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Combinatorics of promoter regulatory elements determines gene expression profiles

Combinatorics of promoter regulatory elements determines gene expression profiles. Yitzhak (Tzachi) Pilpel Priya Sudarsanam George Church DJ Club, Feb. 2001. Goals of study. Identify regulatory networks on a genome-wide scale study the combinatorial nature of transcription regulation

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Combinatorics of promoter regulatory elements determines gene expression profiles

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  1. Combinatorics of promoter regulatory elements determines gene expression profiles Yitzhak (Tzachi) Pilpel Priya Sudarsanam George Church DJ Club, Feb. 2001

  2. Goals of study • Identify regulatory networks on a genome-wide scale • study the combinatorial nature of transcription regulation • Propose causal link between promoter sequence elements and expression patterns

  3. The current methodology for expression - regulatory motif analysis (Tavazoie et al.)

  4. ? Collaboration Co-occurrence (AND) Redundancy (OR) In case of two motifs derived from a cluster

  5. MCB but not SCB SCB but not MCB Time Time Two motifs derived from the same cell-cycle cluster MCB and SCB Normalized expression level Time

  6. In case of multiple clusters that give rise to a motif Is this motif necessarily non-functional ?

  7. Condition-specific TF-TF interaction can be identified (in cell cycle) Forkhead & Mcm1 Mcm1 Forkhead Time Time Time

  8. Expression Assigning promoters to motifs :ScanACE (Hughes et al.)

  9. ScanACE ScanACE A proposed reversed analysis method:

  10. To avoid circularity we generated expression-independent motif data set • 327 - motifs derived from MIPs functional classification (Hughes J et al.) • 40 motifs of known TFs were added (27 overlapped to the MIPs derived motifs)

  11. Expression experiments used • Cell cycle (Cho et al.) • Sporulation (Chu et al.) • Diauxic shift (DeRisi et al.) • Heat shock (Eisen et al.) • Cold shock (Eisen et al.) • Reduction with dtt (Eisen et al.) • MAPK signaling (Roberts et al.) • NER (Jalinski et al.) • Peroxide (Cohen et al.)

  12. Use a Diversity of expression data to diagnose motifs Cell-cycle Sporulation Ndt80 Putative motif

  13. dij Threshold dij (top 5 %) Expression coherence=fraction of i,j pairs with dij <Threshold dij The expression coherence score Gene Set 2 Gene Set 1 * * * * * * * * * *

  14. Identification of functional motifs

  15. New significantly highly scoring motifs For a motif with 300 occurrences in URs the genome, the p-value for an expression coherence score of 0.1 is < 1e-12 P ( p) ~ BinomCDF(p,P,0.05), where p, and P are numbers of correlated pairs and total number of pairs, respectively

  16. For two motifs, RRPE and PAC RRPE PAC

  17. For every combination of N=2,3 motifs Calculate the expression coherence score of the orf that have the N motifs Calculate the expression coherence score of orfs that have every possible subset of N-1 motifs Test (statistically) the hypothesis the score of the orfs with N motifs is significantly higher than that of orfs that have any sub set of N-1 motifs

  18. Ribosomal motifs Rap1-rRPE rRPE-PAC PAC-rPPS2 ...

  19. Cell cycle and sporulation motifs Cell-cycle Sporulation

  20. 1 1 1 2 2 2 1 2 1 1 1 1 * 1 * 1 2 * 2 5 2 2 53 54 5 546 53 546 Motif combinations establish sequence-expression causality

  21. 1 . 8 1 . 5 1 . 2 1-C.C 0 . 9 0 . 6 0 . 3 ' M C B ' ' c y t o k 9 ' ' n d t 8 0 ' ' U m e 6 ' ' m e i o s i s _ 3 ' ' S C B ' ' C L B 2 ' ' F K H 1 S h ' 0 . 2 0 . 4 Expression coherence 0 . 6 0 . 8 1 Cell-cycle Less than a minute on a PowerMac G4 (after pre-processing)

  22. 1 . 8 1 . 5 1 . 2 1-C.C 0 . 9 0 . 6 0 . 3 0 ' M C B ' ' c y t o k 9 ' ' n d t 8 0 ' ' U m e 6 ' ' m e i o s i s _ n 3 ' ' S C B ' ' C L B 2 ' ' F K H 1 S h ' 0 . 2 0 . 4 Expression coherence 0 . 6 0 . 8 1 Sporulation

  23. From the literature: 1)Meiotic role of SWI6 in (Nucleic Acids Res. 1998)2) Role for MCB in sporulation(Nature Genetics 2001) • Different role for MCB and SCB • A potential role of SCB-fkh in giving rise to an Ndt80-type of response • Ndt80’s only synergistic partners in sporulation are cell cycle motifs We add:

  24. NER 'Rap1' 'RPE6' 'PAC' 'rRPE' 'rRSE3' 'rRSE10' 'Abf1' 'REB1' 'CCA' 'RPN4' 'HAP234' 'LFTE17' 'Rap1' 'RPE6' 'PAC' 'rRPE' 'rRSE3' 'rRSE10' 'Abf1' 'REB1' 'CCA' 'RPN4' 'HAP234' 'LFTE17'

  25. What can we infer about specific network architecture ? • Asses the contribution of each motif in a combination • Establish hierarchy motifs • Identify the logical association between motifs: OR for cases of redundancy, and for cases of synergy

  26. A global motif interaction map a1 a1 a2

  27. What can we learns about global interaction ? • Identify central motif players • Suggest regulatory role of un-annotated motifs

  28. Acknowledgments • Priya Sudarsanam • Barak Cohen • John Aach • Aimee Dudley • Jason Hughes • Rob Mitra • Wayne Rindone • Fritz Roth • Uri Keich (UCSF) • George Church

  29. Genes defined by Motif Combination (GMC)

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