Biology subjects

Hurley, J. D.

Publications and source records attributed to Hurley, J. D..

4 recordsLinked to original sources

bioRxiv2025

Anthracyclines induce global changes in cardiomyocyte chromatin accessibility that overlap with cardiovascular disease loci

Breast cancer drugs including anthracyclines (ACs) and Trastuzumab increase the risk for cardiovascular diseases (CVDs) such as atrial fibrillation (AF) and heart failure (HF) that ultimately affect the heart muscle. These CVDs are associated with hundreds of genetic variants in non- coding regions of the genome. However, how these drugs affect the regulatory potential of the non-coding genome of the heart and CVD risk loci is unknown. We therefore measured global chromatin accessibility across iPSC-derived cardiomyocytes from four individuals treated with the ACs, Doxorubicin, Epirubicin, and Daunorubicin, a related non-AC, Mitoxantrone, and the monoclonal antibody Trastuzumab, or a vehicle control for three and 24 hours. We identified 155,557 high-confidence regions of open chromatin across 48 samples where the major sources of variation are associated with drug type and time. Jointly modeling the data revealed three accessibility response signatures denoted as early-acute, early-sustained, and late that correspond to 67,329 regions that open or close in response to drug treatment. Sequences associated with drug-induced chromatin opening contain motifs for DNA damage-associated transcription factors including p53 and ZBTB14, and associate with increases in active histone acetylation and gene expression. 21 AF- and HF-associated SNPs directly overlap with regions associated with drug-induced opening. A shared intronic HF and AF SNP, rs3176326, that is also an eQTL for CDKN1A in heart tissue, associates with increased chromatin accessibility, histone acetylation, and CDKN1A expression in response to all ACs. Our results demonstrate large-scale changes in chromatin accessibility in cardiomyocytes treated with ACs, which correspond to several regions harboring CVD risk loci. Author summaryAnthracyclines are a widely used class of breast cancer drugs that are linked to cardiac toxicity and the development of heart disease in some women. There are hundreds of genetic variants that associate with risk for heart disease; however their role and mechanism of action in drug- induced toxicity is unclear given that most reside in the non-coding genome. We therefore tested the effects of five breast cancer drugs on genome-wide chromatin accessibility using induced pluripotent stem cell-derived cardiomyocytes. We found tens of thousands of chromatin regions that change in accessibility after drug treatment. Regions with increased accessibility contain sequences that have been shown to bind DNA damage-associated transcription factors, and associate with increases in nearby gene expression. We find 21 heart disease-associated genetic variants in regions that increase in chromatin accessibility following treatment. This suggests that cancer drugs have large effects on the non-coding genome of heart cells including at regions associated with heart disease. This research contributes to our understanding of how genetic variants associated with disease exert their effects.

genomics↗

bioRxiv2025

In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer

Antibodies are central mediators of the adaptive immune response, and they are powerful research tools and therapeutics. Antibody discovery requires substantial experimental effort, such as immunization campaigns or in vitro library screening. Predicting antibody-antigen binding a priori remains challenging. However, recent machine learning methods raise the possibility of in silico antibody discovery, bypassing or reducing initial experimental bottlenecks. Here, we report a virtual screen using AlphaFold-Multimer (AF-M) that prospectively identified nanobody binders to MRGPRX2, a G protein-coupled receptor (GPCR) and therapeutic target for the treatment of pseudoallergic inflammation and itch. Using previously reported nanobody-GPCR structures, we identified a set of AF-M outputs that effectively discriminate between interacting and non-interacting nanobody-GPCR pairs. We used these outputs to perform a prospective in silico screen, identified nanobodies that bind MRGPRX2 with high affinity, and confirmed activity in signaling and functional cellular assays. Our results provide a proof of concept for fully computational antibody discovery pipelines that can circumvent laboratory experiments.

biochemistry↗

bioRxiv2023

Antibodies Expand the Scope of Angiotensin Receptor Pharmacology

G protein-coupled receptors (GPCRs) are key regulators of human physiology and are the targets of many small molecule research compounds and therapeutic drugs. While most of these ligands bind to their target GPCR with high affinity, selectivity is often limited at the receptor, tissue, and cellular level. Antibodies have the potential to address these limitations but their properties as GPCR ligands remain poorly characterized. Here, using protein engineering, pharmacological assays, and structural studies, we develop maternally selective heavy chain-only antibody ("nanobody") antagonists against the angiotensin II type I receptor (AT1R) and uncover the unusual molecular basis of their receptor antagonism. We further show that our nanobodies can simultaneously bind to AT1R with specific small-molecule antagonists and demonstrate that ligand selectivity can be readily tuned. Our work illustrates that antibody fragments can exhibit rich and evolvable pharmacology, attesting to their potential as next-generation GPCR modulators.

pharmacology and toxicology↗

bioRxiv2022

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. We designed a set of experiments using a diverse naive synthetic camelid antibody fragment ( nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally tested our models performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the model allowed us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its pharmacological properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

pharmacology and toxicology↗