Method for detecting atopic dermatitis in infants and young children

Abstract

To provide a marker for detecting infant atopic dermatitis, and a method for detecting infant atopic dermatitis using the marker.SOLUTION: A method for detecting infant atopic dermatitis in a subject includes the step of measuring the expression level of at least one gene selected from seven gene groups consisting of IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMICA1 and HNRNPA1 or an expression product thereof in a biological sample taken from the subject.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a method for detecting atopic dermatitis in infants using a marker for atopic dermatitis in infants. Regarding the law. [Background technology]

[0002] Atopic dermatitis (hereinafter also referred to as "AD") occurs mainly in people with a predisposition to atopy. Atopic dermatitis is a skin disease characterized by eczema. The typical symptoms of atopic dermatitis are bilateral and contralateral. It can cause chronic and recurring itching, rash, erythema, etc., as well as keratinization failure, impaired barrier function, dry skin, etc. Atopic dermatitis occurs mostly in infants and tends to improve as the child grows. In recent years, adult-type and intractable atopic dermatitis has also been increasing.

[0003] Newborns / infants who have a genetic predisposition to allergies or atopy may develop infantile eczema. , atopic dermatitis, food allergies, as well as bronchial asthma and allergic rhinitis, etc. It is known that various allergic diseases develop along with the onset of the allergic march. As with allergic diseases, if you develop one disease, you may develop another allergic disease. Allergic reactions in infants and young children are common and treatment is often long-term. There is a need to prevent the onset of the disease.

[0004] Peripheral blood eosinophils are a biomarker for detecting atopic dermatitis. number, serum total IgE level, LDH (lactate dehydrogenase) level, serum thymus and d Activation-Regulated Chemokine (TARC) and S Detects quamous cell carcinoma antigen 2 (SCCA2) (Non-patent Documents 1, 2, 3) and the detection of Staphylococcus aureus in the skin flora. and detecting the expression level of the RNAIII gene dependent on the agrC mutation of the bacterium (Patent Document 1). It has been proposed. However, it remains to be seen whether these markers are applicable to atopic dermatitis in infants. It's not clear.

[0005] On the other hand, analysis of nucleic acids such as DNA and RNA in biological samples has revealed the current state of the human body and even Technologies for investigating future physiological states are being developed. Analysis using nucleic acids is a comprehensive analysis method. It has been established that a wealth of information can be obtained with a single analysis, and it is possible to obtain information on single nucleotide polymorphisms, RNA functions, etc. Based on many research reports on the subject, it has the advantage of being easy to link the analysis results functionally. Nucleic acids derived from living organisms can be extracted from body fluids such as blood, secretions, tissues, etc. Recently, it has been reported that α-glucan is contained in skin surface lipids (SSL). Use of RNA as a sample for biological analysis, and analysis of the epidermis, sweat glands, hair follicles, and sebaceous glands from SSL. It has been reported that the above marker genes can be detected (Patent Document 2). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2019-30272 [Patent Document 2] International Publication No. 2018 / 008319 [Non-patent literature]

[0007] [Non-Patent Document 1] Sugawara et al., Allergy (2002) 57:180-181 [Non-patent document 2] Ohta et al., Ann Clin Biochem.(2012) 49:277-84 [Non-patent document 3] Kato et al., Journal of the Japanese Dermatological Association (2018) 128: 2431-2502 Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention relates to a marker for detecting atopic dermatitis in infants and a method for detecting atopic dermatitis using the marker. The present invention relates to a method for detecting atopic dermatitis in infants and young children. [Means for solving the problem]

[0009] The present inventors have compared the effects of atopic dermatitis on infants and children with normal skin without a predisposition to allergies. SSLs are collected from infants, and the RNA expression status contained in the SSLs is analyzed as sequence information. As a result of comprehensive analysis, it was found that the expression levels of certain genes were significantly different between the two groups. We found that this method can be used as a target to detect atopic dermatitis in infants and young children.

[0010] That is, the present invention relates to the following 1) to 3). 1) Regarding biological samples collected from subjects, IMPDH2, ERI1, FBXW2, A group of seven genes consisting of STK17B, TAGLN2, AMICA1, and HNRNPA1 measuring the expression level of at least one gene selected from the group consisting of: A method for detecting infant atopic dermatitis in a subject, comprising: 2) an oligonucleotide that specifically hybridizes with the gene, or The method for treating atopic dermatitis in infants and young children, which comprises an antibody that recognizes the expression product, Test kits for detection. 3) At least one gene selected from the group of genes shown in Tables B-1 to B-2 below or a marker for detecting infantile atopic dermatitis, comprising the gene encoding the gene or its expression product. [Effects of the Invention]

[0011] According to the present invention, atopic dermatitis in infants can be diagnosed early, with high accuracy, in a simple and non-invasive manner. It is possible to detect it with high sensitivity and specificity. DETAILED DESCRIPTION OF THE INVENTION

[0012] All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety. The bodies are incorporated herein by reference.

[0013] In the present invention, the term "nucleic acid" or "polynucleotide" refers to DNA or RNA. DNA includes cDNA, genomic DNA, and synthetic DNA. , "RNA" includes total RNA, mRNA, rRNA, tRNA, non-co This includes both ding RNA and synthetic RNA.

[0014] In the present invention, the term "gene" refers to double-stranded DNA including human genomic DNA, as well as cDNA. Single-stranded DNA (positive strand) containing the above, and single-stranded DNA (complementary strand) having a sequence complementary to the positive strand. and fragments thereof, and the sequence information of the bases constituting the DNA includes It means something that contains some biological information. In addition, the "gene" in question is not just a "gene" represented by a specific base sequence, but also Homologues (i.e., homologs or orthologs), variants such as genetic polymorphisms, and derivatives Nucleic acids encoding the The names of the genes disclosed herein are listed in NCBI ([www.ncbi.nlm.nih.gov / ]). On the other hand, the official symbols listed in the Gene Ontology (GO) For details, please refer to the Pathway ID listed in String ([string-db.org / ]). cormorant.

[0015] In the present invention, the "expression product" of a gene includes a transcription product and a translation product of the gene. A "transcript" is an RNA that is produced by transcription from a gene (DNA). "Translation product" refers to a protein encoded by a gene that is translated and synthesized based on RNA. means.

[0016] In the present invention, "atopic dermatitis" refers to itchy, moist skin that repeatedly worsens and improves. It refers to a disease whose main pathogenic factor is a rash, and many of its patients are said to have a predisposition to atopy. The predisposing factors for asthma are: i) family history and medical history (bronchial asthma, allergic rhinitis, conjunctivitis, allergic ii) any or more of the following diseases: atopic dermatitis; or ii) a condition that is prone to producing IgE antibodies. There are several predisposing factors. In the present invention, atopic dermatitis in infants and young children refers to atopic dermatitis that occurs from the age of 0 to the age of entering school. The target is infants and toddlers aged 0 to 5 years. In infancy, the rash begins on the head and face. It often descends to the trunk and extremities, and in early childhood, the rash on the face decreases, and the rash mainly affects the neck and joints of the extremities. The difference between atopic dermatitis in infants and adults is that it appears on the mind. In recent years, adult atopic dermatitis has become more chronic than infant atopic dermatitis. It has been reported that abnormal epidermal keratinization is associated with abnormal inflammation (Journal of Allergy and clinical immunology, volume 141, issue 6, June 2018, pageg 2094-2106), report The number of reports is small and the situation is unclear.

[0017] In the present invention, "detection" of infant atopic dermatitis means It means to clarify the existence or non-existence of something, and refers to inspection, measurement, judgment, evaluation, or evaluation support. In this specification, the terms "determination" and "evaluation" are used interchangeably. The term does not include medical assessment or evaluation.

[0018] In this specification, "feature" is synonymous with "explanatory variable" in machine learning, and A gene or its expression product to be used for machine learning selected from the markers for detecting eczema Together they are called "feature genes."

[0019] As shown in the Examples below, SSL of 28 healthy children and 25 children with atopic dermatitis The RNA expression data (read count values) extracted from the Counts were normalized using Normalization (Love MI et al. Genome Biol. 2014). ed count value) to compare the likelihood of atopic dermatitis in children with atopic dermatitis with that of healthy children. By extracting RNA with a p-value corrected by the ratio test (FDR) of less than 0.25, 61 genes with increased expression and 310 genes with decreased expression (total 371 genes (Tables 1-1 to 1-9) In the table, genes indicated with "UP" were found to have higher expression levels in children with atopic dermatitis. The genes with a "down" bell are those with atopic dermatitis. These are genes whose expression levels decrease. Therefore, the genes or their expression products selected from the group of 371 genes are It can be a marker for detecting atopic dermatitis in infants. Of the genes in this group, 318 genes (shown in bold with an * in Tables 1-1 to 1-9) This is a gene that has not been reported to be related to atopic dermatitis to date.

[0020] In addition, data on the expression levels of all SSL-derived RNA detected in the subjects (3486 genes) The Log2(RPM+1) value of the child was used as an explanatory variable, and the differences between healthy individuals and infants with atopic dermatitis were is used as the objective variable, and random forest (Breiman L. Machine Learning) is used as the machine learning algorithm. Learning (2001) 45;5-32) to extract feature genes and construct a prediction model. As shown in the examples below, the top 100 genes in terms of variable importance based on the Gini coefficient (Table 3 -1 to 3-3) were selected as feature genes, and a model using these was developed to evaluate the atopic dermatitis of infants. It was shown that it is possible to predict dermatitis. Therefore, the gene or its expression product selected from the group of 100 genes is It may be a suitable marker for detecting atopic dermatitis in infants. Of the genes in question, 92 genes (marked with an * in bold in Tables 3-1 to 3-3) This gene has not been previously reported to be related to atopic dermatitis, and is a novel As shown in the examples below, these novel atopic dermatitis markers are A predictive model using atopic dermatitis markers can also predict atopic dermatitis in infants and young children. be.

[0021] In addition, the above 371 genes or Using the expression data (Log2(RPM+1) value) of 318 genes, We attempted to build a predictive model using random forests for both infants and young children. It has been shown that it is possible to predict childhood atopic dermatitis

[0022] In addition, the Boruta method (Kursa et al. Fundamental Information maticae (2010) 101;271-286) to extract feature genes (maximum number of trials: 1000, p-value less than 0.01), nine genes (Table 4) were extracted as feature genes. As will be shown in the examples below, a random forest prediction model using this data was used to predict breast milk. It has been shown that it is possible to predict atopic dermatitis in infants. Therefore, the genes selected from the nine gene groups or their expression products are This gene can be a marker for detecting atopic dermatitis in infants and young children. Seven genes (marked with an * in bold in Table 4) have been reported to date to be involved in the treatment of atopic dermatitis. This gene has never been reported to be related to dermatitis, and is a novel marker for atopic dermatitis in infants. As will be shown in the examples below, these novel markers for atopic dermatitis in infants and young children It is also possible to predict atopic dermatitis in infants using a predictive model using this method.

[0023] The 371 gene groups (shown in Tables 1-1 to 1-9) extracted by the above-mentioned expression analysis A) and the genes selected as feature genes by random forest, as shown in Tables 3-1 to 3-3. A group of 100 genes (B) selected as feature genes by the Boruta method The sum (A∪B∪C) of the nine gene groups (C) shown in Table 4 was 441 genes (Table A-1 to A-2) can all be markers for atopic dermatitis in infants and young children, and 383 of these (Tables B-1 to B-2) are new markers for atopic dermatitis in infants and young children.

[0024] [Table A-1]

[0025] [Table A-2]

[0026] [Table B-1]

[0027] [Table B-2]

[0028] IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, and AMI of the present invention The seven genes consisting of CA1 and HNRNPA1 were identified by the random forest analysis described above. A group of 100 genes (B) listed in Tables 3-1 to 3-3 selected as feature genes; The nine genes listed in Table 4 were selected as feature genes by the Boruta method (C). It is a gene (B∩C) common to both, and no relationship to atopic dermatitis has been reported so far. These genes are not present in the genomic DNA (indicated in bold with an * in each table). At least one gene or its expression product selected from the above is used to detect infantile atopic dermatitis. This is particularly useful as a novel marker for detecting atopic dermatitis in infants and young children. Furthermore, among these, IMPDH2, ERI1, and FBXW2 were analyzed by the above-mentioned expression analysis. The genes included in the 371 gene groups (A) listed in Tables 1-1 to 1-9 extracted in A∩B∩C), it is said to be a more preferable new marker for atopic dermatitis in infants. can. Each of these seven genes can be a marker for atopic dermatitis in infants and young children. It is preferable to use two or more, preferably four or more, more preferably six or more in combination. Preferably, all seven species are used, even more preferably.

[0029] In addition, ABHD8, GPT2, PLIN2, FAM100B, YPEL2, M AP1LC3B2, RLF, KIAA0930, UBE2R2, HK2, USF2, PD IA3P, HNRNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7 , ECH1, CASS4, CLEC4A, SNRPD1, SLC7A11 and SNX8 The 23 genes listed in Tables 1-1 to 1-9 were extracted by the above-mentioned expression variation analysis. 371 gene groups (A) and those selected as feature genes by random forest The genes included in the common part of the 100 gene groups (B) listed in Tables 3-1 to 3-3 are conventionally used for atopic dermatitis. From the genes that have not been reported to be related to allergic dermatitis, the above-mentioned IMPDH2, ERI1 and Therefore, the genes selected from these gene groups are At least one gene or its expression product is also used to detect atopic dermatitis in infants. This is useful as a novel marker for atopic dermatitis in infants and young children.

[0030] The above genes that could be markers for infant atopic dermatitis (hereinafter referred to as "target genes") ") has the potential to be a biomarker for detecting atopic dermatitis in infants and young children. a gene having a base sequence substantially identical to the base sequence of the DNA constituting the gene, Here, the term "substantially identical base sequences" refers to sequences that are substantially identical to each other, for example, sequences that are identical to each other, as determined by a homology calculation algorithm. NCBI BLAST was used, expectation = 10; gaps allowed; filtering = ON; match score When searching under the conditions of A = 1; mismatch score = -3, the DNA that constitutes the gene A has a base sequence which is 90% or more, preferably 95% or more, and more preferably 98% or more identical to that of A. It means there is oneness.

[0031] The method for detecting atopic dermatitis in infants of the present invention comprises the steps of: In one embodiment, the target gene is IMPDH2, ERI1, FBXW2, STK17B, Selected from a group of seven genes consisting of TAGLN2, AMICA1, and HNRNPA1 The method includes measuring the expression level of at least one gene or its expression product.

[0032] The biological sample used in the present invention is a sample containing the above-mentioned components that are involved in the onset and progression of atopic dermatitis. Any tissue or biomaterial in which the gene of the invention changes its expression may be used. Specifically, organs, skin, blood prepared from body fluids such as urine, saliva, sweat, skin surface lipids (SSL), tissue exudates, and blood Examples include serum, plasma, feces, hair, etc., and preferably skin or skin surface lipids (SS L), and more preferably skin surface lipids (SSL). In addition, the subjects from whom biological samples are collected are not particularly limited in terms of gender or race, as long as they are infants. However, it is not recommended for infants who require detection of atopic dermatitis or those suspected of developing atopic dermatitis. Infants are preferred.

[0033] Here, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, It is sometimes called sebum. Generally, SSL is secreted from exocrine glands such as sebaceous glands in the skin. It is mainly composed of secretions released from the skin and exists on the surface of the skin as a thin layer covering the surface of the skin. contains RNA expressed in skin cells (see Patent Document 3). Unless otherwise specified, "skin" refers to the epidermis, dermis, hair follicles, sweat glands, and sebaceous glands on the surface of the body. It is a general term for the area that includes tissues such as the lining of the stomach and other glands.

[0034] The SSLs were collected from the subject's skin using a device used to retrieve or remove the SSLs from the skin. Any means that can be used for the absorption of the material may be employed. Preferably, the material is an SSL absorbent material, S SL adhesive material or a tool to scrape SSL from the skin can be used. As for absorbent materials or SSL adhesive materials, materials that have affinity for SSL are particularly suitable. Examples of the material include, but are not limited to, polypropylene and pulp. A more detailed example of the order is the SS on sheet-like materials such as oil blotting paper and oil blotting film. How to absorb L, how to attach SSL to glass plates, tape, etc., how to use a spatula, a scraper, etc. SSL can be scraped off and collected using a rapper or similar method. To improve the properties of the material, SSL absorbents pre-soaked with a highly lipid-soluble solvent may be used. On the other hand, SSL absorbent materials are hindered from adsorption when they contain highly water-soluble solvents or moisture. Therefore, it is preferable to use solvents with high water solubility and low water content. It is preferable to use the material in a dry state. The area of ​​the skin from which SSL is collected is The skin may be any part of the body, such as the head, face, neck, trunk, limbs, etc., but is not particularly limited thereto. Areas where oil is secreted in large amounts, such as facial skin, are preferred.

[0035] The RNA-containing SSL collected from the subject may be stored for a period of time. L should be stored under low temperature conditions as soon as possible after collection to minimize degradation of the RNA contained in it. In the present invention, the RNA-containing SSL is preferably stored at a temperature of 0°C. The temperature is preferably -20±20°C to -80±20°C, more preferably -20 ±10°C to -80±10°C, more preferably -20±20°C to -40±20°C, Preferably, the temperature is -20±10°C to -40±10°C, more preferably -20±10°C, and even more preferably The temperature is preferably −20±5° C. The period of storage of the RNA-containing SSL under the low temperature condition is particularly Although not limited to, it is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably or 6 months or less, for example, 1 day or more and 6 months or less, more preferably 3 months or less, for example More than 3 days and less than 3 months.

[0036] In the present invention, the expression level of a target gene or its expression product is measured using RNase I. cDNA artificially synthesized from A, DNA encoding that RNA, and that RNA The encoded protein, the molecules that interact with the protein, and the molecules that interact with the RNA Here, RNA, DNA or molecules that interact with the DNA are included. Molecules that interact with proteins include DNA, RNA, proteins, polysaccharides, and oligosaccharides. Sugars, monosaccharides, lipids, fatty acids, and their phosphorylation products, alkylation products, sugar adducts, etc., and the above The expression level refers to the expression level of the gene or expression product. It comprehensively refers to abundance and activity.

[0037] In a preferred embodiment of the method of the present invention, SSL is used as the biological sample. In this case, the expression level of the RNA contained in the SSL is analyzed, specifically, the RNA is reverse-translated. After conversion to cDNA by transcription, the cDNA or its amplification product is measured. RNA extraction from SSLs involves the use of methods commonly used for the extraction or purification of RNA from biological samples. methods such as the phenol / chloroform method, AGPC (acid guanidin ium thiocyanate-phenol-chloroform extrac tion) method, or TRIzol®, RNeasy®, QIAzo Column-based methods such as 1 (registered trademark) and special magnetic particles coated with silica The method used is Solid Phase Reversible Immobilization Methods using ion magnetic particles, extraction using commercially available RNA extraction reagents such as ISOGEN, etc. It can be used.

[0038] For the reverse transcription, a primer targeting a specific RNA to be analyzed may be used. For more comprehensive nucleic acid storage and analysis, it is preferable to use random primers. For the reverse transcription, a general reverse transcriptase or a reverse transcription reagent kit can be used. For this purpose, highly accurate and efficient reverse transcriptase or reverse transcription reagent kits are used. M-MLV reverse transcriptase and its variants, Alternatively, commercially available reverse transcriptase or reverse transcription reagent kits, such as PrimeScript (registered trademark), may be used. ) Reverse Transcriptase series (Takara Bio Inc.), Super rScript® Reverse Transcriptase Series (T Hermo Scientific) and others. Trademark)III Reverse Transcriptase, SuperScript( VILO cDNA Synthesis kit (registered trademark) Scientific) is preferably used. The temperature for the extension reaction in the reverse transcription is preferably 42°C ± 1°C, more preferably 42°C ± 1°C. ±0.5°C, more preferably 42°C ±0.25°C, while the reaction time is preferably The time is preferably adjusted to 60 minutes or more, more preferably 80 to 120 minutes.

[0039] When RNA, cDNA, or DNA is used as the target, the expression level can be measured using these methods. PCR method using DNA that hybridizes to the primer, real-time RT-PCR method Nucleic acid amplification methods such as multiplex PCR, SmartAmp, and LAMP , a hybridization method using a nucleic acid that hybridizes to these as a probe ( DNA chip, DNA microarray, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), salt Select from methods for determining the base sequence (sequencing), or a combination of these methods. can be done.

[0040] In PCR, a primer pair targeting a specific DNA to be analyzed is used to amplify that specific DNA. Although it is possible to amplify only the DNA of one species, multiple primer pairs can be used to amplify multiple specific DNA simultaneously. The DNA may be amplified. Preferably, the PCR is a multiplex PCR. Multiplex PCR is a method for generating multiple PCR products by using multiple primer pairs simultaneously in a PCR reaction system. Multiplex PCR is a method for amplifying multiple gene regions simultaneously. (e.g., Ion AmpliSeqTranscriptome Human Gen Using the ELISA Kit (Life Technologies Japan, etc.) This can be implemented. The annealing and extension reaction temperatures in the PCR depend on the primers used. Therefore, it is difficult to generalize, but when using the above multiplex PCR kit, it is preferable to is 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even 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 for the annealing and extension steps depends on the size of the DNA to be amplified. The time can be adjusted depending on the size, etc., but is preferably 14 to 18 minutes. The conditions for the denaturation reaction can be adjusted depending on the DNA to be amplified, but are preferably 95 to 99 ℃ for 10 to 60 seconds. Reverse transcription and PCR at the above temperatures and times are generally This can be performed using a thermal cycler commonly used for PCR.

[0041] The PCR product is purified by size separation. It is preferable to separate the target PCR reaction product into the target PCR reaction product by size separation. DNA can be separated from primers and other impurities by size separation, e.g. By using size separation columns, size separation chips, magnetic beads that can be used for size separation, etc. A preferred example of magnetic beads that can be used for size separation is Ampu re XP etc. Solid Phase Reversible Immobiliza Examples include SPRI magnetic beads.

[0042] Further processing of purified PCR products required for subsequent quantitative analysis For example, purified PCR reaction products may be subjected to Prepare the PCR product in an appropriate buffer solution or use it as a PCR primer in PCR-amplified DNA. Even if the amplicon region is cut or an adapter sequence is added to the amplified DNA, For example, purified PCR reaction products can be prepared in a buffer solution and then used to amplify the amplified DNA. The PCR primer sequence was removed and adapter ligation was performed. The resulting DNA can be amplified as needed to prepare a library for quantitative analysis. These procedures can be carried out, for example, using the SuperScript® VILO cDNA System. 5x IVF supplied with the IVF Enthesis Kit (Life Technologies Japan, Inc.) VILO RT Reaction Mix and Ion AmpliSeq Tran Scriptome Human Gene Expression Kit (Lifetech) 5x Ion AmpliSeq HiF kit included with the Ion Technologies Japan Co., Ltd. i Mix and Ion AmpliSeq Transcriptome Human Using the Gene Expression Core Panel, the primers included with each kit were This can be done according to a protocol.

[0043] Northern blot hybridization was used to identify target genes or their derivatives. When measuring the expression level of a nucleic acid, for example, first, probe DNA is fused to a radioisotope or a fluorescent substance. The labeled DNA is then transferred to a nylon membrane or the like in a conventional manner. The resulting target is then hybridized with RNA from a transfected biological sample. The double strand of DNA and RNA is measured by detecting the signal derived from the label. One method is to

[0044] When measuring the expression level of a target gene or a nucleic acid derived therefrom using RT-PCR, For example, first, cDNA is prepared from RNA derived from a biological sample according to a conventional method, and then the cDNA is used as a template. A pair of primers (the above-mentioned cDNA ( The positive strand that binds to the negative strand and the reverse strand that binds to the positive strand are then hybridized with this. PCR is performed according to standard methods, and the resulting amplified double-stranded DNA is detected. For DNA detection, primers labeled with RI, fluorescent substances, etc. are used to detect the above PC A method for detecting labeled double-stranded DNA produced by performing R can be used. do.

[0045] Measuring the expression level of a target gene or a nucleic acid derived therefrom using a DNA microarray In this case, for example, a small amount of nucleic acid (cDNA or DNA) derived from the target gene of the present invention is attached to a support. Using an array immobilizing at least one species, labeled cDNA or cRNA prepared from mRNA By binding A to the microarray and detecting the label on the microarray, m The amount of RNA expression can be measured. The nucleic acids immobilized on the array are specific (i.e., That is, any nucleic acid that hybridizes to the target nucleic acid (substantially only to the target nucleic acid) is acceptable. The nucleic acid may be a nucleic acid having the entire sequence of a target gene, or a nucleic acid consisting of a partial sequence. Here, the "partial sequence" includes a nucleic acid consisting of at least 15 to 25 bases. Here, stringent conditions are usually around "1x SSC, 0.1% SDS, 37℃". The washing conditions are as follows: 0.5x SS, and the more stringent hybridization conditions are as follows: For hybridization conditions, 0.1% SDS, 42°C is recommended. Hybridization conditions include approximately 100x SSC, 0.1% SDS, and 65°C. Conditions are as per J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Thrd Edition. , Cold Spring Harbor Laboratory Press (2001), etc.

[0046] When measuring the expression level of a target gene or a nucleic acid derived therefrom by sequencing, For example, next-generation sequencers (e.g., Ion S5 / XL System, Life Technologies) The analysis can be performed using a system developed by the Japanese Ministry of Health, Labour and Welfare (MHLW). Based on the number of reads obtained (read count), RNA expression can be quantified.

[0047] The probes or primers used in the above measurement, i.e., the target gene or A primer for specifically recognizing and amplifying a nucleic acid derived therefrom, or the RNA or This includes probes for specifically detecting nucleic acids derived from the It can be designed based on the base sequence that constitutes the target gene. The term "substantially detects the target gene of the present invention or a gene thereof in, for example, Northern blotting" means that the target gene of the present invention or a gene thereof is substantially detected. and in the RT-PCR method, for example, the nucleic acid derived from the The detection or product is the gene or nucleic acid derived therefrom so that only the nucleic acid is amplified. This means that it can be determined to be an acid. Specifically, the target gene of the present invention is a DNA having a base sequence constituting the target gene or a complementary strand thereof. Oligonucleotides containing a certain number of complementary nucleotides can be used. The "complementary strand" is a double-stranded DNA consisting of A:T (U in the case of RNA) and G:C base pairs. The term "complementary" refers to a certain number of consecutive nucleotides in a sequence. It is not limited to a completely complementary sequence in the nucleic acid region, but is preferably 80% or more, more preferably It is sufficient if the base sequence has at least 90% identity, and more preferably at least 95% identity. The identity of the nucleotide sequences can be determined using an algorithm such as the BLAST algorithm. Such oligonucleotides, when used as primers, exhibit specific annealing properties. It is sufficient if the fragment can be fused and elongated, and usually, for example, 10 bases or more, preferably 15 bases or more, More preferably, it is 20 bases or more, and for example, 100 bases or less, preferably 50 bases or less, Preferably, the chain length is 35 bases or less. In this case, specific hybridization is sufficient, and the target gene of the present invention is constituted. At least a part or all of the sequence of DNA (or its complementary strand) consisting of the base sequence For example, it has 10 bases or more, preferably 15 bases or more, and for example, 100 bases or less, preferably Preferably, the chain length is 50 bases or less, more preferably 25 bases or less. Here, the "oligonucleotide" can be DNA or RNA. The probes used for hybridization may be synthetic or natural. , usually labeled ones are used.

[0048] In addition, the translation product (protein) of the target gene of the present invention, and a gene that interacts with the protein If you are measuring molecules, molecules that interact with RNA, or molecules that interact with DNA, Protein chip analysis, immunoassays (e.g., ELISA), mass spectrometry (e.g., LC- MS / MS, MALDI-TOF / MS), one-hybrid method (PNAS 100, 12271-1227 6 (2003)) and the two-hybrid method (Biol. Reprod. 58, 302-311 (1998)). It can be used and can be selected appropriately depending on the subject. For example, when a protein is used as the measurement target, an antibody against the expression product of the present invention is used. The body is contacted with a biological sample, and proteins in the sample that bind to the antibody are detected and their levels are determined. For example, Western blotting is performed by measuring the primary antibody After using the above antibody as a primary antibody, a secondary antibody labeled with a radioisotope, fluorescent substance, enzyme, etc. The primary antibody is labeled with an antibody that binds to the primary antibody, and the resulting labeled substance is used to The signal is measured using a radiation detector, a fluorescence detector, or the like. The antibody against the translation product may be a polyclonal antibody or a monoclonal antibody. These antibodies can be produced according to known methods. Polyclonal antibodies are produced using proteins that have been expressed in E. coli or other bacteria and purified according to standard methods. Alternatively, a partial polypeptide of the protein may be synthesized by a conventional method and then cultured in a non-human animal such as a rabbit. The antibody can be obtained by immunizing a human animal and obtaining the antibody from the serum of the immunized animal in a conventional manner. On the other hand, monoclonal antibodies are proteins that are expressed in E. coli or the like and purified according to standard methods. A partial polypeptide of the protein is immunized into a non-human animal such as a mouse, and the resulting spleen cells are then used to It can be obtained from hybridoma cells prepared by cell fusion with myeloma cells. Alternatively, monoclonal antibodies may be generated using phage display (Griffiths, A .D.; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February y 1998 , pp. 102-108(7)).

[0049] Thus, the target gene of the present invention or its expression product in a biological sample collected from a subject can be detected. The expression level is measured, and infantile atopic dermatitis is detected based on the expression level. Specifically, the detection is performed by measuring the expression level of the target gene of the present invention or its expression product. This is done by comparison with a control level. When analyzing the expression levels of multiple target genes by sequencing, As shown in the figure, the read count value, which is the data on the expression level, and the read count value are used to calculate the total read count between samples. RPM value corrected for differences in the number of columns, and the value converted to a logarithmic value with base 2 (Log2 RPM value) or the logarithm value to the base 2 with an integer 1 added (Log2(RPM+1) value), or Normalized count values ​​using DESeq2 or uses the logarithm value of base 2 (Log2(count+1) value) added with integer 1 as an index. It is also preferable to use fragments p as a quantitative value for RNA-seq. er kilobase of exon per million reads ma pped (FPKM), reads per kilobase of exon p er million reads mapped (RPKM), transcript It may also be a value calculated by ts per million (TPM) or the like. Alternatively, the signal value obtained by the microarray method and its corrected value may be used. In addition, when analyzing the expression level of only a specific target gene using RT-PCR, etc., The expression level of the target gene is converted to a relative expression level based on the expression level of the housekeeping gene. Alternatively, a plasmid containing the target gene region can be used to analyze the target gene. The preferred method is to quantify (absolutely quantify) the number of copies of a gene by digital PCR. The number of copies obtained by the method may also be the number of copies obtained by the method. Here, the "control level" refers to, for example, the level of the target gene or its expression product in a healthy individual. The expression level of a gene in a healthy individual is measured from a population of healthy individuals. It may also be a statistical value (e.g., average value) of the expression level of the target gene or its expression product. If there are multiple genes, determine the reference expression level for each gene or its expression product. is preferred.

[0050] Furthermore, the detection of infant atopic dermatitis in the present invention can be carried out by detecting the target gene of the present invention or its This can also be achieved by increasing / decreasing the expression level of the expression product. The expression levels of target genes or their expression products in a biological sample are determined by the following steps: The cutoff value is determined in advance based on the standard value in healthy individuals. The expression level of the target gene or its expression product is obtained as standard data, and It may be determined appropriately based on statistical values ​​such as the average expression level and standard deviation.

[0051] Furthermore, the expression level of a target gene or its expression product derived from a child with atopic dermatitis, Using the measured expression levels of target genes or their expression products derived from healthy children, We constructed a discriminant (prediction model) to distinguish between children with dermatitis and healthy children, and used this discriminant In other words, it is possible to detect atopic dermatitis in infants and young children. The expression levels of target genes or their expression products derived from healthy children were compared with those of target genes or their expression products derived from healthy children. The expression levels of the product were measured using the teacher sample, and children with atopic dermatitis and healthy children were compared. A discriminant (prediction model) was constructed to distinguish between children with and without atopic dermatitis based on the discriminant. The cutoff value (reference value) for discriminating between healthy children is determined. Dimensionality can be reduced by PCA, and the principal components can be used as explanatory variables. The level of the target gene or its expression product is then measured from the biological sample collected from the subject. Measure the same way, substitute the measured value into the discriminant, and refer to the result obtained from the discriminant. By comparing the values, the presence or absence of infantile atopic dermatitis in the subject can be determined. I can appreciate it.

[0052] The variables used to construct the discriminant equation are explanatory variables and target variables. The expression level of a target gene or its expression product selected by the following method can be used. The objective variable is, for example, whether the sample is from a healthy child or a child with atopic dermatitis. or can be used.

[0053] The selection of features is based on statistically significant differences between the two groups to be discriminated, e.g., expression levels of 2 Using the expression levels of genes that vary significantly between groups (differentially expressed genes) or their expression products In addition, it is possible to use known algorithms such as those used in machine learning to extract features. For example, the following gene expression level can be used. Using the expression levels of genes or their expression products with high variable importance in random forests Or, we extract feature genes using the "Boruta" package in R language, and Expression levels can be used.

[0054] The algorithm used to construct the discriminant is a well-known algorithm such as that used in machine learning. Examples of machine learning algorithms include Random Forest ( Random forest), linear kernel support vector machine (SVM li near), rbf kernel support vector machine (SVM rbf) neural network Neural net, Generalized Linear Model inear model), regularized linear discriminant analysis (Regularized linea discriminant analysis) r discriminant analysis), regularized logistic regression (Re Examples include the use of linear regression (linear regression) and linear regression (linear regression). The validation data is input into the constructed prediction model to calculate the predicted value, and the predicted value is compared with the actual measured value. The best fitting model, e.g., the model with the highest accuracy, is the optimal predictive model. In addition, the detection rate (recall) can be calculated from the predicted value and the actual value. The precision and the harmonic mean F-value are calculated, and the F-value is the best. The model with the largest variance can be selected as the optimal predictive model.

[0055] When using the random forest algorithm to build the discriminant, the predictive model As an index of accuracy, the OOB error rate is used. (Breiman L. Machine Learning (2001) 45;5-32).

[0056] In random forests, a method called bootstrap is used to extract the data from all samples. A sample of about two-thirds of the total number of samples is randomly extracted from the tree, allowing overlaps, and a decision tree is created. The samples that are not extracted at this time are considered to be out of bug (OO) samples. B), which uses a single decision tree to predict the OOB target variable and compare it with the correct label. By comparing the OOB error rate in the decision tree, the error rate can be calculated. The same process was repeated 500 times to obtain the OOB er The average error rate is used as the OOB error rate for the random forest model. or rate.

[0057] The number of decision trees (ntree value) used to build the random forest model is set to the default. The default is 500 pieces, but this can be changed from 1 piece to any number as needed. Furthermore, the number of variables (mtry values) used to create the sample discriminant in one decision tree is by default the square root of the number of explanatory variables, but can be increased or decreased as needed from one to all. It can be changed to any value up to the number of explanatory variables.

[0058] The mtry value can be determined using the R package “caret.” We specified the random forest method in the “aret” package and used eight different mtry values. Try it out and select the mtry value that maximizes accuracy as the optimal mtry value. The number of attempts for the mtry value can be changed to any number of attempts as needed. It is possible.

[0059] When using the random forest algorithm to build the discriminant, the model structure The importance of the explanatory variables used in the construction can be expressed as a numerical value (variable importance). For example, the decrease in the Gini coefficient (Mean Decrease Gini) can be used. can be done.

[0060] The method for determining the cutoff value (reference value) is not particularly limited, and can be determined according to a known method. For example, the ROC (Receiver Operator Criteria) created using the discriminant It can be obtained from the (Lattice Characteristic Curve) In an ROC curve, the vertical axis represents the probability of a positive result in a positive patient (sensitivity) and the horizontal axis represents the probability of a negative result. The false positive rate is calculated by subtracting the probability of a negative result (specificity) from 1 in a patient. The ROC curve shows "true positive (sensitivity)" and "false positive (1-specificity)". Regarding the sensitivity, the value at which "true positive (sensitivity)" - "false positive (1 - specificity)" is maximized (Youden The index) can be used as a cutoff value (reference value).

[0061] As already mentioned, IMPDH2, ERI1, FBXW2, STK17B, and TAGLN 2, seven genes consisting of AMICA1 and HNRNPA1, and 100 genes shown in Tables 3-1 to 3-3, 9 genes shown in Table 4, or The 371 genes represented by -1 to 1-9 were used as feature genes to evaluate the atopic skin of infants and young children. It is possible to build a predictive model that can predict flames. Therefore, a discriminant for separating the group of infants with atopic dermatitis from the group of healthy children is created. In this case, the target genes are IMPDH2, ERI1, FBXW2, STK17B, and TAG One gene selected from seven genes consisting of LN2, AMICA1, and HNRNPA1 Two or more genes, preferably five or more genes, more preferably all seven genes are selected, and the selected genes or Furthermore, when multiple genes are selected, the expression data of the expression products are used. The genes in Tables 3-1 to 3-3 are ranked in descending order of variable importance as feature genes. It is preferable to select the seven genes as shown in Table A and create a discriminant. 441 genes shown in Tables 3-1 to 3-3, 100 genes shown in Table 4 or genes other than the seven genes in the 371 genes shown in Tables 1-1 to 1-9 At least one, 5 or more, 10 or more, 20 or more, 50 or more genes selected from the above, or By appropriately adding expression data of expression products, a discriminant formula was created to identify atopic skin in infants and young children. It is also possible to detect flames. Genes other than the seven genes are listed in Tables 3-1 to 3-3. When selecting from the 100 genes shown, the genes with the highest variable importance are selected. Alternatively, feature genetic analysis is performed using genes ranked within the top 50, preferably within the top 30, of variable importance. Furthermore, genes other than the seven genes may be selected as feature genes. In such cases, the information is indicated in bold with an * in Tables 1-1 to 1-9, 3-1 to 3-3, and 4. It is preferable to select feature genes from the novel atopic dermatitis markers that have been shown. In addition, when adding the 371 genes shown in Tables 1-1 to 1-9, the target genes are IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMICA1 and In addition to the seven genes consisting of HNRNPA1, ABHD8, GPT2, and , PLIN2, FAM100B, YPEL2, MAP1LC3B2, RLF, KIAA0 930, UBE2R2, HK2, USF2, PDIA3P, HNRNPUL1, SEC6 1G, DNAJB11, SDHD, NDUFS7, ECH1, CASS4, IL7R, C A group of 25 genes: LEC4A, AREG, SNRPD1, SLC7A11, and SNX8 At least one selected from the above, preferably a mutation of one of these genes in Tables 3-1 to 3-3 At least one, five or more, ten or more, or twenty or more genes from the most important A discriminant may be prepared by appropriately adding expression data of the expression product. The 25 genes were extracted from the above-mentioned expression variation analysis and are listed in Tables 1-1 to 1-9. 371 gene groups (A) and those selected as feature genes by random forest It is a gene contained in the common part of the 100 gene groups (B) listed in Tables 3-1 to 3-3. . Preferably, the above 7 genes, 371 genes or 318 genes shown in Tables 1-1 to 1-9 Children (shown in bold with an * in Tables 1-1 to 19), 100 shown in Tables 3-1 to 3-3 92 genes (indicated in bold with an * in Tables 3-1 to 3-3), or those shown in Table 4 A discriminant formula using the nine genes listed as feature genes can be proposed. More preferably, the above 7 genes, 100 genes or 92 genes shown in Tables 3-1 to 3-3 genes (indicated in bold with an * in Tables 3-1 to 3-3), or the nine genes shown in Table 4 A discriminant formula using feature genes is given.

[0062] The test kit for detecting atopic dermatitis in infants of the present invention is A test for measuring the expression level of the target gene of the present invention or its expression product in a biological sample. Specifically, the target gene of the present invention or a nucleic acid derived therefrom is contained in the test reagent. An oligonucleotide (e.g., a primer for PCR) that specifically binds (hybridizes) to A reagent for nucleic acid amplification, hybridization, or a target of the present invention, comprising Examples include reagents for immunoassays, including antibodies that recognize gene expression products (proteins). The oligonucleotides, antibodies, etc. contained in the kit are publicly known as described above. It can be obtained by the method described below. In addition to the above antibodies and nucleic acids, the test kit also contains labeling reagents, buffer solutions, coloring substrates, and The secondary antibody, blocking agent, and the necessary equipment for the test, as well as positive and negative controls control reagents used as controls, and equipment for collecting biological samples (e.g., It may include items such as oil removal films for collecting SSL.

[0063] Aspects and preferred embodiments of the present invention are set out below. <1> Regarding biological samples collected from subjects, IMPDH2, ERI1, FBXW2 , STK17B, TAGLN2, AMICA1, and HNRNPA1. measuring the expression level of at least one gene selected from the group or its expression product. A method for detecting infantile atopic dermatitis in a subject, comprising: <2> Selected from a group of three genes consisting of IMPDH2, ERI1 and FBXW2 At least includes measuring the expression level of a gene or its expression product, <1> Atopic dermatitis in infants and young children Methods for detecting genital dermatitis. <3> The expression level of a gene or its expression product is measured by measuring the expression amount of mRNA. <1> or <2> How to do it. <4> The gene or its expression product is RNA contained in the lipids on the skin surface of the subject. <1> ~ <3> Either way. <5> The measured expression levels are compared with reference values ​​for each of the genes or their expression products, and assessing the presence or absence of atopic dermatitis; <1> ~ <4> Either way. <6> The expression level of the gene or its expression product derived from a child with atopic dermatitis and that of a healthy person are compared. The measured expression levels of the gene or its expression product from normal children were used as teacher samples. A discriminant formula was created to distinguish between children with and without eczema, and biological samples were collected from the subjects. The measured value of the expression level of the gene or its expression product obtained from the above is substituted into the discriminant. The results were compared with the reference values ​​to determine the presence or absence of atopic dermatitis in the subjects. Evaluate the presence or absence of <1> ~ <4> Either way. <7> The algorithm for constructing the discriminant is random forest, linear kernel support. Support Vector Machine, Support Vector Machine with RBF kernel, Neural Network The method in <6> is a regularized logistic regression, general linear model, regularized linear discriminant analysis, or regularized logistic regression. <8> The expression levels of all genes in the seven gene groups or their expression products are measured. , <6> or <7> How to do it. <9> In addition to at least one gene selected from the seven gene groups, The 100 types shown in Tables 1 to 3-3 and the 9 types shown in Table 4, excluding the 7 types of genes mentioned above, were The expression level of at least one gene selected from the gene group or its expression product is measured. Ru, <6> ~ <8> Either way. <10> The 100 species shown in Tables 3-1 to 3-3 below were extracted using random forests. The feature genes are <9> How to do it. <11> The nine types shown in Table 4 below are feature genes extracted using the Boruta method. be, <9> How to do it. <12> In addition to at least one gene selected from the seven gene groups, From the gene group excluding the genes contained in the above 7 types among the 371 types shown in -1 to 1-9 The expression level of at least one selected gene or its expression product is measured. <6> ~ <8> Either way. <13> In addition to at least one gene selected from the seven gene groups, the following two The expression level of at least one gene selected from the group of five genes or its expression product is To be measured, <11> or <12> The method of ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1LC3B 2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3P, HN RNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, ECH1, C ASS4, IL7R, CLEC4A, AREG, SNRPD1, SLC7A11 and SN X8. <14> An oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom or an antibody that recognizes the expression product of the gene, <1> ~ <13> Noi A test kit for detecting atopic dermatitis in infants, which is used in either of the methods. <15> At least one gene selected from the 383 gene groups shown in B-1 to B-2 A marker for detecting atopic dermatitis in infants and young children, consisting of a single gene or its expression product. <16> IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMI At least one gene selected from a group of seven genes consisting of CA1 and HNRNPA1 or its expression product, <15> A marker for detecting atopic dermatitis in infants and young children. <17> ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1 LC3B2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3 P, HNRNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, EC Consists of H1, CASS4, CLEC4A, SNRPD1, SLC7A11, and SNX8 At least one gene selected from the group of 23 genes or its expression product, 5> A marker for detecting atopic dermatitis in infants and young children. [Example]

[0064] The present invention will be described in more detail below with reference to examples, but the present invention is not limited to these examples. It's not that.

[0065] Example 1: Expression changes related to infantile atopic dermatitis in RNA extracted from SSL Gene detection 1) SSL collection 28 healthy skin infants (HL) (6 months to 5 years old, male and female) and 28 infants with atopic dermatitis The subjects were 25 infants with atopic dermatitis (AD) (6 months to 5 years old, both male and female). is a person with a rash on the entire face and mild or moderate atopic dermatitis as diagnosed by a dermatologist. Each subject had been diagnosed with allergic dermatitis. An oil-removing filter was applied to the entire face (including the rash area for AD). After collecting sebum using a film (5 x 8 cm, polypropylene, 3M), The film was transferred to a vial and stored at -80°C for approximately one month until use in RNA extraction. .

[0066] 2) RNA preparation and sequencing Cut the oil absorbing film from 1) above to an appropriate size and place it in QIAzol Lysis RNA was extracted using Qiagen Reagent according to the attached protocol. The extracted RNA was used for cDNA synthesis using SuperScript VILO. SIS kit (Life Technologies Japan) at 42°C for 90 minutes Reverse transcription was performed to synthesize cDNA. The primers for the reverse transcription reaction were the same as those included in the kit. The random primers used were from the cDNA obtained, and multiplex PCR was performed. A library containing DNA derived from the 20802 gene was prepared. PCR was performed using the Ion AmpliSeq Transcriptome Human Genomics ne Expression Kit (Life Technologies Japan, Inc.) Then, [99°C, 2 min → (99°C, 15 sec → 62°C, 16 min) × 20 cycles → 4°C, Ho The PCR products were purified using Ampure XP (Beckman, Co., Ltd.). After purification by the HPLC (Luther Co., Ltd.), buffer reconstitution, digestion of primer sequences, and adaptor Ligation, purification, and amplification were performed to prepare a library. The data was loaded onto the Ion 540 Chip and then run on the Ion S5 / XL system (lifecycle Sequencing was performed using a PCR technology developed by Sigma-Aldrich Technologies Japan Co., Ltd.

[0067] 3) Data analysis i) Usage Data The data on the expression level of RNA derived from the subject measured in 2) above (read count value) The expression level data for all sample subjects was corrected using a method called DESeq2. Of these, over 90% of the sample subjects had non-missing expression data. Only six genes were used in the following analysis. The analysis was performed using a method called DESeq2. The normalized count value was used. ii) RNA expression analysis The expression levels of SSL-derived RNA in healthy subjects and AD subjects measured in i above (Normalized Based on the count value, the adjusted p-value by likelihood ratio test was calculated for AD compared with healthy controls. RNAs (differentially expressed genes) with a FDR of less than 0.25 were identified. 0 types of RNA decreased (DOWN), and 61 types of RNA increased (UP) (Table 1-1 to 1- 9).

[0068] [Table 1-1]

[0069] [Table 1-2]

[0070] [Table 1-3]

[0071] [Table 1-4]

[0072] [Table 1-5]

[0073] [Table 1-6]

[0074] [Table 1-7]

[0075] [Table 1-8]

[0076] [Table 1-9]

[0077] The 371 genes shown in Tables 1-1 to 1-9 were analyzed using the public database STRI Gene Ontology (GO) enrichment analysis using NG for biologic As a result, we found that the expression of BP was decreased in AD patients. 144 BPs related to the gene cluster were obtained, and cell death, keratinization, immune response (neutrophils, leukocytes) It has been shown that these terms include those related to myeloid cell activation and lipid metabolism. In addition, 44 BPs associated with the gene clusters with elevated expression were identified, and these BPs were associated with exogenous antigens. The terms related to immune responses to the virus were included (Tables 2-1 to 2-4). 318 genes out of 371 genes shown in 1-1 to 1-9 (marked with * in each table) Regarding the substance (shown in letters), there have been no reports to date suggesting a relationship with atopic dermatitis. Therefore, it was determined that this could be a novel marker for atopic dermatitis.

[0078] [Table 2-1]

[0079] [Table 2-2]

[0080] [Table 2-3]

[0081] [Table 2-4]

[0082] Example 2: Construction of a discriminant model using genes with high variable importance in random forests 1) Usage Data In the same manner as in Example 1, data on the expression level of SSL-derived RNA from subjects (read column) were collected. The RPM values ​​were then converted to RPM values ​​corrected for differences in the total number of reads between samples. However, expression data that are not missing values ​​were obtained for more than 90% of all samples. Only 3486 genes were used in the following analysis. To build the machine learning model, we used a negative binomial distribution. To approximate the RPM values ​​to a normal distribution, we add an integer 1 to the logarithm of base 2 (Log2(R PM+1) values ​​were used.

[0083] 2) Selection of feature genes In order to select feature genes using the random forest algorithm, we 3486 genes for which non-missing expression data were available for more than 90% of the samples in the pool. The Log2(RPM+1) value of the child was used as the explanatory variable, and the healthy subjects (HL) and AD subjects were used as the objective variables. We used the random forest algorithm in the "caret" package of the R language. Specify as the method and find the optimal value of the number of variables (mtry value) used to build a decision tree. The mtry value determined by tuning was used to perform random forwarding. Run the Rest algorithm and calculate the top 100 genes by variable importance based on the Gini coefficient. These 100 genes or genes that have been previously associated with atopic dermatitis have been identified (Tables 3-1 to 3-3). 92 genes that have not been reported to be related to allergic dermatitis (shown in bold with an * in each table) It was selected as a feature gene.

[0084] [Table 3-1]

[0085] [Table 3-2]

[0086] [Table 3-3]

[0087] 3) Model construction The Log2(RPM+1) values ​​of the above 100 or 92 genes were used as explanatory variables, and HL and AD were used as the objective variables. Specify the Forest algorithm as the method and the number of variables used to build a decision tree. The optimum value of (mtry value) was tuned. The mtry value determined by tuning Using the random forest algorithm, we estimated the error rate (OOB error) As a result, the OOB e The error rate was 9.43% for the 92-gene model and 13.21% for the 92-gene model. there were.

[0088] Example 3: Construction of a discrimination model using differentially expressed genes 1) Usage Data In the same manner as in Example 1, data on the expression level of SSL-derived RNA from subjects (read column) were collected. The total number of reads (RPM) was calculated and converted to an RPM value corrected for differences in the total number of reads between samples. To build a machine learning model, we use integers to approximate the RPM values ​​that follow a negative binomial distribution to a normal distribution. The logarithm value of base 2 with 1 added (log2(RPM+1) value) was used.

[0089] 2) Selection of feature genes In Example 2, 371 genes whose expression was significantly altered in AD compared to healthy subjects (HL) were identified. Genes (Table 1-1 to 1-9) or genes that have been reported to be associated with atopic dermatitis 318 unreported genes (marked with * in bold in each table) were used as feature genes. Selected.

[0090] 3) Model construction The Log2(RPM+1) values ​​of the above 371 genes or 318 genes were used as explanatory variables, and H L and AD were used as the objective variables. Specify the forest algorithm as the method and select the variables used to build a decision tree. The optimum value of mtry was tuned. Using the values, we ran the random forest algorithm to calculate the OOB error rate. As a result, the OOB error ra was calculated using the model with 371 genes. The te was 26.42% and the model using 318 genes was 30.19%.

[0091] Example 4: Construction of a discriminant model using feature genes extracted by the Boruta method 1) Usage Data In the same manner as in Example 1, data on the expression level of SSL-derived RNA from subjects (read column) were collected. The RPM values ​​were then converted to RPM values ​​corrected for differences in the total number of reads between samples. However, expression data that are not missing values ​​were obtained for more than 90% of all samples. Only 3486 genes were used in the following analysis. To build the machine learning model, we used a negative binomial distribution. To approximate the RPM values ​​to a normal distribution, we add an integer 1 to the logarithm of base 2 (log2(R PM+1) values ​​were used.

[0092] 2) Selection of feature genes Non-missing expression data were obtained for over 90% of all samples34 The Log2(RPM+1) values ​​of 86 genes were used as explanatory variables, and the objective was to differentiate between healthy subjects (HL) and AD. The algorithm in the "Boruta" package in R was implemented using the maximum The number of runs was set to 1000, and 9 genes with p-values ​​less than 0.01 were calculated (Table 4). The nine genes shown in Figure 1 or those that have been reported to be associated with atopic dermatitis Seven genes that were not present (shown in bold with an * in Table 4) were selected as feature genes.

[0093] [Table 4]

[0094] 3) Model construction The Log2(RPM+1) values ​​of the above 9 or 7 genes were used as explanatory variables, and the correlation between HL and AD was was used as the target variable. The algorithm is specified as the method, and the number of variables used to build a decision tree at one time (mt The optimum value of mtry was tuned. Random forest algorithm was run to calculate the OOB error rate. As a result, the OOB error rate was 9.4 in the model using 9 genes. 3% in the model using seven genes, and 15.09% in the model using seven genes.

Claims

[Claim 1] A method for detecting infant atopic dermatitis in a subject, comprising measuring the mRNA expression level of at least one gene selected from a group of three genes consisting of IMPDH2, ERI1, and FBXW2 in lipids on the skin surface collected from the entire face of an infant who is the subject, wherein the detection comprises comparing the measured expression level of the subject with a reference value for the mRNA expression level of each of the genes, the reference value being used to determine the presence or absence of atopic dermatitis; and indicating that the subject has atopic dermatitis if the measured expression level of the subject is greater than the reference value. method.

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