Epigenetic markers for detecting oxidative stress

Abstract

The present invention relates to a method of identifying oxidative stress (OS) in a test cell, the method comprising: (a) determining the methylation status of at least five genes in a DNA sample obtained from the test cell; (b) comparing the methylation status of the gene from step (a) to the methylation status of the corresponding gene in a control without OS wherein the difference in methylation status of the gene in the test cell compared to the corresponding gene in the control indicates that the cell has OS; and wherein the gene in step (a) is selected from the group consisting of 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, and regulatory regions thereof.

Description

Field of the Invention

[0001] The present invention relates to a method for detecting oxidative stress (OS) in a cell. Specifically, the method is capable of identifying OS in a cell by using a gene panel having at least two genes, wherein the at least two genes are differentially methylated in a cell having OS relative to a cell without OS. More specifically, differential methylation occurs in the gene body and the regulatory region of the gene in the gene panel. Background of the Invention

[0003] Living organisms are constantly subjected to various stresses from the external environment. In order to resist these stresses, they maintain their homeostasis through various regulatory systems. Oxidative stress refers to a serious imbalance between the level of reactive oxygen species (ROS) in cells and their antioxidant defense mechanisms. In order to survive under this stress, living organisms have a system called redox regulation to cope with stress, so as to maintain their homeostasis by regulating the redox status. This system functions to adapt to many external stress factors such as radiation, ultraviolet (UV) rays, environmental pollutants, high fever, low temperature, hypoxia and infectious diseases, as well as oxidative stress from lifestyle-related diseases (such as cancer, diabetes, arteriosclerosis, hypertension and obesity). However, if this regulatory mechanism is destroyed for some reason or other, oxidative stress (OS) occurs. OS can cause cell damage, DNA fragmentation, apoptosis and cell death. Early detection of OS can prevent further damage in living organisms, prompting organisms to receive early treatment or start using protective measures.

[0004] Some methods for early detection of OS are known in the art. However, none of these methods known in the art have been formally used to detect OS in cells using genomic samples of the body.

[0005] Human skin is constantly exposed to oxidative stress and free radicals, such as large amounts of ROS, which are derived not only from common metabolic reactions, but also from continued exposure to air, radiation and ultraviolet rays, environmental pollutants, and physical and / or chemical agents (e.g., cosmetics). Under some conditions, the production of ROS may become so abundant that it may 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 body aging in general, and skin aging in particular. Accordingly, there is a need in the art to detect OS in cells, such as skin cells, to prevent further damage to the cells.

[0006] Some methods for early detection of OS are known in the art. However, none of these methods known in the art have been formally used to detect OS in cells by using genomic samples of the body.

[0007] Human skin is constantly exposed to oxidative stress and free radicals, such as large amounts of ROS, which are derived not only from common metabolic reactions, but also from continued exposure to air, radiation and ultraviolet rays, environmental pollutants, and physical and / or chemical agents (e.g., cosmetics). Under some conditions, the production of ROS may become so abundant that it may 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 body aging in general, and skin aging in particular. Accordingly, there is a need in the art for early detection of OS in cells, such as skin cells, to prevent further damage to the cells.

[0008] Description of the invention

[0009] The present invention attempts to solve the above problems by providing a method using a gene panel having at least two genes that are differentially methylated in cells having OS. In particular, the at least two genes include protein tyrosine phosphatase receptor N2 type (PTPRN2), the differential methylation of which can be used to detect OS in cells.

[0010] Since environmental factors / factors such as ultraviolet exposure, aging, diet, etc. may trigger OS, which can further induce changes in the promoter CpG methylation state of genes by recruiting DNA methyltransferases (DNMTs) and TET enzymes to various promoters, biomarkers that lead to differential methylation in cells with OS are essential for overcoming the problems mentioned above. In particular, genes that can be used as biomarkers for detecting OS in cells include PTPRN2 and other specific genes. PTPRN2 and other specific genes in cells with OS are differentially methylated (i.e., hypomethylated or hypermethylated) compared to the corresponding genes in cells without OS. Accordingly, PTPRN2 and other specific genes can be effectively used to determine whether a cell has OS. Similarly, a gene panel comprising at least PTPRN2 and other specific genes can be used to detect OS in cells because these genes will be differentially methylated compared to cells without OS. This is particularly advantageous because the use of epigenetics provides a means of predicting the onset of OS in cells, thereby allowing OS to be treated as early as possible before causing even more damage to the cells. Furthermore, an epigenetic marker is a long-term biomarker, that is, it is heritable and can also be used to detect OS in the next generation if necessary.

[0011] According to one aspect of the present invention, there is provided a method for identifying oxidative stress (OS) in a test cell, the method comprising:

[0012] (a) determining the methylation status of at least five genes in a DNA sample obtained from a test cell;

[0013] (b) comparing the methylation status of the gene from step (a) with the methylation status of the corresponding gene in a control without OS,

[0014] wherein the difference in methylation status of a gene in the test cell compared to the corresponding gene in the control indicates that the cell has OS; and wherein the gene in step (a) is selected from PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, CACNA1C and their regulatory regions.

[0015] As used herein, the term "cell" refers to a naturally occurring or modified intact living cell. Cells can be isolated from other cells, mixed with other cells in culture, or in a tissue (partial or complete) or organism. In particular, cells can be eukaryotic cells. More particularly, cells can be mammalian cells. The term "mammalian cell" refers to any cell derived from a mammalian subject. Cells can also be cells derived from cultures and amplifications of cells obtained from a subject. Cells can also be genetically modified to express recombinant proteins and / or nucleic acids. Mammalian cells can come from humans and other primates, including non-human primates, such as chimpanzees and other ape and monkey species; farm animals, such as cattle, sheep, pigs, goats and horses; domestic mammals, such as dogs and cats; rodents, such as mice, rats, rabbits, hamsters and guinea pigs; birds, including poultry, wild birds and game birds, such as chickens, turkeys and other gallinaceous birds, ducks, geese, etc. In particular, the subject is a mammal. More particularly, the mammal is selected from the group consisting of mouse, rat, guinea pig, dog, miniature pig, human, cow, sheep, pig, goat, horse, donkey and mule. In particular, the mammalian cell may be a skin cell, a stem cell or a cell derived therefrom. More particularly, the mammalian cell may be a skin cell.

[0016] As used herein, "CpG sites" or "methylation sites" are nucleotides within nucleic acids (DNA or RNA) that are susceptible to methylation, either by naturally occurring events in vivo or by events that chemically methylate nucleotides in vitro. Some of these sites may be hypermethylated and some may be hypomethylated in cells with OS compared to cells without OS.

[0017] As used herein, a "methylated nucleic acid molecule" refers to a nucleic acid molecule that contains one or more nucleotides that are methylated.

[0018] As used herein, "CpG island" describes a DNA sequence segment containing a functionally or structurally deviated CpG density. For example, Yamada et al. have described a set of criteria for determining a CpG island: it must be at least 400 nucleotides in length, have a GC content greater than 50%, and an OCF / ECF ratio greater than 0.6 (Yamada et al., 2004, Genome Research, 14, 247-266). Others have less strictly defined CpG islands as sequences of at least 200 nucleotides in length, with a GC content greater than 50%, and an OCF / ECF ratio greater than 0.6 (Takai et al., 2002, Proc. Natl. Acad. Sci. USA, 99, 3740-3745). In the context of the present invention, the terms "methylation profile", "methylation pattern", "methylation status" or "methylation state" are used herein to describe the status, situation or condition of methylation of a genomic sequence, and such terms refer to the characteristics of a DNA segment at a specific genomic locus associated with methylation. Such properties include, but are not limited to, whether any cytosine (C) residues within the DNA sequence are methylated, the location of methylated C residues, the percentage of methylated C at any particular stretch of residues, and allelic differences in methylation due to, for example, differences in allelic origin.

[0019] The term "methylation state" refers to the state of a specific methylation site (i.e., methylation relative to non-methylation), which means that a residue or methylation site is methylated or unmethylated. Then, based on the methylation state of one or more methylation sites, a methylation profile can be determined. Accordingly, the term "methylation profile" or "methylation pattern" refers to the relative content or absolute content of methylated C residues or unmethylated C residues at any specific residue segment in the genomic material of a biological sample. For example, if a cytosine (C) residue that is not usually methylated in a DNA sequence is methylated, it can be referred to as "hypermethylated"; and if a cytosine (C) residue that is usually methylated in a DNA sequence is not methylated, it can be referred to as "hypomethylated". Similarly, if a cytosine (C) residue in a DNA sequence (e.g., DNA from a sample nucleic acid of a test subject) is methylated compared to another sequence from a different region or different individual (e.g., relative to a standard nucleic acid of a normal nucleic acid or a reference sequence), the sequence is considered to be hypermethylated compared to another sequence. Alternatively, if the cytosine (C) residues within a DNA sequence are non-methylated compared to another sequence from a different region or different individual, the sequence is considered to be hypomethylated compared to the other sequence. These sequences are said to be "differentially methylated". The measurement of differential methylation levels can be accomplished in a variety of ways known to those skilled in the art. As a non-limiting example, one method is to measure the methylation level of each queried CpG site determined by a bisulfite sequencing method.

[0020] As used herein, "methylated nucleotide" or "methylated nucleotide base" refers to the presence of a methyl moiety on a nucleotide base, wherein the methyl moiety is not generally present in a recognized typical nucleotide base. For example, cytosine in its usual form does not contain a methyl moiety on its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. Therefore, cytosine in its usual form may not be considered a methylated nucleotide, while 5-methylcytosine may be considered a methylated nucleotide. In another example, thymine may contain a methyl moiety at position 5 of its pyrimidine ring, however, for the purposes of this article, thymine may not be considered a methylated nucleotide when present in DNA. Typical nucleotide bases for DNA are thymine, adenine, cytosine and guanine. Typical bases for RNA are uracil, adenine, cytosine and guanine. Accordingly, a "methylation site" is a location in a target gene nucleic acid region where methylation has the possibility of occurring. For example, a location containing CpG is a methylation site where cytosine may be methylated or may not be methylated. In particular, the term "methylated nucleotide" refers to a nucleotide carrying a methyl group attached to a position of the nucleotide accessible for methylation. These methylated nucleotides are commonly found in nature, and to date, the methylated cytosines that occur mainly in the context of the dinucleotide CpG, and in the context of CpNpG- and CpNpN-sequences can be considered the most common. In principle, other naturally occurring nucleotides may also be methylated, but they will not be considered for any aspect of the present invention.

[0021] In the context of the present invention, the terms "methylation profile", "methylation pattern", "methylation status" or "methylation state" are used herein to describe the condition, situation or situation of methylation of a genomic sequence, and such terms refer to the properties of a DNA segment at a specific genomic locus that are associated with methylation. Such properties include, but are not limited to, whether any cytosine (C) residues within the DNA sequence are methylated, the location of methylated C residues, the percentage of methylated C at any particular residue stretch, and allelic differences in methylation due to, for example, differences in allelic origin.

[0022] The term "hypermethylation" refers to an average methylation status corresponding to an increased presence of 5-mCyt at one or more CpG dinucleotides within the DNA sequence of the test DNA sample relative to the amount of 5-mCyt found at the corresponding CpG dinucleotides within the normal control DNA sample. In particular, the control refers to cells without OS indication.

[0023] The term "hypomethylation" refers to an average methylation status corresponding to a reduced presence of 5-mCyt at one or more CpG dinucleotides within the DNA sequence of the test DNA sample relative to the amount of 5-mCyt found at the corresponding CpG dinucleotides within the normal control DNA sample. In particular, the control refers to cells without OS indication.

[0024] As used herein, the term "gene" refers to a respective genomic DNA sequence, including any promoter and regulatory sequences of a gene (e.g., enhancers and other gene sequences involved in regulating gene expression), and / or the gene body itself. A gene sequence can be an expressed sequence (e.g., expressed RNA, mRNA, cDNA). Further, when a SNP is known within a gene, the term should be considered to include all sequence variants thereof.

[0025] As used herein, the term "genomic material" refers to a nucleic acid molecule or fragment of the genome of a subject or subject group. In particular, such 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 subject group.

[0026] As used herein, "promoter" or "gene promoter", which can be used interchangeably with the term 'regulatory region' or 'regulatory sequence', refers to a respective 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. In particular, a 'regulatory region' refers to a respective contiguous gene DNA sequence extending from 1.5 kb upstream to 0.5 kb downstream relative to the TSS. In some instances, a 'regulatory region' refers to a respective contiguous gene DNA sequence extending from 1.5 kb upstream to the downstream edge of a CpG island, which overlaps with the region from 1.5 kb upstream to 1.5 kb downstream from the TSS (and in this case, therefore may even extend further beyond 1.5 kb downstream), and a contiguous portion thereof. In particular, with respect to PTPRN2, any CpG dinucleotide of the gene that is coordinately methylated with the 'regulatory region' of the gene has a basic diagnostic / classification utility as disclosed herein.

[0027] As used herein, "DNA sample" refers to DNA extracted from cells according to any aspect of the invention using methods known in the art.

[0028] In particular, when there is detected differential methylation in the test cell, that is, the cell exhibits hypermethylation or hypomethylation at at least one CpG site compared to a control (ie, a cell without an indication of OS), then the test cell has OS.

[0029] In particular, in the method according to any aspect of the present invention, in step (a), 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 particularly, 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.

[0030] In one example, the methylation status of at least 5 genes is determined in step (a). The 5 genes are PTPRN2, MAD1L1, PRDM16, TNXB and HDAC4.

[0031] In another example, the methylation of at least 6 genes is determined in step (a). The 6 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4 and ADARB2.

[0032] In another example, the methylation of at least 7 genes is determined in step (a). The 7 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2 and CDH4.

[0033] In another example, the methylation of at least 8 genes is determined in step (a). The 8 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4 and DIP2C.

[0034] In another example, the methylation of at least 9 genes is determined in step (a). The 9 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2 and SHANK2.

[0035] 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.

[0036] In another example, the methylation of at least 11 genes is determined in step (a). The 11 genes are PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2, SHANK2, CAMTA1 and RPTOR.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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 FRMD4A.

[0043] 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, FRMD4A and MCF2L.

[0044] 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, FRMD4A, MCF2L and FOXP1.

[0045] 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, FRMD4A, MCF2L, FOXP1 and RPS6KA2.

[0046] 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, FRMD4A, MCF2L, FOXP1, RPS6KA2 and SORCS2.

[0047] 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, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2 and NXN.

[0048] 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, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN and TRAPPC9.

[0049] 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, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9 and AUTS2.

[0050] 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, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2 and CACNA1C. More particularly, one of the genes in step (a) is the regulatory region of gene protein tyrosine phosphatase receptor N2 type (PTPRN2) and / or PTPRN2. PTPRN2 is a phosphatidylinositol phosphatase that can dephosphorylate phosphatidylinositol 3-phosphate and phosphatidylinositol 4,5-bisphosphate, which plays an important role in lipid signaling, cell signaling and membrane transport. Differential methylation of the gene body and / or regulatory region of PTPRN2 relative to cells without OS indicates OS in cells. That is, hypomethylation or hypermethylation of PTPRN2 and / or the regulatory region of PTPRN2 indicates OS.

[0051] The gene in step (a) is selected from adenosine deaminase RNA specific B2 (ADARB2), nuclear redox protein (NXN), Sidekick cell adhesion molecule 1 (SDK1), calmodulin binding transcription activator 1 (CAMTA1), MCF.2 cell line derived transforming sequence-like (MCF2L), sortilin-related VPS10 domain-containing receptor 2 (SORCS2), Disco interacting protein 2 homolog C (DIP2C), FERM domain-containing 4A (FRMD4A), histone deacetylase 4 (HDAC4), mitotic spindle assembly checkpoint protein (MAD1L1), PR / SET domain 16 (PRDM16), Ras 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), and) ArfGAP with GTPase domain (AGAP1), L-type voltage-dependent calcium channel alpha 1C subunit (CACNA1C), regulatory-associated protein of MTOR complex 1 (RPTOR), cadherin 4 (CDH4), SEPTIN9 (SEPT9), forkhead box P1 (FOXP1), transcription activator and regulator of development (AUTS2), and SH3 and multiple ankyrin repeat domains 2 (SHANK2).

[0052] In one example, the genes in step (a) may also include the genes provided in Table 3.

[0053] The method according to any aspect of the present invention further comprises the following steps:

[0054] (i) performing bisulfite modification on the DNA sample prior to step (a).

[0055] 'Bisulfite treatment' of genomic DNA, which can be used interchangeably with the term 'bisulfite modification', refers to treating genomic DNA with a deaminating agent such as bisulfite, which can be used to treat all DNA whether methylated or not. In particular, as used herein, the term "bisulfite" encompasses any suitable type of bisulfite, such as sodium bisulfite, or other chemical reagents, which 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 state of DNA, such as US Patent Publication US2010 / 0112595. As used herein, the reagents for "differentially modifying" methylated or unmethylated DNA encompass any reagents that modify methylated and / or unmethylated DNA in the process, by which the product can be distinguished from methylated and unmethylated DNA, thereby allowing the identification of DNA methylation states. Such processes may include, but are not limited to, chemical reactions (e.g., C to U conversion by bisulfite) and enzymatic treatments (e.g., by cleavage of methylation-dependent endonucleases). Thus, an enzyme that preferentially cleaves or digests methylated DNA is one that is able to cleave or digest a DNA molecule with much greater efficiency when the DNA is methylated, whereas an enzyme that preferentially cleaves or digests unmethylated DNA exhibits significantly greater efficiency when the DNA is not methylated.

[0056] Accordingly, prior to performing step (a) according to any aspect of the present invention, the genomic DNA contained / obtained or extracted from the cells is first subjected to bisulfite treatment.

[0057] Alternative methods available in the art may be used instead of bisulfite treatment. The skilled person will understand which other methods are used. In one example, TET-assisted pyridine borane sequencing (TAPS) can be used to detect 5mC and 5hmC (Yibin Liu, et al., Nature Biotechnology, 37: 424–429 (2019).

[0058] The cells used according to any aspect of the present invention are obtained from a biological sample selected from blood, brain, semen and any other tissue or sample (which provides genomic DNA to be used in the method according to any aspect of the present invention). In particular, the biological sample can include any biological material containing DNA obtained from a subject, and can be liquid, solid or both, can be tissue or bone, or body fluids such as blood, lymph, etc. In particular, the biological sample that can be used in the present invention can include biological cells or fragments thereof.

[0059] The term "test" used in conjunction with the term cell herein refers to a cell that is subjected to a method according to any aspect of the invention and is the basis for the analytical application of the invention. Thus, a 'test cell' is a cell or a group of cells tested according to any aspect of the invention, or a profile obtained or generated in this context. Conversely, the term 'reference' or 'control' shall denote a (mostly predetermined) entity for comparison with the test entity. In particular, a 'test cell' refers to a cell of an OS to be tested in which the methylation status must be determined, and a 'control' refers to a cell without an OS in which the methylation status is known and used as a reference.

[0060] As used herein, the term 'epigenetic changes' refers to chemical (e.g., methylation) changes 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 have an impact on genes that are passed from one generation to the next. More particularly, the epigenetic markers for OS in cells are the gene bodies and / or regulatory regions of PTPRN2 and at least one other gene. Relative to cells without OS (i.e., control cells), the differential methylation of these genes in the test cells indicates OS in the test cells. In particular, promoter-specific high / low methylation causes changes in different gene expressions. More particularly, differential methylation of PTPRN2 and other genes causes differential expression of PTPRN2. This gene differential expression indicates OS in the cells tested relative to controls in which there is no differential methylation.

[0061] According to a further aspect of the present invention, a gene experimental panel having at least five genes for detecting the presence of OS in a cell is provided, wherein one of the five genes is PTPRN2, and the other four genes are selected from 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.

[0062] According to yet another aspect of the present invention, there is provided a use of a gene panel having at least five genes for indicating the presence of OS in a cell, wherein one of the five genes is PTPRN2, and the other four genes are selected from 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, and wherein differential methylation of at least five genes indicates the presence of OS in the cell.

[0063] In particular, differential methylation refers to hypermethylation or hypomethylation of a gene. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a graph showing the number of differentially methylated probes in different gene bodies and promoter regions of cells in which artificial OS (high UV for 24 hours) was induced according to Example 1. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (receptor-like protein tyrosine phosphatase) have the highest differentially methylated probe distribution. Other genes that are also differentially methylated in cells with OS are shown.

[0066] Figure 2 is a graph showing the number of differentially methylated probes in different gene bodies and promoter regions of 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 (receptor-like protein tyrosine phosphatase) have the highest differentially methylated probe distribution. Other genes that are also differentially methylated in cells with OS are shown.

[0067] Figure 3 is a graph showing the number of differentially methylated probes in different gene bodies and promoter regions of cells in which artificial OS (high H2O2 for 24 hours) was induced according to Example 2. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (receptor-like protein tyrosine phosphatase) have the highest differentially methylated probe distribution. Other genes that are also differentially methylated in cells with OS are shown.

[0068] Figure 4is a graph showing the number of differentially methylated probes in different gene bodies and promoter regions of 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 (receptor-like protein tyrosine phosphatase) have the highest differentially methylated probe distribution. Other genes that are also differentially methylated in cells with OS are shown.

[0069] Figure 5 is shown in which the cells according to Example 3 are treated with Figure 3 shows the number of differentially methylated probes in different gene bodies and promoter regions of cells induced with artificial OS. As can be seen, the genes MAD1L1 (meiotic spindle arrest component) and PTPRN2 (receptor-like protein tyrosine phosphatase) have the highest differentially methylated probe distribution. Other genes that are also differentially methylated in cells with OS are shown.

[0070] Figure 6 is a graph showing the top 20 overlapping genes under different treatments. Example

[0071] The foregoing describes preferred embodiments, which, as will be appreciated by those skilled in the art, may be subject to changes or modifications in design, construction or operation without departing from the scope of the claims. For example, these changes are intended to be covered by the scope of the claims.

[0072] Example 1

[0073] Oxidative stress in human tissues using UV light

[0074] Artificial oxidative stress was induced in cell culture systems and skin tissue models to analyze the methylation status of promoters.

[0075] The T-Skin model was obtained from Episkin SA, France, and is composed of reconstructed human skin. Each skin model consists of a dermis equivalent covered with a stratified, well-differentiated epidermis derived from normal human keratinocytes. Upon receipt of the skin model, it was recovered by incubation in T-Skin medium overnight at 37°C in a 5% CO2 incubator.

[0076] To induce oxidative stress on human tissue systems, the skin model (5x repetitions) was exposed daily to UV radiation (UVA 24 J / cm 2 +UVB 50mJ / cm 2 )(High UV) for 24 hours.

[0077] In another group, the skin model (5x repetitions) was exposed daily to UV radiation (UVA 12 J / cm 2 +UVB25mJ / cm 2 )(Low UV) for 72 hours.

[0078] Exposure to UV radiation leads to the generation of ROS, which ultimately leads to the development of oxidative stress within the cells. The skin models of the control group (5x replicates) were maintained for 72 hours without any exposure to UV radiation. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0079] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from tissue samples was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0080] Quality control and data processing

[0081] Methylation EPIC array data processing 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, 18. A quality check was performed on the samples to retain probes with a detection P value < 0.01 in one or more samples or an average detection P value < 0.05 across all samples. Samples were then normalized using functional normalization (implemented by the preprocessFunnorm function in minfi) for type bias correction and background correction.

[0082] Before differential methylation analysis, probes with nonspecific binding, cross-reactive probes, probes affected by common SNPs, and probes annotated to chromosomes X and Y were also filtered out. Beta values ​​and M values ​​of the normalized and filtered samples were calculated using the getBeta and getM functions, respectively, and the samples were then subjected to further downstream analysis.

[0083] Differential methylation analysis

[0084] Differential methylation analysis was performed using software packages limma version 3.50.1 and DMRcate version 2.8.5. A comparison matrix was established by comparing each corresponding treatment group and control group, and the empirical Bayesian algorithm was used to fit the M value based on the design and comparison model. Probes with adjusted P values ​​lower than 0.05 were considered as differential methylation positions (DMPs). Annotations were performed using IlluminaHumanMethylationEPICkanno.ilmn12.hg19 and annotatr packages (1.20.0).

[0085] like Figure 1 and Figure 2 As can be seen in Figure 1, one of the top genes differentially methylated in the promoter region is PTPRN2 (protein tyrosine phosphatase receptor N2 type). Other genes that are also differentially methylated are shown in Figure 1 and Figure 2 middle.

[0086] Example 2

[0087] Oxidative stress in human tissues using H2O2

[0088] Another method to induce oxidative stress in human tissues is through hydrogen peroxide treatment, which leads to intracellular ROS generation. Skin models (5x 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. Skin models of the control group (5x replicates) were maintained for 24 hours without any treatment with hydrogen peroxide. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0089] The samples here were subjected to the same quality control and data processing methods as disclosed in Example 1. Further, the data obtained from Example 2 were subjected to the same differential methylation analysis as disclosed in Example 1.

[0090] Results are shown in Figure 3 and 4 middle.

[0091] Example 3

[0092] use Oxidative stress in cell culture systems

[0093] To investigate oxidative stress in cell culture systems, (Evonik, batch number: H-080719) to treat mesenchymal stem cells (MSC), the Contains high amounts of natural anthocyanins associated with antioxidant and anti-inflammatory properties. Bone marrow-derived MSCs were cultured for 1 week in Mesencult ACF Plus Medium with two doses of (4x repeats): 25μg / ml (low) and 100μg / ml (high). Replace every other day with As a control (4x replicates), MSCs were cultured in the absence of The cells were cultured in Mesencult ACF Plus Medium for 1 week. Treatment produces the opposite reaction to OS.

[0094] This is followed by collection of the cell pellet and use Genomic DNA was purified from cell pellets using Blood & Tissue Kit (Qiagen). or NanoDrop TM 2000 for quantification.

[0095] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from cell pellets was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0096] DNA methylation profiling has been shown to be a powerful analytical tool to accurately identify tissues of origin and the effects of environmental factors. It has several advantages as a biomarker classifier because it is a stable marker and it can facilitate quantitative analysis at single nucleotide resolution.

[0097] The samples here were subjected to the same quality control and data processing methods as disclosed in Example 1. Further, the data obtained from Example 3 were subjected to the same differential methylation analysis as disclosed in Example 1.

[0098] For the MSC In the low treatment, 35,532 differentially methylated probes were identified (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 generally hypomethylated at the promoter and gene body with up to 10-15% methylation differences compared to the control group. Among other genes involved in Rho kinase-mediated signaling, cell-to-cell junctions, cell polarity, and vesicle trafficking, one of the top 50 genes identified from the 13,656 unique genes was PTPRN2 (protein tyrosine phosphatase receptor type N2). This is shown in Figure 5 In particular, the top candidates were associated with the Rho-GTPase pathway, the disc-large complex, and histone methyltransferases. The Rho pathway is required for stress fiber formation and focal adhesions (where the cytoskeleton / cells connect integrins to the ECM).

[0099] Results of all Examples (i.e. Examples 1, 2 and 3)

[0100] The significantly differentially methylated genes from Example 1 are juxtaposed with the significantly differentially methylated genes from Examples 2 and 3. The gene list is shown in Table 1 below.

[0101]

[0102]

[0103] 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 (Example 3).

[0104] Three independent experimental setups analyzing the methylation status of promoters during oxidative stress have shown differential methylation of the PTPRN2 promoter and other genes, suggesting that these genes are potential biomarkers of oxidative stress.

[0105] Example 4

[0106] Oxidative stress on human tissues using ultraviolet rays

[0107] Artificial oxidative stress was induced in cell culture systems and skin tissue models to analyze the methylation status of promoters.

[0108] The T-Skin model was obtained from Episkin SA, France, and is composed of reconstructed human skin. Each skin model consists of a dermis equivalent covered with a stratified, well-differentiated epidermis derived from normal human keratinocytes. Upon receipt of the skin model, it was recovered by incubation in T-Skin medium overnight at 37°C in a 5% CO2 incubator.

[0109] To induce oxidative stress on human tissue systems, the skin model (5x repetitions) was exposed to UV radiation (UVA 24 J / cm 2 +UVB 50mJ / cm 2 ) (High UV) and cultured for 24 hours.

[0110] In another group, the skin models (5x repetitions) were exposed to UV radiation (UVA 12 J / cm 2 +UVB 25mJ / cm 2 ) (low UV) and cultured for 24 hours.

[0111] Exposure to UV radiation leads to the generation of ROS, which ultimately leads to the development of oxidative stress within cells. The skin models of the control group (5x replicates) were maintained for 24 hours without any exposure to UV radiation. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0112] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from tissue samples was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0113] Oxidative stress in human tissues using H2O2

[0114] Another method to induce oxidative stress in human tissues is through hydrogen peroxide treatment, which leads to intracellular ROS generation. Skin models (5x 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. Skin models of the control group (5x replicates) were maintained for 24 hours without any treatment with hydrogen peroxide. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0115] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from tissue samples was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0116] Oxidative stress on human tissues using particulate matter 2.5

[0117] Another way to induce oxidative stress in human tissues is through treatment with particulate matter 2.5 (PM2.5), which, through its chemical composition and metals, leads to intracellular ROS generation. The skin model (5x replicates) was treated with two different concentrations of PM2.5 [15 μg / cm 2 (low) and 30 μg / cm 2 (high)] and maintained for 24 hours. The skin models of the control group (5x replicates) were maintained for 24 hours without any treatment with PM2.5. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0118] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from tissue samples was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0119] Oxidative stress in human tissues using glyoxal

[0120] Another method of inducing oxidative stress on human tissue systems is through glyoxal treatment, which triggers oxidative stress by increasing intracellular ROS levels via the production of advanced glycation end products. Skin models (5x replicates) were treated with two different concentrations of glyoxal [0.5 mM (low) and 1 mM (high)] and maintained for 24 hours. Skin models of the control group (5x replicates) were maintained for 24 hours without any treatment with glyoxal. After treatment, the skin models were collected and used Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0121] Using EZ DNA Methylation-Gold TM Kit (Zymo Research), genomic DNA (500 ng) from tissue samples was subjected to bisulfite conversion. Methylation levels were quantified using the Infinium MethylationEPIC v2.0 Kit (Illumina), which can quantify more than 850,000 methylation sites across the genome at single nucleotide resolution.

[0122] Oxidative stress in human tissues using aging

[0123] Another way in which oxidative stress is systematically induced in human tissues is through aging, which leads to the generation of reactive oxygen and nitrogen species (RONS) within cells.

[0124] The skin model was maintained in a deep well plate with culture medium for 7 days (4x replicates), 14 days (4x replicates), and 21 days (4x replicates) to induce senescence of skin tissue. The skin model was collected after 7 days, 14 days, and 21 days, and the Blood & Tissue Kit (Qiagen) was used to purify genomic DNA from tissue samples. or NanoDrop TM 2000 for quantification.

[0125] Quality control and data processing

[0126] A total of 103 samples from 16 different treatments and their respective controls were analyzed. The treatments were grouped into two batches. Batch 1 consisted of all 24-hour treatments at high and low concentrations. All treatments in batch 1 shared the same control group. Table 2 shows sample information about batch 1.

[0127] Table 2. Batch 1 samples

[0128]

[0129]

[0130] Methylation EPIC array data processing was performed in R version 4.2.2 (2021-11-10r83330) 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. A quality check was performed on the samples to retain probes with a detection P value < 0.01 in one or more samples or an average detection P value < 0.05 across all samples. The samples were then normalized using functional normalization (implemented by the preprocesssFunnorm function in minfi) for type bias correction and background correction.

[0131] Before differential methylation analysis, probes with nonspecific binding, cross-reactive probes, probes affected by common SNPs, and probes annotated to chromosomes X and Y were also filtered out. Beta values ​​and M values ​​of the normalized and filtered samples were calculated using the getBeta and getM functions, respectively, and the samples were then subjected to further downstream analysis.

[0132] Differential methylation analysis

[0133] Paired differential methylation analysis (16 pairs in total) was performed using limma package version 3.52.4. Batch 1 samples were analyzed together, while for batch 2 samples, ultraviolet and aging samples were analyzed separately. A contrast matrix was established by comparing each corresponding treatment group and control group, and the empirical Bayesian algorithm was used to fit the M value based on the design and contrast model. The probe with an adjusted P value lower than 0.05 was considered as a differential methylation position (DMP). Illumina Human Methylation EPICanno.ilm10b2.hg19 was used to perform annotation. The genes that overlapped in all treatments were then identified. As shown in Table 3, there were a total of 1250 common genes between 16 comparisons. The row indicates the total number of DMPs that occur about this gene in each comparison.

[0134] Figure 6 The top 20 genes visible in all 16 comparisons are shown.

[0135]

[0136]

[0137]

Claims

1. A method for identifying oxidative stress (OS) in a test cell, the method comprising: (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 gene from step (a) with the methylation status of the corresponding gene in a control without OS, wherein a difference in the methylation status of a gene in the test cell compared to the corresponding gene in the control indicates that the cell has OS; and wherein the gene in step (a) is selected from the group consisting of PTPRN2, MAD1L1, PRDM16, TNXB, HDAC4, ADARB2, CDH4, DIP2C, SHANK2, CAMTA1, RPTOR, RASA3, SDK1, AGAP1, TBCD, SEPT9, FRMD4A, MCF2L, FOXP1, RPS6KA2, SORCS2, NXN, TRAPPC9, AUTS2, CACNA1C and regulatory regions thereof.

2. The method according to claim 1, wherein one of the genes in step (a) is the gene protein tyrosine phosphatase receptor N2 type (PTPRN2), and / or the regulatory region of PTPRN2.

3. The method according to claim 1 or 2, wherein in step (a), the methylation status of at least 10 genes is determined.

4. The method according to any one of the preceding claims, wherein the five genes are PTPRN2, MAD1L1, PRDM16, TNXB and HDAC4.

5. The method according to any one of the preceding claims, wherein the methylation of at least 20 genes is determined.

6. The method of claim 5, 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.

7. The method according to any one of the preceding claims, wherein in step (a) the methylation status of all 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.

8. The method according to any one of the preceding claims, wherein the OS is caused by aging, ultraviolet (UV) light exposure and / or H2O2 exposure.

9. The method according to any one of the preceding claims, further comprising the steps of: (i) performing bisulfite modification on the DNA sample prior to step (a).

10. The method of any one of the preceding claims, wherein the cell is a eukaryotic organism.

11. The method according to any one of the preceding claims, wherein the cell is from a mammal.

12. The method of claim 11, wherein the mammal is a mouse, rat, guinea pig, dog, miniature pig, human, cow, sheep, pig, goat, horse, donkey, and mule.

13. The method according to any one of the preceding claims, wherein the cell is a skin cell, a stem cell or a cell derived therefrom.

14. Use of a gene panel of at least five genes for indicating the presence of OS in a cell, wherein one of the five genes is PTPRN2 and the other four genes are selected from 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.

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