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Inference of DNA methylation patterns in molluscs.

ABSTRACT: Mollusca are the second largest and arguably most diverse phylum of the animal kingdom. This is in sharp contrast to our very limited knowledge concerning epigenetic mechanisms including DNA methylation in this invertebrate group. Here, we inferred DNA methylation patterns by analysing the normalized dinucleotide CG content in protein-coding sequences and identified DNA methyltransferases (DNMT1 and 3) in published transcriptomes and genomes of 140 species across all eight classes of molluscs. Given the evolutionary age and morphological diversity of molluscs, we expected to find evidence for diverse methylation patterns. Our inferences suggest that molluscs possess substantial levels of DNA methylation in gene bodies as a rule. Yet, we found deviations from this general picture with regard to (i) the CpG observed/expected distributions indicating a reduction in DNA methylation in certain groups and (ii) the completeness of the DNMT toolkit. Reductions were evident in Caudofoveata, Solenogastres, Polyplacophora, Monoplacophora, as well as Scaphopoda. Heterobranchia and Oegopsida were remarkable as they lacked DNMT3, usually responsible for de novo methylation, yet showed signs of DNA methylation. Our survey may serve as guidance for direct empirical analyses of DNA methylation in molluscs. This article is part of the Theo Murphy meeting issue 'Molluscan genomics: broad insights and future directions for a neglected phylum'.

SUBMITTER: Manner L

PROVIDER: S-EPMC8059957 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Inference of DNA methylation patterns in molluscs.

Männer Lisa L Schell Tilman T Provataris Panagiotis P Haase Martin M Greve Carola C

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20210405 1825

Mollusca are the second largest and arguably most diverse phylum of the animal kingdom. This is in sharp contrast to our very limited knowledge concerning epigenetic mechanisms including DNA methylation in this invertebrate group. Here, we inferred DNA methylation patterns by analysing the normalized dinucleotide CG content in protein-coding sequences and identified DNA methyltransferases (DNMT1 and 3) in published transcriptomes and genomes of 140 species across all eight classes of molluscs. G ...[more]

PMID: 33813896

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Project description:BackgroundMolluscs are the most diverse marine phylum and this high diversity has resulted in considerable taxonomic problems. Because the number of species in Canadian oceans remains uncertain, there is a need to incorporate molecular methods into species identifications. A 648 base pair segment of the cytochrome c oxidase subunit I gene has proven useful for the identification and discovery of species in many animal lineages. While the utility of DNA barcoding in molluscs has been demonstrated in other studies, this is the first effort to construct a DNA barcode registry for marine molluscs across such a large geographic area.Methodology/principal findingsThis study examines patterns of DNA barcode variation in 227 species of Canadian marine molluscs. Intraspecific sequence divergences ranged from 0-26.4% and a barcode gap existed for most taxa. Eleven cases of relatively deep (>2%) intraspecific divergence were detected, suggesting the possible presence of overlooked species. Structural variation was detected in COI with indels found in 37 species, mostly bivalves. Some indels were present in divergent lineages, primarily in the region of the first external loop, suggesting certain areas are hotspots for change. Lastly, mean GC content varied substantially among orders (24.5%-46.5%), and showed a significant positive correlation with nearest neighbour distances.Conclusions/significanceDNA barcoding is an effective tool for the identification of Canadian marine molluscs and for revealing possible cases of overlooked species. Some species with deep intraspecific divergence showed a biogeographic partition between lineages on the Atlantic, Arctic and Pacific coasts, suggesting the role of Pleistocene glaciations in the subdivision of their populations. Indels were prevalent in the barcode region of the COI gene in bivalves and gastropods. This study highlights the efficacy of DNA barcoding for providing insights into sequence variation across a broad taxonomic group on a large geographic scale.

Project description:DNA methyltransferases establish methylation patterns in cells and transmit these patterns over cell generations, thereby influencing each cell's epigenetic states. Three primary DNA methyltransferases have been identified in mammals: DNMT1, DNMT3A and DNMT3B. Extensive in vitro studies have investigated key properties of these enzymes, namely their substrate specificity and processivity. Here we study these properties in vivo, by applying novel statistical analysis methods to double-stranded DNA methylation patterns collected using hairpin-bisulfite PCR. Our analysis fits a novel Hidden Markov Model (HMM) to the observed data, allowing for potential bisulfite conversion errors, and yields statistical estimates of parameters that quantify enzyme processivity and substrate specificity. We apply this model to methylation patterns established in vivo at three loci in humans: two densely methylated inactive X (Xi)-linked loci (FMR1 and G6PD), and an autosomal locus (LEP), where methylation densities are tissue-specific but moderate. We find strong evidence for a high level of processivity of DNMT1 at FMR1 and G6PD, with the mean association tract length being a few hundred base pairs. Regardless of tissue types, methylation patterns at LEP are dominated by DNMT1 maintenance events, similar to the two Xi-linked loci, but are insufficiently informative regarding processivity to draw any conclusions about processivity at that locus. At all three loci we find that DNMT1 shows a strong preference for adding methyl groups to hemi-methylated CpG sites over unmethylated sites. The data at all three loci also suggest low (possibly 0) association of the de novo methyltransferases, the DNMT3s, and are consequently uninformative about processivity or preference of these enzymes. We also extend our HMM to reanalyze published data on mouse DNMT1 activities in vitro. The results suggest shorter association tracts (and hence weaker processivity), and much longer non-association tracts than human DNMT1 in vivo.

Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

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Inference of DNA methylation patterns in molluscs.

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Inference of DNA methylation patterns in molluscs.

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