JP7655720B2 - Stress marker and method for detecting chronic stress level using same - Google Patents

Description

The present invention relates to a stress marker and a method for detecting chronic stress levels using the same.

Stress is considered to be a change in the mind and body's functions that occurs when people are exposed to various physical, environmental, and psychological stimuli and stress. In modern society, many people, especially those of working age, live with stress. Stress not only has a negative impact on both the psychological and physical aspects and leads to a decline in quality of life (QOL), but is also known to be involved in the onset and exacerbation of various diseases.

Stress is broadly divided into acute stress and chronic stress caused by continuous stress load. Chronic stress is known to be a cause of various illnesses, including mental disorders, and there is a need to understand its mechanism and develop techniques to improve it. In order to develop techniques to improve it, an index to evaluate stress is necessary. From this perspective, chromogranin A (Non-Patent Document 1) has been proposed as an indicator of acute stress in addition to cortisol, and α-klotho (Non-Patent Document 2) and stress markers such as those disclosed in Patent Documents 1 to 4 have been proposed as an indicator of chronic stress. However, most of these stress markers can only be collected by invasive methods, and are not necessarily associated with chronic stress. Therefore, an objective or quantitative evaluation method of chronic stress using biomarkers has not yet been established.

Technology has 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 5 describes the isolation of nucleic acids such as RNA derived from a subject's skin cells from surface skin lipids (SSL) and their use as samples for biological analysis.

JP 2013-150558 A JP 2008-054590 A JP 2007-110912 A JP 2007-306883 A International Publication No. 2018/008319

Stress. 2006,9(3):127-131. J Investig Med, 2019,67(7):1082-1086

The present invention relates to detection of chronic stress levels using biomarkers. The present invention relates to a novel stress marker and a method for detecting chronic stress levels using the same.

In one aspect, the present invention provides a stress marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes.
In another aspect, the present invention provides a method for detecting a chronic stress level in a subject, comprising measuring the expression level of the stress marker in a biological sample taken from the subject.
In another aspect, the present invention provides a method for evaluating a chronic stress controller, comprising measuring the expression level of the stress 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 a chronic stress level, or a kit for evaluating a chronic stress regulator, which is used in the method for detecting a chronic stress level.

The present invention provides a novel stress marker. The stress marker of the present invention enables detection of chronic stress levels. In addition, the stress marker of the present invention can be collected non-invasively from surface skin lipids (SSL), reducing the effort required for collection and the burden on the subject caused by collection.

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

The names of the genes disclosed herein follow the Official Symbols listed in NCBI ([www.ncbi.nlm.nih.gov/]).

In this specification, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, and is sometimes called sebum. In general, SSL mainly contains secretions from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer that covers the skin surface.

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

In this specification, "high stress" (or "subject is under stress") refers to a state in which the stress level is chronically high. In other words, it refers to a high level of chronic stress. The level of chronic stress can be scored based on an interview or questionnaire. For example, in the simplified version of the Occupational Stress Questionnaire (23 items) according to the revised edition of the Stress Check System Implementation Manual based on the Industrial Safety and Health Act (Ministry of Health, Labor and Welfare, 2016), the level of chronic stress can be evaluated as a stress score based on subjective scoring (maximum score 25 points, minimum score 5 points). The lower the stress score from the Occupational Stress Questionnaire, the higher the stress. Thus, in this specification, a chronic stress level corresponding to "high stress" refers to a state in which the stress score on the simplified version of the Occupational Stress Questionnaire (23 items) is 11 points or less, and a chronic stress level corresponding to "low stress" refers to a state in which the stress score is 23 points or more. In this specification, a chronic stress level of "healthy" refers to a state in which the stress score on the simplified version of the Occupational Stress Questionnaire (23 items) is 12 to 25 points.

In this specification, "detection" of chronic stress levels can also be expressed in other terms such as examination, measurement, judgment, or evaluation assistance. The terms "detection," "examination," "measurement," "judgment," or "evaluation" of chronic stress levels in this specification do not include a doctor's diagnosis of a stress-related illness.

(1. Stress Markers)
Conventionally, chronic stress has been evaluated mainly by scoring based on interviews or questionnaires (e.g., the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)). Such evaluation methods are often inferior in objectivity and quantitation, and make it difficult to compare evaluations between studies.

The present inventors have found that a subject's chronic stress level is reflected in the expression level of a specific gene. As shown in the Examples below, genes with different expression levels were found between a high stress group and a low stress group determined based on the simplified version of the Occupational Stress Questionnaire (23 items). Nucleic acids encoding genes that show different expression levels between the two groups, and proteins encoded by the genes, can be used as stress markers. For example, the expression levels of the nucleic acids encoding these genes or the proteins encoded by the genes can be used as indicators to detect chronic stress levels.

Thus, in one aspect, the present invention provides a stress marker for detecting chronic stress levels. The stress marker provided by the present invention (hereinafter also referred to as the marker of the present invention) makes it possible to detect chronic stress levels in individual subjects from which the marker is derived.

The marker of the present invention may include at least one selected from the group consisting of nucleic acids encoding the 197 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.

In one embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding the genes shown in Tables 2 and 3 (hereinafter also referred to as the nucleic acid markers of Tables 2 and 3, respectively) and proteins encoded by the genes (hereinafter also referred to as the protein markers of Tables 2 and 3, respectively). The nucleic acid markers and protein markers of Table 2 may be collectively referred to as the markers of Table 2, and the nucleic acid markers and protein markers of Table 3 may be collectively referred to as the markers of Table 3. Preferably, the markers of Tables 2 and 3 are nucleic acid markers. As shown in the Examples described below, the expression levels of the 49 genes shown in Table 2 increase depending on the chronic stress level of the subject, and the expression levels of the 24 genes shown in Table 3 decrease depending on the chronic stress level of the subject. Therefore, the markers of Table 2 are positive markers whose expression levels increase depending on the chronic stress level of the subject. On the other hand, the markers of Table 3 are negative markers whose expression levels decrease depending on the chronic stress level 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.

In another embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding the 140 genes shown in Table 4 (hereinafter also referred to as the nucleic acid markers of Table 4) and proteins encoded by the genes (hereinafter also referred to as the protein markers of Table 4). The nucleic acid markers and protein markers of Table 4 may be collectively referred to as the markers of Table 4. Preferably, the markers of 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 140 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, chronic stress levels as objective variables, and random forest as a machine learning algorithm.

In a preferred embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding the 16 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. The genes shown in Table 5 are genes commonly included in Table 2 or Table 3 and Table 4.

In a preferred embodiment, the marker of the present invention may further include at least one selected from the group of markers in Table 1 (but other than the markers in Table 5) in addition to at least one selected from the group of markers in Table 5. For example, the marker of the present invention may further include at least one selected from the group of markers in Table 2, Table 3, or Table 4 (but other than the markers in Table 5) in addition to at least one selected from the group of markers in Table 5. For example, the marker of the present invention may include at least one, preferably all, selected from the group of nucleic acid markers in Table 5, and further include at least one selected from the group of nucleic acid markers in Table 2, Table 3, or Table 4 (but other than the nucleic acid markers in Table 5). For example, the marker of the present invention may include at least one, preferably all, selected from the group of protein markers in Table 5, and further include at least one selected from the group of protein markers in Table 2, Table 3, or Table 4 (but other than the protein markers in Table 5).

In another preferred embodiment, all of the nucleic acid markers in Table 2 or Table 3, or all of the protein markers in Table 2 or Table 3, in combination, are used as markers of the present invention.
In another 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 used in combination 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 of Table 1 are used in combination as markers of the present invention.

The stress marker of the present invention can be prepared from a biological sample collected from a subject, such as cells, stratum corneum, tissue (biopsy, etc.), body fluids (blood, etc.), urine, secretions (saliva, SSL, etc.), etc., according to standard methods. For example, a commercially available kit can be used to prepare nucleic acid or protein from a biological sample. Preferably, the marker of the present invention is a nucleic acid marker, and preferred examples of nucleic acid prepared from a biological sample include genomic DNA and RNA such as mRNA.

Examples of subjects from which biological samples containing the markers of the present invention may be collected include mammals, including humans and non-human mammals. Preferably, the subject is a human or non-human mammal in need of detection of chronic stress levels, or a human desiring to detect chronic stress levels. More preferably, the subject is a human.

More preferably, the marker of the present invention is a nucleic acid or protein, even more preferably mRNA, prepared from the SSL of a subject. The site of the skin from which SSL is collected may be any site of the body, such as the head, face, neck, trunk, hands and feet, and preferably a site with high sebum secretion, such as the skin of the face.

To collect SSL from the skin of a subject, any means used for recovering or removing SSL from the skin can be used. 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 and pulp. 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 or tape, and a method of scraping SSL off and collecting it with a spatula, scraper, etc. 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, it is preferable that the SSL absorbent material contains a low content of highly water-soluble solvents and water, since the adsorption of SSL is inhibited if the SSL absorbent material contains highly water-soluble solvents or water. It is preferable to use SSL absorbent material in a dry state.

The collected SSL may be used immediately in the nucleic acid or protein extraction process described below, or may be stored until it is used in the nucleic acid or protein extraction process. 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, even more preferably -20±5°C to -80±5°C. The storage period of 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, even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

For the extraction of nucleic acids or proteins from the collected SSL, methods that are 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 methods using columns such as TRIzol (registered trademark), RNeasy (registered trademark), and QIAzol (registered trademark), methods using special silica-coated magnetic particles, methods using Solid Phase Reversible Immobilization magnetic particles, and extraction using commercially available RNA extraction reagents such as ISOGEN. For protein extraction or purification, commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen) can be used.

(2. Chronic Stress Level Detection Method)
2.1. Detection of Chronic Stress Levels Based on Marker Expression Levels
In another aspect, the present invention provides a method for detecting a chronic stress level in a subject using the stress marker of the present invention. In the method, the chronic stress level of the subject is detected based on the expression level of the marker of the present invention in the subject. The subject to which the chronic stress level detection method according to the present invention is applied is the same as the subject from which the above-mentioned biological sample containing the stress marker of the present invention is collected.

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

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 total proteins.

Preferably, the marker of the present invention used in the chronic stress level detection method of the present invention is SSL-derived mRNA. Preferably, RNA extracted from SSL is converted to cDNA by reverse transcription, and then the cDNA or its amplification product is quantified by the above-mentioned method to measure the expression level of the SSL-derived RNA. For the reverse transcription, a primer targeting a specific RNA to be analyzed may be used, but for more comprehensive nucleic acid storage and analysis, it is preferable to use a random primer. For the reverse transcription, a general reverse transcriptase or a reverse transcription reagent kit can be used. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcription reagent kit is used, and examples thereof include M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptases or reverse transcription reagent kits, such as the PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.) and the SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific). SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both from Thermo Scientific) and the like are preferably used. 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 in which multiple gene regions are amplified simultaneously 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.).

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 temperatures and times described above can be carried out using a thermal cycler commonly used for PCR.

The purification of the reaction products obtained by the PCR is preferably carried out by size separation of the reaction products. By size separation, the target PCR reaction products 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, or magnetic beads that can be used for size separation. A preferred example of magnetic beads that can be used for size separation is Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

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

In one embodiment of the method for detecting a chronic stress level according to the present invention, the expression level of a marker of the present invention in a subject (preferably, the expression level of a marker of the present invention contained in a biological sample collected from the subject) is used as an indicator to detect the chronic stress level of the subject.

As described above, the nucleic acid markers and protein markers in Table 2 are positive markers whose expression levels increase according to the chronic stress level of the subject, while the nucleic acid markers and protein markers in Table 3 are negative markers whose expression levels decrease according to the chronic stress level of the subject. Therefore, if the expression level of the positive marker increases, the subject is detected as being chronically stressed (i.e., the subject's chronic stress level is high stress), and if not, the subject is detected as not being chronically stressed (i.e., the subject's chronic stress level is low stress or healthy). Alternatively, if the expression level of the negative marker decreases, the subject is detected as being chronically stressed, and if not, the subject is detected as not being chronically stressed. Alternatively, the positive marker and the negative marker may be used in combination in the chronic stress level detection method according to the present invention.

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 chronic stress level 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 chronic stress level 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., an 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 chronic stress level), a healthy group (a group of individuals with a chronic stress level in the normal range [e.g., a stress score of 12 to 25 points on the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)]), or a low-stress group (a group of individuals with a chronic stress level in the low-stress range [e.g., a stress score of 23 points or more on the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)]). When multiple types of nucleic acids or proteins are used as the marker of the present invention, it is preferable to determine the standard value for each nucleic acid or protein.

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 chronically stressed, and if not, the subject can be detected as not being chronically stressed. 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 chronically stressed, and if not, the subject can be detected as not being chronically stressed. Also, for example, if the expression level of the positive marker in the subject is preferably 110% or more, more preferably 150% or more, and even more preferably 200% or more relative to the reference value, the subject can be detected as being chronically stressed, and if not, the subject can be detected as not being chronically stressed.

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 chronically stressed, and if not, the subject can be detected as not being chronically stressed. 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 chronically stressed, and if not, the subject can be detected as not being chronically stressed. Also, for example, if the expression level of the negative marker in the subject is preferably 90% or less, more preferably 75% or less, and even more preferably 50% or less of the reference value, the subject can be detected as being chronically stressed, and if not, the subject can be detected as not being chronically stressed.

Alternatively, when multiple nucleic acids or proteins are used in combination as markers, the chronic stress level 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.

(2.2. Detection of Chronic Stress Levels Based on Predictive Models)
In another embodiment of the method for detecting a chronic stress level according to the present invention, the chronic stress level 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.

For example, a predictive model (e.g., a discriminant) for determining the chronic stress level 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 high stress group and a low stress group or a healthy group) as explanatory variables, and data on the chronic stress level (e.g., the presence or absence of high stress) of each individual in the population as the objective variable.

In this specification, "feature" is synonymous with "explanatory variable" in machine learning. In this specification, markers whose expression profiles are used as explanatory variables (features) in machine learning may be referred to as "feature marker(s)." Furthermore, when a feature marker is a nucleic acid marker, it may be referred to as a feature gene.

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 have been normalized. When multiple markers are used as the feature marker group, for example, multiple markers having a high correlation with chronic stress levels can be selected from the markers of the present invention, and each of their expression profiles can be used as an explanatory variable.

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 5 are combined with one or more, preferably all of the nucleic acid markers or protein markers in Tables 2, 3, or 4 (but other than the markers in Table 5) 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.

In a preferred embodiment, expression profiles of individuals with high chronic stress levels (high stress group) and individuals with low chronic stress levels (low stress group) are used as teacher samples for machine learning. A discriminant (prediction model) for dividing individuals into high stress and low stress groups is constructed using the teacher samples. The expression profile of the feature marker(s) can be used as an explanatory variable used in constructing the discriminant. For example, a variable indicating whether the individual from which the feature marker(s) is derived belongs to the high stress group or the low stress group can be used as the objective variable. A cutoff value for discriminating the high stress group can be determined based on the constructed discriminant. Next, the expression profile of the feature marker(s) derived from the subject whose chronic stress level 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 determine whether the subject belongs to the high stress group.

For example, a statistically significant difference between two groups to be discriminated (a high stress group and a low stress group), for example, a marker whose expression profile varies significantly between the two groups, can be used as a feature marker (group), and the expression profile can be used as an explanatory variable. Alternatively, the feature marker (group) can be extracted using a known algorithm such as a machine learning algorithm. For example, as shown in the following example, it can be extracted based on the importance of variables in a random forest. Alternatively, the feature marker (group) can be extracted using the "Boruta" package of the R language, or the like.

Alternatively, when using expression profiles of multiple markers to construct a predictive model, the data may be compressed by dimensionality reduction as necessary before constructing the predictive model. For example, multiple markers are extracted from the nucleic acid markers that code for the group of genes shown in Table 1. Next, principal component analysis is performed on the expression profiles of the extracted markers. A predictive model for determining whether a subject is in the high stress 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 high stress group or the low stress group as the objective variable.

The algorithm used in constructing the predictive 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, Lasso regression, Random Forest, Neural Net, a support vector machine with a linear kernel (SVM (linear)), and a support vector machine with an rbf kernel (SVM (rbf)). Random Forest is preferred as the algorithm used in the present invention.

The constructed prediction model is input with validation data to calculate a predicted value, and the model whose predicted value best matches the actual measured value, for example, the model with the highest accuracy 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. By inputting the expression profile of the markers of the present invention actually measured from a subject into the constructed prediction model, the chronic stress level of the subject can be detected.

(3. Method for evaluating chronic stress control agents)
In a further aspect, the present invention provides a method for evaluating a chronic stress regulator using the stress marker of the present invention. The method evaluates a chronic stress 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 has been administered. 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 chronic stress regulator evaluated in the present invention includes a chronic stress suppressor or a chronic stress enhancer, and is preferably a chronic stress suppressor.

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 chronic stress control agent to be evaluated. When evaluating a chronic stress suppressant, preferably a human or a non-human mammal with a high chronic stress level is used as the subject, and when evaluating a chronic stress enhancer, preferably a human or a non-human mammal with a healthy chronic stress level is used. Preferably, the subject is a human.

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 chronic stress control agent. The test substance may be a naturally occurring substance or a substance artificially synthesized by a chemical or biological method, etc., and 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. Methods for applying the test substance to the subject include oral administration and parenteral administration such as injection, application, spraying, scattering, dripping, pasting, and irradiation, and are not particularly limited. The dose and application period of the test substance applied to the subject can be appropriately set depending on the type and administration form of the test substance.

The procedures for collecting biological samples from subjects, extracting markers from the collected biological samples, and measuring the expression levels of the markers are as described above.

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, depending on the chronic stress level of the subject. Whether or not the test substance is a chronic stress control agent can be evaluated based on the change in the expression level of the marker caused by the test substance. More specifically, a test substance that reduces the expression level of a positive marker or increases the expression level of a negative marker is determined to be a chronic stress suppressor. Conversely, a test substance that increases the expression level of a positive marker or reduces the expression level of a negative marker is determined to be a chronic stress enhancer.

In one embodiment, the effect of the test substance on chronic stress is evaluated by comparing the expression level of the marker of the present invention derived from the subject with a control. 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 reference value for evaluating the chronic stress level described above, etc.

In one example, if the expression level of a positive marker is reduced under application of a test substance compared to a control, the test substance can be determined to be a chronic stress suppressant. More specifically, if the expression level of a positive marker in a subject or subject group to which the test substance is applied is statistically significantly reduced compared to a control, the test substance can be determined to be a chronic stress suppressant. Alternatively, if the expression level of a positive marker in a subject or subject group to which the test substance is applied is reduced to preferably 90% or less, more preferably 75% or less, and even more preferably 50% or less compared to a control, the test substance can be determined to be a chronic stress suppressant.

In another example, if the expression level of a positive marker increases under application of a test substance compared to a control, the test substance can be determined to be a chronic stress enhancer. More specifically, if the expression level of a positive marker in a subject or subject group to which the test substance is applied is statistically significantly increased compared to a control, the test substance can be determined to be a chronic stress enhancer. Alternatively, if the expression level of a positive marker in a subject or subject group to which the test substance is applied is increased by preferably 110% or more, more preferably 150% or more, and even more preferably 200% or more compared to a control, the test substance can be determined to be a chronic stress enhancer.

In yet another example, if the expression level of the negative marker increases under application of the test substance compared to the control, the test substance can be determined to be a chronic stress suppressor. More specifically, if the expression level of the negative marker in the subject or subject group to which the test substance is applied is statistically significantly increased compared to the control, the test substance can be determined to be a chronic stress suppressor. Alternatively, if the expression level of the negative marker in the subject or subject group to which the test substance is applied is increased by preferably 110% or more, more preferably 150% or more, and even more preferably 200% or more compared to the control, the test substance can be determined to be a chronic stress suppressor.

In yet another example, if the expression level of the negative marker is reduced under application of the test substance compared to the control, the test substance can be determined to be a chronic stress enhancer. More specifically, if 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 determined to be a chronic stress enhancer. Alternatively, if the expression level of the negative marker in the subject or subject group to which the test substance is applied is reduced to preferably 90% or less, more preferably 75% or less, and even more preferably 50% or less compared to the control, the test substance can be determined to be a chronic stress enhancer.

(4. Chronic Stress Level Detection Kit and Chronic Stress Control Agent Evaluation Kit)
In a further aspect, the present invention provides a kit for detecting a chronic stress level for detecting a chronic stress level of a subject according to the method for detecting a chronic stress level according to the present invention, and a kit for evaluating a chronic stress controller for evaluating a chronic stress controller according to the method for evaluating a chronic stress controller according to the present invention. In one embodiment, the kit of the present invention comprises a reagent or instrument for measuring the expression level of the stress 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 chronic stress level, guidance explaining the reference value of the expression level of each marker for detecting the chronic stress level, or guidance on a discriminant based on a prediction model and a feature marker to be input thereto. 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), a reagent for extracting the marker of the present invention from the biological sample (e.g., a reagent for nucleic acid purification), a preservative or storage container for the sample collection device after collection of the biological sample, etc.

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

[1] A stress marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes.
[2] The stress marker according to [1], which preferably comprises all of the nucleic acids encoding each of the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, or all of the proteins encoded by each of the genes.
[3] The stress marker according to [1] or [2], 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 stress marker according to [1] or [2], 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 stress marker according to [4], which preferably comprises 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 stress marker according to [4], which preferably comprises 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 stress marker according to [1] or [2], 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 stress marker according to any one of [1] to [7], which is preferably a marker for detecting a chronic stress level.
[9] The stress marker according to any one of [1] to [8], which is preferably a nucleic acid marker.
[10] The stress marker described in [9], wherein the nucleic acid is preferably mRNA extracted from lipids on the surface of the skin.

[11] A method for detecting a chronic stress level in a subject, comprising measuring the expression level of the stress marker according to any one of [1] to [10] in a biological sample collected from the subject.
[12] The method described in [11], preferably further comprising detecting the chronic stress level of the subject based on the expression level of the stress marker.
[13] The stress 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 that the chronic stress level of the subject is high stress when the expression level of the stress marker is increased,
More preferably, the method includes detecting that the chronic stress level of the subject is high stress when the expression level of the stress marker is higher than a reference value.
The method described in [12].
[14] The stress 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 that the chronic stress level of the subject is high stress when the expression level of the stress marker is decreased,
More preferably, the method includes detecting that the chronic stress level of the subject is high stress when the expression level of the stress marker is lower than a reference value.
The method described in [12].
[15] The method described in [12], preferably comprising detecting the subject's chronic stress level based on a predictive model constructed using data on the expression level of the stress marker.
[16] Preferably, the predictive model is constructed by machine learning using data on the expression level of the stress marker obtained from each individual in a population including a high stress group and a low stress group or a healthy group as explanatory variables, and the chronic stress level of each individual in the population as a response variable. The method described in [15].

[17] A method for evaluating a chronic stress regulator, comprising measuring the expression level of the stress marker according to any one of [1] to [10] in a biological sample collected from a subject to which a test substance has been administered.
[18] The method according to [17], wherein the chronic stress control agent is preferably a chronic stress suppressant or a chronic stress enhancer, more preferably a chronic stress suppressant.
[19] The method according to [17] or [18], preferably further comprising evaluating whether the test substance is a chronic stress regulator based on a change in the expression level of the marker caused by the test substance.
[20] The stress 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 that the test substance is a chronic stress suppressor when the expression level of the stress marker under application of the test substance is reduced compared to a control, and determining that the test substance is a chronic stress enhancer when the expression level of the stress marker under application of the test substance is increased compared to a control.
The method described in [19].
[21] The stress 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 includes determining that the test substance is a chronic stress suppressor when the expression level of the stress marker under application of the test substance is increased compared to a control, and determining that the test substance is a chronic stress enhancer when the expression level of the stress marker under application of the test substance is decreased compared to a control.
The method described in [19].

[22] A kit for detecting chronic stress levels used in the method according to any one of [11] to [16] or a kit for evaluating a chronic stress regulator used in the method according to any one of [17] to [21], comprising an oligonucleotide that specifically hybridizes with a nucleic acid that is a stress marker according to any one of [1] to [10], or an antibody that recognizes a protein that is a stress marker according to any one of [1] to [10].

The present invention will be described in more detail below with reference to examples, but the present invention is not limited to these.

Example 1 Construction of a chronic stress level discrimination model using expression-varying genes 1) Selection of subjects and SSL collection 175 healthy men and women in their 20s to 60s were asked to answer the simplified version of the Occupational Stress Simple Questionnaire (23 items) (Ministry of Health, Labor and Welfare, 2016), and stress scores were calculated based on the answers. The lower the stress score, the higher the chronic stress. Nineteen subjects with a stress score of 11 points or less, which is the standard for being recognized as having high stress according to the Ministry of Health, Labor and Welfare standard (Revised version of the Stress Check System Implementation Manual Based on the Industrial Safety and Health Act, Ministry of Health, Labor and Welfare, 2016), were selected as subjects in the high stress group. Meanwhile, 20 subjects with a stress score of 23 points or more were selected as subjects in the low stress group. Sebum was collected from the entire face of each of the 39 subjects in both groups 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.

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 AmpliSeqTranscriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the conditions of [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.), and then buffer reconstitution, digestion of primer sequences, adapter ligation and purification, and amplification were performed 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.). Each read sequence obtained by sequencing was genetically mapped using hg19 AmpliSeq Transcriptome ERCC v1, which is a reference sequence of the human genome, to determine the gene from which each read sequence originated.

3) Data analysis and selection of feature genes The read count of each read obtained by sequencing the SSL-derived RNA of the subject in 2) above was used as the expression level data of each RNA. In order to correct the difference in the total read count 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 3179 genes for which expression level data other than missing values was obtained in 90% or more of the subjects, Student's t test was performed between groups (low stress group and high stress group) based on the expression information, and genes with p<0.05 were extracted. As shown in Table 6, 73 genes whose expression levels fluctuated statistically significantly between groups were obtained. The 49 genes in Table 6A were markers (positive markers) whose expression levels increased due to chronic stress because their expression levels were higher in the high stress group. The 24 genes in Table 6B were markers whose expression levels were decreased by chronic stress (negative markers) because their expression levels were lower in the high stress group. These genes were selected as feature genes to be used in the construction of the following prediction model.

4) Model Construction The expression level data of the feature genes selected in 3) was converted to a logarithmic value of base 2 (Log ₂ (RPM+1) value) by adding an integer 1 to approximate a normal distribution from the RPM value according to the negative binomial distribution. A prediction model for discriminating chronic stress level was constructed using the Log ₂ (RPM+1) values of the expression level data of the obtained 73 feature genes as explanatory variables and the chronic stress level (high stress or low stress) as the objective variable. 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. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the estimated error rate (OOB error rate). As a result, in a model using the expression level data of all 73 genes as variables, the OOB error rate was 25%, indicating that the genes shown in Table 6 can be used as markers for detecting chronic stress levels.

Example 2: Construction of a chronic stress level discrimination model using genes with high random forest variable importance 1) Data used 3,179 types of genes for which expression level data that was not a missing value was obtained for 90% or more of the subjects obtained in 3) of Example 1 were used in the following analysis. In order to approximate the gene expression level data from the RPM value that follows a negative binomial distribution to a normal distribution, the data was converted to a logarithmic value of base 2 by adding an integer 1 (Log ₂ (RPM+1) value).

2) Selection of Feature Genes Using the random forest algorithm, feature genes used in constructing a prediction model were selected. A prediction model for discriminating chronic stress levels was constructed using the Log ₂ (RPM+1) values of the expression level data of 3179 genes obtained in 1) as explanatory variables and chronic stress levels (high stress or low stress) as objective variables. 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 in constructing a decision tree once was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the top 140 genes in terms of variable importance based on the Gini coefficient were calculated (Table 7). These 140 genes were selected as feature genes to be used in constructing a prediction model.

3) Model Construction A prediction model for discriminating chronic stress level was constructed using the Log ₂ (RPM+1) values of the expression level data of the 140 feature genes selected in 2) as explanatory variables and the chronic stress level (high stress or low stress) as the objective variable. 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 once 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, in the model when the expression level data of 140 genes was used as a variable, the OOB error rate was 30%, and it was shown that the genes shown in Table 7 can be used as markers for detecting chronic stress levels.

Example 3 Construction of a chronic stress level discrimination model based on feature genes used in duplicate in Examples 1 and 2 1) Selection of feature genes Among the feature genes used in Examples 1 and 2, the genes used in duplicate between the examples were HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, which were 16 genes. These 16 genes were selected as feature genes to be used in constructing a prediction model.

2) Model Construction A prediction model for discriminating chronic stress level was constructed using the Log ₂ (RPM+1) values of the expression level data of the 16 feature genes selected in 1) as explanatory variables and the chronic stress level (high stress or low stress) as the objective variable. In the "caret" package of the R language, the random forest algorithm was specified as a method, and the optimal value of the number of variables (mtry value) used to construct a decision tree once was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed, and the estimated error rate (OOB error rate) was calculated. As a result, in the model when the expression level data of the 16 genes was used as a variable, the OOB error rate was 15%, and it was shown that the above 16 genes can be used as a marker for detecting chronic stress level.

Example 4 Construction of a chronic stress level discrimination model based on all feature genes used in Examples 1 and 2 1) Selection of feature genes All of the feature genes used in either Example 1 or 2 (a total of 197 types of genes shown in Table 1) were selected as feature genes to be used in constructing a prediction model.

2) Model Construction A prediction model for discriminating chronic stress level was constructed using the Log ₂ (RPM+1) values of the expression level data of the 197 feature genes selected in 1) as explanatory variables and the chronic stress level (high stress or low stress) as the objective variable. 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 once 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, in the model when the expression level data of the 197 genes was used as a variable, the OOB error rate was 20%, and it was shown that the 197 genes in Table 1 can be used as a marker for detecting chronic stress level.

Claims (6)

A method for detecting a chronic stress level in a subject, comprising measuring the expression levels of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes, in a biological sample collected from the subject. The method of claim 1, wherein the nucleic acid is mRNA extracted from lipids on the skin surface. The method of claim 1 or 2, further comprising detecting a chronic stress level of the subject based on the expression level of the nucleic acid or protein. The method according to claim 3, comprising detecting the chronic stress level of the subject based on a prediction model constructed using data on the expression level of the nucleic acid or protein obtained from each individual in a teacher sample population as an explanatory variable and the chronic stress level of each individual in the teacher sample population as a response variable. A method for evaluating a chronic stress regulator, comprising measuring the expression levels of all nucleic acids or all proteins selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by said genes, in a biological sample collected from a subject to which a test substance has been administered. A set of oligonucleotides that specifically hybridize to each of the nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7 ; or
A set of antibodies that recognize each of the proteins encoded by the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7;
A kit for detecting a chronic stress level or a kit for evaluating a chronic stress regulator, which is used in the method according to any one of claims 1 to 5, comprising: