WO2018149186A1 - 一种acpa阴性的ra诊断标志物及其应用 - Google Patents

一种acpa阴性的ra诊断标志物及其应用 Download PDF

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WO2018149186A1
WO2018149186A1 PCT/CN2017/111044 CN2017111044W WO2018149186A1 WO 2018149186 A1 WO2018149186 A1 WO 2018149186A1 CN 2017111044 W CN2017111044 W CN 2017111044W WO 2018149186 A1 WO2018149186 A1 WO 2018149186A1
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antibody
dohh
protein
negative
serum
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French (fr)
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张烜
莫文秀
李永哲
胡朝军
刘国振
武丽君
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中国医学科学院北京协和医院
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Priority to US16/485,102 priority Critical patent/US20190369123A1/en
Publication of WO2018149186A1 publication Critical patent/WO2018149186A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/564—Immunoassay; Biospecific binding assay; Materials therefor for pre-existing immune complex or autoimmune disease, i.e. systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, rheumatoid factors or complement components C1-C9
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/573—Immunoassay; Biospecific binding assay; Materials therefor for enzymes or isoenzymes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/902—Oxidoreductases (1.)
    • G01N2333/90241—Oxidoreductases (1.) acting on single donors with incorporation of molecular oxygen, i.e. oxygenases (1.13)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/10—Musculoskeletal or connective tissue disorders
    • G01N2800/101—Diffuse connective tissue disease, e.g. Sjögren, Wegener's granulomatosis
    • G01N2800/102—Arthritis; Rheumatoid arthritis, i.e. inflammation of peripheral joints

Definitions

  • the invention belongs to the field of biological detection, and particularly relates to an ACPA-negative RA diagnostic marker and an application thereof.
  • Rheumatoid arthritis is a chronic autoimmune disease, mainly caused by inflammation of the local synovial membrane of multiple small joints, which causes local bone destruction in the joint. The disease. In developing countries, rheumatoid arthritis affects nearly 0.5% to 1% of the population. Overall, the incidence of RA in women is higher than in men, and the incidence in the elderly is higher than in young people. The clinical manifestations of rheumatoid arthritis vary widely and can be self-limiting diseases with mild symptoms, or rapid progression of inflammation with joint damage and severe physical disability. Due to differences in disease performance, classification criteria were developed as the basis for disease definition, selection of standardized clinical trials, and comparison of multicenter studies.
  • Autoantibodies have been found in the serum of RA patients for more than 70 years.
  • the rheumatoid factor targets the Fc fragment of human IgG and is the first group of autoantibodies found, including various subtypes such as IgG and IgM.
  • RF is not a specific antibody against RA, and there are also RF in other autoimmune diseases and elderly people. More importantly, RF can also be detected in up to 15% of healthy people.
  • APF anti-peripheral factor antibody
  • AKA anti-keratin antibody
  • citrullinated protein is produced by the PAD enzyme, which is the amino acid lysine which is converted to high citrulline by a chemical reaction.
  • Citrulline and high citrulline are chemically similar but located at different sites of the protein because arginine and lysine are at different sites.
  • high citrulline is only one more formyl.
  • RA is a chronic autoimmune disease
  • the detection of autoantibody markers plays an important role in the diagnosis of diseases.
  • RA is characterized by a large difference in clinical manifestations, which may be mild self-limiting disease, or may be rapid progressive inflammation, joint destruction, and severe functional disability. Differences in the clinical features of RA lead to dramatic differences in patient response.
  • we have no way of predicting the efficacy of a particular treatment for a particular patient because we lack high-performance biomarkers that group RA patients.
  • the discovery of the serum marker ACPA has far-reaching effects, as this is the first time that serum markers can be used. Different disease characteristics of RA are grouped.
  • the present invention provides an ACPA-negative RA diagnostic marker and its use.
  • the diagnostic markers of ACPA-negative RA provided by the present invention are DOHH (Deoxyhypusine dioxygenase), PAGE5 (P antigen family member 5, P antigen family protein 5), DUSP11 (Dual specificity protein phosphatase 11, double Specific protein phosphatase 11) and PTX3 (Pentaxin-related protein, Pentaxin-related protein 3).
  • the invention also provides the use of DOHH, PAGE5, DUSP11, PTX3 or fragments thereof for the preparation of a reagent for the diagnosis of ACPA-negative RA.
  • diagnosis comprises: determining the level of antibody reactive to DOHH, PAGE5, DUSP11, PTX3 or a fragment thereof in a biological sample obtained from a patient exhibiting ACPA-negative RA; optionally,
  • the level of antibody in the biological sample is compared to control data, wherein a detectable increase in antibodies reactive with DOHH, PAGE5, DUSP11 or PTX3 in the sample relative to the control data indicates the likelihood of suffering from ACPA-negative RA Sex.
  • the biological sample is a serum sample.
  • the antibody levels of DOHH, PAGE5, DUSP11 or PTX3 are measured by the following steps, including:
  • DOHH, PAGE5, DUSP11, PTX3 or fragments thereof are deposited or immobilized on a solid support.
  • the support is in the form of a latex bead, a perforated plate or a strip of film.
  • the detection antibody is labeled by a label covalently linked to an enzyme, a fluorescent compound or a metal, or a label having a chemiluminescent compound.
  • the invention also provides an apparatus for identifying the presence or level of an antibody reactive to DOHH, PAGE5, DUSP11, PTX3, or a fragment thereof, or a combination thereof, in a sample from a patient, comprising:
  • DOHH DOHH, PAGE5, DUSP11, PTX3 or a fragment thereof, or a combination thereof;
  • At least one solid support wherein the DOHH, PAGE5, DUSP11, PTX3, or fragments thereof, or a combination thereof, are deposited on the support.
  • the device of the present invention further comprising a detection antibody, wherein the detection antibody is specific to a sample of the patient, and is a reactive antibody to DOHH, PAGE5, DUSP11, PTX3 or a fragment thereof, or a combination thereof, and The detection antibody produces a detectable signal.
  • the sample of the patient is a serum sample.
  • the invention screens 35 antigens with specificity of 90% and sensitivity of more than 25% as the candidate ACPA-negative RA autoantigen by high-density protein chip hybridization with RA serum, and the seven proteins are candidate autoantigens for predicting disease activity.
  • the six proteins are candidate autoantigens that predict therapeutic effects (of which two protein candidate antigens are repeated in different sets of analyses).
  • a protein chip containing 46 candidate RA autoantigens was constructed.
  • one autoantigen can successfully distinguish between low- and moderate-activity and high activity of RA. It is: RRN3, AUC reaches 0.65, and two autoantigens can successfully distinguish ACPA-positive
  • the low-to-low activity and high activity of RA are: RRN3 and PLEKHG2, with AUC of 0.845 and 0.817, respectively.
  • the 6 autoantigens for predicting disease efficacy there is one autoantigen, ERH, which can successfully judge RA for drug treatment.
  • the effect of the predicted AUC is up to 0.733.
  • FIG. 1 Quality control of protein chips.
  • FIG. 2 GST detects the parallelism between all recombinant protein probes on the protein chip Relevance.
  • Figure 3 Local effect map of small sample serum and high density protein chip hybridization.
  • Figure 4 Distribution of signal intensity of Blank and EMPTY on a protein chip.
  • Figure 5 Signal distribution of PTX3 and the like in RA patients and healthy controls and disease control groups.
  • Figure 6 Signal intensity distribution of RRN3 in RA with different disease activity.
  • Figure 7 Signal intensity distribution of two antigens such as RRN3 in ACPA-positive RA with different disease activity
  • Figure 8 Signal intensity distribution and AUC curve of ERH in RA patients with different efficacy.
  • Serum samples serum sample collection and related clinical tests were performed by the Department of Rheumatology and Immunology, Peking Union Medical College Hospital.
  • osteoarthritis serum (osteoarthritis, OA), mean age ⁇ standard deviation: 67.2 ⁇ 16.6;
  • BD Behcet's disease
  • ANCA anti-neutrophil cytoplasmic antibodies
  • RA RA ⁇ RA ⁇ RA ⁇ RA ⁇ RA ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
  • RA sera were tested for serum corresponding antibodies, including ANA 3: ANA-IF (immunofluorescence), DNA-IF (immunofluorescence) and ds-DNA (ELISA), anti-CCP antibody, ACPA Detection (>25 IU/ml is defined as positive), RF detection, AKA and APF detection, MCV detection, GPI detection. All anti-CCP antibodies and/or anti-AKA, APF, and MCV-negative RA patients met the diagnostic criteria for B-ultrasound or nuclear magnetic resonance of RA synovitis. The trial was approved by the Ethics Committee of Peking Union Medical College Hospital.
  • the high-density protein chip and the S. cerevisiae expression recombinant plasmid containing the gene sequence of interest are provided by the laboratory of Professor Zhu Heng of Johns Hopkins University.
  • Each high-density protein chip contains 48 micro-matrices, each containing 992 probe points arranged in a 32*31 array with 2 parallel points for each protein probe on the chip.
  • the protein chip contains 21,827 non-redundant recombinant human proteins.
  • the recombinant protein is derived from the full-length open reading frame (ORF) of the corresponding gene expressed by the Saccharomyces cerevisiae host, and has a glutathione S-transferase (GST) tag at the N-terminus.
  • ORF full-length open reading frame
  • GST glutathione S-transferase
  • a total of 47,616 protein spots per high density protein chip (including positive and negative control points; each protein antigen has two parallel points). These include 21,827 non-redundant recombinant human proteins.
  • a total of 48 microarrays were formed on each chip, and each microarray was arranged in a 32*31 array. Since all recombinant protein probes carry a GST tag at the N-terminus, all anti-GST monoclonal antibodies are used to detect all probes on the chip, ensuring that most of the recombinant proteins on the chip for serum screening can be detected and There is a high parallel between the two parallel points of the same probe. As shown in Figure 1, the GST label positive point detected on the chip is red (white when the signal is saturated).
  • each protein chip There are 48 blocks on each protein chip, and all protein probes in each block are arranged in an array of 32*31, each consisting of two parallel points on the left and right.
  • Each chip contains 21,827 non-redundant group proteins and other control probes. All recombinant proteins carry a GST tag.
  • a and C respectively show the overall effect map of the detection results of the mouse anti-GST monoclonal antibody and the sample of the single block; B shows the signal-to-noise ratio distribution map of all the probe points on the chip.
  • SNR signal-to-noise ratio
  • RA serum and 60 control serum (10 Behcet's serum, 10 aortic sera, 10 SLE serum and 30 healthy human serum) were selected and hybridized with 120 chips to identify candidates by signal acquisition and data analysis.
  • RA self antigen The reaction of autoantibodies in serum with the corresponding autoantigen probes was detected using a PE-Cy5-labeled anti-human IgG antibody.
  • Figure 3 shows representative partial image results of high-density protein chips after reaction with serum, with different protein antigen probes in the box.
  • A, C, E, and G show a schematic diagram of the hybridization of four RA sera to the chip.
  • B, D, F, and H show schematic diagrams of hybridization of four control sera (including healthy controls and disease controls) to the chip.
  • I picture is treatment has Schematic diagram of the effective RA
  • J is a schematic diagram of the treatment of ineffective RA.
  • the two parallel point protein probes in the box of A and B are DOHH; the probes in the box of C and D are DUSP11; the probes in the box of E and F are PTX3; the probes in the box of G and H For PAGE5, the probe in the box of I and J is ERH.
  • RA serum, disease control (BD, SLE, TA) and healthy human serum recognized only a small percentage of the protein on the chip. Even though the normal control sera reacts with the chip, there are multiple detectable positive signals, indicating that autoantibodies also appear in healthy people, but these autoantibodies do not cause disease.
  • the fluorescence signal map of each chip is scanned, and the template and the chip file of the chip, that is, the gail file, are simultaneously dragged into the GenePix Pro 6.0 software for one-to-one correspondence.
  • the signal information of all the probes on each chip collected by GenePix Pro 6.0 software is then converted and imported into an Excel spreadsheet.
  • the foreground signal intensity (F635median) of each probe point is divided by its surrounding background signal strength (B635median) as the signal value of the point.
  • I ij F635median / B635median (I ij represents the signal value of the protein i point in block j).
  • the signal value of the protein antigen probe is closer to 1, indicating that the corresponding autoantibody in the serum is less detectable. The higher the signal value, the stronger the ability of the autoantibody to bind to the target protein antigen probe.
  • the chip data is processed by the in-chip normalization method to normalize the signal on each chip. That is, it is assumed that all target proteins in the chip are randomly placed on the substrate, and only a small part (less than 5%) of the target protein is detected as an autoantigen recognized by the corresponding target autoantibody in the serum, so the signal on the chip The distribution is random and consistent between different blocks.
  • This study sets the median value of all probe point signal values in each block to 1 to normalize the signal values of the probe points in different blocks on the chip.
  • the mean value I average of all point signal values on the entire chip, and the standard deviation SD of the signal values of all signal values less than 1, are calculated, and I average + 5SD is used as a cutoff value to determine whether the probe point on the chip is positive. Then, the information of each serum and each protein antigen probe immunoreactive positive was counted, and the candidate RA autoantigen was determined by a chi-square test (X2) or a Fisher exact test (Fisher exact test).
  • antigens with a specificity of 90% and a sensitivity of not less than 25% are used as candidate RA autoantigens; if screening for candidate markers for predicting disease activity and efficacy, After P ⁇ 0.05 after the square test or Fisher's exact test, the marker was included as a candidate marker.
  • the candidate target autoantigen of interest on the chip is determined by data analysis. Whether the protein probe on the chip is a RA-specific autoantigen, or whether it is a disease-associated or therapeutically relevant autoantigen, the X2 test or Fisher's exact test is used to determine that the protein is a target protein antigen for the ACPA-negative specific reaction in RA. In the present invention, 35 antigens with a specificity of 90% and a sensitivity of more than 25% are used as candidate ACPA-negative RA autoantigens, and 7 proteins are candidate autoantigens for predicting disease activity, and 6 proteins are candidates for predicting therapeutic effects. Antigens (wherein two protein candidate antigens were repeated in different sets of analyses), see Table 1 for details.
  • Table 1-2 Small sample serum and high-density protein chip hybridization screened to 7 candidate autoantigens for predicting disease activity
  • a total of 46 candidate RA autoantigens were screened by analyzing high-density protein chips and small sample serum hybridization results. To verify the specificity and sensitivity of these autoantigens, the present invention produced RA probe antigen chip with low probe density.
  • Table 2 shows the microarray layout of each probe on the RA self antigenic protein chip. The probes on the chip included 46 candidate RA autoantigens screened by the large chip and 5 control IGHG1 probes.
  • All 51 probes on the RA self-antigen protein chip have duplicate double spots.
  • a total of 14 microarrays are ordered on each substrate. Each microarray is isolated by a fence before the hybridization reaction between the serum and the chip, so that each microarray forms a separate space, so each chip can be simultaneously detected.
  • Large sample serum mixed with RA autoantigen chip includes 290 RA serum and 237 control serum (9 OA serum, 38 SLE serum, 39 AS serum, 18 BD serum, 10 ANCA serum, 21 SS serum) And 102 healthy human serum).
  • the information of the probe points in the hybridization result of the RA self-antigen protein chip was collected by Genepix Pro 6.0 software, and the foreground value of each probe point was removed from the background value, which is the signal intensity of the probe point on the chip.
  • the average of the two parallel dot hybridization signals for each probe is the signal value at which the probe hybridizes to the serum and is used for further analysis.
  • Negative control protein wells were included on the prepared protein chip containing 46 RA autoantigens, including 6 blanks (blank control) and 3 EMPTY (negative controls), and the average signal intensity value of the negative control porin was used to perform the protein chip. Quality assessment. As shown in Figure 4, the negative control protein signal intensity values on each block of each chip are separately raised. Take a frequency distribution map of the signal strength value. It can be observed that the signal intensity of Blank and EMPTY is basically around 1 , indicating that the foreground value of this point is almost the same as the background value, indicating that the signal intensity values extracted by these chips are reliable and reasonable.
  • the data of ACPA-negative RA patients and healthy controls and disease control were tested by chi-square test or Fisher's exact test.
  • Each diagnostic marker protein can obtain T score, p value and other parameters.
  • 1000 different samples are selected.
  • the cutoff value can be calculated according to each cutoff value.
  • the ROC curve is plotted with the 1000 points (1-specificity, sensitivity), and the cutoff corresponding to the point where the sum of sensitivity and specificity is the highest is calculated. The value is the optimal cutoff.
  • Table 3 and Figure 5 In the results of hybridization with large sample serum, the sensitivity of the four protein antigens to the ACPA-negative RA serum was greater than 25%, and also different from healthy controls and disease controls.
  • T-test was performed on the data of two groups of RA patients with low-activity and high activity.
  • Each protein associated with predicting disease activity can obtain T score, p value and other parameters.
  • 1000 different ones are selected. Cutoff value, according to each The cutoff value can be used to calculate the sensitivity and specificity.
  • the ROC curve is plotted with the 1000 points (1-specificity, sensitivity), and the AUC is calculated, and the cutoff value corresponding to the point where the sum of sensitivity and specificity is the highest is the optimal cutoff.
  • Table 4 and Figure 6 shows the signal distribution of protein markers in patients with moderate to low activity and highly active patients. It can be observed that patients with high activity have higher expression of autoantigen than patients with moderate to low activity.
  • Table 5 cutoff values and corresponding AUC of two antigens such as RRN3 in ACPA-positive RA with different disease activity
  • T-test was performed on the data of two groups of RA patients who were effective and ineffective. Each protein associated with predicting the efficacy of the disease was given T score, p value and other parameters. Secondly, for each protein, 1000 different cutoff values were selected. Sensitivity and specificity can be calculated according to each cutoff value. The ROC curve is drawn with the 1000 points (1-specificity, sensitivity), and the AUC is calculated, and the point at which the sum of sensitivity and specificity is the highest is calculated. The corresponding cutoff value is the optimal cutoff. The results are shown in Table 6 and Figure 8. When the ERH takes the corresponding optimal cutoff value of 1.201, the corresponding AUC is the largest, which is 0.733. Figure 8 shows the signal distribution of the protein in patients with effective treatment and ineffective treatment. It can be observed that the expression of this autoantigen is significantly higher in patients with therapeutic efficacy than in patients with ineffective treatment.

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Abstract

一种脱氧辅蛋白双加氧酶DOHH或其片段在制备用于诊断抗瓜氨酸多肽抗体阴性的类风湿性关节炎疾病的试剂中的用途。通过高密度蛋白质芯片与RA血清杂交筛选到35种蛋白质为候选ACPA阴性RA自身抗原，鉴定到DOHH,DUSP11,PTX3,PAGE5四种蛋白质抗原在RA的ACPA阴性的血清中具有较高的灵敏度和特异度，其中DOHH可作为ACPA阴性的RA诊断标志物。

Description

一种ACPA阴性的RA诊断标志物及其应用 技术领域
本发明属于生物检测领域，具体涉及一种ACPA阴性的RA诊断标志物及其应用。
背景技术
类风湿性关节炎(Rheumatoid arthritis,RA)是一种慢性的自身免疫性疾病，主要是以引起全身多处小关节局部滑膜的炎症反应，进而造成关节局部的骨质破坏为一组临床表现的疾病。在发展中国家，类风湿性关节炎影响了近0.5％-1％左右的人群。总体来说，RA在女性中的发病率要高于男性，在老年人中的发病率要高于年轻人。类风湿性关节炎的临床表现差异很大，可以是轻微症状的自限性疾病，也可以是快速进展的炎症伴关节破坏及严重身体残疾。由于疾病表现的差异性，人们制定了分类标准作为疾病定义、标准化临床试验入选人群选择以及多中心研究比较的基础。因此1987诞生了由年美国风湿病学会(ACR)制定的RA的分类标准。但在实际应用过程中，由于该分类标准对于关节炎的定义要求严格，致使它对于RA诊断的敏感性不高，有相当一部分早期的RA并没有从中被识别出来。但同时，尤为重要的是新发RA病例有可能从早期的有效干预中获益，以避免进展为慢性、侵蚀状态RA，进一步导致残疾，影响长期生活质量，增加疾病死亡率。因此，对于类风湿性关节炎来说，早期的诊断和治疗是阻止不可逆性关节破坏的关键。于是2009年，ACR/EULAR标准更新，该标准诊断RA的敏感性较高，能够在疾病早期诊断RA并进行治疗，缺点是特异性较差。
在2009年最新的RA的分类标准中，血清中ACPA(抗瓜氨酸多肽抗体)的阳性被纳入诊断标准。这一抗瓜氨酸化的蛋白抗体被认 为是类风湿性关节炎的血清特异性的标志物，它的出现也增加了我们对RA发病机制的理解。但是直至现在，RA的确切病因仍然未知，环境因素和遗传因素被公认为是引起RA临床症状的“扳机”。同时，临床上也存在着一类ACPA抗体阴性的RA患者，并且随着研究者对疾病的认识不断加深，他们逐渐认识到ACPA抗体阳性的RA患者和ACPA抗体阴性的RA患者存在一定的临床异质性。但是由于目前临床上对于ACPA抗体阴性的RA患者缺乏特异性的生物标志物，这使得这类患者的诊断难度大大增加。
RA患者的血清中发现自身抗体已经超过70年。类风湿因子以人类IgG的Fc片段为靶点，是发现的第一组自身抗体，包括IgG和IgM等各类亚型。遗憾的是RF并不是针对RA的特异性抗体，在其他自身免疫病和高龄人群中也会有RF的出现。更重要的是，在高达15％的健康人群中也可以检测到RF。随着研究的深入，1964年和1979年，人们分别发现了RA患者中存在的其它抗体，即抗核周因子抗体(APF)和抗角蛋白抗体(AKA)。尽管这两种抗体对诊断RA的特异性很高，但这些自身抗体很难测到。因此，在日常实践中，RF仍被辅助用于RA的诊断。为此，RF阳性被纳入1987年ACR的RA标准。直到1995年人们才发现抗核周蛋白和抗角蛋白抗体是相同的自身抗体，它们共同的靶点都来自精氨酸残基的去亚胺化所形成的的瓜氨酸残基。于是，2002年通过研发瓜氨酸化肽(CCP)-2技术出现了首个测定ACPA的商业化测试，使ACPA能够作为RA常规检查的生物标志物。对这个独特的自身抗体系统进行深入研究，加深了人们对RA的理解和分类，因此，RF和ACPA都成为ACR/EULAR2010分类标准的一部分。
最近，有文献报道了RA患者血清识别氨基甲酰化蛋白(抗CarP)的新的自身抗体亚型。这个抗体系统独立于ACPA，因为RA患者的血清抗体能够区分瓜氨酸化和氨基甲酰化抗原。相应的，有 相当部分ACPA阴性患者存在抗CarP抗体。在过去的数年，由于RA特异性针对瓜氨酸化蛋白和氨基甲酰化蛋白自身抗体的发现，人们对RA的发病及病因学有了深入的认识。氨基甲酰化和瓜氨酸化都是翻译后修饰，分别引起蛋白氨基甲酰化和瓜氨酸化，使带正电荷的氨基酸为中性氨基酸所替代。瓜氨酸化蛋白由PAD酶产生，而氨基甲酰化蛋白是氨基酸赖氨酸通过化学反应转化为高瓜氨酸。瓜氨酸和高瓜氨酸在化学结构上相似，但位于蛋白质的不同位点，因为精氨酸和赖氨酸在不同的部位。而且，高瓜氨酸只是多了一个甲酰基。尽管一些抗体对两种结构都有反应性，但有些抗CarP抗体对瓜氨酸不产生反应，同样，一些抗瓜氨酸抗体也对CarP不产生反应。
2009年Auger等人用含有8268个蛋白的芯片筛选ACPA阴性的RA病人，成功筛选出PAD4和BRAF两个候选标志物。以涉及蛋白质瓜氨酸化的酶为靶点的抗PAD抗体受到很多关注，因为人们发现这些抗体不只是与靶点结合，还对PAD有激活作用。它能够通过降低该瓜氨酸化酶钙的需要量从而增加PAD4的催化能力。Charpin C等人通过研究发现：RA病人体内存在着抗BRAF催化结构域的抗体，主要集中在416位到766位的氨基酸序列上，而且这一抗体存在于30％的ACPA阴性的RA患者中。同时，抗BRAF抗体阳性的RA病人中有高达33％的病人是ACPA阴性的。4％的AS患者和6％的健康对照中也存在该自身抗体。
如前所述，RA是一种慢性的自身免疫性疾病，自身抗体标记物的检测在疾病的诊断中具有重要作用。RA以临床表现差异大为特征，可以是轻度自限性疾病，也可能是快速进展性炎症、关节破坏及严重的功能残疾。RA临床特征的差异导致患者治疗反应的极大差别。目前，我们没办法预测某项特定治疗对特定患者的疗效，因为我们缺乏对RA患者进行分组的高效能生物标志物。血清标志物ACPA的发现有着深远的影响，因为这是首次可以根据血清标志物 对RA不同的疾病特征进行分组。但ACPA阳性和ACPA阴性的RA患者在疾病的遗传学和环境决定因素、受累关节的分子特征、缓解率以及最重要的对治疗的应答率方面都存在着显著差异。对于很多ACPA阴性的RA患者，能够进行亚组区分的靶点有限，主要示由于缺乏强有力的生物标志物对RA的临床表现进行区分。因此鉴定更多的尤其是ACPA阴性的自身抗体可能有助于揭示RA的发病机制，特别是自身抗体在其中的作用。
由于在发展中国家RA的发病率一直居高不下，中国也是罹患RA一大国家，加之RA的致残率，因此早期正确的识别RA非常重要。鉴定更多ACPA阴性的RA相关的自身抗体标记物和一些能够预测病情活动度和疗效的自身抗原，对于降低RA的致残率十分重要。
发明内容
为了解决上述问题，本发明提供ACPA阴性的RA诊断标志物及其应用。
本发明提供的ACPA阴性的RA诊断标志物为DOHH(Deoxyhypusine dioxygenase，脱氧辅蛋白双加氧酶)，PAGE5(P antigen family member 5，P抗原家族蛋白5)，DUSP11(Dual specificity protein phosphatase 11，双特异性蛋白磷酸酶11)和PTX3(Pentaxin-related protein，Pentaxin相关蛋白3)。
本发明还提供DOHH、PAGE5、DUSP11、PTX3或它们的片段在制备用于诊断ACPA阴性的RA的试剂中的用途。
其中，所述诊断包括：测定获自呈现ACPA阴性的RA的患者的生物样品中对DOHH、PAGE5、DUSP11、PTX3或它们的片段的反应性的抗体的水平；任选地，
与对照数据比较所述生物样品中抗体的水平，其中相对于所述对照数据所述样品中对DOHH、PAGE5、DUSP11或PTX3为反应性的抗体的可检测的提高表明患ACPA阴性的RA的可能性。
其中，所述生物样品为血清样品。
其中，DOHH、PAGE5、DUSP11或PTX3的抗体水平通过以下步骤来测量，包括：
a.使来自患者的生物样品与DOHH、PAGE5、DUSP11、PTX3或它们的片段接触；
b.在生物样品中存在的抗体与DOHH、PAGE5、DUSP11、PTX3或它们的片段之间形成抗体-蛋白质复合物；
c.洗涤来除去任何未结合的抗体；
d.添加被标记的并且对来自生物样品的抗体为反应性的检测抗体；
e.洗涤来除去任何未结合的被标记的所述检测抗体；和
f.将所述检测抗体的标记物转化为可检测信号；其中可检测信号的存在表明所述患者中存在抗DOHH、PAGE5、DUSP11或PTX3的抗体。
其中，所述的DOHH、PAGE5、DUSP11、PTX3或它们的片段沉积或固定在固相支持物上。
其中，所述支持物是乳胶珠子、多孔平板或膜条的形式。
其中，所述检测抗体通过共价连接到酶、具有荧光化合物或金属的标记物、或具有化学发光化合物的标记物来标记。
本发明还提供一种用于鉴定来自患者的样品中对DOHH、PAGE5、DUSP11、PTX3或它们的片段，或它们的组合为反应性的抗体的存在或水平的设备，包括：
a.至少一种DOHH、PAGE5、DUSP11、PTX3或它们的片段，或它们的组合；和
b.至少一种固相支持物，其中所述DOHH、PAGE5、DUSP11、PTX3或它们的片段，或它们的组合沉积在所述支持物上。
本发明所述的设备，进一步包括检测抗体，其中所述检测抗体特异于所述患者的样品中对DOHH、PAGE5、DUSP11、PTX3或它们的片段，或它们的组合为反应性的抗体，并且所述检测抗体产生可检测信号。
其中，所述患者的样品为血清样品。
本发明通过高密度蛋白质芯片与RA血清杂交筛选到特异性达到90％，敏感度大于25％的35个抗原作为候选ACPA阴性的RA自身抗原，7种蛋白质为候选预测疾病活动度的自身抗原，6种蛋白质为候选预测治疗效果的自身抗原(其中有在2种蛋白质候选抗原在不同组的分析中重复出现)。为了验证这些自身抗原的敏感度和特异性，构建了包含46个候选RA自身抗原的蛋白质芯片。然后通过大样本血清(9份OA血清、38份SLE血清、39份AS血清、18份BD血清、10份ANCA血清、21份SS血清及102份健康人，290份RA血清)与自身抗原芯片杂交。通过数据分析，共鉴定到4种蛋白质抗原在RA的ACPA阴性的血清中具有较高的灵敏度和特异度，均为新发现的自身抗原，它们是DOHH,DUSP11,PTX3,PAGE5，其中DOHH作为诊断标志物的敏感性达49.66％，PTX3作为诊断标志物的敏感性达43.54％。7种候选预测疾病活动度的自身抗原中，有1种自身抗原能够成功区分RA的中低度活动与高度活动，它是：RRN3，AUC达0.65，有2种自身抗原能够成功区分ACPA阳性的RA的中低度活动与高度活动，它们是：RRN3和PLEKHG2，AUC分别达0.845和0.817。6种候选预测疾病疗效的自身抗原中，有1种自身抗原，ERH，能够成功判断RA对药物治疗的效果，其预测疗效的AUC可达0.733。
附图说明
图1：蛋白质芯片的质控检测。
图2：GST检测蛋白质芯片上所有重组蛋白质探针平行点之间 的相关性。
图3：小样本血清与高密度蛋白质芯片杂交局部效果图。
图4：Blank和EMPTY的信号强度在蛋白质芯片上的分布。
图5：PTX3等在RA病人和健康对照及疾病对照组中的信号分布。
图6：RRN3在不同病情活动度的RA中信号强度分布。
图7：RRN3等2种抗原在不同病情活动度的ACPA阳性的RA中信号强度分布
图8：ERH在不同疗效的RA病人中信号强度分布图及AUC曲线。
具体实施方式
以下实施例用于说明本发明，但不用来限制本发明的范围。
血清样本(血清样本收集及相关临床检测均由北京协和医院风湿免疫科完成)
本课题研究共使用647份血清，包括：
350份来自RA确诊患者的血清，平均年龄±标准差：45.2±12.5；
9份骨关节炎血清(osteoarthritis,OA)，平均年龄±标准差：67.2±16.6；
48份系统性红斑狼疮血清(systemic lupus erythematosus，SLE)，平均年龄±标准差：36.8±12.4；
28份白塞氏病血清(Behcet’s disease，BD)，平均年龄±标准差：54.2±20.7；
10份ANCA相关性血管炎血清(anti-neutrophil cytoplasmic antibodies,ANCA)，平均年龄±标准差：46.9±16.3；
39份强直性脊柱炎血清(ankylosing-spondylitis,AS)，平均年龄±标准差：38.2±15.1；
21份干燥综合症血清(Sjogren Syndrome,SS)，平均年龄±标准差：52.7±13.2；
10份大动脉炎血清(Takayasu arteritis，TA)，平均年龄±标准差：38.4±13.5；
132份健康人血清，平均年龄±标准差：37.5±12.1；
RA的诊断按照2010年ACR/EULAR确立的标准，OA,SLE,BD,ANCA,AS,SS和TA亦分别满足各自的诊断和/或分类标准。
所有血清由北京协和医院风湿免疫科自2006年至2014年收集，所有疾病血清均来自临床确诊患者，诊断有争议的患者至少通过了临床三名主任医师的会诊后确定诊断。
所有RA血清均进行了血清相应抗体的检测，包括ANA 3项：ANA-IF(免疫荧光法)，DNA-IF(免疫荧光法)和ds-DNA(ELISA法)的检测，抗CCP抗体即ACPA检测(>25IU/ml定义为阳性)，RF检测，AKA和APF检测，MCV检测，GPI检测。所有抗CCP抗体和/或抗AKA,APF,MCV抗体阴性的RA患者均满足RA滑膜炎的B超或核磁的诊断标准。本试验得到了北京协和医院伦理委员会的审核批准。
实施例1高密度蛋白质芯片筛选候选RA自身抗原
高密度蛋白质芯片以及含目的基因序列的酿酒酵母表达重组质粒由美国约翰·霍普金斯大学朱衡教授实验室提供。每张高密度蛋白质芯片上含有48个微矩阵(block)，每个微矩阵包含992个探针点，呈32*31的阵列排列，芯片上每种蛋白质探针有2个平行点。该蛋白质芯片包含21827种非冗余重组人源蛋白质。重组蛋白质来自酿酒酵母宿主表达的相应基因全长开放阅读框(open reading frame，ORF)，其N端具有谷胱甘肽S转移酶(glutathione S-transferase，GST)标签。
使用小鼠抗GST单克隆抗体与芯片杂交验证芯片质量，当芯片 上每两个重复的蛋白点的荧光信号值的相关性达到97％以上时，认为芯片上每两点的可重复性好。
每张高密度蛋白质芯片上共计47616个蛋白点(包括阳性对照点和阴性对照点；每种蛋白抗原具有两个平行点)。其中包括21827种非冗余重组人源蛋白质。每张芯片上所有蛋白共组成48个微阵列(block)，每个微阵列呈32*31的阵列排列。由于所有重组蛋白质探针的N端均带有GST标签，因此采用小鼠抗GST单克隆抗体进行检测芯片上所有探针，确保用于血清筛选的芯片上绝大多数重组蛋白质能被检测到且同一探针的两平行点之间具有较高的平行性。如图1所示，芯片上检测到的GST标签阳性点为红色(信号饱和时显示为白色)。每张蛋白质芯片上有48个block，每个block中所有蛋白探针以32*31的阵列排列，每种探针由左右两个平行点组成。每张芯片上含有21827种非冗余组蛋白质及其他对照探针。所有重组蛋白均带有GST标签。A和C分别显示小鼠抗GST单克隆抗体检测结果的整体效果图和单个block的样图；B显示芯片上所有探针点的信噪比分布图。当两个平行探针点的信噪比(SNR)均大于3时即认为芯片上该探针点可被检测。根据这一标准，96.8％的蛋白质可被检测到(图2)。
实施例2高密度蛋白质芯片与RA及对照组血清杂交
选取60份RA血清及60份对照血清(10份白塞氏病血清，10份大动脉炎血清，10份SLE血清和30份健康人血清)与120张芯片杂交，通过信号采集及数据分析鉴定候选RA自身抗原。用PE-Cy5标记的抗人IgG抗体检测血清中自身抗体与相应自身抗原探针的反应。图3所示为高密度蛋白质芯片与血清反应后的代表性局部图像结果，方框内为差异的蛋白质抗原探针。A、C、E、G显示的是四份RA血清与芯片杂交的示意图。B、D、F、H显示的是四份对照组血清(包括健康对照和疾病对照)与芯片杂交的示意图。I图是治疗有 效的RA的示意图，J图是治疗无效的RA的示意图。A、B图方框中两个平行点蛋白质探针为DOHH；C、D图方框中探针为DUSP11；E、F图方框中探针为PTX3；G、H图方框内探针为PAGE5,I、J图方框内探针为ERH。总体来说，无论是RA血清，还是疾病对照组(BD,SLE,TA)和健康人血清，都只能识别芯片上很少比例的蛋白质。即使正常对照血清反应的芯片也出现了多个可探测到的阳性信号，说明健康人体内也会出现自身抗体，只是这些自身抗体不会引起疾病而已。
扫描得到每张芯片的荧光信号图，将该图与芯片的模板文件即gail文件同时拖到GenePix Pro 6.0软件中进行一一对应。然后将GenePix Pro 6.0软件采集到的每张芯片上所有探针的信号信息转化并导入Excel表格中。每个探针点的前景信号强度(F635median)分别除以其周围背景信号强度(B635median)作为该点的信号值。
即I ij＝F635median/B635median(I ij代表block j中的蛋白质i点的信号值)。蛋白质抗原探针的信号值越趋近于1，说明血清中相应的自身抗体越无法检测到。信号值越高说明自身抗体的结合靶标蛋白质抗原探针的能力越强。
为了消除不同芯片及同一芯片上不同空间对杂交造成的差异，芯片数据的处理采用芯片内归一化(within-chip normalization)的方法对每张芯片上的信号进行归一化。即假设芯片内所有靶标蛋白是随机点制到基片上的，且只有很少部分(小于5％)的靶标蛋白质作为自身抗原被血清中相应的靶标自身抗体识别而被检测到，因此芯片上信号的分布是随机的，不同block之间是一致的。本研究设定每个block中的所有探针点信号值的中位值为1，以此来归一化芯片上不同block内探针点的信号值。
即
Figure PCTCN2017111044-appb-000001
(median(I j)代表block j中所有点信号值的中位值，
Figure PCTCN2017111044-appb-000002
代表归一化后的block j中的蛋白质i 点的信号值)。
在此基础上，根据Hu S,Xie Z,Onishi A,Yu X,Jiang L,Lin J,Rho HS,Woodard C,Wang H,Jeong JS,Long S,He X,Wade H,Blackshaw S,Qian J,Zhu H.Profiling the human protein-DNA interactome reveals ERK2as a transcriptional repressor of interferon signaling.Cell 2009；139:610-622中记载的方法设定cutoff值判断芯片上所有探针点是否为阳性。即计算整张芯片上所有点信号值的均值I average，以及所有信号值小于1的信号值的标准差SD，以I average+5SD为cutoff值，来判断芯片上的探针点是否为阳性。然后统计每份血清与芯片上各蛋白质抗原探针免疫反应阳性的信息，利用非参数检验卡方检验(chi-square test，X2)或Fisher精确检验(Fisher exact test)确定候选RA自身抗原。
在筛选ACPA阴性的RA候选标志物时，将特异性达到90％，敏感度不小于25％的抗原作为候选的RA自身抗原；在筛选预测疾病活动度和疗效的候选标志物时，若经卡方检验或Fisher精确检验后的P<0.05，则该标志物被纳入为候选标志物。
通过数据分析确定芯片上的候选的感兴趣的目标自身抗原。对于芯片上的蛋白质探针是否为RA特异性自身抗原，或是否为病情相关或疗效相关的自身抗原，利用X2检验或Fisher精确检验确定蛋白质为RA中ACPA阴性的特异反应的靶标蛋白质抗原。本发明将特异性达到90％，敏感度大于25％的35个抗原作为候选ACPA阴性的RA自身抗原，7种蛋白质为候选预测疾病活动度的自身抗原，6种蛋白质为候选预测治疗效果的自身抗原(其中有在2种蛋白质候选抗原在不同组的分析中重复出现)，详细信息见表1。
Figure PCTCN2017111044-appb-000003
Figure PCTCN2017111044-appb-000004
表1-2小样本血清与高密度蛋白质芯片杂交筛选到7个候选预测疾病活动度的自身抗原
Figure PCTCN2017111044-appb-000005
表1-3小样本血清与高密度蛋白质芯片杂交筛选到6个候选预测疾病疗效的自身抗原
Figure PCTCN2017111044-appb-000006
实施例3 RA自身抗原蛋白质芯片的构建与血清筛选验证
通过分析高密度蛋白质芯片与小样本血清杂交结果共筛选到46个候选RA自身抗原。为了验证这些自身抗原的特异性和敏感度，本发明制备了低探针密度的RA自身抗原蛋白质芯片。表2为RA自身抗原蛋白质芯片上的各探针的微阵列布局。芯片上的探针包括大芯片筛选到的46个候选RA自身抗原以及5个对照IGHG1探针。
表2 RA自身抗原蛋白质芯片上的各探针的微阵列布局
AK2 IGHG1 ND ATP13A5 ND TBC1D19
RAB35 UBXN10 RAB3D APH1A TNFAIP1 HDAC4
ARL2BP RAI14 RRN3 POLR3B ERH NDRG1
BLANK BLANK BLANK BLANK BLANK GARS
SUGT1 IGHG1 NOL3 ZSCAN20 LSP1 RGCC
EMPTY PAGE5 FGF12 FAM84A DOHH NECAB1
NDEL1 DUSP11 PDCD2 MYLK STK24 METTL21C
IGHG1 STK3 BABAM1 DGKK PTX3 PPFIA4
EMPTY SPANXN2 IGHG1 CHAC2 RNF183 ATXN10
IGHG1 EMPTY CHST11 PLEKHG2 SNX33 BLANK
RA自身抗原蛋白质芯片上所有51个探针都具有重复的双点。每张基片上共点制14个微阵列，在血清与芯片的杂交反应前，用围栏将每一个微阵列隔离开，这样每一个微阵列都形成一个独立的空间，因此每张芯片可同时检测14份血清。与RA自身抗原芯片杂交的大样本血清包括290份RA血清及237份对照血清(9份OA血清、38份SLE血清、39份AS血清、18份BD血清、10份ANCA血清、21份SS血清及102份健康人血清)。利用Genepix Pro 6.0软件采集RA自身抗原蛋白质芯片杂交结果中探针点的信息，每个探针点的前景值除去背景值即为芯片上该探针点的信号强度。取每个探针的两个平行点杂交信号的平均值为该探针与血清杂交的信号值并用于进一步分析。
采用阴性对照孔蛋白信号对此次实验做一个评估检测。在制备的含46个RA自身抗原的蛋白质芯片上含有阴性对照蛋白孔，包括6个人Blank(空白对照)和3个EMPTY(阴性对照)，利用阴性对照孔蛋白的平均信号强度值来进行蛋白质芯片的质量评估。如图4所示，将每张芯片的每个block上的阴性对照蛋白信号强度值分别提 取，做一信号强度值的频率分布图。可以观察到，Blank和EMPTY的信号强度基本都围绕在1左右，表明该点的前景值与背景值几乎相同，说明这些由这些芯片提取的蛋白信号强度值都是可靠合理的。
首先对ACPA阴性的RA病人和健康对照及疾病对照的数据进行卡方检验或Fisher精确检验，每个诊断标志物蛋白可以得到T score，p value等参数；其次对于每个蛋白，选取1000个不同的cutoff值，根据每个cutoff值可以计算灵敏度，特异性，用这1000个点(1-specificity，sensitivity)绘制ROC曲线，并计算AUC，灵敏度和特异性之和最高的那个点所对应的cutoff值为最优的cutoff。结果如表3和图5所示，在与大样本血清杂交反应的结果中，4种蛋白质抗原与ACPA阴性的RA血清免疫反应的敏感性大于25％，同时也具有区别于健康对照和疾病对照的特异性，它们分别是DOHH(Deoxyhypusine dioxygenase，敏感性为49.66％)，PAGE5(P antigen family member 5，敏感性为72.79％)，DUSP11(Dual specificity protein phosphatase 11，敏感性为53.06％)和PTX3(Pentaxin-related protein PTX3，敏感性为43.54％)。图5中所示为这两种蛋白标志物在RA病人和健康对照及疾病对照组中的信号分布图，可以观察到RA病人中这种自身抗体的表达高于对照组。
表3 DOHH等4种蛋白在RA中cutoff值及对应的AUC
Name T Score p value FDR(BH) Q Value Fold Change AUC cutoff Specificity Sensitivity
PAGE5 -3.49914 6.00E-04 0.005056 0.001438 1.11134349 0.627525 1.830343 0.487437 0.727891
PTX3 -3.37216 6.00E-04 0.005056 0.001438 1.13017941 0.594742 2.104885 0.743719 0.435374
DOHH -2.23377 0.020596 0.050632 0.01337 1.08083119 0.599221 2.02812 0.693467 0.496599
DUSP11 -1.79863 2.00E-04 0.002949 0.001038 1.24243701 0.612484 2.112003 0.668342 0.530612
实施例4新发现的预测疾病活动度的抗原在RA自身抗原蛋白芯片中的分析
首先对中低活动度和高度活动的两组RA病人的数据进行T检验，每个与预测疾病活动度相关的蛋白可以得到T score，p value等参数；其次对于每个蛋白，选取1000个不同的cutoff值，根据每个 cutoff值可以计算灵敏度，特异性，用这1000个点(1-specificity，sensitivity)绘制ROC曲线，并计算AUC，灵敏度和特异性之和最高的那个点所对应的cutoff值为最优的cutoff。结果如表4及图6所示，当RRN3这种蛋白取相对应的最佳cutoff值1.55时，其对应的AUC最大，为0.65。图6中所示为蛋白标志物在中低活动度病人和高度活动病人中的信号分布图，可以观察到高度活动的病人中这种自身抗原的表达都高于中低活动度的病人。
表4 RRN3在不同病情活动度的RA中cutoff值及对应的AUC
Figure PCTCN2017111044-appb-000007
对各临床亚组进行进一步的分析发现，在ACPA阳性的RA病人亚组中，除RRN3外，另一蛋白抗原PLEKHG2也能很好的区分不同病情活动度的病人，但这一预测价值仅限于在ACPA阳性的RA病人中(表5，图7)。对ACPA阴性的RA病人亚组分析，没有新的发现。当蛋白抗原RRN3和PLEKHG2的信号cutoff值分别取1.548和1.172时，其所对应的AUC分别为0.845和0.817，具有非常好的临床预测价值。
表5 RRN3等2种抗原在不同病情活动度的ACPA阳性的RA中cutoff值及对应的AUC
Figure PCTCN2017111044-appb-000008
实施例5新发现的预测疾病疗效的抗原在RA自身抗原蛋白芯片中的分析
对治疗有效和治疗无效的两组RA病人的数据进行T检验，每个与预测疾病疗效相关的蛋白可以得到T score，p value等参数；其次对于每个蛋白，选取1000个不同的cutoff值，根据每个cutoff值可以计算灵敏度，特异性，用这1000个点(1-specificity，sensitivity)绘制ROC曲线，并计算AUC，灵敏度和特异性之和最高的那个点所 对应的cutoff值为最优的cutoff。结果如表6及图8所示，当ERH取相对应的最佳cutoff值1.201时，其对应的AUC最大，为0.733。图8中所示为该蛋白在治疗有效病人和治疗无效的病人中的信号分布，可以观察到治疗有效的病人中这种自身抗原的表达明显高于治疗无效的病人。
表6 ERH在不同疗效的RA病人中cutoff值及对应的AUC
Figure PCTCN2017111044-appb-000009
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (7)

  1. 脱氧辅蛋白双加氧酶，即DOHH，或其片段在制备用于诊断抗瓜氨酸多肽抗体阴性的类风湿性关节炎疾病的试剂中的用途。
  2. 如权利要求1所述的用途，其特征在于，所述诊断包括：测定获自呈现抗瓜氨酸多肽抗体阴性的类风湿性关节炎的患者的生物样品中对DOHH或其片段的反应性的抗体的水平；任选地，
    与对照数据比较所述生物样品中抗体的水平，其中相对于所述对照数据所述样品中对DOHH为反应性的抗体的可检测的提高表明患抗瓜氨酸多肽抗体阴性的类风湿性关节炎的可能性。
  3. 如权利要求1或2所述的用途，其中，所述生物样品为血清样品。
  4. 如权利要求1或2所述的用途，其中，DOHH抗体的水平通过以下步骤来测量，包括：
    a.使来自患者的生物样品与DOHH或其片段接触；
    b.在生物样品中存在的抗体与DOHH或其片段之间形成抗体-蛋白质复合物；
    c.洗涤来除去任何未结合的抗体；
    d.添加被标记的并且对来自生物样品的抗体为反应性的检测抗体；
    e.洗涤来除去任何未结合的被标记的所述检测抗体；和
    f.将所述检测抗体的标记物转化为可检测信号；其中可检测信号的存在表明所述患者中存在抗DOHH抗体。
  5. 如权利要求4所述的用途，其中，所述的DOHH或其片段沉积或固定在固相支持物上。
  6. 如权利要求5的用途，其中，所述支持物是乳胶珠子、多孔平板或膜条的形式。
  7. 如权利要求4所述的用途，其中，所述检测抗体通过共价连 接到酶、具有荧光化合物或金属的标记物、或具有化学发光化合物的标记物来标记。
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