JP2022174645A - Biological age prediction method - Google Patents

Abstract

To provide a method for predicting a biological age in a simple manner and with small burden on a subject.SOLUTION: A method for predicting a biological age of a subject includes measuring, for skin surface lipids taken from the subject, the expression of at least one selected from the group consisting of a specific gene selected as an aging change marker and a translation product of the gene.SELECTED DRAWING: None

Description

The present invention relates to a method for predicting biological age.

As the body ages, many functions such as metabolism and exercise capacity decline. The most common indicator of aging is the number of years since birth, or chronological age, but the passage of time is not directly related to aging. For example, even with the same chronological age, some people need nursing care while others enjoy playing sports, so aging does not necessarily occur at the same speed among individuals. Aging is intricately related to internal and external factors such as genetic predisposition and lifestyle, and is caused by various changes at the molecular, cellular, and tissue levels of living organisms.

Biological age has been proposed as an indicator of the progress of aging, which reflects changes at the molecular, cell, and tissue levels of living organisms and is estimated from various functional deterioration of the body. Biological age also reflects a variety of functional declines due to individual aging, such as decreased metabolism, weakened muscle strength, increased disease risk, and formation of age spots and wrinkles. Attempts have been made to measure biological age, which is a more accurate indicator of aging than chronological age.

As a method of calculating the biological age, a method of calculating from the expression level of mRNA in whole blood of a subject has been reported (Non-Patent Document 1). In Non-Patent Document 1, using 7074 samples of 6 epidemiological surveys and 7909 samples of 7 epidemiological surveys, regression analysis was performed independently on 11908 genes with chronological age as the objective variable; Of the genes related to chronological age, 1497 genes that overlap in both analyses, were extracted; Pathways related to aging were detected by these pathway analyses; Construct a regression model with as the objective variable; define the predicted value by the regression model as transcriptome age (Transcriptomic age), and interpret (transcriptome age - chronological age) as reflecting biological age. ing. Non-Patent Document 1 also reports that (transcriptome age-chronological age) is significantly associated with various biological data such as blood pressure, cholesterol level, and BMI. However, the method of using the expression level of mRNA in blood as an index requires blood collection and is an invasive method.

Techniques have been developed for examining the present and future physiological conditions of humans in vivo by analyzing nucleic acids such as DNA and RNA in biological samples. Nucleic acid-based analysis has established a comprehensive analysis method, and a wealth of information can be obtained from a single analysis. It has the advantage that it is easy to tie it effectively. Biological nucleic acids can be extracted from tissues such as blood, body fluids, secretions, and the like. Patent Document 1 discloses that RNA contained in skin surface lipids (SSL) is used as a sample for biological analysis, and that marker genes of epidermis, sweat glands, hair follicles and sebaceous glands are detected from SSL. is described. Little specific knowledge has been obtained about the relationship between gene expression in SSL and gene expression in body fluids such as blood, and it is unknown whether the same genes are equally expressed in blood and SSL.

International Publication No. 2018/008319

Peters, M.J. et al. Nature Communications 6(1):8570 (2015)

TECHNICAL FIELD The present invention relates to an age-related change marker that enables prediction of biological age, and a method for predicting biological age using the age-related change marker.

The present inventor collected SSL from the skin of a subject and comprehensively analyzed the expression state of RNA contained in SSL as sequence information. As a result, the expression level of a specific gene was significantly correlated with chronological age. In addition, the present inventors have found that the expression level of the gene reflects the aging of the living body, and that this can be used as an index to predict the biological age of the subject.

That is, the present invention comprises measuring the expression of at least one selected from the group consisting of the genes shown in Table 3 below and the translation products of the genes, for SSL collected from the subject. To provide a method for predicting a biological age.
The present invention also provides an age-related change marker containing at least one selected from the group consisting of nucleic acids derived from the genes shown in Table 3 below, which are contained in SSL, and translation products of the genes.

The present invention provides age-related change markers. Since the age-related change markers can be collected from SSL, they can be obtained simply and non-invasively. Therefore, according to the present invention, it is possible to determine the biological age without imposing a burden on the subject.

Scatterplot of predicted and observed values for test data in the best predictive model. A diagram showing the relationship between biological age and aging index. Scatterplot of predicted and observed values for test data in the best predictive model. A diagram showing the relationship between biological age and aging index. Scatterplot of predicted and observed values for test data in the best predictive model. A diagram showing the relationship between biological age and aging index.

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

As used herein, the term "nucleic acid" or "polynucleotide" means DNA or RNA. DNA includes cDNA, genomic DNA, and synthetic DNA, and "RNA" includes total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.

In the present invention, the term "gene" refers to double-stranded DNA containing human genomic DNA, single-stranded DNA (positive strand) containing cDNA, and single-stranded DNA (complementary strand) having a sequence complementary to the positive strand. , and fragments thereof, in which some biological information is contained in the sequence information of bases that constitute DNA.
In addition, the "gene" includes not only "gene" represented by a specific nucleotide sequence, but also nucleic acids encoding their homologues (i.e., homologues or orthologs), variants such as genetic polymorphisms, and derivatives. be done.
The names of genes disclosed herein follow the Official Symbol described in NCBI ([www.ncbi.nlm.nih.gov/]).

In the present invention, the "expression product" of a gene is a concept that includes transcription products and translation products of a gene. A "transcription product" is RNA produced by transcription from a gene (DNA), and a "translation product" means a protein encoded by a gene that is translated and synthesized based on RNA.

In the present invention, "chronological age" refers to the number of years since birth. On the other hand, in the present invention, the term "biological age" refers to the progress of aging, which is estimated from deterioration of bodily functions reflecting changes at the molecular, cell, and tissue levels of a living body, expressed as a scale of age. point to For example, if biological age is higher than chronological age, aging is more advanced than average for that chronological age, and if biological age is lower than chronological age, aging is more advanced than average for that chronological age. is also suppressed, and if the biological age is equivalent to the chronological age (neither higher nor lower), aging can be said to be aging corresponding to the chronological age. Here, "aging" is not particularly limited as long as it is a decline in physiological functions that occur in the living body, and includes skin, metabolism, muscles, skeleton, exercise ability, cognitive ability, cardiovascular, endocrine, respiration, digestion, immunity, etc. It is a concept that comprehensively encompasses functional decline and associated morphological changes of the body.
The degree of skin aging (skin age) is exemplified as one that reflects the progress of aging indicated by biological age. The skin is located on the boundary between the body and the outside, and is affected not only by the aging of physiological functions inside the body, but also by the accumulation of chemical and physical stress from the external environment over time. The progress of aging indicated by the academic age is remarkably reflected as age-related changes. Viscoelasticity, surface roughness, degree of saccharification, spot formation and the like are specific examples of aging indices for evaluating skin aging (aging changes). Each index can be evaluated by the following methods commonly used in the field.
(viscoelasticity)
Evaluation by measuring parameters such as R2 (Ua / Uf, recovery rate), R6 (Uv / Ue, viscoelasticity), R7 (Ur / Uf, total recovery rate) using a cute meter (manufactured by Courage + Khazaka) can do. Here, among these parameters, the higher the value of R6 and the smaller the values of R2 and R7, the more advanced the aging of the skin.
(Surface roughness)
Collecting a skin replica and evaluating the three-dimensional shape of the collected replica by measuring various parameters Ra (center line average roughness), Rz (ten-point average roughness), Rmax (maximum height), etc. can be done. Here, it is determined that the larger the values of these parameters, the rougher the skin surface and the more advanced the aging of the skin.
(Saccharification degree)
The amount of advanced glycation end products (AGEs) is measured using an AGE Reader (manufactured by Diagnostics Technologies B.V.), or a stratum corneum extract is prepared from the stratum corneum peeled and collected by a tape stripping method, It can be evaluated by measuring the amount of stratum corneum Nε-carboxymethyllysine (CML) in the stratum corneum extract by liquid chromatography tandem mass spectrometry (LC-MS/MS method). Here, it is judged that the larger the quantitative value of AGEs and CML, the more glycated products are accumulated and the aging of the skin progresses.
(spot formation)
It can be evaluated by a professional panelist's evaluation as a stain score by visual observation or image observation, or by instrumental measurement of the stain area. Here, it is judged that the larger the stain score and the stain area, the more stains are formed on the skin and the aging of the skin is progressing.

In one aspect, the invention provides an age-related change marker. As shown in the examples below, the 189 genes shown in Table 1 below are genes whose expression in skin surface lipids (SSL) is positively correlated with chronological age, and are shown in Table 2 below. The 179 genes shown are genes whose expression in SSL is negatively correlated with chronological age. Therefore, nucleic acids derived from a total of 368 genes shown in Tables 1 and 2 in SSL and the translation products of the genes can each be used as age-related change markers for determining age-related changes. The age-related change marker may be a nucleic acid derived from the gene, a translation product of the gene, or a combination thereof, preferably a nucleic acid.
As shown in Examples below, the age-related change markers of the present invention are related to the immune system, cellular aging, DNA damage response, mitochondrial function, epidermal differentiation, cell cycle, etc., which are known as age-related changes in the skin and body. It can be used as an indicator of biological age, which indicates the degree of progress of aging estimated from deterioration of bodily functions reflecting changes at the level of molecules, cells, and tissues of living organisms.

Of these 368 genes, 83 genes whose expression is positively correlated with chronological age shown in Table 3A below and 80 genes whose expression is negatively correlated with chronological age shown in Table 3B, for a total of 163 genes. is a gene that has not been previously reported to be associated with aging. Therefore, the nucleic acids derived from the 163 genes shown in Table 3 in SSL and the translation products of the genes can each be used as a novel age-related change marker. The novel age-related change marker is at least one, preferably two or more, more preferably three selected from the group consisting of nucleic acids derived from the 163 genes and translation products of the genes. or more, more preferably 5 or more, and more preferably selected from 12 genes: UBE2D2, SMG7, H2AFV, NRD1, MRPL53, GPR157, LOC440173, TPRA1, EVI2B, ERO1L, IGFL2, and ZFAND5 It is a combination containing at least nucleic acids derived from one or more, preferably all genes or translation products of the genes.

Table 4 below shows the 205 genes excluding the genes shown in Table 3 from the genes shown in Tables 1 and 2. Among these, 106 genes shown in Table 4A are genes whose expression is positively correlated with chronological age, and 99 kinds of genes shown in Table 4B are genes whose expression is negatively correlated with chronological age.

The age-related change marker gene also includes a gene having a nucleotide sequence substantially identical to the nucleotide sequence of the DNA constituting the gene, as long as it functions as a marker for determining age-related changes. Here, the substantially identical base sequence, for example, using the homology calculation algorithm NCBI BLAST, expected value = 10; gaps allowed; filtering = ON; match score = 1; mismatch score = -3 It means that it has 90% or more, preferably 95% or more, more preferably 98% or more, and still more preferably 99% or more identity with the base sequence of the DNA constituting the gene when searched with .

Two groups with significant differences in age (biological age) as determined based on the expression of the genes shown in Table 3 in SSL in populations with no significant difference in chronological age, as shown in the Examples below; That is, when the group with a relatively high age (upper 25%, older group) and the group with a relatively lower age (lower 25%, younger group) were compared, the aging of the living body indicated by the group and the aging index of the skin (Example 3).
Specifically, in the group of subjects in their thirties, the older group had a higher R6 and a lower R7. In the population of subjects in their 40s, the older group had lower R7 and higher blemish scores. In the group of subjects in their 50s, the older group had higher Rz and higher stratum corneum CML relative amount.

In addition, as shown in the examples below, among the genes shown in Table 3 in SSL, UBE2D2, SMG7, H2AFV, NRD1, MRPL53, GPR157, LOC440173, TPRA1, EVI2B, and ERO1L, among the genes shown in Table 3, in a population with no significant difference in chronological age , IGFL2, and ZFAND5 2 groups with a significant difference in age (biological age) determined based on the expression of 12 genes, that is, a group with a relatively high age (top 25%, older group) When compared with a relatively low group (bottom 25%, young group), a relationship between the group and the degree of aging progress of the living body indicated by the skin aging index was found (Example 4) .
Specifically, in the population of subjects in their 40s, the older group had higher AGEs. In the population of subjects in their 50s, the older group had higher Ra and higher Rz.

In addition, as shown in the Examples below, there is no significant difference in age (biological age) determined based on the expression of genes shown in Tables 3 and 4 in SSL in a population with no significant difference in chronological age. When comparing two groups, that is, a group with a relatively high age (upper 25%, older group) and a group with a relatively lower age (lower 25%, younger group), the group and the aging index of the skin (Example 2).
Specifically, in the group of subjects in their 40s, the older group had higher Ra, higher Rz, higher Rmax, and higher AGEs. In the population of subjects in their 50s, the older group had higher R6 and higher stratum corneum CML relative amount.

Therefore, the expression of the genes shown in Tables 3 and 4 is considered to reflect biological aging that cannot be captured by chronological age. Therefore, the nucleic acid derived from the gene and the translation product of the gene can each be used as a marker for predicting the biological age representing the progress of aging in a living body. The biological age is preferably the biological age related to the skin, which can also be called the skin age. Therefore, preferably, the nucleic acid derived from the gene and the translation product of the gene can each be used as a marker for predicting skin age.

In the present invention, the biological age of a subject can be predicted based on the expression level of each gene shown in Table 3 or its translation product. In addition, in the present invention, the expression level of each gene shown in Table 3 or its translation product and the expression level of each gene shown in Table 4 or its translation product are used, and based on both expression levels, the subject's biological Prediction of age is possible. In the present invention, the expression levels of the genes shown in Tables 3 and 4 or the translation products thereof can be measured by quantifying the nucleic acids derived from the genes or the translation products of the genes. For example, by quantifying mRNA transcribed from the gene, cDNA prepared from the mRNA, a reaction product from the cDNA, or a translation product (such as a protein) synthesized by translating the mRNA, the gene or The expression level of the translation product can be measured.

Nucleic acids derived from the genes shown in Tables 3 and 4 or translation products of the genes can be prepared from superficial skin lipids (SSL) collected from subjects according to conventional methods. Preferably, the nucleic acid is mRNA prepared from the subject's SSL.

As used herein, "superficial skin lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin and is sometimes referred to as sebum. In general, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer covering the skin surface. SSL contains RNA expressed in skin cells (see US Pat. As used herein, unless otherwise specified, the term "skin" is a general term for areas including stratum corneum, epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands and other glands.

Any means used to collect or remove SSL from the skin can be used to collect the SSL from the subject's skin. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument that scrapes the SSL off the skin, as described below, can be used. The SSL absorbent material or SSL adhesive material is not particularly limited as long as it has affinity for SSL, and examples thereof include polypropylene and pulp. More detailed examples of procedures for collecting SSL from the skin include a method of absorbing SSL into sheet-like materials such as blotting paper and blotting film, a method of adhering SSL to a glass plate, tape, etc., a spatula, a scraper, and the like. and a method of scraping off and recovering SSL. In order to improve the adsorptivity of SSL, an SSL absorbent material previously impregnated with a solvent having high fat solubility may be used. On the other hand, if the SSL absorptive material contains a highly water-soluble solvent or moisture, the adsorption of SSL is inhibited, so it is preferable that the content of the highly water-soluble solvent and moisture is small. The SSL absorbent material is preferably used dry. The site of the skin from which the SSL is collected is not particularly limited, and includes any site of the body such as the head, face, neck, trunk, limbs, etc., and sites with high sebum secretion, such as the skin of the face. is preferred.

The SSL collected from the subject may be stored for a period of time. The collected SSL is preferably stored under low temperature conditions as soon as possible after collection in order to minimize degradation of the contained RNA. The temperature condition for storing the RNA-containing SSL in the present invention may be 0°C or lower, preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C. , More preferably -20 ± 20°C to -40 ± 20°C, more preferably -20 ± 10°C to -40 ± 10°C, more preferably -20 ± 10°C, still more preferably -20 ± 5°C . The storage period of the RNA-containing SSL under the low-temperature conditions is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, More preferably, it is 3 months or less, for example, 3 days or more and 3 months or less.

Extraction of RNA from the collected SSL can be performed by methods commonly used for extraction or purification of RNA from biological samples, such as the phenol/chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or the TRIzol ( (registered trademark), RNeasy (registered trademark), QIAzol (registered trademark) column, method using silica-coated special magnetic particles, Solid Phase Reversible Immobilization using magnetic particles, ISOGEN, etc. Extraction using a commercially available RNA extraction reagent or the like can be used. In addition, for extraction of translation products from SSL, methods commonly used for protein extraction or purification from biological samples, for example, commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen) can be used.

The biological age of a subject can be predicted by examining the expression of one or more genes shown in Table 3 or their translation products using the nucleic acid or translation product prepared by the above procedure. Therefore, in another aspect, the present invention includes measuring the expression of at least one selected from the group consisting of the genes shown in Table 3 or their translation products in SSL collected from the subject. A method for predicting biological age (hereinafter also referred to as the method of the present invention) is provided. In one embodiment, the method of the present invention may further comprise measuring the expression of at least one selected from the group consisting of genes shown in Table 4 or their translation products in SSL collected from a subject. . In another embodiment, the method of the invention is a method of predicting skin age.

A subject in the method of the present invention may be, for example, a human or a non-human mammal, as long as it is necessary to predict the biological age or to know the progress of aging. Preferably, the subject is an animal having SSL on its skin, more preferably a human.
In addition, when the subject is a human, a person with a higher chronological age that is predicted to have a greater variety of deterioration in physical function due to aging, for example, a chronological age of 20 years or older, preferably 30 years or older, more preferably 40 years or older of humans.

In one embodiment, the method of the present invention comprises measuring the expression of at least one gene selected from the group consisting of genes shown in Table 3 (hereinafter also referred to as target gene (group)) in SSL of a subject. include. In another embodiment, the method of the present invention reduces the expression of at least one selected from the group consisting of translation products of genes shown in Table 3 (hereinafter also referred to as target product (group)) in SSL of a subject. Including measuring. In another embodiment, the method of the present invention includes at least one gene selected from the group consisting of the genes shown in Table 3 and at least one gene selected from the group consisting of the genes shown in Table 4 in the subject's SSL. measuring the expression of one gene (hereinafter also referred to as target gene(s)). In another embodiment, the method of the present invention includes at least one selected from the group consisting of the translation products of the genes shown in Table 3 in the SSL of the subject and from the group consisting of the translation products of the genes shown in Table 4 It comprises measuring the expression of at least one selected (hereinafter also referred to as target product(s)). Alternatively, both the expression of the target gene(s) and the expression of the target product(s) may be measured. Preferably, the expression of the target gene(s) is measured.

Preferably, the measurement of expression of the target gene(s) or target product(s) contained in SSL in the method of the present invention is performed using RNA or translation products contained in SSL of a subject. In one embodiment, the method of the present invention may further comprise harvesting the subject's SSL. In one embodiment, the method of the invention may further comprise extracting RNA or translation products from SSL collected from the subject.

By quantifying the amount of target gene-derived RNA contained in subject-derived SSL, for example, RNA extracted from SSL, the expression level of the target gene can be measured. The amount of subject-derived RNA may be measured according to the procedure of gene expression analysis using RNA commonly used in the art. Examples of techniques for gene expression analysis using RNA include converting RNA into cDNA by reverse transcription, and then quantifying the cDNA or its amplified product by real-time PCR, multiplex PCR, microarray, sequencing, chromatography, or the like. method. The expression level of the translation product can be measured using protein quantification methods commonly used in the art, such as ELISA, immunostaining, fluorescence method, electrophoresis, chromatography, and mass spectrometry. The expression level measured may be the absolute level of expression of the target gene(s) or target product(s) in the SSL, or the expression relative to other standards or the total expression level of the gene or translation product. It can be the amount.

In a preferred embodiment, in the method of the present invention, the subject-derived RNA is converted to cDNA by reverse transcription, the cDNA is then subjected to PCR, and the resulting reaction product is purified. For the reverse transcription, primers targeting specific RNAs to be analyzed may be used, but random primers are preferably used for more comprehensive nucleic acid storage and analysis. A common reverse transcriptase or reverse transcription reagent kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcription reagent kit is used, examples of which include M-MLV Reverse Transcriptase and variants thereof, or commercially available reverse transcriptase or reverse transcription reagent kit, Examples include PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.) and SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific). SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both from Thermo Scientific) and the like are preferably used. In PCR of the obtained cDNA, a primer pair targeting a specific DNA to be analyzed may be used to amplify only the specific DNA, but a plurality of primer pairs may be used to amplify a plurality of DNAs. good. Preferably, said PCR is multiplex PCR. Multiplex PCR is a method for simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (eg, Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.).

By adjusting the reaction conditions of the reverse transcription and PCR, the yield of the PCR reaction product is further improved, and the accuracy of analysis using it is further improved. Suitably, in the extension reaction with reverse transcription, the temperature is preferably adjusted to 42°C ± 1°C, more preferably 42°C ± 0.5°C, even more preferably 42°C ± 0.25°C, while The reaction time is adjusted to preferably 60 minutes or more, more preferably 80 to 120 minutes. Also preferably, the temperature of the annealing and extension reaction in the PCR can be appropriately adjusted depending on the primers used. is 62°C ± 0.5°C, more preferably 62°C ± 0.25°C. Therefore, in the PCR, annealing and extension reactions are preferably performed in one step. The time for the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, but is preferably 14 to 18 minutes. The denaturation reaction conditions in the PCR can be adjusted depending on the DNA to be amplified, but are preferably 95-99° C. for 10-60 seconds. Reverse transcription and PCR at temperatures and times as described above can be performed using a thermal cycler commonly used for PCR.

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

Purified PCR reaction products may be subjected to further processing as required for subsequent quantitative analysis. For example, for DNA sequencing, a purified PCR reaction product is prepared into an appropriate buffer solution, a PCR primer region contained in PCR amplified DNA is cleaved, an adapter sequence is added to the amplified DNA, and an adapter sequence is added to the amplified DNA. may be added. For example, a purified PCR reaction product is prepared in a buffer solution, PCR primer sequences are removed from the amplified DNA and adapter ligation is performed, and the resulting reaction product is amplified as necessary for quantitative analysis. of libraries can be prepared. These operations are performed, for example, using 5×VILO RT Reaction Mix attached to SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.). 5×Ion AmpliSeq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Core Panel attached to each kit can be used according to the protocol attached to each kit. For sequencing, a next-generation sequencer (eg, Ion S5/XL system, Life Technologies Japan Co., Ltd.) is preferably used. RNA expression can be quantified based on the number of reads generated by sequencing (read count).

Preferably, the method of the present invention predicts the biological age of a subject based on the expression levels of said target gene(s) or said target product(s) in SSL.
The 83 genes shown in Table 3A are genes whose expression levels increase as aging progresses in the living body. On the other hand, the 80 kinds of genes shown in Table 3B are genes whose expression levels decrease as aging of the living body progresses. The biological age of a subject can be predicted based on the expression level of any one or more of the genes or their translation products shown in Tables 3A and 3B.
In addition, the 106 types of genes shown in Table 4A are genes whose expression levels increase as aging of the living body progresses. On the other hand, the 99 types of genes shown in Table 4B are genes whose expression levels decrease as aging of the living body progresses. Expression of any one or more of the genes or translation products thereof shown in Tables 4A and 4B in addition to the expression level of any one or more of the genes or translation products thereof shown in Tables 3A and 3B as a standard A subject's biological age may be predicted on the basis of the amount.

In one embodiment of the method of the present invention, the expression level of individual target gene(s) or target product(s) is used directly as a parameter to predict biological age. For example, the biological age of a subject can be predicted by comparing the expression level of a target gene or target product contained in SSL measured from a subject with a reference expression level of the target gene or target product. The reference expression level of the target gene or target product can be predetermined based on the relationship between chronological age and the expression level of the target gene or target product. For example, a population is divided into a plurality of groups based on chronological age, and the statistic value (e.g., average value) of the expression level of the target gene or target product in each group is used to determine whether the group belongs to each group. A baseline expression level can be determined. When a plurality of genes or translation products are used as the target gene group or target product group, it is preferable to determine the reference expression level for each gene or translation product. The group is not particularly limited, and a group may be created for each sex or race according to the subject to be predicted. Examples of groups used to calculate the reference expression level include groups with chronological ages in their 20s, 30s, 40s, and 50s. ), a group separated by a finer chronological age range, a group separated by chronological age, and the like. Specific techniques for setting the reference expression level and classifying which group a subject belongs to based on the reference expression level can be carried out as appropriate according to the common general knowledge of those skilled in the art. Then, using the expression level of the target gene or target product as a parameter, the chronological age, chronological age range or chronological age of the group to which the subject is determined to belong by comparison with the reference expression level is predicted as the biological age of the subject. can do. When using the expression levels of multiple genes or translation products selected from the target gene group or target product group as parameters for predicting biological age, the predicted biological age for each parameter A mean value can be calculated and the mean value taken as the subject's biological age.

If the expression level of the target gene or target product in the subject's SSL is an expression level that is determined to belong to a certain chronological age, chronological age range, or chronological age group, the subject's actual chronological age in that group A chronological age, chronological age range, or higher than chronological age can be predicted to indicate that the subject's aging is retarded, ie, the biological age is low. Also, if the actual chronological age of the subject is equivalent to the chronological age, chronological age range or chronological age in the group (not higher or lower), the subject's aging is equivalent to the chronological age, chronological age range or chronological age ie, biological age can be predicted to correspond to chronological age. Furthermore, if the subject's actual chronological age is lower than the chronological age, chronological age range or chronological age in the group, it can be predicted that the subject is aging, ie, is of advanced biological age. When a plurality of target gene clusters or target product clusters are used, a certain proportion, for example, 50% or more, preferably 70% or more, more preferably 90% or more, and still more preferably 100% of the genes or their translation products The biological age of a subject can be predicted based on whether the expression level criteria described above are met.

Alternatively, by comparing the expression level of the target gene or target product contained in the SSL measured from the subject with the expression level of the target gene or target product contained in the SSL measured from the same subject at different times, the subject It is possible to predict the degree of aging progress (eg, progress or inhibition) of aging.

For example, when the target gene or target product is the gene shown in Table 3A or its translation product, an increase in the expression level over time indicates the progress of aging in the subject, while a decrease or no change in the expression level over time indicates Represents inhibition of aging in subjects. When the target gene or target product is the gene shown in Table 3B or its translation product, the decrease in the expression level over time indicates the progress of aging in the subject, while the increase or no change in the expression level over time indicates the subject's Represents inhibition of aging. Further, for example, when the target gene or target product is the gene or its translation product shown in Table 4A, the increase in the expression level over time indicates the progress of aging in the subject, while the decrease or no change in the expression level over time indicates , represents inhibition of aging in the subject. When the target gene or target product is the gene shown in Table 4B or its translation product, the decrease in the expression level over time indicates the aging progress of the subject, while the increase or no change in the expression level over time indicates the subject's Represents inhibition of aging.

In another embodiment, the biological age of a subject is predicted based on a prediction model constructed using expression level data of target gene(s) or target product(s). For example, for each of one or more target genes (groups) or target products (groups) shown in Table 3, the expression data is used as an explanatory variable, and the biological age is predicted by machine learning using chronological age as the objective variable. It is possible to build an optimal prediction model for Alternatively, for each of one or more target gene(s) or target product(s) shown in Table 3 and one or more target gene(s) or target product(s) shown in Table 4, the expression data is An optimal prediction model for predicting biological age may be constructed by machine learning using chronological age as an explanatory variable and objective variable. For example, from among the genes or their translation products shown in Table 3, a plurality of genes having a high absolute value of correlation coefficient between expression level and chronological age are selected as a group of target genes or a group of target products, and their expression data are obtained. can be used as an explanatory variable.

Alternatively, when using expression data of multiple genes or translation products to construct a prediction model, if necessary, by recursive feature elimination (RFE), the importance of feature genes or translation products The prediction model may be constructed after the allocation, feature amount genes or translation products with low importance are eliminated. For example, the genes shown in Table 3 are used as feature amount genes, the feature amount genes are ranked by importance, the feature amount genes with high importance are extracted and used as explanatory variables, and chronological age is used as the objective variable. An optimal predictive model can be constructed for predicting biological age. For example, based on the importance ranking, feature amount genes are extracted from genes ranked within the top 100, preferably within the 75th, more preferably within the 50th. The combination of feature amount genes with high importance is one or more selected from 12 genes of UBE2D2, SMG7, H2AFV, NRD1, MRPL53, GPR157, LOC440173, TPRA1, EVI2B, ERO1L, IGFL2, and ZFAND5, preferably A combination containing all is preferred, and a combination of genes consisting of these 12 types is more preferred.

Algorithms for constructing a prediction model can use well-known algorithms such as algorithms used for machine learning. Examples of machine learning algorithms include linear multiple regression, LASSO (Least Absolute Shrinkage and Selection Operator) regression, ridge regression, elastic net, PLS (Partial Least Squares) regression, decision tree, random forest, linear kernel SVM (Support Vector Machine ), polynomial kernel SVM, and Gaussian kernel SVM. Using the root mean square error (RMSE) between the actual value and the predicted value as an accuracy evaluation index of the prediction model, the model with the smallest RMSE can be selected as the best model from among the constructed models. .

By inputting expression data of the target gene(s) or target product(s) in SSL of the subject into the constructed prediction model, the biological age of the subject can be predicted.

Further disclosed herein are the following materials, methods of manufacture, uses, methods, etc., as exemplary embodiments of the present invention. However, the invention is not limited to these embodiments.

[1] Biology of the subject, including measuring the expression of at least one selected from the group consisting of the genes shown in Table 3 above and the translation products of the genes, for surface lipids collected from the subject A method for predicting a natural age.
[2] The description of [1], which comprises measuring the expression of at least one selected from the group consisting of the genes shown in Table 4 above and the translation products of the genes for surface lipids collected from the subject. the method of.
[3] The method of [1] or [2], which comprises predicting the biological age based on the expression level of the gene or translation product.
[4] predicting biological age using a prediction model based on the expression level of the gene or translation product;
The method of [3], wherein the prediction model is constructed using the expression level of the gene or translation product as an explanatory variable and chronological age as an objective variable.
[5] The method according to any one of [1] to [4], wherein the expression level of the gene is measured by quantifying the mRNA of the gene contained in skin surface lipids collected from the subject. .
[6] The method according to any one of [1] to [5], wherein the biological age is skin age.
[7] An age-related change marker comprising at least one selected from the group consisting of nucleic acids derived from genes shown in Table 3 above, which are contained in sebaceous surface lipids, and translation products of the genes.
[8] The marker of [7], wherein the nucleic acid derived from the gene is the mRNA of the gene.

EXAMPLES The present invention will be described in more detail below based on examples, but the present invention is not limited to these.

Example 1 Analysis of Changes in SSL-RNA with Age 1) Subjects 134 healthy females aged 20 to 60 were used as subjects.

2) SSL Collection Sebum was collected from the entire face of each subject using one oil removing film (5.0 cm×8.0 cm, 3M Company). The blotting film from which the sebum was recovered was stored at -80°C until it was subjected to RNA preparation.

3) Preparation of RNA and Sequencing The blotting film from which the sebum was recovered in 2) above was cut into appropriate sizes, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, reverse transcription was performed at 42° C. for 90 minutes using SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) to synthesize cDNA. Random primers attached to the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the resulting cDNA by multiplex PCR. Multiplex PCR was performed using Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) [99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) × 20 cycles → 4°C, Hold]. The resulting PCR product was purified with Ampure XP (Beckman Coulter, Inc.) and then subjected to buffer reconstitution, primer sequence digestion, adapter ligation and purification, and amplification to prepare a library. The prepared library was loaded into the Ion 540 Chip and sequenced using the Ion S5/XL system (Life Technologies Japan). Genes from which each read sequence was derived were determined by genetic mapping each read sequence obtained by sequencing against hg19 AmpliSeq Transcriptome ERCC v1, which is a reference sequence of the human genome.

4) Data Analysis The expression level data (read count value) of the SSL-derived RNA of the subject obtained in 3) above was corrected using a technique called DESeq2 (Love MI et al. Genome Biol. 2014). However, the expression level data of 21 subjects in whom 4161 or more genes were not detected was excluded, and expression level data that was not a missing value was obtained in 90% or more of the subjects after exclusion. Only 2323 genes were used for the following analysis. For the analysis, count values (Normalized count values) corrected using a technique called DESeq2 were used, and count values of 0 were regarded as missing values.
Based on the SSL-derived RNA expression level (normalized count value) obtained above, genes with a p-value of less than 0.05 by Spearman's rank correlation analysis with chronological age were identified. As a result, 189 genes whose expression levels are positively correlated with chronological age (Tables 5-1 to 5-3), and 179 genes whose expression levels are negatively correlated with chronological age (Tables 6-1 to 6 -3) was obtained.

Using the public database STRING, we searched for biological processes (BP) and Reactome pathways by gene ontology (GO) enrichment analysis. As a result, 389 types of GO terms and 156 types of pathways were identified from the gene group whose chronological age and expression level are positively correlated, and 153 types of GO terms and 118 types of pathways were identified from the gene group whose chronological age and expression level were negatively correlated. A pathway was obtained. These include those related to the immune system, cellular senescence, DNA damage response, mitochondrial function, epidermal differentiation, cell cycle, etc., which are known as age-related changes in the skin and body.

Regarding the genes whose expression levels are correlated with chronological age shown in Tables 5-1 to 5-3 and 6-1 to 6-3, the previously reported literature was checked for relevance to aging, and the results are shown in Table 7. A total of 163 genes, 83 genes whose expression level is positively correlated with chronological age and 80 genes that are negatively correlated with chronological age shown in Table 8, have not been reported to be related to aging. It became clear that it can be a new age-related change marker.

Example 2 Assessment of Biological Age 1 Based on SSL-RNA
1) Acquisition of Skin Aging Index After the SSL collection in Example 1-2), viscoelasticity, surface roughness, degree of saccharification, and spot formation were evaluated as the skin aging index according to the following.
(viscoelasticity)
The test subject's cheeks, eyes, and mouth are used as measurement sites, and parameters related to viscoelasticity R2 (Ua / Uf, recovery rate), R6 (Uv / Ue, viscoelasticity), and R7 are measured using a cutometer (manufactured by Courage + Khazaka). (Ur/Uf, total return rate) was measured.
(Surface roughness)
A skin replica of the outer corner of the eye of the subject was collected by SILFRO (manufactured by Amick), and Ra (center line average roughness), which is a parameter related to the three-dimensional shape of the replica collected using PRIMOS-CR (manufactured by Canfield Scientific), Rz (ten-point average roughness) and Rmax (maximum height) were measured.
(Saccharification degree)
The amount of AGEs around the mouth of the subject was measured using an AGE Reader (manufactured by Diagnostics Technologies B.V.). In addition, the stratum corneum around the mouth of the subject was stripped and collected in three consecutive strips using a film masking tape (2.5 cm x 5.0 cm, manufactured by Teraoka Seisakusho). A stratum corneum extract was prepared according to a conventional method, and the amount of protein was quantified. Next, the amount of stratum corneum Nε-carboxymethyllysine (CML) in the stratum corneum extract was measured by liquid chromatography tandem mass spectrometry (LC-MS/MS method) according to a conventional method, and the relative amount of CML per protein amount was determined. was calculated.
(spot formation)
Three professional judges visually evaluated the number and size of spots on the face of the subject, gave a score (0 to 3, 4 levels), and calculated the average score of the spots.

2) Data used The expression level data (read count value) of SSL-derived RNA from the subject obtained in 3) of Example 1 is RPM (Reads per million mapped reads) corrected for the difference in the total number of reads between samples. Data converted to values were used. However, only the data of 2323 genes for which non-missing expression level data were obtained in 90% or more of all samples were used for the following analysis. In constructing the machine learning model, RPM values converted to base 2 logarithmic values (Log ₂ RPM values) were used as data in order to approximate RPM values following a negative binomial distribution to a normal distribution.

3) Data set division The RNA profile data set obtained from the subject is divided into two groups so that the chronological age distribution is even, and the RNA profile data for 58 people is used as the training data for the chronological age prediction model, and the remaining RNA profile data for 55 subjects was used as test data for evaluating model accuracy.

4) Selection of feature amount genes 189 genes (Tables 5-1 to 5-3) whose expression levels were positively correlated with chronological age in 4) of Example 1 and 179 genes whose expression levels were negatively correlated (Tables 6-1 to 6-3) were used as feature amount genes.

5) Model construction A regression model is constructed using the data (Log ₂ RPM value) of the expression level of the feature gene selected from SSL-derived RNA, which is training data, as an explanatory variable and chronological age as an objective variable. did. For model building, linear multiple regression, LASSO (Least Absolute Shrinkage and Selection Operator) regression, Ridge regression, Elastic net, PLS (Partial Least Squares) regression, decision tree, random forest, linear kernel SVM (Support Vector Machine), polynomial kernel Ten kinds of algorithms of SVM and Gaussian kernel SVM were compared. For these 10 algorithms, model construction was performed by 10-fold cross-validation using training data, and optimal parameters for each algorithm were selected by grid search. At this time, the Root Mean Square Error (RMSE) between the measured value and the predicted value is used as an accuracy evaluation index, and the combination of algorithms and parameters that minimizes the RMSE from among the constructed models is adopted as the optimal model. did. As a result, since the average RMSE of the model using the PLS regression was the smallest, the model constructed using the same algorithm was selected as the optimal model. As a result of verifying the generalization performance of the constructed model using test data, the Pearson's product-moment correlation coefficient between the values predicted by the optimum prediction model of the test data and the actual values was 0.77. The value predicted by this optimal prediction model was taken as the biological age based on SSL-RNA. A scatter plot of predicted values (biological age) and measured values (chronological age) in the test data is shown in FIG.

6) Relevance between biological age and aging index Based on the chronological age, the subjects of the test data in 5) above were divided into 12 people in their 20s, 15 people in their 30s, 14 people in their 40s, and 14 people in their 50s. Aging index obtained in 1) for the upper 25% (older group) and the lower 25% (younger group) where the predicted value (biological age) in 5) above was relatively high within the age group were compared by Student's t-test (Mann-Whitney U-test for visual evaluation). There was no significant difference in chronological age between the older group and the younger group in any age group.
In the twenties and thirties, there was no significant difference or trend of significant difference between the older group and the younger group. In the 40s, the older group had higher Ra, higher Rz, higher Rmax, and higher AGEs (Fig. 2(a)). In the 50s, the older group had a higher R6 and a higher stratum corneum CML relative amount (Fig. 2(b)). In addition, Spearman's rank correlation analysis with chronological age and biological age was performed for items showing differences in each age group. Correlation coefficients and p-values are shown in Tables 9-1 to 9-2. In 5 out of 6 items, the absolute value of the correlation coefficient is larger and the p-value is smaller for biological age than for chronological age, and biological age is more correlated with aging indicators than chronological age. It was recognized that

Example 3 SSL-RNA Based Biological Age Assessment 2
1) Skin Aging Index The aging index obtained in 1) of Example 2 was used.

2) Data used The same data as the data used in 2) of Example 2 were used.

3) Data set division The RNA profile data set obtained from the subject is divided into two groups so that the chronological age distribution is even, and the RNA profile data for 58 people is used as the training data for the chronological age prediction model, and the remaining RNA profile data for 55 subjects was used as test data for evaluating model accuracy.

4) Selection of feature amount genes Of the 189 genes whose expression levels were positively correlated with chronological age and the 179 genes whose expression levels were negatively correlated in 4) of Example 1, the relevance with aging was 163 unreported genes (Tables 7 and 8) were used as feature genes.

5) Model construction Similar to 5) of Example 2, a regression model was constructed using the training data, and 10 types of algorithms were compared. As a result, the average RMSE of the model using PLS regression was the lowest, so this algorithm was selected as the optimal model. As a result of verifying the generalization performance of the constructed model using test data, the Pearson's product-moment correlation coefficient between the values predicted by the optimum prediction model of the test data and the actual values was 0.65. The value predicted by this optimal prediction model was taken as the biological age based on SSL-RNA. A scatter plot of predicted values (biological age) and measured values (chronological age) in the test data is shown in FIG.

6) Relevance between biological age and aging index Based on the chronological age, the subjects of the test data in 5) above were divided into 11 people in their 20s, 13 people in their 30s, 18 people in their 40s, and 13 people in their 50s. For the upper 25% (older group) and the lower 25% (younger group) where the predicted value (biological age) of 5) above was relatively high within the age group, in 1) of Example 2 The mean values of each aging index obtained were compared by Student's t-test (Mann-Whitney U-test for visual evaluation). There was no significant difference in chronological age between the older group and the younger group in any age group.
In the twenties, there was no significant difference or trend of significant difference between the older group and the younger group. In the thirties, the older group had higher R6 and lower R7 (Fig. 4(a)). In the 40s, the older group had lower R7 and higher stain score (Fig. 4(b)). In the 50s, the older group had a higher Rz and a higher stratum corneum CML relative amount (Fig. 4(c)). In addition, Spearman's rank correlation analysis with chronological age and biological age was performed for items showing differences in each age group. Correlation coefficients and p-values are shown in Tables 10-1 to 10-3. Biological age was found to correlate better with aging indicators than chronological age.

Example 4 Assessment of Biological Age 3 Based on SSL-RNA
1) Skin Aging Index The aging index obtained in 1) of Example 2 was used.

2) Data used The same data as the data used in 2) of Example 2 were used.

3) Data set division The RNA profile data set obtained from the subject is divided into two groups so that the chronological age distribution is even, and the RNA profile data for 58 people is used as the training data for the chronological age prediction model, and the remaining RNA profile data for 55 subjects was used as test data for evaluating model accuracy.

4) Selection of feature amount genes Of the 189 genes whose expression levels were positively correlated with chronological age and the 179 genes whose expression levels were negatively correlated in 4) of Example 1, the relevance with aging was 163 unreported genes (Tables 7 and 8) were used as characteristic genes. In the training data, feature selection was performed by recursive feature elimination (RFE) using random forest as an algorithm. Table 11 shows the importance of the 163 genes. RMSE was lowest when using 12 genes of high importance (UBE2D2, SMG7, H2AFV, NRD1, MRPL53, GPR157, LOC440173, TPRA1, EVI2B, ERO1L, IGFL2, and ZFAND5). RNA was used as a feature gene.

5) Model construction Similar to 5) of Example 2, a regression model was constructed using the training data, and 10 types of algorithms were compared. As a result, since the average RMSE of the model using the random forest was the smallest, this algorithm was selected as the optimal model. As a result of verifying the generalization performance of the constructed model using test data, the Pearson's product-moment correlation coefficient between the values predicted by the optimum prediction model of the test data and the actual values was 0.64. The value predicted by this optimal prediction model was taken as the biological age based on SSL-RNA. A scatter plot of predicted values (biological age) and measured values (chronological age) in the test data is shown in FIG.

6) Relevance between biological age and aging index Based on the chronological age, the subjects of the test data in 5) above were divided into 13 people in their 20s, 13 people in their 30s, 16 people in their 40s, and 13 people in their 50s. For the upper 25% (older group) and the lower 25% (younger group) where the predicted value (biological age) of 5) above was relatively high within the age group, in 1) of Example 2 The mean values of each aging index obtained were compared by Student's t-test (Mann-Whitney U-test for visual evaluation). There was no significant difference in chronological age between the older group and the younger group in any age group.
In the twenties and thirties, there was no significant difference or trend of significant difference between the older group and the younger group. In the 40s, AGEs were higher in the older group (Fig. 6(a)). In the 50s, the older group had higher Ra and higher Rz (Fig. 6(b)). In addition, Spearman's rank correlation analysis with chronological age and biological age was performed for items showing differences in each age group. Correlation coefficients and p-values are shown in Tables 12-1 to 12-2. Biological age was found to correlate better with aging indicators than chronological age.

Claims (8)

The biological age of the subject, including measuring the expression of at least one selected from the group consisting of the genes shown in Table 1 below and the translation products of the genes, for lipids on the skin surface collected from the subject. how to predict.

2. The method according to claim 1, comprising measuring the expression of at least one selected from the group consisting of the genes shown in Table 2 below and the translation products of the genes for surface lipids collected from the subject.

3. The method according to claim 1 or 2, comprising predicting the biological age based on the expression level of said gene or translation product. Predicting biological age using a prediction model based on the expression level of the gene or translation product;
4. The method according to claim 3, wherein said predictive model is constructed using the expression level of said gene or translation product as an explanatory variable and chronological age as an objective variable. 5. The method according to any one of claims 1 to 4, wherein the expression level of the gene is measured by quantifying mRNA of the gene contained in superficial skin lipids collected from the subject. A method according to any one of claims 1 to 5, wherein said biological age is skin age. An age-related change marker comprising at least one selected from the group consisting of nucleic acids derived from the genes shown in Table 1 above, which are contained in sebaceous superficial lipids, and translation products of the genes. 8. The marker of claim 7, wherein the nucleic acid derived from said gene is the mRNA of said gene.

Images