Microbial marker related to intractable diarrhea, product and application thereof

Abstract

The invention discloses a microbial marker related to refractory diarrhea, a product and application of the microbial marker, and relates to the technical field of biomedicine. According to the present invention, the microbial marker comprises any one or more of Ruminococcus sp, Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, Rosa mucilaginosa, and Coprococcus catenus, and the microbial marker comprises any one or more of the following components: the Ruminococcus sp, the Bifidobacterium longum, the Lachnospiraceae bacterium, the Klebsiella pneumoniae, the Rosa mucilaginosa, and the Coprococcus catenus. The microbial marker can serve as a detection marker to be applied to diagnosis of refractory diarrhea patients, and when the refractory diarrhea patients are diagnosed, the microbial marker is completely noninvasive and high in accuracy.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and specifically relates to a microbial marker related to refractory diarrhea, a product thereof, and its application. Background Art

[0002] Refractory diarrhea is a type of diarrhea symptom with a longer duration, recurring attacks, and difficulty in being cured. Its main characteristics are a long course of disease, easy recurrence, and difficulty in being controlled by conventional treatments. The causes of adult refractory diarrhea are complex and diverse, including non-infectious intestinal inflammatory diseases (such as inflammatory bowel disease), gastrointestinal tumor diseases, gastrointestinal dysfunction (such as diarrhea-predominant irritable bowel syndrome, functional diarrhea), malabsorption syndrome (such as chronic pancreatitis, liver cirrhosis), and infectious diarrhea (such as intestinal tuberculosis), etc.

[0003] Currently, the methods for diagnosing refractory diarrhea are mainly divided into the following categories: blood tests, fecal tests, hydrogen breath tests, flexible sigmoidoscopy or colonoscopy, and upper gastrointestinal endoscopy.

[0004] In blood tests, a complete blood count, electrolyte measurement, and renal function examination may help to understand the severity of diarrhea; fecal tests can check whether bacteria or parasites cause diarrhea; hydrogen breath tests can check whether the patient has lactose intolerance; medical professionals can extract a small amount of tissue samples from the colon through flexible sigmoidoscopy or colonoscopy to view the intestinal conditions; medical professionals can also check the conditions of the stomach and the upper part of the small intestine through upper gastrointestinal endoscopy.

[0005] Although the above methods for diagnosing refractory diarrhea have certain curative effects on refractory diarrhea, because the prediction, diagnosis, and treatment of refractory diarrhea need to comprehensively consider various factors, currently, no hypothesis can perfectly explain the cause of refractory diarrhea fundamentally. Therefore, it is necessary to provide a new idea and approach for the diagnosis and treatment of refractory diarrhea. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, and provides a microbial marker related to refractory diarrhea, a product thereof, and its application, so as to provide a new idea and approach for the diagnosis and treatment of refractory diarrhea.

[0007] To achieve the above purpose, the technical solution designed by the present invention is as follows:

[0008] In the first aspect, the present invention provides a microbial marker related to refractory diarrhea, and the microbial marker includes Ruminococcus.sp and / or Rothia mucilaginosa.

[0009] Furthermore, the microbial marker also includes one or more of Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, and Coprococcus catus.

[0010] Furthermore, the microbial marker is Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus.

[0011] In a second aspect, the present invention also provides the use of a reagent for detecting a microbial marker in the preparation of a product for diagnosing or screening refractory diarrhea, wherein the microbial marker includes one or more of Bacteroides stercoris, Bacteroides vulgatus, Bacteroides thetaiotaomicron, Ruminococcus, and Lactobacillus salivarius.

[0012] In a third aspect, the present invention also provides a kit, which includes a reagent for detecting the microbial marker.

[0013] In a fourth aspect, the present invention also provides the use of the kit in the preparation of a product for detecting obesity, and / or the use of the detection reagent in the kit in the preparation of a kit for diagnosing refractory diarrhea.

[0014] In a fifth aspect, the present invention also provides a product for diagnosing refractory diarrhea, which includes a primer, a probe, an antibody, an aptamer, or a chip that is specific to the microbial marker.

[0015] In a sixth aspect, the present invention also provides a computer program product related to refractory diarrhea, which is used to execute the risk of diagnosing a subject to be tested with refractory diarrhea, including the following steps:

[0016] 1) Obtain the relative abundance value of each single bacterial species in the feces of the subject to be tested; the single bacterial species is any one of Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceaebacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus;

[0017] 2) Calculate the log odds of the subject to be tested according to the binary logistic regression equation, denoted as the first probability value y;

[0018] 3) Substitute the above first probability value y into formula (2) to calculate the probability that the subject to be tested is a healthy person. Formula (2) is: P = exp(y) / {1 + exp(y)};

[0019] where P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0020] 4) According to the comparison result of the P value and the reference value, diagnose or predict the risk that the subject to be tested has refractory diarrhea.

[0021] Further, the formula of the binary logistic regression equation is:

[0022] y = A + B 1 *x1 + B 2 *x2 + B 3 *x3 + B 4 *x4 + B 5 *x5 + B 6 *x6;

[0023] where A is the intercept term, and B 1 ~B 6 are the regression coefficients of the independent variables; x1~x6 are the relative abundance values of Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceaebacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus in sequence.

[0024] Still further, the A is -0.4226, B 1 is 8.9175, B 2 is 3.5501, B 3is 226.5691, B 4 is -52.6407, B 5 is -737.6975, B 6 is 188.5085.

[0025] Advantages of the present invention:

[0026] 1. By collecting samples from patients with refractory diarrhea and healthy individuals, performing metagenomic sequencing and using bioinformatics for statistical analysis of the sequencing data, the present invention discovers gut microbiota related to the disease, integrates the gut microbiota with disease information, and predicts patients with refractory diarrhea to the greatest extent.

[0027] 2. Through metagenomic sequencing, the present invention first discovers the correlations between Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus and patients with refractory diarrhea, indicating that these bacterial species can be used as predictors of refractory diarrhea. Klebsiella pneumoniae, Coprococcus catus, and Rothia mucilaginosa show significant increases in patients with refractory diarrhea, while Ruminococcus.sp, Bifidobacterium longum, and Lachnospiraceae bacterium show significant decreases in patients with refractory diarrhea.

[0028] Through ROC curve analysis, the above six markers have high specificity and sensitivity as detection variables. Therefore, these six bacterial species can be used as detection markers for the prediction and diagnosis of patients with refractory diarrhea. Using these six genera as detection markers is completely non-invasive and highly accurate.

[0029] 3. The present invention provides a non-invasive and highly accurate detection method. Using these 6 bacterial species as detection markers is completely non-invasive and highly accurate. Through metagenomic sequencing, higher resolution is provided, enabling the analysis of the microbial community to reach the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. The present invention uses a larger sample size for verification to ensure excellent prediction effects for refractory diarrhea.

[0030] 4. The target microorganisms for developing the refractory diarrhea prediction system and early screening kit of the present invention. Currently, there is no mature refractory diarrhea prediction system or early screening kit. The six bacterial species provided by the present invention can be used as target microorganisms for developing these systems, filling the gap in this field.

[0031] 5. The present invention not only provides a means for detecting the intestinal flora of patients with refractory diarrhea, but also uses this to judge the refractory diarrhea and intestinal flora disorder of patients, providing a bacteriological basis and scientific support for later intestinal flora transplantation, and more accurately providing an individualized treatment plan for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 LEfSe score graphs for patients with refractory diarrhea and healthy individuals;

[0033] Figure 2 Box scatter plots of relative abundances for patients with refractory diarrhea and healthy individuals;

[0034] Figure 3 ROC diagnostic curve graph. DETAILED DESCRIPTION OF THE INVENTION

[0035] The present invention will be further described in detail below in conjunction with specific embodiments for the understanding of those skilled in the art.

[0036] Example 1 Screening of microbial markers related to refractory diarrhea

[0037] I. Sample collection

[0038] The sources and inclusion criteria of the refractory diarrhea samples are as follows: from 101 individuals.

[0039] Inclusion criteria:

[0040] 1. Age distribution greater than 18 years old;

[0041] 2. Patients with diarrhea occurring at least 50% of the days in the past 4 weeks, 3 times or more per day.

[0042] Exclusion criteria for refractory diarrhea: 1. Known or planned pregnancy (excluded for men, postmenopausal women or others with negative urine pregnancy test); 2. Previous FMT treatment; 3. Antibiotic treatment within the past 6 weeks; 4. Morphine treatment within the past 4 weeks; 5. Persistent Clostridium difficile infection; 6. Known severe gastrointestinal diseases or gastrointestinal infections (diagnosed as, for example, inflammatory bowel disease and / or gastrointestinal cancer); 7. Previous abdominal surgery history (minor surgical procedures, such as allowing appendectomy); 8. Medications affecting the gastrointestinal tract taken within the past 4 weeks; 9. Previous consultation for thyroid disease (TSH) blood sample disorders, up to 6 months; 10. Known intestinal stenosis; 11. Known severe end-organ diseases.

[0043] The control group consisted of 96 individuals, and the inclusion criteria were as follows:

[0044] 1. The age distribution was greater than 18 years old;

[0045] 2. Did not have diabetes or other metabolic diseases;

[0046] 3. Did not have refractory diarrhea;

[0047] 4. Did not have irritable bowel syndrome or gastrointestinal diseases;

[0048] 5. Did not have other immune system diseases or were not in an immunodeficiency state;

[0049] 6. Did not take antibiotics or probiotic prebiotics, etc. before and during the study.

[0050] The exclusion criteria were the same as those for the refractory diarrhea group.

[0051] The above data were from fecal samples collected by the Union Hospital Affiliated to Fujian Medical University.

[0052] II. DNA Extraction, Library Construction and Sequencing

[0053] 1. Select the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected fecal samples.

[0054] 2. After extraction, use Qubit to detect the DNA concentration, use 1.5% agarose gel electrophoresis to detect the integrity of the extracted genomic DNA, perform quality control on the extracted genomic DNA, and screen out genomic DNA samples with qualified quality (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng).

[0055] 3. For DNA samples with qualified quality, after random fragmentation, end repair, addition of A base, addition of adapters and indexes (index), perform purification and library amplification after adapter ligation, and detect the DNA concentration after amplification (DNA concentration ≥ 40 ng / μL).

[0056] 4. After the library is detected to be qualified, pool different libraries according to the requirements of the effective concentration and the target output data volume and then sequence them on the machine. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0057] III. Screening of Microbial Markers by LEfSe Analysis

[0058] Use KneadData software to perform quality control (based on Trimmomatic) and dehosting (based on Bowtie2) on the raw data from the machine. Use Kraken2 alignment to calculate the number of sequences of the species contained in the sample, and then use Bracken to estimate the actual abundance of the species in the sample. Randomly select 80% of the subjects to be tested (including those in the refractory diarrhea group and the healthy group) as the training set, and the remaining 20% of the samples as the validation set. Then use the LEfSe software to analyze the abundance data of each sample in the training set, and the default screening value of the LDA Score is set to 3.0. The sample information table of all samples is shown in Table 1.

[0059] Table 1 Sample Information Table

[0060]

[0061]

[0062] The results are as Figure 1 shown. Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus are associated with refractory diarrhea. Among them, Klebsiella pneumoniae, Coprococcus catus, and Rothia mucilaginosa show a significant increase in patients with refractory diarrhea, while Ruminococcus.sp, Bifidobacterium longum, and Lachnospiraceae bacterium show a significant decrease in patients with refractory diarrhea.

[0063] Therefore, three microbial markers that are significantly reduced in patients with refractory diarrhea are screened out, namely Ruminococcus.sp, Bifidobacterium longum, and Lachnospiraceae bacterium; three microbial markers that are significantly increased in patients with refractory diarrhea are screened out, namely Klebsiella pneumoniae, Coprococcus catus, and Rothia mucilaginosa.

[0064] Example 2 verifies the reliability of the above six microbial markers

[0065] 1. First, take the remaining 20% of the subjects to be tested in Example 1 (including those in the refractory diarrhea group and the healthy group) as the validation set. Perform binary logistic regression on the abundance data of each sample in this validation set, and then conduct a receiver operating characteristic curve test (ROC curve) analysis to obtain the cutoff value (optimal cut-off value).

[0066] 2. Use IBM SPSS Statistics (v27) statistical software to complete the calculation of specificity and sensitivity and the drawing of the ROC curve. The software first calculates the threshold of the actual measurement value, and then calculates the true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold;

[0067] Specificity (true negative rate) = TN / (TN + FP).

[0068] Sensitivity (true positive rate) = TP / (TP + FN).

[0069] 3. The ROC curve can be constructed through 1 - specificity and sensitivity, and the integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain index, 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 a certain index.

[0070] 4. Directly conduct a receiver operating characteristic curve test (ROC curve) analysis on the relative abundance values of the microbial markers of single strains to obtain the cutoff value (optimal cut-off value). The ROC curve of the prediction score is as Figure 3 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the predicted mimic marker (the marker formed by the combination of six single strains) and single bacteria are shown in Table 2.

[0071] As can be seen from the above: The ROC curve analysis shows that the six microbial markers as detection variables have high specificity and sensitivity, and the AUC of the six microbial markers are all greater than 66%. Therefore, the six single strains can all be used as detection markers for the diagnosis of patients with refractory diarrhea;

[0072] In this application, the six strains are collectively referred to as the six-strain combined marker or six-strain combination or mimic marker. The AUC of the prediction score of the mimic marker is 89.5%, the optimal cut-off value is 0.4152, the sensitivity is 0.85, and the specificity is 0.857. Therefore, using the mimic marker as a detection marker for the diagnosis of refractory diarrhea has a better effect and high accuracy.

[0073] Table 2 Results of ROC diagnostic curve

[0074] Genus name AUC Cut-off value Sensitivity Specificity Mimic biomarker 89.5％ 0.4152 0.85 0.857 Bifidobacterium longum 82.3％ 0.001 0.8 0.81 Ruminococcus 82.1％ 0.0002 0.75 0.857 Lachnospiraceae bacterium 71.9％ 0.0002 0.75 0.667 Rothia mucilaginosa 68.7％ 0.0001 0.524 0.85 Coprococcus eutactus 67.3％ 0.0012 0.55 0.952 Klebsiella pneumoniae 66.7％ 0.0001 0.333 1

[0075] As can be seen from the above results, as shown in the ROC diagnostic curve results in Table 2 above, these 6 microbial markers were discovered for the first time to be related to refractory diarrhea. When predicting with a single strain, Bifidobacterium longum and Ruminococcus have the highest prediction effect on refractory diarrhea, followed by Rothia mucilaginosa and Lachnospiraceae bacterium, and then Klebsiella pneumoniae and Coprococcus eutactus. Moreover, the prediction effect of the six-bacteria combined marker (the combined use of six microbial markers) is the highest.

[0076] Example 3 Establishment of logistic regression model

[0077] a. Establish the model

[0078] Based on the above discovered biomarkers and the proportion of Parkinson's syndrome patients or healthy people in the training set, further, regard the 5 detected strains as mimic markers. On this basis, discuss the linear relationship between the relative abundance values of the 5 single bacteria and the probability of the sample being healthy (or diseased), and calculate the first probability value y of the object to be tested through the binary logistic regression equation:

[0079] y = -0.4226 + 8.9175 * x1 + 3.5501 * x2 + 226.5691 * x3 - 52.6407 * x4 - 737.6975 * x5 + 188.5085 * x6;

[0080] In the formula, y is the first probability value of the object to be tested; x1 is the relative abundance value of Ruminococcus.sp; x2 is the relative abundance value of Bifidobacterium longum; x3 is the relative abundance value of Lachnospiraceae bacterium; x4 is the relative abundance value of Klebsiella pneumoniae; x5 is the relative abundance value of Rothia mucilaginosa; x6 is the relative abundance value of Coprococcus catu.

[0081] b. Calculate the probability value that the object to be tested is a healthy person

[0082] Substitute the above first probability value y into the following formula to calculate the probability that the object to be tested is a healthy person: P = exp(y) / {1 + exp(y)}; where P is the probability value that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y; P can also be written as:

[0083]

[0084] Wherein, P represents the probability that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y.

[0085] c. Verify the model

[0086] Based on the proportion of refractory diarrhea patients or healthy people in the validation set, statistically verify the relevant abundance statistical data of the validation set markers. Among them, the mean determines the central position of the data distribution, the standard deviation reflects the degree of dispersion of the data relative to the mean, and for the q-value, we use the formula of the rank sum test to calculate the statistic. The lower the q-value, the greater the difference between the disease group and the healthy group. Specifically as follows:

[0087] Table 3 Statistical data of the relevant abundances of the validation set markers

[0088]

[0089] Note: In Table 3, E is used to represent the power of 10. For example, 8.221067147555e-05 represents 8.221067147555 * 10 -05 .

[0090] Example 4

[0091] Based on the above embodiments, this embodiment provides a computer program product related to refractory diarrhea, which is used to execute the risk of diagnosing a subject to be tested with refractory diarrhea, including the following steps:

[0092] 1) Obtain the relative abundance value of each single strain in the feces of the object to be tested; the single strain is any one of Streptococcus_salivarius, Bacteroides_ovatus, Bacteroides_vulgatus, Clostridioides_difficile, Clostridium_aldenense, and Schaalia_odontolytica;

[0093] 2) Calculate the log odds of the object to be tested according to the binary logistic regression equation, denoted as the first probability value y;

[0094] y = -0.422 + 8.917 * x1 + 3.5501 * x2 + 226.5691 * x3 - 52.6407 * x4 - 737.6975 * x5 + 188.5085 * x6;

[0095] Among them, y is the first probability value; x1 is the relative abundance value of Ruminococcus.sp; x2 is the relative abundance value of Bifidobacterium longum; x3 is the relative abundance value of Lachnospiraceaebacterium; x4 is the relative abundance value of Klebsiella pneumoniae; x5 is the relative abundance value of Rothia mucilaginosa; x6 is the relative abundance value of Coprococcus catus.

[0096] 3) Substitute the above first probability value y into formula (2) to calculate the probability that the object to be tested is a healthy person. Formula (2) is:

[0097] P = exp(y) / {1 + exp(y)};

[0098] In the formula, P is the probability that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0099] 4) According to the comparison result of the probability P value of a healthy person and the reference value, diagnose or predict the risk that the object to be tested has refractory diarrhea.

[0100] In actual work, when the P value is greater than 0.5, it means that the probability that the person to be tested has refractory diarrhea is small; when the P value is less than 0.5, it means that the probability that the person to be tested has refractory diarrhea is large; when the P value is 0.5, it means that the person to be tested may be a healthy person or may be a patient with refractory diarrhea. At this time, other means need to be further used for detection, and other means include blood routine, judging physical signs and other schemes. Further, when the P value is closer to 0.5, the more other means are needed for detection.

[0101] Example 5

[0102] Based on the products and methods of Example 4, check and verify the health probabilities of healthy people and patients with refractory diarrhea in the validation set. The specific steps are as follows:

[0103] 1) Obtain the relative abundance value of each single strain in the feces of the object to be tested; the single strain is any one of Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceaebacterium, Klebsiella pneumoniae, Rothia mucilaginosa, Coprococcus catus;

[0104] 2) Calculate the log odds of the object to be tested according to the binary logistic regression equation, denoted as the first probability value y;

[0105] y = -0.422 + 8.917 * x1 + 3.5501 * x2 + 226.5691 * x3 - 52.6407 * x4 - 737.6975 * x5 + 188.5085 * x6;

[0106] Where y is the first probability value; x1 is the relative abundance value of Ruminococcus.sp; x2 is the relative abundance value of Bifidobacterium longum; x3 is the relative abundance value of Lachnospiraceaebacterium; x4 is the relative abundance value of Klebsiella pneumoniae; x5 is the relative abundance value of Rothia mucilaginosa; x6 is the relative abundance value of Coprococcus catu.

[0107] 3) Substitute the above first probability value y into formula (2) to calculate the probability P that the object to be tested is a healthy person. Formula (2) is:

[0108] P = exp(y) / {1 + exp(y)};

[0109] In the formula, P is the probability that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y.

[0110] 4) According to the comparison result between the probability P value of a healthy person and the reference value, diagnose or predict the risk that the object to be tested has refractory diarrhea.

[0111] In actual work, when the P value is greater than 0.5, it means that the probability that the person to be tested has refractory diarrhea is small; when the P value is less than 0.5, it means that the probability that the person to be tested has refractory diarrhea is large; when the P value is 0.5, it means that the person to be tested may be a healthy person or may be a patient with refractory diarrhea. In this case, other means need to be used for further detection. Other means include blood routine, judging physical signs and other schemes. Further, the closer the P value is to 0.5, the more other means are needed for detection.

[0112] In actual situations, there are cases that do not fully meet the judgment criteria. The reason is that the fecal samples of the people to be tested may have false positive results or false negative results, and other means need to be used for further detection. Other means are blood routine and judging physical signs.

[0113] Table 4 Related abundance value data of verification set markers

[0114]

[0115]

[0116]

[0117]

[0118] Among them, 5.30E-05 represents 5.30 * 10 -5 .

[0119] Conclusions and explanations:

[0120] 1. From Figures 1 - 3 and Tables 2 to 4, it can be seen that Streptococcus salivarius, Bacteroides ovatus, Bacteroides vulgatus, Clostridioides difficile, Clostridium aldenense, and Schaalia odontolytica can be used as markers related to refractory diarrhea.

[0121] 2. From Table 4, it can be seen that when detecting fecal samples, it is normal to only detect a certain strain or several strains. This is because individuals have differences, and the probability values of the six strains are calculated. That is, even if a certain sample only contains a certain strain, the probability that the test sample has refractory diarrhea can still be calculated in this application.

[0122] 3. From Tables 2 and 4, it can be seen that one or several of Streptococcus salivarius, Bacteroides ovatus, Bacteroides vulgatus, Clostridium aldenense, Schaalia odontolytica, and Clostridioides difficile in this application can be used as microbial markers related to refractory diarrhea. When preparing a kit or a product for detecting the relative abundance value of strains, people can select one or more of the above six strains according to their own needs for preparation.

[0123] 4. From the ROC diagnostic curve results in Table 2 of Example 2, for a single strain, Bifidobacterium longum and Ruminococcus have the highest prediction effect on refractory diarrhea, followed by Roseburia inulinivorans and Lachnospiraceae bacteria, and finally Klebsiella pneumoniae and Faecalibacterium prausnitzii; compared with a single strain, the mimic marker (six-strain combined marker) has the best prediction effect, with an AUC of 89.5% for its prediction scoring method, the best cut-off value of 0.4152, a sensitivity of 0.85, and a specificity of 0.857, and the prediction effect is the best, which can provide a more accurate prediction of refractory diarrhea.

[0124] Other parts not described in detail are all prior arts. Although the above embodiments have described the present invention in detail, they are only some embodiments of the present invention, rather than all embodiments. People can also obtain other embodiments based on these embodiments without creative efforts, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A microbial marker associated with refractory diarrhea, characterized in that: The microbial markers include Ruminococcus.sp and / or Rothia mucilaginosa.

2. The microbial marker according to claim 1, characterized in that: The microbial markers further include one or more of Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, and Coprococcus catus.

3. The microbial marker according to claim 2, characterized in that: The microbial markers are Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceaebacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus.

4. Use of a reagent for detecting microbial markers in the preparation of a product for diagnosing or screening refractory diarrhea, wherein the microbial markers include one or more of Bacteroides stercoris, Bacteroides vulgatus, Bacteroides thetaiotaomicron, Ruminococcus and Lactobacillus salivarius.

5. A kit, characterized in that: The kit comprises a reagent for detecting the microbial marker according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in the preparation of a product for detecting obesity, and / or use of the detection reagent in the kit according to claim 5 in the preparation of a kit for diagnosing intractable diarrhea.

7. A product for diagnosing refractory diarrhea, characterized in that: The product comprises primers, probes, antibodies, aptamers or chips that are specific to the microbial markers described in any one of claims 1 to 3.

8. A computer program product related to refractory diarrhea, characterized in that: The computer program product is used to perform the process of diagnosing the risk of a subject suffering from refractory diarrhea, comprising the following steps: 1) obtaining the relative abundance value of each single bacterial species in the microbial marker of any one of claims 1 to 3; the single bacterial species is any one of Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceae bacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus; 2) Calculate the odds logarithm of the object to be tested according to the binary logistic regression equation, and record it as the first probability value y; 3) Substituting the first probability value y into formula (2) to calculate the probability that the subject to be tested is a healthy person, formula (2) is: P = exp(y) / {1+exp(y)}; Wherein, P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y; 4) diagnosing or predicting the risk of the subject suffering from refractory diarrhea based on the comparison result of the P value with the reference value.

9. The product according to claim 8, characterized in that: The formula for the binary logistic regression equation is: y=A+B1*x1+B2*x2+B3*x3+B4*x4+B5*x5+B6*x6; Among them, A is the intercept term, B1~B6 are the regression coefficients of the independent variables; x1~x6 are the relative abundance values ​​of Ruminococcus.sp, Bifidobacterium longum, Lachnospiraceaebacterium, Klebsiella pneumoniae, Rothia mucilaginosa, and Coprococcus catus respectively.

10. The product according to claim 9, characterized in that: A is -0.4226, B1 is 8.9175, B2 is 3.5501, B3 is 226.5691, B4 is -52.6407, B5 is -737.6975, and B6 is 188.5085.

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