Method for preparing protein marker for differentiating atopic dermatitis

Abstract

Provided are a method for preparing a protein marker for differentiating atopic dermatitis and other skin diseases, and a method for discriminating atopic dermatitis and other skin diseases using said protein marker.　More specifically, the method for preparing a protein marker for differentiating atopic dermatitis and other skin diseases includes collecting, from a sample collected from the skin of a subject, at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2.

Description

Method for preparing protein markers for diagnosing atopic dermatitis

[0001] The present invention relates to a method for preparing a protein marker for distinguishing atopic dermatitis from skin diseases other than atopic dermatitis, a method for distinguishing atopic dermatitis from skin diseases other than atopic dermatitis using the protein marker, a method for preparing a protein marker for detecting the severity of atopic dermatitis, and a method for detecting the severity of atopic dermatitis using the protein marker.

[0002] Atopic dermatitis (hereinafter also referred to as "AD") is an eczematous skin disease that primarily develops in individuals with atopic predisposition, and its primary lesion is an itchy eczema that repeatedly worsens and improves (Non-Patent Document 1). Typical symptoms of atopic dermatitis include chronic and recurrent itching, rash, erythema, etc., occurring bilaterally and contralaterally, as well as dyskeratosis, impaired barrier function, dry skin, etc. Most cases of AD occur in infants and young children and tend to improve with age, but in recent years, adult-onset and intractable AD have also been increasing.

[0003] It is known that newborns or infants with a genetic predisposition to AD (atopic predisposition) develop various allergic diseases with age, including food allergies, bronchial asthma, and allergic rhinitis, in addition to AD (the allergic march). As such, the development of one allergic disease increases the likelihood of developing another, and treatment for these diseases often takes a long time. Therefore, it is important to properly diagnose AD in infancy and start treatment early.

[0004] Globally, the diagnostic criteria of Hanifin & Rajka, established in 1980 (Non-Patent Document 2), and the diagnostic criteria of the U.K. Working Party, established in 1994, are frequently used for diagnosing AD. According to the "Definition and Diagnostic Criteria for Atopic Dermatitis" by the Japanese Dermatological Association, atopic dermatitis is diagnosed regardless of the severity of symptoms if it meets three basic criteria: 1) itching, 2) characteristic rash and distribution, and 3) chronic / recurrent course (Non-Patent Document 1). Suspected cases are classified as acute or chronic eczema, and the diagnosis is based on the patient's age and course. Diagnosing AD is said to be particularly difficult in early infancy, as patients themselves are unable to complain of itching, and the current diagnosis rate for AD is low. Furthermore, particularly in infancy, various skin eruptions often occur, such as seborrheic dermatitis, acne, miliaria, and infantile eczema, which does not have a specific disease name and is not accompanied by itching. Sufficient experience and knowledge are required to differentiate these skin diseases from AD. The Atopic Dermatitis Guidelines list contact dermatitis, seborrheic dermatitis, prurigo simplex, scabies, miliaria, ichthyosis, asteatotic eczema, psoriasis, etc. as the main diseases that should be differentiated from AD. It is important to accurately differentiate AD from these diseases and start appropriate AD treatment.

[0005] As a method for detecting AD using biomarkers, it has been proposed to detect peripheral blood eosinophil counts, serum total IgE levels, LDH (lactate dehydrogenase) levels, serum Thymus and Activation-Regulated Chemokine (TARC), and Squamous Cell Carcinoma Antigen 1 (SCCA1, or Serpin B3) and 2 (SCCA2, or Serpin B4) (Non-Patent Documents 1, 3, 4). AD detection using biomarkers is particularly effective in infants and young children who have difficulty reporting symptoms.

[0006] In particular, it has been reported that serum Serpin B4 is effective in detecting AD in children and adults (Non-Patent Documents 5 and 6). It has also been reported that Serpin B12 is decreased and Serpin B3 is increased in stratum corneum samples taken from AD children (Non-Patent Document 7).

[0007] The pathogenesis of AD is complex, and various factors are known to fluctuate with the disease state. In AD, innate and adaptive immunity are activated by exposure to antigens and irritants due to atopic predisposition or impaired barrier function caused by external factors (Non-Patent Document 8). During the acute phase, alarmins such as IL-33, IL-25, and TSLP, released from keratinocytes in response to stimuli such as antigens, recruit ILC2 and Th2 cells, and induce a Th2-type immune response. Previously reported AD detection markers fluctuate depending on the disease progression of AD, and are therefore useful for understanding the disease progression of AD. However, it is not necessarily known whether they can distinguish AD from other skin diseases. These AD detection markers also include molecules that fluctuate in association with general skin inflammation, which also occurs in other skin diseases. A differential marker that can be used to differentiate AD from other skin diseases must be a molecule that fluctuates specifically in the pathology of AD. Therefore, known AD detection markers may not necessarily serve as differential markers for other skin diseases.

[0008] Furthermore, it is said that a correct assessment of the severity of AD is essential for selecting an appropriate treatment (Non-Patent Document 1). Methods for assessing the severity of AD generally include the Atopic Dermatitis Severity Classification by the Atopic Dermatitis Severity Classification Review Committee of the Japanese Dermatological Association (Non-Patent Documents 9 and 10), Severity Scoring of Atopic Dermatitis (SCORAD) (Non-Patent Document 11), and Eczema Area and Severity Index (EASI) (Non-Patent Document 12). SCORAD and EASI are used internationally. These assessment methods require skilled techniques based on careful visual inspection and palpation. Therefore, an objective marker for AD severity is also desired.

[0009] Recently, it has been reported that RNA contained in skin surface lipids (SSL) can be used as a sample for bioanalysis, and that marker genes for the epidermis, sweat glands, hair follicles, and sebaceous glands can be detected from SSL (Patent Document 1). It has also been reported that marker genes for atopic dermatitis can be detected from SSL (Patent Document 2), that marker proteins for atopic dermatitis can be detected from SSL (Patent Document 3), and that markers for assessing the severity of atopic dermatitis can be detected from SSL (Patent Document 4). However, no markers for distinguishing AD from other skin diseases are known.

[0010] It has been reported that fibrinogen binds to Staphylococcus aureus and is involved in the survival of AD on the skin, and that the predominance of Staphylococcus aureus in the skin flora may be related to the onset of AD (Non-Patent Documents 8, 13, 14). In recent years, it has been reported that fibrinogen triggers granule release from eosinophils, one of the key factors in Type 2 inflammation (Non-Patent Document 15). However, it has not been known that fibrinogen in the skin can be used as a marker to distinguish AD from other skin diseases or to assess the severity of AD. Regarding eosinophils, the number of eosinophils in peripheral blood is known as a biomarker that can serve as a reference for the progression of AD, and EDN (eosinophil-derived neurotoxin), a granule protein contained in eosinophils, has been reported to have potential as a blood biomarker for AD (Non-Patent Document 16). However, it has not been known until now that eosinophil granule proteins in the skin or fibrinogen in the skin can be used as markers for distinguishing AD from other skin diseases or for assessing the severity of AD.

[0011] (Patent Document 1) International Publication No. 2018 / 008319 (Patent Document 2) Japanese Patent Application Laid-Open No. 2020-074769 (Patent Document 3) Japanese Patent Application Laid-Open No. 2021-175958 (Patent Document 4) International Publication No. 2022 / 009988

[0012] (Non-patented document 1) Saeki R, Nichiha Kaisho. 131(13), 2691-2777. (2021) (Non-patented document 2) Hanifin JM et al., Acta Derm-Venereol (Stockh). 60:92(Suppl):44-47. (1980) (Non-patented document 3) Sugawara N et al., Allergy. 57:180-181. (2002) (Non-patented document 4) Ohta S et al., Ann Clin Biochem. 49:277-284. (2012) (Non-patented document 5) Nagao M et al., J Allergy Clin Immunol. 141(5):1934-1936. (2018) (Non-patented document 6) Okawa T et al., Allergology International. 67:124-130. (2018) (Unauthorized document 7) Goleva E et.al., J Allergy Clin Immunol. 146(6):1367-1378. (2020) (Unauthorized document 8) Langan SM et al., Lancet. 396(10247):345-360. (2020) (Non-patented document 9) Aoki, Atomite Dermatitis Severity Classification Committee’s 2nd Report, Journal of the Japan Society for Dermatitis. 111: 2023-2033. (2001) (Non-patented document 10) Yoshida, Atomite Dermatitis Severe Severity Classification Committee Interim Report, Journal of the Japan Skin Society. 108: 1491-1496. (1998) (Non-patented literature 11) No authors listed, Dermatology. 186(1): 23-31. (1993) (Non-patented literature 12) Hanifin JM et al., Exp Dermatol. 10(1): 11-18, (2001) (Non-patented literature 13) Cho SH et al., J Allergy Clin Immunol. 108(2):269-274. (2001) (Non-patented literature 14) Meylan P et al., J Invest Dermatol. 137(12):2497-2504. (2017) (Non-patented literature 15) Coden ME et al., J Immunol.15;204(2):438-448. (2020) (Non-patented reference 16) Kim HS et al., Ann Allergy Asthma Immunol. 119(5):441-445. (2017).

[0013] The present invention relates to the following 1) to 10): 1) A method for preparing a protein marker for distinguishing atopic dermatitis from other skin diseases, which comprises recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 from a sample collected from the skin of a subject. 2) A method for preparing a protein marker for detecting the severity of atopic dermatitis, comprising recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 from a sample collected from the skin of a subject. 3) A method for distinguishing atopic dermatitis from other skin diseases in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in a sample collected from the skin of the subject. 4) A method for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in a sample collected from the skin of the subject. 5) A test kit for distinguishing atopic dermatitis from other skin diseases, which is used in the method described above and contains a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2.6) A test kit for detecting the severity of atopic dermatitis used in the method described above, comprising a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. 7) A protein marker for distinguishing atopic dermatitis from other skin diseases, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. 8) A protein marker for detecting the severity of atopic dermatitis, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. 9) Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 as a protein marker for distinguishing atopic dermatitis from other skin diseases. 10) Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 as a protein marker for detecting the severity of atopic dermatitis.

[0014] The expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in SSL derived from the whole face at one month of age in healthy children, children diagnosed with AD, and children diagnosed with skin diseases other than AD are shown. ROC curves of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in SSL derived from the whole face at one month of age in children diagnosed with AD and children diagnosed with skin diseases other than AD. Figure 1 shows the expression levels of FGG in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 2 shows the expression levels of FGA in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 3 shows the expression levels of FGB in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 4 shows the expression levels of FN1 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 1 shows the expression levels of IGHG1 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 2 shows the expression levels of IGHG2 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 3 shows the expression levels of IGHG3 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 4 shows the expression levels of IGHG4 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 1 shows the expression levels of C3 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus, and children diagnosed with AD. Figure 2 shows the expression levels of C4B in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus, and children diagnosed with AD.Figure 1 shows the expression levels of C7 in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 2 shows the expression levels of CFB in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 3 shows the expression levels of CFH in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 4 shows the expression levels of EPX in SSL derived from the whole face at one month of age in children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD. Figure 1 shows the expression levels of CLC in SSL derived from the whole face of children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD at one month of age. Figure 2 shows the expression levels of RNASE3 in SSL derived from the whole face of children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD at one month of age. Figure 3 shows the expression levels of RNASE2 in SSL derived from the whole face of children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD at one month of age. Figure 4 shows the expression levels of PRG2 in SSL derived from the whole face of children diagnosed with acne, seborrheic dermatitis, miliaria, or eczema without pruritus and children diagnosed with AD at one month of age. 1 shows the expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in SSL derived from the whole face at 1 month of age in healthy children, children diagnosed with mild AD, and children diagnosed with moderate AD. 1 shows the expression levels of PFN1 in SSL derived from the whole face at 1 month of age in healthy children, children diagnosed with AD, and children diagnosed with a skin disease other than AD. 1 shows the expression levels of SPRR2D in SSL derived from the whole face at 1 month of age in healthy children, children diagnosed with mild AD, and children diagnosed with moderate AD. Detailed Description of the Invention

[0015] The present invention relates to a method for preparing a protein marker for distinguishing atopic dermatitis from other skin diseases, a method for distinguishing atopic dermatitis from other skin diseases using the protein marker, a method for preparing a protein marker for assessing the severity of atopic dermatitis, and a method for assessing the severity of atopic dermatitis using the protein marker.

[0016] The present inventors collected SSL from the skin of infants and comprehensively analyzed the expression levels of proteins contained in the SSL of infants diagnosed with AD and infants diagnosed with skin diseases other than AD. As a result, they found that the expression levels of specific proteins significantly differed between the two types of infants, and that this could be used as an indicator to distinguish AD from other skin diseases. They also found that the expression levels of these proteins significantly changed depending on the severity of AD, and that this could be used as an indicator to detect the severity of AD.

[0017] According to the present invention, a protein marker for distinguishing atopic dermatitis from other skin diseases can be collected from the skin of a subject using a simple, minimally invasive or non-invasive technique, or the marker can be used to distinguish atopic dermatitis from other skin diseases or to detect the severity of atopic dermatitis. Therefore, the present invention enables the diagnosis of atopic dermatitis in subjects, including infants, and can contribute to the early treatment of atopic dermatitis.

[0018] All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety.

[0019] The names of proteins disclosed herein follow the Gene Name or Protein Name listed in UniProt ([https: / / www.uniprot.org / ]).

[0020] As used herein, the term "subject" refers to a subject who has some skin symptom, such as erythema, papules, or dryness, and who desires or requires differentiation of atopic dermatitis (AD) from other skin diseases. Subjects include infants, children, and adults, with infants being preferred. "Infants" broadly refers to "children" before the onset of secondary puberty, specifically children under 12 years of age, and preferably refers to infants aged from 0 to 5 years. In the present invention, infants are preferably infants under 6 months of age. "Children" refer to children after the onset of secondary puberty, preferably humans aged 13 to 15 years. "Adults" refer to humans who do not fall under the definition of "infants" or "children," and preferably humans for whom secondary puberty has ended. In the present invention, adults are preferably humans aged 16 years or older, more preferably humans aged 20 years or older.

[0021] As used herein, "atopic dermatitis" (AD) refers to a disease characterized by a pruritic eczema as the primary lesion, which repeatedly worsens and improves, and many of its patients are believed to have a predisposition to atopy. Examples of atopic predisposition include 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. Atopic dermatitis in infants is characterized by a rash that begins on the head or face in infancy and often progresses to the trunk or limbs; from the age of one onward, the rash on the face decreases, and the rash mainly appears on the neck or joints of the limbs.

[0022] The severity of atopic dermatitis is classified, for example, as no symptoms, slight, mild (mild), moderate (moderate), and severe (severe). The severity can be classified, for example, based on the severity assessment method described in the Atopic Dermatitis Treatment Guidelines (Non-Patent Document 1). Examples of severity assessment methods include the Atopic Dermatitis Severity Classification by the Atopic Dermatitis Severity Classification Review Committee of the Japanese Dermatological Association (Non-Patent Documents 9 and 10), Severity Scoring of Atopic Dermatitis (SCORAD) (Non-Patent Document 11), and Eczema Area and Severity Index (EASI) (Non-Patent Document 12). Other severity classification methods described in the Atopic Dermatitis Treatment Guidelines include rash severity assessment, itching assessment, patient assessment, and QOL assessment. In the present invention, the severity of atopic dermatitis is preferably evaluated based on the EASI score. For example, the EASI is a value ranging from 0 to 72 calculated based on the scores for four symptoms (erythema, edema / infiltration / papule, excoriation, and lichenification) at each evaluation site, including the head and neck, trunk, upper limbs, and lower limbs, and the percentage (%) of the area of ​​the evaluation site occupied by the above four symptoms. An EASI score of 1.1 to 7.0 is classified as "mild," an EASI score of 7.1 to 21 is classified as "moderate," and an EASI score of 21.1 to 72 is classified as "severe" (Br J Dermatol. 172(5):1353-1357. (2015)).

[0023] As used herein, "other skin diseases" refers to skin diseases other than atopic dermatitis, preferably acne, seborrheic dermatitis, miliaria, or eczema without itching (a general skin disease that causes eczema but is not diagnosed as a specific disease such as AD, acne, seborrheic dermatitis, or miliaria).

[0024] As used herein, "differentiation between atopic dermatitis and other skin diseases" refers to distinguishing between atopic dermatitis and other skin diseases, and includes distinguishing whether a subject has atopic dermatitis or not, or the likelihood of having atopic dermatitis. Note that, as used herein, the term "differentiation" does not include differentiation by a physician.

[0025] As used herein, unless otherwise specified, "skin" is a general term for an area including tissues such as the stratum corneum, epidermis, dermis, hair follicles, and sweat glands, sebaceous glands, and other glands. Examples of skin sites include skin on any part of the body, such as the head, face, neck, trunk, hands, and feet. The skin site may or may not be a site where some skin condition is present, and may be, for example, a rash or a non-rash area. Examples of samples collected from the skin include skin surface lipids (SSL), the stratum corneum, sweat, skin cleansing solution, skin extract, skin exudate, and the like. Skin surface lipids (SSL) are preferred.

[0026] As used herein, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, and is sometimes called sebum. Generally, SSL mainly contains secretions from exocrine glands such as sebaceous glands in the skin, and is present on the skin surface in the form of a thin layer covering the skin surface.

[0027] As will be described in the Examples below, protein expression analysis was performed on SSL collected from the entire face of infants and young children. As a result, children diagnosed with AD had lower levels of FGG (fibronogen gamma), FGA (fibronogen alpha), FGB (fibronectin), FN1 (fibronectin), IGHG1 (immunoglobulin heavy constant gamma 1), IGHG2 (immunoglobulin heavy constant gamma 2), IGHG3 (immunoglobulin heavy constant gamma 3), and IGHG4 (immunoglobulin heavy constant gamma 4). heavy constant gamma 4), C3 (Complement C3), C4B (Complement C4-B), C7 (Complement component C7), CFB (Complement factor B), CFH (complement factor H), EPX (Eosinophil peroxidase), CLC (Galectin-10), RNASE3 (Eosinophil cationic protein), RNASE2 (Non-secretory ribonuclease) and PRG2 (Bone marrow On the other hand, with regard to PFN1 (Profilin-1, Patent Document 3), which is known to be an AD detection marker, no difference was observed in the expression level of PFN1 in SSL between children diagnosed with AD and children diagnosed with skin diseases other than AD.

[0028] The above results indicate that FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 are useful as markers for distinguishing AD from other skin diseases other than AD.

[0029] Furthermore, children diagnosed with AD were divided into mild and moderate AD based on the EASI score, which is an AD severity classification, and the expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 contained in the SSL of these children were compared, and the expression levels were significantly higher depending on the severity of AD. On the other hand, for SPRR2D (Small proline-rich protein 2D, Patent Document 3), which is known to be an AD detection marker, SPRR2D in SSL did not show any difference in expression level depending on the severity of AD. From the above, it is shown that FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 are further useful as markers for detecting the severity of AD. In the present invention, the detection of the severity of AD is preferably the detection of mild (mild) and moderate (moderate) cases.

[0030] FGG, FGA, FGB, and FN1 are fibrinogen or fibronectin, proteins related to blood coagulation. FGG, FGA, and FGB form complexes through disulfide bonds and function as fibrinogen. Fibrinogen is known to further form complexes with other proteins such as fibronectin. IGHG1, IGHG2, IGHG3, and IGHG4 are immunoglobulin-related proteins. C3, C4B, C7, CFB, and CFH are complement-related proteins important in immune responses. EPX, CLC, RNASE3, RNASE2, and PRG2 are granule proteins contained in eosinophils, which are known to be associated with allergic responses. It is speculated that all of these proteins migrate from the blood to tissues due to increased vascular permeability associated with inflammation and may contribute to the onset of AD, but it was not easily imagined that they would serve as markers to distinguish AD from other skin diseases or as markers of severity to detect the severity of AD.

[0031] Therefore, the present invention provides a protein marker for distinguishing AD from other skin diseases. The present invention also provides a method for preparing a protein marker for distinguishing AD from other skin diseases. The preparation method comprises recovering at least one target protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 (hereinafter also referred to as "target protein") from a sample collected from the skin of a subject. The present invention also provides a method for distinguishing AD from other skin diseases. The method comprises measuring the expression level of the target protein in a sample collected from the skin of a subject.

[0032] The present invention provides a protein marker for detecting the severity of AD. The present invention also provides a method for preparing a protein marker for detecting the severity of AD. The preparation method comprises recovering at least one target protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 (hereinafter also referred to as "target protein") from a sample collected from the skin of a subject. The present invention also provides a method for detecting the severity of AD. The method comprises measuring the expression level of the target protein in a sample collected from the skin of a subject.

[0033] FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 can each be used alone as a marker for distinguishing AD from other skin diseases, but from the viewpoint of improving accuracy, preferably two or more, more preferably three or more, and even more preferably four types may be used in combination. In particular, from the viewpoint of accuracy, it is preferable to select fibrinogens (FGG, FGA, FGB, FN1), complements (C3, C4B, C7, CFB, CFH), immunoglobulins (IGHG1, IGHG2, IGHG3, IGHG4), and eosinophil granule proteins (EPX, CLC, RNASE3, RNASE2, and PRG2) in this order. Among these, a combination of fibrinogens (FGG, FGA, FGB) is more preferred. In the present invention, when FGG, FGA, and FGB are combined, the complex (fibrinogen) formed by the binding of these three types is prepared, and its expression level is measured to differentiate AD from other skin diseases, and the complex is also used as a marker. Other skin diseases that can be differentiated from AD include acne, seborrheic dermatitis, miliaria, and eczema without itching. Among these, when differentiating AD from acne, it is preferable to use at least one selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 as a differentiation marker. When differentiating between AD and seborrheic dermatitis, it is preferable to use at least one selected from FGG, FGA, FGB, FN1, IGHG4, C4B, C7, EPX, RNASE2, and PRG2 as a differential marker.When differentiating between AD and miliaria, it is preferable to use at least one selected from FGG, FGA, FGB, FN1, IGHG1, IGHG4, C3, and C7 as a differential marker.When differentiating between AD and eczema without itching, it is preferable to use at least one selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 as a differential marker.

[0034] FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 can each be used as a marker for detecting the severity of AD, but from the viewpoint of improving accuracy, it is preferable to use a combination of two or more, more preferably three or more, and even more preferably four. In particular, from the viewpoint of accuracy, it is preferable to select fibrinogens (FGG, FGA, FGB, FN1), complements (C3, C4B, C7, CFB, CFH), immunoglobulins (IGHG1, IGHG2, IGHG3, IGHG4), and eosinophil granule proteins (EPX, CLC, RNASE3, RNASE2, and PRG2) in this order. Among these, a combination of fibrinogens (FGG, FGA, FGB) is preferred. Note that in the present invention, when FGG, FGA, and FGB are combined, the present invention also includes preparing a complex (fibrinogen) formed by the binding of these three types, measuring its expression level to detect the severity of AD, and using it as a marker.

[0035] The method for preparing a protein marker for distinguishing AD from other skin diseases, the method for distinguishing AD from other skin diseases, the method for preparing a protein marker for detecting the severity of AD, and the method for detecting the severity of AD according to the present invention (hereinafter collectively referred to as the methods of the present invention) may further comprise collecting a sample from the skin of a subject.

[0036] The subject from whom the sample is collected is not particularly limited by gender or race. A preferred example of a subject is an infant with some kind of skin rash who desires or needs to differentiate AD from other skin diseases. For example, the younger the infant, such as a one-month-old infant, the more difficult it is to detect the itching characteristic of AD, and sufficient experience and knowledge are required to differentiate it from other skin diseases that frequently cause various skin rashes at that age. Therefore, the application of an objective diagnostic marker is preferable because it can lead to early and appropriate AD therapeutic intervention. Another preferred example of a subject is an infant with AD who desires or needs to detect the severity of AD. While understanding the severity is important for selecting an appropriate treatment, blood tests for objectively detecting the severity are often difficult to perform, particularly in infants and children, due to their high invasiveness. Therefore, providing a non-invasive method for detecting the severity is highly significant and preferable.

[0037] Any method can be used to collect a sample from a subject's skin. For example, SSL collection can be preferably performed using an SSL absorbent material, an SSL adhesive material, or an instrument for scraping SSL from the skin, as described below. The SSL absorbent material or SSL adhesive material can be any material that has affinity for SSL, including polypropylene, pulp, etc. More detailed examples of procedures for collecting SSL from the skin include absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, adhering SSL to a glass plate or tape, or scraping SSL off with a spatula, scraper, or the like. To improve SSL adsorption, an SSL absorbent material pre-soaked with a highly lipid-soluble solvent may be used. On the other hand, SSL absorbent materials preferably contain a low content of highly water-soluble solvents or moisture, since the adsorption of SSL is inhibited if they contain highly water-soluble solvents or moisture. It is preferable to use the SSL absorbent material in a dry state.

[0038] The skin sample collected from the subject may be subjected to the subsequent extraction step without storage, or may be stored for a certain period of time. For example, it is preferable to store the collected SSL under low-temperature conditions as soon as possible after collection. The temperature conditions for storing the SSL in the present invention are 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 period for storing the SSL under such low-temperature conditions 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.

[0039] Proteins can be extracted from collected samples using methods commonly used for extracting or purifying proteins from biological samples, such as extraction methods using water, phosphate-buffered saline solution, or solutions containing surfactants such as Triton X-100 or Tween 20, or extraction methods using commercially available protein extraction reagents or kits such as M-PER buffer (Thermo Fisher Scientific), MPEX PTS Reagent (GL science), QIAzol Lysis Reagent (Qiagen), or EasyPep (registered trademark) Mini MS Sample Prep Kit (ThermoFisher Scientific).

[0040] The expression level of a protein measured by the method of the present invention may be determined by measuring the amount or activity of the protein itself or by using an antibody against the protein. Furthermore, when multiple target proteins form a complex, the expression level of the complex or an antibody against the complex may be used. Alternatively, the amount or activity of molecules that recognize or interact with the protein, such as other proteins, sugars, lipids, fatty acids, nucleic acids, and their phosphorylations, alkylations, and sugar adducts, or complexes formed by interaction with any of the above molecules, may be measured. For example, the expression level of fibrinogen may be measured using analytical methods commonly used in clinical testing, such as the thrombin time assay. The calculated expression level of a protein may be based on the absolute amount of the protein in the sample or may be relative to other standards or total protein in the sample, preferably relative to total protein derived from humans.

[0041] In the method of the present invention, the expression level of a protein can be measured by conventional protein detection or quantification methods, such as Western blot, protein chip analysis, immunoassays (e.g., ELISA, etc.), immunochromatography, lateral flow immunoassay, mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), one-hybrid method (PNAS, 100:12271-12276 (2003)), and two-hybrid method (Biol Reprod, 58:302-311 (1998)). For example, the expression level of a protein can be measured by contacting an antibody against the protein with a protein sample and detecting the protein in the sample that binds to the antibody. For example, in the Western blot method, the above-mentioned antibody is used as the primary antibody, and then the primary antibody is labeled with a radioisotope, a fluorescent substance, an enzyme, or the like as the secondary antibody, and the signal derived from the label is measured using a radiation detector, a fluorescence detector, or the like. The primary antibody may be a polyclonal or monoclonal antibody. These antibodies can be commercially available or produced according to known methods. Specifically, polyclonal antibodies can be obtained by immunizing a non-human animal such as a rabbit with a protein expressed and purified in E. coli or a partial polypeptide of the protein, according to standard methods, and then extracting the antibody from the serum of the immunized animal. Monoclonal antibodies can be obtained by immunizing a non-human animal such as a mouse with a protein expressed and purified in E. coli or a partial polypeptide of the protein, according to standard methods, and then fusing the resulting spleen cells with myeloma cells to prepare hybridoma cells. Monoclonal antibodies can also be produced using phage display (Current Opinion in Biotechnology, 9(1):102-108 (1998)). The protein can be used immediately to differentiate AD from other skin diseases or to detect the severity of AD, or it can be stored under standard protein storage conditions until it is used for differentiation or detection of the severity.

[0042] Thus, the expression level of the target protein of the present invention is measured in a sample collected from the skin of a subject, and AD is differentiated from other skin diseases in the subject based on the expression level. In one example, the differentiation is performed by comparing the measured expression level of the target protein of the present invention with a cutoff value (reference value). More specifically, by comparing the expression level of the target protein of the present invention in a sample collected from the skin of a subject with a cutoff value (reference value), it is possible to differentiate between AD and other skin diseases in the subject. As another example, when the expression level of the target protein of the present invention is measured using a method (e.g., immunochromatography) in which a signal is detected by setting a cutoff value (reference value), it is possible to differentiate between AD and other skin diseases in the subject based on a visual judgment or quantified value of the intensity of the detected signal. In yet another embodiment, the expression level of the target protein of the present invention is measured in a sample collected from the skin of a subject, and the severity of AD in the subject is detected based on the expression level. In one example, the detection is performed by comparing the measured expression level of the target protein of the present invention with a cutoff value (reference value). More specifically, the expression level of the target protein of the present invention in a sample collected from the skin of a subject is compared with a cutoff value (reference value), thereby detecting the severity of AD in the subject. As another example, when the expression level of the target protein of the present invention is measured by a method (e.g., immunochromatography) in which a signal is detected by setting a cutoff value (reference value), the severity of AD in the subject can be detected based on a visual determination or quantified value of the intensity of the detected signal.

[0043] Here, the "cutoff value" ("reference value") can be set arbitrarily depending on the purpose, etc. For example, a certain population is divided into a group of people with AD and a group of people with skin diseases other than AD, and a value determined with reference to statistical values ​​such as the average value and standard deviation of the expression level of the target protein of the present invention in each group can be determined as a cutoff value (reference value) for determining whether or not the subject belongs to each group. In addition, when detecting the severity of AD, the AD patient group can be divided into groups with different severity levels (e.g., no symptoms, mild, mild (mild), moderate (moderate), and severe (severe) groups) based on the severity assessment method such as the EASI score described above, and a value determined with reference to statistical values ​​such as the average value and standard deviation of the expression level of the target protein of the present invention in each group can be determined as a cutoff value (reference value) for determining whether or not the subject belongs to each group.

[0044] The cutoff value (reference value) can be determined by various statistical analysis methods. For example, values ​​based on ROC curve (Receiver Operating Characteristic curve) analysis can be exemplified. The ROC curve can be created by determining the probability (%) of a positive result in a positive patient (true positive rate (TPF: True Position Fraction), sensitivity) and the probability (%) of a negative result in a negative patient (specificity) based on the expression level of the target protein in a sample measured from a subject population, and plotting the sensitivity against [100 - specificity] (false positive rate (FPF: False Position Fraction)). The point on the ROC curve to be used as the cutoff value (reference value) can be determined based on various conditions. Generally, in order to increase both sensitivity and specificity (approaching 100%), the cutoff value (reference value) is set to the expression level at the point on an ROC curve with the true positive rate (sensitivity) on the vertical axis (Y axis) and the false positive rate on the horizontal axis (X axis) closest to (0, 100), or to the expression level at the point where [true positive (sensitivity) - false positive (100 - specificity)] is maximized (Youden index). In the method of the present invention, when multiple types of proteins are used as target proteins, it is preferable to determine a cutoff value (reference value) for each protein. Populations may be formed by gender, race, and age.

[0045] For example, if the expression level of the target protein of the present invention in a sample collected from the skin of a subject is higher than a cutoff value (reference value), the subject can be determined to have AD or have a high probability of having AD; if not, the subject can be determined to have a skin disease other than AD or have a high probability of having another skin disease, or not have any skin disease or have a low probability of having any skin disease. Furthermore, when detecting the severity of AD, if the expression level of the target protein of the present invention in a sample collected from the skin of a subject is higher than a cutoff value (reference value) based on a certain AD severity group (e.g., moderate (moderate)), the subject can be determined to have or have a high probability of having AD at or below that AD severity level (e.g., mild (mild) or below). In the method of the present invention, if the expression level of a target protein in a sample is preferably 110% or more, more preferably 150% or more, and even more preferably 200% or more relative to the cutoff value (reference value), the expression level of the target protein can be determined to be higher than the cutoff value (reference value). Alternatively, the difference between the expression level of a target protein in a sample and the cutoff value (reference value) can be determined, for example, by whether or not there is a statistically significant difference between the two.

[0046] Alternatively, when multiple target proteins are used in combination, it is possible to distinguish whether a subject has AD or another skin disease, or to detect the severity of AD, based on whether a certain proportion of those target proteins, for example, 50% or more, preferably 70% or more, more preferably 90% or more, and even more preferably 100%, meet the above-mentioned expression level criteria.

[0047] Furthermore, a discriminant (prediction model) for distinguishing between AD and other skin diseases can be constructed using the expression level of a target protein derived from an AD patient and the measured expression level (expression profile) of a target protein derived from a skin disease other than AD, and this discriminant can be used to distinguish between AD and other skin diseases. That is, a discriminant (prediction model) for separating an AD group from a skin disease group other than AD can be constructed using the expression level of a target protein derived from an AD patient and the measured expression level of a target protein derived from a skin disease other than AD as training samples, and a cutoff value (reference value) for distinguishing between AD and other skin diseases can be determined based on this discriminant. In constructing the discriminant, dimensionality can be reduced by principal component analysis (PCA), and the principal components can be used as explanatory variables. Then, the expression level of the target protein in a sample collected from the skin of the subject is similarly measured, and the measured value is substituted into the discriminant. The result obtained from the discriminant is compared with the cutoff value (reference value), thereby distinguishing between AD and other skin diseases in the subject.

[0048] In addition, when detecting the severity of AD, the expression levels of target proteins from patients with AD of different severity and the measured values ​​(expression profiles) of the expression levels of target proteins from patients with skin diseases other than AD can be used to construct a discriminant (prediction model) for distinguishing between AD of different severity and other skin diseases, and the severity of AD can be detected using this discriminant. That is, the expression levels of target proteins from patients with AD of different severity are used as training samples to construct a discriminant (prediction model) for separating AD groups of different severity (for example, no symptoms, slight symptoms, mild symptoms (mild), moderate symptoms (moderate), severe symptoms (severe) groups), and a cutoff value (reference value) for distinguishing between each AD group of different severity can be determined based on this discriminant. Then, the expression level of target proteins in samples collected from the skin of a subject is measured in the same way, and the measured values ​​obtained are substituted into this discriminant, and the results obtained from this discriminant are compared with the cutoff value (reference value), thereby detecting the severity of AD for the subject.

[0049] The variables used to construct the discriminant equation include explanatory variables and response variables. For example, the expression level (feature) of a target protein selected by the following method can be used as the explanatory variable. For example, the degree of possibility of the sample having AD (whether it is a group with AD or a group with other skin diseases) can be used as the response variable.

[0050] The feature can be a statistically significant difference between the two groups to be discriminated, for example, a protein whose expression level significantly varies between the two groups (expression-varying protein), and its expression level can be used as the feature protein. Also, feature proteins can be extracted using known algorithms such as machine learning algorithms. For example, the expression levels of proteins with high variable importance in the random forest shown below can be used, or the "Boruta" package in the R language can be used to extract feature proteins.

[0051] The algorithm for constructing the discriminant can be a known algorithm such as an algorithm used in machine learning. Examples of machine learning algorithms include random forest, a support vector machine with a linear kernel (SVM linear), a support vector machine with an rbf kernel (SVM rbf), a neural network, a generalized linear model, a regularized linear discriminant analysis, and a regularized logistic regression. Verification data is input into the constructed prediction model to calculate a predicted value, and the model whose predicted value best matches the actual measured value, for example, the model with the highest accuracy, can be selected as the optimal prediction model. Furthermore, the detection rate (Recall), accuracy (Precision), and the F value, which is the harmonic mean of these, are calculated from the predicted values ​​and the measured values, and the model with the largest F value can be selected as the optimal prediction model.

[0052] When using a random forest algorithm to construct a discriminant equation, the OOB error rate can be calculated as an index of the accuracy of the predictive model (Breiman L. Machine Learning (2001) 45;5-32).

[0053] In random forests, a method called the bootstrap method is used to randomly extract approximately two-thirds of the samples from all samples, allowing overlap, to create a classifier called a decision tree. At this time, samples that are not extracted are called out-of-bug (OOB). By predicting the OOB objective variable using one decision tree and comparing it with the correct label, the error rate can be calculated (OOB error rate in the decision tree). The same process is repeated 500 times, and the average value of the OOB error rates in the 500 decision trees can be used as the OOB error rate of the random forest model.

[0054] The number of decision trees (ntree value) used to construct a random forest model is 500 by default, but can be changed to any number as needed. Furthermore, the number of variables (mtry value) used to create a sample discriminant in one decision tree is the square root of the number of explanatory variables by default, but can be changed to any value between 1 and the total number of explanatory variables as needed.

[0055] The "caret" package of the R language can be used to determine the mtry value. Random forest can be specified as a method of the "caret" package, eight mtry values ​​can be tried, and the mtry value that maximizes accuracy can be selected as the optimal mtry value. Note that the number of trials for the mtry value can be changed to any number of trials as needed.

[0056] When a random forest algorithm is used to construct a discriminant, the importance of the explanatory variables used to construct the model can be expressed as a numerical value (variable importance). For example, the decrease in the Gini coefficient (Mean Decrease Gini) can be used as the value of the variable importance.

[0057] When data on a large number of proteins is used to build a prediction model, the data may be compressed by principal component analysis (PCA) as needed before building the prediction model. For example, dimensionality can be reduced by principal component analysis of the quantitative values ​​of proteins, and the main principal components can be used as explanatory variables for building the prediction model.

[0058] The test kit of the present invention for distinguishing atopic dermatitis from other skin diseases or for detecting the severity of atopic dermatitis includes a reagent or instrument for measuring the expression level of a target protein in a sample. For example, the kit may include a reagent for quantifying the target protein (e.g., a reagent for immunological measurement, etc.). Preferably, the kit of the present invention contains a molecule that recognizes the target protein (e.g., an antibody, a protein such as an enzyme, a nucleic acid such as an aptamer, etc.). As described above, the molecules included in the kit can be obtained commercially or by known methods. In addition to the antibody, the kit may also include a labeling reagent, a buffer, a chromogenic substrate, a secondary antibody, a blocking agent, equipment required for the test, and control reagents used as positive and negative controls. Preferably, the kit of the present invention further includes an index or guidance for evaluating the expression level of the target protein. For example, the kit of the present invention may include guidance explaining the cutoff value (reference value) of the expression level of the target protein for distinguishing atopic dermatitis from other skin diseases or detecting the severity of atopic dermatitis. Furthermore, the kit of the present invention may further include an SSL collection device (e.g., the above-mentioned SSL absorbent material or SSL adhesive material), a reagent for extracting proteins from a biological sample, a preservative or storage container for the sample collection device after collection of the biological sample, etc.

[0059] In relation to the above-described embodiment, the present invention further discloses the following aspects.

[0060] <1> A method for preparing a protein marker for distinguishing atopic dermatitis from other skin diseases, comprising recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 from a sample collected from the skin of a subject.

[0061] <2> A method for distinguishing atopic dermatitis from other skin diseases in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 in a sample collected from the skin of the subject.

[0062] <3> The method according to <1> or <2>, wherein the subject is preferably an infant, a child, or an adult, more preferably an infant or a child, even more preferably an infant between 0 and 5 years of age, and even more preferably an infant under 6 months of age. <4> The method according to any one of <1> to <3>, wherein the sample is preferably sebum, the stratum corneum, sweat, a skin cleanser, a skin extract, or a skin exudate, and more preferably lipids on the skin surface. <5> The method according to any one of <1> to <4>, wherein the skin is preferably skin of the head, face, neck, trunk, or hands and feet. <6> The method according to any one of <1> to <5>, wherein the other skin disease is preferably acne, seborrheic dermatitis, miliaria, or non-itchy eczema. <7> The method according to any one of <2> to <6>, further comprising comparing a measured value of the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 with a reference value, and differentiating atopic dermatitis from other skin diseases in the subject.

[0063] <8> A method for preparing a protein marker for detecting the severity of atopic dermatitis, comprising recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 from a sample collected from the skin of a subject.

[0064] <9> A method for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in a sample collected from the skin of the subject.

[0065] <10> The method according to <8> or <9>, wherein the subject is preferably an infant, a child, or an adult, more preferably an infant or a child, even more preferably an infant between 0 and 5 years of age, and even more preferably an infant under 6 months of age. <11> The method according to any one of <8> to <10>, wherein the sample is preferably sebum, stratum corneum, sweat, a skin cleanser, a skin extract, or a skin exudate, and more preferably lipids on the skin surface. <12> The method according to any one of <8> to <11>, wherein the skin is preferably skin of the head, face, neck, trunk, or limbs. <13> The method according to any one of <8> to <12>, wherein the severity is mild, mild (mild), moderate (moderate), or severe (severe), preferably mild (mild) and moderate (moderate). <14> The method according to any one of <9> to <13>, wherein a measured value of the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 is compared with a reference value to detect the severity of atopic dermatitis in a subject.

[0066] <15> A test kit for distinguishing atopic dermatitis from other skin diseases, which is used in the method described above, comprising a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. <16> A test kit for detecting the severity of atopic dermatitis, which is used in the method described above, comprising a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. <17> A protein marker for distinguishing atopic dermatitis from other skin diseases, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. <18> A protein marker for detecting the severity of atopic dermatitis, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2. <19> Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 as a protein marker for distinguishing atopic dermatitis from other skin diseases. <20> Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 as a protein marker for detecting the severity of atopic dermatitis.

[0067] Study Overview This study was approved by the Kao Corporation Ethics Review Board (Reception Number: T282-200115, Study Name: Study on Skin Components in Infants) and the National Center for Child Health and Development Ethics Review Board (Reception Number: 2019-104). One hundred infants, both male and female, born at the National Center for Child Health and Development's Obstetrics Department and attending the center for their one-month checkup were selected as subjects, with 98 subjects excluding two who withdrew their consent during the study period. The study was conducted at the center from August 2020 to May 2021. At one and two months of age, a full-body skin observation and diagnosis were performed by an allergist at the center. Diagnosis of AD by a physician was based on the United Kingdom Working Party (UKWP) criteria [Br J Dermatol, 131:406-416 (1994)], and AD severity was assessed based on the Eczema Area and Severity Index (EASI) [Exp Dermatol, 10:11-18 (2001)]. Furthermore, at 1 month of age, skin surface lipids (SSL) were collected as sebum samples from the entire face of each subject using a single oil-blotting film (5 x 8 cm, polypropylene, Hakugen Earth). The oil-blotting film was stored at -80°C until use for protein analysis.

[0068] Preparation of peptide samples from sebum samples: Oil-blotting film was cut to an appropriate size, and protein precipitates were obtained using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Peptide solutions were obtained using the EasyPep Mini MS Sample Prep Kit (ThermoFisher Scientific) according to the attached protocol. The peptide concentration in the solution was measured using a microplate reader (Corona Electric) according to the Pierce Quantitative Fluorometric Peptide Assay (ThermoFisher Scientific) protocol, and each sample was prepared to a concentration of 30 ng / μL.

[0069] LC-MS / MS Analysis and Data Analysis The sample peptide solution obtained above was subjected to LC-MS / MS analysis under the conditions in Table 1 below.

[0070]

[0071] Spectral data obtained by LC-MS / MS analysis was analyzed using Proteome Discoverer ver. 2.5 (ThermoFisher Scientific). Protein identification was performed using UniProtKB / Swiss-Prot as the reference database, Homo sapiens as the taxonomy, and SequestHT as the search engine. In the search, the enzyme was set to trypsin, the missed cleavage was set to 2, the dynamic modifications were set to oxidation (M), acetyl (N-term, protein N-term), and the static modifications were set to carbamidomethyl (C). Peptides satisfying a false discovery rate (FDR) of p<0.01 were targeted. Label-free quantitative analysis (LFQ, Label-Free Quantification) based on precursor ions was performed on the identified proteins. The protein abundance was calculated based on the peak intensity of the peptide-derived precursor ion, and if the peak intensity was below the detection limit, it was considered a missing value. To correct for experimental bias, protein abundance was normalized using the total peptide amount method. Protein abundance ratios were calculated using the summed abundance-based method. ANOVA (individual-based, t-test) was used to calculate the p-value indicating the significance of differences in abundance between groups. Among the identified proteins, proteins with a false discovery rate (FDR) of 0.1 or higher were excluded from the analysis. Significant differences were determined using the Benjamini-Hochberg method, taking multiple comparisons into account, with an FDR of <0.05. The normalized values ​​were converted to base 2 logarithms, and the Log2 (Abundance + 1) values ​​were used as the quantitative values ​​for each protein.For each selected marker protein, statistical analysis software Prism 8 ver. 3.0 or IBM SPSS Statistics ver. 28.0.0.0 was used. Multigroup comparison analysis was performed using the Kruskal-Wallis followed by Dunn's test, two-group analysis was performed using the Mann-Whitney U test, and trend analysis was performed using the Jonckheere-Terpstra trend test. Graphs were created using BoxPlotR (http: / / shiny.chemgrid.org / boxplotr / ) and FaDA (https: / / shiny-bird.univ-nantes.fr / app / Fada).

[0072] Results At one month of age, seven infants were healthy without facial eczema (HL), and 11 infants were diagnosed with AD (AD). LC-MS / MS analysis of proteins extracted from sebum samples collected from the entire faces of 18 infants identified 1,446 proteins. Analysis using Proteome Discoverer revealed 432 proteins with higher expression levels in AD compared to HL (AD / HL ≥ 2 and FDR < 0.05). These proteins are protein markers that can be used to distinguish between healthy infants without facial eczema and infants with AD, and the specific protein markers of the present invention are protein markers selected from these proteins. Table 2 shows the analysis results of the specific proteins of the present invention.

[0073]

[0074] Extraction of Markers for Differentiating AD from Other Skin Diseases From the 432 marker proteins found above that can distinguish between healthy children without facial eczema and children with AD, markers capable of differentiating AD from other skin diseases according to the present invention were extracted by the following analysis. At one month after birth, 44 children were found to have some kind of skin symptom on their face, such as erythema, papules, or dryness, and were classified as having diseases other than AD, such as acne, seborrheic dermatitis, miliaria, or eczema without itching (a general skin disease that causes eczema but is not diagnosed as a specific disease such as AD, acne, seborrheic dermatitis, or miliaria) (however, children who were subsequently diagnosed with AD at two months of age were excluded, as they may already have shown signs of AD). Therefore, the expression levels of the above 432 proteins were compared for three groups: healthy children without facial rash (HL, n = 7), children with other skin diseases (Other, n = 44), and children with AD (AD, n = 11). As a result, in sebum samples from 1-month-old children, the protein levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 were all significantly higher in children diagnosed with AD than in children with other skin diseases (Kruskal-Wallis followed by Dunn's test). A plot of the quantitative values ​​(Log2(Abundance + 1)) of these proteins is shown in Figure 1. The above results demonstrate that the specific marker proteins according to the present invention shown in Table 2, among the 432 proteins, can function as protein markers for distinguishing atopic dermatitis from other skin diseases.

[0075] Confirmation of the accuracy of distinguishing AD from other skin diseases Using the expression levels in sebum samples from 1-month-olds, the accuracy of distinguishing children with other skin diseases from children diagnosed with AD was evaluated using an ROC curve (Figure 2). Figure 2 shows ROC curves created for each of the protein markers of the present invention shown in Table 2. Table 3 shows the AUC, sensitivity, specificity, and accuracy of the ROC curve showing the discrimination accuracy between the children with other skin diseases (Other, n = 44) and AD children (AD, n = 11). These results showed that the expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 in sebum samples can accurately distinguish between children with other skin diseases and children diagnosed with AD.

[0076]

[0077] Of the 44 children classified as having a condition other than AD at one month of age, 19, 3, 4, and 18 children were classified as having acne, seborrheic dermatitis, miliaria, or infantile eczema without itching, respectively. The protein levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in sebum samples at one month of age were compared (Mann-Whitney U test). Plots of the quantitative protein levels (Log2(Abundance+1)) are shown in Figures 3 to 20. The expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 were all significantly higher in children diagnosed with AD than in children classified as acne or non-itchy infantile eczema (P<0.05). The expression levels of FGG, FGA, FGB, FN1, IGHG4, C4B, C7, EPX, RNASE2, and PRG2 were significantly higher in children diagnosed with AD than in children classified as seborrheic dermatitis (P<0.05). The increases in C3 and CLC tended to be significant (P<0.1). The expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG4, C3, and C7 were significantly higher in children diagnosed with AD than in children classified as miliaria. The increases in IGHG3, C4B, and CFH tended to be significant (P<0.1).

[0078] Markers for detecting the severity of AD Eleven 1-month-old AD patients were divided into mild (mild, EASI = 1.1-7.0, n = 7) and moderate (moderate, EASI = 7.1-21.0, n = 4) based on the EASI score, which is an AD severity classification, and healthy children (HL, n = 7) were also included in these three groups. A plot of the quantitative values ​​(Log2 (Abundance + 1)) of the protein amounts of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in sebum samples from 1 month old children is shown in Figure 21. As a result, for FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2, the median values ​​of each group increased gradually in the order of HL, mild and moderate, and trend analysis was performed to test whether the increase was dependent on the severity, showing that all molecules were significant (P<0.05). These results indicate that the expression of sebum FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 increases in a severity-dependent manner, and that they can function as severity markers.

[0079] Comparative Example 1 Figure 22 shows a plot of the quantitative values ​​(Log2(Abundance+1)) of the protein amount of sebum PFN1 (Profilin-1) in three groups: healthy children (HL, n=7), children with other skin diseases (Other, n=44), and children with AD (AD, n=11). PFN1 is included in the 432 marker proteins that can distinguish between healthy children and children with AD, as described above, and Patent Document 3 has shown that there are differences in expression between healthy children and children with AD. However, the results of this study showed that PFN1 cannot be used as a differential marker.

[0080] Comparative Example 2 Children were divided into mild (mild, EASI = 1.1-7.0, n = 7) and moderate (moderate, EASI = 7.1-21.0, n = 4) groups, and healthy children (HL, n = 7) were also included. A plot of the quantitative values ​​(Log2(Abundance+1)) of the protein amount of SPRR2D (Small proline-rich protein 2D) for these three groups is shown in Figure 23. Patent Document 3 has shown that there is a difference in expression of SPRR2D between healthy children and children with AD, but the results of this study showed that it cannot be used as a severity marker.

Claims

1. A method for preparing a protein marker for distinguishing atopic dermatitis from other skin diseases, comprising recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 from a sample collected from the skin of a subject.

2. A method for distinguishing atopic dermatitis from other skin diseases in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 in a sample collected from the subject's skin.

3. The method of claim 2, further comprising comparing the measured expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 with a reference value, and differentiating atopic dermatitis from other skin diseases in the subject.

4. A method for preparing a protein marker for detecting the severity of atopic dermatitis, comprising recovering at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 from a sample collected from the skin of a subject.

5. A method for detecting the severity of atopic dermatitis in a subject, comprising measuring the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 in a sample collected from the skin of the subject.

6. The method of claim 5, further comprising comparing the measured value of the expression level of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 with a reference value to detect the severity of atopic dermatitis.

7. The method according to any one of claims 1 to 6, wherein the sample is lipids on the surface of the skin.

8. The method of any one of claims 1 to 7, wherein the subject is an infant or child.

9. A test kit for distinguishing atopic dermatitis from other skin diseases, used in the method of any one of claims 2, 3, 7 or 8, which contains a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2.

10. A test kit for detecting the severity of atopic dermatitis, used in the method of any one of claims 5 to 8, comprising a molecule that recognizes at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2.

11. A protein marker for distinguishing atopic dermatitis from other skin diseases, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2.

12. A protein marker for detecting the severity of atopic dermatitis, comprising at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2.

13. Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 as a protein marker for distinguishing atopic dermatitis from other skin diseases.

14. Use of at least one protein selected from FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2 and PRG2 as a protein marker for detecting the severity of atopic dermatitis.

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