WO2019026918A1

WO2019026918A1 - Biomarkers from skin

Info

Publication number: WO2019026918A1
Application number: PCT/JP2018/028702
Authority: WO
Inventors: 匠徳佐藤
Original assignee: 株式会社国際電気通信基礎技術研究所
Priority date: 2017-08-01
Filing date: 2018-07-31
Publication date: 2019-02-07
Also published as: JPWO2019026918A1; JP7445288B2

Abstract

One problem addressed by the present invention is to predict a kidney disease using a skin-derived sample. A device 1 that predicts a kidney disease in a subject and has an acquisition means and a prediction means. The acquisition means acquires a measured value for at least one type of protein selected from among biomarkers included in a skin-derived sample taken from the subject and/or a measured value for at least one type of mRNA selected from among the biomarkers included in the sample taken from the subject. The prediction means predicts the kidney disease on the basis of the measured value(s) acquired by the acquisition means.

Description

Skin biomarkers

The present invention relates to predicting renal disease using a skin-derived sample, predicting metabolic abnormality of phosphorus, predicting activation of FGF23 in a subject, and active ingredients for suppressing functional expression of FGF23. The present invention relates to screening of candidate substances, screening of candidate active ingredients for activating the function of FGF23, and predicting the involvement of FGF23 in diseases.

Diseases may or may not be reversibly treatable (i.e., irreversible). It is essential to maintain good health that during the reversible condition, the abnormality is detected and treated promptly or prevented from becoming even such condition. Also, even in reversible conditions, early detection of the disease leads directly to milder treatment regimens, shorter treatment periods, and better prognosis health. In addition, as represented by heart disease, brain disease, cancer and diabetes, it is well known that abnormalities in one organ or tissue lead to diseases in other organs (generally called complications). In such diseases, it is essential to prevent the disease of one organ or tissue from being caused by another organ or tissue as early as possible.

In all animals including humans, individual organs and tissues are not individual parts, and each form a functional network to achieve quality control at the individual level. The transport of endocrine factors such as hormones by vascular networks distributed throughout the body, and coordinated coordination of each organ function by neural networks are representative examples of "multi-organ linkage system", and systems as physiology and endocrinology It is attached.

On the other hand, the number of end-stage renal failure patients (ESKD) requiring dialysis and kidney transplantation is also on the rise worldwide, and the number of ESKD patients is 430,000 to 1,065,000 in 10 years from 1990 to 2000. The number has increased to at least 1.65 million in 2008 (non-patent document 1). Chronic kidney disease (Chronic Kidney disease: CKD) is a reserve arm of ESKD, but the kidneys are called "silent organs", and even if kidney damage occurs, the condition is less likely to appear in clinical data etc. Renal dysfunction prior to the onset of the disease is currently difficult to detect early.

FGF23 is a hormone that lowers the level of phosphorus in blood, and is known to lower the level of phosphorus in blood by suppressing the reabsorption of phosphorus in the renal proximal tubule and the absorption of phosphorus from the intestinal tract. There is. It is also known that elevated levels of FGF23 cause bone-mineral metabolism disorder (CKD-MBD) associated with chronic kidney disease.

An object of the present invention is to predict kidney disease using a skin-derived sample. An object of the present invention is to predict metabolic disorders of phosphorus using a skin-derived sample. The present invention makes it one task to predict the activation of FGF23 in a subject. One object of the present invention is to screen candidate substances of the active ingredient for suppressing the functional expression of FGF23. One object of the present invention is to screen candidate substances for active ingredients for activating the function of FGF23. The present invention makes it one task to predict the involvement of FGF23 in diseases.

As a result of intensive studies, the inventor found that expression of certain biomarkers changes in the skin depending on the pathological condition in a kidney disease animal model. In addition, it was found that among these biomarkers, those related to FGF23 and those not related were present.

The present invention has been completed based on the above findings, and includes the following aspects.
Item 1.
Apparatus for predicting renal disease in a subject having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring measurement values of at least one mRNA selected from the group consisting of
Prediction means for predicting the renal disease based on the measurement values acquired by the acquisition means.
Item 2.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to item 1, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 3.
The prediction means compares the measured value with a predetermined reference value, and determines that the subject has a renal disease if the measured value is outside the range of the reference value. The device described in.
Item 4.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
The device according to Item 3, wherein it is determined that FGF23 is involved in the renal disease.
Item 5.
A program that, when executed on a computer, causes the computer to perform the following processing to predict renal disease in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition processing for acquiring measurement values of at least one mRNA selected from the group consisting of
The prediction process which predicts the said renal disease based on the measured value acquired by the said acquisition process.
Item 6.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 6. The program according to item 5, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 7.
In the prediction process, the measured value is compared with a predetermined reference value, and if the measured value is out of the range of the reference value, the subject is determined to have a renal disease. The program described in.
Item 8.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
8. The program according to item 7, wherein it is determined that FGF23 is involved in the renal disease.
Item 9.
A method of predicting renal disease in a subject comprising the following steps:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining a measurement value of at least one mRNA selected from the group consisting of
A prediction step of predicting the renal disease based on the measurement value acquired in the acquisition step.
Item 10.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 10. The method according to item 9, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 11.
The prediction step compares the measured value with a predetermined reference value, and determines that the subject has a renal disease if the measured value is out of the range of the reference value. The method described in.
Item 12.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
Item 12. The method according to Item 11, wherein it is determined that FGF23 is involved in the renal disease.
Item 13.
Apparatus for predicting abnormal phosphorus metabolism in a subject, having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring measurement values of at least one mRNA selected from the group consisting of
Prediction means for predicting the phosphorus metabolism abnormality based on the measurement value acquired by the acquisition means.
Item 14.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 14. The device according to item 13, wherein the device is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 15.
The prediction means compares the measured value with a predetermined reference value, and when the measured value is out of the range of the reference value, predicts that the subject has a phosphorus metabolism abnormality, or The device according to 14.
Item 16.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
The device according to Item 15, wherein it is determined that FGF23 is involved in the phosphorus metabolism disorder.
Item 17.
A program that, when run on a computer, causes the computer to perform the following processing for predicting phosphorus metabolism abnormality in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition processing for acquiring measurement values of at least one mRNA selected from the group consisting of
The prediction process which predicts the said phosphorus metabolism abnormality based on the measured value acquired by the said acquisition process.
Item 18.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 Item 18. The program according to Item 17, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 19.
In the prediction processing, the measured value is compared with a predetermined reference value, and when the measured value is out of the range of the reference value, it is predicted that the subject has a phosphorus metabolism abnormality, or The program described in 18.
Item 20.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
20. The program according to Item 19, wherein it is determined that FGF23 is involved in the phosphorus metabolism disorder.
Item 21.
A method of predicting abnormal phosphorus metabolism in a subject comprising the following steps:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining a measurement value of at least one mRNA selected from the group consisting of
A prediction step of predicting the phosphorus metabolism abnormality based on the measurement value acquired in the acquisition step.
Item 22.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 22. The method according to item 21, which is at least one selected from the group consisting of, Col1a1 and Defb8.
Item 23.
The prediction step compares the measured value with a predetermined reference value, and predicts that the subject has a phosphorus metabolism abnormality if the measured value is out of the range of the reference value. The method described in 22.
Item 24.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, And / or if the measured value of one of the biomarkers is outside the range of the reference value, and / or
When the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value
24. The method according to item 23, wherein it is determined that FGF23 is involved in the phosphorus metabolism disorder.
Item 25.
Device for predicting FGF23 activation in a subject, having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring measurement values of at least one mRNA selected from the group consisting of
A prediction unit that predicts the activation of the FGF23 based on the measurement values acquired by the acquisition unit.
Item 26.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The device according to Item 25, which is at least one selected from the group consisting of, Col1a1 and Defb8.
Item 27.
The prediction means compares the measured value with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, And / or when the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or
Item 25. Predicting that FGF23 is activated in the subject, provided that at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range, Or the apparatus described in 26.
Item 28.
A program that, when run on a computer, causes the computer to perform the following processing to predict FGF23 activation in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition processing for acquiring measurement values of at least one mRNA selected from the group consisting of
The prediction process which predicts activation of said FGF23 based on the measured value acquired by the said acquisition process.
Item 29.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 29. The program according to Item 28, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 30.
In the prediction processing, the measured value is compared with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, And / or when the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or
Item 28. Predicting that FGF23 is activated in the subject, provided that at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range, Or the program described in 29.
Item 31.
A method of predicting FGF23 activation in a subject comprising the steps of:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining a measurement value of at least one mRNA selected from the group consisting of
A prediction step of predicting activation of the FGF23 based on the measurement value acquired in the acquisition step.
Item 32.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 32. The method according to Item 31, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 33.
The prediction step compares the measured value with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, And / or when the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or
It is predicted that FGF23 is activated in the subject, provided that at least one biomarker selected from the group consisting of Aldhl2, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of the reference value, Or the method according to 32.
Item 34.
Screening apparatus for candidate active ingredients for suppressing the functional expression of FGF23, having the following means:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A first measurement value acquiring unit for acquiring a measurement value of a biomarker protein of at least one test substance-treated sample and / or a measurement value of mRNA of the protein;
A determination means for determining that the test substance is a candidate substance of the active ingredient based on the measurement value acquired by the first measurement value acquisition means.
Item 35.
From a group consisting of a sample collected from the skin of a subject (except for human beings) not treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A second measurement value acquiring means for acquiring a measurement value of a corresponding biomarker protein in at least one unprocessed sample to be selected and / or a measurement value of mRNA of said protein,
Measured value comparison means for comparing the measured value of the test substance-treated sample with the measured value of the untreated sample,
And have
35. The apparatus according to Item 34, wherein the determination means determines that the test substance is a candidate substance of the active ingredient based on the comparison result of the measurement value comparison means.
Item 36.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to Item 35, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 37.
A screening program which, when executed on a computer, causes the computer to carry out the following processing for screening active substance candidate substances for suppressing the functional expression of FGF23:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A first measurement value acquisition process for acquiring a measurement value of a biomarker protein in at least one test substance-treated sample and / or a measurement value of mRNA of said protein, and
The determination process which determines that the said test substance is a candidate substance of an active ingredient based on the measured value acquired by said 1st measured value acquisition process.
Item 38.
Furthermore, it comprises a sample collected from the skin of a subject not treated with the test substance (except for human beings), a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A second measurement value acquisition process for acquiring a measurement value of a corresponding biomarker protein of at least one untreated sample selected from a group and / or a measurement value of mRNA of said protein,
A measured value comparison process comparing the measured value of the test substance-treated sample with the measured value of the untreated sample;
On the computer,
The program according to Item 37, wherein in the determination process, the test substance is determined to be a candidate substance of the active ingredient based on the comparison result of the measurement value comparison process.
Item 39.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to Item 37 or 38, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 40.
The screening method of the candidate substance of the active ingredient for suppressing the functional expression of FGF23 including the following processes:
(I) A sample collected from the skin of a subject (except for human beings) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Obtaining a measured value of a biomarker protein in at least one test substance-treated sample selected from the group and / or a measured value of mRNA of said protein,
(II) A step of determining that the test substance is a candidate substance of the active ingredient based on the measurement value obtained in the step (I).
Item 41.
Between the steps (I) and (II), the measured value of the test substance-treated sample obtained in the step (I) and the skin of a subject (except for human beings) not treated with the test substance Of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a selected sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And / or comparing the measured value of mRNA of said protein with
Step (II) is a step of determining that the test substance is a candidate substance of the active ingredient based on the comparison result.
The method according to Item 40, further comprising
Item 42.
Before the step (I),
(I) treating a subject (excluding human), a test tissue or test cell derived from skin with a test substance,
(Ii) collecting a sample from the subject, test tissue or test cells treated with the test substance in the step (i);
(Iii) a step of recovering protein and / or mRNA from the sample obtained in the step (ii)
The method according to Item 40 or 41, comprising
Item 43.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The method according to any one of Items 40 to 42, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 44.
Screening apparatus for candidate active ingredients for activating the function of FGF23, having the following means:
Biomarker in a sample collected from the skin of a subject (except for human beings) treated with a test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) A first measurement value acquiring unit for acquiring a measurement value of a protein and / or a measurement value of mRNA of the protein;
A determination means for determining that the test substance is a candidate substance of the active ingredient based on the measurement value acquired by the first measurement value acquisition means.
Item 45.
Biomarker protein of a sample collected from the skin of a subject (except for human beings) not treated with the test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (untreated sample) A second measurement value acquisition unit for acquiring the measurement value of the protein and / or the mRNA of the protein, and
Measured value comparison means for comparing the measured value of the test substance-treated sample with the measured value of the untreated sample,
And have
The screening device according to Item 44, wherein the determination means determines that the test substance is a candidate substance of the active ingredient based on the comparison result of the measurement value comparison means.
Item 46.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to Item 45, which is at least one selected from the group consisting of, Col1a1 and Defb8.
Item 47.
A screening program which, when executed on a computer, causes the computer to carry out the following processing for screening active substance candidate substances for activating the function of FGF23:
Biomarker in a sample collected from the skin of a subject (except for human beings) treated with a test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) A first measurement value acquisition process for acquiring a measurement value of a protein and / or a measurement value of mRNA of the protein, and
The determination process which determines that the said test substance is a candidate substance of an active ingredient based on the measured value acquired by said 1st measured value acquisition process.
Item 48.
Furthermore, the bio of a sample collected from the skin of a subject (except for human beings) not treated with the test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (untreated sample) A second measurement value acquisition process for acquiring a measurement value of a marker protein and / or a measurement value of mRNA of said protein, and
A measured value comparison process comparing the measured value of the test substance-treated sample with the measured value of the untreated sample;
On the computer,
The program according to Item 47, wherein in the determination process, the test substance is determined to be a candidate substance of the active ingredient based on a comparison result of the measurement value comparison process.
Item 49.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to Item 47 or 48, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 50.
The screening method of the active substance candidate substance for activating the function of FGF23 including the following processes:
(I) A sample collected from the skin of a subject (except for human beings) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Obtaining a measured value of a biomarker protein in at least one test substance-treated sample selected from the group and / or a measured value of mRNA of said protein,
(II) A step of determining that the test substance is a candidate substance of the active ingredient based on the measurement value obtained in the step (I).
Item 51.
Between the steps (I) and (II), the measured value of the test substance-treated sample obtained in the step (I) and the skin of a subject (except for human beings) not treated with the test substance Of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a selected sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And / or comparing the measured value of mRNA of said protein with
Step (II) is a step of determining that the test substance is a candidate substance of the active ingredient based on the comparison result.
The method according to Item 50.
Item 52.
Before the step (I),
(I) treating a subject (excluding human), a test tissue or test cell derived from skin with a test substance,
(Ii) collecting a sample from the subject, test tissue or test cells treated with the test substance in the step (i);
(Iii) a step of recovering protein and / or mRNA from the sample obtained in the step (ii)
52. A method according to

item

50 or 51, comprising
Item 53.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The method according to any one of Items 40 to 42, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 54.
Predictor for predicting the involvement of FGF23 in disease comprising the following means:
First measurement value acquiring means for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measurement value of mRNA of the protein, and the first measurement value acquisition means A determination means for determining that FGF23 is involved in the disease based on the measurement value obtained by
Item 55.
A second measurement value acquisition unit for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject not having the disease and / or a measurement value of mRNA of the protein;
Measurement value comparison means for comparing the measurement value acquired by the first measurement value acquisition means with the measurement value acquired by the second measurement value acquisition means;
And have
56. A device according to item 54, wherein the determination means determines that FGF23 is involved in the disease based on the result of the comparison.
Item 56.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 56. The device according to item 55, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 57.
A prediction program which, when executed on a computer, causes the computer to carry out the following processing for predicting the involvement of FGF23 in a disease:
A first measurement value acquisition process for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measurement value of mRNA of the protein, and the first measurement value acquisition process The decision processing which determines that FGF23 is concerned in the said disease based on the measured value acquired by b.
Item 58.
Furthermore, a second measurement value acquisition process for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject not having the disease and / or a measurement value of mRNA of the protein,
A measurement value comparison process comparing the measurement value acquired in the first measurement value acquisition process and the measurement value acquired in the second measurement value acquisition process;
On the computer,
60. The program according to Item 57, wherein in the determination processing, it is determined that FGF23 is involved in the disease based on the result of the comparison.
Item 59.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 60. The program according to Item 57 or 58, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 60.
Methods of predicting FGF23's involvement in disease comprising the following steps:
(I) obtaining a measured value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measured value of mRNA of said protein,
(II) A step of determining that FGF23 is involved in the disease based on the measurement value obtained in the step (I).
Item 61.
Between the steps (I) and (II), the measured value obtained in the step (I) and the measured value of the biomarker protein in a sample collected from the skin of a subject not having the disease And / or comparing the measured value of mRNA of said protein
Further include
Step (II) is a step of determining that FGF23 is involved in the disease based on the result of the comparison.
61. A method according to item 60.
Item 62.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The method according to any one of items 60 to 61, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Item 63.
Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4 dip, I22ra2, Rbfox1, Lrcc2, Col1a1, Col5a1, C1qtnChIh, a C1qtnChIh, a C1qtnCh, a backgrip A skin biomarker for predicting involvement of renal function, abnormal phosphorus metabolism, or FGF23, comprising at least one selected from the group consisting of

According to the present invention, as an effect, it is possible to detect a decrease in renal function using a skin-derived sample. The present invention can predict, as one effect, a metabolic abnormality of phosphorus using a skin-derived sample. The present invention can predict the activation of FGF23 in a subject using a skin-derived sample as one effect. The present invention can screen for a candidate substance of an active ingredient for suppressing functional expression of FGF23 using skin derived specimen as one effect. The present invention can screen for a candidate substance of an active ingredient for activating the function of FGF23 using skin derived specimen as one effect. The present invention can, as one effect, predict the involvement of FGF23 in disease using skin-derived specimens.

FIG. 1 is a schematic view of a system 100 according to first to third embodiments of the present invention. It is a figure showing hardware constitutions of system 100 concerning the 1st-the 3rd embodiment of the present invention. FIG. 6 is a block diagram for explaining the functions of arithmetic units 1 to 3 according to first to third embodiments of the present invention of the present invention. It is a flow chart which shows operation of arithmetic unit 1 concerning the 1st mode of the present invention. It is a flow chart which shows operation of arithmetic unit 2 concerning the 2nd mode of the present invention. It is a flow chart which shows operation of arithmetic unit 3 concerning the 3rd mode of the present invention. FIG. 10 is a schematic view of a system 200 according to the fourth and fifth embodiments of the present invention. It is a figure which shows the hardware constitutions of the system 200 which concerns on the 4th, 5th embodiment of this invention. It is a block diagram for demonstrating the function of the

screening apparatuses

4 and 5 which concern on the 4th, 5th embodiment of this invention of this invention. It is a flowchart which shows operation | movement of the screening apparatus 4 which concerns on the 4th aspect of this invention. It is a flowchart which shows operation | movement of the screening apparatus 5 which concerns on the 5th aspect of this invention. FIG. 16 is a schematic view of a system 300 according to a sixth embodiment of the present invention. It is a figure which shows the hardware constitutions of the system 300 which concerns on the 6th embodiment of this invention. It is a block diagram for demonstrating the function of the arithmetic unit 6 which concerns on the 6th embodiment of this invention of this invention. It is a flowchart which shows operation | movement of the arithmetic unit 6 which concerns on the 6th aspect of this invention. It is an outline figure showing an example of a test kit. FIG. 17A shows the sequences of gRNA and ssODNs. FIG. 17B shows the genotype of mutant mice. The expression of biomarkers in the skin of UNx / HPi, WT3W / HP1W, WT3W / LP1W, WT3W / ND1W mouse models is shown. It is a figure which shows expression of Fgf23 in the skull of E- / M- / L-UNx / HPi and WT3W / HP1W, WT3W / LP1W, WT3W / ND1W. The blood inorganic phosphorus concentration of each model mouse is shown. The comparison result of the expression of the biomarker in Fgf23 gene mutant mouse and WT3W / HP1W, WT3W / LP1W, WT3W / ND1W is shown. 4 shows four types of models dependent or independent on Fgf23 gene functional expression.

The present invention includes the first embodiment for solving the problem of predicting kidney disease using a skin-derived sample. The present invention includes a second embodiment for solving the problem of predicting metabolic abnormality of phosphorus using a skin-derived sample. The present invention includes the third embodiment for solving the problem of predicting the activation of FGF23 in a subject. The present invention includes the fourth embodiment for solving the problem of screening a candidate substance of an active ingredient for suppressing the functional expression of FGF23. The present invention includes the fifth embodiment for solving the problem of screening candidate substances for active ingredients for activating the function of FGF23. The present invention includes the sixth embodiment for solving the problem of predicting the involvement of FGF23 in diseases. The present invention includes the seventh embodiment of the test reagent and the test kit. The present invention includes the eighth embodiment according to the skin biomarker.

I. First to third embodiments
1. Description of Terms First, in the first to third embodiments, terms used in the specification, claims, and summary will be described. Unless otherwise stated, the terms used in the specification, claims and summary with respect to the first to third embodiments conform to the provisions of this section. However, also in the description of the fourth to eighth embodiments, the explanation of this section may be incorporated.

In the present specification, “renal disease” is any abnormality or disease of the kidney causing renal function decline, and is not particularly limited as long as the kidney has some functional or physical disorder. Specifically, among acute renal failure, acute pyelonephritis, acute glomerulonephritis (glomerulonephritis associated with hemolytic streptococcal infection, rapidly progressive glomerulonephritis, etc.), renal disease associated with heart disease Acute renal diseases such as acute diseases (type 1 cardiorenal syndrome etc.); chronic pyelonephritis, reflux nephropathy, interstitial nephritis, polycystic kidney disease, chronic glomerulonephritis (IgA nephropathy, systemic lupus erythematosus glomeruli Nephritis (Lupus Nephritis) etc., Chronic disease among chronic kidney disease associated with heart disease (Type 2 cardio-renal syndrome etc), Chronic nephritis such as diabetic nephropathy, glomerular fibrosis, etc .; Nephrotic syndrome; Renal tumor Chronic kidney disease and the like. Preferably, it is acute kidney disease. In another embodiment, it is preferably renal disease associated with ischemic heart disease, particularly acute renal disease associated with ischemic heart disease (type 1 cardiorenal syndrome, etc.).

In the present specification, “renal function reduction” means, for example, at least one type of kidney disease marker (preferably urine) shown in the following Tables 1-1 to 1-4 which are generally measured by clinical examination in the case of human. The condition where the value is outside the standard value range). As renal disease markers, more preferably serum urea nitrogen, serum creatinine, serum inorganic phosphorus, urinary fibrinogen, creatinine clearance, 24 hour creatinine clearance estimated glomerular filtration rate (eGFR), urea clearance, inulin clearance, sodium thiosulfate clearance , Renal plasma flow rate, filtration rate, sodium excretion rate, lithium excretion rate, phenolsulfonephthalein test, concentration test, dilution test, free water clearance, free water resorption, tubular excretory pole volume, tubular resorption peak volume, It is at least one selected from the group consisting of phosphoric acid resorption rate, β2-microglobulin, and α1-microglobulin.

The above-mentioned kidney disease marker can be measured by a known method disclosed in the Clinical Test Method Revision Edition 32 (Kanai Masamitsu Edited by Kanahara Publishing Co., Ltd.) and the like.

In the present specification, “acute renal failure” is a disease in which renal function is rapidly reduced, for example, those in which the serum creatinine level rapidly increases to 2.0 to 2.5 mg / dl or more (based on the kidney If there is a decline in function, serum creatinine level increases by 50% or more from the previous level), or serum creatinine level is 0.5 mg / dl / day or more, and urea nitrogen is 10 mg / dl / day or more I say something that increases. Acute renal failure includes (1) prerenal acute renal failure due to decreased renal blood flow, (2) renal acute renal failure with impaired renal parenchyma, and (3) renal failure after renal failure Although post-renal acute renal failure is included, preferred acute renal failure for application of the present invention is prerenal acute renal failure and renal acute renal failure, preferably prerenal acute renal failure.

Renal disease associated with heart disease is also called cardiorenal syndrome. Cardiorenal syndromes, including acute and chronic conditions, are classified into multiple types. Of these,

types

1 and 2 start with heart disease, type 1 is acute cardiorenal syndrome, and type 2 is chronic cardiorenal syndrome. Type 1 may be triggered by ischemic heart disease or the like.

Type 1 cardiorenal syndrome refers to any heart disease resulting in one of the following stages 1-3:
Stage 1: Serum creatinine level increases to about 1.5 to 1.9 times the standard value, or in the same individual, serum creatinine level increases by 0.3 mg / dl or more over the previous level, and urine volume Stage 2: The blood pressure is about 0.5 mL / kg / hour over 6 hours to 12 hours: The serum creatinine level increases to 2.0 to 2.9 times the standard value, and the urine volume is 0. Stage 3: The condition of less than 5 mL / kg / hour continues: Does the serum creatinine level increase to about 3 times the standard value, or does the serum creatinine level increase by 4.0 mg / dL or more over the previous level in the same individual , Or if renal replacement therapy is started or eGFR drops to less than 35 mL / min / 1.73 m ^{2 in} patients younger than 18 years, urine volume is 24 in addition to any of the above four conditions. 0.3 mL / kg / hour or more If the condition of less than hour continues or the condition of urinelessness continues for 12 hours or more.

In the present specification, “chronic kidney disease” means, when the subject is a human, according to “CKD Diagnosis Guide 2012 (The Japanese Society of Nephrology), renal disorders (urinary abnormalities such as proteinuria including micro albuminuria), Abnormal urinary sediment, abnormal imaging such as single kidney and multiple cystic kidney, decreased renal function such as increase in serum creatinine level, abnormal electrolyte such as hypokalemia due to renal tubular disorder, abnormal pathology in renal biopsy etc. Etc.), or estimated GFR (glomerular filtration rate) of 60 mL / min / 1.73 m ^{2 or} less is a state in which renal function decline lasts for 3 months or more.

Here, the estimated GFR (eGFR) can be calculated by the estimation formula (eGFR Creat) from the serum creatinine value shown in Table 2 below. In addition, for those with extremely low muscle mass, such as lower extremity amputees, the serum cystatin C estimation formula (eGFRcys) can be applied.

For example, in the case where protein is used as an index, chronic renal disease can be diagnosed when urine protein of 0.15 g / g Cr or more continues by urinalysis three months ago and recent. In addition, it is possible to diagnose chronic kidney disease as a case where diabetes continues more than 3 months ago and 30 mg / g Cr or more continues in the recent albuminuria test.

For children, a standard value of serum creatinine (Cr) is prepared by the enzymatic method in Japanese children, and it can be used to evaluate people with renal dysfunction. For example, the eGFR in% can be represented by the following formula 1 for children between 2 and 11 years old.
[Equation 1]
eGFR (%) = (0.3 x height (m) / subject's serum Cr value) x 100

In addition, in the case of mammals other than humans, such as cats and dogs, chronic kidney disease can be predicted from the average daily water intake or specific gravity of urine.

For example, in the case of humans, the severity of chronic kidney disease can be determined based on Table 3 below (Table 3 is Table 2 of “CKD Diagnosis Guide 2012”).

The disease associated with FGF23 is not limited as long as it is a disease that develops with overexpression of FGF23. Hypophosphatemia rickets / osteomalacia, autosomal dominant hypophosphatemia rickets / osteomalacia, autosomal recessive hypophosphatemia rickets / osteomalacia, X-chromosomal dominant hypophosphate Rickets / osteomalacia, neoplastic rickets / osteomalacia, secondary hyperparathyroidism, abnormal phosphorus metabolism in acute kidney disease or chronic kidney disease, bone-mineral metabolism disorder (CKD-MBD) Etc. can be mentioned.

In the present invention, the "individual" is not particularly limited, but the individual includes humans and mammals other than humans. Examples of mammals other than human include cows, horses, sheep, goats, pigs, dogs, cats, rabbits, monkeys and the like. Preferred are humans, cats and dogs. Also, the age and gender of the individual do not matter.

In addition, the “subject” may or may not be an individual having renal function decline or a history of other renal diseases. In addition, the subject may be an individual with symptoms such as polyuria, thirst, increased water intake, excessive gastric juice, vomiting, hematuria, general fatigue, or even an asymptomatic individual Good. Furthermore, subjects include subjects suspected of having renal disorder or chronic kidney disease according to known diagnostic methods such as medical interviews, urinalysis, blood biochemistry, renal imaging, renal biopsy etc. Be

In the present invention, the "skin-derived sample" is not limited as long as it is derived from the skin of a living body. The skin-derived sample also includes sweat, secretions from the skin, and the like. The method of collecting the skin is also not limited, and examples thereof include a biopsy material, skin adhering to a perforated needle when a hole in a piercing is made, abrasion on the skin surface, and the like.

Furthermore, the sample may be fresh or stored. When the sample is stored, it can be stored in a room temperature environment, a refrigerated environment or a frozen environment, but is preferably frozen.

In the “biomarker” of the present invention, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1b1 shown in Table 4 are included. And at least one selected from the group consisting of Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8, variants thereof, orthologs and the like.

Here, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, & At least one biomarker (referred to as group 1) is likely to be under the control of FGF23. Therefore, when the measured value of one group of biomarkers is out of the range of the reference value, for example, it can be predicted that FGF23 is involved in, for example, renal diseases and other diseases, or abnormal phosphorus metabolism.

On the other hand, at least one biomarker selected from the group consisting of Aldhl2, Col5a1, Col3a1, C1qtnf6, and Col1a1 (referred to as group 2) is likely not to be regulated by FGF23. Therefore, if one group of biomarkers is out of the range of the reference value, and if the measured values of the two groups of biomarkers are within the range of the reference value, for example, renal disease or other diseases, or phosphorus metabolism In the abnormality, it can be predicted more reliably that FGF23 is involved.

Whether or not FGF23 is involved is preferably determined in consideration of both the measured values of one group of biomarkers and the measured values of two groups of biomarkers.

The “measurement value of at least one type of protein selected from the group consisting of biomarkers” refers to a value reflecting the amount or concentration of at least one type of protein selected from the group consisting of biomarkers. When the said measured value is labeled by "amount", although it may be molar or mass, it is preferable to label by mass. In addition, when a value is expressed as “concentration”, it may be a molar concentration or a mass ratio (mass / volume) per a fixed volume of an analyte, but is preferably mass / volume. As the value reflecting the amount or concentration, in addition to the above, the intensity of a signal such as fluorescence or luminescence may be used.

The “measured value of at least one mRNA selected from the group consisting of biomarkers” may be represented by the copy number (absolute amount) of biomarker mRNA present in a certain amount of sample, or β2- It may be a value reflecting the expression level relative to the expression level of housekeeping genes such as microglobulin mRNA, GAPDH mRNA, Maea mRNA and β-actin mRNA. Moreover, it may be expressed by the intensity of signals such as fluorescence and luminescence.

The "predetermined reference value" of the measured value of the biomarker protein is based on the measured value of the biomarker protein in the sample of the individual who has developed renal function decline and / or the measured value of the biomarker protein in the sample of the healthy individual Reference value to be determined. Specifically, it is contained in a measured value of at least one protein selected from the group consisting of biomarkers contained in a skin-derived sample collected from a subject, and / or contained in a sample collected from the subject A group consisting of a measured value (also referred to as a test measured value) of at least one mRNA selected from the group consisting of the biomarkers, and a biomarker contained in a skin-derived sample collected from a healthy individual corresponding to the test measured value And / or a measured value of at least one mRNA selected from the group consisting of a biomarker included in a sample collected from the subject (also referred to as a healthy measurement value) The reference value is determined by known methods on the basis of. Here, "corresponding" is intended to be a homogeneous biomarker.

For example, measurement values of biomarker proteins measured using specimens of a plurality of individuals having decreased renal function and measurements of biomarker proteins measured using specimens of a plurality of healthy individuals are obtained. A value that can most accurately determine the presence or absence of renal function deterioration based on the plurality of values can be used as the “reference value”. Here, "the most accurately determined value" is the sensitivity and / or specificity, the positive predictive value, the negative predictive value, the first quartile range of the quartile range, which are determined from the ROC curve depending on the purpose of the test. It can set suitably based on indices, such as the 2nd quartile (median value) and the 3rd quartile.

For example, among the measured values of the biomarker protein in each sample obtained from a plurality of healthy individuals in one aspect, the highest measured value may be used as the reference value.

In another embodiment, when the reference value is determined based on the measured value of the biomarker protein in the sample of an individual who has developed renal function, the biotechniques in the samples of multiple individuals who have developed renal function have been described. Among the measurement values of the marker protein, the lowest measurement value can be determined as the threshold value.

In another embodiment, the reference value may be a measured value of the biomarker protein itself in a sample of a healthy individual, or an average, median or mode of measured values of a plurality of biomarker proteins of a healthy individual. it can.

Furthermore, as a reference value, a measurement value of a past biomarker protein (which may be a single value or an average value of a plurality of values, which is the same subject and obtained when the subject is in a healthy state) Values, modes, etc. may also be used.

The “reference value” of the measured value of the biomarker mRNA is also determined using the measured value of the biomarker mRNA instead of the measured value of the biomarker protein, as in the “reference value” of the measured value of the biomarker protein described above. can do.

Here, the “range of reference value” means that the reference range is equal to or less than the reference value as long as the biomarker protein has a property that expression and activity increase with the decrease in renal function. If the biomarker protein is of a nature that decreases in expression and activity as renal function declines, the reference range is above the reference value. For example, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, and Col5a1 have decreased expression of renal function. The expression of C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8 decreases with the decrease in renal function.

The "healthy individual" is not particularly limited. The term preferably refers to a human or non-human mammal described in the "individual" section, which does not show abnormal data in biochemical tests, blood tests, urine tests, serological tests, physiological tests and the like. The age and sex of healthy individuals are not particularly limited.

The “plural samples” are two or more, preferably five or more, and more preferably ten or more. These may be specimens collected from different individuals, or may be a plurality of specimens of the same individual different in collection time.

"Multiple values" are measurements of biomarker protein of 2 or more, preferably 5 or more, more preferably 10 or more and biomarker mRNA.

The "plural individuals" are 2 or more, preferably 5 or more, more preferably 10 or more individuals.

The individual who obtains the measurement value of the biomarker protein and the measurement value of the biomarker mRNA to determine the reference value is not necessarily the same as the species, age, sex, etc. of the subject, but is preferably the same. Preferably, the individual is of the same age and / or the same gender as the subject.

The "anti-biomarker antibody" is not particularly limited as long as it specifically binds to at least one protein selected from the group consisting of the aforementioned biomarkers, and at least one protein selected from the group consisting of biomarkers or one of them Any of polyclonal antibodies, monoclonal antibodies, and fragments thereof (for example, Fab, F (ab) ₂ etc.) obtained by immunizing animals other than human with an antibody as an antigen can be used. Also, the class and subclass of immunoglobulin are not particularly limited. It may also be a chimeric antibody. Furthermore, it may be scFv or the like.

As a biomarker protein used as an antigen used to produce an anti-biomarker antibody, all or a part of at least one type of protein selected from the group consisting of the above-mentioned biomarkers can be mentioned.

The “biomarker mRNA detection nucleic acid” in the present invention is not limited as long as it contains at least one mRNA selected from the group consisting of the above-mentioned biomarkers, or a sequence specifically hybridizing with the reverse transcription product of the mRNA. The detection nucleic acid may be DNA or RNA, and the nucleotides contained in the detection nucleic acid may be natural nucleotides or artificially synthesized nucleotides.

The length of the detection nucleic acid is not particularly limited. If the detection nucleic acid is used as a capture probe in a microarray or the like, the sequence hybridizing to the target nucleic acid is preferably about 100 mer, more preferably about 60 mer, and still more preferably It is about 30 mer. The capture probe can be produced using a known oligonucleotide synthesizer or the like. The capture probe may contain a sequence that does not hybridize to the target nucleic acid.

If the detection nucleic acid is a primer used in a PCR reaction, the sequence that hybridizes to the target nucleic acid is preferably about 50 mer, more preferably about 30 mer, and still more preferably about 15 to 25 mer It is. The primers can be produced using a known oligonucleotide synthesizer or the like. The primers may contain sequences that do not hybridize to the target nucleic acid. Also, the primer may be labeled with a fluorescent dye or the like.

In addition to primers, RT-PCR can also use quantitative probes that are degraded during PCR reactions, which are used for real-time quantification of PCR products. The quantitative probe is also not limited as long as it hybridizes to the target nucleic acid. The quantitative probe is preferably a nucleic acid of about 5 to 20 mer, which contains a sequence that hybridizes to a target nucleic acid. Furthermore, it is preferable that a fluorescent dye be labeled at one end of the quantitative probe, and a quencher of the fluorescent dye be labeled at the other end of the quantitative probe.

2. Method of Obtaining Each Measured Value A method of obtaining a measured value of at least one type of protein selected from the group consisting of biomarkers in the present invention and at least one type of mRNA measurement value selected from the group consisting of biomarkers It is not limited as long as the measured value can be obtained. For example, it can be obtained according to the method described below.

2-1. Acquisition of measurement value of protein of biomarker The measurement value of at least one protein selected from the group consisting of biomarkers (hereinafter, sometimes abbreviated as "measurement value of biomarker protein" in the present specification) is obtained In this case, in this step, it is possible to use the anti-biomarker antibody described in the above "1. Description of terms" to obtain the measurement value. Moreover, you may use the test reagent containing the anti-biomarker antibody mentioned later.

As a method of acquiring the measured value of a biomarker protein, it can carry out using well-known ELISA method etc.

In this embodiment, an anti-biomarker antibody for capturing an antigen can be immobilized in advance on a solid phase such as a microplate to form a complex of the immobilized anti-biomarker antibody and a biomarker protein in a sample. . Measuring the amount or concentration of the biomarker protein contained in the sample by detecting the complex immobilized on the solid phase or the complex formed on the solid phase by a method known in the art Can.

The method of fixing the anti-biomarker antibody for antigen capture to the solid phase is not particularly limited. It can be carried out directly or indirectly via another substance using known methods. Examples of direct binding include physical adsorption and the like. Preferably, the anti-biomarker antibody can be physically bound to the microplate directly using an immuno plate or the like.

The material of the solid phase is not particularly limited, and examples thereof include polystyrene and polypropylene. The shape of the solid phase is not particularly limited, and examples thereof include microplates, microtubes, test tubes and the like.

In the present method, following the formation of the complex, an operation of washing the solid phase may be included. In the case of washing, PBS containing a surfactant or the like can be used.

In this method, the detection of the complex is carried out using a detection anti-biomarker antibody labeled with a labeling substance, or an unlabeled anti-biomarker antibody and the unlabeled anti-biomarker antibody are bound to each other. Although it can be carried out using an anti-immunoglobulin antibody etc. labeled with a labeling substance that can be used, it is preferable to use a labeled detection anti-biomarker antibody. Furthermore, it is preferable that the epitope in the biomarker protein of the detection anti-biomarker antibody and the epitope in the biomarker protein of the anti-biomarker antibody for antigen capture are different.

The labeling substance used for the detection anti-biomarker antibody or the labeled anti-immunoglobulin antibody is not particularly limited as long as a detectable signal is generated. For example, fluorescent substances, radioactive isotopes, enzymes and the like can be mentioned. As the enzyme, alkaline phosphatase, peroxidase and the like can be mentioned. Examples of fluorescent substances include fluorescein isothiocyanate (FITC), rhodamine, fluorescent dyes such as Alexa Fluor (registered trademark), fluorescent proteins such as GFP, and the like. As radioactive isotopes, ¹²⁵ I, ¹⁴ C, ³² P and the like can be mentioned. Among them, alkaline phosphatase or peroxidase is preferable as a labeling substance.

The anti-biomarker antibody for detection is obtained by labeling the anti-biomarker antibody with the above-mentioned labeling substance by a labeling method known in the art. Moreover, you may label using a commercially available labeling kit etc. Also, the labeled immunoglobulin antibody may use the same method as the labeling of the anti-biomarker antibody, or a commercially available one may be used.

In this embodiment, the measurement value of the biomarker contained in the sample can be obtained by detecting the signal generated by the labeled substance of the labeled anti-biomarker antibody contained in the complex. Here, "detecting the signal" includes qualitatively detecting the presence or absence of the signal, quantifying the signal intensity, and detecting the intensity of the signal semi-quantitatively. Semi-quantitative detection means that the intensity of a signal is indicated stepwise such as "no signal generation", "weak", "medium", "strong" and the like. In this step, it is preferable to detect the intensity of the signal quantitatively or semi-quantitatively.

The method of detecting a signal can use a well-known method. In this method, a measurement method can be appropriately selected according to the type of signal derived from the above-mentioned labeling substance. For example, when the labeling substance is an enzyme, the signal such as light or color generated by reacting a substrate for the enzyme may be measured by using a known device such as a luminometer or a spectrophotometer. it can.

The substrate for the enzyme can be appropriately selected from known substrates depending on the type of the enzyme. For example, when alkaline phosphatase is used as the enzyme, CDP-Star (registered trademark) (4-chloro-3- (methoxyspiro [1,2-dioxetane-3,2 '-(5'-chloro) trixilo] is used as a substrate. Chemiluminescent substrates such as [3. 3. 1. 13, 7] decane] -4-yl) phenyl phosphate (sodium dibasic), 5-bromo-4-chloro-3-indolyl phosphate (BCIP), 5- Chromogenic substrates such as bromo-6-chloro-indolyl phosphate disodium, p-nitrophenyl phosphate and the like can be mentioned. When the labeling substance is peroxidase, tetramethylbenzidine (TMB) etc. can be mentioned.

When the labeling substance is a radioactive isotope, radiation as a signal can be measured using a known device such as a scintillation counter. When the labeling substance is a fluorescent substance, fluorescence as a signal can be measured using a known device such as a fluorescence microplate reader. In addition, an excitation wavelength and a fluorescence wavelength can be suitably determined according to the kind of fluorescent substance used.

The detection result of the signal can be used as a measurement value of a biomarker protein. For example, in the case of quantitatively detecting the intensity of a signal, the measured value of the signal intensity itself or a value calculated from the measured value of the signal intensity can be used as a measured value of the protein of the biomarker.

2-2. Obtaining a Measured Value of a Biomarker Gene In order to obtain a measured value of at least one mRNA selected from the group consisting of a biomarker (hereinafter sometimes abbreviated as "measured value of a biomarker mRNA" in the present specification) In addition, known methods such as microarray method, RNA-seq analysis method, quantitative RT-PCR method can be used. The probes used in the microarray method may be synthesized by using a self-selected probe or a known probe, or a commercially available microarray chip may be used.

Here, in the present method, either total RNA or mRNA extracted from the sample may be used. Samples used for total RNA and mRNA extraction are collected from an individual and immediately subjected to RNA extraction, or collected from an individual and frozen immediately (preferably under an atmosphere of -196 ° C. or less (quenched in liquid nitrogen ) And stored at -80 ° C. or less until RNA extraction.

The method for extracting total RNA and mRNA from the sample is not particularly limited, and known extraction methods can be used.

The quantification by the microarray method can be performed according to a known method, and the expression amount of the biomarker mRNA may be expressed as a relative expression amount relative to the expression amount of a housekeeping gene, and is expressed as a measurement value of signal intensity of a fluorescent dye or the like be able to.

For quantification by RT-PCR, reverse transcription is performed using total RNA or mRNA extracted from the sample as a template, and the obtained cDNA is used as a template for analysis by real-time PCR using specific primers for the biomarker mRNA. It can be done by Also, in this case, the expression level of the biomarker mRNA may be expressed as a relative expression level relative to the expression level of the housekeeping gene, or may be expressed as a measurement value of the intensity of a signal such as a fluorescent dye.

Also, in the RNA-seq analysis method, mRNA extracted from a sample is fragmented, and this is used as a template to synthesize cDNA by reverse transcription reaction and create a library. The nucleotide sequence of a fragment contained in each library is determined by a next-generation sequencer, the information is mapped to a reference gene sequence, and the amount of mRNA expression is represented as RPKM (Reads Per Killobases per Million). RPKM may be expressed as the intensity of a signal such as a heat map.

The detection result of the above signal can be used as the expression level of biomarker mRNA. For example, in the case of quantitatively detecting the intensity of a signal, the measured value of the signal intensity itself or a value calculated from the measured value of the signal intensity can be used as the expression level of the biomarker mRNA.

As a value calculated from the measurement value of the signal intensity, for example, a value obtained by subtracting the measurement value of the signal intensity of the negative control sample from the measurement value of the signal intensity, the signal intensity of the positive control sample And the combination thereof. Examples of negative control samples include samples of healthy persons. The positive control sample includes a sample containing biomarker mRNA at a predetermined expression level.

Here, at least one mRNA selected from the group consisting of a measured value of at least one protein selected from the group consisting of the biomarkers, and / or a biomarker contained in a sample collected from the subject The measurement value of may be obtained as a measurement value reflecting the function of each biomarker.

3. Hardware Configuration The hardware configuration used in the first to third embodiments will be described using FIGS.

FIG. 1 is a schematic view of a system 100 according to first to third embodiments of the present invention, and FIG. 2 is a block diagram showing a hardware configuration of the system 100. The system 100 refers to arithmetic devices having the same hardware configuration and having different functional configurations as described later, and therefore, the system 100 generically refers to the arithmetic devices having these different functional configurations. For this purpose, it is provided with “

calculation devices

1, 2, 3”, the input unit 8, the display unit 9, the measuring device 5a, and the measuring device 5b.

The

arithmetic units

1, 2, and 3 are, for example, general-purpose personal computers, and include a CPU 101 that performs data processing to be described later, a memory 102 used for a data processing work area, and a recording unit 103 that records processing data. A bus 104 for transmitting data between the respective units, and an interface unit 105 (hereinafter referred to as an I / F unit) for inputting and outputting data with an external device are provided. The input unit 8 and the display unit 9 are connected to the

arithmetic devices

1, 2, 3, the input unit 8 is configured by a keyboard or the like, and the display unit 9 is configured by a liquid crystal display or the like. The input unit 8 and the display unit 9 may be integrated and realized as a display device with a touch panel. The

arithmetic units

1, 2, and 3 do not have to be integrated units, and the CPU 101, the memory 102, the recording unit 103, and the like may be separately provided and connected via a network. Moreover, the apparatus which does not require the operator which abbreviate | omitted the input part 8 or the display part 9 may be sufficient.

In addition, the

arithmetic devices

1, 2, 3, the measuring device 5a, and the measuring device 5b do not necessarily need to be disposed at one place, but a system in which devices provided at different places are communicably connected by a network. May be configured.

In the following description, unless otherwise stated, the processing performed by

arithmetic units

1, 2, 3 is actually based on the program stored in recording unit 103 or memory 102, and CPU 101 of

arithmetic units

1, 2, 3 actually Means the process to be performed. The CPU 101 temporarily stores necessary data (intermediate data in the middle of processing, etc.) using the memory 102 as a work area, and records data to be stored for a long time, such as calculation results, in the recording unit 103 as appropriate.

The measuring device 5a is a device for measuring a protein, and includes a sample storage place 51, a reaction unit 52, and a detection unit 53. The sample collected from the subject set in the sample storage area 51 is aliquoted and incubated in a microplate on which the anti-biomarker antibody for antibody capture placed in the reaction unit 52 is solid phased. After removing the unreacted antigen, if necessary, the detection antibody is aliquoted into a microplate and incubated. If necessary, after removing the unreacted antigen, the substrate for detecting the detection antibody is dispensed to the microplate, the microplate is moved to the detection unit 53, and the signal generated by the reaction of the substrate is measured Be done. Moreover, another embodiment of the measuring device 5a is a device for measuring the measurement value of mRNA by microarray analysis, and the reverse transcription reaction set in the sample storage place 51 is divided on the microarray chip set in the reaction unit 52. After injection, hybridization and washing, the sample is transferred to the detection unit 53 to detect a signal.

Furthermore, another embodiment of the measuring device 5a is a device for measuring the measurement value of mRNA by RT-PCR, in which the reverse transcription reaction set in the sample storage place 51 is placed in the microtube set in the reaction unit 52. Dispense, then dispense quantitative PCR reagents into microtubes. While performing the PCR reaction in the reaction unit 52, the detection unit 53 detects the signal in the tube.

The measuring device 5b is a device for measuring mRNA, and includes a sequence analysis unit 54. The sample subjected to the reaction for RNA-Seq is set in the sequence analysis unit 54, and the sequence analysis is performed in the sequence analysis unit 54.

The measuring

devices

5a and 5b are connected to the

computing devices

1, 2 and 3 by wire or wireless. The measuring device 5a performs A / D conversion of the measured value of the protein, and transmits it as digital data to the

computing devices

1, 2, 3. Similarly, the measuring device 5b A / D converts the measured value of mRNA, and transmits it as digital data to the

computing devices

1, 2, 3. As a result, the

computing devices

1, 2, 3 can acquire the measured value of the protein and the measured value of the mRNA as digital data that can be processed. The measured value of the kidney disease marker is transmitted as digital data from, for example, a medical institution (not shown) via the Internet. Thereby, the

computing devices

1, 2, 3 can acquire the measurement value of the kidney disease marker as digital data.

4. Predicting kidney disease
4-1. Overview In the first embodiment, the measurement value obtained by performing the method of “2. Method for obtaining each measurement value” is used to predict renal disease in a subject.

More specifically, the method for predicting renal disease in a subject in this embodiment is a measurement value of at least one protein selected from the group consisting of biomarkers contained in a skin-derived sample collected from the subject And / or an acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of biomarkers contained in a specimen collected from the subject, and the measurement value acquired in the acquisition step And a prediction step of predicting the renal disease on the basis of the above.

Specifically, in this embodiment, the measured value obtained by the above-mentioned “2. Method of obtaining each measured value” is compared with a predetermined reference value corresponding to each biomarker of the measured value, and the measured value is obtained. Is determined to be outside the reference value range, it can be determined that the subject has a renal disease.

In addition, when the measured value of one group of biomarkers is out of the range of the reference value, it may be determined that FGF23 is involved in the renal disease. In addition, when the measured values of the two groups of biomarkers fall within the range of the reference value, it may be determined that the renal disease is related to FGF23. More preferably, when the measured value of one group of biomarkers is outside the range of the reference value and the measured value of two groups of biomarkers is within the range of the reference value, the renal disease involves FGF23. You may decide to

The process may be performed by the CPU 101 described later, or may be performed by an examiner.

Furthermore, the method for predicting renal disease may include a step of performing treatment (eg, diet therapy such as a low phosphorus diet) to improve the renal disease when it is predicted to be a renal disease. .

4-2. Device for Predicting Renal Function The first embodiment includes a device for predicting renal disease in a subject, which is controlled by the CPU 101 executing the following computing means according to a program described later:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: prediction means for predicting the renal disease based on the measurement value acquired by the acquisition means.

In this embodiment, prediction of renal disease can be performed by the system 100 (FIGS. 1 and 2) provided with the arithmetic device 1 as the above-mentioned device.

FIG. 3 is a block diagram for explaining the function of the arithmetic device 1 according to the first embodiment. The arithmetic device 1 includes a measurement value acquisition unit 11, a reference value acquisition unit 12, a measurement value comparison unit 13, and a prediction unit 14. These functional blocks are realized by, for example, installing the program according to the present invention in the recording unit 103 or the memory 102 of the arithmetic device 1 and executing the program by the CPU 101. The acquisition unit and the prediction unit described in the claims correspond to the measurement value acquisition unit 11 and the prediction unit 14 shown in FIG. 3, respectively. The reference value acquisition unit 12 and the measurement value comparison unit 13 may have any configuration.

In this embodiment, the measured value M11 of the biomarker protein is taken into the arithmetic device 1 from the measuring device 5a, and the measured value M21 of the biomarker mRNA is taken into the arithmetic device 1 from the measuring device 5b. The reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are recorded outside the computing device 1, and are taken into the computing device 1 via the Internet, for example.

The measured value M11 of the biomarker protein and the measured value M21 of the biomarker mRNA may be taken from a medical institution (not shown) via a network. Further, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA may be recorded in advance in the recording unit 103 or the memory 102 of the arithmetic device 1.

In addition, each function block of the measurement value acquisition unit 11, the reference value acquisition unit 12, the measurement value comparison unit 13, and the prediction unit 14 does not necessarily need to be executed by a single CPU, and a plurality of CPUs It may be distributed and processed. For example, the functions of the measurement value acquisition unit 11, the reference value acquisition unit 12, and the measurement value comparison unit 13 are performed by the CPU of the first computer, and the function of the prediction unit 14 is performed by the CPU of another second computer It may be configured as such.

In addition, in order to perform the processes of steps S11 to S17 described in FIG. 4 below, the arithmetic device 1 executes the program according to the present invention in, for example, an executable format (for example, converted from a programming language by a compiler). The arithmetic unit 1 performs processing using the program recorded in the recording unit 103. The program may be installed in the arithmetic device 1 from a tangible computer readable non-temporary storage medium 109 such as a CD-ROM, or the arithmetic device 1 may be installed on the Internet (not shown). You may connect and download the program code of the program via the Internet.

For the explanation of terms used in this section, the above-mentioned I. 4-1. Use the description of.

4-3. Operation of Arithmetic Device and Program The kidney disease prediction method in the first embodiment is the same as the calculation device 1 according to the first embodiment in executing the following kidney disease prediction method of the present invention by the following program Good.

FIG. 4 is a flowchart showing an operation of the arithmetic device 1 according to the first aspect of the present invention. 4 by the measured value acquisition unit 11 shown in FIG. 3, step S12 by FIG. 4 by the reference value acquisition unit 12, step S13 by FIG. 4 by the measured value comparison unit 13, and FIG. The processes of steps S15 and S16 shown in FIG.

In step S11, first, an input of start of acquisition of measurement value M11 of biomarker protein or measurement value M21 of biomarker mRNA from the examiner is received from input unit 8, or measurement from

measurement device

5a or 5b is started. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command of Step S11 corresponds to the acquisition process described in the claims.

Next, in step S12, the reference value acquisition unit 12 acquires the measurement value by the measurement value acquisition unit 11 or according to an input of acquisition start of measurement value or input of comparison start from the input unit 8 by the examiner. In accordance with, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are obtained.

Next, in step S13, the measurement value comparison unit 13 measures the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA, and the reference value R12 of the biomarker protein corresponding to the measurement value of each biomarker, or bio The marker mRNA is compared with the reference value R22.

Next, in step S14, when the comparison result in step S13 is within the range of the reference value of each biomarker, the prediction unit 14 predicts that the subject is not a renal disease (step S15). Further, when the comparison result in step S13 is out of the range of the reference value of each biomarker, the prediction unit 14 predicts that the subject is a renal disease (step S16). Steps S14, S15, and S16 correspond to the prediction processing described in the claims.

The obtained prediction result is displayed on the display unit 9 of the arithmetic device 1 (step S17) or recorded in the recording unit 103 in the arithmetic device 1. Alternatively, it may be displayed on a display unit of a computer terminal outside the arithmetic device 1 connected via the Internet, for example, in a medical institution.

The computer program for predicting kidney disease according to the first embodiment includes a program that causes the CPU 101 of the arithmetic unit 1 to execute the steps S11 to S17. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the arithmetic device can read the program.

5. Prediction of phosphorus metabolism disorder
5-1. Overview In the second embodiment, the measurement value obtained by performing the method of “2. Method for obtaining each measurement value” is used to predict abnormal phosphorus metabolism in a subject.

More specifically, the method of predicting phosphorus metabolism abnormality in a subject in this embodiment comprises measuring at least one protein selected from the group consisting of biomarkers contained in a skin-derived sample collected from the subject Acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of a value and / or a biomarker contained in a specimen collected from the subject, and the measurement value acquired in the acquisition step And a prediction step of predicting the phosphorus metabolism abnormality based on

Specifically, the present embodiment compares the measured values obtained by the above-mentioned “2. Method for obtaining each measured value” with a predetermined reference value corresponding to each measured value, and the measured value is the reference. If out of the value range, the subject can be determined to be abnormal in phosphorus metabolism.

In addition, when the measured value of one group of biomarkers is out of the range of the reference value, it may be determined that FGF23 is involved in the phosphorus metabolism abnormality. In addition, when the measured values of the two groups of biomarkers fall within the range of the reference value, it may be determined that the above-mentioned phosphorus metabolism disorder is related to FGF23. More preferably, when the measured value of one group of biomarkers is outside the range of the reference value and the measured value of two groups of biomarkers is within the range of the reference value, FGF23 is involved in the phosphorus metabolism abnormality. You may decide to

Furthermore, in the method for predicting phosphorus metabolism abnormality, when it is predicted that phosphorus metabolism abnormality is caused, a step of performing treatment (eg, diet therapy such as low phosphorus diet) for improving phosphorus metabolism abnormality is included. May be

5-2. Apparatus for predicting phosphorus metabolism abnormality The second embodiment includes an apparatus for predicting phosphorus metabolism abnormality of a subject, which is controlled by the CPU 101 by executing the following arithmetic function by a program described later:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: prediction means for predicting the phosphorus metabolism abnormality based on the measurement value acquired by the acquisition means.

In the present embodiment, it is possible to predict phosphorus metabolism abnormality by the system 100 (FIG. 1 and FIG. 2) provided with the arithmetic device 1 as the above-mentioned device.

The block diagram for demonstrating the function of the calculating | arithmetic apparatus 2 which concerns on a 2nd embodiment is shown in FIG. The configuration of the arithmetic unit 2 is the same as the above 4-2. It is the same as the arithmetic unit 1 described above.

For the explanation of terms used in this section, the above-mentioned I. 5-1. Use the description of.

5-3. Operation of Arithmetic Device and Program The method for predicting phosphorus metabolism abnormality in the second embodiment is such that the arithmetic device 2 according to the second embodiment executes the following method for predicting phosphorus metabolism abnormality of the present invention by the following program May be

FIG. 5 is a flowchart showing the operation of the arithmetic device 2 according to the second aspect of the present invention. The step S21 in FIG. 5 is performed by the measured value acquiring unit 11 shown in FIG. 3, the step S22 in FIG. 5 is performed by the reference value acquiring unit 12, the step S23 in FIG. The processes of steps S25 and S26 shown in FIG.

In step S21, first, an input of start of acquisition of measurement value M11 of biomarker protein or measurement value M21 of biomarker mRNA from the examiner is received from input unit 8, or measurement from

measurement device

5a or 5b is started. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command of Step S21 corresponds to the acquisition process described in the claims.

Next, in step S22, the reference value acquisition unit 12 acquires the measurement value by the measurement value acquisition unit 11 according to an input of acquisition start of measurement value or input of comparison start from the input unit 8 by the examiner. In accordance with, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are obtained.

Next, in step S23, the measurement value comparison unit 13 measures the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA, and the reference value R12 of the biomarker protein corresponding to the measurement value of each biomarker, or bio The marker mRNA is compared with the reference value R22.

Next, in step S24, when the comparison result in step S23 is within the range of the reference value of each biomarker, the prediction unit 14 predicts that the subject is not abnormal in phosphorus metabolism (step S25). Further, when the comparison result in step S23 is outside the range of the reference value of each biomarker, the prediction unit 14 predicts that the subject is abnormal in phosphorus metabolism (step S26). Steps S24, S25, and S26 correspond to the prediction process described in the claims.

The obtained prediction result is displayed on the display unit 9 of the computing device 2 (step S 27) or recorded in the recording unit 103 in the computing device 2. Alternatively, it may be displayed on a display unit of a computer terminal outside the computing device 2 connected via the Internet, for example, in a medical institution.

A computer program for predicting phosphorus metabolism abnormality according to the second embodiment includes a program that causes the CPU 101 of the arithmetic unit 2 to execute the steps S21 to S27. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the arithmetic device can read the program.

6. Prediction of FGF23 activation
6-1. Overview In the third embodiment, the measurement values obtained by performing the method of “2. Acquisition method of each measurement value” are used to predict the activation of FGF23 in a subject.

More specifically, the method of predicting FGF23 activation in a subject according to this embodiment is a method of at least one protein selected from the group consisting of biomarkers contained in a skin-derived sample collected from a subject An acquisition step of acquiring a measurement value of at least one type of mRNA selected from the group consisting of a measurement value and / or a biomarker included in a sample collected from the subject, and the measurement acquired in the acquisition step And V. predicting the activation of the FGF23 based on the value.

Specifically, the present embodiment compares the measured values obtained by the above-mentioned “2. Method for obtaining each measured value” with a predetermined reference value corresponding to each measured value, and the measured value is the reference. If out of the value range, the subject can be determined to have renal disease.

In addition, when the measured value of one group of biomarkers is out of the range of the reference value, it may be determined that FGF23 is activated in the subject. In addition, when measured values of the two groups of biomarkers fall within the range of the reference value, it may be determined that FGF23 is activated in the subject. More preferably, when the measured value of one group of biomarkers is outside the range of the reference value and the measured value of two groups of biomarkers is within the range of the reference value, FGF23 is activated in the subject You may decide to

Furthermore, in the method for predicting FGF23 activation, when it is predicted that FGF23 activation is to be achieved, a treatment for improving FGF23 activation (eg, administration of anti-FGF antibody, high phosphorus diet, etc.) Dietary treatment may be included.

6-2. Device for predicting activation of FGF23 A third embodiment includes a device for predicting activation of FGF23 in a subject, which is controlled by the CPU 101 executing the following arithmetic function by a program described later: It consists of a measured value of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from subjects, and / or biomarkers contained in samples collected from said subjects Acquisition means for acquiring a measurement value of at least one type of mRNA selected from a group, and prediction means for predicting the activation of the FGF23 based on the measurement value acquired by the acquisition means.

In the present embodiment, it is possible to predict the activation of FGF 23 by the system 100 (FIGS. 1 and 2) provided with the arithmetic device 1 as the above-mentioned device.

The block diagram for demonstrating the function of the arithmetic unit 3 which concerns on a 3rd embodiment is shown in FIG. The configuration of the arithmetic unit 3 is the same as the above 4-2. It is the same as the arithmetic unit 1 described above.

For the explanation of terms used in this section, the above-mentioned I. 6-1. Use the description of.

6-3. Operation of Arithmetic Device and Program The method of predicting FGF23 activation in the third embodiment is as follows: The arithmetic device 3 according to the third embodiment uses the following program to predict the following FGF23 activation method of the present invention It may be executed.

FIG. 6 is a flowchart showing the operation of the arithmetic device 3 according to the third aspect of the present invention. 6 by the measured value acquisition unit 11 shown in FIG. 3, step S32 by FIG. 6 by the reference value acquisition unit 12, step S33 by FIG. 6 by the measured value comparison unit 13, and FIG. The processes of steps S35 and S36 shown in 6 are respectively executed.

In step S31, first, an acceptance start input of measurement value M11 of biomarker protein or measurement value M21 of biomarker mRNA from the examiner is received from input unit 8, or measurement from

measurement device

5a or 5b is started. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command of Step S31 corresponds to the acquisition process described in the claims.

Next, in step S <b> 32, the reference value acquisition unit 12 acquires the measurement value by the measurement value acquisition unit 11 or according to an input of acquisition start of measurement value or input of comparison start from the input unit 8 by the examiner. In accordance with, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are obtained.

Next, in step S33, the measurement value comparison unit 13 measures the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA, and the reference value R12 of the biomarker protein corresponding to the measurement value of each biomarker, or the bio The marker mRNA is compared with the reference value R22.

Next, in step S34, when the comparison result in step S33 is within the range of the reference value of each biomarker, the prediction unit 14 predicts that FGF23 is not activated in the subject (step S35). . Further, when the comparison result in step S33 is out of the range of the reference value of each biomarker, the prediction unit 14 predicts that FGF23 is activated in the subject (step S36). Steps S34, S35, and S36 correspond to the prediction process described in the claims.

The obtained prediction result is displayed on the display unit 9 of the arithmetic device 3 (step S37) or recorded in the recording unit 103 in the arithmetic device 3. Alternatively, it may be displayed on a display unit of a computer terminal outside the arithmetic device 3 connected via the Internet, for example, in a medical institution.

The computer program for predicting the activation of FGF23 according to the third embodiment includes a program that causes the CPU 101 of the arithmetic unit 3 to execute the steps S31 to S37. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the arithmetic device can read the program.

II. Fourth and fifth embodiments
1. Description of Terms In the description of the fourth and fifth embodiments, the terms used will be described.

A "subject" is an individual to whom a test substance is administered, preferably a human is excluded. Examples of mammals other than human include cows, horses, sheep, goats, pigs, dogs, cats, rabbits, monkeys and the like. Preferred are humans, cats and dogs. Also, the age and gender of the individual do not matter.

The subject may or may not be an individual having a history of impaired renal function or other renal disease. Also, the subject may or may not be a subject suffering from a disease associated with FGF23.

In this embodiment, the sample is at least one selected from the group consisting of a sample collected from the skin, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin included. Further, the sample collected from the skin is not limited as long as it originates from the skin of a living body. The skin-derived sample also includes sweat, secretions from the skin, and the like. The method of collecting the skin is also not limited, and it is possible to increase the biopsy material, the skin adhering to the piercing needle when the piercing hole is made, the abrasion of the skin surface, and the like.

In the present embodiment, the “test tissue” is a tissue to which a test substance is to be administered, for example, it is collected from an individual who can be the subject, and survives outside the body by being cultured outside the body. It is an organization. The tissue may be an entire organ or a part of an organ.

In the present embodiment, the “test cell” is a cell to which a test substance is to be administered, for example, it is collected from an individual who can be the subject, and survives outside the body by being cultured in vitro. It is a cell. The cell may be a cell having limited passage such as primary culture, or may be a so-called cultured cell whose passage is maintained. Also, these may be cells produced by genetic engineering.

In the present specification, the “test substance” is a substance that can be a subject to be evaluated as to whether it is a candidate substance of the active ingredient and is not particularly limited. For example, compounds, proteins, peptides, nucleic acids, lipids, carbohydrates, glycolipids, glycoproteins, metals and the like can be mentioned. The administration method of the test substance is also not particularly limited. When a test substance is administered to a test cell or a test tissue, the test substance can be administered, for example, at 1 pg / ml to 1 mg / ml in the culture medium. In addition, in the case of a subject individual, the test substance can be administered at 1 ng / kg to 1 g / kg per day. The period from the administration of the test substance to the collection of the sample is not particularly limited as long as the effect of the test substance can be obtained.

Furthermore, a subject that has been treated with a test substance, a test tissue or a sample collected from test cells may be referred to herein as a "test substance-treated sample". In addition, a subject that has not been treated with a test substance, a subject collected from a subject tissue or a subject cell may be referred to herein as a "naive subject".

In this embodiment, “kidney disease”, “fGF23 related disease”, “biomarker”, “measured value of at least one protein selected from the group consisting of biomarkers”, “selected from the group consisting of biomarkers” Value of at least one type of mRNA, “predetermined reference value”, “healthy individual”, “multiple samples”, “multiple values”, “anti-biomarker antibody”, “biomarker mRNA detection nucleic acid” The explanation is given in the above I.I. 1. A description of the terms of is incorporated herein. In addition, methods for obtaining “a measured value of at least one type of protein selected from the group consisting of biomarkers” and “a measured value of at least one type of mRNA selected from the group consisting of biomarkers” are described above in I. 2. It conforms to.

2. Hardware Configuration The hardware configuration used in the fourth and fifth embodiments will be described with reference to FIGS. 7 and 8. FIG.

FIG. 7 is a schematic view of a system 200 according to fourth and fifth embodiments of the present invention, and FIG. 8 is a block diagram showing a hardware configuration of the system 200. The system 200 refers to a screening apparatus (in the following, having the same hardware configuration and referring to screening apparatuses having different functional configurations as will be described later). For this purpose, the "

screening devices

4 and 5" are provided, the input unit 8, the display unit 9, the measuring device 5a, and the measuring device 5b.

The

screening devices

4 and 5 are, for example, general-purpose personal computers, and include a CPU 101 that performs data processing to be described later, a memory 102 used for a data processing work area, a recording unit 103 that records processing data, And an interface unit 105 (hereinafter referred to as an I / F unit) for inputting and outputting data with an external device. The input unit 8 and the display unit 9 are connected to the

screening devices

4 and 5, the input unit 8 is configured by a keyboard or the like, and the display unit 9 is configured by a liquid crystal display or the like. The input unit 8 and the display unit 9 may be integrated and realized as a display device with a touch panel. Note that the

screening devices

4 and 5 do not have to be integrated devices, and the CPU 101, the memory 102, the recording unit 103, and the like may be separately provided and connected via a network. Moreover, the apparatus which does not require the operator which abbreviate | omitted the input part 8 or the display part 9 may be sufficient.

Also, the

screening devices

4 and 5, the measuring device 5a, and the measuring device 5b do not necessarily have to be disposed at one place, and a system in which devices provided at different places are communicably connected via a network is configured. May be.

In the following description, unless otherwise stated, the processing performed by the

screening devices

4 and 5 actually means the processing performed by the CPU 101 of the

screening devices

4 and 5 based on the program stored in the recording unit 103 or the memory 102. Do. The CPU 101 temporarily stores necessary data (intermediate data in the middle of processing, etc.) using the memory 102 as a work area, and records data to be stored for a long time, such as calculation results, in the recording unit 103 as appropriate.

The measuring

devices

5a and 5b are connected to the

screening devices

4 and 5 by wire or wirelessly. The measuring device 5a performs A / D conversion of the measured value of the protein, and transmits it to the

screening device

4, 5 as digital data. Similarly, the measuring device 5b converts the measured value of mRNA into a digital signal, and transmits it to the

screening devices

4 and 5 as digital data. Thereby, the

screening devices

4 and 5 can acquire the measurement value of the protein and the measurement value of the mRNA as digital data that can be processed. The measured value of the kidney disease marker is transmitted as digital data from, for example, a medical institution (not shown) via the Internet. Thereby, the

screening devices

4 and 5 can acquire the measurement value of the kidney disease marker as digital data.

3. Screening of candidate active ingredients for suppressing functional expression of FGF23
3-1. Overview In the fourth embodiment, an active ingredient for suppressing the functional expression of FGF23 using the measurement values obtained by performing the method of “I. 2. Method for obtaining each measurement value” described above is used. Screening of candidate substances.

More specifically, the method of screening for a candidate substance of the active ingredient for suppressing the functional expression of FGF23 in the subject in the present embodiment is the method of collecting the substance from the skin of the subject (except human) treated with the test substance Of the biomarker protein of at least one test substance-treated sample selected from the group consisting of a tested sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin and And / or acquiring the measurement value of mRNA of the protein, and determining the test substance as a candidate substance of the active ingredient based on the measurement value acquired by the first measurement value acquiring means. .

In this embodiment, preferably, a test sample obtained from the skin of a subject (except for human beings) not treated with the test substance, a test derived from the skin according to the above-mentioned "I. Measurement value of corresponding biomarker protein and / or mRNA of said protein in at least one unprocessed sample selected from the group consisting of a sample collected from a tissue and a sample collected from a test cell derived from skin Including the step of obtaining a measurement value. In addition, the measured value of each biomarker obtained from the test substance-treated sample is compared with the corresponding measured value in the unsampled process, and compared with the measured value of the biomarker of the untreated sample to be treated with the test substance If the measured value of the biomarker of the sample has changed, it can be determined that the test substance is an active ingredient that suppresses the functional expression of FGF23.

Here, the functional expression of FGF23 means that the original function of FGF23 is exhibited. An example of the functional expression of FGF23 is, for example, lowering the concentration of inorganic phosphorus in blood. Moreover, functional expression of FGF23 includes an increase in expression of FGF23.

Whether the change in the measured value of the biomarker of the test substance-treated sample is positive or negative depends on the biomarker.

For example, above I. 1. Among the biomarkers mentioned above, at least one selected from the group consisting of Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1 and Lrrc2 (referred to as the third group) Is up-regulated in FGF23 knockout mice as compared to mice receiving a high phosphorus diet. In addition, at least one type (group 4) selected from the group consisting of Col15a1, Sparc, Col11a1, Clec11a, Serpinb6d, and Defb8 has a decreased expression in FGF23 knockout mice as compared to mice receiving a high phosphorus diet. . Furthermore, at least one type (the above-mentioned two groups) selected from the group consisting of Aldhl2, Col5a1, Col3a1, C1qtnf6, and Col1a1 does not change the expression in FGF23 knockout mice.

Therefore, for the three groups of biomarkers, for example, with the mouse receiving a high phosphorus diet as a subject, the measured value of the biomarker of the test substance-treated sample is increased compared to the measured value of the biomarker of the untreated sample. Then, the test substance can be determined to suppress the functional expression of FGF23. Also, for the 4 groups of biomarkers, if the mouse receiving a high phosphorus diet is the subject, the measured value of the biomarker of the test substance-treated sample is lower than the measured value of the biomarker of the untreated sample. The test substance can be determined to suppress the functional expression of FGF23. Furthermore, in addition to the examination of these biomarkers, when the two groups of biomarkers are similarly examined, if there is no difference between the measured values of the biomarkers of the untreated sample and the measured values of the biomarkers of the test substance treated sample, The test substance can be determined to act specifically on the function of FGF23.

The measured value of the biomarker protein in the test substance-treated sample and / or the measured value of the mRNA of the protein is higher than the measured value of the corresponding biomarker protein of the untreated sample and / or the measured value of the mRNA of the protein For example, when the measured value of the test substance-treated sample is, for example, 115% or more, preferably 130% or more, more preferably 150% or more of the measured value of the untreated sample, the test substance Thus, it can be determined that the measured value of the biomarker protein and / or the measured value of the mRNA of said protein is rising.

The measured value of the biomarker protein in the test substance-treated sample and / or the measured value of the mRNA of said protein is lower than the measured value of the corresponding biomarker protein of the untreated sample and / or the measured value of the mRNA of said protein For example, when the measured value of the test substance-treated sample is, for example, 85% or less, preferably 70% or less, more preferably 50% or less of the measured value of the untreated sample, the test substance Thus, it can be determined that the measured value of the biomarker protein and / or the measured value of the mRNA of said protein is decreasing.

Furthermore, in the screening, before the step (I), the test substance is administered in the administration step of (i) administering the test substance to the subject, the test tissue or the test cell, and (ii) the step (i) The method may include the steps of collecting the sample from the subject, the test tissue or the test cells, and (iii) recovering the protein and / or the mRNA from the sample obtained in the step (ii). Also in this case, the step (ii) and the step (iii) do not necessarily need to be performed continuously in the same organization, and for example, the sample collected in the step (ii) is sent to a third party organization iii) The following may be implemented. In addition, step (iii) and step (I) do not necessarily have to be performed continuously in the same organization, for example, the step of sending the protein and / or mRNA recovered in step (iii) to a third party organization (Iii) The following may be implemented.

3-2. Screening Device A fourth embodiment is a candidate substance of an active ingredient for suppressing the functional expression of FGF23 having the following arithmetic means, which is controlled by the CPU 101 executing the following arithmetic function according to a program described later The screening device 4 includes:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Means for acquiring the measurement value of the biomarker protein of at least one test substance-treated sample and / or the measurement value of mRNA of the protein, and the measurement acquired by the first measurement value acquisition means A determining means for determining that the test substance is a candidate substance of the active ingredient based on a value.

In this embodiment, screening can be performed by the system 200 (FIGS. 7 and 8) provided with the screening device 4 as the above-mentioned device.

FIG. 9 is a block diagram for explaining the function of the screening device 4 according to this embodiment. The screening device 4 includes a first measurement value acquisition unit 31, a second measurement value acquisition unit 32, a measurement value comparison unit 33, and a candidate substance determination unit 34. The second measurement value acquisition unit 32 is an arbitrary configuration. These functional blocks are realized by installing the screening program according to the present invention in the recording unit 103 or the memory 102 of the screening device 4 shown in FIG. 8 and the CPU 101 executing this screening program. The first measured value acquiring unit, the second measured value acquiring unit, the measured value comparing unit, and the determining unit according to the claims are the first measured value acquiring unit 31 and the second measured value illustrated in FIG. 9. The acquisition unit 32, the measurement value comparison unit 33, and the candidate substance determination unit 34 correspond to each other.

In this embodiment, the measured value M31 of the biomarker protein of the test substance-treated sample is taken from the measuring device 5a to the screening device 4, and the measured value M41 of the biomarker mRNA of the test substance-treated sample is measured from the measuring device 5b to the screening device Captured in 4. The measured value M32 of the biomarker protein of the untreated sample and the measured value M42 of the biomarker mRNA of the untreated sample are recorded outside the screening device 4, and are taken into the screening device 4 via the Internet, for example.

The measured value M31 of the biomarker protein of the test substance-treated sample and the measured value M41 of the biomarker mRNA of the test substance-treated sample may be taken from a medical institution (not shown) via a network. In addition, the measurement value M32 of the biomarker protein of the untreated sample and the measurement value M42 of the biomarker mRNA of the untreated sample may be recorded in advance in the recording unit 103 or the memory 102 of the cleaning device 4.

In addition, each functional block of the first measurement value acquisition unit 31, the second measurement value acquisition unit 32, the measurement value comparison unit 33, and the candidate substance determination unit 34 may be executed by a single CPU. It is not necessarily necessary, and processing may be distributed and performed by a plurality of CPUs. For example, the functions of the first measurement value acquisition unit 31, the second measurement value acquisition unit 32, and the measurement value comparison unit 33 are executed by the CPU of the first computer, and the function of the candidate substance determination unit 34 is It may be configured to be executed by the CPU of another second computer.

In addition, the screening device 4 executes the program according to the present invention in, for example, an execution form (for example, converted from a programming language by a compiler and generated) in order to perform the processes of steps S41 to S47 described in FIG. The screening device 4 performs the process using the program recorded in the recording unit 103. The above program may be installed in the screening device 4 from a tangible computer readable non-temporary recording medium 109 such as a CD-ROM, or the screening device 4 may be connected to the Internet (not shown). You may connect and download the program code of the program via the Internet.

3-3. Operation of Screening Device and Program In the fourth embodiment, the screening of candidate substances for the active ingredient for suppressing the functional expression of FGF23 is carried out by the screening device 4 according to the fourth embodiment of Screening of candidate active ingredients for suppressing functional expression of FGF23 may be performed.

FIG. 10 is a flowchart showing the operation of the screening device 4 according to the fourth aspect of the present invention. 10 is performed by the first measurement value acquisition unit 11 shown in FIG. 9, step S42 of FIG. 10 is performed by the second measurement value acquisition unit 32, and step S43 of FIG. 10 is performed by the measurement value comparison unit 33. The candidate substance determining unit 34 executes the processing of steps S45 and S46 shown in FIG.

In step S41, first, an input for starting acquisition of the measured value M31 of the biomarker protein of the test substance-treated sample or the measured value M41 of the biomarker mRNA of the test substance-treated sample from the examiner is received from the input unit 8 Alternatively, upon receiving a measurement start instruction from the

measurement device

5a or 5b, the first measurement value acquisition unit 31 starts acquisition of the measurement value. Step S41 corresponds to the acquisition process described in the claims.

Next, in step S42, the second measurement value acquisition unit 32 acquires the measurement value M32 of the biomarker protein of the untreated sample and the measurement value M42 of the biomarker mRNA of the untreated sample.

Next, in step S43, the measurement value comparison unit 33 compares the measurement value M31 of the biomarker protein of the test substance-treated sample with the measurement value M32 of the biomarker protein of the untreated sample respectively corresponding to the test substance treatment The measured value M41 of the biomarker mRNA of the sample is compared with the measured value M42 of the biomarker mRNA of the untreated sample corresponding to each.

Next, in step S44, if the comparison result in step S43 indicates a change, the candidate substance determination unit 34 determines that the test substance is a candidate substance of the active ingredient (step S45). In addition, when the result of the comparison in step S43 shows no change, the candidate substance determination unit 34 determines that the test substance is not a candidate substance of the active ingredient (step S46). Steps S44, S45, and S46 correspond to the prediction process described in the claims.

The obtained determination result is displayed on the display unit 9 of the screening device 4 (step S47) or recorded in the recording unit 103 in the screening device 4. Alternatively, it may be displayed on a display unit of a computer terminal outside the screening device 4 connected via the Internet, for example, at a medical institution.

The computer program for screening a candidate substance of an active ingredient for suppressing the functional expression of FGF23 according to the fourth embodiment includes a program for causing the CPU 101 of the screening device 4 to execute the steps S41 to S47. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the screening device can read the program.

4. Screening of candidate active ingredients for activating FGF23 function
4-1. Overview In a fifth embodiment, an active ingredient for activating the function of FGF23 using the measurement values obtained by performing the method of “I. 2. Method for obtaining each measurement value” described above is used. Screening of candidate substances.

More specifically, the method of screening for a candidate substance of an active ingredient for activating the function of FGF23 in a subject according to this embodiment is a method for collecting a substance from the skin of a subject (excluding human) treated with a test substance Of the biomarker protein of at least one test substance-treated sample selected from the group consisting of a tested sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin and And / or acquiring the measurement value of mRNA of the protein, and determining the test substance as a candidate substance of the active ingredient based on the measurement value acquired by the first measurement value acquiring means. .

In this embodiment, preferably, a test sample obtained from the skin of a subject (except for human beings) not treated with the test substance, a test derived from the skin according to the above-mentioned "I. Measurement value of corresponding biomarker protein and / or mRNA of said protein in at least one unprocessed sample selected from the group consisting of a sample collected from a tissue and a sample collected from a test cell derived from skin Including the step of obtaining a measurement value. In addition, the measured value of each biomarker obtained from the test substance-treated sample is compared with the corresponding measured value in the unsampled process, and compared with the measured value of the biomarker of the untreated sample to be treated with the test substance If the measured value of the biomarker of the sample has changed, it can be determined that the test substance is an active ingredient that activates the function of FGF23.

Here, the activation of the function of FGF23 means that the original function of FGF23 is activated. An example of functional activation of FGF23 is, for example, lowering the concentration of inorganic phosphorus in blood. Moreover, activation of the function of FGF23 includes an increase in the expression of FGF23.

For example, II. For the three groups of biomarkers described in 4-1, for example, if the normal mouse is a subject, the measured value of the biomarker of the test substance-treated sample is reduced compared to the measured value of the biomarker of the untreated sample. The test substance can be determined to activate the function of FGF23. Also, for the 4 groups of biomarkers, if the measured value of the biomarker of the test substance-treated sample is increased compared to the biomarker measured value of the untreated sample in a normal mouse as a subject, the test substance is The function of FGF23 can be determined as activation. Furthermore, in addition to the examination of these biomarkers, when the two groups of biomarkers are similarly examined, if there is no difference between the measured values of the biomarkers of the untreated sample and the measured values of the biomarkers of the test substance treated sample, The test substance can be determined to act specifically on the function of FGF23.

4-2. Screening Device A fifth embodiment is a candidate substance of an active ingredient for activating the function of FGF23, having the following calculation means, which is controlled by the CPU 101 executing the following calculation function by a program described later The screening device 5 of:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Means for acquiring the measurement value of the biomarker protein of at least one test substance-treated sample and / or the measurement value of mRNA of the protein, and the measurement acquired by the first measurement value acquisition means A determining means for determining that the test substance is a candidate substance of the active ingredient based on a value.

In the present embodiment, screening can be performed by the system 200 (FIGS. 7 and 8) provided with the screening device 5 as the above-mentioned device.

The block diagram for demonstrating the function of the screening apparatus 5 which concerns on this embodiment is shown in FIG. The configuration of the screening device 5 is the same as that of the screening device 4 described in the above II.3-2.

4-3. Operation of Screening Device and Program In the fifth embodiment of the present invention, the screening device 5 according to the fifth embodiment uses the following program to screen the candidate substances of the active ingredient for activating the function of FGF23. Screening of candidate active ingredients for activating the function of FGF23 may be performed.

FIG. 11 is a flowchart showing the operation of the screening device 5 according to the fifth embodiment of the present invention. Note that step S51 in FIG. 11 is performed by the first measurement value acquisition unit 31 shown in FIG. 9, step S52 in FIG. 11 is performed by the second measurement value acquisition unit 32, and step S53 in FIG. The candidate substance determining unit 34 executes the processing of steps S55 and S56 shown in FIG.

In step S51, first, an input for starting acquisition of the measured value M31 of the biomarker protein of the test substance-treated sample or the measured value M41 of the biomarker mRNA of the test substance-treated sample from the examiner is received from the input unit 8 Alternatively, upon receiving a measurement start instruction from the

measurement device

5a or 5b, the first measurement value acquisition unit 31 starts acquisition of the measurement value. Step S51 corresponds to the acquisition process described in the claims.

Next, in step S52, the second measurement value acquisition unit 32 acquires the measurement value M32 of the biomarker protein of the untreated sample and the measurement value M42 of the biomarker mRNA of the untreated sample.

Next, in step S53, the measurement value comparison unit 33 compares the measurement value M31 of the biomarker protein of the test substance-treated sample with the measurement value M32 of the biomarker protein of the untreated sample respectively corresponding to the test substance treatment The measured value M41 of the biomarker mRNA of the sample is compared with the measured value M42 of the biomarker mRNA of the untreated sample corresponding to each.

Next, in step S54, when the result of comparison in step S53 is changing, the candidate substance determination unit 34 determines that the test substance is a candidate substance of the active ingredient (step S55). In addition, when the result of the comparison in step S53 is not changed, the candidate substance determination unit 34 determines that the test substance is not a candidate substance of the active ingredient (step S56). Steps S54, S55, and S56 correspond to the prediction process described in the claims.

The obtained determination result is displayed on the display unit 9 of the screening device 4 (step S57) or recorded in the recording unit 103 in the screening device 4. Alternatively, it may be displayed on a display unit of a computer terminal outside the screening device 4 connected via the Internet, for example, at a medical institution.

The computer program of the screening of the candidate substance of the active ingredient for suppressing the functional expression of FGF23 according to the fourth embodiment includes a program that causes the CPU 101 of the screening device 4 to execute the steps S51 to S57. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the screening device can read the program.

III. Sixth embodiment
1. Description of Terms In the description of the sixth embodiment, the terms used will be described.

The explanation of terms used in this embodiment is the same as that described in I. 1. Is incorporated herein by reference. In addition, methods for obtaining “a measured value of at least one type of protein selected from the group consisting of biomarkers” and “a measured value of at least one type of mRNA selected from the group consisting of biomarkers” are described above in I. 2. It conforms to.

In the present embodiment, the "disease" is not limited. For example, the disease may include any disease or disorder that may develop in each organ of the individual. In addition, it includes abnormalities specific to the specific disease (also referred to as “pre-lesion”) that occur before the disease is reached. Preferred diseases include ischemic diseases such as thrombosis, embolism, and stenosis (in particular, heart, brain, lung, large intestine, etc.); circulatory disorders such as aneurysm, varicose, congestion, hemorrhage, etc. (aorta, vein, lung Liver, spleen, retina etc.); allergic diseases such as allergic bronchitis, glomerulonephritis; degenerative diseases such as dementia, Parkinson's disease, amyotrophic lateral sclerosis, myasthenia gravis, etc. Muscles, etc .; Tumors (benign epithelial tumors, benign non-epithelial tumors, malignant epithelial tumors, malignant non-epithelial tumors); metabolic diseases (glycometabolic disorders, dyslipidemia, electrolytic abnormalities); infections (bacterial) , Viruses, rickettsial, chlamydia, fungi etc., protozoa, parasites etc. The diseases also include diseases causing systemic symptoms. Examples of diseases that cause systemic symptoms include autoimmune diseases such as systemic lupus erythematosus and multiple sclerosis; metabolic abnormalities such as hereditary mucopolysaccharidosis and the like.

2. Hardware Configuration The hardware configuration used in the sixth embodiment will be described using FIG. 12 and FIG.

FIG. 12 is a schematic view of a system 300 according to a sixth embodiment of the present invention. The system 300 refers to arithmetic devices having the same hardware configuration and different functional configurations as described later, and therefore, the system 300 collectively refers to the arithmetic devices having these different functional configurations. For this purpose, it is provided with “the arithmetic unit 6”, the input unit 8, the display unit 9, the measuring device 5a, and the measuring device 5b.

The arithmetic device 6 is constituted by, for example, a general-purpose personal computer, and is located between a CPU 101 for performing data processing described later, a memory 102 used for a data processing work area, a recording unit 103 for recording processing data, And an interface unit 105 (hereinafter referred to as an I / F unit) for inputting and outputting data with an external device. The input unit 8 and the display unit 9 are connected to the arithmetic device 6, the input unit 8 is configured by a keyboard or the like, and the display unit 9 is configured by a liquid crystal display or the like. The input unit 8 and the display unit 9 may be integrated and realized as a display device with a touch panel. The arithmetic device 6 does not have to be an integrated device, and the CPU 101, the memory 102, the recording unit 103, and the like may be separately provided and connected via a network. Moreover, the apparatus which does not require the operator which abbreviate | omitted the input part 8 or the display part 9 may be sufficient.

Further, the arithmetic device 6, the measuring device 5a, and the measuring device 5b are not necessarily arranged at one place, and a system in which devices provided at different places are communicably connected by a network may be configured. .

In the following description, unless otherwise stated, the processing performed by the arithmetic device 6 actually means the processing performed by the CPU 101 of the arithmetic device 6 based on the program stored in the recording unit 103 or the memory 102. The CPU 101 temporarily stores necessary data (intermediate data in the middle of processing, etc.) using the memory 102 as a work area, and records data to be stored for a long time, such as calculation results, in the recording unit 103 as appropriate.

The measuring

devices

5a and 5b are connected to the computing device 6 by wire or wirelessly. The measuring device 5a performs A / D conversion of the measured value of the protein, and transmits the converted value as digital data to the computing device 6. Similarly, the measuring device 5b A / D converts the measured value of mRNA, and transmits it to the computing device 6 as digital data. Thereby, the arithmetic unit 6 can acquire the measurement value of the protein and the measurement value of the mRNA as digital data that can be processed. The measured value of the kidney disease marker is transmitted as digital data from, for example, a medical institution (not shown) via the Internet. Thereby, the arithmetic unit 6 can acquire the measurement value of the kidney disease marker as digital data.

3. Method of predicting the involvement of FGF23 in diseases
3-1. Overview In the sixth embodiment, whether FGF23 is involved in a disease that the subject has, using the measurement values obtained by performing the method of “I.2. Acquisition method of each measurement value”. Predict whether or not.

More specifically, a process for obtaining a measured value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measured value of mRNA of the protein, and the measurement obtained in the obtaining process Determining based on the value that FGF23 is involved in the disease.

Specifically, the present embodiment compares the measured values obtained by the above-mentioned “I.2. Acquisition method of each measured value” with a predetermined reference value corresponding to each measured value, and the measured value When it is outside the reference value range, it can be determined that the disease is related to FGF23.

In addition, when the measured value of one group of biomarkers is out of the range of the reference value, it may be determined that the disease is related to FGF23. Furthermore, if the measured values of the two groups of biomarkers fall within the range of the reference value, it may be determined that the disease is related to FGF23. More preferably, when the measured value of one group of biomarkers is out of the range of the reference value and the measured value of the two groups of biomarkers is within the range of the reference value, May be determined to be involved.

For example, II. 3-1. The three groups of biomarkers mentioned are up-regulated in FGF23 knockout mice as compared to mice inoculated with a high phosphorus diet. Also, the four groups of biomarkers have reduced expression in FGF23 knockout mice as compared to mice inoculated with a high phosphorus diet. Furthermore, at least one type (the above-mentioned two groups) selected from the group consisting of Aldhl2, Col5a1, Col3a1, C1qtnf6, and Col1a1 does not change the expression in FGF23 knockout mice.

Therefore, for the 3 groups of biomarkers, for example, if the measured value is increased in the subject having the disease as compared to the reference value of each biomarker, the functional expression of FGF23 is suppressed in the disease. Can be determined. In addition, for the 4 groups of biomarkers, if the measured value is lower in the subject having the disease as compared to the reference value of each biomarker, the functional expression of FGF23 is suppressed in the disease. It can be decided. Furthermore, in addition to the examination of these biomarkers, when the two groups of biomarkers are examined in the same manner, if the measured value in the subject does not change compared to the reference value, the function of FGF23 in the disease is It can be determined that it is acting specifically.

Furthermore, in a method of predicting whether or not FGF23 is involved in the disease, when it is predicted that FGF23 is involved in the disease, a treatment for improving the functional expression of FGF23 (for example, a low phosphorus diet) Etc.) may be included.

3-2. Device for predicting whether FGF23 is involved in a disease The sixth embodiment relates to the involvement of FGF23 in a disease in which the following means are controlled by the CPU 101 executing the following arithmetic function by a program described later Including a predictor to predict:
First measurement value acquiring means for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measurement value of mRNA of the protein, and the first measurement value acquisition means A determination means for determining that FGF23 is involved in the disease based on the measurement value obtained by

In this embodiment, the involvement of FGF23 in a disease can be predicted by the system 300 (FIGS. 12 and 13) provided with the arithmetic device 6 as the above-mentioned device.

FIG. 14 is a block diagram for explaining the function of the arithmetic device 6 according to the sixth embodiment. The configuration of the arithmetic unit 6 is the same as that of the above-described I. 4-2. It is the same as the arithmetic unit 1 described above.

3-3. Operation of Arithmetic Device and Program The method for predicting the involvement of FGF23 in a disease according to the sixth embodiment is that the arithmetic device 6 according to the sixth embodiment relates FGF23 to the following disease of the present invention by the following program. The prediction method may be performed.

FIG. 15 is a flowchart showing the operation of the arithmetic device 6 according to the sixth aspect of the present invention. The step S61 in FIG. 15 is performed by the measured value acquiring unit 11 shown in FIG. 14, the step S62 in FIG. 15 is performed by the reference value acquiring unit 12, the step S63 in FIG. The processes of steps S65 and S66 shown in 15 are respectively executed.

In step S61, first, an acceptance start input of measurement value M11 of biomarker protein or measurement value M21 of biomarker mRNA from the examiner is received from input unit 8, or measurement from

measurement device

5a or 5b is started. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command of Step S61 corresponds to the acquisition process described in the claims.

Next, in step S62, the reference value acquisition unit 12 acquires the measurement value by the measurement value acquisition unit 11 according to an input of acquisition start of measurement value or input of comparison start from the input unit 8 by the examiner. In accordance with, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are obtained.

Next, in step S63, the measurement value comparison unit 13 measures the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA, and the reference value R12 of the biomarker protein corresponding to the measurement value of each biomarker, or bio The marker mRNA is compared with the reference value R22.

Next, in step S64, when the comparison result in step S63 is within the range of the reference value of each biomarker, the prediction unit 14 predicts that FGF23 is not involved in the disease (step S65). . Further, when the comparison result in step S63 is out of the range of the reference value of each biomarker, the prediction unit 14 predicts that FGF23 is involved in the disease (step S66). Steps S64, S65, and S66 correspond to the prediction process described in the claims.

The obtained prediction result is displayed on the display unit 9 of the arithmetic device 6 (step S67) or recorded in the recording unit 103 in the arithmetic device 6. Alternatively, it may be displayed on a display unit of a computer terminal, for example, in a medical institution, which is connected via the Internet and which is external to the arithmetic device 6.

A computer program according to the sixth embodiment for predicting whether or not FGF23 is involved in a disease includes a program that causes the CPU 101 of the arithmetic unit 6 to execute the steps S61 to S67. The computer program may be stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage format of the program on the recording medium is not limited as long as the arithmetic device can read the program.

In addition, the explanation of the term used in this section is the above-mentioned III. 3-1. Use the description of.

IV. Test Reagent and Test Kit An example of the seventh embodiment of the present invention relates to a test reagent comprising the anti-biomarker antibody used in the above-mentioned "I.2.

As the anti-biomarker antibody, those described above in "I.1. Explanation of terms" can be used.

The test reagent of this embodiment may contain at least one or more anti-biomarker antibodies. When the anti-biomarker antibody is a polyclonal antibody, it may be a polyclonal antibody obtained by immunizing with one type of antigen, or may be obtained by immunizing the same individual with two or more types of antigens in parallel. It may be a polyclonal antibody. Furthermore, each polyclonal antibody obtained by inoculating different animals with two or more kinds of antigens may be mixed. When the anti-biomarker antibody is a monoclonal antibody, it may be a monoclonal antibody produced from one type of hybridoma, but is a monoclonal antibody produced from two or more types of hybridomas, and each monoclonal antibody is Two or more types of multiple monoclonal antibodies that recognize the same or different epitopes may be included. In addition, one or more kinds of polyclonal antibodies and monoclonal antibodies may be mixed and contained.

The form of the anti-biomarker antibody contained in the test reagent is not particularly limited, and may be in a dry state or liquid state such as an antiserum containing anti-biomarker antibody or ascites fluid. In addition, the form of the anti-biomarker antibody may be a dried state or an aqueous solution of a purified anti-biomarker antibody, an immunoglobulin fraction containing the anti-biomarker antibody, or an IgG fraction containing the anti-biomarker antibody.

When the form is a dry state or liquid state of antiserum or ascites fluid containing an anti-biomarker antibody, a stabilizer such as β-mercaptoethanol and DTT; a protective agent such as albumin; polyoxyethylene (20) sorbitan It may contain at least one of surfactants such as monolaurate, polyoxyethylene (10) octyl phenyl ether, and preservatives such as sodium azide. Furthermore, when the form of the anti-biomarker antibody is a purified anti-biomarker antibody, an immunoglobulin fraction containing the anti-biomarker antibody, or a dried state or an aqueous solution of an IgG fraction containing the anti-biomarker antibody, phosphate buffer Buffer component such as liquid; Stabilizing agent such as β-mercaptoethanol and DTT; Protective agent such as albumin; Salt such as sodium chloride; polyoxyethylene (20) sorbitan monolaurate, polyoxyethylene (10) octyl phenyl ether And the like, and may contain at least one preservative such as sodium azide.

In the present invention, the anti-biomarker antibody may be unlabeled or labeled with the above-mentioned labeling substance, but is preferably labeled with the above-mentioned labeling substance. As the labeling substance, those exemplified in the above-mentioned section “I.2. Measurement value acquisition method” can be used. In the present invention, the anti-biomarker antibody for antigen capture may be provided in a state of being immobilized on a solid phase surface or the like. The solid phase and the immobilization are as described above in the section “I.2. Method of obtaining measured value”. Preferably, the solid phase is a microplate.

Furthermore, the test reagent may be provided as a kit.

When the antigen-capture anti-biomarker antibody is previously bound to a solid phase such as a microplate, the test kit of this embodiment comprises a solid phase on which the antigen-capture anti-biomarker antibody is immobilized, and a detection anti-bio And a marker antibody. Furthermore, when the labeling substance is an enzyme, it may contain the substrate solution.

The aforementioned test kit is, for example, a kit as shown in FIG. The test kit 9 includes an external box 94, a microplate 92 on which an antibody for capturing an antigen is immobilized, a first container 91a containing an anti-biomarker antibody for detection labeled with a labeling substance, and a substrate solution that reacts with an enzyme. And the package insert 93 of the test kit. In the attached document 93, the handling method of the test kit, storage conditions and the like can be described. A container or the like containing an aqueous medium for washing may be packaged in the packaging box 94.

Another example of the seventh embodiment relates to a test reagent containing the biomarker mRNA detection nucleic acid used in the above-mentioned "I.2. Method for obtaining measurement value".

As the biomarker mRNA detection nucleic acid, those described above in “I.1. Explanation of terms” can be used.

The test reagent containing the biomarker mRNA detection nucleic acid used for the microarray may be in a lyophilized state or in a solution containing a buffer such as Tris-HCl, EDTA, salt and the like. When there are a plurality of target biomarker mRNAs, it is preferable to place each detection nucleic acid in a separate container. Alternatively, the biomarker mRNA detection nucleic acid may be immobilized on a substrate and provided as a microarray chip. The substrate of the microarray is not particularly limited as long as it can immobilize the detection nucleic acid, and examples thereof include glass, polymers such as polypropylene, and nylon membranes. The method for immobilizing the detection nucleic acid on the substrate can also be performed according to a known method, and for example, a spacer or crosslinker containing a reactive group for immobilizing the detection nucleic acid can be used.

Furthermore, a test reagent containing a biomarker mRNA detection nucleic acid is provided as a kit together with the reagent, a medium such as paper, compact disc or the like on which information of the nucleic acid or information for accessing such information is recorded. It is also good.

The test reagent containing a biomarker mRNA detection nucleic acid used for RT-PCR may be in a lyophilised state or in a solution containing a buffer such as Tris-HCl, EDTA, salts and the like. Also, the primers may be provided in separate containers provided with the forward primer and the reverse primer, or may be provided in a mixed state. Furthermore, even when the quantitative probe is included, the quantitative probe may be provided in a separate container with each primer, and each primer and the quantitative probe may be provided in a mixed state.

Furthermore, a test reagent containing a biomarker mRNA detection nucleic acid may be provided in the form of a kit, which comprises a forward primer and a reverse primer, or a forward primer, a reverse primer and a quantitative probe, and optionally a package insert. Furthermore, the kit may include reagents for quantitative PCR.

The test reagent containing the biomarker mRNA detection nucleic acid may be provided as a kit.

The test reagent and the test kit according to the seventh embodiment can be used to obtain each measurement value in the first to sixth embodiments.

V. Biomarker The eighth embodiment of the present invention is Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l, Elc1 The present invention relates to a method for use as a biomarker of skin for predicting involvement of renal function, abnormal phosphorus metabolism, or FGF23, comprising at least one selected from the group consisting of Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8.

These biomarkers are included in each sample.

EXAMPLES The following will explain the summary of the present invention in more detail by way of examples, but the present invention is not construed as being limited to the examples.

Experimental Example I. Preparation of Fgf23 gene mutant mice
I-1. Construction of gRNA and Cas9 Expression Vectors Using the Optimized CRISPR design tool [published in HP of Massachusetts Institute of Technology, Zhang Lab (http://crispr.mit.edu/)], gRNA sequences (Fgf23g-RNA1, Fgf23-gRNA2 Was designed to synthesize an oligo DNA encoding a gRNA sequence. The target sequences of the Fgf23 gene contained in these sequences are the sequences in which Fgf23-gRNA1 is indicated by GCACGCCCACCAGGA GTCTA (SEQ ID NO: 1) and Fgf23-gRNA2 is indicated by GCCCACCAGGAGTCTAAGGC (SEQ ID No. 2), and the sequence present in exon 1 of the Fgf23 gene is there. These were inserted into the Cas9 expression vector pX330-U6-Chimeric_BB-CBh-hSpCas9 (pX330-Fgf23-gRNA1, pX330-Fgf23-gRNA2). The base sequence of the gRNA insertion site was determined for the obtained vector, and it was confirmed that the gRNA was inserted as designed. In addition, single-stranded oligodeoxynucleotides (ssODNs) were synthesized so as to sandwich each Fgf23 gene target sequence. The donor oligo DNA was designed to have a stop codon immediately below the Leucine coding sequence at the planned cleavage site for Fgf23-gRNA1 and Fgf23-gRNA2 (FIG. 17A). ssODNs for Fgf23-gRNA1, 2:
CGGATAGGCTCTAGCAGTGCCCAAGCTGCAGACAGTGCAAGAGCAGCGCCCAGGAGTCTcctcagaagaactcgtcaagaagCAGGTCCAGTCATGGCACAGCACTGAGTGGCTAATGCTGAGTTTGAATCTGACAA (SEQ ID NO: 3)

I-2. Preparation of Fgf23 Gene Mutant Mice pX330-Fgf23-gRNA1 and pX330-Fgf23-gRNA2 were injected into C57BL / 6N Slc fertilized eggs together with the corresponding ssODNs. The injected fertilized eggs were injected into the fallopian tube of a pseudopregnant ICR female mouse. Genotype was confirmed by PCR and direct sequencing (see Table 5 for primers) of F0 mice. F1 heterozygous mice were generated by mating F0 mice with wild-type mice. F2 heterozygous mice were produced by in vitro fertilization of the obtained F1 heterozygous male mice and wild type mouse females, and F3 mice were produced by in vitro fertilization of the obtained F2 heterozygous female mice and F1 heterozygous male mice. . In addition, F4 mice were prepared by in vitro fertilization of F3 homozygous female mice and F3 heterozygous female mice and F1 heterozygous male mice. For the experiments, homozygotes from the F3 generation onward were used.

Genotype of each mouse was determined by direct sequence.

The sequence of the resulting mutant mouse genotype is shown in FIG. 17B.

Experimental Example II. Production of disease model mice
II-1. Preparation of UNx / HPi Model Mouse and Extraction of Tissue UNx / HPi mouse (one nephrectomy-high phosphorus diet mouse) was prepared by rearing on a high phosphorus diet after single nephrectomy. As a control, mice were raised on a low phosphorus diet after a sham operation.

1-1. After anesthetizing a single nephrectomized mouse (C57BL / 6J, 8-week-old male) with Avertin (250 mg / kg) intraperitoneally, the skin was incised from the back and the right renal arteriovenous vein and ureteral were ligated. It was cut distal to the ligature, and after removing the right kidney, it was closed. For controls, sham surgery was performed. In the sham operation, after exposing the right renal arteriovenous and ureteral tubes, they were closed without ligation. The animals were bred for 4 weeks on a normal diet containing 0.54% inorganic phosphorus (CE-2, CLEA Japan, Inc.) in the sense of waiting for complete recovery from surgical invasion.

1-2. Four weeks after the end of the phosphorus load and tissue removal surgery (12 weeks of age), mice that had one-sided nephrectomy were given a 2% inorganic phosphorus-containing high phosphorus diet (TD. 10662, Oriental Bioservice) (following, renal disease) Also called a group). Sham-operated mice were given a low phosphorus diet (TD. 10662 variant, Oriental Bioservice) containing 0.35% inorganic phosphorus (hereinafter also referred to as Sham group).

This chronic kidney disease model is a modification of the method described in Hu MC et al. (J Am Soc Nephrol 22, 124-136, 2011). Hu MC et al. Added ischemia reperfusion injury to the remaining kidney (left kidney) at the time of half nephrectomy described in (1) above, but in this modification, ischemia reperfusion was not performed.
Tissues were removed after 1 week (E), 4 weeks (M) and 8 weeks (L) after initiation of the high phosphorus diet (low phosphorus diet in the Sham group).

The animals from which the tissue is removed are bled from the orbital to the EDTA-added tube under anesthesia by intraperitoneal administration of Avertin (250 mg / kg), and cervical dislocation is euthanized, and organs and tissues (bone marrow, brain, The skin, heart, kidney, liver, lung, pancreas, skeletal muscle, spleen, testis, thymus, fat, large intestine, stomach, adrenal gland, aorta, eye, ileum, jejunum, pituitary, skull, salivary gland and thyroid) were removed. The excised organs and tissues were measured for wet weight and immediately frozen with liquid nitrogen and stored at -80 ° C. The collected blood was centrifuged at 1200 g for 10 minutes at room temperature. The supernatant plasma was collected after centrifugation and stored at -80.degree.

II-2. Preparation of phosphorus-loaded mouse model and excision of tissue Fgf23 gene mutant mice develop hyperphosphate from postnatal age due to loss of Fgf23 function and die early. In this experiment, a 4-week old Fgf23 mutant mouse (male and female) is used for experiments, and a phosphorus-loaded mouse model is used as a target group, and a WT mouse (C57BL / 6N, 3-week-old male) has a high phosphorus diet for 1 week. I gave it and made it.

2-1. Phosphorus-loaded WT mice (C57BL / 6N, 2 weeks old males) are bred with lactating female mice for 1 week and then weaned to obtain a special phosphorus-containing diet and a 2% inorganic phosphorus-rich high phosphorus diet (TD. 10662, Oriental Bioservice) or A 0.35% inorganic phosphorus-containing low phosphorus diet (TD. 10662 variant, Oriental Bioservice) or a 0.54% inorganic phosphorus-containing normal diet (CE-2, CLEA Japan, Inc.) was given for one week. Each of the above models is hereinafter referred to as WT3W / HP1W, WT3W / LP1W, WT3W / ND1W

2-2. Tissue Extraction The skull and skin were excised one week (4 weeks old) after the start of the special phosphorus-containing diet in WT mice. Fgf23 gene mutant mice (male and female) were 4 weeks old and the skull and skin were removed. The animals from which the tissue was removed were anesthetized with intraperitoneal injection of Avertin (250 mg / kg), blood was collected from the orbital side into a tube added with EDTA, and the skull and skin were removed after euthanasia by cervical dislocation. The excised tissue was weighed and immediately frozen with liquid nitrogen and then stored at -80 ° C. The collected blood was centrifuged at 1200 g for 10 minutes at room temperature. The supernatant plasma was collected after centrifugation and stored at -80.degree.

Experiment III: Measurement of inorganic phosphorus in plasma E- / M- / L-UNx / HPi, E- / M- / L-Sham, 4-week-old Fgf23 mutant mouse, WT3W / HP1W, WT3W / LP1W, WT3W In each mouse of / ND1W, 100 μl each of the cryopreserved plasma sample was used to measure the inorganic phosphorus level in plasma by the enzyme method.

Experimental Example IV. Gene expression analysis in each tissue
IV-1. Extraction of RNA from each tissue Cryopreserved tissue is homogenized in TRIzol Reagent (Thermo Fisher Scientific, MA, USA) for 10 seconds at 15,000 rpm with PT10-35 GT Polytron homogenizer (KINEMATICA, Luzern, Switzerland) After grinding and drying in liquid nitrogen using a mortar and pestle, homogenize for 10 seconds at 15,000 rpm with PT10-35 GT Polytron homogenizer in TRIzol Reagent, or use Cell Destroyer PS1000 or PS2000 (Bio Medical Science Inc., Homogenize at 4 ° C. for 45 seconds at 4,260 rpm using zirconia beads (1.5 mm diameter × 50, 3 mm diameter × 5, 5 mm diameter × 2) of different sizes in Tokyo, Japan). After incubation for 5 minutes at room temperature to separate proteins, 0.2 ml of chloroform was added to 1 ml of TRIzol, and the tube was capped and vortexed vigorously for 15 seconds. After stirring, the mixture was incubated at room temperature for 3 minutes, centrifuged at 12,000 g for 15 minutes at 4 ° C., and the aqueous phase containing RNA was collected in a new tube. An equal volume of 70% ethanol was added to the collected aqueous phase, and after stirring, 700 μl each was applied to an RNeasy mini column (Qiagen), and purified RNA was recovered according to the RNeasy mini kit (Qiagen) standard protocol. The recovered RNA was subjected to quality and concentration confirmation with Nanodrop (Thermo Fisher Scientific, MA, USA).

IV-2. RNA expression analysis (RNASeq)
(1) Acquisition of RNAseq Data Using the above sample, data of RNAseq were acquired according to the following procedure.
a. Quality inspection The quality inspection of the receiving sample was performed in the following items.
-Measurement of concentration using Nanodrop (spectrophotometer)-Measurement of concentration using Agilent 2100 Bioanalyzer-Confirmation of quality
b. Sample Preparation A library for the next generation sequencer HiSeq was prepared according to the following steps using SureSelect Strand-Specific RNA Library Preparation Kit.
i. Recover poly (A) + RNA (mRNA) from Total RNA using oligo (dT) magnetic beads
ii. Fragmentation of RNA
iii. cDNA synthesis
iv. Double-stranded cDNA synthesis
v. End repair, phosphorylation, A tail addition
vi. Indexed Adapter Ligation
vii. 13 cycle PCR
viii. Purification by magnetic beads
c. Data acquisition by next-generation sequencer Using the next-generation sequencer HiSeq 2500 or 4000 (illumina), sequencing was performed according to the following steps.
i. Addition of sequencing reagent Reagent: TruSeq PE Cluster Kit v3-cBot-HS (1 flowcell) <PE-401-3001> (illumina)
Reagents: TruSeq SBS Kit v3-HS (200 cycle) <FC-401-3001> (illumina)
ii. Single base extension reaction
iii. Removal of unreacted base
iv. Incorporation of fluorescent signal
v. Removal of protecting group and fluorescence 2 Cycle ... 3 Cycle ... Repeat the cycle up to 100 cycles.
vi. Perform i to vi up to 100 cycles for reverse chain (Read 2)

(2) Analysis of RNAseq data
(2) -1. Analysis of Next-Generation Sequencer Output Data The following information processing was performed on the output data.
i. Base call: Text data of the base sequence was acquired from the output analysis raw data (image data).
ii. Filtering: Screening of lead data by predetermined filtering was performed. iii. Sorting by Index arrangement: Each sample data was sorted by Index information.

(2) -2. Secondary Analysis of Output Data The data file (Fastq format) obtained by Illumina Hiseq 2500 or 4000 was uploaded onto Galaxy (https://usegalaxy.org/) downloaded to the local server. Then, in order to map each sequence to mouse genome map information mm10, analysis was performed using Bowtie 2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml). The gene for FPKM (RPKM) was calculated by analyzing the BAM file obtained by Bowtie 2 with Cufflinks (http://cole-trapnell-lab.github.io/cufflinks/). That is, in this analysis, all RNAs expressed in each tissue were analyzed.

IV-3. qRT-PCR
CDNA was synthesized according to the standard protocol of Superscrtipt III First-Strand Synthesis Supermix (Thermo Fisher Scientific, MA, USA) using Oligo dT20 primers using 0.5-1 μg of total RNA obtained from skull and skin as a template for cDNA synthesis. The synthesized cDNA is diluted 20-fold with TE buffer (10 mM Tris-HCl pH 8.0, 0.1 mM EDTA), and then LightCycler 480 II (Roche) according to the standard protocol of LightCycler 480 SYBR Green I Master (Roche, Basel, Switzerland) Real-time PCR was performed to measure Cp value. The Cp value obtained for each gene was compared with the Cp value of Maea as a reference gene to quantify the relative expression level of each gene relative to the reference gene. The primer pairs used in real time PCR are as shown in Table 5. All primers were designed by Primer-BLAST (NCBI).

IV-4. Analysis of differentially expressed genes To extract differentially expressed genes, HTSeq-count (parameters -r is pos and -s is no) on the sequence data mapped by Bowtie 2 for each transcript The annotation lead number of was counted. The obtained result is performed with default settings by DESeq 2 (Love, MI, Huber, W. & Anders, S .; Genome biology 15, 550, doi: 10.1186 / s13059-014-0550-8 (2014)) analysis. The E-UNx / HPi vs. E- / M- / L-Sham (n = 3), M-UNx / HPi vs. E- / M- / L-Sham (n = 3), L- The comparison was made with UNx / HPi vs. E- / M- / L-Sham (n = 3).

IV-5. Statistical Analysis Statistical analysis was performed by Student's t-test to obtain a significant difference. And when p value was smaller than 0.05, it defined as significant difference.

IV-6. Results List of genes with differential expression in skin compared to E-UNx / HPi vs. E- / M- / L-Sham (n = 3) (| log ₂ (fold-change) |> 1, p-value <0.05) was obtained by DESeq2 analysis. The expression of these genes was verified by qRT-PCR in the skin of UNx / HPi and WT3W / HP1W, WT3W / LP1W, and WT3W / ND1W mouse models (n = 6-9), as shown in FIGS. And it was shown that there is a difference in expression in 25 genes shown in Table 6. The differential expression was compared to WT3W / LP1W for WT3W / HP1W.

The expression of these 25 genes in the skin of 4-week-old Fgf23 gene mutant mice (male and female) was verified by qRT-PCR (n = 7) in order to evaluate the relationship with functional expression of the Fgf23 gene. In addition, it was confirmed by qRT-PCR that expression of Fgf23 is increased in bones (skulls) of E- / M- / L-UNx / HPi and WT3W / HP1W (FIGS. 19A and B), In addition, as shown in FIG. 20, in the Fgf23 gene mutant mice, the blood inorganic phosphorus concentration is increased, and therefore the control of the Fgf23 gene mutant mice is a high phosphorus load mouse (WT 3 W / w) at the same age (4 weeks old). HP1W, n = 3).

In comparison of Fgf23 gene mutant mice and WT3W / HP1W, statistically significant increase and decrease in expression were observed in several genes as shown in FIGS. 21-1 to 21-7 and Table 6. . In Fgf23 mutant mice, differences in expression due to sex differences were also observed. From the above, it was shown that the genes whose expression is fluctuated in the high phosphorus state skin can be classified into four types of models depending on or independent of Fgf23 gene functional expression shown in FIG.

Specifically, the four types are described as follows.
Genes whose expression is controlled by Model I mechanism:
Expression is elevated in the skin due to renal disease and / or hyperphosphatemia. There are two pathways in which the expression of these genes is suppressed by renal disease and / or hyperphosphorus condition due to the elevated expression of FGF23 in bone and the pathway not affected by the elevated expression of FGF23.

Genes whose expression is regulated by the Model II mechanism:
Renal disease and / or high phosphorus status suppresses expression in the skin. There are two pathways in which the suppression of the expression of these genes is suppressed by renal disease and / or hyperphosphorylation, which is suppressed by the increase in the expression of FGF23 in bone, and the two pathways which are not affected by the expression of FGF23.

Genes whose expression is controlled by the mechanism of Model III:
Renal disease and / or hyperphosphatemia suppresses expression in the skin. The expression of these genes is suppressed by the upregulation of FGF23 in bone due to renal disease and / or hyperphosphatemia.

Genes whose expression is controlled by the mechanism of Model IV:
Renal disease and / or hyperphosphatemia suppresses expression in the skin. The expression of these genes is not affected by the elevated expression of FGF23 in bone due to renal disease and / or hyperphosphate status.

1 Arithmetic unit 1
2 Arithmetic unit 2
3 Arithmetic unit 3
4 Screening device 4
5 Screening device 5
6 Arithmetic device 6
11 measurement value acquisition unit 12 reference value acquisition unit 13 measurement value comparison unit 14 prediction unit 31 first measurement value acquisition unit 32 second measurement value acquisition unit 33 measurement value comparison unit 34 candidate substance determination unit

Claims

Apparatus for predicting renal disease in a subject having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: prediction means for predicting the renal disease based on the measurement value acquired by the acquisition means.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to claim 1, wherein the device is at least one selected from the group consisting of: Col1a1 and Defb8.
The prediction means compares the measured value with a predetermined reference value, and determines that the subject has a renal disease if the measured value is out of the range of the reference value. The device described in 2.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value The device according to claim 3, wherein, if it is, it is determined that FGF23 is involved in the renal disease.
A program that, when executed on a computer, causes the computer to perform the following processing to predict renal disease in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition processing for acquiring a measurement value of at least one type of mRNA selected from the group consisting of and prediction processing for predicting the renal disease based on the measurement value acquired in the acquisition processing.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to claim 5, which is at least one selected from the group consisting of, Col1a1 and Defb8.
The prediction process compares the measured value with a predetermined reference value, and determines that the subject has a renal disease if the measured value is out of the range of the reference value. The program described in 6.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value The program according to claim 7, wherein, if it is, it is determined that FGF23 is involved in the renal disease.
A method of predicting renal disease in a subject comprising the following steps:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining the measurement value of at least one type of mRNA selected from the group consisting of: and predicting the kidney disease based on the measurement value obtained in the acquisition step.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The method according to claim 9, which is at least one selected from the group consisting of: Col1a1 and Defb8.
10. The method according to claim 9, wherein the predicting step compares the measured value with a predetermined reference value, and determines that the subject has a renal disease if the measured value is out of the range of the reference value. The method according to 10.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value The method according to claim 11, wherein if it is determined that FGF23 is involved in the renal disease.
Apparatus for predicting abnormal phosphorus metabolism in a subject, having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: prediction means for predicting the phosphorus metabolism abnormality based on the measurement value acquired by the acquisition means.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The device according to claim 13, wherein the device is at least one selected from the group consisting of: Col1a1, and Defb8.
The prediction means compares the measured value with a predetermined reference value, and when the measured value is out of the range of the reference value, predicts that the subject has a phosphorus metabolism abnormality. Or the apparatus as described in 14.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value The apparatus according to claim 15, wherein, if it is, it is determined that the phosphorus metabolism disorder is related to FGF23.
A program that, when run on a computer, causes the computer to perform the following processing for predicting phosphorus metabolism abnormality in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition processing for acquiring a measurement value of at least one type of mRNA selected from the group consisting of and prediction processing for predicting the phosphorus metabolism abnormality based on the measurement value acquired in the acquisition processing.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to claim 17, which is at least one selected from the group consisting of, Col1a1 and Defb8.
The prediction processing compares the measured value with a predetermined reference value, and when the measured value is out of the range of the reference value, predicts that the subject has a phosphorus metabolism abnormality. Or the program described in 18.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value 20. The program according to claim 19, wherein, if it is, it is determined that FGF23 is involved in the phosphorus metabolism disorder.
A method of predicting abnormal phosphorus metabolism in a subject comprising the following steps:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining the measured value of at least one type of mRNA selected from the group consisting of: and predicting the phosphorus metabolism abnormality based on the measured value obtained in the obtaining step.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 22. The method according to claim 21, which is at least one selected from the group consisting of, Col1a1 and Defb8.
22. The method according to claim 21, wherein the prediction step compares the measured value with a predetermined reference value, and predicts that the subject has a phosphorus metabolism abnormality if the measured value is out of the range of the reference value. Or the method according to 22.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I122ra2, Rbfox1, Lrrc2, Col15a1, Sparc, Col11a1, Clec11a, Serpinb7, When the measured value of one type of biomarker is out of the range of the reference value, and / or the measured value of at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 is in the range of the reference value The method according to claim 23, wherein, if it is, it is determined that the phosphorus metabolism disorder involves FGF23.
Device for predicting FGF23 activation in a subject, having the following means:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Acquisition means for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: prediction means for predicting activation of the FGF23 based on the measurement value acquired by the acquisition means.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to claim 25, which is at least one selected from the group consisting of, Col1a1 and Defb8.
The prediction means compares the measured value with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, When the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 26. The method according to claim 25, wherein FGF23 is predicted to be activated in said subject if at least one biomarker selected is within the range of the reference value. Is the device described in 26.
A program that, when run on a computer, causes the computer to perform the following processing to predict FGF23 activation in a subject:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject An acquisition process for acquiring a measurement value of at least one type of mRNA selected from the group consisting of: and a prediction process for predicting activation of the FGF23 based on the measurement value acquired in the acquisition process.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The program according to claim 28, wherein the program is at least one selected from the group consisting of: Col1a1 and Defb8.
In the prediction processing, the measured value is compared with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, When the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 28. The method according to claim 28, wherein FGF23 is predicted to be activated in said subject if at least one biomarker selected is within the range of reference values. Or the program described in 29.
A method of predicting FGF23 activation in a subject comprising the steps of:
From measured values of at least one protein selected from the group consisting of biomarkers contained in skin-derived samples collected from the subject and / or from biomarkers contained in samples collected from the subject Obtaining a measurement value of at least one type of mRNA selected from the group consisting of: and predicting the activation of the FGF23 based on the measurement value obtained in the acquisition step.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The method according to claim 31, wherein the method is at least one selected from the group consisting of: Col1a1 and Defb8.
The prediction step compares the measured value with a predetermined reference value, and Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1 Sparc, When the measured value of at least one biomarker selected from the group consisting of Col11a1, Clec11a, Serpinb6d, and Defb8 is out of the range of the reference value, and / or from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 34. The method according to claim 31, wherein FGF23 is predicted to be activated in said subject if at least one biomarker selected is within the range of reference values. The method described in 32.
Screening apparatus for candidate active ingredients for suppressing the functional expression of FGF23, having the following means:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Means for acquiring the measurement value of the biomarker protein of at least one test substance-treated sample and / or the measurement value of mRNA of the protein, and the measurement acquired by the first measurement value acquisition means A determining means for determining that the test substance is a candidate substance of the active ingredient based on a value.
From a group consisting of a sample collected from the skin of a subject (except for human beings) not treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Second measurement value acquiring means for acquiring the measurement value of the corresponding biomarker protein in at least one untreated sample to be selected and / or the measurement value of mRNA of said protein, and the measurement value of the test substance-treated sample and Measured value comparison means for comparing the measured values of an untreated sample,
And have
The apparatus according to claim 34, wherein the determination means determines that the test substance is a candidate substance of the active ingredient based on the comparison result of the measurement value comparison means.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The device according to claim 35, wherein the device is at least one selected from the group consisting of: Col1a1 and Defb8.
A screening program which, when executed on a computer, causes the computer to carry out the following processing for screening active substance candidate substances for suppressing the functional expression of FGF23:
Selected from the group consisting of a sample collected from the skin of a subject (except for humans) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A first measurement value acquisition process for acquiring a measurement value of a biomarker protein in at least one test substance-treated sample and / or a measurement value of mRNA of the protein, and the first measurement value acquisition process A determination process of determining that the test substance is a candidate substance of an active ingredient based on measured values.
Furthermore, it comprises a sample collected from the skin of a subject not treated with the test substance (except for human beings), a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin A second measurement value acquisition process for acquiring a measurement value of a corresponding biomarker protein of at least one untreated sample selected from a group and / or a measurement value of mRNA of the protein, and the measurement value of the test substance-treated sample And a measured value comparison process comparing the measured values of the untreated sample,
On the computer,
The program according to claim 37, wherein in the determination process, the test substance is determined to be a candidate substance of the active ingredient based on a comparison result of the measurement value comparison process.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to claim 37 or 38, which is at least one selected from the group consisting of: Col1a1 and Defb8.
The screening method of the candidate substance of the active ingredient for suppressing the functional expression of FGF23 including the following processes:
(I) A sample collected from the skin of a subject (except for human beings) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Obtaining a measured value of a biomarker protein in at least one test substance-treated sample selected from the group and / or a measured value of mRNA of said protein,
(II) A step of determining that the test substance is a candidate substance of the active ingredient based on the measurement value obtained in the step (I).
Between the steps (I) and (II), the measured value of the test substance-treated sample obtained in the step (I) and the skin of a subject (except for human beings) not treated with the test substance Of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a selected sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And / or comparing the measured value of mRNA of said protein with
Step (II) is a step of determining that the test substance is a candidate substance of the active ingredient based on the comparison result.
41. The method of claim 40, further comprising
Before the step (I),
(I) treating a subject (excluding human), a test tissue or test cell derived from skin with a test substance,
(Ii) collecting a sample from the subject, test tissue or test cells treated with the test substance in step (i), and (iii) proteins and / or proteins from the sample obtained in step (ii) 42. The method of claim 40 or 41, comprising the step of recovering mRNA.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The method according to any one of claims 40 to 42, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Screening apparatus for candidate active ingredients for activating the function of FGF23, having the following means:
Biomarker in a sample collected from the skin of a subject (except for human beings) treated with a test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) The test substance is an active ingredient based on a first measurement value acquisition unit that acquires a measurement value of a protein and / or a measurement value of mRNA of the protein, and the measurement value acquired by the first measurement value acquisition unit. Determining means to determine a candidate substance.
Biomarker protein of a sample collected from the skin of a subject (except for human beings) not treated with the test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (untreated sample) A second measured value acquiring means for acquiring a measured value of the protein and / or a measured value of mRNA of the protein, and a measured value comparing means for comparing the measured value of the test substance-treated sample with the measured value of the untreated sample;
And have
45. The screening device according to claim 44, wherein the determination means determines that the test substance is a candidate substance of the active ingredient based on the comparison result of the measurement value comparison means.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The device according to claim 45, wherein the device is at least one selected from the group consisting of: Col1a1 and Defb8.
A screening program which, when executed on a computer, causes the computer to carry out the following processing for screening active substance candidate substances for activating the function of FGF23:
Biomarker in a sample collected from the skin of a subject (except for human beings) treated with a test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) The test substance is an active ingredient based on a first measurement value acquisition process for acquiring a measurement value of a protein and / or a measurement value of mRNA of the protein, and a measurement value acquired in the first measurement value acquisition process. Decision processing to determine that it is a candidate substance of
Furthermore, the bio of a sample collected from the skin of a subject (except for human beings) not treated with the test substance, or a sample collected from a test tissue derived from the skin or a test cell derived from the skin (untreated sample) A second measurement value acquisition process for acquiring a measurement value of a marker protein and / or a measurement value of mRNA of the protein, and a measurement value comparison process for comparing the measurement value of a test substance-treated sample with the measurement value of an untreated sample ,
On the computer,
The program according to claim 47, wherein in the determination process, the test substance is determined to be a candidate substance of the active ingredient based on a comparison result of the measurement value comparison process.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, Bic7 The program according to claim 47 or 48, which is at least one selected from the group consisting of: Col1a1 and Defb8.
The screening method of the active substance candidate substance for activating the function of FGF23 including the following processes:
(I) A sample collected from the skin of a subject (except for human beings) treated with a test substance, a sample collected from a test tissue derived from the skin, and a sample collected from a test cell derived from the skin Obtaining a measured value of a biomarker protein in at least one test substance-treated sample selected from the group and / or a measured value of mRNA of said protein,
(II) A step of determining that the test substance is a candidate substance of the active ingredient based on the measurement value obtained in the step (I).
Between the steps (I) and (II), the measured value of the test substance-treated sample obtained in the step (I) and the skin of a subject (except for human beings) not treated with the test substance Of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a selected sample, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And / or comparing the measured value of mRNA of said protein with
Step (II) is a step of determining that the test substance is a candidate substance of the active ingredient based on the comparison result.
51. The method of claim 50.
Before the step (I),
(I) treating a subject (excluding human), a test tissue or test cell derived from skin with a test substance,
(Ii) collecting a sample from the subject, test tissue or test cells treated with the test substance in the step (i), and (iii) proteins and / or proteins from the sample obtained in the step (ii) 52. The method of claim 50 or 51, comprising the step of recovering mRNA.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The method according to any one of claims 40 to 42, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Predictor for predicting the involvement of FGF23 in disease comprising the following means:
First measurement value acquiring means for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measurement value of mRNA of the protein, and the first measurement value acquisition means A determination means for determining that FGF23 is involved in the disease based on the measurement value obtained by
A second measurement value acquiring means for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject not having the disease and / or a measurement value of mRNA of the protein, and the first Measurement value comparison means for comparing the measurement value acquired by the measurement value acquisition means with the measurement value acquired by the second measurement value acquisition means;
And have
55. The apparatus according to claim 54, wherein the determination means determines that FGF23 is involved in the disease based on the result of the comparison.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 56. The device according to claim 55, wherein the device is at least one selected from the group consisting of: Col1a1 and Defb8.
A prediction program which, when executed on a computer, causes the computer to carry out the following processing for predicting the involvement of FGF23 in a disease:
A first measurement value acquisition process for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measurement value of mRNA of the protein, and the first measurement value acquisition process The decision processing which determines that FGF23 is concerned in the said disease based on the measured value acquired by b.
Furthermore, a second measurement value acquisition process for acquiring a measurement value of a biomarker protein in a sample collected from the skin of a subject not having the disease and / or a measurement value of mRNA of the protein, A measurement value comparison process of comparing the measurement value acquired in the measurement value acquisition process of the first measurement value and the measurement value acquired in the second measurement value acquisition process;
On the computer,
58. The program according to claim 57, wherein said determination processing determines that FGF23 is involved in said disease based on the result of said comparison.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The program according to claim 57 or 58, wherein the program is at least one selected from the group consisting of: Col1a1 and Defb8.
Methods of predicting FGF23's involvement in disease comprising the following steps:
(I) obtaining a measured value of a biomarker protein in a sample collected from the skin of a subject having a disease and / or a measured value of mRNA of said protein,
(II) A step of determining that FGF23 is involved in the disease based on the measurement value obtained in the step (I).
Between the steps (I) and (II), the measured value obtained in the step (I) and the measured value of the biomarker protein in a sample collected from the skin of a subject not having the disease And / or comparing the measured value of mRNA of said protein with
Step (II) is a step of determining that FGF23 is involved in the disease based on the result of the comparison.
61. The method of claim 60.
The biomarkers include Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, I22ra2, Rbfox1, Ldrc2, Aldhl2, Col5a11, Col5a11, Col5a11, C5a11, C5a11, C5a1 The method according to any one of claims 60 to 61, which is at least one selected from the group consisting of: Col1a1 and Defb8.
Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4 dip, I22ra2, Rbfox1, Lrcc2, Col1a1, Col5a1, C1qtnChIh, a C1qtnChIh, a C1qtnCh, a backgrip A skin biomarker for predicting involvement of renal function, abnormal phosphorus metabolism, or FGF23, comprising at least one selected from the group consisting of