A gout-related intestinal flora marker and its application

Abstract

The present invention discloses an intestinal flora marker associated with gout and its application, wherein the intestinal flora marker includes prokaryotes Holdemanella, Acinetobacter, Haemophilus, Weissella, Barnesiella, Paraprevotella, and Collinsella. The present invention first discovered that these 7 intestinal flora genera are associated with gout, and when Haemophilus or a combination of the 7 bacteria is used as a detection marker to predict gout, the accuracy rate is high and the prediction method is non-invasive. It can be seen that the present invention provides a new approach for the diagnosis and treatment of gout patients.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and specifically relates to an intestinal flora marker associated with gout and its application in the diagnosis and treatment of gout. Background Art

[0002] Gout is a recurrent inflammatory disease caused by increased purine biosynthesis and metabolism, excessive uric acid production or poor uric acid excretion, resulting in elevated uric acid in the blood, and precipitation of urate crystals in the synovium, bursa, cartilage and other tissues. With the improvement of living standards, changes in dietary structure and lifestyle, the incidence of gout is increasing rapidly. Although gout does not directly endanger the patient's life, the pain during the onset of the disease, the damage caused to the body, and the cost of treatment have seriously affected the patient's life and work. Moreover, long-term gout can also become an important risk factor for hypertension, hyperlipidemia, cardiovascular disease and cerebrovascular disease.

[0003] The basis of gout is hyperuricemia, but not all patients with hyperuricemia will develop gout. In fact, only 10% of patients with hyperuricemia develop gout. During a gout attack, serum uric acid may also drop to normal levels. At present, the diagnosis of gout mostly relies on clinical manifestations, routine blood and urine tests, blood uric acid determination, joint ultrasound and joint cavity puncture examinations, etc. Although they have their own advantages, they are either invasive examinations, or lack sensitivity or specificity, or have relatively high technical requirements and require rich clinical experience. Therefore, it is very necessary to find markers with high sensitivity and specificity for the diagnosis and early treatment of gout.

[0004] The human intestinal flora is not only involved in uric acid metabolism, but also in the release of inflammatory factors, and plays a key role in the pathogenesis of gout. Given the close connection between dietary health and the incidence of gout, it is very necessary to conduct in-depth research on the intestinal microecology of gout to provide useful exploration for the ultimate clinical treatment and prevention of the disease. Summary of the invention

[0005] The present invention collects samples from gout patients and healthy people, performs 16SrRNA sequencing, and uses bioinformatics to perform statistics on the sequencing data, discovers the intestinal flora associated with gout, further integrates the intestinal flora with disease information, and obtains intestinal flora markers and methods that can predict gout patients, providing a new approach for the diagnosis and treatment of gout.

[0006] The technical solution of the present invention is as follows:

[0007] The first aspect of the present invention provides a gout-related intestinal flora marker, which at least includes Haemophilus Haemophilus .

[0008] Preferably, the intestinal flora marker is composed of prokaryotic organisms Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella , Colinsella Collinsella These 7 types of bacteria.

[0009] The present invention collects stool samples from gout patients and healthy people, extracts, amplifies, builds libraries and performs metagenomic sequencing on the microbial genomic DNA in the stool samples, processes the obtained sequencing data into taxonomic abundance data, and uses the training set data to perform differential bacteria and linear discriminant analysis to obtain 7 types of microorganisms whose abundance is significantly reduced in gout patients: prokaryotes Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella , Colinsella Collinsella , and these 7 microorganisms are the first to be found to be associated with gout in the present invention; the present invention further found through the validation set data that the ROC curve analysis of Haemophilus Haemophilus When used as a detection variable, the specificity and sensitivity are much higher than other genera, and the detection effect is optimal when 7 genera are captured for joint detection. It can be seen that the intestinal flora marker provided by the present invention can be used as a detection marker for the diagnosis of gout patients.

[0010] The second aspect of the present invention provides the use of the above-mentioned intestinal flora markers associated with gout in the preparation of gout diagnostic products.

[0011] In the above application, the product includes but is not limited to primers, reagents, detection kits, gene chips and gout prediction systems, and the product detects the abundance of intestinal flora markers in the fecal samples of the subjects to achieve the diagnosis or auxiliary diagnosis of gout; wherein the intestinal flora markers include at least Haemophilus Haemophilus , which may also include prokaryotes Holdemanella , Acinetobacter Acinetobacter Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella , Colinsella Collinsella Any one or more of the above, it is best when the intestinal flora markers include all 7 of the above microorganisms.

[0012] In the context of the present invention, the term "abundance" refers to a measure of the number of target microorganisms in a biological sample. The quantification of the abundance of a target nucleic acid sequence in a biological sample may be absolute or relative. "Relative abundance" is usually based on one or more internal reference genes, i.e., from a reference strain 16SrRNA gene, such as using universal primers and expressing the abundance of the target nucleic acid sequence as a percentage of total bacterial 16SrRNA gene copies or measured by normalization of Escherichia coli 16SrRNA gene copies. "Absolute abundance" gives the exact number of target molecules by comparison with a DNA standard or by normalization of DNA concentration.

[0013] Preferably, in the above application, the detection kit comprises specific primers for detecting the abundance of the intestinal flora marker in the fecal sample of the subject; more preferably, the specific primers are primers for amplifying the intestinal flora marker 16SrRNA.

[0014] Preferably, in the above application, the gout prediction system uses the abundance of the intestinal flora marker in the subject's fecal sample as a detection variable.

[0015] The third aspect of the present invention provides a method for establishing a gout prediction system based on intestinal flora, specifically as follows Figure 1 As shown, the following steps are included:

[0016] S1. Determine the inclusion criteria for gout patients and healthy subjects;

[0017] S2. Collect stool samples from gout patients and healthy people, extract, amplify, build libraries and perform metagenomic sequencing on the microbial genomic DNA in the stool samples to obtain raw data;

[0018] S3. Use the same quality control and analysis methods to perform quality control on the original data and eliminate unqualified data;

[0019] S4. Use the same method to analyze the relative abundance of intestinal microbial flora;

[0020] S5. Screen out differential bacteria and analyze the effect size through rank sum test and linear discriminant analysis;

[0021] S6. Binary logistic regression test of the true positive rate and true negative rate of the differential bacteria.

[0022] The fourth aspect of the present invention provides the use of the above-mentioned intestinal flora marker in the preparation of a drug for treating gout.

[0023] The fifth aspect of the present invention provides the use of the above-mentioned intestinal flora marker in screening drugs suitable for gout, specifically: screening out the prokaryotic Holdemanella , Acinetobacter Acinetobacter, Haemophilus Haemophilus Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella and Colinsella Collinsella A substance that increases the abundance of at least one microorganism in the culture medium is used as the drug.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] (1) This invention discovered prokaryotes for the first time Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella , Colinsella Collinsella Related to gout, these 7 bacterial genera showed a significant decrease in gout patients.

[0026] (2) The present invention established a prediction system for the possibility of gout using the above 7 bacterial genera, verified the prediction of these 7 bacterial genera, and the results of ROC curve analysis showed that Haemophilus Haemophilus A single bacterium or a combination of seven bacterial genera as a detection variable has high specificity and sensitivity, so it can be used as a detection marker in the diagnosis of gout patients. The test is completely non-invasive and highly accurate.

[0027] (3) Compared with the existing patents 202111492008.0 and 202010023168.X, the present invention uses a larger sample size for verification, and the gout prediction effect is more reliable.

[0028] (4) The present invention provides a means for detecting intestinal flora for gout patients, thereby providing a bacterial-derived basis for determining the patient's gout and intestinal flora disorder, and providing a basis for subsequent intestinal flora transplantation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A technical roadmap for the method of establishing a gout prediction system based on intestinal flora for the present invention.

[0030] Figure 2 This is an experimental flow chart for screening and verifying intestinal flora markers associated with gout in Example 1 of the present invention.

[0031] Figure 3 This is the LEfSe score diagram of the 7 bacterial genera in Example 1.

[0032] Figure 4 It is a box scatter plot of the 7 bacterial genera in Example 1.

[0033] Figure 5 This is the ROC curve diagram in Example 1. DETAILED DESCRIPTION

[0034] In order to better understand the present invention, the content of the present invention is further explained below in conjunction with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0035] Unless otherwise specified, the examples were prepared according to conventional experimental conditions or the conditions recommended by the manufacturer's instructions. All reagents and materials used were commercially available unless otherwise specified.

[0036] Example 1

[0037] In this case, we screened and verified intestinal flora markers that can be used for gout detection. The experimental process is as follows: Figure 2 As shown, the specific operations are as follows:

[0038] (1) Sample collection.

[0039] Fecal samples were collected from 126 gout patients and 360 healthy subjects:

[0040] The gout group samples came from 126 families. The inclusion criteria were: ① age distribution 35-70 years old; ② patients clinically diagnosed with gout, ③ serum uric acid>420μmol / L; the exclusion criteria for the gout group were: ① previous history of bariatric surgery, total colectomy and ileorectal anastomosis, or proctocolectomy; ② acute or chronic infectious diseases, severe trauma, malnutrition, diabetes, malignant tumors, grade 4 heart failure, etc.; ③ other mental illnesses (such as schizophrenia) and other neurodevelopmental disorders; ④ cardiovascular and cerebrovascular diseases diagnosed within 3 months; ⑤ liver dysfunction; ⑥ chronic kidney disease or abnormal renal function; ⑦ alcoholism; ⑧ history of gastrointestinal infection or antibiotic use in the past month; ⑨ the researchers believed that the patients were not suitable for inclusion in this study.

[0041] The samples of the control group were from 360 families, and the inclusion criteria were: ① Age distribution 35-70 years old; ② Good health, no various diseases; ③ Serum uric acid ≤ 420μmol / L; ④ No antibiotics or probiotics were taken in the past month. The exclusion criteria were the same as the gout group.

[0042] The above data comes from stool samples collected by Wuhan hospitals.

[0043] (2) DNA extraction, library construction and sequencing.

[0044] The microbial genomic DNA of the samples was extracted using the CTAB (cetyltrimethylammonium bromide) method, followed by PCR amplification. The amplification primers were used to construct the library using the TruSeq@DNA PCR-Free Sample Preparation Kit, and then sequenced using the Illumina Miseq PE250.

[0045] (3) LEfSe analysis and screening of biomarkers.

[0046] The data was filtered, denoised and spliced ​​to obtain high-quality sequencing data. The Identity standard was set to 100% for clustering and ASV feature sequences were divided. The feature sequence was annotated using the qiime2 plug-in feature-classifier classify-sklearn to generate taxonomic abundance data. 80% of the abundance data was randomly selected for analysis using LEfSe software. The default LDA Score screening value was set to 2.5. The results are as follows: Figure 2 As shown. 7 microorganisms that were significantly reduced in the gout group were screened out Holdemanella , Acinetobacter spp. Acinetobacter ), Haemophilus ( Haemophilus ), Weissella ( Weissella ), Pasteurella spp. Barnesiella ), Parapus spp. Paraprevotella ), Colinsera spp. Collinsella ). The box plots of the seven markers are shown in Figure 3 These seven bacterial genera are the first to be found to be associated with gout.

[0047] (4) Establishment of a predictive scoring system.

[0048] The remaining 20% ​​abundance data were first subjected to binary logistic regression, and then to receiver operating characteristic curve (ROC) analysis to obtain the cutoff value (optimal cutoff value). The sample information table involved in steps (3) and (4) is shown in Table 1.

[0049] Table 1 Sample information table

[0050]

[0051] The specificity and sensitivity calculation and ROC curve drawing were completed using IBM SPSS Statistics (v27) statistical software. The software first calculated the threshold of the actual measurement value, and then calculated the 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), and the ROC curve can be constructed by 1-specificity and sensitivity. The integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of a certain indicator, we first calculated 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 indicator.

[0052] The relative abundance values ​​of individual microbial markers were directly analyzed by receiver operating characteristic curve test (ROC curve). Figure 5 The AUC, optimal cutoff value, sensitivity, and specificity of the seven-bacteria combined and single-bacteria prediction scoring methods are shown in Table 2.

[0053] Table 2 ROC diagnostic curve results

[0054]

[0055] From the above results, it can be seen that Haemophilus spp. Haemophilus ) has a much higher predictive ability for gout than other genera, and the prediction effect of the seven bacteria combined is the highest. The AUC of the prediction scoring method of the seven bacteria combined is 0.928, the best cutoff value is 0.827, the sensitivity is 80.6%, and the specificity is 100.0%.

[0056] In summary, the present invention has discovered for the first time that prokaryotes Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Pasteurella Barnesiella , Parapus Paraprevotella and Colinsella Collinsella It is related to gout patients, specifically manifested as a significantly lower abundance than that in healthy people. When these 7 bacterial genera are used as predictive factors for gout, the accuracy is high and the prediction method is non-invasive. The present invention provides a new approach for the diagnosis and treatment of gout patients.

[0057] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art.

[0058] The above implementation cases are only for illustrating the technical solutions and features of the present invention, and their purpose is to enable people familiar with the technology to implement them better. They cannot be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention are within the protection scope of the present invention, and those not described in detail are prior art.

Claims

1. Use of a reagent for detecting the abundance of intestinal flora markers associated with gout in fecal samples of subjects in the preparation of a gout diagnostic product, characterized in that: The intestinal flora markers are composed of prokaryotes Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Barnesiella , Parapus Paraprevotella , Colinsella Collinsella composition.

2. The use according to claim 1, characterized in that: The products include detection kits and gout prediction systems.

3. The use according to claim 1, characterized in that: The reagent is a primer for detecting 16SrRNA of the intestinal flora marker.

4. The use according to claim 2, characterized in that: The gout prediction system uses the abundance of the intestinal flora marker in the subject's fecal sample as a detection variable.

5. Use of intestinal flora markers associated with gout in screening drugs suitable for gout, characterized in that: The intestinal flora markers are composed of prokaryotes Holdemanella , Acinetobacter Acinetobacter , Haemophilus Haemophilus Weissella Weissella , Barnesiella , Parapus Paraprevotella , Colinsella Collinsella composition.

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