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Post-translational Interactions

A systems-biology approach for finding TSS-specific transcriptional regulation in the same gene Yishai Shimoni Andrea Califano Lab Columbia University. Transcriptional Interactions. POST-TRANSCRIPTIONAL INTERACTIONS. Zhao X et al. (2009) Dev Cell. 17(2):210-21.

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Post-translational Interactions

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  1. A systems-biology approach for finding TSS-specific transcriptional regulation in the same geneYishaiShimoniAndrea Califano LabColumbia University

  2. Transcriptional Interactions POST-TRANSCRIPTIONAL INTERACTIONS Zhao X et al. (2009) Dev Cell. 17(2):210-21. Mani KM et al. (2008) Mol Syst Biol. 4:169 Palomero T et al., Proc NatlAcadSci U S A 103, 18261 (Nov 28, 2006). Margolin AA et al., Nature Protocols; 1(2): 662-671 (2006) Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006). Basso K et al. (2005), Nat Genet.;37(4):382-90. (Apr. 2005) Basso et al. Immunity. 2009 May;30(5):744-52 Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40. Master regulators and mechanism of action Post-translational Interactions Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39 Zhao X et al. (2009) Dev Cell. 17(2):210-21. Wang K et al. (2009) Pac Symp Biocomput. 2009:264-75. Mani KM et al. (2008) Mol Syst Biol. 4:169 Wang K et al. (2006) RECOMB Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377. Carro MS et al. (2010) Nature Jan 21;463(7279):318-25 Mani K et al, (2008) Molecular Systems Biology, 4:169

  3. ARACNe: Reverse Engineering Regulatory Networks • Computing Mutual Information • Start with a large collection of Microarray Gene Expression Profiles • Select two genes, a TF and a candidate target t: • Use expression across multiple experiments to measure TF Target t

  4. ARACNe: Reverse Engineering Regulatory Networks • Computing Mutual Information • Start with a large collection of transcription start site (TSS) activity levels • Select a TF and a candidate target TSS t: • Use expression across multiple experiments to measure TF Target t

  5. ARACNe Filtering indirect interaction: applying Data Processing Inequality TF1 TF2 Target TF1 TF2 Target X

  6. ARACNe TF2 TF1 TF2 TF2 TF1 TF1 TFN TFN TFN T1 T1 T1 TM T2 TM TM Remove Non Statistically Significant Interactions T2 T2 Apply the DPI Compute all pairwise Mutual Information (One of the two nodes must be a TF)

  7. Challenges in applying ARACNe to FANTOM5 data Normalization Joining tag counts into TSSs Using multiple tissues together

  8. MA plot of original tag counts shows need for normalization

  9. Top rank-invariant locations follow power-law relation

  10. MA plot of normalized TSS activity shows good normalization Note: samples were NOT normalized compared to each other, but each one compare to reference

  11. Good normalization observed in multiple random pairs

  12. Samples are separated by tissue

  13. Correlation clustering shows significant negative correlations Strong Negative Correlation Assorted samples Hematopoietic

  14. Possible solution to tissue driven mutual information Calculate MI only form the subset of samples in which both the TF and the target TSS are expressed

  15. In spite of caveats MI agrees with chip-seq http://amp.pharm.mssm.edu/lib/chea.jsp

  16. Interrogating Gene regulatory networks Analyzing data using ARACNe network

  17. MARINa: Master Regulator Inference analysis A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event. TFx Phenotype 1 Phenotype 2 Repressed TFx Targets TFx? Activated TFx Targets TF Regulon If TFx were a Master Regulator of Ph1→Ph2 transformation, then its regulated genes should distribute as follows: Activated TFx Targets Repressed TFx Targets Gene Expression Under-expressed in Ph2 vs. Ph1 Over-expressed in Ph2 vs. Ph1 Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377. Carro MS et al. (2010) Nature Jan 21;463(7279):318-25

  18. Differentially regulated same-gene TSS TSS2 TSS1

  19. Methylation data does not explain alternative TSS usage TSS2 TSS1 CD34+ Adult kidney Fetal lung Pancreatic Islets Smooth Muscles http://www.genboree.org/epigenomeatlas

  20. MARINA initial results for ARHGAP24 differential TSS activity

  21. Future directions • Apply ARACNe to the whole dataset to find differential regulation of TSS activity in the same gene • Apply MARINA to find master regulator of mutually exclusive TSS in the same gene • Use additional algorithms to analyze regulatory networks in developmental stages, and in the time-course data

  22. Acknowledgments • FANTOM5 • Andrea Califano • Mukesh Bansal • Mariano Alvarez • Maria Rodriguez Martinez • Gonzalo Lopez Garcia

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