In vivo mapping of mutagenesis sensitivity of human enhancers.

Journal: Nature
Published Date: Jun 18, 2025

Abstract

Distant-acting enhancers are central to human development. However, our limited understanding of their functional sequence features prevents the interpretation of enhancer mutations in disease. 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.

Authors

  • Michael Kosicki
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Boyang Zhang
    School of Biomedical Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada and Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada. zhangb97@mcmaster.ca.
  • Vivian Hecht
    Department of Genetics, Stanford University, Stanford, CA, USA.
  • Anusri Pampari
    Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
  • Laura E Cook
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Neil Slaven
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Jennifer A Akiyama
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Ingrid Plajzer-Frick
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Catherine S Novak
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Momoe Kato
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Stella Tran
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Riana D Hunter
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Kianna von Maydell
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Sarah Barton
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Erik Beckman
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Yiwen Zhu
    Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
  • Diane E Dickel
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Anshul Kundaje
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Axel Visel
    Department of Energy, Joint Genome Institute, Walnut Creek, California, USA.
  • Len A Pennacchio
    Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. LAPennacchio@lbl.gov.

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