Biology subjects

Harris, H. L.

Publications and source records attributed to Harris, H. L..

4 recordsLinked to original sources

bioRxiv2026

Guanine content in the CTCF motif encodes a vulnerability to persistent, alcohol-induced redistribution

Alcohol consumption is a leading contributor to the global disease burden, yet its molecular consequences remain incompletely understood. Ethanols first metabolite, acetaldehyde, modifies guanine to form DNA adducts and DNA-protein crosslinks that block elongating RNA polymerase II. Because acetaldehyde acts specifically on guanine, we hypothesized that chromatin features would be disrupted in a sequence-dependent rather than stochastic manner. Here we confirm that alcohol exposure displaces elongating RNA polymerase II (RNAPIIs2p) from gene bodies, relocating to promoters. In addition to this confirmation, we found that RNAPIIs2p also relocates to discrete guanine-rich intergenic CTCF sites. Loci with high guanine content at the CTCF motifs zinc finger 5 (ZF5) binding position showed the largest increase in both CTCF and cohesin (RAD21), with CTCF occupancy changes persisting even six days after alcohol withdrawal. Surprisingly, using Hi-C, we found that these changes to CTCF and cohesin do not impact the pronounced structural CTCF loops, but are instead primarily associated with altered guanine-rich enhancer-promoter contacts that connect to lasting changes to gene expression. Beyond our cell-line model, the same sequence rule accounts for altered CTCF occupancy in a mouse starvation (metabolic stress) model and in the placentae after paternal alcohol exposure. Together these findings link alcohol exposure to sequence-directed changes to chromatin whose rules are conserved in alcohol-associated disease.

molecular biology↗

bioRxiv2026

Kilobase-scale compartments enabled by CRUSH reveal regulatory programs across cell types, single-cells, and ancient mammoths

How chromatin is spatially organized in the nucleus has long been studied through the lens of large-scale A/B compartments, but whether these sizes reflect true biological units or analytical artifacts has remained unclear. We find that limits imposed by the conventional eigenvector-based compartment calling have required extreme sequencing depth and coarse resolution, obscuring regulatory-scale organization. We developed CRUSH to iteratively refine compartments to 1 kb resolution without the need for extreme sequencing depth, we show that kilobase-scale A/B segregation (micro-compartments) is evident across cell types. Across these maps, we demonstrate that RNA polymerase II pausing contributes to a sub-genic compartment signature at the transcription start site and that active enhancers almost universally occupy the A compartment. We then show that fine-scale compartment maps can resolve cancer subtype-specific regulatory programs, single-cell tissue identity, and cold-adaptation regulomes in a 52,000-year-old woolly mammoth. These findings establish chromatin compartmentalization as a gene-scale regulatory feature with broad implications for development, disease, and genome evolution. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=176 SRC="FIGDIR/small/719469v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@15c1992org.highwire.dtl.DTLVardef@192f541org.highwire.dtl.DTLVardef@123a737org.highwire.dtl.DTLVardef@1b7292f_HPS_FORMAT_FIGEXP M_FIG C_FIG CRUSH uses iterative resolution walking to identify and correct A/B compartment measurements. This allows identification of A/B compartments at kilobase resolution with low sequencing depth and consistency across methodologies. Kilobase-scale compartments reveal sub-genic compartment organization, compartment identities in single-cells, and a distinct cold-adapted regulome in mammoths.

genomics↗

bioRxiv2024

HiCrayon reveals distinct layers of multi-state 3D chromatin organization

The co-visualization of chromatin conformation with 1D omics data is key to the multi-omics driven data analysis of 3D genome organization. Chromatin contact maps are often shown as 2D heatmaps and visually compared to 1D genomic data by simple juxtaposition. While common, this strategy is imprecise, placing the onus on the reader to align features with each other. To remedy this, we developed HiCrayon, an interactive tool that facilitates the integration of 3D chromatin organization maps and 1D datasets. This visualization method integrates data from genomic assays directly into the chromatin contact map by coloring interactions according to 1D signal. HiCrayon is implemented using R shiny and python to create a graphical user interface (GUI) application, available in both web or containerized format to promote accessibility. HiCrayon is implemented in R, and includes a graphical user interface (GUI), as well as a slimmed-down web-based version that lets users quickly produce publication-ready images. We demonstrate the utility of HiCrayon in visualizing the effectiveness of compartment calling and the relationship between ChIP-seq and various features of chromatin organization. We also demonstrate the improved visualization of other 3D genomic phenomena, such as differences between loops associated with CTCF/cohesin vs. those associated with H3K27ac. We then demonstrate HiCrayons visualization of organizational changes that occur during differentiation and use HiCrayon to detect compartment patterns that cannot be assigned to either A or B compartments, revealing a distinct 3rd chromatin compartment. Overall, we demonstrate the utility of co-visualizing 2D chromatin conformation with 1D genomic signals within the same matrix to reveal fundamental aspects of genome organization. Local version: https://github.com/JRowleyLab/HiCrayon Web version: https://jrowleylab.com/HiCrayon

genomics↗

bioRxiv2022

Predicting A/B compartments from histone modifications using deep learning

Genomes fold into organizational units in the 3D space that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in the A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC curve of 90.9%. Our cell type-specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed analysis of loci where compartment status cannot be accurately predicted from these marks. These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. These ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Finally, we demonstrate the generalizability of our model by predicting compartments in independent tissue samples. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.

bioinformatics↗