Microbial marker for treating recurrent clostridium difficile infection based on coprophilous fungus transplantation

Abstract

The invention discloses a microbial marker for treating recurrent clostridium difficile infection based on coprophilous fungus transplantation and a screening method of the microbial marker. The microbial marker comprises the following components: Dorea longigena, Alistipes putredinis, Citrobacter farmeri, Coprococcos, Eubacter coprostanoliganes, Agatrobacter rectas, Dorea foetida, Blauria obbe, Klebsiella foetida, Blauria foetida, Blauria ovaticus and Gemmigerformation. The microbial marker comprises the following components: Dorea longigena, Alistipes putredinis, Citrobacter farmeri, Citrobacter coprostanoliganes, Citrobacter rectas, according to the invention, through metagenome sequencing and a machine learning algorithm, key microbial markers significantly related to recurrent clostridium difficile 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 recurrent clostridium difficile infection.

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 recurrent Clostridium difficile infection (rCDI) based on intestinal flora transplantation (FMT). Background Art

[0002] Recurrent Clostridioides difficile infection (rCDI) refers to a syndrome in which symptoms resolve after treatment for an initial Clostridioides difficile infection (CDI), but reappear within 2-8 weeks after cessation of treatment, with a positive definitive diagnostic test. The recurrence rate for rCDI ranges from 10% to 25%, and the recurrence rate increases with each relapse. Some patients may even experience recurrent episodes for several years. Symptoms of rCDI are similar to those of the initial infection and include fever, abdominal pain, and watery diarrhea. In severe cases, rCDI may progress to pseudomembranous colitis and toxic megacolon. Recurrences may be related to the high drug resistance of C. difficile spores, impaired immune responses to C. difficile toxins, and new spore exposure. Risk factors for rCDI include age over 65 years, comorbid medical conditions, continued antibiotic use during C. difficile treatment, serum creatinine ≥1.2 mg / dL, and lack of an antibody-mediated immune response to C. difficile toxins. For the treatment of recurrent Clostridium difficile infection, metronidazole or vancomycin can be repeated for the initial recurrence. For multiple recurrences, additional treatments, such as toxin combination therapy, biological therapy, and intravenous immunoglobulin, should be considered in addition to antibiotics. Relapse prevention measures include the appropriate and standardized use of antibiotics, enhanced hand hygiene, and environmental cleanliness.

[0003] In 2013, a study confirmed that gut microbiota transplantation (GMT) has an efficacy rate of up to 90% in treating recurrent Clostridium difficile infection (C. difficile), far exceeding that of traditional antibiotics. Currently, GMT has been widely used to treat recurrent C. difficile infection and shows promising application prospects in areas such as chronic constipation, diarrhea, recurrent C. difficile syndrome, and inflammatory bowel disease. Technically, GMT requires rigorous donor screening, stool preparation, and transplant selection. 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 recurrent Clostridium difficile infection by reconstructing the patient's intestinal flora, the present invention provides a microbial marker and screening method for the treatment of recurrent Clostridium difficile infection syndrome based on intestinal flora transplantation, which can provide a scientific basis and standardized scheme for the treatment of recurrent Clostridium difficile infection with fecal microbiota transplantation.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] Microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation, wherein the microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation are a combination of Dorea longicatena, Alistipesputredinis, Citrobacterfarmeri, Coprococcus comes, Eubacteriumcoprostanoligenes, Agathobacterrectalis, Dorea formicigenerans, Blautia obeum, Klebsiella oxytoca, Bacteroides fragilis, Blautiawexlerae, Bacteroides ovatus, Gemmigerformicilis, Faecalibacterium prausnitzii, Citrobacter freundii, Bacteroides xylanisolvens, and Anaerostipes hadrus.

[0007] A method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation, the method comprising the following steps:

[0008] Primary data collection;

[0009] Quality control data;

[0010] sequence assembly;

[0011] Assemble the sequence Binning to obtain MAG;

[0012] MAG clustering resulted in SGB;

[0013] Microbial markers were screened.

[0014] 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.

[0015] 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.

[0016] 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) using fcluster software to cluster all sample MAGs based on the calculated sequence distance and 5% distance to obtain SGBs.

[0017] According to one aspect of the present invention, the screening for microbial markers includes 1) performing differential analysis: analyzing the difference 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 the treatment of recurrent Clostridium difficile infection based on intestinal flora transplantation.

[0018] 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 treating recurrent Clostridium difficile infection based on intestinal flora transplantation to construct a forest model and create a ROC curve.

[0019] According to one aspect of the present invention, the microbial marker screening method further comprises:

[0020] Step 7: Establish a predictive model to evaluate the predictive ability of microbial markers for response to FMT.

[0021] 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 symptoms of recurrent Clostridium difficile infection during fecal microbiota transplantation treatment are screened out, solving the limitations of traditional therapies such as short-term efficacy, significant individual differences, inability to cure and high recurrence, providing a scientific basis and standardized scheme for fecal microbiota transplantation treatment of recurrent Clostridium difficile infection and providing a breakthrough treatment strategy for clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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.

[0023] Figure 1 The present invention provides a flow chart of the method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation.

[0024] Figure 2 The ROC curves of fecal microbiota transplantation for patients with recurrent Clostridium difficile infection before and after establishment of a random forest method using Bacteroides xylanisolvens, Bacteroides fragilis, and Bacteroides ovatus screened by the present invention are shown.

[0025] Figure 3 The ROC curves of fecal microbiota transplantation before and after treatment of patients with recurrent Clostridium difficile infection were established using random forest methods using Gemmiger formicilis, Eubacterium coprostanoligenes, and Alistipes putredinis screened by the present invention.

[0026] Figure 4 The ROC curves of fecal microbiota transplantation for patients with recurrent Clostridium difficile infection before and after establishment of a random forest analysis were obtained using Blautia obeum, Agathobacter rectalis, Eubacterium coprostanoligenes, Faecalibacterium prausnitzii, Alistipesputredinis, Coprococcus comes, and Citrobacter freundii screened by the present invention.

[0027] Figure 5 The ROC curve of the training set data of the model of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation established by random forest using the microbial markers screened by the present invention.

[0028] Figure 6 The ROC curve of the validation set data of the model of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation established by random forest using the microbial markers screened by the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] Example 1

[0032] The present invention provides microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation, wherein the microbial markers include: Dorealongicatena, Alistipesputredinis, Citrobacter farmeri, Coprococcus comes, Eubacteriumcoprostanoligenes, Agathobacter rectalis, Doreaformicigenerans, Blautia obeum, Klebsiella oxytoca, Bacteroides fragilis, Blautiawexlerae, Bacteroides ovatus, Gemmigerformicilis, Faecalibacterium prausnitzii, Citrobacter freundii, Bacteroides xylanisolvens, and Anaerostipes hadrus combination.

[0033] Example 2

[0034] The present invention provides a method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation, comprising the following steps:

[0035] Step 1. Raw data collection: Collect raw sequencing data of patients with recurrent Clostridium difficile infection who received intestinal flora transplantation before and after treatment.

[0036] Preferably, the raw data of the present invention uses the public database NCBI database, searches the NCBI database for data sets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation, and compares the metagenomic data of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation.

[0037] Step 2: Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0038] 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.

[0039] Step 3: Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0040] 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.

[0041] Step 4: Binning the assembled contigs to obtain MAGs: Binning the assembled contigs to obtain the MAGs of the microorganisms.

[0042] Preferably, metabat2 is used in the assembly sequence step of this experiment to bin the assembled contigs to obtain MAGs of a single sample, and checkM is used to perform quality assessment on the MAGs, filter the MAGs, and retain MAGs with an integrity greater than 50% and a contamination rate less than 5%.

[0043] Step 5: Cluster MAGs to obtain SGBs: Cluster MAGs to obtain SGBs.

[0044] 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.

[0045] Step 6: Screen and obtain microbial markers.

[0046] 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.

[0047] Example 3

[0048] The present invention provides a method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation. Preferably, the method further comprises the following steps: Figure 1 As shown:

[0049] Step 7: Establish a predictive model to evaluate the predictive ability of the selected microbial markers for the response to FMT treatment.

[0050] 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.

[0051] Example 4

[0052] 1. Raw data collection: Collect raw sequencing data from patients with recurrent Clostridium difficile infection who received intestinal flora transplantation before and after treatment.

[0053] During the experiment, the applicant searched the public database NCBI database for datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation as raw data. The dataset numbers are: RJNA637878, PRJNA349197, PRJEB39023, PRJNA701961, PRJEB23524, and PRJEB46780. The raw data included metagenomic datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation.

[0054] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0055] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0056] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0057] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0058] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0059] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0060] 4. Assemble sequence Binning to obtain MAG.

[0061] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0062] 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;

[0065] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0066] 6. Screen and obtain microbial markers.

[0067] 1) Difference analysis: The abundance differences of SGB21166, SGB21204, and SGB21190 before and after treatment were analyzed in all samples (78 cases, 39 before transplantation and 39 after transplantation), the treatment response group (68 cases, 34 before transplantation and 34 after transplantation), and the non-response group (10 cases, 5 before transplantation and 5 after transplantation);

[0068] All samples, response group samples, and non-response group samples were used to perform rank sum tests before and after transplantation on candidate SGB21166, SGB21204, and SGB21190;

[0069] 7. Establish a predictive model to evaluate the ability of the microbial biomarkers Bacteroides xylanisolvens, Bacteroides fragilis, and Bacteroides ovatus to predict response to FMT.

[0070] The random forest model was constructed using the selected SGB21166, SGB21204, and SGB21190, and the ROC curve was created. 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 2 The results showed that the AUC value was 0.725, indicating that SGB21166, SGB21204, and SGB21190 species can be used as markers for judging the transplant effect of patients with recurrent Clostridium difficile, but the effect is not good.

[0071] Example 5

[0072] 1. Raw data collection: Collect raw sequencing data from patients with recurrent Clostridium difficile infection who received intestinal flora transplantation before and after treatment.

[0073] During the experiment, the applicant searched the public database NCBI database for datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation as raw data. The dataset numbers are: RJNA637878, PRJNA349197, PRJEB39023, PRJNA701961, PRJEB23524, and PRJEB46780. The raw data included metagenomic datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation.

[0074] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0075] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0076] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0077] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0078] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0079] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0080] 4. Assemble sequence Binning to obtain MAG.

[0081] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0082] 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.

[0083] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0084] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0085] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0086] 6. Screen and obtain microbial markers.

[0087] 1) Difference analysis: The abundance differences of SGB24861, SGB21891, and SGB20862 before and after treatment were analyzed in all samples (78 cases, 39 cases before transplantation and 39 cases after transplantation), the treatment response group (68 cases, 34 cases before transplantation and 34 cases after transplantation), and the non-response group (10 cases, 5 cases before transplantation and 5 cases after transplantation);

[0088] All samples, response group samples, and non-response group samples were used to perform rank sum tests before and after transplantation on candidate SGB24861, SGB21891, and SGB20862;

[0089] 7. Establish a predictive model to evaluate the ability of the microbial biomarkers Gemmiger formicilis, Eubacterium coprostanoligenes, and Alistipes putredinis to predict response to FMT.

[0090] The random forest model was constructed using the selected SGB24861, SGB21891, and SGB20862, and the ROC curve was created. 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 3 The results showed that the AUC value was 0.825, indicating that SGB24861, SGB21891, and SGB20862 species can be used as markers for judging the transplant effect of patients with recurrent Clostridium difficile, but the effect is not good.

[0091] Example 6

[0092] 1. Raw data collection: Collect raw sequencing data from patients with recurrent Clostridium difficile infection who received intestinal flora transplantation before and after treatment.

[0093] During the experiment, the applicant searched the public database NCBI database for datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation as raw data. The dataset numbers are: RJNA637878, PRJNA349197, PRJEB39023, PRJNA701961, PRJEB23524, and PRJEB46780. The raw data included metagenomic datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation.

[0094] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0095] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0096] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0097] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0098] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0099] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0100] 4. Assemble sequence Binning to obtain MAG.

[0101] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0102] 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.

[0103] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0104] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0105] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0106] 6. Screen and obtain microbial markers.

[0107] 1) Difference analysis: The abundance differences of SGB22762, SGB22551, SGB21891, SGB24634, SGB20862, SGB2193, and SGB20050 before and after treatment were analyzed in all samples (78 cases, 39 before transplantation and 39 after transplantation), the treatment response group (68 cases, 34 before transplantation and 34 after transplantation), and the non-response group (10 cases, 5 before transplantation and 5 after transplantation);

[0108] All samples, response group samples, and non-response group samples were used to perform rank sum tests before and after transplantation on candidate SGB22762, SGB22551, SGB21891, SGB24634, SGB20862, SGB2193, and SGB20050;

[0109] 7. Establish a predictive model to evaluate the ability of the microbial biomarkers Blautia obeum, Agathobacter rectalis, Eubacterium coprostanoligenes, Faecalibacterium prausnitzii, Alistipes putredinis, Coprococcus comes, and Citrobacter freundii to predict response to fecal microbiota transplantation.

[0110] The random forest model was constructed using the selected SGB22762, SGB22551, SGB21891, SGB24634, SGB20862, SGB2193, and SGB20050, and the ROC curve was created. The sample set was divided into a training set and a validation set at a ratio of 7:3, and the ROC curve was drawn based on the statistical results of the validation set. Figure 4 The results showed that the AUC value was 0.796, indicating that SGB22762, SGB22551, SGB21891, SGB24634, SGB20862, SGB2193, and SGB20050 species can be used as markers for judging the transplant effect of patients with recurrent Clostridium difficile, but the effect is not good.

[0111] Example 7

[0112] 1. Raw data collection: Collect raw sequencing data from patients with recurrent Clostridium difficile infection who received intestinal flora transplantation before and after treatment.

[0113] During the experiment, the applicant searched the public database NCBI database for datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation as raw data. The dataset numbers are: RJNA637878, PRJNA349197, PRJEB39023, PRJNA701961, PRJEB23524, and PRJEB46780. The raw data included metagenomic datasets of patients with recurrent Clostridium difficile infection before and after fecal microbiota transplantation.

[0114] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0115] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0116] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0117] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0118] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0119] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0120] 4. Assemble sequence Binning to obtain MAG.

[0121] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0122] 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.

[0123] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0124] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0125] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0126] 6. Screen and obtain microbial markers.

[0127] 1) Difference analysis: The pre- and post-treatment SGB abundance differences were analyzed for all samples (78 cases, 39 before transplantation and 39 after transplantation), the treatment response group (68 cases, 34 before transplantation and 34 after transplantation), and the non-response group (10 cases, 5 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.

[0128] The candidate SGBs were tested for rank sum tests before and after transplantation using all samples, response group samples, and non-response group samples (Wilcox p < 0.05). 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 20 SGBs met the above conditions, as shown in Table 1 below.

[0129] Table 1

[0130] species p-value Enrichment group sgb20050 0.0000000496 FMT_pre sgb20069 0.0000000871 FMT_pre sgb20047 0.000000549 FMT_pre sgb22277 0.00000000243 FMT_post sgb22428 0.0000000151 FMT_post sgb23097 0.00000000635 FMT_post sgb22239 0.000000156 FMT_post sgb20862 0.000000017 FMT_post sgb22193 0.0000000437 FMT_post sgb21891 0.00000000313 FMT_post sgb22551 0.000000123 FMT_post sgb22208 0.0000000308 FMT_post sgb22762 0.0000000226 FMT_post sgb21166 0.0000000222 FMT_post sgb22859 0.0000000299 FMT_post sgb21204 0.000000209 FMT_post sgb24861 0.0000000122 FMT_post sgb24634 0.000000042 FMT_post sgb21190 0.000000141 FMT_post sgb22011 0.0000000268 FMT_post

[0131] 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 the treatment of recurrent Clostridium difficile infection based on intestinal flora transplantation.

[0132] 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 17 species were annotated to specific species information, and their corresponding relationships are shown in Table 2 below:

[0133] Table 2

[0134] SGB ​​number Species information sgb22239 Dorealongicatena sgb20862 Alistipesputredinis sgb20047 Citrobacterfarmeri sgb22193 Coprococcus comes sgb21891 Eubacteriumcoprostanoligenes sgb22551 Agathobacterrectalis sgb22208 Doreaformicigenerans sgb22762 Blautiaobeum sgb20069 Klebsiellaoxytoca sgb21166 Bacteroidesfragilis sgb22859 Blautiawexlerae sgb21204 Bacteroidesovatus sgb24861 Gemmigerformicilis sgb24634 Faecalibacteriumprausnitzii sgb20050 Citrobacterfreundii sgb21190 Bacteroidesxylanisolvens sgb22011 Anaerostipeshadrus

[0135] 7. Establish a predictive model to evaluate the ability of microbial biomarkers to predict response to FMT.

[0136] The 17 SGBs selected above were used to construct a random forest model and create an ROC curve. The sample set was divided into a training set at a ratio of 7:3, and the statistical results were used to draw the ROC curve. Figure 5 The results showed an AUC value of 0.926, indicating that these 17 species can be used as markers to determine the transplant efficacy of patients with recurrent Clostridium difficile infection.

[0137] 8. Prediction model validation: Use new samples to validate microbial markers and prediction models.

[0138] The 17 SGBs selected above were used to construct a random forest model and create an ROC curve. The sample set was divided into a validation set at a ratio of 7:3, and the validation set results were statistically analyzed to draw an ROC curve. Figure 6 The results showed that the AUC value was 0.8, indicating that these 17 species can be used as markers to judge the transplant effect of patients with recurrent Clostridium difficile infection.

[0139] In this example, the AUC value before and after fecal microbiota transplantation in patients with recurrent Clostridium difficile infection established using the 17 SGBs selected above to construct a random forest model was 0.926, which was higher than the AUC values ​​of Bacteroides xylanisolvens, Bacteroides fragilis, and Bacteroides ovatus in Example 4, higher than the AUC values ​​of Gemmigerformicilis, Eubacterium coprostanoligenes, and Alistipes putredinis in Example 5, and higher than the AUC values ​​of Blautia obeum, Agathobacter rectalis, Eubacterium coprostanoligenes, Faecalibacterium prausnitzii, Alistipes putredinis, Coprococcus comes, and Citrobacter freundii in Example 6, indicating that the use of more microbial markers can improve the ROC classification effect.

[0140] 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 symptoms of recurrent Clostridium difficile infection during fecal microbiota transplantation treatment are screened out, solving the limitations of traditional therapies such as short-term efficacy, significant individual differences, inability to cure and high recurrence, providing a scientific basis and standardized scheme for fecal microbiota transplantation treatment of recurrent Clostridium difficile infection and providing a breakthrough treatment strategy for clinical practice.

[0141] 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 recurrent Clostridium difficile infection based on fecal microbiota transplantation, characterized in that: The microbial markers include Dorea longicatena, Alistipes putredinis, Citrobacter farmeri, Coprococcus comes, Eubacteriumcoprostanoligenes, Agathobacter rectalis, Doreaformicigenerans, Blautia obeum, Klebsiella oxytoca, Bacteroides fragilis, Blautiawexlerae, Bacteroides ovatus, Gemmigerformicilis, Faecalibacterium prausnitzii, Citrobacter freundii, Bacteroides xylanisolvens and Anaerostipes hadrus combination.

2. A method for screening microbial markers for the treatment of recurrent Clostridium difficile infection 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.

3. The method for screening microbial markers for treating recurrent Clostridium difficile infection 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.

4. The method for screening microbial markers for treating recurrent Clostridium difficile infection 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.

5. The method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation according to claim 3, characterized in that: include: Binning; MAGs filtering.

6. The method for screening microbial markers for treating recurrent Clostridium difficile infection 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.

7. The method for screening microbial markers for treating recurrent Clostridium difficile infection 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 recurrent Clostridium difficile infection based on intestinal flora transplantation.

8. The method for screening microbial markers for treating recurrent Clostridium difficile infection 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 treating recurrent Clostridium difficile infection based on intestinal flora transplantation to construct a forest model and create a ROC curve.

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

10. Use of the microbial marker according to claim 1 in constructing a prediction model for detecting intestinal flora transplantation in the treatment of recurrent Clostridium difficile infection.

11. Use of the method for screening microbial markers for treating recurrent Clostridium difficile infection based on fecal microbiota transplantation according to any one of claims 3 to 9 in constructing a predictive model for detecting intestinal microbiota transplantation for treating recurrent Clostridium difficile infection.

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