WO2009157251A1 - Procédé de diagnostic du syndrome de dysfonctionnement de l'intégration - Google Patents

Procédé de diagnostic du syndrome de dysfonctionnement de l'intégration Download PDF

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Publication number
WO2009157251A1
WO2009157251A1 PCT/JP2009/057861 JP2009057861W WO2009157251A1 WO 2009157251 A1 WO2009157251 A1 WO 2009157251A1 JP 2009057861 W JP2009057861 W JP 2009057861W WO 2009157251 A1 WO2009157251 A1 WO 2009157251A1
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seq
expression level
gene
gene group
schizophrenia
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PCT/JP2009/057861
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English (en)
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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    • 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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/30—Psychoses; Psychiatry

Definitions

  • the present invention relates to a method for diagnosing schizophrenia using blood as a sample.
  • an object of the present invention is to provide means capable of objectively diagnosing schizophrenia with high accuracy using patient blood as a sample.
  • the inventors of the present invention use blood as a sample, compare the expression levels of about 55,000 genes between healthy individuals and schizophrenic patients, select genes whose expression levels vary significantly, and further describe the invention described later Narrow down to the criteria that we have devised independently, and then use the neural network to increase the variable and cross-validation method to perform the primary selection of the gene group, and the inventors of the present application further select the primary selection of the gene group
  • a low-cost and highly versatile microarray equipped with a gene group to which a large number of candidate genes for classification prediction were added, processed with a neural network in the same manner as described above, and the sensitivity of detection by the constructed classification prediction algorithm The fact that (true positive rate) and specificity (true negative rate) were 80% or more was confirmed using a large number of actual samples, and the present invention was completed.
  • the present invention provides a method for detecting schizophrenia using as an index the expression level of the following gene groups (1) to (10) in a sample isolated from a living body.
  • DLGAP3 SEQ ID NO: 1
  • KCNJ15 SEQ ID NO: 2
  • GPR30 SEQ ID NO: 3
  • NPCR SEQ ID NO: 4
  • TMED1 SEQ ID NO: 5
  • PAFAH2 SEQ ID NO: 6
  • TMEM23 SEQ ID NO: 7
  • PGRMC1 SEQ ID NO: 9)
  • INSL3 SEQ ID NO: 10
  • the present invention provides for the first time a means capable of objectively diagnosing schizophrenia with high accuracy.
  • the present invention uses the expression level of the gene groups (1) to (10) 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 in which the expression level is increased or decreased in schizophrenic patients. Further, in the following examples, determination can be made based on the expression levels of only the above 10 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 levels of other genes such as various genes for normalization in order to ensure measurement accuracy.
  • “based on the expression levels of only the 10 genes” means that the expression levels of only the 10 genes are used as direct variables 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: 34, SEQ ID NO: 42, SEQ ID NO: 77, SEQ ID NO: 81, SEQ ID NO: 98, SEQ ID NO: 109, SEQ ID NO: 122, SEQ ID NO: 165, SEQ ID NO: 200 and SEQ ID NO: It is measured using an oligonucleotide probe having the base sequence indicated by No. 218, and a DNA array on which these probes are immobilized can be preferably used.
  • 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 healthy subjects, which are measured in advance. .
  • 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 healthy individuals. Input the measured expression levels of the above 10 types of genes into 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 Schizophrenia can be detected.
  • the above comparison is preferably performed by multiple regression analysis.
  • a prediction formula multiple regression formula
  • Whether or not the subject has schizophrenia can be determined. This comparison is made, for example, based on the dependent variable calculated for each sample of the known schizophrenia patient group and the healthy subject group, by defining the value of the dependent variable that can preferably classify both groups as a cut-off value.
  • the cut-off value can be appropriately determined by routine statistical processing based on the dependent variables calculated for known schizophrenia patients and healthy individuals.
  • 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 and the healthy person is performed once, it is not necessary to perform the analysis for the known patient / healthy person group every time it is performed, and the prediction formula once obtained. Can also be used in subsequent implementations.
  • 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 detecting schizophrenia using the already obtained multiple regression equation is included in the “detection 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 level of various genes is measured using samples collected from many schizophrenic 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 kinds 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 distal 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 A significant difference test (t-test) is performed between schizophrenia (non-medicine) and healthy subjects.
  • t test two-group test
  • 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 with the data set as an object, and the parameters of the approximate curve and the signal intensity ratio greatly separated were excluded from the analysis target.
  • a DNA microarray equipped with about 55,000 types of probes is expensive, and only one specimen can be processed with one microarray. Therefore, it is desirable to use a lower cost microarray for practical use. Therefore, in the following examples, 216 types of probes were selected from the probes including the 14 types of probes selected above and having a significant difference between schizophrenia and healthy subjects, and mounted on the substrate. Of these 216 types of probes, 202 types of probes other than the 14 types of probes described above are probes that have a significant difference between schizophrenia and healthy subjects, and patients with bipolar disorder who are similar mental disorders. Genes with statistically significant differences among schizophrenic patients were selected.
  • a probe used for global normalization and a management probe (for alignment) were also mounted (details in the following examples).
  • 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 specimens can be simultaneously tested with a single array. Cost, and inspection cost and labor can be greatly reduced.
  • the measurement results of the 14 types of previously specified probes, measured using this practical array, are input to the neural network classification prediction model constructed previously, and the sensitivity and specificity are determined using the above test example.
  • the sensitivity and specificity of the healthy person was less than 80%, and when the sensitivity and specificity were calculated using another test example, both the sensitivity and specificity of schizophrenia (untreated) and healthy persons were calculated. It became less than 80%.
  • the probe with the highest correct answer rate is used.
  • a combination was sought.
  • the above 10 genes were identified. Note that the above 10 types of genes are different from the 14 types of genes specified earlier, and only 2 types of genes overlapped with both.
  • an oligonucleotide probe having the base sequence represented by SEQ ID NO: 42 means an oligonucleotide probe having a base sequence of tcccacatcc ccttgaatat cccaggaaa represented by SEQ ID NO: 42 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 the slide glass, and the 30mer probe is three-dimensionally fixed. It is an excellent microarray, and probes corresponding to about 55,000 human genes 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. As a result, the probe was 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 (trade name) 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 probe of 216 genes described above, a probe used for global normalization (SEQ ID NO: 226 to 525) and a management probe provided by the manufacturer were added to design the following array.
  • CodeLink (trade name) 16-Assay Bioarray classification prediction based on measurement results (neural network) Based on the gene expression information of 60 untreated schizophrenia patients and 56 healthy subjects, we attempted to construct a classification prediction model using a neural network with excellent classification prediction.
  • 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.
  • 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 5 and Table 6 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.
  • 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 56 healthy subjects. Using the expression data of the 10 probes as explanatory variables, a multiple regression analysis was performed on the learning example using commercially available software (SPSS), and a prediction formula was constructed. 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 DLGAP3 (GE54859 SEQ ID NO: 42)
  • X 2 is the gene expression level of KCN15J (GE58277 SEQ ID NO: 77)
  • X 3 is the gene expression level of GPR30 (GE80129 SEQ ID NO: 165)
  • X 4 is the gene expression level of NPCR (GE540583 SEQ ID NO: 34)
  • X 5 is gene expression level TMED1 (GE85017 SEQ ID NO: 200)
  • X 6 the gene expression level of PAFAH2 (GE62881 SEQ ID NO: 122)
  • X 7 is a gene expression level of TMEM23 (GE60313 SEQ ID NO: 98)
  • X 8 is the gene expression level of ABCG1 (GE58)
  • a 1 is 1.00019621196698 A 2 is 0.273175387505458 A 3 is 0.606651443546423 A 4 is -0.659859665599205 A 5 is, -0.287215519193429 A 6 is, -0.271285204843002 A 7 is -0.220049126802913 A 8 is -0.00285057386315785 A 9 is 0.478133475554455 A 10 is -0.169744977943406 Constant C is 0.0429404615746508 It is.
  • Tables 7 and 8 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 92.5% (37/40), Specificity: 92.1% (35/38), Correct answer rate: 92.3% (72/78)

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Abstract

L'invention concerne un nouveau moyen permettant de diagnostiquer avec une grande précision et une grande objectivité le syndrome de dysfonctionnement de l'intégration, à l'aide du sang d'un patient en tant qu'échantillon. L'invention porte sur un procédé de détection du syndrome de dysfonctionnement de l'intégration à l'aide, en tant qu'indications, des quantités d'expression de dix gènes spécifiques dans un échantillon séparé à partir d'un organisme vivant. Conformément à ce procédé, le syndrome de dysfonctionnement de l'intégration peut être diagnostiqué objectivement avec une grande précision. A l'aide d'un certain nombre de spécimens, il se confirme que tant la sensibilité de la détection (c'est-à-dire le taux de vrais positifs) que la spécificité (c'est-à-dire le taux de vrais négatifs) dépassent 80 %. Comme on peut utiliser le sang en tant que spécimen, ce procédé peut être avantageusement mis en œuvre.
PCT/JP2009/057861 2008-06-25 2009-04-20 Procédé de diagnostic du syndrome de dysfonctionnement de l'intégration WO2009157251A1 (fr)

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JP2008166582 2008-06-25
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JP2008226185A JP2010029171A (ja) 2008-06-25 2008-09-03 統合失調症の診断方法

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JP5695328B2 (ja) * 2010-02-24 2015-04-01 国立大学法人大阪大学 統合失調症のかかり易さの検出方法

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