Methods for classifying intestinal inflammatory conditions in avian species

Abstract

The present invention provides a method for classifying the intestinal inflammatory status of an avian subject or a group of avian subjects being tested, comprising comparing the average methylation level within a panel of pre-selected LMRs in genomic DNA isolated from intestinal sample material from an individual avian subject or a group of avian subjects being tested with the average methylation level of the same panel of LMRs in genomic DNA for one or more reference samples having a negative intestinal inflammatory status.

Description

[Technical Field]

[0001] The present invention relates to an epigenetics-based method for classifying intestinal inflammatory states and methods for developing a test system for classifying the intestinal inflammatory state of avian intestinal samples, respectively. [Background technology]

[0002] Gut health is crucial for the welfare and performance of livestock animals, particularly avian species such as poultry / chickens. Enteric diseases and inflammatory processes that affect the structural integrity of the gastrointestinal tract (GIT) result in high economic losses due to reduced weight gain, poor feed conversion efficiency, increased mortality and increased drug costs.

[0003] An intact intestinal barrier provides many physiological and functional characteristics, including nutrient digestion and absorption, host metabolism and energy production, a stable microbiota, mucus layer development, barrier function, and mucosal immune response. As the largest organ in the body, the intestine functions as a selective barrier for allowing nutrients and fluids into the body while excluding unwanted molecules and pathogens. Therefore, proper intestinal function is essential for maintaining optimal health and overall body balance, and represents an important line of defense against foreign antigens from the environment.

[0004] Recently, there has been an increasing interest in the study of intestinal permeability in chickens, leading to different strategies to measure enteritis and the associated intestinal barrier dysfunction. However, none of the proposed strategies are applicable under field conditions or are non-specific for enteritis, making them poor markers for the broiler industry.

[0005] Therefore, a marker or set of markers that can accurately detect enteritis and the associated disturbances in intestinal integrity in avian species would be highly desirable.

[0006] "Epigenetic changes," or alterations in DNA methylation patterns, occur naturally but can also be influenced by several factors, including age, environment / lifestyle, and disease state. DNA methylation regulates gene expression without alteration of genotype, thus resulting in heritable phenotypic changes that do not involve changes in the underlying DNA sequence. Instead, it acts through the chemical modification of DNA by methylating CpG dinucleotides (5'-C-phosphate-G-3'), i.e., regions of DNA in which a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases along its 5'→3' direction. Depending on age and / or environment, many organisms develop tissue-specific methylation patterns punctuated by CpG islands and canyons (hypomethylated / unmethylated, often associated with promoter regions) and low-methylated regions (LMRs). LMRs represent an important feature of the dynamic methylome and typically coincide with regions or sites of transcription factor binding, which may or may not be occupied in response to environmental factors. Environmental influences that alter epigenetic patterns are, for example, diet, disease, microbiota, temperature, and stress. These methylation patterns therefore indicate the interaction of the body / tissue with its environment and are therefore suitable readouts to assess the age or health status of test subjects vs. control groups at a molecular level.

[0007] In view of the above, it was an object of the present invention to provide epigenetic markers that allow for accurate and reliable detection of enteritis in avian subjects or groups of subjects. Summary of the Invention

[0008] The above mentioned objects have been solved by a method according to the present invention. More specifically, the present invention relates to a method for classifying the intestinal inflammatory status of an avian subject or a group of avian subjects to be tested, comprising a comparison of the average methylation level within a panel of preselected LMRs in genomic DNA isolated from intestinal sample material derived / from an individual avian subject or a group of avian subjects to be tested with the average methylation level of the same panel of LMRs in genomic DNA for one or more reference samples with a negative intestinal inflammatory status.

[0009] In another aspect of the invention, there is provided a method for classifying an intestinal inflammatory condition in an avian subject or group of avian subjects to be tested, the method comprising: a. determining test average methylation levels of a panel of preselected low-methylated regions (LMRs) in genomic DNA isolated from intestinal test samples from avian test subjects; b. comparing the test mean methylation level obtained in step (a) with a reference mean methylation level of the same panel of LMRs in genomic DNA isolated from at least one avian intestinal sample negative for an intestinal inflammatory condition; When the test mean methylation level is substantially similar to the reference mean methylation level, the test subject has a negative intestinal inflammatory status, and when the test mean methylation level differs from the reference mean methylation level, the test subject has a positive intestinal inflammatory status.

[0010] The term "significantly similar" in the context of the present disclosure is a similarity observed either by statistical means (i.e., bioinformatics) or by empirical observation.

[0011] In another aspect of the present invention, a method is provided for developing a test system ("classifier") for classifying the intestinal inflammatory state of an avian intestinal sample. In particular, the method comprises: a. detecting hypomethylated regions (LMRs) in genomic DNA in intestinal samples obtained from an avian subject or group of avian subjects with a known intestinal inflammatory condition; b. selecting a panel of LMRs from the LMRs of step (a) that are appropriate for each known intestinal inflammatory state, such that the classification reliability of the selected panel of LMRs is optimized for assignment of the known intestinal inflammatory state; c. Measuring the average methylation level of the selected panel of LMRs for each known intestinal inflammatory condition; d. generating a library of different reference mean methylation levels of the selected panel of LMRs for each known intestinal inflammatory condition; Including, Comparison of the mean methylation level obtained from the gut test sample with the reference mean methylation level of a selected panel of LMRs makes it possible to classify the gut inflammatory status of the gut test sample. DETAILED DESCRIPTION OF THE INVENTION

[0012] The elements of the present invention are described below. As used herein, the terms "of the invention," "in accordance with the invention," "according to the present invention," and the like are intended to refer to all aspects and embodiments of the present invention as described and / or claimed herein. As used herein, the term "comprising" should be interpreted to encompass both "including" and "consisting of," both meanings being specifically intended and therefore individually disclosed embodiments according to the present invention.

[0013] Unless the context dictates otherwise, the above feature descriptions and definitions are not limited to any particular aspect or embodiment of the invention, but apply equally to all aspects and embodiments described. The term "methylation level" refers to the level of a particular methylation site, which can range from 0 (= unmethylated) to 1 (= fully methylated). Therefore, a methylation profile can be determined based on the methylation levels of one or more methylation sites. Thus, the term "methylation profile" or "methylation pattern" refers to the relative or absolute concentration of methylated or unmethylated Cs at any particular stretch of residues in a biological sample. For example, if a typically unmethylated cytosine (C) residue in a DNA sequence is more methylated in the sample, it can be referred to as "hypermethylated." On the other hand, if a typically methylated cytosine (C) residue in a DNA sequence is less methylated, it can be referred to as "hypomethylated." Similarly, if a cytosine (C) residue in a DNA sequence (e.g., a sample nucleic acid) is more methylated when compared to another sequence from a different region or a different individual (e.g., compared to a normal nucleic acid), the sequence is considered to be hypermethylated compared to the other sequence. Alternatively, if a cytosine (C) residue in a DNA sequence is less methylated than another sequence from a different region or a different individual, the sequence is considered to be hypomethylated compared to other sequences. These sequences are said to be "differentially methylated." For example, if the methylation status differs between inflamed and non-inflamed tissues, the sequence is considered to be "differentially methylated." The level of differential methylation can be measured by various methods known to those skilled in the art. One method, as a non-limiting example, is to measure the methylation level of each interrogated CpG site determined by bisulfite sequencing.

[0014] As used herein, "methylated nucleotide" or "methylated nucleotide base" refers to the presence of a methyl moiety on a nucleotide base, which is not present in recognized typical nucleotide bases. For example, cytosine does not contain a methyl moiety in its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at the 5-position of its pyrimidine ring. Thus, cytosine is not a methylated nucleotide, but 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at the 5-position of its pyrimidine ring, but for purposes of this specification, thymine is not considered a methylated nucleotide when present in DNA because it is a typical nucleotide base in DNA. Typical nucleoside bases in DNA are thymine, adenine, cytosine, and guanine. Typical bases in RNA are uracil, adenine, cytosine, and guanine. Correspondingly, a "methylation site" is a position within a target gene nucleic acid region where methylation may occur. For example, positions containing CpG are methylation sites where the cytosine may or may not be methylated.

[0015] As used herein, a "CpG site" or "methylation site" is a nucleotide or sequence of nucleotides within a nucleic acid that is susceptible to methylation, either by naturally occurring events in vivo or by events initiated in vitro to chemically methylate the nucleotide.

[0016] As used herein, a "methylated nucleic acid molecule" refers to a nucleic acid molecule that is / contains one or more nucleotides that are methylated.

[0017] As used herein, "CpG island" refers to a segment of DNA sequence with elevated CpG density. For example, Yamada et al. have described a set of criteria for determining a CpG island. It must be at least 400 nucleotides long, have a GC content of more than 50% and an OCF / ECF ratio of more than 0.6 (Yamada et al., 2004, Genome Research, 14, 247-266). Others have defined a less strict CpG island as a sequence of at least 200 nucleotides long with a GC content of more than 50% and an OCF / ECF ratio of more than 0.6 (Takai et al., 2002, Proc. Natl. Acad. Sci. USA, 99, 3740-3745).

[0018] As used herein, the term "bisulfite" encompasses any suitable type of bisulfite, such as sodium bisulfite, or other chemical agents that can chemically convert cytosine (C) to uracil (U) without chemically modifying methylated cytosine and thus can be used to differentially modify DNA sequences based on the methylation status of the DNA; see, e.g., U.S. Patent Application Publication No. 2010 / 0112595 (Menchen et al.). As used herein, a reagent that "differentially modifies" methylated or unmethylated DNA encompasses any reagent that modifies methylated and / or unmethylated DNA in a process that results in distinguishable products from methylated and unmethylated DNA, thereby enabling identification of DNA methylation status. Such processes may include, but are not limited to, chemical reactions (e.g., conversion of C to U by bisulfite) and enzymatic treatments (e.g., cleavage by a methylation-dependent endonuclease). Thus, an enzyme that preferentially cleaves or digests methylated DNA is an enzyme that can cleave or digest DNA molecules with much greater efficiency when the DNA is methylated, whereas an enzyme that preferentially cleaves or digests unmethylated DNA will show significantly greater efficiency when the DNA is unmethylated.

[0019] In the context of testing for the methylation status at any given methylation site, the present invention also includes any "non-bisulfite-based method" and "non-bisulfite-based quantitative method." Such terms refer to any method for quantifying methylated or unmethylated nucleic acids that does not require the use of bisulfite. The terms also refer to methods for preparing the nucleic acid to be quantified that do not require bisulfite treatment. Examples of non-bisulfite-based methods include, but are not limited to, methods that use one or more methylation-sensitive enzymes to digest nucleic acids and methods that use agents that bind to nucleic acids based on their methylation status to separate nucleic acids. The terms "methyl-sensitive enzyme" and "methylation-sensitive restriction enzyme" refer to DNA restriction endonucleases whose activity depends on the methylation status of their DNA recognition site. For example, some methyl-sensitive enzymes cleave or digest at their DNA recognition sequence only if it is unmethylated. Thus, unmethylated DNA samples are cut into smaller fragments than methylated DNA samples. Similarly, hypermethylated DNA samples are not cleaved. In contrast, there are methyl-sensitive enzymes that cleave at their DNA recognition sequence only if it is methylated. As used herein, the terms "cleave," "cut," and "digest" are used interchangeably.

[0020] The inventors unexpectedly found that the inflammatory status of avian intestinal material can be successfully classified by comparing the average methylation level within a panel of preselected LMRs in genomic DNA isolated from a test intestinal sample with the average methylation level of the same panel of LMRs belonging to one or more reference samples with a negative intestinal inflammatory status. The term "negative intestinal inflammatory status" refers to intestinal material that does not contain signs of an inflammatory process.

[0021] In the context of the present invention, the terms "intestinal inflammation" and "enteritis" are used interchangeably and have the same meaning. The expression "classifying an intestinal inflammation state" refers to both the classification of whether there is an ongoing inflammatory process (YES / NO) and the classification into different inflammation grades (e.g., severe, moderate, or weak inflammation). In particular, a positive intestinal inflammation state is further classified into a severely inflamed, moderately inflamed, or weakly inflamed class based on the average methylation level.

[0022] In certain aspects of the invention, the method comprises: a. isolating genomic DNA from an intestinal sample of a subject or bird population to be tested; b. determining the average methylation level of the panel of preselected LMRs in the genomic DNA obtained in step (a); c. comparing the average methylation level of the panel of preselected LMRs obtained in step (b) with the average methylation level of the same panel of LMRs in genomic DNA belonging to one or more reference samples with a negative intestinal inflammation status; If the average methylation level of the panel LMRs of the test sample is significantly similar to one of the one or more predetermined reference samples, then the intestinal inflammatory state of the avian subject or avian population being tested is similar to the inflammatory state of the respective reference sample.

[0023] In particular, the method a. determining a test average methylation level of a panel of pre-selected low-methylated regions (LMR) in genomic DNA isolated from intestinal test samples from avian test subjects; b. comparing the test mean methylation level obtained in step (a) with a reference mean methylation level of the same panel of LMRs in genomic DNA isolated from at least one avian intestinal sample negative for an intestinal inflammatory condition; If the test mean methylation level is substantially similar to the reference mean methylation level, the test subject has a negative intestinal inflammatory condition, and if the test mean methylation level differs from the reference mean methylation level, the test subject has a positive intestinal inflammatory condition.

[0024] As used herein, the term "preselected panel of LMRs" refers to a panel of LMRs having CpGs that exhibit strand-specific coverage of 5 or more. Furthermore, CpG sites known as single nucleotide polymorphisms (SNPs) can be excluded. In particular, any method known in the art can be used to identify or detect LMRs in genomic DNA. Well-known methods include using programs such as MethylSeekR. In particular, an LMR in genomic DNA has at least three consecutive CpGs and no single nucleotide polymorphisms (SNPs) at any of the CpG positions. Furthermore, an LMR is a region of the genome in which less than 60% of the CpGs in that region are methylated. More specifically, less than 50%, 40%, 30%, 20%, or 10% of the CpGs in an LMR are methylated. Even more specifically, LMRs in genomic DNA are identified based on the methods disclosed at least in Burger, L., (2013) Nucleic Acids Research, 41(16):e155 and / or Stadler, M., (2011) Nature 480, 490-495. LMRs are known to have an average methylation range of 10%-50%, are regions of low CG density, tend to be enriched in H3K4me1, DHS, and p300 / CBP, and / or are located primarily distal to promoters in intergenic or intronic regions. In particular, LMRs are have an average methylation ranging from 10% to 50%; -It is an area of ​​low CG density, - Histone H3 monomethylated at lysine 4 (H3K4me1), DNase I hypersensitive sites (DHS), and the transcriptional coactivators CREB-binding protein (CPB) and p300 are enriched. located primarily distal to the promoter in intergenic or intronic regions, and / or ·There are no single nucleotide polymorphisms (SNPs) at any of the CpG positions.

[0025] Once the LMRs of genomic DNA are identified, a panel of LMRs can be selected. The panel of LMRs can be selected using any method known in the art. In particular, the same panel of LMRs can be used to classify avian intestinal inflammation. That is, the average methylation level of the same panel of LMRs can be used to classify whether the test sample has positive or negative intestinal inflammation, and the positively inflamed intestine can be further classified as severely, moderately, or weakly inflamed.

[0026] In one example, a panel of LMRs for use in accordance with any embodiment of the present invention may be ultimately selected using machine learning techniques such as random forests (Breiman L (2001). "Random Forests". Machine Learning. 45(1):5-32. doi:10.1023 / A:1010933404324). The machine learning system, advantageously a random forest predictor, learns the intestinal inflammatory status of individualized animals based on the average methylation values ​​within genomic LMRs. Said process is further illustrated in the exemplary section.

[0027] The panel of LMRs is advantageously pre-selected such that classification reliability is optimized for assignment of different inflammatory state classes.

[0028] The panel of preselected LMRs can be optimized for accuracy using machine learning techniques such as random forest analysis. In particular, the panel of preselected LMRs is at least two LMRs selected from the following list of LMRs: LMR1 to LMR15.

[0029] [Table 0-1]

[0030] More specifically, the panel of preselected LMRs is at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 LMRs selected from the list of LMRs: LMR1 to LMR 15. Even more specifically, the panel of preselected LMRs is at least 12, the LMRs selected from the list of LMRs: LMR1 to LMR 15.

[0031] In another example, the panel of LMRs can be selected based on other methods, such as the maximum methylation difference between an LMR derived from genomic DNA from at least one avian intestinal sample with a negative intestinal inflammation status and an LMR derived from genomic DNA from at least one avian intestinal sample with a positive intestinal inflammation status. Performing these methods is well within the general knowledge of those skilled in the art. In particular, the preselected panel of LMRs is at least two LMRs selected from the following list of LMRs: LMR16 to LMR30.

[0032] [Table 0-2]

[0033] More specifically, the panel of preselected LMRs is at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 LMRs selected from the list of LMRs: LMR 16 to LMR 30. Even more specifically, the panel of preselected LMRs is at least 12, the LMRs selected from the list of LMRs: LMR 16 to LMR 30. The step of determining the average methylation level within a panel of preselected LMRs in genomic DNA isolated from gut sample material from an individual avian subject or group of avian subjects to be tested may involve a bisulfite conversion process, in which cytosine residues in the genomic DNA are converted to uracil and 5-methylcytosine residues in the genomic DNA are not converted to uracil.

[0034] Whole-genome bisulfite sequencing is a genome-wide analysis of DNA methylation based on sodium bisulfite conversion of genomic DNA, which is then sequenced on a next-generation sequencing platform. The sequences are then aligned to a reference genome to determine the methylation status of CpG dinucleotides based on mismatches resulting from the conversion of unmethylated cytosines to uracils.

[0035] For example, methylation levels can be measured using commercially available Illumina™ sequencing or array platforms.

[0036] The avian subject or group of avian subjects tested may be poultry such as chickens, turkeys, ducks and geese. Preferably, the avian subject or group of avian subjects tested is chicken.

[0037] The intestinal sample material may be intestinal tissue, preferably the ileum or jejunum. Alternatively, the intestinal sample material may be sample material comprising intestinal mucosa, such as faeces.

[0038] In a particular example, the intestinal sample material is a pooled sample derived from the group of avian subjects being tested.

[0039] According to another aspect of the present invention there is provided a method for developing a test system for classifying the intestinal inflammatory status of an avian intestinal sample, comprising: The method is: a. providing one or more intestinal samples obtained from an avian subject or group of avian subjects with a known intestinal inflammatory condition; b. determining the average methylation level of one or more LMRs within the genomic DNA contained in each of the one or more intestinal samples obtained in step (a); c. selecting a panel of LMRs from the one or more LMRs of step (b) such that the classification reliability of the panel of LMRs is optimized for the assignment of each known inflammatory state; d. assigning a panel LMR reference methylation profile for each known intestinal inflammatory condition; Comparison of the mean methylation level obtained from the gut test sample with the panel LMR reference methylation profile allows for classification of the gut inflammatory status of the gut test sample. In particular, provided is a method for developing a test system for classifying the intestinal inflammatory state of an avian intestinal sample, the method comprising: a. detecting hypomethylated regions (LMRs) in genomic DNA in intestinal samples obtained from an avian subject or group of avian subjects with a known intestinal inflammatory condition; b. selecting a panel of LMRs from the LMRs of step (a) that are appropriate for each known intestinal inflammatory state, such that the classification reliability of the selected panel of LMRs is optimized for assignment of the known intestinal inflammatory state; c. Measuring the average methylation level of a selected panel of LMRs for each known intestinal inflammatory condition; d. generating a library of different reference mean methylation levels of the selected panel of LMRs for each known intestinal inflammatory condition; A method in which a comparison of the mean methylation level obtained from the bowel test sample with a reference mean methylation level of a selected panel of LMRs makes it possible to classify the bowel inflammatory status of the bowel test sample.

[0040] In particular, the library of different reference mean methylation levels is based on a single panel of LMRs with different reference mean methylation levels depending on whether the reference samples had positive or negative intestinal inflammation, and further whether the positive intestinal inflammation was severe, moderate, or weak. [Brief explanation of the drawings]

[0041] [Figure 1] Classification error for repeated random forest 3-fold cross-validation with 100 LMRs as starting input, using the command rfcv from the R package random forest. The figure shows that the error becomes very small starting at 13 LMRs. At 6 LMRs, the predictive power decreases and the error increases significantly. [Figure 2] Establishment of chicken DNA methylation random forest classifier for intestinal inflammation conditions as described in the exemplary section. [Example]

[0042] Certain aspects and embodiments of the present invention will now be described, by way of example, with reference to the description, figures, and tables set forth herein. Such examples of methods, uses, and other aspects of the present invention are merely representative and should not be construed as limiting the scope of the invention to only such representative examples. method

[0043] A broiler study was conducted using Ross 308 male broilers fed an industry standard three-phase corn-soybean meal diet formulated to meet all nutritional requirements from days 1-35 (Table 1).

[0044] [Table 1]

[0045] Three physiologically healthy birds were euthanized on days 3, 15, and 35, respectively, to remove spleen, intestinal (ileum), and muscle (pectoralis major) samples for DNA extraction (Invitrogen PureLink genomic DNA isolation kit) and bisulfite sequencing. sample

[0046] Animals were stratified into two groups (inflamed and non-inflamed at three time points each), and DNA was prepared from nine independent animals from each of these two groups, yielding 18 genomic DNA samples. Whole-genome bisulfite sequencing

[0047] Libraries were prepared using the Accel-NGS Methyl-Seq DNA Library Kit from Swift Biosciences. Two sequencing libraries were barcoded into one sequencing lane. Sequencing was performed on an Illumina HiSeq X platform using standard paired-end sequencing protocols with a read length of 105 nucleotides.

[0048] [Table 2]

[0049] Reads were trimmed and mapped with BSMAP 2.5 (Xi Y, Li W. 2009. BSMAP: A Whole-Genome Bisulfite Sequence Mapping Program. BMC Bioinformatics 10:232. doi:10.1186 / 1471-2105-10-232.) using the Gallus gallus genome assembly version 5.0 (https: / / www.ebi.ac.uk / ena / data / view / GCA_000002315.3) as the reference sequence. Duplicates were removed using the Picard tool (http: / / broadinstitute.github.io / picard). Methylation levels were determined by dividing the number of reads with a methylated CpG at a genomic position by the number of all reads covering this position using a Python script (methratio.py) distributed with the BSMAP package. SNP filtering of methylation data

[0050] We excluded all CpGs listed as SNPs in the database dbSNP for the Gallus gallus genome. For the LMR-based random forest, we restricted the analysis to CpGs within hypomethylated regions that showed strand-specific coverage greater than 5 in any of the sequenced samples, resulting in a set of 67,651 LMRs. Establishment of chicken DNA methylation random forest classifier for intestinal inflammation status A random forest predictor (implemented in the R package Random Forest [https: / / cran.r-project.org / web / packages / randomForest / ]) was applied to learn the intestinal inflammation status of animals based on the average methylation value of LMRs. This was done by dividing the entire set of LMRs into chunks of 10,000 LMRs and using each chunk as input for the algorithm by fitting 100 trees using 250 candidate features (LMRs) in each split. All LMRs from each chunk that showed a value greater than 0 for the variable importance "reduction_precision" were pooled. This was repeated 10 times, and for each LMR, the frequency with which it was found in the pooled set of these 10 repetitions was counted. The 100 most frequently occurring LMRs were retained. Next, an iterative random forest 3-fold cross-validation was performed using these 100 LMRs as input, applying the command rfcv in the package Random Forest, which gradually reduced the number of features used as input. The resulting classification error was recorded and the number of LMRs that resulted in non-zero classification error was assessed. This value was found to be an LMR of 13. We determined that 15 LMR was sufficient for near-zero classification. The 15 LMRs with the highest "reduction_precision" values ​​were considered to be the resulting feature set (Table 3). In the second step, the average methylation difference of all LMRs between control and inflamed samples was assessed, and the 15 LMRs showing the highest average methylation difference were retained (Table 4).

[0051] [Table 3]

[0052] [Table 4]

[0053] Table 5 shows an example of a trained random forest constructed using the 15 LMRs from Table 3 using the command "random forest" in the R package random forest. The input data used methylation values ​​for the 15 LMRs in nine con (control) and nine infl (inflammation) samples, with each value calculated as the average across the LMRs. The forest contained 100 trees and used four LMRs per split (parameter mtry). This forest was selected because it correctly classified all 18 samples as control or inflammatory based on internal validation using out-of-bag (OOB) data.

[0054] Depending on the LMR methylation value of each sample, the tree is traversed until a terminal node is reached. This terminal node is assigned either "con" (control) or "infl" (inflammation), and classification is performed in this manner. Consequently, non-terminal nodes contain "NA" (not applicable) in the "Prediction" column, as this does not allow classification of the sample at this point, which is only possible when a terminal node is reached. For each row of the table, the following applies: Field 1 (left_daughter): The number of left daughter nodes; if the value is 0 and there are none Field 2 (right_daughter): The number of right daughter nodes; if not present, the value is 0. Field 3 (split_var): The LMR used to determine which daughter node is selected; Not applicable to terminal nodes (NA) Field 4 (split_point): The value of "split_var" that must be less than the specified value. (to select the left daughter node) or beyond (to select the right daughter node). Field 5 (Status): Terminal node (-1) or non-terminal node (1) Field 6: (Prediction): Classification of terminal node in control (con) or inflammation (infl); NA if node is not terminal

[0055] [Table 5-1]

[0056] Table 5-2

[0057] Table 5-3

[0058] Table 5-4

Claims

1. A method for classifying the intestinal inflammatory condition of a tested avian subject or a group of tested avian subjects, said method comprising: a. determining test mean methylation levels of pre-selected Low-Methylated Regions (LMR) in genomic DNA isolated from intestinal test samples from the test avian subject or group of test avian subjects; b. comparing the test mean methylation level obtained in step (a) with a reference mean methylation level of the same panel of LMRs in genomic DNA isolated from at least one avian intestinal sample negative for an intestinal inflammatory condition; if said test mean methylation level is significantly similar to one of said reference mean methylation levels for one or more of said given avian intestinal samples, said test avian subject or group of test avian subjects have similar inflammatory states for each of said avian intestinal samples, Further, the preselected panel of LMRs comprises at least five LMRs selected from the following list of LMRs: LMR1 through LMR15: Table 1 Alternatively, the preselected panel of LMRs is at least five LMRs selected from the following list of LMRs: LMR16 to LMR30. Table 2

2. The LMR in the genomic DNA is - have an average methylation ranging from 10% to 50%; - Areas with low CG density, - enriched in histone H3 monomethylated at lysine 4 (H3K4me1), DNase I hypersensitive sites (DHS), and transcriptional coactivators CREB-binding protein (CPB) and p300; - located mainly distal to the promoter in intergenic or intronic regions, and - The method of claim 1, wherein there are no single nucleotide polymorphisms (SNPs) at any of the CpG positions.

3. 10. The method of claim 1, wherein the preselected panel of LMRs can be identified using at least one machine learning technique.

4. 10. The method of claim 1, wherein the preselected panel of LMRs is identified using at least one machine learning technique called random forest analysis.

5. 10. The method of claim 1, wherein the average methylation level of the preselected panel of LMRs is determined using bisulfite sequencing.

6. 2. The method of claim 1, wherein the avian subject or group of avian subjects to be tested is a chicken or a group of chickens.

7. 2. The method of claim 1, wherein said avian intestinal sample and intestinal test sample is intestinal tissue, and said intestinal tissue is ileum or jejunum.

8. 2. The method of claim 1, wherein the intestinal test sample is a pooled sample derived from a group of avian subjects to be tested.

9. 1. A method for developing a test system for classifying the intestinal inflammatory state of an avian intestinal sample, said method comprising: a. detecting hypomethylated regions (LMRs) in genomic DNA in intestinal samples obtained from an avian subject or group of avian subjects with a known intestinal inflammatory condition; b. selecting a panel of LMRs from the LMRs of step (a) appropriate for each known intestinal inflammatory condition such that the classification reliability of the selected panel of LMRs is optimized for assignment of the known intestinal inflammatory condition; c. determining the average methylation level of the selected panel of LMRs for each of the known intestinal inflammatory conditions; d. generating a library of different reference mean methylation levels of the selected panel of LMRs for each known intestinal inflammatory condition; A comparison of the mean methylation level obtained from a bowel test sample with the reference mean methylation level of the selected panel of LMRs makes it possible to classify the bowel inflammatory state of the bowel test sample.

10. The LMR in the genomic DNA is - have an average methylation ranging from 10% to 50%; - Areas with low CG density, - enriched in histone H3 monomethylated at lysine 4 (H3K4me1), DNase I hypersensitive sites (DHS), and transcriptional coactivators CREB-binding protein (CPB) and p300; - located mainly distal to the promoter in intergenic or intronic regions, and - The method of claim 9, wherein there are no single nucleotide polymorphisms (SNPs) at any of the CpG positions.

11. 10. The method of claim 9, wherein the selected panel of LMRs is at least five LMRs selected from the following list of LMRs: LMR1 to LMR15. Table 3

12. 10. The method of claim 9, wherein the selected panel of LMRs is at least five LMRs selected from the following list of LMRs: LMR16 to LMR30. Table 4

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