Download Systems biological approach to osteocalst development

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
(Ms)
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 Ms 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