotated to specific bacterial species as microbial markers for fecal microbiota transplantation-based treatment of irritable bowel.

[0012] According to one aspect of the present invention, the SGB that can be annotated to a specific bacterial species is used as a microbial marker for the treatment of irritable bowel disease based on fecal microbiota transplantation to construct a forest model and create a ROC curve.

[0013] According to one aspect of the present invention, the microbial marker screening method further comprises: Step 7: Establish a predictive model to evaluate the predictive ability of microbial markers for response to FMT.

[0014] Advantages of the implementation of the present invention: Through the above-mentioned technical scheme, key microbial markers that are significantly correlated with the improvement of irritable bowel symptoms during fecal microbiota transplantation treatment are screened out, solving the limitations of traditional therapies such as short-term efficacy, significant individual differences and inability to cure the disease, providing a scientific basis and standardized scheme for fecal microbiota transplantation treatment of irritable bowel and providing a breakthrough treatment strategy for clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 The present invention is a flowchart of the method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation.

[0017] Figure 2 The present invention uses sgb21132 to establish the ROC curve of irritable bowel patients before and after fecal microbiota transplantation using random forest.

[0018] Figure 3 The present invention uses sgb21224 and sgb20898, and the ROC curve of irritable bowel patients before and after fecal microbiota transplantation was established by random forest.

[0019] Figure 4 The present invention uses sgb23328 and sgb21279 to establish the ROC curve of irritable bowel patients before and after fecal microbiota transplantation using random forest.

[0020] Figure 5 The present invention uses the 19 selected SGBs to construct a random forest model and creates an ROC curve. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Example 1

[0023] The present invention provides microbial markers for treating irritable bowel based on fecal microbiota transplantation. The microbial markers include: Butyricimonas faecalis, Odoribacter laneus, Parabacteroides merdae, Bacteroideseggerthii, Phocaeicola massiliensis, Phocaeicola dorei, Phocaeicola vulgatus, Bacteroides nordii, Phocaeicola coprocola, Barnesiella viscericola, Muribaculaceae bacterium, Clostridium fessum, Sutterella wadsworthensis, Slackiaisoflavoniconvertens, or a combination thereof.

[0024] Example 2

[0025] The microbial markers for treating irritable bowel based on fecal microbiota transplantation provided by the present invention also include: one or a combination of Bacteroides faecis, Barnesiella intestinihominis, Eubacterium ventriosum, and Collinsella aerofaciens.

[0026] Example 3

[0027] The present invention provides a method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation, the method comprising the following steps: Step 1. Raw data collection: Collect raw sequencing data of patients with irritable bowel disease who received fecal microbiota transplantation before and after treatment.

[0028] Preferably, the raw data of the present invention uses the public database EBI database, searches the EBI database for data sets of irritable bowel patients before and after fecal microbiota transplantation, and compares the metagenomic data of irritable bowel patients before and after fecal microbiota transplantation.

[0029] Step 2: Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0030] Preferably, the data filtering step of the present invention uses FastQC to remove adapter primers and perform quality trimming and uses bowtie2 to filter host sequences by comparing the human host genome, and performs quality control on the raw data.

[0031] Step 3: Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0032] Preferably, the sequence assembly step of the present invention uses metaSPAdes software to assemble the filtered sequencing data into contigs, and then uses metaSPAdes software for secondary assembly after merging.

[0033] Step 4: Binning the assembled contigs to obtain MAGs: Binning the assembled contigs to obtain the MAGs of the microorganisms.

[0034] Preferably, metabat2 is used in the assembly sequence step of this experiment to bin the assembled contigs to obtain MAGs of a single sample. CheckM is used to assess the quality of MAGs, filter MAGs, and retain MAGs with an integrity greater than 50% and a contamination rate less than 5%.

[0035] Step 5: Cluster MAGs to obtain SGBs: Cluster MAGs to obtain SGBs.

[0036] Preferably, the MAG clustering step of the present invention uses Mash software to calculate the sequence distance between each pair of MAGs of all samples, and based on the 5% distance, uses fcluster software to cluster all MAGs of the samples to obtain SGBs.

[0037] Step 6: Screen and obtain microbial markers.

[0038] Preferably, the differential analysis step of the present invention analyzes the difference in SGB abundance before and after treatment for all samples, the response group, and the non-response group. Microorganisms that show significant differences before and after treatment in all samples and the response group, and no significant differences in the non-response group, are selected as a candidate marker set. Within the candidate marker set, species for which species information can be found, after removing duplicates, are selected for annotation.

[0039] Example 4

[0040] The present invention provides a method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation. Preferably, the method further comprises the following steps: Figure 1 As shown: Step 7: Establish a predictive model to evaluate the predictive ability of the selected microbial markers for the response to FMT treatment.

[0041] Preferably, the present invention uses the above selected SGB to construct a random forest model and create a ROC curve. The sample set is divided into a training set and a validation set at a ratio of 7:3, and the validation set results are statistically analyzed to draw the ROC curve.

[0042] Example 5

[0043] 1. During the experiment, the applicant searched the public database EBI database for a dataset of patients with irritable bowel disease before and after fecal microbiota transplantation as raw data. The dataset number is: PRJEB36140. The raw data includes a metagenomic dataset of patients with irritable bowel disease before and after fecal microbiota transplantation.

[0044] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences in the raw data.

[0045] 1) Use FastQC to remove adapter primers and perform quality trimming; 2) Use bowtie2 to filter host sequences by aligning the human host genome.

[0046] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0047] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs; 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0048] 4. Assemble sequence Binning to obtain MAG.

[0049] 1) Binning: Use metabat2 to bin the assembled contigs to obtain MAGs of individual samples; 2) MAGs filtering: checkM was used to evaluate the quality of MAGs, and MAGs with an integrity greater than 50% and a contamination rate less than 5% were retained.

[0050] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0051] 1) Mash software was used to calculate the sequence distances between MAGs of all samples; 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0052] 6. Screen and obtain microbial markers.

[0053] 1) Differential analysis: The differences in sgb21132 abundance before and after treatment were analyzed for all samples (46 cases, 27 before transplantation and 19 after transplantation), the treatment response group (33 cases, 19 before transplantation and 14 after transplantation), and the non-response group (13 cases, 8 before transplantation and 5 after transplantation); All samples, response group samples, and non-response group samples were used to perform rank sum tests on candidate sgb21132 before and after transplantation; 7. Establish a predictive model to evaluate the ability of the microbial marker sgb21132 to predict response to fecal microbiota transplantation.

[0054] Use the selected sgb21132 to build a random forest model and create an ROC curve. The sample set is divided into a training set and a validation set at a ratio of 7:3. The ROC curve is drawn by statistically analyzing the validation set results. Figure 2 The results showed that the AUC value was 1, indicating that this sgb21132 species can be used as a marker to determine the transplant effect of patients with irritable bowel.

[0055] Example 6

[0056] 1. During the experiment, the applicant searched the public database EBI database for a dataset of patients with irritable bowel disease before and after fecal microbiota transplantation as raw data. The dataset number is: PRJEB36140. The raw data includes a metagenomic dataset of patients with irritable bowel disease before and after fecal microbiota transplantation.

[0057] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0058] 1) Use FastQC to remove adapter primers and perform quality trimming; 2) Use bowtie2 to filter host sequences by aligning the human host genome.

[0059] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0060] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs; 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0061] 4. Assemble sequence Binning to obtain MAG.

[0062] 1) Binning: Use metabat2 to bin the assembled contigs to obtain MAGs of individual samples; 2) MAGs filtering: checkM was used to evaluate the quality of MAGs, and MAGs with an integrity greater than 50% and a contamination rate less than 5% were retained.

[0063] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0064] 1) Mash software was used to calculate the sequence distances between MAGs of all samples; 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0065] 6. Screen and obtain microbial markers.

[0066] 1) Differential analysis: The abundance differences of sgb21224 and sgb20898 before and after treatment were analyzed in all samples (46 cases, 27 before transplantation and 19 after transplantation), the treatment response group (33 cases, 19 before transplantation and 14 after transplantation), and the non-response group (13 cases, 8 before transplantation and 5 after transplantation). All samples, response group samples, and non-response group samples were used to perform rank sum tests on candidates sgb21224 and sgb20898 before and after transplantation; 7. Establish a predictive model to evaluate the predictive ability of microbial markers sgb21224 and sgb20898 for response to fecal microbiota transplantation.

[0067] Use the selected sgb21224 and sgb20898 to build a random forest model and create an ROC curve. The sample set is divided into a training set and a validation set at a ratio of 7:3. The ROC curve is drawn by statistically analyzing the validation set results. Figure 3The results showed that the AUC value was 1, indicating that the sgb21224 and sgb20898 species can be used as markers to determine the transplant effect of patients with irritable bowel.

[0068] Example 7

[0069] 1. During the experiment, the applicant searched the public database EBI database for a dataset of patients with irritable bowel disease before and after fecal microbiota transplantation as raw data. The dataset number is: PRJEB36140. The raw data includes a metagenomic dataset of patients with irritable bowel disease before and after fecal microbiota transplantation.

[0070] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0071] 1) Use FastQC to remove adapter primers and perform quality trimming; 2) Use bowtie2 to filter host sequences by aligning the human host genome.

[0072] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0073] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs; 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0074] 4. Assemble sequence Binning to obtain MAG.

[0075] 1) Binning: Use metabat2 to bin the assembled contigs to obtain MAGs of individual samples; 2) MAGs filtering: checkM was used to evaluate the quality of MAGs, and MAGs with an integrity greater than 50% and a contamination rate less than 5% were retained.

[0076] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0077] 1) Mash software was used to calculate the sequence distances between MAGs of all samples; 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0078] 6. Screen and obtain microbial markers.

[0079] 1) Differential analysis: The abundance differences of sgb23328 and sgb21279 before and after treatment were analyzed in all samples (46 cases, 27 before transplantation and 19 after transplantation), the treatment response group (33 cases, 19 before transplantation and 14 after transplantation), and the non-response group (13 cases, 8 before transplantation and 5 after transplantation). The rank sum test before and after transplantation was performed on the candidates sgb23328 and sgb21279 using all samples, response group samples, and non-response group samples; 7. Establish a predictive model to evaluate the predictive ability of microbial markers sgb23328 and sgb21279 for response to fecal microbiota transplantation.

[0080] Use the above selected sgb23328 and sgb21279 to build a random forest model and create an ROC curve. The sample set is divided into a training set and a validation set at a ratio of 7:3. The ROC curve is drawn by statistically analyzing the validation set results. Figure 4 The results showed that the AUC value was 1, indicating that the sgb23328 and sgb21279 species can be used as markers to determine the transplant effect of patients with irritable bowel.

[0081] Example 8

[0082] 1. Raw data collection: Collect raw sequencing data from patients with irritable bowel disease who received fecal microbiota transplantation before and after treatment.

[0083] During the experiment, the applicant searched the public database EBI database for a dataset of patients with irritable bowel disease before and after fecal microbiota transplantation as raw data. The dataset number is: PRJEB36140. The raw data includes a metagenomic dataset of patients with irritable bowel disease before and after fecal microbiota transplantation.

[0084] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0085] 1) Use FastQC to remove adapter primers and perform quality trimming; 2) Use bowtie2 to filter host sequences by aligning the human host genome.

[0086] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0087] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs; 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0088] 4. Assemble sequence Binning to obtain MAG.

[0089] 1) Binning: Use metabat2 to bin the assembled contigs to obtain MAGs of individual samples; 2) MAGs filtering: checkM was used to evaluate the quality of MAGs, and MAGs with an integrity greater than 50% and a contamination rate less than 5% were retained.

[0090] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0091] 1) Mash software was used to calculate the sequence distances between MAGs of all samples; 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0092] 6. Screen and obtain microbial markers.

[0093] 1) Differential analysis: The pre- and post-treatment SGB abundance differences were analyzed for all samples (46 cases, 27 before transplantation and 19 after transplantation), the treatment response group (33 cases, 19 before transplantation and 14 after transplantation), and the non-response group (13 cases, 8 before transplantation and 5 after transplantation). Microorganisms with significant differences before and after treatment in all samples and the response group, but no significant differences in the non-response group, were selected as candidate marker set I. The candidate SGBs were tested for pre- and post-transplantation rank sum tests (Wilcox p < 0.05) using all samples, response group samples, and non-response group samples. SGBs with significant differences in relative abundance before and after transplantation in all samples and response group samples, and no significant differences in relative abundance before and after transplantation in the non-response group, were retained. The results showed that 33 SGBs met the above conditions, as shown in Table 1 below. Table 1

[0094] 2) Screening of microbial markers: Species annotation was performed on the microorganisms in candidate microbial marker set I, and SGB that could be annotated to specific bacterial species were used as microbial markers for fecal microbiota transplantation-based treatment of irritable bowel.

[0095] The microorganisms in the candidate microbial marker set I were annotated to species, and the SGB after removing duplicate species information was taken. The results showed that 23 species were annotated to specific species information, and their corresponding relationships are shown in Table 2 below: Table 2

[0096] 7. Establish a predictive model to evaluate the ability of microbial biomarkers to predict response to FMT.

[0097] The 19 SGBs selected above were used to build a random forest model and create an ROC curve. The sample set was divided into a training set and a validation set at a ratio of 7:3. The ROC curve was drawn based on the statistical results of the validation set. Figure 5 The results showed that the AUC value was 0.958, indicating that these 19 species can be used as markers to determine the transplant effect of patients with irritable bowel.

[0098] Advantages of the implementation of the present invention: Through the above-mentioned technical scheme, key microbial markers that are significantly correlated with the improvement of irritable bowel symptoms during fecal microbiota transplantation treatment are screened out, solving the limitations of traditional therapies such as short-term efficacy, significant individual differences and inability to cure the disease, providing a scientific basis and standardized scheme for fecal microbiota transplantation treatment of irritable bowel and providing a breakthrough treatment strategy for clinical practice.

[0099] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. Microbial markers for the treatment of irritable bowel based on fecal microbiota transplantation, characterized in that: The microbial markers include Butyricimonas faecalis, Odoribacter laneus, Parabacteroides merdae, Bacteroideseggerthii, Phocaeicola massiliensis, Phocaeicola dorei, Phocaeicola vulgatus, Bacteroides nordii, Phocaeicola coprocola, Barnesiella viscericola, Muribaculaceae bacterium, Clostridium fessum, Sutterella wadsworthensis, Slackiaisoflavoniconvertens or a combination thereof.

2. The microbial marker for treating irritable bowel based on fecal microbiota transplantation according to claim 1, characterized in that: The microbial markers further include one or a combination of Bacteroides faecis, Barnesiella intestinihominis, Eubacterium ventriosum or Collinsella aerofaciens.

3. A method for screening microbial markers for the treatment of irritable bowel based on fecal microbiota transplantation, characterized in that: The microbial marker screening method comprises the following steps: Primary data collection; Quality control data; Sequence assembly; Assemble the sequence Binning to obtain MAG; MAG clustering resulted in SGB; Microbial markers were screened.

4. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 3, characterized in that: include: Removal of adapter primers and quality trimming; Host sequence filtering was performed by comparing with the human host genome.

5. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 3, characterized in that: include: Assemble filtered sequencing data into contigs; Perform secondary assembly on the sequencing sequences that were not used for assembly.

6. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 3, characterized in that: include: Binning; MAGs filtering.

7. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 3, characterized in that: include: Calculate the sequence distance of sample MAGs; Clustering of sample MAGs was performed to obtain SGBs.

8. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 3, characterized in that: include: Differential analysis: The SGB abundance differences before and after treatment were analyzed for all samples, the response group, and the non-response group. Microorganisms with significant differences before and after treatment in all samples and the response group, but no significant differences in the non-response group, were extracted as candidate marker set I. Screening of microbial markers: Species annotation was performed on the microorganisms in candidate microbial marker set I, and SGB that could be annotated to specific bacterial species were used as microbial markers for the treatment of irritable bowel based on fecal microbiota transplantation.

9. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to claim 8, characterized in that: The SGB that can be annotated to specific bacterial species is used as a microbial marker for the treatment of irritable bowel based on fecal microbiota transplantation to construct a forest model and create a ROC curve.

10. The method for screening microbial markers for treating irritable bowel based on fecal microbiota transplantation according to any one of claims 3 to 9, further comprising establishing a prediction model.

11. Use of the microbial marker according to any one of claims 1 to 2 in constructing a predictive model for detecting fecal microbiota transplantation in the treatment of irritable bowel.

12. Use of a screening method for microbial markers for the treatment of irritable bowel based on fecal microbiota transplantation according to any one of claims 3 to 10 in constructing a predictive model for detecting the effect of fecal microbiota transplantation on the treatment of irritable bowel.

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