Intestinal microorganism marker for irritable bowel syndrome, application of intestinal microorganism marker and method for constructing detection model

Abstract

The invention provides an irritable bowel syndrome intestinal microbial marker, application thereof and a method for constructing a detection model, and belongs to the technical field of medicine and biomedicine. By utilizing the irritable bowel syndrome intestinal microbial marker, the association between the marker level and IBS symptom severity, the life quality of a patient and the treatment effect can be evaluated, and personalized diet, prebiotics, probiotics or antibacterial drugs and other treatment suggestions are provided for the patient according to the specific intestinal microbial marker composition of the patient. According to the invention, the accuracy and effectiveness of early auxiliary diagnosis, prognosis and treatment of IBS are improved, the understanding of IBS is deepened, and the development of microbiomics in clinical application is promoted. The AUC value of the irritable bowel syndrome detection model reaches 0.9931, which shows that the classifier has good performance when distinguishing positive and negative samples.

Description

Technical Field

[0001] The present invention relates to the field of medicine and biomedical technology, and in particular to an irritable bowel syndrome intestinal microbial marker and an application thereof and a method for constructing a detection model. Background Art

[0002] Irritable bowel syndrome (IBS) is an intestinal functional disorder with chronic or recurrent abdominal pain, diarrhea, abnormal bowel habits and stool characteristics as the main symptoms. Patients often have other functional diseases of the gastrointestinal tract, such as gastroesophageal reflux disease (GERD) and functional dyspepsia (FD). At present, the incidence of IBS is increasing year by year worldwide, and IBS may also cause a variety of complications, such as digestive system diseases (gallbladder disease, abnormal liver function, etc.), psychological disorders (such as further development of anxiety, depression and other diseases), aggravation of sleep disorders and more serious social disorders. These complications will further aggravate the patient's condition and have a serious impact on the patient's quality of life and mental health. Therefore, for IBS patients, early diagnosis and treatment of IBS is very important.

[0003] The diagnosis of IBS mainly relies on medical history, symptomology and exclusion of other diseases. The diagnostic method is quick and simple, but the diagnosis mainly relies on the patient's description of symptoms, which may be affected by the patient's subjective factors, resulting in inaccurate diagnostic results. In addition, the existing diagnostic technology cannot predict the disease progression and prognosis of IBS. In addition, the symptoms of IBS are similar to other intestinal diseases, and the existing diagnostic technology is likely to lead to misdiagnosis or missed diagnosis. Recent studies have found that the imbalance of intestinal microbial communities is closely related to the occurrence and development of IBS.

[0004] Therefore, there is an urgent need for a specific intestinal microbial marker for IBS for early auxiliary diagnosis, prognosis and treatment of IBS. Summary of the invention

[0005] The purpose of the present invention is to provide an intestinal microbial marker for irritable bowel syndrome and its application and a method for constructing a detection model. By analyzing the changes in intestinal microbial markers for irritable bowel syndrome, early irritable bowel syndrome can be accurately diagnosed and targeted treatment plans can be provided.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] The present invention provides an irritable bowel syndrome intestinal microbial marker, characterized in that the irritable bowel syndrome intestinal microbial marker includes the following bacterial species: Bacteroides thetaiotaomicron, Anaerobutyricum halli, Bilophila wadsworthia, Streptococcus sanguinis, Streptococcus anginosus, Actinomyces dentalis, and Mogibacterium diversum.

[0008] The present invention also provides an application of an irritable bowel syndrome intestinal microbial marker, the application including a product for preparing a diagnosis of whether a patient suffers from irritable bowel syndrome or is at risk of suffering from irritable bowel syndrome, a target for the disease assessment and treatment of irritable bowel syndrome, and a construction of an irritable bowel syndrome detection model; the product includes any one of a detection reagent, a detection kit, and a detection chip.

[0009] The present invention also provides a method for constructing an irritable bowel syndrome detection model, comprising the following steps:

[0010] (1) Total DNA was extracted from the feces of patients with irritable bowel syndrome and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function annotation, and difference analysis;

[0011] (2) Using random forest to screen intestinal microbial markers of irritable bowel syndrome, constructing a random forest model, optimizing parameters, and training and testing the constructed random forest model with a training set and a test set to obtain an irritable bowel syndrome detection model;

[0012] (3) The metagenome and abundance information of the patient to be tested are input into the irritable bowel syndrome detection model for identification.

[0013] Preferably, in step (1), the species annotation and composition spectrum analysis includes:

[0014] Alpha diversity analysis, beta diversity analysis, bacterial community sample composition, sample heatmap, and species interaction network analysis.

[0015] Preferably, in step (1), the gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between gene numbers in samples.

[0016] Preferably, in step (1), the functional annotation includes: KEGG data rate functional annotation analysis, HUMAnN3, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

[0017] Preferably, in step (1), the difference analysis includes: Lefse analysis, dominant species difference, multivariate analysis of variance, and rank sum test difference.

[0018] Preferably, in step (2), the parameters for optimizing the parameters are determined according to GridSearchCV.

[0019] The beneficial effects of the present invention compared with the prior art are:

[0020] (1) The present invention extracts biomarkers related to the pathogenesis and progression of irritable bowel syndrome from metagenomic data, uses a random forest algorithm to construct a prediction model, uses a random forest algorithm to construct a random forest model, and trains and tests the constructed random forest model with a training set and a test set to obtain an irritable bowel syndrome detection model. The AUC value of the irritable bowel syndrome detection model of the present invention reached 0.9931, indicating that the classifier performs well in distinguishing positive and negative samples. The CI range is 0.99 to 0.9961, and a reliable evaluation of the AUC value is performed, indicating that the performance stability of the model.

[0021] (2) The irritable bowel syndrome intestinal microbial markers of the present invention can be used to evaluate the association between the marker level and the severity of IBS symptoms, the patient's quality of life and the treatment effect. The present invention can provide patients with personalized treatment recommendations such as diet, prebiotics, probiotics or antibacterial drugs based on the patient's specific intestinal microbial marker composition, which not only improves the accuracy and effectiveness of early auxiliary diagnosis, prognosis and treatment of IBS, but also deepens the understanding of IBS and promotes the development of microbiome in clinical applications. For IBS patients, accurate diagnosis and effective treatment mean that symptoms can be alleviated, quality of life can be improved, and life restrictions caused by symptoms can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 This is a flow chart for constructing the irritable bowel syndrome detection model in Example 1;

[0024] Figure 2 This is a graph showing the results of ten cross-validation runs in Example 1;

[0025] Figure 3 It is the "Mean DecreaseAccuracy" indicator in the random forest model of Example 1;

[0026] Figure 4 is the ROC curve in Example 1;

[0027] Figure 5 for DETAILED DESCRIPTION

[0028] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0029] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0030] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0031] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to the skilled artisan. The present invention description and examples are exemplary only.

[0032] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0033] The present invention provides a method for constructing an irritable bowel syndrome detection model, which specifically comprises the following steps:

[0034] (1) Total DNA was extracted from the feces of patients with irritable bowel syndrome and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function annotation, and difference analysis;

[0035] The species annotation and composition spectrum analysis include:

[0036] Alpha diversity: Shannon, Simpson, Insimpson index analysis;

[0037] beta diversity: Bray-CurtisPCoa / PCA / NMDS / UPGMA analysis;

[0038] Bacteria sample composition, sample heatmap;

[0039] Species interaction network analysis.

[0040] The gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between samples of gene numbers.

[0041] The functional annotations include: KEGG data rate functional annotation analysis, HUMAnN3: quantitative pathway abundance, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

[0042] The difference analysis includes: Lefse analysis, dominant species difference, multivariate variance adonis, MaAsLin, and rank sum test difference.

[0043] (2) Using random forest to screen intestinal microbial markers of irritable bowel syndrome, constructing a random forest model, optimizing parameters, and training and testing the constructed random forest model with a training set and a test set to obtain an irritable bowel syndrome detection model;

[0044] Among them, the screened microbial markers include the following species:

[0045] Bacteroides thetaiotaomicron, Anaerobutyricum halli, Bilophila wadsworthia, Streptococcus sanguinis, Streptococcus anginosus, Actinomyces dentalis, Mogibacterium diversum;

[0046] (3) The metagenome and abundance information of the patient to be tested are input into the irritable bowel syndrome detection model for identification.

[0047] Example 1

[0048] Embodiment 1 of the present invention provides a method for constructing an irritable bowel syndrome detection model, which specifically comprises the following steps:

[0049] Fecal samples were collected from 113 patients with irritable bowel syndrome and 48 healthy controls. The samples were collected to ensure that the patients had not received antibiotics or other related treatments that might affect the intestinal flora. Fecal DNA was extracted using the MagBeads Fecal Genomic Kit. Take 300 mg of fecal sample and 1000 μL Pre-Wash Buffer, vortex until the sample is completely uniform; centrifuge the sample at 14000 rpm for 5 minutes, discard the supernatant, add 980 μL Lysis Buffer F1, 10 μL RNase A Solution and 120 μL Lysis Buffer F2 to the Lysing Matrix E tube, and vortex for 10 minutes; centrifuge at 14000 rpm / min for 5 minutes to separate particle fragments; transfer 900 μL supernatant to a 2.0 mL microcentrifuge tube, add 250 μL PPS, and mix by inverting 20 times; centrifuge at 14000 rpm for 5 minutes, transfer 1000 μL supernatant to a new 2.0 mL microcentrifuge tube, and then add 1000 μL Binding Buffer F and 10 μL Magnetic Beads and vortex to mix; place the test tube on the magnetic stand for 3 minutes to allow the magnetic beads to precipitate, and then discard the supernatant; add 1000 μL Wash to the test tube Buffer F1, shake on a shaker for 3 minutes, place the test tube on the magnetic rack for 1 minute to allow the magnetic beads to precipitate, and then discard the supernatant; add 1000μL Wash Buffer F2 to the test tube, shake on a shaker for 3 minutes, place the test tube on the magnetic rack for 1 minute, and then discard the supernatant; repeat the operation and wash again with Wash Buffer F2; then air-dry the magnetic beads at 55°C; add 100μLTE Buffer and incubate at 55°C for 5 minutes; place the test tube on the magnetic rack until the magnetic beads are precipitated, transfer the supernatant (eluted DNA) to a clean 1.5mL microcentrifuge tube and store it at -20°C for a long time.

[0050] Use an ultrasonic disruptor to randomly break high molecular weight DNA into fragments of about 300 bp, repair the ends of the fragmented DNA to make them blunt ends; connect specific adapter sequences to both ends of the repaired DNA fragments, and remove excess adapters, primers and other impurities through magnetic beads or column purification; use specific primers to PCR amplify the DNA fragments with adapters to enrich a sufficient amount of library fragments; use fastp software to perform quality control on the sequencing data, including removing adapters and low-quality sequences; use Bowtie2 to filter out reads that may come from the host, and then use the Illumina platform for high-throughput sequencing. The sequencing results are sorted according to Figure 1 The species annotation and composition spectrum analysis, gene abundance analysis, functional annotation, and differential analysis processes were analyzed, and the results were input into the random forest model. Random forest was used to screen intestinal microbial markers for irritable bowel syndrome, and a random forest model was constructed. Seven bacterial genera with significant changes in biomarkers between patients with irritable bowel syndrome and healthy people were found, including the following genera: Bacteroides thetaiotaomicron, Anaerobutyricum halli, Bilophila wadsworthia, Streptococcus sanguinis, Streptococcus anginosus, Actinomyces dentalis, and Mogibacterium diversum.

[0051] The random forest model was trained on the training set of patients with irritable bowel syndrome and healthy people using the python software sklearn.Ensemble.RandomForestClassifier module to obtain the random forest detection model; the model training parameters were set as follows: gradient n_estimators = [100,500,1000]; max_depth = [1,2,3,7,9]; max_features = ["log2", "sqrt"];

[0052] The stability and reliability of the random forest model were verified by ten-fold cross validation. The python software GridSearchCV module was used to search for the best parameters. The random forest model was constructed using the best parameters. The irritable bowel syndrome detection model was obtained by training and testing with the training set and test set. The results are shown in the figure. Figure 2 As shown in Table 1.

[0053] Figure 2In the figure, the vertical axis reflects the value of the cross-validation error, which ranges roughly from 0.01 to 0.15. There are several curves in the line graph, which represent different variable selection methods. The black curve clearly shows the trend of the cross-validation error as the number of variables increases. This curve initially shows a downward trend, indicating that the prediction error of the model is gradually decreasing in the process of increasing variables. Both the model training set and the test set have high accuracy, indicating that the model has good results. Among them, the retention performance of seven bacterial genera, including Bacteroides thetaiotaomicron, Anaerobutyricum halli, Bilophila wadsworthia, Streptococcus sanguinis, Streptococcus anginosus, Actinomyces dentalis, and Mogibacterium diversum, is the best.

[0054] Table 1 Random forest model indicators

[0055]

[0056] In Table 1, "Mean DecreaseAccuracy" and "Mean Decrease Gini" are two important indicators in the random forest model. "Mean Decrease Gini" calculates the impact of each variable on the heterogeneity of the observed values ​​at each node of the classification tree, thereby comparing the importance of the variable. The larger the value, the greater the importance of the variable. "MeanDecreaseAccuracy" indicates the degree of reduction in the prediction accuracy of the random forest. The larger the value, the greater the importance of the variable (see the results). Figure 3 ).

[0057] The horizontal axis (X-axis) of the ROC curve represents 1-Specificity, that is, the false positive rate, which ranges from 0 to 1, reflecting the proportion of negative examples that the model mistakenly predicts as positive examples. The vertical axis (Y-axis) represents Sensitivity, that is, the true positive rate, which measures the ability of the model to correctly identify positive examples. The results are shown in Figure 4 .

[0058] Figure 4In the figure, the solid red line clearly depicts the ROC curve of the model, while the shaded part represents the confidence interval (CI). The ROC curve of the present invention is close to the upper left corner, and has a higher true positive rate at each threshold. The area under the curve (AUC) is close to 1, indicating that the classification performance of the model is better. The AUC value of the present invention reaches 0.9931, indicating that the classifier performs well in distinguishing positive and negative samples. The CI ranges from 0.99 to 0.9961, which provides a reliable evaluation of the AUC value, indicating the stability of the model performance.

[0059] The metagenome and abundance information of the patient to be tested are input into the diabetes detection model, and the patient is judged whether he or she is ill according to the predicted score. The judgment criteria are: risk value 0-0.5, low risk; 0.5-0.7 medium risk; 0.7-1 high risk. The results are as follows Figure 5 shown.

[0060] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A intestinal microbial marker for irritable bowel syndrome, characterized in that: The intestinal microbial markers of irritable bowel syndrome include the following species: Bacteroides thetaiotaomicron, Anaerobutyricum halli, Bilophila wadsworthia, Streptococcus sanguinis, Streptococcus anginosus, Actinomyces dentalis, and Mogibacterium diversum.

2. An application of the intestinal microbial marker for irritable bowel syndrome according to claim 1, characterized in that: The application includes the preparation of products for diagnosing whether a patient suffers from irritable bowel syndrome or is at risk of suffering from irritable bowel syndrome, using them as targets for the assessment and treatment of irritable bowel syndrome, and constructing an irritable bowel syndrome detection model; the product includes any one of a detection reagent, a detection kit, and a detection chip.

3. A method for constructing an irritable bowel syndrome detection model, characterized in that: The steps include: (1) Total DNA was extracted from the feces of patients with irritable bowel syndrome and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function annotation, and difference analysis; (2) Use random forest to screen intestinal microbial markers of irritable bowel syndrome, construct a random forest model, optimize parameters, and train and test the constructed random forest model with training sets and test sets to obtain an irritable bowel syndrome detection model; (3) The metagenome and abundance information of the patient to be tested are input into the irritable bowel syndrome detection model for identification.

4. The method for constructing an irritable bowel syndrome detection model according to claim 3, characterized in that: In step (1), the species annotation and composition spectrum analysis includes: Alpha diversity analysis, beta diversity analysis, bacterial community sample composition, sample heatmap, and species interaction network analysis.

5. The method for constructing an irritable bowel syndrome detection model according to claim 3, characterized in that: In step (1), the gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between gene numbers in samples.

6. The method for constructing an irritable bowel syndrome detection model according to claim 3, characterized in that: In step (1), the functional annotation includes: KEGG data rate functional annotation analysis, HUMAnN3, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

7. The method for constructing an irritable bowel syndrome detection model according to claim 3, characterized in that: In step (1), the difference analysis includes: Lefse analysis, dominant species difference, multivariate analysis of variance, and rank sum test difference.

8. The method for constructing an irritable bowel syndrome detection model according to claim 3, characterized in that: In step (2), the parameters for optimizing the parameters are determined according to GridSearchCV.

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