WO2010026821A1 - Procédé de discrimination entre un trouble bipolaire et la schizophrénie - Google Patents

Procédé de discrimination entre un trouble bipolaire et la schizophrénie Download PDF

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WO2010026821A1
WO2010026821A1 PCT/JP2009/061158 JP2009061158W WO2010026821A1 WO 2010026821 A1 WO2010026821 A1 WO 2010026821A1 JP 2009061158 W JP2009061158 W JP 2009061158W WO 2010026821 A1 WO2010026821 A1 WO 2010026821A1
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seq
expression level
schizophrenia
bipolar disorder
gene
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Japanese (ja)
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秀幸 青島
一男 竹村
健太朗 飯嶋
浩志 林
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株式会社エスアールエル
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    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to a method for discriminating between bipolar disorder and schizophrenia.
  • Bipolar disorder (manic-depressive illness) is a mental illness that has a high incidence along with schizophrenia, and repeats a manic state and a depressive state.
  • the lifetime prevalence of bipolar disorder is said to be 0.2-1.6%, often recurring, and is often said to require lifelong drug treatment.
  • an objective diagnostic method using biological markers for schizophrenia or bipolar disorder is established, early diagnosis and early treatment will be possible, and it will be possible to avoid the severity and improve the cure rate.
  • diagnostic methods using biological markers that have been reported so far include a method of diagnosing schizophrenia (schizophrenia) using serum concentration of epidermal growth factor as an index (Patent Document 1).
  • Patent Document 1 a method of diagnosing schizophrenia (schizophrenia) using serum concentration of epidermal growth factor as an index
  • Patent Document 2 there is a method using blood as a sample and using the expression level of a specific gene as an index.
  • these methods cannot accurately diagnose and diagnose schizophrenia and bipolar disorder.
  • an object of the present invention is to provide means capable of objectively diagnosing schizophrenia or bipolar disorder with high accuracy using patient blood as a sample.
  • the inventors of the present application use blood as a sample, compare the expression levels of about 55,000 types of genes between bipolar patients and schizophrenia patients, select genes whose expression levels vary significantly, and further described later
  • a considerable number of classification prediction candidate genes are narrowed down according to the criteria that the inventors have independently devised, and a low-cost and highly versatile microarray equipped with these genes is created, and the expression data measured using this gene is used as a neural network.
  • the classification prediction algorithm is constructed by the variable increase method and the cross-validation method making full use of, and the detection sensitivity (true positive rate) and specificity (true negative rate) are actually over 80% using a large number of samples.
  • the present invention was completed by finding out.
  • the present invention provides a method for discriminating between bipolar disorder and schizophrenia using the expression levels of the following gene groups (1) to (12) in a sample isolated from a living body as an index.
  • FLJ21881 SEQ ID NO: 1
  • DLGAP3 SEQ ID NO: 2
  • FAM20A SEQ ID NO: 3
  • MAX SEQ ID NO: 4
  • ZNF74 SEQ ID NO: 5
  • DIAPH2 SEQ ID NO: 6
  • CR1 SEQ ID NO: 7
  • RAD54B SEQ ID NO: 8)
  • GPR30 SEQ ID NO: 9)
  • SCD5 SEQ ID NO: 10
  • (11) IMAGE: 5785888 SEQ ID NO: 11
  • INSL3 SEQ ID NO: 12
  • the present invention provides for the first time a means capable of objectively diagnosing either bipolar disorder or schizophrenia with high accuracy.
  • the present invention uses the expression level of the gene groups (1) to (12) as an index.
  • the sample for measuring the expression level of each gene is not particularly limited as long as it is a sample isolated from a living body, but as described in detail in the following examples, the gene group is selected using blood as a sample. Therefore, it is preferable to use blood as a sample.
  • the gene group includes those whose expression level is increased and decreased in patients with schizophrenia than those with bipolar disorder. Further, in the following examples, determination can be made based on the expression levels of only the above 12 types of genes that have been confirmed to have a detection sensitivity (true positive rate) and specificity (true negative rate) of 80% or more. preferable.
  • the expression level of other genes such as various genes for normalization in order to ensure measurement accuracy.
  • “based on the expression level of only the 12 types of genes” means that the expression level of only the 12 types of genes is used as a direct variable for classification prediction.
  • the correct answer rate is (a + d) / (a + b + c + d).
  • Measurement of the expression level of each gene in the sample itself can be performed by a known method.
  • the measurement method is not particularly limited, but a method using a single-stranded oligonucleotide probe that hybridizes to the sense strand or antisense strand of each gene, preferably a DNA array on which a DNA probe is immobilized, is simple and preferred.
  • oligonucleotide probes that extract total mRNA from blood, prepare cRNA labeled with biotin from the extracted mRNA, and hybridize with cRNA derived from each gene.
  • CRNA is applied to the immobilized array, the cRNA and probe are hybridized, the array is washed, and the amount of label remaining on the substrate is measured to determine the amount of cRNA, and hence the amount of mRNA, that is, the gene expression level. Can be measured.
  • the probe to be immobilized has a size that specifically hybridizes with cRNA, and usually has a size of about 18 to 50 bases, preferably about 20 to 40 bases.
  • the probe to be immobilized is preferably completely complementary to the region of RNA to which it is hybridized, but the normal hybridization when using a DNA array as specifically described in the following examples. A small number (usually 1 or 2) of mismatches is acceptable as long as it hybridizes under the conditions. Therefore, even when a natural SNP occurs in a gene, it can be measured using the same DNA array.
  • the expression level of the gene group is SEQ ID NO: 18, SEQ ID NO: 44, SEQ ID NO: 61, SEQ ID NO: 90, SEQ ID NO: 97, SEQ ID NO: 120, SEQ ID NO: 125, SEQ ID NO: 161, SEQ ID NO: 167, sequence It is measured using oligonucleotide probes having the nucleotide sequences shown in No. 195, SEQ ID No. 218 and SEQ ID No. 220, and a DNA array on which these probes are immobilized can be preferably used. In addition, according to the measured values using these probes, 10 out of 12 probes have a significant difference (p ⁇ 0.05, t test) in the expression level between schizophrenia and healthy subjects. Met.
  • the determination based on the expression level of the gene group is basically performed by comparing the expression level of the gene group with the expression level of the gene group in known schizophrenia patients and bipolar disorder patients. Done. This comparison is preferably performed by a neural network trained by the variable increment method using the expression level of the gene group in known schizophrenia patients and bipolar disorder patients. Input the expression levels of the above 12 genes measured in the constructed learned neural network (construction method will be described later), output the prediction probability of the group classified into the neural network, and use this prediction probability as a criterion Whether schizophrenia or bipolar disorder can be determined.
  • the above comparison is preferably performed by multiple regression analysis.
  • a prediction formula multiple regression formula
  • the expression level of the above gene group in the subject whose disease should be determined is input to obtain a dependent variable, and the value of this dependent variable is calculated as the dependent of known schizophrenic patients and bipolar disorder patients.
  • the value of the dependent variable that can preferably classify both groups as a cutoff value is, for example, based on the dependent variable calculated for each patient of the known schizophrenia patient group and bipolar disorder patient group, the value of the dependent variable that can preferably classify both groups as a cutoff value. This can be done by comparing the subject's dependent variable with this cutoff value. For example, if the expression level is analyzed in a patient with schizophrenia and the dependent variable is set to be large, if the numerical value of the dependent variable calculated for the subject is greater than the cutoff value, the subject is integrated. Can be predicted to be ataxia.
  • the cut-off value can be appropriately determined by routine statistical processing based on the dependent variables calculated for known schizophrenia patients and bipolar disorder patients.
  • the technique of multiple regression analysis itself is well known, and various software and the like for performing multiple regression analysis are known, and there are many commercially available products. Any software may be used in the present invention.
  • the prediction formula can be determined once the analysis for the known patient is performed. Therefore, it is not necessary to perform the analysis for the known patient group every time it is performed. Can also be used.
  • an analysis method including a step of obtaining a dependent variable of a sample using the obtained multiple regression equation is widely included, and an analysis step for obtaining a multiple regression equation includes Not necessarily included. Therefore, as described above, any method for discriminating a disease using the already obtained multiple regression equation is included in the “discrimination method for performing comparison by multiple regression analysis” in the present invention.
  • the measurement value of the expression level used in the present invention is preferably a value obtained by normalizing the measured signal intensity by a global normalization method as described in the following examples.
  • the global normalization method is a method of calculating the relative expression level by calculating the median value of the expression levels of all genes mounted on the DNA microarray and dividing the expression level of each gene by this median value. .
  • the neural network When performing the method of the present invention using a neural network, the neural network itself is well known and a commercially available neural network can be used. However, although the neural network itself can use a commercial product, in the present invention, there is a feature in the data to be learned by the neural network, and sensitivity (true positive rate) and specificity (true negative rate) can be obtained by learning any data. It is necessary to devise whether both can be increased to 80% or more (described later).
  • An optimal model of a classification prediction model using a neural network can be constructed by a method detailed in the following example, for example. Briefly, for example, the optimum model can be determined as follows. First, the expression levels of various genes are measured using samples collected from a large number of schizophrenia patients, bipolar disorder patients, and healthy individuals. The expression level of the gene can be performed using a DNA microarray as described above. In the following examples, a commercially available DNA microarray equipped with DNA probes of about 55,000 types of human genes was used.
  • data cleansing is performed on the expression level measured using a DNA microarray.
  • the data cleansing can be performed, for example, by excluding probes of genes less than 30% tile or 98% tile or more of the entire expression level.
  • probes other than Quality Flag “Good”, probes of genes located on the Y chromosome, probes set distally from the mRNA 3 ′ end, etc. are excluded, and 10,498 from about 55,000 probes. Narrow down to the probe.
  • the quality “Flag” being “Good” means that the measured expression level is larger than 1.5SD of the background around the spot and can be trusted as the measurement value.
  • the gene located on the Y chromosome is present only in males, it was excluded because the sensitivity and / or specificity of detection might be lowered when females were examined.
  • Probes set distal from the mRNA 3 'end are excluded because they are subject to bias in the preparation of cRNA and are a significant variation in the measured values. Furthermore, preliminary analysis excluded those with a missing value of 25% or more, those with a large difference in expression between men and women, and those with a large difference between batches during array production.
  • the expression level of the gene derived from the RNA hybridized with each probe, measured for each probe selected in this way, is input to the neural network, and the two-group test (t test), that is, the learning example
  • t test A significant difference test (t-test) is performed between schizophrenia (unmedicated) and bipolar disorder groups.
  • a significant difference test is performed between the schizophrenia group and the bipolar disorder group, between the schizophrenia group and the healthy person group, and between the bipolar disorder group and the healthy person group.
  • samples were also narrowed down. That is, the median value of 56 healthy subjects was calculated for each probe, the correlation of each sample was examined for the data set, and the parameters of the approximate curve and the signal intensity ratio greatly separated were excluded from the analysis target.
  • variable increasing method itself is well known, and is performed by adding explanatory variables (measurement results of each probe) one by one and obtaining a combination having a high correlation with the objective variable (correct answer rate).
  • the variable increase method is performed, and the number of probes with the highest correct answer rate is selected. At this time, it is preferable to perform the selection by combining N-fold cross validation methods.
  • the cross-validation method itself is also well known.
  • N-fold cross validation data (measurement results of each probe) is divided into N subsets of approximately the same size, and neural network learning (training) is performed a total of N times while excluding one subset.
  • the data set of the learning example is divided into three subsets, and the neural network performs classification prediction using the probes with significant differences one by one while switching the data sets. The combination of was identified.
  • the inventors of the present application produced a practical microarray in which the 216 types of probes are mounted on a substrate as a low-cost microarray for practical use.
  • the practical array was also equipped with a probe used for global normalization and a management probe (for alignment) (detailed examples below).
  • the global normalization was selected with small variation between arrays.
  • a plurality of chambers (16 in the following embodiment) can be formed on a single substrate, that is, 16 samples can be simultaneously tested with a single array.
  • a DNA microarray equipped with about 55,000 types of probes is expensive, and only one specimen can be processed with one microarray.
  • the cost of preparing the array and the cost and labor of testing are greatly reduced. Can do.
  • the sensitivity and specificity are 80 for both schizophrenia and bipolar disorder. It was confirmed that schizophrenia and bipolar disorder can be discriminated with high sensitivity and high specificity.
  • the method of the present invention can be preferably carried out to examine whether a patient is suspected of having psychiatric disorder, particularly schizophrenia or bipolar disorder, whether it is schizophrenia or bipolar disorder.
  • a method for detecting schizophrenia that is, a method for determining whether schizophrenia is a healthy person
  • Various detection methods for schizophrenia are known (see, for example, the above-mentioned patent document).
  • an oligonucleotide probe having the base sequence represented by SEQ ID NO: 18 means an oligonucleotide probe having a base sequence of attttgcctt cacataccag acatgagaca represented by SEQ ID NO: 18 and having a size of 30 bases.
  • cRNA cRNA was prepared using 0.5 ⁇ g of extracted total RNA.
  • Biotin-labeled cRNA was prepared using an iExpress kit (GE Healthcare Bioscience, Chandler, CA, USA) according to the manufacturer's instructions.
  • the quantification and quality confirmation of the prepared cRNA were performed in the same manner as the quantification and quality confirmation of the extracted total RNA. That is, the absorbance of 230, 260, and 280 nm of cRNA solution diluted 50 times was measured, and the concentration of total RNA was measured. The quality of the cRNA was confirmed using an Agilent 2100 bioanalyzer.
  • Codelink TM 55K Bioarray (GE Healthcare Bioscience) was used. Codelink (trademark) 55K Bioarray is coated with acrylamide with special chemical modification on the surface of glass slide, and 30mer probe is fixed three-dimensionally. It is an excellent microarray, and probes corresponding to about 55000 genes in humans are immobilized.
  • cRNA 10 ⁇ g was prepared with RNase-Free H 2 O to a final volume of 20 ⁇ l, 5 ⁇ l of 5 ⁇ Fragmentation Buffer of iExpress kit was added, and then incubated at 94 ° C. for 20 minutes to fragment the cRNA.
  • the array was fixed using Hybridization® Removal Tool, the hybridization chamber was peeled off, and the array was set on Bioarray® Rack.
  • the Bioarray® Rack with the array set was transferred to a Large® Reagent reservoir containing 0.75 ⁇ TNT® Buffer at 46 ° C. and incubated at 46 ° C. for 1 hour.
  • the Bioarray® Rack was transferred to a Small® Reagent reservoir filled with 3.4 ⁇ ml of Streptavidin-Cy5 diluted solution and incubated at room temperature for 30 minutes. After staining, the Bioarray Rack was transferred to a Large Reagent reservoir filled with 240 ml of 1 ⁇ TNT Buffer, and washed by repeating the operation of incubating at room temperature for 5 minutes 4 times. Next, the Bioarray® Rack was transferred to a Large® Reagent reservoir filled with 0.1 ⁇ SSC / 0.05% Tween-20, washed for 30 seconds, the array was centrifuged and dried, and then stored in the dark until scanning.
  • Array Scanning The washed and dried arrays were scanned with an Agilent Scanner (Agilent Technologies, Santa Clara, CA, USA). The scanner settings were Red PMT [%] 70%, Dye Channel Red (Red is Cy5). The other settings are the default. The scanned array data was saved as a TIF file and digitized.
  • probes other than Quality Flag “Good”, probes located on the Y chromosome, probes set distal from the mRNA 3 ′ end, and the like were excluded. Furthermore, preliminary analysis also excluded those with a missing value of 25% or more, those with a large difference in the expression level between men and women, and those with a large difference between batches during array production. These narrowed down from about 55,000 probes to 10,498 probes.
  • CodeLink (trade name) 55K Bioarray is very expensive, and only one sample can be processed and analyzed. In order to put it to practical use, a microarray that can be analyzed at a lower cost is required. Therefore, the same surface treatment as CodeLink 55K Bioarray is applied, and this is divided into 16 chambers, so that CodeLink (trade name) 16-Assay can process and analyze up to 16 samples at a time.
  • a practical array based on Bioarray (Applied Microarrays) was designed. In addition to the 216 gene probes, probes used for global normalization (SEQ ID NOs: 229 to 527) and management probes by manufacturers were added to design the following array.
  • CodeLink (trade name) 16-Assay Bioarray classification prediction based on measurement results (neural network) Based on gene expression information of 60 untreated schizophrenic patients (schizophrenic patients antipsychotic untreated group) and 48 bipolar disorder patients, we tried to construct a classification prediction model by neural network with excellent classification prediction It was.
  • a classification prediction algorithm is a series of algorithms that can output an optimal solution by inputting a data set whose attributes have been clarified in advance and performing “learning and training”. It is said that an algorithm with high classification accuracy can be constructed from the high learning effect.
  • a part of the normalized data (40 untreated schizophrenia patients, 32 bipolar disorder patients) was input to the ArrayAssist neural network as a learning example, and an algorithm was constructed.
  • N-fold cross validation (N 3)
  • the data set of the learning example was divided into three parts, and the prediction was made using one of the 216 probes with significant difference one by one while changing the data set.
  • the data set of the test example was analyzed using the algorithm learned in this way. Enter the normalized data for the probe set used at the point where Number of Class Accuracy reaches the plateau into the learned algorithm above to verify how well the classification of the test example matches the clinical diagnosis did.
  • Numerous algorithms are constructed by variously changing various parameters of the neural network (learning efficiency, momentum, number of repetitions, number of layers, number of neurons), and learning accuracy by using cross-validation and test examples described above for each. Verification was performed.
  • Table 4 and Table 5 show the prediction results for learning examples and test examples based on this algorithm. Further, FIG. 1 shows the result of Forward Selection for this algorithm.
  • schizophrenia and bipolar disorder can be classified with high accuracy using expression data from 12 probes (Table 2, supra).
  • CodeLink (trade name) 16-Assay Bioarray classification prediction based on measurement results (multiple regression analysis) Similar to the above, classification prediction by multiple regression analysis was attempted using gene expression information of 60 untreated schizophrenia patients and 48 bipolar disorder patients. Using the expression data from the 12 probes as explanatory variables, a multiple regression analysis was performed on the learning example using commercially available software (SPSS) to construct a prediction formula. Multiple regression analysis was performed so that the dependent variable was increased in patients with schizophrenia. Subsequently, the dependent variable was calculated about the said test example using the constructed prediction formula. The obtained prediction formula is as follows.
  • X 1 is the gene expression level of FLJ21881 (GE492524 SEQ ID NO: 18)
  • X 2 is the gene expression level of DLGAP3 (GE54859 SEQ ID NO: 44)
  • X 3 is the gene expression level of FAM20A (GE56606 SEQ ID NO: 61)
  • X 4 is the gene expression level of MAX (GE59858 SEQ ID NO: 90)
  • X 5 is the gene expression level of ZNF74 (GE60153 SEQ ID NO: 97)
  • X 6 is the gene expression level of DIAPH2 (GE62680 SEQ ID NO: 120)
  • X 7 is the gene expression level of CR1 (GE62914 SEQ ID NO: 125)
  • a 1 is -0.166864749248881
  • a 2 is 0.578595208826776
  • a 3 is -0.251720387137894
  • a 4 is 0.285088434152454
  • a 5 is, 0.149134281702735
  • 6 is -0.581754365047968
  • a 7 is 0.965099433225613
  • 8 is -0.237927470298808
  • a 9 is 0.724852821407317
  • a 10 is 0.467110697687733
  • a 11 is 1.55443576023811
  • a 12 is -0.0695649776248728
  • Constant C is -1.20827077326351 It is.
  • Tables 6 and 7 show the results of tabulating the dependent variable 50 as a cutoff value. Moreover, the dependent variable calculated about each sample of a learning example and a test example is shown in FIG.
  • Sensitivity 95.0% (38/40), Specificity: 87.5% (28/32), Correct answer rate: 91.7% (66/72)
  • Sensitivity 85.0% (17/20), Specificity: 81.3% (13/16), Correct answer rate: 83.3% (30/36)

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Abstract

La présente invention concerne un nouveau moyen qui peut diagnostiquer la schizophrénie et un trouble bipolaire distinctement, objectivement et avec une précision élevée en utilisant du sang collecté à partir d’un patient en tant qu’échantillon. La présente invention concerne spécifiquement un procédé pour distinguer la schizophrénie et un trouble bipolaire, qui utilise les taux d’expression de 12 gènes spécifiques dans un échantillon isolé à partir d’un corps vivant en tant que mesures. Le procédé permet le diagnostic distinctif, objectif et très précis de la schizophrénie et d’un trouble bipolaire. Il est effectivement confirmé en utilisant des échantillons multiples que la sensibilité (taux de vrais positifs) et le degré de spécificité (taux de vrais négatifs) de la détection sont de 80 % ou plus. Le procédé utilise du sang en tant qu’échantillon, et peut donc être mis en pratique de manière commode.
PCT/JP2009/061158 2008-09-03 2009-06-19 Procédé de discrimination entre un trouble bipolaire et la schizophrénie WO2010026821A1 (fr)

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Citations (4)

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Publication number Priority date Publication date Assignee Title
WO2003039490A2 (fr) * 2001-11-09 2003-05-15 Office Of Technology Licensing Stanford University Compositions et procedes diagnostiquant et traitant les troubles mentaux
WO2006105516A2 (fr) * 2005-03-31 2006-10-05 The Board Of Trustees Of The Leland Stanford Junior University Compositions et procedes pour le diagnostic et le traitement de troubles neuropsychiatriques
WO2007059064A2 (fr) * 2005-11-12 2007-05-24 The Board Of Trustees Of The Leland Stanford Junior University Methodes associees au fgf2 pour diagnostiquer et traiter une depression
JP2007528704A (ja) * 2003-06-20 2007-10-18 ジーンニュース，インコーポレイテッド 血液中の遺伝子転写産物を検出する方法及びその使用

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WO2003039490A2 (fr) * 2001-11-09 2003-05-15 Office Of Technology Licensing Stanford University Compositions et procedes diagnostiquant et traitant les troubles mentaux
JP2007528704A (ja) * 2003-06-20 2007-10-18 ジーンニュース，インコーポレイテッド 血液中の遺伝子転写産物を検出する方法及びその使用
WO2006105516A2 (fr) * 2005-03-31 2006-10-05 The Board Of Trustees Of The Leland Stanford Junior University Compositions et procedes pour le diagnostic et le traitement de troubles neuropsychiatriques
WO2007059064A2 (fr) * 2005-11-12 2007-05-24 The Board Of Trustees Of The Leland Stanford Junior University Methodes associees au fgf2 pour diagnostiquer et traiter une depression

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TETSURO OMORI ET AL.: "DNA Microarray ni yoru Utsubyo no Atarashii Shindan Marker", PSYCHIATRIA ET NEUROLOGIA JAPONICA, vol. 108, no. 6, 2006, pages 642 - 645 *

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