Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states
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 o...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62252-5 |
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| author | Tomasz 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 Xiaosai Yao |
| author_facet | Tomasz 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 Xiaosai Yao |
| author_sort | Tomasz Włodarczyk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-53fa3c668ce24fe99d473eb2c80afac3 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-53fa3c668ce24fe99d473eb2c80afac32025-08-20T03:05:06ZengNature PortfolioNature Communications2041-17232025-08-0116111910.1038/s41467-025-62252-5Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell statesTomasz Włodarczyk0Aaron Lun1Diana Wu2Minyi Shi3Xiaofen Ye4Shreya Menon5Shushan Toneyan6Kerstin Seidel7Liang Wang8Jenille Tan9Shang-Yang Chen10Timothy Keyes11Aleksander Chlebowski12Adrian Waddell13Wei Zhou14Yangmeng Wang15Qiuyue Yuan16Yu Guo17Liang-Fu Chen18Bence Daniel19Antonina Hafner20Meng He21Alejandro Chibly22Yuxin Liang23Zhana Duren24Ciara Metcalfe25Marc Hafner26Christian W. Siebel27M. Ryan Corces28Robert Yauch29Shiqi Xie30Xiaosai Yao31gRED Computational Sciences, Genentech IncgRED Computational Sciences, Genentech IncDiscovery Oncology, Genentech IncProteomic & Genomic Technologies, Genentech IncDiscovery Oncology, Genentech IncGladstone Institute of Neurological Disease, Gladstone InstitutesgRED Computational Sciences, Genentech IncDiscovery Oncology, Genentech IncDiscovery Oncology, Genentech IncDiscovery Oncology, Genentech IncgRED Computational Sciences, Genentech IncgRED Computational Sciences, Genentech IncgRED Computational Sciences, Genentech IncgRED Computational Sciences, Genentech IncDiscovery Oncology, Genentech IncTranslational Medicine Oncology, Genentech IncCenter for Human Genetics, Department of Genetics and Biochemistry, Clemson UniversitygRED Computational Sciences, Genentech IncDiscovery Oncology, Genentech IncProteomic & Genomic Technologies, Genentech IncDiscovery Oncology, Genentech IncTranslational Medicine Oncology, Genentech IncgRED Computational Sciences, Genentech IncProteomic & Genomic Technologies, Genentech IncCenter for Human Genetics, Department of Genetics and Biochemistry, Clemson UniversityDiscovery Oncology, Genentech IncgRED Computational Sciences, Genentech IncDiscovery Oncology, Genentech IncGladstone Institute of Neurological Disease, Gladstone InstitutesDiscovery Oncology, Genentech IncDiscovery Oncology, Genentech IncgRED Computational Sciences, Genentech IncAbstract 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.https://doi.org/10.1038/s41467-025-62252-5 |
| spellingShingle | Tomasz 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 Xiaosai Yao Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states Nature Communications |
| title | Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states |
| title_full | Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states |
| title_fullStr | Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states |
| title_full_unstemmed | Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states |
| title_short | Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states |
| title_sort | epiregulon single cell transcription factor activity inference to predict drug response and drivers of cell states |
| url | https://doi.org/10.1038/s41467-025-62252-5 |
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