Margaret Werner-Washburne

The translation machinery and 70 kd heat shock protein cooperate in protein synthesis

The function of the yeast SSB 70 kd heatshock proteins (hsp70s) was investigated by a variety of approaches. The SSB hsp70s (Ssb1/2p) are associated with translating ribosomes. This association is disrupted by puromycin, suggesting that Ssb1/2p may bind directly to the nascent polypeptide. Mutant ssb1 ssb2 strains grow slowly, contain a low number of translating ribosomes, and are hypersensitive to several inhibitors of protein synthesis. The slow growth phenotype of ssb1 ssb2 mutants is suppressed by increased copy number of a gene encoding a novel translation elongation factor 1 alpha (EF-1 alpha)-like protein. We suggest that cytosolic hsp70 aids in the passage of the nascent polypeptide chain through the ribosome in a manner analogous to the role played by organelle-localized hsp70 in the transport of proteins across membranes.

“Sleeping Beauty”: Quiescence in Saccharomyces cerevisiae

SUMMARY The cells of organisms as diverse as bacteria and humans can enter stable, nonproliferating quiescent states. Quiescent cells of eukaryotic and prokaryotic microorganisms can survive for long periods without nutrients. This alternative state of cells is still poorly understood, yet much benefit is to be gained by understanding it both scientifically and with reference to human health. Here, we review our knowledge of one “model” quiescent cell population, in cultures of yeast grown to stationary phase in rich media. We outline the importance of understanding quiescence, summarize the properties of quiescent yeast cells, and clarify some definitions of the state. We propose that the processes by which a cell enters into, maintains viability in, and exits from quiescence are best viewed as an environmentally triggered cycle: the cell quiescence cycle. We synthesize what is known about the mechanisms by which yeast cells enter into quiescence, including the possible roles of the protein kinase A, TOR, protein kinase C, and Snf1p pathways. We also discuss selected mechanisms by which quiescent cells maintain viability, including metabolism, protein modification, and redox homeostasis. Finally, we outline what is known about the process by which cells exit from quiescence when nutrients again become available.

The genomics of yeast responses to environmental stress and starvation

Abstract. Unicellular organisms such as yeast have evolved to survive constant fluctuations in their external surroundings by rapidly adapting their internal systems to meet the challenges of each new environment. One aspect of this cellular adaptation is the reorganization of genomic expression to the program required for growth in each environment. The reprogramming of genomic expression can be unveiled using DNA microarrays, which measure the relative transcript abundance of essentially every gene in an organisms genome. Characterizing environmentally triggered gene expression changes provides insights into when, where, and how each gene is expressed and offers a glimpse at the physiological response of the cells to changes in their surroundings. This review will focus on the genomic expression responses of the budding yeast Saccharomyces cerevisiae to diverse environmental changes, highlighting some of the themes that have emerged from the collection of published yeast genomic expression studies. The results of these studies present insights as to how yeast cells sense and respond to each new environment, and suggest mechanisms that this organism uses to survive stressful environmental changes.

Stationary phase in Saccharomyces cerevisiae

Like other microorganisms, the yeast Saccharomyces cerevisiae responds to starvation by arresting growth and entering stationary phase. Because most microorganisms exist under conditions of nutrient limitation, the ability to tolerate starvation is critical for survival. Molecular analyses have identified changes in transcription, translation, and protein modification in stationary‐phase cells. At the level of translation, the pattern of newly synthesized proteins in stationary‐phase cells is surprisingly similar to the pattern of proteins synthesized during exponential growth. When limited for different nutrients, yeast strains may not enter stationary phase but opt for pathways such as pseudohyphal growth. If nutrient limitation continues, the end‐point is likely to be a stationary‐phase cell. Based on the results of recent studies, we propose a model for entry into stationary phase in which G0 arrest is separable from acquisition of the ability to survive long periods of time without added nutrients.

SSC1, an essential member of the yeast HSP70 multigene family, encodes a mitochondrial protein.

SSC1 is an essential member of the yeast HSP70 multigene family (E. Craig, J. Kramer, and J. Kosic-Smithers, Proc. Natl. Acad. Sci. USA 84:4156-4160, 1987). Analysis of the SSC1 DNA sequence revealed that it could encode a 70,627-dalton protein that is more similar to DnaK, an Escherichia coli hsp70 protein, than other yeast hsp70s whose sequences have been determined. Ssc1p was found to have an amino-terminal extension of 28 amino acids, in comparison with either Ssa1p, another hsp70 yeast protein, or Dnak. This putative leader is rich in basic and hydroxyl amino acids, characteristic of many mitochondrial leader sequences. Ssc1p that was synthesized in vitro could be imported into mitochondria and was cleaved in the process. The imported protein comigrated with an abundant mitochondrial protein that reacted with hsp70-specific antibodies. We conclude that Ssc1p is a mitochondrial protein and that hsp70 proteins perform functions in many compartments of the cell.

Characterization of Differentiated Quiescent and Nonquiescent Cells in Yeast Stationary-Phase Cultures

Cells in glucose-limited Saccharomyces cerevisiae cultures differentiate into quiescent (Q) and nonquiescent (NQ) fractions before entering stationary phase. To understand this differentiation, Q and NQ cells from 101 deletion-mutant strains were tested for viability and reproductive capacity. Eleven mutants that affected one or both phenotypes in Q or NQ fractions were identified. NQ fractions exhibit a high level of petite colonies, and nine mutants affecting this phenotype were identified. Microarray analysis revealed >1300 mRNAs distinguished Q from NQ fractions. Q cell-specific mRNAs encode proteins involved in membrane maintenance, oxidative stress response, and signal transduction. NQ-cell mRNAs, consistent with apoptosis in these cells, encode proteins involved in Ty-element transposition and DNA recombination. More than 2000 protease-released mRNAs were identified only in Q cells, consistent with these cells being physiologically poised to respond to environmental changes. Our results indicate that Q and NQ cells differentiate significantly, with Q cells providing genomic stability and NQ cells providing nutrients to Q cells and a regular source of genetic diversity through mutation and transposition. These studies are relevant to chronological aging, cell cycle, and genome evolution, and they provide insight into complex responses that even simple organisms have to starvation.

Multivariate curve resolution of time course microarray data.

BackgroundModeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.ResultsIn this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data.ConclusionProfiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.

Design, construction, characterization, and application of a hyperspectral microarray scanner.

We describe the design, construction, and operation of a hyperspectral microarray scanner for functional genomic research. The hyperspectral instrument operates with spatial resolutions ranging from 3 to 30 microm and records the emission spectrum between 490 and 900 nm with a spectral resolution of 3 nm for each pixel of the microarray. This spectral information, when coupled with multivariate data analysis techniques, allows for identification and elimination of unwanted artifacts and greatly improves the accuracy of microarray experiments. Microarray results presented in this study clearly demonstrate the separation of fluorescent label emission from the spectrally overlapping emission due to the underlying glass substrate. We also demonstrate separation of the emission due to green fluorescent protein expressed by yeast cells from the spectrally overlapping autofluorescence of the yeast cells and the growth media.

The proteomics of quiescent and nonquiescent cell differentiation in yeast stationary-phase cultures

As yeast cultures enter stationary phase in rich, glucose-based medium, differentiation of two major subpopulations of cells, termed quiescent and nonquiescent, is observed. Differences in mRNA abundance between exponentially growing and stationary-phase cultures and quiescent and nonquiescent cells are known, but little was known about protein abundance in these cells. To measure protein abundance in exponential and stationary-phase cultures, the yeast GFP-fusion library (4159 strains) was examined during exponential and stationary phases, using high-throughput flow cytometry (HyperCyt). Approximately 5% of proteins in the library showed twofold or greater changes in median fluorescence intensity (abundance) between the two conditions. We examined 38 strains exhibiting two distinct fluorescence-intensity peaks in stationary phase and determined that the two fluorescence peaks distinguished quiescent and nonquiescent cells, the two major subpopulations of cells in stationary-phase cultures. GFP-fusion proteins in this group were more abundant in quiescent cells, and half were involved in mitochondrial function, consistent with the sixfold increase in respiration observed in quiescent cells and the relative absence of Cit1p:GFP in nonquiescent cells. Finally, examination of quiescent cell-specific GFP-fusion proteins revealed symmetry in protein accumulation in dividing quiescent and nonquiescent cells after glucose exhaustion, leading to a new model for the differentiation of these cells.

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.