DNA-METHYLATION-BASED QUALITY CONTROL OF THE ORIGIN OF ORGANISMS

Abstract

The invention pertains to a method for the identification of the geographic origin of an individual test subject or of an individual group of test subjects, the method comprising the comparison of a test methylation profile obtained from genomic material of the individual test subject or of the individual group of test subjects with one or more predetermined reference methylation profiles each being specific for a distinct geographic origin.

Claims

1. A method for the identification of the geographic origin of an individual test subject or of an individual group of test subjects, the method comprising the comparison of a test methylation profile obtained from genomic material of the individual test subject or of the individual group of test subjects with one or more predetermined reference methylation profiles each being specific for a distinct geographic origin.

2. The method of claim 1, comprising the steps of: a. determining the methylation status of one or more pre-selected methylation sites within the genomic material contained in a biological sample obtained from the individual test subject, or of the individual group of test subjects; b. determining from the methylation status determined in (a) a test methylation profile of the individual test subject, or of the individual group of test subjects; and c. comparing the test methylation profile determined in (b) with one or more predetermined reference methylation profiles, wherein each of the one or more predetermined reference methylation profiles is specific for a distinct geographic origin of subjects or group of subjects which are of the same biological taxon of the individual test subject or individual group of test subjects; wherein if the test methylation profile is significantly similar to one of the one or more predetermined reference methylation profiles, the individual test subject or the individual group of test subjects has a geographical origin similar to the subjects or group of subjects of the one or more predetermined reference methylation profiles.

3. The method of claim 1, wherein the individual test subject or individual group of test subjects is any biological entity having a DNA genome and DNA genome methylation, preferably the methylation site being a CpG site.

4. The method of claim 1, wherein the individual test subject or individual group of test subjects are selected from a prokaryote, or a eukaryote.

5. The method of claim 2, wherein the one or more pre-selected methylation sites in (a) are methylation sites associated with tissue specific gene expression, preferably wherein the pre-selected methylation sites are associated with gene expression of one distinct tissue.

6. The method of claim 5, wherein the tissue is selected from the group consisting of (i) metabolic tissue preferably being gut tissue, (ii) muscular tissue, (iii) skin or feather tissue, and (iv) organ tissue, said organ tissue preferably being hepatic and/or pancreatic tissue.

7. The method of claim 1, wherein the individual test subject, or the individual group of test subjects, are animals.

8. The method of claim 1, wherein the distinct geographic origin is a geographic location that is considered to be the habitat, wherein the individual test subject, or individual group of test subjects, were spawned and/or cultured, or at least cultured for a significant time during their lifetime.

9. The method according to claim 1, wherein the one or more pre-selected methylation sites are within the 20% most differentially methylated genes of the genome of the individual test subject, or individual group of test subjects.

10. A method for quality controlling a suspected geographic origin of an individual test subject, or of an individual group of test subjects, the method comprising the steps of a. determining the methylation status of one or more pre-selected methylation sites within genomic material contained in a biological sample obtained from the individual test subject, or of the individual group of test subjects; b. determining from the methylation status determined in (a) a test methylation profile of the individual test subject, or of the individual group of test subjects; and c. comparing the test methylation profile determined in (b) with a predetermined reference methylation profile, wherein the predetermined reference methylation profile is specific for individual subjects, or individual groups of subjects, of the same biological taxon of the individual test subject or individual group of test subjects, and which were obtained from the suspected geographic origin; wherein if the test methylation profile is significantly similar to the predetermined reference methylation profile, the individual test subject or the individual group of test subjects passes the quality control and the suspected geographical origin is indicated as true geographical origin.

11. A method for assessing one or more environmental parameters of a habitat of an individual test subject, or of an individual group of test subjects, the method comprising the steps of a. determining the methylation status of one or more pre-selected methylation sites within the genomic material contained in a biological sample obtained from the individual test subject, or of the individual group of test subjects; b. determining from the methylation status determined in (a) a test methylation profile of the individual test subject, or individual group of test subjects; and c. comparing the test methylation profile determined in (b) with one or more predetermined reference methylation profiles, wherein the one or more predetermined reference methylation profiles are each specific for individual subjects, or individual groups of subjects, of the same biological taxon of the individual test subject or individual group of test subjects, and which were each obtained from distinct geographic origins; and wherein the distinct geographic origin is distinguished from other distinct geographic origins by one or more environmental parameters; wherein if the test methylation profile is significantly similar to one of the one or more predetermined reference methylation profiles, the individual test subject or the individual group of test subjects is derived from a geographical origin having similar, or preferably equal, environmental parameters to the geographical origin of the individual test subjects or individual group of test subjects of the one of the one or more predetermined reference methylation profiles.

12. A method for confirming or declining an assumed geographic origin of an individual test subject or of an individual group of test subjects, the method comprising the comparison of a test methylation profile obtained from genomic material of the individual test subject or of the individual group of test subjects with one or more predetermined reference methylation profiles each being specific for a distinct geographic origin.

13. A method for developing a test system for confirming an assumed geographic origin of an individual test subject or of an individual group of test subjects, the method comprising the steps of: a. determining the methylation status of one or more methylation sites within genomic material contained in a biological sample obtained from the individual test subject, or of the individual group of test subjects; b. selecting from the one or more methylation sites a reference panel of methylation sites which is characterized by a specific and distinct differential methylation profile for each of the known geographic origins; c. obtaining a test system by assigning a reference methylation profile for each of the known geographic origins; and wherein a comparison of a test methylation profile obtained from a test sample with the reference methylation profiles obtained in (c) allows for confirming the assumed geographic origin of the individual test subject or of the individual group of test subjects from which the test sample was obtained.

14. The method of claim 1, wherein the individual test subject, or the individual group of test subjects is marbled crayfish and/or wherein the distinct geographic origins are geographically distinct waters, these waters preferably being selected from the group consisting of lake(s), river(s) and aquaculture farms.

15. The method of claim 14, wherein the geographically distinct waters are made distinct by one or more environmental parameters selected from the group consisting of pH, water hardness, manganese content, iron content, and aluminum content.

16. The method of any one of claim 14, wherein the method comprises a genome wide methylation analysis or a methylation analysis of a pre-selected panel of methylation sites, the pre-selected panel of methylation sites preferably containing methylation sites within about 500 to 1000, and preferably about 700 genes.

17. The method of claim 16, wherein the panel of methylation sites does not comprise consistently methylated or unmethylated methylation sites.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0115] FIG. 1 shows specific water parameters of four Marbled crayfish population habitats.

[0116] FIG. 2 shows context-specific differential methylation in marbled crayfish populations. (A) Principal component analysis of abdominal muscle (mus., square symbols) and hepatopancreas (hep., circular symbols) samples from Singlis, based on the methylation levels of 56 genes with tissue-specific methylation differences. (B) Principal component analysis of abdominal muscle (mus., square symbols) and hepatopancreas (hep., circular symbols) samples from Reilingen, based on the methylation levels of 35 genes with tissue-specific methylation differences. (C) Principal component analysis of hepatopancreas samples from all locations, based on the methylation levels of 122 genes with location-specific methylation differences. (D) Principal component analysis of abdominal muscle samples from all locations, based on the methylation levels of 22 genes with location-specific methylation differences.

[0117] FIG. 3 shows the validation of context-dependent differential methylation in marbled crayfish. Results are shown for capture-based sequencing and for the corresponding validation experiment with amplicon sequencing, for 4 different genomic regions. Unfilled shapes: abdominal muscle; filled shapess: hepatopancreas;squares: Reilingen; stars: Singlis; circles: Andragnaroa; triangle: Ihosy.

[0118] FIG. 4 are the results of differentially methylated CpG sites in chicken using the function “calculate DiffMeth” from the R package MethylKit on Reduced representation bisulfite sequencing (RRBS) data. The identified differentially methylated CpG sites allowed a robust separation of the three locations in a principle component analysis. After filtering for SNPs: 2.3 - 3.6 million CpG sites. CpG sites with min coverage 10 in all the samples: 623,657, Differentially methylated CpGs:1274 (p-value <0.05).

[0119] FIG. 5 are the results of differentially methylated CpG sites in soho salmon using the function “calculate DiffMeth” from the R package MethylKit on Reduced representation bisulfite sequencing (RRBS) data. The identified differentially methylated CpG sites allowed a robust separation of the two locations in a principle component analysis. CpG sites with min coverage 10 in all the samples after SNP filtering: 610,397, Significant DMRs: 440 (p-value <0.05, diff in methylation>=10%)

EXAMPLES

[0120] Certain aspects and embodiments of the invention will now be illustrated by way of example and with reference to the description, figures and tables set out herein. Such examples of the methods, uses and other aspects of the present invention are representative only, and should not be taken to limit the scope of the present invention to only such representative examples.

Example 1

Habitat Profiles of Four Independent Marbled Crayfish Populations

[0121] To explore the possibility of context-dependent DNA methylation in marbled crayfish, animals from four diverse stable populations were collected. Reilingen (Germany) represents the type locality, a small eutrophic lake in an environmentally protected area. The Singlis (Germany) population is from a larger oligotrophic lake with in a former brown coal mining area. The Andragnaroa (Madagascar) population is located in a river flowing through a forest area at relatively high altitude (1156 m) with soft mountain water. Finally, the Ihosy (Madagascar) population is found in highly turbid water, with high levels of pollution from nearby mining activities. The analysis of physicochemical water parameters showed clean, slightly basic (pH 8.4) water in Reilingen and rather acidic (pH 5.2) water with high levels of Manganese (4792 .Math.g/l) in Singlis. The water in Andragnaroa showed particularly low hardness (0.3 °dH), while the water in Ihosy was characterized by high levels of Aluminium (2967 .Math.g/l) and Iron (2249 .Math.g/l). Altogether, our study thus covered populations that inhabit four diverse habitats from different climatic zones and with different water parameters. These results are shown in FIG. 1,

TABLE-US-00001 Overview of marbled crayfish populations analyzed Geographic location (site name) Coordinates Type Altitude (m) Key features Ground sediment Associated vegetation and fauna Reilingen (Germany) N49°17,649′ E08°32,672′ lake 69 eutrophic lake mud, sand herbaceous grasses, macrophytes, algae, fish, insects, crayfish Singlis (Germany) N51°03.655′ E09°18.710′ lake 168 oligotrophic lake, acidic water sand, pebbles herbaceous grasses, insects Andragnaroa (Madagascar) S21°17.551′ E47°22.292′ river 1083 slow-flowing mountain river mud herbaceous grasses, rice, fish, insects, crabs, crayfish Ihosy (Madagascar) S22°22.512′ E46°06.016′ river 711 slow-flowing, turbid, polluted river mud herbaceous grasses, fish, amphibians, molluscs, insects

Example 2

Identification of a Variably Methylated Gene Set

[0122] It was previously shown that DNA methylation in the marbled crayfish is targeted to gene bodies, relatively stable and largely tissue-invariant (Gatzmann et al., 2018). However, a comparison of 8 whole-genome bisulfite sequencing datasets from different animals, different tissues and different developmental stages also indicated the possibility for a smaller group of genes that showed more variable methylation levels (Gatzmann et al., 2018). This was confirmed by systematic analyses of methylation variance. A variance cutoff of >0.006 identified 846 genes, 149 of which were consistently methylated or unmethylated (mean ratio >0.8 or <0.2, respectively) and excluded from further analysis, thus defining a core set of 697 variably methylated genes. Metric multidimensional analysis based on the methylation levels of these genes separated the hepatopancreas samples from the abdominal muscle samples, which suggested the presence of previously unrecognized tissue-specific methylation patterns.

[0123] In order to analyze the methylation patterns of these genes in a larger number of samples and at higher coverage methylation, a bead-based capture assay was developed. For this assay, DNA samples from 2 different tissues were prepared: hepatopancreas, which represents the main metabolic organ of crayfish and abdominal muscle, the main muscle tissue forming the abdominal tail. Hepatopancreas DNA was prepared from N=47 animals (11-12 per location), while abdominal muscle DNA was prepared from a subset of the same animals (N=26, 12-4 per location). Subgenome capture was found to be both efficient and specific, providing a minimum of 10 million mapped reads per sample under stringent conditions.

[0124] In subsequent steps, genes with more than 50% Ns in their sequence were excluded, which left 623 genes in our analysis. Furthermore, only those CpG sites that were present in all the samples with a sequencing coverage of ≥5x were considered and average methylation levels were calculated only if a gene had ≥5 qualified CpG sites. These criteria were fulfilled for 463 genes. The inventors also excluded invariant genes, i.e., genes that were in the bottom 10% for methylation variance as well as genes with an average methylation level <0.1 or >0.9, resulting in a core set of 361 variably methylated genes (Tab. 2).

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[0125] Importantly, gene ontology analysis was performed to better understand the underlying mechanisms behind our set of variably methylated genes. A significant enrichment on genes with functional characteristics related to GTP-binding proteins (also named G proteins) was observed. G proteins regulating a wide variety of cellular activities, and among others, we detected variably methylated genes playing a role in transcription/translation regulation, response to stress, RNA metabolism, and immune response to pathogens. Together, the functional heterogeneity observed within those 321 variably methylated genes could potentially confer plasticity for the marbled crayfish living under different environmental pressures.

Example 3

Context-Dependent Methylation Patterns in Marbled Crayfish Populations

[0126] In additional steps, we sought to identify specific context-dependent methylation patterns in our core set of 361 variably methylated genes. To identify tissue-specific methylation differences, we applied a Wilcoxon rank sum test for differential (p<0.05 after Benjamini-Hochberg correction) methylation between hepatopancreas and abdominal muscle. For our largest dataset from a single location (Singlis, N=24) this identified 56 genes that allowed a robust separation of the two tissues in a principal component analysis. When the same approach was applied to the second-largest dataset (Reilingen, N=19), it identified 35 differentially methylated genes (28 overlapping with Singlis) that again allowed a robust separation of the two tissues in a principal component analysis. Tissue-specific methylation differences appeared rather moderate for average gene methylation levels, but more pronounced at the CpG level. Of note, tissue-specific methylation differences were highly stable between different populations. Taken together, these findings suggest the existence of localized tissue-specific methylation patterns in marbled crayfish.

[0127] To identify location-specific methylation differences, we applied a Kruskal-Wallis test for differential (p<0.05 after Benjamini-Hochberg correction) methylation between the four locations. For the larger hepatopancreas dataset (N=47), this identified 122 genes that allowed a robust separation of the four locations in a principal component analysis. When the same approach was applied to the smaller abdominal muscle dataset (N=26), it identified 22 differentially methylated genes (21 overlapping with hepatopancreas) that again allowed a robust separation of the four locations in a principal component analysis. Similar to our findings for tissue-specific methylation, location-specific methylation differences appeared moderate for average gene methylation levels, but more pronounced at the CpG level. Also, location-specific methylation differences were highly stable between different locations. These findings suggest the existence of defined location-specific methylation differences among marbled crayfish populations.

Example 4

Validation of Context Dependent Methylation Patterns

[0128] To validate the results for the tissue- and location-specific methylation patterns, markers based on differentially methylated regions (DMRs) within the identified genes, which lead to the separation of the samples, were designed. Both, tissue-specific markers (n=2) and location-specific markers (n=2) were tested with samples from the same two tissues (hepatopancreas and abdominal muscle) and the same four locations (Reilingen, Singlis, Andragnaroa and Ihosy), but from new samples, collected one to two years after the first sampling. The samples were analysed on a PCR based deep sequencing of amplicons. The results confirmed the finding from the capture based subgenome sequencing. With the chosen markers, a separation between the tissues as well as for locations, based on mean methylation ratios per CpG was possible. The mean CpG ratios for the sequenced amplicons were additionally comparable to the mean CpG ratios of the bead-based capture results. Notably, this also confirms that location-specific methylation is stable over time among marbled crayfish populations, resulting in the possibility to define location specific markers to identify the origin of a population and use methylation patterns as a fingerprint for those. These results are shown in FIGS. 2 and 3.

Materials and Methods

[0129] Sampling for bead-based capture assay was carried out in August 2017 for Reilingen, Oktober 2017 for Singlis and as mentioned in Adriantsoa et al., 2019, from October 2017 to March 2018 in Madagascar. Sampling for validation experiment was carried out from March to May 2019 in Germany and Madagascar. Samples were preserved in 100% ethanol and stored in -80° C. until DNA was extracted.

[0130] Genomic DNA was isolated and purified from abdominal muscular and hepatopancreas tissue using a Tissue Ruptor (Qiagen), followed by proteinase K digestion and isopropanol precipitation. The quality of isolated genomic DNA was assessed on a 2200 TapeStation (Agilent).

[0131] Library preparation was carried out as described in the SureSelectXT Methyl-Seq Target Enrichment System for Illumina Multiplexed Sequencing Protocol, Version D0, July 2015. Quality controls were performed, and sample concentrations were measured on a 2200 TapeStation (Agilent). Multiplexed samples were sequenced on a HiSeqX ten system (Illumina).

[0132] Read pairs were quality trimmed and mapped to the 697 genes that showed variable methylation in the whole-genome bisulfite sequencing datasets (Gatzmann et al., 2018) using BSMAP (Xi and Li, 2009). Subsequently, the methylation ratio for each CpG site was calculated using the Python provided with BSMAP. Only those CpG sites that were present in all the samples with a coverage of ≥5x were considered for further analysis. The average methylation level for each gene was calculated only if a gene had at least 5 CpG sites with ≥5x coverage. Furthermore, the genes with following criteria were excluded from subsequent analysis: i) genes that were in the bottom 10% in terms of methylation variance ii) genes with an average methylation level of < 0.1 or > 0.9, and ii) genes with more than 50% Ns in their sequence.

[0133] In order to identify tissue-specific methylation differences, a Wilcoxon rank sum test was applied (hepatopancreas vs. abdominal muscle samples from Singlis and Reilingen) and the p-values were corrected for multiple testing using the Benjamini-Hochberg method. Likewise, to identify location-specific methylation differences, a Kuskal-Wallis test was used, and the p-values were corrected for multiple testing using the Benjamini-Hochberg method. Additionally, dmrseq (Korthauer et al., 2018) was used to identify tissue-specific and location-specific differentially methylated regions within the respective genesets.

[0134] Genomic DNA was bisulfite converted by using the EZ DNA Methylation-Gold Kit (Zymo Research) following the manufacturer’s instructions. Target regions were PCR amplified using region-specific primers (Tab. 3). PCR products were gel-purified using the QIAquick Gel Extraction Kit (Qiagen). Subsequently, samples were indexed using the Nextera XT index Kit v2 Set A (Illumina). The pooled library was sequenced on a MiSeqV2 system using a paired-end 150 bp nano protocol. Sequencing data was analyzed using BisAMP (BisAMP: A web-based pipeline for targeted RNA cytosine-5 methylation analysis, Bormann F, Tuorto F, Cirzi C, Lyko F, Legrand C.Methods. 2019 Mar 1;156:121-127.)

TABLE-US-00003 Primers for Validation Primer Sequence Loc88_R1_fwd 5′-TTATAATATATTAATGGTTTTGATGA-3′ SEQ. ID. NO.:1 Loc88_R1_rev 5′-CACAAAAAACAAAAACTACAAACTC-3′ SEQ. ID. NO.:2 Loc88_R2_fwd 5′-ATTATATTTATATTGGATGGATTTAATTTA-3′ SEQ. ID. NO.:3 Loc88_R2_rev 5′-AAACAAACATCTTATACAATTCTTCTC-3′ SEQ. ID. NO.:4 Loc_460_fwd 5′-GGGTAGATAGAATTATTTTTTTT-3′ SEQ. ID. NO.:5 Loc_460_rev 5′-TTTCCTAAAAACCACATTAAAACAC-3′ SEQ. ID. NO.:6 Tis_595_fwd 5′-TGGAGATAAGTTAGTTTAATTAGGTTATAT-3′ SEQ. ID. NO.:7 Tis_595_rev 5′-AATCATCTTAAAAATTCAAAAAAAA-3′ SEQ. ID. NO.:8 Tis_173_fwd 5′-GAATTATTTTATTTGTGATATTTTTTTAAT-3′ SEQ. ID. NO.:9 Tis_173_rev 5′-ATTAATCCACATAATATTTCACCAC-3′ SEQ. ID. NO.:10

Example 5

Identification of Differentially Methylated CpG Sites in Chicken

[0135] In order to identify differentially methylated CpG sites in the chicken, the function “calculate DiffMeth” from the R package MethylKit was used on the Reduced representation bisulfite sequencing (RRBS) data. 1274 differentially methylated CpGs were identified (p-value < 0.05). Prior to this analysis, the data was filtered for SNPs and a coverage cutoff of minimum 10 per CpG site was applied. The identified differentially methylated CpG sites allowed a robust separation of the three locations in a principle component analysis as shown in FIG. 4.

Material and Methods

[0136] Isolated and purified genomic DNA from breast muscular tissue was provided by different service laboratories in the respective country of sample source. Quality was checked using a 2200 TapeStation (Agilent).

[0137] RRBS library preparation was carried out as described in the Zymo-Seq RRBS™ Library Kit Instruction Manual Ver. 1.0.0. Quality controls were performed, and sample concentrations were measured on a 2200 TapeStation (Agilent). Multiplexed samples were sequenced on a HiSeq 4000 system (Illumina).

[0138] Reads were quality trimmed using trimmomatic version 0.38 and mapped with BSMAP 2.90 to the Gallus gallus genome assembly version 5.0. Methylation ratios were calculated using a python script (methratio.py) distributed with the BSMAP package. All the CpG sites that were associated with sex chromosomes and the CpG sites that overlapped with SNPs for the Gallus gallus genome were filtered out from the further analysis. Differential methylation analysis was performed using the R package MethylKit (Akalin et al. (2012), Genome Biology, 13(10), R87).

Example 6

Identification of Differentially Methylated CpG Sites in Coho Salmon

[0139] In order to identify differentially methylated regions in the coho salmon’s RRBS data, the function “calculate DiffMeth” from the R package MethylKit was used. 440 differentially methylated regions were identified (p-value < 0.05, difference in methylation >= 10%). Prior to this analysis, the data was filtered for SNPs and a coverage cutoff of minimum 10 per CpG site was applied. The identified differentially methylated regions allowed a robust separation of the two locations in a principle component analysis as shown in FIG. 5.

Material and Methods

[0140] RRBS data that was published by Le Luyer et al., 2017 was downloaded from the National Center for Biotechnology Information Sequence Read Archive. Reads were mapped with BSMAP 2.90 to Okis_V2 (GCF_002021735.2) and methylation ratios were determined using a python script (methratio.py) distributed with the BSMAP package. All the CpG sites that overlapped with SNPs were filtered out from the further analysis. Differential methylation analysis, with the breeding environment and sex as covariates, was performed using the R package MethylKit (Akalin et al. (2012), Genome Biology, 13(10), R87).