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

Abstract

The invention discloses a microbial marker for treating autism on the basis of coprophilous fungus transplantation and a screening method of the microbial marker. The microbial marker comprises one or any combination of the following components: Agathobacter recalcitus, Extibacter muris, Hungata hethewayi and Suteraceae bacterium. The invention further discloses a screening method of the microbial marker for treating autism on the basis of coprophilous fungus transplantation. The screening method of the microbial marker for treating autism on the basis of coprophilous fungus transplantation comprises the following steps of: selecting one or any combination of Agathobacter recalcitus and Extibacter muris; through metagenome sequencing and a machine learning algorithm, the key microbial markers significantly related to autism 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 autism.

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 autism spectrum disorder (ASD) based on fecal microbiota transplantation (FMT). Background Art

[0002] Autism is a complex neurodevelopmental disorder characterized by social impairment, delayed language development, and repetitive behaviors. While the etiology of autism remains unclear, research suggests that genetic, environmental, and intestinal microbiome imbalances may contribute to its pathogenesis.

[0003] In recent years, a growing number of studies have revealed the crucial role of the "gut-brain axis" in the development of autism. Gut microbiota influences brain function through pathways such as metabolites (such as short-chain fatty acids and tryptophan metabolites), immune regulation, and neurotransmitters (such as serotonin and gamma-aminobutyric acid). Clinical observations have found that patients with autism often experience gastrointestinal symptoms (such as constipation, diarrhea, and irritable bowel syndrome), and the composition of their gut microbiota differs significantly from that of healthy individuals.

[0004] Currently, treatments for autism mainly include behavioral intervention, drug therapy (such as antipsychotics), and nutritional regulation, but the efficacy is limited, and some drugs have side effects. As an emerging microbial intervention method, fecal microbiota transplantation (FMT) has achieved significant therapeutic effects in diseases such as recurrent Clostridium difficile infection (rCDI) and has gradually been applied to the treatment of neuropsychiatric diseases such as autism. However, the application of fecal microbiota transplantation in the treatment of autism still faces the following challenges: (1) unclear screening criteria for donor microbiota; (2) lack of microbial markers: Bifidobacterium breve, Bifidobacterium dentium, Ruminococcus bromii, and Eggerthellalenta have been publicly associated with the treatment of autism; (3) the mechanism of action is unclear. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the current application of fecal microbiota transplantation in the treatment of autism by reconstructing the patient's intestinal flora, the present invention provides a microbial marker and screening method for the treatment of autism spectrum disorder based on intestinal flora transplantation, which can provide a scientific basis and standardized scheme for the treatment of autism with fecal microbiota transplantation.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] Microbial markers for treating autism based on fecal microbiota transplantation, wherein the microbial markers for treating autism based on fecal microbiota transplantation include one or any combination of Agathobacter rectalis, Extibacter muris, Hungatella hathewayi, and Sutterellaceae bacterium.

[0008] According to one aspect of the present invention, the microbial markers for treating autism based on fecal microbiota transplantation further include: one or a combination of Blautia producta, Clostridium innocuum, Faecalibacterium prausnitzii, and Flavonifractor plautii.

[0009] A method for screening microbial markers for treating autism based on fecal microbiota transplantation, the method comprising the following steps:

[0010] Step 1. Raw data collection: Collect raw sequencing data from autism patients who received gut microbiota transplantation before and after treatment;

[0011] Step 2, quality control data: perform quality control on the raw data and filter out low-quality sequences and host sequences;

[0012] Step 3, sequence assembly: assemble the filtered sequencing data to obtain contigs;

[0013] Step 4: Binning the assembled contigs to obtain MAGs: Binning the assembled contigs to obtain the MAGs of the microorganisms;

[0014] Step 5: Cluster MAGs to obtain SGBs: Cluster MAGs to obtain SGBs;

[0015] Step 6: Screen and obtain microbial markers.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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 that have 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 autism based on intestinal flora transplantation.

[0020] 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 autism based on intestinal flora transplantation to construct a forest model and create a ROC curve.

[0021] According to one aspect of the present invention, the microbial marker screening method further comprises:

[0022] Step 7: Establish a predictive model to evaluate the predictive ability of microbial markers for response to FMT.

[0023] Advantages of the implementation of the present invention: Through the above technical scheme, key microbial markers that are significantly correlated with the improvement of autism symptoms during fecal microbiota transplantation treatment are screened out, solving the limitations of behavioral intervention and nutritional regulation, such as limited and short-term efficacy, and possible side effects of drug therapy (such as antipsychotics), and providing a scientific basis and standardized scheme for the treatment of autism through fecal microbiota transplantation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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.

[0025] Figure 1 This is a flow chart of the method for screening microbial markers for treating autism based on fecal microbiota transplantation of the present invention.

[0026] Figure 2A receiver operating characteristic (ROC) prediction model for the efficacy of fecal microbiota transplantation in patients with autism was established using Agathobacter rectalis as a microbial marker using random forest analysis.

[0027] Figure 3 A ROC prediction model for the efficacy of fecal microbiota transplantation in patients with autism was established using Extibacter_muris and Hungatella_hathewayi as microbial markers using random forest analysis.

[0028] Figure 4 A receiver operating characteristic (ROC) prediction model for the efficacy of fecal microbiota transplantation in patients with autism was established using Sutterellaceae bacteria and Flavonifractor plautii as microbial biomarkers using random forest analysis.

[0029] Figure 5 The ROC prediction model for the effect of fecal microbiota transplantation in autism patients was established using the microbial markers screened by the present invention through random forest. DETAILED DESCRIPTION

[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 autism based on fecal microbiota transplantation, wherein the microbial markers include: one or a combination of Agathobacter rectalis, Extibacter muris, Hungatella hathewayi, and Sutterellaceaebacterium.

[0033] Example 2

[0034] The microbial markers for treating autism based on fecal microbiota transplantation provided by the present invention also include: one or a combination of Blautiaproducta, Clostridiuminnocuum, Faecalibacterium prausnitzii, and Flavonifractorplautii.

[0035] Example 3

[0036] The present invention provides a method for screening microbial markers for treating autism based on fecal microbiota transplantation, which comprises the following steps:

[0037] Step 1. Raw data collection: Collect raw sequencing data of autistic patients who received intestinal flora transplantation before and after treatment.

[0038] Preferably, the raw data of the present invention uses the public database NCBI database, searches the NCBI database for data sets of autistic patients before and after fecal microbiota transplantation, and compares the metagenomic data of autistic patients before and after fecal microbiota transplantation.

[0039] Step 2: Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0040] 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.

[0041] Step 3: Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0042] 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.

[0043] Step 4: Binning the assembled contigs to obtain MAGs: Binning the assembled contigs to obtain the MAGs of the microorganisms.

[0044] 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%.

[0045] Step 5: Cluster MAGs to obtain SGBs: Cluster MAGs to obtain SGBs.

[0046] 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.

[0047] Step 6: Screen and obtain microbial markers.

[0048] 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.

[0049] Example 4

[0050] The present invention provides a method for screening microbial markers for treating autism based on fecal microbiota transplantation. Preferably, the method further comprises the following steps: Figure 1 As shown:

[0051] Step 7: Establish a predictive model to evaluate the predictive ability of the selected microbial markers for the response to FMT treatment.

[0052] Preferably, the present invention performs five cross-validations on the sample population, and each validation uses a random forest model to predict the sampled samples, and the statistical prediction results are used to draw an ROC curve.

[0053] Example 5

[0054] 1. During the experiment, the applicant searched the public database NCBI database for a dataset of autism patients before and after fecal microbiota transplantation as raw data. The dataset number is: PRJNA1010504. The raw data includes a metagenomic dataset of autism patients before and after fecal microbiota transplantation.

[0055] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences in the raw data.

[0056] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0057] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0058] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0059] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0060] 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: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0063] 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.

[0064] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0065] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0066] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0067] 6. Screen and obtain microbial markers.

[0068] 1) Difference analysis: The differences in SGB22652 abundance before and after treatment were analyzed for all samples (48 cases, 26 before transplantation and 22 after transplantation), the treatment response group (39 cases, 21 before transplantation and 18 after transplantation), and the non-response group (9 cases, 5 before transplantation and 4 after transplantation);

[0069] All samples, response group samples, and non-response group samples were used to perform rank sum tests on candidate SGB22652 before and after transplantation.

[0070] 7. Establish a predictive model to evaluate the ability of the microbial marker Agathobacter rectalis to predict response to fecal microbiota transplantation.

[0071] The Agathobacter rectalis selected above was used to perform five cross-validations on the sample population. In each validation, a random forest model was constructed to predict the sampled samples. The statistical prediction results were plotted to draw the ROC curve, as shown in the figure. Figure 2 The results showed an AUC value of 0.922, indicating that Agathobacter rectalis species can be used as a marker to determine the transplantation effect of patients with autism.

[0072] Example 6

[0073] 1. During the experiment, the applicant searched the public database NCBI database for a dataset of autism patients before and after fecal microbiota transplantation as raw data. The dataset number is: PRJNA1010504. The raw data includes a metagenomic dataset of autism patients 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 SGB22267 and SGB22447 before and after treatment were analyzed in all samples (48 cases, 26 before transplantation and 22 after transplantation), the treatment response group (39 cases, 21 before transplantation and 18 after transplantation), and the non-response group (9 cases, 5 before transplantation and 4 after transplantation);

[0088] The rank sum test before and after transplantation was performed on the candidates SGB22267 and SGB22447 using all samples, response group samples, and non-response group samples.

[0089] 7. Establish a predictive model to evaluate the predictive ability of the microbial markers Extibacter_muris and Hungatella_hathewayi for the response to FMT.

[0090] The sample population was cross-validated five times using the Extibacter_muris and Hungatella_hathewayi selected above. A random forest model was constructed for each validation to predict the sampled samples. The ROC curve was plotted based on the statistical prediction results. The curve is shown in Figure 2. Figure 3 The results showed that the AUC value was 0.941, indicating that Extibacter_muris and Hungatella_hathewayi species can be used as markers to determine the transplantation effect of patients with autism.

[0091] Example 7

[0092] 1. During the experiment, the applicant searched the public database NCBI database for a dataset of autism patients before and after fecal microbiota transplantation as raw data. The dataset number is: PRJNA1010504. The raw data includes a metagenomic dataset of autism patients before and after fecal microbiota transplantation.

[0093] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0094] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0095] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0096] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0097] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0098] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0099] 4. Assemble sequence Binning to obtain MAG.

[0100] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0101] 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.

[0102] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0103] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0104] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0105] 6. Screen and obtain microbial markers.

[0106] 1) Difference analysis: The abundance differences of SGB23285 and SGB24595 before and after treatment were analyzed in all samples (48 cases, 26 before transplantation and 22 after transplantation), the treatment response group (39 cases, 21 before transplantation and 18 after transplantation), and the non-response group (9 cases, 5 before transplantation and 4 after transplantation);

[0107] The rank sum test before and after transplantation was performed on the candidates SGB23285 and SGB24595 using all samples, response group samples, and non-response group samples.

[0108] 7. Establish a predictive model to evaluate the ability of the microbial biomarkers Sutterellaceae bacteria and Flavonifractor plautii to predict response to fecal microbiota transplantation.

[0109] The sample population was cross-validated five times using the selected Sutterellaceae bacteria and Flavonifractor plautii. A random forest model was constructed for each validation to predict the sampled samples. The ROC curve was plotted based on the statistical prediction results. Figure 4 The results showed that the AUC value was 0.917, indicating that Sutterellaceae bacteria and Flavonifractor plautii species can be used as markers to determine the transplantation effect of patients with autism.

[0110] Example 8

[0111] 1. Original data collection: Collect original sequencing data of autism patients who received intestinal flora transplantation before and after treatment.

[0112] During the experiment, the applicant searched the public database NCBI database for a dataset of autism patients before and after fecal microbiota transplantation as raw data. The dataset number is: PRJNA1010504. The raw data includes a metagenomic dataset of autism patients before and after fecal microbiota transplantation.

[0113] 2. Quality control data: Perform quality control on the raw data and filter out low-quality sequences and host sequences.

[0114] 1) Use FastQC to remove adapter primers and perform quality trimming;

[0115] 2) Use bowtie2 to align the human host genome and filter the host sequences.

[0116] 3. Sequence assembly: Assemble the filtered sequencing data to obtain contigs.

[0117] 1) Use metaSPAdes software to assemble the filtered sequencing data into contigs;

[0118] 2) The sequencing sequences of each sample that were not used for assembly were merged and then reassembled using metaSPAdes software.

[0119] 4. Assemble sequence Binning to obtain MAG.

[0120] 1) Binning: Using metabat2, the assembled contigs were binned to obtain MAGs of individual samples;

[0121] 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.

[0122] 5. MAG clustering to obtain SGB: MAG is clustered to obtain SGB.

[0123] 1) Mash software was used to calculate the sequence distances between MAGs of all samples;

[0124] 2) Based on the calculated sequence distance, all sample MAGs were clustered using fcluster software at a 5% distance to obtain SGBs.

[0125] 6. Screen and obtain microbial markers.

[0126] 1) Difference analysis: The pre- and post-treatment SGB abundance differences were analyzed for all samples (48 cases, 26 before transplantation and 22 after transplantation), the treatment response group (39 cases, 21 before transplantation and 18 after transplantation), and the non-response group (9 cases, 5 before transplantation and 4 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.

[0127] 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 19 SGBs met the above conditions, as shown in Table 1 below.

[0128] Table 1

[0129] species p-value Enrichment group sgb22394 0.04123 FMT_post sgb22406 0.01416 FMT_post sgb22427 0.00026 FMT_post sgb22428 0.00259 FMT_post sgb22429 0.00322 FMT_post sgb23212 0.02178 FMT_post sgb22267 0.04279 FMT_post sgb22447 0.00673 FMT_post sgb22652 0.01264 FMT_post sgb23285 0.03556 FMT_post sgb24631 0.03218 FMT_post sgb24104 0.04143 FMT_pre sgb21645 0.00124 FMT_pre sgb21822 0.01279 FMT_pre sgb22681 0.00046 FMT_pre sgb23396 0.02248 FMT_pre sgb23417 0.04665 FMT_pre sgb23679 0.02542 FMT_pre sgb24595 0.00899 FMT_pre

[0130] 2) Screening of microbial markers: Species annotation of the microorganisms in candidate microbial marker set I was performed, and SGBs that could be annotated to specific bacterial species were used as microbial markers for the treatment of autism based on intestinal flora transplantation.

[0131] 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 12 species were annotated to specific species information, and their corresponding relationships are shown in Table 2 below:

[0132] Table 2

[0133] SGB ​​number Species information sgb22267 Extibacter_muris sgb22447 Hungatella_hathewayi sgb22652 Agathobacter_rectalis sgb23285 Sutterellaceae_bacterium sgb24631 Faecalibacterium_prausnitzii sgb21645 Clostridium_innocuum sgb21822 Ruminococcus_bromii sgb22681 Blautia_producta sgb23396 Bifidobacterium_breve sgb23417 Bifidobacterium_dentium sgb23679 Eggerthella_lenta sgb24595 Flavonifractor_plautii

[0134] 7. Establish a predictive model to evaluate the ability of microbial biomarkers to predict response to FMT.

[0135] The 12 SGBs selected above were used to build a random forest model, and the sample population was cross-validated 5 times. In each validation, a random forest model was built to predict the sampled samples. The statistical prediction results were used to draw the ROC curve. The curve is as follows Figure 5 As shown, the results showed that the AUC value was 0.902, indicating that these 12 species can be used as markers to judge the transplantation effect of autism patients.

[0136] Advantages of the implementation of the present invention: Through the above technical scheme, key microbial markers that are significantly correlated with the improvement of autism symptoms during fecal microbiota transplantation treatment are screened out, solving the limitations of behavioral intervention and nutritional regulation, such as limited and short-term efficacy, and possible side effects of drug therapy (such as antipsychotics), and providing a scientific basis and standardized scheme for the treatment of autism through fecal microbiota transplantation.

[0137] 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 autism based on fecal microbiota transplantation, characterized by: The microbial marker includes one or a combination of Agathobacter rectalis, Extibacter muris, Hungatella hathewayi, and Sutterella ceaebacterium.

2. The microbial marker for treating autism based on fecal microbiota transplantation according to claim 1, characterized in that: The microbial marker further includes one or a combination of Blautia producta, Clostridium innocuum, Faecalibacterium prausnitzii, and Flavonifractor plautii.

3. A method for screening microbial markers for the treatment of autism 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 autism 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 the human host genome.

5. The method for screening microbial markers for treating autism 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 autism 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 autism 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 autism 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 of the microorganisms in candidate microbial marker set I was performed, and SGBs that could be annotated to specific bacterial species were used as microbial markers for the treatment of autism based on intestinal flora transplantation.

9. The method for screening microbial markers for treating autism 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 autism based on intestinal flora transplantation to construct a forest model and create a ROC curve.

10. The method for screening microbial markers for treating autism 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 intestinal flora transplantation for the treatment of autism.

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

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