Biology of Genomes

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DNA methylation (DNAme) mechanisms are essential for balanced multi-lineage cellular differentiation. While most CpG sites are stably methylated, thousands of hypomethylated regions (HMRs) are scattered across the genome of steady state cells. We have demonstrated that HMR patterns reveal gene transcriptional enhancers of critical importance to both normal and pathological cell phenotypes. Textbook models of gene regulation assume that DNA hypomethylation is necessary for enhancer function and gene transcription, but our recent work demonstrates that HMR dynamics are distinct from chromatin and transcription—enhancer HMRs are established after transcription initiation and can persist long after chromatin accessibility and transcription are lost. Using ATAC-Me, a method developed by our lab to simultaneously profile DNAme and chromatin accessibility, we investigated the coordinated dynamics of DNAme and accessibility during a densely sampled time course of early neural progenitor cell differentiation. We show that chromatin responds quickly, and transiently in some genomic contexts, to induction of differentiation with ~38,000 regions displaying dynamic and transient opening and closing behavior. In contrast, DNAme changes are primarily unidirectional, occurring during a specific window of time for a subset of enhancer loci. We determined site-specific 5-hydroxymethylation using 6-letter sequencing to confirm active removal of methylation at these sites. Overall, we show that a majority of lineage-specifying enhancers undergo periods of DNA demethylation that is temporally distinct from other regulatory events. Furthermore, hypomethylation of these regions persists long after TF binding and chromatin accessibility have dissipated, suggesting that long-lasting hypomethylation of certain enhancers is a historical record of previous activity. These observations challenge the causal relationship between DNAme and transcription. Instead, this work argues that enhancer hypomethylation serves to reinforce cell fate choices, rather than control transcription, to create historical records of a cell’s journey through development.  We are now using this knowledge to understand why perturbations to HMR formation lead to failures in normal cell differentiation, ultimately causing specific disease outcomes like cancer.

Analysing methylation data is challenging, many existing analysis tools are difficult to work with and do not scale well as the number of samples increases. This lack of scalability means that standard analyses, such as identifying differentially methylated regions (DMRs), or summarising methylation fractions over genomic regions, require substantial time and memory – typically necessitating large scale compute infrastructure (e.g. compute clusters, cloud). A recently introduced technology, duet multiomics solution evoC, enables the reading of 5-base and 6-base information from DNA, further increasing the scale and complexity of data that can be extracted from a single sequencing experiment. With this expanded multiomic data-set, the lack of scalability is compounded, hindering the kinds of interactive data analysis that provide rapid and detailed insight. To address this, we present a fast and scalable array-based python package for the analysis of 5 and 6-base genomes (genetics, 5-mC and 5-hmC), leveraging `dask`, with `zarr` as the storage backend, allowing extremely efficient computation, even for datasets that are too large to fit into memory.

We demonstrate how our approach unlocks local analysis for large data cohorts, scaling to thousands of samples, with the major limiting factor being storage space rather than compute. By contrast, existing analysis packages scale poorly and exceed the memory capacity of a typical laptop after ~10 samples. Beyond pure performance benefits, we provide an optimised toolkit of functions to perform exploratory analysis (e.g. plotting, summarisation of methylation information across arbitrary genomic regions, summary statistics) as well as downstream analysis e.g. identifying DMRs and principal component analysis. The design of the package was focussed around providing a computationally efficient and intuitive interface to perform detailed analyses of methylation data, allowing users to quickly move from raw data to actionable insights and publication-ready analyses/figures. It is suited to both interactive analysis and incorporation into data pipelines for optimised processing at any scale. Moving forward into the era of multiomics, the underlying data we use offers powerful integration of additional data modalities, facilitating efficient multiomic analysis.

DNA comprises molecular information stored in genetic and epigenetic bases, both ofwhich are vital to our understanding of biology. In human genomes, an epigenetic modification at the fifth carbon of cytosines bases comprises one fundamental pathway by which genes can be silenced or activated. Methods widely used to detect epigenetic modifications at cytosine bases rely on either deamination of unmodified cytosines to read as thymine or borane reduction of the modified oxidised cytosine bases to read as thymine. As a result, such methods fail to capture common C-to-T mutations, and importantly also fail to distinguish 5-methylcytosine (5mC – mainly found in silenced regions of the genome) from 5-hydroxymethylcytosine (5hmC – enriched in active gene bodies and enhancers). Hence, existing methods are unable to read the complete information stored in our genomes in a single workflow.

duet multiomics solution evoC is a new technology which sequences at single base resolution the complete genetic sequence integrated with modified cytosine from low nanogram amounts of cfDNA. By introducing an important high-fidelity methyltransferase during the workflow, we are able to construct an expanded information table that can be used to deconvolute A,T,C,G, 5-mC and 5-hmC simultaneously in a single read within a DNA molecule. Using synthetic controls and reference DNA samples, we demonstrate that this method can achieve accurate measurement of 5-mC and 5-hmC as well as achieving high accuracy for SNV calling. We then use a mouse embryonic stem cell-line, ES-E14TG2A, to map for the first time a simultaneous reading of the genome and epigenome at high depth. In this data, we show how these epigenetic modifications are segregated across the genome and how a 6-letter (epi-)genome can be used to predict gene expression and chromatin accessibility.

In summary, using our modified approach we demonstrate simultaneous, phased reading of all six genetic and epigenetic bases. This tool provides a more complete picture of the information stored in genomes and will have applications throughout biology and medicine.