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Structure-Function Studies of Two Yeast Homing Endonucleases that Evolved to Cleave Identical Target
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Metabolic engineering of an acid-tolerant yeast strain Pichia kudriavzevii for itaconic acid production
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Developing a broad-range promoter set for metabolic engineering in the thermotolerant yeast Kluyveromyces marxianus
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Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production
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The TaCslA12 gene expressed in the wheat grain endosperm synthesizes wheat-like mannan when expressed in yeast and Arabidopsis
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Flash hydrolysis of yeast (Saccharomyces cerevisiae) for protein recovery
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Programmable polyketide biosynthesis platform for production of aromatic compounds in yeast
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Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model
Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.
Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses
This protocol provides a step-by-step approach to perturb single cells with time-varying stimulation profiles, collect distinct signaling responses, and use these to infer a system of ordinary differential equations to capture and predict dynamics of protein-protein regulation in signal transduction pathways. The models are validated by predicting the signaling activation upon new cell stimulation conditions. In comparison to using standard step-like stimulations, application of diverse time-varying cell stimulations results in better inference of model parameters and substantially improves model predictions. For complete details on the use and results of this protocol, please refer to Jashnsaz et al. (2020).
Repurposing plant hormone receptors as chemically-inducible genetic switches for dynamic regulation in yeast
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Modification of the 4Fe-4S Cluster Charge Transport Pathway Alters RNA Synthesis by Yeast DNA Primas
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Design and Characterization of Rapid Optogenetic Circuits for Dynamic Control in Yeast Metabolic Engineering
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Dynamical Modeling of Optogenetic Circuits in Yeast for Metabolic Engineering Applications
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Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis
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Cell Extracts from Bacteria and Yeast Retain Metabolic Activity after Extended Storage and Repeated Thawing
Cell-free synthetic biology enables rapid prototyping of biological parts and synthesis of proteins or metabolites in the absence of cell growth constraints. Cell-free systems are frequently made from crude cell extracts, where composition and activity can vary significantly based on source strain, preparation and processing, reagents, and other considerations. This variability can cause extracts to be treated as black boxes for which empirical observations guide practical laboratory practices, including a hesitance to use dated or previously thawed extracts. To better understand the robustness of cell extracts over time, we assessed the activity of cell-free metabolism during storage. As a model, we studied conversion of glucose to 2,3-butanediol. We found that cell extracts from Escherichia coli and Saccharomyces cerevisiae subjected to an 18-month storage period and repeated freeze-thaw cycles retain consistent metabolic activity. Here this work gives users of cell-free systems a better understanding of the impacts of storage on extract behavior.