Sleep state marker and method for detecting sleep state using the same

Abstract

To detect a sleep state by using a biomarker.SOLUTION: The present invention discloses a sleep state marker for detecting a sleep state, and a method for detecting a sleep state by using the sleep state marker.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a sleep state marker and a method for detecting a sleep state using the same. [Background technology]

[0002] In recent years, many people have problems with sleep. Insomnia leads to a state in which the quality of sleep is impaired, with problems falling asleep, shallow sleep, waking up early, and a feeling of not having slept enough. Poor sleep quality not only leads to serious illnesses such as myocardial infarction and cerebral infarction, but can also reduce work efficiency due to daytime sleepiness and potentially lead to serious accidents. Improving sleep quality is an important health issue.

[0003] Patent Document 1 discloses transthyretin as a marker for detecting sleep disorders. Patent Document 2 discloses that the expression of the clock gene hPer2 is related to the sleep / wake rhythm. However, an objective or quantitative evaluation method for sleep disorders or insomnia using biomarkers has not yet been established.

[0004] Technologies have been developed to investigate the current and future physiological state of the human body by analyzing nucleic acids such as DNA and RNA in biological samples. Nucleic acids derived from living organisms can be extracted from tissues such as blood, body fluids, secretions, etc. Patent Document 3 describes the isolation of nucleic acids such as RNA derived from skin cells of a subject from surface skin lipids (SSL) and the use of the nucleic acids as samples for biological analysis. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2007-075071 A [Patent Document 2] JP 2005-080603 A [Patent Document 3] International Publication No. 2018 / 008319 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention relates to a novel sleep state marker and a method for detecting a sleep state using the same. [Means for solving the problem]

[0007] In one aspect, the present invention provides a sleep state marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: ATG7, DNAJB6, TBC1D17, ZNF410, TRAPPC5, PSMD3, ABI1, and MARCH6, and proteins encoded by the genes. In another aspect, the present invention provides a method for detecting a sleep state of a subject, comprising measuring an expression level of the sleep state marker in a biological sample collected from the subject. In another aspect, the present invention provides a method for evaluating a sleep-regulating agent, comprising measuring the expression level of the sleep state marker in a biological sample collected from a subject to which a test substance has been administered. In another aspect, the present invention provides a sleep state detection kit or a sleep-regulating agent evaluation kit for use in the sleep state detection method. Effect of the Invention

[0008] The present invention provides a novel sleep state marker. The sleep state marker of the present invention enables detection of a sleep state. In addition, since the sleep state marker of the present invention can be collected non-invasively from the skin surface lipids (SSL), it is possible to reduce the labor of collection and the burden on the subject caused by collection. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] All patents, non-patent publications, and other publications cited herein are hereby incorporated by reference in their entirety.

[0010] The names of the genes disclosed herein are based on the official symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]).

[0011] As used herein, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, also known as sebum. Generally, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer covering the skin surface.

[0012] In this specification, unless otherwise specified, "skin" is a general term for an area including tissues such as the epidermis, dermis, hair follicles, sweat glands, sebaceous glands and other glands on the body surface.

[0013] In this specification, the term "sleep state" refers to the quality of sleep, that is, the degree of satisfaction with sleep regardless of the duration of sleep. Preferably, the term "sleep state" in this specification is reflected in the score of the OSA sleep questionnaire (Yamamoto Yukari et al.: Brain and Mental Medicine 10:401-409, 1999). The OSA sleep questionnaire evaluates the quality of sleep based on an individual's subjective symptoms, that is, the general lifestyle, the physical and mental state before going to bed, and the subjective quality of sleep upon waking. More specifically, the OSA sleep questionnaire comprehensively evaluates the quality of sleep based on five evaluation scales, the first to fifth factors. The first factor is an evaluation of sleepiness upon waking, the second factor is an evaluation of sleep onset and sleep maintenance, the third factor is an evaluation of dreaminess, the fourth factor is an evaluation of fatigue recovery, and the fifth factor is an evaluation of sleep duration. Therefore, the term "sleep state" in this specification is reflected in the score of any one of the five factors evaluated in the OSA sleep questionnaire, namely, sleepiness upon waking, sleep onset and sleep maintenance, dreaminess, fatigue recovery, and sleep time, or the average score of the first to fifth factors. When these scores are high, the sleep state is good (i.e., high quality of sleep), while when the scores are low (for example, when the average score of the first to fifth factors is less than 15), the sleep state is poor (i.e., low quality of sleep). In this specification, low quality of sleep is defined as "insomnia". Meanwhile, in this specification, a "healthy" sleep state refers to a state that does not correspond to insomnia (for example, when the average score of the first to fifth factors is 15 or more), and a "good" sleep state refers to a "healthy" state in which the average score of the first to fifth factors is 20 or more. In this specification, "improvement of sleep" refers to an improvement in the sleep state (sleep quality).

[0014] In this specification, "detection" of a sleep state can be expressed in other terms such as examination, measurement, judgment, or evaluation assistance. The terms "detection," "examination," "measurement," "judgment," or "evaluation" of a sleep state in this specification do not include a diagnosis of a disease such as a sleep disorder by a doctor.

[0015] (1. Sleep state markers) Conventionally, sleep status has been evaluated mainly by scoring based on interviews or questionnaires (e.g., the aforementioned OSA sleep questionnaire). Such evaluation methods can be less objective and quantitative, and make it difficult to compare evaluations between studies.

[0016] The present inventors have found that the sleep state of a subject is reflected in the expression of a specific gene. As shown in the Examples below, genes with different expression levels were found between the good sleep group and the insomnia group determined based on the OSA sleep questionnaire. Nucleic acids encoding genes that show different expression levels between the two groups, and proteins encoded by these genes, can be used as sleep state markers. For example, the sleep state can be detected using the expression levels of the nucleic acids encoding these genes or the proteins encoded by these genes as indicators.

[0017] Therefore, in one aspect, the present invention provides a sleep state marker for detecting a sleep state. The sleep state marker provided by the present invention (hereinafter also referred to as the marker of the present invention) makes it possible to detect the sleep state of an individual subject from which the marker is derived.

[0018] The marker of the present invention may comprise at least one selected from the group consisting of nucleic acids encoding the 78 genes shown in Table 1 below and proteins encoded by the genes. The marker of the present invention may be a nucleic acid encoding a gene shown in Table 1 below (nucleic acid marker), a protein encoded by the gene (protein marker), or a combination thereof, but is preferably a nucleic acid marker. Preferably, the sleep state marker of the present invention is a marker for detecting insomnia.

[0019] [Table 1]

[0020] In one embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding genes shown in Tables 2 and 3 (hereinafter also referred to as nucleic acid markers in Tables 2 and 3, respectively) and proteins encoded by the genes (hereinafter also referred to as protein markers in Tables 2 and 3, respectively). The nucleic acid markers and protein markers in Table 2 may be collectively referred to as markers in Table 2, and the nucleic acid markers and protein markers in Table 3 may be collectively referred to as markers in Table 3. Preferably, the markers in Tables 2 and 3 are nucleic acid markers. As shown in the Examples below, the expression levels of the 18 genes shown in Table 2 increase in response to the deterioration of the sleep state of the subject (deterioration in sleep quality), and the expression levels of the 17 genes shown in Table 3 decrease in response to the deterioration of the sleep state of the subject (deterioration in sleep quality). Therefore, the markers in Table 2 are positive markers whose expression levels increase in response to the deterioration of the sleep quality of the subject. On the other hand, the markers in Table 3 are negative markers whose expression levels decrease in response to the deterioration of the sleep quality of the subject. In the present invention, either the former positive marker or the latter negative marker may be used, or both may be used in combination.

[0021] In another embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding the 49 genes shown in Table 4 (hereinafter also referred to as nucleic acid markers in Table 4) and proteins encoded by the genes (hereinafter also referred to as protein markers in Table 4). The nucleic acid markers and protein markers in Table 4 may be collectively referred to as the markers in Table 4. Preferably, the markers in Table 4 are nucleic acid markers. As shown in the examples described later, the genes shown in Table 4 are genes corresponding to the top 49 variable importance based on the Gini coefficient, extracted when extracting feature genes and constructing a prediction model using data on their expression levels in subjects as explanatory variables, sleep state as a response variable, and random forest as a machine learning algorithm.

[0022] In another embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding two genes shown in Table 5 (hereinafter also referred to as nucleic acid markers of Table 5) and proteins encoded by the genes (hereinafter also referred to as protein markers of Table 5). The nucleic acid markers and protein markers of Table 5 may be collectively referred to as markers of Table 5. Preferably, the markers of Table 5 are nucleic acid markers. As shown in the examples described later, the genes shown in Table 5 are genes extracted as feature genes when the Boruta method is used as the machine learning algorithm.

[0023] In a preferred embodiment, the marker of the present invention comprises at least one selected from the group consisting of nucleic acids encoding the eight genes shown in Table 6 (hereinafter also referred to as nucleic acid markers of Table 6) and proteins encoded by the genes (hereinafter also referred to as protein markers of Table 6). The nucleic acid markers and protein markers of Table 6 may be collectively referred to as markers of Table 6. Preferably, the markers of Table 6 are nucleic acid markers. The genes shown in Table 6 consist of six genes commonly included in Table 2 or Table 3 and Table 4, and two genes commonly included in Table 4 and Table 5.

[0024] In a preferred embodiment, the markers of the present invention may further include at least one selected from the group consisting of markers in Table 1 (but other than the markers in Table 6) in addition to at least one selected from the group consisting of markers in Table 6. For example, the markers of the present invention may further include at least one selected from the group consisting of markers in Table 2, markers in Table 3, or markers in Table 4 (but other than the markers in Table 6) in addition to at least one selected from the group consisting of markers in Table 6. For example, the markers of the present invention may further include at least one, preferably all, selected from the group consisting of nucleic acid markers in Table 6 and at least one selected from the group consisting of nucleic acid markers in Table 2, Table 3, or Table 4 (but other than the nucleic acid markers in Table 6). For example, the markers of the present invention may further include at least one, preferably all, selected from the group consisting of protein markers in Table 6 and at least one selected from the group consisting of protein markers in Table 2, Table 3, or Table 4 (but other than the protein markers in Table 6). In the above case, it is preferable that the at least one marker selected from the group consisting of markers in Table 6 includes all six markers, ATG7, DNAJB6, TBC1D17, ZNF410, TRAPPC5 and PSMD3, which are commonly included in Tables 2 or 3 and 4, all two markers, ABI1 and MARCH6, which are commonly included in Tables 4 and 5, or all eight markers in Table 6.

[0025] In another preferred embodiment, all of the nucleic acid markers of Table 2 or Table 3, or all of the protein markers of Table 2 or Table 3, in combination, are used as markers of the invention. In another preferred embodiment, all of the nucleic acid markers of Tables 2 and 3, or all of the protein markers of Tables 2 and 3, in combination, are used as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 4 are used in combination as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 5 are used in combination as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 6 are used in combination as markers of the present invention. In another preferred embodiment, all of the nuclear acid or protein markers in Table 1 are used in combination as markers of the present invention.

[0026] [Table 2]

[0027] [Table 3]

[0028] [Table 4]

[0029] [Table 5]

[0030] [Table 6]

[0031] The marker of the present invention can be prepared from a biological sample collected from a subject, such as a cell, a stratum corneum, a tissue (biopsy, etc.), a body fluid (blood, etc.), urine, a secretion (saliva, SSL, etc.), etc., according to a conventional method. For example, a commercially available kit can be used to prepare a nucleic acid or a protein from a biological sample. Preferably, the marker of the present invention is a nucleic acid marker, and preferred examples of the nucleic acid prepared from a biological sample include genomic DNA and RNA such as mRNA.

[0032] Examples of subjects from which biological samples containing the markers of the present invention are collected include mammals, including humans and non-human mammals. Preferably, the subject is a human or non-human mammal in need of detecting a sleep state, or a human desiring to detect a sleep state, more preferably a human or non-human mammal in need of detecting insomnia, or a human desiring to detect insomnia. More preferably, the subject is a human.

[0033] More preferably, the marker of the present invention is a nucleic acid or protein, even more preferably an mRNA, prepared from the SSL of a subject. The site of the skin from which the SSL is collected may be any site of the body, such as the head, face, neck, trunk, limbs, etc., and is preferably a site where sebum is secreted in large amounts, such as the skin of the face.

[0034] Any means used for recovering or removing SSL from the skin can be used to collect SSL from the skin of a subject. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument for scraping SSL from the skin, as described below, can be used. The SSL absorbent material or SSL adhesive material is not particularly limited as long as it has an affinity for SSL, and examples thereof include polypropylene, pulp, and the like. More detailed examples of procedures for collecting SSL from the skin include a method of absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, a method of adhering SSL to a glass plate, tape, or the like, and a method of scraping SSL off and collecting it with a spatula, scraper, or the like. In order to improve the adsorption of SSL, an SSL absorbent material that has been previously impregnated with a highly lipid-soluble solvent may be used. On the other hand, if the SSL absorbent material contains a highly water-soluble solvent or moisture, the adsorption of SSL is inhibited, so it is preferable that the content of highly water-soluble solvent or moisture is low. It is preferable to use the SSL absorbent material in a dry state.

[0035] The collected SSL may be used immediately in the nucleic acid or protein extraction step described below, or may be stored until it is used in the nucleic acid or protein extraction step. When stored, the SSL is preferably stored under low temperature conditions. The temperature conditions for storing the SSL are preferably 10° C. or lower, more preferably 5° C. or lower, even more preferably 0° C. or lower, even more preferably −20±20° C. to −80±20° C., even more preferably −20±10° C. to −80±10° C., and even more preferably −20±5° C. to −80±5° C. The period for storing the SSL is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, and even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

[0036] For the extraction of nucleic acids or proteins from the collected SSL, a method commonly used for the extraction or purification of nucleic acids or proteins from biological samples can be used. Examples of nucleic acid extraction or purification methods include the phenol / chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or a method using columns such as TRIzol (registered trademark), RNeasy (registered trademark), or QIAzol (registered trademark), a method using special magnetic particles coated with silica, a method using Solid Phase Reversible Immobilization magnetic particles, and extraction using a commercially available RNA extraction reagent such as ISOGEN. For the extraction or purification of proteins, a commercially available protein extraction reagent such as QIAzol Lysis Reagent (Qiagen) can be used.

[0037] (2. Sleep state detection method) (2.1. Detection of Sleep State Based on Marker Expression Levels) In another aspect, the present invention provides a method for detecting a sleep state of a subject using the sleep state marker of the present invention. In the method, the sleep state of the subject is detected based on the expression level of the marker of the present invention in the subject. Preferably, the method is a method for detecting insomnia. The subject to which the sleep state detection method according to the present invention is applied is the same as the subject from which the above-mentioned biological sample containing the sleep state marker of the present invention is collected.

[0038] In a preferred embodiment, the method for detecting a sleep state according to the present invention comprises measuring the expression level of the marker of the present invention in a biological sample collected from a subject. The type of the biological sample is as described above, preferably SSL. In one embodiment, the method of the present invention may further comprise collecting the SSL of the subject. The procedure for collecting the SSL and the procedure for extracting the marker from the SSL are as described above.

[0039] The expression level of the marker of the present invention can be measured according to a method for quantifying nucleic acids or proteins commonly used in the field. For example, the expression level of a nucleic acid marker may be measured according to a procedure for gene expression analysis commonly used in the field. Examples of gene expression analysis techniques include methods for quantifying DNA or its amplification products by real-time PCR, multiplex PCR, microarray, sequencing, chromatography, etc. When the nucleic acid is RNA, the RNA can be converted into cDNA by reverse transcription and then quantified by the above method. The expression level of a protein marker can be measured using a protein quantification method commonly used in the field, such as ELISA, immunostaining, fluorescence, electrophoresis, chromatography, mass spectrometry, etc. The calculated expression level of a marker may be an expression level based on the absolute amount of the target marker in a biological sample, or a relative expression level to the expression level of other standard substances or the total nucleic acid or protein.

[0040] Preferably, the marker of the present invention used in the method for detecting a sleep state according to the present invention is an SSL-derived mRNA. Preferably, the RNA extracted from the SSL is converted to cDNA by reverse transcription, and then the expression level of the SSL-derived RNA is measured by quantifying the cDNA or its amplification product by the above-mentioned method. For the reverse transcription, a primer targeting a specific RNA to be analyzed may be used, but for more comprehensive storage and analysis of nucleic acids, it is preferable to use a random primer. For the reverse transcription, a general reverse transcriptase or reverse transcription reagent kit can be used. Preferably, a reverse transcriptase or reverse transcription reagent kit with high accuracy and efficiency is used, and examples thereof include M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcription reagent kits, such as PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.), SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific Co., Ltd.), etc. are preferably used. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both Thermo Scientific Co., Ltd.), etc. In PCR of the obtained cDNA, a primer pair targeting a specific DNA to be analyzed may be used to amplify only the specific DNA, or multiple primer pairs may be used to amplify multiple DNAs. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method for simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (e.g., Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan, Inc., etc.).

[0041] By adjusting the reaction conditions of the reverse transcription and PCR, the yield of the PCR reaction product is further improved, and the accuracy of the analysis using the same is further improved. Preferably, in the extension reaction in the reverse transcription, the temperature is preferably adjusted to 42°C ± 1°C, more preferably 42°C ± 0.5°C, and even more preferably 42°C ± 0.25°C, while the reaction time is preferably adjusted to 60 minutes or more, more preferably 80 to 120 minutes. Also preferably, the temperature of the annealing and extension reaction in the PCR is preferably 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even more preferably 62°C ± 0.25°C. Therefore, in the PCR, the annealing and extension reactions are preferably performed in one step. The time of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, etc., but is preferably 14 to 18 minutes. The conditions of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but is preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the above-mentioned temperatures and times can be carried out using a thermal cycler generally used for PCR.

[0042] The purification of the reaction product obtained by the PCR is preferably carried out by size separation of the reaction product. By size separation, the target PCR reaction product can be separated from primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be carried out, for example, by a size separation column, a size separation chip, magnetic beads usable for size separation, etc. A preferred example of magnetic beads usable for size separation includes Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

[0043] The purified PCR reaction product may be subjected to further treatment required for subsequent quantitative analysis. For example, for DNA sequencing, the purified PCR reaction product may be prepared in an appropriate buffer solution, the PCR primer region contained in the PCR-amplified DNA may be cleaved, or an adapter sequence may be further added to the amplified DNA. For example, the purified PCR reaction product may be prepared in a buffer solution, the PCR primer sequence may be removed from the amplified DNA, and adapter ligation may be performed, and the resulting reaction product may be amplified as necessary to prepare a library for quantitative analysis. These operations may be performed, for example, using the 5×VILO RT Reaction Mix included in the SuperScript® VILO cDNA Synthesis kit (Life Technologies Japan, Inc.), and the 5×Ion AmpliSeq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.), according to the protocol included with each kit. For sequencing, a next-generation sequencer (e.g., Ion S5 / XL system, Life Technologies Japan, Inc.) is preferably used. Based on the number of reads generated by sequencing (read count), RNA expression levels can be quantified.

[0044] In one embodiment of the method for detecting a sleep state according to the present invention, the sleep state of a subject is detected using as an indicator the expression level of a marker of the present invention in the subject (preferably, the expression level of a marker of the present invention contained in a biological sample collected from the subject).

[0045] As described above, the nucleic acid markers and protein markers in Table 2 are positive markers whose expression levels increase in response to a decrease in the quality of sleep of a subject, while the nucleic acid markers and protein markers in Table 3 are negative markers whose expression levels decrease in response to a decrease in the quality of sleep of a subject. Thus, if the expression level of the positive marker increases, the sleep state of the subject is detected as bad or insomnia, otherwise, the sleep state of the subject is detected as not bad or insomnia (i.e., the sleep state of the subject is detected as good or healthy). Alternatively, if the expression level of the negative marker decreases, the sleep state of the subject is detected as bad or insomnia, otherwise, the sleep state of the subject is detected as not bad or insomnia (i.e., the sleep state of the subject is detected as good or healthy). Alternatively, the positive marker and the negative marker may be used in combination in the method for detecting a sleep state according to the present invention.

[0046] In one embodiment, the expression level of the marker of the present invention measured from a subject can be compared with a reference value to detect the sleep state of the subject. Alternatively, the expression level of the marker of the present invention can be measured from a subject periodically (e.g., monthly) and the fluctuation of the expression level can be tracked to detect the sleep state of the subject. The reference value can be a predetermined value, for example, a statistical value (e.g., average value) of the expression level of the marker of the present invention. The reference value may be set for each subject. For example, the reference value may be calculated from the statistical value of the expression level of the marker measured multiple times from the same subject. Alternatively, the standard value can be determined based on a statistical value (e.g., average value, etc.) of the expression level of the marker of the present invention measured from a reference population (a group of individuals with any sleep state), a healthy group (a group of individuals with a sleep state in the normal range [e.g., an average value of 15 or more for the scores of factors 1 to 5 of the OSA sleep questionnaire described above]), or a good group (a group of individuals with a sleep state in the good range [e.g., an average value of 20 or more for the scores of factors 1 to 5 of the OSA sleep questionnaire described above]). When multiple types of nucleic acids or proteins are used as the marker of the present invention, it is preferable to determine a standard value for each of the nucleic acids or proteins.

[0047] For example, when the marker of the present invention is a positive marker, if the expression level of the marker is higher than the reference value, the subject's sleep state can be detected as bad or insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state). For example, if the expression level of the positive marker in the subject is statistically significantly higher than the reference value, the subject's sleep state can be detected as bad or insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state). Also, for example, if the expression level of the positive marker in the subject is preferably 110% or more, more preferably 120% or more, and even more preferably 150% or more relative to the reference value, the subject's sleep state can be detected as bad or insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state).

[0048] On the other hand, when the marker of the present invention is a negative marker, if the expression level of the marker is lower than the reference value, the subject's sleep state can be detected as bad or insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state). For example, if the expression level of the negative marker in the subject is statistically significantly lower than the reference value, the subject's sleep state can be detected as bad or insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state). For example, if the expression level of the negative marker in the subject is preferably 90% or less, more preferably 80% or less, and even more preferably 75% or less of the reference value, the subject's sleep state can be detected as not bad or not insomnia, otherwise, the subject's sleep state can be detected as not bad or not insomnia (good or healthy sleep state).

[0049] Alternatively, when multiple nucleic acids or proteins are used in combination as markers, the sleep state of a subject can be detected based on whether a certain percentage of those nucleic acids or 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.

[0050] (2.2. Sleep state detection based on predictive model) In another embodiment of the method for detecting a sleep state according to the present invention, the sleep state of a subject is detected based on a prediction model constructed using data on the expression level of the marker of the present invention (the marker of the present invention contained in a biological sample collected from the subject) (hereinafter referred to as an expression profile). Examples of the expression profile include data on the expression level such as the number of sequencing reads.

[0051] For example, a predictive model (e.g., a discriminant) for discriminating the sleep state of any subject can be constructed by machine learning using the expression profiles of one or more markers (nucleic acids or proteins) obtained from each individual in a teacher sample population (e.g., a population including a healthy or good sleep group, and an insomnia group) as explanatory variables, and data on the sleep state (e.g., healthy, good, or insomnia) of each individual in the population as the objective variable.

[0052] In this specification, the term "feature" is synonymous with "explanatory variable" in machine learning. In this specification, a marker whose expression profile is used as an explanatory variable (feature) in machine learning may be referred to as a "feature marker(s)." In addition, when a feature marker is a nucleic acid marker, it may be referred to as a feature gene.

[0053] The feature marker(s) used in this embodiment may be at least one selected from the group consisting of nucleic acid markers and protein markers in Table 1. The expression profile of the feature marker may be an absolute value or a relative value, or may be normalized. When a plurality of markers are used as the feature marker group, for example, a plurality of markers having a high correlation with the sleep state may be selected from the markers of the present invention, and each of the expression profiles thereof may be used as an explanatory variable.

[0054] In a preferred embodiment, all of the nucleic acid markers in Tables 2 and 3, or all of the protein markers in Tables 2 and 3 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 4 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 5 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 6 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 6 are combined with one or more, preferably all of the nucleic acid markers or protein markers in any of Tables 2, 3, or 4 (except for the markers in Table 6) and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 1 are combined and used as a feature marker group.

[0055] In a preferred embodiment, expression profiles of feature marker(s) in individuals with good sleep (good group) and individuals with bad sleep (insomnia group) are used as teacher samples for machine learning. A discriminant (prediction model) for dividing individuals into a good group and an insomnia group is constructed using the teacher samples. The expression profiles of feature marker(s) can be used as explanatory variables used for constructing the discriminant. For example, a variable indicating whether the individual from which the feature marker(s) originates belongs to the good group or the insomnia group can be used as the objective variable. A cutoff value for discriminating between the good group and the insomnia group can be determined based on the constructed discriminant. Next, the expression profile of the feature marker(s) derived from the subject whose sleep state is to be examined is measured, the measured value obtained is substituted into the discriminant, and the result obtained from the discriminant is compared with the cutoff value to discriminate whether the subject belongs to the insomnia group or not.

[0056] For example, a statistically significant difference between two groups (a good group and an insomnia group) to be discriminated, for example, a marker whose expression profile varies significantly between the two groups, can be used as a feature marker(s), and the expression profile can be used as an explanatory variable. Alternatively, the feature marker(s) can be extracted using a known algorithm such as a machine learning algorithm. For example, as shown in the following examples, the feature marker(s) can be extracted using the variable importance in random forest or the "Boruta" package of the R language.

[0057] Alternatively, when using expression profiles of multiple markers to construct a predictive model, data may be compressed by dimensionality reduction before constructing the predictive model, if necessary. For example, multiple markers are extracted from the nucleic acid markers encoding the gene group shown in Table 1. Then, principal component analysis is performed on the expression profiles of the extracted markers. A predictive model for determining whether a subject is in the insomnia group or not can be constructed by machine learning using one or more principal components calculated by the principal component analysis as explanatory variables and a variable indicating whether the individual from which the explanatory variables are derived is in the good group or insomnia group as a target variable.

[0058] The algorithm for constructing the prediction model can be a known algorithm such as an algorithm used in machine learning. Examples of machine learning algorithms include, but are not limited to, a linear regression model, a lasso regression, a random forest, a neural network, a linear kernel support vector machine (SVM (linear)), and an rbf kernel support vector machine (SVM (rbf)). The algorithm used in the present invention is preferably a random forest.

[0059] The constructed prediction model is input with data for verification to calculate a predicted value, and the model in which the predicted value matches the actual measured value best, for example, the model with the highest accuracy of the predicted value relative to the actual measured value, or the model with the smallest root mean square error (RMSE) of the difference between the predicted value and the actual measured value, can be selected as the optimal model. The expression profile of the feature marker(s) actually measured from the subject can be input to the constructed prediction model to detect the sleep state of the subject.

[0060] (3. Evaluation method of sleep regulators) In a further aspect, the present invention provides a method for evaluating a sleep regulator using the sleep state marker of the present invention. The method evaluates a sleep regulator based on the expression level of the marker of the present invention. Preferably, the method comprises measuring the expression level of the marker of the present invention in a biological sample collected from a subject to which a test substance is applied. In the method, the type of biological sample used is as described above. Preferably, the biological sample is SSL, and the marker of the present invention is SSL-derived mRNA. In one embodiment, the method may further comprise collecting SSL from the subject. The sleep regulator evaluated in the present invention includes an insomnia improving agent or an insomnia inducing agent, and preferably an insomnia improving agent.

[0061] The subject from which the biological sample is collected is a human or a non-human mammal. An appropriate subject can be selected depending on the sleep-regulating agent to be evaluated. When evaluating an insomnia-improving agent, the subject is preferably a human or a non-human mammal with insomnia, and when evaluating an insomnia-inducing agent, the subject is preferably a human or a non-human mammal with good or healthy sleep. Preferably, the subject is a human.

[0062] The test substance used in the evaluation method of the present invention is not particularly limited as long as it is a substance that is desired to be used as a sleep regulator. The test substance may be a naturally occurring substance, or may be a substance artificially synthesized by chemical or biological methods, or may be a compound, composition, or mixture. The form of the test substance is not particularly limited, and may be any form such as solid, semi-solid, gel, liquid, gas, etc., but gel or liquid is preferable. The test substance may also be a physical stimulus such as light irradiation, heat, or massage. The method of applying the test substance to the subject includes oral administration and parenteral administration such as injection, coating, spraying, scattering, dripping, pasting, and irradiation, and is not particularly limited. The dose and application period of the test substance applied to the subject can be appropriately set according to the type and administration form of the test substance.

[0063] The procedures for collecting a biological sample from a subject, extracting a marker from the collected biological sample, and measuring the expression level of the marker are as described above.

[0064] The expression level of the marker of the present invention extracted from the subject increases if it is a positive marker, and decreases if it is a negative marker according to the deterioration of the sleep state of the subject. Based on the change in the expression level of the marker caused by the test substance, it can be evaluated whether the test substance is a sleep regulator. More specifically, a test substance that reduces the expression level of a positive marker or increases the expression level of a negative marker is judged to be a sleep improver or an insomnia improver. Conversely, a test substance that increases the expression level of a positive marker or reduces the expression level of a negative marker is judged to be an insomnia inducer.

[0065] In one embodiment, the expression level of the marker of the present invention derived from the subject is compared with a control to evaluate the effect of the test substance on sleep.More specifically, it is examined whether the expression level of the marker of the present invention under application of the test substance changes compared to the control.The control can include the expression level of the marker of the present invention under non-application of the test substance, the standard value for evaluating the above-mentioned sleep state, etc.

[0066] In one example, when the expression level of the positive marker under application of the test substance is reduced compared to the control, the test substance can be judged as a sleep improver or insomnia improver. More specifically, when the expression level of the positive marker in the subject or subject group to which the test substance is applied is statistically significantly reduced compared to the control, the test substance can be judged as a sleep improver or insomnia improver. Alternatively, when the expression level of the positive marker in the subject or subject group to which the test substance is applied is preferably reduced to 90% or less, more preferably 80% or less, and even more preferably 75% or less compared to the control, the test substance can be judged as a sleep improver or insomnia improver.

[0067] In another example, when the expression level of the positive marker under application of the test substance increases compared to the control, the test substance can be judged as an insomnia inducer. More specifically, when the expression level of the positive marker in the subject or subject group to which the test substance is applied increases statistically significantly compared to the control, the test substance can be judged as an insomnia inducer. Alternatively, when the expression level of the negative marker in the subject or subject group to which the test substance is applied increases preferably by 110% or more, more preferably by 120% or more, and even more preferably by 150% or more compared to the control, the test substance can be judged as an insomnia inducer.

[0068] In yet another example, when the expression level of the negative marker under application of the test substance increases compared to the control, the test substance can be judged as a sleep improver or insomnia improver.More specifically, when the expression level of the negative marker in the subject or subject group to which the test substance is applied increases statistically significantly compared to the control, the test substance can be judged as a sleep improver or insomnia improver.Alternatively, when the expression level of the negative marker in the subject or subject group to which the test substance is applied increases preferably by 110% or more, more preferably by 120% or more, and even more preferably by 150% or more compared to the control, the test substance can be judged as a sleep improver or insomnia improver.

[0069] In yet another example, when the expression level of the negative marker under application of the test substance is reduced compared to the control, the test substance can be judged as an insomnia inducer. More specifically, when the expression level of the negative marker in the subject or subject group to which the test substance is applied is statistically significantly reduced compared to the control, the test substance can be judged as an insomnia inducer. Alternatively, when the expression level of the positive marker in the subject or subject group to which the test substance is applied is preferably reduced to 90% or less, more preferably 80% or less, and even more preferably 75% or less compared to the control, the test substance can be judged as an insomnia inducer.

[0070] (4. Sleep state detection kit and sleep regulator evaluation kit) In a further aspect, a kit for detecting a sleep state for detecting a sleep state of a subject according to the method for detecting a sleep state according to the present invention, and a kit for evaluating a sleep regulator for evaluating a sleep regulator according to the method for evaluating a sleep regulator are provided. In one embodiment, the kit of the present invention comprises a reagent or instrument for measuring the expression level of the sleep state marker of the present invention described above. For example, the kit of the present invention may comprise a reagent for amplifying or quantifying the nucleic acid marker of the present invention (e.g., reverse transcriptase, a reagent for PCR, a primer, a probe, an adapter sequence for sequencing, etc.), or a reagent for quantifying the protein marker of the present invention (e.g., a reagent for immunological measurement, an antibody, etc.). Preferably, the kit of the present invention contains an oligonucleotide that specifically hybridizes with the nucleic acid marker of the present invention (e.g., a primer or a probe for PCR), or an antibody that recognizes the protein marker of the present invention. Preferably, the kit of the present invention comprises an index or guidance for detecting the expression level of the marker of the present invention. For example, the kit of the present invention may comprise guidance explaining the relationship between the increase or decrease in the expression level of each marker and the sleep state, or guidance explaining the reference value of the expression level of each marker for detecting the sleep state. The kit of the present invention may further comprise a biological sample collection device (e.g., the above-mentioned SSL absorbent material or SSL adhesive material), reagents for extracting the markers of the present invention from the biological sample (e.g., reagents for nucleic acid purification), a preservative or storage container for the sample collection device after collection of the biological sample, etc.

[0071] As exemplary embodiments of the present invention, the following substances, manufacturing methods, uses, methods, etc. are further disclosed in this specification, but the present invention is not limited to these embodiments.

[0072] [1] A sleep state marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: ATG7, DNAJB6, TBC1D17, ZNF410, TRAPPC5, PSMD3, ABI1, and MARCH6, and proteins encoded by the genes. [2] The sleep state marker according to [1], preferably comprising all of the nucleic acids encoding each of the following genes: ATG7, DNAJB6, TBC1D17, ZNF410, TRAPPC5, PSMD3, ABI1, and MARCH6, or all of the proteins encoded by each of the genes. [3] The sleep state marker according to [1] or [2], preferably further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 1 and proteins encoded by the genes. [4] The sleep state marker according to [1] or [2], preferably further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Tables 2 and 3, and proteins encoded by the genes. [5] The sleep state marker according to [4], preferably comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 2 and proteins encoded by the genes. [6] The sleep state marker according to [4], preferably comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 3 and proteins encoded by the genes. [7] The sleep state marker according to [1] or [2], preferably further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 4 and proteins encoded by the genes. [8] The sleep state marker according to any one of [1] to [7], which is preferably a marker for detecting insomnia. [9] The sleep state marker according to any one of [1] to [8], which is preferably a nucleic acid marker.

[10] The sleep state marker described in [9], wherein the nucleic acid is preferably mRNA extracted from lipids on the surface of the skin.

[0073]

[11] A method for detecting a sleep state of a subject, comprising measuring the expression level of the sleep state marker according to any one of [1] to

[10] in a biological sample collected from the subject.

[12] The method described in

[11] , which is preferably a method for detecting insomnia.

[13] The method described in

[11] or

[12] , further comprising detecting the sleep state of the subject based on the expression level of the sleep state marker.

[14] The sleep state marker is Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 2 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 2. And the method comprises: Preferably, the method includes detecting the sleep state of the subject as poor or insomnia when the expression level of the sleep state marker is increased, More preferably, the method includes detecting the sleep state of the subject as poor or insomnia when the expression level of the sleep state marker is higher than a reference value. The method described in

[13] .

[15] The sleep state marker is Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 3 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 3, And the method comprises: Preferably, the method includes detecting the sleep state of the subject as poor or insomnia when the expression level of the sleep state marker is decreased, More preferably, the method includes detecting the sleep state of the subject as poor or insomnia when the expression level of the sleep state marker is lower than a reference value. The method described in

[13] .

[16] The method of

[13] , preferably comprising detecting the sleep state of the subject based on a predictive model constructed using data on the expression level of the sleep state marker.

[17] Preferably, the predictive model is constructed by machine learning using data on the expression levels of the sleep state markers obtained from each individual in a population including a healthy or good sleep group and an insomnia group as explanatory variables, and the sleep state of each individual in the population as a response variable. The method described in

[16] .

[0074]

[18] A method for evaluating a sleep-regulating agent, comprising measuring the expression level of the sleep state marker described in any one of [1] to

[10] in a biological sample collected from a subject to which a test substance has been administered.

[19] The method according to

[18] , wherein the sleep control agent is preferably a sleep improver, an insomnia improver or an insomnia inducer.

[20] The method of

[18] or

[19] , preferably further comprising evaluating whether the test substance is a sleep-regulating agent based on a change in the expression level of the marker caused by the test substance.

[21] The sleep state marker is Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 2 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 2. And the method comprises: Preferably, the method includes determining the test substance as a sleep improver or an insomnia improver when the expression level of the sleep state marker under application of the test substance is decreased as compared to a control, and determining the test substance as an insomnia inducer when the expression level of the sleep state marker under application of the test substance is increased as compared to a control. The method described in

[20] .

[22] The sleep state marker is Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 3 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 3, And the method comprises: Preferably, when the expression level of the sleep state marker under application of the test substance is increased as compared to a control, the test substance is judged to be a sleep improving agent or an insomnia improving agent, and when the expression level of the sleep state marker under application of the test substance is decreased as compared to a control, the test substance is judged to be an insomnia inducing agent. The method described in

[20] .

[0075]

[23] A sleep state detection kit for use in the method according to any one of

[11] to

[17] or a sleep regulator evaluation kit for use in the method according to any one of

[18] to

[22] , comprising an oligonucleotide that specifically hybridizes to a nucleic acid that is a sleep state marker according to any one of [1] to

[10] , or an antibody that recognizes a protein that is a sleep state marker according to any one of [1] to

[10] . EXAMPLES

[0076] The present invention will be described in more detail below based on examples, but the present invention is not limited to these examples.

[0077] Example 1: Construction of a sleep state discrimination model using differentially expressed genes 1) Subject selection and SSL collection 175 healthy men and women in their 20s to 60s were asked to fill out an OSA sleep questionnaire, and the top 25% (42 people) with average scores of 20.2 or more for factors 1 to 5 were selected as subjects with good sleep conditions, and the bottom 25% (40 people) with average scores of 14.3 or less were selected as subjects with insomnia. Sebum was collected from the entire face of each of the 82 subjects in both groups using oil blotting film (5 cm x 8 cm, made of polypropylene, 3M). The oil blotting film was transferred to a glass vial and stored at -80°C until it was used for RNA extraction.

[0078] 2) RNA preparation and sequencing The oil blotting film from 1) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. The extracted RNA was reverse transcribed at 42°C for 90 minutes using SuperScript VILO cDNA Synthesis kit (Life Technologies Japan, Inc.) to synthesize cDNA. The random primers included in the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the obtained cDNA by multiplex PCR. Multiplex PCR was performed using Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the following conditions: [99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) × 20 cycles → 4°C, Hold]. The obtained PCR product was purified with Ampure XP (Beckman Coulter, Inc.), followed by buffer reconstitution, digestion of the primer sequence, adapter ligation and purification, and amplification to prepare a library. The prepared library was loaded onto an Ion 540 chip and sequenced using an Ion S5 / XL system (Life Technologies Japan, Inc.). The genes from which each read sequence was derived were determined by genetic mapping of each read sequence obtained by sequencing using the hg19 AmpliSeq Transcriptome ERCC v1, a reference sequence for the human genome.

[0079] 3) Data analysis and feature gene selection The expression level data (read count value) of RNA derived from the subjects measured in 2) above was corrected using a method called DESeq2. However, only 4195 genes for which expression level data that was not missing was obtained from 90% or more of the expression level data of all subjects were used in the following analysis. In the analysis, the count value corrected using a method called DESeq2 (Normalized count value) was used to extract genes with a p-value corrected value (FDR) of less than 0.25 by the likelihood ratio test between the groups (good group and insomnia group). As a result, 35 types of genes whose expression varies statistically significantly between the groups were obtained, as shown in Table 7. The 18 genes in Table 7A had a higher expression level in the insomnia group, so they were markers with a high expression level in the insomnia group (positive markers). The 17 genes in Table 7B had a lower expression level in the insomnia group, so they were markers with a low expression level in the insomnia group (negative markers). These genes were selected as feature genes to be used in the following prediction model construction.

[0080] [Table 7]

[0081] 4) Model construction The expression level data of the feature genes selected in 3) was converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1 to approximate a normal distribution from the RPM values ​​that follow a negative binomial distribution. A prediction model for discriminating sleep states was constructed using the Log2(RPM+1) values ​​of the expression level data of the obtained 35 feature genes as explanatory variables and the sleep state (good sleep group or insomnia group) as the objective variable. The random forest algorithm was specified as the method in the “caret” package of the R language, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 31.7% in the model when the expression level data of all 35 genes were used as variables, indicating that the genes shown in Table 7 can be used as markers for detecting sleep states.

[0082] Example 2: Construction of a sleep state discrimination model using genes with high random forest variable importance 1) Usage Data The 4195 genes for which non-missing expression level data was obtained for 90% or more of the subjects in Example 1-3) were used in the following analysis. In order to approximate the normal distribution from the RPM values ​​that follow a negative binomial distribution, the gene expression level data was converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1.

[0083] 2) Selection of feature genes The random forest algorithm was used to select the feature genes to be used in constructing the prediction model. The Log2(RPM+1) values ​​of the expression level data of 4195 genes obtained in 1) were used as explanatory variables, and the sleep state (good sleep group or insomnia group) was used as the objective variable to construct a prediction model to discriminate the sleep state. The random forest algorithm was specified as a method in the "caret" package of the R language, and the optimal value of the number of variables (mtry value) used to construct one decision tree was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the top 49 genes in terms of variable importance based on the Gini coefficient were calculated (Table 8). These 49 genes were selected as feature genes to be used in constructing the prediction model.

[0084] [Table 8]

[0085] 3) Model construction A prediction model for discriminating between sleep states was constructed using the Log2(RPM+1) values ​​of the expression level data of the 49 feature genes selected in 2) as explanatory variables and the sleep state (good or insomnia) as the objective variable. The random forest algorithm was specified as the method in the R language "caret" package, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 18.3% in the model when the expression level data of the 49 genes were used as variables, indicating that the genes shown in Table 8 can be used as markers for detecting sleep states.

[0086] Example 3: Construction of a sleep state discrimination model using feature genes extracted by the Boruta method 1) Usage Data The 4195 genes for which non-missing expression level data was obtained for 90% or more of the subjects in Example 1-3) were used in the following analysis. In order to approximate the normal distribution from the RPM values ​​that follow a negative binomial distribution, the gene expression level data was converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1.

[0087] 2) Selection of feature genes Using the Log2(RPM+1) values ​​of the expression level data of 4195 genes obtained in 1) as explanatory variables and the sleep state (good or insomnia group) as the objective variable, an algorithm in the "Boruta" package in R was executed to build a prediction model for discriminating between sleep states. The maximum number of trials was set to 1000, and two genes (ABI1, MARCH6) with p-values ​​less than 0.01 were calculated. These two genes were selected as feature genes to be used in building the prediction model.

[0088] 3) Model construction A prediction model for discriminating between sleep states was constructed using the Log2(RPM+1) values ​​of the expression level data of the two feature genes selected in 2) as explanatory variables and the sleep state (good or insomnia) as the objective variable. The random forest algorithm was specified as the method in the R language "caret" package, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 35.4% in the model when the expression level data of the two genes were used as variables. It was shown that the two genes ABI1 and MARCH6 can be used as markers for detecting sleep states.

[0089] Example 4: Construction of a sleep state discrimination model based on the feature genes used in duplicate in Examples 1 to 3 1) Selection of feature genes Among the feature genes used in Examples 1 to 3, the genes used in the same way between the Examples were 8 genes in total, including 6 genes ATG7, DNAJB6, TBC1D17, ZNF410, TRAPPC5, and PSMD3 used in the same way between Examples 1 and 2, and 2 genes ABI1 and MARCH6 used in the same way between Examples 2 and 3. These 8 genes were selected as feature genes to be used in constructing a prediction model.

[0090] 2) Model construction A prediction model for discriminating between sleep states was constructed using the Log2(RPM+1) values ​​of the expression level data of the eight feature genes selected in 1) as explanatory variables and the sleep state (good or insomnia) as the objective variable. The random forest algorithm was specified as the method in the R language "caret" package, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 32.9% in the model when the expression level data of the eight genes were used as variables, indicating that the above eight genes can be used as markers for detecting sleep states.

[0091] Example 5: Construction of a sleep state discrimination model based on all the feature genes used in Examples 1 to 3 1) Selection of feature genes All of the feature genes used in any of Examples 1 to 3 (78 types of genes in total shown in Table 1) were selected as feature genes to be used in constructing a prediction model.

[0092] 2) Model construction A prediction model for discriminating between sleep states was constructed using the Log2(RPM+1) values ​​of the expression level data of the 78 feature genes selected in 1) as explanatory variables and the sleep state (good or insomnia) as the objective variable. The random forest algorithm was specified as the method in the R language "caret" package, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 22.0% in the model when the expression level data of the 78 genes were used as variables, indicating that the 78 genes in Table 1 can be used as markers for detecting sleep states.

Claims

1. A method for detecting the sleep quality of a subject, comprising measuring the expression level of at least one selected from a nucleic acid encoding the ATG7 gene and a protein encoded by the gene in a biological sample collected from the subject.

2. The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of nucleic acids encoding the genes shown in Tables 1A and 1B below, and proteins encoded by the genes. 【Table 1】

3. The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 2 below and proteins encoded by said genes. 【Table 2】

4. The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 3 below and proteins encoded by the genes. 【Table 3】

5. A method described in any one of claims 1 to 4, which is a method for detecting whether sleep quality is poor or not.

6. The method according to any one of claims 1 to 5, wherein the nucleic acid is mRNA extracted from lipids on the skin surface.

7. The method according to any one of claims 1 to 6, further comprising detecting the quality of sleep in the subject based on the expression level of the nucleic acid or protein.

8. The method described in claim 7, comprising detecting that the subject's sleep quality is poor when the expression level of at least one selected from the group consisting of a nucleic acid encoding the ATG7 gene and a protein encoded by the gene is decreased.

9. The method of claim 8, further comprising detecting that the subject's sleep quality is poor when the expression level of at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 1B and proteins encoded by the genes is decreased.

10. The method of claim 7, further comprising detecting the subject's sleep quality based on a predictive model constructed using data on the expression levels of the nucleic acid or protein.

11. A method for evaluating a sleep quality control agent, comprising measuring the expression level of at least one selected from a nucleic acid encoding the ATG7 gene and a protein encoded by the gene in a biological sample collected from a subject to which a test substance has been administered.

12. The method of claim 11 , wherein the sleep quality control agent is a sleep quality improver.

13. A kit for detecting sleep quality or a kit for evaluating a sleep quality control agent, used in the method described in any one of claims 1 to 12, which contains an oligonucleotide that specifically hybridizes with a nucleic acid encoding the ATG7 gene, or an antibody that recognizes a protein encoded by the ATG7 gene.

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