Live fast, die soon: cell cycle progression and lifespan in yeast cells

Gene Expression Analysis of Yeast Strains with a Nonsense Mutation in the eRF3-Coding Gene Highlights Possible Mechanisms of Adaptation

In yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 (Sup45) and eRF3 (Sup35), which are essential for viability. Previous studies have revealed that presence of nonsense mutations in these genes leads to amplification of mutant alleles (sup35-n and sup45-n), which appears to be necessary for the viability of such cells. However, the mechanism of this phenomenon remained unclear. In this study, we used RNA-Seq and proteome analysis to reveal the complete set of gene expression changes that occur during cellular adaptation to the introduction of the sup35-218 nonsense allele. Our analysis demonstrated significant changes in the transcription of genes that control the cell cycle: decreases in the expression of genes of the anaphase promoting complex APC/C (APC9, CDC23) and their activator CDC20, and increases in the expression of the transcription factor FKH1, the main cell cycle kinase CDC28, and cyclins that induce DNA biosynthesis. We propose a model according to which yeast adaptation to nonsense mutations in the translation termination factor genes occurs as a result of a delayed cell cycle progression beyond the G2-M stage, which leads to an extension of the S and G2 phases and an increase in the number of copies of the mutant sup35-n allele.

High nitrogen concentration causes G2/M arrest in Hanseniaspora vineae

Yeasts have been widely used as a model to better understand cell cycle mechanisms and how nutritional and genetic factors can impact cell cycle progression. While nitrogen scarcity is well known to modulate cell cycle progression, the relevance of nitrogen excess for microorganisms has been overlooked. In our previous work, we observed an absence of proper entry into the quiescent state in Hanseniaspora vineae and identified a potential link between this behavior and nitrogen availability. Furthermore, the Hanseniaspora genus has gained attention due to a significant loss of genes associated with DNA repair and cell cycle. Thus, the aim of our study was to investigate the effects of varying nitrogen concentrations on H. vineae's cell cycle progression. Our findings demonstrated that nitrogen excess, regardless of the source, disrupts cell cycle progression and induces G2/M arrest in H. vineae after reaching the stationary phase. Additionally, we observed a viability decline in H. vineae cells in an ammonium-dependent manner, accompanied by increased production of reactive oxygen species, mitochondrial hyperpolarization, intracellular acidification, and DNA fragmentation. Overall, our study highlights the events of the cell cycle arrest in H. vineae induced by nitrogen excess and attempts to elucidate the possible mechanism triggering this absence of proper entry into the quiescent state.

Apple Polyphenol Diet Extends Lifespan, Slows down Mitotic Rate and Reduces Morphometric Parameters in Drosophila Melanogaster: A Comparison between Three Different Apple Cultivars

Plant-derived polyphenols exhibit beneficial effects on physiological and pathological processes, including cancer and neurodegenerative disorders, mainly because of their antioxidant activity. Apples are highly enriched in these compounds, mainly in their peel. The Tuscia Red (TR) apple variety exhibits the peculiar characteristic of depositing high quantities of polyphenols in the pulp, the edible part of the fruit. Since polyphenols, as any natural product, cannot be considered a panacea per se, in this paper, we propose to assess the biological effects of TR flesh extracts, in comparison with two commercial varieties, in a model system, the insect Drosophila melanogaster, largely recognized as a reliable system to test the in vivo effects of natural and synthetic compounds. We performed a comparative, qualitative and quantitative analysis of the polyphenol compositions of the three cultivars and found that TR flesh shows the highest content of polyphenols, and markedly, anthocyanins. Then, we focused on their effects on a panel of physiological, morphometrical, cellular and behavioral phenotypes in wild-type D. melanogaster. We found that all the apple polyphenol extracts showed dose-dependent effects on most of the phenotypes we considered. Remarkably, all the varieties induced a strong relenting of the cell division rate.

Identification of Single Yeast Budding Using Impedance Cytometry with a Narrow Electrode Span

Impedance cytometry is wildly used in single-cell detection, and its sensitivity is essential for determining the status of single cells. In this work, we focus on the effect of electrode gap on detection sensitivity. Through comparing the electrode span of 1 µm and 5 µm, our work shows that narrowing the electrode span could greatly improve detection sensitivity. The mechanism underlying the sensitivity improvement was analyzed via numerical simulation. The small electrode gap (1 µm) allows the electric field to concentrate near the detection area, resulting in a high sensitivity for tiny particles. This finding is also verified with the mixture suspension of 1 µm and 3 µm polystyrene beads. As a result, the electrodes with 1 µm gap can detect more 1 µm beads in the suspension than electrodes with 5 µm gap. Additionally, for single yeast cells analysis, it is found that impedance cytometry with 1 µm electrodes gap can easily distinguish budding yeast cells, which cannot be realized by the impedance cytometry with 5 µm electrodes gap. All experimental results support that narrowing the electrode gap is necessary for tiny particle detection, which is an important step in the development of submicron and nanoscale impedance cytometry.

Consensus clustering for Bayesian mixture models

BACKGROUND

Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness.

RESULTS

In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three 'omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis.

CONCLUSTIONS

Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

A peculiar cell cycle arrest at g2/m stage during the stationary phase of growth in the wine yeas Hanseniaspora vineae

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Cell cycle progress variations among Hanseniaspora species.

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H. vineae shows an unusual cell cycle progress.

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H. vineae undergoes G2/M arrest in stationary phase.

Yeasts of the genus Hanseniaspora gained notoriety in the last years due to their contribution to wine quality, and their loss of several genes, mainly related to DNA repair and cell cycle processes. Based on genomic data from many members of this genus, they have been classified in two well defined clades: the “faster-evolving linage” (FEL) and the “slower-evolving lineage” (SEL). In this context, we had detected that H. vineae exhibited a rapid loss of cell viability in some conditions during the stationary phase compared to H. uvarum and S. cerevisiae. The present work aimed to evaluate the viability and cell cycle progression of representatives of Hanseniaspora species along their growth in an aerobic and discontinuous system. Cell growth, viability and DNA content were determined by turbidity, Trypan Blue staining, and flow cytometry, respectively. Results showed that H. uvarum and H. opuntiae (representing FEL group), and H. osmophila (SEL group) exhibited a typical G1/G0 (1C DNA) arrest during the stationary phase, as S. cerevisiae. Conversely, the three strains studied here of H. vineae (SEL group) arrested at G2/M stages of cell cycle (2C DNA), and lost viability rapidly when enter the stationary phase. These results showed that H. vineae have a unique cell cycle behavior that will contribute as a new eukaryotic model for future studies of genetic determinants of yeast cell cycle control and progression.

Enabling large-scale production of algal oil in continuous output mode

Large-scale algal oil production requires continuous outputs and a trade-off between growth and oil content. Two unrelated marine algae (Nannochloropsis oceanica [CCAP 849/10] and Chlorella vulgaris [CCAP 211/21A]) that showed high oil production under batch culture were studied under controlled semicontinuous cultivation conditions. Three essential attributes maximized oil productivity: (i) downregulation of cell size to maximize light absorption under N limitation; (ii) low nutrient-depletion thresholds to trigger oil induction; (iii) a means of carbohydrate suppression in favor of oil. N. oceanica responded better to input N/P variations and is more suited to continuous oil production. A low N/P ratio was effective in both suppressing carbohydrate and reducing cell size concomitant with oil production. In C. vulgaris, nutrient starvation thresholds for oil were higher and carbohydrate was preferentially induced, which impeded stress-level optimization for oil. These differences, which impact continuous oil production at scale, are driven by species adaptation to specific marine habitats.

VIDEO ABSTRACT

VIDEO ABSTRACT

Cultivation processes to select microorganisms with high accumulation ability

The microbial ability to accumulate biomolecules is fundamental for different biotechnological applications aiming at the production of biofuels, food and bioplastics. However, high accumulation is a selective advantage only under certain stressful conditions, such as nutrient depletion, characterized by lower growth rate. Conventional bioprocesses maintain an optimal and stable environment for large part of the cultivation, that doesn't reward cells for their accumulation ability, raising the risk of selection of contaminant strains with higher growth rate, but lower accumulation of products. Here in this work the physiological responses of different microorganisms (microalgae, bacteria, yeasts) under N-starvation and energy starvation are reviewed, with the aim to furnish relevant insights exploitable to develop tailored bioprocesses to select specific strains for their higher accumulation ability. Microorganism responses to starvation are reviewed focusing on cell cycle, biomass production and variations in biochemical composition. Then, the work describes different innovative bioprocess configurations exploiting uncoupled nutrient feeding strategies (feast-famine), tailored to maintain a selective pressure to reward the strains with higher accumulation ability in mixed microbial populations. Finally, the main models developed in recent studies to describe and predict microbial growth and intracellular accumulation upon N-starvation and feast-famine conditions have been reviewed.

Yeast as a tool to identify anti-aging compounds

In the search for interventions against aging and age-related diseases, biological screening platforms are indispensable tools to identify anti-aging compounds among large substance libraries. The budding yeast, Saccharomyces cerevisiae, has emerged as a powerful chemical and genetic screening platform, as it combines a rapid workflow with experimental amenability and the availability of a wide range of genetic mutant libraries. Given the amount of conserved genes and aging mechanisms between yeast and human, testing candidate anti-aging substances in yeast gene-deletion or overexpression collections, or de novo derived mutants, has proven highly successful in finding potential molecular targets. Yeast-based studies, for example, have led to the discovery of the polyphenol resveratrol and the natural polyamine spermidine as potential anti-aging agents. Here, we present strategies for pharmacological anti-aging screens in yeast, discuss common pitfalls and summarize studies that have used yeast for drug discovery and target identification.

Guidelines and recommendations on yeast cell death nomenclature

Elucidating the biology of yeast in its full complexity has major implications for science, medicine and industry. One of the most critical processes determining yeast life and physiology is cel-lular demise. However, the investigation of yeast cell death is a relatively young field, and a widely accepted set of concepts and terms is still missing. Here, we propose unified criteria for the defi-nition of accidental, regulated, and programmed forms of cell death in yeast based on a series of morphological and biochemical criteria. Specifically, we provide consensus guidelines on the differ-ential definition of terms including apoptosis, regulated necrosis, and autophagic cell death, as we refer to additional cell death rou-tines that are relevant for the biology of (at least some species of) yeast. As this area of investigation advances rapidly, changes and extensions to this set of recommendations will be implemented in the years to come. Nonetheless, we strongly encourage the au-thors, reviewers and editors of scientific articles to adopt these collective standards in order to establish an accurate framework for yeast cell death research and, ultimately, to accelerate the pro-gress of this vibrant field of research.

Non-genetic impact factors on chronological lifespan and stress resistance of baker's yeast

Survival under nutrient limitation is an essential feature of microbial cells, and it is defined by the chronological lifespan. We summarize recent findings, illustrating how crucial the choice of the experimental setup is for the interpretation of data in this field. Especially the impact of oxygen supply differs depending on the culture type, highlighting the differences of alternatives like the retentostat to classical batch cultures. Finally the importance of culture conditions on cell aging and survival in biotechnological processes is highlighted.