A set of marker fecal genera for the diagnosis of obsessive-compulsive disorder in children and adolescents and their applications

Abstract

The present invention discloses a set of marker fecal bacterial genera for diagnosing obsessive-compulsive disorder in children and adolescents, and their applications, belonging to the field of molecular biomedical technology. Based on a comparison and analysis of the intestinal microbiota of patients with obsessive-compulsive disorder and healthy controls, the present invention identifies differential bacterial communities between the two groups. Using high-quality data on the relative abundance of differential bacterial communities between patients with obsessive-compulsive disorder and healthy controls as a training set, the present invention enables risk assessment and early diagnosis of patients with obsessive-compulsive disorder. This approach fills a gap in the early biological diagnosis of the disease, provides medical professionals and patients with more diagnostic and treatment options, and avoids problems such as missed and misdiagnosed diagnoses, thus possessing significant clinical significance.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biomedical technology, and specifically relates to a marker fecal bacteria genus for diagnosing obsessive-compulsive disorder in children and adolescents and its application. Background Art

[0002] Obsessive-compulsive disorder (OCD) is a chronic, disabling mental disorder characterized by recurring and persistent obsessions and / or compulsive behaviors. The World Health Organization ranks OCD as one of the world's ten most disabling diseases. Approximately 30% to 50% of adults with OCD admit that their condition first developed during childhood or adolescence. Adult OCD often develops from personality changes during childhood and adolescence. Childhood and adolescent OCD is a common mental disorder among children and adolescents, often impairing daily living, learning, and interpersonal relationships. Epidemiological studies show that the prevalence of OCD among children and adolescents is 0.5% to 2%. Because cognitive, learning, and social developmental changes in children and adolescents occur within a short period of time, delayed diagnosis and inadequate treatment can have profound consequences for their physical and mental health, both now and in adulthood.

[0003] Currently, there is a lack of reliable biomarkers for the diagnosis of obsessive-compulsive disorder in children and adolescents. The diagnosis is primarily based on observing the patient's behavior and communicating with them to assess their thinking, cognitive abilities, and self-awareness. Because different doctors often have different understandings and perceptions of the disease, the diagnosis of obsessive-compulsive disorder in children and adolescents is inconsistent. This makes it difficult to compare and interpret research results reported by different researchers, hindering risk identification for obsessive-compulsive disorder, significantly increasing the complexity and difficulty of treatment, and worsening prognosis. Therefore, there is an urgent need to establish an objective test for the identification of obsessive-compulsive disorder in children and adolescents. Summary of the Invention

[0004] To address these issues, the present invention aims to effectively differentiate healthy individuals from patients with obsessive-compulsive disorder (OCD) using gut microbiome biomarkers, providing a basis for early clinical diagnosis. This method compares and analyzes the gut microbiota of patients with OCD and healthy controls, identifying differential microbiota between the two groups. Using high-quality data on the relative abundance of differential microbiota between patients with OCD and healthy controls as a training set, the method enables risk assessment and early diagnosis of OCD patients.

[0005] In order to achieve the above objectives, the present invention provides the following technical solutions.

[0006] The present invention provides the use of an intestinal flora detection reagent in the preparation of a product for assisting in the diagnosis of obsessive-compulsive disorder in children and adolescents, characterized in that the product detects a combination of 30 types of intestinal flora in a sample.

[0007] Furthermore, the combination of the 30 intestinal flora is as follows: Bacteroides, Parabacteroides, Sutterella, Subdoligranulum, Mitsuokella, Hydrogenophaga, Monoglobus, Pseudoxanthomonas, Pseudaminobacter, Paenarthrobacter, Nocardioides, Opitutus, Devosia, Bordetella, Bacteria, Agromyces, Aeromicrobium, Luteimonas, Chiayiivirga, Bilophila, Ramlibacter, Haliangium, Altererythrobacter, Lysobacter, IMCC26134, Sandaracinus, Bosea, Erysipelatoclostridium, Niastella, Chryseolinea, and Arenimonas.

[0008] Furthermore, the relative abundance of Bacteroides, Sutterella, Bilophila and Parabacteroides among the 30 intestinal flora detected by the product was shown to be increased in the obsessive-compulsive disorder patient group, while the relative abundance of the remaining flora was decreased in the obsessive-compulsive disorder patient group.

[0009] Furthermore, the sample includes whole blood, serum, plasma, and feces.

[0010] The present invention also provides the use of 30 species of intestinal flora in a drug for treating and / or preventing obsessive-compulsive disorder, characterized in that the intestinal flora includes one or a combination of the following: Bacteroides, Parabacteroides, Sutterella, Subdoligranulum, Mitsuokella, Hydrogenophaga, Monoglobus, Pseudoxanthomonas, Pseudaminobacter, Paenarthrobacter, Nocardioides, Opitutus, Devos, and others. a (Devoiria), Bordetella, Agromyces, Aeromicrobium, Luteimonas, Chiayiivirga, Bilophila, Ramlibacter, Haliangium, Altererythrobacter, Lysobacter, IMCC26134, Sandaracinus, Bosea, Erysipelatoclostridium, Niastella, Chryseolinea, or Arenimonas.

[0011] The present invention also provides a method for using the above-mentioned product for assisting in diagnosing obsessive-compulsive disorder in children and adolescents, which is characterized by comprising the following steps:

[0012] S1: Extract DNA from the sample to be tested, sequence it using a high-throughput sequencing method, and obtain sequencing reads;

[0013] S2: Align the sequencing reads to the human reference genome and remove human DNA sequence reads;

[0014] S3: Align the remaining reads to the microbial genome database to obtain reads that can be aligned to the microbial sequences of 30 intestinal flora diagnostic markers, and count the number of reads;

[0015] S4: For each bacterium, the number is normalized to the full length of the bacterial gene to obtain the relative abundance;

[0016] S5: Use the relative abundance of bacteria as input value and judge the sample to be tested through machine learning classification algorithm.

[0017] Furthermore, the combination of the 30 intestinal flora is as follows: Bacteroides, Parabacteroides, Sutterella, Subdoligranulum, Mitsuokella, Hydrogenophaga, Monoglobus, Pseudoxanthomonas, Pseudaminobacter, Paenarthrobacter, Nocardioides, Opitutus, Devosia, Bordetella, Bacteria, Agromyces, Aeromicrobium, Luteimonas, Chiayiivirga, Bilophila, Ramlibacter, Haliangium, Altererythrobacter, Lysobacter, IMCC26134, Sandaracinus, Bosea, Erysipelatoclostridium, Niastella, Chryseolinea, and Arenimonas.

[0018] Furthermore, the machine learning process uses the probability of disease as the output value.

[0019] Furthermore, the machine learning classification algorithm is the random forest algorithm.

[0020] Furthermore, the sample includes whole blood, serum, plasma, and feces.

[0021] Compared with the prior art, the present invention has the following beneficial effects.

[0022] The present invention proposes for the first time the use of intestinal flora biomarkers for early diagnosis of obsessive-compulsive disorder in children and adolescents, overcoming the limitations of existing diagnostic methods that are affected by the experience of clinicians and the subjective factors of the patients themselves. The present invention can accurately and effectively identify obsessive-compulsive disorder in early childhood and adolescents, saving time and economic costs for the vast patient population. At the same time, the above-mentioned biomarkers can be used as detection targets or detection objectives in the preparation of detection kits. In addition, the above-mentioned biomarkers can be used as targets in the screening of drugs for the treatment and / or prevention of obsessive-compulsive disorder, specifically by screening for specific differential bacterial genera based on individuals, and then developing them into probiotic preparations for children to take in order to alleviate clinical symptoms.

[0023] The obsessive-compulsive disorder-related biomarkers proposed in the present invention are of high value for the early diagnosis of the disease. First, the availability, operability, safety and affordability of stool samples ensure patient compliance. Secondly, the detection of stool samples is based on sequencing technology, and the markers obtained thereby have high sensitivity and specificity. This invention can be applied to clinical work in the future and used in clinical psychology departments for the early diagnosis of obsessive-compulsive disorder in children and adolescents. Patients only need to provide stool samples for testing as required to obtain early diagnostic results. This method is a non-invasive means, and patients can collect samples at home or in medical institutions, filling the gap in the early biological diagnosis of the disease. The present invention can provide more diagnosis and treatment methods for medical workers and patient groups, avoiding problems such as missed diagnosis and misdiagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This figure shows the error rate distribution of a classifier using 10-fold cross-validation according to one embodiment of the present invention. The horizontal axis represents the number of variables used in constructing the random forest model. The vertical axis represents the cross-validation error rate. The black line in the figure represents the mean of the ten cross-validation error rates.

[0025] Figure 2 . A Mean Decrease Gini plot is drawn for 30 intestinal markers screened based on a random forest model according to an embodiment of the present invention. Different shades of color indicate which group the differential species is enriched in.

[0026] Figure 3 . Receiver Operating Characteristic (ROC) curve and area under the curve (AUC) of a training set consisting of obsessive-compulsive disorder patients and healthy controls based on a random forest model (30 intestinal microbial markers) according to one embodiment of the present invention.

[0027] Figure 4 This figure shows the accuracy of test set predictions when constructing a random forest model based on 30 important variables according to one embodiment of the present invention. The horizontal axis represents the grouping, and the vertical axis represents the accuracy of the random forest model's predictions.

[0028] Figure 5 . Receiver Operating Characteristic (ROC) curve and area under the curve (AUC) of a test set consisting of obsessive-compulsive disorder patients and healthy controls based on a random forest model (30 intestinal markers) according to one embodiment of the present invention.

[0029] Figure 6 This figure shows the accuracy of training set predictions when constructing a random forest model based on 30 important variables according to one embodiment of the present invention. The horizontal axis represents the grouping, and the vertical axis represents the accuracy of the random forest model's predictions. DETAILED DESCRIPTION

[0030] The present invention will be described in detail below in conjunction with specific examples. The following examples will contribute to understanding of the present invention, but these examples are only for illustration of the present invention and the present invention is not limited to these contents. The operating methods in the examples are all conventional operating methods in the art.

[0031] Example 1.

[0032] 1. Sample collection and processing.

[0033] The present invention collected fecal samples from subjects including obsessive-compulsive disorder patients (n=49) and healthy controls (n=42). Among them, obsessive-compulsive disorder patients in the present invention must meet the following inclusion criteria: (1) The recruited obsessive-compulsive disorder patients must meet the clinical diagnostic criteria for obsessive-compulsive disorder in DSM-5; (2) The score of the Yale-Brown Obsessive-Compulsive Disorder Symptom Scale for Children must be greater than or equal to 16 points; (3) Patients with obsessive-compulsive disorder for the first time have not taken any other psychiatric drugs; (4) Aged 8 to 17 years old; Gender is not limited; (5) There is no significant change in eating habits in the past six months; (6) Must understand the nature of this study, be able to cooperate with all research content according to the research needs, and sign the informed consent form.

[0034] Exclusion criteria for all subjects included: (1) patients with or with a history of other mental illnesses; (2) patients with a history of organic brain lesions or brain trauma; (3) patients with severe heart, liver, or kidney dysfunction, metabolic diseases, and other serious physical diseases; (4) patients who had taken or used antibiotics or microecological regulators continuously in the past two months; (5) patients with abnormal laboratory routine tests (such as liver function, blood routine, and urine routine); (6) patients with severe suicidal tendencies; (7) patients with known active bacterial, fungal, or viral infections; and (8) vegetarians.

[0035] A control group of 42 healthy subjects matched for age, sex, education, and BMI were recruited from the public. Participation in the study was voluntary and included no history of psychiatric illness, clinical somatic disease, substance abuse, allergies, alcoholism, or unusual dietary habits. Routine physical examinations were normal, and the Yale-Brown Obsessive-Compulsive Scale for Children score was <6. All enrolled children and adolescents with obsessive-compulsive disorder were treated with SSRIs as monotherapy: fluoxetine (20-60 mg / day), sertraline (100-200 mg / day), or fluvoxamine (50-300 mg / day). The specific SSRI and its dosage were determined by the outpatient psychiatric physician based on clinical judgment. Fecal samples were quickly frozen in liquid nitrogen and then stored at -80°C, minimizing repeated freezing and thawing.

[0036] 2. DNA extraction.

[0037] Total DNA was extracted from stool samples using a magnetic bead-based fecal genomic DNA extraction kit. DNA concentration and purity were assessed on a 1% agarose gel. All DNA extracts were stored at −20°C until use.

[0038] 3. PCR amplification.

[0039] (1) Template: diluted genomic DNA.

[0040] (2) Primers: The PCR amplification selection region is the V4 variable region of the bacterial 16S rDNA gene. Specific primers with barcodes are used. The specific sequences are shown in the following table:

[0041] .

[0042] (3) Enzymes and buffers: Use high-efficiency and high-fidelity enzymes for PCR to ensure amplification efficiency and accuracy. PCR reaction system (30 µL):

[0043] .

[0044] PCR reaction procedure:

[0045] .

[0046] (4) Electrophoresis detection of PCR products: PCR products were mixed and purified at equal concentrations according to the concentration of the PCR products. After thorough mixing, the PCR products were purified by electrophoresis using 1×TAE 2% agarose gel. The sequences with a main band size between 400 and 450 bp were selected and the target bands were recovered by cutting the gel.

[0047] 4. Library construction and sequencing: The library was constructed using the Illumina TruSeq DNA PCR-Free Library Preparation Kit. The constructed library was quantified and tested by Qubit. If qualified, it was sequenced using the NovaSeq 6000.

[0048] V. Information Analysis Process: The raw data obtained from sequencing is spliced and filtered, and dirty data is removed to obtain valid data. Based on the valid data, OTU (Operational Taxonomic Unit) clustering and species classification analysis are performed. Based on the OTU clustering results, species annotation is performed on the representative sequence of each OTU to obtain corresponding species information and species-based abundance distribution.

[0049] To further explore differences in community structure between grouped samples, we used Metastat (R Version 2.15.3) to test the species composition and community significance of the grouped samples. To identify species with significant differences between groups, we used the genus-level species abundance table to perform hypothesis testing on the species abundance data between groups. We then used the Benjamini and Hochberg False Discovery Rate method to correct the p-values and obtain q-values. Finally, we used the q-value (FDR) to screen for species with significant differences. In this study, 415 bacterial genera had q < 0.05.

[0050] 6. Use the random forest model to screen potential biomarkers for the risk of obsessive-compulsive disorder.

[0051] To further screen for gut microbial markers of obsessive-compulsive disorder (OCD), we constructed training and test sets of gut microbial markers for OCD subjects and healthy controls based on the "MetaStat analysis technique for screening differences in relative microbial abundance between OCD patients and controls" study. The biomarker levels in the test set samples were then evaluated. In this study, the training set refers to a dataset containing a certain number of fecal samples from OCD and healthy controls to measure the biomarker levels. The remaining samples, regardless of whether they matched the epidemiological data, served as the test set.

[0052] The specific steps include:

[0053] The present invention selected 91 samples (49 patients with obsessive-compulsive disorder and 42 healthy subjects), 39 patients with obsessive-compulsive disorder and 35 healthy subjects as the training set, and the remaining 17 samples (10 patients with obsessive-compulsive disorder and 7 healthy subjects) as the test set (Table 2). The relative abundance information of the differential bacterial communities in the training set was then input into the random forest (RF) classifier, and the classifier was subjected to 10-fold cross-validation. Based on the cross-validation results ( Figure 1 ), when the number of variables reaches 30, the model's error rate is lowest and tends to be stable. Therefore, this model is constructed based on the top 30 differential bacterial genera ranked by importance to predict the risk of obsessive-compulsive disorder. The horizontal axis is: the number of variables used to construct the random forest model. The vertical axis is the cross-validation error (cross-validation error rate). The gray line in the figure is the mean of the error rate of ten cross-validation times. As can be seen from the figure, when the random forest model is constructed with 30 variables, the model's error rate has stabilized. Variable importance is predicted by the random forest. The meaning is: when other variables remain unchanged, after adding a perturbation to a variable and rebuilding the model, the greater the increase in the prediction error rate of the new model, the more important the variable.

[0054] Table 1 Relative abundance information of differential bacterial communities in the training set for predicting obsessive-compulsive disorder

[0055]

[0056] .

[0057] Table 2. Relative abundance information of different bacterial communities in the test set

[0058]

[0059] .

[0060] In the optimal model, Bordetella made the greatest contribution to the model establishment. The contribution values of each genus are detailed in Figure 2 Different colors represent the groups in which the bacterial genus is enriched. The Mean Decrease Gini index calculates the impact of each variable on the heterogeneity of observations at each node in the classification tree, thereby comparing variable importance. A larger value indicates a greater importance of the variable.

[0061] The 30 bacterial genera screened by the RF model were used to calculate the risk of obsessive-compulsive disorder based on their relative abundance and draw the ROC curve ( Figure 3 ), and calculated the area under the ROC curve (AUC). Based on the above analysis, it can be determined that there is a statistical difference in the distribution of microbial communities between the obsessive-compulsive disorder group and the healthy control group. The area under the ROC curve (AUC) of the training set is 0.9011 (95% CI: 83.06%-97.16%; ( Figure 5 Tables 3 and 4 show the combination of 30 biomarkers to predict the disease probability of the training set and the test set, respectively.

[0062] Table 3 Combination of 30 biomarkers to predict disease probability in the training set

[0063] .

[0064] Table 4 Combination of 30 biomarkers to predict disease probability in the test set

[0065] .

[0066] The results showed that the prediction model can effectively distinguish patients with obsessive-compulsive disorder from healthy controls and has a high discriminatory ability in predicting obsessive-compulsive disorder.

Claims

1. Use of an intestinal flora detection reagent in the preparation of a product for assisting in the diagnosis of obsessive-compulsive disorder in children and adolescents, characterized in that: The product detects a combination of 30 intestinal flora in the sample; the combination of the 30 intestinal flora is as follows: Bacteroides, Parabacteroides, Sutterella, Subdoligranulum, Mitsuokella, Hydrogenophaga, Monoglobus, Pseudoxanthomonas, Pseudaminobacter, Paenarthrobacter, Nocardioides, Opitutus, Devosia, Bord etella, Agromyces, Aeromicrobium, Luteimonas, Chiayiivirga, Bilophila, Ramlibacter, Haliangium, Altererythrobacter, Lysobacter, IMCC26134, Sandaracinus, Bosea, Erysipelatoclostridium, Niastella, Chryseolinea, and Arenimonas.

2. The use according to claim 1, characterized in that The product detected 30 intestinal flora, and the relative abundance of Bacteroides, Sutterella, Bilophila and Parabacteroides flora increased in the obsessive-compulsive disorder patient group, while the relative abundance of the remaining flora decreased in the obsessive-compulsive disorder patient group.

3. The use according to claim 1, characterized in that The sample is feces.

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