Biology subjects

Roth, C. J. N.

Publications and source records attributed to Roth, C. J. N..

5 recordsLinked to original sources

bioRxiv2026

Resolving in vitro heterogeneity of host functional responses to HCoV-229E via single-cell analyses

Understanding host-pathogen interactions at the molecular level is essential to elucidating the mechanisms that govern immunity and pathogenesis. While diverse responses to viral infection have been documented, it remains unclear if this variability arises from the complex host milieu or represents an intrinsic property of the infection itself. To investigate this, we performed single-cell (sc) RNA sequencing analysis of infected MRC-5 fibroblast cells with human coronavirus 229E (HCoV-229E), which revealed pronounced variation in viral load across time points and biological replicates. While viral sequences were higher at 24 hours post-infection (hpi), more host genes were differentially expressed at 48 hpi, indicating dynamic responses during infection progression. Cells with higher viral load upregulated expression of unfolded protein response genes and NF-{kappa}B regulators, and downregulated expression of fibroblast identity and cell cycle genes, reflecting a shift toward a stress-adaptive, antiviral state. Network analyses identified NF-{kappa}B signaling as a central regulatory axis, with negative regulators overexpressed in high-viral-load clusters. Additionally, ATAC-seq analysis showed that regions of increased chromatin accessibility were enriched for NF-{kappa}B subunits, further supporting its role in transcriptional control. These findings demonstrate that even within ostensibly uniform cell cultures, viral infection induces distinct heterogeneous transcriptional and regulatory responses.

microbiology↗

bioRxiv2025

AI-Assisted Cryo-ET Workflow for 3D Visualization of Chromatin during Cellular Differentiation

Understanding how chromatin architecture changes during cellular differentiation requires structural methods that can resolve native genomic organization at high resolution. Here, we present an AI-assisted cryo-electron tomography (cryo-ET) and segmentation workflow to quantify chromatin compaction across various stages of motor neuron differentiation from induced pluripotent stem cells (iPSC). By directly imaging extracted and vitrified chromatin, we preserve native structure and avoid artifacts from heavy metal staining and resin embedding. Using three-dimensional (3D) density analysis, we measure chromatin density and capture the progressive increase in chromatin compaction with lineage commitment. This is then correlated with population-averaged Hi-C experiments, observing consistency between the microscale higher order structure of chromatin and global contact patterns. Our approach enables direct visualization of chromatin organization under near-physiological conditions, bridging the gap between structural imaging and genome-wide contact mapping. This platform therefore establishes an AI-assisted experimental framework for linking chromatin architecture to regulatory mechanisms during differentiation.

genomics↗

bioRxiv2025

From 2D to 4D: a Containerized Workflow and Browser to Explore Dynamic Chromatin Architecture

BackgroundCharacterizing the physical organization of the genome is essential for understanding long-range gene reg- ulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two- dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological pro- cesses in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. ResultsHerein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. ConclusionsThe 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visual- ization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

bioinformatics↗

bioRxiv2024

SLUR(M)-py: A SLURM Powered Pythonic Pipeline for Parallel Processing of 3D (Epi)genomic Profiles

Epigenomics has become multi-faceted, with researchers exploring chromatin structure, nucleosome states, and epigenetic modifications, producing large, complex multi-omic data sets. Given this shift, there is de-mand for bioinformatics that leverage high performance computing (HPC) and parallelization to quickly process data. As such, we developed SLUR(M)-py: a pythonic computational platform that leverages the Simple Linux Utility for Resource Management system (SLURM) to process sequencing data. SLUR(M)-py is multi-omic and automates calls to SLURM for processing paired-end sequences from chromatin charac-terization experiments, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C, thereby eliminating the need for multiple analytics pipelines. To demonstrate SLUR(M)-pys utility, we employ ATAC-seq and Hi-C data from viral infection experiments and the ENCODE project, and illustrate its processing speed and completeness, which outpaces current HPC pipelines. We explore the effect of dropping duplicate se-quenced reads in ATAC-seq, demonstrate how SLUR(M)-py can be used for quality control, and how to detect artifacts in Hi-C from viral infection experiments. Finally, we show how features in SLUR(M)-py, like inter-chromosomal analysis, can be used to explore the dynamics of chromosomal contacts in mammalian cells. This multi-omic, system agnostic platform eases the computational burden for researchers and quickly produces accurate, reliable data analytics for the epigenomics community.

bioinformatics↗

bioRxiv2020

Pleiotropy and Epistasis Within and Between Signaling Pathways Defines the Genetic Architecture of Fungal Virulence

Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation. Author summaryDifferent environmental isolates (strains) of the same microbial species can vary greatly in their ability to cause disease, ranging from avirulent to hypervirulent. What makes some strains deadly pathogens, while others are relatively benign? This study describes the characterization of key genetic differences that underlie variation in traits thought to promote virulence in Cryptococcus deneoformans, a wide-spread opportunistic fungal pathogen. Using a combination of quantitative genetic and molecular genetic approaches we dissected the genetic architecture of virulence-related cellular traits (melanin production and the production of a polysaccharide capsule), physiological responses to stress (tolerance of thermal, oxidative, and osmotic stress), and sensitivity to multiple antifungal drugs. Strikingly we find that variation in most of these traits is governed by a small number of genetic differences that modify the function of two major cell signaling networks, cyclic AMP-Protein Kinase A (cAMP-PKA) signaling and a fungal specific MAP-kinase cascade called the high osmolarity glycerol (HOG) pathway. Similar to recent studies in a number of other fungal species, our findings point to an outsize role for a small number of highly pleiotropic signaling pathways in potentiating phenotypic variation both within and between fungal species.

genetics↗