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Networks and Context: Identifying differences between Macrophage and Macrophage Derived Cell Types

Networks and Context: Identifying differences between Macrophage and Macrophage Derived Cell Types. Benner, Subramaniam and Glass. 2003. Macrophage Cell Types. RAW – Macrophage cell line TM - Thioglycolate elicited macrophages BM – Bone Marrow derived macrophages

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Networks and Context: Identifying differences between Macrophage and Macrophage Derived Cell Types

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  1. Networks and Context:Identifying differences between Macrophage and Macrophage Derived Cell Types Benner, Subramaniam and Glass. 2003 UCSD-Bioinformatics & Systems Biology Group

  2. Macrophage Cell Types • RAW – Macrophage cell line • TM - Thioglycolate elicited macrophages • BM – Bone Marrow derived macrophages • ES – Embryonic Stem Cells (not macrophage) UCSD-Bioinformatics & Systems Biology Group

  3. Chromosome 1 UCSD-Bioinformatics & Systems Biology Group

  4. High Throughput Analysis Gene Ontology 15160 16171 16948 22329 104215 20482 232314 12142 13169 71704 21665 20912 67921 19106 11630 22329 12985 23872 20529 12494 16859 66403 1969 15160 16171 16948 22329 104215 20482 232314 12142 13169 71704 21665 20912 67921 19106 11630 22329 12985 23872 20529 12494 16859 66403 1969 15160 16171 16948 22329 104215 20482 232314 12142 13169 71704 21665 20912 67921 19106 11630 22329 12985 23872 20529 12494 16859 66403 1969 Inspect Results Biocarta.org KEGG, others… Significant Categories are Assigned UCSD-Bioinformatics & Systems Biology Group

  5. Differential Response to LPS • Example: NF-kB Responsive Genes (possible Acquired Immunity Response) BM TM UCSD-Bioinformatics & Systems Biology Group

  6. Investigating Differences Cell Cycle Genes Common Transcription Factors UCSD-Bioinformatics & Systems Biology Group

  7. Common cellular and pathway phenotypes lead to distinct regulatory networks in primary B-cells Mock and Subramaniam. 2003 UCSD-Bioinformatics & Systems Biology Group

  8. ALLIANCE FOR CELULAR SIGNALING UCSD-Bioinformatics & Systems Biology Group

  9. Ligand Screen: Perturbing Cells Molecular Biology Lab Microarray analysis Antibody Lab P-proteins Lipid Lab Lipid analysis Protein Lab P-proteins Calcium cAMP Cell Lab in Dallas Produces Cells Treats Cells with Ligands UCSD-Bioinformatics & Systems Biology Group

  10. Summary of Ligand Screen Responses UCSD-Bioinformatics & Systems Biology Group

  11. Reconstructing Networks UCSD-Bioinformatics & Systems Biology Group

  12. Signal Transduction in a Cell from Downward, Nature, August (2001) UCSD-Bioinformatics & Systems Biology Group

  13. Ligand Screen Transcript Analysis • B cell samples prepared by Cell Lab (Dallas). • Cultured for different time periods (.5, 1, 2, and 4 hr) in the presence or absence of ligands before harvesting for total RNA isolation. • Treated and untreated time-course samples hybridized against a spleen reference. • After removing the common spleen denominator, comparison to 0 time point data reflects the changes in mRNA levels due to ligand treatment and/or time in culture. • One of the largest mammalian array sets (33 ligands). • All of the experiments were done in triplicate. Including in controls >450 arrays (Caltech) UCSD-Bioinformatics & Systems Biology Group

  14. Graph association map (4hr) The mitogenic response from the ligands AIG, 40L, I04, LPS, CPG dominate at the center of the plot. This is too dense for a clear view (see histogram to the left). IF, GRH, CGS, PAF, TGF, M3A, 2MA also showed a significant gene response. UCSD-Bioinformatics & Systems Biology Group

  15. Similarity measures between genes under different conditions with respect to expression levels for… … groups of genes  clustering methods … pairs of genes  correlation methods • (x – xmean) (y – ymean) [ (x – xmean)2  (y- ymean)2 ]½ = r2 xy Linear correlation rxy - rxz ryz Partial correlation = rxy.z [(1- r2xz) (1- r2yz)]½ “marginal” global correlation (for ligand j ) r2 all xy - r2 all xy except ligand j UCSD-Bioinformatics & Systems Biology Group

  16. Two-way hierarchical cluster: mean ratio (vs control) of phosphoprotein levels and ligand Several ligands that elicit an ERK response (chemokines + AIG, CD40L) clustered together. UCSD-Bioinformatics & Systems Biology Group

  17. Three main pathways of MAPK and their respective target genes and transcription factors. ERK-MAPK p38 JNK-SAPK ETS.v6 NFATC1 N.MYC1 H3F3A Gadd45a MEF2C CREB1 Gadd45b CHOP C.FOS Gadd45g Max H3F3B Egr1 Bcl2l11 Socs3 CHOP Bcl2l2 CREB3 Diagrams are from … “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) Max JUN STAT1 JUN Egr1 N.MYC1 C.FOS Bcl2l11 STAT1 P53 Bcl2l2 SRF UCSD-Bioinformatics & Systems Biology Group ETS.v5

  18. Level plots “Marginal” correlation of genes in MAPK pathways UCSD-Bioinformatics & Systems Biology Group

  19. Green indicates negative influence on the gene upon removing ligand j Highly responsive genes from MAPK-ERK pathway B cells respond to AIG through the MAPK-ERK pathway. UCSD-Bioinformatics & Systems Biology Group

  20. We see the correlation results of removing ligands CD40L (40L) and interleukin 4 (I04) separately from the pool of 33 ligands. The colors red and green refer to decreases/increases in the subsequent correlation similarity matrix respectively. The absolute differential effects are almost uniform across CD40L (with a slightly smaller marginal difference from the ERK related genes h3f3b, ets-v6,c-fos), in contrast to interleukin 4 which shows darker shades, with the color black showing no differences, except for a few p38 (chop, jun) and JNK-SAPK (gadd45q) related genes. lesser effect in ERK pathway than AIG cytokine stress-related genes UCSD-Bioinformatics & Systems Biology Group

  21. B cells do not show any response to NGF but respond to LPS. Note: LPS has more response genes in p38 & JNK-SAPK than ERK. No marginal changes in the pairwise gene correlations in the MAPK pathways from the addition or subtraction of this ligand NGF. UCSD-Bioinformatics & Systems Biology Group

  22. Marginal Correlations Connection Maps for MAPK Pathways 40L Legend transcription factors target genes only Positive pairwise correlation was more positive by the additional ligand Negative pairwise correlation was less negative by the additional ligand Positive pairwise correlation was more negative by the additional ligand This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [40L] to the low, intermediate-response ligands (n =112, 28 ligands). Negative pairwise correlation was less positive by the additional ligand UCSD-Bioinformatics & Systems Biology Group

  23. Marginal Correlations Connection Maps for MAPK Pathways AIG Legend transcription factors target genes only Positive pairwise correlation was more positive by the additional ligand Negative pairwise correlation was less negative by the additional ligand Positive pairwise correlation was more negative by the additional ligand This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [AIG] to the low, intermediate-response ligands (n =112, 28 ligands). Negative pairwise correlation was less positive by the additional ligand UCSD-Bioinformatics & Systems Biology Group

  24. Marginal Correlations Connection Maps for MAPK Pathways LPS Legend transcription factors target genes only Positive pairwise correlation was more positive by the additional ligand Negative pairwise correlation was less negative by the additional ligand Positive pairwise correlation was more negative by the additional ligand This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [LPS] to the low, intermediate-response ligands (n =112, 28 ligands). Negative pairwise correlation was less positive by the additional ligand UCSD-Bioinformatics & Systems Biology Group

  25. Cell Cycle Kohn Map UCSD-Bioinformatics & Systems Biology Group

  26. UCSD-Bioinformatics & Systems Biology Group

  27. UCSD-Bioinformatics & Systems Biology Group

  28. MYC Connection Map Genetic regulatory module generated by partial correlations critical value = 10-6 UCSD-Bioinformatics & Systems Biology Group

  29. Connection matrix cytosol only Signaling pathways of primary B cell (mouse) UCSD-Bioinformatics & Systems Biology Group

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