WO2011141711A1 - Procédés de sélection de marqueurs de méthylation - Google Patents

Procédés de sélection de marqueurs de méthylation Download PDF

Info

Publication number
WO2011141711A1
WO2011141711A1 PCT/GB2011/000733 GB2011000733W WO2011141711A1 WO 2011141711 A1 WO2011141711 A1 WO 2011141711A1 GB 2011000733 W GB2011000733 W GB 2011000733W WO 2011141711 A1 WO2011141711 A1 WO 2011141711A1
Authority
WO
WIPO (PCT)
Prior art keywords
loci
dna
cell
methylation status
subset
Prior art date
Application number
PCT/GB2011/000733
Other languages
English (en)
Inventor
Andrew Paul Meade
Michael James Wilkinson
Carlos Marcelino Rodriguez Lopez
Original Assignee
Aberystwyth University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Aberystwyth University filed Critical Aberystwyth University
Priority to JP2013509609A priority Critical patent/JP2013526271A/ja
Priority to AU2011251767A priority patent/AU2011251767A1/en
Priority to BR112012028957A priority patent/BR112012028957A2/pt
Priority to EP11722483.2A priority patent/EP2580347A1/fr
Priority to CN201180034459.8A priority patent/CN103080335B/zh
Publication of WO2011141711A1 publication Critical patent/WO2011141711A1/fr

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/6895—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to a method for the compilation and use of a set of target DNA sequences located within the genomic DNA of organisms for the diagnosis and characterisation of exposures that a biological sample has undergone and/or relate to the identity or origin of the tissue(s) or cell(s).
  • stress can be defined as any departure from biotic and abiotic conditions that are optimal for the efficient functioning of a biological tissue or individual.
  • stress when used in the context of this invention, include both abiotic stresses, and biotic and developmental stresses (examples include, but are not limited to, exposure to pathogen-mediated diseases and endogenous 'system breakdown' diseases such as cancer and aging).
  • abiotic stresses include, but are not limited to, heat, osmotic, physical trauma, nutrient deficiency, chemical-induced stresses and physiological stresses such as toxins, prophylactics, fertilisers, and therapeutic or recreational drugs.
  • biotic and developmental stresses include, but are not limited to, exposures to pathogen-mediated diseases, infestation by parasites, endogenous 'system breakdown' diseases such as cancer, and aging.
  • Physiological changes that arise from any given stress are not limited to regulatory changes affecting the initial receptor gene or genes (those that detect the stress) but also include a cascade of other genes (stress response genes and response gene pathways) that are changed as a consequence of changes to the receptor(s).
  • DNA methylation appears to be widespread across many eukaryotic groups including plants, mammals, birds and invertebrates such as nematodes (Finnegan et al obsession 1998; Suzuki S, ef a/., 2007; Gupta S, ef a/., 2006; delGaudio R, ef a/., 1997).
  • DNA methylation has been linked with gene silencing, for example, in Arabidopsis thaliana hyper-methylated alleles of genes SUP, PAI and AG (Jacobson and Meyerowitz, 1997; Melquist et a/., 1999; Jacobson ef a/., 2000) and in Linaria, LCYC (Cubas ef a/., 1999).
  • hypo-methylation of alleles AP3 and FWA in Arabidopsis thaliana displayed ectopic expression (Kinoshita ef a/., 2004).
  • Several reports suggest a role for DNA methylation in the regulation of plant development and cell type (Finnegan ef a/., 2000).
  • DNA methylation is also known to play a role in the epigenetic control of the expression of genes in mammals, including humans, and may be linked with certain tissue types. Determining the degree of methylation of particular genomic DNA target regions of interest is useful in research fields.
  • CpG cytosine- guanine
  • Burkitt's lymphoma Wilms tumor; Prader-Willi/Angelman syndrome; ICF syndrome;
  • Dermatofibroma hypertension; paediatric neurobiology; autism; ulcerative colitis; Fragile X syndrome and Huntingdon's disease. See, for example:
  • Methylation of specific sites has also been identified as being associated with specific stresses and diseases within the genomes of other (non-human) higher organisms. Examples include: In tobacco plants (Nicotiana tabacum), silencing of the type I methyltransferase NtMETI ieads to reduced methylation in the genome. Wada et al., (2004) showed that around 30 genes become upregulated, over half of which are directly implicated in responses to biotic or abiotic stress. Furthermore, Choi et al., (2007) also showed that in tobacco plants the methylation pattern of the pathogen-responsive gene NtGPDL changes within 24h of artificial inoculation with tobacco Mosaic Virus (TMV) and this change is associated with the plant's hypersensitivity reaction (HR).
  • TMV tobacco Mosaic Virus
  • WO 2007/132166 describes the discovery that, in a pregnant woman, certain genes (such as RASSF1A, APC, CASP8, RARB, SCGB3A1 , DAB21 P, PTPN6, THY1 , TMEFF2, and PYCARD) that originate from a foetus are highly methylated, whereas the same genes of maternal origin are unmethylated.
  • genes such as RASSF1A, APC, CASP8, RARB, SCGB3A1 , DAB21 P, PTPN6, THY1 , TMEFF2, and PYCARD
  • US 2007-0292866 relates to methods of detecting global changes in the methylation of human genomic DNA as well as changes in methylation of specific regions of the human genome. The methods allow the diagnosis, prognosis and monitoring of therapeutic treatment for disease and detection of changes in methylation that is a consequence of diet and/or dietary supplements. The stresses are only identifiable separately.
  • US 2004-00291 17 describes a generic invention that allows the identification of differentially methylated DNA loci that can be used as markers for determining the biological effect and/or activity of drugs, chemical substances and/or pharmaceutical compositions.
  • the drugs, chemical substances and/or pharmaceutical compositions can only be investigated individually using epigenetic markers for each separately.
  • DNA methylation in order to identify a particular stress has been limited to markers used in investigation of individual stresses one at a time, or else describe source material (e.g. tissue/organ/cell type) or to establish physiological state.
  • source material e.g. tissue/organ/cell type
  • the current invention provides a system that allows for the identification of sets of markers within genomic DNA that can be used to collectively and repeatedly identify exposure of an individual, tissue, organ or cell to a range of stresses, and/or to diagnose the broad or specific nature of the stress to which the sample has been exposed, and/or to diagnose the physiological state or origin (e.g. cell type or organ) of biological samples.
  • the marker DNA sequences of the invention are hereafter referred to (interchangeably) as 'DNA loci', 'Sentinels' and 'Stress Sentinels' and are capable of identifying stress(es) experienced by a biological sample (including individual organisms, organs, tissues or cells).
  • Stress sentinels each contain sequence motifs that change their DNA methylation status (typically, but not necessarily limited to cytosine residues) after, for example, the organism is exposed to some stresses but not when exposed to other stresses; and/or the cell differentiates into some cell types but not others; and/or depending on organ or tissue from which the sample is taken, and/or with the physiological age or state of development of a cell type, tissue, organ or individual.
  • a collective pattern of DNA methylation when considered across the whole set of stress sentinels allows identification of whether the biological sample is operating under, or 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 stresses that the sample has been exposed to may be known (i.e.
  • stress sentinels The key properties of stress sentinels is that no one sentinel is individually diagnostic of any one stress, source material or physiological state, and that sentinels can be identified using one stress (or source material or physiological state) but still retain diagnostic properties when applied to samples exposed to different stresses (or source material or physiological state).
  • This property provides a marked improvement over existing systems that seek to identify methylation markers that are individually diagnostic of a stress/tissue/developmental state based on surveys of the targeted state judged against a range of control comparator states.
  • the weakness of the existing approach is that it is vulnerable to compromise when wider sampling leading to the discovery of a different state/tissue/cell type that shares the same methylation status thought previously to be diagnostic of the targeted condition.
  • the sentinels markers embrace this eventuality and so is much less likely to be compromised by broader sampling and (unlike existing systems) can diagnose previously uncharacterised responses as being different.
  • the sentinels themselves may or may not arise from DNA associated with coding regions, regulatory regions or introns, and may ur may not be associated with known changes in gene expression.
  • a sentinel site could reside in non-coding regions unassociated with gene expression provided that changes to methylation status between stresses at that site are consistent and reliable.
  • the invention exemplifies this property by reference to a set of sentinel DNA methylation markers (or evidence that they may occur) in Arabidopsis, Mus and human that were selected on the basis of differential methylation (or presumed differential methylation on the basis of differential expression of genes known to be sensitive to methylation changes) in response to one set of stresses and are shown to be able to identify samples exposed to a stress (or tissue type) not used in the markers selection process.
  • Established methods for determining associations or clusters based on binary profiles generated from these markers can be used to provide some guidance on the broad category of stresses to which new (i.e. untrained stress) belongs. These include but are not restricted to multivariate analysis such as Principal Co-ordinate Analysis or diagnostic decision trees or decision trees (see e.g. Analysing Ecological Data (2007), Springer (NY) Ch15 pp259-264, ISBN 978-0-387-45967-7).
  • Sentinel markers derived from regulatory regions of methylation-controlled stress-response genes of Arabidopsis whose expression pattern (and therefore methylation status) is capable of distinguishing at least 78 different stress types.
  • the sentinel principles are further exemplified by training the Arabidopsis loci using a series of subsets of 40 gene markers from the initial set of 78 and using these markers to quantify the frequency at which new stresses are identified as unique (i.e. different from the training set), different from the control but identical to an existing stress (i.e. found in the training set) or identical to the control (false negative).
  • diagnosis means the identification of physiological and phenotypic states of or changes thereof in a cell, tissue, organ or organism.
  • the term "about” means plus or minus 20%, more preferably plus or minus 10%, even more preferably plus or minus 5%, most preferably plus or minus 2%.
  • growth means the living environment in which a cell or tissue or organism is found or is placed into or is cultured in.
  • the term may be related to growth, living, environmental and/or developmental states.
  • growing” and growth mean survival and/or multiplication of a cell or tissue or organism during the period or study or period of interest.
  • a method of identifying a set of DNA loci (stress sentinels) for identifying atypical growth conditions (stresses) comprising the steps of: i. Growing a sample of cells of interest under control growth conditions;
  • step (i) Growing a sample of the same cells of interest as in step (i) in an environment where there is at least one atypical growth condition present;
  • the method includes the step of: ix. Associating the methylation status of the selected subset of loci with the atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci.
  • a method of identifying a plurality of sets of DNA loci (stress sentinels) for identifying particular atypical growth conditions (stresses) comprising the steps of:
  • ii Growing a plurality of cell samples of interest as in step (i) wherein the cells are in an environment where there is at least one atypical growth condition present; iii. Isolating DNA from each cell sample of (i) and (ii);
  • a further step of: ix Associating the methylation status of the selected subset of loci with the atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci.
  • a method of identifying a set of DNA loci for identifying a plurality of atypical growth conditions comprising the steps of: i. Growing a sample of cells of interest under control growth conditions; ii. Growing a plurality of samples of the same cells of interest as in step (i) in an environment where there is at least one atypical growth condition present and wherein each sample is grown in the presence of a different atypical growth condition;
  • an additional step of: ix Associating the methylation status of the selected subset of loci with the atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci.
  • each locus in the subset of loci is not diagnostic of only a single condition.
  • the methylation status of the selected subset of loci is diagnostic of atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci.
  • the methylation status of the selected subset of loci is diagnostic of atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were used to select the loci.
  • the methylation status of the selected subset of loci is diagnostic of (i) atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci; and (ii) atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were used to select the loci.
  • Central to the method of all aspects of the invention is the criteria used to identify and select methylation sites (sentinels) that can be used to diagnose stresses, source material and/or physiological state beyond that used for the selection process. To achieve this, preference is given to selecting markers that typically vary between conditions and/or source samples but are ideally not diagnostic of only a single condition.
  • the selected subset of loci consist of loci that vary between conditions and/or source samples but are not diagnostic of only a single condition and/or source sample.
  • Table 1(a) below illustrates one possible outcome.
  • loci 6 and 7 are eliminated as potential sentinels because they do not vary across the training set of stresses (i.e. loci 6 are all methylated and loci 7 all unmethylated). Loci 3 and 4 do vary and are each diagnostic of one stress (A and F respectively). These could be considered sentinel candidates but would not be ideal since they appear indicative of one stress per loci and are unlikely to feature in the profiles of many, if any, other stresses.
  • the remaining loci are not diagnostic of any one specific stress but collectively do allow separation of all stresses in the training set (see table 1 (b) below).
  • sentinel markers appear in approximately naif of the stresses/sample sources and 'segregate' between the stresses in a complementary manner.
  • the step of methylation detection can be accomplished by using any available method, including but not limited to: Methylation Sensitive Amplified Fragment Analysis (MSAP), a variant of Amplified Fragment Length Polymorphism (AFLP) in which multiple-product profiles generated using isoschizomer restriction enzymes are compared (see Xiong er a/ 1999); treatment of the DNA with sodium bisulfite followed by cycle sequencing (see Frommer et al 1992), pyrosequencing (see Uhlmann et al 2002), high throughput single molecule DNA sequencing (e.g. Zeschnigk ef al 2009), or qPCR (see Munson ei al 2007),HPLC (e.g.
  • MSAP Methylation Sensitive Amplified Fragment Analysis
  • AFLP Amplified Fragment Length Polymorphism
  • methylation status is used in a strictly qualitative manner (i.e. present or absent).
  • methylation status is measured by quantitative data on methylation at loci by, for example, setting decision thresholds or deploying discriminatory analysis systems that are based on quantitative data (such as Principal Component Analysis (PCO)).
  • PCO Principal Component Analysis
  • PCO analysis is a standard technique in which variability across multiple dimensions is measured and new axis drawn such that first axis (known as the first principal component) captures the largest amount of variation in one dimension.
  • the second axis captures the second largest amount of variation across several dimensions in a different axis and is known as the second principal component.
  • the first and second components do not refer to any particular variable and this is a standard analytical strategy.
  • cells of interest we include cells that are derived from an in vitro cell line, and cells derived from or making up a biological material such as a tissue, organ or whole organism.
  • a sample of cells of interest' we include growth of the cells in either in vitro or in vivo conditions.
  • the sample of cells may be grown in vitro in an appropriate piece of laboratory equipment for cell growth.
  • the sample of cells may be grown "in vivo" i.e. an individual organism of interest (such as a plant, animal or human).
  • control growth conditions we include the meaning that the growth conditions are actively controlled (i.e. specified and adjusted) or that the growth conditions are well characterised.
  • the control growth conditions will be determined by how the sample of cells is being grown, active control may be more appropriate for in vitro growth conditions and well characterised control conditions may be more suitable for in vivo growth conditions. However, this may not always be the case.
  • 'atypical growth conditions we mean a growth condition different to the equivalent condition in the control growth condition, for example, a higher temperature in comparison to the control growth conditions.
  • the 'atypical growth condition' can include (but is not limited to) altered individual conditions, such as temperature, pH, light exposure, but can also include, at the whole tissue or organism level, physiological states, disease states, exposure of organisms to chemicals, drugs or biological agents, amongst others.
  • 'phenotypic and/or physiological change' we include measurable changes at any of a cellular, tissue or whole organism level. For example, exposure to a high temperature will have observable effects that will differ at the cell level in comparison to the whole organism level.
  • the change may be qualitative (i.e. a physical response) or quantitative (increases or decreases in an existing phenotypic or physiological feature).
  • the plurality of samples in step (ii) is between 1 and 10,000 stress conditions, preferable between 10 and 500 stress conditions and more preferably between 30 and 100 stress conditions.
  • the loci identified discriminate between samples exposed to the atypical growth condition and control samples or between cell samples used in their selection;
  • steps (vii), (viii) and optional step (ix) may be followed by the further steps of: x.
  • x Associating the subset of loci and their methylation status with a phenotypic and/or physiological response in either the cell sample or a tissue, organ or organism from which the cell sample was derived.
  • the method of any of the first, or second, and third aspects of the invention may comprise the optional steps of: xi. Isolating DNA from a sample of cells wherein the growth conditions of the cells are unknown;
  • step (vii) Identifying the methylation status of the subset of loci identified in step (vii) in the isolated DNA of step (xi);
  • the method of any of the first, second or third aspects of the invention may comprise (subsequent to optional step (x) above) the optional steps of: xi. Isolating DNA from a sample of cells wherein the phenotypic and/or physiological response of the cells (or tissue/organism from which they derived) when exposed to one or more atypical growth conditions is unknown.
  • step (vii) Identifying the methylation status of the subset of loci identified in step (vii) in the isolated DNA of step (xi).
  • step (x) Comparing the methylation status of the loci identified in (vi) to the results of step (x) in order to identify if the cells (or tissue/organism from which they derived) have undergone a phenotypic and/or physiological response to a characterised or uncharacterised atypical growth condition.
  • step (xii) also identifies the type of atypical growth condition that the cell has been exposed to.
  • step (xii) identifies that the cells were exposed to an atypical growth condition that was not tested in step (ii).
  • the method for example step (viii), also identifies the type of physiologies! response to the atypical growth condition that the cell/tissues/organ/individual has been exposed to.
  • step (xiii) identifies that the cells (or tissues or organisms from which they derived) have undergone a phenotypic and/or physiological response after exposure to one or more atypical growth conditions that is different from the response associated with the cells of (i) and (ii).
  • the control growth conditions are the same as the range of biologically normal in vivo growth conditions for the cell of interest. The skilled person will understand that the normal growth conditions will depend on the cell type or organism being investigated. Examples of normal growth conditions constitute any published protocol for the culture of named cell lines or tissues, or even for the survival of organisms of named species or genera or higher taxonomic groups.
  • Examples of normal growth conditions for some of the many specific cell lines that may be used in this 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.
  • the number of DNA loci making up the subset comprising the sentinel markers is preferably between 1 and 5000 sites of DNA methylation in the genome under study, but preferably between 0 and 1000 target regions containing DNA methylation and most preferably between 20 and 100 target regions containing DNA methylation.
  • the different atypical growth conditions in step (ii) of the second aspect are divergent.
  • divergent we mean that the atypical growth conditions are not of the same or similar type or class. For example, temperature and nutrient availability would be considered divergent conditions; whereas exposure to different strains of the same pathogen species would not.
  • the atypical growth conditions may be selected from, amongst others: temperature, physical stress (including increased or decreased atmospheric pressure, directional pressure on the subject, agitation, gravitational force, physical tension), physical wounding, nutrient availability, exposure to chemotoxins, hormones or signalling molecules, exposure to other chemical agents, visible light stress (spectral quality or intensity), exposure to non-visible light irradiation above background levels (e.g.
  • the subset of loci selected in step (vi) are associated with genes for components of cellular stress-response pathways.
  • the loci may be the genes themselves or regulatory regions associated with one or more genes.
  • Such loci may be from exonic or regulatory regions of DNA associated with the expression of functioning of conserved cellular stress response pathways.
  • Examples for plants include (but are not restricted to) genes directly and indirectly involved in the Jasmonic acid pathway (e.g. see Dempsey er al 1999; Wasternack, 2007; Fan ei al 2007; Zhao et al 2007;Reinbothe er al 2009), the Nitric Oxide pathway (e.g. see Wilson et al 2008), the Salicylic acid pathway (Fan et al 2007; Zabala et al 2009), the Ethylen pathway (e.g.
  • the loci are located in genes or regulatory regions for at least one component of every stress-response pathway of the cell of interest.
  • the subset of DNA loci (sentinels) are selected on the basis of clarity and consistency of methylation change, and the pattern of methylation across the sentinels that may be differently methylated when the organism/tissue/cell type is exposed to different atypical growth conditions (typically stresses).
  • the DNA loci collectively allow 'a posteriori' diagnosis of the stress that caused the change in methylation profile. Identification of multiple inductive stresses is made possible because sentinel markers exhibit complementary (non-identical) responses (or non-responses) to different types of stress.
  • the method allows multiple sites that act as sentinels for the overall physiological response of the organism or organ or tissue or cell type to multiple stress elicitors.
  • the DNA loci are selected to be 'generalists' and so can be used to identify Wide rg i ri Q StfSSS dia n . r ! S!S !S based ⁇ thS 0 ⁇ ' ⁇ 3 ⁇ ngH p rn f methylation across all selected sentinel markers, with individual stress being identified by, for example, (but not limited to) multivariate analysis or decision tree analysis.
  • this approach also allows for the diagnosis of samples that exhibit normal, control profiles and those which exhibit atypical but previously uncharacterised divergent profiles suggestive of exposure to an unknown stress.
  • methylation-based loci preferably representing most and ideally all stress- response pathways in the organism.
  • the DNA loci used in the present invention have one or more of these characteristics.
  • the selection of the subset of loci in step (vi) is conducted automatically by a computer program.
  • a generalised example of how a computer program can select loci requires analysis of multiple loci of measured methylation status.
  • One possible embodiment requires a subset of genes and the expression level of the genes in the face of various stresses is collected and methylation status shown. The following approach was then taken to assemble the code for the computer program.
  • the gene expression data was converted into 3 states by representing (coding) a 4-fold or greater change in gene expression as "1” and a 4-fold decrease change as and no change in gene expression as "0". Any stresses that do not cause a change in expression levels in any of the genes cannot be identified and are removed. Stresses possessing an identical expression response cannot be identified individually and so all (or all but one) are removed.
  • N the predefined number of genes to use
  • N is a predefined number of genes to use.
  • the hill climber is run at 10,000 iterations and is run independently 1000 times. If an optimal solution, identifying all stresses uniquely, cannot be found the solution that identifies the most stresses uniquely is returned.
  • a method of identifying one or more sets of DNA loci for identifying one or more atypical growth conditions comprising the steps of either:
  • step (b) combining the results of steps (viii) and (ix) of one or more of the first, second or third aspects of the invention in order to identify one or more sets of DNA loci with one or more atypical growth conditions.
  • a set of DNA loci for identifying one or more atypical growth conditions wherein the set of DNA loci are defined following a method as described herein.
  • a method of identifying one or more atypical growth conditions to which a cell of interest has been exposed comprising the steps of: i. Isolating DNA from a cell of interest wherein the growth conditions of the cell is unknown.
  • ii Identifying the methylation status of a set of loci capable of methylation within the isolated DNA of step (i). methylation statuses for the same set of loci, wherein the set of known methylation statuses are characteristic of one or more stresses,
  • step (iv) also identifies the type of atypical growth condition that the cell has been exposed to.
  • a set DNA loci as defined above in the identification of an atypical growth condition to which a cell has been exposed.
  • the DNA loci and associated diagnostic methylation patterns may be used to identify an atypical growth condition or a cell (or tissue/organism from which it is derived) for a wide range of applications.
  • the identification may include both current and past exposures including where the atypical growth condition is not present in the sample including but not limited to:
  • Exposure to performance-enhancing drugs examples could include, but are not limited to, steroids, nandrolone, Erythropoietin, Nikethamide, Cathine, Clenbuterol, Norandrosterone, Stanozolol and Hydrochlorothiazide.
  • Testing the physiological status of cell lines for biomedical purposes examples could include but are not limited to: assessing the viability, potency and/or totipotency of synthetic or isolated stem cells; evaluating the viability and competency of skin cell lines for grafting; ensuring the homogeneous physiological status of standard cell line cultures relative to a reference standard.
  • Diagnosis of diseases can include those that are asymptomatic, presympomatic or else present ambiguous symptoms.
  • the many possible examples could include diagnosis of different forms of cancer, metabolic disorders, exposure to viral or bacterial infection, including latent infection (e.g. tuberculosis), stress-related diseases/disorders, infection or infestation by parasites.
  • latent infection e.g. tuberculosis
  • stress-related diseases/disorders e.g. tuberculosis
  • infection or infestation by parasites e.g. tuberculosis
  • diseases that could be diagnosed using sentinels include, amongst others:
  • Bacterial diseases that present no or ambiguous symptoms or delayed symptoms such as tuberculosis (symptomatic and asymptomatic infection), meningitis, septicaemia, chlamydia.
  • Viral diseases that present no symptoms, ambiguous symptoms or delayed symptoms Possible examples include, amongst others: HIV, herpes, influenza and ebola.
  • ⁇ Diseases for which methylation changes are already noted and as such this system can aid in the diagnosis, including leukaemia, head and neck cancer, Hodgkin's disease, gastric cancer, prostate cancer, renal cancer, bladder cancer, breast cancer, Burkitt's lymphoma, Wilms tumor, Prader-Willi, Angelman syndrome, ICF syndrome, dermatofibroma, hypertension, paediatric neurobiology.
  • a computer program for selecting a subset of loci of differing methylation status for identifying and distinguishing between atypical growth conditions.
  • 3 SGt of DMA loci for identifying one or more atypical growth conditions to which a cell sample of Arabidopsis thaliana has been exposed, whereby the loci of differing methylation status and/or expression are located within or up to 5 kb upstream/downstream of one or more, for example all, of the following Arabidopsis thaliana genes (see Lister et al. (2008).
  • the set of loci are as defined in the ninth aspect and the loci are used to identify the atypical growth condition or physiological/phenotypic response in a cell of interest from Arabidopsis.
  • the loci are used to identify the atypical growth condition or physiological/phenotypic response in a cell of interest from a plant species other than Arabidopsis.
  • a set of D A ioci for identifying one or more atypical growth conditions to which a human cell sample has been exposed, whereby the loci of differing methylation status and/or expression are located within or up to 5 kb upstream/downstream of one or more, for example all, of the following human genes or chromosome locations (see Eckhardt et al. (2006).
  • PDGFB Platelet-derived growth factor B chain precursor
  • T-box transcription factor (TBX1 )
  • JAG1 According to another aspect of the present invention, there is provided a method for the compilation and use of a set of target DNA sequences located within the genomic DNA of organisms for the diagnosis and characterisation of exposures that a biological sample has undergone and/or relate to the identity or origin of tissue(s) and/or cell(s), the method comprising selecting DNA sequences with a methylation status that varies between some samples but which are not individually diagnostic of only a single condition (exposure that a biological sample has undergone) or source (identity or origin of tissue(s) and/or cell(s)).
  • a method for the compilation and use of a set of target DNA sequences located within the genomic DNA of organisms for the diagnosis and characterisation of exposures that a biological sample has undergone comprising selecting DNA sequences with a methylation status that varies between some samples but which are not individually diagnostic of only a single condition (exposure that a biological sample has undergone).
  • a method of identifying a set of DNA loci (stress sentinels) for identifying atypical growth conditions (stresses) comprising the steps of: (i) Growing a sample of cells of interest under control growth conditions;
  • step (ii) Growing a sample of the same cells of interest as in step (i) in an environment where there is at least one atypical growth condition present;
  • the method includes the step of:
  • a method of identifying a plurality of sets of DNA loci (stress sentinels) for identifying particular atypical growth conditions (stresses) comprising the steps of:
  • step (ii) Growing a plurality of cell samples of interest as in step (i) wherein the cells are in an environment where there is at least one atypical growth condition present;
  • a method of identifying a set of DNA loci for identifying a plurality of atypical growth conditions comprising the steps of: (i) Growing a sample of cells of interest under control growth conditions;
  • step (ii) Growing a plurality of samples of the same cells of interest as in step (i) in an environment where there is at least one atypical growth condition present and wherein each sample is grown in the presence of a different atypical growth condition;
  • the methylation status of the selected subset of loci are collectively diagnostic of atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci.
  • the methylation status of the selected subset of loci are collectively diagnostic of atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were used to select the loci.
  • the methylation status of the selected subset of loci is diagnostic of (i) atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were not used to select the loci; and (ii) atypical growth condition(s) and/or tissue/cell type and/or combinations thereof that were used to select the loci.
  • the methods of the present invention comprise the use of at least about 2, at least about 3, at least about 4, at least about 5, 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, or more different stresses, tissue types or cell types to select the subset of loci.
  • the methods of the present invention comprise the use of between about 1 and about 10,000, preferably about 10 and about 500, more preferably between about 30 and about 100 different stresses, tissue types or cell types to select the subset of loci.
  • each locus in the subset of loci is not diagnostic of only a single condition.
  • each locus in the subset of loci has one or more, for example two or more, three or more, four or more, five or more, six or more, seven or eight of the following characteristics,
  • (vi) include methylation-based loci preferably representing most and ideally all of (a) stress-response pathways in the organism or (b) types of cell(s) and/or tissue(s);
  • loci in the subset of loci are individually diagnostic of any one stress, source material or physiological state.
  • the subset of loci can be identified using one stress, source material or physiological state but still retain diagnostic properties when applied to samples exposed to different stresses or source material or physiological state.
  • each locus in the subset of loci appears in approximately half of the stresses or sample sources used to select the loci and segregates between the stresses in a complimentary manner.
  • Exposure to performance-enhancing drugs examples could include, but are not limited to, steroids, nandrolone, Erythropoietin, Nikethamide, Cathine, Clenbuterol, Norandrosterone, Stanozolol and Hydrochlorothiazide.
  • MSAP methylation-sensitive amplified fragment length polymorphisms
  • Figure 12 Principle Component Analysis diagram based on total trained analysis of the amplified fragment length polymorphisms data from the differentially stressed mouse RAW cells. First two components represent 15.8% of the total variation ( ⁇ ) IL4, ( ⁇ ) LPS, ( A ) Nutritional stress, ( ⁇ ) Control.
  • Figure 16 Genevestigator output showing the effect of 82 stresses on the expression of a random selection of A thaliana markers found to be regulated by methylation. (Lister ef a/ (2008)). Columns represent the 82 stresses.
  • Rows represent a selection of gene markers.
  • the grey tone in each box represents the expression level of the corresponding gene (by row) when A. thaliana is exposed to the corresponding stress (by column). Black indicates no change in expression, light grey is downregulation and dark grey is upregulation.
  • Figure 17 Genevestigator output showing the effect of 9 developmental stages on the expression or a random selection of A thaliana markers found to be regulated by methylation (Lister et a/ (2008)). Columns represent the 9 developmental stages. Rows represent a selection of gene markers. The grey tone in each box represents the expression level of the corresponding gene (by row) from A. thaliana material at each developmental stage (indicated by column).
  • Figure 18 Genevestigator output showing the effect of 40 organ on the expression of a random selection of A thaliana markers found to be regulated by methylation (Lister et al (2008)). Columns represent the 40 organs. Rows represent a selection of gene markers. The grey tone in each box represents the expression level of the corresponding gene (by row) from each A. thaliana organ (by column).
  • Figure 20 A trifurcating tree relating changes in regulation to stresses, two genes are shown leading to 8 potential stresses S1 to S8. "+” denotes upregulation "-” denotes downregulation. If gene 1 is downregulated (-) and gene 2 is upregulated (+), this corresponds to the stress profile of S6.
  • Figure 21 PCoA plot (first principal coordinate against second principal coordinate) illustrating the separation of sentinel profiles (expression profiles normalised to binary format) of 15 loci exposed to 78 different stresses.
  • the stresses were artificially categorised into the following informal groupings: Biotic (black square), chemical (empty square), hormone (empty triangle), light (black triangle), nutrient (empty circle), PCO (black circle), Cold (empty romboid), drought (black romboid), heat (grey romboid), hypoxia (asterix), osmotic (grey square), salt (cross) and wounding (grey circle).
  • Figure 22 PCoA plot (first principal coordinate against second principal coordinate) illustrating the separation of sentinel profiles (expression profiles normalised to binary format) of 36 loci exposed to 78 different stresses.
  • the stresses were artificially categorised into the following informal groupings: Biotic (black square), chemical (empty square), hormone (empty triangle), light (black triangle), nutrient (empty circle), PCO (black circle), Cold (empty romboid), drought (black romboid), heat (grey romboid), hypoxia (asterix), osmotic (grey square), salt (cross) and wounding (grey circle).
  • Figure 23 PCoA plot (first principal coordinate against third principal coordinate) illustrating the separation of sentinel profiles (expression profiles normalised to binary format) of 36 loci exposed to 78 different stresses.
  • the stresses were artificially categorised into the following informal groupings: Biotic (black square), chemical (empty square), hormone (empty triangle), light (black triangle), nutrient (empty circle), PCO (black circle), Cold (empty romboid), drought (black romboid), heat (grey romboid), hypoxia (asterix), osmotic (grey square), salt (cross) and wounding (grey circle).
  • Figure 24 PCoA plot (first principal coordinate against second principal component) illustrating the separation of sentinel profiles (expression profiles normalised to binary format) of 59 loci exposed to 78 different stresses.
  • the stresses were artificially categorised into the following informal groupings: Biotic (black square), chemical (empty square), hormone (empty triangle), light (black triangle), nutrient (empty circle), PCO (black circle), Cold (empty romboid), drought (black romboid), heat (grey romboid), hypoxia (asterix), osmotic (grey square), salt (cross) and wounding (grey circle).
  • Figure 25 Genevestigator output showing the effect of 506 stresses on the expression of 49 markers found to be differentially methylated in humans (Eckhardt et al (2006)). The colour code denotes black as no change in expression, light grey is downregulation and dark grey is upregulation.
  • Figure 26 Genevestigator output showing the effect of 8 age groups on the expression of 49 markers found ⁇ be uiffereniiaiiy methylated m hurrians (Eckhardt ei al (2006)).
  • Figure 27 Genevestigator output showing the effect of 40 organs on the expression of 49 markers found to be differentially methylated in humans (Erkhardt et al. (2006)).
  • Example 1 Methylation-Sensitive Amplified fragment length polymorphisms to identify stresses in Arabidopsis thaliana
  • Candidate Sentinel DNA methylation markers were assembled following exposure of Arabidopsis thaliana (Ecotype: Columbia) cohorts to two divergent external stresses and screening for differential markers in the resultant MSAP (methylation-sensitive amplified polymorphism) profiles. Methylation profiles of stressed test subjects were compared with each other and to a reference (control) population growing under standard experimental conditions. Capability of the Sentinel markers to diagnose plants grown under an uncharacterised stress (exemplification of the method) was established by reference to a third stressed population. Thus, it was possible to assemble and test four sets of candidate sentinels (Sentinels selected from any two of the three stressed populations and tested on the third). Plants were grown in 24 cell trays with 2 plants in each 4 cm x 4 cm cells.
  • the temperature in three out of the four cabinets was set at 22°C; the temperature in the fourth cabinet (for testing temperature as a stress) was set as detailed below.
  • the plants were grown in two separate lots under slightly differing conditions.
  • the supernatant was then transferred to a QIAshredderTM column (with silica gel matrix) and centrifuged at 13,000 rpm for 2 min to remove precipitates and cell debris.
  • the column flow-through was collected and transferred into a fresh tube and mixed with 0.5 volumes of wash buffer and 1 volume of ethanol. This mixture was transferred into a second DNeasy mini spin column and subjected to centrifugation at 8,000 rpm for 1 min. The flow-through was discarded since DNA molecules are retained on the column.
  • the bound DNA was washed twice by passing 500 ⁇ of wash buffer AW through the column by centrifugation at 8,000 rpm for 1 min. Subsequently, the membrane was dried by centrifugation at 13,000 rpm for 1 min after the addition of 100 ⁇ of buffer AE preheated to 65°C and incubation for 5 min at room temperature.
  • the gel When set, the gel was transferred into a horizontal electrophoresis apparatus with the gel comb at the cathode end. The gel comb was removed and sufficient 1 x TAE buffer was added to the electrode chamber to cover the gel by approximately 1 mm.
  • Prepared DNA samples (5 ⁇ DNA: 1 ⁇ blue loading dye [0.23% (w/v) bromophenol blue, 60 mM EDTA, 40% (w/v) sucrose]) were then loaded into the gel wells. HyperLadderll (Bioline, BIO- 33040) size markers were loaded into the flanking lanes. The gels were subjected to electrophoresis at constant voltages ranging from 3-5 V/cm for 15-60 min. The DNA was visualized using a UV transilluminator (320 nm wavelength). Methylation-Sensitive Amplified Fragment Length Polymorphism
  • Methylation-Sensitive Amplified fragment length polymorphism was performed on a randomly selected eight DNA samples per treatment and was based on the AFLP protocol described by Vos et al (1995) but using isoschizomers targeting the same recognition motif.
  • PCR polymase chain reaction
  • the DNA was restricted with 2 restriction enzymes, one rare and one common cutter sensitive to cytosine methylation. Two different restrictions were performed using isoschizomers of the common cutter sensitive to different types of cytosine methylation. All enzymes were obtained from Fermentas, Canada.
  • Mspl enzyme Cuts between the two cytosines of the sequence 5'CCGG 3' and its action is prevented by methylation on the first C but not by methylation on the second C.
  • Hpall enzyme Cuts between the two cytosines of the sequence 5'CCGG 3' and its action is prevented by methylation on the second C but not by methylation on the first C.
  • Adaptors specific to the restriction sites are ligated onto the DNA to allow for the amplification of fragments with generic primers and without the need for sequence information to be obtained first. All enzymes were from Fermentas and the adaptors were from Sigma-Genosys Ltd.
  • the amplification rounds were carried out using one oligonucleotide primer that corresponded to the EcoRI ends and one oligonucleotide primer that corresponded to the Mspl/Hpall ends.
  • the first round of amplification reduces the number of possible fragments by the addition of one extra base at the 3' end of the primer, while the second round of amplification further reduces the amount of possible fragments by the addition of one or two addition bases at the 3' end of the primer.
  • the second round EcoRI primers were labelled 6-Fam (Carboxyfluorescein) to allow visualisation of the products.
  • Each band (amplicon) within the AFLP protocol was considered to be a single allele of a single locus.
  • the allele identity for each locus was first assigned in a simple qualitative manner 1 (present) or 0 (absent) for each replicate individual.
  • a locus was considered to differ between pairs of stress treatments or between the control and a stress treatment if the allelic profile of individuals for the locus differed by three or more individuals (e.g. 1 1 1 1 1111 versus 1 11 11000 would be considered to differ whereas 1 11 1 1 1 1 1 vs 001 1 1 1 1 1 would not). All loci meeting these criteria were considered as possible candidate sentinel markers for subsequent multivariate analyses (see below).
  • Subsets of the selected features were visualised using principle coordinate analysis to determine the optimal number for maximum separation between the four treatments ( Figures 4-7).
  • the organised data was used to calculate total number of bands present out of a potential number of eight for each of the four treatments. Differences between pairs of treatments were calculated and features with the highest differences between these pairs of treatments were selected as sentinels (Table 14).
  • Example 2 Methylation-Sensitive Amplified fragment length polymorphisms to identify stresses in Mus musculus cell lines
  • the bound DNA was washed once by passing 500 ⁇ of wash buffer AW1 through the column by centrifugation at 8,000 rpm for 1 min, then once by passing 500 ⁇ of wash buffer AW2 through the column by centrifugation at 8,000 rpm for 1 min. Subsequently, the membrane was dried by centrifugation at 13,000 rpm for 1 min then the DNA was eluted by the addition of 100 ⁇ of buffer AE and incubation for 1 min at room temperature followed by centrifugation at 8,000 rpm for 1 min.
  • Amplified fragment length polymorphism (AFLP) is based on the protocol described by Vos et at (1995) and was performed as detailed above in Example 1.
  • mice sentinels were selected in the same manner as described above for Arabidopsis thaliana in Example 1.
  • Example 3 Sample method of creating sentinels and their use in Arabidopsis thaliana
  • the first database (the Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis, http://neomorph.salk.edu/epigenome/epigenome.html) contains a list of genes regulated by methylation generated by a recent study that compared the transcriptomes and methylomes of Arabidopsis thaliana wild type and mutants defective in DNA methylation maintenance (met1-3) and establishment (drm1-2 drm2-2 cmt3-11 ) (Lister et al 2008).
  • the program goal was defined as determining the minimum number of genes needed to identify a given number of stresses. In this case, because the expression of all genes selected as candidate sentinels are changed in methylation mutants (see above), a 4- fold change in expression was taken to infer a change in methylation status at these loci.
  • N the predefined number of genes to use
  • Eval takes an ordered set of N genes and returns the number of stresses uniquely identified.
  • a stochastic hill climber was used to identify the optimal number of stresses that can be identified from a given number of genes.
  • a complete solution would be possible for a small number of genes and/or stresses, the computational requirements would increase exponentially as the number of gene or stresses increased, and therefore this approach provides the most useful solution to the problem.
  • step 4 If the solution from steps 2 and 3 yields a better solution, than currently retained, replace the currently retained solution with the one form steps 2 / 3. 5. If all stresses have been uniquely identified report solution and terminate else go to step 2.
  • the hill climber runs for 10,000 iterations and runs independently 1 ,000 times. If an optimal solution, identifying all stresses uniquely, cannot be found the solution that identifies the most stresses uniquely is returned.
  • Results Algorithm A was run using the data described above. The smallest value, N, was established initially at 20 and reduced by 1 until a solution that identified all stresses could not be found. Using this approach 15 was identified as the minimum number of genes needed to identify all 78 stresses. The genes selected and the order they are applied is shown in table 15. Table 16 shows how to diagnose each stress using the gene expression list in table 15. Table 17 summarises how each stress affects these genes in terms of up- or down regulation or no change.
  • Table 17 The number of genes that change expression under a stressed condition, broken down by up regulation and down regulation.
  • Table 18 21 redundant genes and their order, needed to uniquely identify a stresses.
  • a third set of 23 genes was added to the previous 36 genes in order to improve the separation of stresses and to enhance clustering. Table 20.
  • Table 20 23 redundant genes and their order, needed to uniquely identify 77 stresses.
  • a series of selected subsets of markers possessing the appropriate properties can be assembled to detect divergence from the 'normal' physiological state associated with standard control growing conditions and manifest as changes to expression and/or DNA methylation status in these markers.
  • the independence of these sets means that concordance in indicating a divergence from the control state across all sentinels sets provides an increased level of confidence in the accuracy of diagnosis.
  • a further feature of progressive increasing accumulation of sentinel sets and their collective use for multivariate analysis is that the scatter plots becoming increasingly structured ( Figures 21 and 24), with the clustering according to stress type increasing as the number of sentinel markers also increases.
  • Test A took each stress in turn from the validation set and found if it could be uniquely identified from the training set.
  • Test B found the number of groups and number of groups with only one member that was present when combing all the stresses. Stresses were said to be void if they did not show any change in expression for the selected genes. Table 22
  • Test A Results from a validation study, displaying the number of genes, out of 39, that can uniquely identified without being used to train the method.
  • the sentinel loci can be developed to distinguish between stresses, and developmental stages that were not used to identify them.
  • Partial methylomes covering human chromosomes 6, 20 and 22 were published by Eckhardt er a/ (2006). Using this information, 2345 human markers were selected as possible sentinels, of which 40 genes were identified as possessing differing methylation status between organs (Table 22). These 40 genes were cross referenced with Genevestigator software (Zimmerman er a/ 2004 and Zimmerman et 2005), a web-based application that includes changes in expression in different tissues and found also to be differentially expressed in these and other different tissues not included in the methylation study ( Figure 27), confirming their expression is methylation-sensitive.
  • genes of given organisms are activated and a wide range of different expression profiles were observed. These profiles allow for unique profiles to be assembled for most, if not all conditions tested.
  • changed expression is usually (ideally it is always) associated with methylation changes in these sentinel loci, then changes in methylation profiles across these loci would have many applications for medical diagnosis of disease, stress, physiological age and/or status, or even identification of the source of origin of a biological material.
  • the final protocol design would depend on the approach chosen. Once the results from all the Sentinels were obtained they would be introduced into an active database containing the software (algorithms) described in earlier examples which would relate the result to a known stress. If no positive profile match was found, then a PCoA (Principal Coordinate Analysis) could be carried out (as in example 3) in order to associate the epigenetic profile of the sample to a group of stresses as shown above.
  • PCoA Principal Coordinate Analysis
  • methylation status is provided in a quantitative form.
  • the data can be used directly in a quantitative analysis system such as Principal Component Analysis to determine stress clustering patterns.
  • Abscisic Acid has a key role in modulating diverse plant - pathogen interactions. Plant Physiology. 150:1750-1761.
  • Aromatic and di-carboxylates inhibit wound- induced phenolic accumulation in excised lettuce (Lactura sativa L.) leaf tissue. Postharvest Biology and Technology. 46:222-229.
  • EBV Epstein-Barr virus

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Biotechnology (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Molecular Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Artificial Intelligence (AREA)
  • Bioethics (AREA)
  • Immunology (AREA)
  • Software Systems (AREA)
  • Biochemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Microbiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Botany (AREA)
  • Mycology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

La présente invention concerne un procédé d'identification de groupes de loci d'ADN permettant d'identifier des conditions de croissance atypiques et de déterminer l'identité ou la source de cellules d'intérêt. Le procédé peut comprendre l'identification d'une pluralité de groupes de loci d'ADN qui peuvent identifier une pluralité de conditions de croissance atypiques dans des cellules d'origine et/ou de génotype différent. L'invention concerne également des groupes de loci d'ADN et leurs utilisations.
PCT/GB2011/000733 2010-05-12 2011-05-12 Procédés de sélection de marqueurs de méthylation WO2011141711A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
JP2013509609A JP2013526271A (ja) 2010-05-12 2011-05-12 メチル化マーカー選択方法
AU2011251767A AU2011251767A1 (en) 2010-05-12 2011-05-12 Methods for selecting methylation markers
BR112012028957A BR112012028957A2 (pt) 2010-05-12 2011-05-12 métodos para selecionar marcadores de metilação
EP11722483.2A EP2580347A1 (fr) 2010-05-12 2011-05-12 Procédés de sélection de marqueurs de méthylation
CN201180034459.8A CN103080335B (zh) 2010-05-12 2011-05-12 选择甲基化标记的方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB1007944.0 2010-05-12
GBGB1007944.0A GB201007944D0 (en) 2010-05-12 2010-05-12 Methods

Publications (1)

Publication Number Publication Date
WO2011141711A1 true WO2011141711A1 (fr) 2011-11-17

Family

ID=42334674

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2011/000733 WO2011141711A1 (fr) 2010-05-12 2011-05-12 Procédés de sélection de marqueurs de méthylation

Country Status (7)

Country Link
EP (1) EP2580347A1 (fr)
JP (1) JP2013526271A (fr)
CN (1) CN103080335B (fr)
AU (1) AU2011251767A1 (fr)
BR (1) BR112012028957A2 (fr)
GB (1) GB201007944D0 (fr)
WO (1) WO2011141711A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20150082228A (ko) * 2012-09-20 2015-07-15 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
CN110055326A (zh) * 2019-03-08 2019-07-26 宁波大学 预测肾透明细胞癌复发转移的分子标记物及其应用

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105256379A (zh) * 2015-11-23 2016-01-20 武汉大学 一种新的基因组简化甲基化测序文库的制备方法
CN106755534B (zh) * 2017-03-01 2020-06-16 北京林业大学 一种植物dna甲基化组织特异性获取方法
CN107967410B (zh) * 2017-11-27 2021-07-30 电子科技大学 一种面向基因表达与甲基化数据的融合方法
CN113832163B (zh) * 2021-10-20 2023-06-02 浙江师范大学 有效提高植物果实生物量的方法

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB1007944A (en) 1963-09-04 1965-10-22 Spartan Steel & Alloys Ltd Improvements in and relating to the casting of metals and alloys
WO2002070741A2 (fr) * 2001-03-01 2002-09-12 Epigenomics Ag Procedes, systemes et produits programmes informatiques permettant de determiner l'effet biologique et/ou l'activite de medicaments, de substances chimiques et/ou de compositions pharmaceutiques, sur la base de leur effet sur l'etat de methylation de l'adn
WO2005019477A2 (fr) 2003-08-12 2005-03-03 Epigenomics Ag Procedes et compositions permettant de differencier des types de tissus ou de cellules au moyen de marqueurs epigenetiques
WO2006065658A2 (fr) * 2004-12-14 2006-06-22 Genomas, Inc. Procede physiogenomique destine a predire les resultats cliniques de traitements sur des patients
WO2007132166A2 (fr) 2006-05-03 2007-11-22 The Chinese University Of Hong Kong Nouveaux marqueurs de méthylation fœtaux
US20070292866A1 (en) 2005-12-08 2007-12-20 Ambergen, Inc. Diagnosing human diseases by detecting DNA methylation changes
WO2009010776A1 (fr) 2007-07-19 2009-01-22 Aberystwyth University Détection d'une méthylation dans une séquence d'acide nucléique

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB1007944A (en) 1963-09-04 1965-10-22 Spartan Steel & Alloys Ltd Improvements in and relating to the casting of metals and alloys
WO2002070741A2 (fr) * 2001-03-01 2002-09-12 Epigenomics Ag Procedes, systemes et produits programmes informatiques permettant de determiner l'effet biologique et/ou l'activite de medicaments, de substances chimiques et/ou de compositions pharmaceutiques, sur la base de leur effet sur l'etat de methylation de l'adn
US20040029117A1 (en) 2001-03-01 2004-02-12 Alexander Olek Methods, systems and computer program products for determining the biological effect and/or activity of drugs, chemical substances and/or pharmaceutical compositions based on their effect on the methylation status of the DNA
WO2005019477A2 (fr) 2003-08-12 2005-03-03 Epigenomics Ag Procedes et compositions permettant de differencier des types de tissus ou de cellules au moyen de marqueurs epigenetiques
US20060183128A1 (en) 2003-08-12 2006-08-17 Epigenomics Ag Methods and compositions for differentiating tissues for cell types using epigenetic markers
WO2006065658A2 (fr) * 2004-12-14 2006-06-22 Genomas, Inc. Procede physiogenomique destine a predire les resultats cliniques de traitements sur des patients
US20070292866A1 (en) 2005-12-08 2007-12-20 Ambergen, Inc. Diagnosing human diseases by detecting DNA methylation changes
WO2007132166A2 (fr) 2006-05-03 2007-11-22 The Chinese University Of Hong Kong Nouveaux marqueurs de méthylation fœtaux
WO2009010776A1 (fr) 2007-07-19 2009-01-22 Aberystwyth University Détection d'une méthylation dans une séquence d'acide nucléique

Non-Patent Citations (96)

* Cited by examiner, † Cited by third party
Title
"Analysing Ecological Data", 2007, SPRINGER, pages: 259 - 264
"DNA Cloning: Practical Approach", vol. I, II, 1985, MRL PRESS, LTD.
ACEITUNA, F.F., MOSEKYO, N., SEUNG, T.R., GUTIERREZ, R.A.: "The rules of gene expression in plants; Organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thaliana", BMC GENOMICS, vol. 9, 2008, pages 438
ALIMIRAH F ET AL.: "DU-145 and PC-3 human prostate cancer cell lines express andogren receptor: implications for the androgen receptor functions and regulations", FEBS LETT., vol. 580, no. 9, 2006, pages 2294 - 300
AOKI E ET AL.: "Methylation status of the p151NK4B gene in hematopoieotic progenitors and peripheral in myclodysplastic syndromes", LEUKEMIA, vol. 14, no. 4, April 2000 (2000-04-01), pages 586 - 93
ASIMAKOPOULOS F A ET AL.: "ABL1 methylation is a distinct molecular event associated with clonal evolution of chrnic leukemis", BLOOD, vol. 94, no. 7, 1 October 1999 (1999-10-01), pages 2452 - 60
ASSINDER, SJ. ET AL.: "The TGF-beta, P13K/Akt and PTEN pathways: established and proposed biochemical integration in prostate cancer", BIOCHEMICAL SOCIETY TRANSACTIONS., vol. 37, 2009, pages 265 - 272
AUSUBEL, F., BRENT, R., KINGSTON, R.E., MOORE, D.D., SEIDMAN, J.GT., SMITH J.A., STRUHL, K.: "Current protocols in molecular biology", GREEN PUBLISHING ASSOCIATES/WILEY INTERSCIENCES
BIEDLER JL ET AL.: "Morphology and growth, tumorigenicity, and cytogenetics of human neuroblastoma cells in continuous culture", CANCER RES., vol. 33, no. 11, 1973, pages 2643 - 52
BIJLSMA, R., LOESCHCKE, V.: "Environmental Stress, Adaption and Evolution", 1997, BIRKHAU USER VERLAG
BILEZIKIAN ET AL.: "Principles of Bone Biology", 2002, ACADEMIC PRESS, pages: 1506
CALLWAY, R. ET AL.: "Positive interactions among alpine plants increase with stress", NATURE, vol. 417, 2002, pages 844 - 848
CAMPOS-CASTELLO J ET AL.: "The phenomenon of genomic "imprinting" and its implications in clinical neuropediatrics", REV NEUROL, vol. 28, no. 1, 15 January 1999 (1999-01-15), pages 69 - 73
CARLOS M. RODRÍGUEZ LÓPEZ ET AL: "Progressive erosion of genetic and epigenetic variation in callus-derived cocoa (Theobroma cacao) plants", NEW PHYTOLOGIST, vol. 186, no. 4, 1 June 2010 (2010-06-01), pages 856 - 868, XP055001671, ISSN: 0028-646X, DOI: 10.1111/j.1469-8137.2010.03242.x *
CHEN TC ET AL.: "dermatofibroma is a clonal proliferative disease", J CUTAN PATHOL, vol. 27, no. 1, January 2000 (2000-01-01), pages 36 - 9
CHINNUSAMY V ET AL: "Epigenetic regulation of stress responses in plants", CURRENT OPINION IN PLANT BIOLOGY, QUADRANT SUBSCRIPTION SERVICES, GB, vol. 12, no. 2, 1 April 2009 (2009-04-01), pages 133 - 139, XP026013738, ISSN: 1369-5266, [retrieved on 20090127], DOI: DOI:10.1016/J.PBI.2008.12.006 *
CHOI, C., SANO, H.: "Abiotic-Stress induces demethylation and transcriptional activation of a gene encoding a glycerophosphodiesterase like protein in tobacco plants", MOL GENET GENOMICS, vol. 277, no. 5, 2007, pages 589 - 600
CLIFFORD SC ET AL.: "Inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene and allelic losses at chromosome arm 3p in priomary renal cell carcinoma: evidence for a VHL-independent pathway in clear cell renal tumourigenesis", GENES CHROMOSOMES CANCER, vol. 22, no. 3, July 1998 (1998-07-01), pages 200 - 9
DEL GAUDIO, R., DI GIAIMO, R., GERACI, G.: "Genome methylation of the marine annelid worm Chaetopterus variopedatus: methylation of a CpG in an expressed H1 histone gene", FEBS LETTERS, vol. 417, 1997, pages 48 - 52
DEMPSEY ET AL.: "Salicylic Acid and Disease Resistance in Plants", CRITICAL REVIEWS IN PLANT SCIENCES, vol. 18, no. 4, 1999, pages 547 - 631
DEVOTO, TURNER: "Jasmonate-requlated Arabidopsis stress signalling network", PHYSIOLOGIA PLANTARUM, vol. 123, 2005, pages 161 - 172
DICKSON RB ET AL.: "Characterisation of estrogen responsive transforming activity in human breast cancer cell lines", CANCER RES., vol. 46, 1986, pages 1707 - 13
ECKHARDT F, J LEWIN, R CORTESE, VK. RAKYAN, J ATTWOOD, M BURGER, J BURTON, TV. COX, R DAVIES, TA. DOWN: "DNA methylation profiling of human chromosomes 6, 20 and 22", NATURE GENETICS, vol. 38, no. 12, December 2006 (2006-12-01), pages 1378 - 1385
FAANG P ET AL.: "The spectrum of mutations in UBE3A causing Angelman syndrome", HUM MON GENET, vol. 8, no. 1, January 1999 (1999-01-01), pages 129 - 35
FAJKUSOVA L ET AL.: "Detailed mapping of methycuytosine positions at the CpG Island surrounding the Pa prometer at the bcr-abl locus in CML patients and in two cell lines, K562 and BV173", BLOOD CELLS MOL DIS, vol. 26, no. 3, June 2000 (2000-06-01), pages 193 - 204
FAN, J. ET AL.: "Abscisic Acid has a key role in modulating diverse plant - pathogen interactions", PLANT PHYSIOLOGY, vol. 150, 2009, pages 1750 - 1761
FAN, J. ET AL.: "Molecular characterisation of plantain class 1 chitinase gene and its expression in response to infection by Gloeosporium musarum cke and massee and other abiotic stimuli", JOURNAL OF BIOCHEMISTRY, vol. 142, 2007, pages 561570
FERLUGA J ET AL.: "Possible organ and age-related epigenetic factors in Huntingdon's disease and colorectal carcinoma", MED HYPOTHESES, vol. 29, no. 1, May 1989 (1989-05-01), pages 51 - 4
FINNEGAN, E. J., PEACOCK, W. J., DENNIS, E. S.: "Reduced DNA methylation in Arabidopsis thaliana results in abnormal plant development", PROCEEDINQS OF THE NATIONAL ACADEMV OF SCIENCE USA, vol. 93, 1996, pages 8449 - 8454
FRAMMER, M. ET AL.: "A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands", PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCE USA., vol. 89, no. 5, 1992, pages 1827 - 1831
GARCIA JF ET AL.: "Loss of p16 protein expression associated with methylation of the p161NK4A gene is a frequent finding in Hodgkin's disease", LAB INVEST, vol. 79, no. 12, December 1999 (1999-12-01), pages 1453 - 9
GLORIA L ET AL.: "DNA hypomethylation and proliferative activity are increased in the rectal mucosa of patients with longstanding ulcerative colitis", CANCER, vol. 78, no. 11, 1 December 1996 (1996-12-01), pages 2300 - 6
GOH, K. ET AL.: "The human disease network", PROCEEDINGS OF THE NATIONAL ACADEMNY OF SCIENCES USA, vol. 21, 2007, pages 8685 - 8690
GOLDFELD, SM, QUANDT RE, TROTTER HF, ECONOMETRICA, vol. 34, 1966, pages 541
GREENE LA, TISCHLER AS: "Establishment of a noradrenergic clonal line of rat adrenal pheochromocytoma cells with respond to nerve growth factor", PROC. NATL. ACAD. SCI. U.S.A., vol. 73, no. 7, 1976, pages 2428 - 8
GUPTA, S., PATHAK, R. U., KANUNGO, M. S.: "DNA methylation induced changes in chromatin conformation of the prometer of the vitellogenin II gene of Japanese quail during aging", GENE, vol. 377, 2006, pages 159 - 168
HORNSTRA IK ET AL.: "High resolution methylation analysis of the FMR1 gene trinucleotide repeat region in fragile Z syndrome", HUM MOL GENET, vol. 2, no. 10, October 1993 (1993-10-01), pages 1659 - 65
HRUZ, T. ET AL.: "Genevestigator V3: a reference expression database for the meta-analysis of transcriptomes", ADVANCES IN BIOINFORMATICS, 2008, pages 420747
IGARASHI, J. ET AL.: "Quantative analysis of human tissue-specific difference in methylation", BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, vol. 376, 2009, pages 658 - 664
KAGEYAMA ET AL.: "Ultradian oscillators in somite segmentation and other biological events.", COLD SPRING HARB. SYMP. QUANT. BIOL., vol. 72, 2007, pages 451 - 7
KASS DH ET AL.: "Examination of DNA methylation of chromosomal hot spots associated with breast cancer", ANTICANCER RES, vol. 13, no. 5A, September 1993 (1993-09-01), pages 1245 - 51
KATO, M., TAKASHIMA, K., KAKUTANI, T.: "Epigenetic control of CACTA transpoon mobility in Arabidopsis thaliana", GENETICS, vol. 168, 2004, pages 961 - 969
KAZUSA DNA RESEARCH INSTITUTE ET AL: "Sequence and analysis of chromosome 5 of the plant Arabidopsis thaliana", NATURE, NATURE PUBLISHING GROUP, LONDON, GB, vol. 408, no. 6814, 14 December 2000 (2000-12-14), pages 823 - 826, XP002551203, ISSN: 0028-0836, DOI: DOI:10.1038/35048507 *
KLAUCK SM ET AL.: "Molecular genetic analysis of the FMR-1 gene in a large collection of autistic patients", HUM GENET, vol. 100, no. 2, August 1997 (1997-08-01), pages 224 - 9
KLEYMENOVA EV: "Identification of a tumor-specific methylation site in the Wilms tumor suppressor gene", ONCOGENE, vol. 16, no. 6, 12 February 1998 (1998-02-12), pages 713 - 20
KOEN J. F. VERHOEVEN ET AL: "Stress-induced DNA methylation changes and their heritability in asexual dandelions", NEW PHYTOLOGIST, vol. 185, no. 4, 1 March 2010 (2010-03-01), pages 1108 - 1118, XP055001724, ISSN: 0028-646X, DOI: 10.1111/j.1469-8137.2009.03121.x *
LEE SD ET AL.: "Monoclonal endothelial cell proliferation is present in primary but not secondary Pulmonary hypertension", J CLIN INVEST, vol. 101, no. 5, 1 March 1998 (1998-03-01), pages 927 - 34
LEE, Y. ET AL.: "Effects of nickel on DNA methyltransfease activity and genomic DNA methylation levels", MUTAT RES., vol. 415, no. 3, 1998, pages 213 - 8
LIN X ET AL: "SEQUENCE AND ANALYSIS OF CHROMOSOME 2 OF THE PLANT ARABIDOPSIS THALIANA", NATURE, NATURE PUBLISHING GROUP, LONDON, GB, vol. 402, 16 December 1999 (1999-12-16), pages 761 - 768, XP002952930, ISSN: 0028-0836, DOI: DOI:10.1038/45471 *
LISTER R, O'MALLEY RC, TONTI-FILIPPINI J, GREGORY BD, BERRY CC, MILLAR AH, ECKER JR.: "Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis", CELL, vol. 133, no. 3, 8 May 2008 (2008-05-08), pages 523 - 36
LITZ CE ET AL.: "methylation status of the major break-point cluster region in Philadelphia chromosome negative leukemias", LEUKEMIA, vol. 6, no. 1, January 1992 (1992-01-01), pages 35 - 41
LUKENS, L. N., ZHAN, S.: "The plant genome's methylation status and response to stress: implications for plant improvement", CURRENT OPINIONS IN PLANT BIOLOGY, vol. 10, 2007, pages 317 - 322
MADLUNG, A., COMAI, L.: "The effect of stress on genome regulation and structure", ANNALS OF BOTANY, vol. 94, 2004, pages 481 - 495
MANCINI DN ET AL.: "CpG methylation within the 5' regulatory region of the BRCA1 gene is tumor specific and includes a putative CREB binding site", ONCOGENE, vol. 16, no. 9, 5 March 1998 (1998-03-05), pages 1161 - 9
MILLAU, JF. ET AL.: "p53 transcriptional activities: A general overview and some thoughts", MUTATION RESEARCH., vol. 681, 2009, pages 118 - 133
MUNSON, K. ET AL.: "Recovery of bisulfite-converted genomic sequences in the methylation-sensitive qPCR", NUCLEIC ACIDS RESEARCH., vol. 35, 2007, pages 2893 - 2903
MYERS, SM., MULLIGAN, LM.: "The RET receptor is linked to stress response pathways", CANCER RESEARCH, vol. 64, 2004, pages 4453 - 4463
N. STEWARD: "Periodic DNA Methylation in Maize Nucleosomes and Demethylation by Environmental Stress", JOURNAL OF BIOLOGICAL CHEMISTRY, vol. 277, no. 40, 27 September 2002 (2002-09-27), pages 37741 - 37746, XP055001821, ISSN: 0021-9258, DOI: 10.1074/jbc.M204050200 *
NAGATA T ET AL.: "Tobacco BY-2 cell line as the "Hela" cell in the cell biology of higher plants", INTERNATIONAL REVIEW OF CYTOLOGY, vol. 132, 1992, pages 1 - 30
NOSAKA K. ET AL.: "Increasing methylation of the CDKN2A gene is associated with the progression of adult T-cell Leukaemia", CANCER RES, vol. 60, no. 4, 15 February 2000 (2000-02-15), pages 1043 - 8
PESSARAKLI: "The Physiology of Plants Under Stress, Soil and Biotic Factors", 2000, JOHN WILEY AND SONS
PHYSIOLOGY OF ABIOTIC STREES IN PLANTS
PRIGEL-BENNETT A, THEORETICAL COMPUTER SCIENCE, vol. 320, 2004, pages 135 - 153
REINBOTHE, C. ET AL.: "Plant oxylipins: role of jasmonic acid during programmed cell death, defence and leaf sensescence", FEBS JOURANAL., vol. 276, 2009, pages 4666 - 4681
RENNIE PS ET AL.: "Epigenetic mechanisms for progression of prostrate cancer", CANCER METASTASIS REV, vol. 17, no. 4, 1998, pages 401 - 9
REYNA-LOPEZ, G. G., SIMPSON, J., RUIZ-HERRERA, J. R.: "Differences in DNA methylation patterns are detectable during dimorphic transition of fungi by amplification of restriction polymorphisms", MOLECULAR AND GENERAL GENETICS, vol. 253, 1997, pages 703 - 710
SALTVEIT, ME., AND CHOI, YJ.: "Aromatic and di-carboxylates inhibit wound- induced phenolic accumulation in excised lettuce (Lactura sativa L.) leaf tissue", POSTHARVEST BIOLOGY AND TECHNOLOGY., vol. 46, 2007, pages 222 - 229
SAMBROOK ET AL.: "Molecular Cloning: A Laboratory Manual", 2001, COLD SPRING HARBOR LABORATORY PRESS
SAMBROOK, J., RUSSELL: "Molecular cloninq: a laboratory manual.", 2001, COLD SPRING HARBOUR LABORATORY PRESS
SANCHEZ-CESPEDES M ET AL.: "gene prometer hypermethylation tumors and serum of head and neck cancer patients", CANCER RES, vol. 60, no. 4, 15 February 2000 (2000-02-15), pages 892 - 5
SANDHU ET AL.: "Determination of 5-methyle-2 -deoxycytidine in genomic DNA using high performance liquid chromatography-ultravolet detection", JOURNAL OF CHROMATOGRAPH B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES, vol. 377, 2009, pages 1957 - 1961
SARDI I ET AL.: "Molecular genetic alternations of c-myc oncogene in superficial and locally advanced bladder cancer", EUR UROL, vol. 33, no. 4, 1998, pages 424 - 30
STAVREVA DA: "Ultradian hormone stimulation induces glucocorticoid receptor-mediated pulses of gene transcription", NAT CELL BIOL., vol. 11, no. 9, pages 1051 - 3
STEWARD, N. ET AL.: "Expression of ZmMET1, a gene encoding a DNA methyltransfease from maize, is associated not only with DNA replication in actively proliferating cells, but also with altered DNA methylation status in cold- stressed quiescent cells", NUCLEIC ACIDS RES., vol. 28, no. 17, 2000, pages 3250 - 9
STEWARD, N. ET AL.: "Periodic DNA methylation in maize nucleosomes and demethylation by environmental stress", J. BIOL. CHEM., vol. 277, no. 40, 1998, pages 37741 - 6
TAO Q ET AL.: "Epstein-Barr virus (EBV) in endemic Burkitt's lymphoma: molecular analysis of primary tumor tissue", BLOOD, vol. 91, no. 4, 15 February 1998 (1998-02-15), pages 1373 - 81
TIRAOPHON, W. ET AL.: "A stress response pathway from the endoplasmic reticulum to the nucleus requires a novel bifunctional protein kinase/endoribonuclease (Ire 1 p) in mammalian cells", GENES & DEVELOPMENT., vol. 12, 1998, pages 1812 - 1824
TUCK-MULLER ET AL.: "CMDNA hypomethylation and unusual chromosome instability in cell lines from ICF patients", CYTOGENET CELL GENET, vol. 89, no. 1-2, 2000, pages 121 - 8
UHLMANN, K. ET AL.: "Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis", ELECTROPHORESIS, vol. 23, 2002, pages 4072 - 4079
VENTER J C ET AL: "THE SEQUENCE OF THE HUMAN GENOME", SCIENCE, AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, WASHINGTON, DC; US, vol. 291, no. 5507, 16 February 2001 (2001-02-16), pages 1304 - 1351, XP001061683, ISSN: 0036-8075, DOI: DOI:10.1126/SCIENCE.1058040 *
VOS, P., HOGER, R., BLEEKER, M., REIJANS, M., VAN DE LEE, T., HOMES, M., FRIJETERS, A., POT, J., PELEMAN, J., KUIPER, M.: "AFLP: Anew technique for DNA fingerprinting", NUCLEIC ACIDS RESEARCH, vol. 23, 1995, pages 4407 - 4414
WADA, Y. ET AL.: "Association between upregulation of stress-responsive genes and hypomethylation of genomic DNA in tobacco plants", MOL GENET GENOM., vol. 271, no. 6, 2004, pages 658 - 66
WASTERNACK, C.: "Jasmonates: An update on biosynthesis, signal transduction and action in plant stress response, growth and development", ANNALS OF BOTANY., vol. 166, 2007, pages 681 - 697
WHITE, H. ET AL., CLIN. CHEM., vol. 53, 2007, pages 1960 - 1962
WILSON, I. ET AL.: "Nitric oxide synthesis and signalling in plants", PLANT, CELL AND ENVIRONMENT., vol. 31, 2008, pages 622 - 631
WOJDACZ, T., DOBROVIC, A., NUCLEIC ACIDS RES., vol. 35, 2007, pages E41
XLONG, LZ. ET AL.: "Patterns of cytnsine methylation in an elite rice hybrid and its parental lines, detected by a methylation-sensitive amplication polymorphism technique", MOLECULAR AND GENERAL GENETICS, vol. 261, 1999, pages 439 - 446
YANAGISAWI Y ET AL.: "Methylation of the hMLH1 prometer in familial gastric cancer with microsatellite instability", INT J CANCER, vol. 85, no. 1, 1 January 2000 (2000-01-01), pages 50 - 3
YANG, A. ET AL.: "A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements", NUCLEIC ACIDS RESEARCH, vol. 32, no. 3, 2004, pages E38
ZABALA, M. ET AL.: "Antagonism between salicylic and abscisic acid reflects early host-pathogen conflict and moulds plant defence responses", PLANT JOURNAL., vol. 59, 2009, pages 375 - 386
ZESCHNIGH ET AL.: "Imprinted segments in the human genome: different DNA methylation patterns in the Prader-Willi/Angelman syndrome region as determined by the genomic sequencing method", HUMAN MOL GENETICS, vol. 3, no. 6, 1997, pages 387 - 395
ZESCHNIGK ET AL.: "Massive parallel bisulfite sequencing of CG-rich DNA fragments reveals that methylation of many X-chromosomal CpG islands in female blood DNA is complete", HUMAN MOLECULAR GENETICS., vol. 18, 2009, pages 1439 - 1448
ZHAO, JW. ET AL.: "Analysis of gene expression profiles in response to Sclerotinia sclerotiorum in Brassica napus", PLANTA, vol. 227, 2007, pages 13 - 24
ZIMMERMAN P, HIRSCH-HOFFMANN M, HENNIG L, W GRUISSEM: "GENEVESTIGATOR: Arabidopsis Microarray Database and Analysis Toolbox", PLANT PHYSIOLOGY, vol. 136, no. 1, 2004, pages 2621 - 2632
ZIMMERMANN P, HENNIG L, W GRUISSEM: "Gene expression analysis and network discovery using Genevestigator Trents", PLANT SCIENCE, vol. 9, no. 10, 2005, pages 407 - 409
ZRIHAN-LICHT S ET AL.: "DNA methylation status of the MUC1 gene encoding for a breast-cancer-associated protein", INT J CANCER, vol. 62, no. 3, 28 July 1995 (1995-07-28), pages 245 - 51

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20150082228A (ko) * 2012-09-20 2015-07-15 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR20200085928A (ko) * 2012-09-20 2020-07-15 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR102148547B1 (ko) 2012-09-20 2020-08-26 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR102307424B1 (ko) 2012-09-20 2021-09-29 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR20210120124A (ko) * 2012-09-20 2021-10-06 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR102390711B1 (ko) 2012-09-20 2022-04-26 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR20220065034A (ko) * 2012-09-20 2022-05-19 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
KR102587176B1 (ko) 2012-09-20 2023-10-06 더 차이니즈 유니버시티 오브 홍콩 혈장으로부터 태아 또는 종양 메틸롬의 비침습적 결정
CN110055326A (zh) * 2019-03-08 2019-07-26 宁波大学 预测肾透明细胞癌复发转移的分子标记物及其应用
CN110055326B (zh) * 2019-03-08 2022-10-11 宁波大学 预测肾透明细胞癌复发转移的分子标记物及其应用

Also Published As

Publication number Publication date
AU2011251767A1 (en) 2012-12-20
GB201007944D0 (en) 2010-06-30
EP2580347A1 (fr) 2013-04-17
CN103080335A (zh) 2013-05-01
BR112012028957A2 (pt) 2019-09-24
CN103080335B (zh) 2015-04-29
JP2013526271A (ja) 2013-06-24

Similar Documents

Publication Publication Date Title
Sollars et al. Genome sequence and genetic diversity of European ash trees
Buggs et al. Rapid, repeated, and clustered loss of duplicate genes in allopolyploid plant populations of independent origin
Efroni et al. Quantification of cell identity from single-cell gene expression profiles
Dal Santo et al. Grapevine field experiments reveal the contribution of genotype, the influence of environment and the effect of their interaction (G× E) on the berry transcriptome
Stevens et al. Genomic variation among and within six Juglans species
Deschamps et al. Genotyping-by-sequencing in plants
Rong et al. A 3347-locus genetic recombination map of sequence-tagged sites reveals features of genome organization, transmission and evolution of cotton (Gossypium)
Cortijo et al. Widespread inter‐individual gene expression variability in Arabidopsis thaliana
Tuomela et al. Comparative analysis of human and mouse transcriptomes of Th17 cell priming
GB2615272A (en) Determination of base modifications of nucleic acids
WO2011141711A1 (fr) Procédés de sélection de marqueurs de méthylation
Colasuonno et al. From genetic maps to QTL cloning: an overview for durum wheat
Jiang et al. Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids
Lloyd et al. A model-based approach for identifying functional intergenic transcribed regions and noncoding RNAs
Mold et al. Clonally heritable gene expression imparts a layer of diversity within cell types
Ma et al. RNA-seq-mediated transcriptome analysis of a fiberless mutant cotton and its possible origin based on SNP markers
Hunnicutt et al. Unraveling patterns of disrupted gene expression across a complex tissue
Hou et al. Transcriptomic analysis, genic SSR development, and genetic diversity of proso millet (Panicum miliaceum; Poaceae)
Busconi et al. Epigenetic variability among saffron crocus (Crocus sativus L.) accessions characterized by different phenotypes
CN108192893B (zh) 基于转录组测序开发艾纳香ssr引物的方法
Al-Hadeithi et al. Study of plant genetic variation through molecular markers: An overview
Pereira et al. Patterns of DNA methylation changes in elite Eucalyptus clones across contrasting environments
Han et al. Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture
Tomaszewska et al. Oat chromosome and genome evolution defined by widespread terminal intergenomic translocations in polyploids
Smyda-Dajmund et al. Analysis of cytosine methylation in genomic DNA of Solanum× michoacanum (+) S. tuberosum somatic hybrids

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 201180034459.8

Country of ref document: CN

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 11722483

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2013509609

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 10790/DELNP/2012

Country of ref document: IN

Ref document number: 2011722483

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2011251767

Country of ref document: AU

Date of ref document: 20110512

Kind code of ref document: A

REG Reference to national code

Ref country code: BR

Ref legal event code: B01A

Ref document number: 112012028957

Country of ref document: BR

ENP Entry into the national phase

Ref document number: 112012028957

Country of ref document: BR

Kind code of ref document: A2

Effective date: 20121112