Method for detecting severity of atopic dermatitis

Abstract

To detect severity of atopic dermatitis.SOLUTION: Provided are a marker for detecting the severity of atopic dermatitis and a method for detecting the severity of atopic dermatitis by using the marker.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to a marker for detecting the severity of atopic dermatitis and a method for detecting the severity of atopic dermatitis using the same.

Background Art

[0002] Atopic dermatitis (hereinafter also referred to as "AD") is an eczematous skin disease that mainly develops in individuals with an atopic predisposition. Typical symptoms of AD include chronic and recurrent itching, rashes, erythema, etc. that occur bilaterally, as well as parakeratosis, reduced barrier function, dry skin, etc. Many cases of AD develop in infants and tend to improve with growth, but in recent years, adult-type and refractory atopic dermatitis have also been increasing. In AD, various etiologies are complexly involved, resulting in the formation of diverse symptoms and phenotypes, and it is known that exacerbation and remission repeat (Non-Patent Document 1). For example, it has been reported that when moisturization is not continued after remission is induced by topical medications, approximately 40% of AD patients experience symptom relapse within 14 days, and approximately 60% of AD patients experience symptom relapse within 28 days (Non-Patent Document 2). Therefore, in treating AD, it is necessary to accurately grasp the severity of AD, including the diversity of symptoms and phenotypes.

[0003] Traditional methods for evaluating the severity of Alzheimer's disease (AD) include assessments based on the physician's visual observations. These observations encompass a variety of features, including dryness, erythema, scaling, papules, scratch marks, edema, crusting, vesicles, erosions, and pruritic nodules. Scoring indices such as the Eczema Area and Severity Index (EASI) and the Severity Scoring of Atopic Dermatitis (SCORAD) exist to quantify these features. Furthermore, methods using high-performance cameras and probes to obtain objective numerical data on AD symptoms are also employed. On the other hand, patients themselves evaluate their AD based on visual observations and subjective sensations through touch. Scoring indices for this evaluation include the Patient-Oriented Eczema Measure (POEM), Patient-Oriented SCORAD (PO-SCORAD), and Visual Analog Scaling (VAS).

[0004] However, since the aforementioned conventional methods for evaluating the severity of various forms of Alzheimer's disease (AD) each have different evaluation points, evaluation using only one method may not necessarily accurately reflect the true severity of AD. Therefore, it is desirable to comprehensively determine the severity of AD based on the results of evaluations using multiple methods. On the other hand, in reality, it is difficult to use multiple conventional methods for evaluating the severity of AD in combination, considering factors such as the need for a physician's opinion, the cost and availability of measuring equipment, and the burden on the patient associated with self-evaluation.

[0005] In recent years, it has been proposed that the pathogenesis of Alzheimer's disease (AD) should be evaluated not only by phenotypes (phenotypes) that manifest as findings and subjective sensations, but also by endotypes (patent biological mechanisms), in order to aid in the selection of the optimal treatment. In other words, even among AD patients who exhibit similar phenotypes, there is a possibility that they are caused by different molecular mechanisms. It is increasingly believed that by subdividing the pathogenesis of AD patients by combining phenotypes and endotypes, it will be possible to lead to optimal treatment tailored to each individual patient. Currently, objective understanding of the pathogenesis of the disease, or understanding the pathogenesis considering endotypes, often involves using genes or their expression products contained in skin biopsies, blood, stratum corneum, etc., or the presence of specific cell types (these are sometimes collectively referred to as biomarkers). Conventionally, biomarkers proposed for evaluating the presence and severity of Alzheimer's disease (AD) include peripheral blood eosinophil count, serum total IgE levels, lactate dehydrogenase (LDH) levels, and serum thymus and activation-regulated chemokine (TARC) and squamous cell carcinoma antigen 2 (SCCA2) (Non-patent documents 3 and 4). However, the accuracy of these biomarkers is not always sufficient.

[0006] In recent years, technologies have been developed to investigate the current and future physiological state of the human body by analyzing nucleic acids such as DNA and RNA in biological samples. Nucleic acids derived from living organisms can be extracted from bodily fluids such as blood, secretions, and tissues. More recently, it has been reported that RNA contained in skin surface lipids (SSL) can be used as a sample for biological analysis (Patent Document 1). It has also been reported that marker genes for atopic dermatitis can be detected from SSL (Patent Document 2). [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] International Public Gazette No. 2018 / 008319 [Patent Document 2] Japanese Patent Publication No. 2020-074769 [Non-patent literature]

[0008] [Non-Patent Document 1] Kato et al., Journal of the Japanese Dermatological Association, 2018, 128:2431-2502 [Non-Patent Document 2] Lin et al., Adv Ther, 2017, 34:2601-2611 [Non-Patent Document 3] Sugawara et al., Allergy, 2002, 57:180-181 [Non-Patent Document 4] Ohta et al., Ann Clin Biochem, 2012, 49:277-284 [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] The present invention relates to providing a marker for detecting the severity of atopic dermatitis, and a method for detecting the severity of atopic dermatitis using the same. [Means for solving the problem]

[0010] The present invention provides a marker for detecting the severity of atopic dermatitis, comprising at least one selected from the group consisting of the following genes: ADAM15, AGR2, ALPK1, APOD, ATG16L2, CIZ1, CSNK1D, FASN, GSK3A, ITPKB, LSM10, LYNX1, ODC1, PDK4, PLXNC1, PSME2, ​​SASH3, SETD1B, SLC12A6, TSC22D3, TWF1, and VSIR, and the expression products of said genes. The present invention also provides a method for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of a marker for detecting the severity of atopic dermatitis in the subject. The present invention also provides the use of at least one selected from the group consisting of the following genes: ADAM15, AGR2, ALPK1, APOD, ATG16L2, CIZ1, CSNK1D, FASN, GSK3A, ITPKB, LSM10, LYNX1, ODC1, PDK4, PLXNC1, PSME2, ​​SASH3, SETD1B, SLC12A6, TSC22D3, TWF1, and VSIR, as well as the expression products of these genes, as a marker for detecting the severity of atopic dermatitis, or for use in the manufacture of a marker for detecting the severity of atopic dermatitis. [Effects of the Invention]

[0011] The marker for detecting the severity of atopic dermatitis according to the present invention provides an index for detecting the severity of atopic dermatitis. By using this marker, it becomes possible to easily detect the severity of a patient's atopic dermatitis, which in turn enables a correct understanding of the patient's condition and the provision of optimal treatment appropriate to the patient. [Modes for carrying out the invention]

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

[0013] In this specification, the terms “nucleic acid” or “polynucleotide” mean DNA or RNA. DNA includes cDNA, genomic DNA, and synthetic DNA, and RNA includes total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.

[0014] As used herein, the term "gene" refers to double-stranded DNA including human genomic DNA, single-stranded DNA (sense strand) including cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the sense strand, and fragments thereof, which means that some biological information is contained in the sequence information of the bases constituting the DNA. In addition, the "gene" in this specification includes not only the "gene" represented by a specific base sequence, but also its homologs (i.e., homologs or orthologs), variants such as gene polymorphisms, and derivatives.

[0015] In the present invention, the "expression product" of a gene is a concept that includes the transcription product and translation product of the gene. The "transcription product" is RNA generated by transcription from a gene (DNA), and the "translation product" means a protein encoded by the gene that is translationally synthesized based on the RNA.

[0016] As used herein, "skin surface lipids (SSL)" refers to the lipid-soluble fraction present on the surface of the skin, and is sometimes referred to as sebum. Generally, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin and exists on the skin surface in the form of a thin layer covering the skin surface.

[0017] As used herein, "skin" is a general term for a region including the stratum corneum, epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands, unless otherwise specified.

[0018] As used herein, "atopic dermatitis (also referred to as "AD")" refers to a disease mainly caused by itchy eczema that反复 worsens and remits, and many of its patients are considered to have an atopic predisposition. Examples of atopic predisposition include i) family history / past history (any one or a plurality of diseases such as bronchial asthma, allergic rhinitis / conjunctivitis, atopic dermatitis), or ii) a predisposition to easily produce IgE antibodies.

[0019] As used herein, the "severity" of atopic dermatitis (AD) refers not to the presence or absence of AD, but to the level of severity of AD symptoms, and includes not only broad classifications such as mild, moderate, and severe, but also classifications based on more subtle differences. The "severity" of AD can be determined, for example, based on various known evaluation scores for evaluating AD symptoms. In this specification, such an evaluation score is referred to as a "score related to the severity of atopic dermatitis (AD)". Examples of the score related to the severity of AD include the EASI score and the POEM score for systemic skin rash due to AD, the VAS score for skin itching due to AD, the VAS score for skin dryness due to AD (Atopic Dermatitis Treatment Guidelines, published by the Japanese Dermatological Association, Japanese Journal of Dermatology: 128(12), 2431-2502(2018)), and the erythema index for facial erythema due to AD (see JP-A-2018-23756 and Dawson et al., Phys Med Biol, 25(1980)), or a score determined by comprehensively evaluating any two or more selected from these scores and indexes may be used. As the "severity", the score related to the severity of AD itself may be used as the level of severity of AD symptoms.

[0020] As used herein, the "detection" of the severity of AD can also be expressed in terms such as examination, measurement, determination, or evaluation assistance. Note that the terms "detection", "examination", "measurement", "determination", or "evaluation" of the severity of AD in this specification do not include the diagnosis of the severity of AD by a physician.

[0021] (1. Marker for detecting the severity of atopic dermatitis) There is a need for biomarkers that reflect the severity of Alzheimer's disease (AD). Conventional markers for evaluating the presence or absence and severity of AD have mainly been found based on population analysis, i.e., comparisons between groups belonging to different severity levels (e.g., affected group and normal group, or severe group and mild group). However, conventional markers found through these population analyses do not always reflect minor differences in severity within each group, making it difficult to accurately assess the severity of AD using these conventional markers. If the severity of AD patients can be detected more precisely, it will be possible to correctly understand the patient's condition and, consequently, provide the most appropriate treatment for the patient.

[0022] The inventors have discovered that subtle differences in the severity of AD symptoms are reflected in the expression levels of specific genes in those patients. As shown in the examples described below, the relationship between the AD severity scores of subjects based on various conventional indicators and the expression levels of various genes in those subjects was investigated. As a result, genes were found whose expression levels showed a positive or negative correlation with the AD severity scores of the subjects. Such genes or their expression products reflect differences in AD severity in detail and can be used as markers for detecting the severity of AD in subjects. For example, by using the expression levels of such genes or their expression products as indicators, it becomes possible to detect in detail how severe a subject's AD severity is, or whether the severity is worsening or improving.

[0023] Accordingly, in one embodiment, the present invention provides a marker for detecting the severity of AD. In one embodiment, the marker for detecting the severity of AD provided in the present invention (hereinafter also referred to as the marker of the present invention) can be used not only as a marker for broadly classifying the severity of a subject's AD into mild, moderate, and severe, as with conventional markers, but also as a marker for distinguishing between more minor differences in severity. Furthermore, by comparing the severity of AD of a subject detected at different times using the marker of the present invention, it becomes possible to detect changes in the subject's AD severity (e.g., exacerbation or improvement).

[0024] The markers of the present invention may include at least one selected from the group consisting of 22 genes in total, namely the 7 genes shown in Table 1A and the 15 genes shown in Table 1B, and their expression products. The gene names (Gene Symbols) and Gene IDs of the genes shown in Tables 1A and 1B follow the Official Symbols and Gene IDs listed in NCBI ([www.ncbi.nlm.nih.gov / ]). Hereinafter, the genes and expression products shown in Table 1A will be collectively referred to as the markers of Table 1A, and the genes and expression products shown in Table 1B will be collectively referred to as the markers of Table 1B. The markers of the present invention may be the genes shown in Table 1A or Table 1B below, their expression products, or a combination thereof. In one embodiment, the marker of the present invention is a nucleic acid marker such as the DNA of the gene or its transcript, RNA. In another embodiment, the marker of the present invention is a protein marker, which is the translation product of the gene. Preferably, the marker of the present invention is a nucleic acid marker.

[0025] [Table 1]

[0026] The genes shown in Tables 1A and 1B include those consisting of nucleotide sequences registered in NCBI, as well as those consisting of sequences substantially identical to those registered, insofar as they themselves or expression products derived therefrom function as markers for detecting AD severity. Here, substantially identical sequences mean sequences that, for example, when searched using the homology calculation algorithm NCBI BLAST under the conditions of expect value = 10; gap allowed; filtering = ON; match score = 1; mismatch score = -3, have 90% or more, preferably 95% or more, more preferably 98% or more, and even more preferably 99% or more identity with the nucleotide sequence of the gene in question.

[0027] As shown in the examples described later, the markers in Table 1A showed a positive correlation between their expression level and the score related to the severity of AD. In other words, the markers in Table 1A are positive markers whose expression level is positively correlated with the severity of AD. On the other hand, the markers in Table 1B showed a negative correlation between their expression level and the score related to the severity of AD. In other words, the markers in Table 1B are negative markers whose expression level is negatively correlated with the severity of AD. In the present invention, either the former positive markers or the latter negative markers may be used, or both may be used in combination.

[0028] In a preferred embodiment, the marker of the present invention is a marker for detecting the severity of systemic skin lesions caused by AD, for example, a marker capable of detecting the severity of AD corresponding to the EASI score. The marker comprises at least one selected from the group consisting of the following genes: CIZ1, ADAM15, SETD1B, and TWF1, and the expression products of these genes. These markers are positive markers included in Table 1A. The expression levels of these positive markers show a positive correlation with the severity of systemic skin rash due to AD, such as the EASI score.

[0029] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of systemic skin lesions due to AD, for example, a marker capable of detecting the severity of AD corresponding to the POEM score. The marker comprises at least one selected from the group consisting of the following genes: LYNX1 and PSME2, ​​and the expression products of said genes. These markers are positive markers included in Table 1A. The expression levels of these positive markers show a positive correlation with the severity of systemic skin rash due to AD, such as the POEM score.

[0030] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of skin itching due to AD, and is a marker that can detect the severity of skin itching due to AD corresponding to, for example, a VAS score for skin itching. The marker comprises at least one selected from the group consisting of the following genes: ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR, and the expression products of said genes. These markers are negative markers included in Table 1B. The expression levels of these negative markers show a negative correlation with the severity of skin itching due to AD, for example, the VAS score for skin itching.

[0031] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of skin dryness due to AD, and is a marker that can detect the severity of skin dryness due to AD corresponding to, for example, a VAS score for skin dryness. The marker comprises at least one selected from the group consisting of the following genes: TSC22D3, PLXNC1, and SLC12A6, and the expression products of said genes. These markers are negative markers included in Table 1B. The expression levels of these negative markers show a negative correlation with the severity of skin dryness due to AD, for example, the VAS score for skin dryness.

[0032] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of facial erythema due to AD, and is a marker that can detect the severity of facial erythema due to AD corresponding to an erythema index, for example. The marker comprises at least one selected from the group consisting of the following genes: ODC1, AGR2, FASN, APOD, ITPKB, and PDK4, and the expression products of said genes. These markers include positive markers listed in Table 1A and negative markers listed in Table 1B. The expression level of the positive markers shows a positive correlation with the severity of facial erythema due to AD, such as the erythema index. On the other hand, the expression level of the negative markers shows a negative correlation with the severity of facial erythema due to AD, such as the erythema index.

[0033] The markers of the present invention can be prepared by conventional methods from biological samples collected from subjects, such as cells, tissues (biopsy, etc.), body fluids (body fluids such as tissue exudate, blood, serum prepared from blood, plasma, etc.), organs, skin, urine, saliva, sweat, stratum corneum, surface lipids (SSL), stool, hair, etc. For example, commercially available kits can be used to prepare nucleic acids or proteins from biological samples. Preferably, the markers of the present invention are nucleic acid markers, and preferred examples of nucleic acids prepared from biological samples include DNA such as genomic DNA and RNA such as mRNA.

[0034] Examples of subjects from whom biological samples containing the marker of the present invention are collected include mammals, including humans and non-human mammals, and preferably humans. When the subject is human, their sex, age, and race are not particularly limited and may include infants to the elderly. Examples include individuals who have developed Alzheimer's disease (AD), individuals who need or wish to have the severity of their AD detected, and individuals who need or wish to have changes in the severity of their AD detected.

[0035] More preferably, the marker of the present invention is a nucleic acid or protein prepared from the subject's SSL, and more preferably mRNA. The site of skin from which SSL is collected is not particularly limited, and examples include skin from any part of the body such as the head, face, neck, trunk, hands and feet. Areas with high sebum secretion, such as the skin of the head or face, are preferred, and facial skin is more preferred. Furthermore, the site of skin from which SSL is collected may be either a lesion area where AD has developed or an asymptomatic area where AD has not developed, but preferably, a lesion area or an asymptomatic area near a lesion area is preferred. Here, "near a lesion area" refers to an area within 10 cm adjacent to the lesion area.

[0036] Any means used for the recovery or removal of SSL from the skin can be employed to collect SSL from the subject's skin. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument for scraping off SSL from the skin, as described later, can be used. The SSL absorbent material or SSL adhesive material is not particularly limited as long as it is a material that has an affinity for SSL, and examples include polypropylene and pulp. More detailed examples of procedures for collecting SSL from the skin include methods of absorbing SSL onto a sheet material such as oil-blotting paper or oil-blotting film, methods of adhering SSL to a glass plate or tape, and methods of scraping off and recovering SSL with a spatula, scraper, etc. To improve the adsorption of SSL, an SSL absorbent material containing a highly lipid-soluble solvent beforehand may be used. On the other hand, since the adsorption of SSL is inhibited if the SSL absorbent material contains a highly water-soluble solvent or water, it is preferable that the content of highly water-soluble solvents or water is low. It is preferable to use the SSL absorbent material in a dry state.

[0037] The collected SSL may be used immediately in the nucleic acid or protein extraction process described later, or it may be stored until it is used in the nucleic acid or protein extraction process. If stored, the SSL is preferably stored under low temperature conditions. The storage temperature of the SSL may be 0°C or lower, preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C, even more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and even more preferably -20±5°C. The storage period of the SSL is not particularly limited, but is preferably 12 months or less, for example 6 hours to 12 months, more preferably 6 months or less, for example 1 day to 6 months, and even more preferably 3 months or less, for example 3 days to 3 months.

[0038] For the extraction of nucleic acids or proteins from collected SSL, methods commonly used for the extraction or purification of nucleic acids or proteins from biological samples can be used. Examples of nucleic acid extraction or purification methods include the phenol / chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or methods using columns such as TRIzol®, RNeasy®, or QIAzol®, methods using special magnetic particles coated with silica, methods using Solid Phase Reversible Immobilization magnetic particles, and extraction using commercially available RNA extraction reagents such as ISOGEN. For protein extraction or purification, commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen) can be used.

[0039] (2. Method for detecting the severity of atopic dermatitis) In another embodiment, the present invention provides a method for detecting the severity of AD using the marker of the present invention described in 1. above. The method for detecting the severity of AD according to the present invention (hereinafter referred to as the method of the present invention) detects the severity of AD in a subject based on the expression level of the marker of the present invention in the subject. In one embodiment, the method of the present invention detects the severity of AD in a subject, that is, how bad the symptoms are, using the expression level of the marker of the present invention as an indicator. Furthermore, by comparing the severity detected at different times, it becomes possible to detect changes in the subject's AD severity (e.g., worsening or improvement). Therefore, in another embodiment of the method of the present invention, changes in the subject's AD severity (e.g., worsening or improvement of symptoms) are detected using changes in the expression level of the marker of the present invention as an indicator.

[0040] (2.1 Analysis of marker expression) The subjects subjected to the method of the present invention are the same as the subjects from whom biological samples containing the markers of the present invention described above are collected. In a preferred embodiment, the method of the present invention includes measuring the expression level of the markers of the present invention in a biological sample collected from the subject. The type of the biological sample is as described above, and is preferably an SSL. In one embodiment, the method of the present invention may further include collecting the SSL from the subject. The procedure for collecting the SSL and the procedure for extracting the marker from the SSL are as described above.

[0041] The expression level of the marker of the present invention can be measured according to methods for quantifying nucleic acids or proteins commonly used in the art. The expression level of the marker to be measured may be an expression level based on the absolute amount of the target marker in a biological sample, or a relative expression level to other standards or to the expression level of all nucleic acids or all proteins.

[0042] For example, the expression level of nucleic acid markers can be measured according to the gene expression analysis procedures commonly used in the field. Examples of gene expression analysis methods include PCR, multiplex PCR, real-time PCR, hybridization (DNA chip, DNA microarray, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), sequencing, and chromatography, which are methods for quantifying nucleic acids or their amplified products. If the nucleic acid is RNA, it is preferable to convert the RNA to cDNA by reverse transcription before quantifying it using the methods described above.

[0043] The expression level of the protein marker can be measured using protein quantification methods commonly used in the field, such as immunoassays (e.g., Western blotting, ELISA, immunostaining, etc.), fluorescence methods, electrophoresis, protein chips, chromatography, mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), 1-hybrid methods (PNAS, 100, 12271-12276 (2003)), and 2-hybrid methods (Biol. Reprod, 58, 302-311 (1998)). Alternatively, the expression level of the marker of the present invention may be measured by measuring molecules that interact with the nucleic acid or protein that is the marker of the present invention. Examples of molecules that interact with the marker of the present invention include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, and their phosphorylated, alkylated, and glycosidic compounds, as well as complexes of any of the above.

[0044] Preferably, the marker used in the method of the present invention is SSL-derived RNA. In this case, the expression level of RNA contained in SSL is measured. Preferably, the expression level of the SSL-derived RNA is measured by converting the RNA extracted from SSL into cDNA by reverse transcription, and then quantifying the cDNA or its amplification product using the method described above.

[0045] For RNA reverse transcription, primers targeting specific RNAs to be analyzed may be used, but for more comprehensive nucleic acid preservation and analysis, random primers are preferable. General reverse transcriptases or reverse transcription reagent kits can be used for this reverse transcription. Preferably, highly accurate and efficient reverse transcriptases or reverse transcription reagent kits are used, such as M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptases or reverse transcription reagent kits, such as the PrimeScript® Reverse Transcriptase series (Takara Bio Inc.), the SuperScript® Reverse Transcriptase series (Thermo Scientific Inc.), the SuperScript® III Reverse Transcriptase, and the SuperScript® VILO cDNA Synthesis kit (all from Thermo Scientific Inc.). In the extension reaction of the reverse transcription, the temperature is preferably adjusted to 42°C ± 1°C, more preferably to 42°C ± 0.5°C, and even more preferably to 42°C ± 0.25°C, while the reaction time is preferably adjusted to 60 minutes or more, more preferably to 80 to 120 minutes.

[0046] When measuring the expression level of nucleic acid markers using PCR, RNA derived from a biological sample is reverse transcribed into cDNA as needed, and then the DNA derived from the biological sample is amplified using a primer pair. In PCR, only one specific DNA to be analyzed may be amplified using a primer pair that targets that specific DNA, or multiple specific DNAs may be amplified simultaneously using multiple primer pairs. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method of simultaneously amplifying multiple gene regions by using multiple primer pairs simultaneously in the PCR reaction system. Multiplex PCR can be performed using commercially available kits (for example, the Ion AmpliSeqTranscriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.). The temperature for the annealing and extension reactions in the PCR cannot be generalized as it depends on the primers used, but when using the above-mentioned multiplex PCR kit, it is preferably 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 performed in one step. The duration of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, but is preferably 14 to 18 minutes. The conditions for the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but is preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the above temperatures and times can be performed using a thermal cycler commonly used for PCR.

[0047] The purification of the reaction product obtained by the PCR is preferably carried out by size separation of the reaction product. Size separation allows the target PCR reaction product to be separated from primers and other impurities contained in the PCR reaction mixture. DNA size separation can be carried out, for example, by a size separation column, a size separation chip, or magnetic beads that can be used for size separation. Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

[0048] The purified PCR reaction product may be subjected to further processing necessary for subsequent quantitative analysis. For example, the purified PCR reaction product may be prepared into a suitable buffer solution for DNA sequencing, the PCR primer region in the PCR-amplified DNA may be cleaved, or adapter sequences may be further added to the amplified DNA. For instance, the purified PCR reaction product can be prepared into a buffer solution, the amplified DNA can be subjected to removal of PCR primer sequences and adapter ligation, and the resulting reaction product can be amplified as needed to prepare a library for quantitative analysis. These operations can be performed, for example, using the 5×VILO RT Reaction Mix included with the SuperScript® VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.), the 5×Ion AmpliSeq HiFi Mix included with the Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.), and the Ion AmpliSeq Transcriptome Human Gene Expression Core Panel, according to the protocols included with each kit.

[0049] When measuring the expression level of nucleic acid markers using real-time PCR, if necessary, RNA derived from a biological sample is reverse transcribed into cDNA, and then PCR is performed using primers that have been pre-labeled with radioisotopes (RI), fluorescent substances, etc. The resulting labeled double-stranded DNA is detected and quantified.

[0050] When measuring the expression level of nucleic acid markers using Northern blot hybridization, for example, RNA derived from a biological sample is transferred onto a membrane according to a standard procedure, and then probe DNA labeled with an RI, fluorescent substance, etc., is hybridized to the RNA. By detecting the signal derived from the label from the double helix of the formed labeled probe DNA and RNA, the expression level of the nucleic acid marker can be measured.

[0051] When measuring the expression level of nucleic acid markers using DNA microarrays, for example, a microarray is used in which nucleic acids (cDNA or DNA) that specifically hybridize to the target nucleic acid marker are immobilized on a support. By binding nucleic acids (cDNA or cRNA) prepared from a biological sample onto the microarray and detecting the label on the microarray, the expression level of the nucleic acid marker in the biological sample can be measured. The nucleic acid immobilized on the microarray can be any nucleic acid that hybridizes specifically to the target nucleic acid marker (i.e., substantially only to the target nucleic acid marker) under stringent conditions, and may be a nucleic acid having the entire sequence of the nucleic acid marker of the present invention, or a nucleic acid consisting of a partial sequence. Examples of such "partial sequences" include nucleic acids consisting of at least 15 to 25 bases. Here, stringent conditions can be washing conditions of approximately "1×SSC, 0.1%SDS, 37°C", preferably conditions of approximately "0.5×SSC, 0.1%SDS, 42°C", and more preferably conditions of approximately "0.1×SSC, 0.1%SDS, 65°C". Stringent hybridization conditions are described, for example, in J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition, Cold Spring Harbor Laboratory Press (2001).

[0052] When measuring the expression levels of nucleic acid markers using sequencing, a next-generation sequencer (e.g., Ion S5 / XL system, Life Technologies Japan Co., Ltd.) can preferably be used. The expression level of DNA or RNA can be measured based on the number of reads (read count) generated by sequencing.

[0053] When measuring the expression levels of multiple nucleic acid markers by sequencing, the read count described above can be used as expression level data. Alternatively, the RPM (Reads per million mapped reads) value of the read count, corrected for differences in the total number of reads between samples, the logarithm of the RPM value (Log2RPM value, or Log2(RPM+1) value), the count value corrected using DESeq2 (Love MI et al., Genome Biol, 2014) (Normalized count value), or its logarithm (Log2(Normalized count+1) value) can be used as expression level data. Alternatively, as expression level data, commonly used quantitative values ​​for RNA-seq, such as Fragments per kilobase of exon per million reads mapped (FPKM), reads per kilobase of exon per million reads mapped (RPKM), and transcripts per million (TPM), can be used.

[0054] Probes or primers used for measuring nucleic acid markers may, for example, be primers for specifically amplifying the nucleic acid marker of the present invention, or probes for specifically detecting the nucleic acid marker. Here, "specific" means that the nucleic acid can be recognized or detected in such a way that substantially only the marker of the present invention is detected in Northern blotting, for example, or substantially only the marker of the present invention is amplified in PCR, thereby generating a product or detectable substantially derived from the marker of the present invention. These probes or primers can be designed based on the nucleotide sequence of the nucleic acid marker. Specific examples of such probes or primers include oligonucleotides or complementary strands consisting of the whole or partial sequence of the nucleic acid marker of the present invention. The "complementary strand" is not limited to a perfectly complementary sequence, as long as it specifically recognizes the target marker, and may preferably have 80% or more, more preferably 90% or more, even more preferably 95% or more, and even more preferably 98% or more sequence identity. Sequence identity can be determined by algorithms such as NCBI BLAST described above. Examples of primers used for measuring the nucleic acid marker include those that can perform specific annealing and chain extension for the target nucleic acid marker, and which have a chain length of preferably 10 bases or more, more preferably 15 bases or more, even more preferably 20 bases or more, preferably 100 bases or less, more preferably 50 bases or less, and even more preferably 35 bases or less. Examples of probes used for measuring the nucleic acid marker include those capable of specific hybridization with the target nucleic acid marker, preferably having a chain length of 10 bases or more, more preferably 15 bases or more, preferably 100 bases or less, more preferably 50 bases or less, and even more preferably 25 bases or less. The probe or primer can be DNA or RNA, and may be synthetic or natural. Probes used for hybridization are usually labeled.

[0055] When measuring the expression level of a protein marker using an immunoassay, for example, an antibody against the protein marker can be brought into contact with a biological sample, and the protein marker bound to the antibody can be quantified. For example, in Western blotting, a primary antibody against the protein marker is used, followed by labeling the primary antibody with a secondary antibody labeled with an RI, fluorescent substance, enzyme, etc., and then the expression level of the protein marker can be measured by measuring the signal derived from the label. The antibody against the protein marker may be a polyclonal antibody or a monoclonal antibody. These antibodies can be manufactured according to known methods.

[0056] (2.2 Severity detection based on marker expression levels) In one embodiment of the method of the present invention, the severity of AD in a subject is detected based on the expression level of a marker of the present invention derived from the subject (the marker of the present invention contained in a biological sample taken from the subject).

[0057] As described above, the markers in Tables 1A and 1B are markers whose expression levels vary according to the severity of AD. More specifically, the markers in Table 1A are positive markers whose expression levels show a positive correlation with the severity of AD, while the markers in Table 1B are negative markers whose expression levels show a negative correlation with the severity of AD. Therefore, the severity of AD in a subject can be detected using the expression level of the positive or negative marker as an indicator.

[0058] In a preferred example of this embodiment, the severity of AD detected by the method of the present invention is the severity of systemic skin rash due to AD, for example, the severity of AD corresponding to the EASI score. The marker used for this detection is a marker for detecting the severity of systemic skin rash due to AD as described above, for example, a marker that can detect the severity of AD corresponding to the EASI score. These markers are positive markers, and their expression levels show a positive correlation with the severity of systemic skin rash due to AD, for example, the EASI score. The expression level of these markers can be used as an indicator to detect the severity of systemic skin rash due to AD in a subject.

[0059] In another preferred example of this embodiment, the severity of AD detected by the method of the present invention is the severity of systemic skin rash due to AD, for example, the severity of AD corresponding to the POEM score. The markers used for this detection are markers for detecting the severity of systemic skin rash due to AD as described above, for example, markers that can detect the severity of AD corresponding to the POEM score. These markers are positive markers, and their expression levels show a positive correlation with the severity of systemic skin rash due to AD, for example, the POEM score. The expression level of these markers can be used as an indicator to detect the severity of systemic skin rash due to AD in a subject.

[0060] In another preferred example of this embodiment, the severity of AD detected by the method of the present invention is the severity of itching due to AD, for example, the severity of AD corresponding to a VAS score for itching. The marker used for this detection is a marker for detecting the severity of itching due to AD as described above, for example, a marker that can detect the severity of AD corresponding to a VAS score for itching. These markers are negative markers, and their expression levels show a negative correlation with the severity of itching due to AD, for example, a VAS score for itching. The expression level of these markers can be used as an indicator to detect the severity of itching due to AD in a subject.

[0061] In another preferred embodiment of this model, the severity of AD detected by the method of the present invention is the severity of skin dryness due to AD, for example, the severity of AD corresponding to a VAS score for skin dryness. The markers used for detection are markers for detecting the severity of skin dryness due to AD as described above, for example, markers that can detect the severity of AD corresponding to a VAS score for skin dryness. These markers are negative markers, and their expression levels show a negative correlation with the severity of skin dryness due to AD, for example, a VAS score for skin dryness. The expression levels of these markers can be used as indicators to detect the severity of skin dryness due to AD in a subject.

[0062] In another preferred example of this embodiment, the severity of AD detected by the method of the present invention is the severity of facial erythema due to AD, for example, the severity of AD corresponding to the erythema index. The marker used for this detection is a marker for detecting the severity of facial erythema due to AD as described above, for example, a marker that can detect the severity of AD corresponding to the erythema index. If these markers are positive markers, their expression levels show a positive correlation with the severity of facial erythema due to AD, for example, the erythema index. On the other hand, if these markers are negative markers, their expression levels show a negative correlation with the severity of facial erythema due to AD, for example, the erythema index. The expression level of the positive marker or the negative marker can be used as an indicator to detect the severity of facial erythema due to AD in a subject.

[0063] In this embodiment, one or more markers selected from the positive and negative markers in a biological sample taken from a subject can be used as target markers for detection as described above. The positive and negative markers are, as described above, markers that correlate with the EASI score or POEM score related to systemic skin rash due to AD, the VAS score for skin itching due to AD, the VAS score for dry skin due to AD, or the erythema index related to facial erythema due to AD. In this embodiment, it is sufficient to use at least one marker that correlates with any of the above scores or indices as a target marker. Preferably, two or more markers that each correlates with a different score or index are used as target markers. More preferably, markers that correlate with the EASI score, POEM score, VAS score for skin itching due to AD, VAS score for dry skin due to AD, and the erythema index related to facial erythema due to AD are combined and used as target markers. In the method of the present invention, either one of the positive marker and the negative marker may be used as the target marker, or two or more selected from the positive marker and the negative marker may be used in combination as the target marker. Alternatively, a nucleic acid marker or protein marker consisting of all of the positive marker and the negative marker may be used in combination as the target marker.

[0064] In one embodiment, the severity of AD in a subject can be detected by measuring the expression level of a target marker in the subject and comparing the measured expression level of the target marker with a preset reference value. The reference value can be predetermined based on the relationship between the severity of AD (the level of AD symptoms classified based on the score value related to the severity of AD, or the score value related to the severity of AD) and the expression level of the target marker. For example, a population can be divided into multiple groups with different severity levels based on the severity of AD, and a reference value for determining whether a person belongs to each group can be determined by referring to the statistical value (e.g., mean value) of the expression level of the target marker in each group. When multiple types of markers are used as target markers, it is preferable to determine a reference value for each marker. The population can be a group of patients with AD, a group of healthy individuals and patients with AD combined, or a group of patients with a specific severity of AD. Furthermore, depending on the subjects to be detected, groups may be created by age, generation, sex, or race. Examples of groups used to calculate reference values ​​include groups with mild AD (mild group), groups with moderate AD (moderate group), and groups with severe AD (severe group). Alternatively, patient groups based on a more detailed classification of severity may be selected, and reference values ​​may be calculated for each patient group. A healthy control group (group without AD) may also be included. In one example, when detecting the severity of systemic skin rash due to Alzheimer's disease (e.g., the severity of Alzheimer's disease corresponding to the EASI score), a reference value can be calculated from two or more groups of patients with a specific degree of Alzheimer's disease, which are divided into groups based on their EASI scores. In another example, when detecting the severity of systemic skin rash due to Alzheimer's disease (e.g., the severity of Alzheimer's disease corresponding to the POEM score), a reference value can be calculated from two or more groups of patients with a specific degree of Alzheimer's disease, which are divided from the Alzheimer's disease group based on their POEM score. In another example, when detecting the severity of skin itching due to Alzheimer's disease (AD), a reference value can be calculated from two or more groups of patients with a specific AD severity, who are divided into groups based on their VAS scores for skin itching. In another example, when detecting the severity of skin dryness due to Alzheimer's disease (AD), a reference value can be calculated from two or more groups of AD patients with specific AD severity levels, which are divided based on their VAS scores for skin dryness. In another example, when detecting the severity of facial erythema due to Alzheimer's disease (AD), a reference value can be calculated from two or more groups of AD patients with a specific AD severity, each grouped based on an erythema index related to facial erythema.

[0065] When using the positive marker, a higher expression level indicates a more severe case of Alzheimer's disease (AD) in the subject. Conversely, when using the negative marker, a lower expression level indicates a more severe case of AD in the subject.

[0066] The setting of standard values ​​and the specific methods for classifying the severity of symptoms based on said standard values ​​can be carried out appropriately in accordance with the common technical knowledge of those skilled in the art.

[0067] In another embodiment, changes in the severity of AD in a subject (e.g., exacerbation or improvement) can be detected by measuring the expression level of a target marker in the subject at different times and comparing the measured expression levels of the target marker. In one example, changes in the severity of systemic skin lesions due to AD (e.g., the severity of AD corresponding to the EASI score) are detected. The target marker used is the marker for detecting the severity of systemic skin lesions due to AD as described above, for example, a marker that can detect the severity of atopic dermatitis corresponding to the EASI score. In another example, changes in the severity of systemic skin lesions due to AD (e.g., the severity of AD corresponding to the POEM score) are detected. The target marker used is the marker for detecting the severity of systemic skin lesions due to AD as described above, for example, a marker that can detect the severity of atopic dermatitis corresponding to the POEM score. In another example, changes in the severity of skin itching due to AD are detected. Target markers used include those for detecting the severity of skin itching due to AD, such as markers that can detect the severity of atopic dermatitis corresponding to the VAS score for skin itching due to AD. In another example, a change in the severity of skin dryness due to AD is detected. The target marker used is one that can detect the severity of atopic dermatitis corresponding to the VAS score for skin dryness due to AD, as described above. In another example, a change in the severity of facial erythema due to AD is detected. The target marker used is one that can detect the severity of atopic dermatitis corresponding to the erythema index related to facial erythema due to AD, as described above.

[0068] When using a positive marker, an increase in its expression level over time indicates a worsening of the subject's AD symptoms, while a decrease in its expression level over time indicates an improvement in the subject's AD symptoms. In one example, the expression level of the marker in the same subject in a previous measurement is used as the reference value. If the expression level of the positive marker measured from a subject is higher than the reference value, the subject's AD symptoms are detected as having worsened; on the other hand, if the expression level of the positive marker measured from a subject is lower than the reference value, the subject's AD symptoms are detected as having improved. If necessary, the subject's AD symptoms may be determined using conventional methods at the time of the previous measurement. In that case, if the expression level of the positive marker measured from a subject is higher or lower than the reference value, the subject's AD symptoms may be detected as more severe or less severe than the AD symptoms in the previous measurement.

[0069] When using negative markers, an increase in their expression level over time indicates an improvement in the subject's AD severity, while a decrease in their expression level over time indicates an exacerbation of the subject's AD severity. In one example, the expression level of the marker in the same subject in a previous measurement is used as the baseline. If the expression level of the negative marker measured from a subject is higher than the baseline, the subject's AD severity is detected as having improved; conversely, if the expression level of the negative marker measured from a subject is lower than the baseline, the subject's AD severity is detected as having worsened. If necessary, the subject's AD severity may be determined using conventional methods at the time of the previous measurement. In that case, if the expression level of the negative marker measured from a subject is higher or lower than the baseline, the subject's AD severity may be detected as milder or more severe than the AD severity in the previous measurement.

[0070] In one embodiment of the method of the present invention, if the expression level of the marker of the present invention derived from a subject is preferably 91% or less, more preferably 83% or less, and even more preferably 77% or less of the reference value, the expression level of the marker may be judged to be lower than the reference value. If the expression level of the marker of the present invention is preferably 110% or more, more preferably 120% or more, and even more preferably 130% or more of the reference value, the expression level of the marker may be judged to be higher than the reference value. Alternatively, the difference between the expression level of the marker derived from a subject and the reference value can be judged, for example, by whether the two are statistically significant. When multiple markers are used as target markers, the severity of AD can be detected by comparing the expression level of each target marker with the reference value and checking whether the expression level of a certain percentage, for example, 50% or more, preferably 70% or more, more preferably 90% or more, and even more preferably 100%, of the markers differs from the reference value.

[0071] (2.3 AD Severity Detection Based on Predictive Models) In another embodiment of the method of the present invention, the severity of AD in a subject is detected based on a predictive model constructed using data on the expression level of the marker of the present invention derived from the subject (the marker of the present invention contained in a biological sample taken from the subject) (hereinafter referred to as the expression profile). Examples of expression profiles include data on expression levels such as sequencing read counts.

[0072] For example, a predictive model (e.g., a discriminant) for detecting the degree of AD in any given subject can be constructed using machine learning, where the expression profiles of one or more markers (genes or their expression products) obtained from each individual in a training sample population (e.g., a population containing multiple groups with different severity levels) are used as explanatory variables, and a variable representing which severity level each individual in the population belongs to is used as the dependent variable. The constructed predictive model can then be used to detect the degree of AD in a subject, specifically which severity level group they belong to.

[0073] In this specification, "feature" is synonymous with "explanatory variable" in machine learning. In this specification, markers whose expression profiles are used as explanatory variables (features) in machine learning may be referred to as "feature markers (group)." Furthermore, if a feature marker is a gene or its transcript, it may be referred to as a feature gene.

[0074] The feature marker(s) used in this embodiment may be at least one selected from the group consisting of the genes and their expression products shown in Tables 1A and 1B. The expression profiles of the feature markers may be absolute values, relative values, or normalized. When using multiple markers as the feature marker group, for example, several markers with a high correlation to the severity of AD can be selected from the markers of the present invention, and each of their expression profiles can be used as an explanatory variable.

[0075] In one embodiment, all of the genes or their expression products listed in Table 1A are combined and used as a feature marker group. In another embodiment, all of the genes or their expression products listed in Table 1B are combined and used as a feature marker group. In yet another embodiment, all of the genes or their expression products listed in Tables 1A and 1B are combined and used as a feature marker group.

[0076] In a preferred embodiment, the expression profiles of feature markers (or groups of markers) in a population containing two or more groups of AD patients with different severity levels (e.g., asymptomatic AD, and two or more groups selected from mild, moderate, and severe AD) are used as training samples for machine learning. Using these training samples, a discriminant formula (predictive model) is constructed to differentiate the severity of AD in subjects. The expression profiles of feature markers (or groups of markers) can be used as explanatory variables in constructing the discriminant formula. As the dependent variable, for example, a variable representing which severity level of AD patient group the subject from which the feature markers (or groups of markers) originates can be used. Based on the constructed discriminant formula, a cutoff value for determining the severity of AD can be determined. Next, the expression profiles of the feature markers (or groups of markers) originating from the subject are measured, the obtained measurements are substituted into the discriminant formula, and the result obtained from the discriminant formula is compared with the cutoff value to determine the severity of AD in the subject. The cutoff value can be determined according to known methods. For example, the constructed discriminant can be used to obtain the ROC (Receiver Operating Characteristic Curve) curve, and its Youden index can be determined as the cutoff value.

[0077] Alternatively, when using marker expression profiles to build a predictive model, the data may be compressed by dimensionality reduction as needed before building the predictive model. For example, multiple markers can be extracted from the gene groups or their expression products shown in Tables 1A and 1B. Then, principal component analysis can be performed on the expression profiles of the extracted markers. A predictive model for determining the severity of AD in a subject can be constructed using machine learning, with one or more principal components calculated by the principal component analysis as explanatory variables and a variable representing which severity group (e.g., mild or severe) the subject from which the explanatory variables originate belong as the dependent variable.

[0078] Algorithms used in building predictive models can be publicly known, such as those used in machine learning. Examples of machine learning algorithms, though not limited to them, include linear regression models, Lasso regression, Random Forests, Neural Networks, Support Vector Machines (SVM(linear)) with linear kernels, Support Vector Machines (SVM(rbf)) with rbf kernels, Regularized linear discriminant analysis, and Regularized logistic regression.

[0079] The constructed predictive model is populated with validation data to calculate predicted values. The model whose predicted values ​​best match the actual values, for example, the model with the highest accuracy of the predicted values ​​relative to the actual values, can be selected as the optimal model. Alternatively, the recall rate, precision, and their harmonic mean (F-score) can be calculated from the predicted and actual values, and the model with the highest F-score can be selected as the optimal model. By inputting the expression profiles of feature markers (groups) measured from subjects into the constructed predictive model, the degree of AD in those subjects can be detected.

[0080] (3. ADD severity detection kit) In a further embodiment, the present invention provides a kit for detecting the severity of AD in a subject according to the method of the present invention described in Section 2 above. In one embodiment, the kit of the present invention comprises reagents or instruments for measuring the expression level of the markers of the present invention described above. For example, the kit of the present invention may comprise reagents for amplifying or quantifying the nucleic acid markers of the present invention (e.g., reverse transcriptase, PCR reagents, primers, probes, sequencing adapter sequences, etc.), or reagents for quantifying the protein markers of the present invention (e.g., reagents for immunological measurements, antibodies, etc.). Preferably, the kit of the present invention contains oligonucleotides that specifically hybridize with the nucleic acid markers of the present invention (e.g., primers or probes for PCR), or antibodies that recognize the protein markers of the present invention. Preferably, the kit of the present invention comprises an index or guidance for evaluating the expression level of the markers of the present invention. For example, the kit of the present invention may include guidance explaining the symptoms of AD related to each marker (e.g., skin rash, itching, dry skin, facial erythema, etc.), guidance explaining the relationship between increases or decreases in the expression level of each marker and the severity of AD, or guidance explaining reference values ​​for the expression level of each marker for detecting the severity of AD, or guidance on a discriminant formula based on a predictive model and the feature markers to be input thereto. The kit of the present invention may further include a biological sample collection device (e.g., the SSL absorbent material or SSL adhesive material described above), reagents for extracting the markers of the present invention from a biological sample (e.g., nucleic acid purification reagents), a preservative for the sample collection device after biological sample collection, and a storage container.

[0081] Exemplary embodiments of the present invention are further disclosed herein, including the following substances, manufacturing methods, uses, and methods. However, the present invention is not limited to these embodiments.

[0082] [1] A marker for detecting the severity of atopic dermatitis, comprising at least one selected from the group consisting of the following genes: ADAM15, AGR2, ALPK1, APOD, ATG16L2, CIZ1, CSNK1D, FASN, GSK3A, ITPKB, LSM10, LYNX1, ODC1, PDK4, PLXNC1, PSME2, ​​SASH3, SETD1B, SLC12A6, TSC22D3, TWF1, and VSIR, and the expression products of said genes. [2] Preferably, the marker according to [1], wherein the severity of atopic dermatitis is the severity of generalized skin lesions due to atopic dermatitis, the severity of itching of the skin due to atopic dermatitis, the severity of dry skin due to atopic dermatitis, or the severity of facial erythema due to atopic dermatitis. [3] Preferably, the marker according to [1] or [2] comprises at least one selected from the group consisting of the following genes: CIZ1, ADAM15, SETD1B and TWF1, and the expression products of said genes, and is a marker for detecting the severity of systemic skin lesions due to atopic dermatitis. [4] Preferably, the marker described in [3], which is a marker for detecting the severity of atopic dermatitis corresponding to the Eczema Area and Severity Index. [5] Preferably, the marker according to [1] or [2] comprises at least one selected from the group consisting of the following genes: LYNX1 and PSME2, ​​and the expression products of said genes, and is a marker for detecting the severity of systemic skin lesions due to atopic dermatitis. [6] Preferably, the marker described in [5], which is a marker for detecting the severity of atopic dermatitis corresponding to the Patient Oriented Eczema Measure. [7] Preferably, the marker according to [1] or [2] comprises at least one selected from the group consisting of the following genes: ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR, and the expression products of said genes, and is a marker for detecting the severity of skin itching due to atopic dermatitis. [8] Preferably, the marker described in [7] is a marker for detecting the severity of atopic dermatitis corresponding to the Visual Analog Scaling score of skin itching due to atopic dermatitis. [9] Preferably, the marker according to [1] or [2] comprises at least one selected from the group consisting of the following genes: TSC22D3, PLXNC1 and SLC12A6, and the expression products of said genes, and is a marker for detecting the severity of skin dryness due to atopic dermatitis.

[10] Preferably, the marker according to [9], which is a marker for detecting the severity of atopic dermatitis corresponding to the Visual Analog Scaling score of skin dryness due to atopic dermatitis.

[11] Preferably, the marker according to [1] or [2] comprises at least one selected from the group consisting of the following genes: ODC1, AGR2, FASN, APOD, ITPKB, and PDK4 genes, and the expression products of said genes, and is a marker for detecting the severity of facial erythema due to atopic dermatitis.

[12] Preferably, the marker according to

[11] , which is a marker for detecting the severity of atopic dermatitis corresponding to an erythema index related to facial erythema due to atopic dermatitis.

[13] Preferably, a nucleic acid marker, as described in any one of [1] to

[12] .

[14] Preferably, the marker according to

[13] , wherein the nucleic acid is mRNA collected from lipids on the skin surface.

[15] A method for obtaining data for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of any one of the markers described in [1] to

[14] in the subject.

[16] A method for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of any one of the markers described in [1] to

[14] in the subject.

[17] Preferably, the method according to

[16] further comprises detecting the severity of atopic dermatitis in the subject based on the expression level of the marker.

[18] Preferably, the method according to any one of

[15] to

[17] , wherein the marker is the marker described in [3] and the severity is the severity of a systemic skin rash due to atopic dermatitis.

[19] Preferably, the method according to

[18] , wherein the severity is the severity of atopic dermatitis corresponding to the Eczema Area and Severity Index.

[20] Preferably, the method according to any one of

[15] to

[17] , wherein the marker is the marker described in [5] and the severity is the severity of a systemic skin rash due to atopic dermatitis.

[21] Preferably, the method according to

[20] , wherein the severity is the severity of atopic dermatitis corresponding to the Patient Oriented Eczema Measure.

[22] Preferably, the method according to any one of

[15] to

[17] , wherein the marker is the marker described in [7] and the severity is the severity of itching of the skin due to atopic dermatitis.

[23] Preferably, the method according to

[22] , wherein the severity is the severity of atopic dermatitis corresponding to the Visual Analog Scaling score of skin itching due to atopic dermatitis.

[24] Preferably, the method according to any one of

[15] to

[17] , wherein the marker is the marker described in [9] and the severity is the severity of skin dryness due to atopic dermatitis.

[25] Preferably, the method according to

[24] , wherein the severity is the severity of atopic dermatitis corresponding to the Visual Analog Scaling score for dry skin due to atopic dermatitis.

[26] Preferably, the method according to any one of

[15] to

[17] , wherein the marker is the marker described in

[11] and the severity is the severity of facial erythema due to atopic dermatitis.

[27] Preferably, the method according to

[26] , wherein the severity is the severity of atopic dermatitis corresponding to the erythema index for facial erythema due to atopic dermatitis.

[28] Preferably, the marker is at least one selected from the group consisting of the genes shown in Table 1A and the expression products of said genes, and the higher the expression level of said marker, the more severe the atopic dermatitis of the subject is detected to be. This is the method according to any one of the claims

[16] to

[27] .

[29] Preferably, the marker is at least one selected from the group consisting of the genes shown in Table 1B and the expression products of said genes, and the lower the expression level of said marker, the more severe the atopic dermatitis of the subject is detected to be. The method according to any one of any one of

[16] to

[27] .

[30] Preferably, the method according to any one of

[15] to

[27] , comprising measuring the expression levels of the marker in the subject at different times.

[31] Preferably, the marker is at least one selected from the group consisting of the genes shown in Table 1A and the expression products of said genes. If the expression level of the marker measured from the subject is higher than the expression level in the subject in previous measurements, it is detected that the severity of the subject's atopic dermatitis has worsened; or, if the expression level of the marker measured from the subject is lower than the expression level in the subject in previous measurements, it is detected that the severity of the subject's atopic dermatitis has improved.

[30] The method described.

[32] Preferably, the marker is at least one selected from the group consisting of the genes shown in Table 1B and the expression products of said genes. If the expression level of the marker measured from the subject is lower than the expression level in the subject in previous measurements, it is detected that the severity of the subject's atopic dermatitis has worsened; or, if the expression level of the marker measured from the subject is higher than the expression level in the subject in previous measurements, it is detected that the severity of the subject's atopic dermatitis has improved.

[30] The method described. A kit for detecting the severity of atopic dermatitis, used in the method described in any one of items

[15] to

[32] , comprising an oligonucleotide that specifically hybridizes with a nucleic acid that is a marker as described in any one of items [1] to

[12] , or an antibody that recognizes a protein that is a marker as described in any one of items [1] to

[12] .

[34] Use as a marker for detecting the severity of atopic dermatitis of the following genes: ADAM15, AGR2, ALPK1, APOD, ATG16L2, CIZ1, CSNK1D, FASN, GSK3A, ITPKB, LSM10, LYNX1, ODC1, PDK4, PLXNC1, PSME2, ​​SASH3, SETD1B, SLC12A6, TSC22D3, TWF1, and VSIR, and at least one selected from the group consisting of the expression products of said genes.

[35] Use in the production of markers for detecting the severity of atopic dermatitis, selected from the group consisting of the following genes: ADAM15, AGR2, ALPK1, APOD, ATG16L2, CIZ1, CSNK1D, FASN, GSK3A, ITPKB, LSM10, LYNX1, ODC1, PDK4, PLXNC1, PSME2, ​​SASH3, SETD1B, SLC12A6, TSC22D3, TWF1, and VSIR, and the expression products of said genes.

[36] Preferably, the use according to

[34] or

[35] , wherein the severity of the atopic dermatitis is the severity of generalized skin rash due to atopic dermatitis, the severity of itching of the skin due to atopic dermatitis, the severity of dry skin due to atopic dermatitis, or the severity of facial erythema due to atopic dermatitis.

[37] Preferably, the use according to any one of the claims

[34] to

[36] , wherein the marker is a marker for detecting the severity of systemic skin lesions due to atopic dermatitis, and the marker comprises at least one selected from the group consisting of the following genes: CIZ1, ADAM15, SETD1B and TWF1, and the expression products of said genes.

[38] Preferably, the use according to

[37] , wherein the marker is a marker for detecting the severity of atopic dermatitis corresponding to the Eczema Area and Severity Index.

[39] Preferably, the use according to any one of the claims

[34] to

[36] , wherein the marker is a marker for detecting the severity of systemic skin lesions due to atopic dermatitis, and the marker comprises at least one selected from the group consisting of the following genes: LYNX1 and PSME2, ​​and the expression products of said genes.

[40] Preferably, the use according to

[39] , wherein the marker is a marker for detecting the severity of atopic dermatitis corresponding to the Patient Oriented Eczema Measure.

[41] Preferably, the use according to any one of the claims

[34] to

[36] , wherein the marker is a marker for detecting the severity of skin itching due to atopic dermatitis, and the marker comprises at least one selected from the group consisting of the following genes: ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR, and the expression products of said genes.

[42] Preferably, the use according to

[41] , wherein the marker is a marker for detecting the severity of atopic dermatitis corresponding to a Visual Analog Scaling score of skin itching due to atopic dermatitis.

[43] Preferably, the use according to any one of the claims

[34] to

[36] , wherein the marker is a marker for detecting the severity of skin dryness due to atopic dermatitis, and the marker comprises at least one selected from the group consisting of the following genes: TSC22D3, PLXNC1 and SLC12A6, and the expression products of said genes.

[44] Preferably, the use according to

[43] , wherein the marker is a marker for detecting the severity of atopic dermatitis corresponding to a Visual Analog Scaling score for dry skin due to atopic dermatitis.

[45] Preferably, the use according to any one of the claims

[34] to

[36] , wherein the marker is a marker for detecting the severity of facial erythema due to atopic dermatitis, and the marker comprises at least one selected from the group consisting of the following genes: ODC1, AGR2, FASN, APOD, ITPKB, and PDK4 genes, and the expression products thereof.

[46] Preferably, the use according to

[45] , wherein the marker is a marker for detecting the severity of atopic dermatitis corresponding to an erythema index related to facial erythema due to atopic dermatitis.

[47] Preferably, the use according to any one of

[34] to

[46] , wherein the marker is a nucleic acid marker.

[48] ​​Preferably, the use according to

[47] , wherein the nucleic acid is mRNA collected from lipids on the skin surface. [Examples]

[0083] The present invention will be described in more detail below based on examples, but the present invention is not limited thereto. Example 1: Search for a marker for detecting the severity of atopic dermatitis using SSL-derived RNA. 1) Acquisition of a score related to the severity of atopic dermatitis and SSL collection The study included 18 adult males (ages 23-57) with atopic dermatitis (AD). At the time of the initial measurement, the subjects were diagnosed by a dermatologist as having mild to moderate atopic dermatitis. The subjects visited the facility four times, every 14 days, to have their AD severity scores and SSL samples collected. Hereafter, the collected AD severity scores and SSL samples will be referred to as the 1st, 2nd, 3rd, and 4th scores and SSL samples, respectively, based on the order of their visits. As scores related to the severity of AD, the following were used: a physician's systemic EASI score (Hanifin et al., Exp Dermatol, 10, 2001, which scores symptoms from 0 to 72 based on systemic skin rashes); a subject's own systemic POEM score (Charman et al., Arch Dermatol, 140, 2004, which scores symptoms from 0 to 28 based on systemic skin rashes); a systemic VAS score for itchiness (scoring the intensity of itchiness from 0 to 100); a systemic VAS score for dryness (scoring the intensity of dryness from 0 to 100); and a facial erythema index based on facial images taken with a hyperspectral imaging device (Hyperspectral Camera NH-7, Eva Japan Co., Ltd.) (see Japanese Patent Publication No. 2018-23756 and Dawson et al., Phys Med Biol, 25, 1980). In calculating the facial erythema index, the erythema index was calculated for each pixel on the frontal facial image obtained using a hyperspectral imaging device according to formula (1) below. Arbitrary ROIs (Regions of Interest) were defined on the image corresponding to the forehead, above both eyes, and both cheeks, and the average value of the erythema indices at these five ROIs was used as the facial erythema index.

[0084]

number

[0085] Sebum was collected from the entire face of each subject using an oil-absorbing film (5 x 8 cm, made of polypropylene, 3M). The oil-absorbing films were transferred to vials and stored at -80°C for approximately one month until use for RNA extraction.

[0086] 2) RNA preparation and sequencing The oil-absorbing film described in 1) above was cut to an appropriate size, and RNA was transferred to the aqueous layer using QIAzol Lysis Reagent (Qiagen) according to the included protocol. RNA was extracted from the aqueous layer using a commercially available RNA extraction kit with an RNA extraction spin column, according to the included protocol. The extracted RNA was reverse transcribed using the SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) at 42°C for 90 minutes to synthesize cDNA. Random primers included in the kit were used as primers for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR. Multiplex PCR was performed using the Ion AmpliSeqTranscriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) under the conditions [99°C, 2 min → (99°C, 15 sec → 62°C, 16 min) × 20 cycles → 4°C, Hold]. The obtained PCR products were purified using Ampure XP (Beckman Coulter, Inc.), followed by buffer reconstitution, primer sequence digestion, adapter ligation and purification, and amplification to prepare the library. The prepared library was loaded onto an Ion 540 Chip and sequenced using the Ion S5 / XL system (Life Technologies Japan, Inc.). The genes from which each read sequence originated were determined by gene mapping using the human genome reference sequence, hg19 AmpliSeq Transcriptome ERCC v1.

[0087] 3) Data used The read counts of each read obtained by sequencing the SSL-derived RNA of the subjects measured in 2) above were used as data for the expression level of each RNA. Genes whose amplification region in sequencing spanned at least two or more exons were selected as the genes to be analyzed. To correct for differences in total read counts between samples, the read counts of the genes to be analyzed were converted to RPM (Reads per million mapped reads) values. Of these, 4845 genes for which a read count of 20 or more was obtained in more than 90% of the samples were used for the following analysis. Furthermore, to approximate the RPM values ​​to a normal distribution, they were converted to base 2 logarithms (Log2(RPM+1) values) by adding the integer 1. Following the above procedure, expression level data (Log2(RPM+1) values) for 4845 genes were created for the first, second, third, and fourth SSL samples from 18 subjects. These are referred to as the 1st, 2nd, 3rd, and 4th gene expression level data, respectively, based on the order from the first visit.

[0088] 4) Data Analysis i) Searching for genes that correlate with EASI scores Based on the first EASI scores obtained in 1) above for 18 AD patients and the first gene expression level data (Log2(RPM+1) values) for 4845 genes calculated in 3) above for 18 AD patients, the Spearman correlation coefficient Rs between the EASI score and each gene expression level was calculated. Similarly, the Rs for the second to fourth EASI scores and gene expression levels were calculated. The calculated Rs values ​​are referred to as the first to fourth Rs for each gene.

[0089] For each gene, we examined the number of times the p-value (p_val) of Rs for the first to fourth trials was below 0.1 (this number is defined as the A value), and the number of times the p-value of Rs for the first to fourth trials was below 0.05 (this number is defined as the B value). The four genes CIZ1, ADAM15, STED1B, and TWF1, shown in Table 2, had an A value of 4 or a B value of 3 or higher, and showed a high correlation with the EASI score. Since there have been no previous reports suggesting an association between these four genes and atopic dermatitis, they were judged to be potential novel markers for detecting the severity of atopic dermatitis.

[0090] [Table 2]

[0091] ii) Searching for genes that correlate with POEM score Based on the first POEM scores of the 18 AD patients obtained in 1) above, and the first gene expression level data (Log2(RPM+1) values) of 4845 genes from the 18 AD patients calculated in 3) above, the Spearman correlation coefficient Rs between the POEM scores and each gene expression level was calculated. Similarly, the Rs for the second to fourth POEM scores and gene expression levels were calculated. The calculated Rs values ​​are referred to as the first to fourth Rs for each gene.

[0092] For each gene, we examined the number of times the p-value (p_val) of Rs for the first to fourth measurements was below 0.1 (this number is defined as the A value), and the number of times the p-value of Rs for the first to fourth measurements was below 0.05 (this number is defined as the B value). As shown in Table 3, the two genes LYNX1 and PSME had an A value of 4 or a B value of 3 or higher, indicating a high correlation with the POEM score. Since there have been no previous reports suggesting an association between these two genes and atopic dermatitis, they were judged to be potential novel markers for detecting the severity of atopic dermatitis.

[0093] [Table 3]

[0094] iii) Searching for genes that correlate with VAS scores for skin itchiness Based on the VAS scores for the first skin itching of 18 AD patients obtained in 1) above, and the first gene expression level data (Log2(RPM+1) values) for 4845 genes of 18 AD patients calculated in 3) above, the Spearman correlation coefficient Rs between the VAS scores and each gene expression level was calculated. Similarly, the Rs for the second to fourth VAS scores and gene expression levels were calculated. The calculated Rs values ​​are referred to as the first to fourth Rs for each gene.

[0095] For each gene, we examined the number of times the p-value (p_val) of Rs for the first to fourth trials was below 0.1 (this number is defined as the A value), and the number of times the p-value of Rs for the first to fourth trials was below 0.05 (this number is defined as the B value). The seven genes shown in Table 4—ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR—had an A value of 4 or a B value of 3 or higher, indicating a high correlation with itching. Since there have been no previous reports suggesting a link between these seven genes and atopic dermatitis, they were judged to be potential novel markers for detecting the severity of atopic dermatitis.

[0096] [Table 4]

[0097] iv) Searching for genes that correlate with VAS scores for skin dryness Based on the VAS scores for the first skin dryness test obtained in 1) above for 18 AD patients, and the first gene expression level data (Log2(RPM+1) values) for 4845 genes in 18 AD patients calculated in 3) above, the Spearman correlation coefficient Rs between the VAS score and each gene expression level was calculated. Similarly, the Rs for the second to fourth VAS scores and gene expression levels were calculated. The calculated Rs values ​​are referred to as the first to fourth Rs for each gene.

[0098] For each gene, we examined the number of times the p-value (p_val) of Rs for the first to fourth measurements was below 0.1 (this number is defined as the A value), and the number of times the p-value of Rs for the first to fourth measurements was below 0.05 (this number is defined as the B value). The three genes TSC22D3, PLXNC1, and SLC12A6 shown in Table 5 had an A value of 4 or a B value of 3 or higher, indicating a high correlation with dryness. Since there have been no previous reports suggesting a link between these three genes and atopic dermatitis, they were judged to be potential novel markers for detecting the severity of atopic dermatitis.

[0099] [Table 5]

[0100] v) Searching for genes that correlate with the facial erythema index Based on the first-time facial erythema index obtained in 1) above for 18 AD patients and the first-time gene expression level data (Log2(RPM+1) values) for 4845 genes calculated in 3) above for 18 AD patients, the Spearman correlation coefficient Rs between the facial erythema index and each gene expression level was calculated. Similarly, the Rs for the second to fourth facial erythema indices and gene expression levels were calculated. The calculated Rs values ​​are referred to as the first to fourth Rs for each gene.

[0101] For each gene, we examined the number of times the p-value (p_val) of Rs for the first to fourth measurements was below 0.1 (this number is defined as the A value), and the number of times the p-value of Rs for the first to fourth measurements was below 0.05 (this number is defined as the B value). As shown in Table 6A, ODC1 showed a positive correlation with the facial erythema index, while the five genes AGR2, FASN, APOD, ITPKB, and PDK4 shown in Table 6B showed a negative correlation with the facial erythema index. These six genes all had an A value of 4 or a B value of 3 or higher, indicating a high correlation with facial erythema. Since there have been no previous reports suggesting a link between these six genes and atopic dermatitis, they were judged to be potential novel markers for detecting the severity of atopic dermatitis.

[0102] Table 6

Claims

1. A method for detecting the severity of atopic dermatitis in a subject, comprising measuring an expression level of a marker for detecting the severity of atopic dermatitis in lipids on the skin surface collected from the entire face of the subject, the method comprising: (1) the severity of atopic dermatitis corresponds to the Eczema Area and Severity Index of a systemic skin rash caused by atopic dermatitis; The marker for detecting the severity of atopic dermatitis is at least one selected from the group consisting of mRNAs of the following genes: CIZ1, ADAM15, SETD1B, and TWF1; (2) the severity of atopic dermatitis corresponds to the Patient Oriented Eczema Measure of a systemic skin rash caused by atopic dermatitis; The marker for detecting the severity of atopic dermatitis is at least one selected from the group consisting of the mRNAs of the following genes: LYNX1 and PSME2; (3) the severity of atopic dermatitis corresponds to a Visual Analog Scaling score of skin itching caused by atopic dermatitis; The marker for detecting the severity of atopic dermatitis is at least one selected from the group consisting of the mRNAs of the following genes: ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR; (4) the severity of atopic dermatitis corresponds to a Visual Analog Scaling score of dry skin due to atopic dermatitis; The marker for detecting the severity of atopic dermatitis is at least one selected from the group consisting of the mRNAs of the following genes: TSC22D3, PLXNC1, and SLC12A6, or (5) The severity of atopic dermatitis corresponds to the erythema index for facial erythema caused by atopic dermatitis, The marker for detecting the severity of atopic dermatitis is at least one selected from the group consisting of mRNAs of the following genes: ODC1, AGR2, FASN, APOD, ITPKB, and PDK4. method.

2. The method of claim 1 , further comprising detecting the severity of atopic dermatitis in the subject based on the expression level of the marker.

3. The method according to claim 1 or 2, wherein the marker is at least one selected from the group consisting of the mRNAs of the genes shown in Table 1, and the higher the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be. 【Table 1】

4. The method according to claim 1 or 2, wherein the marker is at least one selected from the group consisting of the mRNAs of the genes shown in Table 2, and the lower the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be. 【Table 2】

5. The method of claim 1 or 2, comprising measuring the expression level of the marker in the subject at different times.

6. The marker is at least one selected from the group consisting of mRNAs of genes shown in Table 3, When the expression level of the marker measured in the subject is higher than the expression level in the subject in a previous measurement, the severity of the atopic dermatitis in the subject is detected to have worsened; or, when the expression level of the marker measured in the subject is lower than the expression level in the subject in a previous measurement, the severity of the atopic dermatitis in the subject is detected to have improved. The method of claim 5. 【Table 3】

7. The marker is at least one selected from the group consisting of mRNAs of genes shown in Table 4, When the expression level of the marker measured in the subject is lower than the expression level in the subject in a previous measurement, the severity of the atopic dermatitis in the subject is detected to have worsened; or, when the expression level of the marker measured in the subject is higher than the expression level in the subject in a previous measurement, the severity of the atopic dermatitis in the subject is detected to have improved. The method of claim 5. 【Table 4】

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