Intestinal microbiota markers associated with obstructive sleep apnea and uses thereof

Abstract

The application discloses an intestinal flora marker related to obstructive sleep apnea and application thereof, and the intestinal flora marker is composed of Ruminococcus gnavus, Fusobacterium mortiferum, Lachnospiraceae bacterium and Coprococcus catus. The application firstly finds that the four intestinal flora species are related to obstructive sleep apnea, and when Coprococcus catu or the four bacteria are used as a detection marker to predict obstructive sleep apnea, the accuracy is high and the prediction mode is noninvasive. It can be seen that the application provides a new way for diagnosis and treatment of patients with obstructive sleep apnea.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a gut microbiota biomarker associated with obstructive sleep apnea, and its application in the diagnosis and treatment of obstructive sleep apnea. Background Technology

[0002] Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disorder. Current diagnostic techniques for OSA include polysomnography (PSG), upper airway CT scans, and pharmacologically induced sleep endoscopy. PSG equipment is expensive, complex, and time-consuming, making it difficult to implement in resource-limited areas, leading to missed diagnoses of some OSA patients. The radiation dose of multi-slice spiral CT (approximately 1–3 mSv) limits its application as a long-term follow-up tool. Patients with sensitive pharyngeal reflexes may experience coughing or even laryngospasm during pharmacologically induced sleep endoscopy, requiring the procedure to be performed in the operating room and carrying the risk of complications related to sleep-inducing drugs. Laboratory polysomnography (PSG), as a "single-person, single-session" testing method, suffers from high costs due to significant equipment and manpower investment.

[0003] In conclusion, there is an urgent need for a fast, convenient, and accurate technology for large-scale screening and diagnosis of OSA. Summary of the Invention

[0004] Based on an in-depth analysis of the limitations of existing technologies, this invention collects samples from OSA patients and healthy individuals, performs 16S rRNA sequencing, and uses bioinformatics to statistically analyze the sequencing data. This leads to the discovery of gut microbiota associated with OSA. Furthermore, by integrating gut microbiota with disease information, gut microbiota biomarkers and methods that can predict OSA patients have been obtained, providing a new approach for the diagnosis and treatment of OSA.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a gut microbiota marker associated with OSA, the gut microbiota marker including at least Coprococcus catus.

[0006] Preferably, the intestinal flora markers consist of active Ruminococcus gnavus, dead Fusobacterium mortiferum, Lachnospiraceae bacterium, and regular Coprococcus catus.

[0007] This invention collects fecal samples from OSA patients and healthy individuals, extracts, amplifies, constructs libraries, and performs metagenomic sequencing on the microbial genomic DNA in the fecal samples. The resulting sequencing data is processed into taxonomic abundance data. Using training set data, differential bacterial analysis and linear discriminant analysis were conducted to identify four bacterial species with significantly varying abundance in OSA patients: active *Ruminococcus gnavus*, *Fusobacterium mortiferum*, *Lachnospiraceae bacterium*, and *Coprococcus catus*. These four species are the first to be discovered in this invention as being associated with OSA. Further validation set data revealed that using ROC curve analysis on *Coprococcus catus* as the detection variable showed significantly higher specificity and sensitivity than other bacterial species. The optimal detection results were achieved when all four species were detected in combination, or when three species (active *Ruminococcus* + *Fusobacterium mortiferum* + *Coprococcus catus*) were detected in combination. Therefore, the gut microbiota biomarkers provided by this invention can be used as diagnostic markers for OSA patients.

[0008] The second aspect of this invention provides the application of the above-mentioned OSA-related gut microbiota markers in the preparation of OSA diagnostic products.

[0009] In the above applications, the products include, but are not limited to, primers, reagents, detection kits, gene chips, and OSA prediction systems. The products achieve the diagnosis or auxiliary diagnosis of OSA by detecting the abundance of gut microbiota markers in the fecal samples of the subjects. The gut microbiota markers include at least Coprococcus catus, and may also include any one or more of Ruminococcus gnavus, Fusobacterium mortiferum, and Lachnospiraceae bacterium. It is optimal when the gut microbiota markers include all four species mentioned above.

[0010] In the context of this invention, the term "abundance" refers to a measure of the quantity of a target microorganism in a biological sample. The quantification of the abundance of a target nucleic acid sequence within a biological sample can be absolute or relative. "Relative abundance" is typically based on one or more internal reference genes, i.e., 16S rRNA genes from a reference strain, such as using universal primers and expressing the abundance of the target nucleic acid sequence as a percentage of total bacterial 16S rRNA gene copies or as determined by normalizing E. coli 16S rRNA gene copies. "Absolute abundance" gives the exact number of target molecules by comparison to a DNA standard or by normalizing to DNA concentration.

[0011] Preferably, in the above applications, the detection kit contains specific primers for detecting the abundance of the gut microbiota marker in the subject's fecal sample; more preferably, the specific primers are primers for amplifying the 16S rRNA of the gut microbiota marker.

[0012] Preferably, in the above application, the OSA prediction system uses the abundance of the gut microbiota markers in the subject's fecal sample as the detection variable.

[0013] A third aspect of this invention provides a method for establishing an OSA prediction system derived from gut microbiota, specifically as follows: Figure 1 As shown, it includes the following steps: S1. Determine the inclusion criteria for OSA patients and healthy individuals; S2. Collect fecal samples from OSA patients and healthy individuals, extract, quality control, library construction, and 16S rRNA gene sequencing of microbial genomic DNA from the fecal samples to obtain raw data; S3. Use the same quality control and analysis methods to perform quality control on the raw data and remove unqualified data; S4. Analyze the relative abundance of gut microbiota using the same method; S5. Screen out differentially expressed bacteria and analyze the effect size using rank-sum test and linear discriminant analysis. S6. Binary logistic regression test to determine the true positive rate and true negative rate of the differentially expressed bacteria.

[0014] The fourth aspect of this invention provides the application of the above-mentioned intestinal flora markers in the preparation of medicaments for treating OSA.

[0015] The fifth aspect of the present invention provides the application of the above-mentioned intestinal flora markers in screening drugs suitable for OSA, specifically: screening for substances that promote the increase of the abundance of at least one of the following microorganisms: active Ruminococcus gnavus, dead Fusobacterium mortiferum, Lachnospiraceae bacterium, and Coprococcus catus as said drug.

[0016] The beneficial effects of this invention are as follows: (1) This invention first discovered that active rumenococcus gnavus, dead fusobacterium mortiferum, Lachnospiraceae bacterium, and regular coccus catus are associated with OSA. The two species, dead fusobacterium mortiferum and active rumenococcus gnavus, showed a significant increase in OSA patients, while the two species, Lachnospiraceae bacterium and regular coccus catus, showed a significant increase in healthy individuals.

[0017] (2) The present invention establishes a prediction system for OSA using the above four bacterial species, verifies the predictive formation of these four bacterial species, and the results of ROC curve analysis show that the combination of these four bacterial species or the combination of three bacterial species (active rumenococcus + dead fusobacterium + regular fecal cocci) as detection variables has high specificity and sensitivity. Therefore, it can be used as a detection marker for the diagnosis of OSA patients. The detection is completely non-invasive and highly accurate.

[0018] (3) Compared with the existing patent CN202211411157.4 - a microecological animal model for obstructive sleep apnea syndrome, the present invention uses clinical samples from OSA patients for research. The research results directly provide a combination of intestinal flora detection markers that can be applied to the diagnosis and treatment of OSA, thereby improving the accuracy and reliability of OSA clinical diagnosis and treatment.

[0019] (4) High-throughput sequencing can process multiple samples in batches, shortening the single-sample testing cycle to 24-48 hours. Compared with the "single person, single test" mode of PSG, the unit sample testing cost is greatly reduced. Fecal microbiota detection can quickly distinguish between "OSA" and "healthy people", assisting in the early identification of high-risk patients and meeting the needs of large-scale OSA high-risk population screening.

[0020] (5) This invention provides a means of detecting gut microbiota for OSA patients, and provides a basis for judging the patient's OSA and gut microbiota dysbiosis, and provides a basis for subsequent gut microbiota transplantation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

[0022] Figure 1 A technical roadmap for the method of establishing an OSA prediction system derived from gut microbiota in this invention; Figure 2 This is a flowchart of the experimental process for screening and validating gut microbiota biomarkers related to OSA in Embodiment 1 of the present invention; Figure 3 The LEfSe score graph for the four bacterial species in Example 1. Figure 4 This is a box scatter plot of the four bacterial strains in Example 1.

[0023] Figure 5 This is the ROC curve from Example 1. Detailed Implementation

[0024] To better understand the present invention, the following description, in conjunction with the accompanying drawings and specific embodiments, further clarifies the content of the invention. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0025] Unless otherwise specified, all examples were conducted under standard experimental conditions or as recommended in the manufacturer's instructions. All reagents and materials used are commercially available unless otherwise specified.

[0026] Example 1 This example screened and validated gut microbiota biomarkers that could be used for OSA detection. The experimental procedure is as follows: Figure 2 As shown, the specific operation is as follows: (1) Sample collection.

[0027] Stool samples were collected from 65 OSA patients and 63 healthy individuals: The OSA group sample consisted of 65 OSA patients. The inclusion criteria were: 1. Age range of 10-70 years; 2. Stable vital signs and no major underlying diseases; 3. AHI > 5; 4. Diagnosed with snoring or obstructive sleep apnea syndrome. The exclusion criteria for the OSA group were: 1. Use of antibiotics within the past three months; 2. Use of probiotics, prebiotics, or synbiotics within the past three months. The control group sample consisted of 63 healthy individuals. The inclusion criteria were: 1. Age range of 10-70 years; 2. Stable vital signs and no major underlying diseases; 3. AHI ≤ 5. The exclusion criteria were the same as those for the OSA group.

[0028] The above data comes from fecal samples collected by Meiyitian Biopharmaceutical (Wuhan) Co., Ltd. and the Sleep Medicine Center of Wuhan Central Hospital.

[0029] (2) DNA extraction, library construction and sequencing.

[0030] Microbial genomic DNA was extracted from the samples using the CTAB (hexadecyltrimethylammonium bromide) method. The extracted genomic DNA underwent quality control, and samples meeting quality standards were selected. These selected DNA samples were then randomly fragmented, end-repaired, and A-base ligated. Adapters and indexes were added, and fragment selection yielded a library of approximately 300 bp. Finally, the inserted fragments were sequenced using the BGI platform using a paired-end method.

[0031] (3) LEfSe analysis to screen biomarkers.

[0032] The sequencing data was filtered, denoised, and assembled to obtain high-quality sequencing data. Clustering was performed with the identity criterion set to 100% to classify ASV feature sequences. The feature sequences were annotated using the qiime2 plugin `feature-classifier classify-sklearn` to generate taxonomic abundance data. 75% of the abundance data were randomly selected and analyzed using LEfSe software, with the default LDA Score filter value set to 2.5. The results are as follows... Figure 3 As shown. Two microorganisms, Lachnospiraceae bacterium and Coprococcus catus, were identified as significantly reduced in OSA patients. Box plots of the four markers are shown below. Figure 4 As shown. These four bacterial species are the first to be discovered in this invention that are related to OSA.

[0033] (4) Establishment of a prediction scoring system.

[0034] The remaining 25% abundance data were first subjected to binary logistic regression, and then the receiver operating characteristic (ROC) curve was analyzed to obtain the cutoff value. The sample information involved in steps (3) and (4) is shown in Table 1.

[0035] Table 1 Sample Information Table: IBM SPSS Statistics (v27) software was used to calculate specificity and sensitivity, and to plot the ROC curve. The software first calculates the threshold for the actual measured value, and then calculates the corresponding number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Specificity (true negative rate) = TN / (TN+FP), and sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by subtracting specificity and sensitivity from 1. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, we first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of that indicator.

[0036] The relative abundance values ​​of individual microbial biomarkers were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predicting scores is shown below. Figure 5 As shown, mimicry marker 1 was calculated using a combination of four bacteria; mimicry marker 2 was calculated using active rumenococci, dead fusobacterium, and regular fecal cocci; mimicry marker 3 was calculated using dead fusobacterium and bacteria from the Trichophyceae family; and mimicry marker 4 was calculated using active rumenococci and regular fecal cocci. The AUC, optimal cutoff value, sensitivity, and specificity of the four-bacterial combination, three-bacterial combination, and single-bacterial prediction scoring methods are shown in Table 2.

[0037] Table 2 ROC diagnostic curve results: The results above show that *Coprococcus catus* has a much higher predictive ability for OSA than other bacterial species, and the combined prediction of four bacteria is the most effective. The AUC of the combined prediction scoring method is 1, the optimal cutoff value is 0.631, the sensitivity is 100.0%, and the specificity is 100.0%. The AUC of the combined prediction scoring method of three bacteria (active rumenococcus + dead fusobacterium + *Coprococcus catus*) is 1, the optimal cutoff value is 0.784, the sensitivity is 100.0%, and the specificity is 100.0%.

[0038] In summary, this invention is the first to discover an association between active rumenococcus *Ruminococcus gnavus*, dead *Fusobacterium mortiferum*, bacteria from the Lachnospiraceae family (Lachnospiraceae bacterium), and regular *Coprococcus catus* and obstructive pulmonary disease (OSA). Specifically, the abundance of *Fusobacterium mortiferum* and active *Ruminococcus gnavus* is significantly increased in OSA patients, while the abundance of *Lachnospiraceae bacterium* and *Coprococcus catus* is significantly increased in healthy individuals. Using these four bacteria as predictors of OSA demonstrates high accuracy and a non-invasive predictive method, providing a new approach for the diagnosis and treatment of OSA patients.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0040] The above embodiments are only for illustrating the technical solutions and features of the present invention, and are intended to enable those skilled in the art to implement them better. They should not be used to limit the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention are within the scope of protection of the present invention. The parts not described in detail are prior art.

Claims

1. A gut microbiota biomarker associated with obstructive sleep apnea, characterized in that, The intestinal flora markers consist of active Ruminococcus gnavus, dead Fusobacterium mortiferum, Lachnospiraceae bacterium, and regular Coprococcus catus.

2. The application of the gut microbiota biomarkers associated with obstructive sleep apnea as described in claim 1 in the preparation of obstructive sleep apnea diagnostic products.

3. The application according to claim 1, characterized in that, The products include a testing kit and an obstructive sleep apnea prediction system.

4. The application according to claim 3, characterized in that, The test kit includes reagents for detecting the abundance of the gut microbiota markers in fecal samples from subjects.

5. The application according to claim 4, characterized in that, The reagent is a primer for detecting the 16S rRNA of the gut microbiota marker.

6. The application according to claim 3, characterized in that, The obstructive sleep apnea prediction system uses the abundance of the gut microbiota markers in the subject's fecal samples as the detection variable.

7. The application of gut microbiota biomarkers associated with obstructive sleep apnea in screening drugs suitable for obstructive sleep apnea, characterized in that, The intestinal flora markers consist of active Ruminococcus gnavus, dead Fusobacterium mortiferum, Lachnospiraceaebacterium, and regular Coprococcus catus.

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