Method for detecting the 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 technology]

[0002] Atopic dermatitis (AD) is an eczematous skin disease that primarily affects individuals with atopic predisposition. Typical symptoms of AD include chronic and recurrent itching, rash, erythema, etc., occurring bilaterally and contralaterally, as well as dyskeratosis, impaired barrier function, and dry skin. AD often develops in infants and young children and tends to improve with age. However, in recent years, adult-onset and refractory atopic dermatitis cases have been increasing. It is known that AD is characterized by a complex interplay of various etiologies, resulting in a diversity of symptoms and phenotypes, leading to repeated exacerbations and remissions (Non-Patent Document 1). For example, it has been reported that if topical medications are not continued to moisturize after induction of remission, approximately 40% of AD patients experience a relapse of symptoms within 14 days, and approximately 60% within 28 days (Non-Patent Document 2). Therefore, when treating AD, it is necessary to accurately understand the severity of the disease, including the diversity of symptoms and phenotypes.

[0003] Conventional methods for assessing the severity of AD include evaluation based on visual findings by physicians. These findings include dryness, erythema, scaling, papules, excoriations, edema, crusting, small blisters, erosions, and pruritic nodules. These findings are scored using indices such as the Eczema Area and Severity Index (EASI) and the Severity SCORing of Atopic Dermatitis (SCORAD). Other methods use high-performance cameras and probes to obtain objective numerical values ​​for AD symptoms. Patients can also assess AD themselves based on visual findings and subjective tactile perception. Scoring indices for this assessment include the Patient Oriented Eczema Measure (POEM), Patient Oriented SCORAD (PO-SCORAD), and Visual Analog Scaling (VAS).

[0004] However, because the various conventional methods for assessing the severity of AD described above have different evaluation points, evaluation using only one method may not necessarily correctly evaluate the true severity of AD, and it is therefore desirable to comprehensively determine the severity of AD based on the evaluation results of multiple methods.However, in reality, it is difficult to use multiple conventional methods for assessing the severity of AD in combination from the perspectives of the need for a doctor's opinion, the cost and availability of measuring equipment, and the burden on patients associated with self-assessment.

[0005] In recent years, it has been proposed to evaluate AD pathology not only based on phenotypes (represented by symptoms and subjective symptoms) but also on pathobiological mechanisms (endotypes) to aid in the selection of optimal treatments. In other words, even among AD patients who exhibit similar phenotypes, these may be caused by different molecular mechanisms. It is believed that subdividing the pathology of AD patients by combining phenotypes and endotypes will lead to optimal treatment tailored to individual patients. Currently, objective understanding of disease pathology, or understanding of pathology taking endotypes into account, often relies on the presence of genes or their expression products in skin biopsies, blood, stratum corneum, etc., or the presence of specific cell types (collectively referred to as biomarkers). Previously, biomarkers proposed for assessing the presence or absence of AD and its severity include peripheral blood eosinophil count, serum total IgE level, lactate dehydrogenase (LDH) level, 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 necessarily sufficient.

[0006] In recent years, technologies have been developed to examine the current and future physiological state of the human body by analyzing nucleic acids such as DNA and RNA in biological samples. Biologically derived nucleic acids can be extracted from body fluids such as blood, secretions, tissues, etc. Furthermore, it has recently 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 Publication No. 2018 / 008319 [Patent Document 2] Japanese Patent Application Publication No. 2020-074769 [Non-patent literature]

[0008] [Non-Patent Document 1] Kato et al., Journal of the Japanese Society of Dermatology, 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 Summary of the Invention [Problem to be solved by the invention]

[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 expression products of the genes. The present invention also provides a method for detecting the severity of atopic dermatitis in a subject, the method 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 gene 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 expression products of said genes, as a marker for detecting the severity of atopic dermatitis, or for use in producing a marker for detecting the severity of atopic dermatitis. [Effects of the Invention]

[0011] The marker for detecting the severity of atopic dermatitis of the present invention provides an indicator for detecting the severity of atopic dermatitis. Use of the marker makes it possible to easily detect the severity of atopic dermatitis in a patient, thereby enabling a correct understanding of the patient's condition and the provision of an optimal treatment suitable for the patient. DETAILED DESCRIPTION OF THE INVENTION

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

[0013] As used herein, the terms "nucleic acid" or "polynucleotide" refer to 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" encompasses double-stranded DNA, including human genomic DNA, as well as single-stranded DNA (positive strand) including cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof, and refers to DNA containing some biological information in the sequence information of the bases that make up the DNA. Furthermore, as used herein, "gene" encompasses not only "genes" represented by a specific base sequence, but also their homologs (i.e., homologs or orthologs), mutants such as genetic polymorphisms, and derivatives.

[0015] In the present invention, the term "expression product" of a gene encompasses both transcription products and translation products of the gene. A "transcription product" is RNA generated by transcription from a gene (DNA), and a "translation product" refers to a protein encoded by the gene that is translated and synthesized based on the RNA.

[0016] As used herein, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, sometimes called sebum. Generally, SSL mainly contains secretions 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] In this specification, unless otherwise specified, the term "skin" is a general term for an area including tissues such as the stratum corneum, epidermis, dermis, hair follicles, sweat glands, sebaceous glands and other glands.

[0018] As used herein, "atopic dermatitis (also referred to as "AD")" refers to a disease whose primary pathogenic factor is a pruritic eczema that repeatedly worsens and improves, and many patients are said to have a predisposition to atopy. Predisposition to atopy includes i) a family history or medical history (one or more of the following diseases: bronchial asthma, allergic rhinitis / conjunctivitis, and atopic dermatitis), or ii) a predisposition to 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 evaluation scores are referred to as "scores related to the severity of atopic dermatitis (AD)." Examples of scores relating to the severity of AD include the EASI score and POEM score for whole-body skin rash due to AD, the VAS score for skin itching due to AD, and the VAS score for dry skin due to AD (Atopic Dermatitis Treatment Guidelines, published by the Japanese Dermatological Association, Journal of the Japanese Dermatological Association: 128(12), 2431-2502(2018)). Also, the erythema index for facial erythema due to AD (see JP 2018-23756 A and Dawson et al., Phys Med Biol, 25(1980)) can be used. Alternatively, a score determined by comprehensively evaluating any two or more of these scores and indexes can be used. The score relating to the severity of AD itself can be used as the "severity" of the AD symptoms.

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

[0021] (1. Markers for detecting the severity of atopic dermatitis) There is a need for biomarkers that reflect the severity of AD. Conventional markers for assessing the presence or absence of AD and the severity of the disease have been primarily discovered based on population analyses, i.e., comparisons between groups with different severity levels (e.g., affected and normal groups, or severe and mild groups). However, the conventional markers discovered through these population analyses are not necessarily able to reflect subtle 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 could be detected more precisely, it would be possible to accurately understand the patient's condition and, ultimately, to provide optimal treatment tailored to each patient.

[0022] The present inventors have found that slight differences in the severity of AD in patients are reflected in the expression levels of specific genes in the patients. As shown in the Examples below, the relationship between the score of a subject's AD severity based on various conventional indices and the expression levels of various genes in the subject was investigated. As a result, genes whose expression levels show a positive or negative correlation with the score of a subject's AD severity were found. Such genes or their expression products precisely reflect differences in the severity of AD and can be used as markers for detecting the severity of AD in a subject. For example, the expression level of the gene or its expression product can be used as an indicator to precisely detect the severity of a subject's AD severity, or to detect whether the severity is worsening or improving.

[0023] Thus, in one aspect, the present invention provides a marker for detecting the severity of AD. In one embodiment, the marker for detecting the severity of AD provided by the present invention (hereinafter also referred to as the marker of the present invention) can be used not only as a marker for roughly classifying the severity of AD in a subject into mild, moderate, severe, etc., like conventional markers, but also as a marker for distinguishing between milder differences in severity. Furthermore, by comparing the severity of AD in a subject detected at different times using the marker of the present invention, it becomes possible to detect changes in the severity of AD in the subject (e.g., worsening or improvement).

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

[0025] [Table 1]

[0026] The genes listed in Tables 1A and 1B include those consisting of nucleotide sequences registered with NCBI, as well as those consisting of sequences substantially identical to the registered sequences, so long as the genes themselves or their expression products function as markers for detecting AD severity. Here, "substantially identical sequences" refers to sequences that share 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 when searched using the homology calculation algorithm NCBI BLAST under the following conditions: expectation value = 10; gaps allowed; filtering = ON; match score = 1; mismatch score = -3.

[0027] As shown in the Examples below, the expression levels of the markers in Table 1A were positively correlated with the score related to the severity of AD. That is, the markers in Table 1A are positive markers whose expression levels are positively correlated with the severity of AD. On the other hand, the expression levels of the markers in Table 1B were negatively correlated with the score related to the severity of AD. That is, the markers in Table 1B are negative markers whose expression levels are negatively correlated with the severity of AD. In the present invention, either the former positive marker or the latter negative marker 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 rash caused by AD, for example, a marker capable of detecting the severity of AD corresponding to the EASI score, and includes at least one gene selected from the group consisting of the following genes: CIZ1, ADAM15, SETD1B, and TWF1, and expression products of the genes. These markers are positive markers included in Table 1A. The expression levels of the positive markers positively correlate with the severity of systemic skin rash caused by AD, for example, the EASI score.

[0029] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of systemic skin rash caused by AD, for example, a marker capable of detecting the severity of AD corresponding to the POEM score, and includes at least one gene selected from the group consisting of the following genes: LYNX1 and PSME2, ​​and expression products of the genes. These markers are positive markers included in Table 1A. The expression levels of the positive markers positively correlate with the severity of systemic skin rash caused by AD, for example, the POEM score.

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

[0031] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of dry skin due to AD, for example, a marker capable of detecting the severity of dry skin due to AD corresponding to the VAS score of dry skin, and includes at least one gene selected from the group consisting of the following genes: TSC22D3, PLXNC1, and SLC12A6, and expression products of the genes. These markers are negative markers included in Table 1B. The expression levels of the negative markers negatively correlate with the severity of dry skin due to AD, for example, the VAS score of dry skin.

[0032] In another preferred embodiment, the marker of the present invention is a marker for detecting the severity of facial erythema due to AD, for example, a marker capable of detecting the severity of facial erythema due to AD corresponding to the erythema index, and includes at least one gene selected from the group consisting of the following genes: ODC1, AGR2, FASN, APOD, ITPKB, and PDK4, and expression products of the genes. These markers include positive markers listed in Table 1A and negative markers listed in Table 1B. The expression levels of the positive markers are positively correlated with the severity of facial erythema caused by AD, e.g., the erythema index. On the other hand, the expression levels of the negative markers are negatively correlated with the severity of facial erythema caused by AD, e.g., the erythema index.

[0033] The markers of the present invention can be prepared according to standard methods from biological samples collected from subjects, such as cells, tissues (biopsies, etc.), body fluids (body fluids such as tissue exudates, blood, serum prepared from blood, plasma, etc.), organs, skin, urine, saliva, sweat, stratum corneum, skin 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 which biological samples containing the markers of the present invention can be collected include mammals, including humans and non-human mammals, and preferably humans. When the subject is a human, the gender, age, race, etc., of the subject are not particularly limited, and may include anyone from infants to the elderly. Examples include those who have developed AD, those who need or wish to detect the severity of AD, and those who need or wish to detect changes in the severity of AD.

[0035] More preferably, the marker of the present invention is a nucleic acid or protein, even more preferably mRNA, prepared from the SSL of a subject. The site of skin from which the SSL is collected is not particularly limited and may include skin from any part of the body, such as the head, face, neck, trunk, limbs, etc. Sites 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 the SSL is collected may be either a rash area where AD has developed or a non-rash area where AD has not developed, but preferably a rash area or a non-rash area near the rash area. Here, "near the rash area" refers to an area within 10 cm adjacent to the rash area.

[0036] Any method commonly used for recovering or removing SSL from skin can be used to collect SSL from a subject's skin. Preferably, SSL absorbent materials, SSL adhesive materials, or devices for scraping SSL from skin, as described below, can be used. The SSL absorbent material or SSL adhesive material can be any material that has affinity for SSL, including polypropylene and pulp. More detailed examples of procedures for collecting SSL from skin include absorbing SSL into sheet-like materials such as oil blotting paper or oil blotting film, adhering SSL to glass plates or tape, or scraping SSL off with a spatula or scraper. To improve SSL adsorption, SSL absorbent materials pre-soaked with a highly lipid-soluble solvent may be used. However, SSL absorbent materials preferably contain low amounts of highly water-soluble solvents or moisture, since the presence of highly water-soluble solvents or moisture inhibits SSL adsorption. It is preferable to use SSL absorbent materials in a dry state.

[0037] The collected SSL may be used immediately in the nucleic acid or protein extraction step described below, or may be stored until used in the nucleic acid or protein extraction step. When stored, the SSL is preferably stored under low-temperature conditions. The temperature condition for storing the SSL may be 0°C or below, 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, even more preferably 3 months or less, for example, 3 days to 3 months.

[0038] Nucleic acids or proteins can be extracted from the collected SSL using methods commonly used for extracting or purifying nucleic acids or proteins from biological samples. Examples of nucleic acid extraction or purification methods include the phenol / chloroform method, the acid guanidinium thiocyanate-phenol-chloroform extraction (AGPC) method, methods using columns such as TRIzol®, RNeasy®, and QIAzol®, methods using special silica-coated magnetic particles, methods using solid phase reversible immobilization magnetic particles, and extraction using commercially available RNA extraction reagents such as ISOGEN. Protein extraction or purification can be performed using commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen).

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

[0040] 2.1 Marker Expression Analysis The subject subjected to the method of the present invention is the same as the subject from whom the above-mentioned biological sample containing the marker of the present invention is collected. In a preferred embodiment, the method of the present invention comprises measuring the expression level of the marker of the present invention in a biological sample collected from the subject. The type of the biological sample is as described above, preferably SSL. In one embodiment, the method of the present invention may further comprise collecting SSL from the subject. The procedure for collecting SSL and the procedure for extracting markers from SSL are as described above.

[0041] The expression level of the marker of the present invention can be measured according to a nucleic acid or protein quantification method 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 may be an expression level relative to the expression level of another standard substance, or the expression level of all nucleic acids or all proteins.

[0042] For example, the expression level of a nucleic acid marker may be measured according to a gene expression analysis procedure commonly used in the field. Examples of gene expression analysis techniques include methods for quantifying nucleic acids or their amplification products, such as PCR, multiplex PCR, real-time PCR, hybridization (DNA chips, DNA microarrays, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), sequencing, and chromatography. When the nucleic acid is RNA, it is preferable to convert the RNA into cDNA by reverse transcription and then quantify it using the above method.

[0043] The expression level of a protein marker can be measured using protein quantification methods commonly used in the art, such as immunoassays (e.g., Western blot, ELISA, immunostaining, etc.), fluorescence, electrophoresis, protein chips, chromatography, mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), one-hybrid methods (PNAS, 100, 12271-12276 (2003)), and two-hybrid methods (Biol. Reprod., 58, 302-311 (1998)). Alternatively, the expression level of a marker of the present invention may be measured by measuring molecules that interact with the nucleic acid or protein 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 phosphorylations, alkylations, and sugar adducts thereof, 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 the SSL is measured. Preferably, RNA extracted from the SSL is converted into cDNA by reverse transcription, and the cDNA or its amplification product is then quantified by the above-mentioned method, thereby measuring the expression level of the SSL-derived RNA.

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

[0046] When measuring the expression level of a nucleic acid marker using PCR, RNA derived from a biological sample is reverse transcribed into cDNA as needed, and then DNA derived from the biological sample is amplified using a primer pair. PCR may amplify only one specific DNA to be analyzed using a primer pair targeting the 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 for simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (e.g., Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan, Inc., etc.). The temperatures for the annealing and extension reactions in the PCR depend on the primers used and cannot be generalized; however, when using the multiplex PCR kit described above, the temperatures are 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 carried out in one step. The time for 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 are preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the temperatures and times described above can be carried out 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. By size separation, the target PCR reaction product can be separated from primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be carried out using, for example, a size separation column, a size separation chip, magnetic beads usable for size separation, etc. Preferred examples of magnetic beads usable for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

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

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

[0050] When measuring the expression level of a nucleic acid marker using Northern blot hybridization, for example, RNA derived from a biological sample is transferred onto a membrane according to a standard method, and then probe DNA labeled with RI, a fluorescent substance, or the like is hybridized to the RNA. The expression level of the nucleic acid marker can be measured by detecting a signal derived from the label in the formed double strand of the labeled probe DNA and RNA.

[0051] When measuring the expression level of a nucleic acid marker using a DNA microarray, for example, a microarray is used in which nucleic acids (cDNA or DNA) that specifically hybridize to a target nucleic acid marker are immobilized on a support. Nucleic acids (cDNA or cRNA) prepared from a biological sample are bound to the microarray, and the label on the microarray is detected, thereby measuring the expression level of the nucleic acid marker in the biological sample. The nucleic acid immobilized on the microarray may be any nucleic acid that hybridizes specifically to a target nucleic acid marker (i.e., substantially only to the target nucleic acid marker) under stringent conditions. It may be a nucleic acid having the entire sequence of a nucleic acid marker of the present invention or a nucleic acid consisting of a partial sequence. Examples of such a "partial sequence" include nucleic acids consisting of at least 15 to 25 bases. Stringent conditions include washing conditions such as 1×SSC, 0.1% SDS, and 37°C, preferably 0.5×SSC, 0.1% SDS, and 42°C, and more preferably 0.1×SSC, 0.1% SDS, and 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 level of a nucleic acid marker using sequencing, a next-generation sequencer (e.g., Ion S5 / XL system, Life Technologies Japan, Inc.) can be preferably 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 counts described above can be used as expression level data. Alternatively, the RPM (Reads per million mapped reads) value of the read counts, 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), etc., can be used as expression level data. Alternatively, common 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 as expression level data.

[0054] The probes or primers used to measure nucleic acid markers can be, for example, primers for specifically amplifying or specifically detecting the nucleic acid markers of the present invention. Here, "specific" means that the nucleic acid can be recognized or detected so as to generate a product or detection substance substantially derived from the markers of the present invention, for example, by detecting substantially only the markers of the present invention in Northern blotting or amplifying substantially only the markers of the present invention in PCR. 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 consisting of the entire or partial sequence of the nucleic acid markers of the present invention, or their complementary strands. The "complementary strand" is not limited to a completely complementary sequence, as long as it specifically recognizes the target marker. It is preferable that the sequence have at least 80%, more preferably at least 90%, even more preferably at least 95%, and even more preferably at least 98% sequence identity. Sequence identity can be determined using algorithms such as the NCBI BLAST algorithm described above. Examples of primers used to measure the nucleic acid marker include those that are capable of specific annealing and chain extension to the target nucleic acid marker, and have a chain length of preferably 10 bases or more, more preferably 15 bases or more, even more preferably 20 bases or more, and preferably 100 bases or less, more preferably 50 bases or less, even more preferably 35 bases or less. Examples of probes used to measure the nucleic acid marker include those capable of specific hybridization to the target nucleic acid marker, and preferably having a chain length of 10 bases or more, more preferably 15 bases or more, and preferably 100 bases or less, more preferably 50 bases or less, and even more preferably 25 bases or less. The probe or primer may be DNA or RNA, and may be synthetic or natural. The probe used in hybridization is usually labeled.

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

[0056] (2.2 Disease 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 (a marker of the present invention contained in a biological sample collected from the subject).

[0057] As described above, the markers in Tables 1A and 1B are markers whose expression levels vary depending on the severity of AD. More specifically, the markers in Table 1A are positive markers whose expression levels are positively correlated with the severity of AD, while the markers in Table 1B are negative markers whose expression levels are negatively correlated with the severity of AD. Therefore, the expression level of the positive marker or the negative marker can be used as an indicator to detect the severity of AD in a subject.

[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 caused by AD, for example, the severity of AD corresponding to the EASI score. The markers used in this detection are markers for detecting the severity of systemic skin rash caused by AD as described above, for example, markers 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 caused by 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 caused by 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 caused by AD, for example, the severity of AD corresponding to the POEM score. The markers used in this detection are markers for detecting the severity of systemic skin rash caused by 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 caused by 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 caused by 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 skin itching caused by AD, for example, the severity of AD corresponding to the VAS score of skin itching. The markers used in this detection are markers for detecting the severity of skin itching caused by AD as described above, for example, markers that can detect the severity of AD corresponding to the VAS score of skin itching. These markers are negative markers, and their expression levels show a negative correlation with the severity of skin itching caused by AD, for example, the VAS score of skin itching. The expression levels of these markers can be used as indicators to detect the severity of skin itching caused by AD in a subject.

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

[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 caused by AD, for example, the severity of AD corresponding to the erythema index. The marker used in this detection is a marker for detecting the severity of facial erythema caused by AD as described above, for example, a marker that can detect the severity of AD corresponding to the erythema index. When these markers are positive markers, their expression level shows a positive correlation with the severity of facial erythema caused by AD, for example, the erythema index. On the other hand, when these markers are negative markers, their expression level shows a negative correlation with the severity of facial erythema caused by AD, for example, the erythema index. The expression level of the positive marker or the negative marker can be used as an index to detect the severity of facial erythema caused by AD in a subject.

[0063] In this embodiment, one or more markers selected from the positive markers and the negative markers in a biological sample collected from a subject can be used as target markers to be used as indicators for the above-mentioned detection. As described above, the positive markers and the negative markers are markers correlated with the EASI score or POEM score for systemic skin rash caused by AD, the VAS score for skin itching caused by AD, the VAS score for dry skin caused by AD, or the erythema index for facial erythema caused by AD. In this embodiment, at least one marker correlated with any of the above scores or indexes can be used as the target marker. Preferably, two or more markers correlated with different scores or indexes are used as the target markers. More preferably, a combination of markers correlated with the EASI score, POEM score, VAS score for skin itching caused by AD, VAS score for dry skin caused by AD, and the erythema index for facial erythema caused by AD are used as the target marker. In the method of the present invention, either one of the positive markers or the negative markers may be used as a target marker, or two or more selected from the positive markers and the negative markers may be used in combination as a target marker. Alternatively, a nucleic acid marker or protein marker consisting of all of the positive markers and the negative markers may be used in combination as a target marker.

[0064] In one embodiment, the expression level of a target marker in a subject is measured, and the measured expression level of the target marker is compared with a predetermined reference value, thereby detecting the severity of AD in the subject. The reference value can be determined in advance based on the relationship between the severity of AD (the level of AD symptoms classified based on the score associated with the severity of AD or the score associated with 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 or not a subject belongs to each group can be determined based on the statistical value (e.g., the average 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 the reference value for each marker. The population may be a group of patients with AD, a group combining healthy subjects and patients with AD, or a group of patients with a specific severity of AD. Furthermore, groups may be created by age, generation, gender, or race depending on the subjects to be detected. Examples of groups used to calculate the reference value include a group with mild AD (mild group), a group with moderate AD (moderate group), and a group with severe AD (severe group). Alternatively, patient groups may be selected based on more detailed classification of disease severity, and the reference value 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 a systemic skin rash caused by AD (e.g., the severity of AD corresponding to the EASI score), a reference value can be calculated from two or more groups with a specific AD severity that are grouped based on the EASI score from a group of AD patients. In another example, when detecting the severity of a systemic skin rash caused by AD (e.g., the severity of AD corresponding to the POEM score), a reference value can be calculated from two or more groups with a specific AD severity that are grouped based on the POEM score from a group of AD patients. In another example, when detecting the severity of skin itching due to AD, a reference value can be calculated from two or more groups with a specific AD severity that are grouped based on the VAS score for skin itching from a group of AD patients. In another example, when detecting the severity of dry skin due to AD, a reference value can be calculated from two or more groups with a specific AD severity that are grouped based on the VAS score for dry skin from a group of AD patients. In another example, when detecting the severity of facial erythema due to AD, a reference value can be calculated from two or more groups of AD patients with a specific AD severity, which are grouped based on the erythema index for facial erythema.

[0065] When the positive marker is used, the higher its expression level, the worse the severity of AD in the subject is detected, whereas when the negative marker is used, the lower its expression level, the worse the severity of AD in the subject is detected.

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

[0067] In another embodiment, the expression levels of target markers in a subject are measured at different times, and the measured expression levels of the target markers are compared to detect changes in the severity of AD in the subject (e.g., worsening or improvement). In one example, a change in the severity of a systemic skin rash caused by AD (e.g., the severity of AD corresponding to the EASI score) is detected. As the target marker, a marker for detecting the severity of a systemic skin rash caused by AD as described above, for example, a marker capable of detecting the severity of atopic dermatitis corresponding to the EASI score, is used. In another example, changes in the severity of a systemic skin rash caused by AD (e.g., the severity of AD corresponding to the POEM score) are detected. As the target marker, a marker for detecting the severity of a systemic skin rash caused by AD as described above, for example, a marker capable of detecting the severity of atopic dermatitis corresponding to the POEM score, is used. In another example, changes in the severity of skin itching due to AD are detected. As the target marker, the above-mentioned marker for detecting the severity of skin itching due to AD, for example, a marker that can detect the severity of atopic dermatitis corresponding to the VAS score of skin itching due to AD, is used. In another example, changes in the severity of dry skin due to AD are detected. The target marker used is a marker for detecting the severity of dry skin due to AD, such as a marker that can detect the severity of atopic dermatitis corresponding to the VAS score of dry skin due to AD. In another example, changes in the severity of facial erythema due to AD are detected. As the target marker, a marker for detecting the severity of facial erythema due to AD, such as a marker capable of detecting the severity of atopic dermatitis corresponding to the erythema index for facial erythema due to AD, is used.

[0068] When using a positive marker, an increase in its expression level over time indicates an exacerbation of the AD severity of the subject, while a decrease in its expression level over time indicates an alleviation of the AD severity of the subject. In one example, the expression level of the marker in the same subject in a previous measurement is used as a reference value. If the expression level of the positive marker measured from a subject is higher than the reference value, the AD severity of the subject is detected as having worsened, while if the expression level of the positive marker measured from a subject is lower than the reference value, the AD severity of the subject is detected as having improved. If necessary, the AD severity of the subject can be determined by a conventional method during the previous measurement. In this case, if the expression level of the positive marker measured from a subject is higher or lower than the reference value, the AD severity of the subject can be detected as being more severe or milder than the AD severity in the previous measurement.

[0069] When using a negative marker, an increase in its expression level over time indicates an improvement in the severity of AD in the subject, while a decrease in its expression level over time indicates an exacerbation of the severity of AD in the subject.In one example, the expression level of the marker in the same subject in a previous measurement is used as a reference value.If the expression level of the negative marker measured from a subject is higher than the reference value, the severity of AD in the subject is detected as having improved, while if the expression level of the negative marker measured from a subject is lower than the reference value, the severity of AD in the subject is detected as having worsened.If necessary, the severity of AD in the subject can be determined by a conventional method during the previous measurement.In this case, if the expression level of the negative marker measured from a subject is higher or lower than the reference value, the severity of AD in the subject can be detected as being milder or more severe than the severity of AD in the previous measurement.

[0070] In one embodiment of the method of the present invention, if the expression level of a 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 can be determined to be lower than the reference value. If the expression level of a 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 can be determined to be higher than the reference value. Alternatively, the difference between the expression level of a marker derived from a subject and the reference value can be determined, for example, by whether or not there is a statistically significant difference between the two. When multiple markers are used as target markers, the expression levels of each target marker can be compared with the reference value, and the severity of AD can be detected by examining whether a certain percentage, for example, 50% or more, preferably 70% or more, more preferably 90% or more, and even more preferably 100% of the marker expression levels are different 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 prediction model constructed using data on the expression levels of the markers of the present invention derived from the subject (the markers of the present invention contained in a biological sample collected from the subject) (hereinafter referred to as expression profile). Examples of the expression profile include data on expression levels such as sequencing read counts.

[0072] For example, a prediction model (e.g., a discriminant) for detecting the severity of AD in any subject can be constructed by machine learning using the expression profiles of one or more markers (genes or their expression products) obtained from each individual in a teacher sample population (e.g., a population including multiple groups with different severity levels) as explanatory variables and a variable indicating which severity group each individual in the population belongs to as a response variable.The constructed prediction model can be used to detect the severity of AD in the subject, specifically, to which severity group the subject belongs.

[0073] As used herein, "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 marker(s)." Furthermore, when 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 shown in Table 1A and Table 1B and the expression products of the genes. The expression profile of the feature marker may be an absolute value or a relative value, or may be normalized. When multiple markers are used as the feature marker group, for example, multiple markers having a high correlation with 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 expression products thereof in Table 1A are combined and used as a group of feature markers. In another embodiment, all of the genes or expression products thereof in Table 1B are combined and used as a group of feature markers. In another embodiment, all of the genes or expression products thereof in Tables 1A and 1B are combined and used as a group of feature markers.

[0076] In a preferred embodiment, an expression profile of a feature marker(s) in a population containing two or more groups of AD patients with different severity levels (e.g., but not limited to, asymptomatic AD patients and two or more groups selected from mild, moderate, and severe AD groups) is used as a training sample for machine learning. A discriminant equation (prediction model) for classifying the severity of AD in a subject is constructed using the training sample. The expression profile of the feature marker(s) can be used as an explanatory variable used in constructing the discriminant equation. For example, a variable representing the severity of AD patient group to which the subject from whom the feature marker(s) is derived belongs can be used as a response variable. A cutoff value for discriminating the severity of AD can be determined based on the constructed discriminant equation. The expression profile of the feature marker(s) derived from the subject is then measured, the measured values ​​are substituted into the discriminant equation, and the result obtained from the discriminant equation is compared with the cutoff value to discriminate the severity of AD in the subject. The cutoff value can be determined according to a known method. For example, a receiver operating characteristic curve (ROC) curve can be obtained using the constructed discriminant equation, and the Youden index can be determined as the cutoff value.

[0077] Alternatively, when using marker expression profiles to construct a predictive model, the data may be compressed by dimensionality reduction, if necessary, before constructing the predictive model. For example, multiple markers are extracted from the gene groups or their expression products shown in Tables 1A and 1B. Then, principal component analysis is performed on the expression profiles of the extracted markers. A predictive model for determining the severity of AD in a subject can be constructed by machine learning using one or more principal components calculated by the principal component analysis as explanatory variables and a variable indicating which severity group (e.g., mild or severe) the subject from whom the explanatory variables are derived belongs as a target variable.

[0078] The algorithm used to construct the prediction model may be a known algorithm such as an algorithm used in machine learning. Examples of machine learning algorithms include, but are not limited to, a linear regression model, a lasso regression, a random forest, a neural network, a support vector machine with a linear kernel (SVM(linear)), a support vector machine with an rbf kernel (SVM(rbf)), a regularized linear discriminant analysis, and a regularized logistic regression.

[0079] Verification data is input into the constructed prediction model to calculate a predicted value. The model whose predicted value best matches the actual measured value, for example, the model with the highest accuracy of the predicted value relative to the actual measured value, can be selected as the optimal model. Alternatively, the recall, precision, and F value, which is the harmonic mean of these, can be calculated from the predicted value and the actual measured value, and the model with the highest F value can be selected as the optimal model. By inputting the expression profile of feature marker(s) actually measured from the subject into the constructed prediction model, the severity of AD in the subject can be detected.

[0080] (3. AD severity detection kit) In a further aspect, 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 2. above. In one embodiment, the kit of the present invention comprises a reagent or instrument for measuring the expression level of the marker of the present invention described above. For example, the kit of the present invention may comprise reagents for amplifying or quantifying the nucleic acid marker of the present invention (e.g., reverse transcriptase, PCR reagents, primers, probes, adapter sequences for sequencing, etc.), or reagents for quantifying the protein marker of the present invention (e.g., reagents for immunoassay, antibodies, etc.). Preferably, the kit of the present invention contains an oligonucleotide that specifically hybridizes with the nucleic acid marker of the present invention (e.g., a PCR primer or probe) or an antibody that recognizes the protein marker of the present invention. Preferably, the kit of the present invention comprises an indicator or guidance for evaluating the expression level of the marker of the present invention. For example, the kit of the present invention may include guidance explaining AD symptoms associated with each marker (e.g., skin rash, itchy skin, 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, guidance explaining reference values ​​for the expression level of each marker for detecting the severity of AD, or guidance regarding a discriminant based on a prediction model and feature markers to be input thereto. The kit of the present invention may further include a biological sample collection device (e.g., the above-mentioned SSL absorbent material or SSL adhesive material), reagents for extracting the markers of the present invention from the biological sample (e.g., nucleic acid purification reagents), a preservative or storage container for the sample collection device after collection of the biological sample, etc.

[0081] As exemplary embodiments of the present invention, the following substances, manufacturing methods, uses, methods, etc. are further disclosed herein, but 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 expression products of said genes. [2] Preferably, the severity of atopic dermatitis is the severity of a systemic skin rash caused by atopic dermatitis, the severity of skin itching caused by atopic dermatitis, the severity of dry skin caused by atopic dermatitis, or the severity of facial erythema caused by atopic dermatitis. The marker according to [1]. [3] The marker according to [1] or [2], which preferably comprises at least one selected from the group consisting of the following genes: CIZ1, ADAM15, SETD1B, and TWF1, and expression products of said genes, and is a marker for detecting the severity of systemic skin rash caused by atopic dermatitis. [4] The marker according to [3], which is preferably a marker for detecting the severity of atopic dermatitis corresponding to the Eczema Area and Severity Index. [5] The marker according to [1] or [2], which preferably comprises at least one selected from the group consisting of the following genes: LYNX1 and PSME2, ​​and expression products of said genes, and is a marker for detecting the severity of a systemic skin rash caused by atopic dermatitis. [6] The marker according to [5], which is preferably a marker for detecting the severity of atopic dermatitis corresponding to the Patient Oriented Eczema Measure. [7] The marker according to [1] or [2], which preferably comprises at least one selected from the group consisting of the following genes: ALPK1, ATG16L2, CSNK1D, GSK3A, LSM10, SASH3, and VSIR, and expression products of said genes, and is a marker for detecting the severity of skin itching caused by atopic dermatitis. [8] The marker according to [7], which is preferably a marker for detecting the severity of atopic dermatitis corresponding to the Visual Analog Scaling score of skin itching caused by atopic dermatitis. [9] The marker according to [1] or [2], which preferably comprises at least one selected from the group consisting of the following genes: TSC22D3, PLXNC1, and SLC12A6, and expression products of said genes, and is a marker for detecting the severity of dry skin caused by atopic dermatitis.

[10] The marker according to [9], which is preferably a marker for detecting the severity of atopic dermatitis corresponding to the Visual Analog Scaling score of dry skin caused by atopic dermatitis.

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

[12] The marker according to

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

[13] The marker according to any one of [1] to

[12] , which is preferably a nucleic acid marker.

[14] The marker described in

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

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

[14] in the subject.

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

[14] in the subject.

[17] The method described in

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

[18] The method according to any one of

[15] to

[17] , wherein the marker is preferably the marker according to [3], and the severity is the severity of a systemic rash caused by atopic dermatitis.

[19] The method described in

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

[20] The method according to any one of

[15] to

[17] , wherein the marker is preferably the marker according to [5], and the severity is the severity of a systemic rash caused by atopic dermatitis.

[21] The method according to

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

[22] The method according to any one of

[15] to

[17] , wherein the marker is preferably the marker according to [7], and the severity is the severity of skin itching caused by atopic dermatitis.

[23] The method according to

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

[24] The method according to any one of

[15] to

[17] , wherein the marker is preferably the marker according to [9], and the severity is the severity of dry skin caused by atopic dermatitis.

[25] The method described in

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

[26] The method according to any one of

[15] to

[17] , wherein the marker is preferably the marker according to

[11] , and the severity is the severity of facial erythema caused by atopic dermatitis.

[27] The method described in

[26] , wherein the severity is preferably a severity of atopic dermatitis corresponding to an erythema index for facial erythema caused by atopic dermatitis.

[28] The method according to any one of

[16] to

[27] , wherein the marker is preferably at least one selected from the group consisting of the genes shown in Table 1A above and expression products of the genes, and the higher the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be.

[29] The method according to any one of

[16] to

[27] , wherein the marker is preferably at least one selected from the group consisting of the genes shown in Table 1B above and expression products of the genes, and the lower the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be.

[30] The method according to any one of

[15] to

[27] , preferably comprising measuring the expression level 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 listed in Table 1A and expression products of the genes; When the expression level of the marker measured in the subject is higher than the expression level in the subject in previous measurements, it is detected that the severity of the atopic dermatitis in the subject has worsened; or when the expression level of the marker measured in the subject is lower than the expression level in the subject in previous measurements, it is detected that the severity of the atopic dermatitis in the subject has improved.

[30] The method described in.

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

[30] The method described in.

[33] A kit for detecting the severity of atopic dermatitis, which is used in the method according to any one of

[15] to

[32] , and which contains an oligonucleotide that specifically hybridizes with the nucleic acid that is a marker according to any one of [1] to

[12] , or an antibody that recognizes the protein that is a marker according to any one of [1] to

[12] .

[34] Use of at least one gene 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 expression products of said genes, as a marker for detecting the severity of atopic dermatitis.

[35] Use of at least one gene 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 expression products of said genes, in the production of a marker for detecting the severity of atopic dermatitis.

[36] Preferably, the severity of atopic dermatitis is the severity of a systemic skin rash caused by atopic dermatitis, the severity of skin itching caused by atopic dermatitis, the severity of dry skin caused by atopic dermatitis, or the severity of facial erythema caused by atopic dermatitis. The use of

[34] or

[35] .

[37] The use according to any one of

[34] to

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

[38] The use according to

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

[39] The use according to any one of

[34] to

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

[40] The use according to

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

[41] The use according to any one of

[34] to

[36] , wherein the marker is preferably a marker for detecting the severity of skin itching caused by 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 expression products of the genes.

[42] The use according to

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

[43] The use according to any one of

[34] to

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

[44] The use according to

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

[45] The use according to any one of

[34] to

[36] , wherein the marker is preferably a marker for detecting the severity of facial erythema caused by 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 expression products of the genes.

[46] The use according to

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

[47] The use according to any one of

[34] to

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

[48] ​​The use described in

[47] , wherein the nucleic acid is preferably mRNA extracted from lipids on the surface of the skin. [Example]

[0083] The present invention will be described in more detail below based on examples, but the present invention is not limited to these examples. Example 1: Search for a marker for detecting the severity of atopic dermatitis using SSL-derived RNA 1) Obtaining scores related to the severity of atopic dermatitis patients and collecting SSL data The subjects were 18 adult males (aged 23-57 years) with atopic dermatitis (AD). The subjects were AD patients who had been diagnosed with mild to moderate atopic dermatitis by a dermatologist at the time of the initial measurement. The subjects visited the clinic four times, every 14 days, to receive a score for their AD severity and to collect SSL samples. Hereinafter, the collected AD severity scores and SSL samples will be referred to as the first, second, third, and fourth scores and SSL samples, respectively, based on the order of their visit from the first visit. The following scores were used to assess the severity of AD: the physician-administered EASI score (Hanifin et al., Exp Dermatol, 10, 2001, scoring from 0 to 72 based on symptoms of the entire body skin rash); the subject-administered POEM score (Charman et al., Arch Dermatol, 140, 2004, scoring from 0 to 28 based on symptoms of the entire body skin rash); the subject-administered VAS scores for whole-body skin itching (scoring the intensity of itching from 0 to 100); the subject-administered VAS scores for whole-body skin dryness (scoring the intensity of dryness from 0 to 100); and the facial erythema index (see JP 2018-23756 A and Dawson et al., Phys Med Biol, 25, 1980) based on facial images taken with a hyperspectral imaging device (Hyperspectral Camera NH-7, Eva Japan Co., Ltd.). The facial erythema index was calculated for each pixel on a frontal facial image taken by a hyperspectral imaging device according to the following formula (1): Arbitrary ROIs (Regions of Interest) were defined on the image in areas corresponding to the forehead, above both eyes, and both cheeks, and the average value of the erythema index in the 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 blotting film (5 x 8 cm, polypropylene, 3M). The oil blotting film was transferred to a vial and stored at -80°C for approximately one month until use in RNA extraction.

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

[0087] 3) Usage data The read counts of each read obtained by sequencing the subjects' SSL-derived RNA measured in 2) above were used as expression level data for each RNA. Genes whose amplified regions in sequencing spanned at least two exons were analyzed. To correct for differences in total read counts between samples, the read counts of the analyzed genes were converted to RPM (reads per million mapped reads). Of these, 4,845 genes with read counts of 20 or more in 90% or more of the samples were used for the following analysis. Furthermore, to approximate a normal distribution, the RPM values ​​were converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1. Using these procedures, expression level data (Log2(RPM+1) values) of 4,845 genes were generated for the first, second, third, and fourth SSL samples from 18 subjects. These are referred to as the first, second, third, and fourth gene expression level data, respectively, based on the order of the subjects' initial visit.

[0088] 4) Data analysis i) Search for genes correlated with EASI score Based on the first EASI scores of the 18 AD patients obtained in 1) above and the first gene expression level data (Log2(RPM+1) values) of the 4,845 genes of the 18 AD patients calculated in 3) above, the Spearman correlation coefficient Rs between the EASI score and each gene expression level was calculated. Similarly, the Rs between the EASI scores and gene expression levels for the second to fourth rounds were calculated. The calculated Rs are referred to as the first to fourth Rs for each gene, respectively.

[0089] For each gene, the number of times the p-value (p_val) of the first to fourth Rs was below 0.1 (this number is designated as A-value) and the number of times the p-value of the first to fourth Rs was below 0.05 (this number is designated as B-value) were investigated. The four genes shown in Table 2, CIZ1, ADAM15, STED1B, and TWF1, had an A-value of 4 or a B-value of 3 or greater, and were highly correlated with the EASI score. Because there have been no reports to date suggesting a relationship between these four genes and atopic dermatitis, they were determined to have the potential to become novel markers for detecting the severity of atopic dermatitis.

[0090] [Table 2]

[0091] ii) Search for genes correlated 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 the 4,845 genes of the 18 AD patients calculated in 3) above, the Spearman's correlation coefficient Rs between the POEM score 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 are referred to as the first to fourth Rs for each gene, respectively.

[0092] For each gene, the number of times the p-value (p_val) of the first to fourth Rs was below 0.1 (this number is designated as A-value) and the number of times the p-value of the first to fourth Rs was below 0.05 (this number is designated as B-value) were investigated. The two genes LYNX1 and PSME shown in Table 3 had an A-value of 4 or a B-value of 3 or greater, and were highly correlated with the POEM score. Because there have been no reports to date suggesting a relationship between these two genes and atopic dermatitis, they were deemed to have the potential to become novel markers for detecting the severity of atopic dermatitis.

[0093] [Table 3]

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

[0095] For each gene, the number of times the p-value (p_val) of the first to fourth Rs was below 0.1 (this number is designated as A-value) and the number of times the p-value of the first to fourth Rs was below 0.05 (this number is designated as B-value) were investigated. 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 greater, indicating a high correlation with pruritus. Because there have been no reports suggesting a relationship between these seven genes and atopic dermatitis, they were determined to be potential novel markers for detecting the severity of atopic dermatitis.

[0096] [Table 4]

[0097] iv) Search for genes correlated with VAS scores for dry skin Based on the first VAS scores of dry skin for the 18 AD patients obtained in 1) above and the first gene expression level data (Log2(RPM+1) values) of the 4845 genes for the 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 between the VAS scores and gene expression levels for the second to fourth measurements were calculated. The calculated Rs are referred to as the first to fourth Rs for each gene, respectively.

[0098] For each gene, the number of times the p-value (p_val) of the first to fourth Rs was below 0.1 (this number is designated as A-value) and the number of times the p-value of the first to fourth Rs was below 0.05 (this number is designated as B-value) were investigated. The three genes shown in Table 5, TSC22D3, PLXNC1, and SLC12A6, had an A-value of 4 or a B-value of 3 or greater, indicating a high correlation with dryness. Because there have been no reports to date suggesting a relationship between these three genes and atopic dermatitis, it was determined that they could serve as novel markers for detecting the severity of atopic dermatitis.

[0099] [Table 5]

[0100] v) Search for genes correlated with facial erythema index Based on the first facial erythema index of the 18 AD patients obtained in 1) above and the first gene expression level data (Log2(RPM+1) value) of the 4845 genes of the 18 AD patients calculated in 3) above, the Spearman's correlation coefficient Rs between the facial erythema index and each gene expression level was calculated. Similarly, the Rs between the facial erythema index and gene expression level for the second to fourth times were calculated. The calculated Rs are referred to as the first to fourth Rs for each gene, respectively.

[0101] For each gene, the number of times the p-value (p_val) of Rs from the first to fourth rounds was below 0.1 (referred to as value A) and the number of times the p-value of Rs from the first to fourth rounds was below 0.05 (referred to as value B) were examined. ODC1, shown in Table 6A, 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. A total of six genes had an A value of 4 or a B value of 3 or greater, indicating a high correlation with facial erythema. These six genes have not previously been reported to suggest a relationship with atopic dermatitis, and therefore were deemed to be potential markers for detecting the severity of atopic dermatitis.

[0102]

Table 6

Claims

1. A method for measuring an expression level of a marker for detecting the severity of atopic dermatitis in order to detect the severity of atopic dermatitis in a subject, comprising: The method comprises: Measuring the 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; where: (1) The severity of atopic dermatitis corresponds to the Eczema Area and Severity Index of a systemic 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 for a systemic 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: 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 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 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; The method further comprises: detecting the severity of the atopic dermatitis in the subject based on the expression level of the marker; where: When the marker is at least one selected from the group consisting of mRNAs of genes shown in Table 1, the higher the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be; When the marker is at least one selected from the group consisting of mRNAs of genes shown in Table 2, the lower the expression level of the marker, the worse the severity of the atopic dermatitis of the subject is detected to be. method. 【Table 1】 【Table 2】

2. A method for measuring an expression level of a marker for detecting the severity of atopic dermatitis in order to detect a change in the severity of atopic dermatitis in a subject, comprising: The method comprises: Measuring the 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; where: (1) The severity of atopic dermatitis corresponds to the Eczema Area and Severity Index of a systemic 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 for a systemic 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: 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 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 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; The method further comprises: detecting a change in the severity of the atopic dermatitis in the subject based on the expression level of the marker; where: When the marker is at least one selected from the group consisting of mRNAs of genes shown in Table 3, if the expression level of the marker is higher than the expression level in the subject in previous measurements, the severity of the atopic dermatitis in the subject is detected to have worsened, and if the expression level of the marker is lower than the expression level in the subject in previous measurements, the severity of the atopic dermatitis in the subject is detected to have improved. When the marker is at least one selected from the group consisting of mRNAs of genes shown in Table 4, if the expression level of the marker is lower than the expression level in the subject in previous measurements, the severity of the atopic dermatitis in the subject is detected to have worsened, and if the expression level of the marker is higher than the expression level in the subject in previous measurements, the severity of the atopic dermatitis in the subject is detected to have improved. method. 【Table 3】 【Table 4】

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