JP2022097301A - Stress marker, and method for detecting chronic stress level by using the same - Google Patents

Abstract

To provide a method for detecting a chronic stress level by using a biomarker.SOLUTION: A method for detecting a chronic stress level of a subject includes measuring the expression level of at least one selected from the group consisting of nucleic acids encoding 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 taken from the subject.SELECTED DRAWING: None

Description

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

Stress is considered to be a change in mental and physical functions that occurs when various physical, environmental, and psychological stimuli and loads are received. In modern society, many people, mainly working generations, live with stress. It is known that stress adversely affects the psychological and physical aspects, causes deterioration of quality of life (QOL), and is involved in the onset and exacerbation of various diseases.

Stress is broadly divided into acute stress and chronic stress caused by continuous stress loading. Among them, chronic stress is known to be a factor of various diseases such as mental illness, and it is desired to understand the mechanism and develop improvement techniques. An evaluation index of stress is necessary for developing improvement technology. From this point of view, chromogranin A (Non-Patent Document 1) in addition to cortisol is used for acute stress, and α-croto (non-patent) is used for chronic stress. Stress markers as disclosed in Patent Documents 2) and Patent Documents 1 to 4 have been proposed. However, many of these stress markers can only be harvested in an invasive manner and are not always associated with chronic stress. Therefore, an objective or quantitative evaluation method for chronic stress using biomarkers has not yet been established.

Techniques for investigating the current and future physiological states of human organisms by analyzing nucleic acids such as DNA and RNA in biological samples have been developed. Nucleic acid derived from a living body can be extracted from tissues such as blood, body fluids, secretions and the like. Patent Document 5 describes that nucleic acids such as RNA derived from the skin cells of a subject are separated from lipids (SSL) on the surface of the skin and used as a sample for analysis of a living body.

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

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

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

In one aspect, the invention encodes the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPT M4A, EGR2, ITM2B, and TAF7. Provided are stress markers comprising at least one selected from the group consisting of proteins encoded by the gene.
In another embodiment, the present invention provides a method for detecting a chronic stress level of a subject, which comprises measuring the expression level of the stress marker in a biological sample taken from the subject.
In another embodiment, the present invention provides a method for evaluating a chronic stress control agent, which comprises measuring the expression level of the stress marker in a biological sample collected from a subject to which the test substance is applied.
In another aspect, the present invention provides a kit for detecting a chronic stress level or a kit for evaluating a chronic stress control agent used in the method for detecting a chronic stress level.

The present invention provides a novel stress marker. The stress markers of the present invention allow detection of chronic stress levels. Further, since the stress marker of the present invention can be collected non-invasively from the lipid (SSL) on the surface of the skin, it is possible to reduce the labor of collection and the burden on the subject due to the collection.

All patented, non-patented, and other publications cited herein are incorporated herein by reference in their entirety.

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

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

As used herein, "skin" is a general term for regions including the epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands on the body surface, unless otherwise specified.

As used herein, "high stress" (or "subject is under stress") refers to a condition of chronically high stress levels. That is, it indicates that the level of chronic stress is high. The level of chronic stress can be scored based on interviews and questionnaires. For example, in the simplified version of the simple occupational stress questionnaire (23 items) according to the revised version of the stress check system implementation manual (Ministry of Health, Labor and Welfare, 2016) based on the Industrial Safety and Health Act, the stress score based on subjective scoring (maximum score 25 points, The level of chronic stress can be evaluated as a minimum score of 5 points). The stress score according to the simple occupational stress questionnaire shows that the lower the value, the higher the stress. Therefore, in the present specification, the chronic stress level corresponding to "high stress" means a state in which the stress score of the simplified version of the occupational stress simple questionnaire (23 items) is 11 points or less, and is defined as "low stress". The corresponding chronic stress level means a state in which the stress score is 23 points or more. Further, in the present specification, the term “healthy” as the chronic stress level means a state in which the stress score of the simplified version of the occupational stress simple questionnaire (23 items) is 12 to 25 points.

As used herein, the term "detection" of a chronic stress level can also be paraphrased in terms such as test, measurement, determination or evaluation support. The terms "detection," "test," "measurement," "determination," or "evaluation" of chronic stress levels herein do not include the diagnosis of stressful disease by a physician.

(1. Stress marker)
Conventionally, chronic stress has been evaluated mainly by interviews and scoring based on questionnaires (for example, the above-mentioned simplified version of the simple occupational stress questionnaire (23 items)). Such evaluation methods may be inferior in objectivity and quantitativeness, and make it difficult to compare evaluations between tests.

The present inventor has found that the chronic stress level of a subject is reflected in the expression level of a particular gene. As shown in the examples described later, genes having different expression levels were found between the high stress group and the low stress group determined based on the simplified version of the simple occupational stress questionnaire (23 items). Nucleic acids encoding genes exhibiting different expression levels between these two groups, and proteins encoded by the genes can be used as stress markers. For example, the chronic stress level can be detected by using the expression level of the nucleic acid encoding these genes or the protein encoded by the gene as an index.

Therefore, in one aspect, the invention provides a stress marker for detecting chronic stress levels. The stress markers provided in the present invention (hereinafter, also referred to as the markers of the present invention) make it possible to detect the chronic stress level of an individual subject from which the marker is derived.

The marker of the present invention may include at least one selected from the group consisting of the nucleic acid encoding the 197 gene shown in Table 1 below and the protein encoded by the gene. The marker of the present invention may be a nucleic acid (nucleic acid marker) encoding a gene shown in Table 1 below, a protein encoded by the gene (protein marker), or a combination thereof. , Preferably a nucleic acid marker.

In one embodiment, the markers of the present invention are a nucleic acid encoding a gene shown in Tables 2 and 3 (hereinafter, also referred to as a nucleic acid marker in Tables 2 and 3, respectively), and a protein encoded by the gene (hereinafter, also referred to as a protein). It contains at least one selected from the group consisting of (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 the markers in Table 2, and the nucleic acid markers and protein markers in Table 3 may be collectively referred to as the markers in Table 3. Preferably, the markers in Tables 2 and 3 are nucleic acid markers. As shown in Examples described later, the expression levels of the 49 genes shown in Table 2 increased according to the chronic stress level of the subject, and the 24 genes shown in Table 3 were expressed according to the chronic stress level of the subject. The level drops. Therefore, the markers in Table 2 are positive markers whose expression levels increase with the subject's chronic stress level. On the other hand, the markers in Table 3 are negative markers whose expression levels decrease with the chronic stress level of the subject. In the present invention, either one of the former positive marker and the latter negative marker may be used, or both may be used in combination.

In another embodiment, the markers of the present invention are nucleic acids encoding 140 genes shown in Table 4 (hereinafter, also referred to as nucleic acid markers in Table 4), and proteins encoded by the genes (hereinafter, proteins in Table 4). It contains at least one selected from the group consisting of (also referred to as a marker). The nucleic acid markers and protein markers in Table 4 may be collectively referred to as the markers in Table 4. Preferably, the marker in Table 4 is a nucleic acid marker. As shown in Examples described later, the genes shown in Table 4 are featured gene using the data of the expression level in the subject as the explanatory variable, the chronic stress level as the objective variable, and the random forest as the machine learning algorithm. It is a gene corresponding to the top 140 variable importance based on the Gini coefficient, which was extracted when the prediction model was constructed.

In a preferred embodiment, the markers of the present invention are nucleic acids encoding the 16 genes shown in Table 5 (hereinafter, also referred to as nucleic acid markers in Table 5), and proteins encoded by the genes (hereinafter, protein markers in Table 5). Also includes at least one selected from the group consisting of (also referred to as). The nucleic acid markers and protein markers in Table 5 may be collectively referred to as the markers in Table 5. Preferably, the marker in Table 5 is a nucleic acid marker. The genes shown in Table 5 are genes commonly contained in Table 2 or Table 3 and Table 4.

In a preferred embodiment, the markers of the invention are at least one selected from the group consisting of the markers in Table 5 and at least one selected from the group consisting of the markers in Table 1 (but in Table 5). It may contain something other than a marker). For example, the markers of the present invention are at least selected from the group consisting of the markers in Table 2, the markers in Table 3, or the markers in Table 4, in addition to at least one selected from the group consisting of the markers in Table 5. It may contain one kind (but other than the markers in Table 5). For example, the marker of the present invention contains at least one selected from the group consisting of nucleic acid markers in Table 5, preferably all, and at least selected from the group consisting of nucleic acid markers in Table 2, Table 3 or Table 4. It may contain one kind (however, other than the nucleic acid markers shown in Table 5). For example, the marker of the present invention contains at least one selected from the group consisting of the protein markers in Table 5, preferably all, and at least selected from the group consisting of the protein markers in Table 2, Table 3 or Table 4. It may contain one (but not 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 are used in combination as the markers of the 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 the markers of the invention.
In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 4 are used in combination as the markers of the invention.
In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 5 are used in combination as the markers of the invention.
In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 1 are used in combination as the markers of the invention.

The stress marker of the present invention is obtained from a biological sample collected from a subject, for example, cells, stratum corneum, tissue (biopsy, etc.), body fluid (blood, etc.), urine, secretions (saliva, SSL, etc.), etc. according to a conventional method. Can be prepared. For example, commercially available kits can be used to prepare nucleic acids or proteins from biological samples. Preferably, the marker of the present invention is a nucleic acid marker, and preferred examples of nucleic acids prepared from biological samples include genomic DNA and RNA such as mRNA.

Examples of subjects from which biological samples containing the markers of the invention are collected include mammals, including human and non-human mammals. Preferably, the subject is a human and non-human mammal that requires detection of chronic stress levels, or a human who desires detection of chronic stress levels. More preferably, the subject is a human.

More preferably, the marker of the invention is a nucleic acid or protein prepared from the subject's SSL, more preferably mRNA. Examples of the skin portion from which the SSL is collected include skin at any portion of the body such as the head, face, neck, trunk, and limbs, and a portion with a large amount of sebum secretion, for example, facial skin is preferable.

For the collection of SSL from the skin of a subject, any means used to recover or remove SSL from the skin can be employed. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument that scrapes SSL from the skin, which will be described later, can be used. The SSL absorbent material or the SSL adhesive material is not particularly limited as long as it is a material having an affinity for SSL, and examples thereof include polypropylene and pulp. As a more detailed example of the procedure for collecting SSL from the skin, a method of absorbing SSL into a sheet-like material such as an oil removing paper or an oil removing film, a method of adhering SSL to a glass plate, a tape, etc., a spatula, a scraper, etc. There is a method of scraping off the SSL and collecting it. In order to improve the adsorptivity of SSL, an SSL-absorbing material pre-impregnated with a highly lipophilic solvent may be used. On the other hand, if the SSL-absorbent material contains a highly water-soluble solvent or water, the adsorption of SSL is inhibited, so that it is preferable that the content of the highly water-soluble solvent or water is small. The SSL absorbent material is preferably used in a dry state.

The collected SSL may be immediately used in the nucleic acid or protein extraction step described later, or may be stored until it is used in the nucleic acid or protein extraction step. When stored, SSL is preferably stored under low temperature conditions. The temperature conditions for storing SSL are preferably 10 ° C. or lower, more preferably 5 ° C. or lower, still more preferably 0 ° C. or lower, still more preferably -20 ± 20 ° C. to -80 ± 20 ° C., still more preferably -20 ± 10 ° C. ° C to −80 ± 10 ° C, more preferably −20 ± 5 ° C to −80 ± 5 ° C. The storage period of 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, still more preferably 3 months or less, for example. 3 days or more and 3 months or less.

For the extraction of nucleic acid or protein from the collected SSL, the method usually used for extraction or purification of nucleic acid or protein from a biological sample can be used. Examples of nucleic acid extraction or purification methods include phenol / chloroform methods, AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) methods, or columns such as TRIzol®, RNAy®, and QIAzol®. , A method using special magnetic particles coated with silica, a method using Solid Phase Reversible Immobilization magnetic particles, extraction with a commercially available RNA extraction reagent such as ISOGEN, and the like. Commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen) can be used for protein extraction or purification.

(2. Chronic stress level detection method)
(2.1. Detection of chronic stress level based on marker expression level)
In another aspect, the invention provides a method of detecting a subject's chronic stress level using the stress markers of the invention. In this method, the chronic stress level of a subject is detected based on the expression level of the marker of the present invention in the subject. The subject to which the method for detecting the chronic stress level according to the present invention is applied is the same as the subject from which the biological sample containing the stress marker of the present invention described above 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 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 invention may further comprise collecting the SSL of the subject. The procedure for collecting SSL and the procedure for extracting markers from SSL are as described above.

The expression level of the marker of the present invention can be measured according to the nucleic acid or protein quantification method commonly used in the art. 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 art. Examples of methods for gene expression analysis include methods for quantifying DNA or its amplification products by real-time PCR, multiplex PCR, microarray, sequencing, chromatography and the like. When the nucleic acid is RNA, it can be quantified by the above method after converting RNA into cDNA by reverse transcription. The expression level of a protein marker can be measured using protein quantification methods commonly used in the art, such as ELISA, immunostaining, fluorescence, electrophoresis, chromatography, mass spectrometry and the like. The calculated marker expression level may be an expression level based on the absolute amount of the target marker in a biological sample, or a relative expression level relative to the expression level of another standard gene or all nucleic acids or all proteins. good.

Preferably, the marker of the invention used in the method for detecting chronic stress levels according to the invention is SSL-derived mRNA. Preferably, the expression level of the SSL-derived RNA is measured by converting the RNA extracted from SSL into cDNA by reverse transcription and then quantifying the cDNA or its amplification product by the above method. Primers targeting the specific RNA to be analyzed may be used for the reverse transcription, but random primers are preferably used for more comprehensive nucleic acid storage and analysis. A general reverse transcriptase or reverse transcriptase kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcriptase kit is used, for example, M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcriptase kits. For example, PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Co., Ltd.), SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific) and the like can be mentioned. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both from Thermo Scientific) and the like are preferably used. In the PCR of the obtained cDNA, only the specific DNA may be amplified using a primer pair targeting the specific DNA to be analyzed, or a plurality of DNAs may be amplified using a plurality of primer pairs. good. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method of simultaneously amplifying a plurality of gene regions by simultaneously using a plurality of primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (for example, Ion AmpliSeqTranstriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., 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 PCR reaction product 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., still more preferably 42 ° C. ± 0.25 ° C. The reaction time is preferably adjusted to 60 minutes or longer, more preferably 80 to 120 minutes. Further, 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., still more preferably 62 ° C. ± 0.25 ° C. Therefore, in the PCR, the annealing and extension reactions are preferably carried out 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 and the like, 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, preferably 95 to 99 ° C. for 10 to 60 seconds. Reverse transcription and PCR at temperature and time as described above can be performed using a thermal cycler commonly used for PCR.

Purification of the reaction product obtained by the PCR is preferably performed by size separation of the reaction product. By size separation, the PCR reaction product of interest can be separated from the primers and other impurities contained in the PCR reaction solution. The size separation of DNA can be performed by, for example, a size separation column, a size separation chip, magnetic beads that can be used for size separation, and the like. Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Aple XP.

The purified PCR reaction product may be subjected to further treatment necessary for subsequent quantitative analysis. For example, for sequencing DNA, the purified PCR reaction product can be prepared into a suitable buffer solution, the PCR primer region contained in the PCR amplified DNA can be cleaved, or the adapter sequence can be added to the amplified DNA. May be further added. For example, the purified PCR reaction product is prepared into a buffer solution, the PCR primer sequence is removed and adapter ligation is performed on the amplified DNA, and the obtained reaction product is amplified as necessary for quantitative analysis. Library can be prepared. These operations are performed, for example, 5 × VILO RT Reaction Mix attached to SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.), and Ion AmpliSeq Transcriptome Human Gene Expression Japan Co., Ltd. It can be performed according to the protocol included in each kit using the 5 × Ion AmpliSeq Transcriptome Human Gene Expression Core Panel that comes with the Ion AmpliSeq Transcriptome HiFi Mix. A next-generation sequencer (for example, Ion S5 / XL system, Life Technologies Japan Co., Ltd.) is preferably used for sequencing. RNA expression levels can be quantified based on the number of reads created by sequencing (read count).

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

As mentioned above, the nucleic acid markers and protein markers in Table 2 are positive markers whose expression levels increase with the chronic stress level of the subject, while the nucleic acid markers and protein markers in Table 3 are their expression levels. Is a negative marker that decreases with the subject's chronic stress level. Therefore, if the expression level of the positive marker is increased, the subject is detected to be chronically stressed (ie, the subject's chronic stress level is high stress), otherwise the subject is chronically stressed. It is detected that it is not below (ie, the subject's chronic stress level is low stress or healthy). Alternatively, if the expression level of the negative marker is reduced, the subject is detected to be chronically stressed, otherwise the subject is detected to be chronically unstressed. Alternatively, in the method for detecting a chronic stress level according to the present invention, the positive marker and the negative marker may be used in combination.

In one embodiment, the chronic stress level of the subject can be detected by comparing the expression level of the marker of the present invention measured from the subject with the reference value. Alternatively, the chronic stress level of the subject can be detected by periodically (eg, monthly) measuring the expression level of the marker of the present invention from one subject and tracking the fluctuation of the expression level. As the reference value, a predetermined value, for example, a statistical value (for example, an average value) of the expression level of the marker of the present invention can be used. 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 is a reference group (a group consisting of individuals having an arbitrary chronic stress level) and a healthy group (a group in which the chronic stress level is in the normal range [for example, the simplified version of the above-mentioned simple occupational stress questionnaire (23 items)). The stress score of the group consisting of individuals with a stress score of 12 to 25 points] or the low stress group (the chronic stress level is in the low stress range [for example, the simplified version of the above-mentioned simple occupational stress questionnaire (23 items)) is 23. It can be determined based on the statistical value (for example, the average value) of the expression level of the marker of the present invention measured from the group consisting of individuals having a score or higher. When a plurality of types of nucleic acids or proteins are used as the markers of the present invention, it is preferable to obtain a reference value for each nucleic acid or protein.

For example, if the marker of the invention is a positive marker, if the expression level of the marker is higher than the reference value, the subject can be detected as chronically under stress, otherwise the subject is chronically stressed. It can be detected that it is not under stress. For example, if the expression level of a positive marker in a subject is statistically significantly higher than the reference value, the subject can be detected as chronically under stress, otherwise the subject is chronic. Can be detected as not under stress. Further, for example, if the expression level of the positive marker in the subject is preferably 110% or more, more preferably 150% or more, still more preferably 200% or more with respect to the reference value, the subject is chronically under stress. If not, the subject may be detected as chronically not under stress.

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 chronically under stress, and if not, the subject is chronically. It can be detected that it is not under stress. 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 chronically under stress, otherwise the subject is chronic. Can be detected as not under stress. Further, for example, if the expression level of the negative marker in the subject is preferably 90% or less, more preferably 75% or less, still more preferably 50% or less with respect to the reference value, the subject is chronically under stress. If not, the subject may be detected as chronically not under stress.

Alternatively, when a plurality of nucleic acids or proteins are used in combination as a marker, a certain percentage of those nucleic acids or proteins, for example, 50% or more, preferably 70% or more, more preferably 90% or more, still more preferably 100%. The subject's chronic stress level can be detected based on whether or not the expression level criteria described above are met.

(2.2. Detection of chronic stress level based on predictive model)
In another embodiment of the method for detecting a chronic stress level according to the present invention, data on the expression level of the marker of the present invention (marker of the present invention contained in a biological sample collected from the subject) (hereinafter, expression profile). The chronic stress level of the subject is detected based on the predictive model constructed using (referred to as). Examples of expression profiles include data on expression levels such as the number of sequencing reads.

For example, the expression profile for each of one or more markers (nucleic acid or protein) obtained from each individual in the teacher sample population (eg, a population including a high stress group and a low stress group or a healthy group) is used as an explanatory variable. , A predictive model (eg, discriminant formula) for discriminating the chronic stress level in any subject by machine learning using data on the chronic stress level (for example, the presence or absence of high stress) of each individual in the population as an objective variable. Can be built.

In the present specification, "feature amount" is synonymous with "explanatory variable" in machine learning. In the present specification, a marker whose expression profile is used as an explanatory variable (feature amount) for machine learning may be referred to as a "feature amount marker (group)". When the feature amount marker is a nucleic acid marker, it may be referred to as a feature amount gene.

The feature amount marker (group) used in this embodiment may be at least one selected from the group consisting of the nucleic acid marker and the protein marker in Table 1. The expression profile of the feature amount marker may be an absolute value, a relative value, or may be normalized. When a plurality of markers are used as the feature amount marker group, for example, a plurality of markers having a high correlation with the chronic stress level 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 amount 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 amount 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 amount marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 5 and any one or more, preferably all (but table) of any one or more of the nucleic acid markers or protein markers in Table 2, Table 3 or Table 4. It is used as a feature amount marker group in combination with a marker other than the marker of 5. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 1 are combined and used as a feature amount marker group.

In a preferred embodiment, expression profiles of individuals with high chronic stress levels (high stress group) and individuals with low chronic stress level (low stress group) are used as teacher samples for machine learning. Using the teacher sample, a discriminant (prediction model) for dividing an individual into a high stress group and a low stress group is constructed. As the explanatory variable used for constructing the discriminant, the expression profile of the feature marker (group) can be used. As the objective variable, for example, a variable indicating whether the individual from which the feature amount marker (group) is derived is a high stress group or a low stress group can be used. Based on the constructed discriminant, the cutoff value for discriminating the high stress group can be obtained. Next, the expression profile of the characteristic amount marker (group) derived from the subject whose chronic stress level is to be investigated is measured, the obtained measured value is substituted into the discriminant, and the result obtained from the discriminant is cut. By comparing with the off value, it is determined whether or not the subject is in the high stress group.

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

Alternatively, when the expression profiles of a plurality of markers are used for constructing the predictive model, the predictive model may be constructed after compressing the data by dimension reduction, if necessary. For example, a plurality of markers are extracted from the nucleic acid markers encoding the gene clusters shown in Table 1. Next, principal component analysis is performed on the expression profile of the extracted marker. By machine learning with one or more principal components calculated by the principal component analysis as the explanatory variables and the variable representing whether the individual from which the explanatory variables are derived is the high stress group or the low stress group as the objective variable, the subject It is possible to construct a predictive model for determining whether or not the group is in the high stress group.

As the algorithm for constructing the prediction model, a known algorithm such as an algorithm used for machine learning can be used. Examples of machine learning algorithms are, but not limited to, linear regression models, lassos, random forests, neural nets, and linear kernel support vector machines (SVMs). Algorithms such as linear)), support vector machine of rbf kernel (SVM (rbf)) can be mentioned. Random forest is preferable as the algorithm used in the present invention.

A model in which verification data is input to the constructed prediction model to calculate a prediction value, and the prediction value best matches the measured value, for example, a model having the largest accuracy with respect to the measured value of the predicted value. Alternatively, the model having the smallest root mean square error (RMSE) of the difference between the predicted value and the measured value can be selected as the optimum model. By inputting the expression profile of the marker of the present invention measured from the subject into the constructed predictive model, the chronic stress level of the subject can be detected.

(3. Evaluation method of chronic stress control agent)
In a further aspect, the present invention provides a method for evaluating a chronic stress control agent using the stress marker of the present invention. The method evaluates a chronic stress regulator based on the expression level of the markers of the invention. Preferably, the method comprises measuring the expression level of the marker of the invention in a biological sample taken from a subject to which the test substance has been applied. The types of biological samples used in this method are as described above. Preferably, the biological sample is SSL and the marker of the invention is SSL-derived mRNA. In one embodiment, the method may further comprise collecting the SSL of the subject. Examples of the chronic stress control agent evaluated in the present invention include a chronic stress suppressant or a chronic stress enhancer, and a chronic stress suppressant is preferable.

The subject from which the biological sample is taken is a human or non-human mammal. Appropriate subjects can be appropriately selected according to the chronic stress control agent to be evaluated. As the subject, a human or a non-human mammal having a high chronic stress level is preferably used when evaluating a chronic stress suppressant, and a chronic stress level is preferably used when evaluating a chronic stress enhancer. Healthy humans or non-human mammals are 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 desired to be used as a chronic stress control agent. The test substance may be a naturally occurring substance, a substance artificially synthesized by a chemical or biological method, or a compound, a composition or a mixture. good. 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. Further, the test substance may be a physical stimulus such as light irradiation, heat, or massage. Examples of the method for applying the test substance to the subject include oral administration and parenteral administration such as injection, application, spraying, spraying, dropping, sticking, and irradiation, and the present invention is not particularly limited. The dose and application period of the test substance to be applied to the subject can be appropriately set according to the type and administration form of the test substance.

The procedure for collecting a biological sample from a subject, extracting a marker from the collected biological sample, and measuring the expression level of the marker is as described above.

The expression level of the extracted marker of the present invention derived from the subject is increased if it is a positive marker and decreased 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 depending on the test substance. More specifically, a test substance that lowers the expression level of a positive marker or a test substance that increases the expression level of a negative marker is determined to be a chronic stress suppressant. Conversely, a test substance that increases the expression level of a positive marker or a test substance that lowers the expression level of a negative marker is judged 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 the control. More specifically, it is investigated whether the expression level of the marker of the present invention under the application of the test substance is changed as compared with the control. As a control, the expression level of the marker of the present invention without application of the test substance, the reference value for evaluating the above-mentioned chronic stress level, and the like can be mentioned.

In one example, if the expression level of the positive marker under the application of the test substance is lower than that of the control, the test substance can be judged as a chronic stress suppressant. More specifically, if the expression level of the positive marker in the subject or group of subjects to which the test substance is applied is statistically significantly lower than that of the control, the test substance is considered to be a chronic stress suppressant. Can be judged. Alternatively, if the expression level of the positive marker in the subject or group of subjects to which the test substance is applied is preferably reduced to 90% or less, more preferably 75% or less, still more preferably 50% or less with respect to the control. , The test substance can be determined to be a chronic stress suppressant.

In another example, if the expression level of the positive marker under the application of the test substance is increased compared to the control, the test substance can be determined to be a chronic stress enhancer. More specifically, if the expression level of the positive marker in the subject or group of subjects to which the test substance is applied is statistically significantly increased as compared with the control, the test substance is considered to be a chronic stress enhancer. Can be judged. Alternatively, if the expression level of the positive marker in the subject or group of subjects to which the test substance is applied is preferably increased to 110% or more, more preferably 150% or more, still more preferably 200% or more with respect to the 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 under the application of the test substance is increased compared to the control, the test substance can be determined to be a chronic stress suppressant. More specifically, if the expression level of the negative marker in the subject or group of subjects to which the test substance is applied is statistically significantly increased as compared with the control, the test substance is considered to be a chronic stress suppressant. Can be judged. Alternatively, if the expression level of the negative marker in the subject or group of subjects to which the test substance is applied is preferably increased to 110% or more, more preferably 150% or more, still more preferably 200% or more with respect to the control. , The test substance can be determined to be a chronic stress suppressant.

In yet another example, if the expression level of the negative marker under application of the test substance is lower than that of 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 group of subjects to which the test substance is applied is statistically significantly reduced as compared with the control, the test substance is regarded as a chronic stress enhancer. Can be judged. Alternatively, if the expression level of the negative marker in the subject or group of subjects to which the test substance is applied is preferably reduced to 90% or less, more preferably 75% or less, still more preferably 50% or less with respect 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 invention follows a chronic stress level detection kit for detecting a subject's chronic stress level according to the chronic stress level detection method according to the invention, and a chronic stress according to the method for evaluating a chronic stress control agent according to the present invention. A kit for evaluating a chronic stress control agent for evaluating a control agent is provided. In one embodiment, the kit of the invention comprises a reagent or instrument for measuring the expression level of the stress marker of the invention described above. For example, the kit of the present invention is a reagent for amplifying or quantifying the nucleic acid marker of the present invention (for example, a reverse transcription enzyme, a reagent for PCR, a primer, a probe, an adapter sequence for sequencing, etc.), or a protein marker of the present invention. Can be equipped with reagents for quantifying (eg, reagents for immunological measurements, antibodies, etc.). Preferably, the kit of the invention contains an oligonucleotide (eg, a primer or probe for PCR) that specifically hybridizes to the nucleic acid marker of the invention, or an antibody that recognizes the protein marker of the invention. Preferably, the kit of the invention comprises indicators or guidance for detecting the expression level of the markers of the invention. For example, the kit of the present invention provides guidance explaining the relationship between the increase / decrease in the expression level of each marker and the chronic stress level, or guidance or prediction explaining the reference value of the expression level of each marker for detecting the chronic stress level. It may be equipped with a discriminant based on a model and guidance on a feature amount marker to be input to the discriminant. The kit of the present invention also includes a biological sampling device (for example, the above-mentioned SSL-absorbing material or SL adhesive material), a reagent for extracting the marker of the present invention from a biological sample (for example, a reagent for purifying a nucleic acid), and a biological sample. A preservative for the sampling device after sampling, a storage container, and the like may be further provided.

As an exemplary embodiment of the present invention, the following substances, production methods, uses, methods and the like are further disclosed herein. However, the present invention is not limited to these embodiments.

[1] The following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7-encoding nucleic acids, as well as the genes encoded by the genes. A stress marker comprising at least one selected from the group consisting of proteins.
[2] Preferably, all of the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7. , Or the stress marker according to [1], which comprises all of the proteins encoded by each of the genes.
[3] The stress according to [1] or [2], preferably further comprising at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 1 and the protein encoded by the gene. marker.
[4] preferably, [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. Described stress marker.
[5] The stress marker according to [4], preferably comprising at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 2 and the protein encoded by the gene.
[6] The stress marker according to [4], preferably comprising at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 3 and the protein encoded by the gene.
[7] The stress according to [1] or [2], preferably further comprising at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 4 and the protein encoded by the gene. marker.
[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 according to [9], wherein the nucleic acid is preferably an mRNA collected from a lipid on the surface of the skin.

[11] A method for detecting a chronic stress level of a subject, which comprises 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 according to [11], preferably comprising detecting the chronic stress level of the subject based on the expression level of the stress marker.
[13] The stress marker is
Preferably, it is at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 2 and the protein encoded by the gene.
More preferably, it is at least one selected from the group consisting of lipid-derived mRNAs derived from lipids on the surface of the skin encoding the genes shown in Table 2.
And the method is
Preferably, it comprises detecting that the subject's chronic stress level is high stress when the expression level of the stress marker is increased.
More preferably, it comprises detecting that the subject's chronic stress level is high stress when the expression level of the stress marker is higher than the reference value.
[12] The method according to the description.
[14] The stress marker is
Preferably, it is at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 3 and the protein encoded by the gene.
More preferably, it is at least one selected from the group consisting of mRNA derived from lipids on the surface of the skin encoding the genes shown in Table 3.
And the method is
Preferably, it comprises detecting that the subject's chronic stress level is high stress when the expression level of the stress marker is reduced.
More preferably, it comprises detecting that the subject's chronic stress level is high stress when the expression level of the stress marker is lower than the reference value.
[12] The method according to the description.
[15] The method according to [12], preferably comprising detecting the chronic stress level of the subject based on a predictive model constructed using data on the expression level of the stress marker.
[16] Preferably, the prediction model uses data on the expression level of the stress marker obtained from each individual in a group including a high stress group and a low stress group or a healthy group as an explanatory variable, and each individual in the group. The method according to [15], which is constructed by machine learning with the chronic stress level of the above as an objective variable.

[17] A method for evaluating a chronic stress control agent, which comprises 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 is applied.
[18] The method according to [17], wherein the chronic stress control agent is preferably a chronic stress suppressant or a chronic stress enhancer, and more preferably a chronic stress suppressant.
[19] preferably further comprises assessing whether the test substance is a chronic stress control agent based on changes in the expression level of the marker by the test substance, [17] or [18]. the method of.
[20] The stress marker is
Preferably, it is at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 2 and the protein encoded by the gene.
More preferably, it is at least one selected from the group consisting of lipid-derived mRNAs derived from lipids on the surface of the skin encoding the genes shown in Table 2.
And the method is
Preferably, if the expression level of the stress marker under the application of the test substance is lower than that of the control, the test substance is judged to be a chronic stress inhibitor, and the stress marker under the application of the test substance is used. If the expression level is increased compared to the control, the test substance is considered to be a chronic stress enhancer.
[19] The method according to the description.
[21] The stress marker is
Preferably, it is at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 3 and the protein encoded by the gene.
More preferably, it is at least one selected from the group consisting of lipid-derived mRNAs derived from lipids on the surface of the skin encoding the genes shown in Table 3.
And the method is
Preferably, if the expression level of the stress marker under the application of the test substance is increased as compared with the control, the test substance is judged to be a chronic stress inhibitor, and the stress marker under the application of the test substance is used. If the expression level is lower than that of the control, the test substance is considered to be a chronic stress enhancer.
[19] The method according to the description.

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

Hereinafter, the present invention will be described in more detail based on examples, but the present invention is not limited thereto.

Example 1 Construction of a chronic stress level discrimination model using expression-variable genes 1) Selection of subjects and collection of SSL For 175 healthy men and women in their 20s and 60s, a simplified version of the simple occupational stress questionnaire (23 items) ( The Ministry of Health, Labor and Welfare, 2016) was asked to answer, and the stress score was calculated based on the answer results. The lower the stress score, the higher the chronic stress, which is the standard recognized as high stress by the Ministry of Health, Labor and Welfare standards (Revised version of the stress check system implementation manual based on the Industrial Safety and Health Act, Ministry of Health, Labor and Welfare, 2016). 19 subjects with a stress score of 11 points or less were selected as subjects in the high stress group. On the other hand, 20 subjects having a stress score of 23 points or more were selected as subjects in the low stress group. Sebum was collected from the entire faces of 39 subjects in both groups using a degreasing film (5 cm x 8 cm, made of polypropylene, 3M company). The degreasing film was transferred to a glass vial and stored at −80 ° C. until used for RNA extraction.

2) RNA preparation and sequencing The oil removal 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 a SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) to synthesize cDNA. The random primer included in the kit was used as the primer for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR. Multiplex PCR was performed using IonAmpliSeqTranstriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) at [99 ° C, 2 minutes → (99 ° C, 15 seconds → 62 ° C, 16 minutes) × 20 cycles → 4 ° C, Hold. ] Condition. The obtained PCR product was purified by Aple XP (Beckman Coulter, Inc.), and then buffer reconstruction, primer sequence digestion, adapter ligation and purification, and amplification were performed to prepare a library. The prepared library was loaded into the Ion 540 Chip and sequenced using the Ion S5 / XL system (Life Technologies Japan KK). The gene from which each read sequence was derived was determined by gene mapping each read sequence obtained by sequencing using hg19 AmpliSeq Transcriptome ERCC v1, which is a reference sequence of the human genome.

3) Data analysis and selection of feature gene The read count of each read obtained by sequencing the SL-derived RNA of the subject in 2) above was used as the data of the expression level of each RNA. In order to correct the difference in the total read count between the samples, each read count was converted into an RPM (Reads per million mapped reads) value. However, reads with a read count of less than 10 were treated as missing values. Based on the expression information of 3179 genes for which expression level data that is not a defect value was obtained in 90% or more of the subjects, Student's t-test was performed between groups (low stress group and high stress group), and p. A gene with <0.05 was extracted. The 73 genes shown in Table 6 whose expression levels varied statistically significantly among the groups were obtained. The 49 genes in Table 6A were markers (positive markers) whose expression levels were increased by chronic stress because the expression levels were higher in the high stress group. The 24 genes in Table 6B were markers (negative markers) whose expression levels decreased due to chronic stress because their expression levels were lower in the high stress group. These genes were selected as the feature genes used to construct the following predictive model.

4) Model construction In order to approximate the expression level data of the feature amount gene selected in 3) from the RPM value according to the negative binomial distribution to the normal distribution, the logarithm of the base 2 (Log ₂ (RPM + 1)) to which the integer 1 is added. Value). Using the Log ₂ (RPM + 1) value of the obtained 73 feature gene expression level data as an explanatory variable and the chronic stress level (high stress or low stress) as the objective variable, a predictive model for discriminating the chronic stress level It was constructed. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing 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, the OOB was used in the model when the expression level data of all 73 genes were used as variables. The algorithm was 25%, indicating that the genes shown in Table 6 could be used as markers for detecting chronic stress levels.

Example 2 Construction of a chronic stress level discrimination model using genes with high importance of random forest variables 1) Usage data 90% or more of the subjects acquired in 3) of Example 1 obtained expression level data that is not a missing value. 3179 genes were used in the following analysis. The gene expression level data was converted to the logarithm of the base 2 (Log ₂ (RPM + 1) value) by adding the integer 1 in order to approximate the normal distribution from the RPM value following the negative binomial distribution.

2) Selection of feature gene The feature gene used for building a predictive model was selected using a random forest algorithm. Using the Log ₂ (RPM + 1) value of the expression level data of the 3179 gene obtained in 1) as an explanatory variable and the chronic stress level (high stress or low stress) as the objective variable, a predictive model for discriminating the chronic stress level is used. It was constructed. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the top 140 genes of variable importance based on the Gini coefficient (Table 7). These 140 genes were selected as feature genes to be used for constructing a predictive model.

3) Model construction Chronic stress level using the Log ₂ (RPM + 1) value of the expression level data of the 140 feature genes selected in 2) as the explanatory variable and the chronic stress level (high stress or low stress) as the objective variable. We built a predictive model to determine. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing 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 errator rate was 30% in the model when the expression level data of 140 genes was used as a variable, and it was shown that the genes shown in Table 7 can be used as a marker for detecting the chronic stress level. ..

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

2) Model construction Chronic stress level using the Log ₂ (RPM + 1) value of the expression level data of the 16 feature genes selected in 1) as the explanatory variable and the chronic stress level (high stress or low stress) as the objective variable. We built a predictive model to determine. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing 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 errator rate was 15% in the model when the expression level data of 16 genes was used as a variable, and it was shown that the above 16 genes can be used as a marker for detecting the chronic stress level.

Example 4 Construction of a chronic stress level discrimination model based on all the feature amount genes used in Examples 1 and 2 1) Selection of feature amount genes All of the feature amount genes used in any of Examples 1 and 2. (A total of 197 genes shown in Table 1) were selected as the feature gene used for constructing the predictive model.

2) Model construction Chronic stress level using the Log ₂ (RPM + 1) value of the expression level data of the 197 feature gene selected in 1) as the explanatory variable and the chronic stress level (high stress or low stress) as the objective variable. We built a predictive model to determine. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing 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 errar rate was 20% in the model when the expression level data of 197 genes was used as a variable, indicating that the 197 gene in Table 1 can be used as a marker for detecting the chronic stress level. ..

Claims (12)

Nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7 in biological samples collected from subjects. A method for detecting a chronic stress level of a subject, which comprises measuring the expression level of at least one selected from the group consisting of a protein encoded by the gene. The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of nucleic acids encoding the genes shown in Tables 1A and 1B below, and proteins encoded by the genes.

The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 2 below and the protein encoded by the gene.

The method according to claim 1, further comprising measuring the expression level of at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 3 below and the protein encoded by the gene.

The method according to any one of claims 1 to 4, wherein the nucleic acid is mRNA collected from a lipid on the surface of the skin. The method of any one of claims 1-5, further comprising detecting the chronic stress level of the subject based on the expression level of the nucleic acid or protein. The chronic stress level of the subject is high stress when the expression level of at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 1A and the protein encoded by the gene is increased. 6. The method of claim 6, comprising detecting. The chronic stress level of the subject is high stress when the expression level of at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 1B and the protein encoded by the gene is reduced. 6. The method of claim 6, comprising detecting. The method of claim 6, comprising detecting the chronic stress level of the subject based on a predictive model constructed using data on the expression level of the nucleic acid or protein. The following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF in biological samples collected from subjects to which the test substance was applied. A method for evaluating a chronic stress regulator, which comprises measuring the expression level of at least one selected from the group consisting of a nucleic acid encoding a gene and a protein encoded by the gene. From the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and proteins encoded by the gene. A stress marker comprising at least one selected from the group of The first item of any one of claims 1 to 10, which contains an oligonucleotide that specifically hybridizes with the nucleic acid that is the stress marker according to claim 11 or an antibody that recognizes the protein that is the stress marker according to claim 11. A kit for detecting a chronic stress level or a kit for evaluating a chronic stress control agent used in the above method.