Systematic elucidation and in vivo validation of sequences enriched in hindbrain transcriptional control (original) (raw)

1. Xylena Reed1,2,4,
2. Leila Taher3,4,
3. Zachary E. Stine1,
4. Takeshi Matsui1,
5. Ivan Ovcharenko3,5 and
6. Andrew S. McCallion1,5
7. 1McKusick–Nathans Institute of Genetic Medicine, Department of Molecular and Comparative Pathobiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA;
8. 2Predoctoral Training Program in Human Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA;
9. 3Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
10. ↵4 These authors contributed equally to this work.

Abstract

Illuminating the primary sequence encryption of enhancers is central to understanding the regulatory architecture of genomes. We have developed a machine learning approach to decipher motif patterns of hindbrain enhancers and identify 40,000 sequences in the human genome that we predict display regulatory control that includes the hindbrain. Consistent with their roles in hindbrain patterning, MEIS1, NKX6-1, as well as HOX and POU family binding motifs contributed strongly to this enhancer model. Predicted hindbrain enhancers are overrepresented at genes expressed in hindbrain and associated with nervous system development, and primarily reside in the areas of open chromatin. In addition, 77 (0.2%) of these predictions are identified as hindbrain enhancers on the VISTA Enhancer Browser, and 26,000 (60%) overlap enhancer marks (H3K4me1 or H3K27ac). To validate these putative hindbrain enhancers, we selected 55 elements distributed throughout our predictions and six low scoring controls for evaluation in a zebrafish transgenic assay. When assayed in mosaic transgenic embryos, 51/55 elements directed expression in the central nervous system. Furthermore, 30/34 (88%) predicted enhancers analyzed in stable zebrafish transgenic lines directed expression in the larval zebrafish hindbrain. Subsequent analysis of sequence fragments selected based upon motif clustering further confirmed the critical role of the motifs contributing to the classifier. Our results demonstrate the existence of a primary sequence code characteristic to hindbrain enhancers. This code can be accurately extracted using machine-learning approaches and applied successfully for de novo identification of hindbrain enhancers. This study represents a critical step toward the dissection of regulatory control in specific neuronal subtypes.

Footnotes

• ↵5 Corresponding authors
  E-mail andy{at}jhmi.edu
  E-mail ovcharen{at}nig.gov

• [Supplemental material is available for this article.]

• Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.139717.112.
  Freely available online through the Genome Research Open Access option.

• Received February 28, 2012.

• Accepted June 21, 2012.

• © 2012, Published by Cold Spring Harbor Laboratory Press

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