Modelling fluorescent timers to quantify protein turnover in the cell cycle

Theme: World-Class Underpinning Bioscience

Recently described tandem fluorescent protein timer tags (tFTs) have been used successfully to report on the age of pools of tagged proteins, based on the differential maturation kinetics of a slow-folding red fluorescent protein and a fast-folding green one [1]. We propose to use tFT tags as tools to report on the stability of protein pools, looking at Aurora kinases, which are well known regulators of the cell cycle. Their targeted ubiquitination at mitotic exit has been extensively characterized in the Lindon lab [2,3]. It is not known how turnover of Aurora kinases varies through other phases of the cell cycle, or in different cellular compartments. In pilot experiments in mammalian cells, the colour of a tFT linked to Aurora kinase varied qualitatively as expected when the proteins were stabilized by siRNA-mediated knockdown of an ubiquitin ligase Cdh1, or by mutation of the Aurora degron motif. However it is not clear what can be inferred from these observations, since we do not know in which cells - if any - Aurora turnover is a steady state process. Therefore, the potential of tFT technology for quantitative measurement of protein stability in unperturbed cell populations requires mathematical modelling to underpin it, in order to understand how the determinants of the output signal (synthesis, maturation, degradation) interact with biological phenomena such as the cell cycle.

In collaboration with Dr Graham Ladds (who uses a range of computational techniques to explore biological problems [4-6]), we will develop mathematical models to describe the behaviour of tFTs in the mammalian cell cycle. We propose a collaborative studentship to involve time-resolved fluorescence imaging of tFTs, testing parameters that include rate of synthesis, maturation kinetics of different tFT pairs, and subcellular localization. Some experiments will be carried out in Schizosaccharomyces pombe, where use of cell cycle arrest mutants can generate 'steady states' in the cell cycle. We will develop models to describe how tFT turnover evolves. Quantitative wet experimental data will be fitted to computational simulation to provide the most actuate reconstruction of the biological data. We will then progressively constrain and/or perturb each model to enable us to extract quantitative information on how Aurora kinase half-life varies through the cell cycle and under different growth conditions.