JPWO2019026918A1 - Skin biomarker - Google Patents

Abstract

One of the tasks is to predict renal disease using a skin-derived sample. In order to solve the above problems, a device 1 for predicting a renal disease of a subject is provided, which has the following means: is selected from the group consisting of biomarkers contained in a skin-derived specimen collected from the subject. Acquisition means for acquiring a measurement value of at least one protein and/or a measurement value of at least one mRNA selected from the group consisting of biomarkers contained in a sample collected from the subject, and the acquisition means Prediction means for predicting the renal disease based on the measurement values obtained by.

Description

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

Diseases can be reversibly treatable or not (ie, irreversible). Early detection and treatment of abnormalities during reversible conditions, or prevention of even such conditions, is essential for maintaining good health. Also, even in reversible conditions, early detection of disease is directly linked to milder treatment regimens, shorter treatment periods, and better prognosis health. It is also well known that abnormalities in one organ or tissue lead to diseases in other organs (generally called complications), as typified by heart disease, brain disease, cancer and diabetes. However, in such diseases, it is essential to prevent abnormalities in one organ/tissue from causing diseases in another organ/tissue as early as possible.

In all animals including humans, individual organs and tissues are not individual parts, but each forms a functional network, and quality control is performed at the individual level. The transport of endocrine factors such as hormones through the vascular network that is spread throughout the body, and the coordinated coordination of the functions of each organ by the neural network are typical examples of the "multi-organ connection system", and they are systematic as physiology and endocrinology. It is attached.

On the other hand, the number of patients with end-stage renal disease (ESKD) that requires dialysis or renal transplantation is on the increase worldwide, and the number of ESKD patients has increased from 430,000 to 106.5 million in 10 years from 1990 to 2000. The number has increased to at least 1.65 million by 2008 (Non-Patent Document 1). Chronic Kidney disease (CKD) is a reserve arm of ESKD, but the kidney is called a "silent organ", and even if a renal disorder occurs, its condition is difficult to appear in clinical data, etc. At present, it is difficult to detect renal function decline before the onset of a disease.

FGF23 is a hormone that lowers blood phosphorus concentration, and is known to reduce blood phosphorus concentration by suppressing reabsorption of phosphorus in renal proximal tubules and absorption of phosphorus from the intestinal tract. There is. Further, it is known that an increase in the concentration of FGF23 causes an abnormal bone-mineral metabolism (CKD-MBD) associated with chronic renal disease.

Lysaght MJ:J Am Soc Nephrol. 2002 Jan;13 Suppl 1;S37-40.

An object of the present invention is to predict renal disease using a skin-derived sample. An object of the present invention is to predict phosphorus metabolism abnormality using a skin-derived sample. One object of the present invention is to predict activation of FGF23 in a subject. An object of the present invention is to screen a candidate substance for an active ingredient for suppressing the functional expression of FGF23. An object of the present invention is to screen a candidate substance for an active ingredient for activating the function of FGF23. One object of the present invention is to predict the involvement of FGF23 in diseases.

The present inventor has conducted extensive studies and found that in animal models of renal disease, the expression of certain biomarkers changes in the skin depending on the pathological condition. In addition, it was found that some of these biomarkers were related to FGF23 and some were not.

The present invention has been completed based on this finding, and includes the following aspects.
Item 1.
A device for predicting renal disease in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value 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 biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 2. The device according to Item 1, wherein the device 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 when the measured value is outside the range of the reference value, determines that the subject has renal disease. The device according to.
Item 4.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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 apparatus according to Item 3, wherein it is determined that FGF23 is involved in the renal disease.
Item 5.
A program that, when executed by a computer, causes the computer to perform the following processing for predicting renal disease in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process for acquiring a measurement value of at least one mRNA selected from the group consisting of
A prediction process of predicting the renal disease based on the measurement value acquired in the acquisition process.
Item 6.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 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 outside the range of the reference value, it is determined that the subject has renal disease. The program described in.
Item 8.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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 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 steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring 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 biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 10. The method according to Item 9, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8.
Item 11.
In the prediction step, the measured value is compared with a predetermined reference value, and when the measured value is outside the range of the reference value, it is determined that the subject has renal disease. The method described in.
Item 12.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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.
A device for predicting abnormal phosphorus metabolism in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group consisting of
Prediction means for predicting the abnormal phosphorus metabolism based on the measurement values acquired by the acquisition means.
Item 14.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 14. The device according to Item 13, which 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 outside the range of the reference value, predicts that the subject has abnormal phosphorus metabolism, Item 13 or 14. The device according to 14.
Item 16.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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 reference values
Item 16. The device according to Item 15, which determines that FGF23 is involved in the abnormal phosphorus metabolism.
Item 17.
A program that, when executed by a computer, causes the computer to perform the following processing for predicting abnormal phosphorus metabolism in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process for acquiring a measurement value of at least one mRNA selected from the group consisting of
A prediction process of predicting the abnormal phosphorus metabolism based on the measurement value acquired in the acquisition process.
Item 18.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 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 process, the measurement value is compared with a predetermined reference value, and if the measurement value is outside the range of the reference value, it is predicted that the subject has abnormal phosphorus metabolism, Item 17 or 18. The program according to 18.
Item 20.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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 20. The program according to Item 19, wherein it is determined that FGF23 is involved in the abnormal phosphorus metabolism.
Item 21.
A method of predicting abnormal phosphorus metabolism in a subject, comprising the steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of
A prediction step of predicting the abnormal phosphorus metabolism based on the measurement values acquired in the acquisition step.
Item 22.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 22. The method according to Item 21, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8.
Item 23.
The predicting step compares the measured value with a predetermined reference value, and when the measured value is outside the range of the reference value, predicts that the subject has an abnormal phosphorus metabolism, Item 21 or 22.
Item 24.
Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from the group Il22ra2, Rbfoxe, Rbfox1, Col6S11, Lbfox, Col15Sc, C15cl1, C15cl, Scl, C15cl, C15cl, C15cl1, Cl15a1, Spar11, C15cl1, Cl15a1, Spar11, and C15cl1, Cl15a1, Spar11, C15cl1, Spar11, C15cl1, Scl11, Scl11, C15cl1, Scl11, Scl11, Scl11, Scl11, Scl11, Sc11, c15, c15, c11, c11, c15, c15, c11, c11, c11, c6, c11, c11, c11, c15, c11, c11, c15, c11, c11, c11, c11, c11, c15, c11a11. If the measured value of one biomarker 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 24. The method according to Item 23, wherein it is determined that FGF23 is involved in the abnormal phosphorus metabolism.
Item 25.
Device for predicting activation of FGF23 in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group consisting of
Prediction means for predicting the activation of the FGF23 based on the measurement value acquired by the acquisition means.
Item 26.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 26. The device according to Item 25, wherein the device is at least one selected from the group consisting of:, Col1a1, and Defb8.
Item 27.
The prediction unit compares the measured value with a predetermined reference value, 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 outside the range of reference values, and/or
When at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of reference values, it is predicted that FGF23 is activated in the subject, Item 25 Or 26.
Item 28.
A program that, when executed by a computer, causes the computer to perform the following process for predicting activation of FGF23 in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process for acquiring a measurement value of at least one mRNA selected from the group consisting of
A prediction process of predicting the activation of the FGF23 based on the measurement value acquired in the acquisition process.
Item 29.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 29. The program according to Item 28, which is at least one selected from the group consisting of, Col1a1, and Defb8.
Item 30.
Wherein in the prediction process, compares the measured value with a predetermined reference value, 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 outside the range of reference values, and/or
When at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of reference values, it is predicted that FGF23 is activated in the subject, Item 28 Or the program according to 29.
Item 31.
A method of predicting activation of FGF23 in a subject, comprising the steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring 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 biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 32. The method according to Item 31, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8.
Item 33.
The prediction step, comparing the measured value with a predetermined reference value, 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 outside the range of reference values, and/or
When at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the range of reference values, it is predicted that FGF23 is activated in the subject, Item 31 Or the method described in 32.
Item 34.
A screening device for a candidate substance of an active ingredient for suppressing the functional expression of FGF23, which has the following means:
Selected from the group consisting of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from skin, and a specimen collected from a test cell derived from skin First measurement value acquisition means for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein of at least one test substance-treated sample, and
A determination unit that determines that the test substance is a candidate substance for an active ingredient based on the measurement value acquired by the first measurement value acquisition unit.
Item 35.
From a group consisting of a specimen collected from the skin of a subject (excluding humans) not treated with the test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from a test cell derived from the skin Second measurement value acquisition means for acquiring the measurement value of the corresponding biomarker protein and/or the measurement value of the mRNA of the protein in at least one untreated sample selected, and
Measured value comparison means for comparing the measured value of the test substance treated sample and the measured value of the untreated sample,
Further has
Item 35. The apparatus according to Item 34, wherein the determining unit determines that the test substance is a candidate substance for an active ingredient based on the comparison result of the measurement value comparing unit.
Item 36.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 35. The device according to Item 35, wherein the device is at least one selected from the group consisting of:, Col1a1, and Defb8.
Item 37.
A screening program that causes a computer to perform the following processing for screening a candidate substance of an active ingredient for suppressing the functional expression of FGF23 when the computer executes the screening process:
Selected from the group consisting of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from skin, and a specimen collected from a test cell derived from skin A first measurement value acquisition process for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein in at least one test substance-treated sample, and
A determination process of determining that the test substance is a candidate substance of an active ingredient based on the measurement value acquired in the first measurement value acquisition process.
Item 38.
Furthermore, it consists of a sample collected from the skin of a subject (excluding humans) not treated with the test substance, a sample collected from the test tissue derived from the skin, and a sample collected from the test cell derived from the skin. A second measurement value acquisition process of acquiring the measurement value of the corresponding biomarker protein of at least one untreated sample selected from the group and/or the measurement value of the mRNA of the protein, and
A measurement value comparison process for comparing the measurement value of the test substance treated sample and the measurement value of the untreated sample,
To the computer,
38. The program according to Item 37, wherein in the determination process, the test substance is determined to be a candidate substance for an active ingredient based on the comparison result of the measurement value comparison process.
Item 39.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 39. The program according to Item 37 or 38, which is at least one selected from the group consisting of, Col1a1, and Defb8.
Item 40.
A method for screening a candidate substance for an active ingredient for suppressing the functional expression of FGF23, which comprises the following steps:
(I) Consists of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from test cells derived from the skin Obtaining a measurement value of biomarker protein in at least one test substance-treated sample selected from the group and/or a measurement value of mRNA of the protein,
(II) A step of determining that the test substance is a candidate substance for an active ingredient based on the measured value obtained in the step (I).
Item 41.
Between the steps (I) and (II), the measurement value of the test substance-treated sample obtained in the step (I) and the skin of the subject (excluding human) not treated with the test substance are collected. Value of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And/or further comprising the step of comparing the measured value of mRNA of said protein,
Step (II) is a step of determining that the test substance is a candidate substance for an active ingredient based on the comparison result.
41. The method of paragraph 40, further comprising:
Item 42.
Before the step (I),
(I) a step of treating a subject (excluding human), a test tissue or a test cell derived from skin with a test substance,
(Ii) a step of collecting a sample from the subject, the test tissue or the test cell treated with the test substance in the step (i), and
(Iii) a step of recovering protein and/or mRNA from the sample obtained in the step (ii)
Item 42. The method according to Item 40 or 41.
Item 43.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 43. The method according to any one of Items 40 to 42, which is at least one member selected from the group consisting of, Col1a1, and Defb8.
Item 44.
Screening device for a candidate substance of an active ingredient for activating the function of FGF23, which has the following means:
Biomarker in a sample collected from the skin of a subject (excluding humans) treated with a test substance, or in a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) First measurement value acquisition means for acquiring the measurement value of the protein and/or the measurement value of the mRNA of the protein, and
A determination unit that determines that the test substance is a candidate substance for an active ingredient based on the measurement value acquired by the first measurement value acquisition unit.
Item 45.
Biomarker protein of a sample collected from the skin of a subject (excluding human) that has not been treated with the test substance, or a sample (untreated sample) collected from a test tissue derived from the skin or a test cell derived from the skin Second measurement value acquisition means for acquiring the measurement value of and/or the measurement value of the mRNA of the protein, and
Measured value comparison means for comparing the measured value of the test substance treated sample and the measured value of the untreated sample,
Further has
Item 45. The screening device according to Item 44, wherein the determining unit determines that the test substance is a candidate substance for an active ingredient based on the comparison result of the measurement value comparing unit.
Item 46.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 45. 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 that causes a computer to perform the following processing for screening a candidate substance of an active ingredient for activating the function of FGF23 when the computer executes:
Biomarker in a sample collected from the skin of a subject (excluding humans) treated with a test substance, or in 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 of acquiring a measurement value of protein and/or a measurement value of mRNA of the protein, and
A determination process of determining that the test substance is a candidate substance of an active ingredient based on the measurement value acquired in the first measurement value acquisition process.
Item 48.
Furthermore, the biopsy of a specimen collected from the skin of a subject (excluding humans) that has not been treated with the test substance, or a specimen (untreated specimen) collected from a test tissue derived from the skin or a test cell derived from the skin A second measurement value acquisition process for acquiring the measurement value of the marker protein and/or the measurement value of the mRNA of the protein, and
A measurement value comparison process for comparing the measurement value of the test substance treated sample and the measurement value of the untreated sample,
To the computer,
Item 48. The program according to Item 47, wherein in the determination process, the test substance is determined to be a candidate substance for an active ingredient based on the comparison result of the measurement value comparison process.
Item 49.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 49. The program according to Item 47 or 48, which is at least one selected from the group consisting of, Col1a1, and Defb8.
Item 50.
A method for screening a candidate substance for an active ingredient for activating the function of FGF23, which comprises the following steps:
(I) Consists of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from test cells derived from the skin Obtaining a measurement value of biomarker protein in at least one test substance-treated sample selected from the group and/or a measurement value of mRNA of the protein,
(II) A step of determining that the test substance is a candidate substance for an active ingredient based on the measured value obtained in the step (I).
Item 51.
Between the steps (I) and (II), the measurement value of the test substance-treated sample obtained in the step (I) and the skin of the subject (excluding human) not treated with the test substance are collected. Value of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And/or further comprising the step of comparing the measured value of mRNA of said protein,
Step (II) is a step of determining that the test substance is a candidate substance for an active ingredient based on the comparison result.
Item 50. The method according to item 50.
Item 52.
Before the step (I),
(I) a step of treating a subject (excluding human), a test tissue or a test cell derived from skin with a test substance,
(Ii) a step of collecting a sample from the subject, the test tissue or the test cell treated with the test substance in the step (i), and
(Iii) a step of recovering protein and/or mRNA from the sample obtained in the step (ii)
Item 52. The method according to Item 50 or 51.
Item 53.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 43. The method according to any one of Items 40 to 42, which is at least one member selected from the group consisting of, Col1a1, and Defb8.
Item 54.
A prediction device for predicting the involvement of FGF23 in a disease, which comprises the following means:
First measurement value acquisition means for acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the first measurement value acquisition means A determining means for determining that FGF23 is involved in the disease based on the measurement value obtained by.
Item 55.
Second measurement value acquisition means for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein in the sample collected from the skin of the subject not having the disease, and
A measurement value comparison means for comparing the measurement value acquired by the first measurement value acquisition means and the measurement value acquired by the second measurement value acquisition means,
Further has
The apparatus according to Item 54, wherein the determining unit determines that FGF23 is involved in the disease based on the result of the comparison.
Item 56.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 55. 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 that, when executed by a computer, causes the computer to perform the following process for predicting the involvement of FGF23 in a disease:
First measurement value acquisition process of acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the first measurement value acquisition process A determination process of determining that FGF23 is involved in the disease based on the measurement values obtained in.
Item 58.
Furthermore, a second measurement value acquisition process of acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein in the sample collected from the skin of the subject not having the disease, and
A measurement value comparison process of comparing the measurement value acquired in the first measurement value acquisition process and the measurement value acquired in the second measurement value acquisition process,
To the computer,
Item 58. 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 biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 59. The program according to Item 57 or 58, which is at least one selected from the group consisting of, Col1a1, and Defb8.
Item 60.
A prediction method for predicting the involvement of FGF23 in a disease, which comprises the following steps:
(I) obtaining a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease,
(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 measurement value obtained in the step (I) and the measurement value of the biomarker protein in the sample collected from the skin of the subject who does not have the disease. And/or comparing the measured value of mRNA of said protein
Further including,
Step (II) is a step of determining that FGF23 is involved in the disease based on the result of the comparison,
Item 60. The method according to Item 60.
Item 62.
The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d Item 62. The method according to any one of Items 60 to 61, wherein the method 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, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8 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 one effect, it is possible to detect a decrease in renal function using a skin-derived sample. As one effect, the present invention can predict abnormal metabolism of phosphorus using a skin-derived sample. The present invention can predict the activation of FGF23 in a subject using a skin-derived specimen as one effect. As one of the effects, the present invention can screen a candidate substance of an active ingredient for suppressing the functional expression of FGF23 using a skin-derived sample. As one of the effects of the present invention, a candidate substance of an active ingredient for activating the function of FGF23 can be screened using a skin-derived sample. As one effect, the present invention can predict the involvement of FGF23 in diseases using a skin-derived sample.

It is a general-view figure of the system 100 which concerns on the 1st-3rd embodiment of this invention. It is a figure which shows the hardware constitutions of the system 100 which concerns on the 1st-3rd embodiment of this invention. It is a block diagram for demonstrating the function of the arithmetic units 1-3 which concern on the 1st-3rd embodiment of this 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 flowchart which shows operation|movement of the arithmetic unit 3 which concerns on the 3rd aspect of this invention. It is a general-view figure of the system 200 which concerns on the 4th, 5th embodiment of this 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 apparatus 4 and 5 which concerns on the 4th, 5th embodiment 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. It is a general-view figure of the system 300 which concerns on the 6th Embodiment of this 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 a general-view figure which shows an example of an inspection kit. FIG. 17A shows the sequences of gRNA and ssODNs. FIG. 17B shows the genotype of mutant mice. The expression of the biomarker in the skin of UNx/HPi, WT3W/HP1W, WT3W/LP1W, WT3W/ND1W mouse model is shown. It is a figure which shows the expression of Fgf23 in the skull of E-/M-/L-UNx/HPi, and WT3W/HP1W, WT3W/LP1W, and 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 depending on Fgf23 gene function expression.

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

I. First to third embodiments
1. Explanation of Terms First, in the first to third embodiments, terms used in the present specification, claims and abstract will be explained. Unless otherwise noted, the terms used in the specification, claims, and abstracts of the first through third embodiments are as defined in this section. However, the description of this section may be applied to the description of the fourth to eighth embodiments.

As used herein, the term “renal disease” refers to any abnormality or disease of the kidney that causes decreased renal function, and is not particularly limited as long as the kidney has any functional or physical disorder. Specifically, among acute renal failure, acute pyelonephritis, acute glomerulonephritis (glomerulonephritis associated with hemolytic streptococcal infection, rapidly progressive glomerulonephritis, etc.), and renal diseases 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), chronic diseases associated with heart disease (type 2 cardiorenal syndrome, etc.), diabetic nephropathy, renal glomerular fibrosis, etc.; nephrotic syndrome; renal tumors Chronic kidney disease and the like. Acute renal disease is preferred. Further, as another aspect, a renal disease associated with ischemic heart disease is preferable, and an acute renal disease associated with ischemic heart disease (type 1 cardiorenal syndrome, etc.) is particularly preferable.

In the present specification, the term “decreased renal function” means, for example, in the case of human, at least one renal disease marker (preferably urine) shown in Tables 1-1 to 1-4 below, which is generally measured by clinical examination. (Other than protein) is outside the standard value range. As a renal disease marker, 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, phenolsulfonphthalein test, concentration test, dilution test, free water clearance, free water reabsorption, renal tubular excretion extreme amount, renal tubular reabsorption extreme amount, It is at least one selected from the group consisting of phosphate reabsorption rate, β2-microglobulin, and α1-microglobulin.

The above-mentioned renal disease marker can be measured by a known method published in the 32nd edition of the revised clinical laboratory method (edited by Masamitsu Kanai, Kanahara Publishing Co., Ltd.).

In the present specification, “acute renal failure” is a disease in which renal function is rapidly reduced, and for example, serum creatinine value rapidly increased to 2.0 to 2.5 mg/dl or more (basic renal function). If there is a decline in function, serum creatinine level increased by 50% or more compared to the previous value), or serum creatinine level was 0.5 mg/dl/day or more, and urea nitrogen was at a rate of 10 mg/dl/day or more. It means increasing. Acute renal failure is due to (1) prerenal acute renal failure caused by a decrease in renal blood flow, (2) renal acute renal failure with impaired renal parenchyma, and (3) urinary disturbance after renal failure. Although postrenal acute renal failure is included, the preferred acute renal failure as an application target of the present invention is prerenal acute renal failure and renal acute renal failure, preferably prerenal acute renal failure.

Kidney disease associated with heart disease is also called cardiorenal syndrome. Cardiorenal syndrome includes acute and chronic pathologies and is classified into multiple types. Of these, types 1 and 2 cause heart disease to occur, 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 a condition in which any one of the following stages 1 to 3 is caused by some kind of heart disease:
Stage 1: Serum creatinine level increases by 1.5 to 1.9 times the standard value, or serum creatinine level increases by 0.3 mg/dl or more in the same individual, and urine volume Is about 0.5 mL/kg/hour for 6 to 12 hours 2: Serum creatinine level is increased to about 2.0 to 2.9 times the standard value, and urine volume is 0. Stage 3 where the condition continues below 5 mL/kg/hour: whether serum creatinine level increases to about 3 times the reference level, or whether serum creatinine level increases by 4.0 mg/dL or more in the same individual compared to the previous value , Renal replacement therapy is started, or eGFR is decreased to less than 35 mL/min/1.73 m ^{2 in} the case of a patient younger than 18 years, and urine volume is 24 If the condition continues to be 0.3 mL/kg/hour or more for an hour or more, or if no urine continues for 12 hours or more.

In the present specification, “chronic kidney disease” means that when the subject is a human, according to “CKD Diagnostic Guide 2012 (edited by the Japanese Nephrological Society), renal disorders (urine abnormalities such as proteinuria including microalbuminuria, Abnormal urinary sediment, abnormal images such as single kidney or polycystic kidney disease, decreased renal function such as increased serum creatinine level, abnormal electrolyte such as hypokalemia due to renal tubular disorder, abnormal histopathological examination such as renal biopsy Etc.) or an estimated GFR (glomerular filtration rate) of 60 mL/min/less than 1.73 m ^{2 for a} period of 3 months or more.

Here, the estimated GFR (eGFR) can be calculated by the estimation formula (eGFRcreate) from the serum creatinine value shown in Table 2 below. Further, the estimation formula of serum cystatin C (eGFRcys) can be applied to persons with extremely small muscle mass such as amputees of lower extremities.

For example, when using protein as an index, chronic kidney disease can be diagnosed when urinary protein is 0.15 g/gCr or more in a recent urinalysis 3 months or more. Moreover, it can be diagnosed as a chronic renal disease when the diabetes is 3 months or more before and the recent albuminuria test shows 30 mg/gCr or more.

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

Further, in the case of mammals other than humans such as cats and dogs, it is possible to predict chronic kidney disease from the average daily water withdrawal amount, urine specific gravity and the like.

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

The disease associated with FGF23 is not limited as long as it is a disease that develops with overexpression of FGF23. Hypophosphatemic rickets/osteomalacia, autosomal dominant hypophosphatemic rickets/osteomalacia, autosomal recessive hypophosphatemic rickets/osteomalacia, X-dominant hypophosphorus Cerebral rickets/osteomalacia, neoplastic rickets/osteomalacia, secondary hyperparathyroidism, abnormal phosphorus metabolism in acute renal disease or chronic renal disease, abnormal bone-mineral metabolism (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 humans include cows, horses, sheep, goats, pigs, dogs, cats, rabbits and monkeys. Humans, cats and dogs are preferable. Further, the age and sex of the individual does not matter.

Further, the “subject” may be an individual who has a history of decreased renal function or other renal diseases, or an individual who does not have it. In addition, the subject may be an individual having symptoms such as polyuria, thirst, increased water intake, gastric juice excess, vomiting, hematuria, and general malaise, and may be an asymptomatic individual. Good. Further, the subject also includes a subject suspected of having a renal disorder or chronic renal disease according to a known diagnostic method such as medical interview, urinalysis, blood biochemical examination, renal image diagnosis, and renal biopsy. Be done.

In the present invention, the “skin-derived sample” is not limited as long as it is derived from the skin of a living body. Further, the skin-derived sample includes sweat, skin secretions, and the like. The method for collecting the skin is not limited, and examples thereof include a biopsy material, the skin attached to the perforating needle when the hole of the piercing is opened, and an abrasion product on the skin surface.

Further, the sample may be fresh or may be stored. When the sample is stored, it can be stored in a room temperature environment, a refrigerating environment or a freezing environment, but is preferably frozen storage.

The "biomarker" of the present invention, Hamp2 shown in Table 4, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6 , Spark, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8, and at least one selected from the group consisting of these variants and orthologs.

Here, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rabfox6, Cb11c6, C15cl8, C15cl1, C15cl1, C15cl1, Cl15a1, C15cl1, Cl15a1, Cl15, Cl15a1, C15cl1, Cl15a1, C15cl1, Cl15a1, C15cl1, Cl15a1, Cl15a1, C15cl1, Cl15, Cl15, Cl15a1, Spr, and C15cl. At least one biomarker (referred to as group 1) is likely to be controlled by FGF23. Therefore, when the measured values of the biomarkers of the first group are out of the reference range, it can be predicted that FGF23 is involved in, for example, renal diseases or other diseases, or abnormal phosphorus metabolism.

On the other hand, it is highly possible that at least one biomarker selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 (referred to as group 2) is not controlled by FGF23. Therefore, when the biomarkers of Group 1 are outside the range of the standard value and the measured values of the biomarkers of Group 2 are within the range of the standard value, for example, renal disease or other diseases, or phosphorus metabolism It is possible to more reliably predict that FGF23 is involved in an abnormality.

Whether or not FGF23 is involved is preferably determined in consideration of both the measurement values of the first group of biomarkers and the second group of biomarkers.

The “measured value of at least one protein selected from the group consisting of biomarkers” refers to a value that reflects the amount or concentration of at least one protein selected from the group consisting of biomarkers. When the measured value is indicated by “amount”, it may be by mole or by mass, but it is preferable by notation by mass. When the value is expressed as “concentration”, it may be a molar concentration or a mass ratio (mass/volume) of a sample per a fixed volume, but is preferably mass/volume. In addition to the above, the value reflecting the amount or concentration may be the intensity of signals such as fluorescence and luminescence.

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 fixed amount of a sample, or β2- It may be a value that reflects the relative expression level with respect to the expression levels of housekeeping genes such as microglobulin mRNA, GAPDH mRNA, Maea mRNA and β-actin mRNA. It may also be represented by the intensity of signals such as fluorescence and luminescence.

The "predetermined reference value" of the measurement value of the biomarker protein is based on the measurement value of the biomarker protein in the specimen of the individual who has deteriorated renal function, and/or the measurement value of the biomarker protein in the specimen of the healthy individual. Refers to the standard value. Specifically, the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or included in the sample collected from the subject A group consisting of at least one mRNA measurement value selected from the group consisting of biomarkers (also referred to as a test measurement value) and a biomarker contained in a skin-derived sample collected from a healthy individual corresponding to the test measurement value Measurement value of at least one protein selected from, and/or measurement value of at least one mRNA selected from the group consisting of biomarkers contained in the sample collected from the subject (also referred to as healthy measurement value) ), and the reference value is determined by a known method. As used herein, "corresponding" is intended to be a biomarker of the same species.

For example, a biomarker protein measurement value measured using a plurality of specimens of individuals with impaired renal function and a biomarker protein measurement value measured using a specimen of a plurality of healthy individuals are acquired. A value that can most accurately determine the presence or absence of decreased renal function based on these multiple values can be set as the “reference value”. Here, the “value that can be determined most accurately” is the sensitivity and/or specificity obtained from the ROC curve according to the purpose of the test, the positive predictive value, the negative predictive value, the first quartile of the interquartile range, It can be appropriately set based on indexes such as the second quartile (median) and the third quartile.

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

Further, as another aspect, when determining the reference value based on the measured value of the biomarker protein in the sample of the individual with renal function deterioration, the biomarkers in the samples of multiple individuals with renal function deterioration are determined. Among the measured values of the marker protein, the lowest measured value can be determined as the threshold value.

As another aspect, the reference value may be the measurement value itself of the biomarker protein in the sample of the healthy individual, or the average value of the measurement values of the plurality of biomarker proteins of the healthy individual, the median value or the mode value. it can.

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

The “reference value” of the measurement value of the biomarker mRNA is also determined by using the measurement value of the biomarker mRNA instead of the measurement value of the biomarker protein, similarly to the “reference value” of the measurement value of the biomarker protein. can do.

Here, the “reference value range” means that the reference range is equal to or less than the reference value as long as the biomarker protein has a property of which expression and activity increase with a decrease in renal function. If the biomarker protein has a property of which expression and activity decrease with the decrease of renal function, the reference range is the reference value or more. For example, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a2, and Rhlfo2, Col15a1, Al15, Al15, Al15, Aldhl, Cl15a1, Aldhl, and Aldhl. The expressions of C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8 decrease with the decrease in renal function.

The “healthy individual” is not particularly limited. Preferably, it is a human or a mammal other than the humans described in the "individual" section, which does not show abnormal data in biochemical tests, blood tests, urine tests, serum tests, physiological tests and the like. The age and sex of healthy individuals are not particularly limited.

The “plurality of samples” are two or more, preferably 5 or more, and more preferably 10 or more samples. These may be samples collected from different individuals, or may be multiple samples of the same individual at different collection times.

The “plurality of values” is a measured value of biomarker protein of 2 or more, preferably 5 or more, more preferably 10 or more and a measured value of biomarker mRNA.

The “plurality of individuals” are 2 or more, preferably 5 or more, and more preferably 10 or more individuals.

The species, age, sex, etc. of the subject do not necessarily have to be the same as those of the individual who obtains the measured values of the biomarker protein and the measured values of the biomarker mRNA for determining the reference value, but they are preferably the same species. It is also preferred that said individual is of the same age and/or gender as the subject.

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

Examples of the biomarker protein serving as an antigen used for producing an anti-biomarker antibody include all or part of at least one protein selected from the group consisting of the biomarkers described above.

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

The length of the nucleic acid to be detected is not particularly limited. If the detected nucleic acid is used as a capture probe in a microarray or the like, the sequence that hybridizes with the target nucleic acid is preferably about 100 mer, more preferably about 60 mer, and further preferably 20 to 20 mer. It is about 30 mer. The capture probe can be manufactured using a known oligonucleotide synthesizer or the like. The capture probe may include a sequence that does not hybridize with the target nucleic acid.

If the detection nucleic acid is a primer used in a PCR reaction, the sequence that hybridizes with the target nucleic acid is preferably about 50 mer, more preferably about 30 mer, further preferably about 15 to 25 mer. Is. The primer can be produced using a known oligonucleotide synthesizer or the like. The primer may include a sequence that does not hybridize with the target nucleic acid. Further, the primer may be labeled with a fluorescent dye or the like.

Further, in RT-PCR, besides the primers, a quantification probe that is decomposed during the PCR reaction and used for real-time quantification of PCR products can be used. The quantification probe is not limited as long as it hybridizes with the target nucleic acid. The quantification probe is preferably a nucleic acid of about 5 to 20 mer containing a sequence that hybridizes with the target nucleic acid. Further, it is preferable that one end of the quantification probe is labeled with a fluorescent dye, and the other end of the quantification probe is labeled with a quencher of the fluorescent dye.

2. Method for obtaining each measurement value The measurement value for at least one protein selected from the group consisting of biomarkers in the present invention, and the method for obtaining the measurement value for at least one mRNA selected from the group consisting of biomarkers are There is no limitation 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 Acquisition of measurement value of at least one protein selected from the group consisting of biomarkers (hereinafter, may be abbreviated as “measurement value of biomarker protein” in the present specification) In this case, in this step, it is possible to use the anti-biomarker antibody described in “1. Explanation of terms” above in order to obtain the measured value. In addition, a test reagent containing an anti-biomarker antibody described below may be used.

A known ELISA method or the like can be used as a method for obtaining the measured value of the biomarker protein.

In this embodiment, an anti-biomarker antibody for capturing an antigen can be immobilized on a solid phase such as a microplate in advance to form a complex of the immobilized anti-biomarker antibody and the biomarker protein in the 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. You can

The method of immobilizing the anti-biomarker antibody for capturing the antigen on the solid phase is not particularly limited. It can be carried out directly or indirectly via another substance using known methods. Examples of the direct bond include physical adsorption. Preferably, the immunoplate or the like can be used to directly physically bind the anti-biomarker antibody to the microplate.

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 a microplate, a microtube, and a test tube.

This method may include an operation of washing the solid phase subsequent to the formation of the complex. For washing, PBS containing a surfactant or the like can be used.

In this method, the detection of the complex is carried out by using an anti-biomarker antibody for detection labeled with a labeling substance, or by binding with an unlabeled anti-biomarker antibody and the unlabeled anti-biomarker antibody. It can be carried out using an anti-immunoglobulin antibody or the like labeled with a labeling substance capable of performing the above, but it is preferable to use a labeled anti-biomarker antibody for detection. Moreover, it is preferable that the epitope in the biomarker protein of the anti-biomarker antibody for detection and the epitope in the biomarker protein of the anti-biomarker antibody for antigen capture are different.

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

The anti-biomarker antibody for detection can be obtained by labeling the anti-biomarker antibody with the above-mentioned labeling substance by a labeling method known in the art. Alternatively, labeling may be performed using a commercially available labeling kit or the like. The labeled immunoglobulin antibody may be the same as that used for labeling the anti-biomarker antibody, or may be a commercially available one.

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

As a method for detecting a signal, a known method can be used. In this method, the measuring 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, light generated by reacting a substrate for the enzyme, a signal such as color, can be measured by using a known device such as a luminometer and a spectrophotometer. it can.

The substrate of the enzyme can be appropriately selected from known substrates according to the type of the enzyme. For example, when alkaline phosphatase is used as the enzyme, the substrate is CDP-Star (registered trademark) (4-chloro-3-(methoxyspiro[1,2-dioxetane-3,2'-(5'-chloro)tricloxiro]. A chemiluminescent substrate such as [3.3.1.13,7]decane]-4-yl)phenyl sodium phosphate), 5-bromo-4-chloro-3-indolyl phosphate (BCIP), 5- Color forming substrates such as disodium bromo-6-chloro-indolyl phosphate and p-nitrophenyl phosphate can be mentioned. When the labeling substance is peroxidase, tetramethylbenzidine (TMB) and the like 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. The excitation wavelength and the fluorescence wavelength can be appropriately determined according to the type of fluorescent substance used.

The signal detection result can be used as a measurement value of the biomarker protein. For example, when the signal intensity is quantitatively detected, 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 measured value of the biomarker protein.

2-2. Obtaining measurement value of biomarker gene To obtain measurement value of at least one mRNA selected from the group consisting of biomarkers (hereinafter, may be abbreviated as “measurement value of biomarker mRNA” in the present specification) In addition, known methods such as the microarray method, RNA-seq analysis method, and quantitative RT-PCR method can be used. As the probe used in the microarray method, a probe selected by itself or a known probe may be synthesized and used, or a commercially available microarray chip may be used.

Here, in this method, either total RNA or mRNA extracted from the sample may be used. The sample used for total RNA and mRNA extraction is either immediately collected from an individual and subjected to RNA extraction, or immediately collected from an individual and frozen immediately (preferably in an atmosphere of −196° C. or lower (quenched in liquid nitrogen). Then, it is preferable to store at −80° C. or lower until RNA extraction.

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

Quantification by the microarray method can be performed according to a known method, and the expression level of the biomarker mRNA may be expressed as a relative expression level with respect to the expression level of the housekeeping gene, or as a measured value of signal intensity of a fluorescent dye or the like. be able to.

Quantification by RT-PCR is performed by a real-time PCR method or the like using the total RNA or mRNA extracted from the sample as a template and a reverse transcription reaction using the obtained cDNA as a template and specific primers for biomarker mRNA. It can be done by Further, in this case, the expression level of the biomarker mRNA may be expressed as a relative expression level with respect to the expression level of the housekeeping gene, or may be expressed as a measured value of the intensity of a signal such as a fluorescent dye.

In the RNA-seq analysis method, mRNA extracted from a sample is fragmented, and this is used as a template to synthesize cDNA by a reverse transcription reaction and prepare 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 expression level of mRNA is represented as RPKM (Reads Per Killobases per Million). The RPKM may be expressed as the intensity of a signal such as a heat map.

The detection result of the signal can be used as the expression level of the biomarker mRNA. For example, when the signal intensity is quantitatively detected, 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.

The value calculated from the measured value of the signal intensity is, for example, a value obtained by subtracting the measured value of the signal intensity of the negative control sample from the measured value of the signal intensity, and the measured value of the signal intensity of the signal intensity of the positive control sample. The value obtained by dividing by the measurement value of, and a combination thereof. Examples of the negative control sample include samples from healthy individuals. Examples of the positive control sample include a sample containing biomarker mRNA at a predetermined expression level.

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

3. Hardware Configuration The hardware configuration used in the first to third embodiments will be described with reference to FIGS. 1 and 2.

FIG. 1 is a schematic view of a system 100 according to the 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 will be referred to as a computing device (hereinafter, a computing device having a similar hardware configuration and having a different functional configuration as will be described later). Therefore, the computing devices having these different functional configurations are collectively referred to. Therefore, it is provided with an “arithmetic unit 1, 2, 3”), an input unit 8, a display unit 9, a measuring unit 5a, and a measuring unit 5b.

The arithmetic units 1, 2, and 3 are composed of, for example, general-purpose personal computers, and include a CPU 101 that performs data processing described below, a memory 102 used as a work area for data processing, and a recording unit 103 that records the processed data. A bus 104 for transmitting data between each unit and an interface unit 105 (hereinafter, referred to as an I/F unit) for inputting/outputting data to/from an external device are provided. The input unit 8 and the display unit 9 are connected to the arithmetic units 1, 2, and 3. The input unit 8 is composed of a keyboard and the like, and the display unit 9 is composed of a liquid crystal display and 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 devices, and the CPU 101, the memory 102, the recording unit 103, and the like may be arranged in different places, and these may be connected by a network. Further, the input unit 8 and the display unit 9 may be omitted and a device that does not require an operator may be used.

Further, the arithmetic devices 1, 2, 3, the measuring device 5a, and the measuring device 5b do not necessarily have to be arranged in one place, and a system in which devices provided in different places are connected by a network so that they can communicate with each other. May be configured.

In the following description, the processes performed by the arithmetic units 1, 2, and 3 are based on the programs stored in the recording unit 103 or the memory 102 unless the CPUs 101 of the arithmetic units 1, 2, and 3 actually perform the processes. It means the process to be performed. The CPU 101 temporarily stores necessary data (intermediate data, etc. in the process) using the memory 102 as a work area, and appropriately records data to be stored for a long time such as a calculation result in the recording unit 103.

The measuring device 5a is a device for measuring protein, and includes a sample storage space 51, a reaction part 52, and a detection part 53. The sample collected from the subject set in the sample storage space 51 is dispensed and incubated in the microplate on which the antibody-capturing anti-biomarker antibody is mounted, which is installed in the reaction section 52. After removing unreacted antigen as needed, the detection antibody is dispensed to the microplate and incubated. After removing unreacted antigen as necessary, a 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. To be done. Another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by microarray analysis, in which the reverse transcription reaction product set in the sample storage space 51 is distributed on the microarray chip set in the reaction part 52. After pouring, hybridization and washing, the sample is moved to the detection unit 53 to detect a signal.

Furthermore, another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by RT-PCR, in which the reverse transcription reaction product set in the sample storage space 51 is placed in the microtube set in the reaction part 52. Dispense, and then a quantitative PCR reagent is dispensed into a microtube. While performing the PCR reaction in the reaction section 52, the detection section 53 detects the signal in the tube.

The measurement 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 base sequence is analyzed 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 wirelessly. The measuring device 5a A/D-converts the measured value of the protein and transmits it as digital data to the computing devices 1, 2, and 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, and 3. Thereby, the arithmetic units 1, 2, and 3 can acquire the measured value of protein and the measured value of mRNA as digital data that can be processed. The measurement value of the renal disease marker is transmitted as digital data from, for example, a medical institution (not shown) via the Internet. Thereby, the computing devices 1, 2, and 3 can acquire the measured value of the renal disease marker as digital data.

4. Prediction of renal disease
4-1. Outline In the first embodiment, the renal disease of the subject is predicted using the measurement values obtained by performing the method of “2. Method for obtaining each measurement value” above.

More specifically, the method for predicting a renal disease of a subject in the present 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 sample collected from the subject, and a measurement value acquired in the acquisition step. A prediction step of predicting the renal disease based on the above.

In the present embodiment, specifically, the measured values obtained by the above “2. Method for obtaining measured values” are compared with predetermined reference values corresponding to biomarkers of the respective measured values, and the measured values are compared. Is outside the reference range, the subject can be determined to have renal disease.

Further, when the measured value of the biomarker of the first group 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 biomarkers of the two groups are within the range of the reference value, it may be determined that FGF23 is involved in the renal disease. More preferably, when the measured value of the biomarker of the first group is outside the range of the standard value and the measured value of the biomarker of the second group is within the range of the standard value, FGF23 is involved in the renal disease. You may decide that

The above steps may be performed by the CPU 101, which will be described later, or may be performed by an inspector.

Furthermore, in the method for predicting renal disease, when it is predicted that the disease is renal disease, a step of performing treatment for improving renal disease (for example, diet therapy such as low phosphorus diet) may be included. ..

4-2. Apparatus for Predicting Renal Function The first embodiment includes an apparatus for predicting renal disease of a subject, which is controlled by the CPU 101 executing the following calculation means according to a program described below:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group consisting of, and predicting means for predicting the renal disease based on the measured value acquired by the acquiring means.

In the present embodiment, the renal disease can be predicted by the system 100 (FIGS. 1 and 2) including the arithmetic unit 1 as the above device.

FIG. 3 is a block diagram for explaining the function of the arithmetic unit 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 installing the program according to the present invention in, for example, 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 measured value acquisition unit 11 and the prediction unit 14 illustrated in FIG. 3, respectively. The reference value acquisition unit 12 and the measurement value comparison unit 13 may have any configurations.

In the present embodiment, the measured value M11 of the biomarker protein is loaded from the measuring device 5a into the computing device 1, and the measured value M21 of the biomarker mRNA is loaded from the measuring device 5b into the computing device 1. 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 loaded into the computing device 1 via the Internet, for example.

The measurement value M11 of the biomarker protein and the measurement value M21 of the biomarker mRNA may be taken in 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 of the arithmetic device 1 or the memory 102.

In addition, each of the functional blocks of the measured value acquisition unit 11, the reference value acquisition unit 12, the measured 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 may be used. It may be processed in a distributed manner. 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 executed by the CPU of the first computer, and the functions of the prediction unit 14 are executed by the CPU of another second computer. It may be configured to be performed.

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

The terms used in this section are explained in I. 4-1. The description of is used.

4-3. Operation and Program of Arithmetic Device Even if the arithmetic device 1 according to the first embodiment executes the following method for predicting a renal disease of the present invention, the method for predicting a renal disease according to the first embodiment will be executed. Good.

FIG. 4 is a flowchart showing the operation of the arithmetic unit 1 according to the first aspect of the present invention. Note that the measurement value acquisition unit 11 shown in FIG. 3 performs step S11 of FIG. 4, the reference value acquisition unit 12 performs step S12 of FIG. The processing of steps S15 and S16 shown in FIG.

In step S11, first, the input of the acquisition start of the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA from the examiner is received from the input unit 8 or the measurement start from the measurement device 5a or 5b. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command. 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 in response to the input of the acquisition start or the comparison start of the measurement value by the inspector from the input unit 8. Accordingly, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are acquired.

Next, in step S13, the measurement value comparison unit 13 causes 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 value. The reference value R22 of the marker mRNA is compared.

Next, in step S14, the prediction unit 14 predicts that the subject does not have renal disease when the result of the comparison in step S13 is within the reference value range of each biomarker (step S15). In addition, the prediction unit 14 predicts that the subject has renal disease if the result of the comparison in step S13 is outside the range of the reference value of each biomarker (step S16). Step S14, step S15, and step S16 correspond to the prediction process described in the claims.

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

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

The terms used in this section are explained in I. 4-1. The description of is used.

5. Prediction of abnormal phosphorus metabolism
5-1. Overview In the second embodiment, the abnormal measurement of phosphorus metabolism in a subject is predicted using the measurement values obtained by performing the method of “2. Method for obtaining measurement values” above.

More specifically, the method for predicting abnormal phosphorus metabolism in a subject in the present embodiment measures 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 sample collected from the subject, and the measurement value acquired in the acquisition step And a prediction step of predicting the abnormal phosphorus metabolism based on the above.

In the present embodiment, specifically, the measured values acquired by the above “2. Method for acquiring each measured value” are compared with a predetermined reference value corresponding to each measured value, and the measured value is the reference value. If it is outside the value range, it can be determined that the subject has abnormal phosphorus metabolism.

Further, when the measured values of the biomarkers of the first group are out of the reference range, it may be determined that FGF23 is involved in the abnormal phosphorus metabolism. Further, when the measured values of the biomarkers of the two groups are within the range of the reference value, it may be determined that FGF23 is involved in the abnormal phosphorus metabolism. More preferably, when the measured value of the biomarker of group 1 is outside the range of the standard value and the measured value of the biomarker of group 2 is within the range of the standard value, FGF23 is involved in the abnormal phosphorus metabolism. You may decide that

The above steps may be performed by the CPU 101, which will be described later, or may be performed by an inspector.

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

5-2. Apparatus for Predicting Abnormal Phosphorus Metabolism A second embodiment includes an apparatus for predicting abnormal phosphorus metabolism in a subject, which is controlled by CPU 101 by executing the following arithmetic functions by a program described below:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group, and predicting means for predicting the abnormal phosphorus metabolism based on the measured value acquired by the acquiring means.

In the present embodiment, the system 100 (FIGS. 1 and 2) including the arithmetic unit 1 as the above device can predict abnormal phosphorus metabolism.

FIG. 3 shows a block diagram for explaining the function of the arithmetic unit 2 according to the second embodiment. The configuration of the arithmetic unit 2 is as described in 4-2. This is the same as the arithmetic unit 1 described in.

The terms used in this section are explained in I. 5-1. The description of is used.

5-3. Operation and Program of Arithmetic Device In the method for predicting abnormal phosphorus metabolism according to the second embodiment, the arithmetic device 2 according to the second embodiment executes the following method for predicting abnormal phosphorus metabolism of the present invention by the following program. You may.

FIG. 5 is a flowchart showing the operation of the arithmetic unit 2 according to the second aspect of the present invention. Note that the measurement value acquisition unit 11 shown in FIG. 3 performs step S21 of FIG. 5, the reference value acquisition unit 12 performs step S22 of FIG. 5, the measurement value comparison unit 13 performs step S23 of FIG. The processing of steps S25 and S26 shown in FIG.

In step S21, first, the input of the acquisition start of the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA from the inspector is received from the input unit 8 or the measurement start from the measurement device 5a or 5b. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command. 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 according to the input of the acquisition start or the comparison start of the measurement value from the input unit 8 by the inspector or the measurement value acquisition unit 11. Accordingly, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are acquired.

Next, in step S23, the measurement value comparison unit 13 causes 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 value. The reference value R22 of the marker mRNA is compared.

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

The obtained prediction result is displayed on the display unit 9 of the arithmetic unit 2 (step S27) or recorded on the recording unit 103 in the arithmetic unit 2. Alternatively, it may be displayed on the display unit of a computer terminal, for example, in a medical institution outside the arithmetic unit 2 connected via the Internet.

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

The terms used in this section are explained in I. 5-1. The description of is used.

6. Prediction of FGF23 activation
6-1. Overview In the third embodiment, FGF23 activation in a subject is predicted using the measurement values obtained by performing the method of “2. Method for obtaining measurement values” above.

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

In the present embodiment, specifically, the measured values acquired by the above “2. Method for acquiring each measured value” are compared with a predetermined reference value corresponding to each measured value, and the measured value is the reference value. If it is outside the value range, the subject can be determined to have renal disease.

In addition, when the measured value of the biomarker of the group 1 is outside the range of the reference value, it may be determined that FGF23 is activated in the subject. In addition, when the measured values of the biomarkers of Group 2 are within the range of the reference value, it may be determined that FGF23 is activated in the subject. More preferably, FGF23 is activated in the subject when the measured value of the biomarker of group 1 is outside the range of the standard value and the measured value of the biomarker of group 2 is within the range of the standard value. You may decide.

The above steps may be performed by the CPU 101, which will be described later, or may be performed by an inspector.

Furthermore, in the method for predicting activation of FGF23, when it is predicted that the activation of FGF23, the treatment for improving the activation of FGF23 (for example, administration of anti-FGF antibody, high phosphorus diet, etc. The process of performing diet therapy may be included.

6-2. Apparatus for Predicting Activation of FGF23 The third embodiment includes an apparatus for predicting activation of FGF23 in a subject, which is controlled by the CPU 101 by executing the following arithmetic functions by a program described below: A measurement 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 a biomarker contained in a sample collected from the subject Acquisition means for acquiring the measurement value of at least one mRNA selected from the group, and prediction means for predicting the activation of the FGF23 based on the measurement value acquired by the acquisition means.

In this embodiment, the activation of the FGF 23 can be predicted by the system 100 (FIGS. 1 and 2) including the arithmetic unit 1 as the above device.

FIG. 3 is a block diagram for explaining the function of the arithmetic unit 3 according to the third embodiment. The configuration of the arithmetic unit 3 is the same as the above 4-2. This is the same as the arithmetic unit 1 described in.

The terms used in this section are explained in I. 6-1. The description of is used.

6-3. Operation and Program of Arithmetic Device The method for predicting activation of FGF23 in the third embodiment is the same as the method for predicting activation of FGF23 of the present invention in which the arithmetic device 3 according to the third embodiment uses the following program. You may execute.

FIG. 6 is a flowchart showing the operation of the arithmetic unit 3 according to the third aspect of the present invention. The measurement value acquisition unit 11 shown in FIG. 3 performs step S31 of FIG. 6, the reference value acquisition unit 12 performs step S32 of FIG. 6, the measurement value comparison unit 13 performs step S33 of FIG. The processing of steps S35 and S36 shown in 6 is executed respectively.

In step S31, first, the input of the acquisition start of the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA from the examiner is received from the input unit 8, or the measurement start from the measurement device 5a or 5b is started. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command. Step S31 corresponds to the acquisition process described in the claims.

Next, in step S32, the reference value acquisition unit 12 acquires the measurement value by the inspector in response to the input of the acquisition start of the measurement value or the input of the comparison start from the input unit 8, or the acquisition of the measurement value by the measurement value acquisition unit 11. Accordingly, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are acquired.

Next, in step S33, the measurement value comparison unit 13 causes 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 value. The reference value R22 of the marker mRNA is compared.

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

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

The computer program for predicting 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 hard disk, a semiconductor memory device such as a flash memory, or a recording medium such as an optical disk. The storage format of the program in the recording medium is not limited as long as the arithmetic device can read the program.

The terms used in this section are explained in I. 6-1. The description of is used.

II. Fourth and fifth embodiments
1. Explanation of Terms Terms used in the description of the fourth and fifth embodiments will be explained.

A “subject” is an individual to whom a test substance is administered, and preferably excludes humans. Examples of mammals other than humans include cows, horses, sheep, goats, pigs, dogs, cats, rabbits and monkeys. Humans, cats and dogs are preferable. Further, the age and sex of the individual does not matter.

The subject may or may not have a history of decreased renal function or other renal disease. Further, it may be a subject suffering from a disease associated with FGF23 or a subject not suffering from it.

In this embodiment, the sample is at least one selected from the group consisting of a sample collected from skin, a sample collected from a test tissue derived from skin, and a sample collected from test cells derived from skin. included. The sample collected from the skin is not limited as long as it is derived from the skin of the living body. Further, the skin-derived sample includes sweat, skin secretions, and the like. The method of collecting the skin is not limited, and the biopsy material, the skin attached to the piercing needle when the hole of the piercing is opened, and the abrasion of the skin surface can be increased.

Further, the sample may be fresh or may be stored. When the sample is stored, it can be stored in a room temperature environment, a refrigerating environment or a freezing environment, but is preferably frozen storage.

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

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

In the present specification, the “test substance” is a substance that can be a target for evaluating whether or not it is a candidate substance for an active ingredient, and is not particularly limited. For example, compounds, proteins, peptides, nucleic acids, lipids, sugars, glycolipids, glycoproteins, metals and the like can be mentioned. The test substance administration method is also not particularly limited. When the test substance is administered to the test cells or test tissues, the test substance can be administered to the culture medium at a concentration of 1 pg/ml to 1 mg/ml. Further, in the case of an individual subject, the test substance can be administered so as to be 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 specimen collected from a subject, a test tissue, or a test cell treated with a test substance may be referred to as a “test substance-treated specimen” in the present specification. A specimen collected from a subject, a test tissue, or a test cell that has not been treated with the test substance may be referred to as “untreated specimen” in the present specification.

In the present embodiment, "renal disease", "disease associated with FGF23", "biomarker", "measured value of at least one protein selected from the group consisting of biomarkers", "selected from the group consisting of biomarkers" Measured value of at least one mRNA", "predetermined reference value", "healthy individual", "plural samples", "plural values", "anti-biomarker antibody", "biomarker mRNA detection nucleic acid" For the explanation, see I. 1. The explanation of the term is incorporated herein by reference. In addition, the method for obtaining "measurement value of at least one protein selected from the group consisting of biomarkers" and "measurement value of at least one mRNA selected from the group consisting of biomarkers" is described in the above I. 2. According 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. 7 is a schematic view of a system 200 according to the 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 will be referred to as a screening apparatus (hereinafter, a screening apparatus having a similar hardware configuration and having a different functional configuration as described later), and thus the screening apparatuses having these different functional configurations are collectively referred to. Therefore, it is provided with a “screening device 4, 5”), an input unit 8, a display unit 9, a measuring device 5a, and a measuring device 5b.

The screening devices 4 and 5 are composed of, for example, general-purpose personal computers, and include a CPU 101 that performs data processing described later, a memory 102 that is used as a work area for data processing, a recording unit 103 that records the processed data, and each unit. A bus 104 for transmitting data between them and an interface unit 105 (hereinafter referred to as an I/F unit) for inputting/outputting data to/from an external device are provided. The input unit 8 and the display unit 9 are connected to the screening devices 4 and 5, the input unit 8 includes a keyboard and the like, and the display unit 9 includes a liquid crystal display and 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 arranged in different places, and these may be connected by a network. Further, the input unit 8 and the display unit 9 may be omitted and a device that does not require an operator may be used.

Further, the screening devices 4, 5, the measuring device 5a, and the measuring device 5b do not necessarily have to be arranged in one place, and a system in which devices provided in different places are communicably connected by a network is configured. But it is okay.

In the following description, unless otherwise specified, the processing performed by the screening apparatuses 4 and 5 means the processing actually performed by the CPU 101 of the screening apparatuses 4 and 5 based on the program stored in the recording unit 103 or the memory 102. To do. The CPU 101 temporarily stores necessary data (intermediate data, etc. in the process) using the memory 102 as a work area, and appropriately records data to be stored for a long time such as a calculation result in the recording unit 103.

The measuring device 5a is a device for measuring protein, and includes a sample storage space 51, a reaction part 52, and a detection part 53. The sample collected from the subject set in the sample storage space 51 is dispensed and incubated in the microplate on which the antibody-capturing anti-biomarker antibody is mounted, which is installed in the reaction section 52. After removing unreacted antigen as needed, the detection antibody is dispensed to the microplate and incubated. After removing unreacted antigen as necessary, a 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. To be done. Another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by microarray analysis, in which the reverse transcription reaction product set in the sample storage space 51 is distributed on the microarray chip set in the reaction part 52. After pouring, hybridization and washing, the sample is moved to the detection unit 53 to detect a signal.

Furthermore, another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by RT-PCR, in which the reverse transcription reaction product set in the sample storage space 51 is placed in the microtube set in the reaction part 52. Dispense, and then a quantitative PCR reagent is dispensed into a microtube. While performing the PCR reaction in the reaction section 52, the detection section 53 detects the signal in the tube.

The measurement 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 base sequence is analyzed in the sequence analysis unit 54.

The measuring devices 5a and 5b are connected to the screening devices 4 and 5 by wire or wirelessly. The measuring device 5a A/D-converts the measured value of the protein and sends it to the screening devices 4 and 5 as digital data. Similarly, the measuring device 5b A/D-converts the measured value of mRNA and transmits it as digital data to the screening devices 4 and 5. Thereby, the screening apparatuses 4 and 5 can acquire the measured value of protein and the measured value of mRNA as digital data that can be processed. The measurement value of the renal disease marker is transmitted as digital data from, for example, a medical institution (not shown) via the Internet. Thereby, the screening apparatuses 4 and 5 can acquire the measured value of the renal disease marker as digital data.

3. Screening for candidate substances of 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 is used by using the measurement value obtained by performing the method of “I. 2. Acquisition method of each measurement value” above. Screen for candidate substances.

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

This embodiment is preferably a sample collected from the skin of a subject (excluding human) not treated with a test substance, or a test derived from the skin, according to the above-mentioned “I.2. Specimen collected from tissue, and measurement value of corresponding biomarker protein in at least one untreated specimen selected from the group consisting of specimen collected from test cells derived from skin and/or mRNA of the protein It includes a step of obtaining a measurement value. Further, the measured value of each biomarker obtained from the test substance-treated sample is compared with the measured value during the corresponding untreated sample, and the test substance treatment is performed by comparing the measured value with the biomarker of the untreated sample. If the measured value of the biomarker of the sample changes, it can be determined that the test substance is an active ingredient that suppresses the functional expression of FGF23.

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

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

For example, I. 1. Among the biomarkers described above, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1 and at least one group 3 selected from Lrrc2 (3). Is upregulated in FGF23 knockout mice compared to mice fed a high phosphorus diet. In addition, at least one selected from the group consisting of Col15a1, Sparc, Col11a1, Clell11a, Serpinb6d, and Defb8 (referred to as group 4) has reduced expression in FGF23 knockout mice as compared to mice ingesting a high phosphorus diet. .. Furthermore, the expression of at least one selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 (the above-described 2 groups) does not change even in FGF23 knockout mice.

Therefore, regarding the biomarkers of the 3 groups, for example, in the case of a mouse ingesting a high-phosphorus diet as a test subject, the measured value of the biomarker of the test substance-treated sample is higher than that of the untreated sample. Then, the test substance can be determined to suppress the functional expression of FGF23. Regarding the biomarkers of the 4 groups, if the measurement value of the biomarker of the test substance-treated sample is decreased as compared to the biomarker measurement value of the untreated sample, using a mouse ingesting a high phosphorus diet as a subject. It can be determined that the test substance suppresses the functional expression of FGF23. Furthermore, in addition to the examination of these biomarkers, in the case of examining the biomarkers of the 2 groups in the same manner, if there is no difference between the biomarker measurement value of the untreated sample and the biomarker measurement value of the test substance-treated sample, It can be determined that the test substance specifically acts 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 is Thus, it can be determined that the measured value of the biomarker protein and/or the measured value of the mRNA of the protein is increased.

The measured value of the biomarker protein and/or the measured value of the mRNA of the protein in the test substance-treated sample is lower 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, 85% or less, preferably 70% or less, more preferably 50% or less of the measured value of the untreated sample, the test substance is It can be determined that the measured value of the biomarker protein and/or the measured value of the mRNA of the protein is decreased.

Further, in the screening, before the step (I), (i) an administration step of administering a test substance to a subject, a test tissue or a test cell, (ii) a test substance was administered in the step (i) It may include a step of collecting a sample from a subject, a test tissue, or a test cell, and (iii) a step of recovering a protein and/or mRNA from the sample obtained in the step (ii). Also in this case, the step (ii) and the step (iii) do not necessarily have to be continuously performed in the same engine, and for example, the sample collected in the step (ii) is sent to a third party engine. iii) or later may be implemented. Further, the step (iii) and the step (I) do not necessarily have to be continuously performed in the same institution, and for example, the protein and/or mRNA recovered in the step (iii) may be sent to a third party institution. You may implement (iii) or subsequent ones.

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

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

FIG. 9 is a block diagram for explaining the function of the screening device 4 according to the present 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 has 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 executing the screening program by the CPU 101. The first measurement value acquisition unit, the second measurement value acquisition unit, the measurement value comparison unit, and the determination unit described in the claims are the first measurement value acquisition unit 31 and the second measurement value shown in FIG. 9. It corresponds to the acquisition unit 32, the measured value comparison unit 33, and the candidate substance determination unit 34, respectively.

In this embodiment, the measured value M31 of the biomarker protein of the test substance-treated sample is taken into the screening device 4 from the measuring device 5a, and the measured value M41 of the biomarker mRNA of the test substance-treated sample is measured from the measuring device 5b. Taken in 4. 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 are recorded outside the screening device 4, and are loaded 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 in from a medical institution (not shown) via a network. Further, 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 may be recorded in advance in the recording unit 103 of the cleaning device 4 or the memory 102.

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 is not executed by a single CPU. It is not always necessary and may be processed in a distributed manner 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.

Further, the screening apparatus 4 executes the program according to the present invention in, for example, an execution format (for example, generated by being converted from a programming language by a compiler) in order to perform the processing of steps S41 to S47 described in FIG. 10 below. Preliminarily recorded in the recording unit 103, the screening device 4 performs processing using the program recorded in the recording unit 103. The above program may be installed in the screening device 4 from a tangible recording medium 109 that is computer-readable and is not temporary, such as a CD-ROM, or the screening device 4 may be installed on the Internet (not shown). You may connect and download the program code of a program via the internet.

3-3. Screening Device Operation and Program In the screening of the candidate substance of the active ingredient for suppressing the functional expression of FGF23 in the fourth embodiment, the screening device 4 according to the fourth embodiment uses You may perform the screening of the candidate substance of an active ingredient for suppressing the functional expression of FGF23.

FIG. 10 is a flowchart showing the operation of the screening device 4 according to the fourth aspect of the present invention. Note that the first measurement value acquisition unit 11 shown in FIG. 9 performs step S41 of FIG. 10, the second measurement value acquisition unit 32 performs step S42 of FIG. 10, and the measurement value comparison unit 33 performs step S43 of FIG. The candidate substance determination unit 34 executes the processes of steps S45 and S46 shown in FIG. 10, respectively.

In step S41, first, by accepting an input from the examiner from the input unit 8 to start the acquisition of the measurement value M31 of the biomarker protein of the test substance-treated sample or the measurement value M41 of the biomarker mRNA of the test substance-treated sample, Alternatively, by accepting a measurement start command from the measuring 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 corresponding to each, and treats the test substance. 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, the candidate substance determination unit 34 determines that the test substance is a candidate substance for the active ingredient when the result of the comparison in step S43 changes (step S45). In addition, the candidate substance determination unit 34 determines that the test substance is not a candidate substance for the active ingredient when the result of the comparison in step S43 has not changed (step S46). Step S44, step S45, and step 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 on the recording unit 103 in the screening device 4. Alternatively, it may be displayed on the display unit of a computer terminal outside the screening device 4 connected via the Internet, for example, in a medical institution.

The computer program for screening the candidate substance of the 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 hard disk, a semiconductor memory device such as a flash memory, or a recording medium such as an optical disk. The storage format of the program in the recording medium is not limited as long as the screening device can read the program.

4. Screening candidate substances for active ingredients for activating FGF23 function
4-1. Outline In the fifth embodiment, an active ingredient for activating the function of FGF23 is obtained by using the measurement values obtained by performing the method of “I. Screen for candidate substances.

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

This embodiment is preferably a sample collected from the skin of a subject (excluding human) not treated with a test substance, or a test derived from the skin, according to the above-mentioned “I.2. Specimen collected from tissue, and measurement value of corresponding biomarker protein in at least one untreated specimen selected from the group consisting of specimen collected from test cells derived from skin and/or mRNA of the protein It includes a step of obtaining a measurement value. Further, the measured value of each biomarker obtained from the test substance-treated sample is compared with the measured value during the corresponding untreated sample, and the test substance treatment is performed by comparing the measured value with the biomarker of the untreated sample. If the measured value of the biomarker of the sample changes, it can be determined that the test substance is an active ingredient that activates the function of FGF23.

Here, activation of the function of FGF23 means activation of the original function of FGF23. An example of activation of the function of FGF23 is, for example, to reduce the concentration of inorganic phosphorus in blood. In addition, activation of the function of FGF23 includes an increase in the expression of FGF23.

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

For example, the above II. Regarding the three groups of biomarkers described in 4-1 above, if the measurement value of the biomarker of the test substance-treated sample is decreased as compared with the biomarker measurement value of the untreated sample, for example, using a normal mouse as the subject. The test substance can be determined to have activated the function of FGF23. Regarding the biomarkers of group 4, if the measured value of the biomarker of the test substance-treated sample is higher than that of the untreated sample with the normal mouse as the subject, the test substance is , The function of FGF23 can be determined to be activation. Furthermore, in addition to the examination of these biomarkers, in the case of examining the biomarkers of the 2 groups in the same manner, if there is no difference between the biomarker measurement value of the untreated sample and the biomarker measurement value of the test substance-treated sample, It can be determined that the test substance specifically acts 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 is Thus, it can be determined that the measured value of the biomarker protein and/or the measured value of the mRNA of the protein is increased.

The measured value of the biomarker protein and/or the measured value of the mRNA of the protein in the test substance-treated sample is lower 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, 85% or less, preferably 70% or less, more preferably 50% or less of the measured value of the untreated sample, the test substance is It can be determined that the measured value of the biomarker protein and/or the measured value of the mRNA of the protein is decreased.

Further, in the screening, before the step (I), (i) an administration step of administering a test substance to a subject, a test tissue or a test cell, (ii) a test substance was administered in the step (i) It may include a step of collecting a sample from a subject, a test tissue, or a test cell, and (iii) a step of recovering a protein and/or mRNA from the sample obtained in the step (ii). Also in this case, the step (ii) and the step (iii) do not necessarily have to be continuously performed in the same engine, and for example, the sample collected in the step (ii) is sent to a third party engine. iii) or later may be implemented. Further, the step (iii) and the step (I) do not necessarily have to be continuously performed in the same institution, and for example, the protein and/or mRNA recovered in the step (iii) may be sent to a third party institution. You may implement (iii) or subsequent ones.

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

In this embodiment, screening can be performed by the system 200 (FIGS. 7 and 8) including the screening device 5 as the above device.

FIG. 9 shows a block diagram for explaining the function of the screening apparatus 5 according to the present embodiment. The configuration of the screening device 5 is similar to that of the screening device 4 described in II.3-2 above.

4-3. Screening Device Operation and Program In the screening of the candidate substance of the active ingredient for activating the function of FGF23 in the fifth embodiment, the screening device 5 according to the fifth embodiment uses The screening of the candidate substance for the active ingredient for activating the function of FGF23 of

FIG. 11 is a flowchart showing the operation of the screening device 5 according to the fifth embodiment of the present invention. Note that the first measurement value acquisition unit 31 shown in FIG. 9 performs step S51 of FIG. 11, the second measurement value acquisition unit 32 performs step S52 of FIG. 11, and the measurement value comparison unit 33 performs step S53 of FIG. The candidate substance determination unit 34 executes the processes of steps S55 and S56 shown in FIG. 11, respectively.

In step S51, first, by receiving an input from the examiner from the input unit 8 for starting the acquisition of the measurement value M31 of the biomarker protein of the test substance-treated sample or the measurement value M41 of the biomarker mRNA of the test substance-treated sample, Alternatively, by accepting a measurement start command from the measuring 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 biomarker protein measurement value M32 of the untreated sample and the biomarker mRNA measurement value M42 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 corresponding to each, and treats the test substance. 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, the candidate substance determination unit 34 determines that the test substance is a candidate substance for the active ingredient if the result of the comparison in step S53 has changed (step S55). In addition, the candidate substance determination unit 34 determines that the test substance is not a candidate substance for the active ingredient when the result of the comparison in step S53 has not changed (step S56). Step S54, step S55, and step 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 on the recording unit 103 in the screening device 4. Alternatively, it may be displayed on the display unit of a computer terminal outside the screening device 4 connected via the Internet, for example, in a medical institution.

The computer program for screening the candidate substance of the 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 S51 to S57. The computer program may be stored in a hard disk, a semiconductor memory device such as a flash memory, or a recording medium such as an optical disk. The storage format of the program in the recording medium is not limited as long as the screening device can read the program.

III. Sixth embodiment
1. Explanation of Terms Terms used in the description of the sixth embodiment will be explained.

The terms used in this embodiment are explained in I. 1. Is incorporated herein by reference. In addition, the method for obtaining "measurement value of at least one protein selected from the group consisting of biomarkers" and "measurement value of at least one mRNA selected from the group consisting of biomarkers" is described in the above I. 2. According to.

In this embodiment, the “disease” is not limited. For example, the disease may include any disease or abnormality that may develop in each organ of the individual. It also includes abnormalities (also referred to as "pre-lesion") peculiar to the specific disease that occur before reaching the disease. As diseases, preferably, ischemic diseases such as thrombosis, embolism, and stenosis (especially heart, brain, lung, large intestine, etc.); circulatory disorders such as aneurysm, varicose vein, congestion and hemorrhage (aorta, vein, lung) , Allergic bronchitis, glomerulonephritis, etc.; dementia, Parkinson's disease, amyotrophic lateral sclerosis, myasthenia gravis, etc. degenerative diseases (nerve, skeleton) Muscle, etc.); tumor (benign epithelial tumor, benign non-epithelial tumor, malignant epithelial tumor, malignant non-epithelial tumor); metabolic disease (abnormal glucose metabolism, abnormal lipid metabolism, abnormal electrolytes); infectious disease (bacteria , Viruses, rickettsia, chlamydia, fungi, etc., protozoa, parasites, etc.) and the like. Further, the above-mentioned diseases include diseases that cause systemic symptoms. Examples of the diseases that cause systemic symptoms include autoimmune diseases such as systemic lupus erythematosus and multiple sclerosis; metabolic disorders such as hereditary mucopolysaccharidosis.

2. Hardware Configuration The hardware configuration used in the sixth embodiment will be described with reference to FIGS. 12 and 13.

FIG. 12 is a schematic view of a system 300 according to the sixth embodiment of the present invention. The system 300 will be referred to as a computing device (hereinafter, a computing device having a similar hardware configuration and having a different functional configuration as described later). Therefore, the computing devices having these different functional configurations are collectively referred to. Therefore, it is provided with an “arithmetic unit 6”), an input unit 8, a display unit 9, a measuring unit 5a, and a measuring unit 5b.

The arithmetic unit 6 is composed of, for example, a general-purpose personal computer, and includes a CPU 101 that performs data processing described later, a memory 102 that is used as a work area for data processing, a recording unit 103 that records the processed data, and a unit between each unit. A bus 104 for transmitting data via the I/F unit and an interface unit 105 (hereinafter referred to as an I/F unit) for inputting/outputting data to/from an external device. The input unit 8 and the display unit 9 are connected to the arithmetic unit 6, the input unit 8 is composed of a keyboard and the like, and the display unit 9 is composed of a liquid crystal display and 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 arranged in different places, and these may be connected by a network. Further, the input unit 8 and the display unit 9 may be omitted and a device that does not require an operator may be used.

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

In the following description, unless otherwise specified, 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, etc. in the process) using the memory 102 as a work area, and appropriately records data to be stored for a long time such as a calculation result in the recording unit 103.

The measuring device 5a is a device for measuring protein, and includes a sample storage space 51, a reaction part 52, and a detection part 53. The sample collected from the subject set in the sample storage space 51 is dispensed and incubated in the microplate on which the antibody-capturing anti-biomarker antibody is mounted, which is installed in the reaction section 52. After removing unreacted antigen as needed, the detection antibody is dispensed to the microplate and incubated. After removing unreacted antigen as necessary, a 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. To be done. Another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by microarray analysis, in which the reverse transcription reaction product set in the sample storage space 51 is distributed on the microarray chip set in the reaction part 52. After pouring, hybridization and washing, the sample is moved to the detection unit 53 to detect a signal.

Furthermore, another embodiment of the measuring device 5a is a device for measuring the measured value of mRNA by RT-PCR, in which the reverse transcription reaction product set in the sample storage space 51 is placed in the microtube set in the reaction part 52. Dispense, and then a quantitative PCR reagent is dispensed into a microtube. While performing the PCR reaction in the reaction section 52, the detection section 53 detects the signal in the tube.

The measurement 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 base sequence is analyzed in the sequence analysis unit 54.

The measuring devices 5a and 5b are connected to the computing device 6 by wire or wirelessly. The measuring device 5a A/D-converts the measured value of the protein and sends it to the arithmetic device 6 as digital data. Similarly, the measuring device 5b A/D-converts the measured value of mRNA and transmits it to the computing device 6 as digital data. As a result, the arithmetic device 6 can acquire the measured value of protein and the measured value of mRNA as digital data that can be processed. The measurement value of the renal 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 measured value of the renal disease marker as digital data.

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

More specifically, a step of acquiring a measurement value of biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the measurement acquired in the acquisition step. Determining that FGF23 is involved in the disease based on the value.

In the present embodiment, specifically, the measured values acquired by the above “I.2. Method for acquiring measured values” are compared with predetermined reference values corresponding to the measured values, and the measured values are If it is outside the reference value range, it can be determined that FGF23 is involved in the disease.

Further, when the measured values of the biomarkers of the first group are out of the range of the reference value, it may be determined that FGF23 is involved in the disease. Furthermore, when the measured values of the biomarkers of the two groups are within the range of the reference value, it may be determined that FGF23 is involved in the disease. More preferably, when the measured value of the biomarker of the first group is outside the range of the reference value and the measured value of the biomarker of the second group is within the range of the reference value, the abnormal phosphometabolism and FGF23 in the disease are caused. May be determined to be involved.

For example, the above II. 3-1. The three groups of biomarkers mentioned have increased expression in FGF23 knockout mice compared to mice inoculated with a high phosphorus diet. In addition, the expression of the biomarkers in Group 4 is reduced in FGF23 knockout mice as compared to mice inoculated with a high phosphorus diet. Furthermore, the expression of at least one selected from the group consisting of Aldh11, Col5a1, Col3a1, C1qtnf6, and Col1a1 (the above-described 2 groups) does not change even in FGF23 knockout mice.

Therefore, for the three groups of biomarkers, for example, if the measured value is increased in a subject having a disease as compared with the reference value of each biomarker, the functional expression of FGF23 is suppressed in the disease. You can decide that Further, regarding the biomarkers of the 4 groups, if the measured value is lower than the reference value of each biomarker in a subject having a disease, the functional expression of FGF23 is suppressed in the disease. Can be determined. Furthermore, in addition to the examination of these biomarkers, when the biomarkers of the 2 groups are similarly examined, if the measured value in the subject does not change compared with the reference value, the function of FGF23 in the disease is It can be determined that it is acting specifically.

The above steps may be performed by the CPU 101, which will be described later, or may be performed by an inspector.

Furthermore, in the method for predicting whether FGF23 is involved in the above-mentioned disease, when it is predicted that FGF23 is involved in the disease, a treatment for improving the expression of FGF23 function (for example, a low phosphorus diet). May be included.

3-2. A sixth embodiment of the device for predicting whether or not FGF23 is involved in a disease relates to the involvement of FGF23 in a disease in which the following means are controlled by the CPU 101 executing the following arithmetic functions by a program described below. Including a predictor to predict:
First measurement value acquisition means for acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the first measurement value acquisition means A determining means that determines that FGF23 is involved in the disease based on the measurement value obtained by.

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

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

3-3. Operation and Program of Arithmetic Device The method for predicting the involvement of FGF23 in a disease in the sixth embodiment is such that the arithmetic device 6 according to the sixth embodiment uses the following program to cause FGF23 to participate in the following diseases of the present invention. A method of predicting whether or not there is may be executed.

FIG. 15 is a flowchart showing the operation of the arithmetic unit 6 according to the sixth aspect of the present invention. It should be noted that the measured value acquisition unit 11 shown in FIG. 14 performs step S61 of FIG. 15, the reference value acquisition unit 12 performs step S62 of FIG. 15, the measured value comparison unit 13 performs step S63 of FIG. The processing of steps S65 and S66 shown in 15 is executed.

In step S61, first, the input of the acquisition start of the measurement value M11 of the biomarker protein or the measurement value M21 of the biomarker mRNA from the examiner is received from the input unit 8 or the measurement start from the measurement device 5a or 5b. The measurement value acquisition unit 11 starts acquisition of the measurement value by receiving the command. 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 in response to the input of the acquisition start or the comparison start of the measurement value from the input unit 8 by the inspector. Accordingly, the reference value R12 of the biomarker protein and the reference value R22 of the biomarker mRNA are acquired.

Next, in step S63, the measurement value comparison unit 13 causes 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 value. The reference value R22 of the marker mRNA is compared.

Next, in step S64, the prediction unit 14 predicts that FGF23 is not involved in the disease when the result of the comparison in step S63 is within the reference value range of each biomarker (step S65). .. In addition, the prediction unit 14 predicts that FGF23 is involved in the disease when the result of the comparison in step S63 is outside the reference value range of each biomarker (step S66). Step S64, step S65, and step 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 on the recording unit 103 in the arithmetic device 6. Alternatively, it may be displayed on the display unit of a computer terminal, for example, in a medical institution outside the arithmetic device 6 connected via the Internet.

The 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 steps S61 to S67. The computer program may be stored in a hard disk, a semiconductor memory device such as a flash memory, or a recording medium such as an optical disk. The storage format of the program in the recording medium is not limited as long as the arithmetic device can read the program.

The terms used in this section are explained in the above III. 3-1. The description of is used.

IV. Test Reagent and Test Kit One example of the seventh embodiment of the present invention relates to a test reagent containing the anti-biomarker antibody used in the above-mentioned “I. 2. Method for obtaining measurement value”.

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

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

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

When the above-mentioned form is an antiserum containing anti-biomarker antibody or ascites in a dry or liquid state, a stabilizer such as β-mercaptoethanol or DTT; a protective agent such as albumin; polyoxyethylene (20) sorbitan At least one of surfactants such as monolaurate and polyoxyethylene (10) octyl phenyl ether, and preservatives such as sodium azide may be contained. When the form of the anti-biomarker antibody is a purified anti-biomarker antibody, an immunoglobulin fraction containing the anti-biomarker antibody or an IgG fraction containing the anti-biomarker antibody in a dry state or an aqueous solution, a phosphate buffer is further used. Buffer components such as liquids; stabilizers such as β-mercaptoethanol and DTT; protective agents such as albumin; salts such as sodium chloride; polyoxyethylene (20) sorbitan monolaurate, polyoxyethylene (10) octyl phenyl ether At least one of preservatives such as sodium azide and the like may be included.

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 “I. 2. Method for obtaining measured value” can be used. Further, in the present invention, the anti-biomarker antibody for capturing an antigen may be provided in a state of being immobilized on a solid phase surface or the like. The solid phase and immobilization are as exemplified in the above section “I.2. Method for obtaining measured value”. The solid phase is preferably a microplate.

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

When the anti-biomarker antibody for capturing an antigen is bound to a solid phase such as a microplate in advance, the test kit of the present embodiment includes a solid phase on which an anti-biomarker antibody for capturing an antigen is immobilized and an anti-biomarker for detection. A marker antibody can be included. Further, when the labeling substance is an enzyme, it may contain a substrate solution thereof.

The test kit described above is, for example, a kit as shown in FIG. The inspection kit 9 includes an outer box 94, a microplate 92 on which an antigen-capturing antibody is solid-phased, a first container 91a containing a detection anti-biomarker antibody labeled with a labeling substance, and a substrate solution that reacts with an enzyme. Includes a second container 91b that contains and the package insert 93 of the test kit. The attached document 93 may describe the handling method of the test kit, storage conditions, and the like. A container containing an aqueous medium for cleaning may be included in the outer box 94.

Another example of the seventh embodiment relates to a test reagent containing a 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 in the above "I. 1. Explanation of terms" can be used.

The test reagent containing the biomarker mRNA detecting nucleic acid used in the microarray may be in a freeze-dried state or in a state of being dissolved in a solution containing a buffer such as Tris-HCl, EDTA, salt or the like. When there are a plurality of target biomarker mRNAs, it is preferable to put 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 detected nucleic acid, and is, for example, glass, a polymer such as polypropylene, a nylon film or the like. 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 a crosslinker containing a reactive group for immobilizing the detection nucleic acid can be used.

Further, the test reagent containing the biomarker mRNA detection nucleic acid is provided as a kit together with the reagent, along with a medium such as a paper or a compact disc on which the information of the nucleic acid or the information for accessing the information is recorded. Good.

The test reagent containing the biomarker mRNA detection nucleic acid used for RT-PCR may be in a freeze-dried state or in a state of being dissolved in a solution containing a buffer such as Tris-HCl, EDTA, salt or the like. In addition, the primer may be provided in a state where the forward primer and the reverse primer are put in separate containers, or may be provided in a mixed state. Further, even when a quantitative probe is included, the quantitative probe may be provided in a state in which it is placed in a container different from each primer, or each primer and the quantitative probe may be provided in a mixed state.

Furthermore, the test reagent containing the biomarker mRNA detection nucleic acid may be provided in the form of a kit containing a forward primer and a reverse primer, or a forward primer, a reverse primer and a probe for quantification, and an attachment if necessary. Further, the kit may include reagents for quantitative PCR.

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

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

V. Eighth embodiment of the biomarker present invention, Hamp2, Actn3, Asb11, Cxcl13 , Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, A method for use as a skin biomarker 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.

Hereinafter, the outline of the present invention will be described in more detail with reference to examples, but the present invention is not construed as being limited to the examples.

Experimental Example I. Generation of Fgf23 gene mutant mouse
I-1. Construction of gRNA and Cas9 expression vector
Designed gRNA sequences (Fgf23g-RNA1, Fgf23-gRNA2) using Optimized CRISPR design tool [published on Massachusetts Institute of Technology, Zhang Lab's HP (http://crispr.mit.edu/)] and designed gRNA sequences. An oligo DNA encoding the gene was synthesized. The Fgf23 gene target sequence contained in these sequences is the sequence represented by GCACGCCCACCAGGAGTCTA (SEQ ID NO: 1) for Fgf23-gRNA1 and GCCCACCAGGAGTCTAAGGC (SEQ ID NO: 2) for Fgf23-gRNA2, and is a sequence present in exon 1 of the Fgf23 gene. is there. These were inserted into Cas9 expression vector pX330-U6-Chimeric_BB-CBh-hSpCas9 (pX330-Fgf23-gRNA1, pX330-Fgf23-gRNA2). Regarding the obtained vector, the nucleotide sequence of the gRNA insertion site was determined, and it was confirmed that the gRNA was inserted as designed. In addition, single-stranded oligodeoxynuleotides (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 Fgf23-gRNA1 and Fgf23-gRNA2 cleavage sites (FIG. 17A). ssODNs for Fgf23-gRNA1, 2:
CGGATAGGCTCTAGCAGTGCCCAAGCTGCAGACAGTGCAGAGCACGCCCACCAGGAGTCTcctcagaagaactcgtcaagaagCTAAAGGCAGGTCCCTAGCATTGCACAGCACTGAGTGGCTAATGCTGAGTTTGAAATCTGACA (SEQ ID NO: 3)

I-2. Generation of Fgf23 gene mutant mouse
pX330-Fgf23-gRNA1 and pX330-Fgf23-gRNA2 were injected together with the corresponding ssODNs into C57BL/6N Slc fertilized eggs. The injected fertilized egg was injected into the oviduct of a pseudopregnant ICR female mouse. The genotype of F0 mice was confirmed by PCR and direct sequence (see Table 5 for primers). F1 heterozygous mice were generated by mating F0 and wild type mice. By in vitro fertilization of the obtained F1 heterozygous mouse male and wild-type mouse female, F2 heterozygous mouse was produced, and in vitro fertilization of the obtained F2 heterozygous mouse female and F1 heterozygous mouse male, F3 mouse was produced. . In addition, F4 mice were prepared by in vitro fertilization of F3 homozygous mouse females, F3 heterozygous mouse females and F1 heterozygous mouse males. For the experiment, homozygotes of the F3 generation or later were used.

The genotype of each mouse was determined by direct sequence.

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

Experimental Example II. Preparation of disease model mouse
II-1. Creation of UNx/HPi model mouse and extraction of tissue
UNx/HPi mice (single nephrectomy-high phosphorus diet mice) were prepared by unilateral nephrectomy and breeding on a high phosphorus diet. As a control, a mouse raised on a low phosphorus diet after sham surgery was prepared.

1-1. A single nephrectomized mouse (C57BL/6J, 8-week-old male) was anesthetized by intraperitoneal administration of Avertin (250 mg/kg), the skin was incised from the back, and the right renal arteriovenous vein and the ureter were ligated. The distal side of the ligature was cut, the right kidney was removed, and the abdomen was closed. For controls, sham surgery was performed. In the sham operation, after exposing the right renal arteries and veins and the ureter, the abdomen was closed without ligation. In order to wait for complete recovery from the surgical invasion, the mice were fed with a normal diet containing 0.54% inorganic phosphorus (CE-2, CLEA Japan, Inc.) for 4 weeks.

1-2. Phosphorus load and tissue excision 4 weeks after the operation (12 weeks old), hemi nephrectomized mice were fed a high phosphorus diet containing 2% inorganic phosphorus (TD.10662, Oriental Bioservice) (hereinafter, referred to as renal disease). Also called a group). Sham-operated mice were given a low phosphorus diet containing 0.35% inorganic phosphorus (TD.10662 variant, Oriental Bioservice) (hereinafter also referred to as Sham group).

This chronic renal 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 residual kidney (left kidney) during the above-mentioned one-nephrectomy, but this modification did not perform ischemia-reperfusion.
Tissues were extracted after the initiation of a high phosphorus diet (low phosphorus diet in the Sham group), 1 week (E), 4 weeks (M) and 8 weeks (L).

The animal from which the tissue is to be removed is anesthetized by intraperitoneal administration of Avertin (250 mg/kg), and blood is drawn from the orbit to a EDTA-added tube and euthanized by cervical dislocation, organs and tissues (bone marrow, brain, Skin, heart, kidney, liver, lung, pancreas, skeletal muscle, spleen, testis, thymus, fat, large intestine, stomach, adrenal gland, aorta, eye, ileum, jejunum, pituitary gland, skull, salivary gland and thyroid gland) were removed. Wet weights of the excised organs and tissues were measured, and they were immediately frozen in liquid nitrogen and stored at -80°C. The collected blood was centrifuged at 1200 g for 10 minutes at room temperature. After centrifugation, the plasma of the supernatant was collected and stored at -80°C.

II-2. Creation of phosphorus-loaded mouse model and removal of tissue
Due to the loss of Fgf23 function, Fgf23 gene mutant mice develop a high phosphorus state after birth and die early. This time, using 4-week-old Fgf23 gene mutant mice (male and female) in the experiment, a phosphorus-loaded mouse model was used as this control group, and a WT mouse (C57BL/6N, 3-week-old male) was fed a high phosphorus diet for 1 week. Created by giving.

2-1. Phosphorus-loaded WT mice (C57BL/6N, 2-week-old male) were bred with lactating female mice for 1 week, then weaned, and a high phosphorus diet containing 2% inorganic phosphorus as a special phosphorus-containing diet (TD.10662, Oriental Bioservice) or , A low phosphorus diet containing 0.35% inorganic phosphorus (TD.10662 variant, Oriental Bioservice) or a normal diet containing 0.54% inorganic phosphorus (CE-2, CLEA Japan) was given for 1 week. The above models will be referred to as WT3W/HP1W, WT3W/LP1W, WT3W/ND1W below.

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

Experimental Example III: Measurement of inorganic phosphorus in plasma
E-/M-/L-UNx/HPi, E-/M-/L-Sham, 4-week-old Fgf23 gene mutant mouse, WT3W/HP1W, WT3W/LP1W, WT3W/ND1W The plasma inorganic phosphorus level was measured by an enzymatic method using 100 μl of each plasma sample.

Experimental Example IV. Gene expression analysis in each tissue
IV-1. RNA extraction from each tissue: Each cryopreserved tissue is homogenized in TRIzol Reagent (Thermo Fisher Scientific, MA, USA) with a PT10-35 GT Polytron homogenizer (KINEMATICA, Luzern, Switzerland) at 15,000 rpm for 10 seconds. After crushing in liquid nitrogen using a mortar and pestle and drying, homogenize with PT10-35 GT Polytron homogenizer at 15,000 rpm for 10 seconds in TRIzol Reagent, or Cell Destroyer PS1000 or PS2000 (Bio Medical Science Inc., (Tokyo, Japan) and homogenized at 4° C. for 45 seconds at 4,260 rpm using zirconia beads of different sizes (1.5 mm diameter×50, 3 mm diameter×5, 5 mm diameter×2). Then, to separate proteins, after incubation for 5 minutes at room temperature, 0.2 ml of chloroform was added to 1 ml of TRIzol, the tube was capped, and then 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 amount of 70% ethanol was added to the recovered aqueous phase, and the mixture was stirred, then 700 μl was applied to each RNeasy mini column (Qiagen), and purified RNA was recovered according to the RNeasy mini kit (Qiagen) standard protocol. The quality and concentration of the recovered RNA were confirmed with Nanodrop (Thermo Fisher Scientific, MA, USA).

IV-2. RNA expression analysis (RNASeq)
(1) Acquisition of RNAseq data Using the above sample, RNAseq data was acquired by the following procedure.
a. Quality inspection The quality of the received samples was verified by the following items.
・Concentration measurement using Nanodrop (spectrophotometer) ・Concentration measurement by Agilent 2100 Bioanalyzer ・Confirmation of quality
b. Sample preparation
A library for the next-generation sequencer HiSeq was prepared using the SureSelect Strand-Specific RNA Library Preparation Kit according to the following steps.
i. Poly (A)+ RNA (mRNA) was recovered from total RNA using oligo (dT) magnetic beads.
ii. RNA fragmentation
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 with magnetic beads
c. Data acquisition by next-generation sequencer Using the next-generation sequencer HiSeq 2500 or 4000 (illumina company), the sequence 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 company)
Reagent: TruSeq SBS Kit v3-HS (200 cycle) <FC-401-3001> (illumina)
ii. Single base extension reaction
iii. Removal of unreacted base
iv. Uptake of fluorescent signal
v. Removal of protecting groups and fluorescence
2 Cycles...3 Cycles... are repeated until 100 cycles.
vi. For reverse chain (Read2), perform i to vi up to 100 cycles

(2) Analysis of RNAseq data
(2)-1. Analysis of next-generation sequencer output data The following information processing was performed on the above output data.
i. Base call: The base sequence text data was obtained from the output raw analysis data (image data).
ii. Filtering: Read data was selected by predetermined filtering. iii. Sorting by Index array: Each sample data was sorted by Index information.

(2)-2. Secondary analysis of output data
The data file (Fastq format) obtained with Illumina Hiseq 2500 or 4000 was uploaded to Galaxy (https://usegalaxy.org/) downloaded to the local server. Then, it was analyzed using Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) to map each sequence to mouse genome map information mm10. The FPKM (RPKM) of the gene was calculated by analyzing the BAM file obtained with Bowtie2 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 primer with 0.5 to 1 μg of total RNA obtained from skull and skin as a template for cDNA synthesis. After the synthesized cDNA was diluted 20-fold with TE buffer (10 mM Tris-HCl pH8.0, 0.1 mM EDTA), according to the standard protocol of LightCycler 480 SYBR Green I Master (Roche, Basel, Switzerland), LightCycler480II (Roche) Real-time PCR was carried out at to measure the Cp value. The Cp value obtained for each gene was compared with that of Maea as a reference gene to quantify the relative expression level of each gene with respect to the reference gene. Table 5 shows the primer pairs used in the real-time PCR. All primers were designed by Primer-BLAST (NCBI).

IV-4. Analysis of differentially expressed genes To extract the differentially expressed genes, each transcript is analyzed using HTSeq-count (parameters are -r for pos and -s for no) for the sequence data mapped by Bowtie2. The annotation lead number of was counted. The obtained results were analyzed by DESeq2 (Love, MI, Huber, W. & Anders, S.; Genome biology 15, 550, doi:10.1186/s13059-014-0550-8 (2014)) with default settings. It was 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 For statistical analysis, Student's t-test was performed to obtain a significant difference. And, when the p value was smaller than 0.05, it was defined as significant difference.

IV-6. result
E-UNx/HPi vs. E-/M-/L-Sham (n=3) comparison, gene list with differential expression in skin (|log ₂ (fold-change)| >1, p-value < 0.05) was obtained by DESeq2 analysis. The expression of these genes in the skins of UNx/HPi and WT3W/HP1W, WT3W/LP1W, WT3W/ND1W mouse models was verified by qRT-PCR (n=6-9), and Figs. 18-1 to 18-7. And it was shown that there is a difference in expression among the 25 genes shown in Table 6. The difference in expression of WT3W/HP1W was compared with that of WT3W/LP1W.

In order to evaluate the relationship between these 25 genes and the functional expression of the Fgf23 gene, the expression in the skin of 4-week-old Fgf23 gene mutant mice (male and female) was verified by qRT-PCR (n=7). In addition, it was confirmed by qRT-PCR that the expression of Fgf23 was elevated in the bones (skull bones) 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 mouse, the blood inorganic phosphorus concentration is elevated. Therefore, the control of the Fgf23 gene-mutant mouse was a high phosphorus-loaded mouse of the same age (4 weeks old) (WT3W/ HP1W, n=3).

In the comparison between the Fgf23 gene mutant mouse and WT3W/HP1W, as shown in FIGS. 21-1 to 21-7 and Table 6, statistically significant increases and decreases in expression were observed in several genes. .. In the Fgf23 gene mutant mouse, a difference in expression due to sex difference was also observed. From the above, it was shown that the gene whose expression is changed in the hyperphosphorus state skin can be classified into four types of models depending on Fgf23 gene functional expression shown in FIG. 22.

Specifically, the four types are explained as follows.
Genes whose expression is regulated by the model I mechanism:
Expression is elevated in the skin due to renal disease and/or hyperphosphatemia. Expression of these genes is suppressed by the increased expression of FGF23 in bone due to renal disease and/or hyperphosphatemia, and there are two pathways that are not affected by the increased expression of FGF23.

Genes whose expression is regulated by the model II mechanism:
Expression is suppressed in the skin due to renal disease and/or hyperphosphatemia. The suppression of the expression of these genes includes a pathway that is suppressed by the increased expression of FGF23 in bone due to renal disease and/or a high phosphorus state, and two pathways that are not affected by the increased expression of FGF23.

Genes whose expression is regulated by the model III mechanism:
Expression is suppressed in the skin due to renal disease and/or hyperphosphatemia. Expression of these genes is suppressed by increased expression of FGF23 in bone due to renal disease and/or hyperphosphatemia.

Genes whose expression is regulated by the model IV mechanism:
Expression is suppressed in the skin due to renal disease and/or hyperphosphatemia. Expression of these genes is not affected by elevated FGF23 expression in bone due to renal disease and/or hyperphosphatemia.

1 arithmetic unit 1
2 arithmetic unit 2
3 arithmetic unit 3
4 Screening device 4
5 Screening device 5
6 arithmetic unit 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 (63)

A device for predicting renal disease in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group consisting of, and predicting means for predicting the renal disease based on the measured value acquired by the acquiring means. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d The device according to claim 1, wherein the device is at least one selected from the group consisting of, Col1a1, and Defb8. The predicting means compares the measured value with a predetermined reference value, and when the measured value is outside the range of the reference value, determines that the subject has renal disease. The apparatus according to 2. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. If it is within the range, the device according to claim 3, wherein FGF23 is determined to be involved in the renal disease. A program that, when executed by a computer, causes the computer to perform the following processing for predicting renal disease in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process for acquiring a measurement value of at least one mRNA selected from the group, and a prediction process for predicting the renal disease based on the measurement value acquired by the acquisition process. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d The program according to claim 5, wherein the program is at least one selected from the group consisting of, Col1a1, and Defb8. In the prediction process, the measured value is compared with a predetermined reference value, and if the measured value is outside the range of the reference value, it is determined that the subject has renal disease. 6. The program according to 6. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. If it is within the range, the program according to claim 7, wherein it is determined that FGF23 is involved in the renal disease. A method of predicting renal disease in a subject, comprising the steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of: and a prediction step of predicting the renal disease based on the measurement value acquired in the acquisition step. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 10. The method according to claim 9, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8. 10. The predicting step compares the measured value with a predetermined reference value, and when the measured value is outside the range of the reference value, determines that the subject has renal disease, 10. 10. The method according to 10. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. If it is within the range, it is determined that FGF23 is involved in the renal disease. A device for predicting abnormal phosphorus metabolism in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquiring means for acquiring a measured value of at least one mRNA selected from the group, and predicting means for predicting the abnormal phosphorus metabolism based on the measured value acquired by the acquiring means. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 14. The device of claim 13, wherein the device is at least one selected from the group consisting of:, Col1a1, and Defb8. 14. The prediction means compares the measured value with a predetermined reference value, and predicts that the subject has abnormal phosphorus metabolism when the measured value is outside the range of the reference value. Or the apparatus according to 14. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. 16. The device according to claim 15, wherein if it is within the range, it is determined that FGF23 is involved in the abnormal phosphorus metabolism. A program that, when executed by a computer, causes the computer to perform the following processing for predicting abnormal phosphorus metabolism in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process of acquiring a measurement value of at least one mRNA selected from the group, and a prediction process of predicting the abnormal phosphorus metabolism based on the measurement value acquired by the acquisition process. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d The program according to claim 17, wherein the program is at least one selected from the group consisting of, Col1a1, and Defb8. In the prediction process, the measured value is compared with a predetermined reference value, and if the measured value is outside the range of the reference value, it is predicted that the subject has abnormal phosphorus metabolism. Or the program according to item 18. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. 20. The program according to claim 19, wherein if it is within the range, it is determined that FGF23 is involved in the abnormal phosphorus metabolism. A method of predicting abnormal phosphorus metabolism in a subject, comprising the steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of, and a prediction step of predicting the abnormal phosphorus metabolism based on the measurement value acquired in the acquisition step. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 22. The method of claim 21, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8. 22. The predicting step compares the measured value with a predetermined reference value, and when the measured value is outside the range of the reference value, predicts that the subject has abnormal phosphorus metabolism. Or the method described in 22. Furthermore, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, at least from Il22ra2, Rbfox1, Rabfox1, Col6S11, Lbfox, Col15Sb, C15cl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl, Scl, C15cl1, Scl, and C15cl. When the measurement value of one biomarker is outside the range of the reference value, and/or the measurement value of at least one biomarker selected from the group consisting of Aldh112, Col5a1, Col3a1, C1qtnf6, and Col1a1 is within the reference value range. 24. The method according to claim 23, wherein, when it is within the range, it is determined that FGF23 is involved in the abnormal phosphorus metabolism. Device for predicting activation of FGF23 in a subject having the following means:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject Acquisition means for acquiring a measurement value of at least one mRNA selected from the group, and prediction means for predicting activation of the FGF23 based on the measurement value acquired by the acquisition means. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 26. The device of claim 25, which is at least one selected from the group consisting of:, Col1a1, and Defb8. The prediction unit compares the measured value with a predetermined reference value, 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 outside the range of reference values, and/or from the group consisting of Aldh1l2, Col5a1, Col3a1, C1qtnf6, and Col1a1. 27. The device of claim 25 or 26, which predicts that FGF23 is activated in the subject if the at least one biomarker selected is within the reference range. A program that, when executed by a computer, causes the computer to perform the following process for predicting activation of FGF23 in a subject:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition process of acquiring a measurement value of at least one mRNA selected from the group consisting of: and a prediction process of predicting activation of FGF23 based on the measurement value acquired in the acquisition process. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 29. The program according to claim 28, which is at least one selected from the group consisting of, Col1a1, and Defb8. Wherein in the prediction process, compares the measured value with a predetermined reference value, 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 outside the range of reference values, and/or from the group consisting of Aldh1l2, Col5a1, Col3a1, C1qtnf6, and Col1a1. 30. The program according to claim 28 or 29, which predicts that FGF23 is activated in the subject when at least one biomarker selected is within a reference range. A method of predicting activation of FGF23 in a subject, comprising the steps of:
From the measurement value of at least one protein selected from the group consisting of biomarkers contained in the skin-derived sample collected from the subject, and/or, from the biomarker contained in the sample collected from the subject An acquisition step of acquiring a measurement value of at least one mRNA selected from the group consisting of: and a prediction step of predicting activation of the FGF23 based on the measurement value acquired in the acquisition step. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 32. The method of claim 31, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8. The prediction step, comparing the measured value with a predetermined reference value, 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 outside the range of reference values, and/or from the group consisting of Aldh1l2, Col5a1, Col3a1, C1qtnf6, and Col1a1. 33. The method of claim 31 or 32, wherein FGF23 is predicted to be activated in the subject if at least one biomarker selected is within the reference range. A screening device for a candidate substance of an active ingredient for suppressing the functional expression of FGF23, which has the following means:
Selected from the group consisting of a sample collected from the skin of a subject (excluding humans) treated with a test substance, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin First measurement value acquisition means for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein of at least one test substance-treated sample, and the measurement acquired by the first measurement value acquisition means A determination means for determining that the test substance is a candidate substance for an active ingredient based on the value. From a group consisting of a specimen collected from the skin of a subject (excluding humans) not treated with the test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from a test cell derived from the skin Second measurement value acquisition means for acquiring the measurement value of the corresponding biomarker protein and/or the measurement value of the mRNA of the protein in at least one untreated sample selected, and the measurement value of the test substance-treated sample and Measurement value comparing means for comparing the measurement values of untreated specimens,
Further has
The apparatus according to claim 34, wherein the determining unit determines that the test substance is a candidate substance for an active ingredient based on the comparison result of the measurement value comparing unit. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 36. The device of claim 35, which is at least one selected from the group consisting of:, Col1a1, and Defb8. A screening program that causes a computer to perform the following processing for screening a candidate substance of an active ingredient for suppressing the functional expression of FGF23 when the computer executes the screening process:
Selected from the group consisting of a sample collected from the skin of a subject (excluding humans) treated with a test substance, a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin The first measurement value acquisition process for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein in at least one test substance-treated sample, and the first measurement value acquisition process A determination process of determining that the test substance is a candidate substance for the active ingredient based on the measured value. Furthermore, it consists of a sample collected from the skin of a subject (excluding humans) not treated with the test substance, a sample collected from the test tissue derived from the skin, and a sample collected from the test cell derived from the skin. A second measurement value acquisition process of acquiring a measurement value of the corresponding biomarker protein of at least one untreated sample selected from the group and/or a measurement value of the mRNA of the protein, and the measurement value of the test substance-treated sample And a measurement value comparison process for comparing the measurement values of untreated samples,
To the computer,
38. The program according to claim 37, wherein in the determination process, the test substance is determined to be a candidate substance for an active ingredient based on the comparison result of the measurement value comparison process. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 39. The program according to claim 37, which is at least one selected from the group consisting of, Col1a1, and Defb8. A method for screening a candidate substance for an active ingredient for suppressing the functional expression of FGF23, which comprises the following steps:
(I) Consists of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from test cells derived from the skin Obtaining a measurement value of biomarker protein in at least one test substance-treated sample selected from the group and/or a measurement value of mRNA of the protein,
(II) A step of determining that the test substance is a candidate substance for an active ingredient based on the measured value obtained in the step (I). Between the steps (I) and (II), the measurement value of the test substance-treated sample obtained in the step (I) and the skin of the subject (excluding human) not treated with the test substance are collected. Value of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And/or further comprising the step of comparing the measured value of mRNA of said protein,
Step (II) is a step of determining that the test substance is a candidate substance for an active ingredient based on the comparison result.
41. The method of claim 40, further comprising: Before the step (I),
(I) a step of treating a subject (excluding human), a test tissue or a test cell derived from skin with a test substance,
(Ii) a step of collecting a sample from the subject, a test tissue or a test cell treated with the test substance in the step (i), and (iii) a protein and/or a protein from the sample obtained in the step (ii) 42. The method according to claim 40 or 41, comprising the step of recovering mRNA. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 43. 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 device for a candidate substance of an active ingredient for activating the function of FGF23, which has the following means:
Biomarker in a sample collected from the skin of a subject (excluding humans) treated with a test substance, or in a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) Based on the measurement value obtained by the first measurement value acquisition means for obtaining the measurement value of the protein and/or the measurement value of the mRNA of the protein, and the measurement value obtained by the first measurement value acquisition means, the test substance is an active ingredient. A determination means for determining a candidate substance. Biomarker protein of a sample collected from the skin of a subject (excluding human) that has not been treated with the test substance, or a sample (untreated sample) collected from a test tissue derived from the skin or a test cell derived from the skin Second measurement value acquisition means for acquiring the measurement value of and/or the measurement value of the mRNA of the protein, and measurement value comparison means for comparing the measurement value of the test substance-treated sample and the measurement value of the untreated sample,
Further has
The screening device according to claim 44, wherein the determining unit determines that the test substance is a candidate substance for an active ingredient based on the comparison result of the measurement value comparing unit. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 46. The device of claim 45, which is at least one selected from the group consisting of:, Col1a1, and Defb8. A screening program that causes a computer to perform the following processing for screening a candidate substance of an active ingredient for activating the function of FGF23 when the computer executes:
Biomarker in a sample collected from the skin of a subject (excluding humans) treated with a test substance, or in a sample collected from a test tissue derived from the skin or a test cell derived from the skin (test substance treated sample) Based on the measurement value acquired by the first measurement value acquisition process for acquiring the measurement value of protein and/or the measurement value of mRNA of the protein, and the measurement value acquired by the first measurement value acquisition process, the test substance is an active ingredient. Determination process to determine that the substance is a candidate substance. Furthermore, the biopsy of a specimen collected from the skin of a subject (excluding humans) that has not been treated with the test substance, or a specimen (untreated specimen) collected from a test tissue derived from the skin or a test cell derived from the skin Second measurement value acquisition process for acquiring the measurement value of the marker protein and/or the measurement value of the mRNA of the protein, and the measurement value comparison process for comparing the measurement value of the test substance-treated sample and the measurement value of the untreated sample ,
To the computer,
The program according to claim 47, wherein in the determination processing, it is determined that the test substance is a candidate substance for an active ingredient based on the comparison result of the measurement value comparison processing. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 49. The program according to claim 47 or 48, which is at least one selected from the group consisting of, Col1a1, and Defb8. A method for screening a candidate substance for an active ingredient for activating the function of FGF23, which comprises the following steps:
(I) Consists of a specimen collected from the skin of a subject (excluding humans) treated with a test substance, a specimen collected from a test tissue derived from the skin, and a specimen collected from test cells derived from the skin Obtaining a measurement value of biomarker protein in at least one test substance-treated sample selected from the group and/or a measurement value of mRNA of the protein,
(II) A step of determining that the test substance is a candidate substance for an active ingredient based on the measured value obtained in the step (I). Between the steps (I) and (II), the measurement value of the test substance-treated sample obtained in the step (I) and the skin of the subject (excluding human) not treated with the test substance are collected. Value of the corresponding biomarker protein in at least one untreated sample selected from the group consisting of a sample collected from a test tissue derived from skin, and a sample collected from a test cell derived from skin And/or further comprising the step of comparing the measured value of mRNA of said protein,
Step (II) is a step of determining that the test substance is a candidate substance for an active ingredient based on the comparison result.
The method of claim 50. Before the step (I),
(I) a step of treating a subject (excluding human), a test tissue or a test cell derived from skin with a test substance,
(Ii) a step of collecting a sample from the subject, a test tissue or a test cell treated with the test substance in the step (i), and (iii) a protein and/or a protein from the sample obtained in the step (ii) 52. The method of claim 50 or 51, comprising the step of recovering mRNA. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 43. 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. A prediction device for predicting the involvement of FGF23 in a disease, which comprises the following means:
First measurement value acquisition means for acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the first measurement value acquisition means A determining means for determining that FGF23 is involved in the disease based on the measurement value obtained by. Second measurement value acquisition means for acquiring the measurement value of the biomarker protein and/or the measurement value of the mRNA of the protein in the sample collected from the skin of the subject not having the disease, and the first A measurement value comparison means for comparing the measurement value acquired by the measurement value acquisition means and the measurement value acquired by the second measurement value acquisition means,
Further has
55. The device according to claim 54, wherein the determining means determines that FGF23 is involved in the disease based on the result of the comparison. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 56. The device of claim 55, which is at least one selected from the group consisting of:, Col1a1, and Defb8. A prediction program that, when executed by a computer, causes the computer to perform the following process for predicting the involvement of FGF23 in a disease:
First measurement value acquisition process of acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease, and the first measurement value acquisition process A determination process of determining that FGF23 is involved in the disease based on the measurement values obtained in. Furthermore, a second measurement value acquisition process of acquiring a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject not having the disease, and A measurement value comparison process for comparing the measurement value acquired in the measurement value acquisition process with the measurement value acquired in the second measurement value acquisition process,
To the computer,
The program according to claim 57, wherein in the determination processing, it is determined that FGF23 is involved in the disease based on the result of the comparison. The biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 59. The program according to claim 57 or 58, which is at least one selected from the group consisting of, Col1a1, and Defb8. A prediction method for predicting the involvement of FGF23 in a disease, which comprises the following steps:
(I) obtaining a measurement value of a biomarker protein and/or a measurement value of mRNA of the protein in a sample collected from the skin of a subject having a disease,
(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 measurement value obtained in the step (I) and the measurement value of the biomarker protein in the sample collected from the skin of the subject who does not have the disease. And/or further comprising the step of comparing the measured value of mRNA of said protein,
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 biomarker, Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d 62. The method of any one of claims 60-61, wherein the method is at least one selected from the group consisting of:, Col1a1, and Defb8. Hamp2, Actn3, Asb11, Cxcl13, Ptp4a3, Klhl38, Serpinb6e, Atp2a1, Myot, Nrap, Pde4dip, Il22ra2, Rbfox1, Lrrc2, Col15a1, Aldh1l2, Col5a1, C1qtnf6, Sparc, Col11a1, Clec11a, Col3a1, Serpinb6d, Col1a1, and Defb8 A skin biomarker for predicting involvement of renal function, abnormal phosphorus metabolism, or FGF23, comprising at least one selected from the group consisting of:

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