PgmNr Y3007: Global Analysis of Molecular Fluctuations Associated with Cell Cycle Progression in Saccharomyces cerevisiae.

Authors:
Ben Grys 1 ; Helena Friesen 2 ; Oren Kraus 3 ; Adrian Verster 2 ; Brendan J. Frey 2,3 ; Charles Boone 1,2,4 ; Brenda J. Andrews 1,2,4


Institutes
1) Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; 2) The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada; 3) Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada; 4) Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.


Keyword: Cell Cycle/Growth Control/Metabolism

Abstract:

The regulation of protein expression, turnover, and localization has been recognized as imperative for eukaryotic cell cycle progression. However, there has been no systematic study of proteomic fluctuations throughout the cell cycle in eukaryotes. By combining Synthetic Genetic Array (SGA) technology with high-throughput fluorescence microscopy of the ORF-GFP fusion collection, we have generated image-based data for ~75% of the yeast proteome. Our strategy involves scoring diagnostic fluorescent markers that indicate cell cycle position, which permits the computational classification of yeast cells into one of six predetermined cell cycle stages, and subsequently quantifying protein abundance and localization for each member of the GFP collection, We automated cell cycle classification using a supervised neural network-based approach that functions with ~97% accuracy. With mean GFP-pixel intensity as a metric for protein abundance, we determined how the entire visible budding yeast proteome fluctuates over the course of the cell cycle. We have also adapted our neural network classification method for the automated assignment of GFP-fusion proteins to 21 different subcellular compartments. After applying statistical analyses, we have resolved those GFP-fusion proteins that change in abundance and/or localization in a cell cycle-dependent manner. When combined with cell cycle transcriptional information, and ribosome profiling, this unique platform will provide a resource that can be mined to better characterize existing pathways of cell cycle control, while also identifying novel players in the regulation of cell growth and division. On a broader scale, our dataset will allow us to study pre- and post-translational gene regulation in an ordered and highly conserved biological process, providing a unique opportunity that is not possible with existing eukaryotic data.