JP2011125326A - Method and evaluation kit for evaluating state of muscle fatigue and method for evaluating prevention and recovery effect for muscle fatigue possessed by substance - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for evaluating a fatigue state caused by a muscular exercise load objectively and highly accurately. <P>SOLUTION: The method for evaluating a muscle fatigue state of a subject includes the steps for: measuring a relative expression frequency of a gene in a peripheral blood of the subject in which the relative expression frequency varies proportionally to the muscle fatigue; analyzing the measured relative expression frequency by using a prepared gene expression frequency profile; and evaluating the muscle fatigue state of the subject based on a result of the analysis. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a method for evaluating the exercise fatigue state of a subject, and a method for evaluating the prevention / recovery effect of exercise fatigue possessed by a test substance by using this evaluation method.

  In daily life, moderate exercise is effective for maintaining a good physical condition and improving health, but since exercise is originally a stress stimulus to the body, excessive exercise has a negative effect on the body ( Cause infection, movement disorders, depressive symptoms, chronic fatigue, etc.).

  For example, a particularly important negative effect caused by excessive exercise is a decrease in immune function leading to infection. To date, many cases and studies have been reported regarding fluctuations in immune function before and after exercise. In such reports, the immune function is evaluated by measuring the number of various immunocompetent cells or by measuring the activity of the immunocompetent cells. However, there are often situations where data is not stable.

  In addition to measuring the number and activity of immunocompetent cells, several methods have been proposed, such as a method for measuring biomarkers that fluctuate before and after exercise, but only by measuring these limited factors. It is difficult to comprehensively evaluate complex reactions during exercise load, and it cannot be said that an objective evaluation method for exercise load or fatigue state due to exercise load has been established yet.

  Therefore, in order to enable more accurate assessment of immune function during exercise, it is possible to change the state of immune-competent cells during exercise more accurately, to conventional assessment methods, or to these There is a need to establish a new marker that gives additional information.

  On the other hand, the use of molecular biological information has attracted attention as a technique that can meet such demands. With the recent development of molecular biological information generation technology, particularly in the medical field, the relationship between the presence frequency (gene expression information) of specific RNA molecules in a biological sample and the state of the living body is becoming clear.

  In such medical practice using the frequency of transcripts such as RNA as information for assisting medical treatment, medical treatment is performed through the following stages. Specifically, first, a target disease such as cancer or rheumatism is identified, and patients with the target disease and unaffected healthy persons are recruited, and a biological sample such as blood is obtained under the consent of the patient and the healthy person. To collect. Thereafter, gene expression information obtained from the biological sample is analyzed together with information on the disease state of the patient. The target disease can be analyzed by specifying a transcript that correlates with the disease state. The following documents are known as examples of such inspection methods.

JP 2008-253258 A

  By the way, the use of this molecular biological information is not limited to special physiological changes such as diseases, but individual characteristics and characteristics such as age, gender, and physical characteristics, conditions such as address, living environment, occupation, and working conditions. It is well known that there is a common gene expression pattern in the effect (intensity) of a factor that changes or affects the physiological state, such as the environment and the environment. In addition, the present inventors have confirmed that there are common gene expression patterns in the effects of factors such as exercise fatigue, work fatigue, eating of specific foods and nutrients, eating habits, and lifestyle habits.

  The availability of gene expression information that reflects the effects of such changes in the physiological state of individuals and the factors that affect or affect the physiological state has traditionally resulted in no quantitative indicators or objective judgments. It makes it possible to clearly reveal difficult physiological changes.

  The present invention has been made in view of such a situation, and an object thereof is to provide a method for objectively and highly accurately evaluating a fatigue state due to exercise load.

  Another object of the present invention is to provide a method for evaluating the effect of preventing and recovering from exercise fatigue of test substances such as foods and beverages by using the above evaluation method.

  Another object of the present invention is to provide a solid phase sample for use in the above evaluation method. This solid phase-immobilized sample is prepared by specifically hybridizing to a gene used in the method according to the present invention and immobilizing a probe for detecting the gene on an appropriate solid phase.

  In order to objectively evaluate the fatigue state due to exercise load in a subject, the present inventors express many factors or receptors thereof that are easily obtained as specimens and related to factors that change the physiological state. Focused on peripheral blood. Then, the present inventors comprehensively analyzed mRNA expression of tens of thousands of genes in the analysis of gene groups expressed in peripheral blood, so that the expression frequency changes in proportion to exercise fatigue. As a result of obtaining new knowledge about the gene to be used, and further earnest research and experiments, the present invention has been completed.

  As a result of intensive studies to solve the above problems, the present inventors have found that genes listed in Table 4 below are genes in peripheral blood whose relative expression frequency varies in proportion to exercise fatigue. Can be used to evaluate the exercise fatigue state of a subject having the gene, and the relative expression frequency of the gene group changes before and after intake of a specific food or the like (test substance) in the subject. It was found that the effect of preventing and recovering fatigue of foods and the like can be evaluated by using

  Specifically, according to a first main aspect of the present invention, there is provided a method for evaluating a subject's exercise fatigue state, wherein the relative expression frequency varies in proportion to the exercise fatigue in the peripheral blood of the subject. The step of measuring the relative expression frequency of one or more genes selected from Table 4, the step of analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance, and the analysis And a step of evaluating an exercise fatigue state in the subject based on the result.

  According to such a configuration, it is possible to evaluate the exercise fatigue state of the subject only by collecting blood without giving a special load to the subject. Moreover, according to such a structure, a test subject's exercise fatigue state can be evaluated non-invasively and simply.

  In addition, the method according to the present invention can be used as an objective and simple evaluation method of exercise load for personal health management. Furthermore, the present invention greatly contributes to the improvement of the public health from the viewpoint of preventive medicine. In addition, the method according to the present invention can greatly contribute to the business surrounding sports such as the development of a cool-down method for reducing fatigue caused by exercise and the development of supplements.

  Further, according to one embodiment of the present invention, in such a method, the analyzing step includes calculating the measured relative expression frequency based on a gene expression frequency profile created based on a relative expression frequency before and after exercise load. This is done by comparing.

  In this case, the step of analyzing includes measuring the relative expression frequency before applying the exercise load, during the exercise load, within a predetermined time after stopping the exercise load, after elapse of the predetermined time, or It is preferably performed by comparing with a gene expression frequency profile created based on the relative expression frequency in the combination. The predetermined time set in advance varies depending on the type of exercise.

  According to still another embodiment of the present invention, in such a method, the analyzing step includes measuring the relative expression frequency before exercise load, 4 hours after starting exercise load, 4 hours after stopping exercise load, 20 hours after stopping exercise load, or by comparing with gene expression frequency profile created based on relative expression frequency in these combinations .

  Furthermore, according to another embodiment of the present invention, in such a method, the measuring step is performed before and after the subject is subjected to a treatment for reducing or recovering exercise fatigue in the subject. The relative expression frequency is measured later.

  Further, according to a second main aspect of the present invention, there is provided a method for evaluating an effect of preventing or recovering exercise fatigue of a test substance, which is obtained from peripheral blood collected from a target animal ingested with the test substance. The step of measuring the relative expression frequency of one or more genes selected from Table 4, the step of analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance, and the analysis And a step of evaluating an effect of preventing or recovering exercise fatigue in the target animal of the test substance based on the result.

  According to one embodiment of the present invention, in such a method, the analyzing step comprises calculating the measured relative expression frequency from Table 4 in peripheral blood collected from a target animal before ingesting the test substance. Analysis is performed using a gene expression frequency profile generated based on the relative expression frequency of one or more selected genes.

  According to another embodiment of the present invention, in such a method, the gene expression frequency profile is determined within a predetermined time period before the exercise load is applied, during the exercise load, within a predetermined time after stopping the exercise load. It is generated based on the relative frequency of expression after the passage of time or a combination thereof. In this case, it is preferable that the predetermined time set in advance changes depending on the type of exercise.

  According to still another embodiment of the present invention, in such a method, the gene expression frequency profile is obtained before the exercise load, 4 hours after starting the exercise load, and 4 hours after stopping the exercise load. Then, 20 hours after stopping the exercise load, or based on the relative expression frequency in these combinations.

  According to another embodiment of the present invention, in such a method, the test substance is preferably a food.

  According to a third main aspect of the present invention, there is provided an array for use in the above-described method for evaluating the exercise fatigue state of a subject, at least encoding one or more genes selected from Table 2. There is provided an array characterized in that probes that hybridize with a part of a base sequence under stringent conditions are immobilized at different positions on a solid support.

  It should be noted that the features of the present invention other than those described above and the remarkable actions and effects will become clear to those skilled in the art with reference to the following embodiments and drawings of the present invention.

FIG. 1 is a graph showing a variation pattern of expression levels of all genes used in a method for evaluating an exercise fatigue state according to an embodiment of the present invention. FIG. 2 is a graph showing a variation pattern of the expression level of a selected gene used in the method for evaluating an exercise fatigue state according to an embodiment of the present invention. FIG. 3 is a graph showing changes in genetic markers reflecting fatigue states before and after taking anti-fatigue beverages in an embodiment of the present invention.

Hereinafter, an embodiment and an example according to the present invention will be described with reference to the drawings.
As described above, the method for evaluating the exercise fatigue state of the subject according to the present embodiment is selected from Table 4 in the peripheral blood of the subject whose relative expression frequency varies in proportion to the exercise fatigue, or 1 The step of measuring the relative expression frequency of the above genes, the step of analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance, and the state of exercise fatigue in the subject based on the analysis result And a step of evaluating.

  Here, in one embodiment of the present invention, each of the above genes has an expression level that changes in proportion to exercise fatigue, and specifically reflects the exercise fatigue state as described in the examples described later. Is. In the present specification, the “gene marker” is used almost as an agreement with “gene” and serves as an index for evaluating the state or action of a certain object. A gene-related substance that correlates with quantity. For example, the gene itself includes mRNA that is a transcript, peptide that is a translation, protein that is the final product of gene expression, and the like. The “relative expression frequency” of a gene means a standardized expression level of the gene, and the expression level of the gene at the transcription level (in the case of a transcript or the like) or translation level (polypeptide, (In the case of proteins, etc.).

  In the present embodiment, “exercise fatigue” mainly occurs when muscle exercise is loaded on the body, and refers to local or systemic muscle fatigue. In addition, “exercise fatigue” in the present embodiment includes temporary or chronic muscle fatigue. For example, “exercise fatigue” is caused by various exercises including running, jogging, cycling, and the like, and includes muscle fatigue caused by so-called “exercise”.

  In this embodiment, the exercise fatigue state can be evaluated by comparing and analyzing the relative expression frequency of the subject and the gene expression frequency profile prepared in advance for the sample group in various exercise fatigue states. Alternatively, the exercise fatigue state can be evaluated by comparing and analyzing the gene expression frequency profile of the same subject under normal conditions and the gene expression frequency profile during exercise fatigue load (including during and after exercise load). The exercise fatigue state may be evaluated. In this case, the sample group in the various exercise fatigue states is preferably sampled by classification according to factors such as gender, age, type of exercise, and exercise time (including rest time). The gene expression frequency profile prepared in advance is preferably as the number of accumulated samples increases, and the accuracy of the evaluation method according to the present invention increases. Moreover, in this invention, the structure which can store the data concerning such a storage sample and the gene expression frequency profile prepared beforehand in arbitrary databases can be taken. That is, the present invention can also provide a database for storing such data, and an analysis apparatus that reads and executes the data and a program necessary for comparative analysis. According to such an analysis apparatus, the exercise fatigue state of the subject can be easily evaluated at any time simply by taking out the accumulated data from the database and comparing it with the measured data of the subject.

  In the present embodiment, the analysis of the relative expression frequency of the subject can be performed by comparing the measured relative expression frequency with a gene expression frequency profile created based on the relative expression frequency before and after exercise load. Before and after exercise load, before exercise load, during exercise load, and after exercise load shall be included. Further, in the present embodiment, the analysis of the relative expression frequency of the subject is performed by using the relative expression frequency of the subject before exercise load, during exercise load, immediately after exercise stoppage (within a predetermined time after stopping exercise load), exercise It is preferable to perform this by comparing with a gene expression frequency profile created based on the relative expression frequency after loading (after the elapse of the preset predetermined time) or a combination thereof. In this case, it is preferable that the predetermined time set in advance varies depending on factors such as the type of exercise, the intensity of the exercise, the sex of the subject performing the exercise, and the age. Therefore, “immediately after stopping exercise” means that it changes within 30 minutes, within 20 minutes, within 10 minutes, within 5 minutes, etc. after stopping the exercise, depending on factors such as the type of exercise. And the like change 30 minutes, 20 minutes, 10 minutes, 5 minutes, etc. after stopping the exercise. By setting in this way, even if factors such as the type of exercise change, changes in the exercise fatigue state caused by the exercise can be accurately evaluated.

  Further, in the present embodiment, the evaluation of the exercise fatigue state according to the present invention is performed by comparing the normal expression frequency of the subject with the relative expression frequency after performing a process for reducing or recovering the exercise fatigue for the subject. It can also be done by analysis. Here, processing to reduce or recover from exercise fatigue includes, but is not limited to, food intake, use of equipment or devices that reduce exercise fatigue, and processing of fatigue recovery methods such as cool down. Absent.

  For example, in this embodiment, the gene expression frequency profile of the sample group in various exercise fatigue states is as follows: before exercise load, 4 hours after starting exercise load, 4 hours after stopping exercise load, It can be created by measuring the expression level of the gene group in Table 4 at a time 20 hours after stopping. Moreover, these gene expression frequency profiles can also be classified by combining various elements such as sex and age. The exercise fatigue state according to the present embodiment is evaluated by comparing and analyzing the relative expression frequency of the subject with the gene expression frequency profile as described above. In the present embodiment, the gene group used in the method for evaluating an exercise fatigue state according to the present invention, such as creation of a gene expression frequency profile, is preferably the gene group described in Table 4, but in Table 2, The described gene groups can be used as well.

  Here, in the present embodiment, any analysis means used in the field of molecular biology or bioinformatics such as cluster analysis or various algorithms can be used as a data analysis method. Moreover, in this embodiment, as will be described later, the gene group in Table 2 is classified into seven clusters as a result based on the results of K-means clustering, removal of circadian variation genes, and the like. The classification of the gene group in Table 2 is not limited to such a method, and can be classified by an arbitrary classification method.

  In the present embodiment, as described above, the method for evaluating the effect of preventing or recovering the exercise fatigue of the test substance is selected from Table 4 in the peripheral blood collected from the target animal ingested with the test substance. A step of measuring a relative expression frequency of one or more genes to be analyzed, a step of analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance, and based on the analysis result, And a step of evaluating the effect of recovery from exercise fatigue in the subject animal possessed by the test substance.

  Here, the test substance according to the present embodiment is a substance that can be ingested by the target animal, and includes foods such as food and drink, various supplements, and nutritional supplements. For example, the food product includes, but is not limited to, one having at least one food component selected from the group consisting of energy sources, vitamins, amino acids, antioxidants, peptides, proteins, minerals, and lipids. Is not to be done. The target animal is not limited as long as it can execute the method for evaluating the effect of recovery from exercise fatigue according to this embodiment, but is preferably a human.

  In this embodiment, the array used in the method for evaluating the exercise fatigue state is hybridized under stringent conditions with at least a part of the base sequence encoding one or more genes selected from Table 2. Probes to soy are fixed at different positions on the solid support. In the array according to this embodiment, the probe immobilized on the solid support preferably hybridizes under stringent conditions with at least a part of the base sequence encoding one or more genes selected from Table 4. In other embodiments, it may hybridize under stringent conditions with at least a portion of the base sequence encoding one or more genes selected from Table 2. good.

  The array can be replaced with a PCR plate if necessary. In this case, the probes are arranged at different positions of wells on the plate.

Hereinafter, the present invention will be described in more detail with reference to examples, but the present invention is not limited to these examples.
(Marker gene search and selection method)
Hereinafter, the marker gene searching / selecting method of the present invention will be described.

  The inventors performed expression analysis using a DNA microarray using five healthy subjects as samples and using peripheral blood cell-derived RNA before and after exercise fatigue as a sample. A DNA microarray is a DNA fragment having a base sequence corresponding to a large number of genes immobilized on a support substrate such as glass, and detects DNA or RNA in a sample by hybridization. As long as the gene expression can be comprehensively analyzed, other DNA-immobilized samples (DNA chips, capillaries, membrane filters, etc.) and quantitative methods may be used instead of the DNA microarray. Details of the test system are shown below.

  The subjects of the study were five healthy adult males who explained the contents of the study and obtained consent in writing. Each subject was given an exercise with an intensity of 80% of the maximum oxygen consumption using a bicycle ergometer, 4 points before and after that (0 hr before exercise, immediately after exercise (4 hr), 4 hours after exercise (8 hours), blood was collected 20 hours after exercise (24 hours).

  Table 1 shows the age and physical information of the subject.

  RNA extraction from the blood of the subject was performed using PAXgene Blood RNA System (Qiagen). The quality of RNA extracted from each subject's blood was examined using a bioanalyzer 2100 (manufactured by Agilent), and it was confirmed that there was no degradation. Next, cRNA was amplified from 250 ng of each RNA by an in vitro transcription reaction using Low RNA Input Linear Amp Kit PLUS, One-Color manufactured by Agilent, and simultaneously fluorescently labeled (Cy3 labeling). Subsequently, fluorescence-labeled cRNA was hybridized at 65 ° C. for 17 hours to Whole Human Genome Microarray 4 × 44k manufactured by Agilent. After washing with Agilent's Gene Expression Wash Buffer, the fluorescence image is read with Agilent Scanner (manufactured by Agilent), and the signal intensity of each spot in the fluorescence image is quantified using Agilent image digitization software Feature Extraction. Went.

  The normalization process of the data after digitization was performed using the microarray analysis software GeneSpring GX10 manufactured by Agilent. First, Quantile Normalization was performed so that the signal distribution was the same between the microarrays used for analysis, and correction between the microarrays was performed. Furthermore, between each gene, correction | amendment between genes was performed so that the value before exercise load of each test subject might be set to "1".

  In order to select probes with high signal intensity from all 41000 probes mounted on the microarray, signals are effective in all 20 microarrays of 5 subjects and 4 time points (microarray analysis software GeneSpring GX10 manufactured by Agilent) The probe determined to be determined) was extracted. As a result, 23167 probes were extracted, and the analysis proceeded with these probes as targets.

  Genes that fluctuate due to exercise load were extracted for each fluctuation pattern before and after the load using the K-means clustering method. The analysis was performed using statistical analysis software R. Information unique to each subject was added to each probe to obtain 23167 × 5 probes, time series data of 4 points for each subject was given to these probes, and the probes were classified by the K-means clustering method. The optimal number of clusters to be classified by the K-means clustering method was determined using the Davies-Bouldin Validity Index. Classifying into 3 clusters by clustering in the first stage, and further classifying into clusters of 2, 5 and 4 respectively in the second stage clustering for the gene group included in each of these clusters, resulting in a total of 11 clusters It was.

  The reproducibility between subjects of each probe was evaluated by the same number of probes belonging to each cluster. 3478 probes belonging to the same cluster in 4 or more subjects out of 5 subjects were extracted as good reproducibility genes.

  Laboratory markers such as blood hormones, blood glucose, and blood pressure are known to fluctuate within the day, and cells in the peripheral blood naturally undergo these fluctuations or have their own circadian cycle. there is a possibility. Therefore, the present inventors considered that the probe extracted by the above method may contain genes that fluctuate in the circadian cycle regardless of aging, and they may be noise in analysis.

  In order to identify genes that fluctuate within the day, the following new test was conducted. Blood samples were collected at 3 points of 9:00, 13:00, and 17:00 from 10 subjects who agreed to participate in the study, microarray data was obtained in the same manner as described above, and signal values were corrected. Using GeneSpring GX10, a repeated ANOVA analysis was performed between three groups of 9; 00-13: 00-17: 00, and genes that fluctuated in the day were extracted. (Extraction criteria: Benjamini Hochberg FDR <0.05) When these circadian variation genes were removed from 3478 good reproducibility genes belonging to 11 clusters classified by the two-step K-means method, 7 clusters 1411 probes remained. FIG. 1 shows the extracted probe variation pattern and the number of probes belonging to it, and Table 2 shows the probe annotation list.

  In order to analyze the functional characteristics of these gene clusters, gene ontology analysis and pathway analysis were attempted, but statistically unclear significance could not be obtained. Therefore, we focused on the fact that this analysis is intended for whole blood-derived RNA, and thought that the gene groups that represent the characteristics of various blood cells correspond to these classified Cluster gene groups. Study was carried out. Various blood cell-specific expression gene sets were extracted using NCBI GEO blood cell microarray data set (GSE3982) and multinuclear-mononuclear cell microarray data acquired in-house. Furthermore, the degree of coincidence between the extracted cell-specific gene sets and each cluster-affiliated gene that fluctuates depending on the exercise load was statistically evaluated. The results are shown in Table 3. For Cluster 1 and 2, the accumulation of the multinucleated cell marker and neutrophil marker was significant, and for Cluster 4 the accumulation of the mononuclear cell marker and NK cell marker was significant. Regarding Cluster 5, the accumulation of mononuclear cell markers and T cell markers was significant. Accumulation of polynuclear cell markers for Cluster 3 and mononuclear cell markers for Cluster 6 were significant, but no clear results were obtained for detailed cell types. There was no significant cell marker for Cluster7. From these results, it was suggested that each extracted gene cluster exhibits the characteristics of various cell groups in whole blood.

  The inventors applied a statistical test to these genes in order to extract from these marker gene groups extracted by the K-means clustering method, those that are particularly fluctuating and whose fluctuation is stable among subjects. . At this time, Cluster 7, which is a gene group with little fluctuation, was excluded from the analysis. Probes having a p value of less than 0.001 by repeated ANOVA analysis between time point groups were extracted, and 20 genes were selected as marker genes in order from the smallest p value for each cluster. As a result, the variation pattern of these marker genes and the number of probes belonging to them are shown in FIG.

  Since there is no system that objectively evaluates the fatigue state due to exercise load, there are many foods, beverages, and supplements that are commercially available without reliable data on anti-fatigue effects. We thought that we could evaluate the anti-fatigue effect brought about by food intake using the extracted genetic markers, and conducted the following tests.

  Three healthy healthy subjects who obtained consent to participate in the study were ingested for 4 weeks with a beverage containing imidazole dipeptide, whose evidence is accumulating as a component having an anti-fatigue effect. Bicycle exercise load of 80% of maximum oxygen consumption as in the above test was given before and after drinking, and exercise before exercise load (0 hr), immediately after exercise load (4 hr), after exercise load for 4 hours (8 hr), exercise Blood was collected 20 hours after loading (24 hr), microarray data was obtained by the same method as described above, and signal values were corrected. FIG. 3 shows a variation pattern of the genetic marker reflecting the fatigue state due to the exercise load extracted as described above before and at the time of drinking. Regarding subject 1, the change in cluster 3 was suppressed after ingestion compared to that before ingestion of beverage, but the tendency was slightly increased in clusters 1, 5, and 6, suggesting that there was no beverage ingestion effect. It was. Regarding subject 2, the fluctuation tends to be suppressed after ingestion at 4 hours after exercise compared to before drinking, and for subject 3, either immediately after exercise, after 4 hours of load, or after 20 hours of load. The fluctuations after ingestion tended to be suppressed compared to before ingestion, and it was considered that some of these subjects had an effect of ingesting some beverages. The above results suggested the possibility of evaluating the intake effect of anti-fatigue foods using the extracted marker genes.

  The inventors confirmed the reproducibility of some genes with particularly large fluctuations among gene markers reflecting the fatigue state due to exercise load by the real-time PCR method. As a control gene for correction, a JTB gene having a stable signal value before and after exercise in microarray analysis was used. Table 5 shows the correlation between microarray analysis and real-time PCR results. The reproducibility of the studied genes was extremely high, and results were obtained that ensured the reliability of the extracted gene markers.

(Experimental method)
Hereinafter, experimental methods and substances used to prove the effect of the genetic marker according to an embodiment of the present invention and the definition thereof will be described. In the present embodiment, the following experimental method is used, but the same result can be obtained by using other experimental methods.
(Quantification of genetic markers)
In the present embodiment, when the gene marker is a gene or a transcript of the gene (mRNA), the measurement (quantification) of the expression level of the gene marker is, for example, a DNA chip, microarray method, real-time PCR, Northern blot method, It can be carried out by measuring the amount of mRNA by various molecular biological techniques such as dot blot method and quantitative RT-PCR (quantitative reverse transcription-polymerase chain reaction) method.

  The primer used in the quantitative RT-PCR method is not particularly limited as long as it can specifically detect a gene marker, but an oligonucleotide consisting of 12 to 26 bases is preferable. The base sequence is determined based on the sequence information of each human gene. And the primer which has the determined arrangement | sequence can be produced using a DNA synthesizer, for example.

  On the other hand, when the genetic marker is a translation product (polypeptide) or final product (protein) of the gene, for example, by Western blotting or ELISA using a polyclonal antibody, monoclonal antibody, etc. specific to the genetic marker. The expression level of the gene marker can be measured, but is not limited to these methods, and various methods such as RIA (radioimmunoassay) and EIA (enzyme immunoassay) are used. Can be used.

  “Gene” refers to genetic information represented by a base sequence such as RNA or DNA. Also included are orthologous genes that are conserved among species such as humans, mice, and rats. The gene may function not only as a protein-encoding protein but also as RNA or DNA. A gene generally encodes a protein according to its base sequence, but a protein having a biological function equivalent to that protein (for example, a homologue (such as a homolog or a splice variant), a mutant or a derivative) You may code. For example, a protein encoding a protein whose base sequence is slightly different from the protein indicated by the base sequence based on genetic information, and whose base sequence hybridizes with a complementary sequence of the base sequence based on the genetic information. There may be.

  “RNA” is a concept including not only single-stranded RNA but also single-stranded RNA having a sequence complementary thereto and double-difference RNA composed of these. The concept also includes total RNA, mRNA, and rRNA.

  “DNA chip” and “DNA array” have a structure in which probe DNA is arranged on a substrate, and measure the expression of a plurality of genes by hybridization. This includes not only optically measuring the expression level but also electrical output of the expression level. As the “DNA chip”, for example, GeneChip (trademark) manufactured by Affymetrix can be used. As the “DNA array”, CodeLink Expression Bioarray (trademark) manufactured by Amersham Biosciences can be used. The DNA array includes not only a DNA microarray but also a DNA macroarray.

  The “expression level” is a concept including not only a value obtained by directly measuring the expression level of a gene but also a value converted by a predetermined calculation or statistical method. In addition, “gene expression level”, “expression signal”, “gene expression signal”, “expression signal value”, “gene expression signal value”, “gene expression data”, “expression data”, etc. It is synonymous to indicate the value to be reflected.

  “Gene expression” refers to an aspect of gene expression in a living body expressed by the expression level of a gene, both when expressed by the expression level of one gene and when expressed by the expression level of a plurality of genes Is included. “Expression” is also synonymous with the expression of gene expression in a living body.

  In addition, it cannot be overemphasized that this invention can be deform | transformed variously, It is not limited to one Embodiment mentioned above, A various deformation | transformation is possible in the range which does not change the summary of invention.

Claims (13)

A method for evaluating a subject's exercise fatigue state,
Measuring the relative expression frequency of one or more genes selected from Table 4 in the peripheral blood of the subject, the relative expression frequency of which varies in proportion to exercise fatigue;
Analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance;
Based on the result of the analysis, the step of evaluating the exercise fatigue state in the subject,
A method characterized by comprising: The method of claim 1, wherein
The analyzing step is performed by comparing the measured relative expression frequency with a gene expression frequency profile created based on the relative expression frequency before and after exercise. The method of claim 2, wherein
In the analyzing step, the measured relative expression frequency is measured before the exercise load is applied, during the exercise load, within a predetermined time after the exercise load is stopped, after the predetermined time has elapsed, or a combination thereof. A method characterized by being performed by comparing with a gene expression frequency profile created based on a relative expression frequency. The method of claim 3, wherein
The preset predetermined time varies depending on the type of exercise. The method of claim 2, wherein
In the analyzing step, the measured relative expression frequency is determined before exercise load, 4 hours after starting exercise load, 4 hours after stopping exercise load, and 20 hours after stopping exercise load. Or a comparison with a gene expression frequency profile created based on a relative expression frequency in a combination thereof. The method of claim 1, wherein
The method of measuring the relative expression frequency before and after performing the process of reducing or recovering the exercise fatigue in the subject in the subject. A method for evaluating the effect of preventing or recovering from exercise fatigue of a test substance,
Measuring the relative expression frequency of one or more genes selected from Table 4 in peripheral blood collected from the subject animal ingesting the test substance;
Analyzing the measured relative expression frequency using a gene expression frequency profile prepared in advance;
Based on the analysis result, the step of evaluating the effect of preventing or recovering exercise fatigue in the target animal that the test substance has,
A method characterized by comprising: The method of claim 7, wherein
In the analyzing step, the measured relative expression frequency is based on the relative expression frequency of one or more genes selected from Table 4 in peripheral blood collected from the subject animal before ingesting the test substance. A method characterized by analyzing using a gene expression frequency profile generated. The method of claim 7, wherein
The gene expression frequency profile is generated based on a relative expression frequency before, during, during, or after exercise load, within a predetermined time after stopping the exercise load, after a predetermined time, or a combination thereof. A method, characterized in that The method of claim 9, wherein
The predetermined time set in advance varies depending on the type of exercise. The method of claim 7, wherein
The gene expression frequency profile can be calculated by comparing relative to each other before exercise load, 4 hours after starting exercise load, 4 hours after stopping exercise load, 20 hours after stopping exercise load, or a combination thereof. It is produced | generated based on expression frequency. The method characterized by the above-mentioned. The method of claim 7, wherein
The test substance is a food. An array for use in the method of claim 1, comprising:
A probe that hybridizes under stringent conditions with at least a part of a base sequence encoding one or more genes selected from Table 2 is immobilized at different positions on a solid support. An array.

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