Overview: Functional Genomics Dissections of Transcriptional Networks

1. Overview:Functional Genomics Dissections of Transcriptional Networks Rani Elkon Ron Shamir, Yossi Shiloh

2. I. Reverse-Engineering of Transcriptional Networks

3. ‘Reverse engineering’ of transcriptional networks • Infers regulatory mechanisms from gene expression data • Assumption: co-expression → transcriptional co-regulation → common cis-regulatory promoter elements • Step 1: Identification of co-expressed genes using microarray technology (clustering algs) • Step 2: Computational identification of cis-regulatory elements that are over-represented in promoters of the co-expressed genes • Such methodologies were first demonstrated in yeast

4. Reverse-engineering of the Yeast Cell-Cycle • Expression profiles were recorded in synchronized yeast cells in 10 min intervals over 2 cell cycles. • ~ 500 ORFs showed a periodic expression pattern

5. Reverse-engineering of the Yeast Cell-Cycle (II)

6. Reverse-engineering of the Human Cell-Cycle • Whitfield et al. recorded expression profiles during the progression of human cell cycle. • 874 genes showed periodic expression patterns, and were partitioned into five clusters (G1/S, S, G2, G2/M and M/G1). • We applied promoter analysis to these 5 clusters

7. p = 1.2x10-8 (true positive) 78 promoters (92 hits) p = 1.2x10-11 (152, 203) p = 8x10-4 (20, 25)

8. Enriched TFs in cell cycle-regulated promoters

9. II. Cis-Regulatory Modules

10. Transcriptional Modules I: Co-occurrence • Transcriptional regulation is combinatorial Promoter #1 Promoter #n

11. Defining transcriptional modules: • Co-occurrence • Positional bias (distance) • Orientational bias (order)

12. Positional and Orientational Bias for the RRPE-PAC Module

13. Modules II: Expression Coherence

15. III. Comparative Genomics

16. Conservation of Regulatory Elements Gene “DNA replication licensing factor MCM6”: (G1/S)

17. Human Cell Cycle Revisited • We detected global enrichments that pointed to major TFs in human cell cycle regulation. • However, we did not report on specific target genes due to high rate of false positive hits. • Comparative Genomics greatly boosts the specificity of in-silico detection of regulatory elements. • It now allows us to pinpoint TF targets with high confidence.

19. E2F Human-Mouse Conserved Hits 16,299 human-mouse ortholog promoters (Ensembl)

20. E2F Conserved Hits: Phase Distribution

21. CHR Regulatory Element • Cell-cycle Homology Region • To date, CHR was experimentally identified on 7 cell cycle-regulated promoters: • including CDC2, CCNB1,CCNB2 and CDC25C (major regulators of G2-M)

22. Transcriptional Modules Promoter #1 CHR and NF-Y elements show significant co-occurrence rate (p<10-11) Promoter #n

23. CHR-NF-Y Module 16,299 Hs-Mm ortholog promoters; NFY-CHR putative targets: 71 CHR-NFY: novel transcriptional module with a pivotal role in G2-M regulation

24. G2/M G1/S CHR-NFY Module Dictates Expression that is Specific to G2/M

25. CHR-NFY Module – False Positive Rate Comparative genomics yields highly specific identification of novel CHR-NFY cell-cycle targets

26. Regulation of CyclinB-CDC2 activity Rho GTPases pathways Cytokinesis Regulation of the mitotic spindle assembly Regulation of the kinetochore apparatus NovelCHR-NFY Targets in the G2-M Network

27. IV.Regulation of Gene Expression byMicro-RNAs

28. He and Hannon, 2004

29. Mature miRNA (~ 22 bp) tend to: • Start with a “U” base • Bind their target mRNAs at sites of length 8 bp. • Target site is complementary to positions 1-8 of the mature miRNA. • Assumed to play major regulatory function during development (many show tissue-specific expression pattern)

30. Transfected two miRNAs into Hela human cells and examined changes in mRNA expression profiles: • miR-1: expressed in skeletal muscle • miR-124: expressed in brain • 96 and 174 genes were significantly down-regulated by miR-1 and miR-124, respectively • Comparison with human tissue expression atlas: • Genes down-regulated by miR-1 are expressed at lower levels in skeletal muscle and heart than in other tissues • Genes down-regulated by miR-124 are expressed at lower levels in the brain than in other tissues • Searching for enriched signals in the 3’-UTRs of the down-regulated genes discovered the cognate binding sites

31. Computational identification of putative miRNA targets – scan 3’-UTRs for putative target sites • Anti-Correlation between the expression pattern of miRs and their putative targets (using the human tissue gene expression atlas) • Genes expressed at the same time and place as a miRNA evolved to avoid sites matching the miRNA

32. Comparative analysis of promoter and 3’-UTR regulatory motifs using the human, mouse, rat and dog genomes. • Search for highly conserved motifs (degenerate strings, 6-18 bases) • Motif Conservation Score (MCS): Z score of the proportion of the conserved occurrences of a motif relative to the conservation rate of comparable random motifs. • Promoters (-2 kb to + 2kb relative TSS): • 174 highly conserved motifs (MCS > 6): • 69 – known (out of 123 TRANSFAC motifs, 56%) • 105 potentially novel regulatory elements

33. Demonstrating biological function for the discovered motifs: • Correlate the occurrence of a motif with tissue-specific gene expression (using data from the human tissue expression atlas) • Target sets of 86% (59 out of 69) of the known motifs showed significant tissue-specific expression • 53 out of the 105 (50%) novel motifs • Examine positional bias of the motif hits

34. 3’ UTR signals: • 106 highly conserved motifs (MCS > 6) • Hypothesis: function as binding sites for miRNAs • Many of the discovered motifs show features of miRNA binding sites: • Strong strand biasof the conservation rate • consistent with a role in post-transcriptional regulation, acting at the RNA rather than DNA level • Biased length distribution: strong peak at 8 bp • High rate of “A” in position #8 • Search for matches of the 8-mer motifs to the known human miRs: • in 95% of the cases the matches begins at position 1 or 2 of the mature miRNAs.

35. V. Integrative Analysis

36. Systems-level analysis of the DNA damage response in yeast by an integrated approach that combines: • Genome-wide profiling of TF-promoter binding (ChIP-chip data) • Expression profiling (in deleted and w.t. strains) • Phenotyping sensitivity to DNA damage in deleted strains • Wide scale protein-protein interaction data

37. Systematic screen for TFs involved in the DNA damage response: • 30 (out of 141) TFs based on either: • Expression: differentially expressed after DNA damage • Binding: bind promoters of genes induced by DNA damage • Sensitivity: TF-mutant strain is hyper-sensitive to DNA-damaging agent • TF-promoter binding profiling (Chip-chip) for each of these 30 TFs, without and after exposure to DNA damaging agent

38. Validation of functional roles of the measured TF-promoter binding interactions: • Gene expression profiling in w.t. and deleted strains (27 out of the 30 are non-essential) without and after exposure to DNA damaging agent • “Deletion Buffering”: genes that respond to the damage in w.t. but become unresponsive in a specific TF-deleted strain • Only 11% (37 out of 341) of the observed deletion-buffering events could be explained by direct TF-promoter interaction • The rest are probably mediated by longer, indirect, regulatory pathwayslinking the deleted TF and the buffered gene

39. Physical pathways that explain indirect deletion-buffering events were searched for using Bayesian modeling procedure • Utilized various data sources: • TF-promoter binding data measured in this study • Tf-promoter binding data measured for all yeast TFs (in nominal conditions) • 14K high-throughput protein-protein interactions (in nominal conditions) • The inferred network explains a total of 82 deletion-buffering events.

40. and in human cells? Small-interfering RNA (siRNA)

41. He and Hannon, 2004

42. RNA Interference (RNAi) • A major technological breakthrough in biomedical research • Allows rapid establishment of mammalian cell lines which are stably knocked-down for any gene of interest – pivotal tool in functional genomics • Efforts to establish cell lines in which specific genes are silenced, eventually spanning most of the genome

43. The combination of RNAi and microarrays holds promise as a powerful tool for a systematic, genome-wide, dissection of transcriptional networks in human cells

44. Experiment Goal • Proof of principle that RNAi+microarrays can "deliver" • Focus on transcriptional network induced by DNA damage as a test case

45. Transcriptional network induced by DNA double strand breaks ATM DNA Double Strand Breaks AP-1 CREB NF-kB E2F1 p53 g13 g12 g11 g10 g9 g8 g7 g6 g5 g4 g3 g2 g1

46. Heatmap colors: Red – above average induction Black – average induction Green – below average induction • 26 genes whose activation is: • Strongly reduced in the absence of ATM and Rel-A • Partially reduced in the absence of p53 • ATM-NFκB-dependent cluster, partial role for p53

47. 46 genes whose activation is: • Strongly attenuated in the absence of ATM and p53 • Not affected by the absence of Rel-A • ATM-p53-dependent cluster

48. Response of known NF-κB targets • Knocking down Rel-A subunit of NF-κB abolished the induction of known NF-κB targets • ATM is required for the activation of the NF-κB mediated transcriptional response • p53 plays a positive role in the activation of NF-κB targets (?)

49. Response of known p53 targets • Knocking down p53 attenuated the induction of its known targets • ATM is required for the activation of p53 targets • NF-κB plays an inhibitory role in the induction of some components in the p53 pathway (?)

50. PRIMA - Results