ILANIT 2020

Elevated levels of aggregation prone amino acids hinder growth in wild-type S. cerevisiae laboratory strains

author.DisplayName 1 author.DisplayName 3 author.DisplayName 1,2 author.DisplayName 1
1School of Molecular Cell Biology and Biotechnology, Tel Aviv University, Israel
2Department of Materials Science and Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Israel
3Faculty of Life Sciences, Bar-Ilan University, Israel

Recent work conducted in our group has suggested that single metabolites, such as amino acids and nucleobases, can self-assemble into ordered supramolecular structures that demonstrate chemical and physical properties similar to those of protein aggregates. Furthermore, it has been shown that these amyloid-like structures may have a role in the etiology of inborn error of metabolism (IEM) disorders, wherein deficient enzymatic activity, following a single gene mutation, leads to the accumulation of one or more metabolites. Utilizing yeast as an in vivo model to study aggregation phenomena, we have found that the commonly used wild-type laboratory auxotrophic strain, BY4741, exhibits a non-linear, dose-dependent sensitivity, upon feeding, to increasing amounts of aggregation prone amino acids, compared to the prototrophic strain. We suspect that this is due to the accumulation of metabolites and the consequential formation of metabolite-based aggregates in the cell. These results support the notion that auxotrophic markers may present an inherent bias when studying yeast metabolism, embedding the yeast with a metabolic "disease-like" state. In order to better understand the underlying mechanism behind the observed phenotype, we conducted a genetic screen using the haploid yeast knock-out collection with the BY4741 background. Thus, we have succeeded in identifying genes that upon their deletion rescue the growth of the wild-type strain from the toxicity associated with feeding of aggregation prone amino acids. The identified genes may provide a framework for better understanding the interplay between amino acid metabolism and cellular homeostasis.









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