Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Journal: Cell genomics
PMID: 40020686

Abstract

Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how concentrations of the dosage-sensitive TFs TWIST1 and SOX9 affect regulatory element (RE) chromatin accessibility in facial progenitor cells, achieving near-experimental accuracy. High-affinity motifs that allow for heterotypic TF co-binding and are concentrated at the center of REs buffer against quantitative changes in TF dosage and predict unperturbed accessibility. Conversely, low-affinity or homotypic binding motifs distributed throughout REs drive sensitive responses with minimal impact on unperturbed accessibility. Both buffering and sensitizing features display purifying selection signatures. We validated these sequence features through reporter assays and demonstrated that TF-nucleosome competition can explain low-affinity motifs' sensitizing effects. This combination of transfer learning and quantitative chromatin response measurements provides a novel approach for uncovering additional layers of the cis-regulatory code.

Authors

  • Sahin Naqvi
    Departments of Chemical and Systems Biology and Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA. Electronic address: sahin.naqvi@childrens.harvard.edu.
  • Seungsoo Kim
  • Saman Tabatabaee
    Departments of Chemical and Systems Biology and Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Anusri Pampari
    Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
  • Anshul Kundaje
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Jonathan K Pritchard
    Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA. Electronic address: pritch@stanford.edu.
  • Joanna Wysocka
    Departments of Chemical and Systems Biology and Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: wysocka@stanford.edu.

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