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Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT's activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.
Pubmed ID: 32187373
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Global nonprofit biological resource center (BRC) and research organization that provides biological products, technical services and educational programs to private industry, government and academic organizations. Its mission is to acquire, authenticate, preserve, develop and distribute biological materials, information, technology, intellectual property and standards for the advancement and application of scientific knowledge. The primary purpose of ATCC is to use its resources and experience as a BRC to become the world leader in standard biological reference materials management, intellectual property resource management and translational research as applied to biomaterial development, standardization and certification. ATCC characterizes cell lines, bacteria, viruses, fungi and protozoa, as well as develops and evaluates assays and techniques for validating research resources and preserving and distributing biological materials to the public and private sector research communities.
View all literature mentionsNon-profit plasmid repository dedicated to helping scientists around the world share high-quality plasmids. Facilitates archiving and distributing DNA-based research reagents and associated data to scientists worldwide. Repository contains over 65,000 plasmids, including special collections on CRISPR, fluorescent proteins, and ready-to-use viral preparations. There is no cost for scientists to deposit plasmids, which saves time and money associated with shipping plasmids themselves. All plasmids are fully sequenced for validation and sequencing data is openly available. We handle the appropriate Material Transfer Agreements (MTA) with institutions, facilitating open exchange and offering intellectual property and liability protection for depositing scientists. Furthermore, we curate free educational resources for the scientific community including a blog, eBooks, video protocols, and detailed molecular biology resources.
View all literature mentionsA tool set for pathway-based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.
View all literature mentionsScript distributed with the HT-Seq Python framework for processing RNA-seq or DNA-seq data.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
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