DNA methylome and lncRNAome Analysis Provide Insights Into Mechanism of Genome-dosage Effects in Autotetraploid Cassava

BackgroundDuring newly formed polyploidy, one of the most intriguing aspects is that whole-genome duplication (WGD) increase the dosage of all coding and non-coding genes. However, the molecular implications of genome-dosage effects remain elusive.ResultsWe conducted integrated maps of methylomes and lncRNAomes in autotetraploid cassava (Manihot esculenta Crantz) and its donor parent, both of which were independently clonal propagated for three years. DNA methylation variation of transposable elements (TEs) was observed as widespread in autotetraploid cassava. The hypermethylation of DNA transposons in mCG and mCHH sites may be an effective way to suppress the expression of nearby PCGs in autotetraploid cassava, resulting in similar expression levels for most of PCGs between autotetraploid and diploid cassava. The decreased methylation levels of retrotransposons in mCHG and mCHH sites, which partly attributed to reduction methylation of Cypsy neighboring long intergenic noncoding RNAs in autotetraploid cassava, may be a mechanism that may suppress the expression levels of nearby lncRNA, leading to no significant differences in transcriptome alterations for major of lncRNAs from its diploid parent.ConclusionsThis work highlighted that WGD-induced DNA methylation variation in DNA transposons and retrotransposons may be as direct adaptive responses to dosage of all coding-genes and lncRNAs, respectively.

A Bayesian Hierarchical Model to Estimate DNA Methylation Conservation in Colorectal Tumors

Motivation Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. Results We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation, and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model’s ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Conclusions Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. Availability The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. Supplementary information Supplementary data are available at Bioinformatics online.

Factors Driving DNA Methylation Variation in Human Blood

Epigenetic changes are required for normal development and health, and can also underlie disease states; yet, the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 958 adults is affected by genetic variation, aging, sex and 139 diverse environmental exposures, and investigated whether these effects are direct or mediated by changes in cellular composition, measured by deep immunophenotyping. We show that cellular heterogeneity and DNA sequence variation are the strongest predictors of DNA methylation levels. We identify latent cytomegalovirus infection as a major driver of DNA methylation variation and delineate three distinct effects of aging on DNA methylation, including increased dispersion consistent with epigenetic drift. Our rich dataset provides a unique resource for the design and interpretation of epigenetic studies and highlight critical factors in medical epigenomics studies.

Effects of 24-Epibrassinolide on DNA Methylation Variation in Soybean (Glycine max) Leaf and Root Under Saline-Alkali Stress

Saline-alkali stress is major stress that severely reduces plant growth and productivity, it is necessary to make clear whether exogenous 24-epibrassinolide (EBR) can improve the salt-alkali resistance of soybean (Glycine max) by affecting its DNA methylation. In this study, the effects of EBR on soybean adaptation to saline-alkali stress, genomic DNA methylation level and pattern changes in saline-alkali-stressed leaf and root with or without EBR treatment were compared using methylation-sensitive amplified polymorphism (MSAP). In the results, saline-alkali stress increased DNA methylation levels in leaf and root, with higher respective hemi-methylation and global methylation rates observed in leaf (6.22, 22.24%) than root (5.72, 21.76%). EBR application reduced leaf and root DNA methylation levels, with leaf hemi-methylation rate (6.15%) exceeding that of root (4.25%) and leaf global methylation rate (21.79%) below that of root (22.51%). There were distinct DNA remethylation and demethylation variations across different tissues and treatments, demethylation in leaves was dominant. Meanwhile, untreated saline-alkali-stressed roots exhibited major demethylation-based variations, while remethylation variations predominated post-treatment. Under saline-alkali stress, root remethylation and demethylation rates (6.17, 7.55%, respectively) both exceeded respective leaf rates (5.18 and 7.46%); however, post-EBR treatment, root methylation rate (6.45%) exceeded leaf rate (5.38%), while root demethylation rate (6.13%) fell below leaf rate (6.94%). In conclusion, exogenous EBR application to saline-alkali-stressed soybean can influence leaf and root genomic DNA methylation levels and patterns via distinct tissue-specific methylation mechanisms.

Parental variation in CHG methylation is associated with allelic-specific expression in elite hybrid rice

Heterosis refers to the superior performance of hybrid lines over inbred parental lines. Besides genetic variation, epigenetic differences between parental lines are suggested to contribute to heterosis. However, the precise nature and extent of differences between the parental epigenomes and the reprograming in hybrids that governs heterotic gene expression remain unclear. In this work, we analyzed DNA methylomes and transcriptomes of the widely cultivated and genetically studied elite hybrid rice (Oryza sativa) SY63, the reciprocal hybrid, and the parental varieties ZS97 and MH63, for which high-quality reference genomic sequences are available. We showed that the parental varieties displayed substantial variation in genic methylation at CG and CHG (H = A, C, or T) sequences. Compared with their parents, the hybrids displayed dynamic methylation variation during development. However, many parental differentially methylated regions (DMR) at CG and CHG sites were maintained in the hybrid. Only a small fraction of the DMRs displayed non-additive DNA methylation variation which, however, showed no overall correlation relationship with gene expression variation. By contrast, most of the allelic-specific expression (ASE) genes in the hybrid were associated with DNA methylation, and the ASE negatively associated with allelic-specific methylation (ASM) at CHG. These results revealed a specific DNA methylation reprogramming pattern in the hybrid rice and pointed to a role for parental CHG methylation divergence in ASE, which is associated with phenotype variation and hybrid vigor in several plant species.

Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity

Background Although the genomes of monozygotic twins are practically identical, their methylomes may evolve divergently throughout their lifetime as a consequence of factors such as the environment or aging. Particularly for young and healthy monozygotic twins, DNA methylation divergence, if any, may be restricted to stochastic processes occurring post-twinning during embryonic development and early life. However, to what extent such stochastic mechanisms can systematically provide a stable source of inter-individual epigenetic variation remains uncertain until now. Results We enriched for inter-individual stochastic variation by using an equivalence testing-based statistical approach on whole blood methylation microarray data from healthy adolescent monozygotic twins. As a result, we identified 333 CpGs displaying similarly large methylation variation between monozygotic co-twins and unrelated individuals. Although their methylation variation surpasses measurement error and is stable in a short timescale, susceptibility to aging is apparent in the long term. Additionally, 46% of these CpGs were replicated in adipose tissue. The identified sites are significantly enriched at the clustered protocadherin loci, known for stochastic methylation in developing neurons. We also confirmed an enrichment in monozygotic twin DNA methylation discordance at these loci in whole genome bisulfite sequencing data from blood and adipose tissue. Conclusions We have isolated a component of stochastic methylation variation, distinct from genetic influence, measurement error, and epigenetic drift. Biomarkers enriched in this component may serve in the future as the basis for universal epigenetic fingerprinting, relevant for instance in the discrimination of monozygotic twin individuals in forensic applications, currently impossible with standard DNA profiling.