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Transcription regulation: a genomic network

Transcription regulation: a genomic network. Nicholas Luscombe Laboratory of Mark Gerstein Department of Molecular Biophysics and Biochemistry Yale University Gerstein lab : Haiyuan Yu Snyder lab : Christine Horak Teichmann lab : Madan Babu.

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Transcription regulation: a genomic network

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  1. Transcription regulation: a genomic network Nicholas Luscombe Laboratory of Mark GersteinDepartment of Molecular Biophysics and Biochemistry Yale University Gerstein lab: Haiyuan Yu Snyder lab: Christine Horak Teichmann lab: Madan Babu

  2. Understand Proteins, through analyzing populations Structures(Motions, Composition)Functions(Locations, Interactions)Evolution(Pseudogenes) Work on a diverse range of data Microarrays Sequences Structures

  3. Proteome PubMed Hits Central post-genomic terms Transcriptome

  4. XX XX X XXX Transcriptome: the set of all mRNAs expressed in a cell at a specific time Microarrays: - expression - ChIp-chip Transcriptome Initial Step: genome sequence, genes and genomic features

  5. Overview • ChIp-chip experiments for • 11 transcription factors • - SBF & MBF • Computational studies of • yeast regulatory network

  6. Overview • ChIp-chip experiments for • 11 transcription factors • - SBF & MBF • Computational studies of • yeast regulatory network

  7. SBF and MBF are cell cycle transcription factors G1 SBF MBF M S G2 [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  8. SBF = SCB Binding Factor Swi6p Swi4p SCB G 1 c y c l i n g e n e s SCB = Swi4p-Swi6p cell cycle box Regulator at the G1/S phase of cell cycle Only five known target genes [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  9. MBF = MCB Binding Factor Swi6p Mbp1 MCB S-phase cyclin genes DNA synthesis genes Mlu1 Cell Cycle Box MCB = [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  10. SCB CGCGAAAA MCB ACGCGN SBF binds MCBs and MBF binds SCBs SBF MBF ? SCB MCB [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  11. ChIp-chip Chromatin Immunoprecipitation and DNA chip technology [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  12. Crosslink IP Yeast ChIp-chip Epitope-tagged Untagged Lyse Sonicate Reverse X-links Label Hybridize [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  13. [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  14. Swi4: 183 genes • MBP: 98 genes • Swi4: 163 intergenic regions • Mbp1: 87 intergenic regions • 43 were bound by both [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  15. MBF 11% 22% 5% 9% 8% 14% 6% 4% 1% 7% 2% 41% 22% 24% 24% Cell Wall DNA synthesis Morphogenesis Cell Cycle Transcription Proteolysis Other Unknown Functions of SBF and MBF Gene Targets SBF [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  16. SWI4 HCM1 YOX1 TOS4 PLM2 TYE7 YHP1 YAP5 POG1 TOS8 SBF and MBF target other transcription Factors WTM1 M SPT21 MBF MAL33 RGT1 G1 PDR1 SBF NDD1 GAT2 SOK2 S G2 [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  17. Genomic distribution of TF binding sites

  18. Cell Cycle Control TF’s have distinct Functional Roles Hcm1 Tos4 Tos8 Pog1 Bud Growth Chromosome Segregation and Mitosis Yap5 Yhp1 Yox1 Plm2 Tye7 Protein Synthesis and Metabolism DNA Synthesis and Repair [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  19. Yeast transcriptional regulatory network [Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  20. Overview • ChIp-chip experiments for • 11 transcription factors • - SBF & MBF • Computational studies of • yeast regulatory network

  21. Comprehensive TF dataset 142 transcription factors 3,420 target genes 7,074 regulatory interactions [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  22. Comprehensive Yeast TF network Transcription Factors • Very complex network • But we can simplify: • Topological measures • Network motifs Target Genes [Barabasi, Alon]

  23. Comprehensive Yeast TF network Transcription Factors • Very complex network • But we can simplify: • Topological measures • Network motifs Target Genes [Barabasi, Alon]

  24. Topological measures Indicate the gross topological structure of the network Degree Path length Clustering coefficient [Barabasi]

  25. Incoming degree = 2.1 • each gene is regulated by ~2 TFs Outgoing degree = 49.8 each TF targets ~50 genes Topological measures Number of incoming and outgoing connections Degree [Barabasi]

  26. Regulatory hubs >100 target genes Dictate structure of network Scale-free distribution of outgoing degree • Most TFs have few target genes • Few TFs have many target genes [Barabasi]

  27. 1 intermediate TF Topological measures Number of intermediate TFs until final target Starting TF Final target Indicate how immediate a regulatory response is Average path length = 4.7 = 1 Path length [Barabasi]

  28. 4 neighbours 1 existing link 6 possible links Topological measures Ratio of existing links to maximum number of links for neighbouring nodes Measure how inter-connected the network is Average coefficient = 0.11 Clustering coefficient = 1/6 = 0.17 [Barabasi]

  29. Comprehensive Yeast TF network Transcription Factors • Very complex network • But we can simplify: • Topological measures • Network motifs Target Genes [Alon]

  30. Network motifs Regulatory modules within the network SIM MIM FBL FFL [Alon]

  31. SIM = Single input motifs HCM1 ECM22 STB1 SPO1 YPR013C [Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  32. MIM = Multiple input motifs SBF MBF SPT21 HCM1 [Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  33. FBL = Feed-back loops MBF SBF Tos4 [Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  34. FFL = Feed-forward loops SBF Pog1 Yox1 Tos8 Plm2 [Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

  35. Comprehensive Yeast TF network Transcription Factors • Very complex network • But we can simplify: • Topological measures • Network motifs Target Genes [Barabasi, Alon]

  36. Essentiality of regulatory hubs • Hubs dictate the overall structure of the network • Represent vulnerable points • How important are hubs for viability? • Integrate gene essentiality data Transcription Factors [Yu et al (2004), Trends Genet, 20: 227

  37. hubs (<100 targets) All TFs % TFs that are essential non-hubs (<100 targets) non-hubs (<100 targets) All TFs All TFs Essentiality of regulatory hubs • Essential genes are lethal if deleted from genome • 1,105 yeast genes are essential • Which TFs are essential? Hubs tend to be more essential than non-hubs [Yu et al (2004), Trends Genet, 20: 227

  38. Yeast expression data [Brown] Influence of network on gene expression Transcription Factors • How does the regulatory network affect gene expression? Target Genes [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  39. [Brown, Botstein, Church] Clusteringthe yeast cell cycle mRNA expression level (ratio) Time-> Microarray timecourse of 1 ribosomal protein

  40. Clusteringthe yeast cell cycle Random relationship from 18M

  41. Clusteringthe yeast cell cycle Clusteringthe yeast cell cycle Close relationship from 18M (Associated Ribosomal Proteins)

  42. Time-Shifted Inverted Simultaneous Traditional Global Correlation Local Clustering algorithm identifies further (reasonable) types of expression relationships (Algorithm adapted from local sequence alignment) [Qian et al]

  43. Single input motifs [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  44. Multiple input motifs [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  45. Feed forward motifs [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  46. Summary of expression relationships • Target genes tend to be co-expressed • Co-expression is higher when more TFs are involved • TFs and target genes have complex relationships • Often expression profiles are time-shifted [Yu, Luscombe et al (2003), Trends Genet, 19: 422]

  47. Dynamic Yeast TF network Transcription Factors • Analysed network as a static entity • But network is dynamic • Different sections of the network are active under different cellular conditions • Integrate more gene expression data Target Genes [Luscombe et al, Nature (In press)]

  48. Gene expression data • Genes that are differentially expressed under five cellular conditions • Assume these genes undergo transcription regulation [Luscombe et al, Nature (In press)]

  49. Define differentially expressed genes • Identify TFs that regulate these genes • Identify further TFs that regulate these TFs Backtracking to find active sub-network Active regulatory sub-network [Luscombe et al, Nature (In press)]

  50. cell cycle sub-network • 70 TFs • 280 genes • 550 interactions Network usage under cell cycle complete network • 142 TFs • 3,420 genes • 7,074 interactions [Luscombe et al, Nature (In press)]

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