Biology subjects

Harrell, J. C.

Publications and source records attributed to Harrell, J. C..

6 recordsLinked to original sources

bioRxiv2026

EpiTADformer: A Transformer-Based Model for High-Resolution TAD Boundary Detection Using Epigenomic Signal Embeddings

The human genome is partitioned at different levels of 3D genome organization, with topologically associating domains (TADs) being among the most well-known and biologically important structures. TAD boundary disruption is associated with a wide range of diseases such as cancer, neurological and developmental disorders. Numerous methods have been developed to detect TAD boundaries from chromatin contact maps obtained with Hi-C technology. However, these methods are largely limited by the resolution of Hi-C data, typically 1 Kb to 100 Kb. In contrast, functional DNA loci, collectively referred to as epigenomic data, are profiled at a much higher resolution (100-200 bp for a typical ChIP-seq experiment). To improve the resolution of boundary detection, we hypothesize that the patterns of epigenomic signals associated with regions in proximity to TAD boundaries can serve as embeddings for these genomic regions, defining region similarity. These embeddings, along with their positional relationships, can be effectively modeled using deep learning to achieve more precise boundary prediction. We present EpiTADformer, a transformer-based model that takes as input transcriptional and histone modification signals of neighboring regions centered around TAD boundaries. We demonstrate that EpiTADformer outperforms feedforward neural network, convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) network architectures. These results suggest the positional information of epigenomic signals surrounding TAD boundaries provides a strong predictive signal, enabling improved performance of the transformer model. Our findings highlight the potential of epigenomic signals to serve as region embeddings for refining the epigenomic language of TAD domains and 3D genome organization.

bioinformatics↗

bioRxiv2024

scHiCcompare: an R package for differential analysis of single-cell Hi-C data

Changes in the three-dimensional (3D) structure of the human genome are key indicators of cancer and developmental disorders. Techniques like chromatin conformation capture (Hi-C) have been developed to study these global 3D structures, typically requiring millions of cells and an extremely high sequencing depth (around 1 billion reads per sample) for bulk Hi-C. In contrast, single-cell Hi-C (scHi-C) captures 3D structures at the individual cell level but faces significant data sparsity, marked by a high proportion of zeros. While differential analysis methods exist for bulk Hi-C data, they are limited for scHi-C data. To address this, we developed a method for differential scHi-C analysis, building on existing techniques in the HiCcompare R package. Our approach imputes sparse scHi-C data by considering genomic distances and creates pseudo-bulk Hi-C matrices by summing condition-specific data. The data are normalized using LOESS regression, and differential chromatin interactions are detected via Gaussian Mixture Model (GMM) clustering. Our workflow outperforms existing methods in identifying differential chromatin interactions across various genomic distances, fold changes, resolutions, and sample sizes in both simulated and experimental contexts. This allows for effective detection of cell type-specific differences in chromatin structure, which has meaningful associations with biological and epigenetic features. Our method is implemented in the scHiCcompare R package, available at https://github.com/dozmorovlab/scHiCcompare. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/622369v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b94f54org.highwire.dtl.DTLVardef@72fddorg.highwire.dtl.DTLVardef@1d77702org.highwire.dtl.DTLVardef@c63088_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

excluderanges: exclusion sets for T2T-CHM13, GRCm39, and other genome assemblies

SummaryExclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g., centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. Availability and implementationhttps://bioconductor.org/packages/excluderanges/ ContactMikhail G. Dozmorov (mdozmorov@vcu.edu) Supplementary informationPackage website: https://dozmorovlab.github.io/excluderanges/

bioinformatics↗

bioRxiv2022

Genomic screening reveals UBA1 as a potent and druggable target in c-MYC-high TNBC models

Triple negative breast cancer (TNBC) accounts for over 30% of all breast cancer-related deaths, despite accounting for only 10%-15% of total breast cancer cases. Targeted therapy development has largely stalled in TNBC, underlined by a lack of traditionally druggable addictions like receptor tyrosine kinases (RTKs). Here, through full genome CRISPR/Cas9 screening of TNBC models, we have uncovered the sensitivity of TNBCs to the depletion of the Ubiquitin-Like Modifier Activating Enzyme 1 (UBA1). Targeting UBA1 with the first in-class UBA1 inhibitor TAK-243 induced unresolvable ER-stress and activating transcription factor 4 (ATF4)-mediated upregulation of pro-apoptotic NOXA, leading to cell death. In five patient derived xenograft models (PDXs) of TNBC, TAK-243 therapy led to tumor inhibition or frank tumor regression. In an intracardiac metastatic model of TNBC, TAK-243 markedly reduced metastatic burden. Importantly, there was an order of magnitude greater sensitivity of TNBC lines to TAK-243 compared to normal tissue-derived cells. Lastly, c-MYC expression correlates with TAK-243 sensitivity and cooperates with TAK-243 to induce a stress response and cell death. We posit UBA1 is an important new target in TNBC expressing high levels of c-MYC. SignificanceGenomic screening of TNBC cell lines revealed broad sensitivity to depletion of the E1 ubiquitin enzyme, UBA1. Disrupting UBA1 with the first in-class inhibitor TAK-243 in TNBC models induces ER-stress through an ATF4-NOXA axis that is dependent on c-MYC, leading to apoptosis, in vitro and in vivo, primary tumor growth inhibition and metastatic inhibition.

cancer biology↗

bioRxiv2021

Exploitation of sulfated glycosaminoglycan status for precision medicine of platinums in triple-negative breast cancer

Triple negative breast cancer (TNBC) is a subtype of breast cancer lacking targetable biomarkers. TNBC is known to be most aggressive, and when metastatic is often drug resistant and uncurable. Biomarkers predicting response to therapy improve treatment decisions and allow personalized approaches for TNBC patients. This study explores sulfated glycosaminoglycan (sGAG) levels as a predictor of TNBC response to platinum therapy. sGAG levels were quantified in three distinct TNBC tumor models including cell line-derived, patient-derived xenograft (PDX) tumors, and isogenic models deficient in sGAG biosynthesis. The in vivo antitumor efficacy of Triplatin, a sGAG-directed platinum agent, was compared in these models to the clinical platinum agent, carboplatin. We determined that >40% of TNBC PDX tissue microarray samples have high levels of sGAGs. The in vivo accumulation of Triplatin in tumors as well as antitumor efficacy of Triplatin positively correlated with sGAG levels on tumor cells, whereas carboplatin followed the opposite trend. In carboplatin-resistant tumor models expressing high levels of sGAGs, Triplatin decreased primary tumor growth, reduced lung metastases and inhibited metastatic growth in lungs, liver, and ovaries. sGAG levels served as a predictor of Triplatin sensitivity in TNBC. Triplatin may be particularly beneficial in treating patients with chemotherapy-resistant tumors who have evidence of residual disease after standard neoadjuvant chemotherapy. More effective neoadjuvant and adjuvant treatment will likely improve clinical outcome of TNBC. SignificanceTNBC is a heterogenous disease, often defined by the absence of a therapeutic target. Our recent results show sGAGs may provide a viable biomarker for Triplatin in patients with TNBC, producing a significant advantage over carboplatin in this setting. Selective precision medicine agents, such as Triplatin, that are active against chemotherapy-resistant disease and exploit molecular biomarkers like sGAGs may significantly benefit patients in this setting.

pharmacology and toxicology↗

bioRxiv2020

Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines

Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined. We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated two experimental PDX Hi-C datasets using different library preparation strategies. We evaluated three alignment strategies (Direct, Xenome, Combined) and three processing pipelines (Juicer, HiC-Pro, HiCExplorer) on the quality of Hi-C data. Removal of mouse reads had little-to-no effect on data quality than the results obtained with Direct alignment strategy. Juicer pipeline extracted the most useful information from PDX Hi-C data. However, library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

genomics↗