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Protein aggregation is a hallmark of many diseases but also underlies a wide range of positive cellular functions. This phenomenon has been difficult to study because of a lack of quantitative and high-throughput cellular tools. Here, we develop a synthetic genetic tool to sense and control protein aggregation. We apply the technology to yeast prions, developing sensors to track their aggregation states and employing prion fusions to encode synthetic memories in yeast cells. Utilizing high-throughput screens, we identify prion-curing mutants and engineer "anti-prion drives" that reverse the non-Mendelian inheritance pattern of prions and eliminate them from yeast populations. We extend our technology to yeast RNA-binding proteins (RBPs) by tracking their propensity to aggregate, searching for co-occurring aggregates, and uncovering a group of coalescing RBPs through screens enabled by our platform. Our work establishes a quantitative, high-throughput, and generalizable technology to study and control diverse protein aggregation processes in cells.
Pubmed ID: 29056345 RIS Download
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Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
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