1. Regulatory Genomics Lecture 2 November 2012 Yitzhak (Tzachi) Pilpel 1

2. Course requirements Attendance and participation Two reading assignments A final take home papers reading-based exam website No meeting next week on Nov 15 th 2

3. Expression regulation of genes determines complex spatio-temporal patterns 3

4. Monitor expression during cell cycle Time mRNA expression level G1 S G2 M 4

5. Time-point 1 Time-point 3 Time-point 2 -1.8 -1.3 -0.8 -0.3 0.2 0.7 1.2 123 -2 -1.5 -0.5 0 0.5 1 1.5 123 -1.5 -0.5 0 0.5 1 1.5 123 Time -point Normalized Expression Normalized Expression Normalized Expression Genes can be clustered based on time-dependent expression profiles 5

6. The K-means algorithm Start with random positions of centroids. Iteration = 0 6

7. K-means Start with random positions of centroids. Assign data points to centroids Iteration = 1 7

8. K-means Start with random positions of centroids. Assign data points to centroids. Move centroids to center of assigned points. Iteration = 1 8

9. K-means Start with random positions of centroids. Assign data points to centroids. Move centroids to center of assigned points. Iterate till minimal cost. Iteration = 3 9

10. An expression cluster

11. 1D and 2D clustering of gene expression data

12. Hierarchical clustering

13. How to join sets? f edc b a

14. How to measure a distance between expression profiles? 14 Gene x Gene y t1 t2 t3 Gene x Gene y t4 t5

15. Clustering the data http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletH.html http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html Try these two applets at home (needs java)

16. The common distance matrices 16

17. Promoter Motifs and expression profiles CGGCCCCGCGGA CTCCTCCCCCCCTTCTGGCCAATCA ATGTACGGGTG 17

18. Formaldehyde crosslinks living yeast cells Binding site TF Binding site Inside the yeast nucleus: ChIP - chromatin immunoprecipitation Reversal of the crosslinks to separate DNA segments from proteins, and fluorescence labeling of each pool separately (enriched DNA) hybridization to DNA array of all yeast intergenic sequences (unenriched DNA) TF = epitope tag = TF of interest Harvest and sonicate; results in DNA fragments (some of which are bound to proteins) 18

19. P-value 0.05 35,365 interactions P-value 0.01 12,040 interactions P-value 0.005 8,190 interactions P-value 0.001 3,985 interactions P-value, or confidence level, for each spot in array The total number of protein-DNA interactions in the location analysis data set, using a range of P value thresholds: A P-value was selected which minimizes false positives, at the expense of gaining false negatives. P-value 0.001 19

20. Genome-wide Distribution of Transcriptional Regulators Promoter regions of 2343 of 6270 yeast genes (37%) were bound by 1 or more of the 106 transcriptional regulators (P=.001) Avg.: regulator binds 38 promoter regions At P= 0.001, significantly more intergenic regions bind 4 or more regulators than expected by chance 20

21. Network Motifs 21

22. Network Motifs 22

23. Network Motifs in the Yeast Regulatory Network -Based on algorithmic analyses performed in Matlab; http://jura.wi.mit.edu/cgi- bin/young_public/navframe.cgi?s=17&f=networkmotif 10 3 49 90 81 18 8 23 Protein Gene

24. The Cell Cycle Transcriptional Regulatory Network: Various stages of cell cycle Blue boxes represent sets of genes bound by a common set of regulators. Each box is positioned according to the time of peak expression levels for the genes represented by the box. Ovals represent regulators, connected to genes they regulate Length of arc defines the period of activity of that regulator 24

25. Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators 25

26. Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators 26

27. Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators 27

28. DNAmRNAProtein  Inactive DNA The Central Dogma of Molecular Biology Expressing the genome RNA 28

29. Translation consists of initiation, elongation and termination 5’ 3’STOP Codon Anti-codon 29

30. The ribosome attachment site determines initiation rate E. coli Yeast 30

31. A consensus for S. cerevisiae ribosome attachment sites? position relative to ATG 100% 0% sequence How good is it as a “ribosomal attachment site” ? ribosomal attachment site score 31

32. 5’ 3’ CTG CGC GCG CAG GCG 32

33. Rank ribosomal attachment site score The sequence adaptation score of proteins in yeast CRP good score bad score 33

34. Multiple codons for the same amino acid C1 C2 C3 C4 C5 C6 Serine: UCU UCC UCA UCG AGC AGU Cysteine: UGU UGC Methionine: UGG STOP: UAA, UAG UGA 34

35. G T R Y E C Q A S F D C1C1C1C1C1C1C1C1C1C1C1 C2C2C2C2C2C2C2C2C2C2C2 C1C1C2C1C1C2C1C1C2C1C1 C2C2C2C2C1C1C1C1C1C1C1 C1C1C1C1C1C1C1C2C2C2C2 For a hypothetical protein of 300 amino acids with two-codon each, There are 2^300 possible nucleotide sequences These variants will code for the same protein, and are thus considered “synonymous”. Indeed evolution would easily exchange between them But are they all really equivalent?? 35

36. Selection of codons might affect: Accuracy Throughput Costs Folding RNA-structure 36

37. W i /W max if W i  0 w i = w mean else { dos Reis et al. NAR 2004 The tRNA Adaptation Index (tAI) ATCCCAAAATCGAAT … … … A simple model for translation efficiency Wobble Interaction 37

38. Supply demand and charging 38

39. How the RNA structure influences translation? ? 39

40. No correlation between CAI and protein expression Positive correlation between structure’s energy and expression The 5’ window needs to be un-folded for high expression Protein abundance Protein abundance Conclusions from synthetic library 40

41. Formaldehyde crosslinks living yeast cells Binding site TF Binding site Inside the yeast nucleus: ChIP - chromatin immunoprecipitation Reversal of the crosslinks to separate DNA segments from proteins, and fluorescence labeling of each pool separately (enriched DNA) hybridization to DNA array of all yeast intergenic sequences (unenriched DNA) TF = epitope tag = TF of interest Harvest and sonicate; results in DNA fragments (some of which are bound to proteins) 41

42. A genome-wide method to measure translation efficiency (Ingolia Science 2009) 42

43. Translational response to starvation 43

44. DNAmRNAProtein  Inactive DNA The Central Dogma of Molecular Biology Expressing the genome RNA 44

45. mRNA abundance Option 1Option 2Option 3Option 4 Production degradation 45

46. Relationship between gene expression levels and mRNA decay rates across genes. A study in human population examined decay and steady-state mRNA level variation across people. Found strong negative or positive correlations between mRNA level and decay rates. Fast responding genes show “discordant” relation suggesting that increased expression is often accompanied by increased decay rate

47. The various phases are coupled 47

48. At the hardware level (post- transcription: RNA binding proteins) G1 1 1 1 0 G2 1 0 0 1 G3 0 1 1 1 48

49. At the hardware level (post- transcription: microRNA) G1 1 1 1 0 G2 1 0 0 1 G3 0 1 1 1 RISC 49

50. Yang CGFR 16:397, 2005 50

51. Computational approaches to find microRNA genes MiRscan (Lim, et al. 2003) –Scan to find conserved hairpin structures in both C. elegans and C. briggsae. –Using known microRNA genes (50) as training set. 51

52. What is the effect of over expression of a miR? 52

53. 53 None-Coding RNAs are often co- targeted with their own targets for various cellular needs

54. miR-124 decreases similarly the abundance and translation of mRNA targets 54

55. microRNA expression profiles classify human cancers Lu et al. Nature 435: 834, 2005 Samples (patients) miRs 55

56. Gene expression is noisy 56

57. Fluorescence distribution shapes 57

58. The cell intrinsic and extrinsic contributions to noise 58

59. DNA RNA Protein Regulation by transcription factors RNA Polymerase Ribosome Extrinsic Intrinsic Chromatin remodeling Transcription process Translation process Φ Protein degradation The actual intrinsic and extrinsic sources of noise: Extrinsic – variation in copy numbers of molecules among cells; Intrinsic: stochastic events 59

60. A theoretical approach 60 DNAmRNAProtein

61. The ratio of transcription to translation should affect noise 61

62. Transcription bursts should affect noise 62

63. Can noise be useful?

64. The native net shows longer and more duration-diverse competence periods

65. Native networks does better on a wider range of extracellular [DNA] The trade-off: High competence allows finding solutions, but reduces growth rate

66. Questions about noise What are the sources of noise? How is noise regulated in cells How is it tolerated by the biological systems that need to be noise free? When is noise advantageous /deleterious/ neutral? 66