Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens

Abstract

Noncoding regulatory elements in the human genome can control gene expression through distinct mechanisms, including acting as enhancers, silencers, or topological elements. However, we lack tools to identify all such classes systematically and quantify their effects on gene expression in an endogenous genomic context. Here we develop Swap-seq, a method that combines pooled twin prime editing with flow-sorting to delete hundreds of endogenous regulatory sequences and measure their quantitative effects on the expression of a nearby gene. We apply Swap-seq to dissect the regulatory elements that control PPIF expression in THP-1 monocytes. We quantify the effects of distal and intronic enhancers, characterize CTCF insulators, and discover a silencer that regulates distal genes through histone deacetylase (HDAC)-mediated repression of neighboring enhancers. Hundreds of other genomic elements share its chromatin signatures and transcription factor binding sites, suggesting that this silencer represents a broader class of repressive elements. We leverage Swap-seq data to benchmark sequence-to-function models and show that they often capture the local activity of regulatory elements but fail to link them to distal target genes. Swap-seq thus provides a generalizable tool to discover regulatory elements across mechanistic classes, characterize their effects on gene expression, and generate large-scale datasets for evaluating sequence-to-function models.

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BibTeX RIS

BibTeX

@misc{cai2026systematic,
  title = {{Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens}},
  author = {X. S. Cai and M. T. Montgomery and M. Nagano and A. Nisal and Z. Gu and Z. Chen and K. Obbad and R. Rastogi and Y. Tan and T. Zeng and M. U. Sheth and C. Xia and S. Chiang and A. Kundaje and A. S. Hansen and L. Bintu and J. M. Engreitz},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.08.756856},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.08.756856v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens
AU  - X. S. Cai
AU  - M. T. Montgomery
AU  - M. Nagano
AU  - A. Nisal
AU  - Z. Gu
AU  - Z. Chen
AU  - K. Obbad
AU  - R. Rastogi
AU  - Y. Tan
AU  - T. Zeng
AU  - M. U. Sheth
AU  - C. Xia
AU  - S. Chiang
AU  - A. Kundaje
AU  - A. S. Hansen
AU  - L. Bintu
AU  - J. M. Engreitz
PY  - 2026
DA  - 2026/10/08
PB  - bioRxiv
DO  - 10.64898/2026.10.08.756856
UR  - https://www.biorxiv.org/content/10.64898/2026.10.08.756856v1
AB  - Noncoding regulatory elements in the human genome can control gene expression through distinct mechanisms, including acting as enhancers, silencers, or topological elements. However, we lack tools to identify all such classes systematically and quantify their effects on gene expression in an endogenous genomic context. Here we develop Swap-seq, a method that combines pooled twin prime editing with flow-sorting to delete hundreds of endogenous regulatory sequences and measure their quantitative effects on the expression of a nearby gene. We apply Swap-seq to dissect the regulatory elements that control PPIF expression in THP-1 monocytes. We quantify the effects of distal and intronic enhancers, characterize CTCF insulators, and discover a silencer that regulates distal genes through histone deacetylase (HDAC)-mediated repression of neighboring enhancers. Hundreds of other genomic elements share its chromatin signatures and transcription factor binding sites, suggesting that this silencer represents a broader class of repressive elements. We leverage Swap-seq data to benchmark sequence-to-function models and show that they often capture the local activity of regulatory elements but fail to link them to distal target genes. Swap-seq thus provides a generalizable tool to discover regulatory elements across mechanistic classes, characterize their effects on gene expression, and generate large-scale datasets for evaluating sequence-to-function models.
ER  -

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