Novel method for excavating food allergy intestinal flora target spot

Abstract

The invention discloses a novel method for food allergy intestinal flora target mining, and belongs to the technical field of target mining. The new method comprises the following steps: acquiring whole genome associated GWAS data and intestinal flora related data of food allergy; performing tool variable screening and multi-effect removal on the collected data; according to the collected tool variables, analyzing the data by adopting a bidirectional Mendel randomization method to obtain a preliminary screening result of the causal flora; the preliminary screening result is analyzed, and candidate tool variables are obtained by integrating effect values of multiple analysis methods and judgment of sensitivity analysis; establishing a food allergy animal model; carrying out targeted determination on the screened related flora having a causal relationship with food allergy by adopting a microbiological analysis method; and the active small molecules act on the target flora to determine the reliability of the active small molecules as the food allergy target. According to the method, the intestinal flora and the target flora of food allergy can be determined.

Description

Technical Field

[0001] The present invention relates to the technical field of target mining, and particularly to a new method for mining gut microbiota targets for food allergy. Background Art

[0002] Food allergy is an abnormal immune response of the body to certain proteins present in food. The main reason for its formation is that during the growth process, certain proteins break the immune tolerance of the body and induce the occurrence of food allergy. Research results in recent years have shown that there is an inseparable relationship between gut microbiota and food allergy, mainly reflected in two aspects: the presence of gut microbiota can affect the immune system by influencing T cell differentiation and regulating the maturation of dendritic cells, thereby affecting food allergy; on the other hand, during the metabolic process, gut microbiota will produce metabolites such as short-chain fatty acids, which further affect the intestinal barrier and the occurrence of food allergy. Currently, most of the research on gut microbiota and food allergy is observational experimental research, mainly focusing on using gut microbiota omics analysis methods to observe the gut microbiota diversity of food allergy patients and investigate the prevention and treatment of food allergy by improving microbiota diversity with active ingredients; and after ingesting active ingredients, by observing the changes in some key gut bacteria, such as Clostridium, Bifidobacterium, and Lactobacillus, to prevent and treat food allergy. However, the above observational research methods have certain problems, that is, the causal relationship between gut microbiota and food allergy is not clear, and the research results are easily affected by reverse causality and residual confounding factors, resulting in unclear analysis of relevant research mechanisms and wasting a large amount of resources. Therefore, clear gut microbiota targets for food allergy are a reliable way to solve this problem.

[0003] Therefore, there are still relatively large problems with the existing research methods. Developing a new method to obtain clear gut microbiota targets for food allergy can effectively and specifically solve the problem of food allergy. At the same time, discovering potential drug targets and sensitization mechanisms can bring new opportunities for the prevention and treatment of food allergy. Summary of the Invention

[0004] In view of the above situation, this application uses a virtual-real analysis method that combines bidirectional Mendelian randomization with gut microbiota omics analysis. First, clarify the causal relationship between the two and identify key gut microbiota. Then, use active small molecules to act on the key microbiota, and use microbiota omics analysis to mine potential target microbiota. Through detailed and rigorous analysis and multiple evaluations, fundamentally clarify the correlation between food allergy and gut microbiota, and clarify the gut microbiota-related targets for food allergy. The present invention also provides a small molecule that can affect gut microbiota targets for food allergy. The method of the present invention includes the following steps:

[0005] (1) Step 1: Obtain the genome-wide association study (GWAS) data of food allergy and the data related to gut microbiota;

[0006] (2) Step 2: Screen and deconfound the collected data for instrumental variables;

[0007] (3) Step 3: Analyze the data using the two-sample Mendelian randomization method based on the collected instrumental variables to obtain the preliminary screening results of causal microbiota;

[0008] (4) Step 4: Analyze the preliminary screening results, and comprehensively determine the effect values of multiple analysis methods and the sensitivity analysis to obtain candidate instrumental variables;

[0009] (5) Step 5: Establish an animal model of food allergy

[0010] (6) Step 6: Use the microbiomics analysis method to specifically measure the screened microbiota that has a causal relationship with food allergy;

[0011] (7) Step 7: Act on the target microbiota of bioactive small molecules to determine its reliability as a food allergy target;

[0012] The content of the present invention will be described below with reference to examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Flow chart of two-sample Mendelian randomization analysis of causal relationship;

[0014] Figure 2 Abundance map of target strains. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] To better understand the technical solutions in the present invention, the technical solutions will be clearly and completely described below in combination with the embodiments of the present invention. The present invention provides a method for mining the target of the causal relationship between food allergy and gut microbiota based on two-sample Mendelian randomization combined with microbiomics, including the following steps:

[0016] Step 1: Obtain the genome-wide association study (GWAS) data of food allergy and the data related to gut microbiota, as Figure 1 shown;

[0017] The genome-wide association statistics related to food allergy are sourced from https: / / gwas.mrcieu.ac.uk, which provides information and resources on genome-wide association studies (GWAS) by the MRC Integrative Epidemiology Unit. The GWAS ID of this genome is ebi-a-GCST90018625. The research feature is food allergy, with a sample size of 169,716 and covering 12,453,478 single nucleotide polymorphisms (SNPs).

[0018] The GWAS data related to gut microbiota are sourced from MiBioGen (http: / / mibiogen.gcc.rug.nl). This meta-analysis covered 18,340 participants in 24 cohorts, generating 211 GWAS data statistics related to microbiota taxa, including 9 phyla, 16 classes, 20 orders, 32 families (excluding 3 unknown families), and 119 genera (excluding 12 unknown genera).

[0019] Step 2: Screen and de-multiplex the instrumental variables for the collected data, and the results are shown in Table 2;

[0020] The F-statistic threshold is used to judge weak instrumental variables, and when F < 10, it is defined as a weak instrumental variable. The MR-PRESSO test is used to detect and remove abnormal instrumental variables.

[0021] Step 3: According to the collected instrumental variables, use the two-sample Mendelian randomization method to analyze the data and obtain the preliminary screening results of causal microbiota;

[0022] To explore the causal relationship between food allergy and gut microbiota, the MR analysis selects SNPs shown at the P < 5×10-6 level as tools, based on LD (linkage disequilibrium) R2 < 0.001 within a 10,000 kb window in the 1000G EUR data. Two methods (MR-Egger and random-effects inverse-variance weighted IVW) are used for analysis. When the significant P of any one of the analysis results is < 0.05, there is a causal relationship.

[0023] Step 4: Analyze the preliminary screening results, and comprehensively judge the effect values of multiple analysis methods and the sensitivity analysis to obtain candidate instrumental variables, and the results are shown in Table 3;

[0024] Sensitivity analysis is used to detect potential heterogeneity and pleiotropy. Cochran's Q test is used to identify heterogeneity, and the MR-Egger intercept test analyzes horizontal pleiotropy. When P > 0.05, it is considered that there is no heterogeneity and pleiotropy. The leave-one-out method is used to analyze whether there is bias in the causal estimate or it is driven by a single SNP.

[0025] Table 1 Two-way Mendelian randomization analysis of the causal relationship between gut microbiota and food allergy

[0026]

[0027] Table 2 F values of the instrumental variable SNPs.

[0028]

[0029]

[0030] Table 3 Cochran's Q test for heterogeneity among SNPs

[0031]

[0032] Step 5, Establish an animal model of food allergy

[0033] Female balb / c mice at 4 - 6 weeks old were randomly divided into a blank group (CON), a sensitized model group (FA), an oral tolerance (OT) group, a 30 mg / kg (A3) and a 50 mg / kg (A5) naringin group, with six mice in each group. From D0 to D4, the OT group was intragastrically administered 1 μg of OVA, the A3 group was administered 1 μg of OVA and 30 mg / kg of naringin, and the A5 group was administered 1 μg of OVA and 50 mg / kg of naringin. The CON and FA groups were given an equal volume of PBS. On the 11th and 18th days, except for the CON group, 1 μg of OVA was intraperitoneally injected for sensitization. One week later, from D25 to D39, except for the CON group, each group was challenged with 30 mg of OVA per mouse, and the CON group was given an equal volume of PBS, 4 times a week for a total of 8 times.

[0034] Step 6, Using the metagenomics analysis method, specifically measure the relevant microbiota screened to have a causal relationship with food allergy

[0035] Thirty intestinal content samples were collected for 16S rRNA gene amplicon sequencing. The FastPure Feces DNA Isolation Kit was used to detect the total microbial genomic DNA in the intestinal contents. The hypervariable regions v3-v4 of the bacterial 16S rRNA gene were amplified by a PCR thermal cycler using the primer pair 338F (5'-ATCCTACGGGAGGCAGCAG-3') and 806R (5'-GGATACHVGGGTWTCTAAT-3'). The PCR products were extracted from a 2% agarose gel, purified using a PCR Clean Up Kita, and quantified. Finally, the sample DNA was sequenced using the Illumina PE300 platform, followed by bioinformatics analysis. Step 7, the target flora of the bioactive small molecule was determined for its reliability as a food allergy target, and the results are as Figure 2 shown.

[0036] Data analysis was performed on the Majorbio Cloud Platform (https: / / cloud.majorbio.com) as follows: The mothur software (http: / / www.mothur.org / wiki / Calculators) was used to calculate the flora diversity, and the PERMANOVA non-parametric test was combined to analyze whether the differences in the microbial community structures between sample groups were significant; the LEfSe analysis (Linear discriminant analysis Effect Size) (http: / / huttenhower.sph.harvard.edu / LEfSe) (LDA>2, P<0.05) was used to determine the bacterial taxa with significant differences in abundance from the phylum to the genus level between different groups.

[0037] Based on a new method for mining food allergy intestinal flora targets proposed by the present invention, the following conclusions were obtained:

[0038] The preliminary results of forward Mendelian randomization showed that c.Verrucomicrobiae, o.Verrucomicrobiales, f.Verrucomicrobiaceae, g.Akkermansia, f.Prevotellaceae, g.Lachnospiraceae UCG004, and g.Ruminococcus existing in the intestine were directly related to food allergy. When these microbial communities changed, it could increase the risk of food allergy occurrence; the results of reverse Mendelian randomization showed that p.Actinobacteria, c.Coriobacteriia, o.Coriobacteriales, g.Adlercreutzia, and g.Olsenella were the microbial communities severely affected by food allergy. Further, the microbiomics research of in vivo animal experiments combined with the present invention found that intestinal bacteria f.Lachnospiraceae and g.Lachnospiraceae_NK4A136_group could be used as targets for preventing and treating food allergy, and naringin could act on this target to achieve the purpose of preventing and treating food allergy.

Claims

1. A new method for mining intestinal flora targets for food allergies, characterized by: The following steps are involved: Step 1: Obtain genome-wide association GWAS data and gut microbiota-related data of food allergy; Step 2: Screen the collected data for instrumental variables and remove pleiotropy; Step 3, based on the collected instrumental variables, a bidirectional Mendelian randomization method is used to analyze the data to obtain preliminary screening results of the causal flora; Step 4, analyzing the preliminary screening results, combining the effect values ​​of multiple analysis methods with the determination of sensitivity analysis, and obtaining candidate instrumental variables; Step 5: Establishing a food allergy animal model Step 6: Use microbiome analysis methods to conduct targeted testing of the selected related bacterial communities that have a causal relationship with food allergies Step 7: Active small molecules act on the target bacterial flora to determine its reliability as a food allergy target.

2. A new method for mining intestinal flora targets for food allergies according to claim 1, characterized in that: Based on the bidirectional Mendelian randomization method, we explored potential intestinal flora targets that have a direct causal relationship with food allergies. The application of bidirectional Mendelian randomization can clarify the intestinal flora that increase the occurrence of food allergic reactions, as well as the intestinal flora caused by food allergies. The clear causal relationship lays the foundation for target exploration.

3. A new method for mining intestinal flora targets for food allergies according to claim 1, characterized in that: The method of using microbiome technology to explore bacterial targets, establish in vivo animal experiments, and combine the conclusions of bidirectional Mendelian randomization data to clearly explore relevant intestinal bacterial targets.

4. The novel method for mining food allergy intestinal flora targets according to any one of claims 1 to 3, characterized in that: In step 5, animal experiments use a sensitization group and an oral tolerance group to jointly evaluate the effects of small molecules on the target.

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