Biology subjects

Read, E. L.

Publications and source records attributed to Read, E. L..

5 recordsLinked to original sources

bioRxiv2025

A Comparison of Computational Methods for Modeling Stochastic Collaborative DNA Methylation Dynamics

DNA methylation is a widespread epigenetic modification that is important in gene regulation, cancer, and aging. Methylation occurs primarily on symmetric cytosine-phosphate-guanine (CpG) dinucleotides in vertebrates. Recent findings support that the enzymatic reactions governing both addition and removal of DNA methylation at an individual CpG site are influenced by methylation levels at other CpG sites in the vicinity. Mathematical models that treat this phenomenon have been termed "collaborative" models. These models are computationally challenging due to rare switching events and the combinatorial explosion of site-to-site interactions, hindering integration with data. We compare the efficiency and accuracy of three collaborative DNA methylation modeling approaches: a stochastic simulation algorithm (SSA), an exact Chemical Master Equation (CME), and a mean-field CME. The exact CME model is limited to very small system sizes (few CpGs), with computational expense scaling as 32N for a system of N sites. The SSA approach can accurately handle larger system sizes, with computational expense scaling as N2. The mean-field CME model accurately captures qualitative methylation patterns and is efficient for small (N<100) system sizes, but accuracy and efficiency suffer at larger system sizes, with expense scaling as N5.5. Using the developed numerical approaches, we compute methylation phase diagrams, which reveal how methylation levels and bimodality depend on the DNA-sequence-architecture of CpG clusters, as well as on enzymatic model parameters. We also present phase diagrams derived from the mouse epigenome. These data reveal a complex dependence of cluster methylation patterns on cluster architecture, which is nevertheless well-captured by the relatively simplified model.

biophysics↗

bioRxiv2025

Scalable Inference and Identifiability of Kinetic Parameters for Transcriptional Bursting from Single Cell Data

MotivationStochastic gene expression and cell-to-cell heterogeneity have attracted increased interest in recent years, enabled by advances in single-cell measurement technologies. These studies are also increasingly complemented by quantitative biophysical modeling, often using the framework of stochastic biochemical kinetic models. However, inferring parameters for such models (that is, the kineticrates of biochemical reactions) remains a technical and computational challenge, particularly doing so in a manner that can leverage high-throughput single-cell sequencing data. ResultsIn this work, we develop a Chemical Master Equation reference library-based computational pipeline to infer kinetic parameters describing noisy mRNA distributions from single cell RNA sequencing data, using the commonly applied stochastic telegraph model. Our pipeline also serves as a tool for comprehensive analysis of parameter identifiability, in both a priori (studying model properties in the absence of data) and a posteriori (in the context of a particular dataset) use cases. The pipeline can perform both of these tasks, i.e. inference and identifiability analysis, in an efficient and scalable manner, and also serves to disentangle contributions to uncertainty in inferred parameters from experimental noise versus structural properties of the model. We found that for the telegraph model, the majority of the parameter space is not practically identifiable from single cell RNA sequencing data, and low experimental capture rates worsen the identifiability. Our methodological framework could be extended to other data types in the fitting of small biochemical network models. AvailabilityAll code relevant to this work is available at https://github.com/Read-Lab-UCI/TelegraphLikelihoodInfer.

biophysics↗

bioRxiv2025

Methylation pseudotime analysis for label-free profiling of the temporal chromatin landscape with long-read sequencing

Faithful epigenetic inheritance across cell divisions is essential to maintaining cell identity and involves numerous epigenetic modifications, whose roles in establishing chromatin architecture are less understood. Technological approaches to temporally order epigenetic modifications throughout the cell cycle often face limitations in sequence resolution and rely on potentially damaging mitotic labeling or conversion steps. Herein, we present Methylation Pseudotime Analysis Through read-level Heterogeneity (MPATH), a label- and conversion-free method to infer post-replication DNA strand maturity from methylation patterns across single molecules. We use MPATH to temporally order hydroxymethylation throughout mitotic inheritance, revealing that CpGs within cis-regulatory elements undergo transitions between methylation states at sub-cell-cycle timescales. When applied to long reads generated by NOMe-seq, MPATH uncovered relationships between nucleosome occupancy and DNA maturity. Finally, extension of MPATH to phased reads reveals allele-specific trends in pseudotime distribution associated with X chromosome activity. Our findings suggest that when coupled with multimodal sequencing strategies, MPATH could provide valuable insights into chromatin restoration dynamics.

genomics↗

bioRxiv2024

A Tunable, Ultrasensitive Threshold in Enzymatic Activity Governs the DNA Methylation Landscape

DNA methylation is a widely studied epigenetic mark, affecting gene expression and cellular function at multiple levels. DNA methylation in the mammalian genome occurs primarily at cytosine-phosphate-guanine (CpG) dinucleotides, and patterning of the methylation landscape (i.e., the presence or absence of CpG methylation at a given genomic location) exhibits a generally bimodal distribution. Although much is known about the enzymatic writers and erasers of CpG methylation, it is not fully understood how these enzymes, along with genetic, chromatin, and regulatory factors, control the genome wide methylation landscape. In this study, methylation is analyzed at annotated CpG Islands (CGIs) and independent CpGs as a function of their proximity to other CpG substrates. Analysis is aided by a computationally efficient stochastic mathematical model of methylation dynamics, enabling parameterization from data. We find that methylation exhibits a switch-like dependence on local CpG density. The threshold and steepness of the switch is modified in cell lines in which key enzymes are knocked out. Modeling further elucidates how enzymatic parameters, including catalytic rates and lengthscales of inter-CpG interaction, tune the properties of the switch. Together, the results support a model in which competition between opposing TET1-3 demethylating enzymes and DNA methyltransferases (DNMT3A/B) results in an ultrasensitive switch, analogous to the protein phosphorylation switch (termed zero-order ultra-sensitivity) proposed by Goldbeter and Koshland. Our study provides insight to the mechanisms underlying establishment and maintenance of bimodal DNA methylation landscapes, and further provides a flexible pipeline for gleaning molecular insights to the cellular methylation machinery across cell-specific, epigenomic datasets.

genomics↗

bioRxiv2019

Stochastic Modeling Reveals Kinetic Heterogeneity in Post-replication DNA Methylation

DNA methylation is a heritable epigenetic modification that plays an essential role in mammalian development. Genomic methylation patterns are dynamically maintained, with DNA methyltransferases mediating inheritance of methyl marks onto nascent DNA over cycles of replication. A recently developed experimental technique employing immunoprecipitation of bromodeoxyuridine labeled nascent DNA followed by bisulfite sequencing (Repli-BS) measures post-replication temporal evolution of cytosine methylation, thus enabling genome-wide monitoring of methylation maintenance. In this work, we combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from human embryonic stem cells. We estimate site-specific kinetic rate constants for the restoration of methyl marks on >10 million uniquely mapped cytosines within the CpG (cytosine-phosphate-guanine) dinucleotide context across the genome using Maximum Likelihood Estimation. We find that post-replication remethylation rate constants span approximately two orders of magnitude, with half-lives of per-site recovery of steady-state methylation levels ranging from shorter than ten minutes to five hours and longer. Furthermore, we find that kinetic constants of maintenance methylation are correlated among neighboring CpG sites. Stochastic mathematical modeling provides insight to the biological mechanisms underlying the inference results, suggesting that enzyme processivity and/or collaboration can produce the observed kinetic correlations. Our combined statistical/mathematical modeling approach expands the utility of genomic datasets and disentangles heterogeneity in methylation patterns arising from replication-associated temporal dynamics versus stable cell-to-cell differences.

genomics↗