1. Enhancers and 3D genomics Noam Bar RESEARCH METHODS IN COMPUTATIONAL BIOLOGY

2. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

3. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

4. Non coding DNA  Most of our genome  Important or junk  Holds regulatory elements  Also evolutionary conserved

5. Non coding variation – source of common disorders  Protein coding mutations  Minimally explored

6. Transcription  Reminder, what is transcription

8. Promoter  Some regulatory elements are at close distance to their target genes  Spreads about ~1000 bps upstream to a gene’s TSS  So what about those other conserved elements

10. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

11. Trancsription factors

12. Transcription factors  DNA binding proteins  Recognize 6-12 bp-long sequences  TF occupancy depends on affinity and concentration

13. TF mechanisms  Combinatorial (in most cases)  Diverse types of transcriptional output

14. Additive vs cooperative

15. Co-binding to common cofactors or common complexes

16. Activating chromatin remodeling

17. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

18. Enhancers  Regulator cis elements  Short DNA fragments of several hundred bps, that contain TF binding sequences  Located upstream, downstream or within their target gene (intronic)

20. Gene expression may be tissue specific

22. Why should we look at enhancers?  Variations in regulatory element could lead to the total absence of a gene product  472 out of 1200 SNPs associated with disease, were found in non coding sequences (Visel & Rubin, Berkeley, 2009)

24. Point mutations in the human enhancer

25. Limb enhancer deleted

27. Locating enhancers  Comparative genomics  Molecular methods (ChIP)  Computational methods

28. Comparative genomics  Conserved sequences hold functional properties  Statistical tools  DFS vs BFS

29. enhancer promoter

30. Molecular methods  ChIP technology  Transcriptional coactivator p300  Histone methylation signatures

31. Computational methods  Using gathered data of known enhancers to predict locations of new ones  Machin learning as a tool of finding enhancers

32. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

33. Topological domains  Chromatin interaction identification methods had been used to explore spatial structure  Megabase-sized local chromatin interactions domain

34. Topological domains

35. 3C method chromosome conformation capture  Developed in Harvard in 2002 by Job Dekker

36. Hi-C method

37. Dixon et al study  Hi-C experiment in mouse ES cells, human ES cells, and human IMR90 fibroblasts

38. Genes spreading across a certain domain 5 megabases (5,000,000 bps)

39. Topological domains

41. Topological domains and resulting directional bias

42. Topological domains and transcription control  Topological domains boundaries correlate with regions of the genome displaying classical insulator activity

43. enhancer promoterTarget gene enhancer insulator

44. Conservation of spatial structure Human KBM7 cells Human NHEK cells 3 megabases

45. Conservation of topological boundaries between cell types

47. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

48. Approaching the task  Enhancers don’t always regulate nearest gene  Comparing ChIP data with transcriptome data (RNA-Seq) does not provide the direct evidence for enhancer-promoter interactions

49. ChIA-PET method  Combination of 3C and ChIP methods  Detects interactions of chromatin mediated by a protein of interest

50. Kieffer-Kwon et al, 2013 mouse B & ES cells

53. Outline  Introduction and motivation o Transcription and non coding regulatory elements  Background o Transcription factors o Enhancers o Topological domains  Finding enhancer’s target genes  Conclusion

56. Thank you