WO2006077981A1 - High-throughput functionality evaluation method, program and apparatus - Google Patents

High-throughput functionality evaluation method, program and apparatus Download PDF

Info

Publication number
WO2006077981A1
WO2006077981A1 PCT/JP2006/300875 JP2006300875W WO2006077981A1 WO 2006077981 A1 WO2006077981 A1 WO 2006077981A1 JP 2006300875 W JP2006300875 W JP 2006300875W WO 2006077981 A1 WO2006077981 A1 WO 2006077981A1
Authority
WO
WIPO (PCT)
Prior art keywords
functional
functionality
unknown
sample
evaluation
Prior art date
Application number
PCT/JP2006/300875
Other languages
French (fr)
Japanese (ja)
Inventor
Masahito Suiko
Nobuhiro Fukuda
Masanobu Sakono
Kazuo Nishiyama
Nozomu Eto
Yoichi Sakakibara
Satoshi Kawahara
Masao Yamasaki
Ikuo Yoshihara
Kunihito Yamamori
Kiyoko Nagahama
Original Assignee
Miyazaki Prefecture
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Miyazaki Prefecture filed Critical Miyazaki Prefecture
Publication of WO2006077981A1 publication Critical patent/WO2006077981A1/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/502—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food

Definitions

  • the present invention provides a high throughput functional evaluation method, in particular, a high throughput functional evaluation method relating to health suitable for evaluating functions including complex physiological activity of multi-component materials such as foodstuffs at one time. , Programs, and devices.
  • Japanese Patent Application Laid-Open No. 2002-328124 discloses a high throughput screening method for systematically selecting a combination of compounds having improved characteristics in a biological system.
  • Japanese Patent Publication No. 2003-50401 discloses a high throughput assay system useful for simultaneous implementation of biological or chemical assays.
  • Japanese Unexamined Patent Publication No. 2003-509657 discloses, for example, a high-throughput test that evaluates from the viewpoint of the properties of a common component drug and the properties of an additional component excipient and selects an optimum combination.
  • Toshikazu Yoshikawa ILSI [80] 14 (2004) is an epidemiological study on the prevention of diseases of foodstuffs (1) proposed by Clydesdale as a means of giving scientific basis to the functionality of foodstuffs ( 2) Development of appropriate biomarkers, (3) Clinical intervention tests using human population, and disclosed methods for searching biomarkers expected in the future.
  • the present invention solves the above-mentioned problems, and it is an object of the present invention to provide a high throughput functional evaluation method for evaluating the health functional properties of various materials in a large number of items with high efficiency and high reliability.
  • the present invention solves the above-mentioned problems, and a high throughput function to evaluate the health functions of various materials over multiple items using a central processing unit with high efficiency and high reliability.
  • the purpose is to provide an evaluation method.
  • the present invention aims to provide a high-throughput functionality evaluation program that operates on a computer and evaluates the health and functionality of various materials in a large number of items with high efficiency and high reliability. .
  • the functional evaluation method according to the present invention achieving the above object is a method, a program, and an apparatus for evaluating the functionality of a functional unknown material, and includes the following invention.
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a high throughput functionality evaluation method comprising:
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a functional known sample is added to an evaluation system provided with a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the biomarker expression amount measurement value and functionality And associating the
  • the obtained measured values are processed to compare the biomarker expression amount of the functional known sample correlated with the functionality in the above (2) with the biomarker expression amount of the functional unknown sample, Comprehensively evaluating the functionality of the unknown material
  • a high throughput functionality evaluation method comprising:
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functional value of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the known functional material is associated; (3) Evaluation A functional unknown material is imparted to cultured cells to prepare a functional unknown sample.
  • a high throughput functionality evaluation method comprising:
  • the database preparation method regarding the functional known material characterized by including.
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a high throughput functionality evaluation method comprising:
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
  • a functional unknown material is provided to cultured cells to prepare a functional unknown sample.
  • a high throughput functionality evaluation method comprising:
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
  • a functional unknown material is provided to cultured cells to prepare a functional unknown sample.
  • a high throughput functionality evaluation method comprising:
  • the probability density function is obtained by nonparametric method, and the machine similar to the functional unknown material
  • the method according to [the eighth invention] characterized in that the determination of the material of known ability is performed by Bayesian estimation.
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
  • a functional unknown material is provided to cultured cells to prepare a functional unknown sample.
  • a high throughput functionality evaluation method comprising:
  • the cultured cell is a human-derived cultured cell [1, 2, 3, 4, 5, 6, 8, or 10 invention] described method.
  • a method for evaluating the composite functionality of a functional unknown material comprising:
  • a database storing a data set in which the central processing unit associates the biomarker expression amount of the functional known sample with the functional value of the functional known material based on the biomarker expression amount of the functional unknown sample. Searching and estimating the functional value of the functional unknown material;
  • the central processing unit comprehensively evaluates the functionality of the functional unknown material based on the search result of the step (1) and outputs the general evaluation result.
  • a high throughput functionality evaluation method for unknown functionality material characterized by including.
  • the functional value of the above-mentioned functional known material is characterized in that the measured value force measured by the individual evaluation system is also calculated for each of the functional known materials, and [15th invention] described. the method of. [Seventeenth Invention]
  • the central processing unit collates the biomarker expression amount of the functional known material contained in the database using the biomarker expression amount of the functional unknown sample as a key, and searches a specific data set
  • the method according to [15th invention] characterized in that
  • the central processing unit calculates the learning result by learning the relationship between the measured value of the biomarker expression amount of the functional known sample and the measured value of the functionality of the known material,
  • the central processing unit generalizes the learning result and estimates the functional value of the functional unknown material on the basis of the measured value of the biomarker expression amount of the functional unknown sample. 15] The method of description.
  • the central processing unit is characterized in that calculation of the learning result and estimation of the functional value of the functional unknown material from the biomarker expression amount measurement value of the functional unknown sample by generalization of the learning result are performed by neural network.
  • the method according to [the 18th invention] is characterized in that calculation of the learning result and estimation of the functional value of the functional unknown material from the biomarker expression amount measurement value of the functional unknown sample by generalization of the learning result are performed by neural network.
  • the central processing unit calculates a probability density function that causes the amount of optical marker expression of each functional known sample in the data set,
  • the central processing unit determines the functionality known material similar to the functionality unknown material using the biomarker expression value measurement value of the functionality unknown sample and the probability density function and determines the functionality known material
  • the central processing unit calculates the probability density function by the nonparametric method, and performs estimation of a functional known material similar to the functional unknown material by a Bayesian estimation method. .
  • the central processing unit is a dimer of functional known samples contained in the data set. By clustering the car expression level measurement values, a data set in which the functional measurement values of the functional known material are associated with each class is calculated.
  • the central processing unit determines the class to which the biomarker expression value measurement value of the functional unknown sample belongs,
  • the central processing unit generalizes the functional value of the function known material belonging to the determined class, and estimates the functional value of the functional unknown material. Method described.
  • the central processing unit determines the class of the functionality of the functional unknown material in the self-organizing map, the weight of the competition node associated with the biomarker expression amount measurement value of the functionality known sample, and the functionality.
  • the method according to [23rd invention] characterized in that the determination is performed based on the distance between the unknown sample and the biomarker expression level of the unknown sample.
  • the central processing unit determines the class of functionality of the functional unknown material in the self-organizing map, the coordinates of the competing node associated with the biomarker expression measurement value of the functional known sample, and the functionality unknown. It is characterized in that it is performed based on the Manhattan distance between the coordinates of competing nodes associated with the measured value of the marker expression level of the sample [No.
  • Functional value estimating means for estimating the functional value of a functional unknown material by searching the above-mentioned database based on the biomarker expression amount of the functional unknown sample
  • a high throughput functional evaluation device characterized by including.
  • a functional known sample refers to a sample to be subjected to measurement of a biomarker to which a functional known material is given to a cultured cell for evaluation and expressed.
  • Functionally unknown material refers to a functionally unknown material to be provided to the high throughput functionality evaluation method or apparatus of the present invention.
  • a functional unknown sample refers to a sample to be provided for measurement of a biomarker to which a functional unknown material is given to a cultured cell for evaluation and expressed.
  • Functionalities of foodstuffs refer to the tertiary functions of foodstuffs, that is, physiologically active functions including physical conditioning function.
  • the functionality evaluation method, program and apparatus according to the present invention have the following advantages
  • test material By testing at a time using a plurality of biomarkers, even if the test material is a multicomponent substance such as a food material, in addition to the responsiveness to the individual biomarkers, the test material Can be evaluated quickly and comprehensively. For example, the evaluation results of multiple items such as antioxidative action, antimutagenic action, apoptosis induction action, virus growth suppression action, cancer cell growth suppression action, immune modulation action, etc. are combined to comprehensively evaluate the functionality of the test material. it can.
  • the device can be easily automated, and the evaluation test time can be significantly shortened.
  • FIG. 1 is a flow chart showing an exemplary procedure of the high throughput functionality evaluation method according to the present invention.
  • FIG. 2 is a block diagram of a computer system for performing the high throughput functionality evaluation method according to the present invention.
  • FIG. 3 A flow chart showing a typical procedure in the case of estimating functionality by a learning method in the present invention.
  • FIG. 4 is a diagram of a typical neural network used when estimating functionality by a learning method in the present invention.
  • FIG. 5 is a flow chart showing a typical procedure in the case of estimating functionality by a probability method in the present invention.
  • FIG. 6 is a flow chart showing a typical procedure in the case of estimating functionality by a clustering method in the present invention.
  • FIG. 7 is a graph showing the influence of epigaportal teking gallate (EGCG) and genistin on biomarker expression patterns of Jurkat cells in the present invention.
  • EGCG epigaportal teking gallate
  • FIG. 8 is a graph showing the influence of epigaportal tequingallate (EGCG) and lipoic acid on biomarker expression patterns of Jurkat cells in the present invention.
  • the functional evaluation method to which the present invention is applied is to measure the expression amount of the marker of the unknown sample prepared by applying the functional unknown material to the evaluation cultured cells, process the obtained measured value, and perform the functionality. It is a method that comprehensively evaluates the functionality of unknown materials and displays or outputs the comprehensive evaluation results.
  • the functionality evaluation method according to the present invention can be implemented, for example, according to the flow sheet shown in FIG.
  • the functional evaluation method of the present invention first, the cultured cells for evaluation are cultured, and then functional unknown materials comprising food materials, drugs, drug candidate substances and the like are applied to the cells.
  • the functional evaluation method of the present invention comprises a cell extract, a cell secretion, etc.
  • a functional unknown sample is prepared, and the biomarker expression level of the functional unknown sample is measured, for example, by the method such as integrative immunity.
  • the measured value of the biomarker expression amount measurement value is processed (details will be described later) to evaluate the functionality of the unknown functional material.
  • the functionality of the unknown material is comprehensively evaluated by comparing it with the functionality evaluation data in the database based on the measured value of the expression level of the biomarker expression.
  • Evaluations that can be preferably used in the culture step of the evaluation culture cells shown in FIG. 1 As evaluation culture cells that can be preferably used, Jurkat cells, HL-60 cells, MOLT-4 cells, Huh-7 cells, H epG2 cells, Hep3 B cells, Caco_2 cells, HeLa cells, MCF-7 cells, A431 cells, S1 T cells, Su9T01 cells, HUT101 cells, PLCZPRF_5 cells, Li90 cells, HU VEC cells, HMEC cells, HT17 cells, NIH-3T3 Cells, human derived cell lines such as human, mouse, rat, etc.
  • human leukemia-derived cells such as 3T3-L1 cells, MH134 cells, dRLh_84 cells, RLN-10 cells, PC12 cells, 3Y1 cells or cell lines derived from these cell lines be able to.
  • human leukemia-derived cells and human liver cancer-derived cells are particularly desirable.
  • human leukemia-derived cells include S1 T cells, Su9T01 cells, HUT101 cells, Jurkat cells, HL-60 cells and the like.
  • human liver cancer-derived cells include PLC / PRF-5 cells, Li90 cells, Huh-7 cells, HepG2 cells and the like.
  • the temperature is 37 ° C. or a temperature at which normal mammalian cells grow
  • the concentration of carbon dioxide gas is preferably 5% or a density at which normal mammalian cells grow.
  • the medium established medium for mammalian cells such as D-MEM medium, MEM medium, RPMI 1640 medium, D-MEM / F-12 medium, F-10 medium, F-12 medium, ERDF medium, etc. Or a medium based on them is preferred. It is preferable to use RPMI 1640 medium for human leukemia-derived cells such as Jurkat cells and HL_ 60 cells, and D_MEM medium for human monthly cancer-derived cells such as Huh-7 cells and HepG2 cells. To promote cell growth, fetal calf serum may be added at 10% or at a concentration at which normal mammalian cells grow. If necessary, non-essential amino acids and site strength (FGF, HGF, VEGF, interleukin_2, etc.) can be added. However, in cell lines whose growth is suppressed by serum Use serum free culture.
  • FGF fetal calf serum
  • test material used in the step of donating to cells is not particularly limited, such as food, medicine, medicine candidate substance, etc., but the present invention is not limited to agricultural products, forest products, livestock products, fishery products etc. Application to biological resources and multicomponent materials prepared from them can be expected to have excellent effects.
  • Preferred food materials to be used as test materials are: diverite, tea, soy, sweet potato, pea, komatsuna, spinach, Chinese cabbage, cabbage, lettuce, onion, sweet pepper, chili pepper, mini tomato, eggplant, zucchini, chiyuuri , Corn, cabochya, carrot, burdock, radish, henore monobelly, kumquat, summer sun, hebez, mango, soo mizu, plum, spearmint, sweet basil, Italian parsley, rosemary, stevia, chamomile, perilla, clover , Garlic, shiitake mushroom, oyster mushroom, nameko, maitake mushroom, oyster mushroom, eryngii, enoki mushroom, rice, taro, strawberry, mizuna, leek, melon, peppermint, lemon balm, etc.
  • animal resources such as beef, milk, pork, chicken and eggs, and fermented foods such as kefir, yoghurt and natto, seafood, seafood, and other valuable items such as tea that can be expected to have an effect on health can be mentioned.
  • Preferred test materials in foodstuffs include catechins such as catechins, epicatatequine, epigallocatechin, gallocatenins, force tekingalate, epicathenic gallate, gallocatechin gallate, epigallocatechin gallate (EGCG), etc., daidzein, daidzin, Isoflavones including genistin, genistin, glycicin, glycitin, fluoromononetin, etc., anthocyanins including cyanidin, pelargodizine, delphidizine, etc., quercetin, myricetin, rutin, resveratrol, kenfelol, sesamin, curcumin, limonine , Gamma monoaminobutyric acid (GABA), astaxanthin, galangin, citral, trigonelline hydrochloride, ellagic acid, quinic acid, saponin, capsaicin,
  • the extract is provided from the biological resources, such as food, its lyophilizate and the like to the evaluation culture cells.
  • the extraction solvent water, ethanol, methanol, ethyl acetate, hexane, acetone, a mixed solvent of two or more of them, or the like is suitably used.
  • the extract may be further fractionated and purified by high performance liquid chromatography (HPLC), an oven column, etc., if necessary.
  • the mixing time for causing the extract to act on the cells is 0 hour at the onset of action, 1 hour, 2 hours, 3 hours, 6 hours, 9 hours, 12 hours, 18 hours, 24 hours, 36 hours, 48 hours Time, 72 hours, etc. Force examination. If functionality can be determined within 24 hours, it does not extend beyond that.
  • the concentration of the extract to be fed to the rice field vesicle is 0.5 /., 1 / ⁇ , 1.5 ⁇ , 2 / i M, 3 / i M, 4
  • ⁇ M concentration of ⁇ M, 100 ⁇ M, 200 ⁇ M, 300 ⁇ M, 500 ⁇ M, 1000 ⁇ M and 1000 times and 1/1000 times of these concentrations, it is suitable for the test material and evaluation culture cells.
  • concentration Usually, a range of 0.5 ⁇ m to 1000 / i M is desirable.
  • the concentration may be determined by serial dilution to obtain the optimal experimental results. In the case of acidic or basic extracts, it is desirable to neutralize and then add.
  • the cell and the cell secretion are separated by centrifugation.
  • adherent cells cells are separated from cell secretion by pipetting, and cell secretion is directly used as the test sample.
  • cell debris is collected by density gradient centrifugation, continuous centrifugation, etc. to collect specific organelles such as nuclei, mitochondria, and endoplasmic reticulum, and further organelle extracts are also used as test samples. There is also something to do.
  • Cell disruption can be carried out using a cell disruption device such as Teflon® homogenizer, dross homogenizer, Polytron tie, etc.
  • an antibody specific to a immunomarker-based biomarker is used.
  • an antigen-antibody reaction with a biomarker is used.
  • ELISA ELISA
  • Western blotting antibody chip (antibody array), bead array, immuno chromatography etc.
  • Examples of EL ISAs that are representative immune syndromes include methods described in Ishikawa Eiji et al., "Enzyme Immunoassay 3rd Edition” Medical School, Tokyo, 1987.
  • Western blotting includes the method described in Takatsu Seishi hen, "Antibody Experimental Manual for Protein Research," Yodosha, Tokyo, 2004. As the dinochromats Zuk RF, Ginsberg VK, Houts T, Rabbie
  • antibody chips when used for immunoassays, detection with a specific antibody is performed on a membrane such as a PVDF membrane or a nitrocellulose membrane, a slide glass or a similar substrate. Specific antibody detection is performed on beads in the case of bead arrays and on sticks in the case of immunochromatography.
  • the detection may be performed by a color development method by reaction of an enzyme labeled with an antibody (primary antibody or secondary antibody), a chemiluminescence method, a chemiluminescence method or a fluorescence method wherein a fluorescent dye is directly labeled to an antibody. It is desirable to have a high sensitivity and quantitative quantitative chemiluminescence method.
  • enzymes the use of peroxidases and alkaline phosphatase is preferred.
  • the main functionality of the evaluation at the functionality evaluation stage shown in FIG. 1 is that related to health. Functionality related to health varies depending on the purpose of evaluation and can not always be specified. In the case of foodstuffs, antioxidant activity, antimutagenic activity, apoptosis induction activity, cancer cell metastasis inhibitory activity, cancer cell proliferation inhibitory activity, antistress A variety of functions can be mentioned such as action, immunomodulation action, antiviral action, viral growth suppressive action, arteriosclerosis suppressive action, serum lipid improving action, hypertension preventing action, anti-inflammatory action, anti-obesity action and the like. In particular, evaluation of functions related to cancer prevention, such as antioxidant activity, apoptosis induction activity, and cancer cell proliferation suppression activity, is expected.
  • biomarkers applied to functional evaluation include antioxidant activity, antimutagenic activity, apoptosis induction activity, cancer cell growth inhibitory activity, antistress activity, immunomodulation activity, antiviral activity, virus proliferation. Proteins related to each functionality such as inhibitory activity can be mentioned. Furthermore, together with biomarkers that are related to functionality and whose expression levels change, we handle housekeeping proteins (G6PDH, GAPDH, actin etc.) whose expression levels hardly change as control markers, and including these as biomarkers desirable. For example, the proteins shown in Table 1 can be these biomarker candidates
  • A Housekeeping (control)
  • B Apoptosis inducing action
  • C Antioxidant action
  • enter ⁇ ⁇ Biomarkers can be selected from analysis results such as known information on literature and public databases, proteome analysis, DNA microarray (DNA chip) analysis, etc. Ru.
  • Public databases can include databases that can be searched using PubMed at the National Center for Biotechnology Information (NCBI) and databases that can be searched through the Internet.
  • first-dimensional isoelectric focusing using an IPG strip second-dimensional SDS-PAGE, two-dimensional electrophoresis, image analysis by electrophoresis of electrophoretic patterns, and mass spectrometry of protein spots can be done by analysis and identification
  • fluorescence differential analysis by prelabeling with a fluorescent dye excellent in quantitative property is desirable.
  • DNA microarray DNA chip
  • DNA chip commercially available DNA microarrays (DNA chip) for example, GeneChip probe array (Affymetrix), CodeLink Bioarray (Amersham's biosciences) and the like may be used. Although it is possible, it is preferable to use GeneChip probe array (Affymetrix).
  • any of monoclonal antibodies, polyclonal antibodies, antisera and recombinant antibodies can be used if they have specificity for a biomarker, but use of monoclonal antibodies is preferred.
  • a monoclonal antibody is prepared from Galfre, G., Milst em, and Preparation. Preparation of monoclonal antibodies: strategies and procedures, Methods Enzymol. 1981; 73 (Pt) B): Prepare by the method described in 3-46.
  • As the antigen purified protein, recombinant protein, synthetic peptide and the like can be used.
  • antibodies although commercially available, monoclonal antibodies, polyclonal antibodies, antisera, recombinant antibodies, or other antibodies can be used as long as they have specificity for a biomarker.
  • One of the functional comprehensive evaluations as the final step in FIG. 1 is a database storing biomarker expression levels obtained by evaluating functional known samples, biomarker expression levels of functional unknown samples, and It is a method to carry out by direct comparison of Specifically, a pattern of biomarker expression levels of the functional known sample stored in the database is searched that matches or is similar to the pattern of biomarker expression levels of the functional unknown sample. by If the patterns of the expression levels of the markers match or are similar, it can be evaluated that the functionally unknown material and the functionally known material are functionally similar.
  • the antioxidant, antimutagenic, apoptosis-inducing, cancer cell growth suppressive, anti-stress, immunomodulatory, antiviral, viral growth suppressive effects of functional unknown materials can be estimated.
  • the biomarker expression amount the measured value measured by the above-described method may be used as it is, or a correction value obtained by correcting the measured value may be used.
  • the value after normalization of the measured value may be used as the biomarker expression amount, or the average value of a plurality of measured values may be used as the biomarker expression amount.
  • the database of FIG. 1 accumulates data of known functional materials whose functionality has been confirmed in vivo and in the evaluated cultured cell line.
  • functional known materials whose functions have been confirmed in vivo and in cultured cell lines are provided to the evaluation cultured cells to obtain cell extracts and / or secretions in response to the known functional materials. Then, using the obtained cell extract and / or secretion as a test sample, measure the biomarker expression level by immunoassay.
  • Another method of comprehensive functional evaluation in FIG. 1 is to use a database in which the biomarker expression amount of the functional known sample is associated with the functionality thereof, and from the biomarker expression amount of the functional unknown sample , There is a method to estimate the functionality of the unknown material. For example, as described above, the biomarker expression level of a functional known sample is measured, and the functionality of this functional known material is measured by an individual evaluation system, and a database in which both measured values are associated is created.
  • the functional unknown food or food component is provided to the evaluation culture cells to obtain a cell extract and Z or secretion in response to the functional unknown material, and the obtained cell extract and / or secretion
  • the test sample is used as a test sample, and data on the expression level of biomarkers is obtained by immunology.
  • the functionality of the functional unknown material is estimated from the biomarker expression level of the obtained functional unknown sample using the associated database.
  • database classification and statistical processing are performed by multivariate analysis. Specific examples include learning methods, probabilistic methods, and clustering methods.
  • the functional measurement result by a known individual evaluation system can also be used.
  • the functional measurement methods include antiviral activity such as Replicon assay, TCID method, cancer cell proliferation inhibitory activity such as MTT assay, WST-8 assay etc, DPPH radical scavenging activity measurement, antioxidant such as reporter gene assay Activity, modified Ames method using sulfotransferase, Ames method, micronucleus test method, antimutagenic activity such as Rec atsei, anti-stress activity such as corticosterone, GOT test, TUNE L method, ANNEXIN V method, Apoptosis assays such as DNA ladder method and caspase activity assay can be used.
  • FIG. 2 is a block diagram showing the configuration of a computer system for executing the functionality evaluation method of the present invention.
  • the computer 21 comprises an input / output interface 21a, a central processing unit 21b, and a main storage unit 21c.
  • a network device 22 for exchanging data and programs with another computer system (not shown) via a communication line.
  • a keyboard 23 and a mouse 24 as input devices, and a monitor 25 and a printer 26 as output devices are connected to the input / output interface 21a.
  • an auxiliary storage device 17 such as a hard disk, a flash memory, a magnetic tape device, a magneto-optical disk device, etc. is connected to the input / output interface 11a.
  • the embodiment of the method of the present invention is not limited to the following specific example, and, for example, an integrated circuit mounting apparatus instead of the computer. Or it is also possible to implement using a chip.
  • the central processing unit 21b controls and controls the computer system, and at the same time, learns and generalizes between the biomarker expression amount of the functional known sample and its functional value, each functional known material and functional unknown Derivation of functional value of functional unknown material based on similarity score between materials (biomarker expression probability), clusterin of biomarker expression amount of functional known sample And the correspondence between each class and the functionality value.
  • the temporary or final calculation result by central processing unit 21 b is recorded in main storage unit 21 c or auxiliary storage unit 27 composed of, for example, a DRAM.
  • the main storage device 21c also stores a program for controlling the computer system.
  • Data used for calculation and commands for controlling the computer system can be input to the computer system through the keyboard 23 or the mouse 24. These data and commands can also be input through another computer system (not shown) connected through the network device 22.
  • the estimated result of the functionality of the unknown functional material can be displayed on the monitor 25 or the printer 26. These results can also be output through another computer system (not shown) connected through the network device 22.
  • the procedure for estimating the functionality of a functional unknown material by a learning method is approximately as follows. That is, the expression amount of the marker of the functional known sample and the functional value inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are once stored in the main storage device 21c. Next, the central computing unit 21b learns the relationship between the biomarker expression level and the functional value of the functional known sample. The relationship between the biomarker expression amount obtained by learning and the functional value is stored in the main storage device 21c or the auxiliary storage device 27.
  • the central processing unit 21b When the biomarker expression level of the functional unknown sample is input through the input means, the central processing unit 21b outputs the biomarker expression level and function of the functional known sample stored in the main memory 1 lc or the auxiliary memory 27.
  • the functional value of the functional unknown material is estimated from the relationship of the sex value and output through the output device.
  • FIG. 3 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a learning method.
  • the practice of the present invention by the learning method comprises the steps of: preparing a functional known sample; measuring the biomarker expression level from the functional known sample; measuring the functional value of the functional known material; Step 4 of learning relationship between biomarker expression level and functional value, Step 5 of preparing functional unknown sample, biomarker of functional unknown sample, and step 6 of measuring expression amount of functional unknown material, learning of functional known material Generalization of results and functionality unknown It consists of Step 7 of estimating the functional value of the material, Step 8 of comprehensively evaluating and judging the estimated functional value and processing into a form that can be easily performed, and Step 9 of displaying the comprehensive evaluation result.
  • FIG. 4 shows a method of learning with a neural network.
  • the biomarker expression amount and functionality value of the functional known sample stored in the auxiliary storage device 27 are read into the main memory 21c, the biomarker expression amount is input signal, functionality Using the value as a training signal as a training signal, the weight 42 is adjusted so that the error between the output of the neural network and the functional value is minimized by the error back propagation learning method on the hierarchical neural network.
  • Neural networks and error back-propagation learning methods may include those described in PH H. Winston, Artificial Intelligence Third Edition, Addison Wesley, 1992.
  • the bootstrap method Kerichiro Ishii, The number can be increased appropriately by using Ueda, S. Maeda, Y. Murase, Pattern recognition, Ohmsha, 1998).
  • the estimated value of the functional value of the functional unknown material is the biomarker expression level of the functional known sample stored on the neural network when the biomarker expression level of the functional unknown sample is input to the neural network. Find the generalization of the relationship between and the functional value.
  • the estimated value of functionality is displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
  • the procedure for estimating the functionality of a functional unknown material by the stochastic method is roughly as follows.
  • the biomarker expression amount and the functional value of the functional known sample inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are temporarily stored in the main storage device 21c.
  • the central processing unit 21b determines a probability density function representing the biomarker expression probability in the functional known sample.
  • the obtained probability density function is stored in the main storage unit 21c or the auxiliary storage unit 27.
  • the central processing unit 21b uses the probability density function stored in the main storage unit 11c or the auxiliary storage unit 27 to obtain the functional known material and function.
  • Similarity score with unknown material (probability)
  • the function f is used to estimate the functionality value of the unknown material and output it via the output device.
  • the function f it is possible to use a function that linearly interpolates the functionality value possessed by the functionality known material with the probability that the material is estimated.
  • FIG. 5 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a probabilistic method.
  • the probabilistic method according to the present invention comprises step 1 of preparing a functional known sample, step 2 of measuring the biomarker expression level from the prepared functional known sample, and 2 measuring the functional value of the functional known material.
  • Step 3 determining the probability density function representing the biomarker expression probability of each functional known sample 4; preparing the functional unknown sample 5; and the marker expression amount of the prepared functional unknown sample
  • Step 6 of measuring the degree of similarity, the degree of similarity between each functional known material and the function unknown based on the probability density function obtained in step 4, the degree of similarity obtained in step 7, the degree of similarity calculated in step 7 Step 8 of comprehensively evaluating the functional value based on the probability) and step 9 of displaying the evaluation result.
  • Step 1, Step 2, Step 3, Step 5, and Step 6 of FIG. 5 can be performed in the same manner as when using a neural network.
  • Step 4 of FIG. 5 a Parzen discriminator is used as a method of determining a probability density function representing the biomarker expression probability of each functional known sample (Joriichiro Toriwaki, Recognition Engineering, Corona, Inc.
  • the results of evaluation of the functionality values are displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
  • Functionality of unknown material is determined by clustering method in computer system 21
  • the procedure to estimate is approximately as follows. That is, the expression amount of the marker of the functional known sample and the functional value inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are once stored in the main storage device 21c.
  • the biomarker expression amount of the functional known sample is divided into a plurality of classes by the central processing unit 21b, and a data set in which the functional value is associated with each class is stored in the main storage unit 21c or the auxiliary storage unit 27.
  • the biomarker expression level of the functional unknown sample is input through the input means, the class to which the biomarker expression level belongs is determined, and the functionality value associated with the determined class is output to the output device.
  • FIG. 6 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a clustering method.
  • Step 1 of preparing a functional known sample Step 2 of measuring the biomarker expression level from the prepared functional sample, step 2 of measuring a prepared test sample activity value, and 3 Step 4: Cluster biomarker expression levels of known samples to associate functional value with each class
  • Step 4 Prepare functional unknown sample step 5.
  • Measure biomarker expression level of functional unknown sample Step 6: Measurement Step 7 of determining the class to which the biomarker expression amount belongs, step 8 of generalizing and estimating the functional value from the determined class, step 8 of comprehensively evaluating the functionality from the estimated functionality value, evaluation result It consists of step 10 to display.
  • Step 1, Step 2, Step 3, Step 5, and Step 6 of FIG. 6 can be implemented in the same manner as when using a neural network.
  • Biomarker expression level of functional unknown sample As a method of estimating the expected functionality, Euclidean between the weight possessed by the competitor node of the self-organizing map and the biomarker expression level of the functional unknown sample Distance can be used.
  • Biomarker expression ability of unknown functional sample A method for estimating expected functionality and Then, the Manhattan distance between the coordinates of the competitor node of the self-organizing map corresponding to the biomarker expression amount of the functional unknown sample and the competitor node coordinate of the self-organizing map corresponding to the functional known sample is It can be used.
  • Biomarker expression level of unknown functional sample When estimating the expected functional value, if the biomarker expression level does not match the learning sample prepared from the known functional sample, the functional unknown sample is obtained. Based on the distance between the competing node on the self-organizing map that corresponds to the biomarker expression level of and the competing node located in the vicinity, the functional value of the unknown material is estimated by the function f. As the function f, it is possible to use a function that interpolates the functional value from the slope of the hyperplane on the weight space including the weights possessed by the neighboring nodes.
  • the estimated value of the functional value is displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
  • the food ingredient ⁇ ⁇ pigarocatechin gallate (EGCG) (30 a M) was used as the knowledge material, and genistin (3 ⁇ ) and lipoic acid (ImM) were used as the functional unknown material.
  • the components of each test concentration were applied to the cell culture medium, each material was allowed to act on the cells for 24 hours, and then the cells were collected to obtain a cell extract, which was used as a biomarker test sample.
  • the test sample was electrophoresed first by SDS-PAGE, the proteins were separated, and then transferred to a PVDF membrane. Membrane 5 /.
  • biomarker candidates used for analysis of functional unknown samples markers associated with apoptosis induction (Bel-2, PARP, DFF 45, FADD, CAS), cancer cell proliferation suppression related markers (RB, The expression levels were compared using RB2) and stress-related markers (Hsp-70, Hsp-90).
  • each signal intensity was divided by the signal intensity of Be ⁇ 2 to obtain the biomarker expression amount per unit Be ⁇ 2 expression amount. Furthermore, these values were quantified using the test sample prepared from the cells not treated with the test material as a control and the relative expression level when the control expression level was 1.
  • EGCG epigallocatechin gallate
  • FIG. 7 shows the effects of EGCG and genistin on biomarker expression patterns of Jurkat cells.
  • EGCG is compared with the control 7: 7: RB, 8 : The force by which the expression level of RB2 is reduced to about 1/2.
  • 1: Hsp 70 expression level was hardly changed in EGCG and genistin compared with control.
  • Table 2 Table 2. The control is represented as 1 fc pi age marker expression! :
  • FIG. 8 shows the effects of EGCG and lipoic acid on biomarker expression patterns of Jurkat cells.
  • EGCG which is a functional known material
  • H sp 70, 7: RB, 8: RB2 changes in the expression levels of the three bio markers are compared with the control.
  • lipoic acid is predicted to have EGCG-type functionality and genistin is only when the expression patterns of l: Hsp70, 7: RB, and 8: RB2 are compared. It is judged that it is expected to have different types of functionality.
  • EGCG reduces expression by 10% or more.
  • the expression of Hsp90 was reduced by 10% or more, so the gain was given + 1 for the Hsp 90 item.
  • lipoic acid the expression of Hsp90 is increased by 10% or more, so this item is given 1 point. From the results of this implementation, it was possible to obtain 0.625 points for genistin and 0.25 points for lipoic acid. This average score is a variable with a value of 1 to 1, and as it approaches 1 it can be judged that the fluctuation is of the same quality as that of EGCG. Therefore, it can be estimated from the results of this implementation that it is similar to the EGCG fluctuation due to genistin.
  • the biomarker expression level of the functional known sample is estimated, and the functional value of the functional unknown material is estimated.
  • Human T cell leukemia cells ⁇ [urkat cells were used as cells for measuring biomarker expression level and cancer cell growth inhibition test (individual evaluation system) evaluation cells. Cells were equilibrated with 5% CO gas at 37 ° C. in PRMI 1640 medium containing 10% fetal calf serum (FCS).
  • FCS fetal calf serum
  • the cells were cultured in an incubator.
  • Jurkat cells in the logarithmic growth phase were inoculated at a density of 3 ⁇ 10 5 cells / ml into blastoside dishes, and then functional known materials and functional unknown materials for learning were added.
  • cell lysis buffer PBS containing 1 mM EDTA, 0.005% Tween, 0.5% Triton X-100
  • an anti-human thioredoxin bluetooth antibody 100 ng / ml: 1% BSA-containing PBS
  • an anti-army IgG mouse antibody 200 ng / ml: PBS containing 1% BSA labeled with horseradish peroxidase (HRP) as a secondary antibody 100 / i 1 was added and allowed to react for another hour.
  • the substrate solution was washed 4 times with TPBS, and the substrate solution ⁇ 0.3 mg ABTS [ ⁇ -2,2'-azino_bis_ (3_ethylbenzothiazoline_6_sulfomc acid) aiammonium salt] and 0.03% H 2 O 0.1
  • each absorbance was divided by the absorbance of GAPDH to obtain the amount of biomarker expression per unit GAPDH expression amount. Furthermore, by dividing these values by the biomarker expression level of the control test sample, the biomarker expression level of the test group was obtained as a relative value to the control.
  • Table 8 shows biomarker expression levels when learning known materials were added to Jurkat cells. Further, the biomarker expression level of the functional unknown sample is shown in Table 9.
  • RNA of HCV The genomic RNA of HCV is roughly divided into a core constituting virus particles, a structural protein translational region of envelope and a nonstructural protein translational region functioning for viral genome replication and the like. Ru. This structural protein translation region is replaced by luciferase translation region 'EMCV IRES (brain myocardium virus internal ribosome binding arrangement ⁇ Neomycin resistant gene is substituted for subgenomic replicon RNA to create RNA, human hepatoma cell Huh- It is introduced into the cytoplasm of 7. Huh-7, into which the subgenomic replicon RNA has been introduced, is simultaneously resistant to neomycin to allow selection with Geneticin (G418).
  • HCV replicon RNA produced will be measured by the luciferase assay method described below, and for the passage of this cell, DMEM 10 [GlutaMAX Media Dulbecco 'from GIBCO will be used.
  • D-MEM Modifie d Eagle Medium
  • liquid High glucose, contains sodium pyruvate
  • BS BS Heyclone
  • Penicillin— Streptomycin GIBCO
  • Geneticin invitrog The medium supplemented with E n) is used as the culture medium to which the assay is performed, using DMEM 10 supplemented with 5% of FBS and Penicillin-Streptomycin (however, Geneticin is not added).
  • Norecipherase assay method Luciferase catalyzes a reaction to form luciferin oxide and AMP from luciferase and ATP in the presence of magnesium.
  • the luciferase assay detects the light generated at this time with a luminescence detector, and based on the amount of light obtained, It is a method to evaluate the activity. In the present invention, for convenience, this light amount is referred to as HCV replicon RNA amount.
  • test cells replicon cells
  • Food ingredients and / or food extracts were prepared as sexually unknown materials.
  • EGCG, genistin, lipoic acid, arachidonic acid, crolekmin, daidzein, quercetin, cyanidin, resveratrol, 10t, 12c_CLA were used as functional known materials. It was added to the above 96 well plate according to the final concentration described in Table 1. Thereafter, the cells were further cultured for 72 hours, and allowed to stand at room temperature for 30 minutes or more, after which Luciferase atsei reagent (Promega, Steady-GloTM Luciferase Assay System) was pipetted well with a well. .
  • Luciferase atsei reagent Promega, Steady-GloTM Luciferase Assay System
  • the percentage relative to the control was determined from the luminescence measurement value, and the relative luciferase activity (%) of the test cell at each concentration of the test material was calculated. As described above, the relative luciferase activity (%) reflects the amount of HCV replicon RNA.
  • the obtained results are shown in Table 10.
  • the values shown in Table 10 are the average of the values obtained from multiple measurements, and the maximum value of relative luciferase activity (HCV replicon RNA amount) obtained through all measurements was 2.27.
  • Relative luciferase activity 01 ('Lebricon RNA content)
  • a neural network was constructed to predict relative luciferase activity from the amount of biomarker expression.
  • the neural network was trained to output the relative Noresifusulase activity values shown in Table 10. Since the maximum value of the output of the neural network is 1, the value of the relative luciferase activity value is divided by the maximum value 2.27 obtained by measurement, and this is used as a teacher signal at the time of learning of the neural network. An error back propagation learning method was used for learning of the neural network.
  • the neural network is set as an input neuron 9, an intermediate neuron 4, and an output neuron 1, and the input from an intermediate neuron for threshold adjustment and an output neuron that always outputs 1 is given to the output neuron.
  • the initial value of the weight in the neural network is a random number between 2 ⁇ 0 force and 2. 0, the learning coefficient is set to 0.7, the inertia coefficient is set to 0.4, and the learning frequency is 40 per data. The learning was stopped when reaching 000, 000 times, or when the estimation error was less than 0.1 for all data.
  • Table 11 The output (estimated value) of the neural network shown in FIG. Table 11 also shows the measured value (measured value) of the relative luciferase activity of the substance and the absolute value of the estimation error simultaneously to show the estimated accuracy, and these values are the maximum values obtained in the relative noresifase activity assay. It is the value after dividing by the value 2.27. As shown in Table 11, it was shown that it is possible to estimate the relative luciferase activity at a concentration not used for learning with a specified error or less.
  • the learning shown in Table 9 is performed on a neural network in which learning was performed using a data set obtained by removing daidzein 70 ⁇ and resveratrol 100 ⁇ from the training data sets shown in Table 8 and Table 10.
  • the relative luciferase activity value was estimated by presenting the amount of biomarker 1 expression by the reit, vert, ret, material (corresponding to the functional unknown material).
  • Table 12 shows estimated values of relative luciferase activity and their measured values when the amount of expression of biomolecules shown in Table 9 is presented to a trained neural network.
  • Table 12 Table 1 2. Cub Saishin at 10 WM, Blueberry single leaf extract at 50 g / ml
  • Table 13 After learning with the exception of daidzein 70 ⁇ l and resveratrol 100, when the amount of biomarker expression by these two compounds was given to a neural network, the output shown in Table 13 was obtained. Table 13 also shows the measured values (measured values) for cancer cell growth suppression of the substance and the absolute value of the estimation error at the same time to show the estimation accuracy, and these values are the same as the teacher signal and 1.20. It is the value after dividing. As shown in Table 13, it was shown that it is possible to estimate the cancer cell growth inhibitory effect at concentrations not used for learning with a prescribed error or less.
  • Table 13 Table 1 3. Results of cancer cell growth inhibitory effect estimation results at concentrations not used in science Chemical name Estimated value Measured value Absolute estimation error Daisyin 70 ⁇ 0. 78 0. 70 0. 08
  • Resveratrol 0. 45 0. 38 ⁇ . 07 The learning data shown in Table 6 and Table 8 The materials not used in the learning shown in Table 9 for the neural network that was learned except for 70 ⁇ of daidzein and 100 ⁇ of resveratrol The biomarker expression level according to the material) is presented to estimate the cancer cell growth inhibitory action value.
  • Table 14 shows the estimated value and the measured value of the cancer cell growth inhibitory action when the biomarker expression levels shown in Table 9 are presented on the learned two-user network.
  • the measured value of the cancer cell proliferation inhibitory action is a measurement only once, and it is the value after dividing it by 1.20 similarly to the teacher signal as well as the estimation of 70 ⁇ ⁇ of Dizein and 100 ⁇ of resveratrol.
  • the high-throughput functionality evaluation method, program, and apparatus of the present invention can be used to evaluate foodstuffs, drugs, drug candidates, and the like.
  • it is suitable for comprehensively evaluating the functionality of multi-component substances such as food, and it is a preliminary test before conducting in vivo evaluation tests by animal experiments in developing functional food and developing food for specified health use.
  • it can be suitably used for a preliminary test prior to a human clinical trial.
  • it can be used for functional evaluation tests prior to shipment of biological resource products such as agricultural and forestry forests, and functional evaluation tests for agricultural and aquatic cultivation samples such as livestock and artificially-reared fish and shellfish, and luggage.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Immunology (AREA)
  • Hematology (AREA)
  • Chemical & Material Sciences (AREA)
  • Urology & Nephrology (AREA)
  • Molecular Biology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Microbiology (AREA)
  • Biotechnology (AREA)
  • Pathology (AREA)
  • General Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Cell Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Toxicology (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Disclosed are a method, an apparatus and a program for high-throughput functionality evaluation by which a biologically active functionality of a material can be evaluated from multiple aspects and in terms of multiple items with safety, high efficiency and high reliability. A functionality-unknown material (e.g., a food component) is mixed with cultured human cells. An extract from the cells which have responded to the food component is prepared as a test sample, and the test sample is applied to an evaluation system provided with a plurality of measurement parts having different abilities to evaluate the functionality. In the evaluation test, the expressed amount of a biomarker corresponding to each antibody is determined by immunoassay based on antigen-antibody reaction. The obtained amount can be used for comparison with the expressed amount of a biomarker corresponding to a functionality-known sample, or for estimation of the functionality based on a functionality value corresponding to the expressed amount of the biomarker for the functionality-known sample by using a computer program. Suitable examples of the functionality-unknown material to be evaluated include biomasses such as food materials and other chemical substances.

Description

明 細 書  Specification
高スループット機能性評価方法、プログラム、及び装置  High-throughput functionality evaluation method, program, and apparatus
技術分野  Technical field
[0001] 本発明は、高スループット機能性評価方法、特に食材のごとき多成分系材料の複 合的生理活性を含む機能を一度に評価するのに好適な健康に係わる高スループッ ト機能性評価方法、プログラム、及び装置に関するものである。  The present invention provides a high throughput functional evaluation method, in particular, a high throughput functional evaluation method relating to health suitable for evaluating functions including complex physiological activity of multi-component materials such as foodstuffs at one time. , Programs, and devices.
背景技術  Background art
[0002] これまで、高スループット機能性評価方法あるいは装置として、種々の提案がなさ れている。例えば、特開 2002— 328124号公報は、生物系における改善された特 性を有する化合物の組み合わせを系統的に選択する高スループットスクリーニング 方法を開示している。特表 2003— 504011号公報は、生物学的または化学的アツ セィの同時実施に有用な高スループットアツセィシステムを開示している。特表 2003 509657号公報は、例えば共通成分である医薬の性質と、追加成分である賦形剤 の性質との観点から評価し、最適組み合わせを選択する高ハイスループット試験を 開示している。  Until now, various proposals have been made as high-throughput functionality evaluation methods or apparatuses. For example, Japanese Patent Application Laid-Open No. 2002-328124 discloses a high throughput screening method for systematically selecting a combination of compounds having improved characteristics in a biological system. Japanese Patent Publication No. 2003-50401 discloses a high throughput assay system useful for simultaneous implementation of biological or chemical assays. Japanese Unexamined Patent Publication No. 2003-509657 discloses, for example, a high-throughput test that evaluates from the viewpoint of the properties of a common component drug and the properties of an additional component excipient and selects an optimum combination.
[0003] 一方、吉川敏一 ILSI [80] 14 (2004)は、食材の機能性に科学的根拠を与えること の手段として、 Clydesdaleの提唱する（1)食材の疾病予防に関する疫学的研究、（ 2)適正なバイオマーカーの開発、（3)ヒト集団を利用した臨床的介入試験を挙げ、 今後期待されるバイオマーカー探索手法を開示している。  [0003] On the other hand, Toshikazu Yoshikawa ILSI [80] 14 (2004) is an epidemiological study on the prevention of diseases of foodstuffs (1) proposed by Clydesdale as a means of giving scientific basis to the functionality of foodstuffs ( 2) Development of appropriate biomarkers, (3) Clinical intervention tests using human population, and disclosed methods for searching biomarkers expected in the future.
[0004] 種々の材料、特に食材のような多成分系物質に関し、高スループットで総合的に評 価する試験法及び装置は、前記各文献を含めて、現在までのところ本発明者らが知 る限り知られていないし、実用化にもいたつていない。例えば、これまで実際に行わ れている食材の機能性の評価試験では、個々の食材含有成分から予想される薬理 効果あるいは生理活性、健康維持、疾病予防及び治療機能について、個別的に試 験するにとどまり、総合的な試験はなされていない。  [0004] With respect to various materials, particularly multi-component materials such as foodstuffs, high throughput and comprehensive evaluation methods and apparatus including the aforementioned documents have been known by the present inventors so far. It is not known as much as possible, and has not been put into practical use. For example, in the evaluation test of the functionality of the food which has been actually carried out so far, the pharmacological effect or physiological activity expected from each food material component, health maintenance, disease prevention and treatment function are individually tested. It has not been comprehensively tested.
発明の開示  Disclosure of the invention
発明が解決しょうとする課題 [0005] しかしながら、このような被検材料の成分別、機能別個別試験方法では、多成分を 含む食材の有する多岐にわたる機能性を総合的に評価することは困難である。殊に 、同一条件で同時に測定された個別データでないかぎり、個々の異なる測定データ をいくら集めても、信頼性の高い複合的機能、それらの相互作用の予測は不可能に 近レ、。食材の評価として、モデル動物を用いた in vivo試験は広く行われているが、 ヒトとモデル動物とでは蛋白質や酵素の働きが微妙に異なるので、モデル動物試験 の結果をヒトにそのまま適用できるとは限らない。最終的にヒトによる臨床試験が必要 であるにしても、食材の機能性スクリーニングの段階から、すべてにわたりヒト臨床試 験を実施するのは、安全性、効率、費用の点から実際的ではない。 Problem that invention tries to solve However, it is difficult to comprehensively evaluate a wide variety of functions possessed by foodstuffs containing multiple ingredients by such component-specific or function-specific individual test methods of the test material. In particular, unless it is individual data measured simultaneously under the same conditions, it is impossible to predict reliable complex functions and their interactions, no matter how many different individual measurement data are collected. Although in vivo tests using model animals are widely performed to evaluate foodstuffs, the functions of proteins and enzymes are slightly different between humans and model animals, so that the results of model animal tests can be applied to humans as they are. There is no limit. Although human clinical trials are ultimately required, it is not practical from the safety, efficiency and cost standpoints to conduct human clinical trials from the food ingredient screening stage.
[0006] 本発明は、前記問題点を解消したもので、各種材料の特に健康機能性を多項目に わたり、高効率かつ高信頼性をもって評価する高スループット機能性評価方法を提 供することを目的としている。  The present invention solves the above-mentioned problems, and it is an object of the present invention to provide a high throughput functional evaluation method for evaluating the health functional properties of various materials in a large number of items with high efficiency and high reliability. And
[0007] また本発明は、前記問題点を解消したもので、中央演算装置を用いて、各種材料 の健康機能性を多項目にわたり、高効率かつ高信頼性をもって評価する高スループ ット機能性評価方法を提供することを目的としている。  Further, the present invention solves the above-mentioned problems, and a high throughput function to evaluate the health functions of various materials over multiple items using a central processing unit with high efficiency and high reliability. The purpose is to provide an evaluation method.
[0008] さらに本発明は、コンピュータ上で動作し、各種材料の健康機能性を多項目にわた り、高効率かつ高信頼性をもって評価する高スループット機能性評価プログラムを提 供することを目的としている。  Furthermore, the present invention aims to provide a high-throughput functionality evaluation program that operates on a computer and evaluates the health and functionality of various materials in a large number of items with high efficiency and high reliability. .
課題を解決するための手段  Means to solve the problem
[0009] 前記目的を達成した本発明に係る機能性評価方法は、機能性未知材料の機能性 を評価する方法、プログラム、装置であって、下記の発明を含む。 The functional evaluation method according to the present invention achieving the above object is a method, a program, and an apparatus for evaluating the functionality of a functional unknown material, and includes the following invention.
[0010] [第 1発明] [First Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、  (1) Evaluation A functional unknown material is added to cultured cells to prepare a functional unknown sample.
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、  (2) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression amount of the functional unknown sample;
(3)得られた測定値を処理して、機能性未知材料の機能性を総合評価するステップ と、 (3) A step of processing the obtained measured values to comprehensively evaluate the functionality of the unknown functional material When,
(4)総合評価結果を表示するステップと、  (4) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0011] [第 2発明] [Second Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
( 1 )評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、バイオマーカー発現 量測定値と機能性とを対応付けるステップと、  (2) A functional known sample is added to an evaluation system provided with a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the biomarker expression amount measurement value and functionality And associating the
(3)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、  (3) Evaluation A functional unknown material is imparted to cultured cells to prepare a functional unknown sample.
(4)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、  (4) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(5)得られた測定値を処理して、上記（2)で機能性と対応付けられた機能性既知試 料のバイオマーカー発現量と機能性未知試料のバイオマーカー発現量を照合して、 機能性未知材料の機能性を総合評価するステップと、  (5) The obtained measured values are processed to compare the biomarker expression amount of the functional known sample correlated with the functionality in the above (2) with the biomarker expression amount of the functional unknown sample, Comprehensively evaluating the functionality of the unknown material
(6)総合評価結果を表示するステップと、  (6) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0012] [第 3発明] Third Invention
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
( 1 )評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性値を測定し、機能性既知試料のバイオマーカー発現量測定 値と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製す るステップと、 (3)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (2) A functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functional value of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the known functional material is associated; (3) Evaluation A functional unknown material is imparted to cultured cells to prepare a functional unknown sample.
(4)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (4) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(5)機能性未知試料のバイオマーカー発現量測定値に基づいて上記データベース を検索し、機能性未知材料の機能性値を推定するステップと、 (5) searching the above-mentioned database based on the biomarker expression level measurement value of the functional unknown sample to estimate the functional value of the functional unknown material;
(6)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、  (6) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value;
(7)総合評価結果を表示するステップと、  (7) displaying the comprehensive evaluation result,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0013] [第 4発明] [A Fourth Invention]
(1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定するステップと、 (2) attaching a functional known sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional known sample;
(3)個別評価系により、機能性既知材料の機能性を測定するステップと、 (3) measuring the functionality of the known functional material by an individual evaluation system;
(4)機能性既知試料のバイオマーカー発現量測定値と機能性既知材料の機能性測 定値との関係を対応付けるステップと、  (4) Correlating the relationship between the measured value of biomarker expression level of the functional known sample and the measured value of functionality of the known functional material,
を含むことを特徴とする機能性既知材料に関するデータベース作製方法。  The database preparation method regarding the functional known material characterized by including.
[0014] [第 5発明] [Fifth Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、  (1) Evaluation A functional unknown material is added to cultured cells to prepare a functional unknown sample.
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (2) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression amount of the functional unknown sample;
(3)機能性未知試料のバイオマーカー発現量測定値に基づいて機能性既知試料の バイオマーカー発現量と機能性既知材料の機能性値と対応付けたデータセットを検 索し、機能性未知材料の機能性値を推定するステップと、 (4)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、 (3) Based on the biomarker expression level measurement value of the functional unknown sample, search the data set corresponding to the biomarker expression level of the functional known sample and the functional value of the functional known material, and search for the functional unknown material Estimating the functional value of (4) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value;
(5)総合評価結果を表示するステップと、  (5) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0015] [第 6発明] [Sixth Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、  (2) A functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
(3)上記データベースにおける、機能性既知試料のバイオマーカー発現量測定値と 機能性既知材料の機能性測定値との関係を学習するステップと、  (3) learning the relationship between the biomarker expression level measurement value of the functional known sample and the functional measurement value of the functional known material in the above database;
(4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、  (4) Evaluation A functional unknown material is provided to cultured cells to prepare a functional unknown sample.
(5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (5) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(6)機能性既知試料のバイオマーカー発現量測定値と機能性既知材料の機能性測 定値との関係の学習結果を汎化して、機能性未知試料のバイオマーカー発現量測 定値に基づいて機能性未知材料の機能性値を推定するステップと、 (6) Generalize the learning result of the relationship between the biomarker expression level measurement value of the functional known sample and the functional measurement value of the functional known material, and perform the function based on the biomarker expression level measurement value of the functional unknown sample Estimating the functional value of the property unknown material,
(7)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、  (7) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value;
(8)総合評価結果を表示するステップと、  (8) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0016] [第 7発明] [Seventh Invention]
上記ステップ（3)における機能性既知試料のバイオマーカー発現量測定値と機能 性既知材料の機能性測定値との関係の学習、及び上記ステップ (6)における学習結 果の汎化による機能性未知試料のバイオマーカー発現量測定値からの機能性未知 材料の機能性値の推定をニューラルネットワークにより行うことを特徴とする [第 6発明 ]記載の方法。 Measured value and function of biomarker expression amount of functional known sample in the above step (3) Of the functional value of the functional unknown material from the biomarker expression level measurement value of the functional unknown sample by learning the relationship with the functional measurement value of the sex-known material and generalization of the learning result in the above step (6) The method according to [6th invention], wherein the estimation is performed by a neural network.
[0017] [第 8発明] [Eighth Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、  (2) A functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
(3)上記データベースにおける各機能性既知試料のバイオマーカー発現量が生じる 確率密度関数を求めるステップと，  (3) determining a probability density function that results in the biomarker expression amount of each functional known sample in the above database;
(4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、  (4) Evaluation A functional unknown material is provided to cultured cells to prepare a functional unknown sample.
(5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (5) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(6)機能性未知試料のバイオマーカー発現量測定値に基づいて，上記確率密度関 数力 当該機能性未知材料の各機能性既知材料との類似度合いを求めるステップ と， (6) based on the biomarker expression measurement value of the functional unknown sample, determining the degree of similarity between the probability density function power and the functional unknown material with each known functional material;
(7)上記類似度合いと上記データベースに基づいて機能性未知材料の機能性を総 合評価するステップと，  (7) comprehensively evaluating the functionality of the unknown material based on the degree of similarity and the database;
(8)総合評価結果を表示するステップと、  (8) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0018] [第 9発明] [0018] [Ninth invention]
上記確率密度関数をノンパラメトリック法で求めて、機能性未知材料に類似した機 能性既知材料の決定をベイズ推定法により行うことを特徴とする [第 8発明]記載の方 法。 The probability density function is obtained by nonparametric method, and the machine similar to the functional unknown material The method according to [the eighth invention], characterized in that the determination of the material of known ability is performed by Bayesian estimation.
[0019] [第 10発明]  [Tenth invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、  (2) A functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
(3)上記データベースにおける、機能性既知試料のバイオマーカー発現量測定値を クラスタリングし、各クラスに機能性既知材料の機能性測定値を対応付けるステップと  (3) clustering the biomarker expression level measurement values of the functional known sample in the above database and associating the functional measurement values of the functional known material with each class;
(4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (4) Evaluation A functional unknown material is provided to cultured cells to prepare a functional unknown sample.
(5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、  (5) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(6)機能性未知試料のバイオマーカー発現量測定値に基づいて上記データベース を検索し、当該バイオマーカー発現量測定値がクラスタリングされた各クラスの中のど のクラスに帰属するかを決定するステップと、  (6) searching the above-mentioned database based on the biomarker expression level measurement value of the functional unknown sample, and determining to which class of each class the biomarker expression level measurement value belongs to be clustered; ,
(7)決定されたクラスに基づき、機能性未知試料のバイオマーカー発現量測定値か ら機能性未知材料の機能性値を汎化、推定するステップと、  (7) generalizing and estimating the functional value of the functional unknown material from the measured value of the biomarker expression level of the functional unknown sample based on the determined class;
(8)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、  (8) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value;
(9)総合評価結果を表示するステップと、  (9) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[0020] [第 11発明] クラスタリングを自己組織化マップ法により行うことを特徴とする [第 10発明]記載の 方法。 [Invention 11] The method according to [10th invention], wherein clustering is performed by a self-organizing map method.
[0021] [第 12発明]  [Twelfth Invention]
自己組織化マップにおける機能性未知材料の機能性のクラスの決定を、機能性既 知試料のノィォマーカー発現量測定値に対応付けられた競争ノードが持つ重みと、 機能性未知試料のバイオマーカー発現量測定値とのユークリッド距離に基づき行うこ とを特徴とする [第 11発明]記載の方法。  Determination of the class of functionality of unknown functional material in the self-organizing map, the weight possessed by the competition node associated with the measured value of the marker expression level of the functional known sample, and the expression level of biomarker of the functional unknown sample The method according to [11th invention], characterized in that the measurement is performed based on Euclidean distance with the measured value.
[0022] [第 13発明] [Invention 13]
自己組織化マップにおける機能性未知材料の機能性のクラスの決定を、機能性既 知試料のバイオマーカー発現量測定値に対応付けられた競合ノードの座標と、機能 性未知試料のバイオマーカー発現量測定値に対応付けられた競合ノードの座標間 におけるマンハッタン距離に基づき行うことを特徴とする [第 11発明]記載の方法。  Determination of the functional class of the functional unknown material in the self-organizing map, coordinates of competing nodes associated with the biomarker expression measurement value of the functional known sample, and the biomarker expression amount of the functional unknown sample The method according to [11th invention], characterized in that it is performed based on Manhattan distance between coordinates of competing nodes associated with the measurement value.
[0023] [第 14発明] [Eightteenth Invention]
評価培養細胞がヒト由来培養細胞であることを特徴とする [第 1、 2、 3、 4、 5、 6、 8 又は 10発明]記載の方法。  Evaluation The cultured cell is a human-derived cultured cell [1, 2, 3, 4, 5, 6, 8, or 10 invention] described method.
[0024] [第 15発明] [Fifteenth Invention]
機能性未知材料の複合的な機能性を評価する方法であって、  A method for evaluating the composite functionality of a functional unknown material, comprising:
(1)中央演算装置が機能性未知試料のバイオマーカー発現量に基づいて、機能性 既知試料のバイオマーカー発現量と機能性既知材料の機能性値とを対応付けたデ ータセットを格納したデータベースを検索し、機能性未知材料の機能性値を推定す るステップと、  (1) A database storing a data set in which the central processing unit associates the biomarker expression amount of the functional known sample with the functional value of the functional known material based on the biomarker expression amount of the functional unknown sample. Searching and estimating the functional value of the functional unknown material;
(2)中央演算装置が、上記ステップ（1)の検索結果に基づいて機能性未知材料の機 能性を総合評価するとともに総合評価結果を出力するステップと、  (2) The central processing unit comprehensively evaluates the functionality of the functional unknown material based on the search result of the step (1) and outputs the general evaluation result.
を含むことを特徴とする機能性未知材料に関する高スループット機能性評価方法。  A high throughput functionality evaluation method for unknown functionality material characterized by including.
[0025] [第 16発明] [Sixteenth Invention]
上記機能性既知材料の機能性値は、各機能性既知材料にっレ、て個別評価系によ り測定された測定値力も算出された値であることを特徴とする [第 15発明]記載の方 法。 [0026] [第 17発明] The functional value of the above-mentioned functional known material is characterized in that the measured value force measured by the individual evaluation system is also calculated for each of the functional known materials, and [15th invention] described. the method of. [Seventeenth Invention]
上記ステップ（1)において、中央演算装置は機能性未知試料のバイオマーカー発 現量をキーとして上記データベースに含まれる機能性既知材料のバイオマーカー発 現量を照合し、特定のデータセットを検索結果として算出することを特徴とする [第 15 発明]記載の方法。  In the step (1), the central processing unit collates the biomarker expression amount of the functional known material contained in the database using the biomarker expression amount of the functional unknown sample as a key, and searches a specific data set The method according to [15th invention], characterized in that
[0027] [第 18発明] [Eighteenth Invention]
上記中央演算装置は、機能性既知試料のバイオマーカー発現量測定値と機能性 既知材料の機能性測定値との関係を学習することで学習結果を算出し、  The central processing unit calculates the learning result by learning the relationship between the measured value of the biomarker expression amount of the functional known sample and the measured value of the functionality of the known material,
上記ステップ (1)では、中央演算装置が上記学習結果を汎化し、機能性未知試料 のバイオマーカー発現量測定値に基づいて機能性未知材料の機能性値を推定する ことを特徴とする [第 15発明]記載の方法。  In the step (1), the central processing unit generalizes the learning result and estimates the functional value of the functional unknown material on the basis of the measured value of the biomarker expression amount of the functional unknown sample. 15] The method of description.
[0028] [第 19発明] [Twenty-Ninth Invention]
上記中央演算装置は、上記学習結果の算出及び学習結果の汎化による機能性未 知試料のバイオマーカー発現量測定値からの機能性未知材料の機能性値の推定を ニューラルネットワークにより行うことを特徴とする [第 18発明]記載の方法。  The central processing unit is characterized in that calculation of the learning result and estimation of the functional value of the functional unknown material from the biomarker expression amount measurement value of the functional unknown sample by generalization of the learning result are performed by neural network. The method according to [the 18th invention].
[0029] [第 20発明] [Twentyth Invention]
上記中央演算装置は、上記データセットにおける各機能性既知試料のノくィォマー カー発現量が生じる確率密度関数を算出し，  The central processing unit calculates a probability density function that causes the amount of optical marker expression of each functional known sample in the data set,
上記ステップ（1)において、中央演算装置は、機能性未知試料のバイオマーカー 発現量測定値及び上記確率密度関数を用いて機能性未知材料に類似した機能性 既知材料を決定し、機能性既知材料の機能性値をデータベースから検索して推定 することを特徴とする [第 15発明]記載の方法。  In the step (1), the central processing unit determines the functionality known material similar to the functionality unknown material using the biomarker expression value measurement value of the functionality unknown sample and the probability density function and determines the functionality known material The method according to [Fifteenth Invention], wherein the functional value of is estimated from a database by searching.
[0030] [第 21発明] [0030] [Twenty First Invention]
上記中央演算装置は、上記確率密度関数をノンパラメトリック法で算出し、機能性 未知材料に類似した機能性既知材料の推定をベイズ推定法により行うことを特徴と する [第 20発明]記載の方法。  The central processing unit calculates the probability density function by the nonparametric method, and performs estimation of a functional known material similar to the functional unknown material by a Bayesian estimation method. .
[0031] [第 22発明] 22nd Invention
上記中央演算装置は、上記データセットに含まれる機能性既知試料のバイオマー カー発現量測定値をクラスタリングすることで各クラスに機能性既知材料の機能性測 定値を対応付けたデータセットを算出し、 The central processing unit is a dimer of functional known samples contained in the data set. By clustering the car expression level measurement values, a data set in which the functional measurement values of the functional known material are associated with each class is calculated.
上記ステップ（1)において、中央演算装置は、機能性未知試料のバイオマーカー 発現量測定値が帰属するクラスを決定し、  In the step (1), the central processing unit determines the class to which the biomarker expression value measurement value of the functional unknown sample belongs,
上記ステップ（2)において、中央演算装置は、決定したクラスに帰属する機能既知 材料の機能性値を汎化し、機能性未知材料の機能性値を推定することを特徴とする [第 15発明]記載の方法。  In the step (2), the central processing unit generalizes the functional value of the function known material belonging to the determined class, and estimates the functional value of the functional unknown material. Method described.
[0032] [第 23発明] [The 23rd Invention]
上記中央演算装置は、クラスタリングを自己組織化マップ法により行うことを特徴と する [第 22発明]記載の方法。  The method according to the 22nd invention, wherein the central processing unit performs clustering by a self-organizing map method.
[0033] [第 24発明] [The 24th Invention]
上記中央演算装置は、 自己組織化マップにおける機能性未知材料の機能性のク ラスの決定を、機能性既知試料のバイオマーカー発現量測定値に対応付けられた 競争ノードが持つ重みと、機能性未知試料のバイオマーカー発現量測定値とのユー タリッド距離に基づき行うことを特徴とする [第 23発明]記載の方法。  The central processing unit determines the class of the functionality of the functional unknown material in the self-organizing map, the weight of the competition node associated with the biomarker expression amount measurement value of the functionality known sample, and the functionality. The method according to [23rd invention], characterized in that the determination is performed based on the distance between the unknown sample and the biomarker expression level of the unknown sample.
[0034] [第 25発明] [Twenty-Fifth Invention]
上記中央演算装置は、 自己組織化マップにおける機能性未知材料の機能性のク ラスの決定を、機能性既知試料のバイオマーカー発現量測定値に対応付けられた 競合ノードの座標と、機能性未知試料のノくィォマーカー発現量測定値に対応付けら れた競合ノードの座標間におけるマンハッタン距離に基づき行うことを特徴とする [第 The central processing unit determines the class of functionality of the functional unknown material in the self-organizing map, the coordinates of the competing node associated with the biomarker expression measurement value of the functional known sample, and the functionality unknown. It is characterized in that it is performed based on the Manhattan distance between the coordinates of competing nodes associated with the measured value of the marker expression level of the sample [No.
23発明]記載の方法。 23) The method of description.
[0035] [第 26発明] [The 26th Invention]
機能性未知材料の複合的な機能性を評価する装置であって、  An apparatus for evaluating the complex functionality of unknown functional materials,
機能性既知試料のバイオマーカー発現量と機能性既知材料の機能性値とを対応 付けたデータセットを格納したデータベースにアクセス可能であり、  It is possible to access a database that stores a data set in which biomarker expression levels of functional known samples are associated with functional values of functional known materials,
(1)機能性未知試料のバイオマーカー発現量に基づいて上記データベースを検索 して、機能性未知材料の機能性値を推定する機能性値推定手段と  (1) Functional value estimating means for estimating the functional value of a functional unknown material by searching the above-mentioned database based on the biomarker expression amount of the functional unknown sample
(2)上記機能性値推定手段により推定された機能性値に基づき、機能性未知材料 の機能性を総合評価する総合評価手段と、 (2) The functional unknown material based on the functional value estimated by the functional value estimating means Comprehensive evaluation means to comprehensively evaluate the functionality of
(3)総合評価結果を表示する表示手段と、  (3) Display means for displaying the overall evaluation result,
を含むことを特徴とする高スループット機能性評価装置。  A high throughput functional evaluation device characterized by including.
[0036] 本明細書において、次の用語は下記の意味で用いる。 [0036] In the present specification, the following terms are used with the following meanings.
[0037] (1)機能性既知材料とは、何らかの方法で機能性が明らかになつている機能性材 料であって、本発明の高スループット機能性評価方法又は装置に供し、対応付けモ デルを作成するために使用する材料をいう。  [0037] (1) Functionally known materials are functional materials whose functionality has been clarified in any way, and are used in the high throughput functionality evaluation method or apparatus of the present invention. The material used to create the
[0038] (2)機能性既知試料とは、機能性既知材料を評価培養細胞に与えて発現したバイ ォマーカーの測定に供する試料をレ、う。 (2) A functional known sample refers to a sample to be subjected to measurement of a biomarker to which a functional known material is given to a cultured cell for evaluation and expressed.
[0039] (3)機能性未知材料とは、本発明の高スループット機能性評価方法又は装置に供 する機能性未知の材料をレ、う。 (3) Functionally unknown material refers to a functionally unknown material to be provided to the high throughput functionality evaluation method or apparatus of the present invention.
[0040] (4)機能性未知試料とは、機能性未知材料を評価培養細胞に与えて発現したバイ ォマーカーの測定に供する試料をレ、う。 (4) A functional unknown sample refers to a sample to be provided for measurement of a biomarker to which a functional unknown material is given to a cultured cell for evaluation and expressed.
[0041] (5)食材の機能性とは、食材の三次機能、すなわち体調調節機能を含む生理活性 機能をいう。 (5) Functionalities of foodstuffs refer to the tertiary functions of foodstuffs, that is, physiologically active functions including physical conditioning function.
発明の効果  Effect of the invention
[0042] 本発明に係る機能性評価方法、プログラム、及び装置によれば、下記の利点がある  The functionality evaluation method, program and apparatus according to the present invention have the following advantages
[0043] (1)複数のバイオマーカーを用いて一度に試験することにより、被検材料が食材の ごとき多成分系物質であっても、個々のバイオマーカーに対する応答性に加えて、 被検材料の多機能を迅速かつ総合的に評価できる。例えば、抗酸化作用、抗変異 原作用、アポトーシス誘導作用、ウィルス増殖抑制作用、がん細胞増殖抑制作用、 免疫調節作用など、多項目の評価結果を組み合わせて、被検材料の機能性を総合 評価できる。 (1) By testing at a time using a plurality of biomarkers, even if the test material is a multicomponent substance such as a food material, in addition to the responsiveness to the individual biomarkers, the test material Can be evaluated quickly and comprehensively. For example, the evaluation results of multiple items such as antioxidative action, antimutagenic action, apoptosis induction action, virus growth suppression action, cancer cell growth suppression action, immune modulation action, etc. are combined to comprehensively evaluate the functionality of the test material. it can.
[0044] (2)試験結果の総合評価に当たり、機能性既知材料と照合することにより、信頼性 の高レ、判断を下すことができる。  (2) In the comprehensive evaluation of the test results, it is possible to make a judgment of high reliability by matching the material with known functionality.
[0045] (3)評価培養細胞としてヒト培養細胞を用いる場合は、動物実験による in vivo試 験とは異なり、ヒトを対象にした臨床試験にもっとも近いかたちでの安全で効率的な 機能性の評価が可能になる。 (3) Evaluation When human cultured cells are used as cultured cells, it is safe and efficient in the form closest to human clinical trials, unlike in vivo tests by animal experiments. Functional evaluation becomes possible.
[0046] (4)装置的にオートメーション化が容易であり、評価試験時間も大幅に短縮できる。  (4) The device can be easily automated, and the evaluation test time can be significantly shortened.
[0047] (5)機能性既知試料のバイオマーカー発現量と機能性との対応付け、機能性未知 試料のバイオマーカー発現量からその機能性の推定に、学習法、確率法、クラスタリ ング法の単独又は組み合わせを最適化して適用したプログラムにより、機能性未知 材料の機能性の推定を信頼性高ぐかつ効率的に実施できる。 (5) Correspondence between biomarker expression level and functionality of functional known sample, and estimation of functionality from biomarker expression level of unknown functional sample by learning method, probability method, clustering method A program that is applied alone or in combination with optimization enables reliable and efficient estimation of the functionality of unknown functional materials.
[0048] 本明細書は本願の優先権の基礎である日本国特許出願 2005-013508号の明細書 および/または図面に記載される内容を包含する。 The present specification includes the contents described in the specification and / or the drawings of Japanese Patent Application No. 2005-013508 based on which the priority of the present application is claimed.
図面の簡単な説明  Brief description of the drawings
[0049] [図 1]本発明に係わる高スループット機能性評価方法の典型的な手順を示すフロー 図である。  FIG. 1 is a flow chart showing an exemplary procedure of the high throughput functionality evaluation method according to the present invention.
[図 2]本発明に係わる高スループット機能性評価方法を行う計算機システムのブロッ ク図である。  FIG. 2 is a block diagram of a computer system for performing the high throughput functionality evaluation method according to the present invention.
[図 3]本発明において、学習法により機能性を推定する場合の典型的な手順を示す フロー図である。  [FIG. 3] A flow chart showing a typical procedure in the case of estimating functionality by a learning method in the present invention.
[図 4]本発明において、学習法により機能性を推定するときに利用する典型的なニュ 一ラルネットワークの図である。  FIG. 4 is a diagram of a typical neural network used when estimating functionality by a learning method in the present invention.
[図 5]本発明において、確率法により機能性を推定する場合の典型的な手順を示す フロー図である。  FIG. 5 is a flow chart showing a typical procedure in the case of estimating functionality by a probability method in the present invention.
[図 6]本発明において、クラスタリング法により機能性を推定する場合の典型的な手 順を示すフロー図である。  FIG. 6 is a flow chart showing a typical procedure in the case of estimating functionality by a clustering method in the present invention.
[図 7]本発明において、 Jurkat細胞のバイオマーカー発現パターンに与えるェピガ口 力テキンガレート（EGCG)およびゲニスティンの影響を示すグラフである。  FIG. 7 is a graph showing the influence of epigaportal teking gallate (EGCG) and genistin on biomarker expression patterns of Jurkat cells in the present invention.
[図 8]本発明において、 Jurkat細胞のバイオマーカー発現パターンに与えるェピガ口 力テキンガレート（EGCG)およびリポ酸の影響を示すグラフである。  FIG. 8 is a graph showing the influence of epigaportal tequingallate (EGCG) and lipoic acid on biomarker expression patterns of Jurkat cells in the present invention.
符号の説明  Explanation of sign
[0050] 1 Hsp70 [0050] 1 Hsp70
2 Hsp90 3 DFF45 2 Hsp90 3 DFF45
4 FADD  4 FADD
5 CAS  5 CAS
6 PARP  6 PARP
7 RB  7 RB
8 RB2  8 RB2
21 計算機  21 calculator
21a 入出力インターフェース  21a I / O interface
21b 中央演算装置  21b Central processing unit
21c 主記憶装置  21c Main storage
22 ネットワーク装置  22 Network device
23 キーボード  23 keyboard
24 マウス  24 mouse
25 モニタ  25 monitors
26 プリンタ  26 Printer
27 補助記憶装置  27 Auxiliary storage
41 ニューロン  41 neurons
42 重み  42 weights
43 結合リンク  43 Combined Link
発明を実施するための最良の形態 BEST MODE FOR CARRYING OUT THE INVENTION
次に図面を参照して、本発明の実施の形態を詳述する。本発明を適用した機能性 評価方法は、機能性未知材料を評価培養細胞に付与することで調製された機能性 未知試料のノィォマーカー発現量を測定し、得られた測定値を処理して機能性未知 材料の機能性を総合評価し、総合評価結果を表示或いは出力する方法である。本 発明に係る機能性評価方法は、例えば、図 1に示すフローシートに準じて実施するこ とができる。本発明の機能性評価方法では、先ず評価培養細胞の培養し、その後、 食材、医薬品、医薬品候補物質などからなる機能性未知材料を当該細胞へ付与す る。次に、本発明の機能性評価方法では、細胞抽出物、細胞分泌物などからなる、 機能性未知試料を調製し、例えば統合型ィムノアッセィ等の方法により機能性未知 試料のバイオマーカー発現量を測定する。そして、本発明の機能性評価方法では、 測定したバイオマーカー発現量測定値を処理して (詳細は後述する）、機能性未知 材料の機能性評価を行う。本発明に係る機能性評価方法においては、測定したバイ ォマーカー発現量測定値に基づいてデータベース中の機能性評価データと比較照 合して、機能性未知材料の機能性を総合評価する。 Embodiments of the present invention will now be described in detail with reference to the drawings. The functional evaluation method to which the present invention is applied is to measure the expression amount of the marker of the unknown sample prepared by applying the functional unknown material to the evaluation cultured cells, process the obtained measured value, and perform the functionality. It is a method that comprehensively evaluates the functionality of unknown materials and displays or outputs the comprehensive evaluation results. The functionality evaluation method according to the present invention can be implemented, for example, according to the flow sheet shown in FIG. In the functional evaluation method of the present invention, first, the cultured cells for evaluation are cultured, and then functional unknown materials comprising food materials, drugs, drug candidate substances and the like are applied to the cells. Next, in the functional evaluation method of the present invention, it comprises a cell extract, a cell secretion, etc. A functional unknown sample is prepared, and the biomarker expression level of the functional unknown sample is measured, for example, by the method such as integrative immunity. Then, in the functional evaluation method of the present invention, the measured value of the biomarker expression amount measurement value is processed (details will be described later) to evaluate the functionality of the unknown functional material. In the method for evaluating functionality according to the present invention, the functionality of the unknown material is comprehensively evaluated by comparing it with the functionality evaluation data in the database based on the measured value of the expression level of the biomarker expression.
[0052] 図 1に示す評価培養細胞の培養段階にぉレ、て、好ましく使用することのできる評価 培養細胞としては、 Jurkat細胞、 HL— 60細胞、 MOLT—4細胞、 Huh— 7細胞、 H epG2細胞、 Hep3B細胞、 Caco_ 2細胞、 HeLa細胞、 MCF— 7細胞、 A431細胞 、 S1T細胞、 Su9T01細胞、 HUT101細胞、 PLCZPRF_ 5細胞、 Li90細胞、 HU VEC細胞、 HMEC細胞、 HT17細胞、 NIH— 3T3細胞、 3T3— L1細胞、 MH134 細胞、 dRLh_84細胞、 RLN—10細胞、 PC12細胞、 3Y1細胞などヒト、マウス、ラ ットなど哺乳動物由来細胞株、またはこれらの細胞株から派生する細胞株を挙げるこ とができる。その中で、ヒト白血病由来細胞、ヒト肝がん由来細胞が特に望ましい。ヒト 白血病由来細胞には、 S1T細胞、 Su9T01細胞、 HUT101細胞、 Jurkat細胞、 HL —60細胞などを挙げることができる。ヒト肝がん由来細胞には、 PLC/PRF— 5細胞 、 Li90細胞、 Huh— 7糸田胞、 HepG2細胞などを挙げることができる。  [0052] Evaluations that can be preferably used in the culture step of the evaluation culture cells shown in FIG. 1 As evaluation culture cells that can be preferably used, Jurkat cells, HL-60 cells, MOLT-4 cells, Huh-7 cells, H epG2 cells, Hep3 B cells, Caco_2 cells, HeLa cells, MCF-7 cells, A431 cells, S1 T cells, Su9T01 cells, HUT101 cells, PLCZPRF_5 cells, Li90 cells, HU VEC cells, HMEC cells, HT17 cells, NIH-3T3 Cells, human derived cell lines such as human, mouse, rat, etc. such as 3T3-L1 cells, MH134 cells, dRLh_84 cells, RLN-10 cells, PC12 cells, 3Y1 cells or cell lines derived from these cell lines be able to. Among them, human leukemia-derived cells and human liver cancer-derived cells are particularly desirable. Examples of human leukemia-derived cells include S1 T cells, Su9T01 cells, HUT101 cells, Jurkat cells, HL-60 cells and the like. Examples of human liver cancer-derived cells include PLC / PRF-5 cells, Li90 cells, Huh-7 cells, HepG2 cells and the like.
[0053] 培養条件としては、温度は 37°Cまたは通常の哺乳動物細胞が生育する温度とし、 炭酸ガス濃度は 5%または通常の哺乳動物細胞が生育する濃度が好ましレ、。酸化さ れやすい成分の場合は培養における酸素濃度を低くすることが望まれる。  As culture conditions, the temperature is 37 ° C. or a temperature at which normal mammalian cells grow, and the concentration of carbon dioxide gas is preferably 5% or a density at which normal mammalian cells grow. In the case of components susceptible to oxidation, it is desirable to lower the oxygen concentration in the culture.
[0054] 培地としては、 D— MEM培地、 MEM培地、 RPMI1640培地、 D— MEM/F— 12培地、 F—10培地、 F—12培地、 ERDF培地など、確立された哺乳動物細胞用 培地、またはそれらを基本とした培地が好ましレ、。 Jurkat細胞や HL_ 60細胞などヒ ト白血病由来細胞には RPMI1640培地を、 Huh— 7糸田胞、 HepG2細胞などヒト月干 がん由来細胞には D_ MEM培地の使用が好適である。細胞の成長を促すために は、牛胎児血清を 10%または通常の哺乳動物細胞が生育する濃度で添加してもよ レヽ。必要に応じて、非必須アミノ酸、サイト力イン（FGF, HGF, VEGF,インターロイ キン _ 2など）を添加することもできる。ただし、血清で増殖が抑制される細胞株では 無血清培養を使用する。 [0054] As the medium, established medium for mammalian cells such as D-MEM medium, MEM medium, RPMI 1640 medium, D-MEM / F-12 medium, F-10 medium, F-12 medium, ERDF medium, etc. Or a medium based on them is preferred. It is preferable to use RPMI 1640 medium for human leukemia-derived cells such as Jurkat cells and HL_ 60 cells, and D_MEM medium for human monthly cancer-derived cells such as Huh-7 cells and HepG2 cells. To promote cell growth, fetal calf serum may be added at 10% or at a concentration at which normal mammalian cells grow. If necessary, non-essential amino acids and site strength (FGF, HGF, VEGF, interleukin_2, etc.) can be added. However, in cell lines whose growth is suppressed by serum Use serum free culture.
[0055] 培養には、使用する細胞に適した培他の選定に加えて、細胞数、細胞密度、細胞 周期などの培養条件を選択するために、必要に応じて予備的な培養実験を実施す ること力 S推奨される。 For the culture, in addition to the selection of culture medium suitable for the cells to be used, preliminary culture experiments are carried out as needed to select culture conditions such as cell number, cell density, cell cycle and the like. Force S Recommended.
[0056] 図 1において、細胞への供与段階に使用する被検材料としては、食材、医薬品、医 薬品候補物質など特に限定されないが、本発明は、農産物、林産物、畜産物、水産 物等の生物資源およびそれらから調製された多成分系材料への適用で、優れた効 果が期待できる。  In FIG. 1, the test material used in the step of donating to cells is not particularly limited, such as food, medicine, medicine candidate substance, etc., but the present invention is not limited to agricultural products, forest products, livestock products, fishery products etc. Application to biological resources and multicomponent materials prepared from them can be expected to have excellent effects.
[0057] 被検材料として使用する好ましい食材としては、二ガウリ、お茶、大豆、甘藷、エンド ゥ、小松菜、ほうれん草、白菜、キャベツ、レタス、たまねぎ、ピーマン、唐辛子、ミニト マト、なす、ズッキーニ、キユウリ、トウモロコシ、カボチヤ、ニンジン、ゴボウ、大根、ブ ノレ一べリー、キンカン、 日向夏、へべズ、マンゴー、ソョミズ、ウメ、スペアミント、スウイ ートバジル、イタリアンパセリ、ローズマリー、ステビア、カモミール、シソ、クローバー、 ニンニク、シィタケ、ヒラタケ、ナメコ、マイタケ、ハタケシメジ、エリンギ、エノキダケ、米 、サトイモ、イチゴ、ミズナ、ニラ、メロン、ペパーミント、レモンバームなど、及びこれら の食材の可食部に加え、葉、種子も含む。また、牛肉、牛乳、豚肉、鶏肉、卵などの 動物資源、及びケフィァ、ヨーグルト、納豆などの発酵食材、海産物、健康への効果 が期待できる茶のような嗜好品を挙げることができる。  [0057] Preferred food materials to be used as test materials are: diverite, tea, soy, sweet potato, pea, komatsuna, spinach, Chinese cabbage, cabbage, lettuce, onion, sweet pepper, chili pepper, mini tomato, eggplant, zucchini, chiyuuri , Corn, cabochya, carrot, burdock, radish, henore monobelly, kumquat, summer sun, hebez, mango, soo mizu, plum, spearmint, sweet basil, Italian parsley, rosemary, stevia, chamomile, perilla, clover , Garlic, shiitake mushroom, oyster mushroom, nameko, maitake mushroom, oyster mushroom, eryngii, enoki mushroom, rice, taro, strawberry, mizuna, leek, melon, peppermint, lemon balm, etc. and in addition to edible parts of these foodstuffs, also including leaves and seeds . In addition, animal resources such as beef, milk, pork, chicken and eggs, and fermented foods such as kefir, yoghurt and natto, seafood, seafood, and other valuable items such as tea that can be expected to have an effect on health can be mentioned.
[0058] 食材中の好ましい被検材料としては、カテキン、ェピカテキン、ェピガロカテキン、 ガロカテキン、力テキンガレート、ェピカテキンガレート、ガロカテキンガレート、ェピガ ロカテキンガレート（EGCG)などを含むカテキン類、ダイゼイン、ダイジン、ゲニスティ ン、ゲニスチン、グリシティン、グリシチン、フオルモノネチンなどを含むイソフラボン類 、シァニジン、ペラルゴ二ジン、デルフィ二ジンなどを含むアントシァニン類、ケルセチ ン、ミリセチン、ルチン、レスべラトロール、ケンフエロール、セサミン、クルクミン、リモ ニン、ガンマ一ァミノ酪酸（GABA)、ァスタキサンチン、ガランギン、シトラール、トリゴ ネリン塩酸塩、エラグ酸、キナ酸、サポニン、カプサイシン、ハイド口コルチゾン、ォレ イン酸、ベンジルイソチオシァネート、マンギフエリン、ァピゲニン、ルテオリン、クロ口 ゲン酸、リモネン、スクアレン、レチノール、ロズマリン酸、カフェ酸、リポ酸などの化学 物資、カロテノイド類、ァラキドン酸、リノレン酸などを含む多価不飽和脂肪酸、 9cl lt CLA、 10tl 2cCLAなどを含む共役リノール酸類、その他リバビリン、インターフエ口 ン類など多様な化合物を挙げることができる。ェピガロカテキンガレート、ゲニスティ ン、リポ酸など健康への効果が期待できるものは、特に好ましい。 Preferred test materials in foodstuffs include catechins such as catechins, epicatatequine, epigallocatechin, gallocatenins, force tekingalate, epicathenic gallate, gallocatechin gallate, epigallocatechin gallate (EGCG), etc., daidzein, daidzin, Isoflavones including genistin, genistin, glycicin, glycitin, fluoromononetin, etc., anthocyanins including cyanidin, pelargodizine, delphidizine, etc., quercetin, myricetin, rutin, resveratrol, kenfelol, sesamin, curcumin, limonine , Gamma monoaminobutyric acid (GABA), astaxanthin, galangin, citral, trigonelline hydrochloride, ellagic acid, quinic acid, saponin, capsaicin, hydo-portal cortisone, oleic acid, benzyl Chemistry of isothiocyanate, mangiferin, apigenin, luteolin, crotonic acid, limonene, squalene, retinol, rosmarinic acid, caffeic acid, lipoic acid, etc. There may be mentioned various compounds such as materials, polyunsaturated fatty acids including carotenoids, farakidonic acid, linolenic acid etc., conjugated linoleic acids including 9cl lt CLA, 10 t 1 2c CLA etc, and others such as ribavirin and interferons. Those with potential health effects such as epigallocatechin gallate, genistin and lipoic acid are particularly preferred.
[0059] 被検材料の調製に際しては、生物資源、例えば食材、その凍結乾燥物などから抽 出物を評価培養細胞に供与する。抽出溶媒には、水、エタノール、メタノール、酢酸 ェチル、へキサン、アセトン、あるいはこれらの二つ以上を混合した溶媒等を好適に 用いる。抽出物は、必要に応じて、さらに高速液体クロマトグラフィ（HPLC)、 オーブ ンカラム等により分画、精製してもよい。  At the time of preparation of the test material, the extract is provided from the biological resources, such as food, its lyophilizate and the like to the evaluation culture cells. As the extraction solvent, water, ethanol, methanol, ethyl acetate, hexane, acetone, a mixed solvent of two or more of them, or the like is suitably used. The extract may be further fractionated and purified by high performance liquid chromatography (HPLC), an oven column, etc., if necessary.
[0060] 抽出物と細胞を作用させる混合時間は、作用開始時を 0時間とし、 1時間、 2時間、 3時間、 6時間、 9時間、 12時間、 18時間、 24時間、 36時間、 48時間、 72時間など 力 検討する。 24時間以内で機能性を判定できる場合には、それ以上作用させるに は及ばない。  [0060] The mixing time for causing the extract to act on the cells is 0 hour at the onset of action, 1 hour, 2 hours, 3 hours, 6 hours, 9 hours, 12 hours, 18 hours, 24 hours, 36 hours, 48 hours Time, 72 hours, etc. Force examination. If functionality can be determined within 24 hours, it does not extend beyond that.
[0061] 糸田胞へ供与する抽出物の濃度は、 0. 5 /ι Μ、 1 /ι Μ、 1. 5 μ Μ、 2 /i M、 3 /i M、 4  [0061] The concentration of the extract to be fed to the rice field vesicle is 0.5 /., 1 / ι, 1.5 μΜ, 2 / i M, 3 / i M, 4
/i M、 5 /i M、 7 μ M、 ΙΟ μ M、 15 /i M、 20 /i M、 25 μ M、 30 /i M、 40 /i M、 45 μ M、 50 μ Μ, ΊΟ μ M、 100 μ M、 200 μ M、 300 μ M、 500 μ M、 1000 μ Mおよび これらの濃度の 1000倍および 1/1000倍の濃度の中から、被検材料および評価培 養細胞に適した濃度を選定する。通常、 0. 5 μ Μから 1000 /i Mの範囲が望ましい。 さらに抽出物が、食材抽出物などのようにその濃度が不明な場合には、段階希釈法 にて最適な実験結果が得られる濃度を決定してもよい。酸性または塩基性抽出物の 場合は、中和してから添加するのが望ましい。  / iM, 5 / iM, 7μM, μμM, 15 / iM, 20 / iM, 25μM, 30 / iM, 40 / iM, 45μM, 50μΜ, ΊΟ Among the concentrations of μM, 100 μM, 200 μM, 300 μM, 500 μM, 1000 μM and 1000 times and 1/1000 times of these concentrations, it is suitable for the test material and evaluation culture cells. Select the concentration. Usually, a range of 0.5 μm to 1000 / i M is desirable. Furthermore, if the extract is unknown, such as food extracts, the concentration may be determined by serial dilution to obtain the optimal experimental results. In the case of acidic or basic extracts, it is desirable to neutralize and then add.
[0062] 図 1における被検試料の調製段階では、食材などからの抽出物を供した評価培養 細胞が応答した後、浮遊細胞の場合は、遠心分離により細胞と細胞分泌物を分離す る。接着細胞の場合は、ピペット操作で細胞と細胞分泌物を分離し、細胞分泌物は そのまま被検試料とする。必要に応じて、細胞破砕物は密度勾配遠心、連続遠心な どにより、核、ミトコンドリア、小胞体などの特定の細胞小器官を採取し、さらなる細胞 小器官抽出物としたものも被検試料とすることもある。細胞の破砕は、細胞破砕装置 、例えばテフロン (登録商標）ホモジナイザー、ダウンスホモジナイザー、ポリトロンタイ プホモジナイザー、超音波破砕装置、ビーズ破砕装置などによる破砕、または界面 活性剤、例えば Triton X— 100、 Triton X— 114、 NP— 40、 CHAPS, SB3- 10、コール酸ナトリウム、デォキシコール酸、 CA— 630、 Tween20などによる破砕、 または細胞破砕装置と界面活性剤の併用による破砕から、任意に選択すればょレ、。 必要に応じて、 EDTA等のキレート剤をカ卩えても良い。破砕の後は、遠心分離装置 により細胞抽出物および残さに分離し、細胞抽出物を被検試料とする。 In the preparation stage of the test sample in FIG. 1, after the evaluation culture cells provided with the extract from the foodstuff etc. respond, in the case of floating cells, the cell and the cell secretion are separated by centrifugation. In the case of adherent cells, cells are separated from cell secretion by pipetting, and cell secretion is directly used as the test sample. If necessary, cell debris is collected by density gradient centrifugation, continuous centrifugation, etc. to collect specific organelles such as nuclei, mitochondria, and endoplasmic reticulum, and further organelle extracts are also used as test samples. There is also something to do. Cell disruption can be carried out using a cell disruption device such as Teflon® homogenizer, dross homogenizer, Polytron tie, etc. For example, Triton X-100, Triton X-114, NP-40, CHAPS, SB3-10, Sodium cholate, deoxycholic acid, CA— If it is chosen from crushing by 630, Tween 20 etc., or crushing by a combination of a cell crushing device and a surfactant, it may be selected. If necessary, chelating agents such as EDTA may be added. After disruption, the extract is separated into cell extract and residue by a centrifugal separator, and the cell extract is used as a test sample.
[0063] 図 1における統合型ィムノアッセィ段階では、ィムノアツセィを基盤としたバイオマー カーに特異的な抗体を使用する。好適には、バイオマーカーとの抗原抗体反応を利 用する。ィムノアツセィには、 ELISA、ウェスタンブロッテイング、抗体チップ (抗体ァ レイ）、ビーズアレイ、ィムノクロマトなどを利用する。代表的なィムノアッセィである EL ISAとしては石川栄治ほか、編「酵素免疫測定法 第 3版」医学書院、東京、 1987に 記載された方法が挙げられる。ウェスタンブロッテイングとしては高津聖志ほカ 編「タ ンパク質研究のための抗体実験マニュアル」羊土社、東京、 2004に記載された方法 が挙げられる。ィムノクロマトとしては Zuk RF, Ginsberg VK, Houts T, Rabbie[0063] In the integrative immune step in FIG. 1, an antibody specific to a immunomarker-based biomarker is used. Preferably, an antigen-antibody reaction with a biomarker is used. For ELISA, ELISA, Western blotting, antibody chip (antibody array), bead array, immuno chromatography etc. are used. Examples of EL ISAs that are representative immune syndromes include methods described in Ishikawa Eiji et al., "Enzyme Immunoassay 3rd Edition" Medical School, Tokyo, 1987. Western blotting includes the method described in Takatsu Seishi hen, "Antibody Experimental Manual for Protein Research," Yodosha, Tokyo, 2004. As the dinochromats Zuk RF, Ginsberg VK, Houts T, Rabbie
J, Merrick H, Ullman EF, Fischer MM, SiztoCC, Stiso SN, Litman DJ. Enzyme imunochromatography： a quantitative immunoassay requi ring noinstrumentation. Clin Chem. 1985 Jul ; 31 (7) : 1144— 50に記載さ れた方法が挙げられる。測定部位として、 ELISAの場合は、マイクロプレート上での 特異白勺抗体による検出を行う。マイクロプレートに ίま、 6, 12, 24, 48, 96, 384、及 び 1536ゥエルのプレートなどがある力 96ゥエルが一般的である。ウェスタンブロッ ティングの場合は、膜上で特異的抗体による検出を行う。膜には、 PVDF膜、ニトロセ ルロース膜などを使用できる。 J. Merrick H, Ullman EF, Fischer MM, Sizto CC, Stiso SN, Litman DJ. Enzyme immunochromatography: a quantitative immunoassay requiring ring no instrumentation. Clin Chem. 1985 Jul; 31 (7): 1144-50 Can be mentioned As the measurement site, in the case of ELISA, detection with a specific white rabbit antibody on a microplate is performed. A 96-well force with a plate, such as a 6, 12, 24, 48, 96, 384, and 1536-well plate on a microplate is common. In the case of Western blotting, detection with a specific antibody is performed on the membrane. As the membrane, PVDF membrane, nitrocellulose membrane, etc. can be used.
[0064] 同様に、ィムノアツセィに抗体チップ (抗体アレイ）を用いる場合は、 PVDF膜、ニト ロセルロース膜などの膜、スライドグラスあるいは類似の基盤上で、特異的抗体による 検出を行う。ビーズアレイの場合はビーズ上で、ィムノクロマトの場合はスティック上で 、特異的抗体による検出を行う。検出は、抗体 (一次抗体、二次抗体）に標識した酵 素の反応による発色法、化学発光法、化学蛍光法や抗体に直接蛍光色素を標識し た蛍光法があり、簡便な発色法や感度が高く定量性の良い化学発光法が望ましい。 酵素には、ペルォキシダーゼやアルカリホスファターゼの使用が好適である。 Similarly, when antibody chips (antibody arrays) are used for immunoassays, detection with a specific antibody is performed on a membrane such as a PVDF membrane or a nitrocellulose membrane, a slide glass or a similar substrate. Specific antibody detection is performed on beads in the case of bead arrays and on sticks in the case of immunochromatography. The detection may be performed by a color development method by reaction of an enzyme labeled with an antibody (primary antibody or secondary antibody), a chemiluminescence method, a chemiluminescence method or a fluorescence method wherein a fluorescent dye is directly labeled to an antibody. It is desirable to have a high sensitivity and quantitative quantitative chemiluminescence method. For enzymes, the use of peroxidases and alkaline phosphatase is preferred.
[0065] ィムノアッセィとしては、多数の試料の同時解析、定量解析、分析装置の価格など を考慮した場合、 ELISA法を用いることが特に好ましレ、。  It is particularly preferable to use the ELISA method, considering the simultaneous analysis of a large number of samples, quantitative analysis, the price of an analyzer, etc.
[0066] 図 1に示す機能性評価段階における評価の主たる機能性は、健康に関する機能性 である。健康に関する機能性は、評価目的により異なるので必ずしも特定はできない 、食材の場合、抗酸化作用、抗変異原作用、アポトーシス誘導作用、がん細胞転 移抑制作用、がん細胞増殖抑制作用、抗ストレス作用、免疫調節作用、抗ウィルス 作用、ウィルス増殖抑制作用、動脈硬化抑制作用、血清脂質改善作用、高血圧予 防作用、抗炎症作用、抗肥満など多様な機能を挙げることができる。特に、抗酸化作 用、アポトーシス誘導作用、がん細胞増殖抑制作用など、がんの予防に関連する機 能性の評価が期待されてレ、る。  [0066] The main functionality of the evaluation at the functionality evaluation stage shown in FIG. 1 is that related to health. Functionality related to health varies depending on the purpose of evaluation and can not always be specified. In the case of foodstuffs, antioxidant activity, antimutagenic activity, apoptosis induction activity, cancer cell metastasis inhibitory activity, cancer cell proliferation inhibitory activity, antistress A variety of functions can be mentioned such as action, immunomodulation action, antiviral action, viral growth suppressive action, arteriosclerosis suppressive action, serum lipid improving action, hypertension preventing action, anti-inflammatory action, anti-obesity action and the like. In particular, evaluation of functions related to cancer prevention, such as antioxidant activity, apoptosis induction activity, and cancer cell proliferation suppression activity, is expected.
[0067] 機能性評価に適用するバイオマーカーとしては、抗酸化作用、抗変異原作用、ァ ポトーシス誘導作用、がん細胞増殖抑制作用、抗ストレス作用、免疫調節作用、抗ゥ ィルス作用、ウィルス増殖抑制作用などの各機能性に関係がある蛋白質を挙げるこ とが出来る。さらに、機能性に関わりその発現量が変化するバイオマーカーとともに、 発現量がほとんど変化しないハウスキーピングタンパク質（G6PDH、 GAPDH、 acti nなど）をコントロールマーカーとして取扱い、これらを含めてバイオマーカーとするの が望ましい。例えば、表 1に示すタンパク質がこれらのバイオマーカー候補となり得る  [0067] Examples of biomarkers applied to functional evaluation include antioxidant activity, antimutagenic activity, apoptosis induction activity, cancer cell growth inhibitory activity, antistress activity, immunomodulation activity, antiviral activity, virus proliferation. Proteins related to each functionality such as inhibitory activity can be mentioned. Furthermore, together with biomarkers that are related to functionality and whose expression levels change, we handle housekeeping proteins (G6PDH, GAPDH, actin etc.) whose expression levels hardly change as control markers, and including these as biomarkers desirable. For example, the proteins shown in Table 1 can be these biomarker candidates
[表 1] [table 1]
パイォマーカー候補タンパク質と機能性 Biomarker candidate protein and functionality
Figure imgf000021_0001
Figure imgf000021_0001
Figure imgf000021_0002
Figure imgf000021_0002
A :ハウスキーピング （コントロール） 、 B :アポトーシス誘導作用、 C :抗酸化作  A: Housekeeping (control) B: Apoptosis inducing action C: Antioxidant action
用、 D：がん細胞増殖抑制作用、 E：抗ストレス作用、 F：抗ウィルス作用。 関連す  Use D: Cancer cell growth inhibitory action E: Anti-stress action F: Anti-viral action Related
る機能がある場合〇を記入 バイオマーカーは、文献および公開データベース上の公知情報、プロテオーム解 析、 DNAマイクロアレイ（DNAチップ)解析などの解析結果から選定することができ る。公開データベースには、米国生物工学情報センター（NCBI)にある PubMedを 使用して検索できるデータベースおよびインターネットを通じて検索できるデータべ ースを挙げることができる。 If there is a function, enter ○ ○ Biomarkers can be selected from analysis results such as known information on literature and public databases, proteome analysis, DNA microarray (DNA chip) analysis, etc. Ru. Public databases can include databases that can be searched using PubMed at the National Center for Biotechnology Information (NCBI) and databases that can be searched through the Internet.
[0069] プロテオーム解析は、 IPGストリップを使用した 1次元目等電点電気泳動、 2次元目 SDS— PAGEによる 2次元電気泳動、電気泳動パターンの色素染色によるイメージ 解析、タンパク質スポットの質量分析装置による解析及び同定により行うことができるFor proteome analysis, first-dimensional isoelectric focusing using an IPG strip, second-dimensional SDS-PAGE, two-dimensional electrophoresis, image analysis by electrophoresis of electrophoretic patterns, and mass spectrometry of protein spots Can be done by analysis and identification
。特に、定量性にすぐれた蛍光色素によるプレラベル標識による蛍光ディファレンシ ャル解析が望ましい。 . In particular, fluorescence differential analysis by prelabeling with a fluorescent dye excellent in quantitative property is desirable.
[0070] DNAマイクロアレイ（DNAチップ）解析は、市販の DNAマイクロアレイ（DNAチッ プ）例えば、 GeneChipプローブアレイ（Affymetrix社）、 CodeLink Bioarraay ( アマシャム'バイオサイエンス社)およびこれらに類するものを使用することが出来るが 、 GeneChipプローブアレイ（Affymetrix社）を使用することが好ましい。  [0070] For DNA microarray (DNA chip) analysis, commercially available DNA microarrays (DNA chip), for example, GeneChip probe array (Affymetrix), CodeLink Bioarray (Amersham's biosciences) and the like may be used. Although it is possible, it is preferable to use GeneChip probe array (Affymetrix).
[0071] 本発明に適用する抗体としては、モノクローナル抗体、ポリクローナル抗体、抗血清 、リコンビナント抗体のいずれもバイオマーカーに対する特異性があれば使用可能で あるが、モノクローナル抗体の使用が好ましい。モノクローナル抗体は、実験動物とし てマウスを使用してバイオマーカーに対する抗原を免役した後、 Galfre, G. , Milst em, し. Preparation of monoclonal antibodies： strateoies and procsdur es, Methods Enzymol. 1981 ; 73 (Pt B) : 3 -46.に記載された方法により調 製する。抗原には、精製タンパク質、リコンビナントタンパク質、合成ペプチドなどを使 用できる。  [0071] As antibodies applicable to the present invention, any of monoclonal antibodies, polyclonal antibodies, antisera and recombinant antibodies can be used if they have specificity for a biomarker, but use of monoclonal antibodies is preferred. After immunizing an antigen against a biomarker using a mouse as an experimental animal, a monoclonal antibody is prepared from Galfre, G., Milst em, and Preparation. Preparation of monoclonal antibodies: strategies and procedures, Methods Enzymol. 1981; 73 (Pt) B): Prepare by the method described in 3-46. As the antigen, purified protein, recombinant protein, synthetic peptide and the like can be used.
[0072] また抗体としては、市販されているものであっても、バイオマーカーに対する特異性 があれば、モノクローナル抗体、ポリクローナル抗体、抗血清、リコンビナント抗体の レ、ずれの抗体も使用可能である。  [0072] As antibodies, although commercially available, monoclonal antibodies, polyclonal antibodies, antisera, recombinant antibodies, or other antibodies can be used as long as they have specificity for a biomarker.
[0073] 図 1における最終段階としての機能性総合評価の一つは、機能性既知試料を評価 して得られたバイオマーカー発現量を格納したデータベースと、機能性未知試料の バイオマーカー発現量との直接的な比較により行う方法である。具体的には、機能性 未知試料のバイオマーカー発現量のパターンと一致或いは類似する、データベース に格納された機能性既知試料のバイオマーカー発現量のパターンを検索する。バイ ォマーカー発現量のパターンが一致或いは類似する場合には、機能性未知材料と 機能性既知材料とが機能的に類似していると評価することができる。この評価によつ て、機能性未知材料について、抗酸化作用、抗変異原作用、アポトーシス誘導作用 、がん細胞増殖抑制作用、抗ストレス作用、免疫調節作用、抗ウィルス作用、ウイノレ ス増殖抑制作用などの機能性およびこれらの機能性の組み合わせを推定できる。 [0073] One of the functional comprehensive evaluations as the final step in FIG. 1 is a database storing biomarker expression levels obtained by evaluating functional known samples, biomarker expression levels of functional unknown samples, and It is a method to carry out by direct comparison of Specifically, a pattern of biomarker expression levels of the functional known sample stored in the database is searched that matches or is similar to the pattern of biomarker expression levels of the functional unknown sample. by If the patterns of the expression levels of the markers match or are similar, it can be evaluated that the functionally unknown material and the functionally known material are functionally similar. According to this evaluation, the antioxidant, antimutagenic, apoptosis-inducing, cancer cell growth suppressive, anti-stress, immunomodulatory, antiviral, viral growth suppressive effects of functional unknown materials. And other functionalities and combinations of these functionalities can be estimated.
[0074] なお、ここで、バイオマーカー発現量としては、上述した手法により測定された測定 値をそのまま使用してもよいが、測定値を補正した補正値を使用しても良い。例えば 、測定値を規格化した後の値をバイオマーカー発現量として使用しても良いし、複数 の測定値の平均値をバイオマーカー発現量として使用しても良い。  Here, as the biomarker expression amount, the measured value measured by the above-described method may be used as it is, or a correction value obtained by correcting the measured value may be used. For example, the value after normalization of the measured value may be used as the biomarker expression amount, or the average value of a plurality of measured values may be used as the biomarker expression amount.
[0075] この場合、図 1のデータベースには、 in vivoおよび評価培養細胞系で機能性が 確認されている機能性既知材料のデータを蓄積する。データ取得には、 in vivoお よび培養細胞系で機能性が確認されている機能性既知材料を評価培養細胞に供与 して、機能性既知材料に応答した細胞抽出物及び/または分泌物を得、得られた細 胞抽出物及び/または分泌物を被検試料として、ィムノアッセィによるバイオマーカ 一発現量を測定する。  [0075] In this case, the database of FIG. 1 accumulates data of known functional materials whose functionality has been confirmed in vivo and in the evaluated cultured cell line. For data acquisition, functional known materials whose functions have been confirmed in vivo and in cultured cell lines are provided to the evaluation cultured cells to obtain cell extracts and / or secretions in response to the known functional materials. Then, using the obtained cell extract and / or secretion as a test sample, measure the biomarker expression level by immunoassay.
[0076] 図 1における機能性総合評価の他の方法には、機能性既知試料のバイオマーカー 発現量とその機能性を対応付けしたデータベースを用レ、、機能性未知試料のバイオ マーカー発現量から、機能性未知材料の機能性を推定する方法がある。例えば、前 述のように機能性既知試料のバイオマーカー発現量を測定するとともに、この機能性 既知材料の機能性を個別評価系により測定し、両測定値を対応付けたデータベース を作成する。次に、機能性未知の食材または食材成分を評価培養細胞に供与して、 機能性未知材料に応答した細胞抽出物及び Zまたは分泌物を得、得られた細胞抽 出物及び/または分泌物を被検試料とし、ィムノアッセィによりバイオマーカーの発 現量に関するデータを得る。前記対応付けされたデータベースを用い、得られた機 能性未知試料のバイオマーカー発現量から、機能性未知材料の機能性を推定する 。この推定には、多変量解析によるデータベースの分類、統計処理を行う。具体的に は、学習法、確率法、クラスタリング法などを挙げることができる。  [0076] Another method of comprehensive functional evaluation in FIG. 1 is to use a database in which the biomarker expression amount of the functional known sample is associated with the functionality thereof, and from the biomarker expression amount of the functional unknown sample , There is a method to estimate the functionality of the unknown material. For example, as described above, the biomarker expression level of a functional known sample is measured, and the functionality of this functional known material is measured by an individual evaluation system, and a database in which both measured values are associated is created. Next, the functional unknown food or food component is provided to the evaluation culture cells to obtain a cell extract and Z or secretion in response to the functional unknown material, and the obtained cell extract and / or secretion The test sample is used as a test sample, and data on the expression level of biomarkers is obtained by immunology. The functionality of the functional unknown material is estimated from the biomarker expression level of the obtained functional unknown sample using the associated database. In this estimation, database classification and statistical processing are performed by multivariate analysis. Specific examples include learning methods, probabilistic methods, and clustering methods.
[0077] 本発明においては、健康に関するポジティブな機能性に加えて、副作用や毒性の ごときネガティブ機能性に関しても、バイオマーカーの変化の方向、組合せなどから、 総合的に判定できるようにするのが望ましい。それにより、食材、医薬品、医薬品候 補物質のポジティブな機能性だけでなぐネガティブな機能性も併せて評価すること ができる。 [0077] In the present invention, in addition to positive health-related functionality, side effects and toxicity are As for negative functionality, it is desirable to be able to judge comprehensively from the direction and combination of changes in biomarkers. As a result, it is possible to evaluate not only the positive functionality but also the negative functionality of food products, drugs and drug candidates.
[0078] 本発明における機能性既知材料の機能性データには、公知の個別評価系による 機能性測定結果を利用することもできる。機能性測定法としては、レブリコンアツセィ 、 TCID 法などの抗ウィルス作用、 MTTアツセィ、 WST— 8アツセィなどのがん細 胞増殖抑制作用、 DPPHラジカル消去活性測定、レポータージーンアツセィなどの 抗酸化作用、硫酸転移酵素を用いた Ames変法、 Ames法、小核試験法、 Recアツ セィなどの抗変異原性、コルチコステロン、 GOT試験などの抗ストレス作用、 TUNE L法、 ANNEXIN V法、 DNAラダー法、カスパーゼ活性測定法などのアポトーシス アツセィ等を利用することができる。  As the functional data of the functional known material in the present invention, the functional measurement result by a known individual evaluation system can also be used. The functional measurement methods include antiviral activity such as Replicon assay, TCID method, cancer cell proliferation inhibitory activity such as MTT assay, WST-8 assay etc, DPPH radical scavenging activity measurement, antioxidant such as reporter gene assay Activity, modified Ames method using sulfotransferase, Ames method, micronucleus test method, antimutagenic activity such as Rec atsei, anti-stress activity such as corticosterone, GOT test, TUNE L method, ANNEXIN V method, Apoptosis assays such as DNA ladder method and caspase activity assay can be used.
[0079] 図 2は、本発明の機能性評価方法を実行するためのコンピュータシステムの構成を 示すブロック図である。図 2において、計算機 21は入出力インターフェース 21a、中 央演算装置 21b、主記憶装置 21cを備えている。入出力インターフェース 21aには計 算機 21が他のコンピュータシステム（図示せず）と通信回線を介してデータ、プロダラ ムを交換するためのネットワーク装置 22が接続されている。また、入出力インターフエ ース 21aには、入力装置であるキーボード 23およびマウス 24と、出力装置であるモニ タ 25およびプリンタ 26が接続されている。さらに、入出力インターフェース 11aには、 ハードディスク、フラッシュメモリ、磁気テープ装置、光磁気ディスク装置等の補助記 憶装置 17が接続されてレ、てもよレ、。  FIG. 2 is a block diagram showing the configuration of a computer system for executing the functionality evaluation method of the present invention. In FIG. 2, the computer 21 comprises an input / output interface 21a, a central processing unit 21b, and a main storage unit 21c. Connected to the input / output interface 21a is a network device 22 for exchanging data and programs with another computer system (not shown) via a communication line. Further, a keyboard 23 and a mouse 24 as input devices, and a monitor 25 and a printer 26 as output devices are connected to the input / output interface 21a. Furthermore, an auxiliary storage device 17 such as a hard disk, a flash memory, a magnetic tape device, a magneto-optical disk device, etc. is connected to the input / output interface 11a.
[0080] なお以下に図 2に示す計算機を用いる例を具体的に説明するが、本発明の方法の 実施形態は以下の具体例には限定されず、例えば計算機に代えて集積回路搭載装 置またはチップを用いて実施することも可能である。  Although an example using the computer shown in FIG. 2 will be specifically described below, the embodiment of the method of the present invention is not limited to the following specific example, and, for example, an integrated circuit mounting apparatus instead of the computer. Or it is also possible to implement using a chip.
[0081] 中央演算装置 21bは、コンピュータシステムを制御 ·統括すると同時に、機能性既 知試料のバイオマーカー発現量とその機能性値間の学習と汎化、各機能性既知材 料と機能性未知材料間の類似度合レヽ (バイオマーカー発現確率）に基づく機能性未 知材料の機能性値の導出、機能性既知試料のバイオマーカー発現量のクラスタリン グ及び各クラスと機能性値間の対応付けを行う。中央演算装置 21bによる一時的、ま たは最終的な演算結果は、たとえば DRAMからなる主記憶装置 21cまたは補助記 憶装置 27に記録される。主記憶装置 21cにはコンピュータシステムを制御するプログ ラムも格納する。 The central processing unit 21b controls and controls the computer system, and at the same time, learns and generalizes between the biomarker expression amount of the functional known sample and its functional value, each functional known material and functional unknown Derivation of functional value of functional unknown material based on similarity score between materials (biomarker expression probability), clusterin of biomarker expression amount of functional known sample And the correspondence between each class and the functionality value. The temporary or final calculation result by central processing unit 21 b is recorded in main storage unit 21 c or auxiliary storage unit 27 composed of, for example, a DRAM. The main storage device 21c also stores a program for controlling the computer system.
[0082] 演算に用いられるデータ、およびコンピュータシステムを制御するコマンドは、キー ボード 23またはマウス 24を通じてコンピュータシステムに入力することができる。これ らのデータおよびコマンドはネットワーク装置 22を通じて接続されている他のコンビュ ータシステム（図示せず）を介して入力することもできる。  Data used for calculation and commands for controlling the computer system can be input to the computer system through the keyboard 23 or the mouse 24. These data and commands can also be input through another computer system (not shown) connected through the network device 22.
[0083] 機能性未知材料が持つ機能性の推定結果は、モニタ 25またはプリンタ 26に表示 すること力 Sできる。これらの結果は、ネットワーク装置 22を通じて接続している他のコ ンピュータシステム（図示せず）を通じて出力することもできる。  The estimated result of the functionality of the unknown functional material can be displayed on the monitor 25 or the printer 26. These results can also be output through another computer system (not shown) connected through the network device 22.
[0084] 図 2のコンピュータシステム 21において、学習法により機能性未知材料の機能性を 推定する手順は、およそ以下の通りである。すなわち、入力手段を介して入力された 機能性既知試料のノィォマーカー発現量と機能性値は不揮発性記憶である補助記 憶装置 27に格納されており、これらを一旦主記憶装置 21cに格納する。次に中央演 算装置 21bにより機能性既知試料のバイオマーカー発現量と機能性値間の関係を 学習する。学習により獲得されたバイオマーカー発現量と機能性値間の関係は、主 記憶装置 21cまたは補助記憶装置 27に格納される。機能性未知試料のバイオマー カー発現量が入力手段を介して入力されたとき、中央演算装置 21bは主記憶装置 1 lcまたは補助記憶装置 27に格納された機能性既知試料のバイオマーカー発現量と 機能性値の関係から機能性未知材料の機能性値を推定し出力装置を介して出力す る。  In computer system 21 of FIG. 2, the procedure for estimating the functionality of a functional unknown material by a learning method is approximately as follows. That is, the expression amount of the marker of the functional known sample and the functional value inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are once stored in the main storage device 21c. Next, the central computing unit 21b learns the relationship between the biomarker expression level and the functional value of the functional known sample. The relationship between the biomarker expression amount obtained by learning and the functional value is stored in the main storage device 21c or the auxiliary storage device 27. When the biomarker expression level of the functional unknown sample is input through the input means, the central processing unit 21b outputs the biomarker expression level and function of the functional known sample stored in the main memory 1 lc or the auxiliary memory 27. The functional value of the functional unknown material is estimated from the relationship of the sex value and output through the output device.
[0085] 図 3は、学習法を用いるときの本発明の好適な機能性推定法を示すフローシートで ある。学習法による本発明実施は、機能性既知試料の調製を行うステップ 1、機能性 既知試料からバイオマーカー発現量を測定するステップ 2、該機能性既知材料の機 能性値を測定するステップ 3、バイオマーカー発現量と機能性値の関係を学習するス テツプ 4、機能性未知試料の調製を行うステップ 5、機能性未知試料のバイオマーカ 一発現量を測定するステップ 6、機能性既知材料の学習結果を汎化して機能性未知 材料の機能性値を推定するステップ 7、推定された機能性値を総合評価し判定しや すい形態に加工するステップ 8、総合評価結果を表示するステップ 9からなる。 [0085] Figure 3 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a learning method. The practice of the present invention by the learning method comprises the steps of: preparing a functional known sample; measuring the biomarker expression level from the functional known sample; measuring the functional value of the functional known material; Step 4 of learning relationship between biomarker expression level and functional value, Step 5 of preparing functional unknown sample, biomarker of functional unknown sample, and step 6 of measuring expression amount of functional unknown material, learning of functional known material Generalization of results and functionality unknown It consists of Step 7 of estimating the functional value of the material, Step 8 of comprehensively evaluating and judging the estimated functional value and processing into a form that can be easily performed, and Step 9 of displaying the comprehensive evaluation result.
[0086] 図 4は、学習をニューラルネットワークで行う方法を示す。図 4においては、補助記 憶装置 27に格納されてレ、る機能性既知試料のバイオマーカー発現量と機能性値を 主記憶装置 21cに読み込んだのち、バイオマーカー発現量を入力信号、機能性値 を教師信号とした学習サンプルとし、階層型ニューラルネットワーク上で誤差逆伝播 学習法により、ニューラルネットワークの出力と機能性値間の誤差が極小となるように 重み 42を調節する。ニューラルネットワークおよび誤差逆伝播学習法には、 P.H.Win ston、 Artificial Intelligence Third Edition, Addison Wesley, 1992に記載された方法 を挙げることができる。 [0086] FIG. 4 shows a method of learning with a neural network. In FIG. 4, after the biomarker expression amount and functionality value of the functional known sample stored in the auxiliary storage device 27 are read into the main memory 21c, the biomarker expression amount is input signal, functionality Using the value as a training signal as a training signal, the weight 42 is adjusted so that the error between the output of the neural network and the functional value is minimized by the error back propagation learning method on the hierarchical neural network. Neural networks and error back-propagation learning methods may include those described in PH H. Winston, Artificial Intelligence Third Edition, Addison Wesley, 1992.
[0087] バイオマーカー発現量と機能性値の組み合わせからなる学習サンプル数は、使用 するニューラルネットワークの規模に比べて、学習サンプル数が相対的に少ない場 合には、ブートストラップ法 (石井健一郎、上田修功、前田英作、村瀬洋、パターン認 識、オーム社、 1998)を用いることにより、その数を適宜増やすことができる。  When the number of learning samples consisting of the combination of biomarker expression level and functional value is relatively small compared to the size of the neural network used, the bootstrap method (Kenichiro Ishii, The number can be increased appropriately by using Ueda, S. Maeda, Y. Murase, Pattern recognition, Ohmsha, 1998).
[0088] 機能性未知材料の機能性値の推定値は、機能性未知試料のバイオマーカー発現 量がニューラルネットワークに入力されたとき、ニューラルネットワーク上に記憶された 機能性既知試料のバイオマーカー発現量と機能性値の関係を汎化して求める。  [0088] The estimated value of the functional value of the functional unknown material is the biomarker expression level of the functional known sample stored on the neural network when the biomarker expression level of the functional unknown sample is input to the neural network. Find the generalization of the relationship between and the functional value.
[0089] 機能性の推定値は、図 2のモニタ装置 25、プリンタ 26、またはネットワークを介して 接続した他のコンピュータ（図示せず）上に表示する。  [0089] The estimated value of functionality is displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
[0090] 図 2のコンピュータシステム 21において、確率法により機能性未知材料の機能性を 推定する手順は、おおよそ以下の通りである。入力手段を介して入力された機能性 既知試料のバイオマーカー発現量と機能性値は、不揮発性記憶である補助記憶装 置 27に格納されており、これらを一旦主記憶装置 21cに格納する。次に中央演算装 置 21bにより、機能性既知試料におけるバイオマーカー発現確率を表す確率密度関 数を求める。求められた確率密度関数は、主記憶装置 21cまたは補助記憶装置 27 に格納される。機能性未知試料のバイオマーカー発現量が入力手段を介して入力さ れたとき、中央演算装置 21bは主記憶装置 11cまたは補助記憶装置 27に格納され た確率密度関数から、機能性既知材料と機能性未知材料との類似度合レヽ (確率)を 求め、類似度合い (確率)から関数 fにより機能性未知材料の機能性値を推定し、出 力装置を介して出力する。関数 fには機能性既知材料が持つ機能性値を当該材料と 推定される確率で線形補間する関数を用いることができる。 In the computer system 21 of FIG. 2, the procedure for estimating the functionality of a functional unknown material by the stochastic method is roughly as follows. The biomarker expression amount and the functional value of the functional known sample inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are temporarily stored in the main storage device 21c. Next, the central processing unit 21b determines a probability density function representing the biomarker expression probability in the functional known sample. The obtained probability density function is stored in the main storage unit 21c or the auxiliary storage unit 27. When the biomarker expression level of the functional unknown sample is input through the input means, the central processing unit 21b uses the probability density function stored in the main storage unit 11c or the auxiliary storage unit 27 to obtain the functional known material and function. Similarity score with unknown material (probability) From the degree of similarity (probability), the function f is used to estimate the functionality value of the unknown material and output it via the output device. As the function f, it is possible to use a function that linearly interpolates the functionality value possessed by the functionality known material with the probability that the material is estimated.
[0091] 次に、ノ ィォマーカー発現量と機能性値との関係を確率的事象として捉え、ノィォ マーカー発現量から特定の機能性値が生じる確率を求める場合について説明する。  Next, a case will be described in which the relationship between the expression level of the nomarker and the functional value is regarded as a probabilistic event, and the probability of occurrence of a specific functional value is determined from the expression level of the nomarker.
[0092] 図 5は、確率法を用いるときの本発明の好適な機能性推定法を示すフローシートで ある。本発明による確率法での実施は、機能性既知試料の調製を行うステップ 1、調 製した機能性既知試料からバイオマーカー発現量を測定するステップ 2、該機能性 既知材料の機能性値を測定するステップ 3、各機能性既知試料のバイオマーカー発 現確率を表す確率密度関数を求めるステップ 4、機能性未知試料の調製を行うステ ップ 5、調製された機能性未知試料のノ ォマーカー発現量を測定するステップ 6、 ステップ 4で求めた確率密度関数に基づき各機能性既知材料と機能性未知材料間 の類似度合レ、 (確率）を求めるステップ 7、ステップ 7で求めた類似度合レ、 (確率）に基 づき機能性値を総合評価するステップ 8、評価結果を表示するステップ 9のステップ からなる。  [0092] FIG. 5 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a probabilistic method. The probabilistic method according to the present invention comprises step 1 of preparing a functional known sample, step 2 of measuring the biomarker expression level from the prepared functional known sample, and 2 measuring the functional value of the functional known material. Step 3; determining the probability density function representing the biomarker expression probability of each functional known sample 4; preparing the functional unknown sample 5; and the marker expression amount of the prepared functional unknown sample Step 6 of measuring the degree of similarity, the degree of similarity between each functional known material and the function unknown based on the probability density function obtained in step 4, the degree of similarity obtained in step 7, the degree of similarity calculated in step 7 Step 8 of comprehensively evaluating the functional value based on the probability) and step 9 of displaying the evaluation result.
[0093] 図 5のステップ 1、ステップ 2、ステップ 3、ステップ 5、ステップ 6は、ニューラルネット ワークを用いるときと同様に実施することができる。  Step 1, Step 2, Step 3, Step 5, and Step 6 of FIG. 5 can be performed in the same manner as when using a neural network.
[0094] 図 5のステップ 4において、各機能性既知試料のバイオマーカー発現確率を表す 確率密度関数を求める方法として Parzen識別器 (鳥脇純一郎、認識工学、コロナ社[0094] In Step 4 of FIG. 5, a Parzen discriminator is used as a method of determining a probability density function representing the biomarker expression probability of each functional known sample (Joriichiro Toriwaki, Recognition Engineering, Corona, Inc.
、 1993)が利用できる。 , 1993) are available.
[0095] 各機能性既知材料と機能性未知材料との類似度合い (確率)を求める方法としてべ ィズ推定法 (石井健一郎、上田修功、前田英作、村瀬洋、パターン認識、オーム社、 1998)を用いることができる。機能性未知材料の機能性値を推定する関数 fには、各 機能性既知材料の機能性値を上記類似度合レヽ (確率)で補完する関数を用レ、ること ができる。  [0095] As a method of determining the degree of similarity (probability) between each functional known material and the functional unknown material, a method of estimating the phase (Kenichiro Ishii, Shoko Ueda, Hidesaku Maeda, Hiroshi Murase, Pattern Recognition, Ohmsha, 1998) Can be used. As the function f for estimating the functionality value of the unknown functionality material, a function can be used which complements the functionality value of each functionality known material with the above similarity score (probability).
[0096] 機能性値を評価した結果は、図 2のモニタ装置 25、プリンタ 26、またはネットワーク を介して接続した他のコンピュータ（図示せず）上に表示する。  The results of evaluation of the functionality values are displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
[0097] コンピュータシステム 21においてクラスタリング法により機能性未知材料の機能性を 推定する手順は、およそ以下の通りである。すなわち、入力手段を介して入力された 機能性既知試料のノィォマーカー発現量と機能性値は不揮発性記憶である補助記 憶装置 27に格納されており、これらを一旦主記憶装置 21cに格納する。次に中央演 算装置 21bにより機能性既知試料のバイオマーカー発現量を複数のクラスに分割し 、各クラスに機能性値を対応付けたデータセットを主記憶装置 21cまたは補助記憶 装置 27に格納する。機能性未知試料のバイオマーカー発現量が入力手段を介して 入力されたとき、バイオマーカー発現量が属するクラスを決定し、決定されたクラスに 対応付けられた機能性値を出力装置に出力する。 [0097] Functionality of unknown material is determined by clustering method in computer system 21 The procedure to estimate is approximately as follows. That is, the expression amount of the marker of the functional known sample and the functional value inputted through the input means are stored in the auxiliary storage device 27 which is nonvolatile storage, and these are once stored in the main storage device 21c. Next, the biomarker expression amount of the functional known sample is divided into a plurality of classes by the central processing unit 21b, and a data set in which the functional value is associated with each class is stored in the main storage unit 21c or the auxiliary storage unit 27. . When the biomarker expression level of the functional unknown sample is input through the input means, the class to which the biomarker expression level belongs is determined, and the functionality value associated with the determined class is output to the output device.
[0098] バイオマーカー発現量をクラスタリングし、そのクラスから機能性を推定する場合に ついて説明する。 The case of clustering biomarker expression levels and estimating functionality from the class will be described.
[0099] 図 6は、クラスタリング法を用いるときの本発明の好適な機能性推定法を示すフロー シートである。本発明は、機能性既知試料の調製を行うステップ 1、調製した機能性 既知試料からバイオマーカー発現量を測定するステップ 2、調製した被検試料力 機 能性値を測定するステップ 3、機能性既知試料のバイオマーカー発現量をクラスタリ ングして各クラスに機能性値を対応付けるステップ 4、機能性未知試料の調製を行う ステップ 5、機能性未知試料のバイオマーカー発現量を測定するステップ 6、測定し たバイオマーカー発現量が属するクラスを決定するステップ 7、決定されたクラスから 機能性値を汎化 ·推定するステップ 8、推定された機能性値から機能性を総合評価 するステップ 9、評価結果を表示するステップ 10からなる。  [0099] FIG. 6 is a flow sheet illustrating a preferred functionality estimation method of the present invention when using a clustering method. According to the present invention, Step 1 of preparing a functional known sample, Step 2 of measuring the biomarker expression level from the prepared functional sample, step 2 of measuring a prepared test sample activity value, and 3 Step 4: Cluster biomarker expression levels of known samples to associate functional value with each class Step 4: Prepare functional unknown sample step 5. Measure biomarker expression level of functional unknown sample Step 6: Measurement Step 7 of determining the class to which the biomarker expression amount belongs, step 8 of generalizing and estimating the functional value from the determined class, step 8 of comprehensively evaluating the functionality from the estimated functionality value, evaluation result It consists of step 10 to display.
[0100] 図 6のステップ 1、ステップ 2、ステップ 3、ステップ 5、ステップ 6は、ニューラルネット ワークを用いるときと同様に実施することができる。  Step 1, Step 2, Step 3, Step 5, and Step 6 of FIG. 6 can be implemented in the same manner as when using a neural network.
[0101] 図 6のステップ 4におけるバイオマーカー発現量のクラスタリングを行う方法として、 マーク M.ヴアン.フッレ、 自己組織化マップ—理論.設計.応用、海文堂、 2001に記 載された自己組織化マップ法を用いることができる。  [0101] As a method of performing clustering of biomarker expression levels in step 4 of FIG. 6, a self-organizing map described in Mark M. Vuan. Hulet, self-organizing map-theory, design. The law can be used.
[0102] 機能性未知試料のバイオマーカー発現量力 期待できる機能性を推定する方法と しては、 自己組織化マップの競合ノードが持つ重みと機能性未知試料のバイオマー カー発現量との間のユークリッド距離を用いることができる。  [0102] Biomarker expression level of functional unknown sample As a method of estimating the expected functionality, Euclidean between the weight possessed by the competitor node of the self-organizing map and the biomarker expression level of the functional unknown sample Distance can be used.
[0103] 機能性未知試料のバイオマーカー発現量力 期待できる機能性を推定する方法と しては、機能性未知試料のバイオマーカー発現量に対応する自己組織化マップの 競合ノードの座標と、機能性既知試料に対応する自己組織化マップの競合ノード座 標との間のマンハッタン距離を用いることができる。 [0103] Biomarker expression ability of unknown functional sample A method for estimating expected functionality and Then, the Manhattan distance between the coordinates of the competitor node of the self-organizing map corresponding to the biomarker expression amount of the functional unknown sample and the competitor node coordinate of the self-organizing map corresponding to the functional known sample is It can be used.
[0104] 機能性未知試料のバイオマーカー発現量力 期待できる機能性値を推定するとき 、そのバイオマーカー発現量が機能性既知試料から作成した学習サンプルと一致し ていない場合には、機能性未知試料のバイオマーカー発現量に対応する自己組織 化マップ上の競合ノードと、その近傍に位置する競合ノードとの距離に基づいて、機 能性未知材料の機能性値を関数 fにより推定する。関数 fとして、近傍ノードが持つ重 みを含む重み空間上の超平面の傾きから機能性値を補間推定する関数を用いること ができる。 Biomarker expression level of unknown functional sample When estimating the expected functional value, if the biomarker expression level does not match the learning sample prepared from the known functional sample, the functional unknown sample is obtained. Based on the distance between the competing node on the self-organizing map that corresponds to the biomarker expression level of and the competing node located in the vicinity, the functional value of the unknown material is estimated by the function f. As the function f, it is possible to use a function that interpolates the functional value from the slope of the hyperplane on the weight space including the weights possessed by the neighboring nodes.
[0105] 機能性値の推定値は、図 2のモニタ装置 25、プリンタ 26、またはネットワークを介し て接続した他のコンピュータ（図示せず）上に表示する。  [0105] The estimated value of the functional value is displayed on the monitor device 25 of FIG. 2, the printer 26, or another computer (not shown) connected via a network.
実施例  Example
[0106] く実施例 1〉 Example 1>
(機能性既知試料のバイオマーカー発現量と機能性未知試料のバイオマーカー発 現量を直接対比して、機能性未知材料の機能性を総合評価する例）  (Example of comprehensive evaluation of functionality of unknown functional material by direct comparison of biomarker expression level of functional known sample and biomarker expression level of unknown functional sample)
評価培養細胞としてヒト T細胞白血病細胞 ¾Jurkat細胞を用いた。細胞は RPMI1 640培地に 10%FBSをカ卩えた培地で 37°C、 5% COの条件で培養した。機能性既  Evaluation Human T-cell leukemia cells 3⁄4 Jurkat cells were used as cultured cells. The cells were cultured in RPMI 1640 medium supplemented with 10% FBS at 37 ° C. and 5% CO. Functionality already
2  2
知材料として、食材成分であるェピガロカテキンガレート（EGCG) (30 a M)を使用 し、機能性未知材料としては、ゲニスティン（3 μ Μ)、リポ酸（ImM)を使用した。各 試験濃度の成分を細胞培地に供し、 24時間細胞に各材料を作用させた後細胞を回 収し、細胞抽出物を得、それをバイオマーカー被検試料とした。被検試料は、まず S DS— PAGEにより電気泳動を行レ、、タンパク質を分離し、その後 PVDF膜に転写し た。膜は 5。/。スキムミルクを含む PBSでブロッキングし、その後各バイオマーカーに特 異的な抗体を一次抗体として作用し、ついで西洋わさびパーォキシダーゼ（HRP)ラ ベルした抗マウス IgGを二次抗体として作用した。検出は、 ECL plusを基質として HRPの酵素活性により生じた化学発光をイメージアナライザー LAS— 1000にて解 祈した。 [0107] 機能性未知試料の分析に用いたバイオマーカー候補としては、アポトーシス誘導 作用に関連するマーカー（Bel— 2、 PARP、 DFF45、 FADD、 CAS)、がん細胞増 殖抑制関連マーカー（RB、 RB2)、ストレス関連マーカー（Hsp— 70、 Hsp— 90)を 使用し、その発現量を比較した。シグナル強度として得られた各バイオマーカー発現 量のデータを標準化するために、それぞれのシグナル強度を Be卜 2のシグナル強度 で除し、単位 Be卜 2発現量当たりのバイオマーカー発現量とした。さらに、これらの値 を被検材料を作用させなかった細胞より調製した被検試料をコントロールとし、コント ロールの発現量を 1としたときの相対的な発現量で数値化した。 The food ingredient ピ ガ pigarocatechin gallate (EGCG) (30 a M) was used as the knowledge material, and genistin (3 μΜ) and lipoic acid (ImM) were used as the functional unknown material. The components of each test concentration were applied to the cell culture medium, each material was allowed to act on the cells for 24 hours, and then the cells were collected to obtain a cell extract, which was used as a biomarker test sample. The test sample was electrophoresed first by SDS-PAGE, the proteins were separated, and then transferred to a PVDF membrane. Membrane 5 /. It was blocked with PBS containing skimmed milk, and then each biomarker was treated with a specific antibody as a primary antibody, and then with horseradish peroxidase (HRP) labeled anti-mouse IgG as a secondary antibody. For detection, chemiluminescence generated by the enzyme activity of HRP with ECL plus as a substrate was interpreted with an image analyzer LAS-1000. [0107] As biomarker candidates used for analysis of functional unknown samples, markers associated with apoptosis induction (Bel-2, PARP, DFF 45, FADD, CAS), cancer cell proliferation suppression related markers (RB, The expression levels were compared using RB2) and stress-related markers (Hsp-70, Hsp-90). In order to standardize the data of each biomarker expression amount obtained as the signal intensity, each signal intensity was divided by the signal intensity of Be 卜 2 to obtain the biomarker expression amount per unit Be 卜 2 expression amount. Furthermore, these values were quantified using the test sample prepared from the cells not treated with the test material as a control and the relative expression level when the control expression level was 1.
[0108] 一方、機能性既知材料としては、アポトーシス誘導作用、抗がん作用、抗アレルギ 一作用の機能性を持つことが証明されているェピガロカテキンガレート（EGCG)を使 用し、機能性未知材料であるゲニスティンおよびリポ酸の機能性の評価を行った。  On the other hand, as functional known materials, it is possible to use epigallocatechin gallate (EGCG), which has been proved to have an apoptosis-inducing action, an anticancer action, and an antiallergic action functionality. The functional evaluation of genistin and lipoic acid, which are sexually unknown materials, was conducted.
[0109] 結果を表 2、図 7および図 8に示した。図 7および 8において数字:!〜 8はそれぞれ 次のマーカー蛋白質に対応する。 l:Hsp70、 2:Hsp90、 3:DFF45、 4:FADD、 5 ： CAS, 6:PARP、 7:RB、 8:RB2。図 7は、 Jurkat細胞のバイオマーカー発現パタ ーンに与える EGCGおよびゲニスティンの影響を示す。機能性未知材料としてのゲ ニスティンを、機能性既知材料である EGCGと、 l:Hsp70、 7:RB、 8:RB2に関して のみ比較した場合には、コントロールと比較して EGCGは 7: RB、 8:RB2の発現量が 1/2程度に減少している力 ゲニスティンではあまり変化していなレ、。しかし 1: Hsp 70に関しては、コントロールと比較して発現量が EGCGとゲニスティンでほとんど変 ィ匕していない。  The results are shown in Table 2, FIG. 7 and FIG. In Figures 7 and 8, the numbers:! -8 correspond to the following marker proteins respectively. l: Hsp70, 2: Hsp90, 3: DFF45, 4: FADD, 5: CAS, 6: PARP, 7: RB, 8: RB2. FIG. 7 shows the effects of EGCG and genistin on biomarker expression patterns of Jurkat cells. When comparing genistin as a functional unknown material with EGCG, which is a functional known material, with respect to l: Hsp70, 7: RB, 8: RB2 only, EGCG is compared with the control 7: 7: RB, 8 : The force by which the expression level of RB2 is reduced to about 1/2. However, 1: Hsp 70 expression level was hardly changed in EGCG and genistin compared with control.
[表 2] 表 2.コントロールを 1として表し fcパイ才マーカー発現！:  [Table 2] Table 2. The control is represented as 1 fc pi age marker expression! :
Figure imgf000030_0001
Figure imgf000030_0001
[表 3] 表 3. EGCGにおける発現 Sとの差の平方和およびその平均 ffi
Figure imgf000031_0001
[Table 3] Table 3. Sum of squared difference with expression S in EGCG and its average ffi
Figure imgf000031_0001
[0110] 図 8は、 Jurkat細胞のバイオマーカー発現パターンに与える EGCGおよびリポ酸の 影響を示す。機能性未知材料としてのリポ酸を機能性既知材料である EGCGと 1 :H sp70、 7:RB、 8 :RB2に関してのみ比較した場合、コントロールと比較して 3つのバ ィォマーカーの発現量の変化が EGCGとリポ酸でほぼ一致している。これまでに示し たように、 l:Hsp70、 7:RB、 8 :RB2の 3つのバイオマーカーの発現パターンのみを 比較した場合、リポ酸は EGCG型の機能性を持つことが予測され、ゲニスティンは異 なるタイプの機能性を持つことが予想される、という判定となる。 [0110] FIG. 8 shows the effects of EGCG and lipoic acid on biomarker expression patterns of Jurkat cells. When lipoic acid as a functional unknown material is compared only with EGCG, which is a functional known material, with respect to EGCG and 1: H sp 70, 7: RB, 8: RB2, changes in the expression levels of the three bio markers are compared with the control. There is a close agreement between EGCG and lipoic acid. As described above, lipoic acid is predicted to have EGCG-type functionality and genistin is only when the expression patterns of l: Hsp70, 7: RB, and 8: RB2 are compared. It is judged that it is expected to have different types of functionality.
[0111] し力しな力 Sら、 l:Hsp70、 2:Hsp90、 3:DFF45、 4:FADD、 5: CAS, 6： PARP 、 7:RB、 8 :RB2とより多くのバイオマーカーの発現パターンを比較する場合、例え ば、既知の有効成分である EGCGのバイオマーカー発現パターンを基準パターンと し、ゲニスティン、リポ酸の EGCGとの類似性の解析には、以下のような 2つの解析を 用いることが例として挙げられる。  [0111] Force S et al., L: Hsp70, 2: Hsp90, 3: DFF45, 4: FADD, 5: CAS, 6: PARP, 7: RB, 8: RB2 and more biomarker expression When comparing patterns, for example, the biomarker expression pattern of EGCG, which is a known active ingredient, is used as a reference pattern, and for analysis of similarity between genistin and lipoic acid with EGCG, the following two analyzes are used. Use is mentioned as an example.
[0112] 表 2に示したコントロールを 1とした各バイオマーカーの値を用い、ゲニスティンある いはリポ酸と EGCGの差の平方和の平均値を求めると、ゲニスティンは 0.14、リポ酸 は 0.25となり 0に近いゲニスティンの方力 EGCGと類似性が高いと判断できる。ま た、この実施結果について、コントロール値に対して ±10%以上の変化を示したもの について、発現量の増減があったものとし +あるいは一の符号を与え、 ±10%以内 の変化については土の符号を与えた（表 4)。そして、同符号を付した項目に +1点、 異符号（+から—または—から + )の示した項目に— 1点、その他の場合に 0点を与 え、 EGCGのバイオマーカーを基準としたゲニスティンとリポ酸の変動得点を求めた 。結果を表 4に示す。  Using the value of each biomarker with the control shown in Table 2 as 1, the average value of the sum of squares of the difference between genistin or lipoic acid and EGCG is 0.14 for genistin and 0.25 for lipoic acid. It can be judged that the directionality of genistin close to 0 is highly similar to EGCG. In addition, regarding this execution result, for those showing a change of ± 10% or more with respect to the control value, it is assumed that there is an increase or decrease in expression level and a plus or minus sign is given, and a change within ± 10% The soil symbol is given (Table 4). Then, items given the same sign plus 1 and items with different signs (from + or-or-to +) are given 1 point, and 0 points for the other cases, and the EGCG biomarker is used as a reference. The variation scores of genistin and lipoic acid were determined. The results are shown in Table 4.
[表 4]
Figure imgf000032_0001
[Table 4]
Figure imgf000032_0001
[0113] たとえば、 Hsp90の場合、 EGCGでは発現が 10%以上減少している。ゲユスティ ンでは、 EGCGの場合と同様に、 Hsp90の発現が 10%以上減少しているため、 Hsp 90の項目について + 1点を与えた。それに対し、リポ酸の場合は Hsp90の発現が 1 0%以上増加しているため、この項目に— 1点を与えている。今回の実施結果からは 、ゲニスティンについて 0. 625点、リポ酸について 0. 25点を得ることができた。この 平均得点は _ 1から 1の値をとる変数であり、 1に近いほど EGCGによるものと同質の 変動であると判断できる。したがって、本実施結果からも、ゲニスティンによる変動の 方力 EGCGと類似していると推定できる。 [0113] For example, in the case of Hsp90, EGCG reduces expression by 10% or more. As in the case of EGCG, the expression of Hsp90 was reduced by 10% or more, so the gain was given + 1 for the Hsp 90 item. On the other hand, in the case of lipoic acid, the expression of Hsp90 is increased by 10% or more, so this item is given 1 point. From the results of this implementation, it was possible to obtain 0.625 points for genistin and 0.25 points for lipoic acid. This average score is a variable with a value of 1 to 1, and as it approaches 1 it can be judged that the fluctuation is of the same quality as that of EGCG. Therefore, it can be estimated from the results of this implementation that it is similar to the EGCG fluctuation due to genistin.
[0114] このように、複数のバイオマーカーの発現パターンを、量と質の観点から数学的に 解析することで、未知材料力 Sもつ機能性を推定できる可能性が示された。これらの結 果を総合的に評価すると、 EGCG,ゲニスティン、リポ酸はともにアポトーシスを誘導 することが以前から知られていた力 これに加えて EGCGと類似のバイオマーカー発 現パターンを示したゲニスティンは、 EGCG同様に抗がん作用、抗アレルギー作用 を持つ可能性が示されたこととなる。リポ酸に関しては、より大きなデータベースを準 備すれば、その中より類似のバイオマーカー発現パターンを示す機能性既知材料の 分析結果より、その機能性を評価できると考えることが出来る。これらの実施結果は、 少数のバイオマーカーの解析のみからでは予測不可能であった機能性を多数のバ ィォマーカーの解析とパターン比較により正確に予測できる可能性を示すものである 。好ましいバイオマーカーの数は、少なくとも 4である。  Thus, it has been shown that it is possible to estimate the functionality of unknown material power S by mathematically analyzing the expression patterns of multiple biomarkers in terms of quantity and quality. When these results are comprehensively evaluated, EGCG, genistin and lipoic acid were previously known to all induce apoptosis. In addition to this, genistin showed a similar biomarker expression pattern to EGCG. As with EGCG, it has been shown that it may have anti-cancer and anti-allergic effects. With regard to lipoic acid, if a larger database is prepared, it can be considered that the functionality can be evaluated based on the analysis results of the functional known material showing a similar biomarker expression pattern among them. These implementation results show the possibility that functionality that could not have been predicted just from the analysis of a small number of biomarkers can be accurately predicted by the analysis and pattern comparison of many biomarkers. The preferred number of biomarkers is at least four.
[0115] く実施例 2〉  Example 2>
(機能性既知試料のバイオマーカー発現量とその機能性値との学習結果により、機 能性未知試料のバイオマーカー発現量力 機能性未知材料の機能性値を推定し、 総合評価する例） (Based on the learning results of the biomarker expression level of the functional known sample and its functional value, the biomarker expression level of the functional unknown sample is estimated, and the functional value of the functional unknown material is estimated. Comprehensive evaluation example)
バイオマーカー発現量測定及びがん細胞増殖抑制試験 (個別評価系）の評価用 細胞として、ヒト T細胞白血病細胞^ [urkat細胞を用いた。細胞は、 10%牛胎児血 清 (FCS)含有 PRMI1640培地を用いて、 37°C、 5% COガスで平衡化した COィ  Human T cell leukemia cells ^ [urkat cells were used as cells for measuring biomarker expression level and cancer cell growth inhibition test (individual evaluation system) evaluation cells. Cells were equilibrated with 5% CO gas at 37 ° C. in PRMI 1640 medium containing 10% fetal calf serum (FCS).
2 2 ンキュベータ内で培養した。対数増殖期にある Jurkat細胞を 3 X 105cells/mlの細胞 密度でブラスティックシャーレに接種し、次いで学習用の機能性既知材料及び機能 性未知材料を添加した。 The cells were cultured in an incubator. Jurkat cells in the logarithmic growth phase were inoculated at a density of 3 × 10 5 cells / ml into blastoside dishes, and then functional known materials and functional unknown materials for learning were added.
[0116] 学習用の機能性既知材料としては、 EGCG、ゲニスティン、リポ酸、ァラキドン酸、ク ノレクミン、ダイゼイン、ケルセチン、シァニジン、レスべラトロール、トランス 10シス 12共 役リノール酸（10t，12c_CLA)を用いた。これらの各化合物を表 5に示す終濃度になる ように細胞に添加した。機能性未知材料としては、カブサイシン及びブルーベリ一葉 抽出物を用いた。 Jurkat細胞を 24時間培養した後に、細胞を回収して 4°Cのリン酸緩 衝生理食塩水（PBS)で洗浄し、細胞数を計測した。これらの細胞数をコントロールの 細胞数で除すことでがん細胞増殖抑制作用を示した。結果を表 6に示す。 [0116] As functional known materials for learning, EGCG, genistin, lipoic acid, araquidonic acid, cnorekmin, daidzein, quercetin, cyanidin, resveratrol, trans 10 cis 12 subunit linoleic acid (10 t, 12 c_CLA) Using. Each of these compounds was added to the cells to a final concentration shown in Table 5. Kabsaicin and Blueberry single leaf extract were used as functional unknown materials. After culturing the Jurkat cells for 24 hours, the cells were recovered, washed with phosphate buffered saline (PBS) at 4 ° C., and the number of cells was counted. By dividing the number of these cells by the number of control cells, cancer cell growth inhibitory action was shown. The results are shown in Table 6.
[表 5] 表 5 . 機能性既知材料と終濃度  [Table 5] Table 5. Functionally known materials and final concentrations
Figure imgf000033_0001
Figure imgf000033_0001
[0117] [表 6] 表 6 . 機能性既知材料のがん細胞増殖抑制作用 [0117] [Table 6] Table 6. Tumor cell growth inhibitory effect of known functional materials
(Ο μ Μを .1 としたときの相対比）
Figure imgf000034_0001
(Relative ratio when .PHI. Is .1)
Figure imgf000034_0001
Figure imgf000034_0002
細胞数が 1 X 107 cells/mlとなるように細胞溶解緩衝液（1 mM EDTA, 0.005% Tween , 0.5% Triton X-100を含有する PBS)を加え、穏やかに撹拌した後にプロテアーゼ 阻害剤を加え、その上清を被検試料とした。
Figure imgf000034_0002
Add cell lysis buffer (PBS containing 1 mM EDTA, 0.005% Tween, 0.5% Triton X-100) so that the number of cells is 1 × 10 7 cells / ml, and after gently stirring, protease The inhibitor was added and the supernatant was used as a test sample.
[0119] 被検試料中に含まれる各バイオマーカー量の測定は、 ELISAによって行った。バイ ォマ一力一としては、 thioredoxm, survivin, heat shock protein 70 (HSP70) , X— linked inhibitor of apoptosis protein (XIAP) , Fas-associated death domain protein (FADD) ， thioredoxin reductase 1 (TXNRDl) , heat shock protein 90 (HSP90) , IFN-inducible antiviral protein Mx (MxA) , tumor-associated hydroquinone oxidase (tNOX)の 9禾重 類について測定した。またサンプルを標準化する際に用いる glycelaldehyde-3-phosp hate dehydrogenase (GAPDH)についても測定した。  [0119] The measurement of the amount of each biomarker contained in the test sample was performed by ELISA. As a single molecule, thioredoxm, survivin, heat shock protein 70 (HSP70), X-linked inhibitor of apoptosis protein (XIAP), Fas-associated death domain protein (FADD), thioredoxin reductase 1 (TXNRDl), heat The number of shock proteins 90 (HSP90), IFN-inducible antiviral protein Mx (MxA) and tumor-associated hydroquinone oxidase (tNOX) was measured. In addition, it was also measured about glycelaldehyde-3-phosphate dehydrogenase (GAPDH) used in standardizing the sample.
[0120] 一例として、 thioredoxinの測定について説明する。以下の操作の温度は全て 37°C で行った。抗ヒ hioredoxinマウス抗体（500 ng/ml : 50 mM炭酸緩衝液、 pH9.6)を 96 穴マイクロプレートの各穴に 100 μ 1ずつ添加し、 2時間静置してプレートに固定化し た。 0.05% Tween20含有 PBS (TPBS)で各穴を 1回洗浄した後、 1% BSA含有 PBSを各 穴に 300 μ ΐずつ添カ卩し、 2時間静置しブロッキングを行った。各穴を TPBSで 3回洗浄 した後、 10倍に希釈した細胞抽出液を各穴に 100 / lずつ添加し、 2時間反応させた。 各穴を TPBSで 3回洗浄した後、検出抗体として抗ヒト thioredoxinャギ抗体（100 ng/ml : 1% BSA含有 PBS)を各穴に 100 /i lずつ添加し、さらに 1時間反応させた。各穴を TPB Sで 3回洗浄した後、二次抗体として西洋わさびパーォキシダーゼ (HRP)で標識され ている抗ャギ IgGマウス抗体（200 ng/ml : 1% BSA含有 PBS)を 100 /i 1添加し、さらに 1 時間反応させた。最後に TPBSで 4回洗浄して基質溶液 { 0.3 mg ABTS [ρ-2,2 ' -azino _bis_(3_ethylbenzothiazoline_6_sulfomc acid) aiammonium salt]と 0.03% H O 有 0.1 [0120] As an example, measurement of thioredoxin will be described. The temperatures of the following operations were all at 37 ° C. One hundred μl of an anti-hyoredoxin mouse antibody (500 ng / ml: 50 mM carbonate buffer, pH 9.6) was added to each well of the 96-well microplate, and allowed to stand for 2 hours for immobilization on the plate. After washing each well once with PBS containing 0.05% Tween 20 (TPBS), 300 μl of PBS containing 1% BSA was added to each well and allowed to stand for 2 hours for blocking. After each well was washed 3 times with TPBS, a 10-fold diluted cell extract was added to each well 100 / l and allowed to react for 2 hours. After each well was washed 3 times with TPBS, an anti-human thioredoxin bluetooth antibody (100 ng / ml: 1% BSA-containing PBS) was added as a detection antibody to each well 100 / il and allowed to react for an additional hour. After washing each well three times with TPB S, an anti-army IgG mouse antibody (200 ng / ml: PBS containing 1% BSA) labeled with horseradish peroxidase (HRP) as a secondary antibody 100 / i 1 was added and allowed to react for another hour. Finally, the substrate solution was washed 4 times with TPBS, and the substrate solution {0.3 mg ABTS [ρ-2,2'-azino_bis_ (3_ethylbenzothiazoline_6_sulfomc acid) aiammonium salt] and 0.03% H 2 O 0.1
Mクェン酸緩衝液， pH4}を 100 /i lずつ添加し、 10分間反応させ、 405-490 nmの吸 光度をマイクロプレートリーダーで測定した。他のバイオマーカーについても概略は 同様に行い、使用した抗体類の一覧を表 7に示した。 100 μl of M citrate buffer solution, pH 4} was added, reacted for 10 minutes, and absorbance at 405-490 nm was measured with a microplate reader. The outlines of other biomarkers were the same, and a list of antibodies used is shown in Table 7.
[表 7] 表 7 . バイオマーカーの種類及び検出抗体等の級み合わせ [Table 7] Table 7. Classification of biomarker types and detection antibodies etc.
Figure imgf000036_0001
吸光度として得られた各バイオマーカー発現量のデータを標準化するために、それ ぞれの吸光度を GAPDHの吸光度で除し、単位 GAPDH発現量当たりのバイオマー カー発現量とした。さらに、これらの値をコントロール被検試料のバイオマーカー発現 量で除すことで、試験群のバイオマーカー発現量をコントロールに対する相対値とし て得た。一例として、学習用既知材料を Jurkat細胞に添加した際のバイオマーカー 発現量を表 8に示す。また機能性未知試料のバイオマーカー発現量を表 9に示す。
Figure imgf000036_0001
In order to normalize the data of each biomarker expression amount obtained as absorbance, each absorbance was divided by the absorbance of GAPDH to obtain the amount of biomarker expression per unit GAPDH expression amount. Furthermore, by dividing these values by the biomarker expression level of the control test sample, the biomarker expression level of the test group was obtained as a relative value to the control. As an example, Table 8 shows biomarker expression levels when learning known materials were added to Jurkat cells. Further, the biomarker expression level of the functional unknown sample is shown in Table 9.
[表 8] 表 8. 学習用機能件既知試料によるバイオマーカー発現量 [Table 8] Table 8. Functional expression level of biomarkers by learning function
Figure imgf000037_0001
Figure imgf000037_0001
Figure imgf000038_0001
Figure imgf000038_0001
Figure imgf000039_0001
U
Figure imgf000039_0001
U
101, 1  101, 1
2c-CL  2c-CL
1.39 1. 14 1.06 1. 17 1.12 1.20 1. 17 1.58 i.26 A 10  1.39 1.14 1.06 1.17 1.12 1.20 1.17 1.58 i.26 A 10
wM  wM
化 物名の後の数値は細胞に添加した濃度を表す。 表 9 . カブサイシンを 10 /x M、 ブルーベリ一葉抽出物を 50 / g/inl与えたときの The numerical value after the chemical name represents the concentration added to the cells. Table 9. Cab-saicin at 10 / x M, Blueberry single leaf extract at 50 / g / inl
バイオマーカー允現量  Amount of biomarker production
Figure imgf000040_0001
Figure imgf000040_0001
[0123] レプリコン細胞の調製： HCVのゲノム RNAは、ウィルス粒子を構成するコアとェンべ ロープの構造タンパク質翻訳領域、ウィルスゲノム複製などに機能する非構造タンパ ク質翻訳領域とに大別される。この構造タンパク質翻訳領域をルシフェラーゼ翻訳領 域 ' EMCV IRES (脳心筋ウィルス内部リボゾーム結合配歹 ·ネオマイシン耐性遺伝 子に置換したサブゲノムレプリコン RNAを作成し、得られた RNAをヒト肝がん細胞 Huh -7の細胞質に導入する。サブゲノムレプリコン RNAが導入された Huh-7は、同時にネ ォマイシン耐性能を有するので、 Geneticin (G418)による選択が可能となる。このよう にして得られた細胞を、 HCVレプリコン RNAの産生量の評価に供試する。なお、 HCV レプリコン RNAの産生量は下記に説明するルシフェラーゼアツセィ法により測定する 。この細胞の継体には、 DMEM10[GIBCO社の GlutaMAX Media Dulbecco' s Modifie d Eagle Medium (D-MEM)(l X ), liquid (High glucose, contains sodium pyruvateノ ] ίこ BS (Hyclone社) 10%、 Penicillin— Streptomycin (GIBCO社)、および Geneticin (invitroge n社）を添加した培地を用いる。アツセィを行なう際のアツセィ培地には、 DMEM10に F BSを 5%、および Penicillin- Streptomycinを添加したもの（但し、 Geneticinは加えなレヽ。 )を用いる。 [0123] Preparation of replicon cells: The genomic RNA of HCV is roughly divided into a core constituting virus particles, a structural protein translational region of envelope and a nonstructural protein translational region functioning for viral genome replication and the like. Ru. This structural protein translation region is replaced by luciferase translation region 'EMCV IRES (brain myocardium virus internal ribosome binding arrangement · Neomycin resistant gene is substituted for subgenomic replicon RNA to create RNA, human hepatoma cell Huh- It is introduced into the cytoplasm of 7. Huh-7, into which the subgenomic replicon RNA has been introduced, is simultaneously resistant to neomycin to allow selection with Geneticin (G418). The amount of HCV replicon RNA produced will be measured by the luciferase assay method described below, and for the passage of this cell, DMEM 10 [GlutaMAX Media Dulbecco 'from GIBCO will be used. s Modifie d Eagle Medium (D-MEM) (l x), liquid (High glucose, contains sodium pyruvate) BS BS (Hyclone) 10%, Penicillin— Streptomycin (GIBCO), and Geneticin (invitrog The medium supplemented with E n) is used as the culture medium to which the assay is performed, using DMEM 10 supplemented with 5% of FBS and Penicillin-Streptomycin (however, Geneticin is not added).
[0124] ノレシフェラーゼアツセィ法：マグネシウム存在下で、ルシフヱリンと ATPから酸化ルシ フェリンと AMPを作る反応をルシフェラーゼが触媒する。ルシフェラーゼアツセィ法は 、この時発生する光を発光検出器で検出して、得られた光量に基づいてルシフェラ ーゼ活性を評価する方法である。本発明では便宜上、この光量を HCVレプリコン RN A量とする。 [0124] Norecipherase assay method: Luciferase catalyzes a reaction to form luciferin oxide and AMP from luciferase and ATP in the presence of magnesium. The luciferase assay detects the light generated at this time with a luminescence detector, and based on the amount of light obtained, It is a method to evaluate the activity. In the present invention, for convenience, this light amount is referred to as HCV replicon RNA amount.
[0125] HCVレプリコン RNA産生抑制試験  [0125] HCV replicon RNA production suppression test
白色の 96wellプレートに被検細胞（レプリコン細胞）の懸濁液（2.5 X 104 cells/ml)を 90 μ ΐずつ加え、 37°C、 5%CO存在下、相対湿度 100%の条件で 24時間培養した。機能 A suspension of test cells (replicon cells) (2.5 × 10 4 cells / ml) was added to white 96-well plates by 90 μl each, and 24 ° C at 37 ° C. in the presence of 5% CO and 100% relative humidity. Incubated for time. function
2  2
性未知材料として食材成分及び/または食材抽出物を調製した。機能性既知材料 としては、 EGCG、ゲニスティン、リポ酸、ァラキドン酸、クノレクミン、ダイゼイン、ケルセ チン、シァニジン、レスべラトロール、 10t，12c_CLAを用いた。表 1記載の終濃度に従 い上記 96wellプレートに添加した。この後、さらに 72時間培養し、室温で 30分以上静 置後、ルシフェラーゼアツセィ試薬（Promega社製、 Steady-Glo™ Luciferase Assay S ystem)を ΙΟΟ μ Ι/wellカ卩えて、よくピペッティングした。 5分以上放置してから発光検出 器（ベックマンコールター社製、 DTX800)で発光測定を行った。コントロールとして、 上記被検材料の代わりに DMSOを用いて上記と同様にして調製した反応液について 、同様に発光量を測定した。  Food ingredients and / or food extracts were prepared as sexually unknown materials. EGCG, genistin, lipoic acid, arachidonic acid, crolekmin, daidzein, quercetin, cyanidin, resveratrol, 10t, 12c_CLA were used as functional known materials. It was added to the above 96 well plate according to the final concentration described in Table 1. Thereafter, the cells were further cultured for 72 hours, and allowed to stand at room temperature for 30 minutes or more, after which Luciferase atsei reagent (Promega, Steady-GloTM Luciferase Assay System) was pipetted well with a well. . After leaving for 5 minutes or more, luminescence was measured using a luminescence detector (DTX 800, manufactured by Beckman Coulter, Inc.). As a control, the amount of luminescence was similarly measured for a reaction solution prepared in the same manner as above using DMSO instead of the above-mentioned test material.
[0126] 発光測定値から、コントロールに対する百分率を求め、被検材料の各濃度における 被検細胞の相対ルシフェラーゼ活性 (％)を算出した。前述するように、当該相対ルシ フェラーゼ活性（％)は HCVレプリコン RNA量を反映している。得られた結果は、表 10 に示した。表 10で示した値は複数回の測定から得られた値の平均であり，全測定を 通じて得られた相対ルシフェラーゼ活性（HCVレプリコン RNA量）の最大値は 2.27で あった。 The percentage relative to the control was determined from the luminescence measurement value, and the relative luciferase activity (%) of the test cell at each concentration of the test material was calculated. As described above, the relative luciferase activity (%) reflects the amount of HCV replicon RNA. The obtained results are shown in Table 10. The values shown in Table 10 are the average of the values obtained from multiple measurements, and the maximum value of relative luciferase activity (HCV replicon RNA amount) obtained through all measurements was 2.27.
[表 10] [Table 10]
¾ 1 0 . 相対ルシフェラーゼ活性 01ひ'レブリコン RNA量) 3⁄4 1 0. Relative luciferase activity 01 ('Lebricon RNA content)
Figure imgf000042_0001
Figure imgf000042_0001
[0127] バイオマーカー発現量から相対ルシフヱラーゼ活性を予測するニューラルネットヮ ークを構築した。表 8に示した濃度の機能性既知材料を与えたとき、表 10に示した相 対ノレシフヱラーゼ活性値を出力するようニューラルネットワークを学習させた。ニュー ラルネットワークの出力は最大値が 1であるので、相対ルシフェラーゼ活性値の値を 測定で得られた最大値 2.27で除し、これをニューラルネットワークの学習時の教師信 号とした。ニューラルネットワークの学習には誤差逆伝播学習法を用いた。 [0127] A neural network was constructed to predict relative luciferase activity from the amount of biomarker expression. When given the functional known materials at the concentrations shown in Table 8, the neural network was trained to output the relative Noresifusulase activity values shown in Table 10. Since the maximum value of the output of the neural network is 1, the value of the relative luciferase activity value is divided by the maximum value 2.27 obtained by measurement, and this is used as a teacher signal at the time of learning of the neural network. An error back propagation learning method was used for learning of the neural network.
[0128] ニューラルネットワークの学習の成否を判定するため、表 8、表 10に示した学習用 データセットからダイゼイン 70 μ Μ、レスべラトロール 100 μ Μの 2つを除いたデータセ ットを用いて学習を行った。  [0128] In order to determine the success or failure of learning of the neural network, data sets obtained by removing two of 70% of daidzein and 100μ of resveratrol from the data set for learning shown in Table 8 and Table 10 are used. I learned.
[0129] ニューラルネットワークは入力ニューロン 9、中間ニューロン 4、出力ニューロン 1とし 、閾値調整のため中間ニューロン、出力ニューロンには常に 1を出力するニューロ ンからの入力を与えた。ニューラルネットワーク中の重みの初期値は 2· 0力ら 2. 0 までの間の乱数とし、学習係数は 0. 7、慣性係数は 0. 4に設定し、学習回数が 1デ ータ当たり 40, 000回に達するか、すべてのデータで推定誤差が 0. 1以下になった ときに学習を停止させた。 [0130] 上記条件で学習を行った後に、学習結果の確認のために除いたダイゼイン 70 μ Μ 、 レスべラトロール 100 μ Μの 2つのデータを学習済みのニューラルネットワークに入 力したとき、表 11に示したニューラルネットワークの出力（推定値）が得られた。表 11 には推定精度を示すために当該物質の相対ルシフェラーゼ活性の測定値 (実測値） と推定誤差の絶対値も同時に示しており、これらの値は相対ノレシフヱラーゼ活性測 定実験で得られた最大値 2.27で除した後の値である。表 11に示したとおり、学習に 用いていない濃度での相対ルシフェラーゼ活性を規定誤差以下で推定することが可 能であることが示された。 The neural network is set as an input neuron 9, an intermediate neuron 4, and an output neuron 1, and the input from an intermediate neuron for threshold adjustment and an output neuron that always outputs 1 is given to the output neuron. The initial value of the weight in the neural network is a random number between 2 · 0 force and 2. 0, the learning coefficient is set to 0.7, the inertia coefficient is set to 0.4, and the learning frequency is 40 per data. The learning was stopped when reaching 000, 000 times, or when the estimation error was less than 0.1 for all data. [0130] After performing learning under the above conditions, when two data of Daisyin 70 μΜ and resveratrol 100 μ 除 い removed for confirmation of the learning result are input to the learned neural network, Table 11 The output (estimated value) of the neural network shown in FIG. Table 11 also shows the measured value (measured value) of the relative luciferase activity of the substance and the absolute value of the estimation error simultaneously to show the estimated accuracy, and these values are the maximum values obtained in the relative noresifase activity assay. It is the value after dividing by the value 2.27. As shown in Table 11, it was shown that it is possible to estimate the relative luciferase activity at a concentration not used for learning with a specified error or less.
[表 11] 表 1 1 . 学習に用いていない濃度での相封ルシフェラーゼ活性推定結果
Figure imgf000043_0001
[Table 11] Table 1 1. Results of estimation of the combined luciferase activity at concentrations not used for learning
Figure imgf000043_0001
[0131] 表 8、表 10に示した学習用データセットからダイゼイン 70 μ Μ、レスべラトロール 100 β Μを除いたデータセットを用いて学習を行ったニューラルネットワークに対し、表 9 に示した学習に用レ、てレ、なレ、材料 (機能性未知材料に相当）によるバイオマーカ一 発現量を提示して相対ルシフェラーゼ活性値の推定を行った。表 9に示したバイオマ 一力一発現量を学習済みニューラルネットワークに提示したときの相対ルシフェラー ゼ活性の推定値とその実測値を表 12に示す。なお、相対ノレシフェラーゼ活性の実測 値は 1回のみの測定であり、ダイゼイン 70 μ Μ、レスべラトロール 100 μ Μの推定時と 同じく相対ノレシフェラーゼ活性測定実験で得られた最大値 2.27で除した後の値であ る。表 12に示したとおり、ブルーベリ一葉抽出物 50 x g/mlの相対ルシフェラーゼ活 性については規定誤差以下で推定に成功しており、カプサイシン 10 μ Mについても 1 回目は、規定誤差以下での推定に成功した。 [0131] The learning shown in Table 9 is performed on a neural network in which learning was performed using a data set obtained by removing daidzein 70 μ and resveratrol 100 β from the training data sets shown in Table 8 and Table 10. The relative luciferase activity value was estimated by presenting the amount of biomarker 1 expression by the reit, vert, ret, material (corresponding to the functional unknown material). Table 12 shows estimated values of relative luciferase activity and their measured values when the amount of expression of biomolecules shown in Table 9 is presented to a trained neural network. Note that the actual value of relative noresiferase activity was measured only once, and it was the same as when estimating daidzein 70 μΜ and resveratrol 100 μ It is the value after dividing. As shown in Table 12, the relative luciferase activity of 50 xg / ml of B. vulgaris leaf extract was successfully estimated with less than the specified error, and for the first 10 μM of capsaicin, the estimation was performed less than the specified error. Successful.
[表 12] 表 1 2 . カブサイシンを 10 W M、 ブルーベリ一葉抽出物を 50 g/mlによる [Table 12] Table 1 2. Cub Saishin at 10 WM, Blueberry single leaf extract at 50 g / ml
相対ルシフ Xラーゼ活性推定結 ¾  Relative Lucif Xase activity estimation results
Figure imgf000044_0001
相対ノレシフェラーゼ活性と同様に、がん細胞増殖抑制についてもニューラルネット ワークによる推定を行った。学習には表 8に示したバイオマーカー発現量を入力、表 6に示したがん細胞増殖抑制の 1回目と 2回目の平均値を教師信号とした。二ユーラ ルネットワークの最大出力が 1であるので、教師信号の値は表 6の 1回目と 2回目の平 均値中の最大値 1.18に余裕分 0.02を加えた 1.20で除した値とした。ニューラルネット ワークの構成、及び学習に用いたパラメータは相対ノレシフェラーゼ活性推定時と同じ である。ダイゼイン 70 μ Μ、 レスベラトール 100 を除いて学習を行った後、これら 2 つの化合物によるバイオマーカー発現量をニューラルネットワークに与えたとき、表 1 3の出力が得られた。表 13には推定精度を示すために当該物質のがん細胞増殖抑 制作用の測定値 (実測値)と推定誤差の絶対値も同時に示しており、これらの値は教 師信号と同じく 1.20で除した後の値である。表 13に示したとおり、学習に用いていな い濃度でのがん細胞増殖抑制作用を規定誤差以下で推定することが可能であること が示された。
Figure imgf000044_0001
Similar to relative noresyltransferase activity, we also used neural networks to estimate cancer cell growth inhibition. The biomarker expression level shown in Table 8 was input for learning, and the average value of the 1st and 2nd times of cancer cell growth suppression shown in Table 6 was used as a teacher signal. Since the maximum output of the two-eural network is 1, the value of the teacher signal is divided by 1.20, which is the maximum value 1.18 in the first and second averages in Table 6 plus 0.02 margin. The configuration of the neural network and the parameters used for learning are the same as in the relative noresyltransferase activity estimation. After learning with the exception of daidzein 70 μl and resveratrol 100, when the amount of biomarker expression by these two compounds was given to a neural network, the output shown in Table 13 was obtained. Table 13 also shows the measured values (measured values) for cancer cell growth suppression of the substance and the absolute value of the estimation error at the same time to show the estimation accuracy, and these values are the same as the teacher signal and 1.20. It is the value after dividing. As shown in Table 13, it was shown that it is possible to estimate the cancer cell growth inhibitory effect at concentrations not used for learning with a prescribed error or less.
[表 13] 表 1 3 . 学 に用いていない濃度でのがん細胞増殖抑制作用推定結果 化台物名 推定値 実測値 推定誤差絶対 ダイゼイン 70 Μ 0. 78 0. 70 0. 08 [Table 13] Table 1 3. Results of cancer cell growth inhibitory effect estimation results at concentrations not used in science Chemical name Estimated value Measured value Absolute estimation error Daisyin 70 Μ 0. 78 0. 70 0. 08
レスベラトロール 0. 45 0. 38 ϋ. 07 表 6、表 8に示した学習データ力 ダイゼイン 70 μ Μ、レスべラトロール 100 μ Μを除 いて学習を行ったニューラルネットワークに対し、表 9に示した学習に用いていない 材料 (機能性値未知材料に相当）によるバイオマーカー発現量を提示してがん細胞 増殖抑制作用値の推定を行った。表 9に示したバイオマーカー発現量を学習済み二 ユーラルネットワークに提示したときのがん細胞増殖抑制作用の推定値とその実測値 を表 14に示す。なお、がん細胞増殖抑制作用の実測値は 1回のみの測定であり、ダ ィゼイン 70 μ Μ、レスべラトロール 100 μ Μの推定時と同じく教師信号と同様に 1.20で 除した後の値である。表 14に示したとおり、ブルーベリ一葉抽出物 50 z g/mlのがん 細胞増殖抑制作用について 2回目は規定誤差以下で推定に成功し、 1回目と平均 値についてもほぼ規定誤差での推定に成功した。カプサイシン 10 μ Μについてはす ベて規定誤差以下での推定に成功した。 Resveratrol 0. 45 0. 38 ϋ. 07 The learning data shown in Table 6 and Table 8 The materials not used in the learning shown in Table 9 for the neural network that was learned except for 70 μΜ of daidzein and 100 μΜ of resveratrol The biomarker expression level according to the material) is presented to estimate the cancer cell growth inhibitory action value. Table 14 shows the estimated value and the measured value of the cancer cell growth inhibitory action when the biomarker expression levels shown in Table 9 are presented on the learned two-user network. In addition, the measured value of the cancer cell proliferation inhibitory action is a measurement only once, and it is the value after dividing it by 1.20 similarly to the teacher signal as well as the estimation of 70 μ ゼ of Dizein and 100 μΜ of resveratrol. is there. As shown in Table 14, the cancer cell growth inhibitory effect of 50 Bg / ml of B. vulgari leaf extract succeeded in estimation for the second time with less than the specified error, and for the first time and the average value was also estimated with almost the specified error. did. For 10 μ シ ン of capsaicin, we succeeded in estimation with less than the specified error.
[表 14] 表丄 4 . カブサイシンを 10 W M、 ブルーベリ一葉抽出物を 50 g/mlによる [Table 14] Surface 4. 10 w M of Cub Saishin and 50 g / ml of Bluberry 1 leaf extract
相対がん細胞堪«抑制作用推定結果  Relative cancer cell tolerance suppression result estimation result
Figure imgf000045_0001
Figure imgf000045_0001
このように、機能性未知材料についても、複数のバイオマーカー発現量を測定する だけで、機能性の測定を実際に行わなくても当該材料が持つ複数の機能性を数値と して推定することが可能であることが示された。これらの結果を総合的に評価すると機 能性未知材料であるブルーベリ一葉抽出物にウィルス発がん予防活性があることが 推定された。 As described above, also for the functional unknown material, it is necessary to estimate the plurality of functionality possessed by the material as a numerical value, even if the functionality measurement is not actually performed, by merely measuring the expression level of the plurality of biomarkers. Was shown to be possible. By comprehensively evaluating these results, it was estimated that the extract of Blueberry single leaf, an unknown functional material, has virus carcinogenic preventive activity.
産業上の利用可能性 [0135] 本発明の高スループット機能性評価方法、プログラム、装置は、食材、医薬、医薬 候補物質等の評価に利用できる。特に食材のような多成分系物質の機能性を総合 的に評価するのに適し、機能性食材開発、特定保健用食材の開発に当たり、動物実 験による in vivo評価試験を行う前の予備試験、あるいはヒトを対象にした臨床試験 前の予備試験などに好適に利用できる。加えて、農水林産のごとき生物資源生産物 出荷前の機能性評価試験、家畜、人工飼育魚介類等の農水産栽培試料及び同出 荷物の機能性評価試験等にも利用できる。 Industrial applicability The high-throughput functionality evaluation method, program, and apparatus of the present invention can be used to evaluate foodstuffs, drugs, drug candidates, and the like. In particular, it is suitable for comprehensively evaluating the functionality of multi-component substances such as food, and it is a preliminary test before conducting in vivo evaluation tests by animal experiments in developing functional food and developing food for specified health use. Alternatively, it can be suitably used for a preliminary test prior to a human clinical trial. In addition, it can be used for functional evaluation tests prior to shipment of biological resource products such as agricultural and forestry forests, and functional evaluation tests for agricultural and aquatic cultivation samples such as livestock and artificially-reared fish and shellfish, and luggage.
[0136] 本明細書で引用した全ての刊行物、特許および特許出願をそのまま参考として本 明細書にとり入れるものとする。  All publications, patents and patent applications cited herein are incorporated herein by reference in their entirety.

Claims

請求の範囲 [1] 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (3)得られた測定値を処理して、機能性未知材料の機能性を総合評価するステップ と、 (4)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 [2] 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、バイオマーカー発現 量測定値と機能性とを対応付けるステップと、 (3)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (4)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (5)得られた測定値を処理して、上記（2)で機能性と対応付けられた機能性既知試 料のバイオマーカー発現量と機能性未知試料のバイオマーカー発現量を照合して、 機能性未知材料の機能性を総合評価するステップと、 (6)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 [3] 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性値を測定し、機能性既知試料のバイオマーカー発現量測定 値と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製す るステップと、 (3)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (4)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、(5)機能性未知試料のバイオマーカー発現量測定値に基づいて上記データベース を検索し、機能性未知材料の機能性値を推定するステップと、 (6)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、 (7)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 [4] (1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定するステップと、(3)個別評価系により、機能性既知材料の機能性を測定するステップと、 (4)機能性既知試料のバイオマーカー発現量測定値と機能性既知材料の機能性測 定値との関係を対応付けるステップと、 を含むことを特徴とする機能性既知材料に関するデータベース作製方法。 [5] 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、 (3)機能性未知試料のバイオマーカー発現量測定値に基づレ、て機能性既知試料の バイオマーカー発現量と機能性既知材料の機能性値と対応付けたデータセットを検 索し、機能性未知材料の機能性値を推定するステップと、 (4)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、 (5)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、 (3)上記データベースにおける、機能性既知試料のバイオマーカー発現量測定値と 機能性既知材料の機能性測定値との関係を学習するステップと、 (4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、(6)機能性既知試料のバイオマーカー発現量測定値と機能性既知材料の機能性測 定値との関係の学習結果を汎化して、機能性未知試料のバイオマーカー発現量測 定値に基づいて機能性未知材料の機能性値を推定するステップと、 (7)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、 (8)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 [7] 上記ステップ（3)における機能性既知試料のバイオマーカー発現量測定値と機能 性既知材料の機能性測定値との関係の学習、及び上記ステップ (6)における学習結 果の汎化による機能性未知試料のバイオマーカー発現量測定値からの機能性未知 材料の機能性値の推定をニューラルネットワークにより行うことを特徴とする請求項 6 記載の方法。 [8] 機能性未知材料の複合的な機能性を評価する方法であって、 (1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、 (2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、 (3)上記データベースにおける各機能性既知試料のバイオマーカー発現量が生じる 確率密度関数を求めるステップと， (4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、(6)機能性未知試料のバイオマーカー発現量測定値に基づいて，上記確率密度関 数力 当該機能性未知材料の各機能性既知材料との類似度合いを求めるステップ と， (7)上記類似度合いと上記データベースに基づいて機能性未知材料の機能性を総 合評価するステップと， (8)総合評価結果を表示するステップと、 を含むことを特徴とする高スループット機能性評価方法。 [9] 上記確率密度関数をノンパラメトリック法で求めて、機能性未知材料に類似した機 能性既知材料の決定をベイズ推定法により行うことを特徴とする請求項 8記載の方法 [10] 機能性未知材料の複合的な機能性を評価する方法であって、 Claims [1] A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) adding a functional unknown material to the evaluated culture cell to prepare a functional unknown sample; 2) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression amount of the functional unknown sample, and (3) the obtained measured values And (4) displaying the result of the comprehensive evaluation, and a step of comprehensively evaluating the functionality of the functional unknown material. [2] A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) applying a known material to the evaluated cultured cell to prepare a functional known sample; (2) function Step of attaching a functional known sample to an evaluation system equipped with multiple measurement sites with different sexuality evaluation functions, measuring the biomarker expression amount of the functional known sample, and correlating the biomarker expression amount measurement value with the functionality And (3) step of preparing functional unknown material to evaluation cultured cells and preparing a functional unknown sample, and (4) functional unknown to an evaluation system having a plurality of measurement sites having different functional evaluation functions. Adding a sample, measuring the biomarker expression level of the functional unknown sample, and (5) processing the obtained measured values to obtain a functional known sample associated with the functionality in the above (2). Biomarker expression level and biomarkers of functional unknown samples By matching the expression level, the steps of comprehensive evaluation of the functionality of the functional unknown material, (6) high throughput functional evaluation method characterized by comprising the step of displaying the overall evaluation result. [3] A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) applying a known material to the evaluated cultured cell to prepare a functional known sample; (2) function A functional known sample is added to an evaluation system provided with a plurality of measurement sites having different sex evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional value of the functional known material is measured by an individual evaluation system. Measuring and creating a database correlating the relationship between the biomarker expression level measurement value of the functional known sample and the functional measurement value of the functional known material, (3) Evaluation The functional unknown to the cultured cell A step of applying a material and preparing a functional unknown sample, (4) A functional unknown sample is applied to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and a biomarker of the functional unknown sample Measuring the expression level, (5) functional non- Searching the above-mentioned database based on the biomarker expression level measurement value of the sample to estimate the functional value of the functional unknown material, and (6) the functionality of the functional unknown material based on the estimated functional value A high throughput functionality evaluation method comprising: a step of comprehensive evaluation; and (7) a step of displaying the comprehensive evaluation result. [4] (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample, and (2) Functional evaluation is performed on an evaluation system having a plurality of measurement sites having different functional evaluation functions. Providing a known sample and measuring the biomarker expression level of the functional known sample, (3) measuring the functionality of the known functional material by an individual evaluation system, and (4) measuring the functional known sample Associating the relationship between the biomarker expression level measurement value and the functional measurement value of the functional known material, and a database generation method for the functional known material, characterized by comprising: [5] A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) adding a functional unknown material to the evaluated culture cell to prepare a functional unknown sample; (2) function A functional unknown sample is added to an evaluation system having a plurality of measurement sites having different sex evaluation functions, and the biomarker expression amount of the functional unknown sample is measured; (3) biomarker expression of the functional unknown sample Based on the measured value, search the data set corresponding to the biomarker expression amount of the functional known sample and the functional value of the functional known material, and estimate the functional value of the functional unknown material And (4) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value, and (5) displaying the comprehensive evaluation result, and a high throughput function characterized by: Sex evaluation method. A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) adding a known material to the evaluation culture cell and preparing a functional known sample; (2) functionality evaluation function A functional known sample is added to an evaluation system having a plurality of measurement sites having different values, the biomarker expression amount of the functional known sample is measured, and the functionality of the functional known material is measured by the individual evaluation system, Preparing a database in which the relationship between the biomarker expression level measurement value of the functional known sample and the functional measurement value of the functional known material is created; (3) biomarker expression of the functional known sample in the above database Learning the relationship between the quantity measurement value and the functional measurement value of the functional known material, (4) applying the functional unknown material to the evaluation culture cell, and preparing the functional unknown sample, (5) Functionality evaluation function is different The functional unknown sample is added to an evaluation system having a plurality of measurement sites, and the biomarker expression level of the functional unknown sample is measured, and (6) the biomarker expression level measurement value and function of the functional known sample Generalizing the learning result of the relationship with the functional measurement value of the sex-known material, and estimating the functional value of the functional unknown material based on the biomarker expression amount measurement value of the functional unknown sample; (7) A high throughput functionality evaluation method comprising the steps of: comprehensively evaluating the functionality of an unknown functionality based on the estimated functionality value; and (8) displaying the comprehensive evaluation result. [7] Learning of the relationship between the biomarker expression level measurement value of the functional known sample in the above step (3) and the functional measurement value of the functional known material, and generalization of the learning result in the above step (6) The method according to claim 8, wherein the functional value of the functional unknown material is estimated from the biomarker expression level measurement value of the functional unknown sample by a neural network. [8] A method for evaluating the complex functionality of a functional unknown material, comprising the steps of: (1) applying a known material to the evaluated cultured cell to prepare a functional known sample; (2) function A functional known sample is added to an evaluation system provided with a plurality of measurement sites having different sex evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functionality of the functional known material is determined by an individual evaluation system. Measuring and creating a database correlating the relationship between the biomarker expression level measurement value of the functional known sample and the functional measurement value of the functional known material, and (3) (3) each functional known sample in the above database The step of determining the probability density function in which the amount of biomarker expression occurs, (4) adding the functional unknown material to the evaluation culture cell to prepare the functional unknown sample, (5) multiple functional evaluation functions are different Equipped with measurement site The functional probability sample is added to the evaluation system, the biomarker expression level of the functional unknown sample is measured, and (6) the above probability density function is determined based on the biomarker expression level measurement value of the functional unknown sample. Force Determining the degree of similarity between the functional unknown material and each known functional material, (7) Evaluating the functionality of the unknown functional material on the basis of the above similarity and the above database, (8) 2.) A high throughput functionality evaluation method comprising the steps of: displaying comprehensive evaluation results; [9] The method according to claim 8, characterized in that the probability density function is determined by nonparametric method, and determination of a function known material similar to the functional unknown material is performed by Bayesian estimation. A method of evaluating the complex functionality of an unknown material,
(1)評価培養細胞に機能性既知材料を付与し、機能性既知試料を調製するステップ と、  (1) Evaluation A functional known material is added to cultured cells to prepare a functional known sample,
(2)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性既知試料 を付与し、機能性既知試料のバイオマーカー発現量を測定し、個別評価系により機 能性既知材料の機能性を測定し、機能性既知試料のバイオマーカー発現量測定値 と機能性既知材料の機能性測定値との関係を対応付けたデータベースを作製する ステップと、  (2) A functional known sample is added to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and the biomarker expression amount of the functional known sample is measured, and the functional evaluation material is provided by the individual evaluation system. Measuring the functionality of the sample, and creating a database in which the relationship between the measured value of biomarker expression of the known functional sample and the measured value of the functionality of the known functional material is associated;
(3)上記データベースにおける、機能性既知試料のバイオマーカー発現量測定値を クラスタリングし、各クラスに機能性既知材料の機能性測定値を対応付けるステップと  (3) clustering the biomarker expression level measurement values of the functional known sample in the above database and associating the functional measurement values of the functional known material with each class;
(4)評価培養細胞に機能性未知材料を付与し、機能性未知試料を調製するステップ と、 (4) Evaluation A functional unknown material is provided to cultured cells to prepare a functional unknown sample.
(5)機能性評価機能が異なる複数の測定部位を備えた評価系に、機能性未知試料 を付与し、機能性未知試料のバイオマーカー発現量を測定するステップと、  (5) attaching a functional unknown sample to an evaluation system having a plurality of measurement sites having different functional evaluation functions, and measuring the biomarker expression level of the functional unknown sample;
(6)機能性未知試料のバイオマーカー発現量測定値に基づいて上記データベース を検索し、当該バイオマーカー発現量測定値がクラスタリングされた各クラスの中のど のクラスに帰属するかを決定するステップと、  (6) searching the above-mentioned database based on the biomarker expression level measurement value of the functional unknown sample, and determining to which class of each class the biomarker expression level measurement value belongs to be clustered; ,
(7)決定されたクラスに基づき、機能性未知試料のバイオマーカー発現量測定値か ら機能性未知材料の機能性値を汎化、推定するステップと、  (7) generalizing and estimating the functional value of the functional unknown material from the measured value of the biomarker expression level of the functional unknown sample based on the determined class;
(8)推定された機能性値に基づき機能性未知材料の機能性を総合評価するステツ プと、  (8) comprehensively evaluating the functionality of the unknown material based on the estimated functionality value;
(9)総合評価結果を表示するステップと、  (9) displaying comprehensive evaluation results,
を含むことを特徴とする高スループット機能性評価方法。  A high throughput functionality evaluation method comprising:
[11] クラスタリングを自己組織化マップ法により行うことを特徴とする請求項 10記載の方 法。 11. The method according to claim 10, wherein clustering is performed by a self-organizing map method.
[12] 自己組織化マップにおける機能性未知材料の機能性のクラスの決定を、機能性既 知試料のバイオマーカー発現量測定値に対応付けられた競争ノードが持つ重みと、 機能性未知試料のバイオマーカー発現量測定値とのユークリッド距離に基づき行うこ とを特徴とする請求項 11記載の方法。 [12] Determination of the class of functionality of unknown materials in the self-organizing map The method according to claim 11, characterized in that it is performed based on Euclidean distance between the weight of the competition node associated with the biomarker expression measurement value of the known sample and the biomarker expression measurement value of the functional unknown sample. Method.
[13] 自己組織化マップにおける機能性未知材料の機能性のクラスの決定を、機能性既 知試料のバイオマーカー発現量測定値に対応付けられた競合ノードの座標と、機能 性未知試料のバイオマーカー発現量測定値に対応付けられた競合ノードの座標間 におけるマンハッタン距離に基づき行うことを特徴とする請求項 11記載の方法。  [13] The determination of the class of functionality of unknown functional material in the self-organizing map, the coordinates of the competing node associated with the biomarker expression measurement value of the functional known sample, and the bio of the functional unknown sample The method according to claim 11, which is performed based on Manhattan distance between coordinates of competing nodes associated with the marker expression amount measurement value.
[14] 評価培養細胞がヒト由来培養細胞であることを特徴とする請求項 1、 2、 3、 4、 5、 6 、 8又は 10記載の方法。  [14] The method according to claim 1, 2, 3, 4, 5, 6, 8, or 10, wherein the cultured cell is a human-derived cultured cell.
[15] 機能性未知材料の複合的な機能性を評価する方法であって、  [15] A method for evaluating the complex functionality of a functional unknown material,
(1)中央演算装置が機能性未知試料のバイオマーカー発現量に基づいて、機能性 既知試料のバイオマーカー発現量と機能性既知材料の機能性値とを対応付けたデ ータセットを格納したデータベースを検索し、機能性未知材料の機能性値を推定す るステップと、  (1) A database storing a data set in which the central processing unit associates the biomarker expression amount of the functional known sample with the functional value of the functional known material based on the biomarker expression amount of the functional unknown sample. Searching and estimating the functional value of the functional unknown material;
(2)中央演算装置が、上記ステップ（1)の検索結果に基づいて機能性未知材料の機 能性を総合評価するとともに総合評価結果を出力するステップと、  (2) The central processing unit comprehensively evaluates the functionality of the functional unknown material based on the search result of the step (1) and outputs the general evaluation result.
を含むことを特徴とする機能性未知材料に関する高スループット機能性評価方法。  A high throughput functionality evaluation method for unknown functionality material characterized by including.
[16] 上記機能性既知材料の機能性値は、各機能性既知材料にっレ、て個別評価系によ り測定された測定値力 算出された値であることを特徴とする請求項 15記載の方法。 [16] The functional value of the above-mentioned functional known material is characterized in that the measured value strength measured by an individual evaluation system is calculated for each of the functional known materials separately. Method described.
[17] 上記ステップ（1)において、中央演算装置は機能性未知試料のバイオマーカー発 現量をキーとして上記データベースに含まれる機能性既知材料のバイオマーカー発 現量を照合し、特定のデータセットを検索結果として算出することを特徴とする請求 項 15記載の方法。 [17] In the above step (1), the central processing unit collates the biomarker expression amount of the functional known material contained in the above database using the biomarker expression amount of the functional unknown sample as a key, and a specific data set The method according to claim 15, characterized by calculating as a search result.
[18] 上記中央演算装置は、機能性既知試料のバイオマーカー発現量測定値と機能性 既知材料の機能性測定値との関係を学習することで学習結果を算出し、  [18] The central processing unit calculates the learning result by learning the relationship between the measured value of the biomarker expression amount of the functional known sample and the functional measured value of the functional known material,
上記ステップ (1)では、中央演算装置が上記学習結果を汎化し、機能性未知試料 のバイオマーカー発現量測定値に基づいて機能性未知材料の機能性値を推定する ことを特徴とする請求項 15記載の方法。 In the step (1), the central processing unit generalizes the learning result and estimates the functional value of the functional unknown material based on the measured value of the biomarker expression amount of the functional unknown sample. The method described in 15.
[19] 上記中央演算装置は、上記学習結果の算出及び学習結果の汎化による機能性未 知試料のバイオマーカー発現量測定値からの機能性未知材料の機能性値の推定を ニューラルネットワークにより行うことを特徴とする請求項 18記載の方法。 [19] The central processing unit performs calculation of the learning result and estimation of the functional value of the functional unknown material from the biomarker expression amount measurement value of the functional unknown sample by generalization of the learning result using a neural network. A method according to claim 18, characterized in that.
[20] 上記中央演算装置は、上記データセットにおける各機能性既知試料のバイオマー カー発現量が生じる確率密度関数を算出し， [20] The central processing unit calculates a probability density function that results in the amount of biomarker expression of each functional known sample in the data set,
上記ステップ（1)において、中央演算装置は、機能性未知試料のバイオマーカー 発現量測定値及び上記確率密度関数を用いて機能性未知材料に類似した機能性 既知材料を決定し、機能性既知材料の機能性値をデータベースから検索して推定 することを特徴とする請求項 15記載の方法。  In the step (1), the central processing unit determines the functionality known material similar to the functionality unknown material using the biomarker expression value measurement value of the functionality unknown sample and the probability density function and determines the functionality known material The method according to claim 15, wherein the functional value of is estimated by searching from a database.
[21] 上記中央演算装置は、上記確率密度関数をノンパラメトリック法で算出し、機能性 未知材料に類似した機能性既知材料の推定をベイズ推定法により行うことを特徴と する請求項 20記載の方法。 21. The central processing unit according to claim 20, wherein said central processing unit calculates said probability density function by non-parametric method, and performs estimation of a functional known material similar to the functional unknown material by Bayesian estimation. Method.
[22] 上記中央演算装置は、上記データセットに含まれる機能性既知試料のノ ォマー カー発現量測定値をクラスタリングすることで各クラスに機能性既知材料の機能性測 定値を対応付けたデータセットを算出し、 [22] The above-mentioned central processing unit is a data set in which the functional measurement value of the functional known material is associated with each class by clustering the measured value of the marker expression level of the functional known sample contained in the above data set. Calculate
上記ステップ（1)において、中央演算装置は、機能性未知試料のバイオマーカー 発現量測定値が帰属するクラスを決定し、  In the step (1), the central processing unit determines the class to which the biomarker expression value measurement value of the functional unknown sample belongs,
上記ステップ（2)において、中央演算装置は、決定したクラスに帰属する機能既知 材料の機能性値を汎化し、機能性未知材料の機能性値を推定することを特徴とする 請求項 15記載の方法。  In the step (2), the central processing unit generalizes the functional value of the functional known material belonging to the determined class, and estimates the functional value of the functional unknown material. Method.
[23] 上記中央演算装置は、クラスタリングを自己組織化マップ法により行うことを特徴と する請求項 22記載の方法。 [23] The method according to claim 22, wherein said central processing unit performs clustering by a self-organizing map method.
[24] 上記中央演算装置は、 自己組織化マップにおける機能性未知材料の機能性のク ラスの決定を、機能性既知試料のバイオマーカー発現量測定値に対応付けられた 競争ノードが持つ重みと、機能性未知試料のバイオマーカー発現量測定値とのユー タリッド距離に基づき行うことを特徴とする請求項 23記載の方法。 [24] The central processing unit determines the class of functionality of the functional unknown material in the self-organizing map by determining the weight of the competition node associated with the biomarker expression measurement value of the functionality known sample. The method according to claim 23, characterized in that the method is performed based on the measured distance from the biomarker expression level of the functional unknown sample.
[25] 上記中央演算装置は、 自己組織化マップにおける機能性未知材料の機能性のク ラスの決定を、機能性既知試料のバイオマーカー発現量測定値に対応付けられた 競合ノードの座標と、機能性未知試料のノくィォマーカー発現量測定値に対応付けら れた競合ノードの座標間におけるマンハッタン距離に基づき行うことを特徴とする請 求項 23記載の方法。 [25] The above-mentioned central processing unit associates the determination of the class of functionality of unknown functionality in the self-organizing map with the biomarker expression level measurement value of the functionality known sample The method according to claim 23, characterized in that the method is performed based on the coordinates of the competitor node and the Manhattan distance between the coordinates of the competitor node associated with the marker expression measurement value of the functional unknown sample.
機能性未知材料の複合的な機能性を評価する装置であって、 An apparatus for evaluating the complex functionality of unknown functional materials,
機能性既知試料のバイオマーカー発現量と機能性既知材料の機能性値とを対応 付けたデータセットを格納したデータベースにアクセス可能であり、  It is possible to access a database that stores a data set in which biomarker expression levels of functional known samples are associated with functional values of functional known materials,
(1)機能性未知試料のバイオマーカー発現量に基づいて上記データベースを検索 して、機能性未知材料の機能性値を推定する機能性値推定手段と  (1) Functional value estimating means for estimating the functional value of a functional unknown material by searching the above-mentioned database based on the biomarker expression amount of the functional unknown sample
(2)上記機能性値推定手段により推定された機能性値に基づき、機能性未知材料 の機能性を総合評価する総合評価手段と、  (2) An overall evaluation means for comprehensively evaluating the functionality of the unknown material based on the functionality value estimated by the functionality value estimation means;
(3)総合評価結果を表示する表示手段と、  (3) Display means for displaying the overall evaluation result,
を含むことを特徴とする高スループット機能性評価装置。 A high throughput functional evaluation device characterized by including.
PCT/JP2006/300875 2005-01-21 2006-01-20 High-throughput functionality evaluation method, program and apparatus WO2006077981A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2005013508 2005-01-21
JP2005-013508 2005-01-21

Publications (1)

Publication Number Publication Date
WO2006077981A1 true WO2006077981A1 (en) 2006-07-27

Family

ID=36692361

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2006/300875 WO2006077981A1 (en) 2005-01-21 2006-01-20 High-throughput functionality evaluation method, program and apparatus

Country Status (1)

Country Link
WO (1) WO2006077981A1 (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107028606A (en) * 2017-04-21 2017-08-11 上海耐相智能科技有限公司 Medicinal intelligent monitors loop system
CN107578170A (en) * 2017-09-06 2018-01-12 重庆大学 A kind of fire-fighting system safety evaluation method based on data characteristics selection
CN113777292A (en) * 2021-08-31 2021-12-10 哈尔滨工业大学（深圳） Method for detecting and evaluating tetracycline environmental risk by using paramecium biomarker and IBR (intermediate bulk density receptor)
CN113777291A (en) * 2021-08-31 2021-12-10 哈尔滨工业大学（深圳） Method for detecting and evaluating environmental risk of levofloxacin by using paramecium biomarker and IBR (intermediate bulk density receptor)
CN114720582A (en) * 2021-11-26 2022-07-08 韩山师范学院 Comprehensive evaluation method for aged and yellow wine in different aging years

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004503256A (en) * 2000-06-14 2004-02-05 ビスタジェン　インコーポレイテッド Toxicity classification using liver stem cells
JP2005500032A (en) * 2001-06-15 2005-01-06 インターリューキン ジェネティックス インコーポレイテッド Methods for detecting and treating early onset of aging-related symptoms

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004503256A (en) * 2000-06-14 2004-02-05 ビスタジェン　インコーポレイテッド Toxicity classification using liver stem cells
JP2005500032A (en) * 2001-06-15 2005-01-06 インターリューキン ジェネティックス インコーポレイテッド Methods for detecting and treating early onset of aging-related symptoms

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107028606A (en) * 2017-04-21 2017-08-11 上海耐相智能科技有限公司 Medicinal intelligent monitors loop system
CN107578170A (en) * 2017-09-06 2018-01-12 重庆大学 A kind of fire-fighting system safety evaluation method based on data characteristics selection
CN113777292A (en) * 2021-08-31 2021-12-10 哈尔滨工业大学（深圳） Method for detecting and evaluating tetracycline environmental risk by using paramecium biomarker and IBR (intermediate bulk density receptor)
CN113777291A (en) * 2021-08-31 2021-12-10 哈尔滨工业大学（深圳） Method for detecting and evaluating environmental risk of levofloxacin by using paramecium biomarker and IBR (intermediate bulk density receptor)
CN113777291B (en) * 2021-08-31 2023-09-05 哈尔滨工业大学（深圳） Method for detecting and evaluating environmental risk of levofloxacin by using paramecium biomarkers and IBR (intermediate frequency response)
CN113777292B (en) * 2021-08-31 2023-09-05 哈尔滨工业大学（深圳） Method for detecting and evaluating tetracycline environmental risk by using paramecium biomarker and IBR (intermediate frequency region)
CN114720582A (en) * 2021-11-26 2022-07-08 韩山师范学院 Comprehensive evaluation method for aged and yellow wine in different aging years
CN114720582B (en) * 2021-11-26 2023-10-20 韩山师范学院 Comprehensive evaluation method for old fragrance yellow in different ageing years

Similar Documents

Publication Publication Date Title
Pinu et al. Translational metabolomics: current challenges and future opportunities
Geisslitz et al. Comparative study on gluten protein composition of ancient (einkorn, emmer and spelt) and modern wheat species (durum and common wheat)
Lodge et al. NMR spectroscopic windows on the systemic effects of SARS-CoV-2 infection on plasma lipoproteins and metabolites in relation to circulating cytokines
Ragone et al. Renal cell carcinoma: a study through NMR-based metabolomics combined with transcriptomics
Lawton et al. Analysis of the adult human plasma metabolome
Shao et al. Comprehensive analysis of individual variation in the urinary proteome revealed significant gender differences*[S]
Kussmann et al. Profiling techniques in nutrition and health research
Klueter et al. Taxonomic and environmental variation of metabolite profiles in marine dinoflagellates of the genus Symbiodinium
CN104520435A (en) Interrogatory cell-based assays and uses thereof
CN101313220A (en) Molecular markers of hepatocellular carcinoma and their applications
WO2006077981A1 (en) High-throughput functionality evaluation method, program and apparatus
Cifuentes Foodomics: principles and applications
Moriggi et al. Skeletal muscle proteomic profile revealed gender-related metabolic responses in a diet-induced obesity animal model
Purcell et al. Dietary intake and energy expenditure in breast cancer survivors: a review
JP2008022762A (en) Method, program and apparatus for evaluating high throughput function
Gao et al. Proteomic tissue-based classifier for early prediction of prostate cancer progression
Madrid et al. Survey of commercial food products for detection of walnut (Juglans regia) by two elisa methods and real time pcr
Lecuyer et al. NMR metabolomic profiles associated with long-term risk of prostate cancer
León et al. Foodomics applications
Vardali et al. Recent advances in mycotoxin determination in fish feed ingredients
JP4150761B2 (en) High-throughput functionality evaluation method, program, and apparatus
Zabielska-Koczywąs et al. Proteomic differences in feline fibrosarcomas grown using doxorubicin-sensitive and-resistant cell lines in the chick embryo model
CN102459637A (en) Quality control bioassays for nutriceutical and medicinal products
Kato et al. A perspective on DNA microarray technology in food and nutritional science
Imaizumi et al. Clinical implementation of metabolomics

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 06712100

Country of ref document: EP

Kind code of ref document: A1

WWW Wipo information: withdrawn in national office

Ref document number: 6712100

Country of ref document: EP