sub-project 3 progress report march 2009 simon moon, anna rose, maggie dallman and jaroslav stark

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