Application of plekho2 and npm3 autoantibodies in preparation of reagent kit for detecting deficiency of yin and hyperactivity of fire in hashimoto's thyroiditis

Abstract

The application discloses application of PLEKHO2 and NPM3 autoantibodies in preparation of a kit for detecting hyperactivity of fire due to yin deficiency of Hashimoto's thyroiditis, and the biomarker is at least one of MAP9, IRF9, PLEKHO2, NPM3, DR1, KDM1A, PCGF2, GPCPD1 and IGBP1 related autoantibodies. Among them, the biomarkers for detecting characteristic autoantibodies of the stagnation of liver-Qi of Hashimoto's thyroiditis are MAP9 and IRF9 two related autoantibodies; the biomarkers for detecting characteristic autoantibodies of the hyperactivity of fire due to yin deficiency of Hashimoto's thyroiditis are PLEKHO2 and NPM3 two related autoantibodies; and the biomarkers for detecting characteristic autoantibodies of the deficiency of spleen and kidney yang of Hashimoto's thyroiditis are DR1, KDM1A, PCGF2, GPCPD1 and IGBP1 five related autoantibodies. The serum related autoantibody markers can reflect the syndrome characteristics of Hashimoto's thyroiditis, make the TCM differentiation accurate, and are beneficial to revealing the syndrome essence and molecular connotation of Hashimoto's thyroiditis.

Description

Technical Field

[0001] This invention relates to the fields of immunology and serum antibody omics, and more specifically, to biomarkers for detecting characteristic autoantibodies in different syndromes of Hashimoto's thyroiditis and their applications. Background Technology

[0002] Hashimoto's thyroiditis (HT), also known as Hashimoto's disease (HD) or chronic lymphocytic thyroiditis (CLT), is currently the most common organ-specific autoimmune disease. Clinically, it manifests as diffuse thyroid enlargement and elevated serum thyroid autoantibody levels, and is the most common cause of hypothyroidism. The incidence of HT is rapidly increasing year by year, becoming a common disease that seriously threatens people's health, work, and life. HT often has an insidious onset, with no specific clinical symptoms in the early stages, making it easily missed or misdiagnosed. Most cases present with thyroid nodules, thyroid enlargement, hypothyroidism, or other forms of autoimmune diseases as the first medical visit, and most patients are unaware of their condition, often being discovered during routine physical examinations. Currently, there is a lack of effective drugs for the treatment of HT, especially in the early stages of the disease.

[0003] Traditional Chinese medicine (TCM) has a significant advantage in treating diseases through syndrome differentiation and treatment. In recent years, reports on TCM treatment of hyperthyroidism (HT) have increased significantly, demonstrating remarkable efficacy in improving symptoms and reducing thyroid autoantibodies. Syndromes, as the foundation and core of TCM syndrome differentiation and treatment, encompass both macroscopic symptoms and signs, and microscopic changes in the material basis of the internal organs, tissues, cells, and molecules. With the development of omics technologies such as genomics, proteomics, transcriptomics, and metabolomics, many researchers have recently used omics techniques to screen, analyze, identify, and validate biomarkers, studying the molecular connotations of TCM syndromes. This is crucial for revealing the essence of syndromes at the molecular level, improving the accuracy of syndrome diagnosis, and precisely identifying the molecular pathogenesis and therapeutic targets of syndromes, thus significantly contributing to disease diagnosis and treatment. Studies on the distribution patterns of TCM syndromes in HT patients have found that in the early stage of HT with normal thyroid function, liver qi stagnation syndrome is common; in the hyperthyroid stage, yin deficiency with fire excess syndrome is predominant; and in the hypothyroid stage, spleen and kidney yang deficiency syndrome is common. However, currently, there is a lack of research using omics technologies to study the syndrome characteristics and molecular pathogenesis of HT, as well as the molecular mechanisms of TCM syndrome differentiation and treatment of HT. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a biomarker for detecting autoantibodies characteristic of different syndromes of Hashimoto's thyroiditis and its application, which can reflect the syndrome characteristics of Hashimoto's thyroiditis.

[0005] To achieve the above objectives, one of the technical solutions of the present invention is as follows:

[0006] A biomarker for detecting characteristic autoantibodies in different syndromes of Hashimoto's thyroiditis, wherein the biomarker is at least one of the following autoantibodies: MAP9, IRF9, PLEKHO2, NPM3, DR1, KDM1A, PCGF2, GPCPD1, and IGBP1.

[0007] Furthermore, the biomarker used for detecting characteristic autoantibodies in Hashimoto's thyroiditis with liver qi stagnation is at least one of two related autoantibodies, MAP9 and IRF9; preferably, it is a combination of the two related autoantibodies.

[0008] The biomarker for detecting characteristic autoantibodies in Hashimoto's thyroiditis with yin deficiency and fire excess syndrome is at least one of two related autoantibodies, PLEKHO2 and NPM3; preferably, it is a combination of the two related autoantibodies.

[0009] The biomarkers used for the detection of characteristic autoantibodies in Hashimoto's thyroiditis with spleen and kidney yang deficiency are at least one of five related autoantibodies: DR1, KDM1A, PCGF2, GPCPD1, and IGBP1; preferably, a combination of the five related autoantibodies.

[0010] One of the technical solutions of the present invention is the application of the above-mentioned biomarker in the preparation of a drug for treating Hashimoto's thyroiditis.

[0011] One of the technical solutions of the present invention is the application of the above-mentioned biomarker in the preparation of reagents and kits for detecting characteristic autoantibodies of Hashimoto's thyroiditis syndrome.

[0012] One of the technical solutions of the present invention is: a kit for detecting characteristic autoantibodies of Hashimoto's thyroiditis with liver qi stagnation syndrome, comprising at least one of MAP9 and IRF9 recombinant human proteins, which is used to detect at least one of the two related autoantibodies MAP9 and IRF9 in serum.

[0013] One of the technical solutions of the present invention is: a kit for detecting characteristic autoantibodies in Hashimoto's thyroiditis with yin deficiency and fire excess syndrome, comprising at least one of PLEKHO2 and NPM3 recombinant human proteins. The kit is used to detect at least one of the two related autoantibodies, PLEKHO2 and NPM3, in serum.

[0014] One of the technical solutions of the present invention is: a kit for detecting characteristic autoantibodies of Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome, comprising at least one of five recombinant human proteins: DR1, KDM1A, PCGF2, GPCPD1, and IGBP1. The kit is used to detect at least one of the five related autoantibodies: DR1, KDM1A, PCGF2, GPCPD1, and IGBP1 in serum.

[0015] Furthermore, the kit also includes human IgG standards, horseradish peroxidase-labeled goat anti-human IgG, and reagents commonly used in ELISA technology.

[0016] The beneficial effects of this invention are:

[0017] To investigate the changes in autoantibodies associated with different syndromes of HT, as well as the characteristic protein markers and molecular pathogenesis of these syndromes, this invention selected typical syndromes of liver qi stagnation, yin deficiency with fire excess, and spleen and kidney yang deficiency from patients with different thyroid function states (normal, hyperthyroid, and hypothyroid) using HuProt. TM Human proteome microarray technology (containing over 20,000 newly sequenced recombinant human proteins, corresponding to 16,152 unique protein-coding genes, with a coverage rate of up to 81%, making it the world's highest-throughput human recombinant proteome microarray to date) was used to perform serum antibody proteomics analysis on serum from patients with different HT syndromes and healthy controls. This screened for HT syndrome-related autoantibodies, and then used indirect ELISA to validate and detect potential characteristic autoantibodies in the serum of patients with different Hashimoto's thyroiditis syndromes in a larger sample. The aim was to obtain characteristic proteins that reflect Hashimoto's thyroiditis syndromes, more clearly reveal the molecular connotation of the syndromes, and make TCM syndrome differentiation more precise. This will help guide TCM syndrome differentiation at the molecular level and provide new reference for substantive research on Hashimoto's thyroiditis syndromes.

[0018] Specifically, the technical solution of this invention includes: (1) establishing a unified standard specimen bank and database: the system collects complete basic information, TCM syndrome four diagnostic information and clinical case data of Hashimoto's thyroiditis patients, and collects blood samples that meet the standards according to standard operating procedures. (2) selecting serum samples from patients with different thyroid function states of Hashimoto's thyroiditis, namely, patients with typical syndromes of liver qi stagnation, yin deficiency and fire excess and spleen and kidney yang deficiency. (3) using high-throughput HuProt TMHuman proteome microarray technology was used to analyze the differentially expressed serum-related autoantibodies in patients with different syndromes of Hashimoto's thyroiditis and normal controls. Statistical and bioinformatics methods were used to screen for differentially expressed meaningful syndrome-specific autoantibodies. (4) Further large-sample validation was conducted in patients with different syndromes of Hashimoto's thyroiditis, patients with other thyroid diseases, and normal controls to clarify the sensitivity and specificity of these syndrome-specific autoantibodies in distinguishing different syndrome proteins of Hashimoto's thyroiditis. (5) Development of a syndrome-specific autoantibody detection kit: Based on the differentially expressed characteristic autoantibodies in the serum of patients with different syndromes of Hashimoto's thyroiditis and normal populations, a syndrome-specific protein detection kit was developed to reflect the syndrome characteristics of Hashimoto's thyroiditis, making TCM syndrome differentiation more precise and providing new reference for the substantive study of Hashimoto's thyroiditis syndrome.

[0019] The application described in this invention can detect serum-related autoantibodies characteristic of Hashimoto's thyroiditis syndrome in Traditional Chinese Medicine (TCM). These serum-related autoantibody markers can reflect the syndrome characteristics of Hashimoto's thyroiditis, making TCM diagnosis more precise and helping to reveal the syndrome essence and molecular connotation of Hashimoto's thyroiditis. Attached Figure Description

[0020] Figure 1 This describes the principle of protein chip detection.

[0021] Figure 2 This is the result of the chip scanning quality assessment (protein spotting).

[0022] Note: Figure A shows the chip scan: it shows the overall scan (left) and the magnified view (right). The arrows from left to right 1-4 point to the positive control (GST, concentrations from left to right are 10ng / μL, 50ng / μL, 100ng / μL, and 200ng / μL, respectively), and the rightmost arrow is the negative control (BSA protein); Figure B shows the chip preparation repeatability evaluation results.

[0023] Figure 3 SNR cluster analysis of 59 related autoantibodies that showed significant differences in the Hashimoto's thyroiditis liver qi stagnation syndrome group.

[0024] Note: The horizontal axis represents the sample, and the vertical axis represents the protein (IgG or IgM response type) corresponding to that sample.

[0025] Figure 4 SNR cluster analysis of 147 related autoantibodies that showed significant differences in the yin deficiency and fire excess syndrome group of Hashimoto's thyroiditis.

[0026] Note: The horizontal axis represents the sample, and the vertical axis represents the protein (IgG or IgM response type) corresponding to that sample.

[0027] Figure 5SNR cluster analysis of 152 related autoantibodies that showed significant differences in the Hashimoto's thyroiditis spleen and kidney yang deficiency syndrome group.

[0028] Note: The horizontal axis represents the sample, and the vertical axis represents the protein (IgG or IgM response type) corresponding to that sample.

[0029] Figure 6 Venn plot analysis of characteristic autoantibodies in different syndrome groups of Hashimoto's thyroiditis.

[0030] Note: Bottom left: HT0105: Hashimoto's thyroiditis with liver qi stagnation syndrome (normal thyroid function); Top left: HT0610: Hashimoto's thyroiditis with yin deficiency and fire excess syndrome (hyperthyroidism); Top right: HT1115: Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome (hypothyroidism); Bottom right: HT: Hashimoto's thyroiditis as a whole group.

[0031] Figure 7 Scatter plot and ROC curve of autoantibodies specific to Hashimoto's thyroiditis with liver qi stagnation syndrome.

[0032] Note: HT: Hashimoto's thyroiditis with liver qi stagnation syndrome group, C: normal control group, OTD: other thyroid diseases group.

[0033] Figure 8 ROC curves for individual and combined detection of MAP9 and IRF9 related autoantibodies.

[0034] Figure 9 Scatter plot and ROC curve of specific autoantibodies for the syndrome of yin deficiency and fire excess in Hashimoto's thyroiditis.

[0035] Note: HT: Hashimoto's thyroiditis with yin deficiency and fire excess syndrome group, C: normal control group, OTD: other thyroid diseases group.

[0036] Figure 10 ROC curves for individual and combined detection of two related autoantibodies, PLEKHO2 and NPM3.

[0037] Figure 11 Scatter plot and ROC curve of autoantibodies specific to the spleen and kidney yang deficiency syndrome in Hashimoto's thyroiditis.

[0038] Note: HT: Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome group; C: normal control group; OTD: other thyroid diseases group.

[0039] Figure 12 ROC curves for individual and combined detection of five related autoantibodies: DR1, KDM1A, PCGF2, GPCPD1, and IGBP1. Detailed Implementation

[0040] The specific embodiments of the present invention will be further described in detail below with reference to examples.

[0041] Example 1: Screening for characteristic autoantibodies of different syndromes of Hashimoto's thyroiditis using protein microarray.

[0042] 1. Source of serum samples

[0043] From January 2017 to December 2019, venous serum samples were collected from untreated Hashimoto's thyroiditis patients who were first seen in outpatient and inpatient departments of the First Affiliated Hospital and Third Affiliated Hospital of Henan University of Traditional Chinese Medicine and the First Affiliated Hospital of Zhengzhou University. Fifteen patients with different syndromes of Hashimoto's thyroiditis (five patients each of liver qi stagnation syndrome – normal thyroid function, yin deficiency and fire excess syndrome – hyperthyroidism, and spleen and kidney yang deficiency syndrome – hypothyroidism) were selected as HuProt samples. TM There were no statistically significant differences in gender and age among the patients in the proteomic microarray samples.

[0044] Five healthy controls were selected from the general population at a health checkup center. The healthy controls met the following criteria: did not meet any of the diagnostic criteria for Hashimoto's thyroiditis; thyroid ultrasound, TPOAb, TGAb, FT3, FT4, and TSH laboratory tests were all normal; no history of major illness; and no autoimmune diseases in themselves or their immediate family members. The healthy controls were matched with Hashimoto's thyroiditis cases by sex and age.

[0045] 2. Main instruments and equipment for protein chip experiments

[0046] 120090 type crystal core SlideWasher TM Chip washer-dryer (Beijing Bio-Tech Biotechnology Co., Ltd.), LuxScan chip chip dryer TM 10K-A microarray chip scanner (Beijing Bio-Crystal Biotechnology Co., Ltd.), CyTM3-conjugated AffiniPure Goat Anti-Human IgG (Jackson, Inc., USA), Alexa Fluor 647-conjugated AffiniPure Donkey Anti-Human IgM (Jackson, Inc., USA), Multifuge X1R refrigerated high-speed centrifuge (Thermo Fisher Scientific, USA).

[0047] 3. Protein chip detection principle

[0048] One HuProt sheet was used for each sample. TMProtein chip detection involves specific antibodies (including IgG, IgM, or other types of antibodies) binding to proteins immobilized on the chip. After washing to remove unbound antibodies and other proteins, fluorescently labeled secondary antibodies (Anti-human IgM, Cy5-labeled, 635nm wavelength) and Anti-human IgG fluorescent secondary antibodies (Cy3-labeled, 532nm wavelength) are used, and the signals are quantified using a fluorescence scanner. The signal strength is positively correlated with the antibody affinity and quantity. A schematic diagram of the chip detection principle is shown below. Figure 1 As shown.

[0049] 4. Protein chip quality control

[0050] Prior to high-throughput microarray experiments, quality checks were performed on the microarrays to ensure the reliability of biomarker screening. All proteins on the microarray have a GST (glutathione S-transferase) tag at their N-terminus for purification; therefore, hybridization with the protein microarray using an anti-GST antibody can assess the quality of the high-density proteome microarray. Proteins were expressed and purified using GST fusion, and then fabricated into microarrays, with each protein appearing twice. After microarray fabrication, quality checks were performed using Anti-GST. The protein spotting results are shown below. Figure 2 As shown. From Figure 2 As can be seen, using the foreground value generated by the repeating protein Anti-GST hybridization as the object, the R² of the linear fit was 0.98 through scatter plot analysis, indicating that the overall reproducibility of the chip preparation was good. Using the signal-to-noise ratio generated by the Anti-GST hybridization as the calculation object, the detection rate of protein spots was evaluated through negative control. The results show that the protein detection rate was >99%, indicating that the chip preparation was qualified.

[0051] 5. Protein chip detection experimental steps

[0052] (1) Blocking: Take the stored protein chip out from -80℃, place it on a side-shaking shaker, add blocking solution, and block at room temperature for 3 hours;

[0053] (2) Serum sample incubation: Discard the blocking solution, quickly add serum incubation solution (the serum is diluted 200 times with the incubation solution), place on a side-shaking incubator, and incubate overnight at 4°C;

[0054] (3) Cleaning: Use tweezers to remove the chip (be careful not to touch or scratch the upper surface of the chip), place it on a horizontal shaker, and clean it 3 times with cleaning solution at room temperature, 10 min each time;

[0055] (4) Secondary antibody incubation: Place on a side-swinging shaker and incubate at room temperature for 1 hour with secondary antibody incubation solution (the secondary antibody is diluted 1000 times with the incubation solution). (From this step onwards, be careful to avoid light during operation.)

[0056] (5) Cleaning: Place on a horizontal shaker and clean with cleaning solution at room temperature 3 times, 10 min / time. After completion, clean with ultrapure water at room temperature 2 times, 10 min / time.

[0057] (6) Drying: Place the chip in a chip dryer for centrifugal drying;

[0058] (7) Scanning: Operate according to the scanner's operating specifications and user manual. Set the parameters as follows: Power 90%, PMT value 650. If there are bursts, adjust PMT or Power until there are no bursts during scanning.

[0059] 6. Protein chip experimental data processing and statistical analysis

[0060] The chip scan results were read using GenePix Pro v6.0 software to obtain raw data. The limma package in R was used to process the chip data. To eliminate signal inhomogeneity between different protein spots within the same chip due to inconsistent background values, a background correction method was employed. This was implemented by using the foreground-to-background ratio (F / B) for each protein, and defining the SNR (Signal Noise Ratio) based on this ratio, which is the mean F / B of two repeating proteins. To reduce errors caused by systematic differences between different samples, chips, and experimental procedures, the sample SNR was normalized before data comparison. Statistical analysis was performed based on the normalized data to screen for specific response antibodies that differentiated the experimental group from the control group.

[0061] SPSS 22.0 statistical software was used for data processing and statistical analysis. For continuous variables, the Kolomogorov-Smirnov test was used to test for normality; variables conforming to a normal distribution were... For continuously distributed variables that conform to a normal distribution, t-tests or one-way ANOVA are used for comparisons between groups, and Dunnett's test is used for pairwise comparisons among multiple groups. For continuously distributed variables that do not conform to a normal distribution, the Mann-Whitney U rank-sum test is used for comparisons between two groups, and the χ² test is used for comparisons between categorical variables. 2 The test does not satisfy the χ² test. 2 Fisher's exact test was used for the data under the test conditions. The significance level was set at α = 0.05, and P < 0.05 was considered statistically significant.

[0062] 7. Antibody biomarker screening

[0063] Each chip contained 23,034 protein sites. After removing the ND and Control sites, 23,032 protein sites were retained. Using the normalized SNR value as the calculation object, potential high-specificity antibody biomarkers were screened based on statistical methods. The specific analysis is as follows:

[0064] (1) For any protein, assume that the samples to be compared come from two completely identical populations, and perform the Mann-Whitney U rank-sum test (one-tailed) and characterize it with p-value. Define that when p-value < 0.05, reject the null hypothesis, that is, there is a significant difference between the two.

[0065] (2) For any protein, calculate the fold change between the disease group and the healthy control group, i.e., fold change = mean of the disease group / mean of the healthy control group, which is used to indicate the degree to which the disease group is higher than that of the healthy control group; by definition, fold change ≥ 1.5 is the potential difference, and it is generally believed that the larger the fold change, the more obvious the difference between the two groups.

[0066] (3) To avoid comparisons between negative proteins in different sample groups, before sample comparison, a positive protein judgment threshold was set based on the distribution of protein spot signal SNR values ​​on the chip after normalization. Proteins were defined as positive when IgG-SNR>4 and IgM-SNR>5. Based on this, a cutoff threshold (cutoff-IgG≥4, cutoff-IgM≥5) was set using the SNR of the control group on this protein as the calculation object. The positive rates of the disease group and the healthy control group were calculated separately. The minimum positive rate for the disease group was defined as 50%, meaning the number of positive samples in the disease group on this protein was no less than 3 / 5 or 8 / 15. The maximum positive rate for the healthy control group was 0%, meaning the number of positive samples in the healthy control group on this protein was 0 / 5. The positive rates (sensitivity) of the disease group and (1-specificity) of the healthy control group were calculated separately.

[0067] 8. Results

[0068] 8.1 Autoantibodies related to liver qi stagnation syndrome in Hashimoto's thyroiditis

[0069] By comparing Hashimoto's thyroiditis with liver qi stagnation syndrome (normal thyroid function, HT0105) with a normal healthy control group (NC), a total of 59 autoantibodies with significant differences were screened, including 43 IgG response types and 16 IgM response types (fold change ≥ 1.5, p-value < 0.05). Among them, 33 autoantibodies with fold change ≥ 2 are shown in Table 1.

[0070] Table 1. Autoantibodies showing significant differences between Hashimoto's thyroiditis with liver qi stagnation syndrome (HT0105) and the healthy control group (NC).

[0071]

[0072] SNR clustering analysis was performed on the 59 related autoantibodies that showed significant differences. Figure 3 Based on the unsupervised learning clustering results, the HT0105 group samples and the NC group samples can be distinguished through one-step clustering. Therefore, it can be concluded that the selected proteins achieved a 100% classification accuracy in distinguishing between the Hashimoto's thyroiditis liver qi stagnation syndrome group and the healthy control group.

[0073] 8.2 Autoantibodies related to Yin deficiency and fire excess syndrome in Hashimoto's thyroiditis

[0074] By comparing patients with Hashimoto's thyroiditis (hyperthyroidism, HT0610) and the normal healthy control group (NC), a total of 147 autoantibodies with significant differences were screened, including 53 IgG response types and 94 IgM response types (fold change ≥ 1.5, p-value < 0.05). Among them, 78 autoantibodies with fold change ≥ 2 are shown in Table 2.

[0075] Table 2. Significant differences in related autoantibodies between patients with Hashimoto's thyroiditis (Hysteroid thyroiditis with Yin deficiency and excessive fire syndrome, HT0610) and the healthy control group (NC).

[0076]

[0077]

[0078] SNR clustering analysis was performed on 147 related autoantibodies with significant differences. Figure 4 Based on the unsupervised learning clustering results, the HT0610 group samples and the NC group samples can be distinguished through one-step clustering. Therefore, it can be concluded that the selected proteins achieved a 100% classification accuracy in distinguishing between the Hashimoto's thyroiditis yin deficiency and fire excess syndrome group and the healthy control group.

[0079] 8.3 Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome related autoantibodies

[0080] By comparing patients with Hashimoto's thyroiditis and spleen-kidney yang deficiency syndrome (hypothyroidism, HT1115) with normal healthy controls (NC), a total of 152 autoantibodies with significant differences were screened, including 91 IgG response types and 61 IgM response types (fold change ≥ 1.5, p-value < 0.05). Among them, 73 autoantibodies with fold change ≥ 2 are shown in Table 3.

[0081] Table 3 shows the autoantibodies with significant differences between Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome (HT1115) and the healthy control group (NC).

[0082]

[0083]

[0084] SNR clustering analysis was performed on 152 related autoantibodies with significant differences. Figure 5 Based on the unsupervised learning clustering results, the HT1115 group samples and the NC group samples can be distinguished through one-step clustering. Therefore, it can be concluded that the selected proteins achieved a 100% classification accuracy in distinguishing between the Hashimoto's thyroiditis spleen and kidney yang deficiency syndrome group and the healthy control group.

[0085] 9. Screening for characteristic autoantibodies associated with different syndromes of Hashimoto's thyroiditis

[0086] The above results show that 59 related autoantibodies were screened in the Hashimoto's thyroiditis liver qi stagnation syndrome group (normal thyroid function), 147 related autoantibodies were screened in the yin deficiency and fire excess syndrome group (hyperthyroidism), and 152 related autoantibodies were screened in the spleen and kidney yang deficiency syndrome group (hypothyroidism). Venn diagram analysis was used to identify characteristic related autoantibodies in different syndrome groups of Hashimoto's thyroiditis. The intersection of the four groups was taken; autoantibodies present in the syndrome group but not in other syndrome groups were considered characteristic related autoantibodies of that syndrome group. The results are shown in […]. Figure 6 .Depend on Figure 6 It can be seen that there are 29 characteristic autoantibodies in the Hashimoto's thyroiditis liver qi stagnation syndrome group, 103 characteristic autoantibodies in the Hashimoto's thyroiditis yin deficiency and fire excess syndrome group, and 104 characteristic autoantibodies in the Hashimoto's thyroiditis spleen and kidney yang deficiency syndrome group. The characteristic autoantibodies with fold change ≥5 in each syndrome group are shown in Table 4-6.

[0087] Table 4. Characteristic autoantibodies (fold change ≥ 5) in Hashimoto's thyroiditis with liver qi stagnation syndrome

[0088]

[0089] Table 5. Characteristic autoantibodies (fold change ≥ 5) in Hashimoto's thyroiditis with Yin deficiency and excessive fire syndrome.

[0090]

[0091] Table 6. Characteristic autoantibodies (fold change ≥ 5) in Hashimoto's thyroiditis with spleen and kidney yang deficiency syndrome

[0092]

[0093] From the characteristic autoantibodies selected for different syndrome groups of Hashimoto's thyroiditis mentioned above, autoantibodies with a fold change difference of more than 5 and high expression in thyroid tissues and cells as determined by GenBank database were further selected for validation in a large number of serum samples. The autoantibodies to be validated in the Liver Qi Stagnation syndrome group were TNRC6C, MAP9, and IRF9; those in the Yin Deficiency with Hyperactivity of Fire syndrome group were PLEKHO2 and NPM3; and those in the Spleen and Kidney Yang Deficiency syndrome group were DR1, CTDP1, KDM1A, BRMS1L, PCGF2, GPCPD1, and IGBP1.

[0094] Example 2: Indirect ELISA assay to verify the characteristic autoantibodies associated with different syndromes of Hashimoto's thyroiditis screened out.

[0095] The following autoantibodies were selected for verification: TNRC6C, MAP9, and IRF9 related to the Liver Qi Stagnation syndrome; PLEKHO2 and NPM3 related to the Yin Deficiency and Fire Excess syndrome; and DR1, CTDP1, KDM1A, BRMS1L, PCGF2, GPCPD1, and IGBP1 related to the Spleen and Kidney Yang Deficiency syndrome. The levels of these autoantibodies were detected in a large sample using an indirect ELISA assay to verify their specificity and sensitivity as biomarkers for detecting different syndrome characteristics of Hashimoto's thyroiditis.

[0096] 2.1 Source of serum specimens

[0097] The required sample size was estimated using PASS 15.0 software. Based on the study requirements, the sample size for the Hashimoto's thyroiditis patient group was twice that of the normal control group. Using α = 0.05, 1-β = 0.90, the lowest sensitivity criterion p1 in the patient group was 70%, and the sensitivity p2 in the normal control group was 10%. The mean of the normal control group plus 2SD was used as the cutoff value. The positive rates of the research factor in the Hashimoto's thyroiditis group and the normal control group were calculated based on the cutoff value; the difference should be greater than 10%. The calculations indicated approximately 216 patients were needed in the Hashimoto's thyroiditis group and approximately 108 in the normal control group. Considering dropout and loss to follow-up cases, an additional 10% of cases was needed, ultimately requiring approximately 240 patients in the Hashimoto's thyroiditis group and approximately 120 in the normal control group.

[0098] All selected cases were patients with Hashimoto's thyroiditis who had not received any medication or other treatment. From the collected patients with various syndrome types of Hashimoto's thyroiditis, 95 patients with liver qi stagnation syndrome and normal thyroid function, 60 patients with yin deficiency and fire excess syndrome due to hyperthyroidism, and 85 patients with spleen and kidney yang deficiency syndrome due to hypothyroidism were selected. There were no statistically significant differences in gender or age among the patients with different syndrome types. An additional 60 patients with thyroid diseases other than Hashimoto's thyroiditis were also collected. Normal controls were obtained from healthy individuals at a health checkup center during the same period. Cases in each group were matched by gender and age.

[0099] 2.2 Experimental Procedure

[0100] (1) Commercially available human recombinant proteins TNRC6C, MAP9, IRF9, PLEKHO2, NPM3, DR1, CTDP1, KDM1A, BRMS1L, PCGF2, GPCPD1, and IGBP1 purchased from a biotechnology company were diluted to 250 μg / ml with protein dilution buffer. Each recombinant protein was then diluted to an appropriate concentration using coating buffer as shown in Table 7. 100 μl / well was added to a 96-well microplate using a pipette, taking care to avoid air bubbles and liquid adhesion to the well walls. Separately, human IgG standards were diluted to eight different concentrations (640 ng / ml, 320 ng / ml, 160 ng / ml, 80 ng / ml, 40 ng / ml, 20 ng / ml, 10 ng / ml, 0 ng / ml) using coating buffer as quality controls to adjust for differences between plates, 100 μl / well. The plates were sealed with sealing film and incubated overnight at 4°C.

[0101] (2) Blocking: Discard the coating solution in the microplate, pat dry on absorbent paper, add 200 μl of 2% BSA to each well for blocking, seal with sealing film and place in a 4°C refrigerator overnight.

[0102] (3) Washing the plate: Take out the enzyme-labeled plate that has been blocked overnight, shake off the blocking solution, pat dry, wash the plate 3 times with 1×PBST solution on a 96-well fully automated plate washer, and pat dry.

[0103] (4) Primary antibody (serum) incubation: Dilute serum samples in a 96-well deep plate at a ratio of 1:100 (v / v) with 1×PBST antibody diluent containing 1% BSA (dilute immediately before use). Add the diluted serum sample to the ELISA plate using a 12-well pipette, 100 μl / well. Add 100 μl of serum-free antibody diluent to the blank control well. Seal the plate with a sealing film and incubate in a 37°C water bath for 1 h.

[0104] (5) Washing the plate: Discard the serum diluent in the ELISA plate, pat dry, wash the plate 5 times with 1×PBST solution on a 96-well fully automated plate washer, and pat dry the ELISA plate on absorbent paper.

[0105] (6) Secondary antibody incubation: Dilute horseradish peroxidase-labeled goat anti-human IgG secondary antibody (prepared fresh) with 1×PBST antibody dilution buffer containing 1% BSA at a ratio of 1:10000 (v / v). Add the diluted secondary antibody to the microplate using a 12-well pipette, 100 μl / well. After sealing with sealing film, incubate at 37°C in a half-water bath for 1 h.

[0106] (7) Washing the plate: Discard the secondary antibody dilution in each well of the ELISA plate, pat dry, wash the plate 5 times with 1×PBST solution on a 96-well fully automated plate washer, and pat dry the ELISA plate on absorbent paper.

[0107] (8) Colorimetric reaction: Preheat the microplate reader 30 minutes in advance, and set the sample layout and measurement wavelengths (450nm and 620nm). Take out substrate solution A and substrate solution B from the 4℃ refrigerator and mix them in a 1:1 (v / v) ratio (prepare fresh before use). Add the colorimetric solution 100μl / well to the microplate using a 12-channel pipette and react at room temperature in the dark for 5-10 minutes.

[0108] (9) Termination of reaction: After visual observation of color change, add 50 μl of stop solution to the microplate with a 12-well pipette to terminate the reaction. After complete termination, place the plate in a microplate reader for detection and save the experimental data.

[0109] Table 7. Protein Coating Concentrations

[0110]

[0111] 2.3 Data Processing and Statistical Analysis

[0112] Statistical analysis of the acquired experimental data was performed using SPSS 22.0 and GraphPad Prism 8.0. AUC, sensitivity, specificity, and 95% confidence intervals (95% CI) for each relevant autoantibody were calculated using ROC curves. The screening criterion was defined as AUC > 0.5 and P < 0.05. The maximum Youden index (sensitivity + specificity - 1) with a specificity greater than 90% was selected and defined as the cutoff value. All analyses were performed using two-tailed tests; a p-value less than 0.05 was considered statistically significant.

[0113] 2.4 Results

[0114] 2.4.1 Characteristic autoantibodies related to liver qi stagnation syndrome in Hashimoto's thyroiditis

[0115] The levels of autoantibodies related to Hashimoto's thyroiditis with liver qi stagnation syndrome (normal thyroid function) in a group of serum samples (95 cases of Hashimoto's thyroiditis with liver qi stagnation syndrome, 120 normal controls, and 60 cases of other thyroid diseases) were detected by indirect ELISA. In the liver qi stagnation syndrome group, as shown in Table 8, TNRC6C was excluded based on AUC>0.5 and P<0.05. Its concentration in the serum of Hashimoto's thyroiditis patients and normal controls showed no statistically significant difference. The concentrations of the other two related autoantibodies, MAP9 and IRF9, in the serum of Hashimoto's thyroiditis patients were significantly higher than those in the serum of normal controls, and the differences were statistically significant (P<0.05).

[0116] Table 8. Detection of three types of autoantibodies related to liver qi stagnation syndrome in Hashimoto's thyroiditis.

[0117]

[0118] Scatter plots and ROC curves of the expression of two characteristic autoantibodies, MAP9 and IRF9, in patients with Hashimoto's thyroiditis (liver qi stagnation syndrome), patients with other thyroid diseases (excluding Hashimoto's thyroiditis), and healthy controls are shown below. Figure 7 .Depend on Figure 7 It can be seen that the expression level of related autoantibodies in the HT liver qi stagnation syndrome group was significantly higher than that in the healthy control group and other thyroid disease patient groups.

[0119] ROC curves of MAP9 and IRF9, two characteristic autoantibodies identified by ELISA, in patients with Hashimoto's thyroiditis exhibiting liver qi stagnation syndrome, are shown in the figure below for single antibody detection and combined detection of the two antibodies. Figure 8 It can be seen that the AUC value of the combined detection of the two related autoantibodies is the highest (0.941), indicating that the combined detection of the two related autoantibodies MAP9 and IRF9 has a higher degree of identification of syndrome characteristics.

[0120] 2.4.2 Characteristic autoantibodies associated with the Yin deficiency and fire excess syndrome in Hashimoto's thyroiditis

[0121] The levels of autoantibodies related to the yin deficiency and fire excess syndrome (hyperthyroidism) of Hashimoto's thyroiditis were detected by indirect ELISA in a group of serum samples (60 patients with Hashimoto's thyroiditis and yin deficiency and fire excess syndrome, 120 normal controls, and 60 patients with other thyroid diseases other than Hashimoto's thyroiditis). In the yin deficiency and fire excess syndrome group, as shown in Table 9, the antibody concentrations of PLEKHO2 and NPM3, two related autoantibodies, were significantly higher in the serum of patients with Hashimoto's thyroiditis than in the serum of normal controls, and the differences were statistically significant (P<0.05).

[0122] Table 9. Detection of autoantibodies related to two types of Hashimoto's thyroiditis with yin deficiency and fire excess syndrome.

[0123]

[0124] Scatter plots and ROC curves of the expression of two characteristic autoantibodies, PLEKHO2 and NPM3, in patients with Hashimoto's thyroiditis (Yin deficiency and fire excess syndrome), patients with other thyroid diseases (excluding Hashimoto's thyroiditis), and healthy controls are shown below. Figure 9 .Depend on Figure 9 It can be seen that the expression level of related autoantibodies in the HT Yin deficiency and fire excess syndrome group was significantly higher than that in the healthy control group and other thyroid disease patient groups.

[0125] ROC curves for single antibody detection and combined detection of the two characteristic related autoantibodies, PLEKHO2 and NPM3, obtained by ELISA validation in patients with Hashimoto's thyroiditis exhibiting Yin deficiency and excessive Yang syndrome are shown below. Figure 10 It can be seen that the AUC value of the combined detection of the two related autoantibodies is the highest (0.884), indicating that the combined detection of the two related autoantibodies PLEKHO2 and NPM3 has a higher degree of identification of syndrome characteristics.

[0126] 2.4.3 Characteristic autoantibodies related to spleen and kidney yang deficiency syndrome in Hashimoto's thyroiditis

[0127] The levels of autoantibodies related to Hashimoto's thyroiditis with spleen-kidney yang deficiency syndrome (hypothyroidism) in a group of serum samples (85 cases of Hashimoto's thyroiditis with spleen-kidney yang deficiency syndrome, 120 normal controls, and 60 cases of other thyroid diseases) were detected by indirect ELISA. In the spleen-kidney yang deficiency syndrome group, as shown in Table 10, based on AUC>0.5 and P<0.05, CTDP1 and BRMS1L autoantibodies were excluded, and their concentrations in the serum of Hashimoto's thyroiditis patients and normal controls showed no statistically significant difference. The antibody concentrations of the remaining five related autoantibodies—DR1, KDM1A, PCGF2, GPCPD1, and IGBP1—in the serum of Hashimoto's thyroiditis patients were significantly higher than those in the serum of normal controls, and the differences were statistically significant (P<0.05).

[0128] Table 10. Detection of seven types of autoantibodies related to spleen and kidney yang deficiency syndrome in Hashimoto's thyroiditis.

[0129]

[0130] Scatter plots and ROC curves of the expression of five characteristic autoantibodies—DR1, KDM1A, PCGF2, GPCPD1, and IGBP1—in patients with Hashimoto's thyroiditis due to spleen and kidney yang deficiency, patients with other thyroid diseases besides Hashimoto's thyroiditis, and healthy controls are shown below. Figure 11 .Depend on Figure 11It can be seen that the expression level of related autoantibodies in the HT spleen and kidney yang deficiency syndrome group was significantly higher than that in the healthy control group and other thyroid disease patient groups.

[0131] The ROC curves of five characteristic autoantibodies (DR1, KDM1A, PCGF2, GPCPD1, and IGBP1) identified by ELISA in patients with Hashimoto's thyroiditis due to spleen and kidney yang deficiency are shown in the figure below for single antibody detection and combined detection of the five antibodies. Figure 12 It can be seen that the AUC value of the combined detection of the five related autoantibodies is the highest (0.852), indicating that the combined detection of the five related autoantibodies DR1, KDM1A, PCGF2, GPCPD1, and IGBP1 has a higher degree of identification of syndrome characteristics.

Claims

1. The application of a biomarker in the preparation of reagents or kits for detecting yin deficiency and fire excess syndrome in Hashimoto's thyroiditis, characterized in that, The biomarkers are two autoantibodies, PLEKHO2 and NPM3.

2. The application according to claim 1, characterized in that, The kit includes recombinant human proteins PLEKHO2 and NPM3, and is used to detect two autoantibodies, PLEKHO2 and NPM3, in serum.

3. The application according to claim 2, characterized in that, The kit also includes human IgG standards, horseradish peroxidase-labeled goat anti-human IgG, and reagents commonly used in ELISA technology.

Images