Deciphering the Monocyte-Macrophage Lineage

Differentiation With IPA

Heikki VuorikoskiUniversity of Turku

Institute of BiomedicineDepartment of Anatomy

IPA and How We Use It

Analysis of Big Datasets DNA microarray data, solving the function of ”unknown”

genes Literature Mining

Gene and protein information Data Comparison

DNA microarray data from our experiments vs.public expression data from databases, articles...

Data source 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

Network id Cell lineage Genes Score

Focus genes Top functions

1 DC

A2M, ADAM19, BCL2A1, CCL4, CCL13, CCNA1, CCND2, CCNH, CDKN1A, CSF1, CTSG, CXCL3, EDN1, EGR2, ID2, IL1R2, IL1RAP, IL1RN, INHBA, LGI1, LPL, MMP1, MMP7, MMP12, MSR1, NID, PAX4, PPP1R14A, PTGS1, SLC16A1, SPINK1, SPP1, TGFA, TNC, TPSAB1 54 31

Cellular Growth and Proliferation, Immune Response, Cancer

1 OC

ADM, ASL, ASS, CCNA1, CCNH, CEBPD, CTSL, DIRAS3, DNASE1L3, ECG2, ELA2, FBLN5, FCGR1A, FDX1, FOS, GALP, IL1RN, IL2RA, LEP, LOXL1, LTBP2, MT1B, MT1G, MT2A, MYC, ORM1, PRKAR2B, RPL35, RPS18, SAP30, TGFB1, THBS1, TNF, TP53, XLKD1 37 20

Cancer, Cell Death, Hepatic System Disease

1 EC

ADM, CD1B, CEBPD, CLC, CNTF, CNTFR, CTSG, ELA2, FOS, FSTL1, HMMR, HOMER2, HRAS, IL13, IL17, IL1RN, IL2RA, IRAK1, LXN, MAPK8, RAB33A, RAMP1, SAP30, SCIN, SERPINB1, SERPINB4, SFTPD, SPINK5, SPP1, STAT3, TFPI2, THBS1, TNC, TP53, TRIB3 29 16

Inflammatory Disease, Cell-To-Cell Signaling and Interaction, Cellular Growth and Proliferation

1 M

A2M, ADAM19, BCL2A1, CCND2, CDKN1A, CSF1, CSPG2, CXCL3, EDN1, ELN, ETV4, HAPLN1, HBEGF, ID2, IL1RN, LEF1, LGI1, LPL, MMP1, MMP7, MMP8, MMP12, MSR1, NID, PNN, PTGS1, SAA1, SERPINA1, SOD2, SPINK1, SPP1, TFPI, TGFA , TIMP3, TIMP4 28 19

Cellular Growth and Proliferation, Cancer, Cellular Movement

2 OC

ADAM17, ADM, BIRC3, C3, CCL7, CCL20, CHST4, CXCL13, EGR2, FCGR3A, FPRL1, G0S2, HMGCR, HSPA1A, IFIT1, IL22, IL1R1, IL1R2, IL1RAP, IL1RN, LAD1, LAMB3, LTB, MAP2K6, MMP26, MPO, MT2A, PTEN, S100A8, SAA1, SCARB1, TIMP2, TIMP4, TNF, TPST1 28 16

Cellular Movement, Organismal Injury and Abnormalities, Infectious Disease

2 EC

ADAM19, ADIPOQ, C3, CCL4, CCL13, CCR7, CD38, CD1B, CD1C, CHST4, CST7, CXCL13, GBP1, HSD11B1 , IGFBP4, IL4, IL17, IL1RN, KITLG, LGALS2, LTBR, LYZ , MARCO, MSR1, PIK3R1, RNF128, S100A8, SAMSN1, SCARB1, SLC29A1, STAG2, STAG3, TG, TNF, TPST1 27 15

Immune Response, Cellular Movement, Cell-To-Cell Signaling and Interaction

2 DC

ATF3, ATF4, BRRN1, CCL8, CCL17, CD1A, CD1B, CD1C, CLEC4A, CTLA4, CTNNAL1, CTSK, CTSL, CYBB, DEFB103A, FBP1, FCER2, FGL2, IFNG, IL13, IL15RA, IL1RN, IL2RG, KIAA0555, MAF, MAOA, MMP12, PFKP, PHLDA1, QSCN6, RAB33A, S100A8, SPINT2, STAT6, UBD 26 19

Cell-To-Cell Signaling and Interaction, Hematological System Development and Function, Immune Response

Functional Analysis of the Genes

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 SourceGene Symbol Entrez Gene ID for HumanEntrez Gene ID for MouseEntrez Gene ID for RatIL1A 3552 16175 24493CSF1R 1436 12978 307403HGF 3082 15234 24446IFNB1 3456 15977 24481TNFRSF11B 4982 18383 25341TLR3 7098 142980 364594IFNA 24480CDKN1A 1026 12575 114851PROK1 84432 246691 192205NFATC1 4772 18018 307231TGFB1 7040 21803 59086INPP5D 3635 16331 54259IL9 3578 16198 116558IL11 3589 16156 171040PROK2 60675 50501 192206IL4 3565 16189 287287CD4 920 12504 24932LIF 3976 16878 60584TLR9 54106 81897 338457CSF1 1435 12977 78965IL17 3605 16171 25465EGR1 1958 13653 24330IL6 3569 16193 24498IL3 3562 16187 24495TLR2 7097 24088 310553PTH 5741 19226 24694JUNB 3726 16477 24517ITGB3 3690 16416 29302CALCA 796 12310 24241SRC 6714 20779 83805TLR4 7099 21898 29260CCL3 6348 20302 25542PTK2 5747 14083 25614ITGAV 3685 16410 296456TM7SF4 81501 75766TNF 7124 21926 24835TNFSF11 8600 21943 117516CSF2 1437 12981 116630NFATC2 4773 18019 311658PTHLH 5744 19227 24695IFNG 3458 15978 25712PTK2B 2185 19229 50646BIRC5 332 11799 64041IAPP 3375 15874 24476WT1 7490 22431 24883IL1B 3553 16176 24494

Y-axis: GC RMA File Preprocessor Experiment HOC, Default InterpretationColored by: Time 0 Gene List: IPA OC genes from DC branch (29)

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Y-axis: CD14 Agilent Experiment FE, Default InterpretationColored by: DendriticGene List: IPA OC genes from DC branch (29)

Osteoclast Endothelia Macrophage Dendritic

Tissue Type0,01

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Osteoclast Endothelia Macrophage Dendritic

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Genes categorised as “osteoclast related” in IPA are inspected in our microarray data

• Search and visualize in IPA

• Color with your (or others) expression data

How to Use: Pathway Graphics

How to Use: Co-operation, data sharing

Y-axis: GC RMA File Preprocessor Experiment HOC, Default InterpretationColored by: PLOSL

Error Bars: min-maxGene List: All PLOSL+TREM, DAP12 (54)

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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 co-

workers 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 YouDepartment of Anatomy,University of Turku

Anne SeppänenHusheem Michael Teuvo HentunenTiina Laitala-LeinonenKalervo Väänänen

Department of Medical Microbiology,University of Turku

Milja Möttönen Olli Lassila

Department of Information Technology,University of Turku

Eija NordlundJorma Boberg

Tapio Salakoski

Department of Physiology,University of Turku

Markku Ahotupa

National Public Health Institute, Department of Molecular Medicine,

HelsinkiAnna Kiialainen