Life within a community: benefit to yeast long-term survival

Palková, Zdena; Váchová, Libuše

doi:10.1111/j.1574-6976.2006.00034.x

Cited by 110 publications

(88 citation statements)

References 102 publications

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“…Specific strains of Saccharomyces cerevisiae have been shown to have two robust biological cycles occurring simultaneously, e.g., the metabolic and cell cycles (25,26). The GOALIE framework is validated through analysis of five yeast gene expression datasets, including two YMC time courses involving two different strains grown under two different conditions (YMC1: CEN.PK122 diploid strain, glucose-limited cultures (5) and YMC2: IFO 0233 diploid strain, not glucose limited (7)), a YCC dataset after release from α-factor synchronization (YCC: DBY8724 strain (6)), and observations of the cell cycle under treatment of (HP (27) and (MD (27).…”

Section: Resultsmentioning

confidence: 99%

Reverse engineering dynamic temporal models of biological processes and their relationships

Ramakrishnan

Tadepalli

Watson

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Biological processes such as circadian rhythms, cell division, metabolism, and development occur as ordered sequences of events. The synchronization of these coordinated events is essential for proper cell function, and hence the determination of critical time points in biological processes is an important component of all biological investigations. In particular, such critical time points establish logical ordering constraints on subprocesses, impose prerequisites on temporal regulation and spatial compartmentalization, and situate dynamic reorganization of functional elements in preparation for subsequent stages. Thus, building temporal phenomenological representations of biological processes from genome-wide datasets is relevant in formulating biological hypotheses on: how processes are mechanistically regulated; how the regulations vary on an evolutionary scale, and how their inadvertent disregulation leads to a diseased state or fatality. This paper presents a general framework (GOALIE) to reconstruct temporal models of cellular processes from time-course gene expression data. We mathematically formulate the problem as one of optimally segmenting datasets into a succession of "informative" windows such that time points within a window expose concerted clusters of gene action whereas time points straddling window boundaries constitute points of significant restructuring. We illustrate here how GOALIE successfully brings out the interplay between multiple yeast processes, inferred from combined experimental datasets for the cell cycle and the metabolic cycle. model building and model-checking | temporal data analysis | yeast cell cycle | yeast metabolic cycle | Kripke structures C ells and organisms can be viewed as progressing through sequences of states, as a result of discrete mechanisms. Defining these states and identifying the underlying mechanisms are critical to how we understand biological processes and how we may treat metabolic and developmental disorders. Central to such analysis tools are algorithms for time series analysis using temporal logic formalisms that were originally developed with engineering and computer and systems sciences applications in mind (1, 2, 3).The yeast species Saccharomyces cerevisiae, which has been researched extensively to understand the biology of eukaryotic microorganisms, is a good model organism to illustrate the ideas in this paper. To understand the systems biology of yeast, one may study temporal expression profiles of genes involved in a particular function-for instance, cellular (4) division or metabolism (5) -and create models of the state space dynamics in terms of labeled states and state transition relations. An illustration of this process in shown in Fig. 1. A yeast cell cycle (YCC) model can be created using data generated by Spellman et al. (6) and similarly, a yeast metabolic cycle (YMC) model can be created by combining data generated separately by two other research groups: Tu et al. (5), Klevecz et al. (7). These labeled state transition models are...

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Section: Resultsmentioning

confidence: 99%

Reverse engineering dynamic temporal models of biological processes and their relationships

Ramakrishnan

Tadepalli

Watson

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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“…These clumps were significantly larger at the high salinity, whereas the sizes of cells in the clumps did not change significantly. The ability of microorganisms to organize into multicellular communities, as shown for W. ichthyophaga, can greatly enhance their survival in natural stressful environments as was proposed for yeast populations (37). Similar multicellular structures have also been seen at high salinities in the phylogenetically distant, extremely halotolerant ascomycetous black yeasts, such as Hortaea werneckii (T. Kogej and N. Gunde-Cimerman, unpublished data), Phaeotheca triangularis (15,48), and Trimmatostroma salinum (27), all of the order Dothideales.…”

Section: Discussionmentioning

confidence: 99%

Morphological Response of the Halophilic Fungal Genus Wallemia to High Salinity

Kunčič

Kogej

Drobne

et al. 2010

Appl Environ Microbiol

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The basidiomycetous genus Wallemia is an active inhabitant of hypersaline environments, and it has recently been described as comprising three halophilic and xerophilic species: Wallemia ichthyophaga, Wallemia muriae, and Wallemia sebi. Considering the important protective role the fungal cell wall has under fluctuating physicochemical environments, this study was focused on cell morphology changes, with particular emphasis on the structure of the cell wall, when these fungi were grown in media with low and high salinities. We compared the influence of salinity on the morphological characteristics of Wallemia spp. by light, transmission, and focused-ion-beam/scanning electron microscopy. W. ichthyophaga was the only species of this genus that was metabolically active at saturated NaCl concentrations. W. ichthyophaga grew in multicellular clumps and adapted to the high salinity with a significant increase in cell wall thickness. The other two species, W. muriae and W. sebi, also demonstrated adaptive responses to the high NaCl concentration, showing in particular an increased size of mycelial pellets at the high salinities, with an increase in cell wall thickness that was less pronounced. The comparison of all three of the Wallemia spp. supports previous findings relating to the extremely halophilic character of the phylogenetically distant W. ichthyophaga and demonstrates that, through morphological adaptations, the eukaryotic Wallemia spp. are representative of eukaryotic organisms that have successfully adapted to life in extremely saline environments.

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“…At the same time, they perform metabolically intensive processes, such as the production of extracellular matrix, that while aiding the protection of the colony, require significant nutrient and energy expenditure (21). Switching to the smooth state when such protection is not needed has been proposed as an energy conservation strategy (39). Our results suggest that the copy number imbalance of specific chromosomes is one means by which cells can affect this state change, presumably by altering the dosage of a subset of genes required for the regulation or execution of these biological processes.…”

Section: Discussionmentioning

confidence: 99%

Aneuploidy underlies a multicellular phenotypic switch

Tan

Hays

Cromie

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

106

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Although microorganisms are traditionally used to investigate unicellular processes, the yeast Saccharomyces cerevisiae has the ability to form colonies with highly complex, multicellular structures. Colonies with the "fluffy" morphology have properties reminiscent of bacterial biofilms and are easily distinguished from the "smooth" colonies typically formed by laboratory strains. We have identified strains that are able to reversibly toggle between the fluffy and smooth colony-forming states. Using a combination of flow cytometry and high-throughput restriction-site associated DNA tag sequencing, we show that this switch is correlated with a change in chromosomal copy number. Furthermore, the gain of a single chromosome is sufficient to switch a strain from the fluffy to the smooth state, and its subsequent loss to revert the strain back to the fluffy state. Because copy number imbalance of six of the 16 S. cerevisiae chromosomes and even a single gene can modulate the switch, our results support the hypothesis that the state switch is produced by dosage-sensitive genes, rather than a general response to altered DNA content. These findings add a complex, multicellular phenotype to the list of molecular and cellular traits known to be altered by aneuploidy and suggest that chromosome missegregation can provide a quick, heritable, and reversible mechanism by which organisms can toggle between phenotypes. colony morphology | copy number variation | phenotypic switching | bet-hedging

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Life within a community: benefit to yeast long-term survival

Cited by 110 publications

References 102 publications

Reverse engineering dynamic temporal models of biological processes and their relationships

Reverse engineering dynamic temporal models of biological processes and their relationships

Morphological Response of the Halophilic Fungal Genus Wallemia to High Salinity

Aneuploidy underlies a multicellular phenotypic switch

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