Gut microbial markers for irritable bowel syndrome and their applications

Abstract

This invention discloses a gut microbial biomarker for irritable bowel syndrome (IBS) and its application, belonging to the field of biomedicine. The microbial biomarker includes one or more of the following: *Westernella foodborneella*, *Biliobacterium warfarinii*, *Erysipelothrix rhusiopathiae*, *Bifidobacterium longum*, *Bacteroides polymorpha*, and active *Ruminococcus*. This invention provides a product (such as a kit) comprising detection reagents for detecting the relative abundance of the aforementioned microbial biomarkers. The product provided by this invention has good feasibility and accuracy, and can effectively assess the risk of IBS in adolescents, providing a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to an intestinal microbial marker for irritable bowel syndrome and its application. Background Technology

[0002] Currently, the diagnosis of irritable bowel syndrome (IBS) mainly relies on the Rome criteria based on symptoms for a positive diagnosis, supplemented by tests such as fecal calprotectin to rule out organic diseases. However, this method has the following main limitations: 1) The symptom criteria themselves are incomplete: the Rome IV criteria sacrifice sensitivity (82.1%) for specificity (85.1%), easily missing patients with milder symptoms. Furthermore, its definition centered on "abdominal pain" may not be applicable to Chinese patients whose primary complaint is "abdominal distension"; 2) Lack of objective biomarkers: the diagnosis relies entirely on the patient's subjective description, making it susceptible to recall bias. The high heterogeneity of IBS also makes accurate diagnosis difficult with a single biomarker.

[0003] Therefore, although there are currently diagnostic methods for irritable bowel syndrome (IBS), these methods still have certain limitations. Hence, there is a need in this field to develop a new product to assist in the accurate clinical detection of the intestinal function status of IBS patients in order to meet the diagnostic and treatment needs in different scenarios. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an intestinal microbial biomarker for irritable bowel syndrome and its application, which can accurately diagnose whether a person being tested has irritable bowel syndrome.

[0005] The technical solution provided by this invention is as follows: In a first aspect, a reagent for quantitatively detecting microbial markers is provided for use in the preparation of products for diagnosing irritable bowel syndrome (IBS), wherein the microbial markers include active Ruminococcus gnavus, Erysipelotrichaceae bacterium, and Bilophila wadsworthia, and the active Ruminococcus gnavus, Bilophila wadsworthia, and Erysipelotrichaceae bacterium are significantly increased in the IBS population.

[0006] Secondly, a microbial biomarker associated with irritable bowel syndrome is provided, the microbial biomarker including active Ruminococcus gnavus, Erysipelotrichaceae bacterium, Bilophila wadsworthia, and Erysipelotrichaceae bacterium.

[0007] Thirdly, a product for diagnosing or predicting irritable bowel syndrome is provided, the product comprising one or more of reagents, test strips, aptamers, and chips, wherein the microbial markers in the product are specific and used for quantitative detection of the microbial markers.

[0008] Fourthly, a kit is provided, the kit containing reagents for the quantitative detection of the microbial markers.

[0009] Fifthly, a kit is provided for use in the preparation of products for diagnosing irritable bowel syndrome (IBS), wherein the presence of *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Weissella cibaria* is significantly reduced in IBS patients; and the presence of *Ruminococcus gnavus*, *Bilophila wadsworthia*, and *Erysipelotrichaceae bacterium* is significantly increased in IBS patients.

[0010] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and irritable bowel syndrome (IBS), which can be used to diagnose whether a person under testing has IBS. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the sample through relative abundance values, other methods for quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information), and can also be used to assist in the diagnosis of whether a person under testing has IBS. Users can choose according to their own needs, and these will not be elaborated upon here.

[0011] The beneficial effects of this invention are as follows: 1. This invention newly discovers six intestinal microorganisms associated with irritable bowel syndrome, including: Weissella cibaria, Bilophila wadsworthia, Erysipelotrichaceae bacterium, Bifidobacterium longum, Bacteroides thetaiotaomicron, and Ruminococcus gnavus.

[0012] After research and verification, it was found that one or more of the above six gut microbiota can be used to diagnose whether a person has irritable bowel syndrome (IBS).

[0013] 2. The present invention also provides a reagent and / or kit that can use one or more of the above 6 bacterial species as detection markers (i.e., microbial markers) to diagnose whether the person being tested has irritable bowel syndrome. It is completely non-invasive and highly accurate.

[0014] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to reach the species and even strain level, thereby improving the accuracy and reliability of diagnosis. The six species mentioned can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0015] 3. This invention also provides a product and / or prediction system for diagnosing irritable bowel syndrome (IBS). This product and / or prediction system can diagnose the risk of IBS in a test subject based on the relative abundance values ​​of various bacterial species. The entire diagnostic process is safe, non-invasive, and efficient, demonstrating good feasibility and accuracy. It can effectively assess the risk of IBS in a test subject's (fecal sample), with relatively accurate assessment results. Attached Figure Description

[0016] Figure 1 The graph shows the results of the linear discriminant analysis on the training set. Figure 2 Box plots of relative abundance in irritable bowel syndrome patients and healthy individuals for the validation set; Figure 3 ROC curve of the predicted scores for the validation set. Detailed Implementation

[0017] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0018] This invention provides a gut microbial biomarker for irritable bowel syndrome (IBS) and its application. The sample screening criteria are stringent, and the detection results are accurate and representative. It can be used as a predictive factor for IBS, thereby diagnosing whether a person being tested has IBS. Specifically, the technical concept of this invention is as follows: To address the clinical needs for the diagnosis and detection of irritable bowel syndrome (IBS), this invention collects samples from IBS patients and healthy individuals and uses a standardized experimental testing procedure (specific experimental methods are described in Examples 1-4) to screen out six microorganisms associated with IBS, including: *Weissella cibaria*, *Bilophila wadsworthia*, *Erysipelotrichaceae bacterium*, *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Ruminococcus gnavus*.

[0019] ROC curve analysis showed that the above six biomarkers have high specificity and sensitivity as detection variables, and these six bacterial species can be used as detection biomarkers for the prediction and diagnosis of irritable bowel syndrome.

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0021] Example 1: Sample Collection

[0022] The inclusion criteria for the irritable bowel syndrome (IBS) population group are as follows: 1. No age limit, covering all age groups; 2. Clinically diagnosed with IBS; 3. Stable vital signs.

[0023] The exclusion criteria for the irritable bowel syndrome (IBS) group were as follows: 1. Received IBS treatment within 1 month; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Had any chronic disease, including neurobehavioral disorders; 4. Received medications affecting gastrointestinal motility within 1 week; 5. Had a history of pain from functional dyspepsia, aerophagia, abdominal migraine, or functional constipation; 6. Exhibited growth retardation; 7. Had gastrointestinal obstruction or stricture; 8. Had a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. The researchers deemed the patient unsuitable for inclusion in this study.

[0024] The inclusion criteria for the control group (healthy individuals) are as follows: 1. No age limit, covering all age groups; 2. No irritable bowel syndrome or other neurological diseases; 3. No diabetes or other metabolic diseases; 4. No irritable bowel syndrome or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficient state; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study.

[0025] The exclusion criteria for the control group (healthy individuals) were the same as those for the irritable bowel syndrome group.

[0026] The fecal samples used in this invention were collected from Hubei Province. For both irritable bowel syndrome (IBS) patients and healthy individuals, written informed consent was obtained from both the patients and their legal guardians. Based on the aforementioned inclusion and exclusion criteria, a total of 302 IBS patients and 484 healthy individuals were collected. The statistical results are as follows: Table 1 Sample Information Table .

[0027] Example 2: DNA extraction, library construction, and sequencing 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0028] 2. After extraction, the DNA concentration was detected using Qubit, and the integrity of the extracted genomic DNA was detected by 1.5% agarose gel electrophoresis. The extracted genomic DNA was then subjected to quality control, and qualified genomic DNA samples were selected (DNA concentration ≥20ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0029] 3. For qualified DNA samples, random fragmentation, end repair, A base ligation, adapter and index are added. After adapter ligation, purification and library amplification are performed. After amplification, the DNA concentration is detected (DNA concentration ≥40 ng / μL).

[0030] 4. After the libraries pass the testing, different libraries are pooled according to the effective concentration and target data volume requirements before sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0031] Example 3: LEfSe analysis for screening microbial biomarkers 1. Split the dataset KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the participants (including those in the disease and healthy groups) were randomly selected as the training set, and the remaining 20% ​​of the samples were used as the validation set. The abundance data of each sample in the training set were then analyzed using LEfSe software, with the default LDA Score filter value set to 2. The sample information is shown in Table 1. The results are as follows Figure 1 As shown, the researchers identified six biomarkers that showed significant differences in the irritable bowel syndrome (IBS) group. These included: *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Weissella cibaria* were significantly reduced in the IBS group; while *Ruminococcus gnavus*, *Bilophilawadsworthia*, and *Erysipelotrichaceae bacterium* were significantly increased in the IBS group.

[0032] Example 4: Verifying the reliability of the above 6 microbial biomarkers 1. First, the remaining 20% ​​of the test subjects in Example 1 (including the irritable bowel syndrome group and the healthy group) were used as the validation set. The abundance data of each sample in the validation set were first subjected to binary logistic regression, and then the receiver operating function (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0033] 2. Use R language statistical software to calculate specificity and sensitivity and plot ROC curves. The software first calculates the threshold of the actual measurement value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN + FP) Sensitivity (true positive rate) = TP / (TP + FN).

[0034] The ROC curve can be constructed using 1 minus specificity and sensitivity, and the integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, 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 the certain indicator.

[0035] 4. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 2, the AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of 6 single bacterial species) and individual bacteria are shown in Table 2.

[0036] Table 2. ROC diagnostic curve results of the validation set

[0037] As shown in Table 2, for single bacterial species, Erysipelotrichaceaebacterium had the highest AUC value (approximately 0.881), while Weissella cibaria had the lowest AUC value (approximately 0.689); the AUC value of mimicry marker 6 (approximately 0.969) was higher than that of single bacteria.

[0038] As can be seen from the above, one or more of the six newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the six microbial markers is greater than 65%. Therefore, one or more of the six microbial markers can be used as detection markers for the diagnosis of patients with irritable bowel syndrome.

[0039] In Table 2, mimicry markers 1 to 6 can be collectively referred to as mimicry markers. Mimicry marker 1 is a combination of markers composed of *Bifidobacterium longum*, *Bacteroides polymorpha*, *Biliobacterium warfarinum*, *Erysipelothrix rhusiopathiae*, *Westernella foodae*, and active rumenococci. Mimicry marker 2 is a combination of markers composed of *Bifidobacterium longum*, *Bacteroides polymorpha*, *Biliobacterium warfarinum*, *Erysipelothrix rhusiopathiae*, and *Westernella foodae*. Mimicry marker 3 is a combination of markers composed of *Bacteroides polymorpha*, *Biliobacterium warfarinum*, *Erysipelothrix rhusiopathiae*, *Westernella foodae*, and active rumenococci. Mimicry marker 4 is a combination of markers composed of *Bifidobacterium longum*, *Bacteroides polymorpha*, *Biliobacterium warfarinum*, and *Erysipelothrix rhusiopathiae*. Mimicry marker 5 is a combination of markers composed of *Bifidobacterium longum*, *Bacteroides polymorpha*, and *Biliobacterium warfarinum*. Mimicry marker 6 is a combination of markers composed of *Biliobacterium warfarinum*, *Erysipelothrix rhusiopathiae*, and active rumenococci.

[0040] Example 5: Establishing a Logistic Regression Model Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker, the binary logistic regression algorithm in RStudio software was used to calculate the first disease probability (also known as the logarithm of dominance y) for each sample. Then, the disease probability optimization formula Z=exp(y) / {1+exp(y)} was used to calculate the disease probability Z of the sample under test. Finally, this disease probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establishing a model Based on the biomarkers identified above, and considering the proportion of irritable bowel syndrome (IBS) individuals and patients in the training set, the relative abundance values ​​of the six detected bacterial species were further used as single variables. The linear relationship between the relative abundance values ​​of these six single bacteria and the probability of disease in the samples was then discussed. A binary logistic regression equation was used to calculate the logarithm y (also known as the first probability value y) of the subject's dominance. y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6; Where A is the intercept term, B1 to B6 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium longum, x2 is the relative abundance value of Bacteroides thetaiotaomicron, and x3 is Bilophila wadsworthia. The relative abundance values ​​are: x4 is the relative abundance value of Erysipelotrichaceae bacterium, x5 is the relative abundance value of Weissella cibaria, and x6 is the relative abundance value of Ruminococcus gnavus.

[0041] b. Determine the values ​​of A and B1 to B6 above. After statistical analysis of the sample data, A was -1.6985, B1 was -36.5781, B2 was 20.8833, B3 was 216.6747, B4 was 767.3500, B5 was -2739.0397, and B6 was 6072.4188. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=-1.6985-36.5781×X1+20.8833×X2+216.6747×X3+767.3500×X4-2739.0397×X5+6072.4188×X6; c. Calculate the disease probability Z of the subjects to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the subject is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the subject is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0042] After processing, the formula for calculating the probability Z of disease is: ; d. Validation set data calculation and statistical analysis Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group for each sample was obtained. Then, the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z of the test sample being a patient was then calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a patient with irritable bowel syndrome.

[0043] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0044] Table 3. Relevant data on validation set markers

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] Note: In Table 3, E represents powers of 10. For example, 6.78E-05 means 6.78 * 10^6. -5 .

[0057] Table 4. Statistical data on the abundance of biomarkers in the validation set. .

[0058] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0059] It should be noted that the predicted probabilities of individual samples in Table 3 deviate from the clinical labels. This is a normal statistical phenomenon under continuous probability output by the logistic regression model. Especially when the samples are located in the overlapping area of ​​disease and health status or are affected by factors such as sampling timing, dietary fluctuations, and instantaneous changes in the gut microbiota, the model judgment is not necessarily completely consistent with the gold standard label. This is difficult to completely avoid in cohort cross-validation. Other methods can be used for subsequent detection, such as blood routine tests and diagnostic physical signs.

[0060] e. Results and Analysis Combining the results of Examples 4 and 5, it can be seen that, for single bacterial species, *Erysipelotrichaceae bacterium* has the highest AUC value (approximately 0.881), while *Weissella cibaria* has the lowest AUC value (approximately 0.689). For mimicry markers, mimicry marker 6 has an AUC value of approximately 0.969, an optimal cutoff value of approximately 0.282, a sensitivity of approximately 0.930, and a specificity of 0.979.

[0061] The AUC of the six microbial markers identified in this application is all greater than 65%. One or more of the six microbial markers can be used as detection markers for the diagnosis of patients with irritable bowel syndrome. At the same time, since this invention only requires the collection of stool samples from the test subjects, this invention is not only completely non-invasive, but also highly accurate, and can be used to diagnose whether the test subjects (stool samples) have irritable bowel syndrome.

[0062] Based on the results obtained from the description in Table 3 above and the calculation formula for the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease in the sample to be tested, which is summarized in this application, is basically correct and can be used to diagnose the risk and probability of disease in the sample to be tested. The probability of disease in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal sample of the person to be tested may produce false positive or false negative results. Further testing using other methods is required, including blood routine tests, diagnostic physical signs, etc.

[0063] Example 6: Examining the prevalence of irritable bowel syndrome in a validation set of patients and patients with irritable bowel syndrome. Based on the product and method of Example 5, the probability of disease in healthy individuals and patients with irritable bowel syndrome was examined and verified. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each individual bacterial strain in the intestine; among which, the individual bacterial strains are Weissella cibaria, Bilophila wadsworthia, Erysipelotrichaceae bacterium, Bifidobacterium longum, Bacteroides thetaiotaomicron, and Ruminococcus gnavus. S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6; Where A is the intercept term, B1 to B6 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium longum, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Bilophila wadsworthia, x4 is the relative abundance value of Erysipelotrichaceae bacterium, x5 is the relative abundance value of Weissella cibaria, and x6 is the relative abundance value of Ruminococcus gnavus. S3. Calculate the probability Z of the subject being diagnosed with irritable bowel syndrome based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y; The probability of disease Z can also be expressed as: ; S4. Based on the comparison between the patient's probability Z-score and the reference value, diagnose or predict the risk of the subject having irritable bowel syndrome.

[0064] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has irritable bowel syndrome (IBS); a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient or have IBS, requiring further testing using methods such as mental health assessments and medication analysis. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0065] It should be noted that although this embodiment only lists the methods and means of quantitative detection of the sample to be tested through relative abundance value, other means of quantitative detection of microorganisms are also feasible for the present invention (such as absolute abundance or total microbial load information, etc.), and can also be used to assist in the diagnosis of whether the sample to be tested suffers from irritable bowel syndrome. People can choose the appropriate quantitative detection method of microorganisms according to their own needs, which will not be elaborated here.

[0066] Example 7: Detection Reagent Based on the description of embodiments 1 to 5 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical staff can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has irritable bowel syndrome. Medical staff can also combine multiple single bacterial species together as biomarkers to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has irritable bowel syndrome.

[0067] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing irritable bowel syndrome (IBS) to diagnose whether a sample to be tested has IBS. Simultaneously, the microbial markers can be selected from the six single bacterial species found in this application that are associated with IBS. That is, the microbial markers in the detection reagent can include one or more of the following: *Weissella cibaria*, *Bilophila wadsworthia*, *Erysipelotrichaceae bacterium*, *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Ruminococcus gnavus*.

[0068] Example 8: Reagent Kit This embodiment also provides a kit that may contain the detection reagent described in Example 7 to diagnose whether a sample to be tested suffers from irritable bowel syndrome (IBS). The limitations and technical solutions of the kit in this application can be found in the description of Example 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting IBS, and will not be repeated here.

[0069] Example 9: Products for diagnosing irritable bowel syndrome This application also provides a product for diagnosing irritable bowel syndrome (IBS) to diagnose whether a sample to be tested has IBS. The product is specific to one or more of the six single bacterial species associated with IBS discovered in this application. The product includes primers, probes, antibodies, test strips, aptamers, or chips. Primers, probes, and antibodies can all be used to make reagents and kits, which is a conventional method in the art and will not be described in detail here.

[0070] As can be seen from the descriptions of Examples 1 to 6, when one or more of the six bacterial species described in this application are used as detection markers (i.e., microbial markers) to diagnose whether a person under test has irritable bowel syndrome, it is a technical solution that those skilled in the art can implement to produce corresponding reagents, test strips, aptamers, and chips. That is, reagents, test strips, aptamers, and chips can also be used to assist in the diagnosis of whether a person under test has irritable bowel syndrome. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0071] The method for diagnosing whether a person has irritable bowel syndrome is completely non-invasive and highly accurate. Using the six newly discovered single bacterial species as microbial biomarkers, the creation of corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) should be feasible for those skilled in the art, and will not be elaborated upon here.

[0072] Example 10: Diagnosing whether a test sample is from a patient with irritable bowel syndrome If it is necessary to diagnose whether a person under test has irritable bowel syndrome, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme test, and liver tissue biopsy, medical staff can also use the methods or products described in Examples 1 to 9 above to diagnose the person under test, in order to assist medical staff in making a more accurate judgment: (1) If, after continuous observation over multiple time periods, the content of one or more of the following bacteria is found to be high (compared to the mean and standard deviation of the irritable bowel syndrome group in Table 4), or even shows a significant increasing trend, then the person being tested is more likely to be a patient with irritable bowel syndrome.

[0073] (2) If, after continuous observation over multiple time periods, the person being tested is found to have a high level of one or more of Bifidobacterium longum, Bacteroides thetaiotaomicron, and Weissella cibaria (compared to the mean and standard deviation of the healthy group in Table 4), then the person being tested is more likely to be a healthy person.

[0074] (3) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figure 1 According to Table 4, a probabilistic judgment is made on whether the person being tested is a patient with irritable bowel syndrome; (4) If medical staff want to more accurately determine whether the person to be tested is a patient with irritable bowel syndrome, they can calculate the probability that the person to be tested is a patient with irritable bowel syndrome based on the quantitative detection results of 6 single bacterial species (such as relative abundance values) and with reference to the technical solutions described in Examples 5 to 7.

[0075] Conclusion and explanation: 1. By Figures 1-3 As shown in Table 2, any one of the six newly discovered single bacterial species in this application can serve as a microbial biomarker for irritable bowel syndrome (IBS). Each single bacterial species exhibits both sensitivity and specificity for IBS. Therefore, the microbial biomarker for IBS can be selected from any one or more of the six newly discovered single bacterial species in this application. Specifically: Microbial markers for irritable bowel syndrome can be selected from one or more of the following: Weissella cibaria, Bilophila wadsworthia, Erysipelotrichaceaebacterium, Bifidobacterium longum, Bacteroides thetaiotaomicron, and Ruminococcus gnavus.

[0076] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing intestinal samples. This is because there are individual differences. The probability of disease Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has irritable bowel syndrome.

[0077] 3. Predictive effect: The AUC value of mimicry marker 6 (a marker formed by the combination of Bacillus voricifolius, Erysipelothrix rhusiopathiae, and active Ruminococcus) (approximately 0.969) is higher than that of a single bacterium, and its detection result (AUC value) is not lower than the lowest AUC value of multiple single bacterial species.

[0078] 4. All six newly discovered bacterial species in this application can be used as detection variables. They all have high specificity and sensitivity, and the AUC of all six microbial markers is greater than 65%. One or more of the six microbial markers can be used as detection markers for the diagnosis of patients with irritable bowel syndrome.

[0079] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. The application of a reagent for quantitative detection of microbial markers in the preparation of products for diagnosing irritable bowel syndrome, characterized in that, The microbial markers included active Ruminococcus gnavus, Erysipelotrichaceae bacterium, and Bilophila wadsworthia, which were significantly increased in the irritable bowel syndrome population.

2. The application according to claim 1, characterized in that, The microbial markers also include Weissella cibaria, which is significantly reduced in the irritable bowel syndrome population.

3. The application according to claim 1 or 2, characterized in that, The microbial markers also include Bifidobacterium longum and / or Bacteroides thetaiotaomicron, which are significantly reduced in the irritable bowel syndrome population.

4. The application according to claim 3, characterized in that, The microbial markers are a combination of markers consisting of Weissella cibaria, Bilophila wadsworthia, Erysipelotrichaceae bacterium, Bifidobacterium longum, Bacteroides thetaiotaomicron, and Ruminococcus gnavus. The levels of *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Weissella cibaria* were significantly reduced in the irritable bowel syndrome (IBS) population; while the levels of *Ruminococcus gnavus*, *Bilophila wadsworthia*, and *Erysipelotrichaceae bacterium* were significantly increased in the IBS population.

5. A microbial biomarker associated with irritable bowel syndrome, characterized in that, The microbial markers include active Ruminococcus gnavus, Erysipelotrichaceae bacterium, and Bilophila wadsworthia.

6. The microbial marker according to claim 5, characterized in that, The microbial markers also include one or more of Bifidobacterium longum, Bacteroides thetaiotaomicron, and Weissella cibaria.

7. The microbial marker according to claim 6, characterized in that, The microbial markers are a combination of markers consisting of Weissella cibaria, Bilophila wadsworthia, Erysipelotrichaceae bacterium, Bifidobacterium longum, Bacteroides thetaiotaomicron, and Ruminococcus gnavus.

8. A product for diagnosing or predicting irritable bowel syndrome, characterized in that: The product includes one or more of reagents, test strips, aptamers, and chips, and the product is specific to the microbial biomarkers of any one of claims 5 to 7 and is used for the quantitative detection of the microbial biomarkers of any one of claims 5 to 7.

9. A reagent kit, characterized in that: The kit contains reagents for the quantitative detection of microbial markers as described in any one of claims 5 to 7.

10. The use of the kit of claim 9 in the preparation of a product for diagnosing irritable bowel syndrome (IBS), wherein the presence of *Bifidobacterium longum*, *Bacteroides thetaiotaomicron*, and *Weissella cibaria* is significantly reduced in the IBS population; and the presence of *Ruminococcus gnavus*, *Bilophila wadsworthia*, and *Erysipelotrichaceae bacterium* is significantly increased in the IBS population.

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