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

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

3. Data Analysis
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?
AfCS
2003

4. Reconstructing Networks

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

6. For each gene, define the adjusted “t-statistic” as follows:
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.
For each gene, define the adjusted “t-statistic” as follows:
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.

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

9. Concordance of significantly up (+) or down (-) regulated genes
Concordance of significantly up (+) or down (-) regulated genes mitogenic ligands (FDR = 1%)
“down-regulated” matches
Mosaic plot
(-)
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 (+)

10. 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. 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. Nk=1 {el(x(k)) – xmean)}{el(y(k)) – ymean) = r xy
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)
= r xy
Correlation = r xy/(sx sy)
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.

13. 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. Cross-correlation matrices
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

15. Microarray analysis model using gene expression profiles
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.

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

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

19. “marginal” global correlation (for ligand j )
“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

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

23. “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. mitogenic
chemokines

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

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. Marginal Correlations averaged over Pathway-Specific Genes

30. 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. 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 =

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

33. Genes Correlated by Gene Expression from Legacy Data extracted from Pathway Assist (Stratagene Database)
Mol Cell Biol 1991 Jan;11(1):
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):
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):
Furthermore, expression of antisense ID{2352=CREM} enhances ID{-3796=c-fos} basal and cAMP-induced transcription.

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

36. 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

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

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

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

41. Non-mitogenic ligand response gene correlations

42. MYC Box and related genes

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

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

46. AfCS
2003

47. AfCS
2003