Fatigue marker and method for detecting fatigue using same

Abstract

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

Description

[Technical field]

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

[0002] Fatigue is a state of reduced physical activity accompanied by a unique discomfort and desire for rest caused by physical and mental activity or illness, and is a mechanism by which the body maintains its physical condition and function. Prolonged fatigue leads to a decline in thinking ability, decreased attention, and decreased work efficiency, and can even cause poor physical condition and illness.

[0003] Patent documents 1 to 4 disclose, as markers for evaluating fatigue, translation initiation factor 2α (eIF-2α) phosphorylation-related factors (Patent document 1), eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN) (Patent document 2), as well as various genetic markers (Patent documents 3 and 4). Patent documents 5 to 7 disclose, as markers for evaluating chronic fatigue syndrome, activin βB (Patent document 5), RNase L of about 30 kDa (Patent document 6), as well as various genetic markers (Patent document 7). However, an objective or quantitative evaluation method for fatigue 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 8 describes the isolation of nucleic acids such as RNA derived from the skin cells of a subject from the skin surface lipids (SSL) and the use of the nucleic acids as samples for biological analysis. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2016 / 031581 [Patent Document 2] JP 2013-76691 A [Patent Document 3] JP 2013-150558 A [Patent Document 4] JP 2012-019784 A [Patent Document 5] Special Publication No. 2017-505428 [Patent Document 6] Special Publication No. 2000-516818 [Patent Document 7] JP 2007-228878 A [Patent Document 8] International Publication No. 2018 / 008319 Summary of the Invention [Problem to be solved by the invention]

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

[0007] In one aspect, the present invention provides a fatigue marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, and proteins encoded by the genes. In another aspect, the present invention provides a method for detecting fatigue in a subject, comprising measuring the expression level of the fatigue marker in a biological sample taken from the subject. In another aspect, the present invention provides a method for evaluating a fatigue controlling agent, comprising measuring an expression level of the fatigue 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 kit for detecting fatigue or a kit for evaluating a fatigue control agent, which is used in the method for detecting fatigue. Effect of the Invention

[0008] The present invention provides a novel fatigue marker. The fatigue marker of the present invention enables the detection of fatigue. In addition, since the fatigue marker of the present invention can be collected non-invasively from the skin surface lipids (SSL), it is possible to reduce the labor required for 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, "fatigue" refers to a state of fatigue including physical fatigue, mental fatigue, and cognitive fatigue. For example, the degree of "fatigue" in this specification is reflected in the score of the Chalder Fatigue Questionnaire (CFS), which is recommended by the Centers for Disease Control and Prevention (CDC) to be used for the evaluation of fatigue. The CFS consists of 14 questions regarding physical fatigue and mental fatigue, and can comprehensively evaluate both physical and mental fatigue. A CFS score of 15 points or more, preferably 18 points or more, is considered to be fatigued (high fatigue level) (Narumi D., et al. AIJ Journal of Technology and Design 54 (2017):563-566.). On the other hand, in this specification, "healthy" with respect to fatigue level refers to a state in which the CFS score is less than 15 points, preferably 11 points or less.

[0014] As used herein, "detection" of fatigue can also be expressed in other terms such as examination, measurement, judgment, or evaluation assistance. The terms "detection," "examination," "measurement," "judgment," or "evaluation" of fatigue as used herein do not include a diagnosis of a disease accompanied by fatigue (e.g., chronic fatigue syndrome) by a physician.

[0015] (1. Fatigue Markers) Traditionally, fatigue has been assessed mainly by scoring based on interviews or questionnaires (e.g., the Chalder Fatigue Questionnaire mentioned above). Such assessment methods can be less objective and quantitative, and make it difficult to compare assessments across studies.

[0016] The present inventors have found that the fatigue 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 a healthy group with a particularly low fatigue level of 11 points or less and a fatigue group with a particularly high fatigue level of 18 points or more, as determined based on the Chalder Fatigue Questionnaire. Nucleic acids encoding genes that show different expression levels between the two groups, and proteins encoded by these genes, can be used as fatigue markers. For example, fatigue can be detected using the expression levels of nucleic acids encoding these genes or proteins encoded by these genes as indicators.

[0017] Thus, in one aspect, the present invention provides a fatigue marker for detecting fatigue. The fatigue marker provided by the present invention (hereinafter also referred to as the marker of the present invention) makes it possible to detect fatigue in 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 a nucleic acid encoding the 222 gene shown in Table 1 below and a protein encoded by the gene. 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.

[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 36 genes shown in Table 2 increase in response to fatigue of the subject, and the expression levels of the 179 genes shown in Table 3 decrease in response to fatigue of the subject. Therefore, the markers in Table 2 are positive markers whose expression levels increase in response to fatigue of the subject. On the other hand, the markers in Table 3 are negative markers whose expression levels decrease in response to fatigue 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 23 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 23 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, fatigue 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 the nine 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 the 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 18 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 the markers of Table 6. Preferably, the markers of Table 6 are nucleic acid markers. The genes shown in Table 6 consist of 7 genes (OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1) that are common to Tables 2 or 3, 4, and 5, 9 genes (TYMP, FAM168B, FCHO2, EID1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B) that are common to Tables 2 or 3 and 5, and not included in Table 5, and 2 genes (STARD5, TBC1D23) that are common to Tables 2 or 3 and 5, and not included in Table 4.

[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, the at least one marker selected from the group consisting of markers in Table 6 preferably includes one or more markers selected from the seven genes OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, and CAMSAP1, more preferably two or more markers, even more preferably all seven markers, and even more preferably all 18 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 may be obtained include mammals, including humans and non-human mammals. Preferably, the subject is a human or non-human mammal in need of detection of a fatigue condition, or a human desiring to detect a fatigue condition. 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. How to detect fatigue) 2.1. Detection of Fatigue Based on Marker Expression Levels In another aspect, the present invention provides a method for detecting fatigue in a subject using the fatigue marker of the present invention. In the method, fatigue in the subject is detected based on the expression level of the marker of the present invention in the subject. The subject to which the fatigue detection method according to the present invention is applied is the same as the subject from which the biological sample containing the fatigue marker of the present invention is collected.

[0038] In a preferred embodiment, the method for detecting fatigue according to the present invention comprises measuring the expression level of the marker of the present invention in a biological sample taken 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 taking the SSL of the subject. The procedure for taking 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 to 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 genes, total nucleic acids, or proteins.

[0040] Preferably, the marker of the present invention used in the method for detecting fatigue 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 fatigue according to the present invention, fatigue in a subject is detected using 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) as an indicator.

[0045] As mentioned above, the nucleic acid markers and protein markers in Table 2 are positive markers whose expression levels increase according to the fatigue of the subject, while the nucleic acid markers and protein markers in Table 3 in the subject are negative markers whose expression levels decrease according to the fatigue of the subject.Therefore, if the expression level of the positive marker increases, the subject is detected as being fatigued, otherwise, the subject is detected as not being fatigued.Alternatively, if the expression level of the negative marker decreases, the subject is detected as being fatigued, otherwise, the subject is detected as not being fatigued.Alternatively, the positive marker and the negative marker may be used in combination in the fatigue detection method according to the present invention.

[0046] In one embodiment, fatigue of a subject can be detected by comparing the expression level of the marker of the present invention measured from the subject with a reference value. Alternatively, fatigue of the subject can be detected by periodically (e.g., monthly) measuring the expression level of the marker of the present invention from a single subject and tracking the fluctuation of the expression level. 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 reference value can be determined based on the 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 degree of fatigue) or a healthy group (a group of individuals with a degree of fatigue in the normal range [e.g., a CFS score of less than 15 points as described above]). When multiple types of nucleic acids or proteins are used as the marker of the present invention, it is preferable to determine the reference value for each nucleic acid or protein.

[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 can be detected as being fatigued (i.e., the subject's fatigue level is high); otherwise, the subject can be detected as not being fatigued (i.e., the subject's fatigue level is normal).For example, if the expression level of the positive marker in the subject is statistically significantly higher than the reference value, the subject can be detected as being fatigued; otherwise, the subject can be detected as not being fatigued.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 can be detected as being fatigued; otherwise, the subject can be detected as not being fatigued.

[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 can be detected as being fatigued, otherwise the subject can be detected as not being fatigued.For example, if the expression level of the negative marker in the subject is statistically significantly lower than the reference value, the subject can be detected as being fatigued, otherwise the subject can be detected as not being fatigued.Also, for example, if the expression level of the negative marker in the subject is preferably 90% or less, more preferably 80% or less, even more preferably 75% or less than the reference value, the subject can be detected as being fatigued, otherwise the subject can be detected as not being fatigued.

[0049] Alternatively, when multiple nucleic acids or proteins are used in combination as markers, fatigue in 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. Fatigue detection based on predictive models) In another embodiment of the method for detecting fatigue according to the present invention, the fatigue 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 determining the fatigue state in any subject can be constructed by machine learning using the expression profile of one or more markers (nucleic acid or protein) obtained from each individual in a teacher sample population (e.g., a population including a healthy group and a fatigue group) as explanatory variables, and data on the fatigue state (e.g., healthy or fatigued) in 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 (group) 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 fatigue 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 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 low fatigue levels (healthy group) and individuals with high fatigue levels (fatigue group) are used as teacher samples for machine learning. A discriminant (prediction model) for dividing individuals into a healthy group and a fatigue group is constructed using the teacher samples. The expression profiles of feature marker(s) can be used as explanatory variables used in constructing the discriminant. For example, a variable indicating whether the individual from which the feature marker(s) originates belongs to the healthy group or the fatigue group can be used as the objective variable. A cutoff value for discriminating between the healthy group and the fatigue group can be determined based on the constructed discriminant. Next, the expression profile of the feature marker(s) derived from a subject whose fatigue 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 healthy group or the fatigue group.

[0056] For example, a statistically significant difference between two groups (healthy group and fatigue 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, the predictive model may be constructed after compressing the data by dimensionality reduction, 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 discriminating whether a subject is in the healthy group or the fatigue group 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 healthy group or the fatigue 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 prediction model thus constructed is input with data for verification to calculate a prediction value, and the model in which the prediction value best matches the actual measurement value, for example, the model with the highest accuracy of the prediction value relative to the actual measurement value, or the model with the smallest root mean square error (RMSE) of the difference between the prediction value and the actual measurement value, can be selected as the optimal model. By inputting the expression profile of the feature marker(s) actually measured from the subject into the constructed prediction model, fatigue of the subject can be detected.

[0060] (3. Evaluation method of fatigue control agents) In a further aspect, the present invention provides a method for evaluating a fatigue control agent using the fatigue marker of the present invention. The method evaluates a fatigue control agent 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 has been administered. In the method, the type of biological sample used is as described above. Preferably, the biological sample is an SSL, and the marker of the present invention is an SSL-derived mRNA. In one embodiment, the method may further comprise collecting the SSL of the subject. The fatigue control agent evaluated in the present invention includes a fatigue improving agent or a fatigue enhancing agent, and is preferably a fatigue 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 fatigue control agent to be evaluated. When evaluating a fatigue improving agent, the subject is preferably a human or a non-human mammal with a high degree of fatigue, and when evaluating a fatigue enhancing agent, the subject is preferably a human or a non-human mammal with a normal degree of fatigue. 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 fatigue control agent. The test substance may be a naturally occurring substance, or a substance artificially synthesized by a chemical or biological method, 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 fatigue 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 fatigue control agent. 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 fatigue improving agent. 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 a fatigue enhancing agent.

[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 fatigue. 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. Examples of the control include the expression level of the marker of the present invention under non-application of the test substance, the standard value for the evaluation of fatigue described above, 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 fatigue improving agent. 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 fatigue improving agent. 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 fatigue improving agent.

[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 a fatigue enhancer. 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 a fatigue enhancer. 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 fatigue enhancer.

[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 fatigue improving agent. 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 fatigue improving agent. 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 fatigue improving agent.

[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 a fatigue enhancer. 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 a fatigue enhancer. 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 fatigue enhancer.

[0070] (4. Fatigue detection kit and fatigue control agent evaluation kit) In a further aspect, a kit for detecting fatigue for detecting fatigue in a subject according to the method for detecting fatigue according to the present invention, and a kit for evaluating a fatigue controller for evaluating a fatigue controller according to the method for evaluating a fatigue controller according to the present invention are provided. In one embodiment, the kit of the present invention comprises a reagent or instrument for measuring the expression level of the fatigue 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 fatigue, or guidance explaining the reference value of the expression level of each marker for detecting fatigue. 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 fatigue marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, and proteins encoded by the genes. [2] The fatigue marker according to [1], preferably comprising all of the nucleic acids encoding each of the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, or all of the proteins encoded by each of the genes. [3] Preferably, the fatigue marker according to [1] or [2] further comprises 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] Preferably, the fatigue marker according to [1] or [2] further comprises 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 fatigue 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 fatigue 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] Preferably, the fatigue marker according to [1] or [2] further comprises 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 fatigue marker according to any one of [1] to [7], which is preferably a marker for detecting fatigue. [9] The fatigue marker according to any one of [1] to [8], which is preferably a nucleic acid marker.

[10] The fatigue 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 fatigue in a subject, comprising measuring the expression level of the fatigue marker described in 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 a subject with a high level of fatigue.

[13] The method described in

[11] or

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

[14] The fatigue state marker, 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 comprises detecting the subject as fatigued if the expression level of the fatigue marker is increased, More preferably, the method includes detecting the subject as being fatigued when the expression level of the fatigue marker is higher than a reference value. The method described in

[13] .

[15] The fatigue state marker, 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 further comprises detecting the subject as fatigued if the expression level of the fatigue marker is decreased, More preferably, the method comprises detecting the subject as being fatigued when the expression level of the fatigue marker is lower than a reference value. The method described in

[13] .

[16] The method described in

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

[17] Preferably, the predictive model is constructed by machine learning using data on the expression level of the fatigue marker obtained from each individual in a population including a healthy group and a fatigue group as explanatory variables and the fatigue state of each individual in the population as a response variable. The method described in

[16] .

[0074]

[18] A method for evaluating a fatigue control agent, comprising measuring the expression level of the fatigue 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 fatigue control agent is preferably a fatigue improving agent or a fatigue enhancing agent, more preferably a fatigue improving agent.

[20] The method of

[18] or

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

[21] The fatigue marker, 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 that the test substance is a fatigue improving agent when the expression level of the fatigue marker under application of the test substance is decreased as compared to a control, and determining that the test substance is a fatigue enhancing agent when the expression level of the fatigue marker under application of the test substance is increased as compared to a control. The method described in

[20] .

[22] The fatigue marker, 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 fatigue marker under application of the test substance is increased as compared to a control, the test substance is judged to be a fatigue improving agent, and when the expression level of the fatigue marker under application of the test substance is decreased as compared to a control, the test substance is judged to be a fatigue enhancing agent. The method described in

[20] .

[0075]

[23] A kit for detecting fatigue used in the method according to any one of

[11] to

[17] or a kit for evaluating a fatigue control agent used in the method according to any one of

[18] to

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

[10] , or an antibody that recognizes a protein that is a fatigue marker described in 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 fatigue state discrimination model using differentially expressed genes

[0078] 1) Subject selection and SSL collection 175 healthy men and women in their 20s to 60s were asked to answer the Chalder Fatigue Questionnaire, and those with high fatigue scores of 18 points or more (48 people) were selected as subjects in the fatigue group, and those with low fatigue scores of 11 points or less (42 people) were selected as subjects in the healthy group. Sebum was collected from the entire face of each subject using an 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.

[0079] 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.

[0080] 3) Data Analysis The read counts of each read obtained by sequencing the SSL-derived RNA of the subjects in 2) above were used as the expression level data for each RNA. To correct the difference in total read counts between samples, each read count was converted to an RPM (Reads per million mapped reads) value. However, reads with a read count of less than 10 were treated as missing values. For 3918 genes for which expression level data that was not missing was obtained in 90% or more of the subjects, Student's t test was performed between groups (healthy group and fatigue group) based on the expression information, and genes with p<0.05 were extracted. As shown in Tables 7 and 8, 215 genes with statistically significant changes in expression levels between groups were obtained. The 36 genes in Table 7 had higher expression levels in the fatigue group, so they were markers with high expression levels in the fatigue group (positive markers). The 179 genes in Table 8 had lower expression levels in the fatigue group, so they were markers with low expression levels in the fatigue group (negative markers). These genes were selected as feature genes to be used in the following prediction model construction.

[0081] [Table 7]

[0082] [Table 8]

[0083] 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 was constructed to distinguish between the healthy and fatigue groups using the Log2(RPM+1) values ​​of the expression level data of the obtained 215 feature genes as explanatory variables and the fatigue state (healthy group or fatigue 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 32.2% in the model when the expression level data of all 215 genes were used as variables, and it was shown that the genes shown in Tables 7 and 8 can be used as markers for detecting fatigue.

[0084] Example 2: Construction of a fatigue state discrimination model using genes with high random forest variable importance 1) Usage Data The 3918 types of 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.

[0085] 2) Selection of feature genes A 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 the 3918 genes obtained in 1) were used as explanatory variables, and the fatigue state (healthy group or fatigue group) was used as the objective variable to construct a prediction model to discriminate the fatigue 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 a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the top 23 genes in terms of variable importance based on the Gini coefficient were calculated (Table 9). These 23 genes were selected as feature genes to be used in constructing the prediction model.

[0086] [Table 9]

[0087] 3) Model construction A prediction model for discriminating fatigue state was constructed using the Log2(RPM+1) values ​​of the expression level data of the 23 feature genes selected in 2) as explanatory variables and the fatigue state (healthy group or fatigue 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 14.4% in the model when the expression level data of the 23 genes were used as variables, indicating that the genes shown in Table 9 can be used as markers for detecting fatigue.

[0088] Example 3: Construction of a fatigue state discrimination model using feature genes extracted by the Boruta method 1) Usage Data The 3918 types of genes for which non-missing expression level data was obtained for 90% or more of the subjects in 6) of Example 1 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.

[0089] 2) Selection of feature genes Using the Log2(RPM+1) values ​​of the expression level data of 3918 genes obtained in 1) as explanatory variables and the fatigue state (healthy group or fatigue group) as the objective variable, an algorithm of the "Boruta" package in R language was executed to construct a prediction model for discriminating the fatigue state. The maximum number of trials was set to 1000, and nine genes (OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1, STARD5, and TBC1D23) with a p-value of less than 0.01 were calculated. These nine genes were selected as feature genes to be used in constructing the prediction model.

[0090] 3) Model construction A prediction model for discriminating fatigue state was constructed using the Log2(RPM+1) values ​​of the expression level data of the nine feature genes selected in 2) as explanatory variables and the fatigue state (healthy group or fatigue 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 one decision tree 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 21.1% in the model when the expression level data of the nine genes were used as variables, indicating that the above nine genes can be used as markers for detecting fatigue.

[0091] Example 4: Construction of a fatigue 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 that were used in duplicate between any two or more Examples were OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, a total of 18 genes. These 18 genes were selected as feature genes to be used in constructing a prediction model.

[0092] 2) Model construction A prediction model for discriminating fatigue state was constructed using the Log2(RPM+1) values ​​of the expression level data of the 18 feature genes selected in 1) as explanatory variables and the fatigue state (healthy group or fatigue 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 one decision tree 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 17.8% in the model when the expression level data of the 18 genes were used as variables, indicating that the above 18 genes can be used as markers for detecting fatigue.

[0093] Example 5: Construction of a fatigue 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 (a total of 222 types of genes shown in Table 1) were selected as feature genes to be used in constructing a prediction model.

[0094] 2) Model construction A prediction model for discriminating fatigue state was constructed using the Log2(RPM+1) values ​​of the expression level data of the 222 feature genes selected in 1) as explanatory variables and the fatigue state (healthy group or fatigue 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 one decision tree 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 31.1% in the model when the expression level data of the 222 genes were used as variables, indicating that the 222 genes in Table 1 can be used as markers for detecting fatigue.

Claims

1. A method for detecting fatigue in a subject, comprising measuring, in a biological sample collected from the subject, an expression level of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, and proteins encoded by the genes, or an expression level of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1, STARD5, and TBC1D23, and proteins encoded by the genes.

2. The method of claim 1 , wherein the nucleic acid is mRNA extracted from surface lipids of the skin.

3. The method of claim 1 or 2, further comprising detecting fatigue in the subject based on the expression level of the nucleic acid or protein.

4. The method according to claim 3, comprising detecting fatigue in the subject based on a predictive model constructed using data on the expression levels of the nucleic acid or protein obtained from each individual in a teacher sample population as an explanatory variable and the fatigue state of each individual in the teacher sample population as a response variable.

5. A method for evaluating a fatigue control agent, comprising measuring the expression level of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23, and proteins encoded by the genes, or the expression level of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1, STARD5, and TBC1D23, and proteins encoded by the genes, in a biological sample collected from a subject to which a test substance has been administered.

6. The method according to claim 5 , wherein the fatigue control agent is a fatigue improving agent.

7. A set of oligonucleotides that specifically hybridize to each of the nucleic acids encoding the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23; or A set of oligonucleotides that specifically hybridize to each of the nucleic acids encoding the following genes: OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1, STARD5, and TBC1D23; or A set of antibodies that recognize each of the proteins encoded by the following genes: OPA1, HSPBAP1, TYMP, TOX4, FAM168B, FCHO2, KRTAP19-1, EID1, C1orf21, CYB5B, CAMSAP1, NIPBL, DPYSL3, SNRPB2, SNHG6, MOB1B, STARD5, and TBC1D23; or A set of antibodies that recognize each of the proteins encoded by the following genes: OPA1, HSPBAP1, TOX4, KRTAP19-1, C1orf21, CYB5B, CAMSAP1, STARD5, and TBC1D23; A kit for detecting fatigue or a kit for evaluating a fatigue control agent, which is used in the method according to any one of claims 1 to 6, comprising:

Images