Intestinal microbiota markers related to obesity, products and their applications

Abstract

The present invention discloses intestinal flora markers related to obesity, products and their applications. The intestinal flora markers include Coprococcus catus and Laccospiridae bacteria Lachnospiraceae bacterium and Ruminococcus Ruminococcus sp. . The present invention provides a kit, which includes detection reagents for detecting the intestinal flora markers. The present invention also provides a product for diagnosing obesity and a computer program product related to obesity. The products provided by the present invention have good feasibility and accuracy, can effectively evaluate the risk of obesity, and provide a new tool for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to intestinal flora markers related to obesity, products thereof, and their applications. Background Art

[0002] Obesity is a chronic metabolic disease characterized by excessive accumulation of body fat and abnormal body weight.

[0003] Obesity is mostly caused by the interaction of multiple factors such as genetics and environment. When the calories ingested by the human body are more than the calories consumed, the excess calories are stored in the body in the form of fat, and when the amount exceeds the normal physiological requirement and reaches a certain value, it evolves into obesity. The existence of obesity can trigger chronic non-communicable diseases such as hypertension, diabetes, cardiovascular and cerebrovascular diseases, and tumors, and is an important risk factor for these diseases.

[0004] Obesity can also be classified according to the pathogenesis and etiology, age of onset, adipose tissue pathology, and site of fat accumulation. For example, according to the pathogenesis and etiology, it can be divided into simple obesity and secondary obesity. Among them, primary obesity is the most common type of obesity, accounting for about 95% of the obese population, mainly caused by genetic factors and overnutrition; secondary obesity is a group of diseases caused by endocrine disorders or metabolic disorders, which is rarely seen clinically and only accounts for 2% - 5% of the obese.

[0005] At present, there is still a lack of a clear and effective intestinal flora marker and related products to provide a new idea and approach for the diagnosis and treatment of obesity. Summary of the Invention

[0006] In view of the above technical problems, the present invention provides an intestinal flora marker related to obesity, products thereof, and their applications, so as to provide a new idea and approach for the diagnosis and treatment of obesity.

[0007] The technical solutions provided by the present invention are as follows:

[0008] In the first aspect, an intestinal flora marker related to obesity is provided, including Lachnospiraceae bacterium Lachnospiraceae bacterium , Ruminococcus sp. Ruminococcus sp. and Coprococcus catus Coprococcus catus。

[0009] In the second aspect, the application of a reagent for detecting an intestinal flora marker in the preparation of a product for diagnosing or screening obesity is provided, and the intestinal flora marker includes Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus.

[0010] In a third aspect, a kit is provided, which includes reagents for detecting the intestinal flora markers described in the first aspect.

[0011] In a fourth aspect, there is provided the use of the kit described in the third aspect in the preparation of a product for detecting obesity, and / or the use of the detection reagents in the kit described in the third aspect in the preparation of a kit for diagnosing obesity.

[0012] In a fifth aspect, a product for diagnosing obesity is provided, which includes primers, probes, antibodies, aptamers or chips specific to the intestinal flora markers described in the first aspect.

[0013] In a sixth aspect, a computer program product related to obesity is provided, which is used to execute a method for diagnosing the risk of whether a subject to be tested has obesity. The method includes:

[0014] Obtaining the relative abundance value of each single strain in the feces of the subject to be tested; the single strains include Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus;

[0015] Calculating the logarithm of the odds y of the subject to be tested according to the binary logistic regression equation;

[0016] Calculating the probability P that the subject to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}, where exp() represents the natural exponential function;

[0017] Diagnosing or predicting whether the subject to be tested has obesity or is at risk of having obesity according to the comparison result of the probability P with the reference value.

[0018] In a possible implementation manner, the formula of the binary logistic regression equation is:

[0019]

[0020] where A is the intercept term, and B 1 ~B 3 are the regression coefficients of the independent variables; x1, x2, and x3 are the relative abundance values of Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus in sequence. Lachnospiraceae bacterium Ruminococcus sp., Ruminococcus sp. and Coprococcus catus Coprococcus catus respectively.

[0021] Furthermore, A is -0.5570, B 1 is 8.2195, B 2 is 134.2251, B 3It is 170.8060.

[0022] The beneficial effects of the present invention are as follows:

[0023] 1. The intestinal flora biomarker combination of the present invention includes Lentzea Lachnospiraceae bacterium , Ruminococcus Ruminococcus sp. and Coprococcus catus Coprococcus catus . It has been verified that there is a significant association between these three bacteria and obesity. Specifically: these three bacteria are significantly increased in healthy people. Through ROC curve analysis, the above three biomarkers have high specificity and sensitivity as detection variables. Therefore, these three bacterial species can be used as detection biomarkers for the prediction and diagnosis of obesity patients.

[0024] 2. The kit of the present invention can use these three bacterial species as detection biomarkers to achieve the prediction or diagnosis of obesity, which is completely non-invasive and has high accuracy. Through metagenomic sequencing, higher resolution is provided, enabling the analysis of microbial communities to reach the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. The above three bacterial species can also be used as target microorganisms for developing these systems, filling the gap in this field.

[0025] 3. The present invention also provides a product for diagnosing obesity. This product can calculate the health probability based on the relative abundances of each bacterial species, and then compare it with the reference value to predict or diagnose whether a patient has obesity or is at risk of obesity. This product has good feasibility and accuracy, can effectively evaluate the risk of obesity, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a result graph of linear discriminant analysis;

[0027] Figure 2 It is a box scatter plot of intestinal flora biomarkers;

[0028] Figure 3 It is an ROC curve. DETAILED DESCRIPTION OF THE INVENTION

[0029] In order to evaluate whether the composition of intestinal symbiotic flora can be used as a predictor of obesity, the present invention collected samples from obese patients and healthy people, performed metagenomic sequencing and used bioinformatics to statistically analyze the sequencing data, discovered intestinal flora related to diseases, integrated the intestinal flora with disease information, and maximally predicted obese patients. Through metagenomic sequencing, the present invention discovered Ruminococcus ( Ruminococcus sp. ), Coprococcus catus ( Coprococcus catus ), Lentzea Lachnospiraceae bacteriumCorrelation with obese patients. The abundances of three bacterial species, Ruminococcus ( Ruminococcus sp. ), Coprococcus catus ( Coprococcus catus ), and Lachnospiraceae bacterium ( Lachnospiraceae bacterium ), were significantly lower than those in the control group, indicating that these bacterial species could be used as predictors of obesity.

[0030] During actual work, the staff numbered a certain strain of the genus Ruminococcus detected and denoted it as Ruminococcus sp. CAG:177. Among them, the suffix [CAG:177] in Ruminococcus sp. CAG:177 was numbered by the staff according to their own experimental habits, and this suffix [CAG:177] has no practical significance. People can add suffixes after Ruminococcus sp. according to their own preferences.

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. For experimental methods without specific conditions noted in the embodiments, they are usually in accordance with conventional conditions.

[0032] Example 1: Sample collection

[0033] Collect fecal samples from 69 obese patients and 74 healthy individuals:

[0034] The inclusion criteria for the obese group samples are as follows: 1. Age distribution is 9 - 60 years old; 2. Vital signs are stable; 3. BMI is 25 - 34.9 or female waist circumference > 35 inches, male waist circumference > 40 inches; 4. Diagnosed with obesity or obesity metabolic syndrome.

[0035] Exclusion criteria for the obese group: 1. People who are losing weight; 2. Have taken antibiotics in the past three months; 3. Have used probiotics, prebiotics, or synbiotics in the past month; 4. Have used anti-inflammatory drugs, weight loss drugs, or supplements in the past month; 5. Have a personal medical history of cardiovascular disease, hypertension, cancer, type 1 or type 2 diabetes, and inflammatory gastrointestinal diseases (such as Crohn's disease or colitis); 6. Smoking; 7. Daily alcohol consumption is more than two units; 8. Pregnant or lactating; 9. Irregular menstruation, menopause, or hormone replacement therapy; 10. Exercise more than 300 minutes per week.

[0036] Inclusion criteria for the control group: 1. Age distribution is from 9 to 60 years old; 2. Without diabetes or other metabolic diseases; 3. Without depression or other neurological diseases; 4. Without irritable bowel syndrome or gastrointestinal diseases; 5. Without other immune system diseases or not in an immunodeficiency state; 6. Without taking antibiotics (such as neomycin, rifaximin) or probiotic prebiotics, etc. before and during the study.

[0037] Exclusion criteria are the same as those for the obesity group.

[0038] The above data are from fecal samples collected by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

[0039] Example 2: DNA extraction, library construction and sequencing

[0040] The CTAB (cetyltrimethylammonium bromide) method was used to extract the microbial genomic DNA of the samples, and the extracted genomic DNA was quality inspected to screen out genomic DNA samples with qualified quality. For these screened DNA samples, after random fragmentation, end repair, addition of A bases, addition of adapters and indexes (index), and fragment selection, a library of about 300 bp was obtained. Finally, the paired-end method was used to sequence the inserted fragments on the BGI platform.

[0041] Example 3: Screening biomarkers by LEfSe analysis

[0042] 1. Divide the data set

[0043] In the samples of Example 1, as shown in Table 1, samples were randomly selected as the training set, and the remaining samples were used as the validation set.

[0044] Table 1 Sample information table

[0045]

[0046] 2. Screen intestinal flora biomarkers

[0047] The KneadData software was used to filter, denoise, and splice the off-machine data to obtain high-quality sequencing data. The standard of Identity (sequence consistency) was set to 100% for clustering, and the ASV (Amplicon Sequence Variant) characteristic sequences were divided. The open-source software package qiime2's plugin feature-classifier classify-sklearn was used to annotate the characteristic sequences to generate taxonomic abundance data. 80% of the abundance data was randomly selected for analysis using the LEfSe software, and the screening value of the LDA Score (Linear Discriminant Analysis Score) was set to 2.5 by default. The results are as Figure 1 shown.

[0048] In the figure, the linear discriminant analysis scores of the three bacteria > 2.5, the linear discriminant analysis scores. The said linear discriminant analysis score is an analysis method that can mine and interpret biological markers from high-dimensional data, with significant statistical significance and biological relevance.

[0049] Therefore, three microorganisms, Ruminococcus ( Ruminococcus sp. ), Faecalibacterium prausnitzii ( Coprococcus catus ), and Laccospiraceae bacterium ( Lachnospiraceae bacterium ), which were significantly reduced in obese patients, were screened out. The box scatter plots of the three markers are shown in Figure 2 . These three bacterial species were discovered to be related to obesity for the first time, but Ruminococcus ( Ruminococcus sp. ) has a much higher predictive ability for obesity than other bacterial species, and the combined prediction effect of the three bacteria is the highest.

[0050] Example 4: Construction and verification of a binary regression equation

[0051] 4.1. Construction of a binary regression equation

[0052] 4.11. Based on the above-screened biomarkers, the probability of each sample being predicted as diseased or healthy by the three bacteria was calculated using the binary logistic regression algorithm in the SPSS software, facilitating the subsequent evaluation of the performance of the binary regression model for each bacterium alone.

[0053] 4.12. Construction of a binary logistic regression equation for the relative abundance of the mimic biomarker (combined three bacteria)

[0054] The formula for the binary logistic regression equation is:

[0055]

[0056] Among them, A is the intercept term, B 1 ~B 3Regression coefficient for the independent variable; x1, x2, and x3 are the Laciniospora, Lachnospiraceae bacterium , Ruminococcus Ruminococcus sp. , and Coprococcus eutactus Coprococcus catus relative abundance values, respectively.

[0057] By fitting the data, the values of each variable are obtained, and then the following formula is obtained:

[0058]

[0059]

[0060] On this basis, the health probability of the object to be tested can be calculated, and it can be diagnosed or predicted whether the object to be tested has obesity or is at risk of having obesity. The method is as follows:

[0061] Obtain the relative abundance value of each single strain among the 3 gut microbiota markers of the object to be tested;

[0062] Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation;

[0063] Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}, where exp() represents the natural exponential function;

[0064] Diagnose or predict whether the object to be tested has obesity or is at risk of having obesity according to the comparison between the probability P and the reference value.

[0065] 4.2. Verification of the binary logistic regression equation

[0066] 4.21. Calculation and statistical analysis of the verification set data

[0067] Based on the data of the verification set, calculate the relative abundance of the mimic markers (combined with 3 bacteria) of each sample in the disease group and the healthy group, then use the aforementioned binary logistic regression method to obtain the logarithm of the odds y of the object to be tested, and then calculate the probability that the object to be tested is a healthy person. The results are shown in Table 2 and Table 3.

[0068] Table 3 shows the mean and standard deviation of the relative abundance of each strain. The mean relative abundance determines the central position of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The q value is calculated using the formula of the rank sum test. The lower the q value, the greater the difference between the disease group and the healthy group.

[0069] Table 2. Related data of the verification set markers

[0070]

[0071] Note: In Table 2, 6.10E-05 means 。

[0072] Table 3 Statistical data of the relative abundances of the validation set markers

[0073]

[0074] Note: In Table 3, the mean refers to the mean relative abundance, and the same applies to the standard deviation.

[0075] 4.3. Validation results

[0076] Based on the data in Table 2, a receiver operating characteristic curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cut-off value). The IBM SPSS Statistics (v27) statistical software was used to complete the calculation of specificity and sensitivity and the drawing of the ROC curve. The software first calculates the threshold of the actual measured 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). The ROC curve can be constructed through 1 - specificity and sensitivity, and the integral of the ROC curve is the AUC (Area Under the Curve, the area covered under the ROC curve). In order to calculate the specificity and sensitivity of a certain index, in this embodiment, the Youden coefficient is first calculated (Youden index = sensitivity + specificity - 1), and the specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of a certain index.

[0077] The relative abundance values of individual microbial markers were directly subjected to a receiver operating characteristic curve test (ROC curve) analysis to obtain the cutoff value (optimal cut-off value). The AUC of the prediction scoring method for the combination of three bacteria is 93.3%, the optimal cut-off value is 0.4378, the sensitivity is 0.933, and the specificity is 0.857. The ROC curve of the prediction score is as Figure 3 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the prediction scoring methods for the combination of three bacteria and single bacteria are shown in Table 4.

[0078] Table 4 Results of the ROC diagnostic curve

[0079]

[0080] From the above results, it can be seen that the above 3 gut microbiota markers were discovered for the first time and are related to obesity. Among them, the single-bacteria prediction effect of _Conchostraca_ on obesity is the highest, followed by the single-bacteria prediction effects of _Ruminococcus_ and _Coprococcus catus_.

[0081] As can be seen from Table 4, the AUC of the mimetic biomarker for predicting obesity is 93.3%, the optimal cut-off value is 0.4378, the sensitivity is 0.933, and the specificity is 0.857, which is higher than other biomarkers. That is to say, it is confirmed that the performance of the model with the combination of three bacteria is higher than other models, and it has good feasibility and accuracy. The calculated optimal cut-off value of the mimetic biomarker is 0.4378, and the larger this value is, the higher the probability of being healthy. Through this binary logistic regression equation, the risk of obesity can be effectively evaluated, providing a new tool for clinical diagnosis.

[0082] Example 5

[0083] Based on the above embodiments, this embodiment provides a computer program product related to obesity, which is used to execute a method for diagnosing the risk of whether a subject to be tested has obesity, including the following steps:

[0084] 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 Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus; Lachnospiraceae bacterium 、Ruminococcus sp. and Coprococcus catus;

[0085] 2) Calculate the logarithm of the odds y of the subject to be tested according to the binary logistic regression equation;

[0086]

[0087] In the formula, x1, x2, and x3 are the relative abundance values of Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus in sequence;

[0088] 3) Calculate the probability P of the subject to be tested being a healthy person according to y, P = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0089] 4) According to the comparison between the probability P value of a healthy person and the reference value, diagnose or predict the risk of the subject to be tested having obesity.

[0090] In actual work, when the P value is greater than 0.5, it means that the probability of the subject to be tested having obesity is small; when the P value is less than 0.5, it means that the probability of the subject to be tested having obesity is large; when the P value is 0.5, it means that the person to be tested may be a healthy person or an obesity patient. At this time, other means need to be further used for detection, and the other means are blood routine and judging physical signs. Further, the closer the P value is to 0.5, the more other means are needed for detection.

[0091] Example 6

[0092] Based on the products and methods of Example 5, check and verify the health probabilities of healthy people and obese patients in the validation set. The specific steps are as follows:

[0093] 1) Collect fecal samples of the persons to be tested and detect the relative abundance values of single strains in the feces; among them, the single strains are any one of Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus;

[0094] 2) Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation;

[0095]

[0096] In the formula, x1, x2, and x3 are the relative abundance values of Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus in turn;

[0097] 3) Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0098] 4) According to the comparison 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 obesity.

[0099] In actual work, when the P value is greater than 0.5, it means that the probability that the object to be tested has obesity is small; when the P value is less than 0.5, it means that the probability that the object to be tested has obesity is large; when the P value is 0.5, it means that the object to be tested may be a healthy person or may be an obese patient. At this time, other means need to be further used for detection, and the other means are blood routine and judging physical signs. Further, the closer the P value is to 0.5, the more other means need to be used for detection.

[0100] Conclusions and explanations:

[0101] 1. From Figures 1 - 3 and Tables 2 - 4, it can be seen that Lachnospiraceae bacterium, Ruminococcus sp., and Coprococcus catus can be used as related markers for obesity.

[0102] 2. As can be seen from Table 2, when detecting fecal samples, it is normal to only detect a certain strain or several strains of bacteria. This is because individuals have differences. The probability value of the three strains of bacteria is calculated. That is, even if a certain sample only contains a single strain of bacteria, the probability that the sample to be tested has obesity can still be calculated in this application.

[0103] 3. Single-bacteria prediction effect: The single-bacteria prediction effect of Lentzea on obesity is the highest, followed by Ruminococcus and Coprococcus catus.

[0104] 4. Mimic biomarker prediction effect: The combination of three bacteria, Lentzea, Ruminococcus, and Coprococcus catus can all be used as biomarkers for predicting obesity. The AUC of the mimic biomarker for predicting obesity is 93.3%, the optimal cut-off value is 0.4378, the sensitivity is 0.933, and the specificity is 0.857. The prediction effect is the best and can provide a more accurate prediction of obesity.

[0105] The above are only the preferred specific embodiments 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 technical scope disclosed by the present invention should all be included within the scope of protection of the invention.

Claims

1. A combination of intestinal flora markers associated with obesity, characterized in that: The intestinal flora marker combination is a marker combination composed of Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus.

2. Use of a reagent for detecting a combination of intestinal flora markers in the preparation of a product for diagnosing obesity, wherein the combination of intestinal flora markers is a marker combination consisting of Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus.

3. A computer program product related to obesity, characterized in that: The computer program product is used to diagnose the risk of a subject suffering from obesity, comprising the following steps: Obtaining the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Lachnospiraceaebacterium, Ruminococcus sp. and Coprococcus catus; Substituting the relative abundance values ​​of the Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus into a binary logistic regression equation, and calculating the logarithm y of the odds of the object to be tested; According to y, calculate the probability P that the subject to be tested is a healthy person, P = exp (y) / {1 + exp (y)}, exp () represents the natural exponential function; According to the comparison result of the probability P and the reference value, it is diagnosed or predicted whether the subject to be tested suffers from obesity or has the risk of suffering from obesity.

4. The computer program product according to claim 3, characterized in that: The formula for the binary logistic regression equation is: Among them, A is the intercept term, B1~B3 are the regression coefficients of the independent variables; x1, x2 and x3 are Lachnospiraceae Bacterium , Ruminococcus Ruminococcus sp. Coprococcus Coprococcus catus The relative abundance value of .

5. The computer program product according to claim 4, characterized in that: A is -0.5570, B1 is 8.2195, B2 is 134.2251, and B3 is 170.8060.

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