Transcript CISBIC meeting March 2009 Sub Project 3 - Workspace

Sub-Project 3 Progress Report
March 2009
Simon Moon, Anna Rose, Maggie Dallman and Jaroslav Stark
Recap
TLR 4
Interaction
Notch
Recap: Experimental Method
RNA
Real-time PCR
Microarray
BMDC
Macrophages
+ Jagged1
+/- LPS
+ control
Supernatant
ELISA
Recap: Interaction
Modelling microarray data
Rate of change
of expression of
a gene
Transcription factor
activity
dx i
 i  Si f (t)   i x i
dt
Basal rate

Sensitivity
Decay rate
Example Cluster: IL10 Jagged
Modelling IL-10 degradation
•Stimulate cells with our ligands
•Treat at 4 hours with
Actinomycin D: an inhibitor of
transcription.
•Observe decay of mRNA using
RT-PCR
•Modelled using simple ODE
models featuring mRNA
stabilization and destabilization
Unbound
Protein
Stable Protein
mRNA Complex
Unbound
mRNA
Integration of the sub-projects
Role of glycostructures of C. jejuni in the immune response
•DCs and macrophages are the one of the first cell types of
the immune system to sense the presence of pathogenic
bacteria
•They have a wide range of pattern recognition receptors,
like the TLRs, that trigger expression of cytokines upon
binding of a ligand.
•Investigation of the role of the glycostructures of C. jejuni in
the immune response using C. jejuni mutants from subproject 1.
Integration of the sub-projects
Role of glycostructures of C. jejuni in the immune response
TNF expression in DCs after
infection with C. jejuni
300
Fold change in gene expression
• Murine BMDCs were infected
with various amounts of C.
jejuni for three hours and
changes in gene expression
measured by real-time PCR.
• To date, WT, PglB (no N-linked
glycosylation) and cj1439
(acapsular) were used.
• Cytokines like TNF, IL-6 and
IL-10 were higher with the
acapsular mutant than WT.
250
200
MOI=100
MOI=20
150
MOI=10
MOI=1
100
50
0
WT
PglB
1439
LPS
Prediction of Splice variants from Exon array data
A collaboration with Sylvia Richardson
• Sylvia Richardson’s group developed a new algorithm
to predict the presence of splice variants from Exon
microarray data.
• Algorithm takes into account that some probes bind to
more than one gene.
• Prediction should be more accurate than other
methods.
• Used our microarray data (4hr time point) to predict
splice variants.
• Predictions were verified with RT-PCR.
Prediction of Splice variants from Exon array data
Probability
A collaboration with Sylvia Richardson
Level of gene expression
Prediction of Splice variants from Exon array data
Probability
A collaboration with Sylvia Richardson
Gel picture
Level of gene expression
Public Engagement in Science etc.
•Next Generation Project (NGP)
•Masterclass in Biomedical Sciences for A level
students
•Sat 7th March: ERASysBio Workshop: Towards
European Standards for PhD Training in Systems
Biology
Future plans
•Continue Sub-project integration: Sub-project 1, 2 and 4
•Continuation of work on IL10 degradation modelling
•Use Gaussian processes to obtain confidence intervals
for parameter estimates.
•Investigation of phosphorylation states of proteins in
signalling pathways
Recap: Notch Signalling
Deltex
?
Nrarp
Fringe
MINT
CoA
Target genes
RBP-J
CoRs
S3 g-secretase
S2 ADAM
Metalloprotease activity
RBP-J
Numb