CN112029880B - Microorganism for detecting myasthenia gravis and application - Google Patents
Microorganism for detecting myasthenia gravis and application Download PDFInfo
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- CN112029880B CN112029880B CN202010968288.7A CN202010968288A CN112029880B CN 112029880 B CN112029880 B CN 112029880B CN 202010968288 A CN202010968288 A CN 202010968288A CN 112029880 B CN112029880 B CN 112029880B
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Abstract
The invention discloses a microorganism for detecting myasthenia gravis and application thereof, wherein the microorganism shows significant difference between a patient suffering from myasthenia gravis and a healthy person, and the microorganism is specifically selected from Bactroides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis. The diagnosis of myasthenia gravis using Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis as the test variable has a higher efficacy.
Description
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 (Myasthenia gravis, MG) is a chronic autoimmune disease in which the transmission function between nerve-muscle junctions (synapses) is impaired, mainly mediated by antibodies to acetylcholine receptors (Acetylcholine Receptor, achR), cellular immunity and complement involvement leading to disruption of the normal structure of the postsynaptic membrane. Clinically, the symptoms are that the affected muscle groups are weak and easy to fatigue, and the symptoms are relieved after rest and application of cholinesterase inhibitors. Myasthenia gravis can occur at any age. The incidence of symptoms of MG in childhood is 1-5/10 ten thousand people (Lai C, tseng h.national width platform-Based Epidemiological Study of Myasthenia Gravis in Taiwan [ J ]. Neuroepidemic, 2010,35 (1): 66-71.), and the relative peak age of onset is under 10 years (Mullaney P, smith r.the Natural History and Ophthalmic Involvement in Childhood Myasthenia Gravis at The Hospital for Sick Children [ J ]. Ophtalmology, 2000,107 (3): 504-510). Myasthenia gravis is classified into five types according to the modified Osservan typing. Type I (oculoplastomy), which is manifested by paralysis of the oculoplastynes, is the most common type, and some children suffering from infantile diseases may be accompanied by limited eye movement, strabismus, or compound vision. Type IIa (mild systemic type), slow progression, involvement of extraocular muscles, involvement of throat muscles, good response to cholinesterase inhibitors, and low mortality. Type IIb (moderate systemic type) extends from the involvement of the extraocular muscles and the pharyngeal muscles to systemic muscles, which are generally unaffected and often insensitive to cholinesterase inhibitors. Type III (acute rapid progression type) is usually sudden onset, rapid progression within weeks to months, early onset of respiratory muscle involvement, severe limb and trunk muscle involvement, poor cholinesterase inhibitor response, frequent incorporation of thymoma, and high mortality. Type IV (chronic severe): the primary disease is type I or type IIa, the disease is suddenly worsened after 2 years or longer, the reaction to cholinesterase inhibitor is not obvious, and the chest adenoma is often combined, and the prognosis is poor. Type V (myopic) skeletal muscle atrophy and weakness occur within half a year of onset. Eye muscle type MG is one of the most common types, with about 37.5% of infants progressing to systemic myasthenia gravis during the course of the disease (Generalized Myasthenia Gravis, GMG).
To make MG diagnosis clear, a detailed medical history, careful physical examination, and necessary auxiliary examination are required. Diagnosis of childhood MG remains a challenge because non-specific symptoms may exist, or the test results are mildly abnormal.
The human distal gut is colonized with the most dense and diverse microbiota-gut flora in humans, the genome load of which is about 99% of the total human genes, and is therefore known as the "second genome" of humans (Qin J, li R, raes J, et al a hmnan gut microbial gene catalogue established by metagenomic sequencing J. Nayure,2010,464 (7285):59-65), which has an extremely important impact on our health. The imbalance in the composition and function of these intestinal microorganisms is closely related to human health. With the rapid development of science and technology, 16S rDNA gene sequencing and whole genome shotgun sequencing are the main methods for researching the relative abundance and evolutionary relationship of intestinal flora. These techniques have greatly driven research in intestinal microbiology, helping students to better explore the structure and function of the intestinal flora and the signaling pathways associated with disease development and progression. Research on microorganisms related to diseases also provides a new means for diagnosing, preventing and treating 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 the myasthenia gravis.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect the invention provides a detection reagent which detects the abundance of a microbial marker selected from one or more of bacteriodes massiliensis, providencia alcalifaciens, paraprevotella unclassified, ralstonia unclassified, vibrio alginolyticus, prevotella bivia or Megamonas rupellensis.
Further, the microbial marker is selected from any two or more of providencia_ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, 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.
In a second aspect 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 taking the abundance of a microbial marker as an input variable, determining whether a subject has myasthenia gravis or determining the risk of a subject having myasthenia gravis by analyzing the abundance of a microbial marker compared to a diagnostic threshold; the microbial markers include Bactroides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis.
In a fourth aspect the invention provides a composition comprising a substance that reduces the abundance of bacterioides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas rupellensis.
A fifth aspect of the invention provides the use of any one of the following:
1) Use of an agent according to the first aspect of the invention for the preparation of a product for diagnosing myasthenia gravis;
2) Use of a product according to the second aspect of the invention for the manufacture of a tool for diagnosing myasthenia gravis;
3) The use of a composition according to the fourth aspect of the invention in the manufacture of a medicament for the treatment of myasthenia gravis;
4) Use of the microbial markers bacterides massiliensis, providencia alcalifaciens, paraprevotella unclassified, ralstonia unclassified, vibrio alginolyticus, prevotella bivia and/or Megamonas rupellensis 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 that the Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis is related to myasthenia gravis for the first time, the abundance of the Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ unclassified, vibrio _ unclassified, vibrio _bivia or Megamonas_rupellensis is significantly different in myasthenia gravis patients and healthy people, and the ROC curve analysis has higher specificity and sensitivity as a detection variable, so that the Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis can be used as a detection marker for diagnosing the myasthenia gravis patients. The method takes the Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia and/or the Megamonas_rupellensis as detection markers, and has the advantages of no wound, high accuracy, high specificity and high sensitivity.
Drawings
FIG. 1 is a violin diagram of an α diversity and β diversity distribution; wherein a-C is a profile of alpha diversity based on shannon index at the gate (a), genus (B) and species (C) level; D-F is a distribution of beta diversity at the gate (D), genus (E) and species (F) levels based on the Bray-Curtis distance.
Fig. 2 is PcoA at the relative abundances of all participants at different taxonomic levels, where plots a-F are PcoA at the relative abundances at the phylum, class, order, family, genus, species taxonomic levels, respectively, red and blue triangles represent MG and HC, respectively.
Detailed Description
In order to evaluate whether the composition of intestinal symbiotic flora can be used as a predictor of myasthenia gravis, the invention collects samples of the myasthenia gravis patients and healthy people, performs whole genome sequencing and uses bioinformatics to count sequencing data, discovers the intestinal flora related to diseases, integrates the intestinal flora with disease information, and predicts the myasthenia gravis patients to the greatest extent. The invention discovers that the abundance of Bactroides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis in myasthenia gravis patients and healthy people shows a significant difference through whole genome sequencing for the first time, and shows that Bactroides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis can be used as a predictor of myasthenia gravis.
The term "difference in abundance" refers to a higher or lower level of a microorganism obtained in a patient suffering from myasthenia gravis compared to the in vivo level of a normal or control target. For the purposes of the present invention, "difference in abundance" is considered to be a phenomenon that occurs when there is a 1.5-fold or more, about 4-fold or more, about 6-fold or more, about 10-fold or more difference in the level of microorganisms taken from a normal or diseased subject, or from each stage of a subject with a disease.
In the present invention, any method known in the art may be used to detect or determine the level of a microbial marker. These methods include, but are not limited to, methods utilizing sequence amplification of primers, immunological methods using antigen-antibody reactions. 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, synergistic PCR, real-time PCR, differential PCR, cDNA end rapid amplification, inverse polymerase chain reaction, vector-mediated PCR, thermal asymmetric interleave 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, euclidean immunodiffusion method, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence-activated cell sorter, protein chip, or the like, but the scope of the present invention is not limited thereto.
According to embodiments of the invention, stool samples from healthy individuals and myasthenia gravis patients can be analyzed in batches using high throughput sequencing. Based on the high throughput sequencing data, healthy people are aligned with myasthenia gravis patients, thereby determining specific nucleic acid sequences associated with the myasthenia gravis patients.
The invention provides a computational model for predicting myasthenia gravis. As will be appreciated by the skilled artisan, there are a variety of ways to use the measurement of two or more markers to improve the diagnosis of a problem.
Biomarkers may be assayed individually or, in one embodiment of the invention, they may be assayed simultaneously, for example using chip or bead-based array technology. The concentration of the biomarkers is then interpreted independently, for example using individual entrapment of each marker, or a combination thereof.
As will be appreciated by the skilled artisan, 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 the one or more other markers are mathematically combined and the combined values are correlated with the underlying diagnostic problem. The determination of the 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 a value can easily be correlated with, for example, an individual's risk for myasthenia gravis or with other intentional diagnostic uses that aid in assessing a patient with myasthenia gravis, based on the underlying diagnostic problem. In a preferred manner, such a logarithmic function is obtained as follows: a) classifying individuals into groups, such as normal individuals, individuals at risk of myasthenia gravis, patients with myasthenia gravis, and the like, b) identifying markers that differ significantly between these groups by univariate analysis, c) logistic regression analysis to evaluate the markers for use in evaluating the independent differential values of these different groups, and d) constructing a logarithmic function to combine the independent differential 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 method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based method (i.e., logistic regression, CART, random forest method, boosting/bagging method), generalized linear model (i.e., logistic regression), principal component-based method (i.e., SIMCA), generalized superposition model, fuzzy logic-based method, neural network and genetic algorithm-based method. The skilled artisan will not have problems in selecting an appropriate statistical method to evaluate the marker combinations of the present invention and thereby obtain an appropriate 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, rule-based discriminant analysis), kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based method (i.e., logistic regression, CART, random forest method, boosting method), or generalized linear model (i.e., logistic regression).
The area under the receiver operating curve (AUC) is an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of the diagnostic method is best described by its Receiver Operating Characteristics (ROC). ROC plots are line graphs derived from all sensitivity/specificity pairs that continuously change the decision threshold over the entire data range observed.
The clinical performance of a laboratory test depends on its diagnostic accuracy or the ability to correctly classify subjects into a clinical Guan Ya group. Diagnostic accuracy measures the ability of a test to correctly discern two different conditions of a subject under investigation. Such conditions are, for example, health and disease or disease progression versus disease-free 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 positive + number of false negative test results) ]. This is also referred to as positive for the presence of a disease or condition. It is calculated only from the affected subgroups. On the x-axis is a false positive score, or 1-specificity [ defined as (number of false positive results)/(number of true negative + 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 results profiles) had a ROC line graph passing through the upper left corner, a true positive score of 1.0, or 100% (perfect sensitivity), and a false positive score of 0 (perfect specificity). One theoretical line plot for the test that did not distinguish (the results of the two groups were equally distributed) was a 45 ° diagonal from the lower left corner to the upper right corner. Most line graphs fall between these two extremes. Qualitatively (if the ROC line plot falls well below the 45 ° diagonal, then this is easily corrected by reversing the "positive" criterion from "greater than" to "less than" or vice versa.) the closer the line plot 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 area under the ROC curve (AUC). Conventionally, this area is always ≡ 0.5 (if this is not the case, the decision rules can be reversed to make this the case). The range of values is between 1.0 (test values perfectly separating the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on the sensitivity at a specific part of the line graph, such as the point closest to the diagonal or at 90% specificity, but also on the whole line graph. This is a quantitative, descriptive representation of how the ROC line diagram is near perfect (area=1.0).
The 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 a specificity of 75%, 80%, 85%, or still preferably 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 cell population, or an amount of tissue or body fluid, is obtained from a subject. A "patient sample" may often include cells from an animal, but the term may also refer to non-cellular biological material, such as non-cellular portions of blood, saliva, or urine that 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 sample, and the like. A biological sample or tissue sample may refer to 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 tract, intestinal tract and genitourinary tract; tears, saliva; and organs. The sample may comprise frozen tissue. The term "sample" also encompasses any material derived from further processing of such a sample. Deriving a sample 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 isolation and/or purification of specific nucleic acids, proteins, other cytoplasmic components, or nuclear components. As a preferred embodiment, the sample is a fecal sample.
The terms "patient," "subject," and "individual" are used interchangeably herein and refer to an animal, particularly a human, for which it is desired to analyze the presence or amount of a biomarker in a biological sample obtained therefrom. In some embodiments, the subject is in need of diagnosis of a disease or disorder, such as myasthenia gravis, wherein the biological sample is analyzed using assays conventional in the art. The term "subject" or "patient" as used herein also refers to human and non-human animals. The term "non-human animal" includes all vertebrates, for example, 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 animals or pets; and non-mammals such as chickens, amphibians, reptiles, and the like. In one embodiment, the subject is a human.
The invention will now be described in further detail with reference to the drawings and examples. The following examples are only illustrative of the present invention and are not intended to limit the scope of the invention. The experimental procedure, in which specific conditions are not noted in the examples, is generally followed by conventional conditions.
Example 1 screening for intestinal flora associated with myasthenia gravis
1. Study and sample collection
55 children's myasthenia gravis patients and 36 Healthy Controls (HC) of the corresponding ages and sexes collected in the first hospital myasthenia gravis treatment center in Shi-Jia-Zhuang City of Hebei were studied. Sample information is shown in table 1.
Diagnostic criteria: (1) clinical manifestations of eyelid ptosis, double vision, strabismus; (2) neostigmine assay positive: (3) acetylcholine receptor antibody positivity: (4) electromyography: the facial nerve attenuates at low frequency and does not increase at high frequency. One of (1) + (2) or (3) or (4) can be diagnosed explicitly.
Parting: new clinical typing and quantitative myasthenia gravis score (QMG) criteria were proposed by reference to the american myasthenia gravis association (MGFA) in 2000.
Inclusion criteria: the patient is clearly diagnosed as myasthenia gravis type, and meets the diagnosis standard.
Exclusion criteria: (1) age <2 years 10 months or no age information; (2) Antibiotics other than beta-lactams were used within 3 months; (3) administering other drugs/hormones for treating diseases; (4) using anti-inflammatory drugs or unknown Chinese herbal medicines.
Table 1 sample clinical features
2. DNA extraction and sequencing
DNA was extracted from the samples using the DNA extraction kit, and the procedure was performed according to instructions. The concentration of DNA was measured using a fluorometer or microplate reader (e.g., qubit Fluorometer, invitrogen) and the integrity and purity of the samples were measured using agarose gel electrophoresis (agarose gel concentration: 1%V, voltage: 150V, electrophoresis time: 40 min). The genomic DNA was randomly disrupted using Covaris and fragmented genomic DNA with an average size of 200-400bp was screened with magnetic beads. The resulting DNA fragment was subjected to end repair, 3 '-end was subjected to adenylation, and a linker was attached to the end of the 3' -end adenylated fragment, followed by PCR amplification. The PCR products were purified using magnetic beads. The double-stranded PCR product was subjected to heat distortion and circularization with a splint oligonucleotide sequence, single-stranded loop DNA (SsCir DNA) was formatted to construct the final library, and quality control was performed on the library quality. Amplifying the library by phi29 to obtain a DNA Nanosphere (DNB) with a molecular copy number of more than 300. The resulting DNBs were added to the network wells on the chip (immobilized on an arrayed silicon chip) and double-ended sequences with read length of 100bp/150bp were obtained by combining probe-anchored polymerization (cPAS) and double-ended sequencing by multiple displacement amplification (MDA-PE).
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) The contamination of human genome sequences was removed, low quality reads and sequence-adapted sequences were screened using FastP (REF 21) and its default parameters, reads were aligned with human genome (Hg 38) using Bowtie2 (REF 22), and paired reads that were not aligned with human genome were screened using Samtools as clean reads for subsequent analysis.
4. Classification annotation and functional annotation
High quality reads were mapped to the mpa_v20 marker gene database using Metlan2, resulting in a classification abundance map at different classification levels for each sample. The results of all samples were combined using merge_meta_tables.py and combined abundance spectra at different species levels were obtained using 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. Then, the abundances of all samples were pooled using humann2_join_ Tables, humann2_renorm_table and humann2_split_strayfied_table, respectively, and normalized and hierarchically classified annotated for abundance. In addition, KEGG and GO enrichment analysis was performed using humann2_regroup_table and humann2.
5. Statistical analysis
All abundance results were differentially analyzed using the 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-value (FDR) for screening species and pathways that clearly exhibit significant differences. Alpha diversity was calculated for each sample using Shannon index. Under the same input, β diversity was calculated using the Vegan packet in R with the parameter 'method=dist_method'. ROC curves were also plotted using pROC analysis of R and AUC areas thereof were calculated.
Principal Component Analysis (PCA) is carried out on the classification map, the eig result of the PCA is calculated by using an Ade4 software package of R, the dudi.pca function is used for obtaining feature vectors of different PCs, the li result is used for obtaining coordinates of each sample on a PC axis, and the cl is used for obtaining contribution percentage of each PC in the total variance.
To relate the differential species to the clinical phenotype of the sample, the Spearman correlation between the characteristic and the clinical phenotype was calculated using the corr.tes method in the R-package according to the parameters 'method=spearman, use=parilwise, adjust=bh'.
6. Results
The α -diversity and β -diversity at different classification levels based on Shannon index were not significantly different between patient and healthy population (fig. 1).
PCA and PcoA results showed no significant aggregation profile in patients and healthy persons (fig. 2).
Analysis of species variability results showed 20 species exhibiting significant differences, 11 of which ROC detection AUC values >0.7, as shown in table 3. The results of the combined diagnostic analysis on the 20 differential bacterial groups are shown in Table 4. The contents and diagnostic efficacy of Bactoides_ massiliensis, providencia _ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas_rupellensis are shown in Table 2, which shows that the use of the above bacteria for diagnosing myasthenia gravis has higher accuracy and specificity. The combination diagnostic efficacy of the above flora was analyzed and found that any two combinations of providencia_ alcalifaciens, paraprevotella _ unclassified, ralstonia _ unclassified, vibrio _ alginolyticus, prevotella _bivia or Megamonas rupellensis have higher diagnostic efficacy, which indicates that the above 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 values |
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 verification of genomic sequencing accuracy
Samples 19 of myasthenia gravis and 13 of healthy subjects were collected as in example 1, and the patient information is shown in Table 5.
Table 5 sample clinical features
The differential flora, prevotella copri, clostridium bartlettii, fusobacterium mortiferum, helicobacter cinaedi, was randomly selected for sequencing validation and calculated for diagnostic efficacy in myasthenia gravis.
The results showed that the AUC values of Prevotella copri, clostridium bartlettii, fusobacterium mortiferum, helicobacter cinaedi were 0.736842105, 0.672064777, 0.821862348, 0.615384615, respectively, comparable to the results of the previous assays, indicating accurate sequencing data for the metagenome.
The above description of the embodiments is only for the understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that several improvements and modifications can be made to the present invention without departing from the principle of the invention, and these improvements and modifications will fall within the scope of the claims of the invention.
Claims (9)
1. Use of a detection reagent for the preparation of a product for diagnosing myasthenia gravis, characterized in that the detection reagent detects the abundance of intestinal flora, including prevotella_bivia and Megamonas rupellensis.
2. The use according to claim 1, wherein the reagent is a reagent for detecting the abundance of intestinal flora by means of 16S rRNA sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescent in situ hybridization, microarray and PCR-ELISA methods.
3. The use according to claim 1 or 2, wherein the agent is a primer, probe, antisense oligonucleotide, aptamer or antibody specific for the intestinal flora.
4. The use according to claim 1, wherein the myasthenia gravis is childhood myasthenia gravis.
5. Use of a product comprising a detection reagent for detecting abundance of intestinal flora, the intestinal flora comprising Prevotella bivia and Megamonas rupellensis, for the manufacture of a tool for diagnosing myasthenia gravis.
6. The use according to claim 5, wherein the reagent is a reagent for detecting the abundance of intestinal flora by 16S rRNA sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescent in situ hybridization, microarray and PCR-ELISA methods.
7. The use according to claim 5 or 6, wherein the agent is a primer, probe, antisense oligonucleotide, aptamer or antibody specific for the intestinal flora.
8. The use according to claim 5 or 6, wherein the product further comprises reagents for extracting nucleic acid molecules from the sample.
9. The use according to claim 5, wherein the myasthenia gravis is childhood myasthenia gravis.
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