1. Using IPA to study immune cells CSC workshop on Pathway Analysis Espoo, November 12, 2007 Helena Ahlfors Turku Centre for Biotechnology

2. Lehtonen & Ahlfors et al. Journal of Leukocyte Biology, 2007

3. Aim of the study Identification of cell-type specific differences in gene expression profiles of monocytes, macrophages (Mf) and dendritic cells (DCs)

4. Experimental setting Monocyte Macrophage GM-CSF + IL-4 Dendritic cell Buffy coat Ficoll gradient Percoll gradient depletion of T and B cells Gene expression profiling with Affymetrix HG-U133A arrays

5. CD14 CD1b DC-SIGN =CD209 Monocyte 3d M  7d M  3d DC7d DC Known Mf and DC markers

6. Monocyte 0d3d 7d Dendritic cell Macrophage 405 376 824 342 441 265 733 389 + GM-CSF, IL-4 + GM-CSF Number of regulated genes Filtering criteria: not NC not AA SLR ≥ 1 reproducible differences

7. Genes regulated both at 3-d and 7-d time point 354 236 76 120 342 194 * Mf vs DC *

8. TGF  20 15 10 5 FZD2 642642 100 80 60 40 20 WNT5A 10 8 6 4 2 SOCS1 16 12 8 4 Fc  R1A Fc  R1A 30 20 10 1 0.1 0.01 C3 TCF7L2 1.0 0.6 0.2 Mo M  DC M  DC 3d 7d Mo M  DC M  DC 3d 7d Mo M  DC M  DC 3d 7d Validation of the expression of a selected set of genes by quantitative RT-PCR

9. IPA / Canonical pathways Mf DC

10. Conclusions I GM-CSF and IL-4 regulate the expression of almost 900 genes during the differentiation of macrophages and DCs. Altogether 196 genes were differentially regulated in macrophages and DCs throughout the 7-day differentiation. Most of these genes code for factors involved in the signaling from cell surface to nucleus. Several novel genes with unknown molecular function were identified. These genes may cooperate with the previously known differentiation promoting factors and thus have an essential role in macrophage and DC differentiation process. Ingenuity Pathways Analysis revealed that canonical pathways of particular interest are differentially regulated in macrophages and DCs.

11. Kumar et al. Molecular Systems Biology, 2007, In press

12. Aim of the study Examine how the BCR-dependent intracellular signaling network adapts to targeted perturbations induced through siRNA-mediated depletion of select signaling intermediates.

13. BCR signaling network

14. Experimental setting mock CaMKIIPyk2PLCγPKCδ Transfecting the cells with siRNA oligos Stimulating the cells with anti- IgG for 30 min Gene expression profiling with Illumina mouse-6 beadchips B cell

15. Alterations in phosphorylation profiles

16. Array results mock PLCγ CaMKII Pyk2 PKCδ 21 0 22 55 93 0 35 0 44 9 Filtering criteria: SLR ≥ 1 reproducible differences

17. IPA results

18. PKCδ PLCγ Pyk2 IPA / network 1 CaMKII mock Network 1: Cancer, Cell cycle, Skeletal and Muscular Disorders

19. Conclusions II The depletion of any given component from the BCR signaling network resulted in significant alterations in phophorylation profiles of the other intermediates. This effect was not localized but extended over to intermediates that were not within the canonical signaling pathways to which each of the depleted molecules belonged. The depletion of individual nodes also reflected alterations in signal processing with corresponding alterations in the cellular phenotypic response as several tens of genes were either strongly induced or downregulated. The most significant gene regulatory network identified by IPA was a Myc-centric network with a proposed function in cell cycle regulation.

20. Acknowledgements Turku Centre for Biotechnology Prof Riitta Lahesmaa National Public Health Institute Prof Ilkka Julkunen Anne Lehtonen Ville Veckman Minja Miettinen International Centre for Genetic Engineering and Biotechnology (ICGEB), Delhi, India Prof Kanury V.S. Rao Dhiraj Kumar Ravichandran Srikanth Finnish Microarray Centre Miina Miller Päivi Junni Tiia Heinonen National Graduate School of Informational and Structural Biology

22. IPA results