Method for preparing protein marker for predicting risk of developing atopic dermatitis in infants

Abstract

Provided are a method for preparing a protein marker for predicting the risk of developing atopic dermatitis or the risk of allergen sensitization in infants, and a method for predicting the risk of developing atopic dermatitis or the risk of allergen sensitization using said protein marker.　More specifically, the method for preparing a protein marker for predicting the risk of developing atopic dermatitis includes recovering at least one kind of 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 an infant subject.

Description

Method for preparing protein markers for predicting the risk of developing atopic dermatitis in infants

[0001] The present invention relates to a method for preparing a protein marker for predicting the risk of developing atopic dermatitis in infants and a method for predicting the risk of developing atopic dermatitis using the protein marker, as well as a method for preparing a protein marker for predicting the risk of allergen sensitization and a method for predicting the risk of allergen sensitization using the protein marker.

[0002] In recent years, the number of people suffering from allergic diseases has been on the rise, with approximately one in two people in Japan suffering from some form of allergic disease, such as hay fever, atopic dermatitis, food allergies, etc. These diseases are caused by a complex combination of genetic and environmental factors, and they repeatedly worsen and improve with age, lifestyle, and environmental changes, significantly reducing the quality of people's daily lives.

[0003] Atopic dermatitis (AD) and food allergies are common in infancy, followed by the onset of symptoms such as asthma and rhinitis (the allergic march), which become chronic in adulthood and are difficult to alleviate (Non-Patent Documents 1 and 2). It has been revealed that the allergic march develops through sensitization via inflamed skin with weakened barrier function (Non-Patent Documents 3 and 4). It has also been reported that AD that develops in infancy is involved in the subsequent development of the allergic march, such as food allergy and asthma (Non-Patent Documents 5 and 6). In IgE-mediated food allergies, sensitization occurs when allergen-specific IgE antibodies are induced in vivo by the entry of food allergens, which then bind to high-affinity IgE receptors on mast cells. Generally, the state of sensitization can be objectively assessed by quantifying the allergen-specific IgE antibody titer in the blood using a blood test. The presence of allergen-specific IgE antibodies indicates sensitization to the allergen in question, and although it is not necessarily the allergen associated with the onset of food allergy, it is an important indicator of the onset of food allergy. Sensitization to multiple allergens is considered to be a risk factor for various diseases, such as food allergies and asthma, and early polysensitization in particular has been reported to be associated with the onset of multiple diseases (Non-Patent Document 7). Therefore, it is considered important to identify the risk of AD and allergen sensitization in infants as early as possible and to visit a medical institution to start appropriate treatment in order to prevent the progression of the allergic march.

[0004] AD is a disease characterized by a pruritic eczema that repeatedly worsens and improves (Non-Patent Document 8). In particular, AD in early infancy is often delayed because patients themselves are unable to complain of itching, making it difficult for parents to recognize their child's itching or eczema. This often results in delays in treatment due to inability to visit a medical institution in a timely manner. Meanwhile, infants often undergo regular health checkups, providing doctors, midwives, public health nurses, and others with the opportunity to assess their health status. However, it is currently difficult to thoroughly examine the skin on the spot, determine the risk of developing AD, and encourage consultation with a specialist. Therefore, if parents, non-allergy specialists, midwives, public health nurses, and others could identify the risk of developing AD using objective biomarkers, it is believed that this would encourage timely consultation with a specialist.

[0005] As a method for detecting AD using biomarkers, it has been proposed to detect peripheral blood eosinophil count, serum total IgE level, LDH (lactate dehydrogenase) level, 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 8 to 10). AD detection using biomarkers is particularly effective in infants and young children who have difficulty reporting symptoms.

[0006] On the other hand, the effectiveness of biomarkers for detecting atopic dermatitis may vary depending on the patient's age, for example, whether they are children or adults. For example, it has been reported that the above-mentioned serum TARC has lower sensitivity and specificity in children under 2 years of age compared to children aged 2 years or older (Non-Patent Document 11). Serum IL-18 (Non-Patent Document 12) has been reported as an effective marker for detecting infantile AD. It has also been reported that serum Serpin B4 is effective in detecting pediatric and adult AD (Non-Patent Documents 13 and 14). It has also been reported that a decrease in Serpin B12 and an increase in Serpin B3 were observed in stratum corneum samples taken from AD patients (Non-Patent Document 15).

[0007] The pathogenesis of AD is complex, and various factors are known to fluctuate with the progression of the disease. In AD, innate and adaptive immunity are activated by exposure to antigens or irritants due to atopic predisposition or impaired barrier function caused by external factors (Non-Patent Document 16). 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 progression of AD disease, and are therefore useful for understanding the progression of AD disease. However, it is unknown whether they can predict the risk of developing AD. These AD detection markers also include molecules that fluctuate as a result of the development of AD pathology. On the other hand, markers that predict the risk of developing AD are thought to fluctuate early in the pathogenesis and affect subsequent pathogenesis. Therefore, known AD detection markers may not necessarily serve as AD risk prediction markers that can detect early signs of AD.

[0008] 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), and that marker proteins for atopic dermatitis can be detected from SSL (Patent Document 3). However, no markers are known that can objectively predict the risk of developing AD and the risk of allergen sensitization in infants.

[0009] It has been reported that fibrinogen binds to Staphylococcus aureus and is involved in its survival in AD skin, and that the predominance of Staphylococcus aureus in the skin flora may be related to the onset of AD in infants (Non-Patent Documents 16-18). Furthermore, recent reports have suggested that fibrinogen triggers granule release from eosinophils, a key factor in Type 2 inflammation (Non-Patent Document 19). However, it has not been known that fibrinogen in the skin can be used as a predictive marker for the risk of developing AD or allergen sensitization. Regarding eosinophils, peripheral blood eosinophil counts are known as biomarkers 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 20). However, it has not been known that eosinophil granule proteins in the skin can be used as predictive markers for the risk of developing AD or allergen sensitization.

[0010] (Patent Document 1) International Publication No. 2018 / 008319 (Patent Document 2) JP 2020-074769 A (Patent Document 3) JP 2021-175958 A

[0011] (Non-patented document 1) Baba Minoru, Allergy Immunology, 11: 736-743 (2004) (Non-patented document 2) Czarnowicki T et al., J Allergy Clin Immunol. 139(6):1723-1734. (2017) (Non-patented document 3) Lack G, J Allergy Clin Immunol. 121(6):1331-1336. (2008) (Non-patented document 4) Matsumoto K et al., Allergol Int. 62(3):291-296. (2013) (Non-patented document 5) Davidson WF et al., J Allergy Clin Immunol. 143(3):894-913. (2019) (Non-patented document 6) Ravnborg N et al., J Am Acad Dermatol. 84(2):471-478. (2021) (Non-patented document 7) Maiello N et al., Children. 9(4), 450. (2022) (Non-patented document 8) Saeki R, Journal of the Japanese Society for Child Health. 131(13), 2691-2777, (2021) (Non-patented document 9) Sugawara N et al., Allergy. 57:180-181. (2002) (Non-patented document 10) Ohta S et al., Ann Clin Biochem. 49:277-284. (2012) (Non-patented document 11) Fujisawa R, Journal of the Japanese Society for Child Health. 19(5):744-757. (2005) (Non-patented document 12) Ohnishi H et al., Allergology International. 52:123-130. (2003) (Non-patented document 13) Nagao M et al., J Allergy Clin Immunol. 141(5):1934-1936. (2018) (Non-patented document 14) Okawa T et al., Allergology International. 67:124-130. (2018) (Non-patented document 15) Goleva E et al., J Allergy Clin Immunol. 146(6):1367-1378.(2020) (Non-patented literature 16) Langan SM et al., Lancet. 396(10247):345-360. (2020) (Non-patented literature 17) Cho SH et al., J Allergy Clin Immunol. 108(2):269-274. (2001) (Non-patented literature 18) Meylan P et al., J Invest Dermatol. 137(12):2497-2504. (2017) (Non-patented literature 19) Coden ME et al., J Immunol. 15;204(2):438-448. (2020) (Non-patented literature 20) Kim HS et al., Ann Allergy Asthma Immunol. 119(5):441-445. (2017).

[0012] The present invention relates to the following 1) to 10): 1) A method for preparing a protein marker for predicting the risk of developing 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 an infant subject. 2) A method for predicting the risk of developing atopic dermatitis in an infant, 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 infant subject. 3) A test kit for predicting the risk of developing atopic dermatitis in an infant, which is used in the method described above, and 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. 4) A protein marker for predicting the risk of developing atopic dermatitis in infants, 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. 5) 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 marker for predicting the risk of developing atopic dermatitis in infants. 6) A method for preparing a protein marker for predicting the risk of allergen sensitization, 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 an infant subject.7) A method for predicting the risk of allergen sensitization in an infant, 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 infant subject. 8) A test kit for predicting the risk of allergen sensitization in an infant, which is used in the method described above, and 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. 9) A protein marker for predicting the risk of allergen sensitization in infants, 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. 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 marker for predicting the risk of allergen sensitization in infants.

[0013] Figure 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 of children with AD and children without AD at 1 month of age. 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 of children with AD and children without AD at 1 month of age. Figure 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 of sensitized positive children (class 3 or higher) and sensitized negative children at 1 month of age. 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 of sensitized positive children (class 3 or higher) and sensitized negative children at 1 month of age. ROC curves for 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 for children sensitized to multiple allergens and children sensitized negative. Expression levels of PPIA in SSL derived from the whole face at 1 month of age for children with AD and children without AD, and the ROC curve for PPIA. Expression levels of PFN1 in SSL derived from the whole face at 1 month of age for children sensitized (class 3 or higher) and children sensitized negative, and the ROC curve for PFN1. Detailed Description of the Invention

[0014] The present invention relates to a method for preparing a protein marker for predicting the risk of developing atopic dermatitis or the risk of allergen sensitization in infants and young children, and to providing a method for predicting the risk of developing atopic dermatitis or the risk of allergen sensitization using the protein marker.

[0015] The inventors collected SSL from the skin of infants and conducted comprehensive analysis of the protein expression levels contained in the SSL before the onset of AD from children who did not develop AD or who later developed AD, and in the SSL before the onset of sensitization from sensitization-negative children or children who later became sensitization-positive. As a result, they found that the expression levels of specific proteins were significantly different between the two types of children, and that this can be used as an indicator to predict the risk of developing AD or the risk of sensitization to allergens in infants.

[0016] According to the present invention, protein markers for predicting the risk of developing atopic dermatitis or predicting the risk of allergen sensitization can be collected from the skin of an infant subject using a simple, minimally invasive or non-invasive technique, or the markers can be used to predict the risk of developing atopic dermatitis or the risk of allergen sensitization in an infant. Thus, the present invention enables assessment of the risk of developing atopic dermatitis or the risk of allergen sensitization in infants at an early stage after birth. Furthermore, the methods of the present invention can contribute to early treatment of atopic dermatitis in infants.

[0017] All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety.

[0018] The names of the proteins disclosed herein follow the Gene Name or Protein Name listed in UniProt ([https: / / www.uniprot.org / ]).

[0019] In this specification, the term "infant" broadly refers to a "child" before the onset of secondary puberty, specifically a concept including children aged 12 or younger, and preferably refers to infants aged from 0 to the age of entering school, specifically 0 to 5 years old. In the present invention, infants are preferably infants under 6 months old.

[0020] 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 skin rash that begins on the head or face in infancy and often progresses to the trunk or limbs. From the age of one, the facial rash diminishes, and the rash primarily appears on the neck or joints of the limbs.

[0021] As used herein, "risk of developing AD" refers to susceptibility to AD, and "prediction of risk of developing AD" includes predicting whether or not an infant is likely to develop AD in the future, or the likelihood of this happening. The future may be, for example, one month, two months, five months, or longer.

[0022] In this specification, "allergens" include foods (e.g., eggs, milk, meats (beef, chicken, pork, etc.), grains (soybeans, rice, wheat, buckwheat, sesame, peanuts, etc.), crustaceans (shrimp, crab, etc.)), house dust, mold, fungi (Malassezia, etc.), mites (Dermatophagoides pteronyssinus, Dermatophagoides farinae, etc.), pollen (cedar, cypress, alder, etc.), animals (cats, dogs, etc.), insects (moths, cockroaches, etc.), etc. In the present invention, allergens are preferably foods and mites.

[0023] As used herein, "risk of allergen sensitization" refers to the susceptibility to sensitization, i.e., the likelihood of an increase in allergen-specific IgE antibodies in the blood, and "prediction of risk of allergen sensitization" includes predicting whether or not an infant is likely to become sensitized to an allergen in the future, or the likelihood of this happening. The future refers to, for example, one month later, two months later, five months later, or a longer period. As used herein, the criterion for a positive allergen sensitization is Class 1 (0.35 UA / mL) or higher, more preferably Class 2 (0.70 UA / mL) or higher, and more preferably Class 3 (3.50 UA / mL) or higher, in a specific IgE test.

[0024] 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 AD symptoms are 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.

[0025] 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.

[0026] As will be described in the Examples below, protein expression analysis was performed on SSL collected from the entire face of infants at one month after birth. As a result, children who developed AD one month after collection 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, for PPIA (Peptidyl-prolyl cis-trans isomerase A, Patent Document 3), which is known to be an AD detection marker, no difference was observed in the expression level of PPIA in SSL between children who developed AD one month after collection and children who did not develop AD at one month after birth. Furthermore, in sensitized positive children who tested positive for allergen sensitization 5 months after collection, the expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, CFB, CFH, EPX, CLC, RNASE2, RNASE3 and PRG2 were significantly higher at 1 month of age compared to sensitized negative children, and the expression level of C7 showed a significant tendency to be higher at 1 month of age in sensitized positive children.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 allergen-sensitized and sensitization-negative children at one month of age.

[0027] The above results indicate that FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE2, RNASE3 and PRG2 are useful as markers for predicting the risk of developing atopic dermatitis in infants or the risk of allergen sensitization in infants.

[0028] 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. All of these proteins migrate from the blood to tissues due to increased vascular permeability associated with inflammation, and it is speculated that they may contribute to the onset of AD and subsequent sensitization, but it was not easy to imagine that they could serve as predictive markers for the risk of developing AD or the risk of allergen sensitization in infants.

[0029] Therefore, the present invention provides a protein marker for predicting the risk of developing AD in infants. The present invention also provides a method for preparing a protein marker for predicting the risk of developing AD in infants. 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 an infant subject. The present invention also provides a method for predicting the risk of developing AD. The method comprises measuring the expression level of the target protein in a sample collected from the skin of an infant subject.

[0030] The present invention further provides a protein marker for predicting the risk of allergen sensitization in infants. The present invention also provides a method for preparing a protein marker for predicting the risk of allergen sensitization in infants. The preparation method comprises recovering a target protein from a sample collected from the skin of an infant subject. The present invention also provides a method for predicting the risk of allergen sensitization. The method comprises measuring the expression level of the target protein in a sample collected from the skin of an infant subject.

[0031] 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 predicting the risk of AD onset or the risk of allergen sensitization in infants, but from the viewpoint of improving accuracy, preferably two or more, more preferably three or more, may be used in combination. From the viewpoint of accuracy, it is preferable to select fibrinogens (FGG, FGA, FGB, FN1), immunoglobulins (IGHG1, IGHG2, IGHG3, IGHG4), complement (C3, C4B, C7, CFB, CFH), and eosinophil granule proteins (EPX, CLC, RNASE3, RNASE2, and PRG2) in this order. Among these, a combination of fibrinogens (FGG, FGA, FGB) is more preferable. 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 predict the risk of developing AD or the risk of allergen sensitization, and using it as a marker.

[0032] The method for preparing a protein marker for predicting the risk of developing AD or the risk of allergen sensitization in infants according to the present invention, and the method for predicting the risk of developing AD or the risk of allergen sensitization (hereinafter collectively referred to as the methods of the present invention) may further comprise collecting a sample from the skin of an infant subject.

[0033] The infant subject from which the sample is collected is not particularly limited in terms of gender, race, etc., as long as it is an infant. Preferred examples of infant subjects include infants who desire or need to predict the risk of developing AD or the risk of allergen sensitization. For example, the younger the infant, such as a one-month-old infant, the more preferable it is, since early detection of the risk of developing AD or allergen sensitization can lead to early therapeutic intervention.

[0034] Any method can be used to collect samples from the skin of infant subjects. 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 methods such as absorbing SSL into sheet-like materials such as oil blotting paper or oil blotting film, adhering SSL to glass plates or tape, and scraping SSL off with a spatula, scraper, etc. To improve SSL adsorption, an SSL absorbent material pre-soaked with a highly lipid-soluble solvent may be used. However, since the presence of highly water-soluble solvents or moisture inhibits SSL adsorption, it is preferable for the SSL absorbent material to contain a low content of highly water-soluble solvents or moisture. The SSL absorbent material is preferably used in a dry state.

[0035] The sample collected from the skin of an infant subject may be subjected to the subsequent extraction process without preservation, or may be stored for a certain period of time. For example, when the collected SSL is to be stored for a certain period of time, it is preferable to store it under low-temperature conditions as soon as possible after collection. The temperature conditions for storing the SSL in the present invention are sufficient as long as they 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.

[0036] 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).

[0037] 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.

[0038] In the method of the present invention, the expression level of a protein can be measured by a protein detection or quantification method such as Western blot, protein chip analysis, immunoassay (e.g., ELISA), immunochromatography, lateral flow immunoassay, mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), one-hybrid method (PNAS, 100:12271-12276 (2003)), or 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 can be 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 according to standard methods. Meanwhile, monoclonal antibodies can be obtained from hybridoma cells prepared 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. 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 predict the risk of developing AD or the risk of allergen sensitization, or it can be stored under standard protein storage conditions until use in the prediction.

[0039] Thus, the expression level of the target protein of the present invention is measured in a sample collected from the skin of an infant subject, and the risk of developing AD or the risk of allergen sensitization in the infant is predicted based on the expression level. In one example, the prediction is made 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 an infant subject with a cutoff value (reference value), it is possible to predict the possibility or likelihood of developing AD in the infant, or the possibility or likelihood of developing allergen sensitization. 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 predict the possibility or likelihood of developing AD in the infant, or the possibility or likelihood of developing allergen sensitization, based on a visual judgment or quantification of the intensity of the detected signal.

[0040] Here, the "cut-off value" ("reference value") can be set arbitrarily depending on the purpose, etc. Examples of the "cut-off value" ("reference value") include the expression level of the target protein of the present invention in a sample from a population of AD-free children or sensitization-negative children. For example, as the expression level in AD-free children or sensitization-negative children, a value determined with reference to statistical values ​​such as the average value and standard deviation of the expression level of the target protein in a sample measured from a population of AD-free children or sensitization-negative children can be used.

[0041] Another example of the "cutoff value" ("reference value") is a value determined based on the expression level of the target protein in samples measured from a population of infants and toddlers including children with and without AD, or children with and without sensitization. The cutoff value (reference value) can be determined by various statistical analysis methods. For example, a value 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 population of infants, 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.

[0042] For example, if the expression level of the target protein of the present invention in a sample collected from the skin of an infant subject is higher than a cutoff value (reference value), the infant can be determined to have a possibility of developing AD or allergy sensitivity, or a high possibility of developing AD or allergy sensitivity; if not, the infant can be determined to have no possibility of developing AD or allergy sensitivity, or a low possibility of developing AD or allergy sensitivity. For example, if the expression level of the target protein in the sample is statistically significantly higher than the cutoff value (reference value), the infant can be determined to have a possibility of developing AD or allergy sensitivity, or a high possibility of developing AD or allergy sensitivity; if not, the infant can be determined to have no possibility of developing AD or allergy sensitivity, or a low possibility of developing AD. Furthermore, for example, if the expression level of the target protein in the sample is preferably 110% or more, more preferably 150% or more, and even more preferably 200% or more of the cutoff value (reference value), the infant can be determined to have a possibility of developing AD or allergy sensitivity, or a high possibility of developing AD or allergy sensitivity; if not, the infant can be determined to have no possibility of developing AD or allergy sensitivity, or a low possibility of developing AD or allergy sensitivity.

[0043] Alternatively, when multiple target proteins are used in combination, the risk of developing AD or the risk of allergen sensitization in infants can be predicted 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%, meets the above-mentioned expression level criteria.

[0044] Furthermore, using the expression levels of target proteins in children with AD or sensitized children and the measured values ​​(expression profiles) of the expression levels of target proteins in children without AD or sensitized children, a discriminant (prediction model) can be constructed to distinguish between the presence or absence of AD onset or allergen sensitization, or the likelihood of AD onset or allergen sensitization, and the discriminant can be used to predict the risk of AD onset or allergen sensitization. That is, using the expression levels of target proteins in children with AD or sensitized children and the measured values ​​of the expression levels of target proteins in children without AD or sensitized children as teacher samples, a discriminant (prediction model) can be constructed to separate the AD onset group or sensitization positive group from the AD non-onset group or sensitization negative group, and a cutoff value (reference value) for predicting the risk of AD onset or allergen sensitization can be determined based on the discriminant. In addition, in creating the discriminant, dimensionality reduction can be performed using principal component analysis (PCA), and the principal components can be used as explanatory variables. The expression level of the target protein in a sample collected from the skin of an infant subject is then similarly measured, the obtained measurement value is substituted into the discriminant formula, and the result obtained from the discriminant formula is compared with the cutoff value (reference value), thereby making it possible to predict the risk of developing AD or the risk of allergen sensitization in the infant.

[0045] 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 level of risk of developing AD or risk of allergen sensitization of the sample (whether the sample is an AD-onset group or an AD-non-onset group, or a sensitization-positive group or a sensitization-negative group) can be used as the response variable.

[0046] 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.

[0047] 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.

[0048] 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).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] The test kit of the present invention for predicting the risk of atopic dermatitis in infants or the risk of allergen sensitization in infants includes a reagent or instrument for measuring the expression level of a target protein in a sample. For example, the kit of the present invention may include a reagent for quantifying the target protein (e.g., a reagent for immunoassay, etc.). Preferably, the kit of the present invention contains a molecule that recognizes the target protein (e.g., a protein such as an antibody or 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 above molecules, the kit may also contain a labeling reagent, a buffer solution, 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 predicting the risk of developing AD or the risk of allergen sensitization. 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, a container for protein extraction, etc.

[0055] In relation to the above-described embodiment, the present invention further discloses the following aspects.

[0056] <1> A method for preparing a protein marker for predicting the risk of developing 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 an infant or child subject.

[0057] <2> A method for predicting the risk of developing atopic dermatitis in an infant, 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 infant subject.

[0058] <3> A method for preparing a protein marker for predicting the risk of allergen sensitization, 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 an infant or toddler subject.

[0059] <4> A method for predicting the risk of allergen sensitization in an infant, 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 infant subject.

[0060] <5> The method according to any one of <1> to <4>, wherein the infant is preferably an infant between 0 and 5 years old, more preferably an infant under 6 months old. <6> The method according to any one of <1> to <5>, wherein the sample is preferably sebum, stratum corneum, sweat, skin cleansing solution, skin extract, or skin exudate, more preferably lipids on the skin surface. <7> The method according to any one of <1> to <6>, wherein the skin is preferably skin of the head, face, neck, trunk, or limbs. <8> The method according to any one of <3> to <7>, wherein the allergen is preferably food, grain, crustacean, house dust, mold, fungus, mite, pollen, animal, or insect, more preferably food or mite. <9> The method according to any one of <2> and <4> to <8>, 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 to predict the risk of developing atopic dermatitis in an infant or predict the risk of allergen sensitization in an infant.

[0061] <10> A test kit for predicting the risk of developing atopic dermatitis in an infant, 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. <11> A test kit for predicting the risk of allergen sensitization in an infant, 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. <12> A protein marker for predicting the risk of developing atopic dermatitis in infants, 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> A protein marker for predicting the risk of allergen sensitization in infants, 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. <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 marker for predicting the risk of developing atopic dermatitis in infants. <15> 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 marker for predicting the risk of allergen sensitization in infants.

[0062] Test Example 1: Study Overview This study was conducted with the approval of the Kao Corporation Human Research Ethics Committee (Reception Number: T282-200115, Study Name: Study on Skin Components in Infants) and the National Center for Child Health and Development Ethics Review Committee (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 consent during the study period. The study was conducted at the center from August 2020 to May 2021. At one, two, and six months of age, a full-body skin observation and diagnosis were performed by an allergist at the center. Physician diagnosis of AD 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)]. At 1 month of age, skin surface lipids (SSL) were collected as sebum samples from each subject's entire face 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. In addition, to assess the presence or absence of allergen sensitization, blood samples were taken at 6 months of age and allergen-specific IgE antibody titers (to Dermatophagoides farinae, milk, egg white, ovomucoid, peanuts, soybeans, and wheat) were analyzed by blood tests at a clinical laboratory.

[0063] 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.

[0064] 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.

[0065]

[0066] Proteome Discoverer ver. 2.5 (ThermoFisher Scientific) was used to analyze the spectral data obtained by LC-MS / MS analysis. For protein identification, the reference database was set to UniProtKB / Swiss-Prot, the taxonomy was set to Homo sapiens, and the search engine was SequestHT. 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) based on precursor ions was performed on the identified proteins. Protein abundance was calculated based on the peak intensity of the peptide-derived precursor ion, and peak intensities below the detection limit were considered missing values. 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 p values ​​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 to obtain Log2 (Abundance + 1) values, which were used as the quantitative values ​​for each protein. For each selected marker protein, statistical analysis software Prism8 ver. 3.0 was used, and multigroup comparison analysis was performed using the Kruskal-Wallis test followed by Dunn's test, and two-group analysis was performed using the Mann-Whitney U test.Graphs were created using BoxPlotR (http: / / shiny.chemgrid.org / boxplotr / ) and FaDA (https: / / shiny-bird.univ-nantes.fr / app / Fada, PLoS One. 2021 Dec 20;16(12):e0261083).

[0067] Results: Proteins extracted from sebum samples collected from the entire face of seven healthy children (HL) and 11 children with AD (AD) at one month of age were comprehensively analyzed. Healthy children were defined as children who had not been diagnosed with AD or any other skin disease at one, two, or six months of age. Analysis of sebum samples from 18 children 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 children and AD children, 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.

[0068]

[0069] Extraction of AD Risk Prediction Markers From the 432 marker proteins identified above that can distinguish between healthy and AD-afflicted children, markers capable of predicting the risk of developing AD according to the present invention were extracted using the following analysis. Sebum samples collected at one month of age were analyzed to determine whether early detection of children who will develop AD and those who will not subsequently develop AD was possible. Of the subjects, 26 children were not diagnosed with AD at one, two, or six months of age (AD-free children) (including children diagnosed with healthy or non-AD skin diseases at one month of age). Meanwhile, 30 children were not diagnosed with AD at one month of age but were diagnosed with AD at two months of age, and another 30 children were diagnosed with AD at six months of age. The expression levels of the above 432 proteins were compared between two groups: AD-free children (n = 26) and children diagnosed with AD at two or six months of age (AD-afflicted children, n = 60). As a result, in sebum samples taken at one month of age, the protein levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, CFB, CFH, EPX, CLC, and RNASE2 were all significantly higher in children with AD compared to children without AD (Mann-Whitney U test, P<0.05). RNASE3 and PRG2 also tended to be significantly higher (P<0.1). Next, the expression levels of the above 432 proteins were compared between two groups: children without AD (n=26) and children diagnosed with AD at two months of age (children with AD, n=30). As a result, in sebum samples taken at one month of age, the protein expression levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, C7, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 were significantly higher in children with AD than in children without AD (Mann-Whitney U test, P<0.05). Figure 1 shows the protein expression levels of children without AD (n=26) and children diagnosed with AD at two months of age (children with AD, n=30).The above results indicate that the expression levels of the specific marker proteins according to the present invention shown in Table 2, among the 432 proteins, are already increased prior to the onset of AD, and can function as protein markers for predicting the risk of developing atopic dermatitis.

[0070] Confirmation of the accuracy of discriminating AD risk Using the expression level in sebum samples from 1 month old, the accuracy of discriminating between AD-free children (n = 26) and children diagnosed with AD at 2 months old (AD-onset children, n = 30) was evaluated using an ROC curve. Figure 2 shows the ROC curves created for each of the protein markers for predicting the risk of AD onset of the present invention shown in Table 2. Table 3 shows the sensitivity, specificity, and accuracy when the expression level in the AUC and Youden index of the ROC curve is used as the cutoff value (reference value). These results show 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 taken at one month of age can be used as indicators to accurately distinguish between infants who will not develop AD and those who will develop AD one month later.

[0071]

[0072] Extraction of markers for predicting risk of allergen sensitization From the 432 marker proteins found above that can distinguish between healthy infants and infants with AD, markers capable of predicting the risk of allergen sensitization according to the present invention were extracted by the following analysis. Sebum samples taken at 1 month of age were analyzed to determine whether it is possible to early detect infants who do not develop allergen-specific IgE antibodies at 6 months of age (sensitization-negative infants) and infants who do develop allergen-specific IgE antibodies (sensitization-positive infants). Of the subjects, 45 infants (sensitization-negative infants, n = 45) showed allergen-specific IgE antibody titers of less than 0.35 UA / mL (negative according to the specific IgE criteria) for all allergens (Dermatophagoides farinae, cow's milk, egg white, ovomucoid, peanut, soy, and wheat) in blood tests taken at 6 months of age. On the other hand, 48 children showed levels of 0.35 UA / mL or higher against at least one allergen, of which 8 children had levels of 0.35 UA / mL to 0.69 UA / mL, which would result in a false positive for Class 1, 20 children had levels of 0.70 UA / mL to 3.49 UA / mL, which would result in a positive for Class 2, and 20 children had levels of 3.50 UA / mL or higher, which would result in a positive for Class 3 or higher. All of the children who showed a positive result for Class 3 or higher also showed a positive result for egg white-specific IgE. The expression levels of the above 18 proteins were compared between two groups: sensitized-negative children (n=45) and sensitized-positive children (children who showed a positive result for Class 2 or higher, n=40). The results showed that the protein levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, CFB, CFH, EPX, CLC, RNASE3, RNASE2, and PRG2 in sebum samples from sensitized children at one month of age were significantly higher than those in sensitized children (Mann-Whitney U test, all P<0.05). C7 also tended to be significantly higher (P<0.1). Furthermore, the quantitative values ​​(Log2(Abundance+1)) in sebum samples from sensitized children at one month of age from sensitized children (n=45) and sensitized children (n=20) who showed a positive result of Class 3 or higher are plotted in Figure 3.The protein levels of FGG, FGA, FGB, FN1, IGHG1, IGHG2, IGHG3, IGHG4, C3, C4B, CFB, CFH, EPX, CLC, RNASE2, and PRG2 in sebum samples from 1-month-old children were all significantly higher in sensitized-positive children (children who showed a positive result of Class 3 or higher) than in sensitized-negative children (Mann-Whitney U test, P<0.05), and C7 and RNASE3 tended to be significantly higher (P<0.1). From the above results, it was shown that the expression levels of the specific marker proteins according to the present invention shown in Table 2 are already increased at a time point prior to the establishment of allergen sensitization, and can function as protein markers for predicting the risk of allergen sensitization.

[0073] Confirmation of Accuracy of Discrimination of Allergen Sensitization Risk Using expression levels in sebum samples from 1-month-olds, the accuracy of discriminating between infants in whom allergen-specific IgE antibodies are not induced 5 months after collection (sensitization-negative infants, n = 45) and infants in whom allergen-specific IgE antibodies are induced 5 months after collection (sensitization-positive infants with a positive result of Class 3 or higher, n = 20) was evaluated using an ROC curve. Figure 4 shows ROC curves created for each of the protein markers for predicting allergen sensitization risk of the present invention shown in Table 2. Table 4 shows the sensitivity, specificity, and accuracy when the AUC of the ROC curve and the expression level in the Youden index are used as the cutoff value (reference value). 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 taken at one month of age can be used as indicators to accurately distinguish between infants who will subsequently (five months later) test negative for allergen sensitization and those who will test positive.

[0074]

[0075] Furthermore, there were nine children who were sensitized (children who showed a positive result of Class 2 or higher against two or more allergens) in whom two or more allergen-specific IgE antibodies were induced. Figure 5 shows the ROC curve for discriminating between two groups of sensitized-negative children (n = 45) and children who were sensitized to multiple allergens (n ​​= 9). Table 5 shows the AUC of the ROC curve, and the sensitivity, specificity, and accuracy when the expression level in the Youden index was used as the cutoff value (reference value). These results show 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 taken at one month of age can be used as indicators to discriminate with even greater accuracy between infants who subsequently (five months later) test negative for allergen sensitization and those who test positive for sensitization to multiple allergens.

[0076]

[0077] Comparative Example 1 Plots of the quantitative protein amounts (Log2(Abundance+1)) of sebum PPIA (Peptidyl-prolyl cis-trans isomerase A) of children who were not diagnosed with AD at 1 month, 2 months, or 6 months of age (AD-free children, n = 26) and children who were not diagnosed with AD at 1 month of age but were diagnosed with AD at 2 months of age (AD-developed children, n = 30) are shown in Figure 6, along with an ROC curve. PPIA is included in the 432 marker proteins that can distinguish between healthy children and AD patients, as described above, and Patent Document 3 has shown that there are differences in expression between healthy children and AD children. However, the results of this study indicate that it cannot be used as a marker for predicting the risk of developing AD.

[0078] Comparative Example 2 Figure 7 shows plots of the quantitative values ​​(Log2(Abundance+1)) of sebum PFN1 (Profilin-1) protein amount for children in whom allergen-specific IgE antibodies were not induced (sensitization-negative children, n=45) and children in whom allergen-specific IgE antibodies were induced (sensitization-positive children with a positive result of Class 3 or higher, n=20), as well as the ROC curve. 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 also shown that there are differences in expression between healthy children and children with AD. However, the results of this study showed that it cannot be used as a marker for predicting the risk of allergen sensitization.

Claims

1. A method for preparing a protein marker for predicting the risk of developing 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 an infant subject.

2. A method for predicting the risk of developing atopic dermatitis in an infant, 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 infant subject.

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 to predict the risk of developing atopic dermatitis in an infant.

4. The method according to any one of claims 1 to 3, wherein the sample is lipids on the surface of the skin.

5. The method according to any one of claims 1 to 4, wherein the infant is an infant under 6 months of age.

6. A method for preparing a protein marker for predicting the risk of allergen sensitization, 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 an infant subject.

7. A method for predicting the risk of allergen sensitization in an infant, 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 taken from the skin of the infant subject.

8. The method of claim 7, 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 to predict the risk of allergen sensitization in an infant.

9. The method according to any one of claims 6 to 8, wherein the sample is lipids on the surface of the skin.

10. The method according to any one of claims 6 to 9, wherein the infant is an infant under 6 months of age.

11. The method according to any one of claims 6 to 10, wherein the allergen is a food or a mite.

12. A test kit for predicting the risk of developing atopic dermatitis in infants, used in the method of any one of claims 2 to 5, 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.

13. A protein marker for predicting the risk of developing atopic dermatitis in infants and young children, 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.

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 marker for predicting the risk of developing atopic dermatitis in infants and young children.

15. A test kit for predicting the risk of allergen sensitization in infants, used in the method according to any one of claims 7 to 11, 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.

16. A protein marker for predicting the risk of allergen sensitization in infants and young children, 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.

17. 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 marker for predicting the risk of allergen sensitization in infants and young children.

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