molecular interactions based on chapter 4 of post-genome bioinformatics by minoru kanehisa, oxford...
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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.
1) Element
MoleculeGene
2) Binary Relation
Molecular interactionGenetic interactionOther types of relations
3) Network
Pathway
AssemblyNeighbour Cluster
Hierarchical TreeGenome
Representation of the same graph by: (a) a drawing of nodes and edges, (b) a linked list, and (c) an adjacency matrix.
(a)
A
B
DC
E
F
(b) A B
B A C D
C B E
D B E
E C D F
F E
A B C D E F
A
B 1
C 0 1
D 0 1 0
E 0 0 1 1
F 0 0 0 0 1
(c)
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.
(A, a) (A, b) (A, c) (A, d) (A, e) (A, f)
(B, a) (B, b) (B, c) (B, d) (B, e) (B, f)
(C, a) (C, b) (C, c) (C, d) (C, e) (C, f)
(D, a) (D, b) (D, c) (D, d) (D, e) (D, f)
(E, a) (E, b) (E, c) (E, d) (E, e) (E, f)
(b)
(a) Pathway 1 Pathway 2A
B
C
D
E
a
b c
d
A
A
B-a
B-b
C-d
D-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).
A - a
B - b
C - c
D - d
. . .
. . .
Clusteringalgorithm
A BC
D
E G
H
K
F
I
J
A BC
D
E G
H
K
F
I
J
a bc
d
e g
h
k
f
i
j
a bc
d
e g
h
k
f
i
j
(a)Graph 1 Correspondences Graph 2
(b) A-B-C-D-E-F-G-H-I-J-K
: : :
A-c-b-d-e-f-h-g-j-k-i
Examples of binary relations
Type of relation Contents ExamplesFactual relation
Similarity relation
Functional relation
Links between databaseentities
Computed similarityComputed complementarity
Molecular reactionsMolecular interactionsGenetic interactions
Chromosomal relationsEvolutionary relations
Factual data and its publication informationNucleotide sequence and translated aminoacid sequenceProtein sequence and 3D structure
Sequence similarity: 3D stuctural similarity3D structural complementarity
Substrate-product relationsMolecular pathways; molecular assembliesPositively co-expressed genesNegatively co-expressed genesCorrelation of gene locations (operons)Orthologous and paralogous genes
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.
H3C
O
O
OHC00022
O
OH
CH3
NH2
1.4.1.1
O
OH
CH3
NH2
2.6.1.21 5.1.1.1
OH
O
O
HOO
C00036
O
OH
NH2
O
OH
C00049
1.4.3.16
C00133
4.1.1.124.1.1.3
Query relaxation. Nodes E and E’ are considered to be equivalent according to the grouping G.
A B
B C G
C D
B E E E’
E’ F
A B C D
E F
Network data representation in KEGG
Network type KEGG data Content RepresentationPathwayAssembly
Genome
Cluster
Pathway map
Genome mapComparative genomemap
Expression map
Metabolic pathway, regulatory pathway, andmolecular assembly
Chromosomal location of genes
Differential gene expression profile bymicroarrays
GIF image map
Java applet
Java applet
NeighbourPathway AssemblyGenome
Hierarchical tree
Orthologue grouptable
Gene catalogueMolecular catalogueTaxonomyDisease catalogue
Functional unit of genes in a pathway orassembly, together with orthologous relationof genes and chromosomal relation of genes
Hierarchical classification of genesHierarchical classification of moleculesHierarchical classification of organismsHierarchical classification diseases
HTML table
Hierarchical text
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 hisG hisD hisC hisB hisH hisA hisF hisI
yefM yzzB
(b) Metabolic pathway
Pentose phosphate cycle
Purine metabolism
HISTIDINE METABOLISM
2.4.2.17 3.6.1.31 3.5.4.19 5.3.1.16 2.4.2.- 4.2.1.19 2.6.1.9 3.1.3.15
3.5.1.-
2.6.1.-Phosphoribulosyl-Formimino-AICAR-P
Phosphoribosyl-Formimino-AICAR-P
Phosphoribosyl-AMP
Phosphoriboxyl-ATPPRPP
5P-D-1-ribulosyl-formimine
Imidazole-Glicerol-3P
Imidazole-acetole P
L-Histidinol-P
1.1.1.23
2.1.1.-
6.3.2.11
2.1.1.22
6.3.2.11
3.4.13.5
3.4.13.20
3.4.13.3
4.1.1.22
4.1.1.281.4.3.61.2.1.31.141353.5.2.-3.5.3.5
N-Formyl-L-aspartate
Imidazoloneacetate
Imidazole-4-acetate
Imidazoleacetaldehyde Histamine
Carnosine
Aneserine
1.1.1.23
6.1.1
1-Methyl-L-histidine
L-Hisyidinal
L-Hisyidinal
5P Ribosyl-5-amino 4-Imidazole carboxamide(AICAR)
L-Histidine
Hercyn
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.
……..NE, TYROSINE AND TRYPTOPHAN BIOSYNTHESIS Tyrosine metabolism
Alkaloid biosynthesis I
2.6.1.9 2.6.1.57
2.6.1.1 2.6.1.5
6.1.1.1
1.4.3.2
2.6.1.9 2.6.1.57
2.6.1.1 2.6.1.5
4.1.1.48 4.2.1.20
4.2.1.20
4.2.1.20
Tryptophanmetabolism
5.3.1.242.4.2.184.1.3.272.5.1.19
2.7.1.71
1.1.99251.1.1.25
4.2.1.10
4.2.1.11
1.1.99251.1.1.24
Quniate
4.2.1.91
4.2.1.51
2.6.1.57
2.6.1.572.6.1.92.6.1.5
1.4.1.20 2.6.1.1
1.4.3.2
6.1.1.20
4.2.1.91
4.2.1.51
1.14.16.1
1.3.1.43
Tyr-tRNA
4-Hydroxy-phenylpyruvate
Prephenate
Tyrosine
Pretyrosine
RNA Phenylalanine
5.4.99.5
4.6.1.4
Anthranilate
Histidine
N-(5-Phospho--v-ribosyl)-anthranilate
1-(2- Carboxy-Phenylamino)-1-deoxy-D-ribulose5-phosphate
(3-Indolyl)-Glycerolphosphate
Indole
L-Tryptophan
4.1.3.-
Folatebiosynthesis
Ubiquinone biosynthesis
Chorismate
4-Aminobenzoate
4.6.1.3
3-deoxy-D-arabino-heptonate
3-Dehydro-quinate
4.2.1.10
3-Dehydro-shikimate
Protocatechuate
Shikimate
Phenylpyruvate
SCOP hierarchical tree
1. All alpha
2. All beta
3. 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
8. Peptides
9. Designed proteins
10. Non-protein
Grand challenge problems
Protein folding problems Organism reconstruction problem
Prediction Structure prediction - to predict protein 3Dstructure from amino acid sequence
Network prediction - to predict entirebiochemical network from completegenome sequence
Knowledge Known protein 3D structures Known biochemical pathways andassemblies
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.
66 6 6
56
6
33
3
3
3
3
3
8
5
54
7
8
10
21
23
NADPH
NADPH
D-Ribulose-5P
D-Ribose-5P
PentosePhosphatepathway
6-Phospho-
D-gluconate
D-Glucono-1,5-Lactone-6P
D-Glucose-6P
D-Glucose
CO2D-Xylulose-5P
D-Fructose-6P
D-Fructose-1,6P2
D-Sedoheptulose-7P
NADH
ATP
Glycerone-PGlyceraldehyde-3P
Glycerae-1,3P2
Glycerate-3P
Glycerate-2P
Phophoenolpyruvate
ATP
Pyruvate
NADH
Lipoamide
CO2
S-Acetyl-dihydrolipoamide
Dihydro-lipoamide
Acetyl-CoA
6
44
6
55
88
12
21
25
NADH
Lipoamide
CO2
S-Acetyl-dihydrolipoamide
Dihydro-lipoamide
Succinyl-CoA
4
NADH
(S )-MalateFunarate
4
FADH2
GTP 21CoA
CoA CoA
NADH
Isocitrate
Citrate
21CoA
Oxaloacetate
Citrate cycle(TCA cycle)
2-Oxo-glutarate
Succinate
Glycolysis viewed as a network of enzymes (gene products). Each box is an enzyme with its EC number inside.
2.7.1.2
5.3.1.1
2.7.1.401.2.1.51
1.2.4.1
1.8.1.4
2.3.1.126-S-Acetyl-dihydrolipoamide
Citrate cycle(TCA cycle) Phosphoenolpyruvate
Acetyl-CoA
Dihydrolipoamide Lipoamide
Pyruvate
4.2.1.11
5.4.2.1
Glycerate-2P
2.7.2.3
Glycerate-3P
Gycerate-1, 3P2
1.2.1.12
4.1.2.13
2.7.1.113.1.3.11
Glycerone-PGlyceraldehyde-3P
D-Fructose-1,6P2
D-Fructose-6P
5.3.1.9
2.7.1.69PentosePhosphatecycle
D-Glucose-6P
D-Glucose(extracellular)
D-Glucose
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 2Enzymic reaction
Protein 1 Protein 2
Gene(Molecular template)
Gene expression
Binary relations:
Positional cloning Genome comparisons
Gene-gene (indirect) interactions DNA chips
Protein-protein (direct) interactions Protein chips
Substrate-product relations Biochemical knowledge
Hierarchial relations Sequence analysis
A strategy for network reconstruction from genomic information.
Reference knowledge(e.g. KEGG)
Predicted network byorthologue identification
Predicted network bypath computation
Gene cataloguein the genome
Genetic and chemical blueprints of life.
Blueprint Entity Information
Genetic blueprint of life Genome Centralized Static
Chemical blueprint of life Network of interactingmolecules in the cell
Distributed Dynamic
Principles of the biochemical network encoded in the genome.
Hierarchy - conservation and diversification
(a)(b)
Duality - chemical logic and genetic logic
(c) (d)
Low resolution network
High resolution network
Divergent inputs Divergent outputs
Conserved pathway
+ =
Chemicalnetwork
Enzymenetwork
Metabolicnetwork
Protein-proteininteraction network
Gene regulatorynetwork
Biological examples of complex systems
System Node Edge
Protein 3D structure Atom Atomic interaction
Organism Molecule Molecular interaction
Brain Cell Cellular interaction
Ecosystem Organism Organism interaction
Civilization Human Human interaction
From Sequence to FunctionComparison of bioinformatics aproaches for functional prediction
Era Experiments Database Computational method
1977 gene cloning sequence sequence similarity search sequencing
1995 whole genome pathway pathway reconstruction sequencing 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: AimsSuzie 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.IL-6 Cytokine Receptor Family.
IL-6IL-11
CNTF LIFOSMCT
OSM
IL-6R CNTFR
IL--11RLIFRgp130gp130 gp130 OSMR
Function
Proliferation/maturation of megakaryocytes
Expansion of hemopoietic progenitor cells in the AGM
Induce terminal differentiation of M1 cells
Inhibit differentiation of ES cells
Stimulate proliferation of fibroblasts
Increase expression of TIMP-1, ICAM-1 and VCAM-1
Proliferation/differentiation of vascular endothelial cells
Elevate LDL receptors in hepatocytes
Induce synthesis of acute phase proteins in the liver
Inhibit lipoprotein lipase, resulting in fat depletion
Induce bone resorption, stimulate osteoblast activity
Induce proliferation/differentiation of T-lymphocytes
Promote survival or differentiation of neurons
Cytokine
OSM, LIF, IL-6, IL-11
OSM
OSM, LIF, IL-6, CT-1
OSM, LIF, CT-1, CNTF
OSM
OSM, LIF, IL-6, IL-11
OSM
OSM
OSM, LIF, IL-6, IL-11, CNTF, CT-1
OSM, LIF, IL-6, IL-11
OSM, LIF, IL-6, IL-11
OSM, LIF, IL-6
OSM, LIF, IL-6, IL-11, CNTF, CT-1
Physiological Functions of IL-6 family membersPhysiological Functions of IL-6 family members
Effects of IL-6, LIF, OSM, CNTF and Effects of IL-6, LIF, OSM, CNTF and IL-11 on MCF-7 cell proliferation.IL-11 on MCF-7 cell proliferation.
Con
trol
IL-6
LIF
OS
M
CN
TF
IL-1
1
n = 9 expts.
14
0
20
40
60
80
100
120
Cel
l No.
(%
Con
trol
)
* p < 0.001# p < 0.01+ p < 0.02
*
#
#
+
Effects of OSM on breast cancer cells.Effects of OSM on breast cancer cells.
• OSMR 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
S
GRB2
OSM
JAK1
YY
YY
JAK1P P
P
PP
P
P
PSTAT3
P
P
P
P
Nucleus
Cytoplasm
P
P
P
P
TranscriptionFactors
SHC
SOS RAS
RAF
MEK
MAPK
?
P
P
P
Cell Membrane
OSM SignallingOSM SignallingOSMR
orLIFR
gp130
STAT3
STAT3Transcription
(ERK1/2)
Signalling by IL-6 Type CytokinesSignalling 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
• 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 growth factors and receptorsEGF family of growth factors and receptors
EGF family of receptorsEGF 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 signallingEGF signalling
EGF
PI3K SrcRasPLC-
MAPK
Cell Proliferation
EGFREGFR,ErbB-2,3 or 4
Effects of OSM and EGF on proliferation Effects of OSM and EGF on proliferation of MCF-7 cells.of MCF-7 cells.
Co
ntro
l
EG
F
OS
M
OS
M+
EG
F
0
20
40
60
80
100
120C
ell
Nu
mb
er (
% C
on
tro
l)
*
N=10
SummarySummary
• Effects of OSM on breast cancer cells enhanced by EGF– Inhibition of proliferation
– Decreased clonogenicity
– Cell cycle suppression
– Decreased ER expression
– Differentiation
• Mechanism?