Biomic Study of Human Myloid Leukemia Cell (HL
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Transcript Biomic Study of Human Myloid Leukemia Cell (HL
Systems Biology
Hsueh-Fen Juan (阮雪芬)
NTUT
Aug 29, 2003
Yuki Juan’s Systems Biology Lab
Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
juan SBL
Case study: effect of RGD-peptides in
breast cancer
Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
juan SBL
Case study: effect of RGD-peptides in
breast cancer
Traditional Biology &
Systems Biology
Traditional biology :
– Single genes or proteins
Systems biology:
– Simultaneously study the complex
interaction of many levels of biological
information to understand how they work
together
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Genomic DNA
mRNA
Proteins
Functional
proteins
Informational pathways
Informational networks
Systems Biology and
Omics Data
Genetic
Transcriptomic
Systems Biology
Proteomic
Metabonomic
Drug discovery
Development process
Understanding drug toxicology
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The Aims of Systems
Biology
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What are the basic structures and
properties of a biological network?
How does a biological system behave
over time under various conditions?
How does a biological system maintain
its robustness and stability?
How can we modify or construct
biological systems to achieve desired
properties?
To Understand Biological
Systems
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System
System
System
System
structure identification
behavior analysis
control
design
Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
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Case biology study: effect of RGDpeptides in breast cancer
System Structure
Identification
Network structure identification
– KEGG and EcoCyc
Parameter identification
– Genetic algorithms
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KEGG
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http://www.genome.ad.jp/kegg/
Pathway in KEGG
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EcoCyc
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http://www.ecocyc.org/
Pathway in EcoCyc
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Genetic Algorithms
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Based on the underlying genetic process
They are replicated and passed onto the
next generation with selection depending on
fitness.
Genetic information can be changed through
genetic operations.
Three Main Operations in
GA
Selection
Crossover
Mutation
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Genetic Algorithms
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System Behavior Analysis
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Simulation
Analysis methods
Software Tools for Systems
Biology and Their Workflow
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Relationship among software tools
Workflow and software tools
Relationship among
Software Tools
Genome/proteome
database
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Experimental data
database
Simulator
Experimental data
interface
System analysis
module
Measurement
systems
System profile
database
System structure
database
Parameter optimiztion
module
Visualization module
Hypotheses generation
experiment planning
module
Workflow and Software
Tools
Expression profile data
Two-hybrid data
RT-PCR data, etc.
Parameter
optimizer
Simulator
Gene regulation network
Metabolic cascade network
Signal transduction network
Hypothesis
generator
Dynamic system
analysis
Robustness stability,
bifurcation, etc
Design pattern
analysis
Design patter
extraction
A set of plausible hypothesis
Predictions of gene and interactions
Experiment design
Assistance system
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Biological experiments
Experiment plans
Robustness of Biological
System
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System control
Redundancy
Modular design
Structural stability
System Control
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Feedforward Control
Feedback Control
Feedforward Control and
Feedback Control
Feedforward control
input
Controller
Effector
output
Effector
output
Feedback control
input
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Controller
Heat Shock Response with
Feedforward and Feedback
Control
Heat Shock
Normal
Protein
hsp
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dnaK
dnaJ
grpE
GroES
GroEL
dnaK
dnaJ
GroES
GroEL
Misfolded
Protein
rpoH
sE
s70
dnaK
32
dnaJ s
grpE
s32
Es32
Redundancy in MAP
kinase cascade
Raf, Mos
MEK1,2/MKK1,2
MAPK/ERK
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MEKK1, MLK3
ASK1, TAK1
SEK1, 2/MKK4,7
MKK3,6
SAPK/JNK
p38
Transcription
Modular Design
Component:
– An elemnetary unit of the system
– Genes and proteins
Device:
– An minimum unit of the functional assembly
– Transcription complexes and replication complexes
Module:
– A large cluster of devices
– Organells and gene regulatory circuits for the cell cycle
System
– A top-level assembly of modules
– A cell or entire animal
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Structural Stability
Play important roles in development
Temporal arrangement of signaling in
– the JAK/STAS signaling pathway
– pattern formation in Drosophila involving
Ubx and Dpp
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Genome, Proteome, and
Systeome
Dynamics information
High resolution
image
Expression profile
Protein interactions, etc.
Basic model information System dynamics
information
Basic structure
Gene network model
Metabolic pathway model
Signal transduction model
Parameters
Components information
Proteome
Genome
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System dynamics
analysis
Mutation analysis
Drug sensitivity
analysis
Individual genetic variations
Individual sequence variation
Individual heterochromatin
variation
Individual
Systeome
Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
juan SBL
The systems biology study: sffect of
RGD-peptides in breast cancer
Introduction to Protein—
protein Interaction
Protein-protein interactions are intrinsic
to every cellular process.
Form the basis of phenomena
-DNA replication and transcription
-Metabolism
-Signal transduction
-Cell cycle control
-Secretion
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PPI
Knowledge of interacting proteins
Provide insight into the
function of important
genes
Elucidates relevant
pathways
Facilitates the identification of potential
drug targets
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Use in developing novel therapeutics
The Study of Protein-protein
Interaction by Mass
Spectrometry
bait
?
?
S14
?
?
SDSPAGE
*
*
*
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MASS
*
Peptide Mass
Fingerprinting
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Yeast Two-hybrid System
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Useful in the study of various interactions
The technology was originally developed during
the late 1980's in the laboratory Dr. Stanley
Fields (see Fields and Song, 1989, Nature).
Yeast Two-hybrid Assay
GAL4 DNAbinding
GAL4 DNAactivation
domain
domain
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Nature, 2000
Yeast Two-hybrid Assay
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Library-based
yeast two-hybrid
screening
method
Nature, 2000
Protein-protein
Interactions on the Web
Yeast
http://depts.washington.edu/sfields/yplm/data/index.html
http://portal.curagen.com
http://mips.gsf.de/proj/yeast/CYGD/interaction/
http://www.pnas.org/cgi/content/full/97/3/1143/DC1
http://dip.doe-mbi.ucla.edu/
http://genome.c.kanazawa-u.ac.jp/Y2H
C. Elegans
http://cancerbiology.dfci.harvard.edu/cancerbiology/ResLabs/Vidal/
H. Pylori
http://pim/hybrigenics.com
Drosophila
http://gifts.univ-mrs.fr/FlyNets/Flynets_home_page.html
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Yeast Protein Linkage
Map Data
http://depts.washington.edu/sfields/yplm/data
New protein-protein interactions in yeast
Stanley Fields Lab
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GeneScape
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http://portal.curagen.com
PathwayCalling: Protein interaction
and pathway Analysis
Munich Information Center
for Protein Sequences
http://mips.gsf.de/
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MIPS: a database for genomes and protein
sequences
The MIPS Comprehensive Yeast Genome Database
(CYGD) aims to present information on the
molecular structure and functional network.
Yeast Interacting
Proteins Database
http://genome.c.kanazawa-u.ac.jp/Y2H
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Suiseki
is a system for the
extraction of
protein-protein
interactions from
large collections of
scientific text
•DNA replication
•The Immune System
•The E2F transcription factor
•The talin/viniculin/actin system
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http://www.pdg.cnb.uam.es/suiseki/
Suiseki
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Suiseki
Regulate
Activate
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Information Extraction
(IE)
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A vast amount of data on proteinprotein interactions residues in the
published literature, which never been
entered into databases.
IE have been applied to gaining
information on protein-protein
interactions.
Mining Literature for
Protein-protein
Interactions
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Extraction of the
Interactions
The nouns and verbs are taken from a
hand constructed list containing nouns
such as activation, phosphorylation or
interaction, and verbs such as
activates, binds or phosphorylates.
Rules are applied directly to the text
by string comparison.
Comp Funct Genom 2001, 2, 196-206
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Extraction of the
Interactions
The sentence “The expressed p53
protein showed nuclear localization
and its expression was associated with
an induction of p21 and bax
expression” relates p53 with p21 and
bax but does not imply a physical
interaction between them.
Comp Funct Genom 2001, 2, 196-206
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Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
juan SBL
The systems biology study: sffect of
RGD-peptides in breast cancer
Linkage of a Basic System-Biology
Research Cycle with Drug
Discovery and Treatment Cycles
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Nature 2002, 420, 206.
Mammalian Systemmicrobial-nutritionalxenobiotic Interactions
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Nature 2003, 2, 668.
Possible Interactions
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Nature 2003, 2, 668.
The Dynamic Pachinko
Model of Metabolism
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Nature 2003, 2, 668.
Outline
Introduction
To understand biological systems
Protein—protein interaction
Drug Discovery
juan SBL
Case study: effect of RGDpeptides in breast cancer
Effect of RGD-peptides in
breast cancer
juan SBL
Introduction
cDNA microarray
Proteomics
Bioinformatics
Introduction
Yuki Juan’s Systems Biology Lab
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SCIENCE, 2001, 294, 82-85
The Structure of an
Integrin
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Hynes in 1987 to
emphasize the role of
these RGD receptors
in integrating the
extracellular matrix
outside the cell with
the actin-containing
cytoskeleton inside
the cell.
The Interactions of Integrins
with Other Proteins on both
Sides of the Lipid Bilayer
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Schematic Model of the
Protein-protien Interactions of
a Focal Adhesion Complex
Signal are presumably
transmitted into the
nucleus, where they
stimulate the
transcription of gene
involved in cell growth
and proliferation
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Integrin-activated Survival
Signals
Ras
Shc
Raf
Grb2/Sos
MEK
MAPK
Cell survival
Integrins
FAK
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PI 3-kinase
Trends in Cell Biology, 1997, 7, 146-150
How RGD Trigger Apoptosis?
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By integrin-mediated signal?
Directly interact with the protein in
cytosol?
How RGD Trigger Apoptosis?
a. Cell survive
b. RGD trigger apoptosis
via integrin
c. Cell apoptosis by
activating procaspase-3
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Nature, 1999, 397, 534-539
RGD and Cell Death
RGD(Arg-Gly-Asp) is the smallest motif that
bind with the integrin receptor on the cell surface
and play important role in cell cycle.
Control
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Aggregation
Cell Death
Our Study
Yuki Juan’s Systems Biology Lab
Our Study
Human breast cancer cell MCF-7
Genomic Study
Proteomics
Bioinformatics
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Cell
Apoptosis
The Structures of RGD
Mimetic Peptides
NH
H2N
NH
O
O
OH
HN
HN
O
HN
Gly Asp
Arg
O
HN
Trp
O
Tpa
N
Pro
O
S
Asp
Arg
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Gly
CysNH
S
H2N
O
Cyclic-RGD
O
NH
RGD
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control
control
1mM
0.5mM
5mM
1mM
cRGD
cDNA Microarray
Yuki Juan’s Systems Biology Lab
Introduction to
Microarray
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After the draft of Human genome project was
published and the powerful high–throughput
microarray technology is available, the discovery of
discriminating gene patterns becomes important.
cDNA microarray technology is a powerful approach
to accurately measure changes in global mRNA
expression levels.
This technique has been used to discover novel
genes, determine gene functions, evaluate drugs,
dissect pathways, and classify clinical samples.
A Framework of Microarray
Analysis
Experiments
Designing
Microarrary
Analysis
Data Preprocessing
(Normalization &
Data Filtering )
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Data
Analysis
Image
Analysis
cDNA Microarray
C-RGD, 6hr
C-RGD, 24hr
C-RGD, 48hr
C-RGD, 72hr
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Apoptosis
Total
34 genes, but after filtering
there are only 19 genes
Total 11 genes have expression
fold >2 (up or down changes)
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Apoptosis Regulator
U60519
U97075
AF051941
U13738
AF005775
U60521
Z48810
AAF19819
U67319
U28976
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AF015450
Apoptosis Regulator
Description
Genebank
accession
No.
6h
24 h
48 h
72 h
Fold Change Fold Change Fold Change Fold Change
Group 1
caspase 10, apoptosis-related cysteine protease U60519
-
-
-
0.471
CASP8 and FADD-like apoptosis regulator
U97075
nucleoside diphosphate kinase type 6 (inhibitor
of p53-induced apoptosis-alpha)
AF051941
-
-
-
0.355
-
-
-
0.376
Group 2
caspase 3, apoptosis-related cysteine protease
U13738
-
2.301
-
-
CASP8 and FADD-like apoptosis regulator
AF005775
-
2.272
-
-
U60521
-
-
2.519
-
Z48810
2.615
-
2.796
2.819
Group 3
caspase 9, apoptosis-related cysteine protease
Group 4
caspase 4, apoptosis-related cysteine protease
Group 5
inhibitor of apoptosis protein
AAF19819
-
-
-
5.249
caspase 7, apoptosis-related cysteine protease
U67319
-
-
-
2.19
caspase 4, apoptosis-related cysteine protease
U28976
-
-
-
2.603
AF015450
-
-
-
6.912
Group 6
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CASP8 and FADD-like apoptosis regulator
10
p1
Normalized Intensity
(log scale)
10
p1
Normalized Intensity
(log scale)
10
1
1
1
0.1
0.1
0.1
time (hour)
0.01
6
10
24
48
6
72
p1
Normalized Intensity
(log scale)
time (hour)
0.01
10
24
48
6
p1
Normalized Intensity
(log scale)
10
1
1
1
0.1
0.1
0.1
time (hour)
0.01
6
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24
48
72
time (hour)
0.01
6
24
48
72
time (hour)
0.01
72
p1
Normalized Intensity
(log scale)
24
48
72
p1
Normalized Intensity
(log scale)
time (hour)
0.01
6
24
48
72
Using Linear Model to Construct
Gene Network
Linear Model
Δyi t
wi,j y j t bi , i; t
Δt
j
y t
W y t B, t
t
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Y ~ T ~ T ~
W
Y Y Y
t
1
D’haeseleer, 2000
Weights Matrix of
Apoptosis Regulator
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Weights
2.670363
2.068236
-1.889373
-1.427408
-0.81632
-0.761848
0.753277
0.682257
0.646907
0.636552
0.632796
0.594627
-0.55848
0.543142
-0.527872
0.518056
0.508007
0.499483
Gene 1
AF015450
AAF19819
AF015450
AAF19819
AF005775
U13738
AF015450
U13738
Z48810
AF015450
AF005775
AF005775
Z48810
AAF19819
U60521
U28976
U60521
U13738
Gene 2
U60521
U60521
AAF19819
AAF19819
AF005775
AF005775
U60519
AAF19819
AF005775
AF005775
Z48810
AAF19819
AAF19819
U60519
U60521
U60521
AF005775
Z48810
Gene Network of
Apoptosis Regulator
caspase 10, apoptosis-related
cysteine protease
(U60519)
CASP8 and FADD-like
apoptosis regulator
(AF015450)
+0.753277
caspase 9, apoptosis-related
cysteine protease
(U60521)
+2.670363
6
+2.068236
-1.889373
+0.636552
CASP8 and FADD-like
apoptosis regulator
(AF005775)
inhibitor of apoptosis protein
(AAF19819)
+0.682257
-0.761848
+0.646907
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caspase 4, apoptosis-related
cysteine protease
(Z48810)
caspase 3, apoptosis-related
cysteine protease
(U13738)
Signal Transducer
1
BE336944
X52599
L24494
M64347
M12783
AF107885
U52112
M83575
L13857
AI692949
AW663903
AJ222700
M57399
U31176
X03438
AI885899
AB009249
U73737
U66406
AF266504
AW887370
AB039723
M77227
AF010312
M35878
NM_005429
AAC17439
U28054
L13858
L34641
X14253
NM_004791
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AF035835
D63395
U94888
J03071
D87845
L13857
M21188
NM_004114
X14885
X70340
2
J03071
X15215
S75361
M37483
U14187
D12614
NM_005130
AF179274
AB017365
AF035835
D25328
NM_003242
S81439
AB017364
L13858
L27475
AF068868
M34480
NM_005928
AF005271
X53038
AI127370
AF002986
M37763
AF107885
AF081513
AJ000185
AF251118
D10202
U12535
AF026692
X14253
X51602
AF119815
U72338
AF041240
M37435
AB000509
AI634668
S77035
AF107885
U52112
AF056087
AF019634
X76079
Weights Matrix of Signal
Transducer
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Weights
-0.875598
0.834468
-0.78655
-0.760695
-0.728793
0.645775
0.608239
0.558504
0.538674
0.530764
0.455149
0.453129
-0.452645
0.449854
0.444301
-0.442259
0.437687
0.429097
Gene1
NM_003242
M37435
L27475
M21188
D63395
NM_003242
NM_003242
L27475
D63395
U72338
AF041240
AI634668
AB017364
NM_003242
M21188
M37435
M21188
NM_003242
Gene2
L27475
L27476
L27477
L27478
L27479
NM_005429
NM_005430
NM_005431
NM_005432
L27475
L27476
L27477
L27478
U12535
AF251118
U52112
U12535
AF251118
Gene Network of Signal
Transducer
vascular endothelial growth factor C
(NM_005429)
+0.608239
insulin-degrading enzyme
(M21188)
+0.538674
epidermal growth factor
receptor pathway substrate 8
(U12535)
Notch (Drosophila)
homolog 4
(D63395)
+0.645775
+0.444301
Interleukin-1 Superfamily z
(AF251118)
+0.558504
+0.449854
-0.760695
-0.728793
transforming growth factor,
beta receptor II (70-80kD)
(NM_003242)
-0.875598
Human interleukin-1 beta
converting enzyme gene, 5' flank.
(L27475)
+0.834468
colony stimulating
factor 1 (macrophage)
( M37435)
msh (Drosophila) homeo box
homolog 1 (formerly homeo box 7)
(AI634668)
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+0.453129
-0.452645
+0.530764
+0.455149
Human platelet activating factor
acetylhydrolase, brain isoform, 45
kDa subunit (LIS1) gene, exon 7.
(U72338)
frizzled (Drosophila) homolog 2
(AB017364)
hypocretin (orexin)
neuropeptide precursor
(AF041240)
Proteomics
Yuki Juan’s Systems Biology Lab
Two-dimensional Gel Approach
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Nature 2000, 405, 837-846
Control vs c-RGD (6hr)
Control
cRGD
1
97000
66000
45000
30000
20100
14400
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3.2
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
3.2
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
Control vs c-RGD (24hr)
c-RGD
Control
97000
17
66000
2
45000
3
4
18
30000
5
6
8
9
10
15 12 11 13
16
14
20100
14400
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7
3.2
4.0
5.0
5.5
6.0
3.2
7.0
8.0
9.0 10.0
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
Control vs c-RGD (48hr)
Control
c-RGD
97000
66000
19 20
45000
21
22
30000
20100
14400
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3.2
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
Control vs c-RGD (72hr)
Control
c-RGD
23
97000
66000
24 25
45000
26
30000
20100
14400
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3.2
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
3.2
4.0
5.0
5.5
6.0
7.0
8.0
9.0 10.0
Proteomics Results
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1. Semenogelin I protein precursor
(SGI)
2. Cell division protein kinase 6
3. Zinc finger protein 74 isoform 4
4. Keratin
5. Similar to presomitic mesoderm
specific gene
6. Unnamed protein product
7. RNA-binding protein regulatory
subunit
8. Similar to Claudin-6 (Skullin)
9. Unnamed protein product
10. Similar to Per-hexamer repeat
protein 5
11. Similar to L1 repetitive element
ORF
12. Hypothetical protein
13. Zinc finger protein 189 ISOFORM 2
Red color: up-regulated
White color: down-regulated
14. Hypothetical protein
15. Similar to stretch response protein
553
16. Zinc finger protein 83
17. Immunoglobulin heavy chain
variable region
18. Hypothetical protein
19. Cytokeratin 8
20. Cytokeratin 8
21. Zinc-alpha-2-glycoprotein
precursor
22. Keratin 18
23. Platelet-activating factor
acetylhydrolase precursor
24. Topoisomerase II alpha
25. 13kD differentiation-associated
protein
26. Purified protein derivative-specific
T-cell receptor beta chain
Bioinformatics
Yuki Juan’s Systems Biology Lab
RGD Peptides Can Be Used
in Many Diseases
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Thrombosis
Osteoporosis
Cancer
Any one else
??
Blood Clots Form
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Blood clots form
when platelets
adhere to one
another through
fibrinogen bridges
that bind to the
platelet integrin
Clustering Analysis of
Proteins
http://uranus.csie.ntu.edu.tw:9000/index.jsp
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RGD-containing Proteins
in Swiss-Prot Database
In Swiss-Prot database, there are 738
human RGD-containing proteins which
containing 5 caspase proteins .
– Caspase 1, caspase 2, caspase 3 and
caspase7, caspase 8.
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RGD-containing Proteins in
Swiss-Prot Database
Heat shock protein Dna
Chaperone DnaJ
Alzheimer's disease
SM22
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Leiomyoma
Conclusion and
Discussion
Yuki Juan’s Systems Biology Lab
Conclusion and
Discussion
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Cyclic RGD exerts more potency than that of liner RGD
on the inhibiting cell growth.
The cyclic RGD exerts 8-10 times potency more than
that of liner RGD peptide in inhibiting proliferation and
inducing clustering of MCF-7 cells.
Cyclic RGD can induce the apoptosis of MCF7. We
showed many caspases involved in this apoptosis and
constructed the caspase pathway.
Conclusion and
Discussion
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CASP8 and FADD-like apoptosis regulator, caspase 9 and
inhibitor of apoptosis protein formed the positive and
negative feedback control system.
Vascular endothelial growth factor C and human
interleukin-1 beta converting enzyme gene have the
important positions in the gene network because they will
affected many other genes.
Clustering tool maybe could predict some novel functions in
RGD-containing proteins.
Outlook
cDNA microarray
Proteomics
Apoptosis pathway
Cellular mechanism
Bioinformatics
juan SBL
Drug discovery
Summary
juan SBL
Systems biology is a new and
emerging field in biology.
Systems biology requires a range of
new analysis techniques,
measurement technologies,
experimental methods, software tools.
Systems Biology will be the dominant
paradigm in biology.
juan SBL
Thank you!
juan SBL