JP2013526271A - Methylation marker selection method - Google Patents

Abstract

  The present invention relates to a method for identifying a group of DNA loci for identifying an atypical growth condition and for identifying an attribute or source of a cell of interest. The method can include identifying multiple groups of DNA loci that specify multiple atypical growth conditions in cells of different origin and / or genotype. Also provided are DNA locus groups and uses thereof.

Description

Cross reference of related applications

  The present invention claims priority from UK patent application number 1007944.0 filed on May 12, 2010. The contents of UK Patent Application No. 1007944.0 are hereby incorporated by reference in their entirety.

  The present invention relates to a method for compiling and using target DNA sequences located within an organism's genomic DNA for the diagnosis and characterization of exposures received by a biological sample and / or tissue (or plurality). Of tissue) or cells (or cells).

  All biological samples (from individual cells to complex multicellular eukaryotic plants and animals) are continuously exposed to environmental factors that affect cellular processes. These environmental factors can take many different forms of exposure, including physical exposure, chemical exposure, biological (eg, pest) exposure, exposure to viruses, and exposure to radioactivity. When these exposures reduce the efficient functionality of biological tissues, they are called stress.

  In a broad sense, stress can be defined as any deviation from biological and abiotic conditions that are optimal for the efficient functionality of a biological tissue or individual.

  Thus, stress (or stresses) in the sense used in the context of the present invention includes abiotic stress and biological and developmental stress (eg, endogenous “systems such as pathogen-mediated diseases, cancer and aging”). Such as, but not limited to, exposure to “breakdown” diseases).

  For example, several authors have listed a number of abiotic stresses that affect plants and have described the physical responses elicited in plants by exposure to these stresses. (For example, Orcutt and Nilsen 2000); Padmanabh 2005). Similarly, a book edited by Bijlsma and Loeschcke (1997) describes a wide range of environmental stresses and investigates their impact on evolutionary processes. Goh et al. (2007) lists a number of human disease stress groups and reports on their efficacy on gene expression. An extensive list of stresses in several animal and plant species such as humans, mice, and Arabidopsis is provided by Hruz et al. (2008) on the Genevestigator website (https://www.genevestigator.com)

  Examples of abiotic stress include heat, osmotic pressure, physical trauma, nutritional deficiencies, chemically induced stress, and physiological stress, such as harmful substances, preventive drugs, fertilizers, therapeutic drugs, or entertainment There are drugs, but it is not limited to these.

Examples of organisms and developmental stress include, but are not limited to, pathogen-mediated diseases, erosion by parasites, endogenous “system breakdown” diseases such as cancer.

  There are also examples where standard stress provides health and other benefits to an organism, ie positive stress. Among the many examples of positive stress, in the alpine plant community, close proximity brings the benefits of further adaptation rather than the losses normally associated with plant-to-plant competitive stress There is a study by Callaway et al. (2002) that showed this. Dhabhar and McEwen (1998) show that short-term inhibitors significantly improve delayed type hypersensitivity in human skin, and stress-induced leukocyte transport into the skin has been observed in the study He argued that it would be an intermediate for improvement. Poss and Tonegawa (1997) studied the regulation and activity of mouse heme oxygenase 1 (Hmox1), a gene that is classically up-regulated in stressed mammalian cells. They provided evidence that up-regulation of this gene provided cellular protection against oxidative stress and therefore provided a positive benefit from stress exposure.

  Given the numerous parameters that affect the optimal performance of any biological system and the inherently variable nature of the environment in which almost all living organisms exist, each organism is exposed to various forms of stress during its lifetime. There is no way to avoid it.

  Individual organisms are a combination of patience (for sub-optimal stress that can be overcome) and / or avoidance (for all unacceptable stresses and for many of the stresses that reduce operational efficiency) Can adapt to stress efficacy.

  When organisms encounter unavoidable stresses, mechanisms to overcome those stresses include biological adaptations, which typically take advantage of system duplication and function better under new conditions. It is generally considered to be a change to a metabolic pathway that adopts / enhances the pathway (Madlung et al., 2004). Such alterations can be achieved in a number of ways, including the regulation of gene transcriptional control through differential cytosine methylation in both the upstream regulatory region and the gene itself (eg Aceituno et al., 2008).

  Physiological mutations that occur from a given stress are not limited to changes that affect the original receptor gene or multiple such genes (those that detect stress), but others that have been changed as a result of changes to the receptor A cascade of genes (stress response genes and response gene pathways).

  There is considerable redundancy in the stress response genes / pathways employed by various stress elicitors. For example, the jasmonate pathway is involved in Arabidopsis responses to both biological and abiotic stress (see Devoto and Turner 2005 and Westernack 2007 for a discussion of this interaction).

  DNA methylation is widely found in many eukaryotic populations including invertebrates such as plants, mammals, birds and linear animals (Finnegan et al., 1998; Suzuki S et al., 2007; Gupta S, et al., 2006; delGaudio R, et al., 1997). In plants, DNA methylation is gene silencing, such as gene silencing in the hypermethylated alleles of Arabidopsis genes SUP, PAI, and AG (Jacobson and Meyerowitz, 1997, malequist et al., 1999, Jacobson et al. , 2000) and gene silencing in linaria LCYC (Cubas et al., 1999). However, hypomethylation of Arabidopsis alleles AP3 and FWA exhibited ectopic expression (Kinoshita et al., 2004). There are several reports that show the role of DNA methylation in plant development and cell type control (Finnegan et al., 2000).

  DNA methylation is also known to play a role in epigenetic regulation of gene expression in mammals such as humans and can be associated with some tissue type. Determining the degree of methylation of a particular gene DNA target region of interest is useful in the research field.

  In humans and other mammals, methylcytosine is found primarily in cytosine guanine (CpG) dinucleotides. Genomic DNA methylation plays an important role in gene regulation. The earliest and common genetic alterations observed in human malignancies, such as cancer, include atypical methylation of so-called CpG islands, particularly CpG islands located in the 5 'regulatory region of genes. This changes the expression of such genes.

  From the association of DNA methylation and gene regulation, as a diagnostic tool for diagnosing the specific state of developmental progression, the identification of cells, tissues or organs, the development of disease or other forms of stress, or exposure to it, Increasing research interest has focused on using the presence and extent of DNA methylation at specific sites in the genome.

  The stress response pathway itself affects the methylation of genes and related regulatory regions that act in maintenance or housekeeping pathways. Since many different stresses can activate the same stress response gene, just because there is a methylation change associated with the expression of any of these genes, the stress itself that caused the activation is individually It is not a diagnosis. In this context, such sites are less important when assigning causes (individual stress) directly to responses (inducing methylation or demethylation). This is because the same change in DNA methylation can be caused by several different stresses. For this reason, the focus of attempts to use DNA methylation to diagnose diseases and other forms of stress is to gather new sites for each disease or stress and to identify specific diseases or stresses. It is to find a new site to be diagnosed individually or collectively.

There are numerous studies that have established an association between DNA methylation of a single or multiple sites within a gene or related control region and a particular human disease. Among them, leukemia; head and neck cancer; Hodgkin's disease; stomach cancer; prostate cancer; kidney cancer; bladder cancer; breast cancer; Burkitt's lymphoma; Wilms tumor; Prader-Willi / Angelman syndrome; ICF syndrome; Pediatric neurobiology; autism; ulcerative colitis; fragile X syndrome; and Huntington's disease. For example, see below.

Leukemia Aoki E et al. (2000); Nosaka K. et al. (2000); Asimakopoulos FA et al. (1999); Fajkusova L et al. (2000); Litz CE et al. (1992)

Head and neck cancer Sanchez-Cespedes M et al (2000)

Hodgkin's disease Garcia JF et al. (1999)

Gastric cancer Yanagisawi Y et al. (2000)

Prostate cancer Rennie PS et al. (1998-99)

Renal cancer Clifford SC et al. (1998)

Bladder cancer Sardi I et al. (1998)

Breast cancer Mancini DN et al. (1998); Zrihan-Licht S et al. (1995); Kass DH et al. (1993)

Burkitt lymphoma Tao Q et al. (1998)

Wilms tumor Kleymenova EV et al. (1998)

Prader Willie / Angelman syndrome Zeschnigh et al. (1997); Faang P et al. (1999)


ICF syndrome Tuck-Muller et al. (2000)

Dermatofibroma Chen TC et al. (2000)

Hypertension Lee SD et al. (1998)

Pediatric neurobiology Campos-Castello J et al. (1999)

Autism Klauck SM et al. (1997)

Ulcerative colitis Gloria L et al. (1996)

Vulnerable syndrome X Hornstra IK et al. (1993)

Huntington's disease Ferluga J et al. (1989)

  Site-specific methylation has also been recognized in the genomes of other (non-human) higher organisms as being associated with specific stresses and diseases. Examples include the following:

  In tobacco plants (Nicotiana tabacum), silencing the type I methyltransferase NtMET1 reduces methylation in the genome. Wada et al., (2004) showed that about 30 genes were up-regulated, more than half of which were directly associated with responses to biological or abiotic stress. In addition, Choi et al., (2007) showed that in tobacco plants, the methylation pattern of the pathogenic responsive gene NtGPDL changed within 24 hours of artificial inoculation with tobacco mosaic virus (TMV), and this change was associated with plant hypersensitivity. It was shown to be related to sexual reaction (HR).

  In maize (Zea mays), low temperature stress appears to reduce methyltransferase transcription and necessarily DNA methylation (Stewart et al, (2000)). In a subsequent study (Stewart et al, (2002)), the same group used HPLC analysis, but low temperature stress caused a greater than 10% reduction in global methylation, but at first glance, the Ac / Ds transposon I confirmed that it was limited.

  Methylation has also been associated with resistance to stress from high concentrations of heavy metals. For example, Lee et al., (1998) presented that hamster cell lines that showed increased activity of methyltransferase (and thus greater methylation) showed good resistance to heavy metals.

  For a discussion of methylated loci associated with stress in a wide range of organisms, see Peng and Zhang (2009) and Hruz et al, (2008) and the Genevestigator website (https: //www.genevestigator. com).

  The methods used in the prior art to elucidate DNA methylation patterns for diagnostic purposes are currently aimed at diagnosing individual environmental exposures, ie specific diseases, specific conditions of development, or specific stresses To identify and elucidate genetic loci. WO2007 / 132166 shows that fetal genes (RASSF1A, APC, CASP8, RARB, SCGB3A1, DAB21P, PTPN6, THY1, TMEFF2, and PYCARD, etc.) are hypermethylated in pregnant women, while maternally derived It describes the discovery that the gene is not methylated. The study described how detecting one or more of these methylated fetal genes in a biological sample from a pregnant woman serves as a universal indicator of the presence of fetal DNA in the sample. Says. Non-invasive analytical processes monitor fetal DNA quality and quantity, and these fetal methylation markers have been described as being particularly useful as positive controls.

  US2006-0183128 (also published as WO2005 / 019477) describes a genome-wide epigenetic map that identifies epigenetic markers for distinguishing tissues. This approach involves a correlation between methylation variable CpG positions (MVP) and genomic DNA sample types. The epigenetic map has wide utility such as identification of sample types and identification between sample types. Analysis of the epigenetic properties of one or a group of nucleic acid sequences only allows the determination of the origin of the nucleic acid in the context of the epigenetic map, not the determination of the environmental exposure / stress profile.

  US2007-0292866 relates to a method for detecting global changes in methylation of human genomic DNA and changes in methylation of specific regions of the human genome. The method allows for disease diagnosis, prognosis, and therapy monitoring, and detection of methylation changes that are the result of diet and / or dietary supplements. The stress can only be identified separately.

  US2004-0029117 allows the identification of differentially methylated DNA loci that can be used as markers for determining the biological action and / or activity of drugs, chemicals and / or pharmaceutical compositions A comprehensive invention is described. The drug, chemical and / or pharmaceutical composition can only be investigated individually, using epigenetic markers for each separately.

  Thus, so far, the use of DNA methylation to identify specific stresses has been limited to markers used in investigating individual stresses one at a time, or else source materials ( For example, to identify tissue / organ / cell type) or to establish a physiological state.

  As a result, there is a need for a more efficient system that studies a variety of stresses, and in particular, verifies multiple stresses in a single analysis. Ideally, the same analysis should be able to distinguish the source material and physiological state.

  The present invention is used to collectively and repeatedly identify that an individual, tissue, organ or cell is exposed to various stresses and / or the general stress of which a sample has been exposed, Or allows the identification of markers in genomic DNA that can be used to diagnose specific properties and / or to diagnose the physiological state or origin (eg, cell type or organ) of a biological sample Provide a system.

  The marker DNA sequences of the present invention are hereinafter referred to (in exchange) as “DNA loci”, “sentinels”, “stress sentinels”, which are biological samples (including individual organisms, organs, tissues or cells). Can identify the stress (or multiple stresses) experienced. Each stress sentinel contains a sequence motif, which is, for example, after an organism is exposed to one stress and not when it is exposed to another stress; If differentiated into a cell type but not the other; and / or depending on the organ or tissue from which the sample was taken; and / or the physiological age or occurrence of the cell type, tissue, organ, or individual Depending on the state, its DNA methylation state (usually but not necessarily limited to cytosine residues) is changed. Thus, the overall pattern of DNA methylation, when examined across the stress sentinel group, enables the following identification: whether the biological sample is acting under one or more stresses; or Whether it has been exposed to one or more stresses; and / or can be used to diagnose the origin of the sample or the physiological state of the source material. The stress to which the sample has been exposed, whether it is a known stress (ie already characterized by a stress sentinel) or an unknown stress (not characterized by a stress sentinel) ) Good. The source of the physiological state or origin of the sample (tissue, organ, or cell type) will generally be inferred by comparison with a standard reference sample (eg, associated with DNA from hepatocytes) A match with the DNA methylation profile means that the sample is from a liver sample).

  An important characteristic of stress sentinels is that no single sentinel individually diagnoses any single stress, source material, or physiological state, and a sentinel is a single stress (or source material or physiological state) Although it can be identified using, it still retains diagnostic properties when applied to a sample exposed to another stress (or source material or physiological condition). While existing systems aim to identify methylation markers that individually diagnose stress / tissue / occurrence status based on target status studies determined against a wide range of control status, In contrast, the characteristics described above provide a significant improvement. Existing approaches tend to be useless when extensive sampling reveals that different states / tissues / cell types share the same methylation status previously thought to diagnose the target condition It has a weak point. In contrast, sentinel markers encompass this situation and are much less likely to be useless due to extensive sampling, diagnosing a response that was previously uncharacterized (unlike existing systems) as different. be able to.

  The sentinel itself may or may not be from the DNA associated with the coding region, regulatory region, or intron, and may or may not be associated with a known change in gene expression. Furthermore, the sentinel site may be present in a non-coding region that is not associated with gene expression if the change to methylation status at that site is consistent and reliable during stress.

  The present invention exemplifies this property with reference to the sentinel DNA methylation marker groups in Arabidopsis, mice, and humans (or the evidence that they can occur), but these sentinel DNA methylation marker groups Are selected based on differential methylation in response to a stress group (or putative differential methylation based on differential expression of genes known to be sensitive to methylation changes) It has been shown that samples exposed to stress (or tissue type) that are not used in the marker selection process can be identified.

  Use established methods for determining associations or clusters based on binary profiles generated from these markers to provide some guidance on the broader stress categories to which new stresses (ie untrained stresses) belong can do. This includes, but is not limited to, multivariate analysis such as principal coordinate analysis, diagnostic decision trees, decision trees (eg Analyzing Ecological data (2007), Springer (NY) Ch 15 pp259-264 , ISBN 978-0-387-45967-7).

  Furthermore, a group of sentinel markers have been described, but these sentinel markers have an expression pattern (and hence methylation status) that can distinguish at least 78 different stress types, and the methylation-controlled stress response of Arabidopsis thaliana Derived from the regulatory region of the gene. That this approach will be sensitive indicates the fact that some of the stresses are very mild (induced changes in less than 5 of 78 investigated genes).

  In addition, a subset of 40 genetic markers from the original 78 genetic marker group of Arabidopsis thaliana was used and the new stress was unique (ie different from the training group), different from the control Using these markers to quantify the frequency that is identified as the same as the existing stress (ie, in the training group) or the same as the control (false negative), the Arabidopsis locus Explain the sentinel principle by training. Also shown is the sentinel's ability to identify tissue types using groups of human loci known to be differentially methylated in different tissues. It has been shown that selecting and using sentinels for a limited group of tissues allows the diagnosis of tissues not used to identify sentinels. In this context, diagnosis means identifying the physiological and phenotypic state of a cell, tissue, organ, or organism, or identifying changes in them.

  In the present specification, embodiments are described in such a manner that a clear and concise specification can be described, but the embodiments may be variously combined and divided without departing from the present invention. It is intended and will be understood as such.

  In this specification, the term “about” means plus or minus 20%, more officially means plus or minus 10%, more preferably plus or minus 5%, most preferably Preferably it means plus or minus 2%.

  As used herein, the term “growth” refers to the living environment in which cells, tissues, or organisms are found, placed, or cultured. For example, the term may relate to growth, life, environmental and / or developmental situations. The terms “grow” and “growth” refer to the survival and / or propagation of a cell, tissue, or organism during a period, study, or period of interest.

In the first aspect of the present invention, there is provided a method for identifying a DNA locus (stress sentinel) group for identifying an atypical growth condition (stress), the method comprising:
(i) growing a sample of the cell of interest under control growth conditions;
(ii) growing a sample of the same cell as the cell of interest in step (i) in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset of loci is an isolated DNA from step (ii) that is different from the isolated DNA from step (i) A process characterized by different methylation states; and
(vii) identifying a subset of the loci selected in step (vi);
(viii) associating the methylation status of the selected subset of loci with the atypical growth conditions (a plurality of atypical growth conditions) and / or the cell sample of interest;
including.

Optionally, the method
(ix) the methylation status of a subset of the selected loci, the atypical growth conditions (multiple atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
including.

In a second aspect of the present invention, there is provided a method for identifying a plurality of DNA loci (stress sentinel) groups for identifying a specific atypical growth condition (stress), the method comprising:
(i) growing a plurality of samples of cells of interest under control growth conditions, wherein the plurality of cells of interest have different genotypes;
(ii) growing a plurality of samples of the cell of interest as in step (i), wherein the cell is in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) compare the methylation status of the isolated DNA from steps (i) and (ii) or compare the methylation status of the isolated DNA from steps (i) and (iii) Or comparing the methylation status of the isolated DNA from steps (ii) and (iii) above;
(vi) selecting a subset of loci, wherein the subset is the isolated DNA from step (ii) and the isolated DNA from step (i) and / or (iii) The methylation status of the locus is different and / or the methylation status of the locus is different from the isolated DNA from step (i) in the isolated DNA from step (iii) Characterized by a process;
(vii) identifying a subset of the loci selected in step (vi);
(viii) associating said selected subset of loci and their methylation status with said atypical growth conditions and / or cell sample of interest;
including.

Optionally, the method further includes
(ix) the methylation status of a subset of the selected loci, the atypical growth conditions (multiple atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
including.

In a third aspect of the present invention, there is provided a method for identifying a DNA locus group for identifying a plurality of atypical growth conditions, the method comprising:
(i) growing a sample of the cell of interest under control growth conditions;
(ii) A step of growing a plurality of samples of the same cell as the cell of interest in step (i) in an environment having at least one atypical growth condition, wherein each sample is grown in a different atypical growth condition. A process;
(iii) isolating DNA from each cell / sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, said subset comprising an isolated DNA from each sample (ii) that is different from the isolated DNA from said step (i) Characterized by the difference between the process;
(vii) identifying a subset of the loci selected in step (vi);
(viii) associating the subset of loci and their methylation status with different atypical growth conditions of step (ii);
including.

If necessary,
(ix) the methylation status of a subset of the selected loci, the atypical growth conditions (multiple atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
There is.

  Preferably, each locus of the subset of loci does not diagnose only a single condition.

Preferably, the methylation status of a selected subset of loci has not been used to select said locus, said atypical growth conditions (a plurality of atypical growth conditions), and / or tissue / Diagnose cell types and / or combinations thereof.

  Preferably, the methylation status of a selected subset of loci is selected from said atypical growth conditions (a plurality of atypical growth conditions) and / or tissue / cell type used to select said loci , And / or combinations thereof.

Preferably, the methylation status of a selected subset of loci is: (i) the atypical growth conditions (a plurality of atypical growth conditions) that were not used to select the locus, and / or Or a tissue / cell type and / or a combination thereof; (ii) the atypical growth conditions (a plurality of atypical growth conditions) and / or tissue / cell type used to select the locus And / or combinations thereof are diagnosed.

  Central to the method of all aspects of the invention is the use of methylation sites (sentinel) that can be used for diagnosing stress, source material, and / or physiological conditions, and more particularly for the selection process. A criterion used to identify and select. To obtain this, it is preferred to select markers that are generally diverse between conditions and / or between raw material samples, ideally not only diagnosing a single condition. Thus, it is not the state of a few condition-specific loci that are linked to diagnosis, but rather a differential methylation pattern across multiple sentinels. A table showing this principle is shown below.

  Thus, in the method of the present invention, the selected subset of loci are diverse loci between conditions and / or between source samples, so that only a single condition and / or source sample is diagnosed. It will be appreciated that it is preferably composed of non-locus loci.

  There are 6 stresses (AF) and 8 loci (1-8) in the hypothetical situation, and the methylation status for various stresses is scored as methylated (+) or unmethylated (-) doing. Table 1 (a) below shows one possible result.

  Of all 8 loci, loci 6 and 7 are not different between the stress training groups (ie, locus 6 is all methylated and locus 7 is not all methylated) The potential sentinel is removed.

  Locus 3 and 4 are different and each diagnoses one stress (A and F, respectively). These are considered candidates for sentinels, but are not ideal because they appear to have a single stress on the locus, and if there are other stresses, they are not likely to stand out in a number of stress profiles.

  The remaining loci do not diagnose any one specific stress, but collectively allow for the separation of all stresses in the training group (see Table 1 (b) below).

  If another stress was further introduced (G, indicated by an arrow), this stress was not used in the selection process but could be identified with the selected sentinel marker, but not with the deleted one .

  Thus, ideal sentinel markers appear in nearly half of the stress / sample sources and “separate” the stresses in a complementary manner.

  The methylation detection step may be performed using any existing method, for example, methylation-sensitive amplified fragment analysis (MSAP), that is, a multi-product profile generated using an isoschizomer restriction enzyme. Amplified fragment length polymorphism (AFLP) to be compared (see Xiong et al, 1999); DNA sequencing with sodium bisulfite followed by cycle sequencing (Frommer et al. 1992), pyro sequencing (Uhlmann et al. al. 2002), ultrafast single-molecule DNA sequencing (eg Zeschnigk et al 2009), or qPCR (see Munson et al 2007), HPLC (eg Sandhu et al 2009), mass spectrometry (eg Igarashi et al 2009) (but not limited to) or by performing high-resolution melting analysis directly (WO2009 / 010776) May be. In one embodiment, the methylation state is used strictly qualitatively (ie, in the presence or absence). In another embodiment, methylation status is measured by quantitative data about locus methylation, for example by setting a threshold for determination, deploying a discriminant analysis system based on quantitative data, etc. (Principal component analysis (PCO) etc.).

  PCO analysis is a standard technique in which variation across multiple dimensions is measured and a new axis is drawn so that the first axis (known as the first principal component) captures the maximum amount of variation in one dimension. . The second axis captures the second largest amount of variation across several dimensions on another axis, which is known as the second principal component. Thus, the first and second components do not exhibit any particular variable, which is a standard analytical strategy.

  The expression “sample of the cell of interest” also includes cells obtained from cell lines in vitro, cells obtained from biological materials such as tissues, organs or whole organisms, or cells forming them.

  The expression “grow a sample of the cell of interest” includes the growth of the cell in an in vitro or in vivo condition. For example, a sample of cells may be grown in vitro in appropriate laboratory equipment for cell growth. Alternatively, a sample of cells may be grown “in vivo”, ie, in the individual organism of interest (such as a plant, animal, or human).

  The expression “control growth conditions” includes the meaning that the growth conditions are actively controlled (ie specified and adjusted) or that the characteristics of the growth conditions are clarified. The control growth conditions depend on how the sample of cells is grown, active control is more appropriate for in vitro growth conditions, and well-defined control conditions are more appropriate for in vivo growth conditions . However, this is not always the case.

  The expression “atypical growth conditions” means growth conditions that are different from the equivalent conditions in the control growth conditions, such as higher temperatures compared to the control growth conditions.

  The expression “atypical growth conditions” includes (but is not limited to) individual conditions that have been altered, such as, but not limited to, temperature, pH, light irradiation, etc. Includes exposure to conditions, disease states, chemicals, drugs, biological agents, and the like.

  The expression “phenotype and / or physiological change” includes a measurable change either at the cellular, tissue or whole organism level. For example, exposure to high temperatures will have observable effects that differ at the cellular and whole organism levels.

  The change may be qualitative (eg, physical response) or quantitative (existing phenotype or increase or decrease in physiological characteristics).

  In one embodiment of the second or third aspect of the present invention, the plurality of samples in the step (ii) are 1 to 10,000 stress conditions, preferably 10 to 500 stress conditions. More preferably, it is 30 to 100 stress conditions.

  Preferably, for example, the locus identified in step (iii) is distinct between samples exposed to atypical growth conditions and control samples, or between cell samples used for selection. is there.

In any of the first, second, and third aspects of the present invention, the following steps are performed after steps (vii), (viii), and step (ix) as necessary.

(x) associating a subset of said loci and their methylation status with a phenotype and / or physiological response in said cell sample or tissue, organ or organism from which said cell sample was obtained.

In one embodiment, the method of any of the first, second, and third aspects of the present invention may be

(xi) isolating DNA from a sample of cells, wherein the growth conditions of the cells are unknown;
(xii) identifying the methylation status of the subset of the loci identified in step (vii) in the isolated DNA of step (xi);
(xiii) In order to identify whether the sample of cells of step (xi) has been exposed to atypical growth conditions, the methylation status of the locus identified in step (xii) is determined by the step (viii ) To compare with the results of

May be included.

In another embodiment, the method of any of the first, second and third aspects of the invention is optionally followed (following step (x) as necessary)

(xi) isolating DNA from a sample of cells, the phenotype of said cell (or the tissue / organism from which it was obtained) and / or when exposed to one or more atypical growth conditions Or the physiological response is unknown, the process;
(xii) identifying the methylation status of the subset of the loci identified in step (vii) in the isolated DNA of step (xi);
(xiii) In order to identify whether the cell (or the tissue / organism from which it was obtained) had a phenotypic and / or physiological response to atypical growth conditions that are clearly or unclear Comparing the methylation status of the locus identified in step (vi) with the result of step (x);

May be included.

  Suitably, said step (xii) also identifies the type of atypical growth conditions to which said cells have been exposed.

  Advantageously, said step (xii) identifies that said cell has been exposed to atypical growth conditions not tested in said step (ii).

  Suitably, said method, for example said step (viii), also identifies the type of physiological response to atypical growth conditions to which said cell / tissue / organ / individual has been exposed.

  Advantageously, said step (xiii) comprises one or more of said cells (or tissue or organism from which said cells were obtained) different from said response associated with said step (i) and (ii) cells. Identifies having a phenotypic and / or physiological response after exposure to atypical growth conditions.

  Preferably, the control growth conditions are in the same range as the range of biologically normal in vivo growth conditions of the cell of interest. One skilled in the art will appreciate that the normal growth conditions depend on the cell type or organism being investigated. Examples of normal growth conditions are published protocols for the culture of designated cell lines or tissues, or even for the survival of organisms of the designated species or genus or higher taxon Configure.

  Examples of normal growth conditions for some of the numerous specific cell lines that may be used in the present invention include, but are not limited to:

  Prostate cancer DU145 Alimirah F, et al (2006). "DU-145 and PC-3 human prostate cancer cell lines express andogren receptor: implications for the androgen receptor functions and regulations". FEBS Lett. 580 (9): 2294-300 .

  Breast cancer MCF-7 Dickson RB et al (1986) "Characterisation of estrogen responsive transforming activity in human breast cancer cell lines". Cancer Res. 46: 1707-13.

  Leukemia THP-1 Dickson RB et al (1986). "Characterisation of estrogen responsive transforming activity in human breast cancer cell lines". Cancer Res. 46: 1707-13.

  Neuroblastoma SHSY5Y Biedler JL et al (1973) Morphology and growth, tumorigenicity, and cytogenetics of human neuroblastoma cells in continuous culture.Cancer Res. 33 (11): 2643-52.

  Osteosarcoma Saos-2 Biedler JL et al (1973) Morphology and growth, tumorigenicity, and cytogenetics of human neuroblastoma cells in continuous culture.Cancer Res. 33 (11): 2643-52.

Rat SP12 Greene LA and Tischler AS (1976). "Establishment of a noradrenergic clonal line of rat adrenal pheochromocytoma cells with respond to nerve growth factor". Proc. Natl. Acad. Sci. U.S.A. 73 (7): 2428-8.

Mouse MC3T3 Bilezikian et al (2002). Principles of Bone Biology. San Diego: Academic Press. 1506.

Tobacco BY-2 cell line as the "Hela" cell in the cell biology of higher plants. International Review of Cytology. 132: 1-30.

  The number of DNA loci comprising the subset comprising the sentinel marker is preferably from 1 to 5000 sites of DNA methylation in the genome under study, or preferably 10 to 10 comprising DNA methylation. 1000 target regions, and most preferably 20-100 target regions including DNA methylation.

  For convenience, various atypical growth conditions in step (ii) of the second aspect are different. The expression “different” means that the atypical growth conditions are not of the same or similar type or kind. For example, temperature and nutrient availability would be considered as different conditions, and exposure to different types of the same pathogenic species is not considered different conditions.

  The atypical growth conditions may in particular be selected from the following: temperature, physical stress (increase / decrease atmospheric pressure, directional pressure on the object, agitation, gravity, physical tension), physical damage , Nutrient availability, exposure to chemical toxins and hormones, signaling molecules, exposure to other chemicals, visible light stress (spectrum quality and intensity), invisible light above ground level (eg, X-rays, Exposure to gamma rays, infrared, ultraviolet, microwave, etc., exposure to pathogens or parasites, exposure to other organisms or other components of the same species, exudates from other organisms or individuals, the target Feeding / eating by other organisms on the organism, water stress (over or under), exposure to signals or conditions that stimulate the object to enter resting state, oxygen stress, or , Exposure to excessive or suboptimal amounts of free radicals.

  Preferably, the subset of loci selected in step (vi) is associated with genes for components of the cellular stress response pathway. The locus may be the gene itself or a regulatory region associated with one or more genes.

  Such a locus may be from an exon or control region of DNA associated with the expression of a conserved cellular stress response pathway function. Examples for plants include the jasmonate pathway (see eg Dempsey et al 1999; Wasternack, 2007; Fan et al 2007; Zhao et al 2007; Reinbothe et al 2009), the nitric oxide pathway (eg Wilson et al 2008), Genes directly or indirectly involved in the salicylic acid pathway (Fan et al 2007; Zabala et al 2009), the ethylene pathway (see for example Zhao et al 2007), the abscisic acid pathway (Zabala et al 2009; Fan et al 2009), and Examples include (but are not limited to) genes involved in the control of phenolic compounds (see, for example, Saltveit and Choi, 2007).

  Examples for human and mammalian organisms include the p53 master transcription pathway (see, eg, Millau et al 2009); the P13K pathway (see, eg, Assinder et al 2009; Maria et al 2009), the PTEN-AKT3 signaling cascade (eg, Madhunapantula and Robertson 2009), endoplasmic reticulum stress response gene hire1p and related products (Tirasophon et al 1998) and cascade gene associated with RET transmembrane receptor (Myers and Mulligan 2004) (but not limited to these). Absent).

  Conveniently, the locus is located in a gene or regulatory region for at least one component of each stress response pathway of the cell of interest.

  A subset of the DNA loci (sentinels) are distinct and consistent in methylation changes, and differ in methylation when organisms / tissues / cell types are exposed to various atypical growth conditions (typically stress) Selected based on the methylation pattern across the sentinel. The DNA locus enables “inductive diagnosis” to diagnose stresses that have changed methylation profiles in a population. A large number of evoked stresses can be identified because sentinel markers exhibit a complementary (non-identical) response (or no response) to different types of stress. The method allows multiple sites to act as sentinels for the overall physiological response of an organism or organ or tissue or cell type to multiple stress inducers.

  Conveniently, the DNA locus is selected to be “universal” and can be used to identify a wide range of unrelated stresses. Stress diagnosis is based on the entire methylation pattern across all selected sentinel markers, and individual stresses are identified by, for example (but not limited to) multivariate analysis or decision tree analysis. Importantly, this approach can also be used to diagnose samples that exhibit normal control profiles and samples that exhibit atypical but previously uncharacterized differences that suggest exposure to unknown stress. Enables diagnosis.

  These sentinels have several essential characteristics, which are characterized by the ability to diagnose characterized or uncharacterized stress, random selection of the genome, or specific stress Characteristically related target region, or a feature that enhances its ability to be estimated from the whole genome (where the noise-signal ratio can reduce the resolution of stress discrimination).

Preferred features of the stress sentinel are shown below.
1. For the same tissue type from the same organ, the methylation state is reproducibly changed in response to the same stress in the same manner.
2. Ideally, the methylation status between replicate samples taken from the same tissue from the same individual at the same time does not show variation, or preferably less than 20%.
3. It produces qualitative methylation changes associated with different stress states that are easily detected.
4). Changes methylation status in response to certain stresses but not other stresses (preferably about 15-85% of stress, more preferably about 25-75% of stress Preferably, it is varied for 40-60% of the stress).
5. It includes methylation-based loci, preferably representing most, ideally all, of the stress response pathways of an organism.
6). Separately assembled for each tissue / organ type of the organism.
7). Unless intended for use only with a particular tissue, organ, or developmental stage, there is ideally no variability between tissues / development stages, preferably less than 10%.

  Preferably, the DNA locus used in the present invention has one or more of these characteristics.

  Advantageously, the selection of the subset of loci in step (vi) is performed automatically by a computer program.

  To give a general example of how a computer program can select a locus, it is necessary to analyze a large number of loci of the measured methylation status. One possible embodiment requires that a subset of genes and their expression levels compared to various stresses are collected to indicate methylation status. The following approach was taken to assemble the code for the computer program.

Data Represents 4 times or more change in gene expression as “1”, change as less than 1 or 4 as “−1”, and no change in gene expression as “0” (encoding) The gene expression data was converted into three stages.

  Stresses that do not change expression levels in any of the genes cannot be identified and are eliminated. Stresses with similar expression responses cannot be identified individually and are therefore all (or all but one) removed.

Algorithm (A)
Determine the minimum number of genes required to identify a given number of stresses. Since the algorithm is probabilistic and incomplete, there is no guarantee that it will produce an optimal answer, but it will always produce an answer that is very close to the optimal value. The algorithm is also sufficiently consistent, allowing statistical comparisons between experiments.
the term:
N, the predetermined number of genes used.
Eval, takes an ordered set of N genes and returns the number of uniquely identified stresses.

Hill Climber (see, for example, Goldfeld, SM, Quandt RE and Trotter HF (1966) Econometrica 34 541; Prigel-Bennett A (2004) Theoretical Computer Science 320, 135-153)
1. Generate a random group of N genes. N is the predetermined number of genes used.
2. Replace one of the N genes in the group with a gene not currently in the group.
3. In the function Eval, the group of genes from step 2 is used to find the number of uniquely identified stresses.
4). If the solution from steps 2 and 3 yields a better solution than that currently held, replace the currently held solution with the solution from step 2/3.
5. If all the stresses are uniquely identified, report the solution and exit, otherwise go to step 2.

  The hill climber is executed with 10,000 iterations and is executed 1000 times independently. If all stresses are uniquely identified and no optimal solution is found, a solution that uniquely identifies most stress is returned.

In a fourth aspect of the present invention, there is provided a method for identifying one or more DNA loci for identifying one or more atypical growth conditions, the method comprising:
(a) Simultaneously performing one or more of the first, second or third aspects of the present invention to associate one or more DNA loci with one or more atypical growth conditions Process to make or
(b) a step in one or more of the first, second, or third aspects of the invention (viii) to identify one or more DNA loci group under one or more atypical growth conditions (viii ) And (ix) are combined.

  In a fifth aspect of the present invention, a group of DNA loci for specifying one or more atypical growth conditions is provided, the group of DNA loci being defined according to a method as described herein.

In a sixth aspect of the invention, there is provided a method for identifying one or more atypical growth conditions to which a cell of interest has been exposed, said method comprising:
(i) a step of isolating DNA from the cell of interest, wherein the growth conditions of the cell are unknown;
(ii) identifying the methylation state of a group of loci that can be methylated in the isolated DNA of step (i);
(iii) comparing the methylation state of the locus as identified in step (ii) with a known methylation state group for the same locus group, the known methylation state group Is a process exhibiting one or more stress characteristics;
(iv) identifying whether the cell of interest has been exposed to atypical growth conditions (a plurality of atypical growth conditions).

  Suitably, step (iv) also identifies the type of atypical growth conditions to which the cells have been exposed.

  In a seventh aspect of the invention, it is provided to use a group of DNA loci as defined above for the identification of atypical growth conditions to which cells have been exposed.

  The use or method of the sixth and seventh aspects, wherein the exposure to the atypical growth conditions continues.

  The use or method of the sixth and seventh aspects, wherein the exposure to the atypical growth conditions has occurred in the past (ie at some time prior to the test).

The DNA locus and associated diagnostic methylation pattern may be used to identify atypical growth conditions or cells (or tissues / organs from which the cells were obtained) for a wide range of applications. The identification may include both current and past exposures, including when the atypical growth conditions are not present in the sample, including but not limited to:
-Diagnosis of symptomatic or asymptomatic disease (see below for details)
-Acquired infection susceptibility-Allergies-Aging-Exposure to medicines and toxicants (prior exposure does not require the presence of active ingredients)
-Exposure to athletic performance-enhancing drugs. Examples include, but are not limited to, steroids, nandrolone, erythropoietin, niketamide, kachin, clenbuterol, norandrosterone, stanozolol, and hydrochlorothiazide.
-Exposure of organisms to pesticides-Checking the status of commercial cell lines-Testing the physiological status of cell lines for biomedical purposes. Examples of this include investigating the viability, potency, and / or totipotency of synthetic or isolated stem cells; assessing the potency and potency of skin cell lines for transplantation; uniformity of standard cell line cultures relative to a reference standard Such as, but not limited to, ensuring a healthy physiological state.
-Preclinical testing-testing for unwanted or favorable, atypical reactions of cell lines or organs or organisms to prophylactic, medical, or toxic biochemical reagents.

Diagnosis of the disease includes the diagnosis of asymptomatic, pre-onset or other ambiguous symptoms. The numerous examples may include various forms of diagnosis such as cancer, metabolic abnormalities, exposure to viral or bacterial infections including latent infections (eg tuberculosis), stress related diseases / disorders, infection or infestation by parasites. . Examples of diseases that can be diagnosed using sentinel include, among others:
-Bacterial diseases, such as tuberculosis (symptomatic and asymptomatic infection), meningitis, sepsis, chlamydia, with no symptoms, vague symptoms, or delayed symptoms.
-All types of cancer, including those in the asymptomatic stage or in remission.
-Viral diseases that do not present symptoms, are ambiguous, or have delayed symptoms. Examples include HIV, herpes, influenza, and Ebola.
-Methylation changes have already been pointed out, so that the system is useful for diagnosis such as leukemia, head and neck cancer, Hodgkin's disease, stomach cancer, prostate cancer, kidney cancer, bladder cancer, breast cancer, Burkitt lymphoma Wilms tumor, Prader-Willi / Angelman syndrome, ICF syndrome, dermal fibroma, hypertension, pediatric neurobiology, etc.

  In an eighth aspect of the invention, a computer program is provided for selecting a subset of loci of different methylation states for identifying and distinguishing atypical growth conditions.

In an eighth aspect of the present invention, there is provided a DNA locus group for identifying one or more atypical growth conditions to which an Arabidopsis cell sample has been exposed, wherein the genes have different methylation status and / or expression. The locus is located in one or more of the following Arabidopsis genes, for example, all in or up to 5 kb upstream / downstream (see Lister et al (2008)).

AT2G15800
AT2G38240
AT5G54720
AT1G76650
AT3G31540
AT3G28820
AT5G02580
AT1G07120
AT1G61800
AT1G48110
AT5G28235
AT5G28430
AT5G28760
AT3G53300
AT5G46200
AT3G57260
AT4G02330
AT3G05400
AT5G25110
AT3G28770
AT5G62750

  In a further aspect of the present invention, the method according to any one of the preceding aspects, wherein the locus group is defined in the ninth aspect, wherein the locus is the atypical growth condition or physiological in the cell of interest of Arabidopsis thaliana Used to identify / phenotype response. Alternatively, the locus is used to identify the atypical growth condition or physiological / phenotypic response in cells of interest from plant species other than Arabidopsis.

In a further aspect of the invention, a group of DNA loci are provided for identifying one or more atypical growth conditions to which a human cell sample has been exposed, wherein the loci of different methylation status and / or development are , Located in one or more of the following human genes or chromosomal loci, eg, all or up to 5 kb upstream / downstream (Eckhardt et al. (2006))

ZNRF3
SLC7A4
Myosin-18B (MYO18B)
Glycoprotein Ib (platelet), beta polypeptide
RP1-47A17.8
OncostatinM (OSM)
CTA-299D3.6
CTA-941F9.6
Cytohesin-4
PIB5PA
SUSD2
chr 22: 35,256,796-35,277,053, NCBI 36
RP3-438O4.2
RP4-756G23.1
Somatostatin receptor type 3 (SSTR3)
Bcl-2 interacting killer (BIK)
GAS2L1
RP3-355C18.2
Gamma-parvin
CARMA 3
TMPRSS6
Platelet-derived growth factor B chain precursor (PDGFB)
CELSR1 Cadherin
T-box transcription factor (TBX1)
Q6ZRW2_HUMAN
NKG2DL4 (RAET1E)
DAAM2
RP1-47M23.1
RP11-397G17.1
Nesprin-1 (Syne-1)
Myogenic repressor I-mf (MDFI)
RP11-174C7.4
CMAH
PKHD1
Glutathione peroxidase 5
GRIK2
SLC22A1
RP11-235G24.1
TBX18
TGM3
RIN2
SLC24A3
Q9ULE8_HUMAN
C20orf117
Breast carcinoma amplified sequence 4 (BCAS4)
Nuclear factor of activated Tcells (NFATC2)
SCUBE1
JAG1

  According to another aspect of the present invention, a method for compiling and using a target DNA sequence group located within an organism's genomic DNA for diagnosis and characterization of exposures received by a biological sample, and / or An attribute or origin of a tissue (or tissues) or cells (or cells) is provided, the method comprising selecting a DNA sequence having a methylation state, wherein the methylation state is between samples Methods that vary but do not individually diagnose a single condition (exposure received by a biological sample) or source (attributes and origins of tissue (or tissues) or cells (or cells)) It is.

  According to another aspect of the present invention, a group of target DNA sequences located within the genomic DNA of an organism for diagnosis and characterization of the attribute or origin of the tissue (or tissues) or cells (or cells) is provided. A method for compiling and using is provided, the method comprising selecting a DNA sequence having a methylation state, wherein the methylation state varies from sample to sample but to a single source (tissue (or multiples). It is a method that does not individually diagnose tissue) or cell (or cell) attributes and origins).

  According to another aspect of the present invention, there is provided a method for compiling and using target DNA sequences located within an organism's genomic DNA for diagnosis and characterization of exposures received by a biological sample, The method includes selecting a DNA sequence having a methylation state, wherein the methylation state varies from sample to sample, but is subject to a single condition (exposure received by a biological sample) or a single condition (a biological sample is subject to reception). It is a method that does not diagnose individual exposures).

According to another aspect of the invention, a target DNA sequence located within the genomic DNA of an organism for diagnosis and characterization of the attribute or origin of tissue (or tissues) and / or cells (or cells) A method is provided for compiling and using a group, the method comprising:
(i) isolating DNA from two or more samples of cells of known but different attributes or origins;
(ii) identifying the methylation status of a methylatable locus in the isolated DNA of each sample;
(iii) comparing the methylation status of isolated DNA from each sample;
(iv) selecting a subset of loci, characterized by a methylation status that varies between samples but does not individually diagnose a single attribute or origin; and
including.

In another aspect of the present invention, there is provided a method for identifying a DNA locus (stress sentinel) group for identifying an atypical growth condition (stress), the method comprising:
(i) growing a sample of the cell of interest under control growth conditions;
(ii) growing a sample of the same cell as the cell of interest in step (i) in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset of loci is an isolated DNA from step (ii) that is different from the isolated DNA from step (i) The methylation status is different and each locus in the subset changes the methylation status in response to certain atypical growth conditions, but relative to other atypical growth conditions A process that does not.

Optionally, the method
(vii) methylation status of a subset of the selected loci, the atypical growth conditions (a plurality of atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
including.

In another aspect of the present invention, there is provided a method for identifying a DNA locus (stress sentinel) group for identifying a specific atypical growth condition (stress), the method comprising:
(i) growing a plurality of samples of cells of interest under control growth conditions, wherein the plurality of cells of interest have different genotypes;
(ii) growing a plurality of samples of the cell of interest as in step (i), wherein the cell is in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) compare the methylation status of the isolated DNA from steps (i) and (ii) or compare the methylation status of the isolated DNA from steps (i) and (iii) Or comparing the methylation status of the isolated DNA from steps (ii) and (iii) above;
(vi) selecting a subset of loci, wherein the subset is a locus that is different from the isolated DNA from step (i) or (iii) in the isolated DNA from step (ii); And / or the DNA from step (iii) is different in the methylation state of the locus from the isolated DNA from step (i). And each locus in the subset changes the methylation status in response to certain atypical growth conditions, but not to other atypical growth conditions; and
including.

If necessary,
(viii) methylation status of a subset of the selected loci, the atypical growth conditions (multiple atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
There is more.

In another aspect of the present invention, there is provided a method of identifying a group of DNA loci for identifying a plurality of atypical growth conditions, the method comprising:
(i) growing a sample of the cell of interest under control growth conditions;
(ii) A step of growing a plurality of samples of the same cell as the cell of interest in step (i) in an environment having at least one atypical growth condition, wherein each sample is grown in a different atypical growth condition. A process;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset of loci is the isolated DNA from each sample of step (ii) and the isolated DNA from step (i) It is characterized by the different methylation status of the loci, and each locus in the subset changes the methylation status in response to certain atypical growth conditions, while other atypical growths A process that does not do so for conditions.

If necessary,
(vii) methylation status of a subset of the selected loci, the atypical growth conditions (a plurality of atypical growth conditions) that were not used to select the loci, and / or tissues / Associating with cell types and / or combinations thereof;
There is more.

Preferably, the atypical growth conditions (multiple atypical growth conditions), wherein the methylation status of the selected subset of loci has not been used collectively to select the loci, and / or Or diagnose the tissue / cell type and / or combinations thereof.

  Preferably, the atypical growth conditions (multiple atypical growth conditions) and / or tissue wherein the methylation status of the selected subset of loci is used collectively to select the loci Diagnose / cell type and / or combinations thereof.

Preferably, the methylation status of a selected subset of loci is: (i) the atypical growth conditions (a plurality of atypical growth conditions) that were not used to select the locus, and / or Or a tissue / cell type and / or a combination thereof; (ii) the atypical growth conditions (a plurality of atypical growth conditions) and / or tissue / cell type used to select the locus And / or combinations thereof are diagnosed.

  Preferably, the method of the invention uses at least about 2, at least about 3, at least about 4, at least about 5 different stresses, tissue types or cell types to select a subset of said loci. At least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 15, at least about 20, at least about 30, at least about 40, at least about 50 Including using or more.

  For example, in some embodiments, the methods of the present invention employ 1 to about 10,000, preferably about 10 to about, different stresses, tissue types or cell types to select a subset of said loci. Use of 500, more preferably from about 30 to about 100.

  As described herein, in preferred embodiments, each locus in the subset of loci does not diagnose only a single condition.

Preferably, each locus in said subset of loci has one or more of the following characteristics, such as two or more, three or more, four or more, five or more, six or more, seven or eight: Have one.
(i) For the same tissue type from the same organ, the methylation state is reproducibly changed in response to the same stress in the same manner.
(ii) The methylation state between replicate samples taken from the same tissue from the same individual at the same time does not show any variation or preferably shows a variation of less than 20%.
(iii) produce qualitative methylation changes associated with different easily detected stress conditions or attributes or origins of cells (multiple cells) and / or tissues (multiple tissues).
(iv) Produces significant quantitative methylation changes associated with different easily detected stress conditions or attributes or origins of cells (multiple cells) and / or tissues (multiple tissues).
(v) changes methylation status in response to certain stresses but not other stresses (preferably for 15-85% of stress, more preferably 25-75% of stress) Most preferably, it is varied for 40-60% of the stress).
(vi) (a) methyl, which represents the organism's stress response pathway, (b) the cell (s) and / or tissue (s) types, preferably most, ideally all Includes genetic loci.
(vii) Assembled separately for each tissue / organ type of the organism.
(viii) There is no variability between tissues / development stages, preferably less than 10%, unless intended for use only with specific tissues, organs, or developmental stages.

  Preferably, none of the loci in the subset of loci individually diagnose any single stress, source material, or physiological condition.

  Preferably, the subset of loci can be identified using a single stress, source material, or physiological condition, but when applied to a sample exposed to a different stress, source material, or physiological condition. Still retains diagnostic properties.

  Preferably, each locus of the subset of loci appears in approximately half of the stress or sample source used to select the locus, and separates the stresses in a complementary manner.

Further provided by the present invention is one or more groups of DNA sequences or one or more subsets of loci identified by the methods described herein. It is also provided that one or more subsets of the one or more target DNA sequences or loci are used for the applications described herein, for example as described elsewhere herein. As used in one or more of the following applications:-Diagnosis of symptomatic or asymptomatic disease (see below for details)
-Acquired infection susceptibility-Allergies-Aging-Exposure to medicines and toxicants (prior exposure does not require the presence of active ingredients)
-Exposure to athletic performance-enhancing drugs. For example, steroids, nandrolone, erythropoietin, niketamide, katin, clenbuterol, norandrosterone, stanozolol, and hydrochlorothiazide-biological exposure to pesticides-checking the status of commercial cell lines-testing the physiological status of cell lines for biomedical purposes To do. Examples of this include investigating the viability, potency, and / or totipotency of synthetic or isolated stem cells; assessing the potency and potency of skin cell lines for transplantation; uniformity of standard cell line cultures relative to a reference standard Such as, but not limited to, ensuring a healthy physiological state.
-Preclinical testing-to test for undesirable or favorable, atypical responses of cell lines or organs or organisms to prophylactic, medical, or toxic biochemical reagents.

  The invention is illustrated in the accompanying figures and examples. It should be understood that the teachings of the examples should in no way be construed as limiting the scope of the invention.

FIG. 1: Principal component analysis (PCO) A diagram based on analysis of methylation-sensitive amplified fragment analysis (MSAP) data from different stressed Arabidopsis thaliana. The top two components represent 23.2% of the total variation (for untrained data). (S = salt stress, N = temperature stress, H = herbivorous, C = control). Figure 2: PCO of polymorphic MSAP marker between salinity, temperature and control sample only. The top two components represent 24.8% of the variation (trained for C, N, and S). (S = salt stress, N = temperature stress, H = herbivorous, C = control). Fig. 3: PCO of the polymorphic MSAP marker among all stresses (sentinel group) The top two components represent 32.7% of the variation. (S = salt stress, N = temperature stress, H = herbivorous, C = control). Figure 4: Data analyzed by statistical pairwise comparison between the four treatments. Top 100 significant amplicons from each of 6 comparisons of all 381 amplicons. The top two components of the total variation are 15.2%. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 5: Data analyzed by statistical pairwise comparison between the four treatments. Top 50 significant amplicons from each of 6 comparisons of all 217 amplicons. The top two components cover 17.9% of the total variation. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 6: Data analyzed by statistical pair comparison between the four treatments. Top 25 significant amplicons from each of the six comparisons of all 116 amplicons. The top two components of the total variation are 20.5%. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 7: Data analyzed by statistical pair comparison between the four treatments. Top 10 significant amplicons from each of the 6 comparisons of all 52 amplicons. The top two components cover 25.4% of all variations. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 8: Data analyzed by differences between the four processes. Top 100 amplicons that showed significant differences from each of the 6 comparisons of all 380 amplicons. The top two components cover 23.5% of the total variation. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 9: Data analyzed by differences between the four processes. Top 50 amplicons that showed significant differences from each of the 6 comparisons of all 206 amplicons. The top two components of the total variation are 28.3%. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 10: Data analyzed by differences between the four processes. Top 25 amplicons that showed significant differences from each of the 6 comparisons of all 112 amplicons. The top two components cover 33.3% of all variations. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 11: Data analyzed by differences between the four processes. Top 10 amplicons that showed significant differences from each of the 6 comparisons of all 53 amplicons. The total variation covered by the top two components is 39.2%. (S = salt stress, N = temperature stress, H = herbivorous, C = control). FIG. 12: Principal component analysis based on full trained analysis of amplified fragment length polymorphism data from differently stressed mouse RAW cells. In the total variation, the top two components represent 15.8%. (■) IL4, (♦) LPS, (▲) nutritional stress, (●) control. FIG. 13: Principal component analysis based on control of amplified fragment length polymorphism data from differently stressed mouse RAW cells, IL4, and trained analysis of nutritional stress. The top two components represent 20.7% of the total variation. (■) IL4, (♦) LPS, (▲) nutritional stress, (●) control. FIG. 14: Principal component analysis based on trained analysis of amplified fragment length polymorphism data control, IL4, and LPS data from differently stressed mouse RAW cells. The top two components represent 19.8% of the total variation. (■) IL4, (♦) LPS, (▲) nutritional stress, (●) control. FIG. 15: Principal component analysis based on control of amplified fragment length polymorphism data from differently stressed mouse RAW cells, LPS, and trained analysis of nutritional stress. The top two components represent 19.9% of the total variation. (■) IL4, (♦) LPS, (▲) nutritional stress, (●) control. FIG. 16: Genestigator results (Lister et al (2008)) showing the effect of 82 stresses on the expression of randomly selected Arabidopsis markers found to be controlled by methylation. Indicating stress, rows indicate selection of genetic markers, gray in each box represents the expression level of the corresponding gene (row) when the Arabidopsis is exposed to the corresponding stress (column). Exposed to (column), black indicates no change in expression, light gray is down-regulated, dark gray is up-regulated. Figure 17: Genestigator results (Lister et al (2008)) showing the effect of nine developmental stages on the expression of randomly selected Arabidopsis markers, which can be seen to be controlled by methylation. Rows indicate selection of genetic markers, gray in each box represents the level of expression of the corresponding gene (row) from Arabidopsis at each stage of development (shown in the column). Figure 18: Genestigator results showing the effect of 40 organs on the expression of randomly selected Arabidopsis markers found to be controlled by methylation (Lister et al (2008). 40 columns). Rows indicate selection of genetic markers, gray in each box represents the expression level of the corresponding gene (row) from each Arabidopsis organ. Figure 19: Classification of 27 stresses using 3 sentinels, each representing 3 states (gray continuous linkage: up-regulated or hypomethylated sentinel; gray intermittent linkage: down-regulated or hypermethylated sentinel; black linkage: No change in development or sentinel methylation level). FIG. 20: A trifurcated tree relating changes in control to stress, showing that two genes are linked to eight stresses S1-S8. “+” Indicates upward control, and “−” indicates downward control. When gene 1 is down-regulated (-) and gene 2 is up-regulated, this corresponds to the stress profile of S6. FIG. 21: PCoA plot (first major coordinate versus second major coordinate) showing the classification of sentinel profiles (expression profiles normalized to a binary format) of 15 loci exposed to 78 different stresses. The stresses were artificially classified into the following informal groups: organisms (black squares), chemistry (white squares), hormones (white triangles), light (black triangles), nutrition (white circles), PCO (black circles) Low temperature (white rhombus), dry (black rhombus), heat (gray rhombus), hypoxia (star shape), osmotic pressure (gray square), salt (cross), and damage (gray circle). FIG. 22: PCoA plot (first principal coordinate versus second principal coordinate) showing the classification of sentinel profiles (expression profiles normalized to a binary format) of 36 loci exposed to 78 different stresses. The stresses were artificially classified into the following informal groups: organisms (black squares), chemistry (white squares), hormones (white triangles), light (black triangles), nutrition (white circles), PCO (black circles) Low temperature (white rhombus), dry (black rhombus), heat (gray rhombus), hypoxia (star shape), osmotic pressure (gray square), salt (cross), and damage (gray circle). FIG. 23: PCoA plot (first principal coordinate versus third principal coordinate) showing the classification of sentinel profiles (expression profiles normalized to a binary format) of 36 loci exposed to 78 different stresses. The stresses were artificially classified into the following informal groups: organisms (black squares), chemistry (white squares), hormones (white triangles), light (black triangles), nutrition (white circles), PCO (black circles) Low temperature (white rhombus), dry (black rhombus), heat (gray rhombus), hypoxia (star shape), osmotic pressure (gray square), salt (cross), and damage (gray circle). FIG. 24: PCoA plot (first major coordinate vs. second major coordinate) showing the classification of sentinel profiles (expression profiles normalized to a binary format) of 59 loci exposed to 78 different stresses. The stresses were artificially classified into the following informal groups: organisms (black squares), chemistry (white squares), hormones (white triangles), light (black triangles), nutrition (white circles), PCO (black circles) Low temperature (white rhombus), dry (black rhombus), heat (gray rhombus), hypoxia (star shape), osmotic pressure (gray square), salt (cross), and damage (gray circle). Figure 25: Genestigator results showing the effect of 506 stresses on the expression of 49 markers that were found to be differentially methylated in humans (Eckhardt et al (2006)). In the color code, black indicates no change in expression, light gray is down-regulated, and dark gray is up-regulated. FIG. 25 (continued) FIG. 25 (continued) FIG. 25 (continued) FIG. 26: Genestigator results (Eckhardt et al (2006)) showing the effect of 8 age groups on the expression of 49 markers that were found to be differentially methylated in humans. FIG. 27: Genestigator results showing the effect of 40 organs on the expression of 49 markers found to be differentially methylated in humans (Eckhardt et al (2006)). Figure 27 (continued)

Examples Methods used in molecular biology applicable to the examples below are known to those skilled in the art. Comprehensive articles describing conventional molecular biology, microbiology, and recombinant DNA technology known to those skilled in the art include, for example: Sambrook et al., Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York (2001); Glover ed., DNA Cloning: Practical Approach, Volumes I and II, MRL Press, Ltd., Oxford, UK (1985); and Ausubel, F., Brent, R., Kingston, RE, Moore, DD, Seidman, J.GT., Smith JA, Struhl, K. Current protocols in molecular biology.Green Publishing Associates / Wiley Intersciences, New York.

Example 1-Methylation-sensitive amplified fragment length polymorphism for stress identification in Arabidopsis

Assembling candidate sentinel DNA methylation markers, exposing a cohort of Arabidopsis thaliana (ecotype: Columbia) to two different external stresses, and differential markers in the resulting MSAP (methylation sensitive amplification polymorphism) profile Inspect. The methylation profiles of the stressed test subjects were compared with each other and with a reference (control) population grown under standard experimental conditions. The ability of the sentinel marker to diagnose plants grown under uncharacterized stress (exemplification of the method) was confirmed by reference to a stressed third population. In this way, four groups of candidate sentinels could be assembled and tested (sentinel selected from two of the three stressed populations and tested against a third population).

Plant Growth Two plants are grown in each cell of a tray having 24 4 cm × 4 cm cells. Remove one cell for bottom water supply. Thus, there were 46 plants for each treatment. Seeds were germinated at 4 ° C., grown for 1 week in a greenhouse and then transferred to experimental conditions in 4 controlled growth boxes. The plant body was grown at a day length of 8 hours (about 70 μmol / m ² / s) to suppress flowering.

  Three of the four growth boxes were set at 22 ° C .; a fourth growth box (tested with temperature as stress) was set as follows. The plants were grown in two separate lots under slightly different conditions.

DNA extraction All DNA extractions were performed using Qiagen kits according to the manufacturer's instructions. A DNeasy® plant mini kit was used to extract DNA from Arabidopsis samples from a total of 46 plants for each treatment. All reagents listed below are from this kit.

  Approximately 100 mg of plant tissue was disrupted in liquid nitrogen in a 1.5 ml centrifuge tube using scissors. Immediately without melting the tissue, 400 μl lysis buffer AP1 and 4 μl ribonuclease A preheated to 65 ° C. were added to each tube. The contents were mixed by inversion and incubated at 65 ° C. for 10 minutes with occasional mixing every 2-3 minutes.

  Following this, 130 μl of AP2 buffer was added to each sample and each tube was incubated on ice for 5 minutes to precipitate proteins and polysaccharides. The tube was then centrifuged at 13,000 rpm for 5 minutes to precipitate the viscous lysate.

  The supernatant was transferred to a QIAshredder ™ column (with silica gel matrix) and centrifuged at 13,000 rpm for 2 minutes to remove precipitates and cell debris. The fluid in the column was collected and transferred to a new tube and mixed with a volume of 0.5 wash buffer and a volume of 1 ethanol. This mixture was transferred to a second DNeasy mini rotary column and centrifuged at 8,000 rpm for 1 minute. Since the DNA molecules are retained on the column, the fluid was discarded. 500 μl of washing buffer AW was passed through the column and centrifuged at 8,000 rpm for 1 minute to wash the adhering DNA twice.

  Subsequently, 100 μl of buffer AE preheated to 65 ° C. was added and incubated at room temperature for 5 minutes, and then the membrane was dried by centrifugation at 13,000 rpm for 1 minute.

Agarose gel quantified DNA aliquots (1-5 μl) were subjected to 1% (w / v) agarose gel electrophoresis to determine the quality and quantity of DNA present. The gel was prepared by dissolving an appropriate amount of agarose in an appropriate volume of 1 (TAE buffer (40 mM Tris Acetate, 1 mM EDTA) and then heating in a microwave until the agarose melted. The solution is cooled to 50 ° C. and then added with a 10 mg / ml ethidium bromide solution to a final concentration of 0.35 μg / ml. After that, an appropriate comb is placed on a casting tray on which a loading well is created. Injected.

  When set, the gel moved to a horizontal electrophoresis apparatus equipped with a gel comb at the cathode end. The gel comb was removed and a sufficient amount of 1 (TAE buffer was added to the electrode chamber to cover the gel with approximately 1 mm. Prepared DNA sample (5 μl DNA: 1 μl blue loading dye [0.23% (w / v) Bromophenol blue, 60 mM EDTA, 40% (w / v) sucrose]) was loaded into the gel well, HyperLadder II (Bioline, BIO-33040) size marker was loaded into the adjacent lane. Electrophoresis was performed at a constant voltage in the range of cm for 15 to 60 minutes, and the DNA was visualized using an ultraviolet transilluminator (wavelength 320 nm).

Methylation-sensitive amplified fragment length polymorphism
Methylation-sensitive amplified fragment length polymorphisms for 8 DNA samples randomly selected per treatment based on the AFLP protocol described by Vos et al (1995), but using isoschizomers targeted to the same recognition motif (AFLP) was performed.

  The basis of this technique is to detect restriction fragments of genomic DNA by polymerase chain reaction (PCR) amplification. This makes it possible to create a fingerprint from DNA of any origin or complexity using a limited set of general purpose primers, without the need to know the sequence in advance. This method can be applied to methylation detection through the use of restriction enzymes that are sensitive to methylation.

  The DNA was restricted by two restriction enzymes, one rare cutter and one common cutter, sensitive to cytosine methylation. Two different limitations were made using isoschizomers that are sensitive to different types of cytosine methylation in a common cutter. All enzymes were obtained from Fermentas, Canada.

  Mspl enzyme: cleaves between two cytosines of sequence 5′CCGG3 ′. Its action is inhibited by methylation on the first C, but not by methylation on the second C.

  Hpall enzyme: cleaves between two cytosines of sequence 5′CCGG3 ′. Its action is inhibited by methylation on the second C, but not by methylation on the first C.

  An adapter specific for the restriction site is ligated to the DNA, making it possible to amplify the fragment with universal primers without having to first obtain sequence information. All the enzymes were obtained from Fermentas and the adapters were obtained from Sigma-Genosys.

  The adapter mixture was made by combining 1 nM EcoRI adapter and 10 nM MspI / HpaII adapter.

  The amplification round is performed using one oligonucleotide primer corresponding to the EcoRI end and one oligonucleotide primer corresponding to the MspI / Hpall end. The first round of amplification reduces the number of fragments by adding one extra base at the 3 ′ end of the primer, and the second round of amplification is one extra at the 3 ′ end of the primer or The amount of fragments is further reduced by adding two bases. The second round EcoRI primer is labeled with 6-Fam (carboxyfluorescein) to allow visualization of the product.

  The product of the selective amplification step was sent to Macrogen, Korea, where it was fractionated by capillary electrophoresis on an Applied Biosystems gene analyzer. The results were visualized and interpreted using GeneMapper analysis software and exported to Microsoft Excel for further analysis. Kovach, W. (1998); MVSP-A Multivariate Statistical Package for Windows, Version 3.0. Pentraeth, Wales, UK: MVSP as described by Kovach Computing Services (http: //www.kovcomp.co.u/ mvsp /) or using Multitab 15 (http://www.minitab.com/en-GB/default.apsx?WT.srch=1&WT.mc_id=SE004815) Principal coordinate analysis).

  Each band (amplicon) within the AFLP protocol was considered to be a single allele at a single locus. For each treatment, the allelic attribute of each locus was first assigned to each replicated individual as 1 (yes) or 0 (no) in a simple quantitative manner. If the allelic profile of an individual differs by more than 3 individuals for a locus, the locus is considered to differ between a pair of stress treatments or between a control and stress treatment (eg, 11111111 and 11111000 are considered different, but 11111111 and 00111111 were not considered different. If all loci met this criteria, they were considered candidates for sentinel markers for subsequent multivariate analysis (see below).

  There were four rounds of training to assemble sentinel DNA methylation markers. For this reason, comparisons between treatments were made in four groups of three treatments including controls (Table 11). Subsequently, the data from these four groups were combined to form a group integrating the sentinels and applied to all samples.

Principal coordinate analysis was used to analyze the results from the trained sentinel.

Results all markers (no selective training)
When training was limited to identifying polymorphic loci across all conditions (combination training), 959 loci were recorded, all of which exhibited some polymorphic level among individuals. When using the data to compile a multivariate analysis, there was a reasonable separation in the stressed population, and plants exposed to the same stress tended to form clusters (PCO, figure 1). However, there was also overlap between individuals who received different stress treatments, especially between temperature stress and herbivory or controls. The top two components were 23.2% of the total variation.

  Using only markers that met the criteria for sentinel selection with two stresses and controls improved the separation between trained stresses while reducing the number of markers used. For example, selecting sentinels for control samples and salinity and temperature stress reduced the number of markers used from 959 to 525. Nevertheless, the separation between these groups improved (there was no overlap between the training groups), and the rate of variation represented by the top two principal components increased slightly to 24.8%.

  Importantly, individuals exposed to untrained stress (vegetation) also showed very clear separation, with only one sample (H8) being ambiguous (FIG. 2).

  When all three stresses were included in the training, the number of loci decreased to 137, but the separation between the stresses was significantly improved and there were no ambiguity individuals (FIG. 3). Furthermore, the rate of variation represented by the top two principal components in all variations increased significantly (to 32.7%).

Statistical Approach for Sentinel Selection Computation of organized data was performed in the R environment (R environment) using a package obtained from the R website (www.r-project.org). Comparisons were made between the paired processes to determine which features (bands) were statistically most relevant to distinguish the four processes. Using three different methods, Random forest, Welch t-statistic, area under the curve (AUC) to select features and combine the results from all three to distinguish the different processes The most suitable feature group was set (Table 13).

  The selected subset of features was visualized using principal coordinate analysis to determine the optimal number for maximum separation between the four processes (FIGS. 4-7).

Difference Approach Using the organized data, the total number of bands present was calculated from the potential number of 8 for each of the four treatments. The difference between treatment pairs was calculated and the feature with the largest difference between these treatment pairs was selected as the sentinel (Table 14).

  The selected subset of features was visualized using principal coordinate analysis to determine the optimal number for maximum separation between the four processes (FIGS. 8-11). Here, the number of sentinel markers has decreased, and a clear distinction between groups has been maintained, but the ratio to the whole has changed from 16.1% when using the top 100 markers (FIG. 8) to the top 10 It increased to 27% (Figure 11) when the marker was used as a sentinel.

Example 2-Methylation-sensitive amplified fragment length polymorphism for stress identification in mouse cell lines

Cell culture conditions The effect of external stress on the epigenome of mouse monocyte macrophage cell RAW 264.7 (ECACC No. 91062702) is examined. Cells were seeded and cultured in 32-well microplates at 2.5 (10 ⁵ cells / ml) and cultured for 20 hours at 37 ° C. and 5% carbon dioxide until harvest. Unless otherwise stated, the medium used was DMEM supplemented with 2 mM L-glutamine and 10% fetal calf serum.

Three stresses were applied to these populations of cells.
1. (I) 20 mg / ml interleukin 4 (IL4)
2. (L) 1 mg / ml lipopolysaccharide (LPS)
3. (S) No L-glutamine from the medium

  All DNA extractions were performed using Qiagen kits according to the manufacturer's instructions. A DNeasy blood tissue kit was used to extract DNA from macrophage samples. All reagents mentioned below are from the DNeasy kit.

  Media was removed from the culture wells by inversion and replaced with 200 μl PBS, 20 μl proteinase, and 200 μl buffer AL (without ethanol). The contents were mixed by vortexing and incubated at 56 ° C. for 10 minutes. Following this, 200 μl of ethanol (95-100%) was added to each sample and the plate was mixed by vortexing. This mixture from each well was transferred to a DNeasy mini spin column and centrifuged at 8,000 rpm for 1 minute.

  Since the DNA molecules are retained on the column, the fluid was discarded. Pass 500 μl of wash buffer AW1 through the column at 8,000 rpm for 1 minute to wash the adhering DNA once, pass 500 μl of wash buffer AW2 through the column at 8,000 rpm for 1 minute. The attached DNA was washed once. Subsequently, the membrane was dried by centrifugation at 13,000 rpm for 1 minute, and 100 μl of buffer AE was added to elute the DNA. After culturing at room temperature for 1 minute, the membrane was centrifuged at 8,000 rpm for 1 minute.

  DNA quality and quantity are tested as detailed in Example 1 above.

Amplified fragment length polymorphism Amplified fragment length polymorphism (AFLP) is based on the protocol described by Vos et al (1995) and was performed as detailed in Example 1 above.

Sentinel selection Mouse sentinel was selected in the same manner as described above for Arabidopsis thaliana in Example 1.

Results all markers (no selective training)
When training was limited to identifying polymorphic loci across all conditions (combination training), 734 loci were recorded, all of which exhibited some polymorphic level among individuals. When using the data to compile a multivariate analysis, there was a reasonable separation in the stressed population and cell lines exposed to the same stress tended to cluster (PCO, below) FIG. 12). Only 15.8% revealed the top two components for total variation.

  Selecting the marker that showed the most consistent difference between the two stresses and the control sample maintained (or slightly improved) the distinction between the stress groups, but covered the first and second principal components The plots trained for two of the three stresses entered a narrow range of 19.8 to 20.7% of the total variation (FIGS. 13-15).

  Thus, by selecting a subset of polymorphic markers that highlight the differences between the two stress treatments, the third stress could be similarly separated in PCO performed using all polymorphic markers.

  Furthermore, in the trained sentinel group, the top two principal components involved in group distinction correspond to more of the total variability in the trained sentinel group than in the plot using all markers, and hence the variation in “noise” There were few. However, the trained sentinel group contains several loci that vary both within and between treatments and therefore would not be useful when used alone for stress diagnosis.

Example 3-Sample Method for Making Sentinel and Its Use in Arabidopsis

Selection of the target gene for use as a sentinel was performed by cross-referencing two complementary databases.

  The first database (the Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis, http://neomorph.salk.edu/epigenome/epigenome.html) is the wild type of Arabidopsis and DNA methylation maintenance (met1-3) List of methylation-controlled genes created by recent studies comparing transcriptomes and methylomes with mutants defective with and established (drm1-2, drm2-2, cmt3-11) (Lister et al 2008). When the expression level of these genes increases, followed by a global loss of DNA methylation in the mutant compared to the wild type, these genes are controlled by DNA methylation and most are down-regulated by methylation. I can guess. Thus, if there is a change in expression under any of the stresses tested compared to the control, or during either the developmental stage or the tissue, it causes a change in the methylation profile due to the changed conditions And they will be suitable as candidate sentinels.

  Genevestigator (https://www.genevestigator.com) provided information from microarray analysis on the expression of 484 genes in Arabidopsis under a range of specific stresses, organs, and developmental stages (FIGS. 16-18).

  A gene was classified as up-regulated under stress if it showed 4-fold expression compared to a control when exposed to a given stress. Downregulation was defined as a one-fourth decrease in expression compared to controls. “1” indicates that the gene expression has changed by a factor of 4 or more, and “−1” indicates that the gene expression has changed to a quarter or less, and “0” indicates that the gene expression has not changed The output data was converted into three stages. Thus, in the following analysis, the abundance of cDNA is used as a raw correlation of DNA methylation status, thus providing a worst-case indicator of the potential of methylated sentinels at these loci. Direct measurement of DNA methylation will reduce the manifestation of useless variability. Because mRNA is subject to sudden and unstable changes in its abundance (Stavreva et al 2009; Kageyama et al 2007), and each individual locus has several potentials that can themselves be used as sentinels. This is because it contains a typical methylation site.

  Stresses 4 and 75 were not identified because they did not cause changes in expression levels in any of the genes and were therefore removed from the analysis. Stresses S51, S52, and S57 could not be individually identified because they had similar expression responses and S51 and S52 were removed (S57 was left in the analysis). Of the original 82 stresses, 78 profiles were therefore used for the analysis.

  A total of 39,688 gene-stress pair combinations were tested with 484 genes and 82 stresses for each. Of these pairs, 1806 (4.56%) showed more than a 4-fold change in expression, 931 (2.35%) were upregulated under stress, and 875 (2.21%) Down controlled.

Computer programs Software development and programming Like all Bayesian interpretations, the algorithms for the analysis are probabilistic and incomplete; therefore, there is no guarantee that the program will produce a definite answer, but certainty. The answer is often generated, and even if it is not, a reliable answer is always provided that is close enough for comparison purposes.

Task definition and algorithm design The purpose of the program was defined as determining the minimum number of genes needed to identify a given number of stresses. In this case, since the expression of all genes selected as candidate sentinels changes with methylation mutations (see above), a 4-fold change in expression was considered indicative of a change in methylation status at these loci.

Algorithm (A)
N, the predetermined number of genes used.
Eval, takes an ordered set of N genes and returns the number of uniquely identified stresses.

The number of stresses that can be classified using a gene representing three different stages (-1: downregulation, 0: no change, 1: upregulation) is three. Thus, the logical number of stresses identified from a group of gene expression profiles is the cube of the genes in the group. Further, according to the combination formula, N ³ combinations can be made by taking some N elements without overlapping each other (see FIG. 19 as an example).

  A computer program was created to identify the minimum number of genes that can classify 81 stresses from 488 genes. A trifurcated tree is used to represent the solution, where the nodes of the tree correspond to genes and the branches indicate the response to stress, indicating whether it is up-regulated, down-regulated, or unchanged in control (FIG. 20). ). Two genes have been shown to be associated with eight stresses S1-S8. “+” Indicates upward control, and “−” indicates downward control. If gene 1 is down-regulated (−) and gene 2 is up-regulated (+), this corresponds to the stress profile of S6.

  Probabilistic hill climbers were used to identify the optimal number of stresses that could be identified from a given number of genes. Although a complete solution may be possible for a small number of genes and / or stress, this approach is the most useful solution to this problem because the computational requirements increase geometrically as the number of genes or stress increases. Provide a solution.

Hill climber Generate a random group of N genes. N is the predetermined number of genes used.
2. Replace one of the N genes in the group with a gene not currently in the group.
3. In function Eval, the group of genes from step 2 is used to find the number of uniquely identified stresses.
4). If the solution from steps 2 and 3 yields a better solution than that currently held, replace the currently held solution with the solution from step 2/3.
5. If all the stresses are uniquely identified, report the solution and exit, otherwise go to step 2.

  The hill climber is executed with 10,000 iterations and is executed 1000 times independently. If all stresses are uniquely identified and no optimal solution is found, a solution that uniquely identifies most stress is returned.

Results Algorithm A was performed using the above data. The minimum value, N, was initially set to 20 and was decreased by 1 until no solution could be found that identifies all the stresses. Using this approach, the minimum number of genes required to identify all 78 stresses was identified as 15. The selected genes and the order in which they are applied are shown in Table 15. Table 16 shows how each stress is diagnosed using the gene expression list of Table 15. Table 17 summarizes how each stress affects these genes for up-regulation, down-regulation, or no change.

  In order to create an overlapping group of genes that can identify the 78 stresses, the above procedure was repeated to remove the genes in Table 15 to obtain a pool of available genes. Stress 80 was identified only by the one gene used for the first analysis and was subsequently removed because it could not be identified. N was found by starting at 15 and incrementing by 1 until a solution was found. The lowest value of N that can identify all 77 stresses was 21. Table 18 shows the 21 duplicate genes and their order required to identify 77 stresses.

  Similarities between different stresses were determined by principal coordinate Euclidean analysis (PCoA) (Gower, 1966) based on the above expression profile using GeneAlex software (Peakall and Slouse 2005). This analysis showed a certain level of stress grouping (FIG. 21).

  To improve separation and grouping, a second group of 21 genes was added to the previous 15 genes (Table 18).

  This creates the matrix shown in Table 19, where gene sentinels 1-21 did not change in response to genes S1-82 (-1), down-regulated (0), or up-regulated. (1).

  Again, similarities between different stresses were determined by principal coordinate Euclidean analysis (PCoA) as described above. This second group of sentinel genes showed a high level of clustering by stress grouping.

  Thus, these data indicate that a group of sentinels identified based on 39 stresses can identify exposure to these stresses, and the amount of stress that was not used to identify sentinels. Part exposure can also be identified. Furthermore, stress clustering suggests that an enormous classification of detected unknown stresses could be optimized.

  To improve stress isolation and clustering, a third group of 23 genes was added to the previous 36 genes. Table 20.

  Again, similarities between different stresses were determined by principal coordinate Euclidean analysis (PCoA) as described above. The second group of genes showed a high level of clustering among the stress groupings (Figure 24).

  This creates the matrix shown in Table 21, where gene sentinels 1-21 were not changed in response to genes S1-82 (-1), down-regulated (0), or up-regulated. (1).

  The drawing shown below shows a representation on a two-dimensional space that is part of the information obtained by PCoA. In fact, by creating volumes up to six dimensions, a space was created where stress groups could be separated. This cannot be shown graphically, but this information can be used in the computer.

  This data shows that a program developed to demonstrate the sentinel principle generated three consecutively trained groups of Arabidopsis sentinel genes, each containing 15, 21, and 23 genes. Is shown. Each sentinel group can clearly diagnose exposure to 78 investigated stresses as expression / methylation distinguishable from control samples.

  In this way, a series of selected subsets of markers with the appropriate properties are assembled to detect differences from “normal” physiological states associated with standard control growth conditions and expression in these markers And / or can be manifested as changes to the DNA methylation status. The fact that these groups are independent is consistent across all sentinel groups for showing differences from the control state, and that this match increases the level of confidence in the accuracy of the diagnosis. means. A further feature is that sentinel groups progressively increase and accumulate, and that they are used collectively for multivariate analysis, with clustering by stress type increasing as the sentinel markers increase, and scatter plots. That is more and more systematized (FIGS. 21 and 24).

  When applied to replicate each stress sample (as in Examples 1 and 2), increasing the number of markers in this way is the noise caused by variations between samples exposed to the same stress. Minimize. Thus, cluster analysis, or other decision algorithms, correctly classifies previously untested stresses and stress responses into appropriate groupings.

Validation To validate the predictive power of the method, sample half of the stress randomly without replacement and create 1000 data groups. Half of the sampled stress was classified as the training group, and the unsampled stress was called the validation group.

  Each of the training groups was analyzed four times with Algorithm A with N set to 10, 20, 30, and 40.

  Two tests were performed on the results (Table 22). Test A took each stress from the validation group and examined whether it could be uniquely identified from the training group. Test B examined the number of groups and the number of groups with only one member when all the stresses were combined. If there was no change in expression for the selected gene, the stress was negated.

Example 4-Stress characterization for biological samples

The sentinel locus can be generated for the distinction of stress and the developmental stage that was not used for stress identification.

  A partial methylome covering human chromosomes 6, 20, and 22 was published by Eckhardt et al (2006). Using this information, 2345 human markers were selected as sentinel candidates, of which 40 genes were identified as having different methylation states by organ (Table 22). These 40 genes are cross-referenced in Genevestigator software (Zimmerman et al 2004 and Zimmerman et 2005) and web-based applications, but include changes in expression in different tissues and are included in these tissue and methylation studies This gene was found to be differentially expressed in another tissue that was not found (FIG. 27), and its expression was confirmed to be sensitive to methylation.

  In addition, when the expression of these genes is being studied at various stages of development and when the cells / individuals are exposed to a wide range of stimuli, drug treatments, diseases, and genetic modifications, the genes of a given organism Is activated and a wide variety of expression profiles are observed. These profiles make it possible to build a unique profile for the most part even if not all conditions can be tested.

  Genevestigator can be used to determine that these markers are responsive to 506 stimuli in humans, including chemical exposure, organ transplantation, disease, environmental stress, etc. (FIG. 16). And 25). This analysis was also performed in 8 different age groups in humans (FIGS. 17 and 26) and finally in 227 human cell types (FIGS. 18 and 27) using sentinel expression levels.

  If altered expression is usually (ideally always) associated with methylation changes at these sentinel loci, changes in the methylation profile across these loci are associated with disease, stress, physiological age and / or condition. Many applications such as medical diagnosis and identification of the origin of biological substances will be applicable.

  To apply this sentinel group to the sample, various approaches using existing technologies already in use in laboratories, hospitals, medical diagnostic offices, etc. will be available. Techniques for scoring the methylation status of the sentinel include bisulfite treatment of genomic DNA followed by PCR amplification of the sentinel using specific primers for each of the sentinel loci, then (1) high resolution melting (HRM) analysis (Wojdacz and Dbrovic 2007; White et al 2007), (2) Bisulfite-specific sequencing (Frommer et al 1992), or (3) Bisulfite-specific pyrosequencing (Yang et al 2004) There is a technique for performing (but not limited to). The design of specific primers for each sentinel locus will be a routine task for those skilled in the art.

  All three approaches have advantages and disadvantages, but allow high throughput sample analysis, can be automated, and can generate digital data in a format that can be easily analyzed.

  The final protocol design will depend on the approach chosen. Once the results from all the sentinels are obtained, the results are introduced into an active database containing the software (algorithm) described in the above examples, and the results are associated with known stresses. If no valid profile match is found, a PCoA (Principal Coordinate Analysis) could be performed to associate the sample epigenetic profile with the stress group as described above (as in Example 3). ).

  In the example shown in Table 22, the methylation state is presented quantitatively. Simple thresholds (eg, value> 50% = 1; value <50% = 0) can be applied to these values used in PCoA as in Example 3. Alternatively, the stress clustering pattern can be determined using the data directly in a quantitative analysis system such as principal component analysis.

Table 23-Examples of genes found to be differentially methylated Mapping 470 differentially methylated (P <0.001) amplicons onto genome annotations revealed analyzed tissue / It enables identification of genes that are differentially methylated by cell type. The table shows 48 genes that exhibit differential methylation using their respective average methylation values for each tissue. The color code indicates black = none, light gray = uneven, white = hypermethylation. Modified Eckhardt et al., 2006.

  It should be understood that various changes and modifications to the presently preferred embodiments described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the present invention and without diminishing its attendant advantages. Accordingly, such changes and modifications are intended to be within the scope of the appended claims.

  The contents of all references cited herein are hereby incorporated by reference in their entirety.

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Claims (41)

  A method for compiling a target DNA sequence group located in an organism's genomic DNA for diagnosis and characterization of exposures received by a biological sample and / or a tissue (or multiple tissues) or cells In connection with the attribute or origin of (or multiple cells), the method comprises selecting a DNA sequence having a methylation state, wherein the methylation state varies from sample to sample, but is received by a biological sample. A method that does not individually diagnose only a single exposure or the attributes and origins of tissue (or tissues) and / or cells (or cells). A method for compiling a set of target DNA sequences located within the genomic DNA of an organism for diagnosis and characterization of the attribute or origin of the tissue (or tissues) or cells (or cells),
(i) isolating DNA from two or more samples of cells of known but different attributes or origins;
(ii) identifying the methylation status of a methylatable locus in the isolated DNA of each sample;
(iii) comparing the methylation status of isolated DNA from each sample;
(iv) selecting a subset of loci, wherein the subset is characterized by the methylation status of each locus, and the methylation status varies from sample to sample but has a single attribute or origin The process is not diagnosed individually, and
Including methods. A method for identifying a DNA locus (stress sentinel) group for identifying an atypical growth condition (stress),
(i) growing a sample of the cell of interest under control growth conditions;
(ii) growing at least one sample of the same cell as the cell of interest of step (i) in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset is an isolated DNA from step (ii) that is different from the isolated DNA from step (i) And each locus in the subset changes the methylation status in response to certain atypical growth conditions, but not for other atypical growth conditions. Not including the step. (i) the atypical growth conditions (a plurality of atypical growth conditions) and / or tissue / cell types and / or combinations thereof that were not used to select the locus; and (ii) The atypical growth conditions (a plurality of atypical growth conditions), and / or tissue / cell types, and / or combinations thereof used to select the loci, a subset of the selected loci The method according to any of the preceding claims, wherein the methylation status of is collectively diagnosed.   The method comprises at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, different stresses, tissue types or cell types to select a subset of the loci. Use at least about 7, at least about 8, at least about 9, at least about 10, at least about 15, at least about 20, at least about 30, at least about 40, at least about 50, or more A method according to any of the preceding claims, comprising: Each locus in the subset of loci, or each selected DNA sequence,
(i) For the same tissue type from the same organ, reproducibly change the methylation status in response to the same stress in a similar manner;
(ii) show no variation or preferably less than 20% variation in methylation status between replicate samples taken from the same tissue from the same individual at the same time;
(iii) produce qualitative methylation changes associated with different easily detected stress conditions or attributes or origins of cells (multiple cells) and / or tissues (multiple tissues);
(iv) produce distinct stress states that are easily detected, or significant quantitative methylation changes associated with the attribute or origin of the cell (s) and / or tissue (s);
(v) changes methylation status in response to certain stresses but not other stresses (preferably for 15-85% of stress, more preferably 25-75% of stress) Most preferably for 40-60% of the stress);
(vi) (a) a stress response pathway of an organism, (b) methyl (preferably most), preferably all, of the cell (s) and / or tissue (s) types Including loci based on crystallization;
(vii) assembled separately for each tissue / organ type of an organism;
(viii) There is no variability between tissues / development stages, preferably less than 10%, unless intended for use only with specific tissues, organs, or developmental stages,
A method according to any preceding claim, having one or more of the following features, for example two or more, three or more, four or more, five or more, six or more, seven or eight.   Each locus of the subset of loci, or each selected DNA sequence, appears in approximately half of the stress or sample source used to select the locus, leading to a complementary separation of the stress or sample A method according to any of the claims. A method for identifying a group of DNA loci for identifying one or more atypical growth conditions,
(i) growing a sample of the cell of interest under control growth conditions;
(ii) growing a sample of the same cell as the cell of interest in step (i) in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset is an isolated DNA from step (ii) that is different from the isolated DNA from step (i) Preferably, each locus in the subset changes methylation status in response to certain atypical growth conditions, but for other atypical growth conditions Otherwise, the process and
(vii) identifying the subset of loci selected in step (vi);
(viii) associating the methylation status of the selected subset loci with the atypical growth conditions (multiple atypical growth conditions) and / or the cell sample of interest;
Including methods. A method for specifying a plurality of DNA loci for specifying atypical growth conditions,
(i) growing a plurality of samples of cells of interest under control growth conditions, wherein the plurality of cells of interest have different genotypes;
(ii) growing a plurality of samples of the cell of interest as in step (i), wherein the cell is in an environment with at least one atypical growth condition;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) compare the methylation status of the isolated DNA from steps (i) and (ii) or compare the methylation status of the isolated DNA from steps (i) and (iii) Or comparing the methylation status of the isolated DNA from steps (ii) and (iii) above;
(vi) selecting a subset of loci, wherein the subset is a locus that is different from the isolated DNA from step (i) or (iii) in the isolated DNA from step (ii); And / or the DNA from step (iii) is different in the methylation state of the locus from the isolated DNA from step (i). And preferably each locus in the subset changes the methylation state in response to certain atypical growth conditions, but not to other atypical growth conditions; and
(vii) identifying the subset of loci selected in step (vi);
(viii) associating the subset of loci and their methylation status with the atypical growth conditions and / or cell sample of interest used to select the locus;
Including methods. A method for identifying a group of DNA loci for identifying a plurality of atypical growth conditions,
(i) growing a sample of the cell of interest under control growth conditions;
(ii) A step of growing a plurality of samples of the same cell as the cell of interest in step (i) in an environment having at least one atypical growth condition, wherein each sample is grown in a different atypical growth condition. A process;
(iii) isolating DNA from each cell sample of steps (i) and (ii);
(iv) identifying the methylation state of the locus that can be methylated in the isolated DNA of step (iii);
(v) comparing the methylation status of the isolated DNA from steps (i) and (ii);
(vi) selecting a subset of loci, wherein the subset is an isolated DNA from each sample of step (ii) that is different from the isolated DNA from step (i) The methylation status is different, preferably each locus in the subset changes the methylation status in response to certain atypical growth conditions, but not to other atypical growth conditions. For the process, not so;
(vii) identifying a subset of the loci selected in step (vi);
(viii) associating the subset of loci and their methylation status with different atypical growth conditions of step (ii);
Including methods. (ix) the methylation status of a subset of the selected loci, the atypical growth conditions (multiple atypical growth conditions) that were not used to select the loci, and / or tissues / The steps associated with cell types, and / or combinations thereof,
The method according to any one of claims 8 to 10, further comprising as necessary.   (ix) the step of associating a subset of said loci and their methylation status with a phenotype and / or physiological response in said cell sample or tissue, organ or organism from which said cell sample was obtained 12. The method according to any of claims 8 to 11, further comprising depending on:   The plurality of samples in the step (ii) is 1 to 10,000 stresses, and includes 10 to 500 stresses and / or 30 to 100 stresses. the method of. (xi) isolating DNA from a sample of cells, wherein the growth conditions of the cells are unknown;
(xii) identifying the methylation status of the subset of the loci identified in step (vii) in the isolated DNA of step (xi);
(xiii) In order to identify whether the sample of cells of step (xi) has been exposed to atypical growth conditions, the methylation status of the locus identified in step (xii) is determined by the step (viii ) To compare with the results of
14. The method according to any one of claims 8 to 13, further comprising: (xi) isolating DNA from a sample of cells, the phenotype of said cell (or the tissue / organism from which it was obtained) and / or when exposed to one or more atypical growth conditions Or the physiological response is unknown, the process;
(xii) identifying the methylation status of the subset of the loci identified in step (vii) in the isolated DNA of step (xi);
(xiii) In order to identify whether the cell (or the tissue / organism from which it was obtained) had a phenotypic and / or physiological response to atypical growth conditions that are clearly or unclear Comparing the methylation status of the locus identified in step (vi) with the result of step (x);
14. The method according to any one of claims 8 to 13, further comprising:   16. A method according to claim 14 or 15, wherein step (xiii) also identifies the type of atypical growth conditions to which the cells have been exposed.   16. The method of claim 14 or 15, wherein step (xiii) identifies that the cell has been exposed to atypical growth conditions that were not tested in step (ii).   The method according to any of the preceding claims, wherein the subset of DNA loci comprises 1 to 5000 loci, 10 to 1000 loci and / or 20 to 100 loci.   The method according to any of the preceding claims, wherein the control growth conditions are the same as the biologically normal in vivo growth conditions of the cell of interest.   The method according to any one of claims 8 to 18, wherein the various atypical growth conditions in step (ii) are different.   Temperature, physical stress (including atmospheric pressure increase / decrease, directional pressure on objects, agitation, gravity, physical tension), physical damage, nutrient availability, exposure to chemical toxins and hormones, signaling molecules, etc. Exposure to chemicals, visible light stress (spectrum quality and intensity), exposure to non-visible light above ground level (eg, X-rays, gamma rays, infrared, ultraviolet, microwave, etc.), pathogens or Exposure to parasites, exposure to other organisms or other components of the same species, exudates from other organisms or individuals, feeding / feeding by other organisms on the target organism, water stress (excessive or Under), exposure to signals or conditions that stimulate the object to enter resting state, oxygen stress, or exposure to excess or sub-optimal amounts of free radicals. Length conditions are selected, the method according to any one of the preceding claims.   The method according to any of claims 8 to 21, wherein the subset of loci selected in step (vii) is associated with genes or regulatory regions for components of a cellular stress response pathway.   The method according to claim 22, wherein the locus is located in a gene for at least one component of each stress response pathway of the cell of interest.   24. A method according to any of claims 8 to 23, wherein the selection of a subset of loci in step (vi) is performed automatically by a computer program. A method of identifying one or more groups of DNA loci for identifying one or more atypical growth conditions,
(a) simultaneously performing the method of any preceding claim to associate one or more DNA loci groups with one or more atypical growth conditions; or
(b) combining the results of steps (viii) and (ix) of any preceding claim to identify one or more DNA loci groups under one or more atypical growth conditions;
A method comprising any of the steps.   A group of DNA loci for identifying one or more atypical growth conditions, as defined in any preceding claim. A method for identifying one or more atypical growth conditions to which a cell of interest has been exposed, comprising:
(i) a step of isolating DNA from the cell of interest, wherein the growth conditions of the cell are unknown;
(ii) identifying the methylation state of a group of loci that can be methylated in the isolated DNA of step (i);
(iii) comparing the methylation state of the locus as identified in step (ii) with a known methylation state group for the same locus group, the known methylation state group Is a process exhibiting one or more stress characteristics;
(iv) identifying whether the cell of interest has been exposed to atypical growth conditions (a plurality of atypical growth conditions).   28. The method of claim 27, wherein step (iv) also identifies the type of atypical growth conditions to which the cells have been exposed.   Use of DNA loci for identifying atypical growth conditions to which cells have been exposed, using DNA loci as defined in claim 25 or as defined in claim 27 or 28 .   30. Use according to claim 29, wherein exposure to said atypical growth conditions continues.   30. Use according to claim 29, wherein the exposure to said atypical growth conditions has occurred in the past.   The atypical growth conditions include symptomatic or asymptomatic disease, acquired infection susceptibility, allergies, aging, exposure to drugs and toxicants, exposure to exercise capacity enhancers, exposure to pesticides, commercial cell lines Check the status of cells, test the physiological status of cell lines for biomedical purposes (examine the viability, potential, and / or totipotency of synthetic or isolated stem cells; potential of skin cell lines for transplantation 32. Use according to any of claims 29-31, comprising assessing and capacity; including ensuring a uniform physiological state of a standard cell line culture relative to a reference standard), and pre-clinical testing of drugs. .   In the diagnosis of diseases including cancer, metabolic disorders, viral or bacterial infections, stress-related diseases / disorders, parasite infection or invasion, Prader Willie / Angelman syndrome, ICF syndrome, dermal fibroma, hypertension, pediatric neurobiology 32. Use according to any of claims 29 to 31.   A computer program for selecting a subset of loci of different methylation states to identify and identify atypical growth conditions. A group of DNA loci for identifying one or more atypical growth conditions to which an Arabidopsis cell sample has been exposed, wherein the loci of different methylation status and / or expression are of the following Arabidopsis genes: A group of DNA loci located in one or more, for example, all, or up to 5 kb upstream / downstream thereof.
AT2G15800
AT2G38240
AT5G54720
AT1G76650
AT3G31540
AT3G28820
AT5G02580
AT1G07120
AT1G61800
AT1G48110
AT5G28235
AT5G28430
AT5G28760
AT3G53300
AT5G46200
AT3G57260
AT4G02330
AT3G05400
AT5G25110
AT3G28770
AT5G62750   36. The locus group is defined as in claim 35, wherein the locus is used to identify the atypical growth condition or physiological / phenotypic response in an Arabidopsis cell of interest. 28. The method according to 27.   The locus is defined as in claim 35 and is used to identify the atypical growth condition or physiological / phenotypic response in a cell of interest from a plant species other than Arabidopsis. The method described in 1. A group of DNA loci for identifying one or more atypical growth conditions to which a human cell sample has been exposed, wherein the loci of different methylation status and / or development are: A group of DNA loci located in one or more of them, for example, all or up to 5 kb upstream / downstream thereof.
ZNRF3
SLC7A4
Myosin-18B (MYO18B)
Glycoprotein Ib (platelet), beta polypeptide
RP1-47A17.8
OncostatinM (OSM)
CTA-299D3.6
CTA-941F9.6
Cytohesin-4
PIB5PA
SUSD2
chr 22: 35,256,796-35,277,053, NCBI 36
RP3-438O4.2
RP4-756G23.1
Somatostatin receptor type 3 (SSTR3)
Bcl-2 interacting killer (BIK)
GAS2L1
RP3-355C18.2
Gamma-parvin
CARMA 3
TMPRSS6
Platelet-derived growth factor B chain precursor (PDGFB)
CELSR1 Cadherin
T-box transcription factor (TBX1)
Q6ZRW2_HUMAN
NKG2DL4 (RAET1E)
DAAM2
RP1-47M23.1
RP11-397G17.1
Nesprin-1 (Syne-1)
Myogenic repressor I-mf (MDFI)
RP11-174C7.4
CMAH
PKHD1
Glutathione peroxidase 5
GRIK2
SLC22A1
RP11-235G24.1
TBX18
TGM3
RIN2
SLC24A3
Q9ULE8_HUMAN
C20orf117
Breast carcinoma amplified sequence 4 (BCAS4)
Nuclear factor of activated Tcells (NFATC2)
SCUBE1
JAG1   A method substantially as herein described with reference to the foregoing examples and drawings.   Use as substantially described herein with reference to said examples and drawings.   A group of DNA loci substantially as described herein with reference to the Examples and Figures.