Composition for diagnosing parkinson's disease and method for diagnosing parkinson's disease by using same

Abstract

The present invention relates to a composition for diagnosing Parkinson's disease, comprising an agent for detecting a protein consisting of a combination of GLUT3 and any one or more selected from the group consisting of USP14, α-synuclein, and AIMP2, or a gene encoding the protein. The composition for diagnosing Parkinson's disease and a diagnostic kit comprising the composition, of the present invention, can be used to perform detection or diagnosis by distinguishing Parkinson's disease patient groups through a simple blood test. Furthermore, by primarily applying same to patients showing prodromal symptoms of Parkinson's disease, such as olfactory dysfunction, sleep disorders, and constipation, the composition and the diagnostic kit can be used as a preliminary test to determine whether to perform neurological examinations and brain imaging tests at large hospitals. Also, the composition and the diagnostic kit can be used as a means for quantifying the effect of treating Parkinson's disease.

Description

Composition for diagnosing Parkinson's disease and method for diagnosing Parkinson's disease using the same

[0001] The present invention was made under the support of the Ministry of Science and ICT of the Republic of Korea under the research project number 2021M3H4A4079521, and the research management specialized organization of the project is the National Research Foundation of Korea, the research project name is “Nanomaterial Technology Development”, the research project name is “Development of isothermal amplification electrochemical molecule and digital fluorescence imaging immunodiagnostic device”, the main organization is Sungkyunkwan University, and the research period is 2022.02.01-2023.01.31.

[0002] The present invention was made under the support of the Ministry of Health and Welfare of the Republic of Korea under the research number HU22C0143000022, the research management specialized institution of the said project is the Korea Health Industry Development Institute, the research project name is “Dementia Overcoming Research and Development Project (Ministry of Welfare)”, the research project name is “Identification of the cause / mechanism of Lewy body dementia and lesion propagation brain map of AIMP2 target based on alpha-synuclein aggregate formation / propagation promotion system”, the main institution is Sungkyunkwan University Industry-Academic Cooperation Foundation, and the research period is 2022.04.01-2022.12.28.

[0003] This patent application claims priority to Republic of Korea Patent Application No. 10-2023-0083059, filed with the Korean Intellectual Property Office on June 27, 2023, the disclosure of which is incorporated herein by reference.

[0004] The present invention relates to a composition for diagnosing Parkinson's disease by detecting the expression level of one or more proteins selected from the group consisting of GLUT3, USP14, α-synuclein and AIMP2 in blood or a gene encoding the protein.

[0005]

[0006] Parkinson's disease is a degenerative brain disease that causes movement disorders due to the destruction of dopaminergic neurons in the substantia nigra, the central part of the brainstem. Patients with Parkinson's disease experience not only motor impairment but also non-motor disorders, including olfactory disturbances, sleep disorders, depression, mania, and cognitive impairment. In today's rapidly aging society, the number of Parkinson's patients is rapidly increasing. According to disease statistics from the National Health Insurance Service, the number of Parkinson's patients in Korea increased approximately 2.5-fold over the past decade, from 39,265 in 2004 to 96,499 in 2016.

[0007] Diagnosis of Parkinson's disease (PD) relies heavily on the quantitative scoring of Parkinson's-associated motor and non-motor symptoms based on the Unified Parkinson's Disease Rating Scale (UPDRS), a neurological assessment by a specialist. However, misdiagnosis rates in the early stages of Parkinson's disease have been reported to reach 25% (Thomas G. Beach et al., 2018). While DaTscan brain imaging, which captures the axons of dopaminergic neurons, can detect dopaminergic cell loss early, it is limited to large hospitals equipped with expensive equipment, limiting patient accessibility.

[0008] For patients with Parkinson's disease, the development of molecular diagnostic markers and the development of miniature diagnostic devices utilizing these markers are crucial for easily accessible and accurate diagnosis. Molecular diagnostic markers can be used not only to diagnose the disease but also to quantify symptom improvement in clinical trials and assess treatment prognosis. Therefore, further research in this area is necessary.

[0009] Numerous papers and patents are referenced and cited throughout this specification. The disclosures of these cited papers and patents are incorporated herein by reference in their entirety to provide a clearer understanding of the state of the art and the scope of the present invention.

[0010]

[0011] The present inventors have diligently sought to develop a diagnostic method that enables the early detection and diagnosis of Parkinson's disease, while also enabling a simple, minimally invasive diagnosis. As a result, the present invention was completed by demonstrating that early diagnosis of Parkinson's disease is possible by detecting the expression levels of one or more genes selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2 in the blood.

[0012] Accordingly, the purpose of the present invention is to provide a composition for diagnosing Parkinson's disease.

[0013] Another object of the present invention is to provide a Parkinson's disease diagnostic kit.

[0014] Another object of the present invention is to provide a method for providing information for diagnosing Parkinson's disease.

[0015] Another object of the present invention is to provide a method for screening a Parkinson's disease treatment agent.

[0016] Other objects and advantages of the present invention will become more apparent from the detailed description, claims and drawings below.

[0017]

[0018] According to one aspect of the present invention, the present invention provides a composition for diagnosing Parkinson's disease, further comprising an agent for detecting a combination of any one or more proteins selected from the group consisting of GLUT3 (glucose transporter 3), USP14 (ubiquitin-specific protease 14), α-synuclein, and AIMP2 (aminoacyl-tRNA synthetase complex interacting multifunctional protein-2), an aggregate of the proteins, or a gene encoding the proteins.

[0019] According to another aspect of the present invention, the present invention provides a composition for diagnosing Parkinson's disease, comprising an agent for detecting GLUT3 (glucose transporter 3) protein, an aggregate of the protein, or a gene encoding the protein.

[0020] The present inventors have devoted extensive research efforts to identify a diagnostic method that can detect the possibility of developing Parkinson's disease, enable early diagnosis, and enable simple, minimally invasive diagnosis of Parkinson's disease. As a result, we have discovered that early diagnosis of Parkinson's disease is possible by detecting in blood GLUT3, a protein composed of one or more combinations selected from the group consisting of GLUT3, USP14 (ubiquitin-specific protease 14), α-synuclein, and AIMP2 (aminoacyl-tRNA synthetase complex interacting multifunctional protein-2), or a gene encoding said protein.

[0021] The term "aggregate" in the present invention may include a polymer of protein, a fibril, a fibril precursor, a plaque, a protein monomer and polymer combined with another protein, a protein combined with fat or carbohydrate, a protein combined with nucleic acid, and a decomposed monomer combined with blood cells, and the fibril may include an oligomer of an abnormal protein.

[0022] In addition, the above aggregates may be formed by the aggregation of IDPs (Intrinsically Disordered Proteins) or misfolded proteins due to various causes such as DNA sequence mutations, problems in the DNA transcription / translation stages, environmental stresses such as temperature, pH, and oxidative stress, and aging.

[0023] Using the composition of the present invention, a simple blood test can be used to identify and diagnose Parkinson's disease patients. Furthermore, by preferentially applying it to patients exhibiting prodromal symptoms of Parkinson's disease, such as olfactory disturbances, sleep disturbances, and constipation, it can be used as a preliminary test to determine whether to proceed with neurological or brain imaging tests at a large hospital. It can also be utilized as a means of quantifying the effectiveness of Parkinson's treatment.

[0024] In the present invention, the term "diagnosis" means determining the susceptibility of a subject to a specific disease or condition or determining whether a subject currently has a specific disease or condition.

[0025] The composition for diagnosing Parkinson's disease of the present invention may include a preparation for detecting a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein.

[0026] The above-mentioned agent for detecting GLUT3, USP14, α-synuclein and AIMP2 proteins can detect not only the normal structure of the proteins but also aggregate forms (oligomers, amyloid aggregates) that appear in disease states.

[0027] Unless otherwise stated, the agent for detecting a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein and AIMP2 of the present invention can be used for quantification and analysis of both the normal structure and the denatured aggregate structure of the protein.

[0028] However, the method of distinguishing between normal and denatured structures of the four proteins and measuring them can also be expanded and utilized for diagnosis.

[0029] When using a composition for diagnosing Parkinson's disease including a preparation for detecting the GLUT3 protein of the present invention, Parkinson's disease can be diagnosed with high sensitivity and specificity.

[0030] When using a diagnostic composition for Parkinson's disease comprising an agent detecting GLUT3, USP14, α-synuclein, and AIMP2 proteins of the present invention, Parkinson's disease can be diagnosed with remarkably high sensitivity and specificity. This means that while Parkinson's disease can be diagnosed using only an agent detecting GLUT3 protein in plasma, the diagnostic accuracy for Parkinson's disease is significantly improved when combined with agents detecting USP14, α-synuclein, and AIMP2 proteins.

[0031] The protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein and AIMP2 of the present invention or a gene encoding the same may be present in a biological sample isolated from a subject, more specifically, for example, blood, serum or plasma.

[0032] In one embodiment of the present invention, the protein comprising a combination of any one or more selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2 refers to a protein in a blood sample isolated from a human body.

[0033] The conventional method for diagnosing Parkinson's disease is a quantitative scoring method for Parkinson's disease-related motor and non-motor symptoms based on the Unified Parkinson's Disease Rating Scale (UPDRS), which is performed by a neurologist. However, the misdiagnosis rate in the early stages of Parkinson's disease is reported to be as high as 25% (Thomas G. Beach et al., 2018), limiting its application for early diagnosis. While DaTscan brain imaging can be used to diagnose dopaminergic cell loss early, it is only available at large hospitals equipped with expensive imaging equipment, limiting patient accessibility.

[0034] In contrast, when using a composition for diagnosing Parkinson's disease according to one embodiment of the present invention, there is an advantage in that early diagnosis of Parkinson's disease can be made quickly, simply, and accurately by measuring the expression level of a protein consisting of a combination of any one or more selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2, or a gene encoding the protein, in blood, serum, or plasma separated from a subject.

[0035] In one embodiment of the present invention, the agent for detecting the protein of the present invention is at least one selected from the group consisting of an antibody, an aptamer, a ligand, a peptide nucleic acid (PNA), and an oligopeptide.

[0036] “Protein detection” of the present invention refers to a process of confirming the presence and expression level of GLUT3, USP14, α-synuclein or AIMP2 proteins, which are Parkinson’s disease diagnostic markers of the present invention, in blood, which is a biological sample, in order to diagnose Parkinson’s disease. Methods for measuring or comparing the expression level of the above protein include, but are not limited to, protein chip analysis, immunoassay, ligand binding assay, MALDI-TOF (Matrix Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry) analysis, SELDI-TOF (Sulface Enhanced Laser Desorption / Ionization Time of Flight Mass Spectrometry) analysis, radioimmunoassay, radioimmunodiffusion, aukteroni immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, complement fixation assay, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry (LC-MS), LC-MS / MS (liquid chromatography-mass spectrometry / mass spectrometry), Western blotting, dot blotting, and ELISA (enzyme linked immunosorbent assay).

[0037] The term "antibody" of the present invention refers to a substance that specifically binds to an antigen and causes an antigen-antibody reaction. For the purposes of the present invention, the antibody refers to an antibody that specifically binds to GLUT3, USP14, α-synuclein, or AIMP2 protein. The antibodies of the present invention include polyclonal antibodies, monoclonal antibodies, and recombinant antibodies. The antibodies can be readily produced using techniques well known in the art. For example, polyclonal antibodies can be produced by methods well known in the art, including a process of injecting an animal with the GLUT3, USP14, α-synuclein, or AIMP2 protein antigen and collecting blood from the animal to obtain serum containing the antibody. Such polyclonal antibodies can be produced from any animal, such as a goat, rabbit, sheep, monkey, horse, pig, cow, or dog. In addition, monoclonal antibodies can be produced using the hybridoma method well known in the art (see Kohler and Milstein (1976) European Journal of Immunology 6:511-519) or the phage antibody library technique (see Clackson et al, Nature, 352:624-628, 1991; Marks et al, J. Mol. Biol., 222:58, 1-597, 1991). Antibodies produced by the above methods can be separated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, and affinity chromatography. In addition, the antibodies of the present invention include not only complete forms having two full-length light chains and two full-length heavy chains, but also functional fragments of antibody molecules. A functional fragment of an antibody molecule refers to a fragment that possesses at least an antigen-binding function, and includes Fab, F(ab'), F(ab')2, and Fv.

[0038] The term "aptamer" of the present invention refers to an oligonucleic acid or peptide molecule, and the general contents of aptamers are described in detail in the literature [Bock LC et al., Nature 355(6360):5646(1992); Hoppe-Seyler F, Butz K "Peptide aptamers: powerful new tools for molecular medicine". J Mol Med. 78(8):42630(2000); Cohen BA, Colas P, Brent R. "An artificial cell-cycle inhibitor isolated from a combinatorial library". ProcNatl Acad Sci USA. 95(24): 142727(1998)].

[0039] The term "ligand" in the present invention refers to a low-molecular-weight substance capable of binding to GLUT3, USP14, α-synuclein, or AIMP2 proteins to form a complex and generate a signal. It may also be interpreted to comprehensively include antibodies.

[0040] The term "PNA (Peptide Nucleic Acid)" of the present invention refers to an artificially synthesized polymer similar to DNA or RNA, and was first introduced in 1991 by Professors Nielsen, Egholm, Berg, and Buchardt of the University of Copenhagen, Denmark. While DNA has a phosphate-ribose sugar backbone, PNA has a repeated N-(2-aminoethyl)-glycine backbone linked by peptide bonds, which greatly increases its binding affinity and stability to DNA or RNA, and is used in molecular biology, diagnostic analysis, and antisense therapy. PNA is described in detail in the literature [PE Nielsen, M Egholm, RH Berg, O Buchardt, "Sequence-selective recognition of DNA by strand displacement with a thymine-substituted polyamide", Science 254(5037):1497-500 (1991)].

[0041] In one embodiment of the present invention, the agent for detecting the gene of the present invention is at least one selected from the group consisting of a primer, a probe, and an antisense nucleotide. Since information on the GLUT3, USP14, α-synuclein, or AIMP2 proteins according to the present invention is known, those skilled in the art will be able to easily design primers, probes, or antisense nucleotides that specifically bind to the mRNA of the gene encoding the protein based on this information.

[0042] The term "gene detection" of the present invention refers to a process of measuring the amount of mRNA by confirming the presence and expression level of mRNA of genes encoding GLUT3, USP14, α-synuclein, or AIMP2 proteins in a biological sample for the purpose of diagnosing Parkinson's disease. Analysis methods for this include, but are not limited to, reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), Northern blotting, and DNA chips.

[0043] The term "primer" in the present invention refers to a fragment that recognizes a target gene sequence, and includes a pair of forward and reverse primers, but is preferably a pair of primers that provide analysis results with specificity and sensitivity. When the nucleic acid sequence of the primer is a sequence that does not match the non-target sequence present in the sample, and thus the primer only amplifies the target gene sequence containing the complementary primer binding site and does not cause non-specific amplification, high specificity can be imparted.

[0044] The term "probe" in the present invention refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through said binding. The type of the probe is not particularly limited as long as it is a substance commonly used in the art, but may preferably be PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA, or DNA. More specifically, the probe includes a biomaterial derived from or similar to a living organism or manufactured in vitro, and may be, for example, an enzyme, a protein, an antibody, a microorganism, an animal or plant cell and organ, a nerve cell, DNA, and RNA. DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins may include antibodies, antigens, enzymes, peptides, etc.

[0045] The term "LNA (Locked nucleic acids)" of the present invention refers to a nucleic acid analog containing a 2'-O, 4'-C methylene bridge [J Weiler, J Hunziker and J Hall Gene Therapy (2006) 13, 496-502]. LNA nucleosides contain common nucleic acid bases of DNA and RNA and can form base pairs according to the Watson-Crick base pairing rules. However, due to the 'locking' of the molecule caused by the methylene bridge, LNA does not form an ideal shape in Watson-Crick bonding. When LNA is included in a DNA or RNA oligonucleotide, LNA can pair with a complementary nucleotide chain more quickly and increase the stability of the double helix.

[0046] The term "antisense" in the present invention refers to an oligomer having a sequence of nucleotide bases and an intersubunit backbone that allows the antisense oligomer to hybridize with a target sequence within RNA by Watson-Crick base pairing, typically allowing the formation of an mRNA and RNA:oligomer heteroduplex within the target sequence. The oligomer may have exact sequence complementarity or approximate sequence complementarity to the target sequence.

[0047]

[0048] According to another aspect of the present invention, the present invention provides a Parkinson's disease diagnostic kit comprising a Parkinson's disease diagnostic composition according to another aspect of the present invention described above.

[0049] The term "kit" of the present invention may be, but is not limited to, an RT-PCR kit, a DNA chip kit, an ELISA kit, a protein chip kit, a rapid kit, or an MRM (Multiple reaction monitoring) kit.

[0050] The diagnostic kit for Parkinson's disease of the present invention may further comprise one or more other component compositions, solutions, or devices suitable for the analysis method. For example, the diagnostic kit may further comprise essential elements necessary for performing a reverse transcription polymerase reaction. The reverse transcription polymerase reaction kit comprises a pair of primers specific for a gene encoding a marker protein. The primers are nucleotides having a sequence specific to the nucleic acid sequence of the gene, and may have a length of about 7 to 50 bp, more preferably about 10 to 30 bp. It may also comprise a primer specific to the nucleic acid sequence of a control gene. In addition, the reverse transcription polymerase reaction kit may comprise a test tube or other appropriate container, a reaction buffer (with various pH and magnesium concentrations), deoxynucleotides (dNTPs), an enzyme such as Taq polymerase and reverse transcriptase, DNase, RNase inhibitor DEPC water, sterile water, etc.

[0051] Additionally, the diagnostic kit of the present invention may include essential elements necessary for performing a DNA chip. The DNA chip kit may include a substrate to which cDNA or oligonucleotides corresponding to a gene or fragment thereof are attached, and reagents, preparations, enzymes, etc. for producing a fluorescently labeled probe. The substrate may also include cDNA or oligonucleotides corresponding to a control gene or fragment thereof.

[0052] Additionally, the diagnostic kit of the present invention may include essential elements necessary for performing an ELISA. The ELISA kit includes an antibody specific for the protein. The antibody is an antibody with high specificity and affinity for the marker protein and little cross-reactivity with other proteins, and may be a monoclonal antibody, polyclonal antibody, or recombinant antibody. The ELISA kit may also include an antibody specific for a control protein. In addition, the ELISA kit may include reagents capable of detecting bound antibodies, such as labeled secondary antibodies, chromophores, enzymes (e.g., conjugated to antibodies), and their substrates or other substances capable of binding to antibodies.

[0053] The "Parkinson's disease diagnostic kit" of the present invention includes the "Parkinson's disease diagnostic composition" which is another aspect of the present invention described above, and thus, redundant descriptions are omitted to avoid excessive complexity of the description in this specification.

[0054]

[0055] According to another aspect of the present invention, the present invention provides a method for providing information for diagnosing Parkinson's disease, comprising the following steps:

[0056] (a) measuring the expression level of GLUT3 protein, aggregates of the protein, or a gene encoding the protein in a blood sample isolated from a human body; and

[0057] (b) A step of providing information that if the expression level is increased compared to a normal blood control sample, there is a high possibility of Parkinson's disease.

[0058]

[0059] In one embodiment of the present invention, a method for providing information for diagnosing Parkinson's disease is provided, wherein step (a) is step (a-1) below, and step (b) is step (b-1) below:

[0060] (a-1) a step of measuring the expression level of a protein comprising a combination of any one or more selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein;

[0061] (b-1) A step of providing information that there is a high possibility of Parkinson's disease when, compared to a normal blood control sample, the expression level of a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein is increased.

[0062]

[0063] In one embodiment of the present invention, the step of measuring the protein level of the present invention is performed using at least one agent selected from the group consisting of antibodies, aptamers, ligands, peptide nucleic acids (PNAs), and oligopeptides. Specifically, for example, the expression of GLUT3, USP14, α-synuclein, or AIMP2 proteins in a blood sample to be diagnosed and a normal blood control sample can be measured and compared using an antibody that specifically binds to the GLUT3, USP14, α-synuclein, or AIMP2 protein of the present invention. A method is used in which the antibody and the GLUT3, USP14, α-synuclein, or AIMP2 protein in the biological sample form an antigen-antibody complex, and the same is detected.

[0064] The term "antigen-antibody complex" of the present invention refers to a combination of a protein antigen and an antibody that recognizes it, for confirming the presence or absence of a corresponding gene in a biological sample. The antigen-antibody complex can be detected using methods known in the art, such as spectroscopic, photochemical, biochemical, immunochemical, electrical, absorbance, chemical, and various other methods.

[0065] In one embodiment of the present invention, the step of measuring the expression level of the gene of the present invention is performed using at least one agent selected from the group consisting of a primer, a probe, and an antisense nucleotide. Specifically, for example, the measurement or comparison of the expression level of the gene encoding the GLUT3, USP14, α-synuclein, or AIMP2 protein of the present invention may use, but is not limited to, reverse transcriptase polymerase reaction, competitive reverse transcriptase polymerase reaction, real-time reverse transcriptase polymerase reaction, RNase protection assay, Northern blotting, or DNA chip. Through the above measurement methods, the mRNA expression level in a normal control group and the mRNA expression level in an individual to be diagnosed for disease onset can be confirmed, and by comparing the expression levels, the onset of Parkinson's disease can be diagnosed or predicted.

[0066] The "method for providing information for diagnosing Parkinson's disease" of the present invention relates to a diagnostic method performed using the composition for diagnosing Parkinson's disease, which is another aspect of the present invention described above. Duplicate content is cited, and duplicate description is omitted to avoid excessive complexity of the description in this specification.

[0067]

[0068] According to another aspect of the present invention, the present invention provides a method for screening a Parkinson's disease treatment agent, comprising the following steps:

[0069] a) administering a drug candidate to a Parkinson's disease animal model having increased levels of GLUT3 protein, aggregates of the protein, or expression levels of a gene encoding the protein in the blood;

[0070] b) a step of collecting blood from the animal model after step a) and measuring the protein or mRNA expression level in the collected blood; and

[0071] c) A step of determining that the drug candidate is a treatment for Parkinson's disease when the level of GLUT3 protein or gene expression in the collected blood shows a significant decrease due to administration of the drug candidate.

[0072]

[0073] The administration of step a) of the present invention can be administered orally or parenterally, and a person skilled in the art can select an appropriate administration route depending on the characteristics of the drug candidate substance.

[0074]

[0075] According to one embodiment of the present invention, a method for screening a Parkinson's disease treatment agent is provided, wherein step a) is the following step a-1), and step c) is the following step c-1):

[0076] a-1) A step of administering a drug candidate to a Parkinson's disease animal model in which the expression level of a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2 in the blood, an aggregate of the protein, or a gene encoding the protein is increased;

[0077] c-1) A step of determining that the drug candidate is a treatment for Parkinson's disease when the level of expression of a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2 in the collected blood, an aggregate of the protein, or a gene encoding the protein shows a significant decrease by administration of the drug candidate.

[0078]

[0079] The features and advantages of the present invention are summarized as follows:

[0080] (a) The present invention provides a composition for diagnosing Parkinson's disease.

[0081] (b) The present invention provides a kit for diagnosing Parkinson's disease.

[0082] (c) The present invention provides a method for providing information for diagnosing Parkinson's disease.

[0083] (d) The present invention provides a method for screening a Parkinson's disease treatment agent.

[0084] (e) By utilizing the composition and diagnostic kit for Parkinson's disease of the present invention, early diagnosis of Parkinson's disease can be made very quickly and easily through a blood test.

[0085]

[0086] Figure 1 shows the age distribution of Parkinson's disease patients and normal control group (Parkinson's disease patient group n = 80, normal control group n = 78)

[0087] Figure 2 is a graph showing the quantitative analysis of protein markers (GLUT3, USP14, α-synuclein, and AIMP2) in plasma from Parkinson's disease patients and age-matched normal controls based on sandwich ELISA (quantitative data are expressed as mean ± standard error, and significance was determined by unpaired student t test, ** p < 0.01, *** p < 0.001).

[0088] Figure 3 is a graph showing the quantitative analysis of the protein marker DJ-1 in plasma from Parkinson's disease patients and age-matched normal controls based on sandwich ELISA (quantitative data are expressed as mean ± standard error, and significance was determined by unpaired student t test, ** p < 0.01, *** p < 0.001).

[0089] Figure 4 shows the results of ROC curve analysis and diagnostic accuracy analysis for plasma protein markers (GLUT3, USP14, α-synuclein, and AIMP2). It is a graph of ROC curve analysis and sensitivity / 1-specificity for the plasma concentration values ​​of each protein marker in normal controls / Parkinson's disease patients. Figure 5 shows the results of ROC curve analysis and diagnostic accuracy analysis for a plasma protein marker (DJ-1). It is a graph of ROC curve analysis and sensitivity / 1-specificity for the plasma concentration values ​​of each protein marker in normal controls / Parkinson's disease patients.

[0090] Figure 6 shows the results of a multi-marker panel ROC curve analysis and diagnostic accuracy analysis based on logistic regression analysis. This graph shows the ROC curve analysis based on the logarithmic regression function calculated for the diagnostic accuracy analysis of AIMP2 alone or in combination with α-synuclein, GLUT3, and USP14 markers.

[0091] Figure 7 shows the results of a multi-marker panel ROC curve analysis and diagnostic accuracy analysis based on logistic regression analysis. This graph shows the ROC curve analysis based on the logarithmic regression function calculated for diagnostic accuracy analysis of GLUT3 alone or in combination with α-synuclein, AIMP2, and USP14 markers.

[0092] Figure 8 is a diagram showing the production and verification of AIMP2 aggregate antigen:

[0093] A: Western blot analysis results for GST-AIMP2 recombinant protein (0) and GST-AIMP2 aggregates (7) obtained through 7-day aggregation reaction; B: Amyloid structure analysis using Thioflavin T fluorescence analysis for the normal structure of recombinant protein GST-AIMP2 (GST-AIMP2 monomer) and GSTAIMP2 aggregates (GST-AIMP2 fibril); and C: Coomassie blue staining results for GST-AIMP2 aggregate antigen (1: BSA, 2: size marker, and 3: GST-AIMP2 aggregate).

[0094] Figure 9 is a diagram showing the production of single antibody clones specific for each AIMP2 aggregation form and the results of isotype analysis thereof:

[0095] A: Eight antibody-producing clones that bind to AIMP2 monomers and / or AIMP2 aggregates; and B: Antibody isotype analysis results for the eight clones.

[0096] Figure 10 is a diagram showing the results of analyzing the reactivity of AIMP2 antibody clones to the AIMP2 normal structure (monomer) and aggregate structure:

[0097] A: Dot blot analysis results of antibodies purified from eight clones; and B: Western blot analysis results of antibodies purified from eight clones.

[0098] Figure 11 is a table comprehensively summarizing the AIMP2 stereostructure-specific reactivity of eight AIMP2 antibody clones.

[0099] Figure 12 shows the sequence analysis results of antibodies produced from AIMP2 aggregate conformation-specific antibody clones (5B2 and 6C7):

[0100] Green: amino acid sequences of CDH1, CDH2, and CDH3; blue: amino acid sequences of CDRL1, CDRL2, and CDRL3; gray: N-terminal amino acid sequences other than those analyzed by Igblast; gray and underlined: constant regions within the amino acid sequences of the complete heavy and light chains when producing humanized chimeric antibodies; and red boxes: regions of difference in the amino acid sequences of the antibodies of the two clones.

[0101] Figure 13 shows the sequence analysis results of antibodies produced from AIMP2 monomer conformation-specific antibody clones (3C2 and 7H9):

[0102] Green: amino acid sequences of CDH1, CDH2, and CDH3; blue: amino acid sequences of CDRL1, CDRL2, and CDRL3; gray: N-terminal amino acid sequences other than those analyzed by Igblast; gray and underlined: constant regions within the amino acid sequences of the complete heavy and light chains when producing humanized chimeric antibodies; and red boxes: regions of difference in the amino acid sequences of the antibodies of the two clones.

[0103] Figure 14 shows the sequence analysis results of an antibody produced from an antibody clone (13H9) that binds to both AIMP2 monomer and aggregate conformations:

[0104] Green: CDH1, CDH2, and CDH3 amino acid sequences; blue: CDRL1, CDRL2, and CDRL3 amino acid sequences; gray: N-terminal amino acid sequences other than those analyzed by Igblast; and gray and underlined: constant regions within the amino acid sequences of the complete heavy and light chains when producing humanized chimeric antibodies.

[0105] Figure 15 is an image showing the results of confirming the total protein amount through Ponceau staining after binding plasma proteins of age-matched control group and Parkinson's disease patient group to a nitrocellulose membrane, and confirming the amount of AIMP2 aggregates present in each plasma sample through dot blot using 5B2 antibody.

[0106] Figure 16 is a graph showing the concentration values ​​of AIMP2 aggregate proteins present in each plasma sample calculated based on the 5B2 dot blot signal intensity according to the concentration of recombinant AIMP2 aggregate proteins confirmed from the standard of Figure 15 (n = 24 control groups and 24 PD groups).

[0107] Figure 17 shows an ROC curve analysis to evaluate the effectiveness of plasma AIMP2 aggregate concentration as a diagnostic marker for Parkinson's disease (n=24 controls and 24 PD). The AUC, cut-off value, and diagnostic sensitivity and specificity values ​​are shown below the graph.

[0108]

[0109] Hereinafter, the present invention will be described in more detail through examples. These examples are intended solely to illustrate the present invention more specifically, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples, in accordance with the gist of the present invention.

[0110]

[0111] Example

[0112] Example 1: Preparation of plasma samples from Parkinson's disease patients and controls

[0113] Blood samples were donated from 80 patients with Parkinson's disease and 78 age-matched normal controls from Hanyang University Hospital. The study received approval from the Institutional Review Board (IRB) of Hanyang University Hospital, approval number HY-2021-06-040. As shown in Figure 1 and Table 1 below, the average age was 64 years, and there was no significant difference in age between the two groups.

[0114]

[0115] Summary table of age and sex distribution of normal control and Parkinson's disease patient groups Normal control Parkinson's disease patient Age average 64.15 64.93 Sex M = 36 M = 41 F = 42 F = 39

[0116] Blood samples were collected in sodium heparin tubes (7 cc), centrifuged at 3000 rpm for 10 minutes at 4°C, and 1 cc of plasma was placed in EP tubes and stored at -70°C. The plasma was subsequently used for plasma protein marker analysis.

[0117]

[0118]

[0119] Example 2: Sandwich ELISA (enzyme-linked immunosorbent assay) analysis and absolute quantification

[0120] Candidate diagnostic marker proteins for Parkinson's disease, including aminoacyl-tRNA synthetase complex interacting multifunctional protein-2 (AIMP2), ubiquitin-specific protease 14 (USP14), glucose transporter 3 (GLUT3), DJ-1, and α-synuclein, were quantified using sandwich ELISA.

[0121] The coating solution was prepared by diluting the capture antibody 1:1000 to 1:2000 in coating buffer. All buffers, including the coating buffer, were from an ELISA buffer kit (CNB0011, Invitrogen). The coating solution was applied to 96-well plates at 100 μL per well. The plates were covered and incubated overnight (12 to 18 hours) at 2 to 8°C. After incubation, the solution in the wells was aspirated and washed once with 200 μL of wash buffer per well. After washing, the plates were inverted and blotted on absorbent paper to remove excess liquid. The plates were blocked for 1 hour at room temperature with 200 μL of blocking buffer per well, aspirated after 1 hour, and excess liquid was removed as much as possible. Standards and plasma samples were diluted with blocking buffer to a final volume of 100 μL and prepared in advance. 100 μL of the standard and plasma samples were added to the designated wells. The plates were incubated for 1 hour at room temperature using a shaker (~500 rpm). After 1 hour, 200 μL of wash buffer per well was aspirated and washed five times. After washing, the plates were inverted to thoroughly remove excess liquid. A detection antibody solution was prepared by diluting the detection antibody 1:1000 to 1:2000 in blocking buffer. 100 μL of the detection antibody solution was added to each well. The plates were incubated for 2 hours at room temperature using a shaker (~500 rpm). After 2 hours of incubation, 200 μL of wash buffer per well was aspirated and washed five times. After washing, turn it over to remove excess liquid.Streptavidin-HRP solution containing blocking buffer was prepared by diluting 1:5,000. 100 μL of Streptavidin-HRP solution was added to each well. The wells were incubated at room temperature for 30 minutes on a shaker (~500 rpm). After 30 minutes of incubation, the wells were aspirated and washed five times with 200 μL of wash buffer. After washing, the wells were inverted to remove excess liquid. After removal, 100 μL of TMB substrate solution was added to each well. The wells were incubated for 30 minutes at room temperature. After 30 minutes of incubation, an additional 100 μL of stop solution was added to each well. The absorbance was measured at 450 nm within 30 minutes of adding the stop solution. Results were calculated using log-log or a 4-parameter curve fit.

[0122] The results are shown in Fig. 2, Fig. 3 and Table 2.

[0123]

[0124] Mean and standard error of the concentrations of proteomic markers in plasma from normal and Parkinson's patients based on ELISA analysis. Marker Plasma concentration (pg / μl) Normal group Parkinson's patients GLUT 3 1.65 ± 0.06 2.63 ± 0.10 USP 14 1.09 ± 0.04 1.61 ± 0.07 α-synuclein 0.22 ± 0.02 1.44 ± 0.32 AIMP 25.54 ± 0.19 9.34 ± 0.36 DJ-11.59 ± 0.21 1.57 ± 0.13

[0125]

[0126] As shown in Figures 2 and 3, the expression levels of the remaining four proteins (GLUT3, USP14, α-synuclein, and AIMP2) except DJ-1 were confirmed to be significantly increased in the blood of Parkinson's disease patients.

[0127] Table 2 above shows the average plasma concentrations of four proteins (GLUT3, USP14, α-synuclein, and AIMP2) in Parkinson's disease patients and age-matched normal controls. The average plasma concentration of GLUT3 was 1.68 pg / μl in the normal group and 2.49 pg / μl (48% increase) in the Parkinson's disease patient group. The average plasma concentration of USP14 was 1.17 pg / μl in the normal group and 1.66 pg / μl (41% increase) in the Parkinson's disease patient group. The average plasma concentration of α-synuclein was 0.14 pg / μl in the normal group and 1.92 pg / μl (13-fold increase) in the Parkinson's disease patient group. The average plasma concentration of AIMP2 was measured to be 6.38 pg / μl in the normal group and 10.44 pg / μl (63% increase) in the Parkinson's disease patient group.

[0128]

[0129]

[0130] Example 3: Analysis of diagnostic sensitivity and specificity of individual markers using ROC (Receiver Operating Characteristic) curve analysis.

[0131] Based on the concentration values ​​of protein markers in the plasma of Parkinson's disease patients and normal controls (Figures 2 and 3), ROC curve analysis was performed to evaluate whether individual markers could distinguish between normal controls and Parkinson's disease patients.

[0132] The OD values ​​obtained through the ELISA analysis of Example 2 were normalized, and then the OD values ​​or actual concentration values ​​for the control group were entered in column A using the GraphPad Prism program, and the OD values ​​for PD were entered in column B. After entering the values, the Analyze button was clicked in the data table window and the Receiver-Operator Characteristic Curve was selected. In the ROC dialog box, the columns containing the control and Parkinson's disease results were specified, and whether the result value was a fraction or percentage was set and the analysis was performed. The analysis results obtained were the Area Under the Curve (AUC) value, sensitivity, specificity, and the ROC curve. The cut-off value can be obtained as a table of the coordinates of the ROC curve in the Prism program, and the three parts of the cut-off value, sensitivity, and specificity from the curve were imported into Excel. Sensitivity and specificity were selected and plotted as a line chart. The numerical value at the point where the two graphs intersect was taken as the cut-off value, and the sensitivity and specificity values ​​at that point were interpreted as the sensitivity and specificity for this ROC curve.

[0133] The results are shown in Figures 4, 5 and Table 3.

[0134]

[0135] Summary table of diagnostic sensitivity / specificity values ​​for AUC values ​​and cut-off values ​​as diagnostic markers by ROC curve analysis for each marker Marker AUC Cut-off value (pg / μl) Sensitivity (%) Specificity (%) GLUT 3 0.77 1 3 1.90 766.67 7 1.43 USP 14 0.75 5 11.19 566.70 66.70 α-Synuclein 0.75 7 0.17 50 66.67 66.67 AIMP 2 0.85 4 97.46 0 78.5 7 8 0.95 DJ-10.68 5 91.01 161.02 62.22

[0136] As shown in Figures 4 and 5 and Table 3 above, individual markers showed relatively high area under the curve (AUC) values, and AIMP2 showed the best AUC value (0.8549). The cut-off value that could maximize sensitivity and specificity was identified, and α-synuclein, GLUT3, and USP14 showed a sensitivity of 66%. DJ-1 showed a specificity of 62%, and α-synuclein and USP14 showed a specificity of 66%, while GLUT3 showed a relatively high specificity of 71%. AIMP2, which showed the best AUC value, showed a sensitivity of 78.57% and a specificity of 80.95% based on a cut-off value of 7.46 ng / μL.

[0137]

[0138] Example 4: Multi-marker log regression analysis and ROC curve analysis by multi-marker panel (1)

[0139] We constructed a multi-marker panel by combining the Parkinson's disease diagnostic markers AIMP2 and α-synuclein with the newly discovered GLUT3 and USP14, and verified through logistic regression analysis whether the constructed multi-marker panel could increase the diagnostic accuracy of Parkinson's disease.

[0140] For each antibody, the normalized OD value or actual concentration value obtained through the ELISA of Example 2 was listed in one row in Excel for the control group and the Parkinson's disease group, and then entered in another column as 'Control = 0, Parkinson's disease = 1'. The SPSS program was run and pasted into the data view window. In the variable view window, the meaning of each column was entered, and for the column entered as 'Control = 0, Parkinson's disease = 1', '0 is control, 1 is Parkinson's disease' was added to the value label. When Analysis, Regression Analysis, and Binary Logistic are clicked in order, the Binary Logistic window will appear, where Control = 0, Parkinson's disease = 1 are entered as the dependent variable, and AIMP2, αSyn, GLUT3, and USP14, which are independent variables, are moved to the block cell. In the method, 'Leave as entered', which enters all variables at the same time. Clicking Save will open a window where you can select predicted values, influence, and residuals. By default, I selected probability, membership diagnosis, Cook's distance, and standardization, and then clicked Continue. After the analysis was complete, the items selected in the "Save" menu were moved to the "Data View" window, where I confirmed that the selections had been saved. "Probability (P)" stores the predicted probability of an event for each case. "Group Membership (G)" displays the group to which each case is assigned based on the predicted probability. "Standardization (N)" or standardized residuals are standardized by dividing the residuals by their standard deviations and serve as an important criterion for outlier detection. "Cook's distance" identifies observations with high influence. Next, I clicked the Options menu, checked the four items: classification diagram, Hosmer-Lemeshow goodness of fit, iterative calculation process, and confidence interval for exp, and clicked Continue. I clicked OK and reviewed the analysis results.The analysis results can be used to obtain the AUC, sensitivity, specificity, and cut-off values ​​from the ROC curve. In addition, the Hosmer and Lemeshow table can be used to determine the goodness of fit of the logistic regression model as a significance probability value. In addition, the odds ratio or odds ratio was calculated using the formula Odds (OR) = P / (1-P). Through this, the estimated regression equation for NPD / PD in the logistic regression analysis could be constructed.

[0141] The log probability function for each marker panel by logistic regression analysis is shown in Table 4 below.

[0142]

[0143] Log probability function for each marker panel by logistic regression analysis Panel log function [ln(P / 1-P)]AIMP2-5.994 + 0.8567X AIMP2 AIMP2, αSyn-9.041 + 0.9499X AIMP2 + 6.487X αsyn AIMP2, αSyn, GLUT3-15.46 + 1.019X AIMP2 + 7.965X αsyn + 2.627X GLUT3 AIMP2, αSyn, GLUT3, USP14-21.41 + 1.192X AIMP2 + 9.811X αsyn + 2.940X GLUT3 + 2.658X USP14

[0144] The results are shown in Fig. 6 and Table 5.

[0145] As shown in Figure 6, the optimal log regression function was derived by increasing the number of markers combined, such as AIMP2 alone, AIMP2 and α-synuclein panel, AIMP2, α-synuclein and GLUT3 panel, and AIMP2, α-synuclein, GLUT3 and USP14 panel. ROC curve analysis was performed based on the log regression function derived from each multi-marker panel.

[0146]

[0147] Summary table of AUC, cut-off value, diagnostic sensitivity and specificity of ROC curve based on logistic regression analysis and log-odds function by multimarker panel. Panel AUC Cut-off (Probability) Sensitivity (%) Specificity (%) AIMP2 0.89 74 0.48 0 58 5.00 87.18 AIMP2, αSyn 0.96 0 3 0.42 7 3 8 8.75 89.74 AIMP2, αSyn, GLUT3 0.97 9 8 0.45 3 6 9 1.25 9 2.31 AIMP2, αSyn, GLUT3, USP 1 4 0.98 78 0.46 8 59 3.75 9 4.87

[0148] As a result, as shown in Fig. 6 and Table 5 above, the AUC values ​​increased in all multi-marker panels compared to the AIMP2 marker alone, and the panel combining all four markers showed the maximum AUC value of 0.9878. In addition, when the sensitivity and specificity of each panel were analyzed using the probability value that can obtain the maximum sensitivity and specificity as the cut-off value, the sensitivity was 85% and the specificity was 87.18% for AIMP2 alone, while the diagnostic accuracy of Parkinson's disease was significantly improved with a sensitivity of 93.75% and a specificity of 94.87% for the four-marker panel at a cut-off value of 0.4685.

[0149]

[0150] Example 5: Multi-marker log regression analysis and ROC curve analysis by multi-marker panel (2)

[0151] The inventors constructed a multi-marker panel by combining the newly discovered GLUT3 and USP14 with the Parkinson's disease diagnostic markers AIMP2 and α-synuclein. Logistic regression analysis was used to verify whether the constructed multi-marker panel could increase the diagnostic accuracy of Parkinson's disease. The same logistic regression analysis method as in Example 4 was used. The log-odds function for each marker panel based on logistic regression analysis is shown in Table 6 below.

[0152]

[0153] Log probability function for each marker panel by logistic regression analysis Panel log function [ln(P / 1-P)]GLUT3-4.416 + 2.151X GLUT3 GLUT3, AIMP2-11.30 + 2.500X GLUT3 + 0.8427X AIMP2 GLUT3, αSyn-6.123 + 2.152X GLUT3 + 4.256X αsyn GLUT3, USP14-6.790 + 1.999X GLUT3 + 2.083X USP14 GLUT3, AIMP2, αSyn-15.46 + 2.627X GLUT3 + 1.019X AIMP2 + 7.965X αsyn GLUT3, AIMP2, USP14-14.17 + 2.469X GLUT3 + 0.8595X AIMP2 + 2.152X USP14 GLUT3, αSyn, USP14-8.380 + 1.995X GLUT3 + 4.363X αsyn + 1.999X USP14 GLUT3, AIMP2, αSyn, USP14-21.41 + 2.940X GLUT3 + 1.192X AIMP2 + 9.811X αsyn + 2.658X USP14

[0154]

[0155] The results are shown in Figure 7 and Table 7.

[0156] As shown in Figure 7, the optimal log regression function was derived by increasing the number of markers combined into GLUT3 alone, GLUT3 and AIMP2 panel, GLUT3 and α-synuclein panel, GLUT3 and USP14 panel, GLUT3, AIMP2 and α-synuclein panel, GLUT3, AIMP2 and USP14 panel, GLUT3, α-synuclein and USP14 panel, and GLUT3, AIMP2, α-synuclein and USP14 panel. ROC curve analysis was performed based on the log regression function derived from each multi-marker panel.

[0157]

[0158] Summary table of AUC, cut-off value, diagnostic sensitivity and specificity of ROC curve based on logistic regression analysis and log-odds function by multi-marker panelPanelAUCCut off(Probability)Sensitivity (%)Specificity (%)GLUT30.83750.341775.3272.84GLUT3, AIMP20.94820.641488.8989.61GLUT3, αSyn0.91700.556788.1684.15GLUT3, USP140.87870.438179.4977.50GLUT3, AIMP2, αSyn0.97980.790993.5991.25GLUT3, AIMP2, USP140.96190.681387.3486.08GLUT3, αSyn, USP140.93690.612687.1885.00GLUT3, AIMP2, αSyn, USP140.98780.838296.1593.75

[0159] As a result, as shown in Table 7 above, the AUC values ​​increased in all multi-marker panels compared to the GLUT3 marker alone, and the panel combining all four markers showed the maximum AUC value of 0.9878. In addition, when the sensitivity and specificity of each panel were analyzed using the probability value that can obtain the maximum sensitivity and specificity as the cut-off value, the sensitivity was 75.32% and the specificity was 72.84% for GLUT3 alone, while the diagnostic accuracy of Parkinson's disease was confirmed to have significantly increased with a sensitivity of 96.15% and a specificity of 93.75% for the four-marker panel at a cut-off value of 0.8382.

[0160]

[0161] Example 6: Identification of antibodies that specifically bind to AIMP2 monomers and / or AIMP2 aggregates.

[0162] 6-1. Production and verification of AIMP2 aggregates

[0163] To construct a single antibody clone that labels AIMP2 aggregates, the present inventors constructed a recombinant AIMP2 aggregate as an antigen. A vector encoding the GST-AIMP2 recombinant protein was transformed into BL21 cells, cultured, eluted using GST beads, and purified to obtain the GST-AIMP2 recombinant protein. The purified GST-AIMP2 recombinant protein (GST-AIMP2 monomer) was incubated in a test tube at 37°C and 250 rpm for 7 days to form high molecular weight aggregates (GST-AIMP2 fibrils). For reference, GST-truncated AIMP2 (monomer) and AIMP2 aggregates (AIMP2 fibrils) were obtained by dialyzing the GST-AIMP2 and GST-AIMP2 aggregates and treating with 100 μl of Glutathione Sepharose 4B (GE Heathcare #17-0756-01) and 10 μl of Precision protease (GE Heathcare #27-0843-01) and incubating overnight. The prepared GST-AIMP2 and GST-AIMP2 aggregates were subjected to Western blot analysis using an AIMP2-specific antibody (Proteintech Group rabbit antibody), and the GST-AIMP2 aggregates were loaded onto an SDS-PAGE gel and confirmed by Coomassie blue staining. As a result, GST-AIMP2 was detected, and it was confirmed that GST-AIMP2 aggregates (Day 7) were formed (Fig. 8A). Additionally, the amyloid structure of GST-AIMP2 aggregates was verified by thioflavin T fluorescence analysis (Fig. 8B).

[0164]

[0165] 6-2. Hybridoma cell line production and antibody analysis

[0166] As in Example 6-1 above, ELISA analysis was performed using GST-AIMP2 of Figure 8C, which formed aggregates in the form of amyloid structures, as an antigen, and it did not react to GST but bound to the GST-AIMP2 normal structure (monomer) or GST-AIMP2 aggregates (fibril).

[0167] Eight hybridoma clones, 3C2, 5B2, 6C7, 7G2, 10F3, 12D12, and 13H9, producing antibodies were selected (Fig. 9A). The culture supernatants of each hybridoma clone were collected, treated with Protein G IgG binding buffer (Thermo Fisher Scientific), and the antibodies produced by each clone were purified using a Capturem Protein G Maxiprep Column (Cat. No. 635727). The isotypes of the antibodies of each purified clone were analyzed (Fig. 9B).

[0168]

[0169] 6-3. Analysis of AIMP2 stereostructure-specific reactivity by clone

[0170] 6-3-1. Dot blot analysis

[0171] To verify the binding ability of each antibody produced for each of the eight clones selected in the above example to the three-dimensional structure of AIMP2, dot blot analysis was performed. Specifically, GST, GST-AIMP2 (Monomer), and GST-AIMP2 aggregates were diluted to the same concentration of 0.1 mg / ml with 1X PBS, printed on a nitrocellulose membrane, vacuumed, loaded with Ponceau, removed again, and blocked with 5% skimmed milk. The blocked membrane was washed three times with TBS-T, and the antibodies of each clone purified in the above example were reacted with the primary antibody at 4°C for 1 hour (rabbit AIMP2 antibody and GST antibody from Proteintech Group were used as controls). The membrane was washed three times with TBS-T and reacted with the secondary antibody (5000:1) at RT for 30 minutes. Afterwards, the blot was developed using ECL. As a result, unlike the ELISA results, 10F3 and 12D12 were found to be not suitable AIMP2 antibody clones because they also bound to GST. In addition, the rabbit AIMP2 antibody from Proteintech Group used as a control antibody labeled both the normal and aggregated structures of AIMP2 without selectivity, the antibody of the 7G2 clone showed no binding ability to either the AIMP2 monomer (normal structure) or aggregates, and the antibodies of the 10F3 clone and the 12D12 clone were found to recognize GST (Fig. 10a). In addition, the antibody clones of 5B2 and 6C7 bound relatively well to AIMP2 aggregates, and the antibody clones of 3C2, 7H9, and 13H9 showed the same level of binding signals to both the normal and aggregated structures of AIMP2 (Fig. 10a).

[0172]

[0173] 6-3-2. Western blot analysis

[0174] To verify antibody recognition for denatured forms of the protein, GST, GST-AIMP2 (Monomer), and GST-AIMP2 aggregates were separated by size on SDS-PAGE using Western blot analysis, and then the selective reactivity was analyzed using antibodies purified from each antibody clone. As a result, similar to the dot blot analysis results of Example 6-3-1 above, the conventional Proteintech Group antibody not only labeled the GST-AIMP2 normal structure (monomer) of about 60 kDa, but also detected all high-molecular-weight GST-AIMP2 aggregate structures. In the case of the 5B2 and 6C7 clones, which showed selectivity for AIM2 aggregates in the dot blot analysis, they were found to selectively label high-molecular-weight AIMP2 aggregates in particular, and in particular, the antibody of the 5B2 clone showed higher selectivity for AIMP2 aggregates than the antibody of the 6C7 clone (Fig. 10b). In addition, the 3C2 and 7H9 clones detected signals only for the normal structure of AIMP2 (60 kDa), indicating that they were clones that showed selectivity for the normal structure (monomer) of AIMP2 rather than the aggregates (Fig. 10b). In addition, the antibody purified from the 13H9 clone labeled all AIMP2 regardless of the structure (Fig. 10b).

[0175] As a result of synthesizing the above dot blot analysis results and Western analysis results, the 10F3 clone and the 12D12 clone also bound to GST, and the 7G2 clone was not a suitable AIMP2 antibody clone due to its low binding affinity to AIMP2. However, the 3C2 and 7H9 clones were identified as AIMP2 normal structure-specific single antibody-producing clones, 5B2 and 6C7 were identified as AIMP2 aggregate structure-specific single antibody-producing clones, and the 13H9 clone was identified as AIMP2 structure-nonspecific single antibody-producing clones.

[0176] It could be distinguished by production clones (Fig. 11).

[0177]

[0178] 6-4. CDS amino acid sequence analysis of AIMP2 antibody

[0179] In order to obtain the cDNA sequence of each antibody encoded by each antibody clone, the sequence of the antibody variable region was obtained by referring to the existing paper (Lena Meyer et al. A simplified workflow for monoclonal antibody sequencing, PlosOne, 2019). Specifically, for the cDNA extracted from each hybridoma cell, the variable regions of the light and heavy chains were amplified by PCR using the primers in Table 8 below, cloned into a T vector, and then the T vector including the light chain variable region and heavy chain variable region was sequenced to obtain the cDNA sequence, and the variable region sequence was analyzed using IgBlast (Igblast: https: / www.ncbi.nlm.nih.gov / igblast / ).

[0180] The amino acid sequence of the CDR (complementarity determining region) was also analyzed.

[0181] Primer NameForward or reversePrimer SequenceISPCRUniversal F primer5' aagcagtggtatcaacgcagag 3'mIGK PCRR primer for kappa chain5' acattgatgtcttttggggtagaag 3'mIGL PCRR primer for lamda chain5' atcgtacacaccagtgtggc 3'mIGHG PCRR primer for heavy chain5' gggatccagagttccaggtc 3'

[0182] As a result, the cDNA sequences of the 5B2 and 6C7 clones, which showed specific reactivity to AIMP2 aggregates, were analyzed, and the amino acid sequences of the CDR regions were confirmed to be identical, and only one of the amino acids encoding the light chain was different (Figure 12). Accordingly, the entire antibody sequence, including the sequence of the constant region, was also constructed when making a humanized IgG using this (Figure 12). In the same way, the sequences of the variable regions of the antibodies of the 3C2 and 7H9 clones, which showed specific binding ability to the normal AIMP2 structure, were analyzed, and the two clones also shared the same CDR amino acid sequences, and the differences were only found in two amino acid sequences in the variable region of the light chain (Figure 13). Similarly, the entire sequence of a humanized antibody polymerized with the constant region of human IgG was also constructed (Figure 13). In addition, the antibody clone 13H9, which detects both the normal AIMP2 structure and aggregates, showed an antibody amino acid sequence that was distinct from the other four clones above (Figure 14). The amino acid sequence of the variable region, the CDR amino acid sequence, and the entire amino acid sequence of the humanized antibody are also described in Figure 14.

[0183]

[0184] Example 7: Analysis of the expression of AIMP2 aggregates in the serum of control and Parkinson's disease patients.

[0185] To analyze the expression of AIMP2 aggregates in the sera of control and Parkinson's disease patients, dot blot and ROC curve analyses were performed.

[0186] NC membranes were prepared by immersing them in TBS buffer, and the prepared NC membranes were set in a Bio-Dot microfiltration apparatus (Bio-Rad) and connected to a vacuum tube. After loading the samples into each well, the Bio-Dot microfiltration apparatus was detached and the NC membranes were washed three times for 5 minutes each with TBS-T buffer (TBS buffer containing 5% Tween 20). The NC membranes were blocked with 2.5% skim milk (TBS-T base) for 1 hour. The NC membranes were incubated with primary antibodies (O / N). They were washed three times for 10 minutes each with TBS-T buffer, and incubated for 1 hour and 30 minutes with HRP-conjugated secondary antibodies corresponding to the primary antibodies. The membranes were washed three times for 10 minutes each with TBS-T buffer, and the results were confirmed in a darkroom using ECL solution. After normalizing the quantitative values ​​obtained through dot blot, ROC curve analysis was performed using the GraphPad prism program.

[0187] The results are shown in Figures 15 to 17.

[0188] As shown in Figures 15 and 16, the expression of AIMP2 as well as AIMP2 aggregates was significantly higher in Parkinson's disease patients than in the control group. As shown in Figure 17, the ROC curve analysis results showed an AUC value of 0.731, confirming that AIMP2 aggregate concentration can be used as a marker for diagnosing Parkinson's disease. The optimal cut-off value was 2.6 pg / μl, and the sensitivity and specificity at this value were confirmed to be 58.3% and 87.5%, respectively.

[0189]

[0190] The sequence list used in the present invention is as shown in the table below.

[0191] <h2 style=";text-align:left;direction:ltr">서열번호서열 명칭서열1ISPCR - Universal F primer5' aagcagtggtatcaacgcagag 3'2mIGK PCR - R primer for kappa chain5' acattgatgtctttggggtagaag 3'3mIGL PCR - R primer for lambda chain5' atcgtacacaccagtgtggc 3'4mIGHG PCR - R primer for heavy chain5' gggatccagagttccaggtc 3'55B2 / 6C7-CDRH1GFTFSDFY65B2 / 6C7-CDRH2SRNKANDYTT75B2 / 6C7-CDRH3ARDAFYGYGA MDY85B2 / 6C7-CDRL1KSIRKY95B2 / 6C7-CDRL2SGS105B2 / 6C7-CDRL3QQHNEYPYT115B2 / 6C7-VHMKLWLNWVFL LTLLHGIQCE VKLVESGGGL VQPGGSLRLS CATSGFTFSD FYMEWVRQPP GKRLEWIAAS RNKANDYTTE YSGSTLQSGI PSRFSGSGSG TDFTLTISSL EPEDFAMYYC QQHNEYPYTF GGGTKLEIK136C7-VLMRFQVQVLGL LLLWISGAQC DVQITQSPSY LAASPGETIT INCRASKSIR KYLAWYQEKP GKTNKLLIYS GSTLQSGIPS RFSGSGSGID FTLTISSLEP EDFAMYYCQQ HNEYPYTFGG GTKLEIK145B2 / 6C7-Heavy chainMKLWLNWVFL LTLLHGIQCE VKLVESGGGL VQPGGSLRLS CATSGFTFSD FYMEWVRQPP GKRLEWIAAS RNKANDYTTE YSESVKGRFI VSRDISQSIL<h2 style=";text-align:left;direction:ltr">YLQMNALRAE DTAIYYCARD AFYGYGAMDY WGQGTSVTVS SASTKGPSVF PLAPCSRSTS ESTAALGCLV KDYFPEPVTV SWNSGALTSG VHTFPAVLQS SGLYSLSSVV TVPSSNFGTQ TYTCNVDHKP SNTKVDKTVE RKCCVECPPC PAPPVAGPSV FLFPPKPKDT LMISRTPEVT CVVVDVSHED PEVQFNWYVD GVEVHNAKTK PREEQFNSTF RVVSVLTVVH QDWLNGKEYK CKVSNKGLPA PIEKTISKTK GQPREPQVYT LPPSREEMTK NQVSLTCLVK GFYPSDISVE WESNGQPENN YKTTPPMLDS DGSFFLYSKL TVDKSRWQQG NVFSCSVMHE ALHNHYTQKS LSLSPGK155B2-Light chainMGNQFLPGHS LDMRFQVQVL GLLLLWISGA QCDVQITQSP SYLAASPGET ITINCRASKS IRKYLAWYQE KPGKTNKLLI YSGSTLQSGI PSRFSGSGSG TDFTLTISSL EPEDFAMYYC QQHNEYPYTF GGGTKLEIKR TVAAPSVFIF PPSDEQLKSG TASVVCLLNN FYPREAKVQW KVDNALQSGN SQESVTEQDS KDSTYSLSST LTLSKADYEK HKVYACEVTH QGLSSPVTKS FNRGEC166C7-Light chainMRFQVQVLGL LLLWISGAQC DVQITQSPSY LAASPGETIT INCRASKSIR KYLAWYQEKP GKTNKLLIYS GSTLQSGIPS RFSGSGSGID FTLTISSLEP EDFAMYYCQQ HNEYPYTFGG GTKLEIKRTV AAPSVFIFPP SDEQLKSGTA SVVCLLNNFY PREAKVQWKV DNALQSGNSQ ESVTEQDSKD STYSLSSTLT LSKADYEKHK VYACEVTHQG LSSPVTKSFNRGEC173C2 / 7H9-CDRH1GFSLSTSGMG183C2 / 7H9-CDRH2IWWDDDK193C2 / 7H9-CDRH3ARIANSWFAY203C2-CDRL1KSVSTSAYSY217H9-CDRL1KSVSTSGYTY223C2 / 7H9-CDRL2LAS233C2 / 7H9-CDRL3QHSRELPPT243C2-VHMGRLTSSFLL LIVPAYVLSQ VTLKESGPGI LQPSQTLSLT CSFSGFSLST SGMGVGWIRQ PSGKGLEWLA HIWWDDDKYY NTALKSGLTI SKDTSKNQVF LKIASVDTAD TATYYCARIA NSWFAYWGQG TLVTVSA257H9-VHMGDKCAVMGR LTSSFLLLIV PAYVLSQVTL KESGPGILQP SQTLSLTCSF SGFSLSTSGM GVGWIRQPSG KGLEWLAHIW WDDDKYYNTA LKSGLTISKD TSKNQVFLKI ASVDTADTAT YYCARIANSW FAYWGQGTLV TVSA263C2-VLMGSCQEPKKH PLFQLSEMET DTLLLWVLLL WVPGSTGDIV LTQSPASLAV SLGQRATISC RASKSVSTSA YSYMHWYQQK PGQPPKLLIY LASNLDSGVP ARFSGSGSGT DFTLNIHPVE EEDAATYYCQ HSRELPPTFG AGTKLELK277H9-VLMETDTLLLWV LLLWVPGSTG DIVLTQSPAS LAVSLGQRAT ISCRASKSVS TSGYTYMHWY QQKPGQSPKL LIYLASNLES GVPARFSGSG SGTDFTLNIH PVEEEDAATY YCQHSRELPP TFGAGTKLEL K283C2-Heavy chainMGRLTSSFLL LIVPAYVLSQ VTLKESGPGI LQPSQTLSLT CSFSGFSLST SGMGVGWIRQ PSGKGLEWLA HIWWDDDKYY NTALKSGLTI SKDTSKNQVF LKIASVDTAD TATYYCARIA NSWFAYWGQG TLVTVSAAST KGPSVFPLAP CSRSTSESTAALGCLVKDYF PEPVTVSWNS GALTSGVHTF PAVLQSSGLY SLSSVVTVPS SNFGTQTYTC NVDHKPSNTK VDKTVERKCC VECPPCPAPP VAGPSVFLFP PKPKDTLMIS RTPEVTCVVV DVSHEDPEVQ FNWYVDGVEV HNAKTKPREE QFNSTFRVVS VLTVVHQDWL NGKEYKCKVS NKGLPAPIEK TISKTKGQPR EPQVYTLPPS REEMTKNQVS LTCLVKGFYP SDISVEWESN GQPENNYKTT PPMLDSDGSF FLYSKLTVDK SRWQQGNVFS CSVMHEALHN HYTQKSLSLS PGK297H9-Heavy chainMGDKCAVMGR LTSSFLLLIV PAYVLSQVTL KESGPGILQP SQTLSLTCSF SGFSLSTSGM GVGWIRQPSG KGLEWLAHIW WDDDKYYNTA LKSGLTISKD TSKNQVFLKI ASVDTADTAT YYCARIANSW FAYWGQGTLV TVSAASTKGP SVFPLAPCSR STSESTAALG CLVKDYFPEP VTVSWNSGAL TSGVHTFPAV LQSSGLYSLS SVVTVPSSNF GTQTYTCNVD HKPSNTKVDK TVERKCCVEC PPCPAPPVAG PSVFLFPPKP KDTLMISRTP EVTCVVVDVS HEDPEVQFNW YVDGVEVHNA KTKPREEQFN STFRVVSVLT VVHQDWLNGK EYKCKVSNKG LPAPIEKTIS KTKGQPREPQ VYTLPPSREE MTKNQVSLTC LVKGFYPSDI SVEWESNGQP ENNYKTTPPM LDSDGSFFLY SKLTVDKSRW QQGNVFSCSV MHEALHNHYT QKSLSLSPGK303C2-Light chainMGSCQEPKKH PLFQLSEMET DTLLLWVLLL WVPGSTGDIV LTQSPASLAV SLGQRATISC RASKSVSTSA YSYMHWYQQK PGQPPKLLIY LASNLDSGVPARFSGSGSGT DFTLNIHPVE EEDAATYYCQ HSRELPPTFG AGTKLELKRT VAAPSVFIFP PSDEQLKSGT ASVVCLLNNF YPREAKVQWK VDNALQSGNS QESVTEQDSK DSTYSLSSTL TLSKADYEKH KVYACEVTHQ GLSSPVTKSF NRGEC317H9-Light chainMETDTLLLWV LLLWVPGSTG DIVLTQSPAS LAVSLGQRAT ISCRASKSVS TSGYTYMHWY QQKPGQSPKL LIYLASNLES GVPARFSGSG SGTDFTLNIH PVEEEDAATY YCQHSRELPP TFGAGTKLEL KRTVAAPSVF IFPPSDEQLK SGTASVVCLL NNFYPREAKV QWKVDNALQS GNSQESVTEQ DSKDSTYSLS STLTLSKADY EKHKVYACEV THQGLSSPVT KSFNRGEC3213H9-CDRH1DYSITSGYS3313H9-CDRH2IHYRGST3413H9-CDRH3ATWDYRLAY3513H9-CDRL1SQHSTYT3613H9-CDRL2LKKDGSH3713H9-CDRL3GVGDTIKEQF VYV3813H9-VHMRVLILLCLF TAFPGILSDV QLQESGPDLV KPSQSLSLTC TVTDYSITSG YSWHWIRQFP GNKLEWMAYI HYRGSTNYNP SLKSRISITR DTSKNQFFLQ LNSVTTEDTA TYYCATWDYR LAYWGQGTLV TVSA3913H9-VLMGNLSVSAEI SSTCIMAWTP LFFFFVLHCS GSFSQLVLTQ SSSASFSLGA SAKLTCTLSS QHSTYTIEWY QQQPLKPPKY VMELKKDGSH STGDGIPDRF SGSSSGADRY LSISNIQPED EAIYICGVGD TIKEQFVYVF GGGTKVTVL4013H9-Heavy chainMRVLILLCLF TAFPGILSDV QLQESGPDLV KPSQSLSLTC TVTDYSITSG YSWHWIRQFP GNKLEWMAYIHYRGSTNYNP SLKSRISITR DTSKNQFFLQ LNSVTTEDTA TYYCATWDYR LAYWGQGTLV TVSAASTKGP SVFPLAPCSR STSESTAALG CLVKDYFPEP VTVSWNSGAL TSGVHTFPAV LQSSGLYSLS SVVTVPSSNF GTQTYTCNVD HKPSNTKVDK TVERKCCVEC PPCPAPPVAG PSVFLFPPKP KDTLMISRTP EVTCVVVDVS HEDPEVQFNW YVDGVEVHNA KTKPREEQFN STFRVVSVLT VVHQDWLNGK EYKCKVSNKG LPAPIEKTIS KTKGQPREPQ VYTLPPSREE MTKNQVSLTC LVKGFYPSDI SVEWESNGQP ENNYKTTPPM LDSDGSFFLY SKLTVDKSRW QQGNVFSCSV MHEALHNHYT QKSLSLSPGK4113H9-Light chainMGNLSVSAEI SSTCIMAWTP LFFFFVLHCS GSFSQLVLTQ SSSASFSLGA SAKLTCTLSS QHSTYTIEWY QQQPLKPPKY VMELKKDGSH STGDGIPDRF SGSSSGADRY LSISNIQPED EAIYICGVGD TIKEQFVYVF GGGTKVTVLG QPKAAPSVTL FPPSSEELQA NKATLVCLIS DFYPGAVTVA WKADSSPVKA GVETTTPSKQ SNNKYAASSY LSLTPEQWKS HRSYSCQVTH EGSTVEKTVA PTECS

Claims

1. A composition for diagnosing Parkinson's disease, further comprising an agent for detecting a combination of one or more proteins selected from the group consisting of GLUT3 (glucose transporter 3), USP14 (ubiquitin-specific protease 14), α-synuclein, and AIMP2 (aminoacyl-tRNA synthetase complex interacting multifunctional protein-2), an aggregate of the proteins, or a gene encoding the proteins.

2. A composition for diagnosing Parkinson's disease, comprising an agent for detecting GLUT3 protein, an aggregate of the protein, or a gene encoding the protein.

3. A composition for diagnosing Parkinson's disease, further comprising an agent for detecting a combination of any one or more proteins selected from the group consisting of USP14, α-synuclein, and AIMP2, aggregates of the proteins, or a gene encoding the proteins, in the second paragraph.

4. A composition for diagnosing Parkinson's disease, wherein the protein in paragraph 1 is a protein in a blood sample isolated from a human body.

5. A composition for diagnosing Parkinson's disease, wherein the agent for detecting the protein in the first paragraph is at least one selected from the group consisting of an antibody, an aptamer, a ligand, a PNA (peptide nucleic acid), and an oligopeptide.

6. A composition for diagnosing Parkinson's disease, wherein the agent for detecting the gene in the first paragraph is at least one selected from the group consisting of a primer, a probe, and an antisense nucleotide.

7. A kit for diagnosing Parkinson's disease, comprising a composition according to any one of claims 1 to 6.

8. A method for providing information for diagnosing Parkinson's disease, including the following steps: (a) measuring the expression level of GLUT3 protein, aggregates of the protein, or a gene encoding the protein in a blood sample isolated from a human body; and (b) A step of providing information that if the expression level is increased compared to a normal blood control sample, there is a high possibility of Parkinson's disease.

9. A method for providing information for diagnosing Parkinson's disease, wherein step (a) is step (a-1) below, and step (b) is step (b-1) below: (a-1) A step of measuring the expression level of a protein comprising a combination of any one or more selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein in a blood sample isolated from a human body; (b-1) A step of providing information that the possibility of Parkinson's disease is high when the expression level of a protein comprising a combination of at least one selected from the group consisting of GLUT3; and USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein is increased compared to a normal blood control sample.

10. A method for providing information for diagnosing Parkinson's disease, wherein the step of measuring the protein level in paragraph 8 is performed using one or more agents selected from the group consisting of antibodies, aptamers, ligands, PNA (peptide nucleic acid) and oligopeptides.

11. A method for providing information for diagnosing Parkinson's disease, wherein the step of measuring the expression level of the gene in paragraph 8 is performed using at least one agent selected from the group consisting of a primer, a probe, and an antisense nucleotide.

12. A method for screening a Parkinson's disease treatment agent, comprising the following steps: a) administering a drug candidate to a Parkinson's disease animal model having increased levels of GLUT3 protein, aggregates of the protein, or expression levels of a gene encoding the protein in the blood; b) a step of collecting blood from the animal model after step a) and measuring the protein or mRNA expression level in the collected blood; and c) A step of determining that the drug candidate is a treatment for Parkinson's disease when the level of GLUT3 protein, aggregates of the protein, or gene expression level encoding the protein in the collected blood shows a significant decrease due to administration of the drug candidate.

13. In the 12th paragraph, the step a) is the following step a-1), and the step c) is the following step c-1), a screening method for a Parkinson's disease treatment agent: a-1) A step of administering a drug candidate to a Parkinson's disease animal model in which the expression level of a protein comprising a combination of at least one selected from the group consisting of GLUT3 in the blood, USP14, α-synuclein, and AIMP2, an aggregate of the protein, or a gene encoding the protein is increased; c-1) A step of determining a drug candidate as a treatment for Parkinson's disease when a protein comprising a combination of at least one selected from the group consisting of GLUT3, USP14, α-synuclein, and AIMP2 in the collected blood, an aggregate of the protein, or the expression level of a gene encoding the protein shows a significant decrease by administration of the drug candidate.

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