NetBioSIG2012 kostiidit

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Traditionally the gene expression pathway was regarded as being composed of independent steps, from RNA transcription to protein translation. To-date there is increasing evidence for coupling between the different processes of the pathway, specifically between transcription and splicing. Given the extensive cross-talk between these processes, we derived a transcription-splicing integrated network. The nodes of the network included experimentally verified human proteins belonging to three groups of regulators: Transcription factors (TFs), splicing factors (SFs) and kinases. The nodes were wired by instances of predicted transcriptional and alternative splicing regulation. Analysis of the network indicated a pervasive cross-regulation among the nodes, specifically; SFs were significantly more often regulated by alternative splicing relative to the two other subgroups, while TFs were more extensively controlled by transcriptional regulation. In particular, we found a significant preference of specific pairs of TF-TF and SF-SF to regulate their target genes, SFs being the most regulated group via independent and combinatorial binding of SFs. Consistent with the extensive cross-regulation among the splicing and transcription factors, the subgroup of kinases within the network had the highest density of predicted phosphorylation sites. The prevalent regulation of the regulatory proteins was further supported by computational analysis of the protein sequences, demonstrating the propensity of these proteins to be highly disordered relative to other proteins in the human proteome. Overall, our systematic study reveals that an organizing principle in the logic of integrated networks favor the regulation of regulatory proteins by the specific regulation they conduct. Based on these results we propose a new regulatory paradigm, postulating that fine-tuned gene expression regulation of the master regulators in the cell is commonly achieved by cross-regulation.

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  • Gene expression pathway, many regulatory points on the way to create a protein from DNAWe focued on AS and transcription
  • We take experimentally verifies SF motifsAS events – 2 sources
  • Weak but necesery
  • NetBioSIG2012 kostiidit

    1. 1. A transcription-splicing integrated network reveals pervasive cross-regulation among regulatory proteins Network Biology SIG ISMB 2012 Long Beach CA Idit Kosti Computational Biology Lab Technion, Haifa, Israel
    2. 2. Regulation of the gene expression pathway DNA Promoter intron exon Transcription RNA Splicing Alternative Splicing Protein Translation P Phosphorylation Posttranslational modification
    3. 3. TranscriptionDNA Promoter intron exon Transcription• The first step leading to gene expression.• Transcription is regulated by transcription factors (TFs) that are bound to the promoter region of the gene.
    4. 4. Alternative splicingRNA Alternative Splicing• Alternative splicing (AS) creates a huge protein variety from a small number of genes.• 95% of the genes have at least one AS event.• AS is regulated by splicing factors (SFs).
    5. 5. PhosphorylationProtein Translation P Phosphorylation• Protein phosphorylation plays a significant role in a wide range of cellular processes• Phosphorylation occurs at phosphorylation sites and facilitated by kinases.
    6. 6. In our network we focus ontranscription and alternative splicing regulation in human
    7. 7. The splicing-transcription co-regulatory network SF 20 Splicing Factors Transcription SF (gene/protein) regulation TF SF TF 90 Transcription Factors (gene/protein) K K 147 Kinases (gene only)Kosti I., Radivojac P., Mandel-Gutfreund Y., An integrated regulatory networkreveals pervasive cross-regulation among transcription and splicing factors,PLoS Computational Biology, in press.
    8. 8. Predicting TF binding sites using TRANSFAC A [13 13 3 1] TRANSFAC PSSMs C [13 39 5 53] G [17 2 37 0] T [11 0 9 0]Predication of TFBS using PSSM Human TCGACGCCTCACGTGTTCCTCCTGG and conservation Mouse ATCGCACTGCACGTGGGATCTGATC Significant hits in promoter region TFBS table, UCSC genone broswer
    9. 9. The splicing-transcription co-regulatory network SF 20 Splicing Factors Transcription SF (gene/protein) regulation TF SF TF 90 Transcription Factors (gene/protein) Alternative Splicing regulation K K 147 Kinases (gene only)
    10. 10. Predicting SF binding sites using SFmap sfmap.technion.ac.il UCUU Experimentally defined binding YCAY motifs YGCUKY GAAGAA Human UCGACGCCUUCCUUCUCUUUCCUCCU Predication of SFBS using motif, conservation and multiplicity Mouse AUCGCACUGUCUUAUCGGAUCUGAUC Significant hits in AS region Paz I. et al., Nucleic Acids Res. 2010
    11. 11. Regulation on AS events changes according to event types Cassette exon Alternative 5’ splice site Alternative 3’ splice site
    12. 12. How do we wire the network? 1 2 3 A X X 3 2 X A 1
    13. 13. Our network behaves like a regulatory networkHighly clustered Sparse Sparseness=0.046 p-value =1.09e-61 Outdegree Frequency Power law outdegree distribution Outdegree
    14. 14. Cross-regulation vs. Cross-talk regulation TF TF SF Kcross-talk regulation (regulation across functional group)cross-regulation (regulation within the functional group)
    15. 15. Transcription regulation is highest among TFs Transcription Regulation pv= 1.2E-3 pv = 3.8E-7 Number of inedges 3654 SF TF Kinase pv < 0.05
    16. 16. Splicing regulation is highest among SFs Splicing Regulation pv= 2.7E-3 Number of inedges pv= 2.3E-4 97 SF TF Kinase P-value < 2.2e-16
    17. 17. Similar gene length, number of exons and number of AS events Gene length Number of exons per factor 20000 30000 40000 30 Number of exons per factor 25 Gene length (nt) 20 15 10000 10 5 0 0 SF TF Kinase SF TF Kinase Frequency from target group SFsNumber of alternative TFssplicing events per factor Kinases Number of alternative splicing events
    18. 18. Random networks showed insignificant inedges density Splicing regulation Transcription regulationInedge average Inedge average SF TF Kinase SF TF Kinase
    19. 19. Experimental binding data supports splicing regulation trend RNA QKI splicing Transcription activity PTB FOX2 SF2/ASF 0 2 4 6 8 10 12 -Log10(pvalue)
    20. 20. Cross regulation vs. cross-talk regulation TF TF SF K
    21. 21. Same trend, different organisms Transcription regulation Human Drosophila Yeast pv= 1.2e-3 pv= 9.2e-10SF TF SF TF SF TF Marbach et al. Genome Res. 2011 Pelechano et al., PLoS Genetics 2009
    22. 22. Same trend, different organisms Splicing regulation Guy Plaut Human Drosophila Number of inedges pv= 2.3e-4 Number of inedges pv= 1.7e-11 SF TF SF TF
    23. 23. Screening using expressionshow for Tissue specific networks data the muscleregulatory behavior same and heart tissues
    24. 24. Tissue specific networks show the same regulatory behavior Splicing Regulation Transcription RegulationNumber of inedges Number of inedges SF TF SF TF SF TF SF TF 40 TFs 11 SFs 33 TFs 14 SFs
    25. 25. The splicing-transcription co-regulatory network SF 20 Splicing Factors Transcription SF (gene/protein) regulation TF SF TF 90 Transcription Factors (gene/protein) Alternative Splicing regulation K K 147 Kinases (gene only)
    26. 26. is highest among Kinases Phosphorylation Regulation Fraction of protein with predicted phosphorylation sitePredragRadivojac SF TF Kinase
    27. 27. Cross-regulation vs. cross-talk regulation TF TF SF K
    28. 28. The role of cross talk between splicing and transcription regulation SRP20 SRP55 PAX 6 SC35 SF2ASF 9G8
    29. 29. Regulatory proteins tend to be highlyregulated by the specific regulation they carry out. TF TF SF K
    30. 30. Thanks!TechnionYael Mandel GutfreundGuy PlautInbal PazIris DrorMartin AkermanAnd all lab membersIndiana UniversityPredrag Radivojac And you for your attention!

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