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Deciphering the MonocyteMacrophage Lineage Differentiation With IPA Heikki Vuorikoski University of Turku Institute of Biomedicine Department of Anatomy IPA and How We Use It Analysis of Big Datasets Literature Mining DNA microarray data from our experiments vs.public expression data from databases, articles... Data source Gene and protein information Data Comparison DNA microarray data, solving the function of ”unknown” genes E.g. ”osteoclast” related information Pathway Graphics Co-operation projects Information sharing Monocyte-macrophage System (MMS) Plasticity CD14+ monocytes isolated from human peripheral blood can differentiate into bone resorbing osteoclasts (OCs), endothelial cells (ECs), dendritic cells (DCs) and macrophages (Ms) Common key factors for different cell lineage differentiation includes M-CSF, c-fos, GM-CSF, and IL-4 Capability of transdifferentiation: immature DCs can transdifferentiate into OCs DCs and Ms into each other immature DCs into EC-like cells Systems Biology Approach to Cell Lineage Differentiation Methods: Microarray gene expression profiling Human OCs grown on plastic and bone In silico promoter region analysis of OC specific genes In silico transcription factor model prediction Microarray data mining analyses GO, Pathway analysis OC Differentiation Assay Time series analysis with Affymetrix HG-U133A Functional Analysis of the Genes Ne twork i d 1 1 C e ll l in eage DC OC Ge n e s A2M, ADAM19, BC L2A1, C C L4, C C L13, C C NA1, C C ND2, C C NH, C DKN1A, CS F1, C TSG, C XC L3, EDN1, EGR2, ID2, IL1R2, IL1RAP, IL1RN, INHBA, LGI1, LPL, MMP1, MMP7, MMP12, MS R1, NID, PAX4, PPP1R14A, PTGS 1, SLC16A1, SPINK1, S PP1, TGFA, TNC, TPSAB1 ADM, ASL, ASS, C C NA1, C C NH, CEBPD, CTS L, DIRAS3, DNAS E1L3, ECG2, ELA2, FBLN5, FCGR1A, FDX1, FOS, GALP, IL1RN, IL2RA, LEP, LOXL1, LTBP 2, MT1B, MT1 G, MT2 A, MYC, ORM1, PRKAR2B, RPL35, RPS18, S AP30, TGFB1, THBS 1, TNF, TP 53,XLKD1 2 OC 2 EC ADM, C D1B, C EBPD, C LC, CNT F, CNT FR, C TSG, ELA2, FO S, FSTL1, HMMR, HOMER2, HRAS, IL13, IL17,IL1RN, IL2RA, IRAK1, LXN, MAPK8, RAB33A, RAMP1, SAP30, SCIN, SERP INB1, SERP INB4, SFTPD, SP INK5, SPP1, STAT3, T FP I2, THBS 1, TNC, T P 53,TRIB3 A2M, ADAM19, BC L2A1, C C ND2, C DKN1A, CS F1, CSPG2, C XC L3, EDN1, ELN, ETV4, HAPLN1, HBEGF, ID2, IL1RN, LEF1, LGI1, LPL, MMP1, MMP7, MMP 8, MMP12, MS R1, NID, P NN, PTGS 1, SAA1, SERPINA1, SOD2, S PINK1, S PP1, T FPI, TGFA, TIMP 3, TIMP 4 ADAM17, ADM, BIRC 3, C3, CCL7, CCL20,CHST4, C XC L13, EGR2, FCGR3A, FPRL1, G0S 2, HMGCR, HSPA1A, IFIT1 , IL22,IL1R1, IL1R2, IL1RAP, IL1RN, LAD1, LAMB3, LTB, MAP 2K6, MMP 26, MP O, MT2 A, PTEN, S 100A8, SAA1, SCARB1, TIMP 2, TIMP 4,TNF, TPS T1 ADAM19, ADIPOQ, C3, CCL4, C C L13, C C R7, C D38, C D1B, CD1C, CHST4, CS T7, C XC L13, GBP 1, HSD11B1, IGFBP 4, IL4, IL17, IL1RN, KIT LG, LGALS 2, LT BR, LYZ, MARCO, MS R1, P IK3R1, RNF128, S 100A8, SAMSN1, SCARB1, SLC29A1, STAG2, S TAG3, TG, TNF, TPS T1 DC ATF3, ATF4, BRRN1, CCL8, C C L17, C D1A, CD1B, C D1C, C LEC 4A, CTLA4, CTNNAL1, CT SK, CTSL, CYBB, DEFB103A, FBP1, FCER2, FGL2, IFNG, IL13, IL15RA, IL1RN, IL2RG, KIAA0555, MAF, MAOA, MMP12, PFKP, PHLDA1, Q SC N6, RAB33A, S100A8, SPINT2, STAT6, UBD 1 1 2 EC M S core 54 Focu s ge ne s 31 Top fun cti on s Cellular Growth and P roliferat io n, Immune Response, Cancer 37 20 29 16 Cancer, Cell Death, Hepatic System Disease Inflammatory Disease, Cell-ToCell Signaling and Interact ion, Cellular Growth and P roliferat io n 19 Cellular Growth and P roliferat io n, Cancer, Cellular Movement 28 28 16 27 15 26 19 Cellular Movement , Organismal Injury and Abnormalit ei s, Infect ious Disease Immune Response, Cellular Movement , Cell-To-Cell Signaling and Interact ion Cell-To-Cell Signaling and Interact ion, Hematological System Development and Funct ion, Immune Response The Functional Analysis of a network identified the biological functions and/or diseases that were most significant to the genes in the network. Genes in bold are up-regulated and in italic down-regulated. How to Use: Literature Mining How to use: Data Comparison Data from external sources, e.g. articles Import to IPA Comparison analysis with your own data How to Use: Data Source 100 Gene Symbol IL1A CSF1R HGF IFNB1 TNFRSF11B TLR3 IFNA CDKN1A PROK1 NFATC1 TGFB1 INPP5D IL9 IL11 PROK2 IL4 CD4 LIF TLR9 CSF1 IL17 EGR1 IL6 IL3 TLR2 PTH JUNB ITGB3 CALCA SRC TLR4 CCL3 PTK2 ITGAV TM7SF4 TNF TNFSF11 CSF2 NFATC2 PTHLH IFNG PTK2B BIRC5 IAPP WT1 IL1B Entrez Gene IDEntrez for Human Gene IDEntrez for Mouse Gene ID for Rat 3552 16175 24493 1436 12978 307403 3082 15234 24446 3456 15977 24481 4982 18383 25341 7098 142980 364594 24480 1026 12575 114851 84432 246691 192205 4772 18018 307231 7040 21803 59086 3635 16331 54259 3578 16198 116558 3589 16156 171040 60675 50501 192206 3565 16189 287287 920 12504 24932 3976 16878 60584 54106 81897 338457 1435 12977 78965 3605 16171 25465 1958 13653 24330 3569 16193 24498 3562 16187 24495 7097 24088 310553 5741 19226 24694 3726 16477 24517 3690 16416 29302 796 12310 24241 6714 20779 83805 7099 21898 29260 6348 20302 25542 5747 14083 25614 3685 16410 296456 81501 75766 7124 21926 24835 8600 21943 117516 1437 12981 116630 4773 18019 311658 5744 19227 24695 3458 15978 25712 2185 19229 50646 332 11799 64041 3375 15874 24476 7490 22431 24883 3553 16176 24494 Genes categorised as “osteoclast related” in IPA are inspected in our microarray data 10 1 0,1 0,01 0 Y-axis: Colored by: Gene List: 5 7 9 11 Time 15 GC RMA File Preprocessor Experiment HOC, Default Interpretation Time 0 IPA OC genes from DC branch (29) 100 10 1 • Search and visualize in IPA 0,1 0,01 Osteoclast Y-axis: Colored by: Gene List: • Color with your (or others) expression data Endothelia CD14 Agilent Experiment FE, Default Interpretation Dendritic IPA OC genes from DC branch (29) Macrophage Tissue Type Dendritic How to Use: Pathway Graphics How to Use: Co-operation, data sharing 100 Normalized Intensity (log scale) 10 1 0,1 0,01 0 Y-axis: Colored by: 5 GC RMA File Preprocessor Experiment HOC, Default Interpretation PLOSL 7 9 Error Bars: min-max Gene List: All PLOSL+TREM, DAP12 (54) 11 Time 15 Conclusions Big datasets are easily handled with the software Integration to other analysis programs is easy Doesn’t require advanced computing skills (“biologistettavissa”) Data analysis and data sharing between coworkers is easy IPA is not an excuse to stop wet-lab work, but it is valuable tool for interpreting the data coming from the lab. Thank You Department of Anatomy, University of Turku Anne Seppänen Husheem Michael Teuvo Hentunen Tiina Laitala-Leinonen Kalervo Väänänen Department of Medical Microbiology, University of Turku Milja Möttönen Olli Lassila Department of Information Technology, University of Turku Eija Nordlund Jorma Boberg Tapio Salakoski Department of Physiology, University of Turku Markku Ahotupa National Public Health Institute, Department of Molecular Medicine, Helsinki Anna Kiialainen