scholarly journals Analysis of Genome DNA Methylation at Inherited Coat Color Dilutions of Rex Rabbits

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Chen ◽  
Shuaishuai Hu ◽  
Ming Liu ◽  
Bohao Zhao ◽  
Naisu Yang ◽  
...  

Background: The dilution of color in rabbits is associated with many different genetic mechanisms that form different color groups. A number of previous studies have revealed potential regulatory mechanisms by which epigenetics regulate pigmentation. However, the genome-wide DNA methylation involved in animal coat color dilution remains unknown.Results: We compared genome-wide DNA methylation profiles in Rex rabbit hair follicles in a Chinchilla group (Ch) and a diluted Chinchilla group (DCh) through whole-genome bisulfite sequencing (WGBS). Approximately 3.5% of the cytosine sites were methylated in both groups, of which the CG methylation type was in greatest abundance. In total, we identified 126,405 differentially methylated regions (DMRs) between the two groups, corresponding to 11,459 DMR-associated genes (DMGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these DMGs were principally involved in developmental pigmentation and Wnt signaling pathways. In addition, two DMRs were randomly selected to verify that the WGBS data were reliable using bisulfite sequencing PCR, and seven DMGs were analyzed to establish the relationship between the level of DNA methylation and mRNA expression using qRT-PCR. Due to the limitation of small sample size, replication of the results with a larger sample size would be important in future studies.Conclusion: These findings provide evidence that there is an association between inherited color dilution and DNA methylation alterations in hair follicles, greatly contributing to our understanding of the epigenetic regulation of rabbit pigmentation.

2020 ◽  
Author(s):  
Yang Chen ◽  
Shuaishuai Hu ◽  
Ming Liu ◽  
Bohao Zhao ◽  
Naisu Yang ◽  
...  

Abstract Background Dilution of color in rabbits is associated with many different genetic mechanisms that form different color groups. A number of previous studies have revealed potential regulatory mechanisms by which epigenetics regulate pigmentation. However, the genome-wide DNA methylation involved in animal coat-color dilution remains unknown.Results We compared genome-wide DNA methylation profiles in Rex rabbit hair follicles in a Chinchilla group (Ch) and a diluted Chinchilla group (DCh) through whole-genome bisulfite sequencing (WGBS). Approximately 3.5% of the cytosine sites were methylated in both groups, of which the CG methylation type was in greatest abundance. In total, we identified 126,405 differentially methylated regions (DMRs) between the two groups, corresponding to 11,459 DMR-associated genes (DMGs). Gene ontogeny (GO) and KEGG pathway analysis revealed that these DMGs were principally involved in developmental pigmentation and Wnt signaling pathways. In addition, 2 DMRs were randomly selected to verify that the WGBS data were reliable using bisulfite treatment (BSP), and 7 DMGs were analyzed to establish the relationship between the level of DNA methylation and mRNA expression using qRT-PCR.Conclusion These findings provide evidence that there is an association between inherited color dilution and DNA methylation alterations in hair follicles, greatly contributing to our understanding of the epigenetic regulation of rabbit pigmentation.


2021 ◽  
Author(s):  
Xin Chen ◽  
Qingrun Zhang ◽  
Thierry Chekouo

Abstract Background: DNA methylations in critical regions are highly involved in cancer pathogenesis and drug response. However, to identify causal methylations out of a large number of potential polymorphic DNA methylation sites is challenging. This high-dimensional data brings two obstacles: first, many established statistical models are not scalable to so many features; second, multiple-test and overfitting become serious. To this end, a method to quickly filter candidate sites to narrow down targets for downstream analyses is urgently needed. Methods: BACkPAy is a pre-screening Bayesian approach to detect biological meaningful clusters of potential differential methylation levels with small sample size. BACkPAy prioritizes potentially important biomarkers by the Bayesian false discovery rate (FDR) approach. It filters non-informative sites (i.e. non-differential) with flat methylation pattern levels accross experimental conditions. In this work, we applied BACkPAy to a genome-wide methylation dataset with 3 tissue types and each type contains 3 gastric cancer samples. We also applied LIMMA (Linear Models for Microarray and RNA-Seq Data) to compare its results with what we achieved by BACkPAy. Then, Cox proportional hazards regression models were utilized to visualize prognostics significant markers with The Cancer Genome Atlas (TCGA) data for survival analysis. Results: Using BACkPAy, we identified 8 biological meaningful clusters/groups of differential probes from the DNA methylation dataset. Using TCGA data, we also identified five prognostic genes (i.e. predictive to the progression of gastric cancer) that contain some differential methylation probes, whereas no significant results was identified using the Benjamin-Hochberg FDR in LIMMA. Conclusions: We showed the importance of using BACkPAy for the analysis of DNA methylation data with extremely small sample size in gastric cancer. We revealed that RDH13, CLDN11, TMTC1, UCHL1 and FOXP2 can serve as predictive biomarkers for gastric cancer treatment and the promoter methylation level of these five genes in serum could have prognostic and diagnostic functions in gastric cancer patients.


Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 191
Author(s):  
Yuanyuan Chen ◽  
Fengjiao Xu ◽  
Cong Pian ◽  
Mingmin Xu ◽  
Lingpeng Kong ◽  
...  

In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, “small sample size problem”, diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer’s disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer’s disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 608-608
Author(s):  
Matthew J. Walter ◽  
Dong Shen ◽  
Jin Shao ◽  
Li Ding ◽  
Marcus Grillot ◽  
...  

Abstract Abstract 608 Myelodysplastic syndrome (MDS) genomes are characterized by global DNA hypomethylation with concomitant hypermethylation of gene promoter regions compared to CD34+ cells from normal bone marrow samples. Currently, the underlying mechanism of altered DNA methylation in MDS genomes and the critical target genes affected by methylation remain largely unknown. The methylation of CpG dinucleotides in humans is mediated by DNA methyltransferases, including DNMT1, DNMT3A, and DNMT3B. DNMT3A and DNMT3B are the dominant DNA methyltransferases involved in de novo DNA methylation and act independent of replication, whereas DNMT1 acts predominantly during replication to maintain hemimethylated DNA. The function of these proteins in cancer cells is less well defined. Our group recently found that DNMT3A mutations are common in de novo acute myeloid leukemia (62/281 cases, 22%) and are associated with poor survival (Ley, et al, unpublished), providing a rationale for examining the mutation status of DNMT3A in MDS patients. MDS cases (n=150) were classified according to the French-American-British (FAB) system. The patients included refractory anemia (RA; n=67), RA with ringed sideroblasts (RARS; n=5), RA with excess blasts (RAEB; n=72), and RA with excess blasts in transformation (RAEB-T; n=6). The median International Prognostic Scoring System (IPSS) score was 1 (range 0–3), and the median myeloblast count was 4 (range 0–28%). We designed and validated 28 primer pairs covering the coding sequences and splice sites of all 23 exons for DNMT3A. Paired DNA samples were obtained from the bone marrow (tumor) and skin (normal) of each patient so that somatic mutations could be distinguished from inherited variants/polymorphisms. 17,120 reads were produced by capillary sequencing, providing at least 1X coverage for 82.6% of the target sequence (low/no coverage was obtained for 2 out of 28 amplicons). A semiautomated analysis pipeline was used to identify sequence variants and we restricted our analysis to nonsynonymous and splice site nucleotide changes. All mutations were confirmed by independent PCR and sequencing. We identified nonsynonymous DNMT3A mutations in 12/150 bone marrow samples (8% of cases). All the mutations were heterozygous (10 missense, 1 nonsense, 1 frameshift) and were computationally predicted (by SIFT and/or PolyPhen2) to have deleterious functional consequences. DNMT3A mRNA is expressed in normal CD34+ bone marrow cells and was expressed in all MDS patient samples tested (n=28), independent of mutation status. There was no difference in the expression level of total DNMT3A mRNA in CD34+ cells harvested from mutant (n=3) vs. non-mutant MDS samples (n=25). Amino acid R882, located in the methyltransferase domain of DNMT3A, was the most common mutation site, accounting for 4/12 mutations. The clinical characteristics of the 12 patients with DNMT3A mutations were similar to those of the 138 patients without mutations. Specifically, DNMT3A mutations were present in all MDS FAB subtypes (excluding CMML which was not tested) and in patients with IPSS scores ranging from 0–3. Mutations were not associated with a specific karyotype. In addition, there was no correlation between mutation detection and the myeloblast count of the banked bone marrow specimen, suggesting that mutations were not missed due to the cellular heterogeneity in the samples. We compared the overall (OS) and event-free survival (EFS) of the 12 patients with DNMT3A mutations vs. 138 patients without a mutation and observed a significantly worse OS in patients with mutations (p=0.02), with a median survival of 433 and 945 days, respectively. There was a trend towards worse EFS for patients with mutations (p=0.05). A multivariate analysis for outcomes could not be performed due to the small sample size of patients with mutations, indicating that a larger cohort from a clinical trial will be needed to properly address the affect of DNMT3A mutations on outcomes. The small sample size also precluded us from addressing whether the response to the hypomethylating agents 5-azacytidine or decitabine correlated with the mutation status of DNMT3A. If validated in larger cohort studies, we propose that DNMT3A mutation status could help risk stratify de novo MDS patients for more aggressive treatment early in their disease course. Disclosures: Westervelt: Novartis: Honoraria; Celgene: Honoraria, Speakers Bureau. DiPersio:Genzyme: Honoraria.


2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Chen ◽  
Qingrun Zhang ◽  
Thierry Chekouo

DNA methylations in critical regions are highly involved in cancer pathogenesis and drug response. However, to identify causal methylations out of a large number of potential polymorphic DNA methylation sites is challenging. This high-dimensional data brings two obstacles: first, many established statistical models are not scalable to so many features; second, multiple-test and overfitting become serious. To this end, a method to quickly filter candidate sites to narrow down targets for downstream analyses is urgently needed. BACkPAy is a pre-screening Bayesian approach to detect biological meaningful patterns of potential differential methylation levels with small sample size. BACkPAy prioritizes potentially important biomarkers by the Bayesian false discovery rate (FDR) approach. It filters non-informative sites (i.e., non-differential) with flat methylation pattern levels across experimental conditions. In this work, we applied BACkPAy to a genome-wide methylation dataset with three tissue types and each type contains three gastric cancer samples. We also applied LIMMA (Linear Models for Microarray and RNA-Seq Data) to compare its results with what we achieved by BACkPAy. Then, Cox proportional hazards regression models were utilized to visualize prognostics significant markers with The Cancer Genome Atlas (TCGA) data for survival analysis. Using BACkPAy, we identified eight biological meaningful patterns/groups of differential probes from the DNA methylation dataset. Using TCGA data, we also identified five prognostic genes (i.e., predictive to the progression of gastric cancer) that contain some differential methylation probes, whereas no significant results was identified using the Benjamin-Hochberg FDR in LIMMA. We showed the importance of using BACkPAy for the analysis of DNA methylation data with extremely small sample size in gastric cancer. We revealed that RDH13, CLDN11, TMTC1, UCHL1, and FOXP2 can serve as predictive biomarkers for gastric cancer treatment and the promoter methylation level of these five genes in serum could have prognostic and diagnostic functions in gastric cancer patients.


2020 ◽  
Vol 13 ◽  
pp. 251686572093214
Author(s):  
Valeska Stonawski ◽  
Jakob Roetner ◽  
Tamme W Goecke ◽  
Peter A Fasching ◽  
Matthias W Beckmann ◽  
...  

Background: Maternal depressive symptoms are a common phenomenon during pregnancy and are related to negative outcomes for child development and health. Modifications in child DNA methylation are discussed as an underlying mechanism for the association between prenatal depressive symptoms and alterations in child outcomes. However, formerly reported genome-wide associations have yet to be replicated. Methods: In an epigenome-wide association study (EWAS), alterations of DNA methylation related to maternal prenatal depressive symptoms were investigated in buccal cell samples from 174 children (n = 52 exposed to prenatal depressive symptoms; 6-9 years old) of the German longitudinal study FRAMES-FRANCES. Whole blood samples from the independent, age-comparable ARIES subsample of the ARIES/ALSPAC study (n = 641; n = 159 exposed to prenatal depressive symptoms; 7-8 years old) were examined as a confirmation sample. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. DNA methylation was analyzed with the Infinium Human Methylation 450k BeadChip. Modifications in single CpGs, regions, and biological pathways were investigated. Results were adjusted for age and birth outcomes as well as postnatal and current maternal depressive symptoms. Analyses were performed for the whole sample as well as separated for sex. Results: The EWAS yielded no differentially methylated CpG or region as well as no accordance between samples withstanding correction for multiple testing. In pathway analyses, no overlapping functional domain was found to be enriched for either sample. A comparison of current and former findings suggests some overlapping methylation modifications from infancy to childhood. Results suggest that there might be sex-specific differential methylation, which should be further investigated in additional studies. Conclusions: The current, mainly nonsignificant, results challenge the assumption of consistent modifications of DNA methylation in children exposed to prenatal depressive symptoms. Despite the relatively small sample size used in this study, this lack of significant results may reflect diverse issues of environmental epigenetic studies, which need to be addressed in future research.


2021 ◽  
Author(s):  
Aurelia Wilberforce ◽  
Giulio Valentino Dalla Riva

Myalgic Encephalomyelitis, also known as Chronic Fatigue Syndrome (ME/CFS), is a debilitating illness characterised by severe fatigue and associated with immune dysfunction. Previous studies of DNA methylation (epigenetic changes that can affect the gene transcription) have found evidence of changes in immune cells for ME/CFS. However these studies have been limited by their small sample size, precluding the ability to detect changes to methylation of smaller magnitude. Therefore, to achieve a larger sample size and detect small changes to DNA methylation, we aggregate three comparable datasets and analyse them in unison. We find 10,824 differentially methylated genes, with a very small average change. We then turn our attention to the network structure of the Protein-Protein interaction, which we built from the currently known interactions of relevant proteins, and localising the network cartography framework, we identify 184 hub genes. A distinct structuring emerges, with different hub types playing differing, meaningful, biological roles. Supporting previous theories about ME/CFS, Gene ontology enrichment analysis of these hubs reveal that they are involved in immune system processes, including response to TGF-β and LPS, as well as mitochondrial functioning. We also show that dopaminergic signalling may potentially contribute to immune pathology in ME/CFS. Our results demonstrate the potentiality of network cartographic approaches in shedding light on the epigenetic contribution to the immune dysregulation of ME/CFS.


2013 ◽  
Vol 16 (4) ◽  
pp. 767-781 ◽  
Author(s):  
Miguel E. Rentería ◽  
Marcel W. Coolen ◽  
Aaron L. Statham ◽  
R. Seong Min Choi ◽  
Wenjia Qu ◽  
...  

Imprinting control regions (ICRs) play a fundamental role in establishing and maintaining the non-random monoallelic expression of certain genes, via common regulatory elements such as non-coding RNAs and differentially methylated regions (DMRs) of DNA. We recently surveyed DNA methylation levels within four ICRs (H19-ICR, IGF2-DMR, KvDMR, and NESPAS-ICR) in whole-blood genomic DNA from 128 monozygotic (MZ) and 128 dizygotic (DZ) human twin pairs. Our analyses revealed high individual variation and intra-domain covariation in methylation levels across CpGs and emphasized the interaction between epigenetic variation and the underlying genetic sequence in a parent-of-origin fashion. Here, we extend our analysis to conduct two genome-wide screenings of single nucleotide polymorphisms (SNPs) underlying either intra-domain covariation or parent-of-origin-dependent association with methylation status at individual CpG sites located within ICRs. Although genome-wide significance was not surpassed due to sample size limitations, the most significantly associated SNPs found through multiple-trait genome-wide association (MQFAM) included the previously described rs10732516, which is located in the vicinity of the H19-ICR. Similarly, we identified an association between rs965808 and methylation status within the NESPAS-ICR. This SNP is positioned within an intronic region of the overlapping genes GNAS and GNAS-AS1, which are imprinted genes regulated by the NESPAS-ICR. Sixteen other SNPs located in regions apart from the analyzed regions displayed suggestive association with intra-domain methylation. Additionally, we identified 13 SNPs displaying parent-of-origin association with individual methylation sites through family-based association testing. In this exploratory study, we show the value and feasibility of using alternative GWAS approaches in the study of the interaction between epigenetic state and genetic sequence within imprinting regulatory domains. Despite the relatively small sample size, we identified a number of SNPs displaying suggestive association either in a domain-wide or in a parent-of-origin fashion. Nevertheless, these associations will require future experimental validation or replication in larger and independent samples.


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