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CISBAN and telomere maintenance and shortening, BBSRC Systems Biology Workshop

December 17, 2008 Leave a comment Go to comments

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Amanda Greenall: Telomere binding proteins are conserved between yeat and higher eukaryotes. The capping proteins are very important, because they prevent the telomeres from being recognized as double-strand breaks. They work on cdc13, which is the functional homologue of POT1 in humans. A point mutation cdc13-1 allows them to study telomere uncapping. When grown above 27 degrees Celcius, the cdc13-1 protein becomes non-functional, and fall off. This uncapping causes telomere loss and cell-cycle arrest. So, they do further study into the checkpoint response that happens when telomeres are uncapped. Yeast is a good model, as many of the proteins involved in humans have direct analogs in yeast. They did a series of transcriptomics experiments to determine how gene expression is affected when telomeres are uncapped. They did 30 arrays, and the data was analysed using limma. 647 differentially-expressed genes were identified (418 upregulated (carbohydrate metabolism, energy generation, response to OS), and 229 downregulated (amino acid and ribosome biogenesis, RNA metabolism, etc)). The number of differentially-expressed genes increase with time. For example, 259 of the genes were involved in DNA damage response.

They became quite interested in BNA2, which is an enzyme which catalyses de novo NAD+ biosynthesis. Why is it upregulated? It seems over-expression of BNA2 enhances survival of cdc13-1 strains (using spot tests). Nicotinamide biosynthetic genes are altered when telomeres are uncapped in yeast and humans. The second screen was a robotic screen to identify ExoX and/or pathways affecting responses to telomere uncapping. Robots were used to to large-scale screens that can measure systematic cdc13-1 genetic interactions. One of the tests was the up-down assay, which allows them to distinguish Exo1-like and Rad9-like suppressors. Carry on with the spot tests until have worked through the entire library of strains.

Darren Wilkinson: a discrete stochastic kinetic model has been built to model the cellular response to uncapping. (J Royal Soc Interface, 4(12):73-90), and in Biomodels. Encoded in SBML and simulated in BASIS (web-based simulation engine). You can use the microarray data to infer networks of interactions. Such top-down modelling can often be done with Dynamic Bayesian Networks (DBNs) for discretised data and sparse Dynamic Linear Models (DLMs) for (normalized) continuous data. A special case of DLM is the sparse vector auto-regressive model of order 1, known as the sparse VAR(1) model, and this appears to be effective for uncovering dynamic network interactions (see Opgen-Rhein and Strimmer, 2007). They use a simple version of this model. They use a RJ-MCMC algorithm to explore both graphical structure and model parameters. When the RJ-MCMC is performed, it's quite hard to visualize. They do a plot of the marginal probability that an edge exists. This can also be summarised by choosing an arbitrary threshold and then plotting the resulting network. You can change the thickness of the edges so they match the marginal probability associated with each edge. This picture is then easier for biologists to analyse, and allows them to narrow down their search for important genes. He also performed analysis over the robotic genetic screens. There are usually about 1000 images per experiment, each with 384 spots, and therefore image analysis needs to be automated. Want to pick out those strains that are genetically interacting with the query mutation. For interactions to be useful concept in practice, you need the networks to be sparse. With HTP data, we have sufficient data to be able to re-scale the data in order to enforce this sparsity. A scatter-plot of double against single will show them all lying along a straight line (under a model of genetic independence). Points above and below the regression line are phenotypic enhancers and suppressors, respectively.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. :)

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