CN111254207A - Intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people and application thereof - Google Patents
Intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people and application thereof Download PDFInfo
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
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- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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- C12Q1/6869—Methods for sequencing
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- G16B40/20—Supervised data analysis
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention belongs to the technical field of biological medicines, and particularly relates to an intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people and application thereof. The invention provides an intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people, which consists of microorganisms shown by at least one gene in SEQ ID NO. 1-5, wherein the microbial gene is obviously enriched or reduced in human intestinal tracts. The invention also provides application of the detection reagent in preparing a differential detection kit for autoimmune hepatitis and healthy people, and application of the detection reagent in establishing an intestinal microorganism model for differentiating autoimmune hepatitis and healthy people. Therefore, the microorganism gene discrimination model of the invention realizes good discrimination capability in autoimmune hepatitis and healthy people.
Description
Technical Field
The invention belongs to the technical field of biological medicines, and particularly relates to an intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people and application thereof.
Background
Autoimmune hepatitis (AIH) is a chronic, non-specific inflammation of the liver mediated by the immune system. Epidemiological statistics show that the Chinese autoimmune liver disease rate is 20/100000, and the average annual incidence rate of northern Europe white people is 1.07-1.9/100000. The etiology of this disease is related to a variety of genetic and environmental factors, but has not yet been fully elucidated. The risk of cirrhosis and/or hepatocellular carcinoma (HCC) can be increased due to the long-term untreated autoimmune hepatitis, and the death rate of various complications such as intractable ascites, upper gastrointestinal hemorrhage and the like which are common in the decompensation period of cirrhosis is high, which seriously threatens the health of people in China, so that the rapid and timely diagnosis of the autoimmune hepatitis is very important. Although the diagnostic criteria for autoimmune hepatitis have been basically established and validated, a number of atypical autoimmune hepatitis have been difficult to achieve definitive diagnosis, including autoantibody negative AIH due to autoantibody variation, drug-induced AIH, Overlap Syndrome (OS), and post-transplant AIH. In addition, the gold standard for diagnosis of autoimmune hepatitis is liver tissue biopsy, and the invasive diagnosis method is not completely accepted by the patients and family members at present. Therefore, a noninvasive diagnosis model for distinguishing the autoimmune hepatitis from healthy people is established, the early and noninvasive diagnosis of the autoimmune hepatitis is realized, and the noninvasive diagnosis model has great significance for early discovery, early diagnosis and early treatment of the autoimmune hepatitis.
The human intestinal micro-ecosystem is closely related to health and disease. The implantation number in the human intestinal tract is huge (10)14) Complex (over 1000 bacteria) microbial community (about 1.5 kg). The total cell amount is almost 10 times of the number of human self-cells, and the total gene amount is 150 times of the number of human self-genes. In the process of symbiosis and co-evolution of the intestinal flora and the host, the intestinal flora plays an important role in the aspects of regulating digestion and absorption, immune reaction, metabolism and the like of the host. The intestinal microecological disorder can promote the occurrence and development of chronic diseases including liver cirrhosis and liver cirrhosisHepatocellular carcinoma, pancreatic carcinoma, colorectal carcinoma, and the like.
The key functional bacteria in the intestinal microorganisms can become a novel biomarker of human diseases. The characteristics of intestinal microbiology or a discriminative model established based on intestinal microorganisms are increasingly being widely reported and recognized as a tool for differentiating specific diseases or tumors. The intestinal type (Enterotypes) of the intestinal flora can reflect the susceptibility of people to diseases, and the intestinal flora is prompted to have potential early warning and diagnosis effects. Qin J et al first reported the correlation between intestinal microbiology and type 2 diabetes using metagenomics, indicating that 23 species may be intestinal microbial markers for distinguishing type 2 diabetes. Qin N, Li A and the like (Nature journal 2014) analyze the intestinal microecological structure of cirrhosis patients of Chinese population by a metagenomic sequencing technology, identify 15 markers of cirrhosis specificity on the microbial gene and functional level, and create a high-accuracy cirrhosis patient distinguishing index. Ren Z and the like indicate that the intestinal microecology is more sensitive to the acute rejection reaction of liver transplantation, and the change of the intestinal microecology can be used for predicting the early acute rejection reaction after the liver transplantation and can also be an auxiliary target for improving the acute rejection injury after the liver transplantation. Wang Z et al (Nature,2011) indicate that gut-microecological-dependent lecithin metabolism promotes the progression of cardiovascular disease, which provides a theoretical basis for novel diagnostic and therapeutic strategies for cardiovascular disease. Oh PL et al (AJT,2012) found that changes in intestinal microecology are closely related to small intestine transplant rejection and could be a potential diagnostic marker of transplant rejection. More importantly, in 2019, Ren Z and the like establish a non-invasive diagnosis model for primary liver cancer, which is composed of 30 microbial markers, for the first time, and cross-region verification is realized. Yu J et al disclose and validate microbial markers of colorectal cancer in different ethnic patients, indicating that microbial markers are a payable, non-invasive early diagnostic marker for colorectal cancer. Thus, gut microbiota may be a powerful tool for the diagnosis of different diseases. However, no model of gut microbiology for distinguishing autoimmune hepatitis from healthy people has been reported.
Disclosure of Invention
The invention provides an intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people, which consists of microorganisms shown by at least one gene in SEQ ID NO. 1-5, wherein the microbial gene is obviously enriched or reduced in human intestinal tracts.
>OTU442
CCTACGGGTGGCAGCAGTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCGACGCCGCGTGGAGGAAGAAGGTCTTCGGATTGTAAACTCCTGTTGTTGAGGAAGATAATGACGGTACTCAACAAGGAAGTGACGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTAGTAGTC
>OTU151
CCTACGGGTGGCTGCAGTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGAAGGATGATTGCCCTATGGGTTGTAAACTTCTTTTATATGGGAATAAAGTATTCCACGTGTGGGATTTTGTATGTACCATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTTTTAAGTCAGCGGTGAAAGTCTGTGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGATACCCTAGTAGTC
>OTU678
CCTACGGGTGGCTGCAGTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCGACGCCGCGTGGAGGAAGAAGGTCTTCGGATTGTAAACTCCTGTTGTTGGGGAAGATAATGACGGTACCCAACAAGGAAGTGACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGgggCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTACGGCAAGTCTGATGTGAAATCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGGACTAGAGTGTCGGAGGGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGA GGAACACCAGTGGCGAAGGCGGCTTACTGGACGATTACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTAGTAGTC
>OTU136
CCTACGGGTGGCTGCAGTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCGACGCCGCGTGAGCGATGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCACCGGCTAAATACGTGCCAGCAGCCGCGGTAATACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCCTGTAGTC
>OTU105
CCTACGGGTGGCTGCAGTGAGGAATATTGGTCAATGGGCGAGAGCCTGAACCAGCCAAGTAGCGTGAAGGATGACTGCCCTATGGGTTGTAAACTTCTTTTATATGGGAATAAAGTTTTCCACGTGTGGAATTTTGTATGTACCATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGATACCCTTGTAGTC
In addition, the invention also provides a detection reagent which comprises a primer for detecting 5 microbial genes shown in SEQ ID NO. 1-5.
Preferably, the primer sequence is SEQ ID NO 6-7, and the primer sequence is as follows:
primer Primers
Sequencing region V3+ V4: 338F-806R
An upstream primer: 338F ACTCCTACGGGAGGCAGCA
A downstream primer: 806R GGACTACHVGGGTWTCTAAT
The invention also provides application of the detection reagent in preparing a differential detection kit for autoimmune hepatitis and healthy people, and application of the detection reagent in establishing an intestinal microorganism model for differentiating autoimmune hepatitis and healthy people, wherein the detection reagent is suitable for detecting one or more of 5 microorganism genes shown in SEQ ID NO. 1-5.
The microorganism distinguishing model is suitable for distinguishing autoimmune hepatitis patients from healthy people.
The stool of said subject is examined to determine whether the sample contains said microbial genes and whether a microbial gene model can be established that distinguishes autoimmune hepatitis from healthy populations.
The total DNA of the microorganism is extracted by collecting the fecal sample of the group object, the 16S rDNAmseq sequencing of the microorganism DNA is completed, and whether 5 microorganism genes of SEQ ID NO:1-5 genes exist is detected.
Further, 16SrDNA Miseq sequencing of intestinal flora was performed by collecting fecal samples from the subjects in the group, extracting total DNA from the microorganisms. Establishing a microorganism distinguishing model of autoimmune hepatitis and healthy people based on high-throughput sequencing data, and establishing a probability of disease (POD) index of the autoimmune hepatitis; the POD index can be used for calculating the distinguishing capability of the POD index and carrying out verification.
The method specifically comprises the following steps:
(1) collecting fecal samples of group-entry objects (autoimmune hepatitis patients and healthy people), completing extraction of microbial total DNA in the fecal samples according to a DNA standard extraction method, and completing 16SrDNA high-throughput sequencing work of intestinal flora on an Illumina MiSeq platform;
(2) based on high throughput sequencing data, the best 5 microbial gene markers for a microbial differentiation model were identified in a cohort of models, between 37 autoimmune hepatitis and 78 healthy people, based on a random forest model, by a ten-fold cross-validation algorithm.
(3) Based on the 5 microbial gene markers, the prevalence of autoimmune hepatitis (POD) index was calculated by using the ratio of randomly generated decision trees.
(4) The microorganism distinguishing model has the distinguishing capability of 83.25% between 37 cases of autoimmune hepatitis and 78 cases of healthy people, the POD index is obviously increased in patients with autoimmune hepatitis, and the two groups have significant difference (p is less than 0.001).
Therefore, the microorganism gene discrimination model of the invention realizes good discrimination capability in autoimmune hepatitis and healthy people.
In addition, a kit for distinguishing an intestinal microorganism model of autoimmune hepatitis and a healthy population is also provided, comprising primers for detecting 5 microorganism genes represented by SEQ ID NOs 1 to 5 of claim 1.
Also provided is an intestinal microbial model for distinguishing autoimmune hepatitis from healthy people, the model being established in cohorts based on high-throughput sequencing data, the model being established with a probability of autoimmune hepatitis (POD) index; the POD index can be used for calculating the distinguishing capability of the POD index and carrying out verification.
The method comprises the following specific operation steps:
(1) the study design of the present invention is shown in FIG. 1, in accordance with the design principles of prospective clinical trials. The study protocol was approved by the ethical committee of the first subsidiary hospital of zhengzhou university. All enrolled patients signed study protocol informed consent and clinical specimen collection informed consent.
(2) Each of the patients with autoimmune hepatitis and healthy persons who were enrolled provided a fresh stool sample, which was divided into 10 portions by weight of 200mg each by the investigator and immediately frozen in a freezer at-80 ℃. The extraction method of total DNA of fecal bacteria was performed according to the kit instructions.
(3) Amplification and DNA library construction of the total DNA sample of the fecal bacteria are completed, and 16S rDNA sequencing is completed on an IlluminaMiseq sequencing platform. All output sequences complete basic pre-processing and basic bioinformatic analysis.
(4) Randomly selecting the same amount of sequence numbers from all samples, splicing into corresponding 16SrDNA gene sequence classification Units (OTUs) according to a UPARSE transfer path, and collecting and sorting the generated OTUs gene sequences of all samples. Based on microbial gene sequences, the RDP classifier version 2.6 was used for annotation.
(5) OTUs frequency files for microbial genetic markers were calculated based on representative sequences generated from high throughput sequencing data. These OTUs were used in a correlation study to identify OTUs abundance between autoimmune hepatitis patients and healthy populations. The microbial gene markers were statistically analyzed for differences between autoimmune hepatitis patients and healthy populations using the Wilcoxon test method.
(6) In the training set of the microorganism distinguishing model, 37 autoimmune hepatitis patients and 78 healthy people are used, screened OTUs abundance files are used, and a ten-fold cross validation algorithm (except that "import is TRUE", and software parameters are default) is adopted in a random forest model (R software 3.4.1 and random forest software package 4.6-12) to screen microorganism gene markers. With 10 trials of ten times cross validation, a cross validation error curve was obtained, where the smallest cross validation error point was used as the cut-off value. The minimum cross-validation error value plus the standard deviation of the corresponding value is the cut-off value. A set of 5 or less OTUs markers with an error rate less than the cut-off value was screened, the set of the smallest number of OTUs was selected as the optimal set of microbial genetic markers, and finally the optimal 5 microbial genetic markers for this model were identified (fig. 2). The gene sequences of 5 selected microbial OTUs markers are shown in SEQ ID NO 1-5.
(7) A Probability of disease (POD) index is calculated by using a ratio of randomly generated decision trees. The decision tree prediction sample is 'AIH', and the set parameter prediction is as follows: proximitity is T, norm. And (3) a random forest model constructed in the LOO mode is used for predicting the POD index of each sample in the queue, and finally, the average predicted POD index of each sample is calculated.
(8) Receiver Operating Curves (ROCs) were calculated using the pROC tool in the R3.3.0 package and used to evaluate the microorganism discrimination model, and the area under the curve (AUC) was used to assign the effect value of ROC.
(9) The differential ability of the microorganism differential model between 37 autoimmune hepatitis and 78 healthy people reaches 83.25% (figure 3), the POD index is obviously increased in liver cancer patients, and the two groups have significant difference (p is less than 0.001) (figure 4).
Therefore, the microorganism gene discrimination model of the invention realizes good discrimination capability in autoimmune hepatitis and healthy people.
Drawings
FIG. 1 is a study design and application of an intestinal microbial model for differentiating autoimmune hepatitis from healthy people.
FIG. 2 shows the optimal intestinal microbial gene markers identified by a ten-fold cross-validation method based on a random forest model.
FIG. 3 the differential potency achieved by the microbial gene differential model in a cohort of 37 autoimmune hepatitis and 78 healthy people;
FIG. 4. expression difference of the Prevalence (POD) index between the two groups in a cohort of 37 autoimmune hepatitis and 78 healthy people;
Detailed Description
The invention is further illustrated with reference to the following examples, to which, however, the invention is not restricted.
The methods used in the following examples are conventional methods unless otherwise specified. The materials or reagents required in the following examples are commercially available in the open, unless otherwise specified.
The invention extracts the total DNA of microorganisms by collecting the fecal samples of the grouped objects, and carries out 16SrDNA Miseq sequencing on intestinal flora. Establishing a microorganism distinguishing model of autoimmune hepatitis and healthy people in a training set based on high-throughput sequencing data, and establishing a probability of disease (POD) index of the autoimmune hepatitis; the POD index can be used for calculating the distinguishing capability of the POD index and carrying out verification.
The operation steps are as follows:
(1) the study design of the present invention is shown in FIG. 1, in accordance with the design principles of prospective clinical trials. The study protocol was approved by the ethical committee of the first subsidiary hospital of zhengzhou university. All enrolled patients signed study protocol informed consent and clinical specimen collection informed consent.
(2) Each of the patients with autoimmune hepatitis and healthy persons who were enrolled provided a fresh stool sample, which was divided into 10 portions by weight of 200mg each by the investigator and immediately frozen in a freezer at-80 ℃. The extraction method of total DNA of fecal bacteria was performed according to the kit instructions.
(3) Amplification and DNA library construction of the total DNA sample of the fecal bacteria are completed, and 16S rDNA sequencing is completed on an IlluminaMiseq sequencing platform. All output sequences complete basic pre-processing and basic bioinformatic analysis.
(4) Randomly selecting the same amount of sequence numbers from all samples, splicing into corresponding 16SrDNA gene sequence classification Units (OTUs) according to a UPARSE transfer path, and collecting and sorting the generated OTUs gene sequences of all samples. Based on microbial gene sequences, the RDP classifier version 2.6 was used for annotation.
(5) OTUs frequency files for microbial genetic markers were calculated based on representative sequences generated from high throughput sequencing data. And identifying the OTUs abundance of the autoimmune hepatitis patients and healthy people according to grouping conditions. The microbial gene markers were statistically analyzed for differences between autoimmune hepatitis patients and healthy populations using the Wilcoxon test method.
(6) In the cohort of microorganism differentiation models, 37 autoimmune hepatitis patients and 78 healthy people were screened, and screening of microorganism gene markers was performed in a random forest model (R software 3.4.1 and random forest software package 4.6-12) using tenfold cross-validation algorithm (except that "import" TRUE "was set, and software parameters were defaulted) using screened OTUs abundance files. With 10 trials of ten times cross validation, a cross validation error curve was obtained, where the smallest cross validation error point was used as the cut-off value. The minimum cross-validation error value plus the standard deviation of the corresponding value is the cut-off value. A set of 5 or less OTUs markers with an error rate less than the cut-off value was screened, the set of the smallest number of OTUs was selected as the optimal set of microbial genetic markers, and finally the optimal 5 microbial genetic markers for this model were identified (fig. 2). The gene sequences of 5 selected microbial OTUs markers are shown in SEQ ID NO 1-5.
(7) A Probability of disease (POD) index is calculated by using a ratio of randomly generated decision trees. The decision tree prediction sample is 'AIH', and the set parameter prediction is as follows: proximitity is T, norm. A random forest model constructed in the LOO mode is used to predict the POD index of each sample, and finally an average predicted POD index of each sample is calculated.
(8) Receiver Operating Curves (ROCs) were calculated using the pROC tool in the R3.3.0 package and used to evaluate the microorganism discrimination model, and the area under the curve (AUC) was used to assign the effect value of ROC.
(9) The differential ability of the microorganism differential model between 37 cases of autoimmune hepatitis and 78 cases of healthy people reaches 83.25% (figure 3), the POD index is obviously increased in patients with autoimmune hepatitis, and the two groups have significant difference (p is less than 0.001) (figure 4).
Therefore, the microorganism gene discrimination model of the invention realizes good discrimination capability in autoimmune hepatitis and healthy people.
Sequence listing
<110> first subsidiary Hospital of Zhengzhou university
<120> intestinal microbial marker for distinguishing autoimmune hepatitis from healthy people and application thereof
<130>20-212069-00015465
<160>7
<170>SIPOSequenceListing 1.0
<210>1
<211>440
<212>DNA
<213> enteric Microorganism (Microorganism)
<400>1
cctacgggtg gcagcagtgg ggaatattgc acaatggggg aaaccctgat gcagcgacgc 60
cgcgtggagg aagaaggtct tcggattgta aactcctgtt gttgaggaag ataatgacgg 120
tactcaacaa ggaagtgacg gctaactacg tgccagcagc cgcggtaaaa cgtaggtcac 180
aagcgttgtc cggaattact gggtgtaaag ggagcgcagg cggtgcggca agtctgatgt 240
gaaagcccgg ggctcaaccc cggtactgca ttggaaactg tcgtactaga gtgtcggagg 300
ggtaagcgga attcctagtg tagcggtgaa atgcgtagat attaggagga acaccagtgg 360
cgaaggcggc ttactggacg ataactgacg ctgaggctcg aaagcgtggg gagcaaacag 420
gattagatac cctagtagtc 440
<210>2
<211>460
<212>DNA
<213> enteric Microorganism (Microorganism)
<400>2
cctacgggtg gctgcagtga ggaatattgg tcaatggacg agagtctgaa ccagccaagt 60
agcgtgaagg atgattgccc tatgggttgt aaacttcttt tatatgggaa taaagtattc 120
cacgtgtggg attttgtatg taccatatga ataaggatcg gctaactccg tgccagcagc 180
cgcggtaata cggaggatcc gagcgttatc cggatttatt gggtttaaag ggtgcgtagg 240
cggcctttta agtcagcggt gaaagtctgt ggctcaaccg taaaattgca gttgaaactg 300
gcagtcttga gtacagtaga ggtgggcgga attcgtggtg tagcggtgaa atgcttagat 360
atcacgaaga actccgattg cgaaggcagc tcactagact gttactgaca ctgatgctcg 420
aaagtgtggg tatcaaacag gattagatac cctagtagtc 460
<210>3
<211>440
<212>DNA
<213> enteric Microorganism (Microorganism)
<400>3
cctacgggtg gctgcagtgg ggaatattgc acaatggggg aaaccctgat gcagcgacgc 60
cgcgtggagg aagaaggtct tcggattgta aactcctgtt gttggggaag ataatgacgg 120
tacccaacaa ggaagtgacg gctaactacg tgccagcagc cgcggtaata cgtagggggc 180
aagcgttatc cggatttact gggtgtaaag ggagcgcagg cggtacggca agtctgatgt 240
gaaatcccgg ggctcaaccc cggtactgca ttggaaactg tcggactaga gtgtcggagg 300
ggtaagtgga attcctagtg tagcggtgaa atgcgtagat attaggagga acaccagtgg360
cgaaggcggc ttactggacg attactgacg ctgaggctcg aaagcgtggg gagcaaacag 420
gattagatac cctagtagtc 440
<210>4
<211>440
<212>DNA
<213> enteric Microorganism (Microorganism)
<400>4
cctacgggtg gctgcagtgg ggaatattgc acaatggggg aaaccctgat gcagcgacgc 60
cgcgtgagcg atgaagtatt tcggtatgta aagctctatc agcagggaag aaaatgacgg 120
tacctgacta agaagcaccg gctaaatacg tgccagcagc cgcggtaata cgtatggtgc 180
aagcgttatc cggatttact gggtgtaaag ggagcgtaga cggtgtggca agtctgatgt 240
gaaaggcatg ggctcaacct gtggactgca ttggaaactg tcatacttga gtgccggagg 300
ggtaagcgga attcctagtg tagcggtgaa atgcgtagat attaggagga acaccagtgg 360
cgaaggcggc ttactggacg gtaactgacg ttgaggctcg aaagcgtggg gagcaaacag 420
gattagatac ccctgtagtc 440
<210>5
<211>460
<212>DNA
<213> enteric Microorganism (Microorganism)
<400>5
cctacgggtg gctgcagtga ggaatattgg tcaatgggcg agagcctgaa ccagccaagt 60
agcgtgaagg atgactgccc tatgggttgt aaacttcttt tatatgggaa taaagttttc 120
cacgtgtgga attttgtatg taccatatga ataaggatcg gctaactccg tgccagcagc 180
cgcggtaata cggaggatcc gagcgttatc cggatttatt gggtttaaag ggagcgtaga 240
tggatgttta agtcagttgt gaaagtttgc ggctcaaccg taaaattgca gttgatactg 300
gatatcttga gtgcagttga ggcaggcgga attcgtggtg tagcggtgaa atgcttagat 360
atcacgaaga actccgattg cgaaggcagc tcactggact gcaactgaca ctgatgctcg 420
aaagtgtggg tatcaaacag gattagatac ccttgtagtc 460
<210>6
<211>19
<212>DNA
<213> Artificial Sequence (Artificial Sequence)
<400>6
actcctacgg gaggcagca 19
<210>7
<211>20
<212>DNA
<213> Artificial Sequence (Artificial Sequence)
<400>7
Claims (10)
1. An intestinal microbiogenic marker for distinguishing autoimmune hepatitis from healthy people, characterized by: comprises a microorganism shown by at least one gene in SEQ ID NO. 1-5, and the microorganism is obviously enriched or reduced in intestinal tracts.
2. A detection reagent for detecting the intestinal microbial gene marker of claim 1, which comprises a primer for detecting at least one gene of SEQ ID NO. 1-5 of claim 1.
3. The detection reagent according to claim 2, wherein: the primer sequence is SEQ ID NO 6-7.
4. Use of the detection reagent of claim 2 for preparing a differential detection kit for autoimmune hepatitis and healthy people, said detection reagent being suitable for detecting the gene of the intestinal microorganism of claim 1.
5. Use of the detection reagent of claim 2 for establishing an intestinal microorganism model for distinguishing autoimmune hepatitis from healthy people, said detection reagent being suitable for detecting the gene of the intestinal microorganism of claim 1.
6. Use according to claim 4 or 5, characterized in that: the stool of said subject is examined to determine whether the sample contains said microbial genes and whether a microbial gene model can be established that distinguishes autoimmune hepatitis from healthy populations.
7. Use according to claim 6, characterized in that: extracting total DNA of microorganisms by collecting fecal samples of the grouped objects, and sequencing the 16S rDNA Miseq of the intestinal flora; establishing a microorganism distinguishing model of autoimmune hepatitis and healthy population patients in a queue based on high-throughput sequencing data, and establishing an autoimmune hepatitis Prevalence (POD) index; the POD index can be used for calculating the distinguishing capability of the POD index and carrying out verification.
8. The application according to claim 4 or 5, comprising in particular:
(1) collecting fecal samples of group-entry objects, wherein the group-entry objects comprise 37 autoimmune hepatitis patients and 78 healthy people, extracting total DNA of microorganisms in the fecal samples according to a standard DNA extraction method, and completing 16S rDNA high-throughput sequencing work of intestinal flora on an Illumina MiSeq platform;
(2) based on high-throughput sequencing data, in a cohort of microorganism differentiation models, between 37 cases of autoimmune hepatitis and 78 cases of healthy people, based on a random forest model, through a ten-fold cross validation algorithm, the optimal 5 microorganism gene markers for the model were identified;
(3) calculating an autoimmune hepatitis Prevalence (POD) index by using a ratio of randomly generated decision trees based on 5 microbial gene markers;
(4) the microorganism distinguishing model has the distinguishing capability of 83.25% between 37 cases of autoimmune hepatitis and 78 cases of healthy people, the POD index is obviously increased in patients with autoimmune hepatitis, and the two groups have significant difference (p is less than 0.001).
9. A kit for distinguishing autoimmune hepatitis from healthy people, comprising primers for detecting 5 microbial genes represented by SEQ ID NOs: 1-5 according to claim 1.
10. An intestinal microbial model for distinguishing autoimmune hepatitis from a healthy population, the model being established in cohorts based on high throughput sequencing data, the model being established with a probability of developing autoimmune hepatitis (POD) index; the POD index can be used for calculating the distinguishing capability of the POD index and carrying out verification.
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