CN112029880A - Microorganism for detecting myasthenia gravis and application thereof - Google Patents

Abstract

The invention discloses a microorganism for detecting myasthenia gravis and application thereof, wherein the microorganism is a microorganism which shows significant difference between a patient with myasthenia gravis and a healthy person, and is specifically selected from Bacteroides _ massilisensis, Providencia _ alcalifactions, Pararevolutella _ subclassified, Ralstonia _ subclassified, Vibrio _ alginolyticus, Prevotella _ bivia or Megammons _ rupelens. The diagnosis of myasthenia gravis using Bacteroides _ massilisensis, Providencia _ alcalifaciens, Pararevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ algyrinthulius, Prevotella _ bivia or Megammas _ rucllensis as the detection variable has a high efficacy.

Description

Microorganism for detecting myasthenia gravis and application thereof

Technical Field

The invention belongs to the technical field of biology, and relates to a microorganism for detecting myasthenia gravis and application thereof.

Background

Myasthenia Gravis (MG) is a chronic autoimmune disease with dysfunction in nerve-muscle junction (synapse) transmission, mainly mediated by Acetylcholine Receptor (AchR) antibodies, cellular immunity and complement involvement leading to disruption of the normal structure of the postsynaptic membrane. The clinical manifestations are weakness of the affected muscle group, easy fatigue, and alleviation and relief after rest and application of cholinesterase inhibitor. Myasthenia gravis can arise at any age. The incidence of MG symptoms in Childhood is 1-5/10 ten thousand people (Lai C, Tseng H. national position-Based Epidemiological Study of Myastemia Gravis in Taiwan [ J ]. Neuroepidemiology,2010,35(1):66-71.), and The relative peak age of onset is less than 10 years old (Mulleney P, Smith R. The national History and Ophthalic invasion in Childhond Myastemia Gravis at The host for ceramic [ J ]. Ophthalimunology, 2000,107(3): 504-510.). Myasthenia gravis is classified into five types according to the improved Osserman type. Type I (oculomoid) it is manifested as ophthalmoplegia, which is the most common type, and some children may be accompanied by restricted eye movement, strabismus or diplopia. Type IIa (mild general type) develops slowly, the extraocular muscles are involved, meanwhile, the muscles of throat can be involved, the reaction to cholinesterase inhibitor is good, and the fatality rate is low. Type IIb (moderate systemic type) extends from the involvement of the muscles of the eye and throat to the muscles of the whole body, the respiratory muscles are generally not involved and are often insensitive to cholinesterase inhibitors. Type III (acute rapidly progressive type) frequently occurs suddenly, progresses rapidly within weeks to months, and has respiratory muscle involvement in early stage, severe limb muscle and trunk muscle involvement, poor cholinesterase inhibitor response, frequently combined thymoma and high mortality rate. Type IV (chronic severe): the disease is I type or IIa type at the beginning, the disease condition is suddenly worsened after 2 years or longer, the reaction to cholinesterase inhibitor is not obvious, the thymoma is often combined, and the prognosis is poor. Type V (muscular atrophy type) the skeletal muscle atrophy and weakness appear in half a year after onset of disease. Ocular muscle type MG is the most common type, with about 37.5% of patients progressing to Generalized Myasthenia Gravis (GMG) during the course of the disease.

To clarify the diagnosis of MG, detailed medical history, careful physical examination and necessary auxiliary examination are required. Diagnosis of MG in children remains a challenge, as non-specific symptoms may be present, or the test results are mildly abnormal.

The human distal intestine colonizes the most dense and diversified microflora in the human body, namely the intestinal flora, and the genetic load of the genome of the intestinal flora accounts for about 99 percent of the total gene amount of the human body, so that the human distal intestine is called as the second genome of the human (Qin J, Li R, Raes J, et al. A hmnan gum microbial gene mapping [ J ] Nayure,2010,464(7285):59-65), and the part of genes have extremely important influence on the health of people. The imbalance in the composition and function of these intestinal microorganisms is closely related to human health. With the rapid development of scientific technology, 16S rDNA gene sequencing and whole genome shotgun sequencing become the main methods for studying the relative abundance and evolutionary relationship of intestinal flora. The technologies greatly promote the research of intestinal microbiology, and help the scholars to better explore the structure and the function of intestinal flora and signal paths related to the occurrence and the development of diseases. The research on the microorganisms related to the diseases also provides a new means for the diagnosis, prevention and treatment of the diseases.

Disclosure of Invention

The invention aims to find a microorganism marker related to myasthenia gravis so as to realize diagnosis, prevention and treatment of myasthenia gravis.

In order to achieve the purpose, the invention adopts the following technical scheme:

in a first aspect of the invention there is provided a detection reagent which detects the abundance of a microbial marker selected from one or more of bacteriodes _ massilisensis, Providencia _ alcalifactens, paramrevolutella _ subclassified, Ralstonia _ subclassified, Vibrio _ algyrinthulius, Prevotella _ bivia or Megamonas _ rubellensis.

Further, the microorganism marker is selected from any two or more of Providencia alcalifactens, parapyrevolella unclassified, Ralstonia unclassified, Vibrio _ algingomyticus, Prevotella bivia, or Megamonas rupellensis.

Further, the reagent is a reagent for detecting the abundance of the microbial marker by using 16S rRNA sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray and PCR-ELISA methods.

Further, the agent is a primer, probe, antisense oligonucleotide, aptamer, or antibody specific for the microbial marker.

A second aspect of the invention provides a product comprising an agent as hereinbefore described.

Further, the product also includes reagents for extracting nucleic acid molecules from the sample.

A third aspect of the present invention provides a system for predicting myasthenia gravis, the system determining whether a subject has myasthenia gravis or determining a risk of the subject having myasthenia gravis by comparing an abundance of a biomarker with a diagnostic threshold value, the abundance being analyzed as an input variable; the microbial markers include Bacteroides _ massilisensis, Providencia _ alcalifactens, paramrevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ Alignosytics, Prevotella _ bivia, or Megammas _ rupelensis.

In a fourth aspect of the invention, there is provided a composition comprising a substance which reduces the abundance of bacteria _ massilisensis, Providencia _ alicifaciens, paramrevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ algyrinthulius, Prevotella _ bivia or Megamonas _ rupellensis.

A fifth aspect of the invention provides a use as claimed in any one of:

1) the use of a reagent according to the first aspect of the invention for the manufacture of a product for the diagnosis of myasthenia gravis;

2) use of a product according to the second aspect of the invention in the manufacture of a means for diagnosing myasthenia gravis;

3) the composition of the fourth aspect of the invention is used for preparing a medicament for treating myasthenia gravis;

4) use of the microbial markers Bacteroides _ massilisensis, Providencia _ alcalifactions, paramrevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ alginolyticus, Prevotella _ bivia and/or Megamonas _ rupelensis for constructing a computational model for predicting myasthenia gravis.

Further, the myasthenia gravis is childhood myasthenia gravis. The invention has the advantages and beneficial effects that:

the invention discovers for the first time that Bacteroides _ maliense, Providencia _ aliciformes, Paraprevotella _ unclassified, Ralstonia _ unclassified, Vibrio _ Alignosticus, Prevotella _ bivia or Megamonas _ rupelensis are related to myasthenia gravis, the abundance of which shows significant difference between patients with myasthenia gravis and healthy people, and ROC curve analysis has higher specificity and sensitivity as a detection variable, so that Bacteroides _ maliense, Providencia _ aliciformes, Paravotella _ unclassified, Ralstonia _ unclassified, Vibrio _ Alignostimulus, Prevotella _ viva or Megalonas _ billius can be applied to diagnosis of myasthenia gravis. The detection markers are Bacteroides _ massilisensis, Providencia _ alcalifactions, Pararevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ algyrinthulius, Prevotella _ bivia and/or Megamonas _ rubellensis, and the detection markers are completely noninvasive, high in accuracy, specificity and sensitivity.

Drawings

Fig. 1 is a violin diagram of the alpha and beta diversity distribution; wherein A-C is a distribution plot of alpha diversity at the phylum (A), genus (B) and species (C) levels based on the shannon index; D-F is a distribution plot of beta diversity at the phylum (D), genus (E) and species (F) levels based on the Bray-Curtis distance.

Figure 2 is PcoA of the relative abundance of all participants at different categorical levels, where panels a-F are PcoA of the relative abundance at phylum, class, order, family, genus, categorical levels, respectively, and the red and blue triangles represent MG and HC, respectively.

Detailed Description

In order to evaluate whether the composition of the intestinal symbiotic flora can be used as a prediction factor of the myasthenia gravis, the invention discovers the intestinal flora related to diseases by collecting samples of patients with the myasthenia gravis and healthy people, performing whole genome sequencing and counting sequencing data by using bioinformatics, integrates the intestinal flora with the disease information, and predicts the patients with the myasthenia gravis to the maximum extent. The invention discovers for the first time that Bacteroides _ massilisensis, Providencia _ aliclifaciens, Paraprevotella _ unclassified, Ralstonia _ unclassified, Vibrio _ Alignosticus, Prevotella _ bivia or Megamonas _ rupelensis have significant difference in abundance in patients with myasthenia gravis and healthy persons through whole genome sequencing, and explains that Bacteroides _ massilisensis, Providencia _ aliclifaciens, Paraprevotella _ unclassified, Raptonia _ unclassified, Vibrio _ alginidacus, Prevotella _ bivia or Megamonas _ rupelensis can be used as a predictor of myasthenia gravis.

The term "abundance difference" refers to a higher or lower level of microorganisms in a patient with myasthenia gravis as compared to the in vivo level of a normal or control target. For the purposes of the present invention, an "abundance difference" is considered to be a phenomenon that occurs when the level of microorganisms taken from a normal or disease-suffering subject, or from each stage of a disease-suffering subject, differs by 1.5-fold or more, about 4-fold or more, about 6-fold or more, about 10-fold or more.

In the present invention, any method known in the art can be used to detect a microbial marker or to determine the level of a microbial marker. These methods include, but are not limited to, a method of sequence amplification using primers, and an immunological method using an antigen-antibody reaction. Among them, the method of sequence amplification using the primer may be, for example, Polymerase Chain Reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hot start PCR, nested PCR, PCR amplification, real-time PCR, differential PCR, rapid amplification of cDNA ends, reverse polymerase chain reaction, vector-mediated PCR, thermal asymmetric cross PCR, ligase chain reaction, repair chain reaction, transcription-mediated amplification, autonomous sequence replication, selective amplification reaction of a target base sequence. The immunological method using the antigen-antibody reaction may be, for example, western blotting, enzyme-linked immunosorbent assay, radioimmunoassay, radioimmunodiffusion, euclidean immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter, protein chip, etc., but the scope of the present invention is not limited thereto.

According to embodiments of the present invention, stool samples from healthy people and myasthenia gravis patients can be analyzed in bulk using high throughput sequencing. Based on high-throughput sequencing data, healthy people are compared with myasthenia gravis patients, and specific nucleic acid sequences related to the myasthenia gravis patients are determined.

The invention provides a calculation model for predicting myasthenia gravis. As the skilled artisan will appreciate, there are a variety of ways to use the measurement of two or more markers to improve the diagnostic problem.

Biomarkers can be determined individually, or in one embodiment of the invention, they can be determined simultaneously, for example using a chip or bead-based array technology. The concentration of the biomarkers is then interpreted independently, for example using individual retention of each marker, or a combination thereof.

As the skilled artisan will appreciate, the step of associating a marker level with a certain likelihood or risk may be implemented and realized in different ways. Preferably, the measured concentrations of the protein and one or more other markers are mathematically combined and the combined value is correlated with the underlying diagnostic problem. The determination of marker values may be combined by any suitable prior art mathematical method.

Preferably, the mathematical algorithm applied in the marker combination is a logarithmic function. Preferably, the result of applying such a mathematical algorithm or such a logarithmic function is a single value. Such values can easily be associated, depending on the underlying diagnostic question, for example with the individual's risk for myasthenia gravis or with other intentional diagnostic uses that help to assess patients with myasthenia gravis. In a preferred manner, such a logarithmic function is obtained as follows: a) classifying individuals into groups, e.g., normal persons, individuals at risk of myasthenia gravis, patients with myasthenia gravis, etc., b) identifying markers that differ significantly between these groups by univariate analysis, c) logarithmic regression analysis to assess independent difference values of the markers that can be used to assess these different groups, and d) constructing a logarithmic function to combine the independent difference values. In this type of analysis, the markers are no longer independent, but represent a combination of markers.

The logarithmic function used to correlate marker combinations with disease preferably employs algorithms developed and obtained by applying statistical methods. For example, suitable statistical methods are Discriminant Analysis (DA) (i.e., linear, quadratic, regular DA), Kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (i.e., logistic regression), principal component-based methods (i.e., SIMCA), generalized additive models, fuzzy logic-based methods, neural network-and genetic algorithm-based methods. The skilled person will not have problems in selecting a suitable statistical method to evaluate the marker combinations of the invention and thereby obtain a suitable mathematical algorithm. In one embodiment, the statistical method used to obtain the mathematical algorithm used in assessing myasthenia gravis is selected from DA (i.e., linear, quadratic, regular discriminant analysis), Kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting methods), or generalized linear models (i.e., logarithmic regression).

The area under the receiver operating curve (AUC) is an indicator of the performance or accuracy of the diagnostic procedure. The accuracy of a diagnostic method is best described by its Receiver Operating Characteristics (ROC). ROC plots are line graphs of all sensitivity/specificity pairs derived from continuously varying decision thresholds across the entire data range observed.

The clinical performance of a laboratory test depends on its diagnostic accuracy, or the ability to correctly classify a subject into a clinically relevant subgroup. Diagnostic accuracy measures the ability to correctly discriminate between two different conditions of the subject under investigation. Such conditions are, for example, health and disease or disease progression versus no disease progression.

In each case, the ROC line graph depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity for the entire range of decision thresholds. On the y-axis is the sensitivity, or true positive score [ defined as (number of true positive test results)/(number of true positives + number of false negative test results) ]. This is also referred to as a positive for the presence of a disease or condition. It is calculated from the affected subgroups only. On the x-axis is the false positive score, or 1-specificity [ defined as (number of false positive results)/(number of true negatives + number of false positive results) ]. It is an indicator of specificity and is calculated entirely from unaffected subgroups. Because the true and false positive scores are calculated completely separately using test results from two different subgroups, the ROC line graph is independent of the prevalence of disease in the sample. Each point on the ROC line graph represents a sensitivity/1-specificity pair corresponding to a particular decision threshold. One test with perfect discrimination (no overlap of the two result distributions) has a ROC line graph that passes through the upper left corner where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). A theoretical line graph for an undifferentiated test (the results of the two groups are equally distributed) is a 45 ° diagonal from the lower left to the upper right. Most line graphs fall between these two extremes. (if the ROC line graph falls well below the 45 ° diagonal, this is easily corrected by reversing the criteria for "positive" from "greater to" less than "or vice versa.) qualitatively, the closer the line graph is to the upper left corner, the higher the overall accuracy of the test.

One convenient goal to quantify the diagnostic accuracy of a laboratory test is to express its performance by a single numerical value. The most common global metric is the area under the ROC curve (AUC). Conventionally, this area is always ≧ 0.5 (if not, the decision rule can be reversed to do so). The range of values was between 1.0 (test values that perfectly separated the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on a particular part of the line graph, such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how the ROC plot is close to perfect (area 1.0).

Overall assay sensitivity will depend on the specificity required to carry out the methods disclosed herein. In certain preferred settings, a specificity of 70% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk of myasthenia gravis is based on specificity of 75%, 80%, 85%, or preferably also 90% or 95%.

The term "patient sample" or "biological sample" as used herein refers to a fluid sample, a cell sample, a tissue sample, or an organ sample obtained from a patient. In some embodiments, a cell or population of cells, or an amount of tissue or body fluid, is obtained from a subject. Often a "patient sample" may include cells from an animal, but the term may also refer to acellular biological material, such as the acellular portion of blood, saliva, or urine, which may be used to detect the presence or class of microorganisms. Biological samples include, but are not limited to: biopsy, scrape (e.g., oral scrape), whole blood, plasma, serum, urine, saliva, cell culture, biopsy, mucosal sample, stool, intestinal lavage, joint fluid, cerebrospinal fluid, bile sample, respiratory secretions (e.g., sputum), bronchoalveolar lavage, and the like. A biological sample or tissue sample may refer to a tissue or fluid isolated from an individual, including, but not limited to, for example, blood, plasma, serum, urine, stool, sputum, spinal fluid, pleural fluid, lymph fluid; the outer layers of the skin, respiratory, intestinal and genitourinary tracts; tears, saliva; and organs. The sample may comprise frozen tissue. The term "sample" also encompasses any material derived from further processing such samples. Derivative samples may include, for example, nucleic acids or proteins extracted from the sample; or nucleic acids or proteins obtained by subjecting the sample to techniques such as nucleic acid amplification or reverse transcription of mRNA, or separation and/or purification of specific nucleic acids, proteins, other cytoplasmic or nuclear components. As a preferred embodiment, the sample is a stool sample.

The terms "patient," "subject," and "individual" are used interchangeably herein and refer to an animal, particularly a human, for which it is desirable to analyze a biological sample obtained therefrom for the presence or amount of a microbial marker. In some embodiments, the subject is in need of diagnosis of a disease or disorder, such as myasthenia gravis, wherein a biological sample is analyzed using assays routine in the art. The term "subject" or "patient" as used herein also refers to both human and non-human animals. The term "non-human animal" includes all vertebrates, e.g., mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any domestic or pet animal; and non-mammals, such as chickens, amphibians, reptiles, and the like. In one embodiment, the subject is a human.

The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental methods in the examples, in which specific conditions are not specified, are generally carried out under conventional conditions.

Example 1 screening of intestinal flora associated with myasthenia gravis

1. Study subject and sample Collection

55 patients with myasthenia gravis of children and 36 Healthy Controls (HC) of the corresponding age and sex were collected at the myasthenia gravis treatment center of the first hospital, Shijiazhuan, Hebei province. The sample information is shown in table 1.

Diagnostic criteria: (1) clinical manifestations of eyelid ptosis, diplopia and strabismus; (2) positive neostigmine test: (3) acetylcholine receptor antibody positive: (4) electromyogram: the facial nerve attenuates low frequencies with no increase in high frequencies. The compound (1) + (2) or (3) or (4) can be clearly diagnosed.

Typing: reference was made to the american association for Myasthenia Gravis (MGFA) in 2000 to propose a new standard form for clinical typing and quantitative myasthenia gravis score (QMG).

Inclusion criteria were: the patient is definitely diagnosed as eye muscle type myasthenia gravis and accords with the diagnosis standard.

Exclusion criteria: (1) age <2 years old 10 months or no age information; (2) antibiotics except beta-lactams are used within 3 months; (3) taking other drugs/hormones to treat the disease; (4) anti-inflammatory drugs or unknown herbal medicines are used.

TABLE 1 clinical characteristics of samples

2. DNA extraction and sequencing

DNA was extracted from the sample using a DNA extraction kit and the procedure was as described in the instructions. The concentration of DNA is checked using a Fluorometer or a microplate reader (e.g., Qubit Fluorometer, Invitrogen), and the integrity and purity of the sample is checked using agarose gel electrophoresis (agarose gel concentration: 1% V, voltage: 150V, electrophoresis time: 40 min). Covaris was used to randomly break the genomic DNA and magnetic beads were used to select fragmented genomic DNA of average size 200-400 bp. The resulting DNA fragment was subjected to end repair, the 3 'end was adenylated, and a linker was ligated to the end of the 3' end adenylated fragment, followed by PCR amplification. The PCR product was purified using magnetic beads. Performing thermal deformation on the double-stranded PCR product, performing cyclization by using a splint oligonucleotide sequence, formatting single-stranded circular DNA (SsCir DNA) to construct a final library, and performing quality control on the library. The library was amplified with phi29 to yield DNA Nanospheres (DNBs) with a molecular copy number of over 300. The obtained DNBs are added into mesh pores on a chip (fixed on an arrayed silicon chip), and a double-end sequence with the read length of 100bp/150bp is obtained by combining a probe anchoring polymerization technology (cPAS) and a double-end sequencing method (MDA-PE) of multiple displacement amplification.

3. Quality control

And performing quality control processing on the measured data to finally obtain high-quality data for subsequent analysis, wherein the quality control steps are as follows: 1) filtering low-quality reads; 2) decontaminate human genome sequences, screen low quality reads and sequence adapters using FastP (REF21) and its default parameters, align reads to the human genome (Hg38) using Bowtie2(REF22), and screen pairs that cannot align to the human genome using Samtools as clean reads for use in subsequent analyses.

4. Classification and functional Annotation

High quality reads were mapped to the mpa _ v20 marker gene database using metalan 2, resulting in a class abundance map for different class levels for each sample. Py, combine the results of all samples using merge _ melan _ tables and obtain combined abundance spectra at different species levels using an internal script. On the other hand, high quality reads were mapped to uniref90 and chocophlan using humann2 to obtain gene abundance and pathway abundance maps. The abundances of all samples were then combined using human 2_ Join _ Tables, human 2_ renorm _ table, and human 2_ Split _ clustered _ table, respectively, and the abundances were normalized and hierarchically classified for annotation. In addition, KEGG and GO enrichment analyses were performed using humann2_ regroup _ table and humann 2.

5. Statistical analysis

All abundance results were analyzed for differences using wilcox. test two. side function in R, depending on the grouping of samples. The P value in each result will be corrected according to the BH method to obtain q values (FDR) for screening of species and pathways that exhibit significant differences. The α diversity of each sample was calculated using the Shannon index. With the same input, the Vegan packet in R with the parameter 'method _ dist _ method' was used to calculate β diversity. ROC curves were also plotted using the pROC analysis of R and AUC areas were calculated.

And (3) carrying out Principal Component Analysis (PCA) on the classification map, calculating an eig result of the PCA by using an Ade4 software package of R, obtaining feature vectors of different PCs by using a dudi.

To correlate differential species with the clinical phenotype of the sample, Spearman correlation between features and clinical phenotype was calculated using the corr. tes method in the R package, according to the parameters 'method ═ Spearman, use ═ pairwise, adjust ═ BH'.

6. Results

The different categorical levels of alpha and beta diversity based on Shannon index did not differ significantly between patients and healthy populations (figure 1).

The PCA and PcoA results show no significant aggregation profile in patients and healthy persons (fig. 2).

Species differential results analysis showed that there were 20 species exhibiting significant differences, of which 11 were ROC AUC values >0.7, as shown in table 3. The results of the combined diagnostic analysis of the 20 different bacterial populations are shown in table 4. Wherein, the contents and the diagnostic efficacy of Bacteroides _ massilisensis, Providencia _ alcalifactions, Pararevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ algyrinthulius, Prevotella _ bivia or Megammas _ rubellensis are shown in Table 2, which indicates that the diagnosis of myasthenia gravis using the above bacteria has higher accuracy and specificity. The combined diagnostic efficacy of the flora is analyzed, and the combination of any two of Providencia alcalifactions, Paraprevotella subclassified, Ralstonia subclassified, Vibrio _ algingomyticus, Prevotella bivia or Megamnas _ rupellensis is found to have higher diagnostic efficacy, so that the flora alone or in combination as a detection index can effectively distinguish myasthenia gravis patients from healthy people.

TABLE 2 content of intestinal flora and diagnostic efficacy

TABLE 3 differential flora and AUC values

species	AUC value
		Bacteroides_massiliensis	0.671
Paraprevotella_unclassified	0.691
		Prevotella_bivia	0.697
Prevotella_copri	0.805
		Prevotella_stercorea	0.816
Lachnospiraceae_bacterium_2_1_46FAA	0.728
		Clostridium_bartlettii	0.741
Dialister_succinatiphilus	0.710
		Megamonas_funiformis	0.731
Megamonas_hypermegale	0.731
		Megamonas_rupellensis	0.699
Megamonas_unclassified	0.704
		Fusobacterium_mortiferum	0.715
Ralstonia_unclassified	0.6
		Sutterella_parvirubra	0.681
Sutterella_wadsworthensis	0.727
		Helicobacter_cinaedi	0.639
Providencia_alcalifaciens	0.609
		Vibrio_alginolyticus	0.609
Pyramidobacter_piscolens	0.732

TABLE 4 Combined diagnostic AUC values

Example 2 validation of genome sequencing accuracy

19 samples of myasthenia gravis and 13 samples of healthy persons were collected in the same manner as in example 1, and the patient information is shown in Table 5.

TABLE 5 sample clinical characteristics

The differential bacteria Prevotella _ copri, Clostridium _ bartlettii, Fusobacterium _ mortierum and Helicobacter _ cinaedi were randomly selected for sequencing verification, and the diagnostic efficacy of the differential bacteria Prevotella _ copri, Clostridium _ bartlettii and Helicobacter _ cinaedi in the application to myasthenia gravis was calculated.

The results show that the AUC values of Prevotella _ copri, Clostridium _ bartlettii, Fusobaterium _ mortierum and Helicobacter _ cinaedi are 0.736842105, 0.672064777, 0.821862348 and 0.615384615 respectively, which are equivalent to the results of the above detection, and indicate that the sequencing data of the metagenome is accurate.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims (10)

1. A detection reagent that detects the abundance of a biomarker selected from one or more of Bacteroides _ massilisensis, Providencia _ alcalifactions, paramrevolutella _ unclassified, Vibrio _ algorityticus, Prevotella _ bivia, or Megamonas _ rubensis.

2. An agent according to claim 1, wherein the microbial marker is selected from any two or more of Providencia alcalifasciens, parapyrevolella unclassified, Ralstonia unclassified, Vibrio _ algyrinyticus, Prevotella bivia or Megamonas rupellensis.

3. The reagent according to claim 1 or 2, wherein the reagent is a reagent for detecting the abundance of a microbial marker by using 16S rRNA sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray and PCR-ELISA methods.

4. An agent according to claim 1 or 2, wherein the agent is a primer, probe, antisense oligonucleotide, aptamer or antibody specific for the microbial marker.

5. A product comprising the agent of any one of claims 1 to 3.

6. The product of claim 5, further comprising a reagent for extracting nucleic acid molecules from the sample.

7. A system for predicting myasthenia gravis, wherein the system determines whether a subject has myasthenia gravis or determines a risk of the subject having myasthenia gravis by comparing an abundance of a biomarker with a diagnostic threshold value, using the abundance of the biomarker as an input variable; the microbial markers include Bacteroides _ massilisensis, Providencia _ alcalifactens, paramrevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ Alignosytics, Prevotella _ bivia, or Megammas _ rupelensis.

8. A composition comprising a substance that reduces the abundance of bacteria _ maleicans, Providencia _ aliciferas, paramrevolutionand rapistonia _ unclassified, Vibrio _ algyrinthulium, Prevotella _ bivia and/or Megamonas _ rupellensis.

9. Use according to any one of the following:

1) use of the agent of any one of claims 1 to 4 for the manufacture of a product for diagnosing myasthenia gravis;

2) use of a product according to claim 5 or 6 for the preparation of a tool for diagnosing myasthenia gravis;

3) use of a composition according to claim 8 for the preparation of a medicament for the treatment of myasthenia gravis;

4) use of the microbial markers Bacteroides _ massilisensis, Providencia _ alcalifactions, paramrevolutella _ unclassified, Ralstonia _ unclassified, Vibrio _ alginolyticus, Prevotella _ bivia and/or Megamonas _ rupelensis for constructing a computational model for predicting myasthenia gravis.

10. The use of claim 9, wherein the myasthenia gravis is childhood myasthenia gravis.

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