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.

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.

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/ .

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.

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

UNLABELLED: 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/

"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.

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.

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.

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.

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.

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.

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.

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.

Engineering of a redox protein for DNA-directed assembly.

Engineered enzyme conjugate of the small laccase enzyme from Streptomyces coelicolor and zinc finger DNA binding domain from Zif268 is demonstrated to bind double stranded DNA in a site specific manner while retaining enzymatic activity.

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.

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.

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.

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.