 Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell statesTomasz Włodarczyk,
Aaron Lun,
Diana Wu,
Minyi Shi,
Xiaofen Ye,
Shreya Menon,
Shushan Toneyan,
Kerstin Seidel,
Liang Wang,
Jenille Tan,
Shang-Yang Chen,
Timothy Keyes,
Aleksander Chlebowski,
Adrian Waddell,
Wei Zhou,
Yangmeng Wang,
Qiuyue Yuan,
Yu Guo,
Liang-Fu Chen,
Bence Daniel,
Antonina Hafner,
Meng He,
Alejandro Chibly,
Yuxin Liang,
Zhana Duren,
Ciara Metcalfe,
Marc Hafner,
Christian W. Siebel,
M. Ryan Corces,
Robert Yauch (),
Shiqi Xie () and
Xiaosai Yao ()
Nature Communications, 2025, vol. 16, issue 1, 1-19 Abstract: Abstract Transcription factors (TFs) and transcriptional coregulators are emerging therapeutic targets. Gene regulatory networks (GRNs) can evaluate pharmacological agents and identify drivers of disease, but methods that rely solely on gene expression often neglect post-transcriptional modulation of TFs. We present Epiregulon, a method that constructs GRNs from single-cell ATAC-seq and RNA-seq data for accurate prediction of TF activity. This is achieved by considering the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. ChIP-seq data allows motif-agonistic activity inference of transcriptional coregulators or TF harboring neomorphic mutations. Epiregulon accurately predicted the effects of AR inhibition across different drug modalities including an AR antagonist and an AR degrader, delineated the mechanisms of a SMARCA4 degrader by identifying context-dependent interaction partners, and prioritized drivers of lineage reprogramming and tumorigenesis. By mapping gene regulation across various cellular contexts, Epiregulon can accelerate the discovery of therapeutics targeting transcriptional regulators. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62252-5 Ordering information: This journal article can be ordered from DOI: 10.1038/s41467-025-62252-5 Access Statistics for this article Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie More articles in Nature Communications from Nature |
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