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Molecular interactions
Based on Chapter 4 of
Post-genome Bioinformatics
by Minoru Kanehisa,
Oxford University Press, 2000
Network representation. A network (graph) consists of a set of elements
(vertices) and a set of binary relations (edges). Biological knowledge and
computational results are represented by different types of network data.
2) Binary Relation
1) Element
Molecular interaction
Genetic interaction
Other types of relations
Molecule
Gene
3) Network
Assembly
Pathway
Genome
Neighbour
Cluster
Hierarchical Tree
Representation of the same graph by: (a) a drawing of nodes and
edges, (b) a linked list, and (c) an adjacency matrix.
(a)
(b)
A
B
C
D
E
F
A
B
C
D
E
F
(c)
B
A
B
B
C
E
C D
E
E
D F
A B C D E F
A
B
C
D
E
F
1
0
0
0
0
1
1 0
0 1 1
0 0 0 1
Biological examples of network comparisons.
Pathway vs. Pathway
Pathway vs. Genome
Genome vs. Genome
Cluster vs. Pathway
Pathway alignment is a problem of graph isomorphism:
(a) a maximum common induced subgraph and (b) a maximum clique.
Pathway 1
(a)
E
Pathway 2
A
a
B
b
c
C
d
D
A
B-a
B-b
C-d
D-f
A
(b)
(A,
(B,
(C,
(D,
(E,
a)
a)
a)
a)
a)
(A,
(B,
(C,
(D,
(E,
b)
b)
b)
b)
b)
(A,
(B,
(C,
(D,
(E,
c)
c)
c)
c)
c)
(A,
(B,
(C,
(D,
(E,
d)
d)
d)
d)
d)
(A,
(B,
(C,
(D,
(E,
e)
e)
e)
e)
e)
(A,
(B,
(C,
(D,
(E,
f)
f)
f)
f)
f)
A heuristic algorithm for biological graph comparison. It searches for
clusters of correspondences, as shown in (a), which is similar in spirit
to sequence alignment, shown in (b).
Graph 1
(a)
A
C
D
B
A
B
C
D
.
.
G
E
I
Correspondences
K
H
F
J
A
C
D
E
I
H
.
.
a
b
c
d
.
.
Graph 2
a
d
i
k
h
a
c
i
J
j
(b)
A-B-C-D-E-F-G-H-I-J-K
: : :
A-c-b-d-e-f-h-g-j-k-i
k
h
F
b
g
e
K
f
j
d
G
b
g
e
Clustering
algorithm
B
c
f
Examples of binary relations
Type o f relation
Factual relation
Contents
Link s between database
entiti es
Examples
Factual data and it s publi cation information
Nucleotide sequence and trans la ted ami no
acid sequence
Protein sequence and 3D structure
Simila rit y relation
Computed simil arity
Computed complementarit y
Sequenc e simil arit y: 3D stuctural simi larit y
3D structural co mplementarit y
Func tiona l relation
Mole cular reactions
Mole cular interactions
Gene tic interactions
Subs trate-product relations
Mole cular pathway s; molecular assembli es
Positi vely co-expre ssed gene s
Negatively co-exp ressed genes
Correlation o f gene locations (operons )
Orthologous and pa ralogous gene s
Chromosomal relations
Evolu tiona ry relations
An example of computing possible reaction paths from pyruvate
(C00022) to L-alanine (C00041) given a set of substrate-product
binary relations, or a given list of enzymes.
O
O
O
1.4.1.1
H3C
OH
NH2
OH
C00022
CH3
2.6.1.21
5.1.1.1
O
CH3
OH
4.1.1.3
C00133
4.1.1.12
NH2
O
O
OH
OH
O
1.4.3.16
OH
NH2
O HO
C00036
C00049
O
Query relaxation. Nodes E and E’ are considered to be
equivalent according to the grouping G.
A
B
B
B
C
C
E
E’
G
A
B
C
E
F
D
E
F
E’
D
Network data representation in KEGG
Network type
Pathway
Assembly
KEGG d ata
Pathway map
Content
Metaboli c pathway, regu la tory pathway, and
molecular assembly
Representation
GIF image map
Geno me
Geno me map
Comparative geno me
map
Chromosomal l ocation o f genes
Java applet
Cluster
Expre ssion map
Dif ferential gene expr ession p rofil e by
mi croarrays
Java applet
Neighbou r
Orthologue g roup
Pathway Assembly table
Geno me
Func tiona l un it of gene s in a pathway or
assembly, toge ther with orthologous relation
of gen es and chromosomal r elation o f gen es
HTML table
Hierarchical tree
Hierarchical classification of gene s
Hierarchical classification of molecules
Hierarchical classification of organ isms
Hierarchical classification diseases
Hierarchical text
Gene catalogue
Mole cular catalogue
Taxono my
Disease ca talogue
Genome-pathway comparison, which reveals the correlation of
physical coupling of genes in the genome - operon structure (a) and
functional coupling (b) of gene products in the pathway
(a) E. coli genome
hisL
yefM
hisG
hisD
hisC
hisB
hisH
hisA
hisF
hisI
yzzB
(b) Metabolic pathway
HISTIDINE METABOLISM
Pentose phosphate cycle
5P-D-1-ribulosylformimine
3.5.1.-
Phosphoribosyl-AMP
PRPP
3.6.1.31
2.4.2.17
3.5.4.19
Phosphoriboxyl-ATP
PhosphoribulosylFormimino-AICAR-P
5.3.1.16
2.6.1.-
2.4.2.-
PhosphoribosylFormimino-AICAR-P
Imidazoleacetole P
4.2.1.19
ImidazoleGlicerol-3P
2.6.1.9
3.1.3.15
L-Histidinol-P
5P Ribosyl-5-amino 4Imidazole carboxamide
(AICAR)
1.1.1.23
1-MethylL-histidine
3.4.13.5
Aneserine
6.3.2.11
Purine metabolism
3.4.13.3
3.5.3.5
Imidazolone
acetate
3.5.2.-
1.14135
Imidazole4-acetate
3.4.13.20
Imidazole
acetaldehyde
1.2.1.3
Histamine
1.4.3.6
L-Hisyidinal
2.1.1.-
2.1.1.22
Carnosine
N-Formyl-Laspartate
L-Hisyidinal
4.1.1.22
4.1.1.28
6.3.2.11
1.1.1.23
6.1.1
Hercyn
L-Histidine
Hierarchy-pathway comparison, which reveals the correlation of evolutionary
coupling of genes (similar sequences or similar folds due to gene duplications)
and functional coupling of gene products in the pathway.
SCOP hierarchical tree……..NE, TYROSINE AND TRYPTOPHAN BIOSYNTHESIS
1.
2.
3.
All alpha
All beta
Alpha and beta (a/b)
3.1 beta/alpha (TIM)-barrel
3.2 Cellulases
. . . . . . .
3.74 Thiolase
3.75 Cytidine deaminase
4. Alpha and beta (a+b)
5. Multi-domain (alpha and beta)
6. Membrane and cell surface pro
7. Small proteins
RNA
8. Peptides
9. Designed proteins
10. Non-protein
2.5.1.19
3-deoxyD-arabinoheptonate
1.3.1.43
4.2.1.51
3-Dehydroquinate
4.2.1.10
4.2.1.11
1.1.9925
Quniate
2.6.1.57
Pretyrosine
4.2.1.91
1.4.1.20
6.1.1.20
2.6.1.5
Phenylalanine
Phenylpyruvate
2.6.1.1
2.6.1.9
2.6.1.57
4.1.3.27
Histidine
1.1.9925
4-Aminobenzoate
3-Dehydro- Protocatechuate
shikimate
Folate
biosynthesis
2.6.1.9
2.6.1.57
1.4.3.2
2.6.1.1
2.6.1.5
2.6.1.9
2.6.1.57
Prephenate
4.2.1.51
Indole
4.2.1.91
5.4.99.5
2.4.2.18
N-(5-Phosphob-v-ribosyl)anthranilate
4.1.3.-
4.2.1.10
2.6.1.5
4.2.1.20
1.4.3.2
4.6.1.4
2.6.1.1
4-Hydroxyphenylpyruvate
1.14.16.1
Shikimate
1.1.1.25
Alkaloid biosynthesis I
6.1.1.1
Tyrosine
Anthranilate
4.6.1.3
1.1.1.24
Tyr-tRNA
Chorismate
2.7.1.71
Tyrosine metabolism
Ubiquinone biosynthesis
4.2.1.20
5.3.1.24
4.1.1.48
1-(2- CarboxyPhenylamino)1-deoxy-D-ribulose
5-phosphate
4.2.1.20
(3-Indolyl)Glycerol
phosphate
L-Tryptophan
Tryptophan
metabolism
Grand challenge problems
Protein folding problems
Organism reconstruction problem
Prediction
Structure prediction - to predict protein 3D
structure from amino acid sequence
Network prediction - to predict entire
biochemical network from complete
genome sequence
Knowledge
Known protein 3D structures
Known biochemical pathways and
assemblies
Knowledge based prediction
Threading
Network reconstruction
Ab initio prediction
Energy minimization
Path computation
Prediction of perturbed states
Protein engineering
Pathway engineering
Glycolysis, the TCA cycle , and the pentose phosphate pathway, viewed as a
network of chemical compounds. Each circle is a chemical compound with
the number of carbons shown inside.
NADPH
D-Glucose-6P
D-Glucose
6
6
D-Fructose-6P
6
D-Fructose-1,6P2
6
6
D-Xylulose-5P CO
2
5
4
Glycerone-P
3
3
NADH
Funarate
(S )-Malate
4
4
4
Citrate
Succinate
4
21
GTP
21
21
25
CO2
12
Succinyl-CoA
Dihydrolipoamide
Isocitrate
8
6
CoA
8
NADH
Glycerae-1,3P2
3
Glycerate-3P
3
Glycerate-2P
3
CoA
glutarate
Lipoamide
3
21
NADH
5 2-Oxo-
S-Acetyldihydrolipoamide
Oxaloacetate
6
CoA
CoA
6
23
10
Acetyl-CoA
Dihydrolipoamide
CO2
8
S-Acetyldihydrolipoamide
8
NADH
D-Sedoheptulose-7P
Glyceraldehyde-3P
NADH
ATP
Phophoenolpyruvate
ATP
3 Pyruvate
Lipoamide
6-PhosphoD-gluconate
NADPH
5 D-Ribulose-5P
5
7
Citrate cycle
(TCA cycle)
FADH2
D-Glucono-1,5Lactone-6P
D-Ribose-5P
Pentose
Phosphate
pathway
Glycolysis viewed as a network of enzymes (gene products).
Each box is an enzyme with its EC number inside.
D-Glucose
(extracellular)
D-Glucose
2.7.1.69
D-Glucose-6P
2.7.1.2
5.3.1.9
D-Fructose-6P
3.1.3.11
2.7.1.11
D-Fructose-1,6P2
4.1.2.13
Glycerone-P
Glyceraldehyde-3P
5.3.1.1
1.2.1.12
Gycerate-1, 3P2
2.7.2.3
Glycerate-3P
5.4.2.1
Glycerate-2P
4.2.1.11
Citrate cycle
(TCA cycle)
Phosphoenolpyruvate
Acetyl-CoA
2.7.1.40
1.2.1.51
2.3.1.12
6-S-Acetyl-dihydrolipoamide
Dihydrolipoamide
1.8.1.4
1.2.4.1
Lipoamide
Pyruvate
Pentose
Phosphate
cycle
A generalized concept of protein-protein interactions.
Direct protein-protein interaction
Protein 1
Protein 2
Binding, modification,
Cleavage, etc.
Indirect protein-protein interaction
Protein 1
Protein 2
Enzymic reaction
Protein 1
Protein 2
Gene expression
Gene
(Molecular template)
A strategy for network reconstruction from genomic information.
Reference knowledge
(e.g. KEGG)
Gene catalogue
in the genome
Predicted network by
orthologue identification
Predicted network by
path computation
Binary relations:
Positional cloning
Genome comparisons
Gene-gene (indirect) interactions
DNA chips
Protein-protein (direct) interactions
Substrate-product relations
Protein chips
Biochemical knowledge
Hierarchial relations
Sequence analysis
Genetic and chemical blueprints of life.
Bluep rint
Entity
Info rmation
Gene tic bluep rint of lif e
Geno me
Centralized
Static
Chemic al bluep rint of lif e
Network of interacting
molecules in the cell
Distributed
Dyna mic
Principles of the biochemical network encoded in the genome.
Hierarchy - conservation and diversification
(a) Low resolution network
(b) Divergent inputs
Divergent outputs
Conserved pathway
High resolution network
Duality - chemical logic and genetic logic
(c) Chemical Enzyme
network network
+
Metabolic
network
=
(d) Protein-protein
interaction network
Gene regulatory
network
Biological examples of complex systems
System
Node
Edge
Protein 3D structure
Atom
Atomi c interaction
Organ ism
Mole cule
Mole cular interaction
Brain
Cell
Cellular interaction
Ecosystem
Organ ism
Organ ism i nteraction
Civili zation
Human
Human interaction
From Sequence to Function
Comparison of bioinformatics aproaches for functional prediction
Era
Experiments Database
Computational method
1977 gene cloning
sequencing
sequence
sequence similarity search
1995 whole genome
sequencing
pathway
pathway reconstruction
path computation
pathway = wiring diagram
Functional Reconstruction Problem
(Sequence -> organism)
1. Genome is a blueprint of life
(Dolly’s cloning principle)
Genome + Environment
(Nucleus)
2. Network of molecular interactions in the entire cell is a
blueprint of life - Genome is only a warehouse of parts
(Principle of molecular interaction)
Germ Cell Line
Pathway Assembly
DNA Damage
Suzie Grant’s Study: Aims
• Examine the effects of oncostatin-M (OSM)
in combination with Epidermal Growth
Factor (EGF)
• Delineate the signalling pathway
responsible for the effects induced by OSM
in breast cancer cells.
IL-6 Cytokine Receptor Family.
gp130
IL-6
IL-11
CNTF
IL-6R
IL--11R
CNTFR
gp130
LIF
OSM
CT
LIFRb
OSM
gp130
OSMR b
Physiological Functions of IL-6 family members
Function
Cytokine
Proliferation/maturation of megakaryocytes
OSM, LIF, IL-6, IL-11
Expansion of hemopoietic progenitor cells in the AGM
OSM
Induce terminal differentiation of M1 cells
OSM, LIF, IL-6, CT-1
Inhibit differentiation of ES cells
OSM, LIF, CT-1, CNTF
Stimulate proliferation of fibroblasts
OSM
Increase expression of TIMP-1, ICAM-1 and VCAM-1
OSM, LIF, IL-6, IL-11
Proliferation/differentiation of vascular endothelial cells
OSM
Elevate LDL receptors in hepatocytes
OSM
Induce synthesis of acute phase proteins in the liver
OSM, LIF, IL-6, IL-11, CNTF, CT-1
Inhibit lipoprotein lipase, resulting in fat depletion
OSM, LIF, IL-6, IL-11
Induce bone resorption, stimulate osteoblast activity
OSM, LIF, IL-6, IL-11
Induce proliferation/differentiation of T-lymphocytes
OSM, LIF, IL-6
Promote survival or differentiation of neurons
OSM, LIF, IL-6, IL-11, CNTF, CT-1
Effects of IL-6, LIF, OSM, CNTF and
IL-11 on MCF-7 cell proliferation.
100
#
+
#
80
* p < 0.001
# p < 0.01
+ p < 0.02
60
40
*
20
IL-11
CNTF
OSM
14
LIF
0
IL-6
n = 9 expts.
Control
Cell No. (% Control)
120
Effects of OSM on breast cancer cells.
• OSMRb and gp-130 are expressed in breast cancer cell lines and
primary tumour samples
• Inhibition of proliferation of ER + and - breast cancer cell lines
• Decreased clonogenicity
• Inhibition of cell cycle progression
– Reduced S phase fraction
– Increased G0/G1 phase fraction
• Alterations in mRNA expression
– Decrease ER and PRLR expression
– Increased EGFR expression
• Phenotypic changes consistent with differentiation-induction
– Morphology
– Lipid accumulation
– Apoptosis
OSM Signalling
OSM
OSMRb
or
LIFRb
gp130
Cell Membrane
P
JAK1
PY
P
Cytoplasm
JAK1
STAT3 P
YP
YP
Y
P
P
?
GRB2
SOS
RAS
P SHC
RAF
P
MEK
P
STAT3
P
P
P
P
Nucleus
MAPK (ERK1/2)
P
STAT3
Transcription
Factors
P
P
P
Transcription
S
Signalling by IL-6 Type Cytokines
• In M1 cells, STAT3 is critical for IL-6 induced growth regulation and
differentiation.
Nakajima et al., EMBO J, 15, 1996
• Growth inhibition of A375 cells by OSM/IL-6 is STAT3 dependant.
Kortylewski et al., Oncogene, 18, 1999
• In myeloma cells IL-6 up regulates mcl-1 through the JAK/STAT not ras/MAPK
pathway.
Puthier et al., Eur. J. Immunol., 29, 1999
• OSM activates STAT3 and ERK 2 in GOS3 cells. Blockade of MEK 1 partially
inhibits the effects of OSM on these cells.
Halfter et al., MCBRC, 1, 1999
• In adipocytes, LIF induces differentiation via the MAPK pathway.
Aubert et al., JBC, 274, 1999
• Growth of KS cells stimulated by OSM/IL-6 is mediated by ERK 1/2 and
negatively regulated by p38.
Murakami-Mori et al., BBRC, 264, 1999
• OSM activates MAPK through a JAK 1 dependant pathway in HeLa cells.
Stancato et al., MCB, 17, 1997
EGF family of growth factors and receptors
• Epidermal growth factor (EGF) is a polypeptide growth factor
• Mitogenic for mammary epithelium and breast cancer cells
• Overcomes effects of several breast inhibitors such as
tamoxifen and dexamethasone
• Binds the EGFR/ErbB-1, a receptor with intrinsic tyrosine
kinase activity
• Signalling via an EGFR homodimer or EGFR heterodimer
with ErbB-2,-3 or -4
– Heterodimer of EGFR and ErbB-2 preferred
EGF family of receptors
• EGFR/ErbB-1
– Overexpressed in about 30% of breast tumours
– Expression correlates inversely with ER
– Predicts aggressive disease/poor prognosis
• ErbB-2 (HER2/neu)
– Overexpressed in many types of cancer
– Correlates with aggressive disease and shorter disease free survival
in breast cancer patients
– Most oncogenic of all ErbB family members
– Orphan receptor
• ErbB-3
– Contains a non-functional kinase
– No correlation b/w expression in tumours and prognosis
• ErbB-4
– Few clinical studies
EGF signalling
EGFR,
ErbB-2,
3 or 4
PI3K
EGF
EGFR
Ras
PLC-g
MAPK
Cell Proliferation
Src
Effects of OSM and EGF on proliferation
of MCF-7 cells.
Cell Number (% Control)
120
100
80
60
*
40
20
N=10
0
OSM+EGF
OSM
EGF
Control
Summary
• Effects of OSM on breast cancer cells enhanced by EGF
–
–
–
–
–
Inhibition of proliferation
Decreased clonogenicity
Cell cycle suppression
Decreased ER expression
Differentiation
• Mechanism?