In vivo mapping of mutagenesis sensitivity of human enhancers

Michael Kosicki, Boyang Zhang, Vivian Hecht, Anusri Pampari, Laura E. Cook, Neil Slaven, Jennifer A. Akiyama, Ingrid Plajzer-Frick, Catherine S. Novak, Momoe Kato, Stella Tran, Riana D. Hunter, Kianna Maydell, Sarah Barton, Erik Beckman, Yiwen Zhu, Diane E. Dickel, Anshul Kundaje, Axel Visel () and Len A. Pennacchio ()
Additional contact information
Michael Kosicki: Lawrence Berkeley National Laboratory
Boyang Zhang: Stanford University
Vivian Hecht: Stanford University
Anusri Pampari: Stanford University
Laura E. Cook: Lawrence Berkeley National Laboratory
Neil Slaven: Lawrence Berkeley National Laboratory
Jennifer A. Akiyama: Lawrence Berkeley National Laboratory
Ingrid Plajzer-Frick: Lawrence Berkeley National Laboratory
Catherine S. Novak: Lawrence Berkeley National Laboratory
Momoe Kato: Lawrence Berkeley National Laboratory
Stella Tran: Lawrence Berkeley National Laboratory
Riana D. Hunter: Lawrence Berkeley National Laboratory
Kianna Maydell: Lawrence Berkeley National Laboratory
Sarah Barton: Lawrence Berkeley National Laboratory
Erik Beckman: Lawrence Berkeley National Laboratory
Yiwen Zhu: Lawrence Berkeley National Laboratory
Diane E. Dickel: Lawrence Berkeley National Laboratory
Anshul Kundaje: Stanford University
Axel Visel: Lawrence Berkeley National Laboratory
Len A. Pennacchio: Lawrence Berkeley National Laboratory

Nature, 2025, vol. 643, issue 8072, 839-846

Abstract: Abstract Distant-acting enhancers are central to human development1. However, our limited understanding of their functional sequence features prevents the interpretation of enhancer mutations in disease2. Here we determined the functional sensitivity to mutagenesis of human developmental enhancers in vivo. Focusing on seven enhancers that are active in the developing brain, heart, limb and face, we created over 1,700 transgenic mice for over 260 mutagenized enhancer alleles. Systematic mutation of 12-base-pair blocks collectively altered each sequence feature in each enhancer at least once. We show that 69% of all blocks are required for normal in vivo activity, with mutations more commonly resulting in loss (60%) than in gain (9%) of function. Using predictive modelling, we annotated critical nucleotides at the base-pair resolution. The vast majority of motifs predicted by these machine learning models (88%) coincided with changes in in vivo function, and the models showed considerable sensitivity, identifying 59% of all functional blocks. Taken together, our results reveal that human enhancers contain a high density of sequence features that are required for their normal in vivo function and provide a rich resource for further exploration of human enhancer logic.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-025-09182-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:643:y:2025:i:8072:d:10.1038_s41586-025-09182-w

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-025-09182-w

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().