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Inferring Connection Maps from AfCS Experimental Data and Legacy Data

Inferring Connection Maps from AfCS Experimental Data and Legacy Data. Alliance for Cellular Signaling. AfCS. 2003. COMPONENTS Parts-List. Context-Specific. INTERACTIONS AND NETWORKS. COMPUTATIONAL MODELS. Data Analysis. AfCS. 2003.

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Inferring Connection Maps from AfCS Experimental Data and Legacy Data

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

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

  3. Data Analysis AfCS 2003 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?

  4. Reconstructing Networks

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

  6. Significance analysis of microarrays* (SAM)(R. Tibshirani, G. Chu 2002) For each gene, define the adjusted “t-statistic” as follows: 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. treated - untreated •  mean of replicates   standard deviation for the gene  + adjustment factor 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.

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

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

  9. 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?

  10. 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.

  11. Similarity measures between genes under different conditions with respect to expression levels for… … groups of genes  clustering methods … pairs of genes  correlation methods Covariance = • Nk=1 {el(x(k)) – xmean)}{el(y(k)) – ymean) = rxy Correlation = r xy/(sxsy) Where, el(x(k)) indicates the expression level of gene x under condition k. xmean is the expression level of gene x over N different conditions. sx is the standard deviation for gene x.

  12. 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

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

  14. Microarray analysis model using gene expression profiles P P Protein mRNA mRNA mRNA mRNA DNA Gene A Gene B Gene C Gene D 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.

  15. 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)

  16. 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)

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

  18. “marginal correlation” “marginal” global correlation (for ligand j ) difference in correlation = r2 all xy - r2 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

  19. “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

  20. 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.

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

  22. “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

  23. mitogenic chemokines

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

  25. Marginal Correlations averaged over Pathway-Specific Genes

  26. Marginal Correlations averaged over Pathway-Specific Genes

  27. Marginal Correlations averaged over Pathway-Specific Genes

  28. Marginal Correlations averaged over Pathway-Specific Genes

  29. 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

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

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

  32. Genes Correlated by Gene Expression from Legacy Data extracted from Pathway Assist (Stratagene Database) 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}. 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). 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. 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.

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

  34. 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. Full view of two-way dendrogram

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

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

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

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

  39. MYC Box and related genes

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

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

  42. AfCS 2003

  43. AfCS 2003

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