Download Document

The TIR domain Family of Immediate-Early
Host Defence Proteins
TIR Domain
Ig Domain
Death Domain
Leucine Rich
Repeat
Extracellular
sI L1R II
sST2
sI L1R I
PlantPathogenResistanceProteins
B 15R
IL1R II
N -protein R RP1
ST2
IL1R I
A cP
IL18A cP IL18R
IL1R rP
Tlr1
Tlr2
Tlr3
Tlr4
Tlr5
Tlr6 Tlr7
Tlr8 Tlr9 Tlr10
Tolls 1-8
A 46R A 52R
MyD 88
MyD 88
Viral Gene
Products
Human Cytokine Receptors
Cytoplasmic
Human Tolls
Dros ophila
L6
R RP5
Schematic of TIR signalling
Cells as computational devices
Temperature
Nutrients Chemical Stress
Mechanical
Hormones
Secretion
Division
locomotion
Differentiation
Apoptosis
• Contains 1 copy of the genome
• Contains ca 1010- 1011 protein molecules in
a volume of ca 1 picolitre
• Contains ca 104 different proteins
present at 104 - 107 copies/cell
• The proteins plus small molecules and
ions form a spatially distributed, dissipative
computational network capable of robust
self regulation within a narrow range of
physical environments.
• The network is non-linear- binary (?)
• The network is embodied as a semi-solid
state device - most reactions do not occur
in free solution
The Computational Network is Built from Noisy
Components
Nuclear Concentration of NFkB Measured by
Confocal Imaging of Anti-relA Labelled Fibroblasts
Protein concentration in cells is controlled in part by
rate of gene transcription
transcription is stochastic
The concentration of any given protein must therefore
vary between different cells in clonal population
Since most if not all proteins participate in the control
network, this will in turn affect control of gene
expression differentially in each individual cell
Theoretical Analysis suggests that the Segment Polarity network in Flies is Binary
Random Graph Representation of Signal Transduction Network
Nodes = Reactants
Edges = Reactions
Implementation
1.
2.
3.
Chemical kinetics -ODEs
Cellular Automata
Software agents
Basic considerations
1.
2.
3.
4.
5.
Edges/nodes
Edge/node distribution
Rules
Bit depth
Word length
Signal Transduction Pathways in a Network ?
Input
Promoter
Input
Perturbation of Internal State of Network Can Mimic Agonist
Example Mechanisms
1.
Titre out inhibitor
2.
Increase concentration above
Kd for activator
brightness
TK-RL
Positive pools are detectable by both screen strategies
0.9
0.8
0.7
Dual-Luciferase assay
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.8
IL8-luc
positive pool
negative pool
145
145
125
125
105
105
85
85
65
65
45
45
25
25
5
EGFP assay
5
50
550
1050
1550
area
2050
50
550
1050
15 50
area
2050
Summary of clones isolated from 3% of
library
Clone ID
Gene name
Orientation Clone
ID
Proposed mech. of action
2E3
N10/NUR77
Sense
Inhibition of survival signals
3E1
RelA
Sense
Induction of NFkB
responsive genes
4D8
hTrb-1(hTrb-2, hTrb-3)
Sense (3’ UTR)
Inhibition of AP-1 activation
2B2
2G1
19-6.2
23-5.8
IFN
Sense
Induces TRAIL expression
which activates
Both apoptosis and NFkB
19-3.7
Prothymosin 4
Antisense (full)
Thymosin beta is an antiinflammatory protein
83-3.8
Ferritin
Sense
Redox effect on NFkB
91-2.8
Mitochondrial NADPH
Oxidase
Antisense
Redox
53-1.7
Homer1B
Sense 3’ UTR
Scaffold
126
Genomic clone + est
Adaptation of Mammalian Expression Screen to
High throughput Robotic Platform
cDNA
Ligate Expression
Vector
MEGAPIX
96 pin
high speed
picking head
Transform
E. Coli
Library of 3x 10 6
independant
transformants
Library in trays
ca 3,000
colonies per tray
Q-BOT
working copy
Assay by
mammalian
transfection
Cherry pick source wells
for positive pools from
master library
Construct
20,833 pools
of 48 from
one copy and
make Minpreps
in bar coded
96 well plates
Identify source well
by rows and columns
Backup copy
2 copies of
gridded library
106 clones each
in bar coded
384 well plates
1 clone per well
Store at -80
VERIFY ACTIVITY
SEQUENCE INSERT
Output from High Throughput Screen showing Candidate
Positive Pools - each Pool from 48 Library Wells
1.
2.
3.
4.
5.
1/1000 clones regulate pIL8
pIL8 has 4 TF sites
1/4000 per site
3,000,000 clones in library
Each mRNA is represented by
ca 10 clones
6. 200 unique cDNAs per site
7. The signal transduction network
is highly overlapping.
8. Few if any components will
be unique to a gene , class of
genes or agonist
9. Specifity is likely to be a higher
order property - ie “words” rather
than letters.
10. What is the word length ?
Conclusions/Speculations
1. Signal Transduction networks are likely binary
2. Data suggest ca 200 elements of network “control”
a given transcription factor in the IL-8 promoter
3. Since each element is in turn coded by a gene ---4. Many elements must be used generically
5. The notion that there are dedicated/specfic pathways
and pathway components cannot be correct
6. Programs that are specific may use generic elements
by combining them into longer “words”.