{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"A transcription factor-responsive enhancer discovery platform for targeted immunotherapy","authors":"Jia, Y.; Chen, C.-Y.; Zhu, B.; Wu, Z.; Wu, Y.-C.; Wang, R.; Salamah, A. I.; Halder, S.; Liu, Y.-N.; Scimeca, L.; Yin, H.; Sabu, T.; Antwi, E. B.; Wang, Y.; Wu, M.-R.","author_corresponding":"Ming-Ru Wu","author_corresponding_institution":"Dana-Farber Cancer Institute and Harvard Medical School","doi":"10.64898\/2025.12.08.693012","date":"2025-12-10","version":"1","type":"new results","license":"cc_by_nc_nd","category":"synthetic biology","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2025\/12\/10\/2025.12.08.693012.source.xml","abstract":"Synthetic enhancers with high specificity are crucial for therapeutic gene control. However, experimental screens and machine learning-guided design typically require context-specific datasets, limiting generalizability. Because transcription factor (TF) activity reflects cellular state, TF-responsive enhancer libraries offer a universal starting point. Here, we developed TREND (transcription factor-responsive enhancer discovery), a massively parallel reporter assay of [~]2.7 million enhancer-barcode constructs representing 57,715 designs. TREND covers 1,068 motif-annotated proteins, including 729 confirmed TFs across 49 DNA-binding domain families. Applied to ovarian cancer, TREND identified enhancers that discriminate cancer from normal epithelial cells. These enhancers enabled protein-interaction-based AND-gate circuits with reduced OFF-state leakage and amplified ON-state output, driving tumor-restricted expression of combinatorial immune effectors and robust antitumor responses in murine ovarian cancer models. TREND also identified T-cell activation-responsive enhancers with greater inducibility and lower basal activity than conventional NFAT-motif-based elements. Together, TREND provides a generalizable framework for context-specific enhancer discovery and therapeutic gene regulation.","funder":"NA","published":"NA","server":"bioRxiv"},{"title":"Harnessing transcription factor activity for programmable therapeutic gene regulation","authors":"Jia, Y.; Chen, C.-Y.; Zhu, B.; Wu, Z.; Wu, Y.-C.; Wang, R.; Salamah, A. I.; Halder, S.; Liu, Y.-N.; Guo, Y.; Chen, Y.; Scimeca, L.; Yin, H.; Sabu, T.; Antwi, E. B.; Wang, Y.; Wu, M.-R.","author_corresponding":"Ming-Ru Wu","author_corresponding_institution":"Dana-Farber Cancer Institute and Harvard Medical School","doi":"10.64898\/2025.12.08.693012","date":"2026-01-12","version":"2","type":"new results","license":"cc_by_nc_nd","category":"synthetic biology","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2026\/01\/12\/2025.12.08.693012.source.xml","abstract":"Synthetic enhancers with high specificity are crucial for therapeutic gene control. However, experimental screens and machine learning-guided design typically require context-specific datasets, limiting generalizability. Because transcription factor (TF) activity reflects cellular state, TF-responsive enhancer libraries offer a universal starting point. Here, we developed TREND (transcription factor-responsive enhancer discovery), a massively parallel reporter assay of [~]2.7 million enhancer-barcode constructs representing 57,715 designs. TREND covers 1,068 motif-annotated proteins, including 729 confirmed TFs across 49 DNA-binding domain families. Applied to ovarian cancer, TREND identified enhancers that discriminate cancer from normal epithelial cells. These enhancers enabled protein-interaction-based AND-gate circuits with reduced OFF-state leakage and amplified ON-state output, driving tumor-restricted expression of combinatorial immune effectors and robust antitumor responses in murine ovarian cancer models. TREND also identified T-cell activation-responsive enhancers with greater inducibility and lower basal activity than conventional NFAT-motif-based elements. Together, TREND provides a generalizable framework for context-specific enhancer discovery and therapeutic gene regulation.","funder":"NA","published":"NA","server":"bioRxiv"},{"title":"TREND: A generalizable synthetic enhancer discovery platform for targeted immunotherapy","authors":"Jia, Y.; Chen, C.-Y.; Zhu, B.; Wu, Z.; Wu, Y.-C.; Wang, R.; Salamah, A. I.; Halder, S.; Liu, Y.-N.; Guo, Y.; Chen, Y.; Scimeca, L.; Yin, H.; Sabu, T.; Wang, Y.; Antwi, E. B.; Wang, Y.; Wu, M.-R.","author_corresponding":"Ming-Ru Wu","author_corresponding_institution":"Dana-Farber Cancer Institute and Harvard Medical School","doi":"10.64898\/2025.12.08.693012","date":"2026-05-07","version":"3","type":"new results","license":"cc_by_nc_nd","category":"synthetic biology","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2026\/05\/07\/2025.12.08.693012.source.xml","abstract":"Synthetic enhancers with high specificity are crucial for therapeutic gene control. However, experimental screens and machine learning-guided design typically require context-specific datasets, limiting generalizability. Because transcription factor (TF) activity reflects cellular state, TF-responsive enhancer libraries offer a universal starting point. Here, we developed TREND (transcription factor-responsive enhancer discovery), a massively parallel reporter assay of [~]2.7 million enhancer-barcode constructs representing 57,715 designs. TREND covers 1,068 motif-annotated proteins, including 729 confirmed TFs across 49 DNA-binding domain families. Applied to ovarian cancer, TREND identified enhancers that discriminate cancer from normal epithelial cells. These enhancers enabled protein-interaction-based AND-gate circuits with reduced OFF-state leakage and amplified ON-state output, driving tumor-restricted expression of combinatorial immune effectors and robust antitumor responses in murine ovarian cancer models. TREND also identified T-cell activation-responsive enhancers with greater inducibility and lower basal activity than conventional NFAT-motif-based elements. Together, TREND provides a generalizable framework for context-specific enhancer discovery and therapeutic gene regulation.","funder":"NA","published":"NA","server":"bioRxiv"}]}