Biology subjects

Downing, T. L.

Publications and source records attributed to Downing, T. L..

9 recordsLinked to original sources

bioRxiv2026

XIST Is a Key Modulator Associated with the Adhesome Network

A long non-coding RNA (lncRNA) known as the X-inactivation specific transcript (XIST) plays a central role in X chromosome inactivation - a transcriptional process that silences one of the two X chromosomes in females to ensure dosage compensation between males and females. Much research has been conducted on how XIST regulates X chromosome transcription critical to embryonic development, but recent studies suggest a non-canonical role for XIST in regulating cancer stem cells and cellular plasticity. As cell adhesion and adhesome genes are integral to the regulation of cancer stemness, we explored the previously unrecognized link between XIST and the adhesome network. By performing gene expression and gene ontology analysis on XIST-knockdown ovarian cancer cells, our study showed that XIST loss altered adhesome gene expression and downstream adhesion pathways. Using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets, we identified distinct correlations between XIST lncRNA and adhesome genes across normal and cancer tissue samples, which are associated with cell stemness. Furthermore, network analysis suggests that XIST may interact with specific adhesome genes within the cell nucleus. This interaction may have significant functional implications, as demonstrated by the hazard ratio analysis of XIST and adhesome gene expression in relation to clinical outcomes. Overall, our results show that among well-annotated functional lncRNAs, XIST appears to be a modulator strongly associated with the adhesome network and cell stemness. Our findings thus support a novel link between lncRNA-mediated epigenetic regulation of cell adhesion genes, highlighting XIST as a key regulator contributing to the adhesome network. Significance StatementThis study identified that XIST, a long non-coding RNA essential for X-chromosome dosage compensation and embryonic development, plays a significant role in modulating the adhesome network. We found that XIST knockdown affected adhesion pathways in ovarian cancer cells, whereas XIST expression is strongly correlated with adhesome gene expression across all tissues. We observed that the interaction between XIST and adhesome genes changes significantly between tumors and normal tissues, and this altered interaction is associated with certain cancer outcomes. These findings reveal a possible link between lncRNA-mediated regulation and adhesome control that is associated with cell stemness signatures and the emergence of cancerous tissues.

cell biology↗

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

Nucleosome placement and polymer mechanics explain genomic contacts on 100kbp scales

The 3d organization of the genome -- in particular, which two regions of DNA are in contact with each other -- plays a role in regulating gene expression. Several factors influence genome 3d organization. Nucleosomes (where [~] 100 basepairs of DNA wrap around histone proteins) bend, twist and compactify chromosomal DNA, altering its polymer mechanics. How much does the positioning of nucleosomes between gene loci influence contacts between those gene loci? And, to what extent are polymer mechanics responsible for this? To address this question, we combine a stochastic polymer mechanics model of chromosomal DNA including twists and wrapping induced by nucleosomes with two data-driven pipelines. The first estimates nucleosome positioning from ATACseq data in regions of high accessibility. Most of the genome is low-accessibility, so we combine this with a novel image analysis method that estimates the distribution of nucleosome spacing from electron microscopy data. There are no fit parameters in the biophysical model. We apply this method to IL6, IL15, CXCL9, and CXCL10, inflammatory marker genes in macrophages, before and after inflammatory stimulation, and compare the predictions with contacts measured by conformation capture experiments (4C-seq). We find that within a 500 kilo-basepairs genomic region, polymer mechanics with nucleosomes can explain 71% of close contacts. These results suggest that, while genome contacts on 100kbp-scales are multifactorial, they may be amenable to mechanistic, physical explanation. Our work also highlights the role of nucleosomes, not just at the loci of interest, but between them, and not just the total number of nucleosomes, but their specific placement. The method generalizes to other genes, and can be used to address whether a contact is under active regulation by the cell (e.g., a macrophage during inflammatory stimulation).

biophysics↗

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↗

bioRxiv2022

Activation of a bacterial flow sensor by hypochlorous acid from stimulated neutrophils

Neutrophils kill bacteria by producing an array of lethal molecules, including hypochlorous acid (HOCl). The extent to which pathogens detect HOCl from neutrophils and defend themselves has not been understood. We report that the opportunistic pathogen Pseudomonas aeruginosa responds to activated neutrophils by upregulating the flow regulated operon (Fro), which was previously shown to be activated by fluid flow. We found that fro is upregulated in a mouse infection model where neutrophil influx occurs. This upregulation is induced in vitro by HOCl and its secondary product taurine chloramine but not by other neutrophil defense factors including LL-37, histones, or H2O2. HOCl induces the FroR-dependent upregulation of methionine sulfoxide reductases that relieve otherwise lethal oxidative stress. Fro expression is regulated by FroR's anti-sigma factor FroI, which contains the highest density of methionines and cysteines of all anti-sigma factors. The second-order rate constants of HOCl are highest with these residues, raising the possibility that the activation of fro could involve oxidation of FroI. These findings suggest a model in which flow transports oxidizing molecules that activate the fro operon, establishing an early warning system for P. aeruginosa that improves its survival against host immune defenses and persistence during infection.

microbiology↗

bioRxiv2021

Locally-correlated kinetics of post-replication DNA methylation reveals processivity and region-specificity in DNA methylation maintenance

DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of maintenance methylation activity among neighboring CpGs is critical to collective stability over cellular generations, however the mechanism of this coupling is unclear. We used mathematical models and stochastic simulation to analyze data from experiments that probe genome-wide methylation of nascent DNA postreplication in cells. We find that DNA methylation maintenance rates on individual CpGs are locally correlated, and the degree of this correlation varies by genomic regional context. Applying theory of one-dimensional diffusion of proteins along DNA, we show that exponential decay of methylation rate correlation with genomic distance is consistent with enzyme processivity. Our results provide quantitative evidence of genome-wide methyltransferase processivity in vivo. From the experiment-derived correlations, we estimate that an individual methyl-transferase methylates neighbor CpGs in sequence if they are 36 basepairs apart, on average. But other mechanisms of coupling dominate for inter-CpG distances past ~ 100 basepairs. Our study demonstrates that quantitative insights into enzymatic mechanisms can be obtained from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modeling.

biophysics↗

bioRxiv2020

Epigenome Prediction of Gene Expression using a Dynamical System Approach

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at https://github.com/kordk/stoch_epi_lib.

systems biology↗

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↗