WO2021230379A1 - パーキンソン病の検出方法 - Google Patents

パーキンソン病の検出方法 Download PDF

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WO2021230379A1
WO2021230379A1 PCT/JP2021/018511 JP2021018511W WO2021230379A1 WO 2021230379 A1 WO2021230379 A1 WO 2021230379A1 JP 2021018511 W JP2021018511 W JP 2021018511W WO 2021230379 A1 WO2021230379 A1 WO 2021230379A1
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gene
expression
disease
parkinson
expression level
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English (en)
French (fr)
Japanese (ja)
Inventor
裕也 上原
高良 井上
信孝 服部
臣二 斉木
真一 上野
遙香 竹重
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Kao Corp
Juntendo Educational Foundation
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Kao Corp
Juntendo Educational Foundation
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Priority to US17/924,640 priority Critical patent/US20230183806A1/en
Priority to EP21805028.4A priority patent/EP4151752A4/en
Priority to CN202180035027.2A priority patent/CN115605608A/zh
Publication of WO2021230379A1 publication Critical patent/WO2021230379A1/ja
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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
    • G01N33/6896—Neurological disorders, e.g. Alzheimer's disease
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/28—Neurological disorders
    • G01N2800/2835—Movement disorders, e.g. Parkinson, Huntington, Tourette

Definitions

  • the present invention relates to a method for detecting Parkinson's disease using a Parkinson's disease marker.
  • Parkinson's disease is pathologically a progressive neurodegenerative disease mainly composed of formation of Lewy bodies mainly composed of ⁇ -synuclein aggregates, degeneration of dopamine nerve cells in the substantia nigra of the midbrain, and cell death. It is a disease mainly caused by motor disorders such as muscle stiffness, tremor, tremor, and walking disorders. Parkinson's disease is the second most common neurodegenerative disease after Alzheimer's disease, and the prevalence is said to be 120 to 130 out of 100,000, and it is estimated that there are about 140,000 patients in Japan.
  • Parkinson's disease there is no curative treatment for Parkinson's disease, and it is considered important to maintain QOL by controlling the symptoms by symptomatic treatment such as L-DOPA supplementation.
  • symptomatic treatment such as L-DOPA supplementation.
  • the subjective symptoms of movement disorders appear after the middle stage, and it is required to diagnose the disease at an early stage and to seek early intervention.
  • Biomarkers for detecting Parkinson's disease include detection of ⁇ -synuclein accumulation, detection of microRNA derived from circulating serum (Patent Document 1), and measurement of the concentration ratio of tyrosine to phenylalanine in blood. That (Patent Document 2) and the like have been proposed.
  • Patent Document 1 the formation of ⁇ -sinucrane aggregates is observed in the skin of Parkinson's disease patients as in the brain (Non-Patent Document 1). It has been reported that skin diseases and symptoms such as liquor appear (Non-Patent Document 2), and it is considered that there is some relationship between Parkinson's disease and the condition of the skin, but the scientific relationship is completely unknown.
  • RNA contained in skin surface lipids is used as a sample for analysis of living organisms.
  • Patent Document 3 It has been reported that marker genes for epidermis, sweat glands, hair follicles and sebaceous glands can be detected from SSL (Patent Document 3).
  • Patent Document 1 Japanese Patent Application Laid-Open No. 2019-506183
  • Patent Document 2 Japanese Patent Application Laid-Open No. 2016-75644
  • Patent Document 3 International Publication No. 2018/008319
  • Non-Patent Document 1 Rodriguez-Leyva I et al. Ann Clin Transl Neurol. 2014 (modified)
  • Non-Patent Document 2 Ravn AH et al. Clin Cosmet Investig Dermatol. 2017
  • the present invention relates to the following 1) to 3).
  • the subject comprises a step of measuring the expression level of at least one gene selected from four gene groups consisting of SNORA16A, SNORA24, SNORA50 and REXO1L2P or an expression product thereof. How to detect Parkinson's disease.
  • a test kit for detecting Parkinson's disease used in the method of 1), which contains an oligonucleotide that specifically hybridizes with the gene or an antibody that recognizes an expression product of the gene.
  • a marker for detecting Parkinson's disease which comprises at least one gene selected from the gene groups shown in Tables 3-1 to 3-4 and Tables 6-1 to 6-2 or an expression product thereof.
  • a confusion matrix that plots the predicted and measured values of the optimal prediction model in the test data.
  • the present invention relates to providing a marker for detecting Parkinson's disease and a method for detecting Parkinson's disease using the marker.
  • the present inventors have found that the expression level of a specific gene is between the two. It was found that Parkinson's disease can be detected using this as an index.
  • Parkinson's disease can be detected at an early stage with high accuracy, sensitivity and specificity in a simple and non-invasive manner.
  • nucleic acid or “polynucleotide” means DNA or RNA.
  • DNA includes any of cDNA, genomic DNA, and synthetic DNA
  • RNA includes any of total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.
  • the "gene” is a double-stranded DNA containing human genomic DNA, a single-stranded DNA containing cDNA (positive strand), and a single-stranded DNA having a sequence complementary to the positive strand (complementary strand). , And those fragments that include some biological information in the sequence information of the bases constituting the DNA.
  • the "gene” includes not only “genes” represented by specific base sequences, but also nucleic acids encoding homologues (that is, homologs or orthologs), mutants such as gene polymorphisms, and derivatives. Will be done. The names of the genes disclosed herein follow the Official Symbol described in NCBI ([www.ncbi.nlm.nih.gov/]). On the other hand, regarding Gene Ontology (GO), Pathway ID.
  • the "expression product" of a gene is a concept including a transcript and a translation product of the gene.
  • the "transcription product” is RNA produced by being transcribed from a gene (DNA), and the “translation product” means a protein encoded by a gene, which is translated and synthesized based on RNA.
  • Parkinson's disease is mainly caused by degeneration of substantia nigra dense layer dopamine nerve cells, and slowly and progressively develops three signs of exercise (resting tremor, atrophy, bradykinesia and akinesia). It means idiopathic and progressive disease.
  • detection of Parkinson's disease means to clarify the presence or absence of Parkinson's disease, and can be paraphrased by the terms test, measurement, judgment, evaluation or evaluation support.
  • judgment or evaluation in the present specification does not include judgment or evaluation by a doctor.
  • the expression level of SSL-derived RNA shown in Examples described later was significantly increased (UP) or decreased (UP) or decreased in Parkinson's disease patients with respect to healthy subjects. It is a gene selected from the 33 genes listed in Table A below, which was found to be DOWN), and is a gene for which no association with Parkinson's disease was previously known (in the table, in bold). show.).
  • RNAs extracted from the SLs of subjects in two trials (Study 1: healthy subjects / patients with Parkinson's disease 15 each, Study 2: healthy subjects / patients with Parkinson's disease 50 each). Based on the value obtained by converting the expression level data (read count value) into an RPM value corrected for the difference in the total number of reads between samples and converting the RPM value into a logarithmic value of the base 2 (Log 2 RPM value). , We identified RNAs with a p value of 0.05 or less in Student's t-test in patients with Parkinson's disease compared to healthy subjects (Test 1: 111 genes with increased expression and 68 genes with decreased expression (179 in total).
  • a gene selected from the group consisting of 179 genes and 859 genes (total 1005 genes excluding duplication) or an expression product thereof can be a Parkinson's disease marker for detecting Parkinson's disease, and among them, it is shown in Table A.
  • a gene selected from the group consisting of 33 genes or an expression product thereof is a preferred Parkinson's disease marker.
  • the "P value” is a statistic that is more extreme than the statistic actually calculated from the data under the null hypothesis in the statistical test. Shows the probability. Therefore, it can be considered that the smaller the "P value” is, the more significant the difference is between the comparison targets.
  • the gene indicated by “UP” is a gene whose expression level is increased in Parkinson's disease patients
  • the gene indicated by “DOWN” is a gene whose expression level is decreased in Parkinson's disease patients.
  • the gene group whose expression was varied above was found to include those related to Parkinson's disease (hsa05012) by searching for biological processes (BP) and KEGG pathway by Gene Ontology (GO) enrichment analysis (see below). See Table 2).
  • the genes shown in Tables 3-1 to 3-4 below are genes for which no relationship with Parkinson's disease has been reported so far. Therefore, at least one gene selected from these gene groups or an expression product thereof is a novel Parkinson's disease marker for detecting Parkinson's disease, and in particular, SNORA16A, SNORA24, and SNORA50 common to Test 1 and Test 2.
  • At least one gene selected from the group consisting of REXO1L2P or an expression product thereof is preferable as a novel Parkinson's disease marker. More preferably, two or more species selected from the group, further preferably three or more species, and even more preferably all four species. Further, it is preferable to contain at least SNORA24 which is commonly contained in Table A and Table B below.
  • the Normalized count value was used as the data of the expression level of RNA extracted from the SLs of the subjects of the above two tests, and the corrected value of the p value by the likelihood ratio test was used in the Parkinson's disease patient as compared with the healthy subject (the corrected value of the p value by the likelihood ratio test.
  • RNA with FDR When RNA with FDR) of 0.25 or less was identified, 74 genes with increased expression and 209 genes with decreased expression were identified in Test 1, for a total of 283 genes (Tables 4-1 to 4-8), and in Test 2, expression was increased. A total of 459 genes (Tables 4-9 to 4-20) can be obtained, with 151 genes and 308 underexpression genes.
  • 7 genes ANXA1, AQP3, EMP1, KRT16, POLR2L, SERPINB4, SNORA24
  • 10 genes are decreased (ATP6V0C, BHLHE40, CCL3, CCNI, CXCR4, EGR2).
  • GABARAPL1, RHOA, RNASEK, SERINC1 totaling 17 types (Table B).
  • a gene selected from the group consisting of such 283 genes and 459 genes (total of 725 genes excluding duplication) or an expression product thereof can be a Parkinson's disease marker for detecting Parkinson's disease, and among them, it is shown in Table B.
  • the gene selected from the group consisting of 17 genes or its expression product is a preferred Parkinson's disease marker, and among these, 11 genes shown in Table C below, which are common to the genes shown in Table A above.
  • a gene or expression product thereof selected from the group consisting of groups is a more preferred marker for Parkinson's disease.
  • the genes shown in Tables 6-1 to 6-2 below are genes for which no relationship with Parkinson's disease has been reported so far. Therefore, at least one gene selected from these gene groups or an expression product thereof is a novel Parkinson's disease marker for detecting Parkinson's disease, and in particular, SNORA24 (in the table, which is common to Test 1 and Test 2). Bold type) or its expression product is preferred as a novel Parkinson's disease marker.
  • the above-mentioned gene that can be a Parkinson's disease marker (hereinafter, also referred to as "target gene") is substantially the same as the base sequence of the DNA constituting the gene as long as it can be a biomarker for detecting Parkinson's disease. Genes having the base sequence of are also included.
  • the method for detecting Parkinson's disease of the present invention is the expression of at least one gene selected from the group consisting of a target gene, SNORA24A, SNORA24, SNORA50 and REXO1L2P, or an expression product thereof, in a biological sample collected from a subject. Includes the step of measuring the level.
  • examples of the subject for collecting a biological sample include mammals including humans and non-human mammals, and humans are preferable.
  • the sex, age, race, etc. are not particularly limited and may include infants to elderly people.
  • the subject is a human who needs or desires detection of Parkinson's disease.
  • the subject is a person suspected of developing Parkinson's disease or a person genetically predisposed to Parkinson's disease.
  • the biological sample used in the present invention may be a tissue or biomaterial whose expression changes with the development and progression of Parkinson's disease.
  • Specific examples include organs, skin, blood, urine, saliva, sweat, stratum corneum, body fluids such as skin surface lipids (SSL), tissue exudates, serum prepared from blood, plasma, others, stool, hair, etc.
  • the skin, stratum corneum or superficial skin lipid (SSL) is preferable, and skin superficial lipid (SSL) is more preferable.
  • the part of the skin from which the SSL is collected is not particularly limited, and examples thereof include the skin of any part of the body such as the head, face, neck, trunk, and limbs, and the part where sebum is secreted, for example, the head or face. Skin is preferred, and facial skin is more preferred.
  • SSL lipid on the surface of the skin
  • SSL lipid on the surface of the skin
  • SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and is present on the surface of the skin in the form of a thin layer covering the skin surface.
  • SSL contains RNA expressed in skin cells (see Patent Document 3 above).
  • skin is a general term for regions including tissues such as the stratum corneum, epidermis, dermis, hair follicles, and sweat glands, sebaceous glands and other glands, unless otherwise specified.
  • any means used for the recovery or removal of SSL from the skin can be adopted.
  • an SSL absorbent material, an SSL adhesive material, or an instrument that scrapes SSL from the skin, which will be described later, can be used.
  • the SSL absorbent material or the SSL adhesive material is not particularly limited as long as it is a material having an affinity for SSL, and examples thereof include polypropylene and pulp.
  • a method of absorbing SSL into a sheet-like material such as an oil removing paper or an oil removing film, a method of adhering SSL to a glass plate, a tape, etc., a spatula, a scraper, etc.
  • a method of scraping off the SSL and collecting the SSL can be mentioned.
  • an SSL-absorbing material pre-impregnated with a highly lipophilic solvent may be used.
  • the SSL-absorbent material contains a highly water-soluble solvent or water, the adsorption of SSL is inhibited, so that it is preferable that the content of the highly water-soluble solvent or water is small.
  • the SSL absorbent material is preferably used in a dry state.
  • the part of the skin from which the SSL is collected is not particularly limited, and examples thereof include the skin of any part of the body such as the head, face, neck, trunk, and limbs, and the part where sebum is secreted, for example, the skin of the face. Is preferable.
  • RNA-containing SSL collected from the subject may be stored for a certain period of time. It is preferable that the collected SSL is stored under low temperature conditions as soon as possible after collection in order to suppress the decomposition of the contained RNA as much as possible.
  • the temperature condition for storing the RNA-containing SSL in the present invention may be 0 ° C. or lower, preferably ⁇ 20 ⁇ 20 ° C. to ⁇ 80 ⁇ 20 ° C., more preferably ⁇ 20 ⁇ 10 ° C. to ⁇ 80 ⁇ 10 ° C. It is more preferably ⁇ 20 ⁇ 20 ° C. to ⁇ 40 ⁇ 20 ° C., further preferably ⁇ 20 ⁇ 10 ° C.
  • the storage period of the RNA-containing SSL under the low temperature condition is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less. More preferably, it is 3 months or less, for example, 3 days or more and 3 months or less.
  • the target for measuring the expression level of the target gene or its expression product is a cDNA artificially synthesized from RNA, a DNA encoding the RNA, a protein encoded by the RNA, and an interaction with the protein.
  • Examples include molecules that interact with RNA, molecules that interact with DNA, and the like.
  • examples of molecules that interact with RNA, DNA or protein include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, their phosphorylates, alkylated products, sugar adducts and the like, and Examples thereof include any of the above complexes.
  • the expression level comprehensively means the expression level and activity of the gene or expression product.
  • SSL is used as a biological sample.
  • the expression level of RNA contained in SSL is analyzed, and specifically, after RNA is converted to cDNA by reverse transcription. , The cDNA or its amplification product is measured.
  • methods commonly used for extracting or purifying RNA from biological samples such as the phenol / chloroform method, AGPC (acid guanidinium thiocyanate-phenyl-chloroform extraction) method, or TRIzol®. ), RNAy®, QIAzol® and other columns, silica-coated special magnetic particles, Solid Phase Reversible Immobilization magnetic particles, ISOGEN and the like on the market. Extraction with an RNA extraction reagent or the like can be used.
  • Primers targeting the specific RNA to be analyzed may be used for the reverse transcription, but random primers are preferably used for more comprehensive nucleic acid storage and analysis.
  • a general reverse transcriptase or reverse transcriptase kit can be used for the reverse transcription.
  • a highly accurate and efficient reverse transcriptase or reverse transcriptase kit is used, for example, M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcriptase kits.
  • PrimeScript registered trademark
  • Reverse Transcriptase series Takara Bio Co., Ltd.
  • SuperScript registered trademark Reverse Transcriptase series (Thermo Scientific) and the like can be mentioned.
  • the temperature is preferably adjusted to 42 ° C. ⁇ 1 ° C., more preferably 42 ° C. ⁇ 0.5 ° C., still more preferably 42 ° C. ⁇ 0.25 ° C., while the reaction time is preferably adjusted. It is preferably adjusted for 60 minutes or more, more preferably 80 to 120 minutes.
  • the method for measuring the expression level is typified by the PCR method using a DNA that hybridizes to these as a primer, the real-time RT-PCR method, the multiplex PCR, the SmartAmp method, the LAMP method, and the like.
  • Nucleic acid amplification method, hybridization method using nucleic acid hybridizing to these as a probe DNA chip, DNA microarray, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.
  • method for determining base sequence Sequencing or a combination of these methods can be selected.
  • PCR only the specific DNA may be amplified using a primer pair targeting the specific DNA to be analyzed, or a plurality of DNAs may be amplified using a plurality of primer pairs.
  • the PCR is multiplex PCR.
  • Multiplex PCR is a method of simultaneously amplifying a plurality of gene regions by simultaneously using a plurality of primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (for example, Ion AmpliSeqTranstriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.).
  • the temperature of the annealing and extension reactions in the PCR cannot be unequivocally determined because it depends on the primers used, but when the above multiplex PCR kit is used, it is preferably 62 ° C ⁇ 1 ° C, more preferably 62 ° C ⁇ 0. It is 5.5 ° C, more preferably 62 ° C ⁇ 0.25 ° C. Therefore, in the PCR, the annealing and extension reactions are preferably carried out in one step.
  • the time of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified and the like, but is preferably 14 to 18 minutes.
  • the conditions of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, preferably 95 to 99 ° C. for 10 to 60 seconds. Reverse transcription and PCR at temperature and time as described above can be performed using a thermal cycler commonly used for PCR.
  • the reaction product obtained by the PCR is purified by size separation of the reaction product.
  • size separation By size separation, the desired PCR reaction product can be separated from the primers and other impurities contained in the PCR reaction solution.
  • DNA size separation can be performed by, for example, a size separation column, a size separation chip, magnetic beads that can be used for size separation, or the like.
  • Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Aple XP.
  • SPRI Solid Phase Reversible Immobilization
  • the purified PCR reaction product may be subjected to further treatment necessary for subsequent quantitative analysis.
  • the purified PCR reaction product can be prepared into a suitable buffer solution, the PCR primer region contained in the PCR amplified DNA can be cleaved, or the adapter sequence can be added to the amplified DNA. May be further added.
  • the purified PCR reaction product is prepared into a buffer solution, the PCR primer sequence is removed and adapter ligation is performed on the amplified DNA, and the obtained reaction product is amplified as necessary for quantitative analysis. Library can be prepared.
  • the probe DNA is first labeled with a radioactive isotope, a fluorescent substance, or the like, and then the obtained labeled DNA is used.
  • cDNA is first prepared from RNA derived from a biological sample according to a conventional method, and the target gene of the present invention is used as a template.
  • a pair of primers prepared for amplification (a normal chain that binds to the above-mentioned cDNA (-chain) and a reverse chain that binds to the + chain) are hybridized with this.
  • the PCR method is performed according to a conventional method, and the obtained amplified double-stranded DNA is detected.
  • a method for detecting the labeled double-stranded DNA produced by performing the above PCR using a primer labeled with RI, a fluorescent substance, etc. in advance is used. Can be done.
  • an array in which at least one of the nucleic acids (cDNA or DNA) derived from the target gene of the present invention is immobilized on a support is used.
  • Labeled cDNA or cRNA prepared from mRNA can be bound onto a microarray and the expression level of mRNA can be measured by detecting the label on the microarray.
  • the nucleic acid immobilized on the array may be any nucleic acid that specifically hybridizes (that is, substantially only to the nucleic acid of interest) under stringent conditions, for example, all of the target genes of the present invention.
  • nucleic acid may be a nucleic acid having a sequence or a nucleic acid consisting of a partial sequence.
  • partial sequence includes nucleic acids consisting of at least 15 to 25 bases.
  • stringent conditions can usually be a cleaning condition of about "1 x SSC, 0.1% SDS, 37 ° C.”
  • stricter hybridization conditions are "0.5 x SSC, 0.1".
  • more severe hybridization conditions include“ 0.1 ⁇ SSC, 0.1% SDS, 65 ° C. ”.
  • Hybridization conditions are described in J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Thrd Edition, Cold Spring Harbor Laboratory Press (2001) and the like.
  • RNA expression can be quantified based on the number of reads created by sequencing (read count).
  • a probe or primer used for the above measurement that is, a primer for specifically recognizing and amplifying the target gene of the present invention or a nucleic acid derived from it, or a primer for specifically detecting the RNA or a nucleic acid derived from it.
  • Probes fall into this category, but they can be designed based on the base sequences that make up the target gene.
  • “specifically recognizing” means that, for example, in Northern blotting, substantially only the target gene of the present invention or a nucleic acid derived from the target gene can be detected, and in, for example, in the RT-PCR method, substantially only the nucleic acid.
  • the detection or product can be determined to be the gene or nucleic acid derived from it so that is amplified.
  • an oligonucleotide containing a certain number of nucleotides complementary to a DNA consisting of a base sequence constituting the target gene of the present invention or a complementary strand thereof can be used.
  • the “complementary strand” refers to the other strand of a double-stranded DNA consisting of A: T (U in the case of RNA) and G: C base pairs to one strand.
  • “complementary” is not limited to the case where the sequence is completely complementary in the fixed number of continuous nucleotide regions, and is preferably 80% or more, more preferably 90% or more, still more preferably 95% or more bases. It suffices to have the sameness on the sequence.
  • the identity of the base sequence can be determined by an algorithm such as BLAST.
  • BLAST oligonucleotide
  • Those having a chain length of 50 bases or less, more preferably 35 bases or less can be mentioned.
  • specific hybridization it suffices if specific hybridization can be performed, and it has at least a part or all of a DNA (or a complementary strand thereof) consisting of a base sequence constituting the target gene of the present invention, for example.
  • a chain length of 10 bases or more, preferably 15 bases or more, and for example, 100 bases or less, preferably 50 bases or less, more preferably 25 bases or less is used.
  • the "oligonucleotide” can be DNA or RNA, and may be synthesized or natural. Alternatively, the probe used for hybridization is usually labeled.
  • the molecule that interacts with the protein when measuring the translation product (protein) of the target gene of the present invention, the molecule that interacts with the protein, the molecule that interacts with RNA, or the molecule that interacts with DNA, protein chip analysis and immunoassay (immunassay) For example, ELISA, etc.), mass spectrometry (eg, LC-MS / MS, MALDI-TOF / MS), 1-hybrid method (PNAS 100, 12271-12276 (2003)) and 2-hybrid method (Biol. Reprod. 58). , 302-311 (1998)) can be used and can be appropriately selected according to the target.
  • immunoassay immunoassay
  • a protein when used as a measurement target, it is carried out by contacting an antibody against the expression product of the present invention with a biological sample, detecting a polypeptide in the sample bound to the antibody, and measuring the level thereof. ..
  • the above antibody is used as the primary antibody, and then the primary antibody is used as the secondary antibody by using an antibody that binds to the primary antibody labeled with a radioactive isotope, a fluorescent substance, an enzyme or the like. Labeling is performed, and signals derived from these labeling substances are measured with a radiation measuring device, a fluorescence detector, or the like.
  • the antibody against the translation product may be a polyclonal antibody or a monoclonal antibody.
  • the polyclonal antibody is immunized against non-human animals such as rabbits by using a protein expressed and purified in Escherichia coli or the like according to a conventional method or by synthesizing a partial polypeptide of the protein according to a conventional method. It can be obtained from the serum of the immunized animal according to a conventional method.
  • a protein expressed and purified in Escherichia coli or the like according to a conventional method or a partial polypeptide of the protein is immunized against a non-human animal such as a mouse, and the obtained spleen cells and myeloma cells are fused into cells.
  • Monoclonal antibodies may also be prepared using phage display (Griffiths, AD; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February 1998, pp. 102-108 (7)).
  • the expression level of the target gene of the present invention or the expression product thereof in the biological sample collected from the subject is measured, and Parkinson's disease is detected based on the expression level. Detection is specifically performed by comparing the measured expression level of the target gene of the invention or its expression product with the control level.
  • the read count value which is the data of the expression level
  • the RPM value obtained by correcting the difference in the total number of reads between the samples.
  • the value obtained by converting the RPM value into the log value of the base 2 (Log 2 RPM value), the count value corrected using DESeq2 (Normalized count value), or the log value of the base 2 obtained by adding the integer 1 (Log 2).
  • Counter Counter + 1 value is preferably used as an index.
  • it is commonly used as a quantitative value of RNA-seq, such as fractions per kilobase of exon per million reads mapped (FPKM), reads per kilobase of exon per million readM (FPKM), etc. It may be a value. Further, it may be a signal value obtained by the microarray method and a correction value thereof.
  • a method of converting the expression level of the target gene into a relative expression level based on the expression level of the housekeeping gene or an analysis method.
  • control level includes, for example, the expression level of the target gene or its expression product in a healthy person.
  • the expression level of a healthy person may be a statistical value (for example, an average value) of the expression level of the gene or its expression product measured from a healthy person population.
  • the detection of Parkinson's disease in the present invention can also be performed by increasing / decreasing the expression level of the target gene of the present invention or its expression product.
  • the expression level of the target gene or its expression product in the biological sample derived from the subject is compared with the cutoff value (reference value) of each gene or its expression product.
  • the cutoff value can be determined as appropriate based on statistical values such as the average value and standard deviation of the expression level based on the expression level of the target gene or its expression product in healthy subjects obtained as reference data in advance. good.
  • a discriminant formula for separating Parkinson's disease patients and healthy subjects by using the measured values of the expression level of the target gene or its expression product derived from Parkinson's disease patients and the expression level of the target gene derived from healthy subjects or its expression products. (Prediction model) can be constructed and Parkinson's disease can be detected by using the discrimination formula.
  • dimensional compression can be performed by principal component analysis (PCA), and the main component can be used as an explanatory variable.
  • the level of the target gene or its expression product is similarly measured from the biological sample collected from the subject, the obtained measured value is substituted into the discriminant, and the result obtained from the discriminant is compared with the reference value. By doing so, the presence or absence of Parkinson's disease in the subject can be evaluated.
  • the algorithm for constructing the discriminant a known algorithm such as an algorithm used for machine learning can be used.
  • machine learning algorithms include random forest (Random forest), linear kernel support vector machine (SVM linear), rbf kernel support vector machine (SVM rbf), neural network (Regular net), and general linear model (Generalized linear). Model), regularized linear discriminant analysis, regularized logistic regression, and the like.
  • Data for verification is input to the constructed prediction model to calculate the prediction value, and the model in which the prediction value best matches the measured value, for example, the detection rate (Recall), accuracy (Precision), and accuracy (Precision) from the predicted value and the measured value.
  • the F value which is the harmonic mean of these, can be calculated, and the model with the largest F value can be selected as the optimum prediction model.
  • the method for determining the cutoff value is not particularly limited and can be determined according to a known method. For example, it can be obtained from a ROC (Receiver Operating Characteristic Curve) curve created by using a discriminant.
  • ROC Receiveiver Operating Characteristic Curve
  • the vertical axis plots the probability of getting a positive result in a positive patient (sensitivity)
  • the horizontal axis plots the probability of getting a negative result in a negative patient (specificity) minus 1 (false positive rate).
  • “true positive (sensitivity)” and “false positive (1-specificity)” shown in the ROC curve “true positive (sensitivity)"-"false positive (1-specificity)” is the maximum value (Youden). index) can be a cutoff value (reference value).
  • the other 29 genes shown in Table A are used. High by appropriately adding expression data of at least one gene selected from the above or its expression product, preferably based on the variable importance shown in Table 8 below, by adding an appropriate number of genes with high variable importance. A discriminant showing the detection rate and accuracy can be created, and Parkinson's disease can be detected with higher accuracy.
  • EGR2, RHOA, CCNI, RNASEK, CSF2RB, SERP1, ANKRD12, and SLC25A3 are preferable, and 12 types including 4 types of CD83, CXCR4, ITGAX, and UQCRH are preferable, and KCNQ1OT1, CCL3, It is preferable to add 18 kinds including 6 kinds of C10orf116, SERPINB4, LCE3D and CNFN, and further preferably all 29 kinds.
  • expression data of at least one gene selected from the genes shown in Table B or an expression product thereof as a target gene to be used when creating a discriminant formula for separating a Parkinson's disease patient group and a healthy person group.
  • the test kit for detecting Parkinson's disease of the present invention contains a test reagent for measuring the expression level of the target gene of the present invention or an expression product thereof in a biological sample isolated from a patient.
  • a test reagent for nucleic acid amplification and hybridization or a reagent containing an oligonucleotide (for example, a primer for PCR) that specifically binds (hybridizes) to the target gene of the present invention or a nucleic acid derived thereto.
  • examples thereof include a reagent for immunological measurement containing an antibody that recognizes an expression product (protein) of the target gene of the present invention.
  • the oligonucleotides, antibodies and the like included in the kit can be obtained by a known method as described above.
  • the test kit includes labeling reagents, buffers, color-developing substrates, secondary antibodies, blocking agents, instruments and controls necessary for testing, and tools for collecting biological samples (for example, a grease removing film for collecting SSL) and the like can be included.
  • the subject comprises a step of measuring the expression level of at least one gene selected from four gene groups consisting of SNORA16A, SNORA24, SNORA50 and REXO1L2P or an expression product thereof. How to detect Parkinson's disease in.
  • the method for detecting Parkinson's disease according to ⁇ 1> which comprises at least measuring the expression level of the SNORA24 gene or an expression product thereof.
  • the method of ⁇ 1> or ⁇ 2>, wherein the expression level of the gene or its expression product is a measurement of the expression level of mRNA.
  • ⁇ 4> The method according to any one of ⁇ 1> to ⁇ 3>, wherein the gene or its expression product is RNA contained in the lipid on the skin surface of the subject.
  • ⁇ 5> The method according to any one of ⁇ 1> to ⁇ 4>, wherein the measured value of the expression level is compared with the reference value of each gene or its expression product to evaluate the presence or absence of Parkinson's disease.
  • ⁇ 6> A discriminant formula for separating Parkinson's disease patients and healthy subjects using the measured values of the expression level of the gene or its expression product derived from a Parkinson's disease patient and the expression level of the same gene or its expression product derived from a healthy person as a teacher sample.
  • the measured value of the expression level of the gene or its expression product obtained from the biological sample collected from the subject is substituted into the discrimination formula, and the obtained result is compared with the reference value to be tested.
  • the expression level of at least one gene selected from the following 29 gene groups or an expression product thereof is measured.
  • the expression level of at least one gene selected from the following ten gene groups or an expression product thereof is measured.
  • the expression level of at least one gene selected from the following 16 gene groups or an expression product thereof is measured.
  • ⁇ 11> In addition to at least one gene selected from the above four gene groups, at least one selected from the gene groups shown in Tables 3-1 to 3-4 below and Tables 6-1 to 6-2 below.
  • the method of ⁇ 6> or ⁇ 7> wherein the expression level of one gene (excluding the above four genes) or its expression product is measured.
  • ⁇ 12> In addition to at least one gene selected from the above four gene groups, 1005 species shown in Tables 1-1 to 1-27 below and 725 species shown in Tables 4-1 to 4-20 below.
  • the method of ⁇ 6> or ⁇ 7>, wherein the expression level of at least one gene selected from the gene group excluding the above four genes or an expression product thereof is measured.
  • Parkinson used in any of the methods ⁇ 1> to ⁇ 10> which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived from the gene, or an antibody that recognizes an expression product of the gene.
  • a test kit for detecting diseases ⁇ 14> Use of at least one gene selected from the gene groups shown in Tables 3-1 to 3-4 below and 6-1 to 6-2 below or an expression product thereof as a marker for detecting Parkinson's disease.
  • ⁇ 15> Use of at least one gene selected from the four gene groups consisting of SNORA16A, SNORA24, SNORA50 and REXO1L2P or an expression product thereof as a marker for detecting Parkinson's disease.
  • ⁇ 16> A marker for detecting Parkinson's disease, which comprises at least one gene selected from the gene groups shown in Tables 3-1 to 3-4 below and 6-1 to 6-2 below, or an expression product thereof.
  • a marker for detecting Parkinson's disease of ⁇ 16> which comprises at least one gene selected from a group of four genes consisting of SNORA16A, SNORA24, SNORA50 and REXO1L2P or an expression product thereof.
  • Example 1 Detection of Parkinson's disease using RNA extracted from SSL 1
  • SSL collection test was performed twice in Test 1 and Test 2 below.
  • Study 1 The subjects were 15 healthy subjects (male and female aged 40 to 89 years) and 15 patients with Parkinson's disease (PD) (male and female aged 40 to 89 years).
  • Test 2 50 healthy subjects (male 40-89 years old) and 50 PDs (male 40-89 years old) were the subjects.
  • PD had been previously diagnosed with Parkinson's disease (Hohen & Yahr stage I or II) by a neurologist.
  • RNA preparation and sequencing The oil removal film from 1) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, cDNA was synthesized by reverse transcription at 42 ° C. for 90 minutes using a SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.). The random primer included in the kit was used as the primer for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR.
  • RNA expression analysis 1 In the data on the expression level of RNA derived from the subject (read count value) measured in 2) above, the data with a read count of less than 10 is treated as a missing value, and the difference in the total number of reads between the samples is corrected to the RPM value. After the conversion, the missing values were compensated by using a method called Singular Value Decomposition (SVD) imputation. However, only the genes for which 80% or more of the expression level data of the subjects in all the samples had the expression level data that was not a missing value were used in the following analysis. In the analysis, the RPM value (Log 2 RPM value) obtained by converting the RPM value of the read count into the logarithmic value of the base 2 was used in order to approximate the RPM value following the negative binomial distribution to the normal distribution.
  • Singular Value Decomposition Singular Value Decomposition
  • RNA was identified.
  • 111 kinds of RNA were upregulated in PD (Tables 1-1 to 1-3) and 68 kinds were downregulated (Tables 1-4 to 1-5) as compared with healthy subjects.
  • 565 RNAs were upregulated (Tables 1-6 to 1-19) and 294 species were downregulated (Tables 1-20 to 1-27).
  • 18 types of RNA were commonly up-expressed and 15 types of RNA were down-expressed (genes shown in bold in the table).
  • RNA expression analysis 2 The data (read count value) of the expression level of RNA derived from the subject measured in 2) above was corrected using a method called DESeq2. However, samples in which 4161 or more genes have not been detected are excluded, and genes for which expression level data that is not a missing value is obtained in 90% or more of the sample subjects among the expression level data of the subjects in all the excluded samples. Only used for the following analysis. For the analysis, a count value (normalized count value) corrected by using a method called DESeq2 was used.
  • the p-value correction value (FDR) by the likelihood ratio test is 0.25 in PDs as compared with healthy subjects.
  • the following expression-variable RNAs were identified.
  • 74 types of RNA were upregulated in PD (Tables 4-1 to 4-2) and 209 types were downregulated (Tables 4-3-4-8) as compared with healthy subjects.
  • 151 types of RNA were upregulated (Tables 4-9 to 4-12), and 308 types were downregulated (Tables 4-13 to 4-20).
  • 7 types of RNA were commonly up-expressed and 10 types of RNA were down-expressed (genes shown in bold in the table).
  • Example 2 Creation and verification of discrimination model 1
  • Data used as in the RNA expression analysis 1 of Example 1 in the data (read count value) of the expression level of SSL-derived RNA from the subject, the data having a read count of less than 10 is treated as a missing value and between the samples. After converting to the RPM value corrected for the difference in the total number of reads, the missing value was compensated by using a method called Singular Value Decomposition (SVD) imputation. However, only the genes for which expression level data that is not a missing value was obtained in 80% or more of the samples were used in the following analysis. In constructing the machine learning model, the RPM value (Log 2 RPM value) converted into the logarithmic value of the base 2 was used in order to approximate the normal distribution to the RPM value following the negative binomial distribution.
  • Singular Value Decomposition Singular Value Decomposition
  • RNA profile data obtained from the subjects of Test 1
  • the RNA profile data for a total of 20 healthy subjects and 10 PDs were used as training data for the PD prediction model, and the remaining 10 subjects were used.
  • the RNA profile data was used as test data to be used to evaluate the model accuracy.
  • the RNA profile data for a total of 80 healthy subjects and 40 PDs were used as training data for the PD prediction model, and the RNA profile data for the remaining 20 subjects were used as a model. It was used as test data used for accuracy evaluation.
  • RNA expression analysis 1 of Example 1 18 types of RNA whose expression was commonly increased and 15 types of RNA whose expression was decreased in PD patients in Test 1 and Test 2 as compared with healthy subjects ( (Genes shown in bold in Tables 1-1 to 1-27) are selected as feature amount genes, and these expression level data are converted to main components by principal component analysis, and then the first to tenth main components are selected. It was used as an explanatory variable.
  • 4 types of SNORA16A, SNORA24, SNORA50, and REXO1L2P were selected as feature gene genes from 18 types of RNA whose expression was commonly increased and 15 types of RNA whose expression was decreased in PD patients in Test 1 and Test 2. After converting the expression level data of the above into principal components by principal component analysis, the first to fourth principal components were used as explanatory variables.
  • the value of each principal component obtained from the feature amount gene expression level (Log2RPM value) of the test data was input to the model after training, and the target prediction value of each prediction item was calculated.
  • the detection rate (Recall), accuracy (Precision), and F value, which is the harmonic mean of them, were calculated from the predicted value and the measured value, and the model with the largest F value was selected as the optimum prediction model.
  • Results Table 7 shows the algorithm, detection rate, accuracy, and F value of the prediction target items. Further, FIG. 1 shows a confusion matrix in which the predicted values and the actually measured values in the optimum prediction model are plotted in the test data. The numerical values in the figure indicate the number of samples in each quadrant. Table 8 shows the results of calculating the variable importance of each feature gene when the model was constructed using a random forest.
  • the F1 of the model using the four types of SNORA16A, SNORA24, SNORA50, and REXO1L2P is 0.67 in test 1, 0.75 in test 2, and 0.76 when test 1 + test 2 are integrated, and PD can be predicted. It was shown to be.
  • the F1 of the model using a total of 33 types of increased RNA and 15 decreased RNA in PD patients was 0.91 in Test 1, 0.80 in Test 2, and 0.82 when Test 1 + Test 2 were integrated. , It was shown that more accurate PD prediction is possible.
  • Example 3 Creation and verification of discrimination model 2
  • Data used as in the RNA expression analysis 2 of Example 1 the data (read count value) of the expression level of SSL-derived RNA from the subject was corrected using a method called DESeq2.
  • DESeq2 a count value corrected by using a method called DESeq2 was used.
  • RNA profile data for a total of 15 healthy subjects and 6 PDs were used as training data for the PD prediction model, and the remaining healthy subjects 4
  • the RNA profile data for a total of 5 people, one name and one PD, were used as test data to be used for evaluation of model accuracy.
  • the RNA profile data for a total of 72 healthy subjects and 35 PDs were used as training data for the PD prediction model, and the remaining 13 healthy subjects and 11 PDs were used.
  • RNA profile data for a total of 24 people was used as test data to be used for evaluation of model accuracy.
  • RNA expression analysis 2 of Example 1 17 types of RNA (Tables 4-1 to 4) whose expression was commonly increased or decreased in PD patients in Test 1 and Test 2 as compared with healthy subjects. (Genes shown in bold in -20) were selected as feature amount genes, and these expression level data were converted into main components by principal component analysis, and then the first to fourth main components were used as explanatory variables.
  • the value of each principal component obtained from the characteristic amount gene expression level (value obtained by adding 1 to the Normalized count value and making it a logarithmic value of the base 2) of the test data is input to the model after training, and each The target predicted value of the predicted item was calculated.
  • Results Table 9 shows the algorithm used, detection rate, accuracy, and F value of the prediction target items.
  • Example 4 Creation and verification of discrimination model 3
  • Data used as in the RNA expression analysis 2 of Example 1 the data (read count value) of the expression level of SSL-derived RNA from the subject was corrected using a method called DESeq2.
  • DESeq2 a method for which expression level data that is not a missing value is obtained in 90% or more of the sample subjects among the expression level data of the subjects in all the excluded samples. Only used for the following analysis.
  • a count value (normalized count value) corrected by using a method called DESeq2 was used.
  • RNA profile data for a total of 15 healthy subjects and 6 PDs were used as training data for the PD prediction model, and the remaining healthy subjects 4
  • the RNA profile data for a total of 5 people, one name and one PD, were used as test data to be used for evaluation of model accuracy.
  • the RNA profile data for a total of 72 healthy subjects and 35 PDs were used as training data for the PD prediction model, and the remaining 13 healthy subjects and 11 PDs were used.
  • RNA profile data for a total of 24 people was used as test data to be used for evaluation of model accuracy.
  • RNA expression analysis 2 of Example 1 19 types of RNA (genes shown in Table 6-1) whose expression was increased or decreased in PD patients in Test 1 as compared with healthy subjects, or healthy subjects.
  • Test 2 30 types of RNA (genes shown in Table 6-2) whose expression was increased or decreased in PD patients were selected as characteristic amount genes, and these expression level data were converted into main components by main component analysis. After the conversion, the first to fourth principal components were used as explanatory variables.
  • the value of each principal component obtained from the characteristic amount gene expression level (value obtained by adding 1 to the Normalized count value and making it a logarithmic value of the base 2) of the test data is input to the model after training, and each The target predicted value of the predicted item was calculated.
  • Results Tables 10 and 11 show the algorithms used, detection rates, accuracy, and F-numbers of the prediction target items.
  • Example 5 Creation and verification of discrimination model 4
  • Data used as in the RNA expression analysis 1 of Example 1 in the data (read count value) of the expression level of SSL-derived RNA from the subject, the data having a read count of less than 10 is treated as a missing value and between the samples. After converting to the RPM value corrected for the difference in the total number of reads, the missing value was compensated by using a method called Singular Value Decomposition (SVD) imputation. However, only the genes for which expression level data that is not a missing value was obtained in 80% or more of the samples were used in the following analysis. For the construction of the machine learning model, the RPM value (Log 2 RPM value) converted into the logarithmic value of the base 2 was used in order to approximate the normal distribution of the RPM value following the negative binomial distribution.
  • Singular Value Decomposition Singular Value Decomposition
  • RNA profile data obtained from the subjects of Test 1
  • the RNA profile data for a total of 20 healthy subjects and 10 PDs were used as training data for the PD prediction model, and the remaining 10 subjects were used.
  • the RNA profile data was used as test data to be used to evaluate the model accuracy.
  • the RNA profile data for a total of 80 healthy subjects and 40 PDs were used as training data for the PD prediction model, and the RNA profile data for the remaining 20 subjects were used as a model. It was used as test data used for accuracy evaluation.
  • RNA expression analysis 1 of Example 1 21 types of RNA (genes shown in Table 3-1) whose expression was increased or decreased in PD patients in Test 1 as compared with healthy subjects, or healthy subjects.
  • Test 2 92 types of RNA (genes shown in Tables 3-2 to 3-4) whose expression was increased or decreased in PD patients were selected as characteristic amount genes, and these expression level data were analyzed as the main component. After conversion to the main component by, the first main component to the fourth main component were used as explanatory variables.
  • the value of each principal component obtained from the feature amount gene expression level (Log2RPM value) of the test data was input to the model after training, and the target prediction value of each prediction item was calculated.
  • the detection rate (Recall), accuracy (Precision), and F value, which is the harmonic mean of them, were calculated from the predicted value and the measured value, and the model with the largest F value was selected as the optimum prediction model.
  • Results Tables 12 and 13 show the algorithms used, detection rates, accuracy, and F-numbers of the prediction target items.

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CN117051102B (zh) * 2023-10-12 2024-01-26 上海爱谱蒂康生物科技有限公司 生物标志物组合在制备预测帕金森病的产品中的应用

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