1. 2003 Inferring Connection Maps from AfCS Experimental Data and Legacy Data

2. 2003 COMPONENTS Parts-List INTERACTIONS AND NETWORKS COMPUTATIONAL MODELS Alliance for Cellular Signaling Context-Specific 2003

3. Our experiments measure genes, proteins and key metabolites. What are the underlying biological relationships amongst these entities? The cell functions as an integrated system involving all these players. How can we analyze our data to reveal this interconnectedness? Data Analysis 2003

4. Reconstructing Networks

5. 2003 Signal Transduction in a Cell from Downward, Nature, August (2001)

6. 2003 Significance analysis of microarrays * (SAM) (R. Tibshirani, G. Chu 2002) Objective: The replicated expression for each gene is taken for the 4hr time condition (untreated vs ligand) to determine whether the gene is statistically differentially up- or down- regulated. The t-statistics for all the genes are ordered and noted. The labels are then permutated and the t-statistic is calculated again. After many iterations, the cumulative t-statistics is averaged for each gene. Finally, for a given false positive rate, [called “False Discovery Rate” or FDR], the significant genes are selected. For each gene, define the adjusted “t-statistic” as follows:  treated -  untreated  + adjustment factor   mean of replicates   standard deviation for the gene

7. 2003

8. “mitogenic” ligands FDR = 1% FDR = 35% FDR = 18% FDR = 1%- 3% Two-way dendrogram using significantly expressed genes (4hr) 2670 unique genes

9. 2003 Concordance of significantly up (+) or down (-) regulated genes mitogenic ligands (FDR = 1%) 756 (-) 1082 (+) 337 (-) 135 (-) 553 (-) 147 (-) “down-regulated” matches “up-regulated” matches 3 (-) 446 (-) 887 (+) 96 (-) Mosaic plot 578 (+) 73 (+) 597 (+) 117 (+) 47 (+) 477 (+) 117 (+) 4 (+)6 (+)3 (+) 796 (-) 854 (+) 5 (+)4 (+) 3 (-) 10 (+) 1 (-) 3 (-) 2 (-) 3 (-) 72 (+) 18 (+) 341 (-) 143 (-) 152(-) 80(+) 108 (+) 171 (-) 163 (+) 151 (-) 119 (-) Discordance matrix Example: CD40L had 756 down-regulated and 1082 up-regulated genes. Those which were similarly regulated in AIG: 337 down 578 up. 72 (-)

10. 2003 Beyond Clustering How can we obtain biological information from array data at the level of individual genes and correlations in expression between genes? Can we use the correlations to build a connection network that reflects correlations in expression? Is there biological significance to this?

11. 2003 Two-way hierarchical cluster: mean ratio (vs control) of phosphoprotein levels and ligand Note: the ligands that elicit an ERK response (chemokines + AIG, CD40L) clustered together. A correspondence plot below also showed the grouping.

12. 2003 Similarity measures between genes under different conditions with respect to expression levels for… … groups of genes  clustering methods … pairs of genes  correlation methods Covariance =  N k=1 {el(x(k)) – x mean )}{el(y(k)) – y mean ) = r xy Correlation = r xy /(  x  y  Where, el(x(k)) indicates the expression level of gene x under condition k. x mean is the expression level of gene x over N different conditions.  x is the standard deviation for gene x.

13. 2003 Transcription factor encoded by fos is stabilized by ERK and continues to affect other IE genes such as jun from Nature Cell Biology August 2002

14. 2003 Schematic interpretation of ERK signal duration for IE gene product for fos Cross-correlation matrices Transcription response from “non-ERK” ligands Transcription response from “ERK” response ligands

15. 2003 Microarray analysis model using gene expression profiles DNA Gene AGene BGene CGene D Protein mRNA P P Signal transduction is most likely regulated on the protein level, but the downstream signal on the transcriptional level is the resultant output from the upstream (outside the nucleus) signal input. The signal information processing complexity is now increased on the transcription level but some information flows upstream and oscillates in an input/output fashion.

16. 2003 Beyond Clustering Mechanisms for inducing high correlation between genes in their expression profiles –A direct interaction –An indirect interaction (the regulatory information of gene A product is transferred through the expressions of some other genes to induce the expression of gene B) –Regulation by a common gene (the expression of genes A and B are regulated by a common gene)

17. 2003 Mitogen-Activated Protein Kinase Pathways Mediated by ERK, JNK, and p38 Protein Kinases G. L. Johnson and R. Lapadat Science 2002 December 6; 298: 1911-1912. (in Review)

18. 2003 Transcriptional effects downstream from proteins recruited in MAPK cascades (Hazzalin, et al,Nature Cell Biology (2002)

19. 2003 “marginal correlation” “marginal” global correlation (for ligand j ) difference in correlation = r 2 all xy - r 2 all xy except ligand j Red indicates positive influence on the gene upon removing ligand j Green indicates negative influence on the gene upon removing ligand j

20. 2003 “Marginal” correlation IE genes downstream from MAPK Ligand n=33 Idea: indicates the “leverage” on the global correlation coefficient for a gene for the particulat ligand

21. 2003 Marginal Correlations between Genes Provides a “biologically”-driven approach to discriminating ligand responses at the gene and gene-product level. Serves as a pathway driven hypothesis generation method for QRTPCR. Suggests ideal double ligand experiments to explore major signaling pathways that lead to downstream gene expression changes.

22. 2003 “Marginal” correlation signatures IE genes downstream from MAPK Ligand n=33  Correlation coefficient green = negative red = positive Mitogenic ligand

23. 2003 “Marginal” correlation signatures IE genes downstream from MAPK Ligand n=33  Correlation coefficient green = negative red = positive chemokines No obvious pattern so consider data reduction

24. 2003 mitogenicchemokines

25. 2003 For the case of ligand 2MA… cAMP responsive element modulator

26. 2003 Marginal Correlations averaged over Pathway-Specific Genes

27. 2003 Marginal Correlations averaged over Pathway-Specific Genes

28. 2003 Marginal Correlations averaged over Pathway-Specific Genes

29. 2003 Marginal Correlations averaged over Pathway-Specific Genes

30. 2003 transcription factor binding sites immediately upstream from “immediate- early” genes fos & jun (Hazzalin, et al,Nature Cell Biology (2002) = expression measured indirectly in ligand AfCS experiment

31. 2003 Difference in IE gene cross-correlations from ligands that involve ERK pathway Critical level p =0.00001 Partial correlations Ligands that stimulate ERK Note: junB expression wasn’t detected

32. 2003 Difference in IE gene cross-correlations from ligands that involve ERK pathway Critical level p =0.00001 Partial correlations ERK CREM a h k Possible interpretation of a gene regulatory network

33. 2003 J Biol Chem 1998 Nov 20;273(47):31327-36 The transcription factors ID{-3321=Elk-1} and ID{-11291=Serum Response Factor} are necessary for GH-stimulated transcription of ID{-3796=c-fos} through the Serum Response Element (SRE). Proc Natl Acad Sci U S A 1991 Jun 15;88(12):5448-52 Furthermore, expression of antisense ID{2352=CREM} enhances ID{-3796=c-fos} basal and cAMP-induced transcription. Neurol Res 2000 Mar;22(2):138-44 In the non-trauma patients 36% expressed ID{-3796=c-fos} and 73% expressed ID{-6204=c-jun} mRNA, with all patients studied expressing ID{-3796=c-Fos} and ID{-6204=c- Jun} proteins. Mol Cell Biol 1991 Jan;11(1):192- 201 We observe that the expression of endogenous ID{-6204=c-jun} and ID{-6205=jun B} genes is induced by E1A, which directly transactivates the promoters of ID{-3796=c-fos}, ID{-6204=c- jun}, and ID{-6205=jun B}. Genes Correlated by Gene Expression from Legacy Data extracted from Pathway Assist (Stratagene Database)

34. 2003 Connections at the Protein Level from Legacy Data extracted using Pathway Assist (Stratagene)

35. 2003

36. Full view of two-way dendrogram Two-Way Dendrogram from AfCS ligand screen using the probes (genes) relating to the “immediate-early” genes (with additional genes that encode MAPK proteins involved in the cascade). Summary: The transcription profiles of these selected genes distinguished the “mitogenic” ligands (AIG, CPG, CD40L, IL-4, IL10, LPS) from the “non-mitogenic” at the 2hr / 4hr time period. Since the upstream MAPK-ERK pathway is involved in cell proliferation this would be expected under ideal experimental conditions. The fact that a distinct two-way “bicluster” (mitogenic ligands are clustered to the IE genes from MAPK-ERK) as a first-pass result of the microarray experiment is highly encouraging. This “semi-supervised” approach indicates our expression data is biologically informative.

37. 2003 Kohn’s Mammalian Cell Cycle Map (with AfCS genes)

38. 2003 Kohn’s Mammalian Cell Cycle Map (with AfCS genes)

39. 2003 Kohn’s Mammalian Cell Cycle Map (with AfCS genes)

40. 2003

41. Non-mitogenic ligand response gene correlations Mitogenic ligand response gene correlations

42. 2003 MYC Box and related genes

43. 2003 MYC Connection Map Genetic regulatory module generated by partial correlations critical value = 10 -6

44. 2003 Literature-derived expression-based connection maps for all AfCS proteins AfCS proteins with no known connections

45. 2003