Author
Listed:
- 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
Stanford University)
- Axel Visel
(Lawrence Berkeley National Laboratory
University of California
US Department of Energy Joint Genome Institute)
- Len A. Pennacchio
(Lawrence Berkeley National Laboratory
US Department of Energy Joint Genome Institute
University of California)
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.
Suggested Citation
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. Hun, 2025.
"In vivo mapping of mutagenesis sensitivity of human enhancers,"
Nature, Nature, vol. 643(8072), pages 839-846, July.
Handle:
RePEc:nat:nature:v:643:y:2025:i:8072:d:10.1038_s41586-025-09182-w
DOI: 10.1038/s41586-025-09182-w
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