Nasal microbe key groups for different genders of human beings

Abstract

The invention relates to nasal microbe key groups of different genders of human beings. The present invention provides primers and / or probes for the specific identification of key populations of nasal microorganisms, wherein the key populations of nasal microorganisms comprise a microorganism or a combination of a plurality of microorganisms from a female key population of nasal microorganisms and / or a male key population of nasal microorganisms. The invention also provides methods of screening and identifying key populations of microorganisms. The identified nasal cavity microorganism key class group plays a key role in the stability and the function of the nasal cavity microorganism structure.

Description

Technical Field

[0001] The present invention relates to the field of microbial and disease research. Specifically, the present invention relates to the research on the relationship between key groups of nasal microbiota and respiratory diseases. Background Art

[0002] Human microbiota exists on the body surface, especially in the gut, vagina, oral cavity, respiratory tract, skin, etc., including bacteria, fungi, archaea, and viruses, but is mainly composed of bacteria. The nasal cavity is an important barrier closely related to our external air-borne environment, and the commensal flora is an important part of it, which is crucial for nasal environmental homeostasis and function. In the nasal microbiome, most of the bacterial components belong to the phyla Actinobacteria, Firmicutes, and Proteobacteria, among which the genera Corynebacterium, Propionibacterium, and Staphylococcus have relatively high abundances; there are also complex fungal and viral components. However, at present, due to the small sample size and single sequencing method, the research on the nasal microbiome is very limited.

[0003] Respiratory diseases seriously affect human health worldwide. According to the Global Burden of Disease Study, as of 2019, respiratory diseases affected billions of people's lives and accounted for more than 10% of all disability-adjusted life years. More and more studies have shown that the nasal microbiome is closely related to various different respiratory diseases, and it is recognized by more researchers as the health goalkeeper of the respiratory system. The nasal environment is characterized by a lack of nutrients and limited surface adhesion, and there are a large number of opportunistic pathogens stored, such as Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae, etc. How the complex symbiotic network in the microbial system drives their dynamic changes is still unknown.

[0004] Keystone microbial taxa are species with high connectivity in the microbial system. Individually or together, they have a significant impact on the structure and function of the microbial system, while having little relation to their species abundance in the population. Keystone microbial taxa play a unique and crucial role in the microbial system, and their removal can lead to drastic changes in the structure and function of the microbial community. In the study of the human microbiome, it has been found that keystone microbial communities are highly associated with human disease-related functions, such as inflammation, colon cancer and gastric cancer, starch degradation, and microbial homeostasis. In the human oral cavity, the keystone microbial taxon Porphyromonas gingivalis affects the growth and development of the entire oral microbiome by influencing the immune system, causing dysregulation of the microbial community, triggering destructive changes in the normal homeostatic host-microbial interactions in the periodontal tissue, and inducing oral inflammations such as periodontitis. In the human gut microbiome, the keystone taxon Bacteroides thetaiotaomicron can enhance the virulence of enterohemorrhagic Escherichia coli by altering the morphology of metabolites in the gut; the colon keystone taxon Bacteroides fragilis is closely associated with colon cancer. The gastric keystone taxon Helicobacter pylori is closely related to various digestive tract diseases, such as gastritis, peptic ulcer, and lymphoproliferative gastric lymphoma. Summary of the Invention

[0005] Research has found that nasal microbiota play an important role in the homeostasis and function of the respiratory tract environment. The present invention studies the relationship between the dynamic changes of nasal microbiota, especially keystone nasal microbial taxa, and respiratory diseases.

[0006] In some aspects, the present invention analyzes the metagenomic sequencing data of the nasal microbiome through a large population cohort, constructs a nasal microbial symbiotic network using a new method, further screens and identifies the combination of keystone nasal microbial taxa of different genders, and studies the possibility of using this keystone microbial community as a therapeutic intervention target for related respiratory diseases.

[0007] In some aspects, the present invention clarifies the keystone taxa of the human nasal microbial system and the method for screening and identifying keystone taxa by constructing a microbial symbiotic network, addressing the current gaps in keystone nasal microbial taxa and the lack of research on nasal microbial functions; furthermore, the present invention explores the potential application of the combination of keystone nasal microbial taxa as a therapeutic intervention target for respiratory-related diseases.

[0008] In some aspects, the present invention encompasses the following.

[0009] 1. Primers and / or probes for specifically identifying key groups of nasal microorganisms, wherein the key groups of nasal microorganisms include at least one of the following microorganisms: (1) Key groups of female nasal microorganisms: Phycomyces blakesleeanus, Neisseria sicca, Moraxella osloensis, Finegoldia s1, Anaerococcus provencensis, Lactobacillus iners, Pleurotus salmoneostramineus, Meyerozyma caribbica, Annulohypoxylon stygium, Stenotrophomonas geniculata; (2) Key groups of male nasal microorganisms: Stenotrophomonas maltophilia, Clarireedia sp. SE16F4, Staphylococcus warneri, Pedobacter nutrimenti, Actinomyces oris, Austropuccinia_psidii, Echinosphaeria canescens, Staphylococcus haemolyticus, Anaerococcus nagyae, Morchella septimelata, Neisseria sicca, Cutibacterium humerusii, Neisseria subflava.

[0010] 2. The primer and / or probe described in Item 1, wherein the microorganism includes at least one of the following microorganisms: (1) Key groups of female nasal cavity microorganisms: Neisseria sicca_D_MAG_3580, Finegoldia s1_MAG_3853, Moraxella_A osloensis_A_MAG_3428, Anaerococcus provencensis_MAG_868, Lactobacillus iners_MAG_25, Stenotrophomonas geniculate_MAG_3558; (2) Key groups of male nasal cavity microorganisms: Stenotrophomonas maltophilia_L_MAG_3559, Staphylococcuswarneri_MAG_825, Pedobacter nutrimentis_MAG_3755, Actinomyces_oris_A_MAG_2360, Staphylococcus haemolyticus_MAG_847, Anaerococcus nagyae_MAG_938, Neisseria_sicca_B_MAG_3577, Cutibacterium humerusii_MAG_3187, Neisseria subflava_MAG_3575.

[0011] 3. The primer and / or probe described in Item 1 or 2, wherein the primer and / or probe is a primer and / or probe specifically targeting the rDNA of the microorganism.

[0012] 4. The primer and / or probe described in any one of Items 1 - 3, wherein the primer and / or probe includes a combination of primers and / or probes specifically targeting two or more microorganisms.

[0013] 5. Use of the primer and / or probe described in any one of Items 1 - 4 in the preparation of a kit for evaluating or treating respiratory diseases.

[0014] 6. The use described in Item 5, wherein the evaluation or treatment of respiratory diseases includes detecting the presence of a treatment intervention target for respiratory diseases, or evaluating the risk of developing respiratory diseases, optionally further including supplementing one or more of the microorganisms or administering a drug that inhibits one or more of the microorganisms.

[0015] 7. A kit containing the primer and / or probe described in any one of Items 1 - 4.

[0016] 8. A method for screening and identifying key groups of microorganisms, wherein the method includes:

[0017] (1) Obtain the relative abundance information of the total microorganisms including bacteria and fungi from the sample,

[0018] (2) Calculate the species correlation score information and the corresponding p-value information using four statistics, namely MI, Bray-Curtis, COAT, and HUGE, to obtain the microbial species correlation score, and

[0019] (3) Screen the key taxa in the microbial network according to the integrated influence value, i.e., IVI (Integrated Value of Indluence).

[0020] 9. The method according to item 8, wherein step (1) includes:

[0021] Perform metagenomic sequencing on the DNA from the sample, construct a metagenome-assembled genome set, and obtain the relative abundances of bacteria and fungi in each sample.

[0022] Perform abundance filtering on the obtained relative abundances of bacteria and fungi.

[0023] Adopt the method of weighted similarity network fusion to merge the filtered relative abundances of bacteria and fungi to obtain the relative abundance information of the total microorganisms.

[0024] 10. The method according to item 8 or 9, wherein the method further includes:

[0025] Determine the role of different microbial key taxa in maintaining the stability of the microbial system by calculating the robustness and stability of the network after removing the key nodes in the network, and

[0026] Optionally, screen the representative strains of the key taxa based on the assembly score calculated according to the following formula:

[0027] Assembly score = completeness - 5 × contamination + 0.5 × log(N50).

[0028] 11. A biomarker for evaluating or treating respiratory diseases, which includes the key nasal microbial taxa defined in item 1 or 2.

[0029] 12. Use of the key nasal microbial taxa defined in item 1 or 2 in the preparation of a biomarker for evaluating or treating respiratory diseases.

[0030] In some aspects, the present invention screened and identified key taxonomic group combinations of nasal microbiota of different genders, which can serve as potential intervention targets for the treatment of respiratory diseases. In some embodiments, the present invention identified the following key taxonomic groups of nasal microbiota: (1) Key bacterial taxonomic groups in the female nasal cavity, which include a combination of one or more of the following microorganisms, or are composed of the following microorganisms: Neisseria sicca_D_MAG_3580, Finegoldia s1_MAG_3853, Moraxella_A osloensis_A_MAG_3428, Anaerococcus provencensis_MAG_868, Lactobacillus iners_MAG_25, Stenotrophomonas geniculate_MAG_3558; (2) Key bacterial taxonomic groups in the male nasal cavity, which include a combination of one or more of the following microorganisms, or are composed of the following microorganisms: Stenotrophomonas maltophilia_L_MAG_3559, Staphylococcus warneri_MAG_825, Pedobacter nutrimentis_MAG_3755, Actinomyces_oris_A_MAG_2360, Staphylococcus haemolyticus_MAG_847, Anaerococcus nagyae_MAG_938, Neisseria_sicca_B_MAG_3577, Cutibacterium humerusii_MAG_3187, Neisseria subflava_MAG_3575. In this article, "MAG_xx_yy" represents a specific strain number, and the respective strain-specific gene sequences are described in detail below.

[0031] In some aspects, the present invention relates to a method for screening and identifying key taxa in complex microbial systems based on metagenomic sequencing, and the method optionally includes one or more of the following steps: obtaining relative abundance information of total microorganisms (including bacteria and fungi) by the method of weighted similarity network fusion; calculating species correlation score information and corresponding p-value information using four statistics (MI, Bray-Curtis, COAT, HUGE), combining the four correlation score information, and at the same time using the weighted Simes method to combine the p-value information corresponding to the 4 methods, and finally obtaining the filtered microbial species correlation score after p-value filtering; further, a network attack method is adopted. First, calculate the IVI value of each node in the network, then sort according to the size of the IVI value, and sequentially remove the corresponding network nodes from the network from large to small, and calculate the natural connectivity of the network; correspondingly, calculate the natural connectivity of randomly removing the corresponding number of removed nodes 1000 times (bootstrap) as a control, calculate the p-value, and screen the key taxa of the microbial network according to the p-value.

[0032] In some aspects, the present invention analyzes the metagenomic sequencing data of the nasal microbiome to construct a nasal microbial symbiotic network and screen and identify the key taxa of the nasal microbial system.

[0033] In some embodiments, the present invention relates to a method for constructing a microbial symbiotic network and screening and identifying the key taxa of a microbial system (such as the key taxa of nasal microorganisms).

[0034] In some embodiments, the method of the present invention optionally includes one or more of the following exemplary steps:

[0035] (a) Sample collection: Collect nasal swab samples from healthy adult individuals and extract sample DNA.

[0036] (b) Metagenomic sequencing: Use the extracted DNA sample to construct a sequencing library and perform paired-end metagenomic sequencing on the DNBSEQ sequencing platform.

[0037] (c) Metagenomic assembly: Perform quality control on the sequenced data, remove host DNA to obtain high-quality sequencing fragments; further, through assembly, binning, detecting genome contamination degree and integrity, removing redundancy, and annotation, obtain a set of bacterial non-redundant metagenomic assembled genomes.

[0038] (d) Obtaining relative abundance information of nasal cavity microorganisms: Align the high-quality sequencing fragments in the previous step with the bacterial reference genome set and the fungal reference genome set to determine the relative abundance information of nasal cavity microorganisms (including bacteria and fungi); specifically, the bacterial reference genome set is the non-redundant bacterial metagenomic assembled genome set obtained in the previous step; the fungal reference genome set comes from the genome database of the National Center for Biotechnology Information in the United States and contains a total of 39,559 species genomes;

[0039] (e) Constructing the nasal cavity microbial network: Calculate the correlation between microbial species through a combined method to construct the nasal cavity microbial network; specifically, first use the weighted similarity network fusion method to merge the bacterial relative abundance information and the fungal relative abundance information to obtain the relative abundance information of the overall microorganisms (including bacteria and fungi); then, use four statistics, MI, Bray-Curtis, COAT, and HUGE, to calculate the species correlation score information and the corresponding p-value information, merge the four correlation score information, and use the weighted Simes method to merge the p-value information corresponding to the 4 methods. After p-value filtering, the filtered microbial species correlation score is finally obtained;

[0040] (f) Identification of key nasal cavity microbial groups: Adopt a network attack method. First, calculate the IVI value of each node in the network, then sort according to the size of the IVI value, and sequentially remove the corresponding network nodes from the network from large to small, and calculate the natural connectivity of the network; correspondingly, calculate the natural connectivity of the corresponding removed node numbers by randomly removing 1000 times (bootstrap) as a control, calculate the p-value, and then screen the key nasal cavity microbial groups according to the p-value.

[0041] In some aspects, the present invention relates to the use of nasal cavity microorganisms as potential intervention targets for the treatment of respiratory diseases. In some aspects, the present invention relates to the use of nasal cavity microorganisms for evaluating respiratory diseases. In some aspects, the present invention relates to biomarkers for evaluating or treating respiratory diseases, which include the key nasal cavity microbial groups described herein. In some aspects, the present invention relates to the application of the key nasal cavity microbial groups described herein in the preparation of biomarkers for evaluating or treating respiratory diseases.

[0042] In some embodiments, the evaluation described herein, understood in a broad sense, may include, for example, diagnosing a disease, assessing the risk of developing a disease, evaluating the effectiveness of a candidate drug, and / or evaluating the therapeutic effect of a disease treatment. In some embodiments, a biomarker refers to an indicator that can mark changes or potential changes in the structure or function of a system, organ, tissue, cell, and subcellular structure. As is known to those skilled in the art, there are various different biomarkers in the human body, and with the continuous progress of molecular biology techniques, the types of biomarkers are also increasing, including specific molecules (such as tumor-specific antigens), gene mutations, cell markers (such as circulating tumor cells), proteins or metabolites, microorganisms, etc. In some embodiments, the biomarkers for evaluating or treating respiratory diseases include the presence and / or abundance of one or more microorganisms described herein. In some embodiments, the biomarkers include in vivo and / or in vitro biomarkers. In some embodiments, detecting biomarkers includes in vivo and / or in vitro detection. In some embodiments, the presence and / or abundance of microorganisms include the presence and / or abundance of in vivo and / or in vitro (isolated) microorganisms. In some embodiments, through the biomarkers, the occurrence and risk of respiratory diseases, the effectiveness of candidate drugs, and / or the therapeutic effect of disease treatment can be evaluated. In some embodiments, the effectiveness or therapeutic effect of a drug is determined by detecting whether the drug restores the presence and / or abundance of the biomarker to the equilibrium state when healthy after administering the drug. In some embodiments, drugs can be screened by detecting the presence and / or abundance of the biomarker.

[0043] In some embodiments, the biomarkers, microorganisms, key microbial taxa, primers, probes, and / or kits described herein include those directed to any combination of any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 microorganisms listed herein (preferably a combination of microorganisms with a high integrated influence index (IVI)).

[0044] In some aspects, the present invention relates to screening and identifying key microbial taxa using a network attack method based on the correlation matrix of the overall microbial network. In some embodiments, according to the results of the present invention, whether it is the key microbial taxa in the male nasal cavity or the key microbial taxa in the female nasal cavity, their integrated influence index (IVI) in their respective microbial networks is high, indicating that these key taxa have a relatively high influence in the microbial network; at the same time, according to the natural connectivity results calculated by removing nodes based on IVI, the removal of key taxa will cause a sharp disruption to the stability of the microbial network; in summary, the key microbial taxa in the nasal cavity play a key role in the stability and function of the nasal microbial structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 . Bar chart of IVI values of key microbial taxa in females. For each microbe, the upper part in the figure represents the IVI value in the male microbial network, and the lower part represents the IVI value in the female network.

[0046] Figure 2 . Bar chart of IVI values of key microbial taxa in males. For each microbe, the upper part in the figure represents the IVI value in the male microbial network, and the lower part represents the IVI value in the female network. Detailed implementation manners

[0047] Key microbial taxa refer to species with high correlation in the microbial population, which alone or together have a significant impact on the structure and function of the microbial system. These taxa play a unique and crucial role in the microbial community, and their removal will lead to a sharp change in the structure and function of the microbial community. The key taxa of the human microbiota are closely related to diseases, which lays a foundation for using key taxa as therapeutic intervention targets for related diseases.

[0048] In this article, respiratory diseases include diseases with lesions in the respiratory system such as the trachea, bronchi, lungs, and chest cavity. In mild cases of respiratory system lesions, there are mostly coughs, chest pains, and affected breathing. In severe cases, there is difficulty breathing, hypoxia, and even death due to respiratory failure. In this article, respiratory diseases can include respiratory tract infections, which include any infectious diseases involving parts of the respiratory tract, and such diseases are generally further divided into upper respiratory tract infections (URI, URTI) or lower respiratory tract infections (LRT, LRTI).

[0049] In this article, potential disease treatment intervention targets refer to key microbial taxa related to diseases through specific drug targets, which can restore the composition and function of the microbial community to the balanced state when healthy, so as to achieve the purpose of treatment. The identification of key taxa has significant clinical benefits, because it can promote the development of new treatment methods for multi-microbial or complex probiotic diseases by focusing treatment strategies on a limited number of bacterial targets that stabilize the microbial community. In addition, if a complex multi-microbial disease is proven to be driven by key pathogens or a limited number of microbes acting in this way, then new and targeted diagnostic tools can be developed. For example, the key taxon of the human oral microbiota, Porphyromonas gingivalis, can cause inflammatory tissue destruction and lead to imbalance or dysregulation of the community, thus facilitating the further growth of this keystone taxon; at the same time, studies have shown that specifically removing Porphyromonas gingivalis from the periodontal biofilm can reverse the changes in community microbial dysregulation, indicating that through specific targeted treatment of this key taxon, it may be possible to effectively treat oral inflammations such as periodontitis.

[0050] According to the research results of the present invention, the inventors found that there are obvious differences in the key groups of human nasal microbiota between men and women, including two aspects of differences. First, there are differences in species. The 13 key groups screened in the male nasal microbiota network are almost completely different from the 10 key groups screened in the female nasal microbiota network. Second, there are huge differences in the IVI (Integrated Influence Index) values of the key groups of female nasal microbiota in the female microbiota network and in the male network. Similarly, there are also huge differences in the IVI (Integrated Influence Index) values of the key groups of male nasal microbiota in the female microbiota network and in the male network. According to the research background investigation, gender is a significant factor affecting many diseases. Respiratory diseases seriously affect human health worldwide. Research shows that there are obvious differences in the sensitivity and severity of respiratory infections between men and women. For most respiratory infections, men have a higher infection frequency and severity at all age stages. Research shows that nasal microbiota is closely related to different respiratory diseases, and the nasal microbiota is the goalkeeper of respiratory health. In women, most nasal microbiota have a higher relative abundance, and the microbial interaction network in women has higher robustness and stronger antagonism. This indicates that the differences in the key groups of male and female nasal microbiota are likely to be highly correlated with the differences in the sensitivity and severity of respiratory infections between men and women. Further research found that the gender-specific evolutionary characteristics of the key groups of microbiota are strongly related to their key functions in the microbiota networks of their respective genders. For example, the key group of male nasal microbiota, Stenotrophomonas maltophilia_L, is a multi-drug resistant bacterium related to the occurrence of respiratory infections and is the most influential key microbiota in the male microbiota network. It has various antibiotic resistance-related transport pumps, which may make men more likely to be infected, making it have great potential as an intervention target for related respiratory diseases; the key microbiota of female nasal microbiota, Neisseria sicca_D, has a key influence in the female nasal microbiota network. When women receive positive gene adaptation conditions to cope with stress conditions in specific niches (such as iron limitation), this may help form a more stable nasal microbiota community to resist infection.

[0051] According to the research results of the present invention, these key groups of nasal microbiota have strong application potential as intervention treatment targets for respiratory infections. Moreover, further, different intervention treatment plans for respiratory infections of different genders can be designed based on the differences between men and women, so as to achieve precise intervention treatment of respiratory infections.

[0052] Accordingly, the present invention screened and identified key taxonomic groups of nasal microbiota in different genders, and further elaborated on the possibility of key microbiota in the nasal microbiota system as therapeutic intervention targets for respiratory-related diseases.

[0053] In some embodiments, the nasal microbiota of the key taxonomic groups screened by the present invention may include microorganisms containing genes encoding rRNA or fragments thereof, wherein the genes encoding rRNA or fragments thereof contain sequences having at least 90% sequence identity (e.g., having at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 98.7%, at least 99% or 100% sequence identity) with the gene sequences identified by the present invention. In some embodiments, the rRNA may include, for example, 16S rRNA of bacteria or 18S rRNA of fungi, preferably the rRNA sequences described herein. As is known to those skilled in the art, microorganisms can be taxonomically classified and / or identified based on the sequences of genes encoding microbial ribosomal RNA (rRNA) (e.g., 16S rRNA of bacteria or 18S rRNA of fungi) or fragments thereof, which gene sequences are also referred to as ribosomal DNA sequences (rDNA). In some embodiments, primers and / or probes can be prepared for microbial rDNA (e.g., 16S rDNA of bacteria or 18S rDNA of fungi) or fragments thereof for assessing the presence and / or abundance of microorganisms. In some embodiments, any method known to those skilled in the art can be used to prepare primers and / or probes for known sequences. In some embodiments, the present invention deeply analyzed the gene sequences of the key taxonomic groups of nasal microbiota, so as to be able to identify, evaluate or analyze the key taxonomic groups of nasal microbiota through primers and / or probes for microorganisms (e.g., primers and / or probes for microbial rDNA or fragments thereof).

[0054] In some embodiments, the present invention provides primers and / or probes for specifically identifying key groups of the nasal microbiota (especially specific strains identified by the present invention), and kits containing the primers and / or probes. In some embodiments, the primers and / or probes are primers and / or probes specifically targeting the microorganisms identified by the present invention, for example, primers and / or probes specifically targeting the rDNA of the microorganisms identified by the present invention. As is known to those skilled in the art, methods for designing and preparing primers and / or probes specifically targeting known DNA and / or RNA sequences are widely known in the art, and any suitable method can be adopted. In some embodiments, the present invention can rapidly and accurately detect the presence of the corresponding gene by a PCR instrument, a chemiluminescence fully automatic instrument, etc. In some embodiments, the kit of the present invention includes PCR detection reagents, and may also include a purification step and corresponding purification devices and / or reagents. In some embodiments, the kit of the present invention may contain one or more of the following: primers for performing PCR amplification and / or probes for performing hybridization. In some embodiments, the primers and / or probes are used for targeted amplification or specific detection of the specific genes (such as rDNA) of the microorganisms identified by the present invention in a sample. In some embodiments, the kit of the present invention may contain one or more of the following: reaction and / or purification devices, such as magnetic beads, which are used for hybridization reactions, PCR amplification, and / or purification of amplification products; purification reagents, which are used for hybridization reactions and purification of PCR amplification products; sealing reagents, such as mineral oil, which are used for thermal sealing of the PCR mixture; washing reagents, such as water, which are used for washing pipettes; elution reagents, such as water, which are used for eluting adsorbed substances from the purification device. In some embodiments, the kit of the present invention may contain one or more of the following: nucleic acid probes for detecting the specific genes (such as rDNA) of the microorganisms identified by the present invention, the nucleic acid probes can be DNA probes, RNA probes, and / or other nucleic acid-based probes, the nucleic acid probes can be combined with fluorescent probes such as green fluorescence GFP, red fluorescence DsRed, mCherry, etc., can be combined with chromogenic compounds such as horseradish peroxidase (HRP), and can also be combined with small molecule compounds such as digoxin; antibodies against small molecule compounds such as digoxin, which are used for detecting probes carrying small molecule compounds such as digoxin, and the antibodies can bind to substrates of fluorescent probes and / or chromogenic compounds such as horseradish peroxidase; substrates of chromogenic compounds such as horseradish peroxidase, which are used for detecting horseradish peroxidase and performing qualitative and / or quantitative analysis; blocking agents such as BSA, skim milk; cleaning agents such as SSC buffer, etc. In some embodiments, the reagents in the kit of the present invention can be placed in separate containers respectively, or the same reagents can be placed in the same container. For example, if the purification reagent, the washing reagent, and / or the elution reagent are the same, they can be placed in the same container.In some embodiments, the kit comprises one or more reaction vessels for performing hybridization reactions and / or PCR reactions. In some embodiments, the hybridization reaction and / or PCR reagents may include reagents necessary for performing hybridization reactions and / or PCR reactions, such as polymerases, dNTPs, buffers. In some embodiments, any commercially available and suitable hybridization reaction and / or PCR reaction mixture may be used. In some embodiments, the mixture may be placed in one or more containers, preferably one container.

[0055] On the other hand, the present invention provides a method for screening and identifying key taxa in complex microbial systems based on metagenomic sequencing, which optionally includes one or more of the following steps: obtaining relative abundance information of total microorganisms (including bacteria and fungi) by the method of weighted similarity network fusion; calculating species correlation score information and corresponding p-value information using four statistics (MI, Bray-Curtis, COAT, HUGE), merging the four correlation score information, and simultaneously merging the p-value information corresponding to the four methods using the weighted Simes method, and finally obtaining filtered microbial species correlation scores through p-value filtering; further, a network attack method is adopted. First, calculate the IVI value of each node in the network, then sort according to the size of the IVI value, and sequentially remove the corresponding network nodes from the network from large to small, and calculate the natural connectivity of the network; correspondingly, calculate the natural connectivity of randomly removing the corresponding number of removed nodes 1000 times (bootstrap) as a control, calculate the p-value, and screen key taxa of the microbial network according to the p-value.

[0056] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below. The instruments, reagents, software, etc. used in the embodiments herein are all commercially available products unless otherwise specifically stated.

[0057] Example 1

[0058] 1.1 Sample collection

[0059] The population cohort of the example was derived from a previous set of research cohorts (Zhu, J. et al., Che, 2021. Over 50,000 Metagenomically Assembled Draft Genomes for the Human Oral Microbiome Reveal New Taxa. Genomics, Proteomics & Bioinformatics 143. doi: 10.1016 / j.gpb.2021.05.001). Nasal swab sub-samples were collected from 1,593 healthy adult individuals, including 439 males and 807 females, with an average age of 29.9 (±5.13) years. After collection, the nasal samples were transported frozen and rapidly transferred to -80°C for storage, and DNA extraction was performed to obtain the extracted DNA samples. DNA extraction was carried out using the MagPure Stool DNA KF Kit B (MD5115, Magen) (Yang et al., 2020) kit.

[0060] 1.2 Metagenomic sequencing and screening

[0061] The extracted DNA samples were used to construct sequencing libraries, and paired-end metagenomic sequencing (read length 150 bp) was performed on the DNBSEQ sequencing platform. The data generated by the sequencing were filtered using the fastp v0.20.1 software (quality-controlled, removing adapter-contaminated sequences, low-quality sequences, and host genome-contaminated sequences). The filtered data were aligned to the human reference genome GRCh38 using the Bowtie2 2.4.2 software to remove host genome-contaminated sequences and obtain high-quality sequences.

[0062] 1.3 Construction of metagenome-assembled genomes (MAGs)

[0063] Use the MEGAHIT v1.2.9 software or the SPAdes v3.15.2 software to perform single-sample metagenomic assembly on the high-quality sequencing reads after "1.2 Metagenomic Sequencing and Screening" to obtain the assembled contigs; use the DAS Tool 1.1.2 method to perform binning on the contigs obtained from the previous step of assembly to obtain the genomes (bins); use the CheckM v1.1.3 software to evaluate and filter the genomes obtained after the previous binning. Referring to the method described in (Stewart et al., 2018), for genomes with a completeness of 80% or more and a contamination rate of less than 10%, that is, medium-high-quality genomes, perform the next analysis. Use the dRep v3.0.1 software to remove redundancy from the genome set after the previous filtering (the first clustering ANI is 90%, and the second clustering ANI is 99%). Finally, 974 non-redundant MAGs are obtained, including 718 high-quality microbial species genomes (completeness greater than 90% and contamination rate less than 5%) and 256 medium-quality microbial genomes (completeness greater than 80% and contamination rate less than 10%). Further, use GTDB-TK v1.5.1 to annotate the species information of the 974 non-redundant MAGs obtained through the GTDB database (nucleotide alignment rate of more than 95%). Finally, 232 microbial species information is obtained.

[0064] Specifically, in the present invention, for the species classification at the phylum level, the similarity of the alignment is more than 65%, and the alignment coverage rate is more than 70% as the critical value for the species classification at the phylum level. For the species classification at the genus level, the similarity of the alignment is more than 85% as the critical value for the species at the genus level, and for the similarity of the alignment is more than 95% as the critical value for the species classification at the species and strain levels.

[0065] 1.4 Obtain microbial relative abundance information

[0066] Based on the bacterial non-redundant MAGs in the 974 non-redundant MAGs obtained from "1.3 Constructing the Metagenomic Assembled Genome Set", use the CoverM software to obtain the relative abundance information of bacteria in each sample. Use the kraken2 method to align the high-quality sequencing reads after "1.2 Metagenomic Sequencing and Screening" to the NCBI database to obtain the relative abundance information of fungi in each sample.

[0067] 1.5 Construct a microbial co-occurrence network

[0068] To avoid the artificial influence of random noise on network construction, the relative abundances of microbial species need to be filtered. Specifically, species with low relative abundance and low occurrence rate need to be filtered out. Abundance filtering is performed on the bacterial relative abundance and fungal relative abundance obtained according to "1.4 Obtaining Microbial Relative Abundance Information". Specifically, a threshold of 0.0001 is set to filter the bacterial relative abundance, and a threshold of 0.001 is set to filter the fungal relative abundance. At the same time, for the species occurrence rate of bacteria and fungi, a threshold of 0.1 is set for filtering. Here, the species occurrence rate refers to the ratio of bacteria and fungi appearing in the total 1593 samples.

[0069] To construct the overall cross-kingdom microbial network, the relative abundance information of bacteria and the relative abundance information of fungi need to be merged. Referring to the literature (Mac Aogáin et al., 2021), the weighted similarity network fusion method is used to merge the filtered bacterial relative abundance information and fungal relative abundance information to obtain the relative abundance information of the overall microorganisms (including bacteria and fungi).

[0070] We use a combined method to calculate the correlation of microbial species. Specifically, four statistics, MI (mutual information), Bray-Curtis dissimilarity, COAT (composition-adjusted thresholding) (Cao et al., 2018), and HUGE (High-dimensional Undirected Graph Estimation) (Zhao et al., 2012) are used respectively. The microbial relative abundance information obtained by "1.4 Obtaining the Relative Abundance Information of the Overall Microorganisms" is used to calculate the species correlation score information and the corresponding p-value information. Then, the method described in (Faust et al., 2012) is used to merge the four correlation score information obtained in the previous step, and the weighted Simes method is used to merge the p-value information corresponding to the 4 methods. A p-value of 0.001 is set to filter the combined correlation scores, and finally the filtered microbial species correlation scores are obtained. The correlation value is jointly determined by four correlation statistical methods, and the correlation method, that is, the positive or negative of the value, is determined by the two correlation statistical methods of COAT and HUGE.

[0071] The p-value is calculated using the bootstrap and permutation methods, which specifically include the following steps: For each method, first, the samples are resampled 1000 times using the bootstrap, and after each resampling, the correlation scores of the microbial network are recalculated; simultaneously, the network is randomly shuffled using the permutation the same number of times, and then the correlation scores of the microbial network are calculated.

[0072] Specifically, for the construction of the overall microbial network of the female nasal cavity, the overall microbial relative abundance information of the samples of 807 female individuals is used to construct the female nasal cavity microbial symbiotic network based on the above method; for males, the overall microbial relative abundance information of the samples of 439 male individuals is used to construct the male nasal cavity microbial symbiotic network based on the above method. Specifically, the absolute value of the correlation score represents the strength of the association between species, and the positive and negative values represent the association attributes of the species. A positive value can represent a cooperative relationship, and a negative value can represent an antagonistic relationship.

[0073] 1.6 Identification and screening of key microbial groups

[0074] We use the IVI (Integrated Value of Influence) index to screen and identify key microbial groups. The IVI index is a comprehensive method for evaluating the influence of nodes within a network. First, we calculate the IVI index of the nodes in the microbial network correlation score matrix obtained from "1.5 Construction of the microbial symbiotic network", and then screen the key groups in the microbial network according to the IVI.

[0075] In the embodiments of the present invention, the Integrated Value of Indluence (IVI) is a method for identifying and measuring the influence of nodes within a complex network. The IVI integrates 6 important network centrality indicators, including degree centrality, Cluster Rank, neighborhood connectivity, local Hindex, betweenness centrality, and collective influence, to measure the influence of nodes in a complex network; the IVI can capture all topological dimensions of the complex network while eliminating positional bias to identify the most influential nodes and their functional modules throughout the network. The IVI is a universal indicator that does not depend on the directionality or weighting of the network and can calculate directed networks and weighted networks, with wide applicability.

[0076] Specifically, it includes the following steps:

[0077] 1.6.1 Identification of key microbial groups in the female nasal cavity

[0078] First, for 807 nasal samples from females, DNA extraction, sequencing, quality control of sequencing reads, genome assembly, quality assessment and binning, species annotation were performed according to the methods described in 1.1 - 1.5 to obtain relative abundance information, construct a microbial overall interaction network, and then identify and screen key groups of nasal cavity microorganisms according to the method in "1.6 Identification and Screening of Key Microbial Groups".

[0079] The results showed that the key groups of female nasal cavity microorganisms obtained in the present invention included the following 10 species, namely Phycomyces blakesleeanus, Neisseria sicca_D, Finegoldia s1, Moraxella_Aosloensis_A, Anaerococcusprovencensis, Lactobacillus iners, Pleurotussalmoneostramineus, Meyerozyma caribbica, Annulohypoxylon stygium, and Stenotrophomonas geniculate. Figure 1 The IVI values of the 10 key groups of female nasal cavity microorganisms and their IVI values in the microbial network of male samples are shown. We can observe that the IVI values of the key groups of female nasal cavity microorganisms are relatively low in the male network, which indirectly indicates the differences in nasal cavity microorganisms between males and females.

[0080] 1.6.2 Identification of Key Groups of Male Nasal Cavity Microorganisms

[0081] First, for 439 nasal samples from males, DNA extraction, sequencing, quality control of sequencing reads, genome assembly, quality assessment and binning, species annotation were performed according to the methods described in 1.1 - 1.5 to obtain relative abundance information, construct a microbial overall interaction network, and then identify and screen key groups of nasal cavity microorganisms according to the method in "1.6 Identification and Screening of Key Microbial Groups".

[0082] The results showed that the key groups of male nasal cavity microorganisms obtained in the present invention included the following 12 species, namely Stenotrophomonas maltophilia_L, Clarireedia sp. SE16F4, Staphylococcus warneri, Pedobacter nutrimentis, Actinomyces oris_A, Austropuccinia psidii, Echinosphaeria canescens, Staphylococcus haemolyticus, Anaerococcus nagyae, Morchella septimelata, Neisseria sicca_B, Cutibacterium humerusii, and Neisseria subflava. Figure 2 The IVI values of 13 key groups of male nasal cavity microorganisms and their IVI values in the microbial network of female samples are shown. We can observe that the IVI values of the key groups of male nasal cavity microorganisms are relatively low in the female network, which indirectly indicates the differences in nasal cavity microorganisms between men and women.

[0083] The 16S rRNA sequences (bacteria) or 18S rRNA sequences (fungi) identified in the key group species of nasal cavity microorganisms are as follows:

[0084] Key groups of female nasal cavity microorganisms

[0085] The 18S rRNA sequence of Phycomyces blakesleeans is as follows:

[0086] >18S_rRNA::NW_017265148.1:326920-328749(-)

[0087]

[0088]

[0089] The 16S rRNA sequence of Neisseria sicca is as follows:

[0090] >16S_rRNA::NZ_CP072524.1:63398-64934(+)

[0091]

[0092]

[0093] The 16S rRNA sequence of Moraxella osloensis is as follows:

[0094] >16S_rRNA::NZ_CP014234.1:791019-792546(-)

[0095]

[0096]

[0097] The 16S rRNA sequence of Finegoldia s1 is as follows:

[0098] >16S_rRNA::s__unclassified_MAG_3853_44:76207-76279(+)

[0099]

[0100] The 16S rRNA sequence of Anaerococcus provencensis is as follows:

[0101] >16S_rRNA::HG003688.1:27303-28828(+)

[0102]

[0103]

[0104] The 16S rRNA sequence of Lactobacillus iners is as follows:

[0105] >16S_rRNA::CP045664.1:398371-399939(-)

[0106]

[0107]

[0108] The 18S rRNA sequence of Pleurotus salmoneostramineus is as follows:

[0109] >18S_rRNA::BEWF01000223.1:3209-5014(+)

[0110]

[0111]

[0112] The 18S rRNA sequence of Meyerozyma caribbica is as follows:

[0113] >18S_rRNA::BADS01000001.1:222537-224337(+)

[0114]

[0115]

[0116] The 18S rRNA sequence of Annulohypoxylon stygium is as follows: >18S_rRNA::QLPL01000063.1:819-1984(+)

[0117]

[0118] The 16S rRNA sequence of Stenotrophomonas geniculata is as follows: >16S_rRNA::AJLO02000004.1:165416-165599(+)

[0119]

[0120] Key groups of male nasal cavity microbiota

[0121] The 16S rRNA sequence of Stenotrophomonas maltophilia is as follows:

[0122] >16S_rRNA::NZ_LS483377.1:360635-362176(+)

[0123]

[0124]

[0125] The 18S rRNA sequence of Clarireedia sp. SE16F4 is as follows:

[0126] >18S_rRNA::LLKF01000155.1:7015-8228(-)

[0127]

[0128] The 16S rRNA sequence of Staphylococcus warneri is as follows:

[0129] >16S_rRNA::NZ_CP032159.1:972764-974314(+)

[0130]

[0131]

[0132] The 16S rRNA sequence of Pedobacter nutrimenti is as follows:

[0133] >16S_rRNA::NZ_QKLU01000016.1:644-2163(-)

[0134]

[0135] The 16S rRNA sequence of Actinomyces oris is as follows:

[0136] >16S_rRNA::NZ_CP066060.1:1795883-1797435(-)

[0137]

[0138] The 18S rRNA sequence of Austropuccinia psidii is as follows:

[0139] >18S_rRNA::JALGQZ010000040.1:35629-37430(-)

[0140]

[0141]

[0142] The 18S rRNA sequence of Echinosphaeria canescens is as follows:

[0143] >18S_rRNA::JAOXNW010067824.1:1232-2399(-)

[0144]

[0145] The 16S rRNA sequence of Staphylococcus haemolyticus is as follows:

[0146] >16S_rRNA::NZ_CP013911.1:1636688-1638236(-)

[0147]

[0148] The 16S rRNA sequence of Anaerococcus nagyae is as follows:

[0149] >16S_rRNA::NZ_QVEU01000005.1:104798-106323(+)

[0150]

[0151]

[0152]

[0153]

[0154] The 16S rRNA sequence of Neisseria sicca is as follows:

[0155] >16S_rRNA::NZ_CP072524.1:63398-64934(+)

[0156]

[0157]

[0158] The 16S rRNA sequence of Cutibacterium humerusii([Propionibacterium]humerusii) is as follows: >16S_rRNA::NZ_CP017040.1:2160427-2161949(+)

[0159]

[0160]

[0161] The 16S rRNA sequence of Neisseria subflava is as follows:

[0162] >16S_rRNA::NZ_CP039887.1:191200-192736(+)

[0163]

[0164]

[0165] The rRNA sequence fragments identified in MAGs are as follows:

[0166] Finegoldia s1_MAG_3853

[0167] >16S_rRNA::s__unclassified_MAG_3853_44:76207-76279(+)

[0168]

[0169] >23S_rRNA::s__unclassified_MAG_3853_36:0-274(+)

[0170]

[0171] >5S_rRNA::s__unclassified_MAG_3853_10:164-275(+)

[0172]

[0173] Moraxella_A osloensis_A_MAG_3428

[0174] >16S_rRNA::s__Moraxella_A_osloensis_A_MAG_3428_157:0-108(-)

[0175]

[0176] >23S_rRNA::s__Moraxella_A_osloensis_A_MAG_3428_117:8696-9034(-)

[0177]

[0178] >5S_rRNA::s__Moraxella_A_osloensis_A_MAG_3428_117:8486-8595(-)

[0179]

[0180] Anaerococcus provencensis_MAG_868

[0181] >16S_rRNA::s__Anaerococcus_provencensis_MAG_868_18:26960-27143(+)

[0182]

[0183] Lactobacillus iners_MAG_25

[0184] >5S_rRNA::s__Lactobacillus_iners_MAG_25_151:5788-5900(-)

[0185]

[0186] Stenotrophomonas geniculate_MAG_3558

[0187] >5S_rRNA::s__Stenotrophomonas_geniculata_MAG_3558_14:7-108(+)

[0188]

[0189] Key groups of male nasal cavity microbiota

[0190] Staphylococcus warneri_MAG_825

[0191] >5S_rRNA::s__Staphylococcus_warneri_MAG_825_25:37323-37432(+)

[0192]

[0193]

[0194] Actinomyces_oris_A_MAG_2360

[0195] >5S_rRNA::s__Actinomyces_oris_A_MAG_2360_66:7270-7346(-)

[0196]

[0197] Staphylococcus haemolyticus_MAG_847

[0198] >5S_rRNA::s__Staphylococcus_haemolyticus_MAG_847_15:38017-38085(+)

[0199]

[0200] Anaerococcus nagyae_MAG_938

[0201] >16S_rRNA::s__Anaerococcus_nagyae_MAG_938_2:47989-48059(+)

[0202]

[0203] Neisseria_sicca_B_MAG_3577

[0204] >16S_rRNA::s__Neisseria_sicca_B_MAG_3577_166:6339-6461(+)

[0205]

[0206] Cutibacterium humerusii_MAG_3187

[0207] >5S_rRNA::s__Cutibacterium_humerusii_MAG_3187_23:27389-27461(-)

[0208]

[0209] Example 2

[0210] To verify the key roles of the identified and screened key taxa in the nasal microbial system, we used the "network destruction" method, that is, by calculating the robustness and stability of the network after removing the key nodes in the network, to clarify the roles of key taxa of different genders in maintaining the stability of the nasal microbial system as potential intervention targets for the treatment of related diseases.

[0211] According to the methods described in 1.1 - 1.5 of Example 1, nasal microbial networks of different genders were obtained respectively; according to the method described in 1.6 of Example 1, key taxa of different genders screened according to IVI were obtained. For the key taxa, they were sorted in descending order according to the IVI value, and nodes were removed from largest to smallest. After each node was removed, the natural connectivity was calculated according to the method described in (Morone and Makse, 2015). At the same time, we randomly removed network nodes and calculated the corresponding natural connectivity of the network, repeating the random removal 1000 times each time. Further, the p-value was calculated through the results of random network node removal and removal according to the IVI index. Specifically, for the calculation of the p-value, by calculating the number of times the natural connectivity in 1000 random removals was greater than the natural connectivity removed according to IVI among the same number of network node removals, and then dividing by 1000.

[0212] Here, the natural connectivity specifically refers to a measure of robustness in complex networks, which originates from the inherent structural properties of the network and is a measure of the overall robustness. The functions and performance of complex networks depend on their robustness, that is, the ability of the network to maintain its connectivity when losing some nodes. Natural connectivity has strong discrimination when measuring the robustness of complex networks and can sensitively show changes in robustness.

[0213] Here, the screening of representative strains of key bacteria is based on the assembly score, and the assembly score of key bacteria is calculated based on the following formula:

[0214] Assembly score = Completeness - 5 × Contamination + 0.5 × log(N50)

[0215] According to Table 1, the results of calculating the natural connectivity by removing nodes according to IVI show that the p-value results indicate that the removal of key taxa in the female nasal microbial network causes a sharp decline in the natural connectivity of the network and a decrease in the stability and robustness of the microbial network, indicating serious damage to the microbial network, proving that key taxa play an important role in maintaining the stability and functional integrity of the microbial network; for the statistical results of the assembly of key species in Table 1 (Table 2), the one with the highest score in each key species was selected as the representative species; similarly, according to Table 3, the p-value results show that in the male nasal microbial network, the removal of key taxa also causes a sharp decline in the natural connectivity of the network, causing serious damage to the microbial network. For the statistical results of the assembly of key species in Table 3 (Table 4), the one with the highest score in each key species was selected as the representative species. In Table 2 and Table 4, "MAG_xx_yy" represents the specific strain number, and the specific gene sequences of their respective strains are described in detail below.

[0216] In summary, it shows that the key groups of nasal cavity microorganisms play a crucial role in the stability and function of the nasal cavity microbial structure, and representative strains of key species are selected.

[0217] Table 1 IVI values and p-values of key microbial flora in the nasal cavity of females

[0218] Serial number Species name IVI value p-value Mean relative abundance 1 Phycomyces blakesleeanus 100.00 0.221 1.07E-04 2 Neisseria sicca_D 90.65 0.061 1.17E-04 3 Moraxella A osloensis A 87.51 0.017 2.69E-04 4 Finegoldia_s1 87.10 0.009 7.47E-05 5 Anaerococcus provencensis 84.38 0.012 3.16E-04 6 Lactobacillus iners 75.96 0.005 1.29E-04 7 Pleurotus salmoneostramineus 70.65 0.004 3.03E-04 8 Meyerozyma caribbica 68.16 0.002 2.45E-04 9 Annulohypoxylon stygium 67.92 0.002 2.01E-04 10 Stenotrophomonas geniculata 67.29 0 1.85E-04

[0219] Table 2 Genome quality of key strains of nasal cavity microorganisms in females

[0220]

[0221] Table 3 IVI values and p-values of key microbial flora in the nasal cavity of males

[0222]

[0223] Table 4 Genome quality of key strains of nasal cavity microorganisms in males

[0224]

Claims

1. Primers and / or probes for specifically identifying key groups of nasal cavity microorganisms, wherein the key groups of nasal cavity microorganisms include at least one of the following microorganisms: (1) Key groups of female nasal cavity microorganisms: Phycomyces blakesleeanus, Neisseria sicca, Moraxella osloensis, Finegoldia s1, Anaerococcus provencensis, Lactobacillus iners, Pleurotus salmoneostramineus, Meyerozyma caribbica, Annulohypoxylon stygium, Stenotrophomonas geniculata; (2) Key groups of male nasal cavity microorganisms: Stenotrophomonas maltophilia, Clarireedia sp. SE16F4, Staphylococcus warneri, Pedobacter nutrimenti, Actinomyces oris, Austropuccinia_psidii, Echinosphaeria canescens, Staphylococcus haemolyticus, Anaerococcus nagyae, Morchella septimelata, Neisseria sicca, Cutibacterium humerusii, Neisseria subflava.

2. The primer and / or probe according to claim 1, wherein the microorganism comprises at least one of the following microorganisms: (1) Key groups of female nasal cavity microorganisms: Neisseria sicca_D_MAG_3580, Finegoldia s1_MAG_3853, Moraxella_A osloensis_A_MAG_3428, Anaerococcus provencensis_MAG_868, Lactobacillus iners_MAG_25, Stenotrophomonas geniculate_MAG_3558; (2) Key groups of male nasal cavity microorganisms: Stenotrophomonas maltophilia_L_MAG_3559, Staphylococcus warneri_MAG_825, Pedobacter nutrimentis_MAG_3755, Actinomyces_oris_A_MAG_2360, Staphylococcus haemolyticus_MAG_847, Anaerococcus nagyae_MAG_938, Neisseria_sicca_B_MAG_3577, Cutibacterium humerusii_MAG_3187, Neisseria subflava_MAG_3575.

3. The primer and / or probe according to claim 1 or 2, wherein the primer and / or probe is a primer and / or probe specifically targeting the rDNA of the microorganism; Preferably, the primer and / or probe comprises a combination of primers and / or probes specifically targeting two or more microorganisms.

4. Use of the primer and / or probe according to any one of claims 1-3 in the preparation of a kit for evaluating or treating respiratory diseases.

5. The use according to claim 4, wherein the evaluation or treatment of respiratory diseases includes detecting the presence of a treatment intervention target for respiratory diseases, or evaluating the risk of developing respiratory diseases, optionally further including supplementing one or more of the microorganisms or administering a drug that inhibits one or more of the microorganisms.

6. A kit comprising the primer and / or probe according to any one of claims 1-3.

7. A method for screening and identifying key groups of microorganisms, wherein the method comprises: (1) Obtaining relative abundance information of the total microorganisms including bacteria and fungi from a sample, (2) Calculating species correlation score information and corresponding p-value information using four statistics, namely MI, Bray-Curtis, COAT, and HUGE, to obtain microorganism species correlation scores, and (3) Screening key groups in the microorganism network according to the integrated influence value, i.e., IVI (Integrated Value of Indluence).

8. The method according to claim 7, wherein step (1) comprises: Performing metagenomic sequencing on DNA from a sample, constructing a metagenomic assembled genome set, and obtaining the relative abundances of bacteria and fungi in each sample; Performing abundance filtering on the obtained relative abundances of bacteria and fungi; Using the method of weighted similarity network fusion to combine the filtered relative abundances of bacteria and fungi to obtain the relative abundance information of the overall microorganisms.

9. The method according to claim 7 or 8, wherein the method further comprises: Determining the role of different microbial key taxa in maintaining the stability of the microbial system by calculating the robustness and stability of the network after removing key nodes from the network, and Optionally, screening representative strains of key taxa based on the assembly score calculated according to the following formula: Assembly score = completeness - 5 × contamination + 0.5 × log(N50).

10. A biomarker for evaluating or treating respiratory diseases, comprising the key nasal microbial taxa defined in claim 1 or 2.

11. Use of the key nasal microbial taxa defined in claim 1 or 2 in the preparation of a biomarker for evaluating or treating respiratory diseases.

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