This proposal aims to provide crucial training for the candidate?s long-term career plan to study how cellular quiescence is established through decision-making processes. The decision to undergo quiescence in response to stress or developmental signals is a fundamental and understudied property of living systems. Failure to maintain quiescence can lead to cell proliferation disorders in humans, such as fibrosis or cancer. Quiescence entry is triggered when multiple nutrient- and stress-sensing signaling pathways arrest the cell cycle machinery. However, the molecular mechanisms that coordinate stress response pathways with the cell cycle during quiescence remain largely unclear. This is, in part, due to the difficulties to simultaneously quantify multiple stress pathways at the single cell level in vivo. To solve this limitation, the candidate will use a microfluidics- fluorescent imaging system that tracks up to six different pathways simultaneously during the transition from proliferation into quiescence. Using this approach, the coordination between stress responses and the cell cycle machinery can be quantified with unprecedented temporal resolution in the model organism S. cerevisiae. A computational platform based on machine learning and time series analysis will be used to process the large imaging data derived from tracking six biomarkers simultaneously in single cells. An initial version of this framework found that during the onset of quiescence the nuclear levels of the conserved DNA-replication kinase Cdc7 are dynamically regulated. This approach also identified that the nuclear levels of the stress-activated transcriptional repressor Xbp1 define how the cell cycle is stopped during quiescence entry. Combining this computational approach with biochemical techniques will determine the molecular mechanisms for the establishment of cellular quiescence by modulation of stress responses and the cell cycle machinery. The candidate is to acquire crucial training in computational biology during the K99 phase of this proposal to complement his previous training in biochemistry, cell biology and yeast genetics. The candidate will be mentored by a leader in computational biology Dr. Gaudenz Danuser, whose lab develops advanced machine learning and time series analysis to study cellular signal transduction. This proposal harnesses the commitment of an entire bioinformatics core facility and the training environment of a world-class research institution at UTSW. Establishing a unique computational and imaging framework, combined with biochemical approaches for the study of quiescence, will support the candidate?s transition to an independent research academic position and will lead to the discovery of biomedically relevant principles of quiescence and cell cycle regulation.
The precise integration of stress signals during the establishment of cellular quiescence is crucial to prevent cell proliferation disorders, such as fibrosis and cancer. However, it is unclear how stress responses are integrated during quiescence, in part, because of a lack of methods for simultaneously quantifying multiple stress pathways in single cells. To solve this problem, the proposed work will combine machine learning approaches with six color fluorescent microscopy to reveal the mechanisms by which stress responses coordinate quiescence.
