Epigenetic markers for detecting oxidative stress

Abstract

The present invention provides a method for identifying oxidative stress (OS) in a test cell, comprising the steps of: (a) determining the methylation status of at least five genes in a DNA sample obtained from a test cell; (b) comparing the methylation status of the genes from step (a) with the methylation status of the corresponding genes in a control without OS; a difference in the methylation status of the gene in the test cell compared to the corresponding gene in the control is indicative of a cell having OS; The genes in step (a) were PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, and CACNA1C; and regulatory regions thereof.

Description

[Technical field]

[0001] The present invention relates to a method for detecting oxidative stress (OS) in cells.Specifically, the method can identify OS in cells by using a gene panel that comprises at least two genes that are differentially methylated in cells with OS compared to cells without OS.More specifically, the differential methylation occurs in the gene body and regulatory region of the genes in the gene panel. [Background technology]

[0002] Living organisms are constantly exposed to various stresses from the external environment. To withstand such stresses, organisms maintain their homeostasis by various regulatory systems. Oxidative stress refers to a severe imbalance between the levels of reactive oxygen species (ROS) in cells and their antioxidant defense mechanisms. To withstand this stress, organisms have a system called redox regulation that deals with stress and maintains their homeostasis by regulating the redox state. This system functions to adapt to oxidative stress due to many external stressors such as radiation, ultraviolet (UV) rays, environmental pollutants, high heat, low temperature, hypoxia, and infections, as well as lifestyle-related diseases such as cancer, diabetes, arteriosclerosis, hypertension, and obesity. However, if this regulatory mechanism is disrupted for any reason, oxidative stress (OS) occurs. OS can lead to cell damage, DNA fragmentation, apoptosis, and cell death. Early detection of OS can prevent further damage in the organism, which causes the organism to receive early treatment or start using protection.

[0003] There are several methods known in the art for early detection of OS. However, none of these methods known in the art have been formally used to detect OS in cells by using a body genomic sample.

[0004] Human skin is constantly exposed to oxidative stress and free radicals, such as large amounts of ROS, resulting from normal metabolic reactions as well as continuous exposure to air, radiation and UV rays, environmental pollutants, and physical and / or chemical agents (e.g., cosmetics). Under some conditions, the production of ROS can be so great that it can contribute to the pathogenesis of, for example, psoriasis or skin cancer. Oxidative damage caused by free radicals such as ROS is also a major cause of physical aging in general, and of skin in particular. Therefore, there is a need in the art for the detection of OS in cells to prevent further damage to cells, for example, skin cells.

[0005] There are several methods known in the art for early detection of OS. However, none of these methods known in the art have been formally used to detect OS in cells by using a body genomic sample.

[0006] Human skin is constantly exposed to oxidative stress and free radicals, such as large amounts of ROS, resulting from normal metabolic reactions as well as continuous exposure to air, radiation and UV rays, environmental pollutants, and physical and / or chemical agents (e.g., cosmetics). Under some conditions, the production of ROS can be so great that it can contribute to the pathogenesis of, for example, psoriasis or skin cancer. Oxidative damage caused by free radicals such as ROS is also a major cause of physical aging in general, and of skin in particular. Therefore, there is a need in the art for early detection of OS in cells to prevent further damage to cells, for example, skin cells. Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention attempts to solve the above problems by providing a method using a gene panel with at least two genes that are differentially methylated in cells with OS. Specifically, at least two genes, including protein tyrosine phosphatase receptor type N2 (PTPRN2), the differential methylation of which can be used to detect OS in cells.

[0008] Environmental factors / agents such as UV light exposure, aging, and diet can induce OS, which can further induce changes in the promoter CpG methylation status of genes by recruiting DNA methyltransferases (DNMTs) and TET enzymes to various promoters, so biomarkers that result in differential methylation in cells with OS are essential to solve the above problems. Specifically, genes that can be used as biomarkers to detect OS in cells include PTPRN2 and other specific genes. PTPRN2 and other specific genes are differentially methylated (i.e., hypomethylated or hypermethylated) in cells with OS compared to the corresponding genes in cells without OS. Thus, PTPRN2 and other specific genes can be effectively used to determine whether a cell has OS. Similarly, a gene panel including at least PTPRN2 and other specific genes can be used to detect OS in cells, since these genes are differentially methylated compared to cells without OS. This is particularly advantageous because the use of epigenetics provides a means to predict the emergence of OS in cells, thus allowing for earlier treatment of OS before more damage is caused to the cell. Furthermore, epigenetic markers are long-term biomarkers, i.e., they are heritable and can also be used to detect OS in subsequent generations if necessary. [Means for solving the problem]

[0009] According to one aspect of the invention, there is provided a method for identifying oxidative stress (OS) in a test cell, comprising the steps of: (a) determining the methylation status of at least five genes in a DNA sample obtained from a test cell; (b) comparing the methylation status of the genes from step (a) with the methylation status of the corresponding genes in a control without OS; A difference in the methylation status of the genes in the test cells compared to the corresponding genes in the control is indicative of a cell having OS, the genes in step (a) being PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, CACNA1C, and their regulatory regions.

[0010] As used herein, the term "cell" refers to a naturally occurring modified intact living cell. A cell may be isolated from other cells, mixed with other cells in culture, or in a tissue (partially or intact), or in an organism. Specifically, a cell may be a eukaryotic cell. More specifically, a cell may be a mammalian cell. The term "mammalian cell" refers to any cell derived from a mammalian subject. A cell may also be a cell derived from the culture and propagation of cells obtained from a subject. A cell may also be genetically modified to express recombinant proteins and / or nucleic acids. Mammalian cells may be derived from humans and other primates, including non-human primates such as chimpanzees, other apes, and monkey species, farm animals such as cows, sheep, pigs, goats, and horses, domestic mammals such as dogs and cats, rodents such as mice, rats, rabbits, hamsters, and guinea pigs, birds including chickens, turkeys, and other poultry, domestic, wild, and game birds such as ducks, geese, and the like. Specifically, the subject is a mammal. More specifically, the mammal is selected from the group consisting of mouse, rat, guinea pig, dog, mini pig, human, cow, sheep, pig, goat, horse, donkey, and mule. Specifically, the mammalian cell may be a skin cell, a stem cell, or a cell derived therefrom. More specifically, the mammalian cell may be a skin cell.

[0011] As used herein, a "CpG site" or "methylation site" is a nucleotide in a nucleic acid (DNA or RNA) that is subject to methylation, either by naturally occurring events in vivo or by events that initiate chemical methylation of the nucleotide in vitro. Some of these sites may be hypermethylated and some may be hypomethylated in cells with OS compared to cells without OS.

[0012] As used herein, a "methylated nucleic acid molecule" refers to a nucleic acid molecule that contains one or more nucleotides that are methylated.

[0013] "CpG island" as used herein describes a segment of DNA sequence that contains functionally or structurally deviant CpG density. For example, Yamada et al. describe a set of standards for determining CpG islands. CpG islands must be at least 400 nucleotides long, have a GC content of more than 50%, and an OCF / ECF ratio of more than 0.6 (Yamada et al., 2004, Genome Research, 14, pp. 247-266). Others define CpG islands less strict as sequences at least 200 nucleotides long with a GC content of more than 50% and an OCF / ECF ratio of more than 0.6 (Takai et al., 2002, Proc. Natl. Acad. Sci. USA, 99, pp. 3740-3745). In the context of the present invention, the terms "methylation profile", "methylation pattern", "methylation state" or "methylation status" are used herein to describe the state, status or condition of methylation of a genomic sequence, and these terms refer to the characteristics of a DNA segment at a particular genomic locus that are related to methylation. Such characteristics include, but are not limited to, the presence or absence of methylation of any of the cytosine (C) residues in this DNA sequence, the positions of the methylated C residues, the percentage of methylated C in any particular stretch of residues, and allelic differences in methylation due to, for example, differences in the origin of the alleles.

[0014] The term "methylation state" refers to the state of a particular methylation site, meaning that the residue or methylation site is methylated or unmethylated (i.e., methylated vs. unmethylated). A methylation profile may then be determined based on the methylation state of one or more methylation sites. Thus, the term "methylation profile" or "methylation pattern" refers to the relative or absolute concentration of methylated or unmethylated C residues in any particular stretch of residues in the genomic material of a biological sample. For example, if a cytosine (C) residue that is typically unmethylated in a DNA sequence is methylated, this may be referred to as "hypermethylation". On the other hand, if a cytosine (C) residue that is typically methylated in a DNA sequence is unmethylated, this may be referred to as "hypomethylation". Similarly, if a cytosine (C) residue in a DNA sequence (e.g., DNA of a sample nucleic acid from a test subject) is methylated compared to another sequence in a different region or different individual (e.g., compared to a normal nucleic acid, or a standard nucleic acid of a reference sequence), the sequence is considered to be hypermethylated compared to the other sequence. Alternatively, if a cytosine (C) residue in a DNA sequence is not methylated compared to another sequence from a different region or a different individual, the sequence is considered to be hypomethylated compared to other sequences. These sequences are said to be "differentially methylated". Measurement of the level of differential methylation may be performed by various methods known to those skilled in the art. One method is, as a non-limiting example, to measure the methylation level of each matched CpG site, as determined by bisulfite sequencing.

[0015] As used herein, "methylated nucleotide" or "methylated nucleotide base" refers to the presence of a methyl moiety on a nucleotide base, which is not normally present in the recognized typical nucleotide base. For example, cytosine does not contain a methyl moiety in its pyrimidine ring in its normal form, but 5-methylcytosine contains a methyl moiety at the 5th position of its pyrimidine ring. Thus, cytosine may not be considered a methylated nucleotide in its normal form, and 5-methylcytosine may be considered a methylated nucleotide. In another example, thymine may contain a methyl moiety at the 5th position of its pyrimidine ring, but for the purposes herein, thymine may not be considered a methylated nucleotide when present in DNA. Typical nucleotide bases of DNA are thymine, adenine, cytosine, and guanine. Typical bases of RNA are uracil, adenine, cytosine, and guanine. Correspondingly, a "methylation site" is a location in a target gene nucleic acid region where methylation may occur. For example, a location containing CpG is a methylation site where cytosine may or may not be methylated. Specifically, the term "methylated nucleotide" refers to a nucleotide that carries a methyl group attached to the nucleotide position available for methylation. These methylated nucleotides are usually found in nature, and to date, methylated cytosine, which occurs mainly in association with the dinucleotide CpG but also in association with CpNpG and CpNpN sequences, can be considered the most common. In principle, other naturally occurring nucleotides can also be methylated, but these are not considered in relation to any aspect of the present invention.

[0016] In the context of the present invention, the terms "methylation profile", "methylation pattern", "methylation state" or "methylation status" are used herein to describe the state, status or condition of methylation of a genomic sequence, and these terms refer to the characteristics of a DNA segment at a particular genomic locus that are related to methylation. Such characteristics include, but are not limited to, the presence or absence of methylation of any of the cytosine (C) residues in this DNA sequence, the positions of the methylated C residues, the percentage of methylated C in any particular stretch of residues, and allelic differences in methylation due to, for example, differences in the origin of the alleles.

[0017] The term "hypermethylation" refers to an average methylation state that corresponds to an increased presence of 5-mCyt at one or more CpG dinucleotides in a DNA sequence of a test DNA sample compared to the amount of 5-mCyt found at the corresponding CpG dinucleotide in a normal control DNA sample. Specifically, the control refers to cells that do not exhibit OS.

[0018] The term "hypomethylation" refers to an average methylation state that corresponds to a decreased presence of 5-mCyt at one or more CpG dinucleotides in a DNA sequence of a test DNA sample compared to the amount of 5-mCyt found at the corresponding CpG dinucleotide in a normal control DNA sample. Specifically, the control refers to cells that do not exhibit OS.

[0019] As used herein, the term "gene" refers to the corresponding genomic DNA sequence, including any promoter and regulatory sequences (e.g., enhancers and other gene sequences involved in regulating gene expression), and / or the body of the gene itself. A gene sequence may be an expressed sequence (e.g., expressed RNA, mRNA, cDNA). Furthermore, when a SNP is identified within a gene, the term should be construed to include all sequence variants thereof.

[0020] As used herein, the term "genomic material" refers to a nucleic acid molecule or fragment of the genome of a subject or group of subjects. Specifically, such a nucleic acid molecule or fragment is DNA or RNA or a hybrid thereof, and most preferably is a molecule of the DNA genome of a subject or group of subjects.

[0021] As used herein, "promoter" or "gene promoter", which is used interchangeably with the term "regulatory region" or "regulatory sequence", refers to the corresponding contiguous gene DNA sequence extending from 1.5 kb upstream to 1.5 kb downstream relative to the transcription start site (TSS), or a contiguous portion thereof. Specifically, "regulatory region" refers to the corresponding contiguous gene DNA sequence extending from 1.5 kb upstream to 0.5 kb downstream relative to the TSS. In some cases, "regulatory region" refers to the corresponding contiguous gene DNA sequence extending from 1.5 kb upstream to the downstream end of the CpG island that overlaps with the region 1.5 kb upstream to 1.5 kb downstream of the TSS (and thus may extend even more than 1.5 kb downstream in such cases), or a contiguous portion thereof. Specifically, with respect to PTPRN2, any CpG dinucleotide of the gene that is coordinately methylated with the "regulatory region" of the gene has substantial diagnostic / classification utility as disclosed herein.

[0022] As used herein, a "DNA sample" refers to DNA extracted from a cell according to any aspect of the invention using methods known in the art.

[0023] Specifically, if differential methylation is detected in the test cell, i.e., the cell exhibits hypermethylation or hypomethylation at at least one CpG site compared to a control (i.e., a cell not exhibiting OS), the test cell has OS.

[0024] Specifically, in step (a) of the method according to any aspect of the invention, the methylation status of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17, 18, 19, 20, 21, 22, 23, or 24 genes is determined. More specifically, in step (a), the methylation status of at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17, 18, 19, 20, 21, 22, 23, 24, or 25 genes is determined.

[0025] In one example, the methylation status of at least five genes is determined in step (a). The five genes are PTPRN2, MAD1L1, PRDM16, TNXB, and HDAC4.

[0026] In another example, the methylation of at least six genes is determined in step (a). The six genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, and ADARB2.

[0027] In another example, the methylation of at least seven genes is determined in step (a). The seven genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, and CDH4.

[0028] In another example, the methylation of at least eight genes is determined in step (a). The eight genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, and DIP2C.

[0029] In another example, the methylation of at least nine genes is determined in step (a). The nine genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, and SHANK2.

[0030] In another example, the methylation of at least 10 genes is determined in step (a). The 10 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, and CAMTA1.

[0031] In another example, the methylation of at least eleven genes is determined in step (a). The eleven genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, CAMTA1, and RPTOR.

[0032] In another example, the methylation of at least 12 genes is determined in step (a). The 12 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, CAMTA1, RPTOR, and RASA3.

[0033] In a further example, the methylation of at least 13 genes is determined in step (a). The 13 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, CAMTA1, RPTOR, RASA3, and SDK1.

[0034] In a further example, the methylation of at least 14 genes is determined in step (a). The 14 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, and AGAP1.

[0035] In a further example, the methylation of at least 15 genes is determined in step (a). The 15 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, and TBCD.

[0036] In a further example, the methylation of at least 16 genes is determined in step (a). The 16 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, and SEPT9.

[0037] In a further example, the methylation of at least 17 genes is determined in step (a). The 17 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, and Multi-Frame4A.

[0038] In a further example, the methylation of at least 18 genes is determined in step (a). The 18 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, and MCF2L.

[0039] In a further example, the methylation of at least 19 genes is determined in step (a). The 19 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Form4A, MCF2L, and FOXP1.

[0040] In a further example, the methylation of at least 20 genes is determined in step (a). The 20 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, and RPS6KA2.

[0041] In a further example, the methylation of at least 21 genes is determined in step (a). The 21 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, and SORCS2.

[0042] In another example, the methylation of at least 22 genes is determined in step (a). The 22 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, and NXN.

[0043] In another example, the methylation of at least 23 genes is determined in step (a). The 23 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, and TRAPPC9.

[0044] In another example, the methylation of at least 24 genes is determined in step (a). The 24 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, and AUTS2.

[0045] In another example, the methylation of at least 25 genes is determined in step (a). The 25 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, and CACNA1C. More specifically, one of the genes in step (a) is the gene protein tyrosine phosphatase receptor type N2 (PTPRN2), and / or the regulatory region of PTPRN2. PTPRN2 is a phosphatidylinositol phosphatase that can dephosphorylate phosphatidylinositol 3-phosphate and phosphatidylinositol 4,5-diphosphate, which play an important role in lipid signaling, cell signaling, and membrane transport.Compared to cells without OS, the differential methylation of the gene body and / or regulatory region of PTPRN2 indicates OS in cells.That is, the hypomethylation or hypermethylation of PTPRN2 and / or the regulatory region of PTPRN2 indicates OS.

[0046] The genes in step (a) were adenosine deaminase RNA-specific B2 (ADARB2), nucleoredoxin (NXN), sidekick cell adhesion molecule 1 (SDK1), calmodulin-binding transcription activator 1 (CAMTA1), transforming sequence-like from MCF.2 cell line (MCF2L), sortilin-related VPS10 domain-containing receptor 2 (SORCS2), Disco-interacting protein 2 homolog C (DIP2C), FERM domain-containing 4A (Multi-domain 4A), histone deacetylase 4 (HDAC4), mitotic spindle assembly checkpoint protein (MAD1L1), PR / SET domain 16 (PRDM16), and Ras The protein is selected from the group consisting of GTPase-activating protein 3 (RASA3), ribosomal protein S6 kinase A2 (RPS6KA2), tubulin folding cofactor D (TBCD), tenascin-X (TNXB), transport protein particle complex subunit 9 (TRAPPC9), ArfGAP with GTPase domain (AGAP1), calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C), regulatory associated protein 1 of the MTOR complex (RPTOR), cadherin 4 (CDH4), SEPTIN9 (SEPT9), forkhead box P1 (FOXP1), activator of transcription and developmental regulator (AUTS2), and SH3 and multiple ankyrin repeat domains 2 (SHANK2).

[0047] In one example, the genes in step (a) may also include genes provided in Table 3.

[0048] A method according to any aspect of the present invention comprises the steps of: (i) further comprising the step of subjecting the DNA sample to bisulfite modification prior to step (a).

[0049] "Bisulfite treatment" of genomic DNA, which is used interchangeably with the term "bisulfite modification", refers to the treatment of genomic DNA with a deaminating agent such as bisulfite, which can be used to treat all DNA, regardless of whether it is methylated or not. Specifically, the term "bisulfite" as used herein includes any suitable type of bisulfite, such as sodium bisulfite, or other chemical agents that can chemically convert cytosine (C) to uracil (U) without chemically modifying methylated cytosine, and thus can be used to differentially modify DNA sequences based on the methylation status of DNA, e.g., US Patent Publication No. 2010 / 0112595. As used herein, a reagent that "differentially modifies" methylated or unmethylated DNA includes any reagent that modifies methylated and / or unmethylated DNA in a process in which distinguishable products are generated from methylated and unmethylated DNA, thereby allowing identification of DNA methylation status. Such processes may include, but are not limited to, chemical reactions (such as C to U conversion with bisulfite) and enzymatic treatments (such as cleavage with methylation-dependent endonucleases). Thus, an enzyme that preferentially cleaves or digests methylated DNA is an enzyme that is able to cleave or digest a DNA molecule with much higher efficiency when the DNA is methylated, whereas an enzyme that preferentially cleaves or digests unmethylated DNA shows significantly higher efficiency when the DNA is unmethylated.

[0050] Therefore, before step (a) according to any aspect of the invention is carried out, the genomic DNA contained in / obtained or extracted from the cell is first bisulfite treated.

[0051] Instead of bisulfite treatment, alternative methods available in the art may be used. Those skilled in the art will understand which other methods should be used. In one example, TET-assisted pyridine borane sequencing (TAPS) may be used to detect 5mC and 5hmC (Yibin Liu et al., Nature Biotechnology, 37:424-429 (2019)).

[0052] The cells used according to any embodiment of the present invention are obtained from a biological sample selected from the group consisting of blood, brain, sperm, and any other tissue or sample that provides genomic DNA for use in the method according to any embodiment of the present invention. In particular, the biological sample may include any biological material obtained from a subject that contains DNA, and may be a liquid, solid, or both, tissue or bone, or a bodily fluid such as blood or lymph. In particular, the biological sample useful in the present invention may include biological cells or fragments thereof.

[0053] The term "test" as used herein in conjunction with the term cell refers to a cell that has been subjected to a method according to any embodiment of the present invention and is the basis for the analytical application of the present invention. A "test cell" is therefore a cell or group of cells that is being tested according to any embodiment of the present invention, or a profile obtained or generated in such a context. In contrast, the term "reference" or "control" is intended to indicate a primarily predefined entity that is used for comparison with a test entity. In particular, a "test cell" refers to a cell that is being tested for an OS whose methylation status has to be determined, whereas a "control" refers to a cell that does not contain an OS, whose methylation status is already known and used as a reference.

[0054] The term "epigenetic changes" as used herein refers to chemical (e.g., methylation) or protein (e.g., histone) changes that occur to a gene body or its promoter. Through epigenetic changes, environmental factors such as diet, stress, and prenatal nutrition can affect genes that are transmitted from one generation to the next. More specifically, the epigenetic markers for OS in cells are the gene body and / or regulatory regions of PTPRN2 and at least one other gene. Differential methylation of these genes in the test cells compared to cells without OS (i.e., control cells) indicates OS in the test cells. Specifically, promoter-specific hypermethylation / hypomethylation leads to changes in the expression of various genes. More specifically, differential methylation of PTPRN2 and other genes leads to differential expression of PTPRN2. Such differential expression of genes compared to the control indicates OS in the tested cells in the absence of differential methylation.

[0055] According to a further aspect of the present invention, there is provided a gene panel for detecting the presence of OS in a cell, comprising at least five genes, one of the five genes being PTPRN2 and the remaining four genes being: A panel of genes is provided, the gene panels being selected from the group consisting of MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, and CACNA1C.

[0056] According to another aspect of the present invention, there is provided a use of a gene panel comprising at least five genes for indicating the presence of OS in a cell, wherein one of the five genes is PTPRN2 and the remaining four genes are: selected from the group consisting of MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, Multi-Frame4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, and CACNA1C; Use is provided, wherein differential methylation of at least five genes indicates the presence of OS in the cell.

[0057] Specifically, differential methylation refers to the hypermethylation or hypomethylation of a gene. [Brief description of the drawings]

[0058] [Figure 1] Graph showing the number of differentially methylated probes in various gene body and promoter regions in cells induced with artificial OS (high UV for 24 hours) according to Example 1. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (similar to receptor-like protein tyrosine phosphatase) have the highest distribution of differentially methylated probes. Other genes that are similarly differentially methylated in cells with OS are shown. [Diagram 2] 1 is a graph showing the number of differentially methylated probes in various gene body and promoter regions in cells induced with artificial OS (low UV for 72 hours) according to Example 1. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (similar to receptor-like protein tyrosine phosphatase) have the highest distribution of differentially methylated probes. Other genes that are similarly differentially methylated in cells with OS are shown. [Diagram 3]1 is a graph showing the number of differentially methylated probes in various gene body and promoter regions in cells induced with artificial OS (high H2O2 for 24 hours) according to Example 2. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (similar to receptor-like protein tyrosine phosphatase) have the highest distribution of differentially methylated probes. Other genes that are similarly differentially methylated in cells with OS are shown. [Figure 4] 1 is a graph showing the number of differentially methylated probes in various gene body and promoter regions in cells induced with artificial OS (low H2O2 for 24 hours) according to Example 2. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (similar to receptor-like protein tyrosine phosphatase) have the highest distribution of differentially methylated probes. Other genes that are similarly differentially methylated in cells with OS are shown. [Diagram 5] 1 is a graph showing the number of differentially methylated probes in various gene body and promoter regions in cells in which artificial OS according to Example 3 is induced using Medox®. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (similar to receptor-like protein tyrosine phosphatase) have the highest distribution of differentially methylated probes. Other genes that are also differentially methylated in cells with OS are shown. [Figure 6] Graph showing the top 20 overlapping genes in various treatments. EXAMPLES

[0059] The above describes preferred embodiments, which may be subject to changes or modifications in design, construction, or operation, as will be understood by those skilled in the art, without departing from the scope of the claims. These variations are intended to be covered, for example, by the claims. [Example 1] Oxidative stress in human tissues induced by UV light To analyze the methylation status of the promoter, artificial oxidative stress was induced in cell culture systems and skin tissue models.

[0060] T-Skin models composed of reconstructed human skin were obtained from Episkin SA (France). Each skin model consists of a dermis equivalent covered by a stratified, well-differentiated epidermis derived from normal human keratinocytes. After receiving the skin models, they were allowed to recover by incubating overnight in T-Skin culture medium at 37°C in a 5% CO2 incubator.

[0061] To induce oxidative stress in a human tissue system, the skin model (five replicates) was exposed to UV radiation (UVA 24 J / cm2 + UVB 50 mJ / cm2) (high UV) for 24 hours daily.

[0062] In another group, the skin model (five replicates) was exposed to UV radiation (daily UVA 12 J / cm2 + UVB 25 mJ / cm2) (low UV) for 72 hours daily.

[0063] Exposure to UV irradiation generates ROS, which ultimately leads to the development of oxidative stress in cells. A control set of skin models (five replicates) was maintained for 72 hours without exposure to UV irradiation. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using DNeasy Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen or NanoDrop™ 2000.

[0064] Genomic DNA (500ng) from tissue samples was subjected to bisulfite conversion using EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0065] Quality control and data processing

[0066] Processing of methylation EPIC array data was performed in R version 4.1.2 (2021-11-01) using minfi version 1.40.0. Raw intensity data (IDAT) were imported into R (4.1.2) and processed using the minfi (1.4.0) Bioconductor package. Sample quality checks were performed to retain probes with detection P values ​​less than 0.01 in one or more samples or with an average detection P value less than 0.05 across all samples. Samples were then normalized using function normalization (performed by the preprocessFunnorm function in minfi) for type-bias correction and background correction.

[0067] Prior to differential methylation analysis, we also filtered out probes with non-specific binding, cross-reactive probes, probes affected by common SNPs, and probes annotated to chromosomes X and Y. The beta and M values ​​of the normalized and filtered samples were calculated using the getBeta and getM functions, respectively, before the samples were subjected to further downstream analysis. Analysis of differential methylation Differential methylation analysis was performed using the packages limma version 3.50.1 and DMRcate version 2.8.5. A contrast matrix was set up by comparing each corresponding treatment group with the control group, and an empirical Bayes algorithm was used to fit M values ​​based on the design and contrast models. Probes with adjusted P values ​​less than 0.05 were considered as differentially methylated positions (DMPs). Annotation was performed using IlluminaHumanMethylationEPICkanno.ilmn12.hg19 and the annotatr package (1.20.0).

[0068] As seen in Figures 1 and 2, one of the top genes differentially methylated within the promoter region is PTPRN2 (protein tyrosine phosphatase receptor type N2). Other genes that were also significantly differentially methylated are shown in Figures 1 and 2. [Example 2] Oxidative stress in human tissues caused by H2O2 Another method of inducing oxidative stress in human tissue systems is by hydrogen peroxide treatment, which generates ROS in cells. Skin models (five replicates) were treated with two different concentrations of hydrogen peroxide [100 μM (low) and 200 μM (high)] for 2 hours and maintained for 24 hours. A control set of skin models (five replicates) was maintained for 24 hours without treatment with hydrogen peroxide. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using the DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0069] The same quality control and data processing methods were performed on the samples in this example as disclosed in Example 1. In addition, the same differential methylation analysis as disclosed in Example 1 was performed on the data obtained from Example 2.

[0070] The results are shown in Figures 3 and 4. [Example 3] Oxidative stress on cell culture systems by Medox® To investigate oxidative stress in cell culture, mesenchymal stem cells (MSCs) were treated with Medox® (Evonik, batch: H-080719), which contains many natural anthocyanins associated with antioxidant and anti-inflammatory properties. Bone marrow-derived MSCs were cultured for one week in Mesencult ACF Plus medium with two doses of Medox® (low, 25 μg / ml) and 100 μg / ml (high) (four replicates). The medium with Medox® was changed every other day for one week. As a control (four replicates), MSCs were cultured for one week in Mesencult ACF Plus medium without Medox® treatment. Medox® treatment is expected to result in an opposite response to OS.

[0071] After this, the cell pellet was collected and genomic DNA was purified from the cell pellet using the DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0072] Genomic DNA (500ng) from cell pellets was subjected to bisulfite conversion using the EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0073] DNA methylation profiling has proven to be a powerful analytical tool to accurately identify tissue origin and the influence of environmental factors. It has several advantages as a biomarker classifier since it is a stable marker and can facilitate quantitative analysis at single nucleotide resolution.

[0074] The same quality control and data processing methods were performed on the samples in this example as disclosed in Example 1. In addition, the same differential methylation analysis as disclosed in Example 1 was performed on the data obtained from Example 3.

[0075] Low treatment to Medox® on MSCs identified 35,532 differentially methylated probes (p<0.05), of which 15,368 probes were hypermethylated and 20,164 probes were hypomethylated. These probes were associated with 13,656 unique genes that were globally hypomethylated across promoters and gene bodies with methylation differences of up to 10-15% compared to the control group. One of the top 50 genes identified from the 13,656 unique genes was PTPRN2 (protein tyrosine phosphatase receptor type N2), among other genes involved in Rho kinase-mediated signaling, cell-cell junctions, cell polarity, and vesicular trafficking. This is shown in Figure 5. Specifically, the top candidates were associated with the Rho-GTPase pathway, disc-large complex, and histone-methyltransferases. The Rho pathway is required for stress fiber formation and focal adhesions (where the cytoskeleton / cell connects integrins with the ECM).

[0076] Results for all Examples (i.e., Examples 1, 2, and 3)

[0077] The significantly differentially methylated genes from Example 1 were aligned with the significantly differentially methylated genes from Examples 2 and 3. The list of genes is shown in Table 1 below.

[0078] [Table 1] Table 1. Differentially methylated genes in five categories: (i) high UV (Example 1), (ii) low UV (Example 1), (iii) high H2O2 (Example 2), (iv) low H2O2 (Example 2), and (v) high Medox® (Example 3).

[0079] Three independent experimental settings analyzing the promoter methylation status during oxidative stress showed differential methylation of the PTPRN2 promoter and other genes, suggesting that these genes are potential biomarkers of oxidative stress. [Example 4] Oxidative stress in human tissues induced by UV light To analyze the methylation status of the promoter, artificial oxidative stress was induced in cell culture systems and skin tissue models.

[0080] T-Skin models composed of reconstructed human skin were obtained from Episkin SA (France). Each skin model consists of a dermis equivalent covered by a stratified, well-differentiated epidermis derived from normal human keratinocytes. After receiving the skin models, they were allowed to recover by incubating overnight in T-Skin culture medium at 37°C in a 5% CO2 incubator.

[0081] To induce oxidative stress in a human tissue system, skin models (five replicates) were exposed to UV irradiation (UVA 24 J / cm2 + UVB 50 mJ / cm2) (high UV) and incubated for 24 hours.

[0082] In another group, the skin models (five replicates) were exposed to UV irradiation (daily UVA 12 J / cm2 + UVB 25 mJ / cm2) (low UV) and incubated for 24 hours.

[0083] Exposure to UV irradiation generates ROS, which ultimately leads to the development of oxidative stress in cells. A control set of skin models (five replicates) was maintained for 24 hours without exposure to UV irradiation. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using DNeasy Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen or NanoDrop™ 2000.

[0084] Genomic DNA (500ng) from tissue samples was subjected to bisulfite conversion using EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0085] Oxidative stress in human tissues caused by H2O2

[0086] Another method of inducing oxidative stress in human tissue systems is by hydrogen peroxide treatment, which generates ROS in cells. Skin models (five replicates) were treated with two different concentrations of hydrogen peroxide [100 μM (low) and 200 μM (high)] for 2 hours and maintained for 24 hours. A control set of skin models (five replicates) was maintained for 24 hours without treatment with hydrogen peroxide. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using the DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0087] Genomic DNA (500ng) from tissue samples was subjected to bisulfite conversion using EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0088] Oxidative stress on human tissues due to particulate matter 2.5

[0089] Another way to induce oxidative stress in human tissue systems is by particulate matter 2.5 (PM2.5) treatment, which generates ROS in cells due to its chemical components and metals. Skin models (five replicates) were treated with two different concentrations of PM2.5 [15 μg / cm2 (low) and 30 μg / cm2 (high)] and maintained for 24 hours. A control set of skin models (five replicates) was maintained for 24 hours without treatment with PM2.5. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using the DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0090] Genomic DNA (500ng) from tissue samples was subjected to bisulfite conversion using EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0091] Glyoxal-induced oxidative stress in human tissues

[0092] Another method of inducing oxidative stress in human tissue systems is by glyoxal treatment, which induces oxidative stress by increasing the level of ROS in cells by producing advanced glycation end products. Skin models (five replicates) were treated with two different concentrations of glyoxal [0.5 mM (low) and 1 mM (high)] and maintained for 24 hours. A control set of skin models (five replicates) was maintained for 24 hours without treatment with glyoxal. After treatment, the skin models were harvested and genomic DNA was purified from the tissue samples using DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0093] Genomic DNA (500ng) from tissue samples was subjected to bisulfite conversion using EZ DNA Methylation-Gold™ Kit (Zymo Research). Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantitatively analyze over 850,000 methylation sites across the genome at single nucleotide resolution.

[0094] Oxidative stress in human tissues during aging

[0095] Another way of inducing oxidative stress in human tissue systems is through aging, which generates reactive oxygen and nitrogen species (RONS) within cells.

[0096] The skin models were maintained in deep well plates containing culture medium for 7 days (4 replicates), 14 days (4 replicates), and 21 days (4 replicates), respectively, to induce aging of skin tissue. The skin models were harvested after 7 days, 14 days, and 21 days, respectively, and genomic DNA was purified from the tissue samples using DNeasy® Blood & Tissue Kit (Qiagen). Genomic DNA was quantified using PicroGreen® or NanoDrop™ 2000.

[0097] Quality control and data processing

[0098] A total of 103 samples from 16 different treatments and their matched controls were analyzed. Treatments were grouped into two batches. Batch 1 consisted of full 24-hour treatments at high and low concentrations. All treatments in batch 1 shared the same control group. Table 2 shows the sample information for batch 1.

[0099] [Table 2]

[0100] Processing of methylation EPIC array data was performed in R version 4.2.2 (2021-11-10 r83330) using minfi version 1.42.0. Raw intensity data (IDAT) were imported into R (4.2.2) and processed using the minfi(1.42.0) Bioconductor package. Sample quality checks were performed to retain probes with detection P values ​​less than 0.01 in one or more samples or with an average detection P value less than 0.05 across all samples. Samples were then normalized using the function normalization (performed by the preprocesssFunnorm function in minfi) for type bias correction and background correction.

[0101] Prior to differential methylation analysis, we also filtered out probes with non-specific binding, cross-reactive probes, probes affected by common SNPs, and probes annotated to chromosomes X and Y. The beta and M values ​​of the normalized and filtered samples were calculated using the getBeta and getM functions, respectively, before the samples were subjected to further downstream analysis.

[0102] Analysis of differential methylation

[0103] Pairwise differential methylation analysis (total of 16 pairs) was performed using the limma package version 3.52.4. Samples from batch 1 were analyzed together, whereas UV light and aged samples were analyzed separately for samples from batch 2. A contrast matrix was set up by comparing each corresponding treatment group with the control group, and an empirical Bayes algorithm was used to fit M values ​​based on the design model and contrast model. Probes with adjusted P values ​​less than 0.05 were considered as differentially methylated positions (DMPs). Annotation was performed using IlluminaHumanMethylationEPICanno.ilmn10b2.hg19. Overlapping genes were then identified in all treatments. As can be seen in Table 3, a total of 1250 genes are common between the 16 comparisons. The rows indicate the total number of DMPs found for that gene per comparison. Figure 6 shows the top 20 genes identified in all 16 comparisons.

[0104] [Table 3A] Table 3. List of 1250 genes differentially methylated in all treatments

[0105] [Table 3B]

[0106] [Table 3C]

Claims

1. A method for identifying oxidative stress (OS) in test cells, (a) A step of determining the methylation status of at least five genes in the DNA sample obtained from the test cells, (b) a step of comparing the methylation state of the gene from step (a) with the methylation state of the corresponding gene in an OS-free control, A method wherein the difference in the methylation state of the gene in the test cells compared to the corresponding gene in the control indicates a cell having OS, and the five genes are PTPRN2, MAD1L1, PRDM16, TNXB, and HDAC4, and their regulatory regions.

2. The method according to claim 1, wherein methylation of at least 20 genes is determined.

3. The method according to claim 2, wherein the 20 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, FRMD4A, MCF2L, FOXP1, and RPS6KA2.

4. The method according to claim 1 or 2, wherein in step (a), the methylation status of all the genes PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, and CACNA1C is determined.

5. The aforementioned OS is affected by aging, ultraviolet (UV) light exposure, and / or H 2 O 2 The method according to claim 1, which is brought about by exposure.

6. (i) The method according to claim 1, further comprising the step of performing bisulfite modification on the DNA sample prior to step (a).

7. The method according to claim 1, wherein the cell is a eukaryote.

8. The method according to claim 1, wherein the cells are of mammalian origin.

9. The method according to claim 8, wherein the mammal is a mouse, rat, guinea pig, dog, miniature pig, human, cow, sheep, pig, goat, horse, donkey, and mule.

10. The method according to claim 1, wherein the cells are skin cells, stem cells, or cells derived therefrom.

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