Microecological quantitative detection method and application thereof

Abstract

The present application relates to a microecological quantitative detection method and its application, the method comprising: collecting microorganism detection data; clustering at the sequence level with each gene as a unit; obtaining a plurality of homologous sequence clustering clusters of each gene according to the clustering result; through biological analysis, each target microorganism is corresponded to one or more combinations of clustering clusters; for the selected target microorganism, the corresponding single or multiple set clustering cluster is searched and evaluated for specificity; the specific conserved region is combined in pairs, and primers are designed; the conserved region pairs of the successfully designed primers are artificially synthesized for gene sequence, positive quality control standards are prepared according to the set different concentration gradient, and a standard curve is drawn; most of the target microorganisms can be detected to develop reagents, kits and instruments, and complex scientific research findings in many microecological fields can be quickly transformed into detection products.

Description

Technical Field

[0001] This invention relates to the field of microbial detection technology, and more specifically, to a quantitative detection method for microecology and its application. Background Technology

[0002] Microorganisms are ubiquitous on Earth, influencing the entire biosphere. They play a major role in regulating the biogeochemical systems of almost every environment on our planet, including some of the most extreme environments, from frozen landscapes and acidic lakes to hydrothermal vents on the deep seabed, as well as some of the most familiar environments to humans, such as the human gut, mouth, reproductive tract, and skin. In the natural environment, microorganisms often do not exist in a single species. To better adapt to the environment and perpetuate life, they cooperate through complementary gene metabolic pathways among different species, and there is also a process of microevolution, which causes single strains to constantly mutate and produce mixed communities of multiple strains. This mixed community is called a microecology, also known as a microbial community, microbiome, etc.

[0003] The most well-known and important microecology among humans is the human gut microbiota. A large number of symbiotic microorganisms exist within the human gut, influencing human health through various pathways, especially their metabolites, which play a crucial role in promoting health and the occurrence and development of diseases. Gut microbiota research has found that diseases such as diabetes, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), obesity, and depression are all related to the gut microbiota. Recent research suggests that Alzheimer's disease may be related to gut microbiota, and gut microbiota balance is becoming an important indicator of human health. Maintaining the dynamic balance of the gut microbiota helps the body resist various diseases and maintain a healthy state. Maintaining this balance primarily involves keeping the number of gut microbiota within a stable and healthy range. To better predict disease risk through gut microbiota analysis, specific microbial testing is particularly necessary.

[0004] The gut microbiota is dynamic and can be altered by various factors, such as dietary habits, exercise, medications, and gut microbiota transplantation therapy, as well as functional foods like probiotics, prebiotics, and postbiotics, which have become popular in the health and medical field in recent years. What kind of gut microbiota requires active intervention and modification? To what extent should this modification be implemented for optimal health? These questions highlight that gut microbiota testing is the cornerstone of medical and health management practices in this area. Currently, the globally accepted quantitative detection methods for gut microbiota include bacterial 16S rRNA gene sequencing, metagenomic sequencing, and microbial product detection. However, these methods are either costly and complex to operate, or they offer limited detection content and narrow coverage, failing to meet the specific needs of the healthcare field for gut microbiota testing. If gut microbiota testing, the area with the largest global research investment, faces such challenges, then the detection of gut microbiota in other environments is even more lacking in convenient, rapid, professional, effective, and cost-effective methods. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a quantitative detection method for microecology, and to provide an application of the quantitative detection method for microecology, in view of the above-mentioned defects of the prior art.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] A quantitative detection method for microbial ecosystems is constructed, comprising the following steps:

[0008] Step 1: Collect microbial detection data from one or more samples for the micro-ecological environment that needs to be tested;

[0009] The second step is to cluster the collected samples at the sequence level, using genes as the unit.

[0010] Step 3: Based on the clustering results, obtain clusters of multiple homologous sequences for each gene;

[0011] Step 4: Through biological analysis, each target microorganism is mapped to a combination of one or more clusters;

[0012] Step 5: For the selected target microorganisms, perform conserved region retrieval and specificity assessment on the corresponding single or multiple union clusters; pair conserved regions with specificity and design primers.

[0013] Step 6: Artificially synthesize the gene sequences of the conserved regions of the successfully designed primers, prepare positive control standards according to different concentration gradients, and plot standard curves.

[0014] The microecological quantitative detection method of the present invention, wherein the primer design process in the fifth step includes the following method:

[0015] Multiple sequence alignment was performed on all gene sequences of the selected target microorganism.

[0016] Find conserved regions in the multiple sequence alignment results; the length of the conserved region should be greater than 18 bp.

[0017] All the identified conservative regions are paired up to form conservative region pairs, with the two conservative regions in each pair being 80 to 400 bp apart.

[0018] The sequence contained in each conserved region pair is compared with the sequence set of all genes in the third step. Conserved region pairs whose specificity does not meet the set threshold are filtered out based on the comparison results.

[0019] Primers were designed for the filtered conserved regions.

[0020] The microecological quantitative detection method of the present invention further includes a method for primer design processing in the fifth step;

[0021] If no conservative region pair passes the filter or if primers cannot be successfully designed for the filtered conservative region pair, then the current cluster is considered unacceptable, and the next cluster is selected to repeat the primer design process.

[0022] If primers cannot be successfully designed for any of the clusters, the selected target microorganisms are considered to be difficult to detect by a single primer target, and they are processed according to the set processing method.

[0023] The microecological quantitative detection method of the present invention, wherein the setting and processing method includes:

[0024] The method terminates or jumps to step four to reselect or define the correspondence between the selected target microorganism and the sequence gene cluster.

[0025] The microecological quantitative detection method of the present invention, wherein the setting and processing method includes:

[0026] After relaxing the definition criteria of the conserved region, relaxing the criteria for filtering the specificity of the conserved region, or relaxing the requirements for primer sensitivity and specificity, primer design should be carried out again.

[0027] The quantitative detection method for microecology described in this invention, wherein the fifth step further includes a method:

[0028] Bioinformatics evaluation of primer sensitivity and specificity is conducted. When both sensitivity and specificity are acceptable, the primer design for the target microorganism is considered successful.

[0029] Primer sensitivity is the coverage of the primer sequence to all sequences within the target microorganism; primer specificity is the proportion of all genes in the third step that the primer sequence can cover that originate from the target microorganism.

[0030] The microbial ecological quantitative detection method of the present invention, wherein, in the first step, the microbial detection data includes metagenomic sequencing data and / or gene-targeted sequencing data.

[0031] The microecological quantitative detection method of the present invention includes, in the four steps, the biological analysis method comprising: obtaining gene sequence clusters through direct correspondence by species classification, correspondence by gene function, or correlation analysis of gene sequence cluster profiles and sample phenotypic information profiles.

[0032] The microecological quantitative detection method of the present invention further includes, in the sixth step, a method:

[0033] The actual amplification results of the positive control standard are fitted to obtain the absolute quantitative value Y, as follows: Y = aX + b, where X is the PCR amplification Ct value, and a and b are the fitting results of the actual detection data of the positive control standard.

[0034] An application of a quantitative detection method for microecology, wherein the quantitative detection method for microecology described above is applied in the detection of human intestinal microecology and / or reproductive tract microecology.

[0035] A human gut microbiota reagent, wherein the positive control standard in the reagent is prepared by the microbiota quantitative detection method described above.

[0036] A reproductive tract microecology testing reagent, wherein the positive control standard in the reagent is prepared by the microecological quantitative detection method described above.

[0037] The beneficial effects of this invention are as follows: This invention proposes a quantitative detection method for microorganisms applicable to various types of microecological samples. Using this method, detection reagents, kits, and instruments can be developed for the vast majority of target microorganisms. It can rapidly transform complex scientific research findings in many microecological fields into detection products, which can be applied to all aspects of daily life, such as medical treatment, health management, pharmaceuticals, food nutrition, and environmental protection. Furthermore, the method described in this invention can be further extended to the detection of non-microecological targets, such as gene detection in humans, animals, and plants. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort:

[0039] Figure 1 This is a flowchart of a preferred embodiment of the microecological quantitative detection method of the present invention;

[0040] Figure 2 This is a flowchart illustrating the design of a preferred embodiment of the microecological quantitative detection method of the present invention.

[0041] Figure 3 This is a standard curve result diagram of the target microorganism 1 in the microecological quantitative detection method of the preferred embodiment of the present invention;

[0042] Figure 4 This is a commonly used gene sequence clustering tool in the microecological quantitative detection method of the preferred embodiment of the present invention;

[0043] Figure 5 The microecological quantitative detection method of the preferred embodiment of the present invention commonly uses multiple sequence alignment tools;

[0044] Figure 6 This is a schematic diagram of the intestinal microecological target in the microecological quantitative detection method of the present invention according to a preferred embodiment;

[0045] Figure 7 This is a schematic diagram of the multiple sequence alignment results of the microbial ecological quantitative detection method of the present invention when the target microorganism is a classification target;

[0046] Figure 8 This is a schematic diagram of the microecological quantitative detection method of the present invention for multiple sequence alignment of specific species such as pathogens and certain functional genes;

[0047] Figure 9 This is a schematic diagram of the amplification curve of the Veillonellaceae target in the microecological quantitative detection method of the present invention according to a preferred embodiment;

[0048] Figure 10 This is a schematic diagram of the amplification curve of the Lactobacillus target in the microecological quantitative detection method of the present invention according to a preferred embodiment of the present invention;

[0049] Figure 11 This is a schematic diagram of the amplification curve of the Bacteroides target in the microecological quantitative detection method of the present invention according to a preferred embodiment;

[0050] Figure 12This is a schematic diagram of the amplification curve of the Enterococcus target in the microecological quantitative detection method of the present invention, which is a preferred embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, a clear and complete description will be provided below in conjunction with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0052] The preferred embodiment of the microecological quantitative detection method of the present invention and its application, such as Figure 1 As shown, it includes the following steps:

[0053] S01: Collect microbial detection data from one or more samples for the micro-ecological environment that needs to be tested;

[0054] For the micro-ecological environment that needs to be detected, collect as much microbial detection data as possible from a large number of samples, including metagenomic sequencing data, gene-targeted sequencing data such as 16S rRNA, etc. The selected samples should be representative and cover all possible situations of the target microorganisms.

[0055] S02: All collected samples are clustered at the sequence level, using genes as the unit (e.g., ...). Figure 2 (as shown);

[0056] Sequence clustering here can employ various known tools, such as CD-HIT, UCLUST (or USEARCH), OrthoFinder, and clustering methods that calculate sequence similarity based on BLAST alignment results, etc. (e.g.) Figure 3 (as shown);

[0057] S03: Based on the clustering results, obtain clusters of multiple homologous sequences for each gene;

[0058] The aforementioned steps yield clusters of numerous homologous sequences for each gene, as each gene may have similar copies in the genomes of numerous strains of various microorganisms. For ease of subsequent discussion, we will simply refer to each cluster as a gene sequence cluster, and use "cluster A1" to represent the first gene sequence cluster of gene A, "cluster A2" to represent the second gene sequence cluster of gene A, "cluster B1" to represent the first gene sequence cluster of gene B, and so on. Using all gene sequence clusters as a reference database, we can obtain the abundance count of each sample in each gene sequence cluster, thereby obtaining a gene sequence cluster profile (e.g., ...). Figure 2 (As shown)

[0059] S04: Through biological analysis, each target microorganism is mapped to a combination of one or more clusters;

[0060] The biological analysis methods used here are not limited. They can be based on direct correspondence through species classification (e.g., genes like 16S rRNA that can be used for species classification), or on correspondence based on gene function (e.g., common genes of the target microorganism, antibiotic resistance genes, toxin genes, metabolite synthesis genes, etc.). They can also be obtained by association analysis of gene sequence cluster profiles and sample phenotypic information profiles, as well as other biological analysis methods. The correspondence between the target microorganism and the gene sequence cluster is in a combined form, including AND and OR relationships. For example, "Target Microorganism 1 = Cluster A1 * Cluster A2 + Cluster B3" means that the target microorganism "Target Microorganism 1" can be represented by the union of the two gene sequence clusters "Cluster A1 and Cluster A2" or by "Cluster B3" alone, and so on.

[0061] S05: For the selected target microorganisms, perform conserved region retrieval and specificity assessment on the corresponding single or multiple union clusters; pair conserved regions with specificity and design primers.

[0062] Specifically, for a selected target microorganism, the single or multiple gene sequence clusters obtained from the union of the aforementioned steps are processed sequentially as follows:

[0063] Perform multiple sequence alignment (MSA) on its entire gene sequence (generated in step S02). Commonly used tools include... Figure 4 As shown.

[0064] In the multiple sequence alignment results, conserved regions are searched for, with a length of at least 18 bp.

[0065] To facilitate PCR primer design, all identified conserved regions are combined into conserved region pairs, which consist of two conserved regions spaced 80–400 bp apart. Note that the same conserved region is allowed to appear in multiple conserved region pairs.

[0066] Then, the sequences contained in each conserved region pair are compared with the complete sequence set of all genes in step S03 (the main method is BLAST in Table 1) to filter out conserved region pairs with poor specificity.

[0067] Primers are designed on the filtered conserved region pairs. If no conserved region pair with good specificity exists, or if a conserved region pair exists but primers cannot be designed successfully, the current gene sequence cluster is considered unacceptable, and the next gene sequence cluster is selected to repeat the current step (S05). If primers can be designed successfully, the bioinformatics evaluation of primer sensitivity and specificity continues.

[0068] Here, primer sensitivity refers to the coverage of the primer sequence over all sequences within the target microorganism; primer specificity refers to the proportion of all sequences of all genes in step S03 that the primer sequence can cover that originate from the target microorganism; only when both sensitivity and specificity are acceptable is the primer design for the target microorganism considered successful.

[0069] If primers cannot be successfully designed for any of the gene sequence clusters in this step, it is considered that the target microorganism is difficult to detect by a single primer target. It is recommended to return to step S04 to reselect or define the correspondence between the target microorganism and the sequence gene cluster.

[0070] If primer design for a specific gene sequence cluster is necessary for some reason, the following approaches can be considered: First, relax the definition of conserved regions; for example, a position where the same base appears in 80% (default is 100%) of the entire sequence is considered a conserved position. Second, relax the criteria for filtering specificity of conserved regions. Third, relax the requirements for primer sensitivity and specificity.

[0071] S06: Artificially synthesize gene sequences for the conserved regions of successfully designed primers, prepare positive control standards according to different concentration gradients, and plot standard curves.

[0072] Preferably, the number of concentration gradients is 5, that is, there are 5 positive control standards, denoted as PC1, PC2, PC3, PC4, and PC5.

[0073] Preferably, the concentrations of the positive control standards PC1, PC2, PC3, PC4, and PC5 are 10 ppm. 8 10 7 10 6 10 5 10 4 Copy / microliter.

[0074] As a preferred option, the actual amplification results of the positive control standard are fitted (see...). Figure 5 The absolute quantitative value Y is obtained by formulating Y = aX + b, where X is the PCR amplification Ct value, and a and b are the fitting results of the actual detection data of the positive quality control standard.

[0075] This invention proposes a quantitative detection method for microorganisms applicable to various types of microecological samples. Using this method, detection reagents, kits, and instruments can be developed for the vast majority of target microorganisms. It can rapidly transform complex scientific research findings in many microecological fields into detection products, which can be applied to all aspects of daily life, such as medical treatment, health management, pharmaceuticals, food nutrition, and environmental protection. The method described in this invention can also be further extended to the detection of non-microecological targets, such as gene detection in humans, animals, and plants.

[0076] The purpose of this invention is to fill the technological gap and solve related technical problems in the current field of microecological detection. It proposes a quantitative detection method and application for microecological systems, capable of designing and evaluating detection targets for any target microorganism (referring to the microbial targets to be detected, mainly including bacteria, fungi, and viruses, and may also include certain functional genes, but referred to as "target microorganisms" in this paper) in various types of microecological environments. Based on the detection results, a foundation can be provided for developing highly targeted and accurate quantitative detection reagents, kits, and even detection devices. Taking the human gut microbiota as an example, this invention can effectively quantitatively detect target microorganisms related to gut microbiota dysregulation, thereby assessing human gut health and contributing to gut health management.

[0077] An application of a quantitative detection method for microecology, wherein the above-mentioned quantitative detection method for microecology is applied in the detection of human gut microecology and / or reproductive tract microecology;

[0078] This invention also discloses the application of the above-mentioned quantitative detection method of microecology in the detection of human intestinal microecology and reproductive tract microecology, which can be used to detect intestinal microecological disorders and reproductive tract microecological disorders, respectively, thereby assessing the human microecological health status.

[0079] A human gut microbiota reagent, wherein the positive control standard in the reagent is prepared by the microbiota quantitative detection method described above.

[0080] A reproductive tract microecology testing reagent, wherein the positive control standard in the reagent is prepared by the microecological quantitative detection method described above. Detailed implementation method:

[0082] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein;

[0083] A1. Collection of human microecological samples and construction of gene sequence clusters

[0084] We collected the latest published Unified Human Gut Genome (UHGG) and reference bacterial genome data from publicly available internet databases.

[0085] UHGG contains 286,997 genomes fully relevant to the human gut microbiota and can be downloaded from http: / / ftp.ebi.ac.uk / pub / databases / metagenomics / mgnify_genomes / human-gut / v1.0 / all_genomes / . Reference bacterial genome data are from the NCBI database, located at ftp: / / ftp.ncbi.nlm.nih.gov / genomes / refseq / bacteria / and ftp: / / ftp.ncbi.nlm.nih.gov / genomes / refseq / archaea / .

[0086] In addition, we searched all human microbiome samples in the NCBI SRA (Sequence Read Archive) database (https: / / www.ncbi.nlm.nih.gov / sra) and downloaded more than 2,400 recently published human microbiome metagenomic samples collected from healthy people of different ages around the world.

[0087] Based on this data, we constructed the MBCN database (www.microbiota.cn), which contains numerous homologous protein clusters (COGs). For each COG, we clustered the DNA sequences of each gene within it, obtaining gene sequence clusters according to the default parameters of CD-HIT.

[0088] A2. Selection of target microorganisms in the human gut microbiota

[0089] Analyzing the gut microbiota data of healthy individuals obtained from A1, we selected commonly present gut microbiota targets in the population, such as... Figure 6 As shown, the labeled targets are the selected target microorganisms:

[0090] A3. Selection of target microorganisms in the human reproductive tract microecology

[0091] Based on the analysis of reproductive tract microecological data in A1, the following categories of microorganisms commonly found in the population were selected:

[0092] Actinobacteriota Phylum Actinobacteria Bacteroidota Bacteroidetes Fusobacteria Fusobacteria Lactobacillus Lactobacillus Streptococcus Streptococcus Enterococcus Enterococcus Aerococcus genus Balloonbacteria Staphylococcus Staphylococcus Gemella Geminids Veillonellaceae Veillonaceae Mycoplasmataceae Mycoplasma Clostridia Clostridium Chlamydiaceae Chlamydia Proteobacteria Proteobacteria Fungi Candida and other fungi Trichomonas_vaginalis Trichomonas vaginalis

[0093] A4. Examples of multiple sequence alignment and conserved region alignment for gene sequence clusters.

[0094] Preferably, when the target microorganism is a taxonomic target, gene sequence clusters can be generated targeting the 16S rRNA gene. In this case, the multiple sequence alignment results are as follows: Figure 7 As shown, the conserved region is relatively long (the first row in the figure is marked with an asterisk, which indicates that all sequences are 100% identical), which facilitates subsequent primer design.

[0095] As a preferred approach, when targeting specific species such as pathogens or functional genes, gene sequence clusters can be generated based on the relevant genes. In this case, the multiple sequence alignment results are as follows: Figure 8 As shown in the multiple sequence alignment results of one of the gene sequence clusters of the pheS gene, it can be seen that the conserved region is relatively short, requiring the design of primers that span a longer region and relax the conditions.

[0096] A5. Primer design for target microorganisms in the human gut microbiota

[0097] Using the method described in this patent, the primer design is shown in the table below:

[0098]

[0099]

[0100]

[0101] The probe design is shown in the table below:

[0102]

[0103]

[0104] A6. Primer design for target microorganisms in the human reproductive tract microecology

[0105] Using the method described in this patent, the primer design is shown in the table below:

[0106]

[0107]

[0108]

[0109] The probe design is shown in the table below:

[0110]

[0111]

[0112] A7. Plotting Standard Curves for Target Microorganisms in Human Gut Microbiota (Part 1) Experimental Materials:

[0113] 12 synthesized linearized plasmid DNAs containing the target gene

[0114] Taqman qPCR reaction solution

[0115] The kit of this invention (which contains four mixed targets: probe primer 1, probe primer 2, probe primer 3, and probe primer 4)

[0116] (II) Experimental Methods:

[0117] 1. Preparation of positive control standards

[0118] ① Mix 12 synthesized linearized plasmid DNAs containing the target gene in equal volumes, with a concentration of 10^8 copies / μL, denoted as PC1.

[0119] ② PC1 was sequentially diluted 10-fold according to its concentration gradient, resulting in concentrations of 10^7 copies / μL, 10^6 copies / μL, 10^5 copies / μL, and 10^4 copies / μL, which were designated as PC2, PC3, PC4, and PC5, respectively.

[0120] 2. qPCR amplification

[0121] qPCR amplification was performed using PC1, PC2, PC3, PC4, PC5, and deionized water as templates, with TaqMan analysis used as the negative control. Each template was amplified in triplicate for each target mixture.

[0122] ① Plotting the standard curve for probe primer 1

[0123] Take 1 μL of template, 10 μL of PCR reaction solution from the kit of this invention, 2 μL of probe primer 1, and 7 μL of deionized water and add them to the PCR reaction tube. Mix well by pipetting, centrifuge, and place on a real-time PCR instrument for reaction. Perform three replicate experiments for each positive control standard of different concentrations.

[0124] The PCR amplification reaction procedure is as follows: digestion at 37°C for 2 min; pre-denaturation at 95°C for 30 s; denaturation at 95°C for 10 s, followed by maintenance at 60°C for 30 s and collection of fluorescence signal for 40 cycles.

[0125] ② The steps for plotting the standard curves for probe primers 2, 3, and 4 are the same as those for plotting the standard curve for probe primer 1.

[0126] (III) Experimental Results

[0127] In the test results, positive standards should meet the linearity requirements of the standard curve; otherwise, they are considered invalid, and errors in instruments, reagents, amplification conditions, etc., should be checked.

[0128] legend:

[0129] The amplification curve of the Veillonellaceae target in probe primer 1 is shown below. Figure 9 As shown;

[0130] The amplification curve of the Lactobacillus target in probe primer 2 is as follows: Figure 10 As shown;

[0131] The amplification curve of the Bacteroides target in probe primer 3 is as follows: Figure 11 As shown;

[0132] The amplification curve of the Enterococcus target in probe primer 4 is as follows: Figure 12 As shown;

[0133] Conclusions: ① R² > 0.99, indicating that the standard curve generated by our kit has a linear relationship, meaning it has accurate template concentration recognition capability and effective detection. ② Through calculation, our amplification efficiency Eff = 10^(–1 / k)–1, ranging from 90% to 110%, therefore, the amplification of our kit is close to ideal. ③ In summary, our detection kit provides reliable results within the copy number detection limit of 10^8–10^4.

[0134] Reference range of target microorganisms in human gut microbiota for healthy individuals

[0135]

[0136] Reference range of target microorganisms in the human reproductive tract microecology for healthy individuals

[0137]

[0138]

[0139] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for quantitative detection of microecological features, characterized in that, Includes the following steps: Step 1: Collect microbial detection data from one or more samples for the micro-ecological environment that needs to be tested; The second step is to cluster the collected samples at the sequence level, using genes as the unit. Step 3: Based on the clustering results, obtain clusters of multiple homologous sequences for each gene; Step 4: Through biological analysis, each target microorganism is mapped to a combination of one or more clusters; Step 5: For the selected target microorganisms, perform conserved region retrieval and specificity assessment on the corresponding single or multiple union clusters; pair conserved regions with specificity and design primers. Step 6: Artificially synthesize the gene sequences of the conserved regions of the successfully designed primers, and prepare positive control standards according to different concentration gradients. The primer design process in the fifth step includes the following methods: Multiple sequence alignment was performed on all gene sequences of the selected target microorganism. Find conserved regions in the multiple sequence alignment results; the length of the conserved region should be greater than 18 bp. All the identified conservative regions are paired up to form conservative region pairs, with the two conservative regions in each pair being 80~400bp apart. The sequence contained in each conserved region pair is compared with the sequence set of all genes in the third step. Conserved region pairs whose specificity does not meet the set threshold are filtered out based on the comparison results. Primers were designed for the filtered conserved region pairs. The primer design process in the fifth step also includes methods; If no conservative region pair passes the filter or if primers cannot be successfully designed for the filtered conservative region pair, then the current cluster is considered unacceptable, and the next cluster is selected to repeat the primer design process. If primers cannot be successfully designed for any of the clusters, it is considered that the selected target microorganism is difficult to detect by a single primer target, and it should be processed according to the set processing method. The sixth step also includes a method: The actual amplification results of the positive control standard are fitted to obtain the absolute quantitative value Y, as follows: Y = aX + b, where X is the PCR amplification Ct value, and a and b are the fitting results of the actual detection data of the positive control standard.

2. The method for quantitative detection of microecology according to claim 1, characterized in that, The setting processing method includes: The method terminates or jumps to step four to reselect or define the correspondence between the selected target microorganism and the sequence gene cluster.

3. The method for quantitative detection of microecology according to claim 1, characterized in that, The setting processing method includes: After relaxing the definition criteria of the conserved region, relaxing the criteria for filtering the specificity of the conserved region, or relaxing the requirements for primer sensitivity and specificity, primer design should be carried out again.

4. The method for quantitative detection of microecology according to any one of claims 1-3, characterized in that, The fifth step also includes a method: Bioinformatics evaluation of primer sensitivity and specificity is conducted. When both sensitivity and specificity are acceptable, the primer design for the target microorganism is considered successful. Primer sensitivity is the degree to which the primer sequence covers the entire sequence within the target microorganism; Primer specificity is the proportion of all genes in the third step that the primer sequence can cover that originate from the target microorganism.

5. The method for quantitative detection of microecology according to any one of claims 1-3, characterized in that, In the first step, the microbial detection data includes metagenomic sequencing data and / or gene-targeted sequencing data.

6. The method for quantitative detection of microecology according to any one of claims 1-3, characterized in that, The biological analysis method in the four steps includes: obtaining gene sequence clusters through direct correspondence by species classification, correspondence by gene function, or correlation analysis of gene sequence cluster profiles and sample phenotypic information profiles.

7. An application of a quantitative detection method for microecology, characterized in that, The application of the microecological quantitative detection method as described in any one of claims 1-6 in the detection of human intestinal microecology and / or reproductive tract microecology.

8. A human intestinal microecological reagent, characterized in that, The positive control standard in the reagent is prepared by the microecological quantitative detection method as described in any one of claims 1-6.

9. A reproductive tract microecological detection reagent, characterized in that, The positive control standard in the reagent is prepared by the microecological quantitative detection method as described in any one of claims 1-6.

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