scholarly journals MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

2022 ◽  
Author(s):  
Patrick Hüther ◽  
Jörg Hagmann ◽  
Adam Nunn ◽  
Ioanna Kakoulidou ◽  
Rahul Pisupati ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Statistical Power ◽  
Large Scale ◽  
Developmental Stages ◽  
Signal To Noise Ratio ◽  
Statistical Tests ◽  
Whole Genome ◽  
Differentially Methylated Regions ◽  
Variable Nature ◽  
Nucleotide Resolution

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore.

scholarly journals Whole-genome fetal and maternal DNA methylation analysis using MeDIP-NGS for the identification of differentially methylated regions

2016 ◽  
Vol 98 ◽  
Author(s):  
ANNA KERAVNOU ◽  
MARIOS IOANNIDES ◽  
KYRIAKOS TSANGARAS ◽  
CHARALAMBOS LOIZIDES ◽  
MICHAEL D. HADJIDANIEL ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Chorionic Villus ◽  
Maternal Blood ◽  
Maternal Plasma ◽  
Chorionic Villus Sampling ◽  
Whole Genome ◽  
Differentially Methylated Regions ◽  
Epigenetic Marker ◽  
Novel Approach ◽  
Targeted Enrichment

SummaryDNA methylation is an epigenetic marker that has been shown to vary significantly across different tissues. Taking advantage of the methylation differences between placenta-derived cell-free DNA and maternal blood, several groups employed different approaches for the discovery of fetal-specific biomarkers. The aim of this study was to analyse whole-genome fetal and maternal methylomes in order to identify and confirm the presence of differentially methylated regions (DMRs). We have initially utilized methylated DNA immunoprecipitation (MeDIP) and next-generation sequencing (NGS) to identify genome-wide DMRs between chorionic villus sampling (CVS) and female non-pregnant plasma (PL) and peripheral blood (WBF) samples. Next, using specific criteria, 331 fetal-specific DMRs were selected and confirmed in eight CVS, eight WBF and eight PL samples by combining MeDIP and in-solution targeted enrichment followed by NGS. Results showed higher enrichment in CVS samples as compared to both WBF and PL samples, confirming the distinct methylation levels between fetal and maternal DNA for the selected DMRs. We have successfully implemented a novel approach for the discovery and confirmation of a significant number of fetal-specific DMRs by combining for the first time MeDIP and in-solution targeted enrichment followed by NGS. The implementation of this double-enrichment approach is highly efficient and enables the detailed analysis of multiple DMRs by targeted NGS. Also, this is, to our knowledge, the first reported application of MeDIP on plasma samples, which leverages the implementation of our enrichment methodology in the detection of fetal abnormalities in maternal plasma.

1 Whole-genome bisulfite sequencing of bovine gametes and invivo-produced pre-implantation embryos

2019 ◽  
Vol 31 (1) ◽  
pp. 126
Author(s):  
J. E. Duan ◽  
Z. Jiang ◽  
F. Alqahtani ◽  
I. Mandoiu ◽  
H. Dong ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Epigenetic Regulation ◽  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Differentially Methylated Regions ◽  
Whole Genome Bisulfite Sequencing ◽  
Cpg Sites ◽  
Genome Wide ◽  
Genome Bisulfite Sequencing

Dynamic changes in DNA methylation are crucial in the epigenetic regulation of mammalian embryogenesis. Global DNA methylation studies in the bovine, however, remain mostly at the immunostaining level. We adopted the single-cell whole-genome bisulfite sequencing method to characterise stage-specific genome-wide DNA methylation in bovine sperm, individual oocytes derived invivo and invitro, and invivo-developed embryos at the 2-, 4-, 8-, and 16-cell stages. This method allowed us to theoretically cover all CpG sites in the genome using a limited number of cells from single embryos. Pools of 20 sperm were selected from a bull with proven fertility. Single oocytes (n=6) and embryos (n=4 per stage) were collected from Holstein cows (n=10). Single-cell whole-genome bisulfite sequencing libraries were prepared and sequenced using the Illumina HiSEqn 4000 platform (Illumina, San Diego, CA, USA). Sequencing reads were filtered and aligned to the bovine reference genome (UMD 3.1.1) using Bismark (Krueger and Andrews 2011Bioinformatics27, 1571-1572, DOI: 10.1093/bioinformatics/btr167).A 300-bp tile-based method was applied to bin the genome into consecutive windows to facilitate comparison across samples. The DNA methylation level was calculated as the fraction of read counts of the total number of cytosines (methylated) in the total read counts of reported cytosines and thymines (methylated and unmethylated), only if more than 3 CpG sites were covered in this tile. Gamete-specific differentially methylated regions were identified when DNA methylation levels were greater than 75% in one type of gamete and less than 25% in the other with false discovery rate-corrected Fisher’s exact test P-values of less than 0.05. The major wave of genome-wide DNA demethylation was complete at the 8-cell stage when de novo methylation became prominent. Sperm and oocytes had numerous differentially methylated regions that were enriched in intergenic regions. Differentially methylated regions were also identified between invivo- and invitro-matured oocytes. Moreover, X chromosome methylation followed the global dynamic patterns. Virtually no (less than 1.5%) DNA methylation was found in mitochondrial DNA. Finally, using our RNA sequencing data generated from the same developmental stages (Jiang et al. 2014 BMC Genomics 15, 756; DOI: 10.1186/1471-2164-15-756), we revealed an inverse correlation between gene expression and promoter methylation. Our study provides the first fully comprehensive analysis of the global dynamics of DNA methylation in bovine gametes and single early embryos using single-cell whole-genome bisulfite sequencing. These data provide insights into the critical features of the methylome of bovine embryos and serve as an important reference for embryos produced by assisted reproduction, such as IVF and cloning, and a model for human early embryo epigenetic regulation.

scholarly journals Prenatal smoke effect on mouse offspring Igf1 promoter methylation from fetal stage to adulthood is organ and sex specific

2020 ◽  
Vol 318 (3) ◽  
pp. L549-L561
Author(s):  
Zhijun Zeng ◽  
Karolin F. Meyer ◽  
Khosbayar Lkhagvadorj ◽  
Wierd Kooistra ◽  
Marjan Reinders-Luinge ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Promoter Methylation ◽  
Large Scale ◽  
Developmental Stages ◽  
Association Studies ◽  
Inverse Correlation ◽  
Cpg Sites ◽  
Increased Risk ◽  
Organ Specific ◽  
Health And Disease

Prenatal smoke exposure (PSE) is associated with reduced birth weight, impaired fetal development, and increased risk for diseases later in life. Changes in DNA methylation may be involved, as multiple large-scale epigenome-wide association studies showed that PSE is robustly associated with DNA methylation changes in blood among offspring in early life. Insulin-like growth factor-1 (IGF1) is important in growth, differentiation, and repair processes after injury. However, no studies investigated the organ-specific persistence of PSE-induced methylation change of Igf1 into adulthood. Based on our previous studies on the PSE effect on Igf1 promoter methylation in fetal and neonatal mouse offspring, we now have extended our studies to adulthood. Our data show that basal Igf1 promoter methylation generally increased in the lung but decreased in the liver (except for 2 persistent CpG sites in both organs) across three different developmental stages. PSE changed Igf1 promoter methylation in all three developmental stages, which was organ and sex specific. The PSE effect was less pronounced in adult offspring compared with the fetal and neonatal stages. In addition, the PSE effect in the adult stage was more pronounced in the lung compared with the liver. For most CpG sites, an inverse correlation was found for promoter methylation and mRNA expression when the data of all three stages were combined. This was more prominent in the liver. Our findings provide additional evidence for sex- and organ-dependent prenatal programming, which supports the developmental origins of health and disease (DOHaD) hypothesis.

88 VARIABLE DNA METHYLATION PROFILES AT IMPRINTED LOCI IN BOVINE EARLY PRE-IMPLANTATION EMBRYOS

2013 ◽  
Vol 25 (1) ◽  
pp. 192
Author(s):  
A. M. O'Doherty ◽  
D. Magee ◽  
M. E. Beltman ◽  
S. Mamo ◽  
D. Rizos ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Developmental Stages ◽  
Transcript Abundance ◽  
Bovine Embryo ◽  
Differentially Methylated Regions ◽  
Cell Stage ◽  
Imprinted Gene ◽  
Genomic Imprints ◽  
Time Point

The DNA methylation imprints, at maternally imprinted gene differentially methylated regions, are established during the postnatal growth stage of oogenesis, with paternal imprints being acquired in the perinatal prospermatagonia. Murine DNA methylation marks, at imprinted loci, are widely regarded to be resistant to post-fertilization demethylation events that occur in the paternal pronucleus of the zygote and to passive demethylation of the maternally derived genomic content from cleavage to the 16-cell stage. However, the DNA methylation profile of bovine imprinted genes following fertilization remains unknown. The objective of the current study was to analyze the methylation dynamics at several imprinted gene differentially methylated regions during bovine embryo development. In addition, a previously published RNA-seq database (Mamo et al. 2011 Biol. Reprod.) was mined for transcript abundance of genes associated with establishing and maintaining genomic imprints. Single in vivo blastocysts (Day 7), hatched ovoid embryos (Day 14), filamentous embryos (Day 17), and implanting conceptii (Day 25) were collected (n = 4–9, per time point) from beef heifers. Genomic DNA was isolated and bisulfite modified, using the EZ DNA methylation direct kit (Zymo, Irvine, CA, USA), and used as template in bisulfite PCR reactions. The PCR products were verified by agarose gel electrophoresis and subsequently pyrosequenced. Observed methylation values were most highly variable in Day 7 blastocysts, with values ranging between 13 and 44% (IGF2R), 5 and 63% (PEG10), 7 and 59% (MEST), 3 and 61% (SNRPN), 12 and 64% (PLAGL1), and 20 and 32% (H19). There was a marked reduction in variability as embryonic development progressed, with values at Day 25 ranging from 37 to 41% (IGF2R), 34 to 38% (PEG10), 31 to 37% (MEST), 36 to 40% (SNRPN), 17 to 26% (PLAGL1), and 25 to 30% (H19). Statistical analysis (Levene’s test for equal variance) of methylation values for each gene at each time point confirmed that the methylation values observed in Day 7 embryos were significantly variable (P < 0.05) when compared with later developmental stages. Concordant with this finding, RNA transcript levels of associated methylation machinery genes DNMT3A, DNMT3B, and TRIM28 progressively increased from Day 7 to 13 and subsequently decreased from Day 13 to 16. Taken together our results demonstrate that in cattle DNA methylation marks, at imprinted loci, are highly variable at the blastocyst stage and are progressively stabilized with increasing days post-fertilization. This stabilization of imprint is coordinated with a window of increased levels of associated methylation machinery transcripts. Work presented here provides evidence of a novel mechanism for bovine embryonic DNA methylation imprint maintenance. This work was funded by SFI grant number 07/SRC/B1156.

scholarly journals Recent progress towards understanding the role of DNA methylation in human placental development

Reproduction ◽  
2016 ◽  
Vol 152 (1) ◽  
pp. R23-R30 ◽  
Author(s):  
Tina Bianco-Miotto ◽  
Benjamin T Mayne ◽  
Sam Buckberry ◽  
James Breen ◽  
Carlos M Rodriguez Lopez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Pregnancy Complications ◽  
Large Scale ◽  
Developmental Stages ◽  
Epigenetic Modifications ◽  
Placental Development ◽  
Functional Changes ◽  
Genome Wide

Epigenetic modifications, and particularly DNA methylation, have been studied in many tissues, both healthy and diseased, and across numerous developmental stages. The placenta is the only organ that has a transient life of 9 months and undergoes rapid growth and dynamic structural and functional changes across gestation. Additionally, the placenta is unique because although developing within the mother, its genome is identical to that of the foetus. Given these distinctive characteristics, it is not surprising that the epigenetic landscape affecting placental gene expression may be different to that in other healthy tissues. However, the role of epigenetic modifications, and particularly DNA methylation, in placental development remains largely unknown. Of particular interest is the fact that the placenta is the most hypomethylated human tissue and is characterized by the presence of large partially methylated domains (PMDs) containing silenced genes. Moreover, how and why the placenta is hypomethylated and what role DNA methylation plays in regulating placental gene expression across gestation are poorly understood. We review genome-wide DNA methylation studies in the human placenta and highlight that the different cell types that make up the placenta have very different DNA methylation profiles. Summarizing studies on DNA methylation in the placenta and its relationship with pregnancy complications are difficult due to the limited number of studies available for comparison. To understand the key steps in placental development and hence what may be perturbed in pregnancy complications requires large-scale genome-wide DNA methylation studies coupled with transcriptome analyses.

scholarly journals Whole Genome Bisulfite Sequencing of Purified Mouse Promyelocytes Reveals Differentially Methylated Regions in Cells Expressing PML-Rara

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3531-3531
Author(s):  
Shamika Ketkar-Kulkarni ◽  
Christopher B Cole ◽  
David H. Spencer ◽  
Angela M. Verdoni ◽  
Nichole Havey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Latent Period ◽  
Bisulfite Sequencing ◽  
De Novo ◽  
Chromosome 8 ◽  
Whole Genome ◽  
Chromosome 4 ◽  
Differentially Methylated Regions ◽  
Genome Bisulfite Sequencing

Abstract Acute promyelocytic leukemia (APL) is an AML subtype that is characterized by aberrant expansion of immature myeloid progenitors and precursors that are arrested at the promyelocyte stage. Almost all APL cases are characterized by the t(15;17)(q22;q11.2) translocation that creates the PML-RARA fusion oncogene. Human APL cells are known to have a canonical expression signature and a specific methylation phenotype that is unique to this form of AML. Our laboratory previously created a mouse model of APL by expressing a human PML-RARA cDNA from the mouse Cathepsin G (Ctsg) locus (Ctsg-PML-RARA), which activates human PML- RARA expression in early myeloid progenitor cells, with peak expression in promyelocytes. After a long latent period (6-12 months), ~60% of these mice develop a clonal, APL-like myeloid malignancy. The long latent period is probably due to the requirement for cooperating mutations that synergize with PML-RARA to accelerate the disease. Human APL samples have a unique gene expression signature that distinguishes them from all other subtypes of AML. We evaluated RNA-Seq data derived from Poly A+ enriched cDNAs obtained from purified promyelocytes derived from 3 young (6 week old) WT and 3 Ctsg-PML-RARA mice. We identified 779 annotated genes that are significantly dysregulated in murine promyelocytes expressing PML-RARA with a log2 fold change >= 2 and P<0.05. Some of these genes included Spib/Pu.1, Pou2af1, Jak2, Runx1, and many others. We also identified a set of 24,018 RNAs in promyelocytes that were defined as novel transcripts. This set contains 7,413 lncRNAs with an FPKM value of >= 2. Differential expression analysis yielded 56 dysregulated lncRNA regions in PML-RARA expressing promyelocytes. To explore the association between gene dysregulation and DNA methylation in promyelocytes, we carried out whole-genome bisulfite sequencing using DNA derived from the purified promyelocytes of a 6 week old Ctsg-PML-RARA mouse, and a WT littermate. We generated a total of approximately 800 million sequencing reads, of which 78% mapped uniquely to the reference genome (mm9); we were able to map ~19 million CpGs with at least 10x coverage. Differential methylation analysis performed on ~4.5 million 1 Kb windows spanning the entire genome identified 17,633 differentially methylated regions with a mean difference of >= 25% and a q-value of < 0.01, the vast majority of which (17,264, 98%) were hypomethylated in the Ctsg-PML-RARA promyelocytes. These windows overlap several known genes, including Runx1, Jak2, Dnmt3a, Gata2, and the Hoxa and Hoxb gene clusters. Using more strict criteria (> 50% mean methylation difference), we identified 87 differentially methylated regions of at least 2 Kb in size. Of these 87 distinct regions, 74 (85%) were hypomethylated in PML-RARA promyelocytes, and 13 were hypermethylated; examples of both as shown in Figure 1. These data strongly suggest that PML-RARA has at least two distinct mechanisms by which it can modify DNA methylation. In regions where CpGs are hypomethylated, PML-RARA may be blocking the normal methylation of CpGs by the de novo DNA methyltransferases Dnmt3a and/or Dnmt3b. In contrast, PML-RARA may be directing de novo methyltransferases to act on the hypermethylated regions. Regardless, these data, when coupled with comprehensive chromatin accessibility mapping and complete RNA sequencing data, should provide new insights into the mechanisms used by PML-RARA to alter gene expression and initiate APL. Figure1. Examples of differentially methylated regions. Black=WT cells. Red=PML-RARA expressing cells. Each CpG in the region is represented as a dot. Scale is 0-100% methylated at each position. Top panel: a region on chromosome 8 that is hypomethylated in PML-RARA expressing promyelocytes. Bottom panel: a region on chromosome 4 that is hypermethylated in PML-RARA expressing promyelocytes. Figure1. Examples of differentially methylated regions. Black=WT cells. Red=PML-RARA expressing cells. Each CpG in the region is represented as a dot. Scale is 0-100% methylated at each position. Top panel: a region on chromosome 8 that is hypomethylated in PML-RARA expressing promyelocytes. Bottom panel: a region on chromosome 4 that is hypermethylated in PML-RARA expressing promyelocytes. Disclosures No relevant conflicts of interest to declare.

scholarly journals Gene Content Evolution in the Arthropods

2018 ◽  
Author(s):  
Gregg W.C. Thomas ◽  
Elias Dohmen ◽  
Daniel S.T. Hughes ◽  
Shwetha C. Murali ◽  
Monica Poelchau ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Large Scale ◽  
Gene Families ◽  
Protein Domain ◽  
Whole Genome ◽  
Genome Sequences ◽  
Arthropod Evolution ◽  
Animal Diversity ◽  
Physiological Adaptations ◽  
Protein Domain Evolution

AbstractBackgroundArthropods comprise the largest and most diverse phylum on Earth and play vital roles in nearly every ecosystem. Their diversity stems in part from variations on a conserved body plan, resulting from and recorded in adaptive changes in the genome. Dissection of the genomic record of sequence change enables broad questions regarding genome evolution to be addressed, even across hyper-diverse taxa within arthropods.ResultsUsing 76 whole genome sequences representing 21 orders spanning more than 500 million years of arthropod evolution, we document changes in gene and protein domain content and provide temporal and phylogenetic context for interpreting these innovations. We identify many novel gene families that arose early in the evolution of arthropods and during the diversification of insects into modern orders. We reveal unexpected variation in patterns of DNA methylation across arthropods and examples of gene family and protein domain evolution coincident with the appearance of notable phenotypic and physiological adaptations such as flight, metamorphosis, sociality and chemoperception.ConclusionsThese analyses demonstrate how large-scale comparative genomics can provide broad new insights into the genotype to phenotype map and generate testable hypotheses about the evolution of animal diversity.

scholarly journals Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent

2019 ◽  
Vol 112 (3) ◽  
pp. 295-304 ◽  
Author(s):  
Yaohua Yang ◽  
Lang Wu ◽  
Xiao-Ou Shu ◽  
Qiuyin Cai ◽  
Xiang Shu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Statistical Power ◽  
Prediction Models ◽  
Statistical Tests ◽  
Critical Role ◽  
White Blood Cells ◽  
Cpg Sites

Abstract Background DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk. Methods Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women’s Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided. Results Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10–7, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes. Conclusion Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

Epigenetics of Hematopoiesis, Stem Cell Reprogramming, and Cancer

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-47-SCI-47
Author(s):  
Andrew P. Feinberg
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Conflicts Of Interest ◽  
Cpg Island ◽  
Nuclear Lamina ◽  
Whole Genome ◽  
Differentially Methylated Regions ◽  
Cancer Epigenetics ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing

Abstract Since the discovery of altered DNA methylation in cancer in 1982, most studies of cancer epigenetics have focused on epimutations which could serve as surrogates of mutation. In the past decade, we and our collaborators have been leading efforts to develop whole-genome approaches to epigenetic analysis of human disease, that include novel approaches to array-based analysis and whole-genome bisulfite sequencing. This has led to the first whole-genome bisulfite sequencing methylation map of the cancer genome. Surprising results have been the discovery of CpG island shores and large hypomethylated blocks corresponding to nuclear lamina/heterochromatin regions, and accounting for the vast majority of epigenetic alterations in cancer. These results point to the possibility that at least solid tumors represent epigenetically a single process with a common molecular characteristic; namely increased epigenetic plasticity that allows selection of the tumor cells at the expense of the host. This view can explain most of the hallmarks of cancer, as well as offering a strategy for powerful new approaches to risk detection and treatment. This whole-genome epigenetic approach has also revealed that the classical lineage structure of hematopoiesis is very well reflected in DNA methylation, but that there are very surprising dynamic changes in this process, with waves of both increasing and decreasing methylation changes. This work has also revealed a molecular basis for phenotypic memory in induced pluripotent stem cells, as well as a strong connection between reprogramming differentially methylated regions, or R-DMRs, and tissue-specific differentially methylated regions, or T-DMRs. Disclosures: No relevant conflicts of interest to declare.

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