DIVERSITY OF SIDEROPHORE-PRODUCING BACTERIAL CULTURES FROM CARLSBAD CAVERNS NATIONAL PARK (CCNP) CAVES, CARLSBAD, NEW MEXICO

Siderophores are microbially-produced ferric iron chelators. They are essential for microbial survival, but their presence and function for cave microorganisms have not been extensively studied. Cave environments are nutrient-limited and previous evidence suggests siderophore usage in carbonate caves. We hypothesize that siderophores are likely used as a mechanism in caves to obtain critical nutrients such as iron. Cave bacteria were collected from Long-term parent cultures (LT PC) or Short-term parent cultures (ST PC) inoculated with ferromanganese deposits (FMD) and carbonate secondary minerals from Lechuguilla and Spider caves in Carlsbad Caverns National Park (CCNP), NM. LT PC were incubated for 10-11 years to identify potential chemolithoheterotrophic cultures able to survive in nutrient-limited conditions. ST PC were incubated for 1-3 days to identify a broader diversity of cave isolates. A total of 170 LT and ST cultures,18 pure and 152 mixed, were collected and used to classify siderophore production and type and to identify siderophore producers. Siderophore production was slow to develop (>10 days) in LT cultures with a greater number of weak siderophore producers in comparison to the ST cultures that produced siderophores in <10 days, with a majority of strong siderophore producers. Overall, 64% of the total cultures were siderophore producers, which the majority preferred hydroxamate siderophores. Siderophore producers were classified into Proteobacteria (Alpha-, Beta-, or Gamma-), Actinobacteria, Bacteroidetes, and Firmicutes phyla using 16S rRNA gene sequencing. Our study supports our hypothesis that cave bacteria have the capability to produce siderophores in the subsurface to obtain critical ferric iron.

Principles of Intentional Mentoring

This essay elucidates four universal principles that have been used in a psychosocial mentoring approach to help mentors and mentees to understand themselves, learn productive reframing, build their personal and family narratives, discover their paths forward, and make decisions that fit who they are, their values, and what they love.

Distinct histone methylation and transcription profiles are established during the development of cellular quiescence in yeast

Background

Quiescent cells have a low level of gene activity compared to growing cells. Using a yeast model for cellular quiescence, we defined the genome-wide profiles of three species of histone methylation associated with active transcription between growing and quiescent cells, and correlated these profiles with the presence of RNA polymerase II and transcripts.

Results

Quiescent cells retained histone methylations normally associated with transcriptionally active chromatin and had many transcripts in common with growing cells. Quiescent cells also contained significant levels of RNA polymerase II, but only low levels of the canonical initiating and elongating forms of the polymerase. The RNA polymerase II associated with genes in quiescent cells displayed a distinct occupancy profile compared to its pattern of occupancy across genes in actively growing cells. Although transcription is generally repressed in quiescent cells, analysis of individual genes identified a period of active transcription during the development of quiescence.

Conclusions

The data suggest that the transcript profile and histone methylation marks in quiescent cells were established both in growing cells and during the development of quiescence and then retained in these cells. Together, this might ensure that quiescent cells can rapidly adapt to a changing environment to resume growth.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3509-9) contains supplementary material, which is available to authorized users.

Aging and the survival of quiescent and non-quiescent cells in yeast stationary-phase cultures

In this chapter, we argue that with careful attention to cell types in stationary-phase cultures of the yeast, S. cerevisiae provide an excellent model system for aging studies and hold much promise in pinpointing the set of causal genes and mechanisms driving aging. Importantly, a more detailed understanding of aging in this single celled organism will also shed light on aging in tissue-complex model organisms such as C. elegans and D. melanogaster. We feel strongly that the relationship between aging in yeast and tissue-complex organisms has been obscured by failure to notice the heterogeneity of stationary-phase cultures and the processes by which distinct cell types arise in these cultures. Although several studies have used yeast stationary-phase cultures for chronological aging, the majority of these studies have assumed that cultures in stationary phase are homogeneously composed of a single cell type. However, genome-scale analyses of yeast stationary-phase cultures have identified two major cell fractions: quiescent and non-quiescent, which we discuss in detail in this chapter. We review evidence that cell populations isolated from these cultures exhibit population-specific phenotypes spanning a range of metabolic and physiological processes including reproductive capacity, apoptosis, differences in metabolic activities, genetic hyper-mutability, and stress responses. The identification, in S. cerevisiae, of multiple sub-populations having differentiated physiological attributes relevant to aging offers an unprecedented opportunity. This opportunity to deeply understand yeast cellular (and population) aging programs will, also, give insight into genomic and metabolic processes in tissue-complex organism, as well as stem cell biology and the origins of differentiation.

Identification of a small molecule yeast TORC1 inhibitor with a multiplex screen based on flow cytometry

TOR (target of rapamycin) is a serine/threonine kinase, evolutionarily conserved from yeast to human, which functions as a fundamental controller of cell growth. The moderate clinical benefit of rapamycin in mTOR-based therapy of many cancers favors the development of new TOR inhibitors. Here we report a high-throughput flow cytometry multiplexed screen using five GFP-tagged yeast clones that represent the readouts of four branches of the TORC1 signaling pathway in budding yeast. Each GFP-tagged clone was differentially color-coded, and the GFP signal of each clone was measured simultaneously by flow cytometry, which allows rapid prioritization of compounds that likely act through direct modulation of TORC1 or proximal signaling components. A total of 255 compounds were confirmed in dose-response analysis to alter GFP expression in one or more clones. To validate the concept of the high-throughput screen, we have characterized CID 3528206, a small molecule most likely to act on TORC1 as it alters GFP expression in all five GFP clones in a manner analogous to that of rapamycin. We have shown that CID 3528206 inhibited yeast cell growth and that CID 3528206 inhibited TORC1 activity both in vitro and in vivo with EC(50)'s of 150 nM and 3.9 μM, respectively. The results of microarray analysis and yeast GFP collection screen further support the notion that CID 3528206 and rapamycin modulate similar cellular pathways. Together, these results indicate that the HTS has identified a potentially useful small molecule for further development of TOR inhibitors.

A multiple network learning approach to capture system-wide condition-specific responses

MOTIVATION

Condition-specific networks capture system-wide behavior under varying conditions such as environmental stresses, cell types or tissues. These networks frequently comprise parts that are unique to each condition, and parts that are shared among related conditions. Existing approaches for learning condition-specific networks typically identify either only differences or only similarities across conditions. Most of these approaches first learn networks per condition independently, and then identify similarities and differences in a post-learning step. Such approaches do not exploit the shared information across conditions during network learning.

RESULTS

We describe an approach for learning condition-specific networks that identifies the shared and unique subgraphs during network learning simultaneously, rather than as a post-processing step. Our approach learns networks across condition sets, shares data from different conditions and produces high-quality networks that capture biologically meaningful information. On simulated data, our approach outperformed an existing approach that learns networks independently for each condition, especially for small training datasets. On microarray data of hundreds of deletion mutants in two, yeast stationary-phase cell populations, the inferred network structure identified several common and population-specific effects of these deletion mutants and several high-confidence cases of double-deletion pairs, which can be experimentally tested. Our results are consistent with and extend the existing knowledge base of differentiated cell populations in yeast stationary phase.

AVAILABILITY AND IMPLEMENTATION

C++ code can be accessed from http://www.broadinstitute.org/~sroy/condspec/ .

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.

Scalable learning of large networks

Cellular networks inferred from condition-specific microarray data can capture the functional rewiring of cells in response to different environmental conditions. Unfortunately, many algorithms for inferring cellular networks do not scale to whole-genome data with thousands of variables. We propose a novel approach for scalable learning of large networks: cluster and infer networks (CIN). CIN learns network structures in two steps: (a) partition variables into smaller clusters, and (b) learn networks per cluster. We optionally revisit the cluster assignment of variables with poor neighbourhoods. Results on networks with known topologies suggest that CIN has substantial speed benefits, without substantial performance loss. We applied our approach to microarray compendia of glucose-starved yeast cells. The inferred networks had significantly higher number of subgraphs representing meaningful biological dependencies than random graphs. Analysis of subgraphs identified biological processes that agreed well with existing information about yeast populations under glucose starvation, and also implicated novel pathways that were previously not known to be associated with these populations. [Includes supplementary material].

Budding yeast SSD1-V regulates transcript levels of many longevity genes and extends chronological life span in purified quiescent cells

Ssd1 is an RNA-binding protein that affects literally hundreds of different processes and is polymorphic in both wild and lab yeast strains. We have used transcript microarrays to compare mRNA levels in an isogenic pair of mutant (ssd1-d) and wild-type (SSD1-V) cells across the cell cycle. We find that 15% of transcripts are differentially expressed, but there is no correlation with those mRNAs bound by Ssd1. About 20% of cell cycle regulated transcripts are affected, and most show sharper amplitudes of oscillation in SSD1-V cells. Many transcripts whose gene products influence longevity are also affected, the largest class of which is involved in translation. Ribosomal protein mRNAs are globally down-regulated by SSD1-V. SSD1-V has been shown to increase replicative life span currency and we show that SSD1-V also dramatically increases chronological life span (CLS). Using a new assay of CLS in pure populations of quiescent prototrophs, we find that the CLS for SSD1-V cells is twice that of ssd1-d cells.

Learning structurally consistent undirected probabilistic graphical models

In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the statistical dependency structure than directed graphical models. Unfortunately, structure learning of undirected graphs using likelihood-based scores remains difficult because of the intractability of computing the partition function. We describe a new Markov random field structure learning algorithm, motivated by canonical parameterization of Abbeel et al. We provide computational improvements on their parameterization by learning per-variable canonical factors, which makes our algorithm suitable for domains with hundreds of nodes. We compare our algorithm against several algorithms for learning undirected and directed models on simulated and real datasets from biology. Our algorithm frequently outperforms existing algorithms, producing higher-quality structures, suggesting that enforcing consistency during structure learning is beneficial for learning undirected graphs.

Inference of functional networks of condition-specific response--a case study of quiescence in yeast

Analysis of condition-specific behavior under stressful environmental conditions can provide insight into mechanisms causing different healthy and diseased cellular states. Functional networks (edges representing statistical dependencies) inferred from condition-specific expression data can provide fine-grained, network level information about conserved and specific behavior across different conditions. In this paper, we examine novel microarray compendia measuring gene expression from two unique stationary phase yeast cell populations, quiescent and non-quiescent. We make the following contributions: (a) develop a new algorithm to infer functional networks modeled as undirected probabilistic graphical models, Markov random fields, (b) infer functional networks for quiescent, non-quiescent cells and exponential cells, and (c) compare the inferred networks to identify processes common and different across these cells. We found that both non-quiescent and exponential cells have more gene ontology enrichment than quiescent cells. The exponential cells share more processes with non-quiescent than with quiescent, highlighting the novel and relatively under-studied characteristics of quiescent cells. Analysis of inferred subgraphs identified processes enriched in both quiescent and non-quiescent cells as well processes specific to each cell type. Finally, SNF1, which is crucial for quiescence, occurs exclusively among quiescent network hubs, while non-quiescent network hubs are enriched in human disease causing homologs.

A system for generating transcription regulatory networks with combinatorial control of transcription

We have developed a new software system, REgulatory Network generator with COmbinatorial control (RENCO), for automatic generation of differential equations describing pre-transcriptional combinatorics in artificial regulatory networks. RENCO has the following benefits: (a) it explicitly models protein-protein interactions among transcription factors, (b) it captures combinatorial control of transcription factors on target genes and (c) it produces output in Systems Biology Markup Language (SBML) format, which allows these equations to be directly imported into existing simulators. Explicit modeling of the protein interactions allows RENCO to incorporate greater mechanistic detail of the transcription machinery compared to existing models and can provide a better assessment of algorithms for regulatory network inference.

AVAILABILITY

RENCO is a C++ command line program, available at http://sourceforge.net/projects/renco/

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.

A Hidden-State Markov Model for Cell Population Deconvolution

Microarrays measure gene expression typically from a mixture of cell populations during different stages of a biological process. However, the specific effects of the distinct or pure populations on measured gene expression are difficult or impossible to determine. The ability to deconvolve measured gene expression into the contributions from pure populations is critical to maximizing the potential of microarray analysis for investigating complex biological processes. In this paper, we describe a novel approach called the multinomial hidden Markov model (MHMM) that produces: (i) a maximum a posteriori estimate of the fraction represented by each pure population and (ii) gene expression values for each pure population. Our method uses an unsupervised, probabilistic approach for handling missing data points and clusters genes based on expression in pure populations. MHMM, used with several yeast datasets, identified statistically significant temporal dynamics. This method, unlike the linear decomposition models used previously for deconvolution, can extract information from different types of data, does not require a priori identification of pure gene expression, exploits the temporal nature of time series data, and is less affected by missing data.

Cell-Directed Assembly of Lipid-Silica Nanostructures Providing Extended Cell Viability

Amphiphilic phospholipids were used to direct the formation of biocompatible, uniform silica nanostructures in the presence of Saccharomyces cerevisiae and bacterial cell lines. The cell surfaces organize multilayered phospholipid vesicles that interface coherently with the silica host and help relieve drying stresses that develop with conventional templates. These host structures maintain cell accessibility, addressability, and viability in the absence of buffer or an external fluidic architecture. The cell surfaces are accessible and can be used to localize added proteins, plasmids, and nanocrystals. Prolonged cell viability combined with reporter protein expression enabled stand-alone cell-based sensing.

Multivariate curve resolution of time course microarray data

Background

Modeling 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.

Results

In 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.

Conclusion

Profiles 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.

Isolation of quiescent and nonquiescent cells from yeast stationary-phase cultures

Quiescence is the most common and, arguably, most poorly understood cell cycle state. This is in part because pure populations of quiescent cells are typically difficult to isolate. We report the isolation and characterization of quiescent and nonquiescent cells from stationary-phase (SP) yeast cultures by density-gradient centrifugation. Quiescent cells are dense, unbudded daughter cells formed after glucose exhaustion. They synchronously reenter the mitotic cell cycle, suggesting that they are in a G(0) state. Nonquiescent cells are less dense, heterogeneous, and composed of replicatively older, asynchronous cells that rapidly lose the ability to reproduce. Microscopic and flow cytometric analysis revealed that nonquiescent cells accumulate more reactive oxygen species than quiescent cells, and over 21 d, about half exhibit signs of apoptosis and necrosis. The ability to isolate both quiescent and nonquiescent yeast cells from SP cultures provides a novel, tractable experimental system for studies of quiescence, chronological and replicative aging, apoptosis, and the cell cycle.

Release of extraction-resistant mRNA in stationary phase Saccharomyces cerevisiae produces a massive increase in transcript abundance in response to stress

A rapid transcript increase due to the release of extraction-resistant mRNAs from yeast cells in response to stress is described.

Background

As carbon sources are exhausted, Saccharomyces cerevisiae cells exhibit reduced metabolic activity and cultures enter the stationary phase. We asked whether cells in stationary phase cultures respond to additional stress at the level of transcript abundance.

Results

Microarrays were used to quantify changes in transcript abundance in cells from stationary phase cultures in response to stress. More than 800 mRNAs increased in abundance by one minute after oxidative stress. A significant number of these mRNAs encode proteins involved in stress responses. We tested whether mRNA increases were due to new transcription, rapid poly-adenylation of message (which would not be detected by microarrays), or potential release of mature mRNA present in the cell but resistant to extraction during RNA isolation. Examination of the response to oxidative stress in an RNA polymerase II mutant, rpb1-1, suggested that new transcription was not required. Quantitative RT-PCR analysis of a subset of these transcripts further suggested that the transcripts present in isolated total RNA from stationary phase cultures were polyadenylated. In contrast, over 2,000 transcripts increased after protease treatment of cell-free lysates from stationary phase but not exponentially growing cultures. Different subsets of transcripts were released by oxidative stress and temperature upshift, suggesting that mRNA release is stress-specific.

Conclusions

Cells in stationary phase cultures contain a large number of extraction-resistant mRNAs in a protease-labile, rapidly releasable form. The transcript release appears to be stress-specific. We hypothesize that these transcripts are associated with P-bodies.

An automated, pressure-driven sampling device for harvesting from liquid cultures for genomic and biochemical analyses

Here we describe an automated, pressure-driven, sampling device for harvesting 10 to 30 ml samples, in replicate, with intervals as short as 10 s. Correlation between biological replicate time courses measured by microarrays was extremely high. The sampler enables sampling at intervals within the range of many important biological processes.

Hyperspectral microarray scanning: impact on the accuracy and reliability of gene expression data

Background

Commercial microarray scanners and software cannot distinguish between spectrally overlapping emission sources, and hence cannot accurately identify or correct for emissions not originating from the labeled cDNA. We employed our hyperspectral microarray scanner coupled with multivariate data analysis algorithms that independently identify and quantitate emissions from all sources to investigate three artifacts that reduce the accuracy and reliability of microarray data: skew toward the green channel, dye separation, and variable background emissions.

Results

Here we demonstrate that several common microarray artifacts resulted from the presence of emission sources other than the labeled cDNA that can dramatically alter the accuracy and reliability of the array data. The microarrays utilized in this study were representative of a wide cross-section of the microarrays currently employed in genomic research. These findings reinforce the need for careful attention to detail to recognize and subsequently eliminate or quantify the presence of extraneous emissions in microarray images.

Conclusion

Hyperspectral scanning together with multivariate analysis offers a unique and detailed understanding of the sources of microarray emissions after hybridization. This opportunity to simultaneously identify and quantitate contaminant and background emissions in microarrays markedly improves the reliability and accuracy of the data and permits a level of quality control of microarray emissions previously unachievable. Using these tools, we can not only quantify the extent and contribution of extraneous emission sources to the signal, but also determine the consequences of failing to account for them and gain the insight necessary to adjust preparation protocols to prevent such problems from occurring.

Genomic analysis of stationary-phase and exit in Saccharomyces cerevisiae: gene expression and identification of novel essential genes

Most cells on earth exist in a quiescent state. In yeast, quiescence is induced by carbon starvation, and exit occurs when a carbon source becomes available. To understand how cells survive in, and exit from this state, mRNA abundance was examined using oligonucleotide-based microarrays and quantitative reverse transcription-polymerase chain reaction. Cells in stationary-phase cultures exhibited a coordinated response within 5-10 min of refeeding. Levels of >1800 mRNAs increased dramatically (>or=64-fold), and a smaller group of stationary-phase mRNAs decreased in abundance. Motif analysis of sequences upstream of genes clustered by VxInsight identified an overrepresentation of Rap1p and BUF (RPA) binding sites in genes whose mRNA levels rapidly increased during exit. Examination of 95 strains carrying deletions in stationary-phase genes induced identified 32 genes essential for survival in stationary-phase at 37 degrees C. Analysis of these genes suggests that mitochondrial function is critical for entry into stationary-phase and that posttranslational modifications and protection from oxidative stress become important later. The phylogenetic conservation of stationary-phase genes, and our findings that two-thirds of the essential stationary-phase genes have human homologues and of these, many have human homologues that are disease related, demonstrate that yeast is a bona fide model system for studying the quiescent state of eukaryotic cells.

"Sleeping beauty": quiescence in Saccharomyces cerevisiae

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.

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 genome sequence of the filamentous fungus Neurospora crassa

Neurospora crassa is a central organism in the history of twentieth-century genetics, biochemistry and molecular biology. Here, we report a high-quality draft sequence of the N. crassa genome. The approximately 40-megabase genome encodes about 10,000 protein-coding genes--more than twice as many as in the fission yeast Schizosaccharomyces pombe and only about 25% fewer than in the fruitfly Drosophila melanogaster. Analysis of the gene set yields insights into unexpected aspects of Neurospora biology including the identification of genes potentially associated with red light photobiology, genes implicated in secondary metabolism, and important differences in Ca2+ signalling as compared with plants and animals. Neurospora possesses the widest array of genome defence mechanisms known for any eukaryotic organism, including a process unique to fungi called repeat-induced point mutation (RIP). Genome analysis suggests that RIP has had a profound impact on genome evolution, greatly slowing the creation of new genes through genomic duplication and resulting in a genome with an unusually low proportion of closely related genes.

Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays

Microarray analysis is a critically important technology for genome-enabled biology, therefore it is essential that the data obtained be reliable. Current software and normalization techniques for microarray analysis rely on the assumption that fluorescent background within spots is essentially the same throughout the glass slide and can be measured by fluorescence surrounding the spots. This assumption is not valid if background fluorescence is spot-localized. Inaccurate estimates of background fluorescence under the spot create a source of error, especially for low expressed genes. We have identified spot-localized, contaminating fluorescence in the Cy3 channel on several commercial and in-house printed microarray slides. We determined through mock hybridizations (without labeled target) that pre-hybridization scans could not be used to predict the contribution of this contaminating fluorescence after hybridization because the change in spot-to-spot fluorescence after hybridization was too variable. Two solutions to this problem were identified. First, allowing 4 h of exposure to air prior to printing on to Corning UltraGAPS slides significantly reduced contaminating fluorescence intensities to approximately the value of the surrounding glass. Alternatively, application of a novel, hyperspectral imaging scanner and multivariate curve resolution algorithms, allowed the spectral contributions of Cy3 signal, glass, and contaminating fluorescence to be distinguished and quantified after hybridization.

Comparative analysis of multiple genome-scale data sets

The ongoing analyses of published genome-scale data sets is evidence that different approaches are required to completely mine this data. We report the use of novel tools for both visualization and data set comparison to analyze yeast gene-expression (cell cycle and exit from stationary phase/G(0)) and protein-interaction studies. This analysis led to new insights about each data set. For example, G(1)-regulated genes are not co-regulated during exit from stationary phase, indicating that the cells are not synchronized. The tight clustering of other genes during exit from stationary-phase data set further indicates the physiological responses during G(0) exit are separable from cell-cycle events. Comparison of the two data sets showed that ribosomal-protein genes cluster tightly during exit from stationary phase, but are found in three significantly different clusters in the cell-cycle data set. Two protein-interaction data sets were also compared with the gene-expression data. Visual analysis of the complete data sets showed no clear correlation between co-expression of genes and protein interactions, in contrast to published reports examining subsets of the protein-interaction data. Neither two-hybrid study identified a large number of interactions between ribosomal proteins, consistent with recent structural data, indicating that for both data sets, the identification of false-positive interactions may be lower than previously thought.

The genomics of yeast responses to environmental stress and starvation

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 organism's 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.

Analysis of the pdx-1 (snz-1/sno-1) region of the Neurospora crassa genome: correlation of pyridoxine-requiring phenotypes with mutations in two structural genes

We report the analysis of a 36-kbp region of the Neurospora crassa genome, which contains homologs of two closely linked stationary phase genes, SNZ1 and SNO1, from Saccharomyces cerevisiae. Homologs of SNZ1 encode extremely highly conserved proteins that have been implicated in pyridoxine (vitamin B6) metabolism in the filamentous fungi Cercospora nicotianae and in Aspergillus nidulans. In N. crassa, SNZ and SNO homologs map to the region occupied by pdx-1 (pyridoxine requiring), a gene that has been known for several decades, but which was not sequenced previously. In this study, pyridoxine-requiring mutants of N. crassa were found to possess mutations that disrupt conserved regions in either the SNZ or SNO homolog. Previously, nearly all of these mutants were classified as pdx-1. However, one mutant with a disrupted SNO homolog was at one time designated pdx-2. It now appears appropriate to reserve the pdx-1 designation for the N. crassa SNZ homolog and pdx-2 for the SNO homolog. We further report annotation of the entire 36,030-bp region, which contains at least 12 protein coding genes, supporting a previous conclusion of high gene densities (12,000-13,000 total genes) for N. crassa. Among genes in this region other than SNZ and SNO homologs, there was no evidence of shared function. Four of the genes in this region appear to have been lost from the S. cerevisiae lineage.

The highly conserved, coregulated SNO and SNZ gene families in Saccharomyces cerevisiae respond to nutrient limitation

SNZ1, a member of a highly conserved gene family, was first identified through studies of proteins synthesized in stationary-phase yeast cells. There are three SNZ genes in Saccharomyces cerevisiae, each of which has another highly conserved gene, named SNO (SNZ proximal open reading frame), upstream. The DNA sequences and relative positions of SNZ and SNO genes have been phylogenetically conserved. This report details studies of the expression of the SNZ-SNO gene pairs under various conditions and phenotypic analysis of snz-sno mutants. An analysis of total RNA was used to determine that adjacent SNZ-SNO gene pairs are coregulated. SNZ2/3 and SNO2/3 mRNAs are induced prior to the diauxic shift and decrease in abundance during the postdiauxic phase, when SNZ1 and SNO1 are induced. In snz2 snz3 mutants, SNZ1 mRNA is induced prior to the diauxic shift, when SNZ2/3 mRNAs are normally induced. Under nitrogen-limiting conditions, SNZ1 mRNAs accumulate in tryptophan, adenine, and uracil auxotrophs but not in prototrophic strains, indicating that induction occurs in response to the limitation of specific nutrients. Strains carrying deletions in all SNZ-SNO gene pairs are viable, but snz1 and sno1 mutants are sensitive to 6-azauracil (6-AU), an inhibitor of purine and pyrimidine biosynthetic enzymes, and methylene blue, a producer of singlet oxygen. The conservation of sequence and chromosomal position, the coregulation and pattern of expression of SNZ1 and SNO1 genes, and the sensitivity of snz1 and sno1 mutants to 6-AU support the hypothesis that the associated proteins are part of an ancient response to nutrient limitation.

Yeast bcy1 mutants with stationary phase-specific defects

Entry into the stationary phase requires the yeast BCY1 gene, which encodes the regulatory subunit of the cAMP-dependent protein kinase (cAPK). New bcy1 mutants, constructed by in vitro mutagenesis of the 3'-region encoding the cAMP-binding domains, were classified as early or late-acting mutants based on viability studies. The late-acting bcy1 mutants accumulated fewer stationary phase-specific Bcy1p isoforms and had decreased cAPK activity. This late-acting class is novel and dies after 7 days in culture, later than two previously reported stationary phase mutants, ubi4 and ard1.

Expressed sequences from conidial, mycelial, and sexual stages of Neurospora crassa

In the Neurospora Genome Project at the University of New Mexico, expressed sequence tags (ESTs) corresponding to three stages of the life cycle of the filamentous fungus Neurospora crassa are being analyzed. The results of a pilot project to identify expressed genes and determine their patterns of expression are presented. 1,865 partial complementary DNA (cDNA) sequences for 1,409 clones were determined using single-pass sequencing. Contig analysis allowed the identification of 838 unique ESTs and 156 ESTs present in multiple cDNA clones. For about 34% of the sequences, highly or moderately significant matches to sequences (of known and unknown function) in the NCBI database were detected. Approximately 56% of the ESTs showed no similarity to previously identified genes. Among genes with assigned function, about 43.3% were involved in metabolism, 32.9% in protein synthesis and 8.4% in RNA synthesis. Fewer were involved in defense (6%), cell signalling (3.4%), cell structure (3.4%) and cell division (2.6%).

A stationary-phase gene in Saccharomyces cerevisiae is a member of a novel, highly conserved gene family

The regulation of cellular growth and proliferation in response to environmental cues is critical for development and the maintenance of viability in all organisms. In unicellular organisms, such as the budding yeast Saccharomyces cerevisiae, growth and proliferation are regulated by nutrient availability. We have described changes in the pattern of protein synthesis during the growth of S. cerevisiae cells to stationary phase (E. K. Fuge, E. L. Braun, and M. Werner-Washburne, J. Bacteriol. 176:5802-5813, 1994) and noted a protein, which we designated Snz1p (p35), that shows increased synthesis after entry into stationary phase. We report here the identification of the SNZ1 gene, which encodes this protein. We detected increased SNZ1 mRNA accumulation almost 2 days after glucose exhaustion, significantly later than that of mRNAs encoded by other postexponential genes. SNZ1-related sequences were detected in phylogenetically diverse organisms by sequence comparisons and low-stringency hybridization. Multiple SNZ1-related sequences were detected in some organisms, including S. cerevisiae. Snz1p was found to be among the most evolutionarily conserved proteins currently identified, indicating that we have identified a novel, highly conserved protein involved in growth arrest in S. cerevisiae. The broad phylogenetic distribution, the regulation of the SNZ1 mRNA and protein in S. cerevisiae, and identification of a Snz protein modified during sporulation in the gram-positive bacterium Bacillus subtilis support the hypothesis that Snz proteins are part of an ancient response that occurs during nutrient limitation and growth arrest.

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 G(o) arrest is separable from acquisition of the ability to survive long periods of time without added nutrients.

Protein synthesis in long-term stationary-phase cultures of Saccharomyces cerevisiae

We are interested in characterizing the process of entry into and the maintenance of the stationary phase. To identify proteins that are induced during growth to stationary phase, we examined protein synthesis in long-term stationary-phase cultures using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). Although the total rate of protein synthesis declined when growth ceased after the postdiauxic phase, the pattern of proteins synthesized remained similar throughout the experimental period (28 days), except at the diauxic shift. At the diauxic shift most proteins detectable by 2D-PAGE undergo a transient reduction in their relative rate of synthesis that ends when cells resume growth during the postdiauxic phase. We conclude from this that the transient repression of protein synthesis at the diauxic shift is not directly associated with stationary-phase arrest. A number of proteins that are synthesized after exponential phase have been identified by 2D-PAGE. These proteins could be divided into three temporal classes depending upon when their synthesis became detectable. One postexponential protein, designated p35, was induced later than all other proteins, and its relative rate of synthesis increased throughout stationary phase. Unlike most postexponential proteins, p35 was not regulated by heat shock or glucose repression. We also observed that a direct correlation between steady-state mRNA accumulation and protein synthesis for another postexponential protein (Ssa3p) or two closely related constitutive proteins (Ssa1p and Ssa2p) did not exist. We conclude from this result that synthesis of proteins in stationary phase is regulated by mechanisms other than the control of steady-state mRNA accumulation.

Isolation and characterization of AAP1. A gene encoding an alanine/arginine aminopeptidase in yeast

The yeast AAP1 gene, encoding a putative amino-peptidase, was isolated based on its ability to suppress the temperature-sensitive growth on nonfermentable carbon sources of spr5, a stationary phase regulatory mutant. AAP1 was physically mapped to chromosome VIII between PUT2 and CUP1. Sequence analysis of the AAP1 gene showed a 1581-nucleotide open reading frame capable of encoding a 59-kilodalton protein. The protein encoded by this open reading frame exhibits approximately 40% sequence identity to human, rat, and mouse aminopeptidases. In limited regions, sequence identity between Aap1 and the mammalian aminopeptidases ranges from 53% to 93%. Insertional inactivation of the AAP1 gene resulted in a decrease in glycogen accumulation and the loss of the major band of arginine/alanine aminopeptidase activity. Strains carrying the AAP1 gene on a high copy plasmid show an increase in the major arginine/alanine aminopeptidase activity, a dramatic increase in glycogen accumulation, and an increase in transcription from a vector carrying lacZ fused to the promoter of a gene (SSA3) expressed during post-diauxic and stationary phases of the culture cycle. We conclude that although the AAP1 gene is not essential for viability, the Aap1 protein positively affects glycogen accumulation in yeast.

Stationary phase in the yeast Saccharomyces cerevisiae

Growth and proliferation of microorganisms such as the yeast Saccharomyces cerevisiae are controlled in part by the availability of nutrients. When proliferating yeast cells exhaust available nutrients, they enter a stationary phase characterized by cell cycle arrest and specific physiological, biochemical, and morphological changes. These changes include thickening of the cell wall, accumulation of reserve carbohydrates, and acquisition of thermotolerance. Recent characterization of mutant cells that are conditionally defective only for the resumption of proliferation from stationary phase provides evidence that stationary phase is a unique developmental state. Strains with mutations affecting entry into and survival during stationary phase have also been isolated, and the mutations have been shown to affect at least seven different cellular processes: (i) signal transduction, (ii) protein synthesis, (iii) protein N-terminal acetylation, (iv) protein turnover, (v) protein secretion, (vi) membrane biosynthesis, and (vii) cell polarity. The exact nature of the relationship between these processes and survival during stationary phase remains to be elucidated. We propose that cell cycle arrest coordinated with the ability to remain viable in the absence of additional nutrients provides a good operational definition of starvation-induced stationary phase.

Heat shock-mediated cell cycle blockage and G1 cyclin expression in the yeast Saccharomyces cerevisiae

For cells of the yeast Saccharomyces cerevisiae, heat shock causes a transient inhibition of the cell cycle-regulatory step START. We have determined that this heat-induced START inhibition is accompanied by decreased CLN1 and CLN2 transcript abundance and by possible posttranscriptional changes to CLN3 (WHI1/DAF1) cyclin activity. Persistent CLN2 expression from a heterologous promoter or the CLN2-1 or CLN3-1 alleles that are thought to encode cyclin proteins with increased stability eliminated heat-induced START inhibition but did not affect other aspects of the heat shock response. Heat-induced START inhibition was shown to be independent of functions that regulate cyclin activity under other conditions and of transcriptional regulation of SWI4, an activator of cyclin transcription. Cells lacking Bcy1 function and thus without cyclic AMP control of A kinase activity were inhibited for START by heat shock as long as A kinase activity was attenuated by mutation. We suggest that heat shock mediates START blockage through effects on the G1 cyclins.

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.

Bcy1, the regulatory subunit of cAMP-dependent protein kinase in yeast, is differentially modified in response to the physiological status of the cell

The regulatory subunit of cAMP-dependent protein kinase in yeast, encoded by the BCY1 gene, is known to be required under certain conditions such as growth on nonfermentable carbon sources and entry into stationary phase. We have identified novel isoforms of Bcy1 in cells under these conditions. The isoforms are distinguishable by their migration on one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and 2-dimensional nonequilibrium pH gradient gel electrophoresis. The isoforms observed by one-dimensional SDS-PAGE bind cAMP, as determined by [32P]8-azido-cAMP labeling (diagnostic of Bcy1 protein). Proteins isolated from cells grown to stationary phase in rich medium exhibit five antibody-reactive bands, by one-dimensional SDS-PAGE immunoblot analysis, with apparent molecular masses of 50, 52, 55, 59 and 61 kDa. Total Bcy1 protein increases at least 8-fold between exponential and stationary phase. Analysis of proteins from a variety of yeast mutants indicated that 1) many of the observed modifications of Bcy1 are dependent upon the presence of the Ser-145 phosphorylation site; 2) the appearance of the 59- and 61-kDa bands is dependent upon the presence of Yak1 kinase; and 3) Bcy1 protein is modified even in the absence of cAMP-dependent protein kinase catalytic subunits. Cells carrying the bcy1(ala145) allele exhibit non-wild type growth, indicating that these modifications may be functionally significant.

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.

Yeast Hsp70 RNA levels vary in response to the physiological status of the cell

Yeast Hsp70 genes constitute a multigene family in which at least five of the nine members are heat inducible. Hsp70 RNA levels also vary dramatically during stationary arrest and sporulation. During growth to stationary phase, SSB1-SSB2 and SSC1 RNAs decreased in abundance as cell density increased. In contrast, SSA1-SSA2 RNA levels increased before the diauxic shift and then decreased as cells approach stationary phase. SSA3 RNA was detected only after the diauxic shift and accumulated to high levels as cells entered stationary phase. This accumulation was reversed by addition of glucose. Studies with cyr1 mutants indicated that SSA3 RNA accumulation is stimulated by decreasing intracellular cyclic AMP concentrations. When cells were incubated in sporulation medium, most Hsp70 RNAs, with the exception of SSA1-SSA2 RNA, decreased in abundance. This finding contrasted with the SSA1-SSA2 pattern observed during growth to stationary phase. SSA3 RNA was not detected during growth in acetate-based medium but accumulated after several hours. SSA3 RNA accumulation was higher in sporulating cells than in nonsporulating cells and was reversed by addition of glucose.

Expression of members of the Saccharomyces cerevisiae hsp70 multigene family

The hsp70 multigene family of Saccharomyces cerevisiae is a complex multigene family, composed of members exhibiting complex patterns of regulation. Expression of some members is induced after a heat shock, whereas expression of others is repressed. Some members of the family are expressed during exponential growth. One gene, SSA3, shows an unusual pattern of expression during approach to stationary phase. While most RNAs decrease in abundance, SSA3 RNA levels dramatically increase. The constitutive expression of SSA3 in cells lacking adenylate cyclase activity suggests that cAMP modulates SSA3 expression.

A subfamily of stress proteins facilitates translocation of secretory and mitochondrial precursor polypeptides

Depletion of a subset of 70K stress proteins in yeast mutants shows that they are involved in the post-translational import of precursor polypeptides into both mitochondria and the lumen of the endoplasmic reticulum. The identification of such a basic function may explain the remarkable evolutionary conservation of the gene family encoding these proteins.

Complex interactions among members of an essential subfamily of hsp70 genes in Saccharomyces cerevisiae

Saccharomyces cerevisiae contains a large family of genes related to hsp70, the major heat shock-inducible gene of Drosophila melanogaster. One subfamily, identified by sequence homology, contains four genes, SSA1, SSA2, SSA3, and SSA4 (formerly YG100, YG102, YG106, and YG107, respectively). Previous studies showed that strains containing mutations in SSA1 and SSA2 are temperature sensitive for growth. SSA4, which is normally heat inducible and not expressed during vegetative growth, is expressed at high levels in ssa1 ssa2 strains at 23 degrees C. We constructed mutations in SSA3 and SSA4 and analyzed strains carrying mutations in the four genes. Strains carrying mutations in SSA3 SSA4 or SSA3 and SSA4 were indistinguishable from the wild type. However, ssa1 ssa2 ssa4 strains were inviable. SSA3, like SSA4, is a heat-inducible gene that is not normally expressed at 23 degrees C. Nevertheless, an intact copy of SSA3 regulated by the constitutive SSA2 promoter was capable of rescuing a ssa1 ssa2 ssa4 strain. This indicates that SSA3 encodes a functional protein and that the SSA1, SSA2, SSA3, and SSA4 gene products are functionally similar.