Screening method of non-alcoholic fatty liver disease treatment drug targets and application thereof

Abstract

The invention discloses a non-alcoholic fatty liver disease treatment drug target screening method and application thereof, and the method comprises the following steps: screening a drug-acceptable gene, obtaining cis-eQTL data of the drug-acceptable gene, obtaining whole genome correlation research data of a non-alcoholic fatty liver disease, and constructing an end database and a verification database. Screening potential drug targets having a causal relationship with the non-alcoholic fatty liver disease by using a Mendel randomization analysis method, and determining a target drug target by using a co-localization analysis method; potential drug targets related to NAFLD pathogenesis are screened and identified by using a Mendel randomization analysis method, and a causal relationship is established between exposure and results, so that the accuracy of the drug targets is improved, common driving factors between the potential drug targets and NAFLD risks are confirmed by performing co-localization analysis, and the risk of the NAFLD is determined. And screening, identifying and determining a drug target for treating the NAFLD so as to realize targeted treatment of the NAFLD.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a method for screening drug targets for treating non-alcoholic fatty liver disease and an application thereof. Background Art

[0002] Non-alcoholic fatty liver disease (NAFLD) refers to a pathological state in which the fat content in the liver increases without heavy alcohol consumption. NAFLD is closely related to metabolic syndromes such as obesity, type 2 diabetes, and hyperlipidemia.

[0003] Currently, the measures for treating NAFLD mainly include lifestyle changes, drug therapy and surgical treatment. Among them, drug therapy is limited to the control of co-existing diseases such as diabetes and hyperlipidemia, as well as the control of liver inflammation and fibrosis. There are no drug targets for NAFLD, which greatly limits the development of NAFLD drugs.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The technical task of the present invention is to address the above deficiencies and provide a method for screening drug targets for the treatment of non-alcoholic fatty liver disease and its application, so as to achieve targeted treatment of NAFLD by screening, identifying and determining drug targets for the treatment of NAFLD, thereby solving the problem of poor treatment effect of NAFLD.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] According to one aspect of the present application, a method for screening drug targets for the treatment of non-alcoholic fatty liver disease is provided, comprising: screening drugable genes and obtaining cis-eQTL data of the drugable genes; obtaining genome-wide association study data of non-alcoholic fatty liver disease, and constructing an outcome database and a verification database; using a Mendelian randomization analysis method to screen potential drug targets that have a causal relationship with non-alcoholic fatty liver disease; and using a co-localization analysis method to determine the target drug targets.

[0008] In some embodiments, the druggable gene is a 4302 druggable gene located on an autosome.

[0009] In some embodiments, the Mendelian randomization analysis method comprises: using the cis-eQTL data of the genomic position of the druggable gene within ±100 kb as a genetic instrumental variable, and performing a Mendelian randomization analysis using the data of the outcome database.

[0010] In some embodiments, the Mendelian randomization analysis is a two-sample Mendelian randomization analysis using an inverse variance weighted model, MR-Egge regression, and a weighted median model.

[0011] In some embodiments, the Mendelian randomization analysis and the colocalization analysis are both performed using the R package.

[0012] In some embodiments, the method further comprises conducting a safety assessment on the target drug target and performing clinical validation.

[0013] In some embodiments, the safety assessment is to evaluate the association between potential drug targets and risk factors associated with metabolic diseases and metabolic syndrome-related diseases using Mendelian randomization analysis.

[0014] In some embodiments, the metabolic disease-related risk factors include: total blood cholesterol, blood triglycerides, blood high-density lipoprotein, blood low-density lipoprotein, blood apolipoprotein A1, blood apolipoprotein B, blood lipoprotein a, systolic blood pressure, diastolic blood pressure, fasting blood glucose, fasting insulin, glycated hemoglobin, body mass index, waist circumference, hip circumference, waist-to-hip ratio; the metabolic syndrome-related diseases include: essential hypertension, obesity, hyperlipidemia, type 2 diabetes, myocardial infarction, ischemic stroke, and atherosclerotic heart disease.

[0015] According to another aspect of the present application, a method for screening drug targets for treating non-alcoholic fatty liver disease is provided for use in screening drugs for treating non-alcoholic fatty liver disease.

[0016] In some embodiments, the drug target is the druggable gene CD33.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are:

[0018] 1. By using Mendelian randomization analysis methods, potential drug targets related to the onset of NAFLD were screened and identified. The cis-eQTL and NAFLD risk association data were combined to establish a causal relationship between exposure and outcome, thereby improving the accuracy of drug targets.

[0019] 2. Conducting co-localization analysis to identify common drivers between potential drug targets and NAFLD risk will help determine the causal relationship between drug targets and NAFLD disease and exclude potential confounding factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 Schematic diagram of the application method flow chart.

[0022] Figure 2 A Manhattan plot of co-localization analysis in one embodiment of the present application.

[0023] Figure 3 This is a schematic diagram of the causal relationship between CD33 inhibitors and risk factors associated with metabolic diseases in one embodiment of the present application.

[0024] Figure 4 This is a schematic diagram of the causal relationship between CD33 inhibitors and metabolic syndrome-related diseases in one embodiment of the present application.

[0025] Figure 5 This is a volcano plot of liver transcriptomics in one embodiment of this application.

[0026] Figure 6 This is a GO enrichment pathway diagram of liver transcriptomics in one embodiment of the present application.

[0027] Figure 7 This is a KEGG enrichment pathway diagram of liver transcriptomics in one embodiment of the present application.

[0028] Figure 8 This is a liver pathology staining diagram in one embodiment of the present application, wherein Figure a is HE staining of a patient with simple fatty degeneration; Figure b is HE staining of a patient with non-alcoholic fatty liver disease; and Figure c is Masson staining of a patient with non-alcoholic fatty liver disease.

[0029] Fig. 9 This is the result of CD33 immunohistochemical staining in one embodiment of the present application, wherein Figure a is the simple fatty degeneration group; Figure b is the non-alcoholic fatty liver disease group; Figure c is the statistical analysis of the CD33 immunohistochemical positive cell ratio between groups a and b, P < 0.05 indicates statistical significance. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0031] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0032] See also Figure 1 This embodiment provides a method for screening drug targets for treating non-alcoholic fatty liver disease, comprising the following steps:

[0033] S100, screening druggable genes and obtaining cis-eQTL data of the druggable genes.

[0034] In one embodiment, step S100 includes S101 - S102 .

[0035] S101, screening for druggable genes.

[0036] In one embodiment, druggable genes located on autosomes were screened from the HGNC database. The druggable genes included 1,375 protein therapeutic target genes in clinical development, 646 protein genes related to drug targets and compounds, and 2,281 protein genes related to key drug target family members. The above three types of genes were combined to obtain 4,302 druggable genes.

[0037] S102, obtaining cis-eQTL data of the druggable gene.

[0038] In one embodiment, cis-eQTL data of 31,684 peripheral blood samples with statistical significance (FDR < 0.05, ± 1 Mb per probe) in the whole genome provided by the eQTLGen database were extracted, and the cis-eQTL data of the 31,684 peripheral blood samples were quality controlled, and finally cis-eQTL data of 2,664 druggable genes were obtained.

[0039] Among them, the quality control method is as follows.

[0040] F-statistic filtering: exclude SNPs with F-statistic < 10, that is, exclude weakly associated SNPs;

[0041] Linkage disequilibrium filtering: Use the 1000Genomes European reference panel, select r 2 SNPs with a linkage disequilibrium level lower than 0.1 were selected to ensure their validity as instrumental variables.

[0042] Steiger filtering: Remove genes whose variance in the trait is greater than the exposure variable to avoid over-interpretation of the results.

[0043] S200, obtain genome-wide association study data for non-alcoholic fatty liver disease (NAFLD) and construct outcome database and validation database.

[0044] In one embodiment, the outcome database is a genome-wide association study data of NAFLD, the outcome database comes from a genome-wide meta-analysis, and the outcome database includes four cohorts, namely, UK Biobank, Estonian Biobank, eMERGE, and Finland, which have electronic health records of NAFLD. The four cohorts in the outcome database contain a total of 8,434 NAFLD cases and 770,180 controls.

[0045] In one embodiment, referring to Table 1, the validation database includes 4,761 NAFLD cases and 373,227 healthy controls without NAFLD. A sensitivity analysis was performed in the validation database, excluding other secondary liver pathological conditions that coexisted, and correcting for confounding factors such as body mass index (BMI) and alcohol intake.

[0046] Table 1 Outcome database and validation database

[0047]

[0048] S300, using Mendelian randomization analysis method to screen potential drug targets with causal relationship with NAFLD risk.

[0049] In one embodiment, the cis-eQTL data of the genomic position of the druggable gene obtained in step S100 within the range of ±100 kb is used as a genetic instrument variable.

[0050] In one embodiment, a Mendelian randomization analysis is performed using genome-wide association study data of NAFLD in an outcome database as outcome data.

[0051] In one embodiment, the Two Sample MR R package was used to perform Mendelian randomization data analysis, and high-quality instrumental variables were screened. Single nucleotide polymorphisms (SNPs) with weak effects were excluded by calculating the F statistic (F-statistic < 10). 2<0.1, based on the 1000Genomes Europe reference panel) were used as instrumental variables, and genes with high exposure to NAFLD characteristics were removed by Steiger filtering. The MR estimate of each SNP was calculated by the Wald ratio method, and the inverse variance weighted model (IVW), MR-Egger regression, and weighted median model were used for Mendelian randomization analysis to evaluate the causal relationship between druggable gene CD33 gene expression and NAFLD risk and the robustness of the results. Among them, the IVW model was used for the main analysis, and the causal estimate and its 95% confidence interval were calculated. The potential pleiotropy in the Mendelian randomization analysis was detected and corrected by MR-Egger regression. Sensitivity analysis was performed by weighted median model to verify the robustness of the IVW model results. Bonferroni correction was used to evaluate the significance threshold of multiple test exposures.

[0052] Through Mendelian randomization analysis, potential drug targets with a causal relationship with the risk of NAFLD were screened out, among which the potential drug targets were druggable genes CD33, MFGE8, TEK, and LILRA3, as shown in Table 2 for details.

[0053] Table 2 Results of Mendelian randomization analysis

[0054]

[0055]

[0056] In one embodiment, a p value less than 1.90E-5 (p=0.05 / 2644) is defined as a significant difference, and the potential drug targets with these significant differences are verified in a verification database, and the potential drug targets are further screened as the druggable gene CD33.

[0057] S400, Identification of drug targets using colocalization analysis.

[0058] Co-localization analysis of GWAS data related to gene CD33 and GWAS data related to NAFLD was performed to determine that the drug target is CD33.

[0059] In one embodiment, colocalization analysis of NAFLD risk was performed using the R package coloc with default prior probabilities: p1 = 1E-4, p2 = 1E-4, where p1 and p2 are predefined probabilities that the SNP in the test region is substantially associated with gene expression, NAFLD risk, or both. Among them, p1 = 1E-4, which means p1 = 1x10 -4 , which means the p-value is 0.0001.

[0060] In one embodiment, the posterior probability derived from the co-localization analysis corresponds to one of the following five hypotheses: PPH0, SNPs are not associated with either trait; PPH1, SNPs are associated with gene expression but not with NAFLD risk; PPH2, associated with NAFLD risk but not with gene expression; PPH3, associated with NAFLD risk and gene expression but driven by different SNPs; PPH4 is associated with NAFLD risk and gene expression, driven by common SNPs.

[0061] In one embodiment, the significance threshold of co-localization is set to PPH4>0.80, and genes co-localized with NAFLD can be considered as potential drug target genes, excluding potential confounding factors.

[0062] Since one gene corresponds to multiple SNPs and one SNP has a specific rs ID, the gene CD33 corresponds to multiple rs IDs. The Manhattan plot of co-localization analysis is shown in Figure 1. Figure 2 As shown in the figure, Figure a shows the position of the rs ID (rs1710398) corresponding to a SNP in multiple SNPs of the gene CD33, and Figure b shows the position of the rs ID (rs1710398) corresponding to this SNP in the NAFLD-related SNPs association region. The horizontal axis in the figure is the position of chromosome chr19, and the vertical axis is the p value. Through co-localization analysis of the GWAS data of CD33 and NAFLD, the druggable gene CD33 and NAFLD have the same and significant SNP co-localized at the target CD33, further illustrating the causal relationship between the target CD33 and the outcome. It can be concluded that the target drug target is the druggable gene CD33.

[0063] S500, conducts safety assessment and clinical verification of the druggable gene CD33.

[0064] S501, safety assessment.

[0065] By analyzing the association between CD33 gene expression and risk factors related to metabolic diseases and metabolic syndrome-related diseases, we evaluated whether inhibiting CD33 gene function would lead to an increased risk of other metabolic-related diseases.

[0066] Among them, metabolic disease-related risk factors include: total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoproteins A1 and apolipoproteins B, lipoprotein A, systolic blood pressure, diastolic blood pressure, fasting glucose, fasting insulin, glycated haemoglobin, body mass index, waist circumference, hip circumference, waist-to-hip ratio; metabolic syndrome-related diseases include: essential hypertension Hypertension, Obesity, Hyperlipidemia, Type 2 diabetes, Myocardial infarction, Ischemic Stroke, and atherosclerotic heart disease.

[0067] Figure 3-4 A schematic diagram of the causal relationship between CD33 inhibitors and metabolic disease-related risk factors and metabolic syndrome-related diseases is shown. As can be seen from the figure, there is no confirmed causal relationship between CD33 proxy gene inhibitors and lipid metabolism markers, blood pressure and glucose-related markers (p>2.17E-3). Among them, CD33 proxy gene inhibitors are significantly negatively correlated with atherosclerotic heart disease (OR=0.997, 95%CI: 0.996-0.999, p=1.67E-3).

[0068] S502. Clinical verification.

[0069] In one embodiment, in order to further verify the results of Mendelian randomization and co-localization, liver biopsies were performed on obese patients with NAFLD who were scheduled to undergo metabolic weight loss surgery at Tianjin Medical University General Hospital while undergoing sleeve gastrectomy. RNA sequencing (RNA-seq) was performed on the remaining liver biopsy specimens of 41 patients. Figure 5 The volcano plot of liver transcriptomics is shown, and the results demonstrate that CD33 expression is significantly different between non-NASH patients and NASH patients at the liver RNA-seq level, and CD33 is significantly upregulated in NASH patients.

[0070] In one embodiment, the above results were cross-validated by immunohistochemistry and quantitative polymerase chain reaction (qPCR) technology to ensure the accuracy and reliability of RNA-seq data and co-localization analysis results.

[0071] Figure 6-7 The GO enrichment pathway map and KEGG enrichment pathway map of liver transcriptomics are shown. It can be seen from the figure that the onset of non-alcoholic steatohepatitis (NASH) is related to chronic inflammation and abnormal immune cell function.

[0072] Figure 8 These are liver pathological staining images (a: HE staining of patients with simple fatty degeneration; b: HE staining of patients with NASH; c: Masson staining of patients with NASH). It can be seen from the pictures that simple fatty degeneration is the mildest type of NAFLD.

[0073] Fig. 9 CD33 immunohistochemical staining is shown (a: simple fatty degeneration group; b: NASH group; d: statistical analysis of the ratio of CD33 immunohistochemical positive cells between the two groups, P < 0.05 indicates statistical significance). It can be seen from the figure that there is a difference in CD33 expression between the non-NASH group and the NASH group, and it is statistically significant (P < 0.05).

[0074] Through the above specific implementations, those skilled in the art can easily implement the present invention. However, it should be understood that the present invention is not limited to the above specific implementations. On the basis of the disclosed implementations, those skilled in the art can arbitrarily combine different technical features to achieve different technical solutions.

Claims

1. A method for screening drug targets for the treatment of non-alcoholic fatty liver disease, characterized in that: include: Screening druggable genes and obtaining cis-eQTL data of the druggable genes; Obtain genome-wide association study data for non-alcoholic fatty liver disease and construct outcome database and validation database; Use Mendelian randomization analysis to screen potential drug targets with causal relationship with non-alcoholic fatty liver disease; Use co-localization analysis to identify drug targets of interest.

2. The method for screening drug targets for the treatment of non-alcoholic fatty liver disease according to claim 1, characterized in that: The druggable genes are 4302 druggable genes located on autosomes.

3. The method according to claim 1, characterized in that The Mendelian randomization analysis method includes: using the cis-eQTL data of the genomic position of the druggable gene within ±100kb as a genetic instrumental variable, and performing a Mendelian randomization analysis using the data of the outcome database.

4. The method for screening drug targets for treating non-alcoholic fatty liver disease according to claim 3, characterized in that: The Mendelian randomization analysis was performed using an inverse variance weighted model, MR-Egge regression, and a weighted median model for two-sample Mendelian randomization analysis.

5. The method for screening drug targets for treating non-alcoholic fatty liver disease according to claim 1, characterized in that: The Mendelian randomization analysis and the colocalization analysis were both performed using the R package.

6. The method for screening drug targets for treating non-alcoholic fatty liver disease according to claim 1, characterized in that: The method also includes: conducting a safety assessment on the target drug target and performing clinical verification.

7. The method for screening drug targets for treating non-alcoholic fatty liver disease according to claim 6, characterized in that: The safety assessment was performed using Mendelian randomization analysis to evaluate the association between potential drug targets and risk factors associated with metabolic diseases and metabolic syndrome-related diseases.

8. The method for screening drug targets for treating non-alcoholic fatty liver disease according to claim 7, characterized in that: The metabolic disease-related risk factors include: total blood cholesterol, blood triglycerides, blood high-density lipoprotein, blood low-density lipoprotein, blood apolipoprotein A1, blood apolipoprotein B, blood lipoprotein a, systolic blood pressure, diastolic blood pressure, fasting blood glucose, fasting insulin, glycated hemoglobin, body mass index, waist circumference, hip circumference, waist-to-hip ratio; the metabolic syndrome-related diseases include: essential hypertension, obesity, hyperlipidemia, type 2 diabetes, myocardial infarction, ischemic stroke, and atherosclerotic heart disease.

9. Use of the method for screening drug targets for treating non-alcoholic fatty liver disease according to any one of claims 1 to 8 in screening drugs for treating non-alcoholic fatty liver disease.

10. The use according to claim 9, wherein the drug target is the druggable gene CD33.

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