sub-project 3 progress report march 2009 simon moon, anna rose, maggie dallman and jaroslav stark
TRANSCRIPT
Sub-Project 3 Progress Report
March 2009
Simon Moon, Anna Rose, Maggie Dallman and Jaroslav Stark
Recap
TLR 4Notch
Interaction
Recap: Experimental Method
+ Jagged1
+ control
BMDCMacrophages
+/- LPS
RNAReal-time PCRMicroarray
SupernatantELISA
Recap: Interaction
Modelling microarray data
dx idt
i Si f (t) ix i
Rate of change of expression of
a gene
Basal rate
Transcription factor activity
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-projectsRole 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 sub-project 1.
Integration of the sub-projectsRole of glycostructures of C. jejuni in the immune response
• 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.
Prediction of Splice variants from Exon array dataA 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 dataA collaboration with Sylvia Richardson
Level of gene expression
Pro
babi
lity
Prediction of Splice variants from Exon array dataA collaboration with Sylvia Richardson
Level of gene expression
Pro
babi
lity
Gel picture
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
S2 ADAMMetalloprotease
S3 -secretase activity
CoRs
Fringe
Numb
MINT
Nrarp
Deltex ?
RBP-J
Target genes
Mam
l
CoA
RBP-J
p300