Diarrhea gut microbial markers and uses thereof

Abstract

The application discloses a diarrhea intestinal microorganism marker and application thereof, and belongs to the field of biological medicine. The microorganism marker comprises one or more of Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus and Fusobacterium mortiferum. The application provides a product (such as a kit) comprising detection reagents for detecting the relative abundance of the microorganism marker. The product provided by the application has good feasibility and accuracy, can effectively evaluate whether a teenager has a risk of diarrhea, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, and specifically relates to a gut microbial biomarker for diarrhea and its application. Background Technology

[0002] Diarrhea can lead to dehydration and malnutrition. Currently, the main diagnostic methods for diarrhea include clinical assessment and colonoscopy: 1) Clinical assessment focuses on medical history collection and stool characteristic grading (such as the Bristol stool type), which is convenient to use in primary screening and epidemiological surveys at the grassroots level. However, its results are highly dependent on the patient's subjective recollection of the frequency and characteristics of defecation, making it difficult to distinguish the etiological differences between functional diarrhea and organic lesions; 2) Colonoscopy is the gold standard for diagnosing inflammatory bowel disease and colorectal tumors, but it has limitations such as being invasive, expensive, and requiring complicated bowel preparation, making it unsuitable for mild cases and large-scale screening at the grassroots level.

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

[0004] To address the aforementioned technical problems, this invention provides a gut microbiota marker for diarrhea and its application, which can accurately diagnose whether a person being tested has diarrhea.

[0005] The technical solution provided by this invention is as follows: In a first aspect, a reagent for the quantitative detection of microbial markers is provided for use in the preparation of diarrhea-related products, wherein the microbial markers include active Ruminococcus gnavus, Campylobacter concisus, and Fusobacterium mortiferum, which are significantly increased in patients with diarrhea.

[0006] In a second aspect, a gut microbial biomarker for diagnosing or predicting diarrhea is provided, the biomarker comprising Coprococcus catus, Ruminococcus gnavus, Fusobacterium mortiferum, Rothia mucilaginosa, Streptococcus salivarius, and Campylobacter concisus.

[0007] Thirdly, a gut microbial biomarker for diagnosing or predicting diarrhea is provided, said microbial biomarker being any combination of the following: a) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Campylobacter concisus, and Fusobacterium mortiferum; b) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Rothia mucilaginosa, Campylobacter concisus, and Fusobacterium mortiferum. c) The microbial markers are a combination of markers consisting of Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum.

[0008] Thirdly, a product for diagnosing or predicting diarrhea 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.

[0009] Fourthly, a kit for diagnosing or predicting diarrhea is provided, the kit containing reagents for quantitative detection of the microbial markers.

[0010] Fifthly, a kit is provided for use in the preparation of products for diagnosing diarrhea, wherein the *Coprococcus catus* and *Streptococcus salivarius* are significantly reduced in patients with diarrhea; and the *Ruminococcus gnavus*, *Rothia mucilaginosa*, *Campylobacter concisus*, and *Fusobacterium mortiferum* are significantly increased in patients with diarrhea.

[0011] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and diarrhea, which can be used to diagnose whether a person being tested has diarrhea. 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 diagnosing whether a person being tested has diarrhea. Users can choose according to their own needs, and these will not be elaborated upon here.

[0012] The beneficial effects of this invention are as follows: 1. This invention newly discovers six intestinal microorganisms associated with diarrhea, including: Streptococcus salivarius, Ruminococcus gnavus, Fusobacterium mortiferum, Rothia mucilaginosa, Campylobacter concisus, and Coprococcus catus.

[0013] After research and verification, it was found that one or more of the above six intestinal microorganisms can be used to diagnose whether a person has diarrhea.

[0014] 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 diarrhea. It is completely non-invasive and highly accurate.

[0015] 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.

[0016] 3. This invention also provides a product for diagnosing diarrhea. This product can diagnose the risk of diarrhea in a person being tested 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 diarrhea in a person being tested (based on their stool sample), and the assessment results are relatively accurate. Attached Figure Description

[0017] Figure 1 The graph shows the results of the linear discriminant analysis on the training set. Figure 2 Box plots of relative abundance in diarrhea patients and healthy individuals for the validation set; Figure 3 The ROC curve for the prediction score of the validation set. Detailed Implementation

[0018] 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.

[0019] Diarrhea is a clinical syndrome characterized by increased frequency of bowel movements (≥3 times per day), loose stools (water content >85%), or stools containing mucus, pus, or blood. It encompasses acute diarrhea (duration <2 weeks), persistent diarrhea (2–4 weeks), and chronic diarrhea (>4 weeks). Based on etiology, it can be classified into infectious diarrhea (bacterial, viral, parasitic), non-infectious diarrhea (food intolerance, drug reaction, functional bowel disorder), and inflammatory bowel disease-related diarrhea, among which acute infectious diarrhea is the most common.

[0020] The core danger of diarrhea lies in the massive loss of body fluids and electrolytes caused by diarrhea, which can rapidly lead to dehydration, hypokalemia, and metabolic acidosis, especially potentially fatal to infants and the elderly. Long-term chronic diarrhea can lead to protein-energy malnutrition, micronutrient deficiencies, and decreased immune function, and significantly increase the risk of growth retardation and cognitive impairment in children, making it a significant public health problem threatening the health of grassroots populations in my country.

[0021] Data from the Chinese Center for Disease Control and Prevention shows that approximately 800 million people suffer from diarrhea each year in my country, with an annual incidence rate as high as 1.5 to 2.0 times per child under the age of 5. In economically underdeveloped areas, diarrhea remains one of the top five causes of death among children.

[0022] Diarrhea is not a single disease, but a syndrome with highly heterogeneous etiologies. Common types can be classified by etiology as viral (such as rotavirus and norovirus), bacterial (such as Escherichia coli, Shigella, and Salmonella), and parasitic (such as Giardia and Entamoeba histolytica). According to clinical characteristics, it can be classified as secretory, exudative, osmotic, and motility-dysfunctional diarrhea.

[0023] The causes of diarrhea can be attributed to the combined effects of multiple factors, including pathogenic microbial infection, intestinal flora imbalance, food allergy or intolerance, gut-brain axis dysfunction, adverse drug reactions, and systemic diseases (such as hyperthyroidism), leading to an imbalance in intestinal secretion and absorption and abnormal intestinal motility. If not intervened in time, acute diarrhea can progress to chronic diarrhea, significantly increasing the risk of serious complications such as severe dehydration, electrolyte imbalance, intestinal perforation, reactive arthritis, and hemolytic uremic syndrome.

[0024] For patients with diarrhea, the main diagnostic methods currently include clinical assessment and colonoscopy: 1) Clinical assessment focuses on medical history collection and stool characteristics grading (such as the Bristol stool type), which is convenient to use in primary screening and epidemiological surveys at the grassroots level. However, its results are highly dependent on the patient's subjective recollection of the frequency and characteristics of defecation, and it is difficult to distinguish the etiological differences between functional diarrhea and organic lesions; 2) Colonoscopy is the gold standard for diagnosing inflammatory bowel disease and colorectal tumors, but it has limitations such as being invasive, expensive, and requiring complicated bowel preparation, and is not suitable for mild cases and large-scale screening at the grassroots level.

[0025] Therefore, although there are currently many diagnostic protocols for diarrhea, these protocols still have certain limitations in terms of etiological identification, ease of operation, and universality. Thus, there is a need in this field to develop a new product to assist in the accurate detection of the etiology of diarrhea or the assessment of intestinal function, and to meet the diagnostic and treatment needs in different scenarios.

[0026] This invention provides a microbial biomarker related to diarrhea and its application. It not only utilizes samples from people of all ages, but also provides a large sample size, resulting in accurate and representative test results. This biomarker can be used as a predictor of diarrhea, thereby diagnosing whether a person being tested has diarrhea. Specifically, the technical concept of this invention is as follows: To address the clinical needs for the diagnosis and detection of diarrhea, this invention collects samples from diarrhea patients and healthy individuals and uses a standardized experimental testing procedure (specific experimental methods are described in Examples 1-4) to screen out six diarrhea-related microorganisms, including: Streptococcus salivarius, Ruminococcus gnavus, Fusobacterium mortiferum, Rothia mucilaginosa, Campylobacter concisus, and Coprococcus catus.

[0027] 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 diarrhea patients.

[0028] 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.

[0029] Example 1: Sample Collection

[0030] Inclusion criteria for the diarrhea group: 1. No age limit, covering all age groups; 2. Clinically diagnosed with diarrhea; 3. Stable vital signs.

[0031] Exclusion criteria for the diarrhea group: 1. Received diarrhea treatment within 1 month; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Have any chronic disease, including neurobehavioral disorders; 4. Received medication that affects gastrointestinal motility within 1 week; 5. Have a history of pain from functional dyspepsia, aerophagia, abdominal migraine, or functional constipation; 6. Showed growth retardation; 7. Have gastrointestinal obstruction or stricture; 8. Have a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. The researchers deemed the patient unsuitable for inclusion in this study.

[0032] Inclusion criteria for the control group (healthy individuals): 1. No age limit, covering all age groups; 2. No depression or other neurological disorders; 3. No diabetes or other metabolic diseases; 4. No diarrhea 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.

[0033] The exclusion criteria for the control group (healthy individuals) were the same as those for the diarrhea group.

[0034] The fecal samples used in this invention were collected from Hubei Province. Written informed consent was obtained from both diarrhea patients and healthy individuals, as well as their legal guardians (if any). Based on the aforementioned inclusion and exclusion criteria, a total of 341 diarrhea patients and 430 healthy individuals were collected. The statistical results are as follows: Table 1 Sample Information Table .

[0035] Example 2: DNA extraction, library construction, and sequencing

[0036] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0037] 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).

[0038] 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).

[0039] 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.

[0040] Example 3: LEfSe analysis for screening microbial biomarkers

[0041] 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 group and the healthy group) 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 screened out six biomarkers that were significantly increased in the diarrhea group, including *Streptococcus salivarius*, *Ruminococcus gnavus*, *Fusobacterium mortiferum*, *Rothia mucilaginosa*, *Campylobacter concisus*, and *Coprococcus catus*.

[0042] Example 4: Verifying the reliability of the above 6 microbial biomarkers

[0043] 1. First, the remaining 20% ​​of the test subjects in Example 1 (including the diarrhea 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 characteristic (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0044] 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).

[0045] 3. The ROC curve can be constructed using 1 minus specificity and sensitivity. 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.

[0046] 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 marker 1 (a marker formed by a combination of 6 single bacterial species) and individual bacteria are as follows: Table 2. ROC diagnostic results of validation set markers .

[0047] As shown in Table 2, for single bacterial species, Streptococcus salivarius has the highest AUC value (approximately 0.906), while Campylobacter contortus has the lowest AUC value (approximately 0.690). The AUC value of mimicry marker 1 (a marker formed by a combination of 6 single bacterial species) (approximately 1) is higher than that of single bacteria.

[0048] 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 all 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 diarrhea patients.

[0049] In Table 2, mimicry markers 1 to 7 can be collectively referred to as mimicry markers. Mimicry marker 1 is a combination of markers consisting of obvious fecal cocci, salivarius streptococci, active rumenococci, *Roseidon mucosae*, *Campylobacter simplicissima*, and *Fusobacterium catarrhalis*. Mimicry marker 2 is a combination of markers consisting of active rumenococci, *Roseidon mucosae*, *Campylobacter simplicissima*, and *Fusobacterium catarrhalis*. Mimicry marker 3 is a combination of markers consisting of obvious fecal cocci, salivarius streptococci, active rumenococci, and *Fusobacterium catarrhalis*. Mimicry marker 4 is a combination of markers consisting of active rumenococci, *Roseidon mucosae*, and *Campylobacter simplicissima*. Mimicry marker 5 is a combination of markers consisting of obvious fecal cocci, salivarius streptococci, *Roseidon mucosae*, and *Campylobacter simplicissima*. Mimicry marker 6 is a combination of markers consisting of active rumenococci, *Campylobacter simplicissima*, and *Fusobacterium catarrhalis*. Mimicry marker 7 is a combination of markers consisting of obvious fecal cocci and *Roseidon mucosae*.

[0050] Example 5: Establishing a Logistic Regression Model

[0051] 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 the proportion of diarrhea 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 the six single bacteria and the probability of disease in the samples was then discussed. The logarithm y (also known as the first probability value y) of the dominance of the test subject was calculated using a 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 Rothiamucilaginosa, x2 is the relative abundance value of Campylobacter concisus, x3 is the relative abundance value of Streptococcus salivarius, x4 is the relative abundance value of Coprococcus catus, x5 is the relative abundance value of Ruminococcus gnavus, and x6 is the relative abundance value of Fusobacterium mortiferum.

[0052] b. Determine the values ​​of A and B1 to B6 above. After statistical analysis of the sample data, A was -0.4891, B1 was 2516.7839, B2 was 23.7761, B3 was -106.513, B4 was -11.5764, B5 was 8872.6983, and B6 was 171.2074. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=-0.4891+2516.7839×X1+23.7761×X2-106.513×X3-11.5764×X4+8872.6983×X5+171.2074×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.

[0053] 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.

[0054] Table 4 shows the mean and standard deviation of the relative abundance of each bacterial species. The mean relative abundance determines the central location of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The p-value is a statistic calculated using the formula of the rank-sum test. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0055] Table 3. Relevant data on validation set markers

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] .

[0066] Table 4. Relevant data on validation set markers

[0067] Note: e represents a power of 10. For example, 7.39713361073e-05 means 7.39713361073 * 10^ ... -5 Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similarly defined.

[0068] 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.

[0069] e. Results and Analysis Based on the results of Examples 4 and 5, it can be seen that, for single bacterial species, *Streptococcus salivarius* had the highest AUC value (approximately 0.906), while *Campylobacter simplicissima* had the lowest AUC value (approximately 0.690). The AUC value of mimicry marker 1 (a marker formed by a combination of 6 single bacterial species) (approximately 1) was higher than that of single bacterial species.

[0070] The AUC of the six microbial markers identified in this application is all greater than 70%. One or more of the six microbial markers can be used as detection markers for the diagnosis of diarrhea patients. At the same time, since this invention only requires the collection of stool samples from the person to be tested, this invention is not only completely non-invasive, but also highly accurate, and can be used to diagnose whether the person to be tested (stool sample) has diarrhea.

[0071] 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.

[0072] Example 6: Examining the probability of disease in patients in the validation group and patients with diarrhea

[0073] Based on the product and method of Example 5, the probability of disease in healthy individuals and patients with diarrhea 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 strain in the intestine; wherein, the individual strains include Campylobacter concisus, Rothiamucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum.

[0074] 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 Rothiamucilaginosa, x2 is the relative abundance value of Campylobacter concisus, x3 is the relative abundance value of Streptococcus salivarius, x4 is the relative abundance value of Coprococcus catus, x5 is the relative abundance value of Ruminococcus gnavus, and x6 is the relative abundance value of Fusobacterium mortiferum.

[0075] S3. Calculate the probability Z of the subject being a diarrhea patient 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-value and the reference value, diagnose or predict the risk of the subject having diarrhea.

[0076] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has diarrhea; 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 with diarrhea or simply have diarrhea. In this case, further testing using other methods, such as mental health testing and drug testing, is necessary. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0077] 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 has diarrhea. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0078] Example 7: Detection Reagent

[0079] 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 diarrhea. 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 diarrhea.

[0080] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing diarrhea to diagnose whether a sample to be tested has diarrhea; at the same time, the microbial markers can be selected from the six single bacterial species related to diarrhea discovered in this application, that is, the microbial markers in the detection reagent can include one or more of Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum.

[0081] Example 8: Reagent Kit

[0082] This embodiment also provides a reagent kit, which may contain the detection reagent described in Example 7, to diagnose whether a sample to be tested has diarrhea; the limitations and technical solutions of this application for the reagent kit can be referred to the description of Example 7 above, and will not be repeated here. Similarly, the above reagent kit can also be used in the preparation of diarrhea detection products, and will not be repeated here.

[0083] Example 9: Products for diagnosing diarrhea

[0084] This application also provides a product for diagnosing diarrhea, to diagnose whether a sample to be tested has diarrhea; the product is specific to one or more of the six single bacterial species related to diarrhea discovered in this application, and the product includes primers, probes, antibodies, test strips, aptamers or chips, wherein primers, probes and antibodies can be used to make reagents and kits, which are conventional methods in the art and will not be described in detail here.

[0085] As can be seen from the descriptions of Examples 1 to 5, 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 being tested has diarrhea, 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 diagnosing whether a person being tested has diarrhea. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0086] The method for diagnosing whether a person is suffering from diarrhea 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.

[0087] Example 10: Diagnosing whether a test sample belongs to a patient with diarrhea

[0088] If it is necessary to diagnose whether a person being tested has diarrhea, 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 1-9 above to diagnose the person being tested, 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 diarrhea group in Table 4), or even shows a significant increasing trend, then the person being tested is more likely to be a patient with diarrhea.

[0089] (2) If, after continuous observation over multiple time periods, the content of one or more of Coprococcus catus and Streptococcus salivarius is found to be significantly higher (compared to the mean and standard deviation of the diarrhea group in Table 4), or even shows a significant increasing trend, then the person being tested is more likely to be a healthy person.

[0090] (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 preliminary judgment is made on whether the person to be tested is a patient with diarrhea; (4) If medical staff want to determine more accurately whether the person to be tested is a diarrhea patient, they can calculate the probability that the person to be tested is a diarrhea patient based on the quantitative detection results of 6 single bacterial species (such as relative abundance values) and the technical solutions described in Examples 5 to 7.

[0091] Conclusion and explanation: 1. By Figures 1-3As shown in Table 2, any one of the six newly discovered single bacterial species in this application can be used as a microbial biomarker for diarrhea. Each single bacterial species has both sensitivity and specificity for diarrhea. Therefore, the microbial biomarker for diarrhea can be selected from any one or more of the six newly discovered single bacterial species in this application. Specifically: One or more of the following: Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum.

[0092] 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 diarrhea.

[0093] 3. Predictive effect: The AUC value (approximately 1) of the mimicry marker 1 (a marker formed by the combination of 6 single bacterial species) is higher than that of a single bacterial species. When other microorganisms are added to one or several single bacterial species for testing, the test result (AUC value) will not be lower than the lowest AUC value of multiple single bacterial species.

[0094] 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 diarrhea patients.

[0095] 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 diarrhea, characterized in that, The microbial markers include active Ruminococcus gnavus, Campylobacter concisus, and Fusobacterium mortiferum, which are significantly increased in patients with diarrhea.

2. The application according to claim 1, characterized in that, The microbial markers also include Rothia mucilaginosa and / or Coprococcus catus, wherein Coprococcus catus is significantly reduced in patients with diarrhea and Rothia mucilaginosa is significantly increased in patients with diarrhea.

3. The application according to claim 1 or 2, characterized in that, The microbial markers also include Streptococcus salivarius, which is significantly reduced in patients with diarrhea.

4. The application according to claim 3, characterized in that, The microbial markers are a combination of markers consisting of Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum. The Coprococcus catus and Streptococcus salivarius are significantly reduced in patients with diarrhea. The active rumenococcus (Ruminococcus gnavus), Rothia mucilaginosa, Campylobacter concisus, and Fusobacterium mortiferum were significantly increased in patients with diarrhea.

5. A gut microbiota marker for diagnosing or predicting diarrhea, characterized in that, The microbial markers include Coprococcus catus, Ruminococcus gnavus, Fusobacterium mortiferum, Rothia mucilaginosa, Streptococcus salivarius, and Campylobacter concisus.

6. A gut microbiota marker for diagnosing or predicting diarrhea, characterized in that, The microbial biomarker is any one of the following combinations: a) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Campylobacter concisus, and Fusobacterium mortiferum; b) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Rothia mucilaginosa, Campylobacter concisus, and Fusobacterium mortiferum. c) The microbial markers are a combination of markers consisting of Campylobacter concisus, Rothia mucilaginosa, Coprococcus catus, Streptococcus salivarius, Ruminococcus gnavus, and Fusobacterium mortiferum.

7. A product for diagnosing or predicting diarrhea, 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 claim 5 or 6 and is used for the quantitative detection of the microbial biomarkers of claim 5 or 6.

8. A kit for diagnosing or predicting diarrhea, characterized in that: The kit contains reagents for the quantitative detection of the microbial markers described in claim 5 or 6.

9. The use of the kit of claim 8 in the preparation of a product for diagnosing diarrhea, wherein the *Coprococcus catus* and *Streptococcus salivarius* are significantly reduced in patients with diarrhea; and the *Ruminococcus gnavus*, *Rothia mucilaginosa*, *Campylobacter concisus*, and *Fusobacterium mortiferum* are significantly increased in patients with diarrhea.

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