Application of intestinal microorganisms in bone parameter prediction and bone metabolism markers

Abstract

The invention discloses application of intestinal microorganisms in bone parameter prediction and bone metabolism markers, and belongs to the technical field of medicines and microorganisms. The technical problem of lack of early prediction and early drug intervention of osteoporosis in the prior art is solved. The invention provides application of enteric microorganisms in bone parameter prediction. The enteric microorganisms comprise parabacteroides coumarini. On the other hand, the invention also provides an application of the enteric microorganism as a bone metabolism marker, and the enteric microorganism comprises parabacteroides coumarini. According to the method, the bone parameters can be predicted through the abundance of the parabacteroides coumarini.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to an application of intestinal microorganisms in the prediction of bone parameters and bone metabolism markers. Background Art

[0002] According to the data of China's seventh census, the population of my country aged 60 and above has reached 260 million, of which about 190 million are aged 65 and above, accounting for 13.5% of the total population. Osteoporosis is a common and important health problem for the elderly. As time goes by, the aging of society will become increasingly serious, and the prevention and treatment of osteoporosis is imminent.

[0003] Although significant progress has been made in the prevention and treatment of osteoporosis, poor patient compliance and adverse reactions to drug treatment still exist. Therefore, studying the pathophysiological process of osteoporosis and finding potential new treatment options for osteoporosis remain urgent tasks.

[0004] In terms of mechanism, osteoporosis is divided into primary osteoporosis and secondary osteoporosis. Primary osteoporosis is divided into postmenopausal osteoporosis, senile osteoporosis and idiopathic osteoporosis. Secondary osteoporosis is caused by diseases and / or drugs that affect bone metabolism and other causes.

[0005] Osteoporosis is manifested in histopathology as abnormal bone metabolism, specifically, bone loss caused by bone resorption being greater than bone formation. Bone formation is mainly mediated by osteoblasts, while bone resorption is mainly mediated by osteoclasts. The process of maintaining a dynamic balance and continuous renewal between the two is called bone remodeling. The greatest significance of studying bone metabolism for the diagnosis and treatment of osteoporosis lies in exploring the mechanism of osteoporosis.

[0006] It is of great significance to find markers to predict the occurrence of osteoporosis, targets or strategies for the treatment of osteoporosis, and further understand the interaction between bones and other organs. Although intestinal bacteria transplantation has been used in the clinical treatment of other diseases, it has not yet been used in the clinic to treat osteoporosis. Summary of the invention

[0007] In view of the above analysis, the present invention aims to provide an application of intestinal microorganisms in the prediction of bone parameters and bone metabolism markers, so as to solve the technical problem of lack of early prediction and early drug intervention of osteoporosis.

[0008] The purpose of the present invention is mainly achieved through the following technical solutions:

[0009] The present invention provides an application of intestinal microorganisms in predicting bone parameters, wherein the intestinal microorganisms include Parabacteroides archaea.

[0010] In one possible design, bone mass parameters are predicted based on the abundance of P. gibberellus in stool samples in relation to bone mineral density.

[0011] In a possible design, the theoretical bone density of stool samples is calculated based on the relationship model between the abundance of Parabacteroides gibberellus and bone density, and the status of bone parameters is judged based on the comparison results between the theoretical bone density and the actual measured bone density.

[0012] In one possible design, the method for establishing a model of the relationship between the abundance of Parabacteroides gibberellus and bone density includes:

[0013] First, the dependent variable was determined to be the relative abundance of Parabacteroides giuseppeii, and the outcome variable was determined to be bone density. Then, the relevant data on the relative abundance of Parabacteroides giuseppeii and bone density results were collected. The collected data were cleaned, including processing missing values, outliers, and duplicate values.

[0014] In one possible design, the collected data on the relative abundance of Parabacteroides gibberellus and bone density results were first subjected to univariate analysis and multivariate analysis, and the variables screened out in the univariate analysis were incorporated into the multivariate logistic regression model; then, the maximum likelihood method was used to estimate the regression coefficients, and the stepwise regression method was used to optimize the model variables, and finally a relationship model between the abundance of Parabacteroides gibberellus and bone density was obtained.

[0015] In one possible design, the abundance of Parabacteroides gibberellus was positively correlated with bone density.

[0016] On the other hand, the present invention also provides a use of intestinal microorganisms as bone metabolism markers, wherein the intestinal microorganisms include Parabacteroides archaea.

[0017] In one possible design, Parabacteroides gibberellus could be used in combination with conventional excipients in its associated products.

[0018] In a possible design, in the related product of Parabacteroides gibberellus, the amount of live bacteria of Parabacteroides gibberellus is more than 85%, and the number of live bacteria contained in each 1g of the related product of Parabacteroides gibberellus is greater than 1×10 9 indivual.

[0019] In one possible design, the related products of Parabacteroides gibberellus include frozen enterobacteria liquid and freeze-dried enterobacteria capsules.

[0020] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0021] (1) The present invention establishes the connection between intestinal flora and osteoporosis and the connection between bone metabolism and intestinal metabolism, identifies the possible responsible strains, achieves early detection of osteoporosis, and provides a therapeutic target for early drug intervention of osteoporosis.

[0022] (2) The present invention provides theoretical support and potential strain selection for the application of intestinal bacteria transplantation in osteoporosis.

[0023] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the embodiments of the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0025] Figure 1 The indicated data are 16s rDNA sequencing using fecal samples from old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice);

[0026] Figure 2 The fifth lumbar vertebra (L5) samples of old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice) were examined by micro-CT to evaluate their bone microstructure;

[0027] Figure 3 Shown is the correlation analysis using the abundance of Parabacteroides archaeopteris detected in old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice) and the bone volume fraction of the fifth lumbar vertebrae. DETAILED DESCRIPTION

[0028] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0029] The present invention provides an application of intestinal microorganisms in the prediction of bone parameters; the intestinal microorganism is Parabacteroides goldsteinii. Parabacteroides goldsteinii belongs to Bacteroidota, Bacteroidia, Bacteroidales, Tannerellaceae, Parabacteroides, and Parabacteroides_goldsteinii; Parabacteroides goldsteinii is characterized by being round, having a particle size of 1-2 mm, having a grayish white front, a smooth and bright surface, and a moist texture, and being a non-pigmented Gram-negative bacillus.

[0030] The present invention can predict bone parameters based on the relationship between the abundance of Parabacteroides gibberellus in stool samples and bone density.

[0031] The present invention can calculate the theoretical bone density of a stool sample according to a relationship model between the abundance of Parabacteroides gibberellus and bone density, and judge the status of bone parameters according to a comparison result between the theoretical bone density and the actually measured bone density.

[0032] Experiments have shown that the expression of Parabacteroides_goldsteinii in elderly mice is lower than that in young mice, and it has a strong positive correlation with bone density (bone quality). Parabacteroides_goldsteinii can be used as an indicator for assessing bone quality.

[0033] It should be pointed out that the method for establishing the relationship model between the abundance of Parabacteroides gibberellus and bone density includes:

[0034] First, the dependent variable was determined to be the relative abundance of Parabacteroides guckii, and the outcome variable was bone density; then the relevant data on the relative abundance of Parabacteroides guckii and bone density results were collected; the collected data were cleaned, including processing missing values, outliers, and duplicate values. In addition, the collected data on the relative abundance of Parabacteroides guckii and bone density results were subjected to univariate and multivariate analysis, and the variables screened out in the univariate analysis were included in the multivariate logistic regression model; then, the maximum likelihood method was used to estimate the regression coefficient, and the stepwise regression method was used to optimize the model variables, and finally the relationship model between the abundance of Parabacteroides guckii and bone density was obtained.

[0035] On the other hand, the present invention also provides a use of intestinal microorganisms as bone metabolism markers, wherein the intestinal microorganisms include Parabacteroides archaea.

[0036] It should be noted that the Parabacteroides archaeopteris of the present invention can be used in combination with conventional excipients in its related products.

[0037] In the related products of Parabacteroides gibberellus, the live bacteria content of Parabacteroides gibberellus is more than 85%, and the number of live bacteria contained in each 1g of Parabacteroides gibberellus related products is>1×10 9 indivual.

[0038] The related products of Parabacteroides gibberellus include frozen enterobacteria liquid and freeze-dried enterobacteria capsules; frozen enterobacteria liquid and freeze-dried enterobacteria capsules can achieve the purpose of preventing osteoporosis.

[0039] It should be emphasized that the process of predicting bone parameters by intestinal microorganisms of the present invention includes the following steps:

[0040] S1. Collect samples;

[0041] Stool samples are collected from the target population, for example, people over 50 years old or those who require self-testing for osteoporosis, and the collected samples are used to analyze the composition of intestinal microorganisms.

[0042] S2. DNA extraction and DNA sequencing of collected samples:

[0043] The intestinal flora detection method using 16s rDNA sequencing includes the following sub-steps:

[0044] S21. Extraction of microbial DNA from stool samples;

[0045] S22, PCR amplification after DNA extraction;

[0046] S23. PCR amplification followed by high-throughput sequencing to identify and quantify different microbial species in the intestine.

[0047] In the above S2 step, the DNA extraction process includes the following sub-steps:

[0048] S211, sample preparation;

[0049] The stool samples collected by step S1 were stored in a refrigerator at -80°C to prevent DNA degradation.

[0050] S212, cell disruption;

[0051] The sample is taken out, cell disruption buffer is added, and the cells in the sample are disrupted using a homogenizer or mortar to destroy the cell membrane and release DNA.

[0052] S213, protease treatment;

[0053] Protease is added to the disrupted sample to degrade the protein in the sample and thereby remove protein interference.

[0054] S214, DNA extraction using phenol / chloroform;

[0055] Add an equal volume of phenol / chloroform mixture to the sample, mix well and centrifuge to separate the mixture into two layers, the upper layer being the DNA supernatant.

[0056] S215, DNA precipitation;

[0057] Transfer the DNA supernatant to a new centrifuge tube, add an equal volume of cold isopropanol, gently invert to mix, and wait for the DNA to gradually precipitate.

[0058] S216, washing the DNA precipitate;

[0059] Wash the DNA pellet with 70% ethanol to remove isopropanol and other impurities.

[0060] S217, drying and resuspending;

[0061] After centrifugation, aspirate the ethanol, dry at room temperature, and add an appropriate amount of ddH2O to resuspend the DNA.

[0062] For the above DNA extraction process, the DNA extraction results are as follows: (1) Purity detection: Use a spectrophotometer to measure the OD260 / OD280 ratio of the DNA solution to evaluate the purity of the DNA; the OD260 / OD280 ratio of pure DNA is 1.8. (2) Concentration determination: Calculate the concentration of DNA by measuring the OD260 value. The concentration determination formula is: DNA concentration (μg / mL) = OD260 value x 50μg / mL x dilution multiple. (3) Molecular size detection: Take a small amount of DNA for agarose gel electrophoresis to evaluate the molecular size and integrity of the DNA. (4) Enzyme digestion experiment: Take an appropriate amount of DNA for restriction endonuclease digestion experiment to detect whether the DNA is suitable for subsequent molecular biology experiments. (5) DNA storage: The extracted DNA is stored at 2-8°C for a short term. If it needs to be stored for a long term, it should be placed at -20°C.

[0063] In the above step S22, the process of PCR amplification after DNA extraction is as follows:

[0064] S221. Take an appropriate amount of sample DNA in a centrifuge tube and dilute the sample to 1 ng / μl with sterile water. Use the diluted genomic DNA as a template and use specific primers with barcodes according to the selection of the sequencing region.

[0065] High-Fidelity PCR Master Mix with GC Buffer and high-efficiency and high-fidelity enzymes are used for PCR amplification to ensure amplification efficiency and accuracy.

[0066] The primer corresponding regions include: 16S V4 region primers (515F and 806R): identification of bacterial diversity; 18S V4 region primers (528F and 706R): identification of eukaryotic microbial diversity; ITS1 region primers (ITS5-1737F and ITS2-2043R): identification of fungal diversity; in addition, the amplification regions also include: 16S V3-V4 / 16S V4-V5 / 16SV5-V7; Archaea 16S V4-V5 / Archaea 16SV8; 18S V9 and ITS2 regions.

[0067] S222, mixing and purification of PCR products;

[0068] The PCR products were detected by electrophoresis using 2% agarose gel; the qualified PCR products were purified by magnetic beads and quantified by enzyme labeling, and equal amounts were mixed according to the concentration of the PCR products. After sufficient mixing, the PCR products were detected by electrophoresis using 2% agarose gel, and the target bands were recovered using the gel recovery kit provided by Qiagen.

[0069] S223, library construction and sequencing;

[0070] use The library was constructed using the DNA PCR-Free Sample Preparation Kit. The constructed library was quantified by Qubit and Q-PCR. After the library was qualified, it was sequenced using NovaSeq6000.

[0071] In the above step S23, high-throughput sequencing is performed after PCR amplification, using a 16s rDNA sequencing detection method, including the following steps:

[0072] S231. Perform high-throughput sequencing on the constructed library. Commonly used platforms include NovaSeq PE250, Illumina MiSeq, and HiSeq.

[0073] S232, data analysis;

[0074] It includes quality control, OTU (operational taxonomic unit) clustering, species annotation, alpha diversity analysis, beta diversity analysis and difference analysis. Among them, OTU clustering: groups sequences by sequence similarity, and each OTU represents an operational taxonomic unit. Species annotation: compares OTU with the database to determine its taxonomic status. Alpha diversity analysis: evaluates the diversity of microorganisms in the sample. Commonly used indicators include Chao1, Shannon, and Simpson. Beta diversity analysis: evaluates the differences in microbial composition between different samples. Commonly used analysis methods include PCoA and NMDS. Difference analysis: such as LEfSe analysis, is used to find microorganisms with significant differences between different groups.

[0075] S233, result interpretation;

[0076] Based on the analysis results, the relationship between the composition and diversity of intestinal flora and diseases was obtained.

[0077] The bone quality detection method of the present invention includes single photon absorptiometry (SPA), dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), and quantitative ultrasound measurement.

[0078] The bone quality can be obtained through the above-mentioned bone quality detection method and then evaluated. For example, a patient's relative abundance of Parabacteroides archaeopteris was found to be 0.01% through 16S rDNA sequencing, which is a low level, so it is predicted that the patient has a high risk of osteoporosis.

[0079] Taking DXA as an example, the patient underwent DXA examination, and the results showed that the bone density BMD of the femoral neck was 0.463g / cm2 and the T value was -3.5, and he was diagnosed with osteoporosis.

[0080] It should be noted that the DXA detection process of the present invention includes the following steps:

[0081] S1. Patient preparation;

[0082] Patients are asked to remove any metal objects that may affect the measurement.

[0083] S2, positioning;

[0084] The patient is placed on the examination table of the DXA scanner, usually in the supine position.

[0085] S3, scan;

[0086] The scanner emits X-rays and a computer analyzes the attenuation of the X-rays.

[0087] S4. Result analysis;

[0088] The software analyzes the data and generates a report including BMD, T-score, and Z-score.

[0089] S5. Report interpretation;

[0090] The doctor makes a diagnosis and treatment recommendations based on the results of the DXA report and the patient's clinical condition.

[0091] General steps of QCT testing:

[0092] S1. Patient preparation;

[0093] The patient is asked to remove any metal objects that may interfere with the scan and to assume the appropriate position, usually supine, as directed by the technician.

[0094] S2, positioning and scanning;

[0095] The patient is placed in the CT scanner, making sure the part to be scanned (such as the lumbar spine or hip) is within the scanning field, and a calibration phantom may be used to ensure the accuracy of the image.

[0096] S3. Image acquisition: Use a CT scanner to obtain cross-sectional images of the patient.

[0097] S4. Image processing: The scanned images need to be processed by specialized QCT analysis software, which usually includes image calibration, segmentation, and determination of the region of interest (ROI).

[0098] S5. Data analysis: The software analyzes the images and calculates bone density, usually expressed in mg / cm3. QCT can measure the density of cortical bone and cancellous bone separately.

[0099] S6. Interpretation of results: The doctor or technician will evaluate the patient's bone density based on the QCT results and compare it with normal values ​​or the bone density of people of the same age.

[0100] S7. Report writing: Write a report based on the analysis results, which may include bone density values, T scores, Z scores, and an assessment of fracture risk.

[0101] S8. Follow-up instructions: Based on the QCT results, the doctor may recommend specific lifestyle changes, medications, or other interventions to the patient.

[0102] The bone quality assessment of the present invention includes determining the amount of bone, the degree of bone loss, and the fracture risk assessment.

[0103] Bone mass: Taking DXA as an example, after a patient undergoes DXA examination, the result shows that the bone mineral density (BMD) of the femoral neck is 0.463 g / cm 2 , the T-value is -3.5, and the diagnosis is osteoporosis (the T-value is calculated by comparing the bone mineral density measured in the patient with a normal reference value and standard deviation (SD). Normal bone mass: T-value ≥ -1.0; Low bone mass: -2.5 < T-value < -1.0; Osteoporosis: T-value ≤ -2.5). Taking QCT as an example, after a patient undergoes QCT examination, the result shows that the lumbar spine bone mineral density is 70 mg / cm 3 , and the diagnosis is osteoporosis (the QCT diagnostic criteria for the lumbar spine: Usually, the average value of the cancellous bone mineral density of the first and second lumbar vertebrae is used for diagnosis. The absolute value of bone mineral density ≥ 120 mg / cm3 indicates normal bone mineral density, the absolute value of bone mineral density in the range of 80 - 120 mg / cm3 indicates low bone mass, and the absolute value of bone mineral density ≤ 80 mg / cm 3 indicates osteoporosis).

[0104] Degree of bone loss: Taking DXA as an example, after a patient undergoes the first DXA examination, the result shows that the bone mineral density (BMD) of the femoral neck is 0.463 g / cm 2 , the T-value is -3.5, and the diagnosis is osteoporosis. After the second DXA with memory addition, the result shows that the bone mineral density (BMD) of the femoral neck is 0.784 g / cm 2 , the T-value is -1.9, and the diagnosis is osteopenia; bone loss is inhibited (bone mass rebounds).

[0105] Fracture risk assessment: The fracture risk of the patient is assessed in combination with the FRAX score.

[0106] In S3, data analysis is performed after DNA sequencing;

[0107] In step S3, bioinformatics tools are used to analyze the sequencing results obtained in step S2 to obtain the abundance of Parabacteroides goldsteinii; and the change in the abundance of Parabacteroides goldsteinii is used as a microbial biomarker related to osteoporosis (an increase in the abundance of Parabacteroides goldsteinii means better bone quality; a decrease in the abundance of Parabacteroides goldsteinii means poor bone quality).

[0108] In step S3, the above bioinformatics tools include: OTU clustering, which requires the use of the Uparse algorithm, species annotation of OTU / ASV sequences, species annotation analysis with the SILVA138.1 SSUrRNA database using the Mothu method, and calculation of Observed_otus / ASV, Chao1, Shannon, Simpson, ace, Goods-coverage, PD_whole_tree indices using Qiime software (Version 1.9.1).

[0109] In step S3, the analysis process of the sequencing results is as follows:

[0110] S31, sequencing data processing;

[0111] Separate the data of each sample from the offline data according to the Barcode sequence and PCR amplification primer sequence. After truncating the Barcode and primer sequence, use FLASH (v1.2.11) to splice the reads of each sample. The spliced ​​sequence is the original Tags data (Raw Tags). The spliced ​​Raw Tags need to be strictly filtered to obtain high-quality Tags data (Clean Tags). Refer to the Tags quality control process of Qiime (V1.9.1) and perform the following operations:

[0112] a) Tags truncation: Cut Raw Tags from the first low-quality base site where the number of consecutive low-quality bases (the default quality threshold is <= 19) reaches the set length (the default length is 3);

[0113] b) Tag length filtering: The tags are truncated to obtain a tag data set, and the tags whose continuous high-quality base length is less than 75% of the tag length are further filtered out. The tags obtained after the above processing need to be processed to remove chimera sequences. The tag sequences are compared with the species annotation database to detect chimera sequences, and finally the chimera sequences are removed to obtain the final effective data (Effective Tags).

[0114] S32, OTU clustering, ASV denoising and species annotation;

[0115] If OTU clustering is selected, the Uparse algorithm is used to cluster all Effective Tags of all samples. By default, the sequences are clustered into OTUs (Operational Taxonomic Units) with a consistency of 97%. At the same time, the representative sequence of OTU will be selected. According to the algorithm principle, the sequence with the highest frequency in OTU is selected as the representative sequence of OTU. If the noise reduction method is selected to generate ASV, Deblur (v1.1.0) is used to compare the Hamming distances between sequences in the sample with the upper error curve, and the amplicon sequence variant (ASV) is obtained by combining the greedy algorithm.

[0116] The OTU / ASV sequences were annotated with species, and the species annotation analysis was performed using the Mothur method and the SSUrRNA database of SILVA138.1 (the threshold was set to 0.8-1), and taxonomic information was obtained and the community composition of each sample was statistically analyzed at each classification level: phylum, class, order, family, genus, and species. MAFFT (v7.490) software was used for rapid multiple sequence alignment to obtain the phylogenetic relationship of all OTU / ASV representative sequences; finally, the data of each sample was homogenized, and the sample with the least amount of data was used as the standard for homogenization. The subsequent Alpha diversity analysis and Beta diversity analysis were based on the data after homogenization.

[0117] S33, Sample complexity analysis (Alpha Diversity);

[0118] Qiime software (Version 1.9.1) was used to calculate the Observed_otus / ASV, Chao1, Shannon, Simpson, ace, Goods-coverage, and PD_whole_tree indices. R software (Version 4.1.2) was used to draw the dilution curve, Rank abundance curve, species accumulation curve, and use R software to analyze the differences between groups in the Alpha diversity index. The analysis of the differences between groups in the Alpha diversity index will be carried out using parametric and non-parametric tests. If there are only two groups, T-test and Wilcox test will be used. If there are more than two groups, Tukey test and Kruskal-Wallis test will be used.

[0119] S34, Alpha diversity index includes:

[0120] The indices for calculating community richness include: Chao-the Chao1estimator; ACE-the ACE estimator.

[0121] The indices for calculating community diversity are:

[0122] Shannon-the Shannon index;Simpson-the Simpson index;

[0123] The sequencing depth indices are: Coverage-the Good's coverage;

[0124] The indices of phylogenetic diversity are: PD_whole_tree-PD_whole_tree index.

[0125] S34, multi-sample comparative analysis (Beta Diversity);

[0126] Qiime software (Version 1.9.1) was used to calculate Unifrac distance and construct UPGMA sample clustering tree. PCA, PCoA and NMDS graphs were drawn using R software (Version 4.1.2). PCA and PCoA analysis used the stats package and ggplot2 package of R software, and NMDS analysis used the vegan package of R software. R software was used to analyze the differences between groups of Beta diversity index, and parametric and non-parametric tests were performed respectively. If there were only two groups, T-test and Wilcox test were used. If there were more than two groups, Tukey test and Kruskal-Wallis test were used.

[0127] LEfSe analysis used LEfSe software, and the default setting of LDA Score screening value was 4. Metastats analysis used Mothur software to perform permutation test between groups at each classification level (Phylum, Class, Order, Family, Genus, Species) to obtain p-values, and then used the Benjamini and Hochberg False Discovery Rate method to correct the p-values ​​to obtain q-values. Anosim, MRPP and Adonis analyses used the anosim function, mrpp function and adonis function of the R vegan package, respectively. AMOVA analysis used the amova function of mothur software. Analysis of species with significant differences between groups used R software to perform inter-group T-tests and plot graphs.

[0128] Through the above analysis process, the structural composition and abundance data of the intestinal flora, including the relative abundance of Parabacteroides archaea, were finally obtained.

[0129] S4, association study;

[0130] The composition of intestinal microorganisms (abundance of Parabacteroides archaea) was analyzed in association with bone density, bone microstructure, and bone metabolism markers to determine the risk of osteoporosis in the subjects. If necessary, the patients can be personalized evaluated by clinical indicators (such as age, gender, dietary habits, etc.).

[0131] In the above step S4, the association analysis between the composition of intestinal microorganisms (abundance of Parabacteroides gibberellus) and bone density, bone microstructure, and bone metabolism markers specifically includes the following processes:

[0132] S41. Data preparation;

[0133] Collect relevant data, including the relative abundance of Parabacteroides gibberellus and bone density results obtained by individual intestinal flora sequencing; clean the collected data, including processing missing values, outliers and duplicate values.

[0134] S42. Recruit a large number of subjects from normal and healthy population (100 cases, and the more the number, the more accurate the prediction model), expand the sample size, and conduct the above-mentioned tests and statistical results; with the support of large sample data, conduct a correlation analysis between the relative abundance of Parabacteroides archaea and bone density.

[0135] S43, selecting the Pearson correlation analysis method as the correlation analysis method;

[0136] The value of the correlation coefficient is calculated by the formula. The value is between -1 and 1. A value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates no obvious linear relationship.

[0137] S44. Interpretation of results;

[0138] Based on the value and sign of the correlation coefficient, explain the relationship between the variables.

[0139] S45, significance test;

[0140] Conduct statistical significance tests to determine whether the associations are statistically significant.

[0141] S46, data visualization;

[0142] Use scatter plots to show the relationship between variables and help you understand the correlation intuitively.

[0143] S47, application;

[0144] The target population is sequenced for intestinal flora to obtain the relative abundance of Parabacteroides archaea, and then the formula in the Pearson correlation analysis method in S43 is used to calculate the theoretical bone density and compare it with the actual measured bone density. If the actual measured bone density is higher than the theoretical calculated bone density, it is normal; if the actual measured bone density is lower than the theoretical calculated bone density, there is a risk of osteoporosis.

[0145] Because there are many factors that affect intestinal flora and bone mass, for example, the intestinal flora and bone mass of a 50-year-old and an 80-year-old are different, so after calculating the risk of osteoporosis using conventional methods, corrections can be made to their age, gender, etc. For example, a 50-year-old should be calculated according to the standard for a 50-year-old, and an 80-year-old should be calculated according to the standard for an 80-year-old.

[0146] S5. Establish prediction model and verify it:

[0147] Through statistical methods, such as multivariate logistic regression analysis, an osteoporosis prediction model based on intestinal microbial composition was established; and the accuracy and reliability of the prediction model were verified on an independent sample set.

[0148] In the above step S5, the process of establishing the prediction model is:

[0149] S511. Determine the research objectives;

[0150] The outcome variable (dependent variable) that you want to predict is clearly defined, namely osteoporosis (relative abundance of Parabacteroides archaeopteris), and the outcome variable (dependent variable) is a binary variable, such as yes / no, with / without disease.

[0151] S512, data preparation;

[0152] Collect relevant data, clean it, and handle missing values ​​and outliers.

[0153] S513, variable selection:

[0154] Univariate analysis: First, a univariate logistic regression analysis was performed on each independent variable to screen out variables with a P value less than a specific threshold (such as 0.05 or 0.1).

[0155] Multivariate analysis: Incorporate the variables selected in the univariate analysis into the multivariate logistic regression model. Sometimes it is necessary to consider the interaction between variables.

[0156] S514, Model establishment:

[0157] The regression coefficients were estimated using the maximum likelihood method. Stepwise regression methods (forward selection, backward elimination, or stepwise method) were used to optimize the model variables.

[0158] S52. Model evaluation:

[0159] S521, overall model significance;

[0160] The overall significance of the model was evaluated by likelihood ratio test and Wald test.

[0161] S522, goodness of fit;

[0162] The Hosmer-Lemeshow test was used to assess the goodness of fit of the model.

[0163] S523, predictive ability;

[0164] The AUC value of the model was calculated to evaluate the model's discrimination ability.

[0165] S524, model optimization;

[0166] Adjust the model as needed, which may include adding or removing variables or accounting for interaction terms.

[0167] S525, result interpretation;

[0168] Explain the coefficient, OR value and its 95% confidence interval for each variable, and use Exp(B) to explain the impact of the independent variable on the dependent variable.

[0169] S526, model verification;

[0170] Verify the robustness of the model through cross-validation, bootstrap methods, or independent datasets.

[0171] The present invention has been clinically applied to predict osteoporosis in a target population.

[0172] The specific process is: first recruit 100 healthy subjects and collect their feces, use 16SrDNA to detect the abundance of their Parabacteroides archaea, and use DXA to detect their bone density; then calculate the correlation and formula as the basis for subsequent predictions.

[0173] Then test the abundance of Parabacteroides gibberellus in the target population, insert it into the formula obtained in step S611, calculate its theoretical bone density, and compare it with the actual measured bone density. If the actual measured bone density is higher than the theoretical calculated bone density, it is normal; if the actual measured bone density is lower than the theoretical calculated bone density, there is a risk of osteoporosis.

[0174] After verification in clinical trials, the research results can be transformed into clinical applications; including the development of early screening and prevention strategies for osteoporosis based on intestinal microbial detection, continuous monitoring of changes in intestinal microorganisms, and exploration of new biomarkers and intervention measures.

[0175] Compared with the prior art, the present invention establishes the connection between intestinal flora and osteoporosis and the connection between bone metabolism and intestinal metabolism, determines the possible responsible strains, achieves early detection of osteoporosis, and provides a therapeutic target for early drug intervention of osteoporosis.

[0176] The present invention provides theoretical support and potential strain selection for realizing the application of intestinal bacteria transplantation in osteoporosis.

[0177] Based on the results of osteoporosis prediction by specific intestinal microorganisms (Parabacteroides gibberellus), the intestinal microorganisms are used to prepare Parabacteroides gibberellus related products, which specifically include the following processes:

[0178] The process of preparing products related to Parabacteroides gibberellus using intestinal microorganisms involves the following steps:

[0179] S1. Donor screening;

[0180] Healthy donors are selected and undergo rigorous health checks and screening to ensure that the donor feces are free of harmful pathogens and have a diverse and stable intestinal flora.

[0181] S2. Collection and processing of stool samples;

[0182] Collect stool samples from healthy donors and start the preparation process of enterobacteria capsules within the specified time (usually no more than 6 hours); the stool samples need to be visually inspected according to the Bristol stool classification system, and the appropriate stool type is selected for subsequent processing.

[0183] In the above step S2, the appropriate feces type refers to the feces type according to the Bristol feces classification system.

[0184] Choose ones that are relatively complete in shape, not too watery or too hard, and have no obvious abnormalities such as blood or mucus.

[0185] S3. Isolation and cultivation of microorganisms;

[0186] The stool sample is homogenized, filtered and centrifuged using a sterile solution to separate and concentrate the stool sample into an enterobacteria stock solution. The purpose is to separate the solid and liquid parts of the stool, extract the microorganisms, and discard the stool residue that does not contain microorganisms. If necessary, a cryoprotectant is added or freeze-dried.

[0187] The homogenization process includes the following sub-steps:

[0188] S311, sample mixing;

[0189] Use sterile tools to mix the stool sample with an appropriate buffer (such as PBS) to ensure sample homogeneity.

[0190] S312, mechanical homogenization;

[0191] Physically agitate the sample using a homogenizer or blender to further ensure sample homogeneity.

[0192] S313, fecal sample filtering process;

[0193] The filtration process of stool samples is one of the key steps in the pretreatment of stool samples. It helps to remove solids and macromolecular particles in the samples, thereby obtaining purer and more homogeneous samples for subsequent analysis and experiments.

[0194] Sample collection: Collect fresh stool samples and process them as quickly as possible to avoid changes in microbial activity and metabolite degradation.

[0195] Sample homogenization: Add sterile PBS buffer to the stool sample and use a mechanical shaker to shake until the solid sample is fully dissolved.

[0196] Centrifugation: Centrifuge at high speed for 10 min in a refrigerated centrifuge to separate the solid particles from the liquid portion.

[0197] Filtration: Filter the supernatant after centrifugation through a 0.45 μm filter to remove smaller unbroken cells and particles. In some cases, filters with different pore sizes may be used, depending on the requirements of the experiment.

[0198] Collect the filtrate: Collect the filtrate after filtration, which is the sample suspension, which can be used for subsequent DNA or RNA extraction.

[0199] Cryopreservation: Freeze the sample suspension at a temperature below -20°C for subsequent experimental use.

[0200] S32. The filtering conditions include:

[0201] Temperature: The filtration process is usually carried out at 4°C to maintain sample stability.

[0202] Centrifugation speed: High-speed centrifugation (such as 8000G to 12000G) helps separate the solid and liquid phases.

[0203] Centrifugation time: usually 10 minutes, the specific time may be adjusted according to experimental conditions.

[0204] Filter membrane pore size: The commonly used filter membrane pore size is 0.45μm, but sometimes 0.22μm or other pore size filters are used as needed.

[0205] S33, the centrifugation process includes:

[0206] The centrifugation process is used to separate the solid and liquid parts of the stool. Preparation before centrifugation: Mix the stool sample with an appropriate buffer (such as PBS) to help separate the solid and liquid parts.

[0207] Homogenization: Use a mechanical shaker to shake the mixed sample to ensure that the stool sample is thoroughly mixed with the buffer.

[0208] Centrifugation conditions: Temperature: Usually performed at 4°C to maintain sample stability.

[0209] Speed: high speed centrifugation, such as 8000G to 12000G, the specific speed depends on the experimental requirements.

[0210] Time: Usually centrifuge for 10 minutes, but the specific time may be adjusted according to experimental conditions.

[0211] Post-centrifugation steps: After centrifugation, the supernatant is usually transferred to a new centrifuge tube and centrifuged again to further purify the sample. The second centrifugation may be at a higher speed, such as 10,000 g, and the time may be 10 minutes.

[0212] The supernatant after filtration and centrifugation may need to be filtered through a membrane filter to remove smaller particles.

[0213] Collection: Collect the filtrate after filtration for subsequent DNA or RNA extraction.

[0214] S34, microbial culture process, comprising the following steps:

[0215] S341. Select culture medium: Select appropriate culture medium according to the type of microorganism and growth requirements. The culture medium can be solid or liquid and usually contains a carbon source, a nitrogen source, minerals and vitamins.

[0216] S342. Prepare the culture medium: mix the ingredients according to the formula proportions, heat to dissolve the agar solidifying agent, and then autoclave to ensure sterility.

[0217] S343, Inoculation: Inoculate the microorganisms onto the culture medium; inoculation is carried out under sterile conditions by streaking, spreading or in liquid culture medium.

[0218] S344, culture: the inoculated culture medium is placed in a constant temperature incubator for culture. The culture conditions (such as temperature, pH, oxygen supply) need to be adjusted according to the type of microorganism.

[0219] S345. Observation and separation: Observe the growth of microorganisms during the culture process, such as colony morphology and color; separate and purify the colonies when necessary.

[0220] S346. Identification: Identify the cultured microorganisms through microscopic observation, biochemical testing or molecular biological methods (such as 16SrRNA gene sequencing).

[0221] S347. Preservation: The cultured pure strains of microorganisms shall be properly preserved, and cryoprotectants shall be added when necessary for subsequent research or application. Cryoprotectants shall be added after the enterobacteria stock solution is fully prepared and the next step is to prepare the drug or preserve it.

[0222] S4. Drug formulation design;

[0223] Drug formulations are designed based on the type of disease being treated.

[0224] The enterobacteria medicine of the present invention comprises enterobacteria frozen bacterial liquid and enterobacteria freeze-dried capsule.

[0225] It should be noted that in the above S4, the frozen enterobacteria solution specifically includes enterobacteria culture, protective agent, buffer and nutrients or growth factors. Among them, the enterobacteria culture is the main component of the frozen enterobacteria solution, which comes from intestinal microorganisms cultured in the laboratory, and in the present invention, it refers to Parabacteroides gibberellii. The activity of the bacteria (Parabacteroides gibberellii) is greater than or equal to 80%, and the number of viable cells per 1 ml of the solution is greater than (5×10 8 ) samples from the same donor and the same batch should have consistent bacterial flora composition.

[0226] Regarding the above protective agent: during the freezing process, the protective agent can prevent cells from being damaged by ice crystal formation. The protective agent of the present invention includes: skim milk: usually used at a concentration of 10%; sucrose: usually used at a concentration of 12%; glycerol; dimethyl sulfoxide (DMSO): can also prevent the death of microorganisms during freezing.

[0227] The buffer of the present invention is PBS (phosphate buffered saline), which is used to maintain the pH value and provide a stable environment.

[0228] In the above S4, the components of the enterobacteria freeze-dried capsule include intestinal microorganisms, protective agents, nutrients or growth factors and capsule shells.

[0229] Regarding the intestinal microorganisms in the freeze-dried enterobacteria capsules: they are the main components of the freeze-dried enterobacteria capsules, which are usually derived from the intestinal microorganisms of healthy donors cultured in the laboratory. In the present invention, they refer to Parabacteroides gibberellii, and the amount of live bacteria exceeds 85%; and the contents of the capsules should also be taken for bacterial activity monitoring and viable cell counting, wherein the bacterial activity should be not less than 70%, and the number of live bacteria per 1g of the contents> (1×10 9 ) and the anaerobic bacteria content needs to be >90%.

[0230] Protective agent in enterobacteria freeze-dried capsule: Protective agent is used to protect microorganisms from damage during freezing and freeze-drying. The protective agent of the present invention includes glycerol, water-soluble starch, oligosaccharide, and mannitol. For example, the amount of glycerol can be between 0.5 and 1.5 parts, water-soluble starch between 8.0 and 12.0 parts, oligosaccharide between 1.0 and 3.0 parts, mannitol between 0.5 and 1.5 parts, and water 80 to 90 parts.

[0231] Targeting the nutrients or growth factors in the enterobacteria freeze-dried capsules, such as oligofructose, oligomaltose, trehalose and stachyose, to promote the colonization of the flora in the intestine.

[0232] Regarding the capsule shell in the enterobacteria freeze-dried capsule: enteric-coated capsules, such as acid-resistant hydroxypropyl methylcellulose capsules, are usually used to ensure that the contents are released in the small intestine.

[0233] S5. Quality control;

[0234] The prepared enterobacteria drugs are subjected to a series of quality tests, including microbial counting, flow cytometry, pathogenic bacteria testing, etc., to ensure the safety, effectiveness and stability of the products.

[0235] S6. Clinical trials;

[0236] Preclinical studies are conducted on animal models to verify the safety and effectiveness of the drug. Subsequently, human clinical trials are conducted to further evaluate the efficacy and safety of the drug, and subsequent monitoring and research are improved to ensure the safety and effectiveness of long-term use.

[0237] It should be pointed out that the above-mentioned intestinal microorganism is Parabacteroides goldsteinii; Parabacteroides goldsteinii belongs to the phylum Bacteroidota, class Bacteroidia, order Bacteroidales, family Tannerellaceae, genus Parabacteroides, and species Parabacteroides goldsteinii; the characteristics of Parabacteroides goldsteinii are: round, with a particle size of 1-2mm, grayish white on the front, smooth and bright surface, moist texture, and are non-pigmented Gram-negative rods.

[0238] Example 1

[0239] This example uses the above-mentioned intestinal microorganisms in the osteoporosis prediction process to explore the relationship between Parabacteroides gibberellus and osteoporosis.

[0240] The present invention first detected the relative abundance of Parabacteroides archaeopteris in young mice and old mice, such as Figure 1 As shown; then the bone microstructure of young mice and old mice was measured respectively, as shown Figure 2 As shown, finally, the correlation analysis is performed on the two, as shown in Figure 3 As shown. Among them, Figure 1 The figure shows 16s rDNA sequencing of fecal samples from old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice). The results showed that the relative abundance of Parabacteroides archaeopteris was higher in young mice than in old mice, and the difference was statistically significant. Figure 2The figure shows the evaluation of bone microarchitecture using micro-CT of the fifth lumbar vertebrae (L5) of old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice). The results show that the bone volume fraction of young mice is greater than that of old mice, and the difference is statistically significant. Figure 3 The figure shows the correlation analysis between the abundance of Parabacteroides gibberellus and the bone volume fraction of the fifth lumbar vertebra detected in old mice (18-month-old C57 / BL6 mice) and young mice (2-month-old C57 / BL6 mice). The results showed that the abundance of Parabacteroides gibberellus and the bone volume fraction of the fifth lumbar vertebra showed a strong positive correlation.

[0241] It should be noted that the expression of Parabacteroides_goldsteinii in aged mice is lower than that in young mice, and it has a strong positive correlation with bone quality. Parabacteroides_goldsteinii can be used as an indicator for assessing bone quality.

[0242] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An application of intestinal microorganisms in predicting bone parameters, characterized in that: The intestinal microorganisms include Parabacteroides archaeopteris.

2. The use of intestinal microorganisms in predicting bone parameters according to claim 1, characterized in that: Bone parameters were predicted based on the abundance of Parabacteroides archaeopteris in stool samples and its relationship with bone mineral density.

3. The use of intestinal microorganisms in predicting bone parameters according to claim 2, characterized in that: The theoretical bone density of stool samples was calculated based on the relationship model between the abundance of Parabacteroides gibberellus and bone density, and the status of bone parameters was determined based on the comparison results between the theoretical bone density and the actual measured bone density.

4. The use of intestinal microorganisms in predicting bone parameters according to claim 3, characterized in that: The method for establishing the relationship model between the abundance of Parabacteroides archaeopteris and bone density comprises: First, the dependent variable was determined to be the relative abundance of Parabacteroides giuseppeii, and the outcome variable was determined to be bone density. Then, the relevant data on the relative abundance of Parabacteroides giuseppeii and bone density results were collected. The collected data were cleaned, including processing missing values, outliers, and duplicate values.

5. The use of intestinal microorganisms in predicting bone parameters according to claim 4, characterized in that: First, the collected data on the relative abundance of the Parabacteroides gibberellus and bone density results were subjected to univariate analysis and multivariate analysis, and the variables screened out in the univariate analysis were incorporated into the multivariate logistic regression model; then, the maximum likelihood method was used to estimate the regression coefficient, and the stepwise regression method was used to optimize the model variables, and finally the relationship model between the abundance of the Parabacteroides gibberellus and bone density was obtained.

6. The use of intestinal microorganisms in predicting bone parameters according to claim 3, characterized in that: The abundance of the bacteriodes was positively correlated with bone density.

7. A use of intestinal microorganisms as bone metabolism markers, characterized in that: The intestinal microorganisms include Parabacteroides archaeopteris.

8. The use of intestinal microorganisms as bone metabolism markers according to claim 3, characterized in that: The Parabacteroides archaeopteris can be used in combination with conventional excipients in its related products.

9. The use of intestinal microorganisms as bone metabolism markers according to claim 7, characterized in that: In the related products of Parabacteroides giusena, the live bacteria amount of Parabacteroides giusena is more than 85%, and the live bacteria count per 1g of the related products of Parabacteroides giusena is greater than 1×10 9 indivual.

10. The use of intestinal microorganisms as bone metabolism markers according to claim 8, characterized in that: The related products of the Parabacteroides archivus include frozen enterobacteria liquid and freeze-dried enterobacteria capsules.

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