Proteome-wide screens in Saccharomyces cerevisiae using the yeast GFP collection

Tunable transcription factor library for robust quantification of regulatory properties in Escherichia coli

Predicting the quantitative regulatory function of transcription factors (TFs) based on factors such as binding sequence, binding location, and promoter type is not possible. The interconnected nature of gene networks and the difficulty in tuning individual TF concentrations make the isolated study of TF function challenging. Here, we present a library of Escherichia coli strains designed to allow for precise control of the concentration of individual TFs enabling the study of the role of TF concentration on physiology and regulation. We demonstrate the usefulness of this resource by measuring the regulatory function of the zinc-responsive TF, ZntR, and the paralogous TF pair, GalR/GalS. For ZntR, we find that zinc alters ZntR regulatory function in a way that enables activation of the regulated gene to be robust with respect to ZntR concentration. For GalR and GalS, we are able to demonstrate that these paralogous TFs have fundamentally distinct regulatory roles beyond differences in binding affinity.

The Capture of a Disabled Proteasome Identifies Erg25 as a Substrate for Endoplasmic Reticulum Associated Degradation

Studies in the yeast Saccharomyces cerevisiae have helped define mechanisms underlying the activity of the ubiquitin-proteasome system (UPS), uncover the proteasome assembly pathway, and link the UPS to the maintenance of cellular homeostasis. However, the spectrum of UPS substrates is incompletely defined, even though multiple techniques-including MS-have been used. Therefore, we developed a substrate trapping proteomics workflow to identify previously unknown UPS substrates. We first generated a yeast strain with an epitope tagged proteasome subunit to which a proteasome inhibitor could be applied. Parallel experiments utilized inhibitor insensitive strains or strains lacking the tagged subunit. After affinity isolation, enriched proteins were resolved, in-gel digested, and analyzed by high resolution liquid chromatography-tandem MS. A total of 149 proteasome partners were identified, including all 33 proteasome subunits. When we next compared data between inhibitor sensitive and resistant cells, 27 proteasome partners were significantly enriched. Among these proteins were known UPS substrates and proteins that escort ubiquitinated substrates to the proteasome. We also detected Erg25 as a high-confidence partner. Erg25 is a methyl oxidase that converts dimethylzymosterol to zymosterol, a precursor of the plasma membrane sterol, ergosterol. Because Erg25 is a resident of the endoplasmic reticulum (ER) and had not previously been directly characterized as a UPS substrate, we asked whether Erg25 is a target of the ER associated degradation (ERAD) pathway, which most commonly mediates proteasome-dependent destruction of aberrant proteins. As anticipated, Erg25 was ubiquitinated and associated with stalled proteasomes. Further, Erg25 degradation depended on ERAD-associated ubiquitin ligases and was regulated by sterol synthesis. These data expand the cohort of lipid biosynthetic enzymes targeted for ERAD, highlight the role of the UPS in maintaining ER function, and provide a novel tool to uncover other UPS substrates via manipulations of our engineered strain.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

High-Throughput Microscopy-Based Screening in Saccharomyces cerevisiae

The budding yeastSaccharomyces cerevisiaehas served as the pioneer model organism for virtually all genome-scale methods, including genome sequencing, DNA microarrays, gene deletion collections, and a variety of proteomic platforms. Yeast has also provided a test-bed for the development of systematic fluorescence-based imaging screens to enable the analysis of protein localization and abundance in vivo. Especially important has been the integration of high-throughput microscopy with automated image-processing methods, which has allowed researchers to overcome issues associated with manual image analysis and acquire unbiased, quantitative data. Here we provide an introduction to automated imaging in budding yeast.

Building a KATalogue of acetyllysine targeting and function

Acetylation is a dynamic post-translational modification that is attached to protein substrates by lysine acetyltransferases (KATs) and removed by lysine deacetylases (KDACs). While these enzymes are best characterized as histone modifiers and regulators of gene transcription, work in a number of systems highlights that acetylation is a pervasive modification and suggests a broad scope for KAT and KDAC functions in the cell. As we move beyond generating lists of acetylated proteins, the acetylation field is in dire need of robust tools to connect acetylation and deacetylation machineries to their respective substrates and to dissect the function of individual sites. The Saccharomyces cerevisiae model system provides such a toolkit in the context of both tried and true genetic techniques and cutting-edge proteomic and cell imaging methods. Here, we review these methods in the context of their contributions to acetylation research thus far and suggest strategies for addressing lingering questions in the field.

Population perspectives on functional genomic variation in yeast

Advances in high-throughput sequencing have facilitated large-scale surveys of genomic variation in the budding yeast,Saccharomyces cerevisiae These surveys have revealed extensive sequence variation between yeast strains. However, much less is known about how such variation influences the amount and nature of variation for functional genomic traits within and between yeast lineages. We review population-level studies of functional genomic variation, with a particular focus on how population functional genomic approaches can provide insights into both genome function and the evolutionary process. Although variation in functional genomics phenotypes is pervasive, our understanding of the consequences of this variation, either in physiological or evolutionary terms, is still rudimentary and thus motivates increased attention to appropriate null models. To date, much of the focus of population functional genomic studies has been on gene expression variation, but other functional genomic data types are just as likely to reveal important insights at the population level, suggesting a pressing need for more studies that go beyond transcription. Finally, we discuss how a population functional genomic perspective can be a powerful approach for developing a mechanistic understanding of the processes that link genomic variation to organismal phenotypes through gene networks.