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Machine learning classification of cell-specific cardiac enhancers uncovers developmental subnetworks regulating progenitor cell division and cell fate specification

Friday, November 08, 2013 — Poster Session IV

2:00 p.m. – 4:00 p.m.

FAES Academic Center (Upper-Level Terrace)




  • S. M. Ahmad
  • B. W. Busser
  • D. Huang
  • E. J. Cozart
  • S. Michaud
  • X. Zhu
  • N. Jeffries
  • A. Aboukhalil
  • M. L. Bulyk
  • I. Ovcharenko
  • A. M. Michelson


The Drosophila heart is composed of two distinct cell types, the contractile cardial cells (CCs) and the surrounding non-muscle pericardial cells (PCs), whose development is regulated by a network of conserved signaling molecules and transcription factors (TFs). Here we used machine learning with array-based chromatin immunoprecipitation (ChIP) data and TF sequence motifs to computationally classify cell type-specific cardiac enhancers. Large-scale testing of predicted enhancers at single cell resolution revealed the value of ChIP data for modeling cell type-specific activities. Furthermore, clustering the top-scoring classifier sequence features identified novel cardiac and cell-type specific regulatory motifs. For example, we found that the Myb motif learned by the classifier is critical for CC activity, and the Myb TF acts in concert with two forkhead domain TFs and Polo kinase to regulate cardiac progenitor cell divisions. In addition, differential motif enrichment and cis-trans genetic studies revealed that the Notch signaling pathway TF Suppressor of Hairless (Su(H)) discriminates PC and CC enhancer activities. Collectively, these studies elucidate molecular pathways used in the regulatory decisions for proliferation and differentiation of cardiac progenitor cells, implicate Su(H) in regulating cell fate decisions of these progenitors, and document the utility of enhancer modeling in uncovering developmental regulatory subnetworks.

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