Protocols and programs for high-throughput growth and aging phenotyping in yeast

Cultivating efficiency: high-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource-intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics. Yet, their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 µM and 0.4 µM, respectively. Complete inhibition of growth was achieved at 38 µM arsenite for C. bolteae and 338 µM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

IMPORTANCE

We present a workflow that facilitates the production and analysis of growth curves for anaerobic microbes using small-footprint microplate readers and an R script. This workflow is a cost and space-effective solution to most high-throughput solutions for collecting growth data from anaerobic microbes. This technology can be used for applications where high throughput would advance discovery, including microbial isolation, bioprospecting, co-culturing, host-microbe interactions, and drug/toxin-microbial interactions.

MiniRead: A simple and inexpensive do-it-yourself device for multiple analyses of micro-organism growth kinetics

Fitness in micro-organisms can be proxied by growth parameters on different media and/or temperatures. This is achieved by measuring optical density at 600 nm using a spectrophotometer, which measures the effect of absorbance and side scattering due to turbidity of cells suspensions. However, when growth kinetics must be monitored in many 96-well plates at the same time, buying several 96-channel spectrophotometers is often beyond budgets. The MiniRead device presented here is a simple and inexpensive do-it-yourself 96-well temperature-controlled turbidimeter designed to measure the interception of white light via absorption or side scattering through liquid culture medium. Turbidity is automatically recorded in each well at regular time intervals for up to several days or weeks. Output tabulated text files are recorded into a micro-SD memory card to be easily transferred to a computer. We propose also an R package which allows (1) to compute the nonlinear calibration curves required to convert raw readings into cell concentration values, and (2) to analyze growth kinetics output files to automatically estimate proxies of growth parameters such as lag time, maximum growth rate, or cell concentration at the plateau.

Exogenous glucosylglycerol and proline extend the chronological lifespan of Rhodosporidium toruloides

Glucosylglycerol (GG) is an osmolyte found in a few bacteria (e.g., cyanobacteria) and plants grown in harsh environments. GG protects microbes and plants from salinity and desiccation stress. In the industry, GG is synthesized from a combination of ADP-glucose and glycerol-3-phosphate in a condensation reaction catalyzed by glucosylglycerol phosphate synthase. Proline, on the other hand, is an amino acid-based osmolyte that plays a key role in cellular reprograming. It functions as a protectant and a scavenger of reactive oxygen species. Studies on lifespan extension have focused on the use of Saccharomyces cerevisiae. Rhodosporidium toruloides, also known as Rhodotorula toruloides, is a basidiomycetous oleaginous yeast known to accumulate lipids to more than 70% of its dry cell weight. The oleaginous red yeast (R. toruloides) has not been intensely studied in the lifespan domain. We designed this work to investigate how GG and proline promote the longevity of this red yeast strain. The results obtained in our study confirmed that these molecules increased R. toruloides' viability, survival percentage, and lifespan upon supplementation. GG exerts the most promising effects at a relatively high concentration (100 mM), while proline functions best at a low level (2 mM). Elucidation of the processes underlying these favorable responses revealed that GG promotes the yeast chronological lifespan (CLS) through increased catalase activity, modulation of the culture medium pH, a rise in ATP, and an increase in reactive oxygen species (ROS) accumulation (mitohormesis). It is critical to understand the mechanisms of these geroprotector molecules, particularly GG, and the proclivity of its lifespan application; this will aid in offering clarity on its potential application in aging research.

Cultivating efficiency: High-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics but their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 μM and 0.4 μM, respectively. Complete inhibition of growth was achieved at 38 μM arsenite for C. bolteae, and 338 μM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

Atg1-dependent phosphorylation of Vps34 is required for dynamic regulation of the phagophore assembly site and autophagy in Saccharomyces cerevisiae

Macroautophagy/autophagy is a key catabolic pathway in which double-membrane autophagosomes sequester various substrates destined for degradation, enabling cells to maintain homeostasis and survive under stressful conditions. Several autophagy-related (Atg) proteins are recruited to the phagophore assembly site (PAS) and cooperatively function to generate autophagosomes. Vps34 is a class III phosphatidylinositol 3-kinase, and Atg14-containing Vps34 complex I plays essential roles in autophagosome formation. However, the regulatory mechanisms of yeast Vps34 complex I are still poorly understood. Here, we demonstrate that Atg1-dependent phosphorylation of Vps34 is required for robust autophagy activity in Saccharomyces cerevisiae. Following nitrogen starvation, Vps34 in complex I is selectively phosphorylated on multiple serine/threonine residues in its helical domain. This phosphorylation is important for full autophagy activation and cell survival. The absence of Atg1 or its kinase activity leads to complete loss of Vps34 phosphorylation in vivo, and Atg1 directly phosphorylates Vps34 in vitro, regardless of its complex association type. We also demonstrate that the localization of Vps34 complex I to the PAS provides a molecular basis for the complex I-specific phosphorylation of Vps34. This phosphorylation is required for the normal dynamics of Atg18 and Atg8 at the PAS. Together, our results reveal a novel regulatory mechanism of yeast Vps34 complex I and provide new insights into the Atg1-dependent dynamic regulation of the PAS.Abbreviations: ATG: autophagy-related; BARA: the repeated, autophagy-specific Co-IP: co-immunoprecipitation; GFP: green fluorescent protein; IP-MS: immunoprecipitation followed by tandem mass spectrometry; NTD: the N-terminal domain; PAS: phagophore assembly site; PtdIns3P: phosphatidylinositol-3-phosphate; PtdIns3K: phosphatidylinositol 3-kinase; SUR: structurally uncharacterized region; Vps34[KD]: Vps34D731N.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

The role of NAD and NAD precursors on longevity and lifespan modulation in the budding yeast, Saccharomyces cerevisiae

Molecular causes of aging and longevity interventions have witnessed an upsurge in the last decade. The resurgent interests in the application of small molecules as potential geroprotectors and/or pharmacogenomics point to nicotinamide adenine dinucleotide (NAD) and its precursors, nicotinamide riboside, nicotinamide mononucleotide, nicotinamide, and nicotinic acid as potentially intriguing molecules. Upon supplementation, these compounds have shown to ameliorate aging related conditions and possibly prevent death in model organisms. Besides being a molecule essential in all living cells, our understanding of the mechanism of NAD metabolism and its regulation remain incomplete owing to its omnipresent nature. Here we discuss recent advances and techniques in the study of chronological lifespan (CLS) and replicative lifespan (RLS) in the model unicellular organism Saccharomyces cerevisiae. We then follow with the mechanism and biology of NAD precursors and their roles in aging and longevity. Finally, we review potential biotechnological applications through engineering of microbial lifespan, and laid perspective on the promising candidature of alternative redox compounds for extending lifespan.

Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex

BACKGROUND

Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation.

RESULTS

We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery.

CONCLUSIONS

The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.

Development of a method for heat shock stress assessment in yeast based on transcription of specific genes

All living cells, including yeast cells, are challenged by different types of stresses in their environments and must cope with challenges such as heat, chemical stress, or oxidative damage. By reversibly adjusting the physiology while maintaining structural and genetic integrity, cells can achieve a competitive advantage and adapt environmental fluctuations. The yeast Saccharomyces cerevisiae has been extensively used as a model for study of stress responses due to the strong conservation of many essential cellular processes between yeast and human cells. We focused here on developing a tool to detect and quantify early responses using specific transcriptional responses. We analyzed the published transcriptional data on S. cerevisiae DBY strain responses to 10 different stresses in different time points. The principal component analysis (PCA) and the Pearson analysis were used to assess the stress response genes that are highly expressed in each individual stress condition. Except for these stress response genes, we also identified the reference genes in each stress condition, which would not be induced under stress condition and show stable transcriptional expression over time. We then tested our candidates experimentally in the CEN.PK strain. After data analysis, we identified two stress response genes (UBI4 and RRP) and two reference genes (MEX67 and SSY1) under heat shock (HS) condition. These genes were further verified by real-time PCR at mild (42°C), severe (46°C), to lethal temperature (50°C), respectively.

Genome-wide screens in yeast models towards understanding chronological lifespan regulation

Cellular models such as yeasts are a driving force in biogerontology studies. Their simpler genome, short lifespans and vast genetic and genomics resources make them ideal to characterise pro-ageing and anti-ageing genes and signalling pathways. Over the last three decades, yeasts have contributed to the understanding of fundamental aspects of lifespan regulation including the roles of nutrient response, global protein translation rates and quality, DNA damage, oxidative stress, mitochondrial function and dysfunction as well as autophagy. In this short review, we focus on approaches used for competitive and non-competitive cell-based screens using the budding yeast Saccharomyces cerevisiae, and the fission yeast Schizosaccharomyces pombe, for deciphering the molecular mechanisms underlying chronological ageing. Automation accompanied with appropriate computational tools allowed manipulation of hundreds of thousands of colonies, generation, processing and analysis of genome-wide lifespan data. Together with barcoding and modern mutagenesis technologies, these approaches have allowed to take decisive steps towards a global, comprehensive view of cellular ageing.

SPOCK, an R based package for high-throughput analysis of growth rate, survival, and chronological lifespan in yeast

Plate reader-based methods for high-throughput measurement of growth rate, cellular survival, and chronological lifespan are a compelling addition to the already powerful toolbox of budding yeast Saccharomyces cerevisiae genetics. These methods have overcome many of the limits of traditional yeast biology techniques, but also present a new bottleneck at the point of data-analysis. Herein, we describe SPOCK (Survival Percentage and Outgrowth Collection Kit), an R-based package for the analysis of data created by high-throughput plate reader based methods. This package allows for the determination of chronological lifespan, cellular growth rate, and survival in an efficient, robust, and reproducible fashion.

Scalable continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities

Enzyme orthologs sharing identical primary functions can have different promiscuous activities. While it is possible to mine this natural diversity to obtain useful biocatalysts, generating comparably rich ortholog diversity is difficult, as it is the product of deep evolutionary processes occurring in a multitude of separate species and populations. Here, we take a first step in recapitulating the depth and scale of natural ortholog evolution on laboratory timescales. Using a continuous directed evolution platform called OrthoRep, we rapidly evolve the Thermotoga maritima tryptophan synthase β-subunit (TmTrpB) through multi-mutation pathways in many independent replicates, selecting only on TmTrpB's primary activity of synthesizing L-tryptophan from indole and L-serine. We find that the resulting sequence-diverse TmTrpB variants span a range of substrate profiles useful in industrial biocatalysis and suggest that the depth and scale of evolution that OrthoRep affords will be generally valuable in enzyme engineering and the evolution of biomolecular functions.

Targeting Cdc42 with the anticancer compound MBQ-167 inhibits cell polarity and growth in the budding yeast S. cerevisiae

The Rho GTPase Cdc42 is highly conserved in structure and function. Mechanical or chemical cues in the microenvironment stimulate the localized activation of Cdc42 to rearrange the actin cytoskeleton and establish cell polarity. A role for Cdc42 in cell polarization was first discovered in the budding yeast Saccharomyces cerevisiae, and subsequently shown to also regulate directional motility in animal cells. Accordingly, in cancer Cdc42 promotes migration, invasion, and spread of tumor cells. Therefore, we targeted Cdc42 as a therapeutic strategy to treat metastatic breast cancer and designed the small molecule MBQ-167 as a potent inhibitor against Cdc42 and the homolog Rac. MBQ-167 inhibited cancer cell proliferation and migration in-vitro, and tumor growth and spread in-vivo in a mouse xenograft model of metastatic breast cancer. Since haploid budding yeast express a single Cdc42 gene, and do not express Rac, we used this well characterized model of polarization to define the contribution of Cdc42 inhibition to the effects of MBQ-167 in eukaryotic cells. Growth, budding pattern, and Cdc42 activity was determined in wildtype yeast or cells expressing a conditional knockdown of Cdc42 in response to vehicle or MBQ-167 treatment. As expected, growth and budding polarity were reduced by knocking-down Cdc42, with a parallel effect observed with MBQ-167. Cdc42 activity assays confirmed that MBQ-167 inhibits Cdc42 activation in yeast, and thus, bud polarity. Hence, we have validated MBQ-167 as a Cdc42 inhibitor in another biological context and present a method to screen Cdc42 inhibitors with potential as anti-metastatic cancer drugs.

High-resolution yeast quiescence profiling in human-like media reveals complex influences of auxotrophy and nutrient availability

Yeast cells survive in stationary phase culture by entering quiescence, which is measured by colony-forming capacity upon nutrient re-exposure. Yeast chronological lifespan (CLS) studies, employing the comprehensive collection of gene knockout strains, have correlated weakly between independent laboratories, which is hypothesized to reflect differential interaction between the deleted genes, auxotrophy, media composition, and other assay conditions influencing quiescence. This hypothesis was investigated by high-throughput quiescence profiling of the parental prototrophic strain, from which the gene deletion strain libraries were constructed, and all possible auxotrophic allele combinations in that background. Defined media resembling human cell culture media promoted long-term quiescence and was used to assess effects of glucose, ammonium sulfate, auxotrophic nutrient availability, target of rapamycin signaling, and replication stress. Frequent, high-replicate measurements of colony-forming capacity from cultures aged past 60 days provided profiles of quiescence phenomena such as gasping and hormesis. Media acidification was assayed in parallel to assess correlation. Influences of leucine, methionine, glucose, and ammonium sulfate metabolism were clarified, and a role for lysine metabolism newly characterized, while histidine and uracil perturbations had less impact. Interactions occurred between glucose, ammonium sulfate, auxotrophy, auxotrophic nutrient limitation, aeration, TOR signaling, and/or replication stress. Weak correlation existed between media acidification and maintenance of quiescence. In summary, experimental factors, uncontrolled across previous genome-wide yeast CLS studies, influence quiescence and interact extensively, revealing quiescence as a complex metabolic and developmental process that should be studied in a prototrophic context, omitting ammonium sulfate from defined media, and employing highly replicable protocols.

Sentinel interaction mapping - a generic approach for the functional analysis of human disease gene variants using yeast

Advances in sequencing technology have led to an explosion in the number of known genetic variants of human genes. A major challenge is to now determine which of these variants contribute to diseases as a result of their effect on gene function. Here, we describe a generic approach using the yeast Saccharomyces cerevisiae to quickly develop gene-specific in vivo assays that can be used to quantify the level of function of a genetic variant. Using synthetic dosage lethality screening, 'sentinel' yeast strains are identified that are sensitive to overexpression of a human disease gene. Variants of the gene can then be functionalized in a high-throughput fashion through simple growth assays using solid or liquid media. Sentinel interaction mapping (SIM) has the potential to create functional assays for the large majority of human disease genes that do not have a yeast orthologue. Using the tumour suppressor gene PTEN as an example, we show that SIM assays can provide a fast and economical means to screen a large number of genetic variants.

Automated Continuous Evolution of Proteins in Vivo

We present automated continuous evolution (ACE), a platform for the hands-free directed evolution of biomolecules. ACE pairs OrthoRep, a genetic system for continuous targeted mutagenesis of user-selected genes in vivo, with eVOLVER, a scalable and automated continuous culture device for precise, multiparameter regulation of growth conditions. By implementing real-time feedback-controlled tuning of selection stringency with eVOLVER, genes of interest encoded on OrthoRep autonomously traversed multimutation adaptive pathways to reach desired functions, including drug resistance and improved enzyme activity. The durability, scalability, and speed of biomolecular evolution with ACE should be broadly applicable to protein engineering as well as prospective studies on how selection parameters and schedules shape adaptation.

An Optimized Competitive-Aging Method Reveals Gene-Drug Interactions Underlying the Chronological Lifespan of Saccharomyces cerevisiae

The chronological lifespan of budding yeast is a model of aging and age-related diseases. This paradigm has recently allowed genome-wide screening of genetic factors underlying post-mitotic viability in a simple unicellular system, which underscores its potential to provide a comprehensive view of the aging process. However, results from different large-scale studies show little overlap and typically lack quantitative resolution to derive interactions among different aging factors. We previously introduced a sensitive, parallelizable approach to measure the chronological-lifespan effects of gene deletions based on the competitive aging of fluorescence-labeled strains. Here, we present a thorough description of the method, including an improved multiple-regression model to estimate the association between death rates and fluorescent signals, which accounts for possible differences in growth rate and experimental batch effects. We illustrate the experimental procedure-from data acquisition to calculation of relative survivorship-for ten deletion strains with known lifespan phenotypes, which is achieved with high technical replicability. We apply our method to screen for gene-drug interactions in an array of yeast deletion strains, which reveals a functional link between protein glycosylation and lifespan extension by metformin. Competitive-aging screening coupled to multiple-regression modeling provides a powerful, straight-forward way to identify aging factors in yeast and their interactions with pharmacological interventions.

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

BACKGROUND

Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited.

RESULTS

Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation.

CONCLUSIONS

We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

Phenotypic assays in yeast and zebrafish reveal drugs that rescue ATP13A2 deficiency

Mutations in ATP13A2 (PARK9) are causally linked to the rare neurodegenerative disorders Kufor-Rakeb syndrome, hereditary spastic paraplegia and neuronal ceroid lipofuscinosis. This suggests that ATP13A2, a lysosomal cation-transporting ATPase, plays a crucial role in neuronal cells. The heterogeneity of the clinical spectrum of ATP13A2-associated disorders is not yet well understood and currently, these diseases remain without effective treatment. Interestingly, ATP13A2 is widely conserved among eukaryotes, and the yeast model for ATP13A2 deficiency was the first to indicate a role in heavy metal homeostasis, which was later confirmed in human cells. In this study, we show that the deletion of YPK9 (the yeast orthologue of ATP13A2) in Saccharomyces cerevisiae leads to growth impairment in the presence of Zn2+, Mn2+, Co2+ and Ni2+, with the strongest phenotype being observed in the presence of zinc. Using the ypk9Δ mutant, we developed a high-throughput growth rescue screen based on the Zn2+ sensitivity phenotype. Screening of two libraries of Food and Drug Administration-approved drugs identified 11 compounds that rescued growth. Subsequently, we generated a zebrafish model for ATP13A2 deficiency and found that both partial and complete loss of atp13a2 function led to increased sensitivity to Mn2+. Based on this phenotype, we confirmed two of the drugs found in the yeast screen to also exert a rescue effect in zebrafish-N-acetylcysteine, a potent antioxidant, and furaltadone, a nitrofuran antibiotic. This study further supports that combining the high-throughput screening capacity of yeast with rapid in vivo drug testing in zebrafish can represent an efficient drug repurposing strategy in the context of rare inherited disorders involving conserved genes. This work also deepens the understanding of the role of ATP13A2 in heavy metal detoxification and provides a new in vivo model for investigating ATP13A2 deficiency.

Cell organelles and yeast longevity: an intertwined regulation

Organelles are dynamic structures of a eukaryotic cell that compartmentalize various essential functions and regulate optimum functioning. On the other hand, ageing is an inevitable phenomenon that leads to irreversible cellular damage and affects optimum functioning of cells. Recent research shows compelling evidence that connects organelle dysfunction to ageing-related diseases/disorders. Studies in several model systems including yeast have led to seminal contributions to the field of ageing in uncovering novel pathways, proteins and their functions, identification of pro- and anti-ageing factors and so on. In this review, we present a comprehensive overview of findings that highlight the role of organelles in ageing and ageing-associated functions/pathways in yeast.

The flavonoid 4,4'-dimethoxychalcone promotes autophagy-dependent longevity across species

Ageing constitutes the most important risk factor for all major chronic ailments, including malignant, cardiovascular and neurodegenerative diseases. However, behavioural and pharmacological interventions with feasible potential to promote health upon ageing remain rare. Here we report the identification of the flavonoid 4,4'-dimethoxychalcone (DMC) as a natural compound with anti-ageing properties. External DMC administration extends the lifespan of yeast, worms and flies, decelerates senescence of human cell cultures, and protects mice from prolonged myocardial ischaemia. Concomitantly, DMC induces autophagy, which is essential for its cytoprotective effects from yeast to mice. This pro-autophagic response induces a conserved systemic change in metabolism, operates independently of TORC1 signalling and depends on specific GATA transcription factors. Notably, we identify DMC in the plant Angelica keiskei koidzumi, to which longevity- and health-promoting effects are ascribed in Asian traditional medicine. In summary, we have identified and mechanistically characterised the conserved longevity-promoting effects of a natural anti-ageing drug.

Ameliorating the Metabolic Burden of the Co-expression of Secreted Fungal Cellulases in a High Lipid-Accumulating Yarrowia lipolytica Strain by Medium C/N Ratio and a Chemical Chaperone

Yarrowia lipolytica, known to accumulate lipids intracellularly, lacks the cellulolytic enzymes needed to break down solid biomass directly. This study aimed to evaluate the potential metabolic burden of expressing core cellulolytic enzymes in an engineered high lipid-accumulating strain of Y. lipolytica. Three fungal cellulases, Talaromyces emersonii-Trichoderma reesei chimeric cellobiohydrolase I (chimeric-CBH I), T. reesei cellobiohydrolase II (CBH II), and T. reesei endoglucanase II (EG II) were expressed using three constitutive strong promoters as a single integrative expression block in a recently engineered lipid hyper-accumulating strain of Y. lipolytica (HA1). In yeast extract-peptone-dextrose (YPD) medium, the resulting cellulase co-expressing transformant YL165-1 had the chimeric-CBH I, CBH II, and EG II secretion titers being 26, 17, and 132 mg L^-1, respectively. Cellulase co-expression in YL165-1 in culture media with a moderate C/N ratio of ∼4.5 unexpectedly resulted in a nearly two-fold reduction in cellular lipid accumulation compared to the parental control strain, a sign of cellular metabolic drain. Such metabolic drain was ameliorated when grown in media with a high C/N ratio of 59 having a higher glucose utilization rate that led to approximately twofold more cell mass and threefold more lipid production per liter culture compared to parental control strain, suggesting cross-talk between cellulase and lipid production, both of which involve the endoplasmic reticulum (ER). Most importantly, we found that the chemical chaperone, trimethylamine N-oxide dihydride increased glucose utilization, cell mass and total lipid titer in the transformants, suggesting further amelioration of the metabolic drain. This is the first study examining lipid production in cellulase-expressing Y. lipolytica strains under various C/N ratio media and with a chemical chaperone highlighting the metabolic complexity for developing robust, cellulolytic and lipogenic yeast strains.

A simple and inexpensive quantitative technique for determining chemical sensitivity in Saccharomyces cerevisiae

Chemical sensitivity, growth inhibition in response to a chemical, is a powerful phenotype that can reveal insight into diverse cellular processes. Chemical sensitivity assays are used in nearly every model system, however the yeast Saccharomyces cerevisiae provides a particularly powerful platform for discovery and mechanistic insight from chemical sensitivity assays. Here we describe a simple and inexpensive approach to determine chemical sensitivity quantitatively in yeast in the form of half maximal inhibitory concentration (IC50) using common laboratory equipment. We demonstrate the utility of this method using chemicals commonly used to monitor changes in membrane traffic. When compared to traditional agar-based plating methods, this method is more sensitive and can detect defects not apparent using other protocols. Additionally, this method reduces the experimental protocol from five days to 18 hours for the toxic amino acid canavanine. Furthermore, this method provides reliable results using lower amounts of chemicals. Finally, this method is easily adapted to additional chemicals as demonstrated with an engineered system that activates the spindle assembly checkpoint in response to rapamycin with differing efficiencies. This approach provides researchers with a cost-effective method to perform chemical genetic profiling without specialized equipment.

Statistical Package for Growth Rates Made Easy

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

Natural variation of chronological aging in the Saccharomyces cerevisiae species reveals diet-dependent mechanisms of life span control

Aging is a complex trait of broad scientific interest, especially because of its intrinsic link with common human diseases. Pioneering work on aging-related mechanisms has been made in Saccharomyces cerevisiae, mainly through the use of deletion collections isogenic to the S288c reference strain. In this study, using a recently published high-throughput approach, we quantified chronological life span (CLS) within a collection of 58 natural strains across seven different conditions. We observed a broad aging variability suggesting the implication of diverse genetic and environmental factors in chronological aging control. Two major Quantitative Trait Loci (QTLs) were identified within a biparental population obtained by crossing two natural isolates with contrasting aging behavior. Detection of these QTLs was dependent upon the nature and concentration of the carbon sources available for growth. In the first QTL, the RIM15 gene was identified as major regulator of aging under low glucose condition, lending further support to the importance of nutrient-sensing pathways in longevity control under calorie restriction. In the second QTL, we could show that the SER1 gene, encoding a conserved aminotransferase of the serine synthesis pathway not previously linked to aging, is causally associated with CLS regulation, especially under high glucose condition. These findings hint toward a new mechanism of life span control involving a trade-off between serine synthesis and aging, most likely through modulation of acetate and trehalose metabolism. More generally it shows that genetic linkage studies across natural strains represent a promising strategy to further unravel the molecular basis of aging.

A Sake yeast strain deficient in producing the protein building block serine lives longer than other yeast strains, especially when exposed to high glucose. A team led by Carole Linster at the University of Luxembourg found a broad variability of lifespan when analyzing more than fifty Saccharomyces cerevisiae strains isolated from around the world. Combining hundreds of lifespan measurements with genotype data from a progeny obtained by crossing the long-lived Sake strain and a short-lived collection strain, they identified two genes playing a pivotal role in causing the contrasting aging behavior of the parents: RIM15, when glucose was limiting and SER1, when glucose was plenty. RIM15 is part of a signaling cascade also regulating aging in mammals; SER1 revealed that a blockage in serine synthesis reprograms metabolism to favor glucose storage and long life.

PHENOS: a high-throughput and flexible tool for microorganism growth phenotyping on solid media

Background

Microbial arrays, with a large number of different strains on a single plate printed with robotic precision, underpin an increasing number of genetic and genomic approaches. These include Synthetic Genetic Array analysis, high-throughput Quantitative Trait Loci (QTL) analysis and 2-hybrid techniques. Measuring the growth of individual colonies within these arrays is an essential part of many of these techniques but is useful for any work with arrays. Measurement is typically done using intermittent imagery fed into complex image analysis software, which is not especially accurate and is challenging to use effectively. We have developed a simple and fast alternative technique that uses a pinning robot and a commonplace microplate reader to continuously measure the thickness of colonies growing on solid agar, complemented by a technique for normalizing the amount of cells initially printed to each spot of the array in the first place. We have developed software to automate the process of combining multiple sets of readings, subtracting agar absorbance, and visualizing colony thickness changes in a number of informative ways.

Results

The “PHENOS” pipeline (PHENotyping On Solid media), optimized for Saccharomyces yeasts, produces highly reproducible growth curves and is particularly sensitive to low-level growth. We have empirically determined a formula to estimate colony cell count from an absorbance measurement, and shown this to be comparable with estimates from measurements in liquid. We have also validated the technique by reproducing the results of an earlier QTL study done with conventional liquid phenotyping, and found PHENOS to be considerably more sensitive.

Conclusions

“PHENOS” is a cost effective and reliable high-throughput technique for quantifying growth of yeast arrays, and is likely to be equally very useful for a range of other types of microbial arrays. A detailed guide to the pipeline and software is provided with the installation files at https://github.com/gact/phenos.

Electronic supplementary material

The online version of this article (10.1186/s12866-017-1143-y) contains supplementary material, which is available to authorized users.

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.

Dietary restriction and lifespan: Lessons from invertebrate models

Dietary restriction (DR) is the most robust environmental manipulation known to increase active and healthy lifespan in many species. Despite differences in the protocols and the way DR is carried out in different organisms, conserved relationships are emerging among multiple species. Elegant studies from numerous model organisms are further defining the importance of various nutrient-signaling pathways including mTOR (mechanistic target of rapamycin), insulin/IGF-1-like signaling and sirtuins in mediating the effects of DR. We here review current advances in our understanding of the molecular mechanisms altered by DR to promote lifespan in three major invertebrate models, the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster.

Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions

Interactions between microbes are central to the dynamics of microbial communities. Understanding these interactions is essential for the characterization of communities, yet challenging to accomplish in practice. There are limited available tools for characterizing diffusion-mediated, contact-independent microbial interactions. A practical and widely implemented technique in such characterization involves the simultaneous co-culture of distinct bacterial species and subsequent analysis of relative abundance in the total population. However, distinguishing between species can be logistically challenging. In this paper, we present a low-cost, vertical membrane, co-culture plate to quantify contact-independent interactions between distinct bacterial populations in co-culture via real-time optical density measurements. These measurements can be used to facilitate the analysis of the interaction between microbes that are physically separated by a semipermeable membrane yet able to exchange diffusible molecules. We show that diffusion across the membrane occurs at a sufficient rate to enable effective interaction between physically separate cultures. Two bacterial species commonly found in the cystic fibrotic lung, Pseudomonas aeruginosa and Burkholderia cenocepacia, were co-cultured to demonstrate how this plate may be implemented to study microbial interactions. We have demonstrated that this novel co-culture device is able to reliably generate real-time measurements of optical density data that can be used to characterize interactions between microbial species.

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Phenotypic heterogeneity of genetically identical cells can generate nonheritable variation in a population. Is this heterogeneity favorable for microbes? In a changing environment, the answer is a definite yes. While scholars have argued that stochastically generated variation precedes genetic changes and thereby facilitate the evolution of complex traits, this idea has remained disputed, not least because of the shortage of experimental studies. We address this long-standing and controversial issue by integrating synthetic biology, laboratory experimental evolution, and genomic analyses. We explicitly tested the mechanisms whereby phenotypic heterogeneity may promote evolvability. Our work demonstrates that phenotypic heterogeneity facilitates evolutionary rescue from deteriorating environmental stress by generating individuals with exceptionally high fitness. Remarkably, elevated phenotypic heterogeneity evolves as a direct response to stress and thereby it promotes evolution of rare combinations of mutations. These results indicate that phenotypic heterogeneity might have an important role in the evolution of key innovations.

Multiple Origins of the Pathogenic Yeast Candida orthopsilosis by Separate Hybridizations between Two Parental Species

Mating between different species produces hybrids that are usually asexual and stuck as diploids, but can also lead to the formation of new species. Here, we report the genome sequences of 27 isolates of the pathogenic yeast Candida orthopsilosis. We find that most isolates are diploid hybrids, products of mating between two unknown parental species (A and B) that are 5% divergent in sequence. Isolates vary greatly in the extent of homogenization between A and B, making their genomes a mosaic of highly heterozygous regions interspersed with homozygous regions. Separate phylogenetic analyses of SNPs in the A- and B-derived portions of the genome produces almost identical trees of the isolates with four major clades. However, the presence of two mutually exclusive genotype combinations at the mating type locus, and recombinant mitochondrial genomes diagnostic of inter-clade mating, shows that the species C. orthopsilosis does not have a single evolutionary origin but was created at least four times by separate interspecies hybridizations between parents A and B. Older hybrids have lost more heterozygosity. We also identify two isolates with homozygous genomes derived exclusively from parent A, which are pure non-hybrid strains. The parallel emergence of the same hybrid species from multiple independent hybridization events is common in plant evolution, but is much less documented in pathogenic fungi.

The genus Candida is one of the leading causes of fungal morbidity in humans. Many pathogenic Candida species are diploid, and do not have have a full sexual cycle. The evolutionary origin of Candida orthopsilosis is unclear. Here, we use whole genome sequencing of 27 C. orthopsilosis isolates from around the world to show that C. orthopsilosis arose from hybridization (or mating) of two distinct parental species. Unusually, the hybridization event did not occur only once; we identify at least four events, and we suggest that hybridization is ongoing. The “species” C. orthopsilosis therefore does not have one single origin. We have identified one of the parental lineages involved, but the other remains elusive. Our results suggest that inter-species hybridization has an evolutionary advantage. However, unlike in plant pathogens, it does not appear to result in increased virulence of C. orthopsilosis.

Growth inhibition of S. cerevisiae, B. subtilis, and E. coli by lignocellulosic and fermentation products

This paper describes the effect of several inhibiting components on three potential hosts for the bio-based production of methyl propionate, namely, wild-type Escherichia coli and Bacillus subtilis, and evolved Saccharomyces cerevisiae IMS0351. The inhibition by the lignocellulose-derived products 5-hydroxymethyl-2-furaldehyde, vanillin, and syringaldehyde and the fermentation products 2-butanol, 2-butanone, methyl propionate, and ethyl acetate has been assessed for these strains in defined medium. Multiple screenings were performed using small-scale cultures in both shake flasks and microtiter plates. Technical drawbacks revealed the limited applicability of the latter in this study. The microbial growth was characterized by means of a lag-time model, and the inhibitory thresholds were determined using product-inhibition models. The lignocellulose-derived products were found to be highly inhibitory, and none of the strains could grow in the presence of 2.0 g L-1 of product. From the fermentation products tested, methyl propionate had the most severe impact resulting in complete inhibition of all the strains when exposed to concentrations in the range of 12-18 g L-1. In general, S. cerevisiae and B. subtilis were comparatively more tolerant than E. coli to all the fermentation products, despite E. coli's lower sensitivity towards vanillin. The results suggest that, overall, the strains investigated have good potential to be engineered and further established as hosts for the bio-based production of methyl esters.

Trends and Challenges in Pesticide Resistance Detection

Pesticide resistance is a crucial factor to be considered when developing strategies for the minimal use of pesticides while maintaining pesticide efficacy. This goal requires monitoring the emergence and development of resistance to pesticides in crop pests. To this end, various methods for resistance diagnosis have been developed for different groups of pests. This review provides an overview of biological, biochemical, and molecular methods that are currently used to detect and quantify pesticide resistance. The agronomic, technical, and economic advantages and drawbacks of each method are considered. Emerging technologies are also described, with their associated challenges and their potential for the detection of resistance mechanisms likely to be selected by current and future plant protection methods.

Quantifying differences in cell line population dynamics using CellPD

BACKGROUND

The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge.

RESULTS

CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training. CellPD requires one input (a spreadsheet) and generates multiple outputs including parameter estimation reports, high-quality plots, and minable XML files. We validated CellPD's estimates by comparing it with a previously published tool (cellGrowth) and with Microsoft Excel's built-in functions. CellPD correctly estimates the net growth rate of cell cultures and is more robust to data sparsity than cellGrowth. When we tested CellPD's usability, biologists (without training in computational modeling) ran CellPD correctly on sample data within 30 min. To demonstrate CellPD's ability to aid in the analysis of high throughput data, we created a synthetic high content screening (HCS) data set, where a simulated cell line is exposed to two hypothetical drug compounds at several doses. CellPD correctly estimates the drug-dependent birth, death, and net growth rates. Furthermore, CellPD's estimates quantify and distinguish between the cytostatic and cytotoxic effects of both drugs-analyses that cannot readily be performed with spreadsheet software such as Microsoft Excel or without specialized computational expertise and programming environments.

CONCLUSIONS

CellPD is an open source tool that can be used by scientists (with or without a background in computational or mathematical modeling) to quantify key aspects of cell phenotypes (such as cell cycle and death parameters). Early applications of CellPD may include drug effect quantification, functional analysis of gene knockout experiments, data quality control, minable big data generation, and integration of biological data with computational models.

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

BACKGROUND

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology.

RESULTS

To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis.

CONCLUSIONS

PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

Large-Scale Survey of Intraspecific Fitness and Cell Morphology Variation in a Protoploid Yeast Species

It is now clear that the exploration of the genetic and phenotypic diversity of nonmodel species greatly improves our knowledge in biology. In this context, we recently launched a population genomic analysis of the protoploid yeast Lachancea kluyveri (formerly Saccharomyces kluyveri), highlighting a broad genetic diversity (π = 17 × 10⁻³) compared to the yeast model organism, S. cerevisiae (π = 4 × 10⁻³). Here, we sought to generate a comprehensive view of the phenotypic diversity in this species. In total, 27 natural L. kluyveri isolates were subjected to trait profiling using the following independent approaches: (i) analyzing growth in 55 growth conditions and (ii) investigating 501 morphological changes at the cellular level. Despite higher genetic diversity, the fitness variance observed in L. kluyveri is lower than that in S. cerevisiae. However, morphological features show an opposite trend. In addition, there is no correlation between the origins (ecological or geographical) of the isolate and the phenotypic patterns, demonstrating that trait variation follows neither population history nor source environment in L. kluyveri. Finally, pairwise comparisons between growth rate correlation and genetic diversity show a clear decrease in phenotypic variability linked to genome variation increase, whereas no such a trend was identified for morphological changes. Overall, this study reveals for the first time the phenotypic diversity of a distantly related species to S. cerevisiae. Given its genetic properties, L. kluyveri might be useful in further linkage mapping analyses of complex traits, and could ultimately provide a better insight into the evolution of the genotype–phenotype relationship across yeast species.

Water-Transfer Slows Aging in Saccharomyces cerevisiae

Transferring Saccharomyces cerevisiae cells to water is known to extend their lifespan. However, it is unclear whether this lifespan extension is due to slowing the aging process or merely keeping old yeast alive. Here we show that in water-transferred yeast, the toxicity of polyQ proteins is decreased and the aging biomarker 47Q aggregates at a reduced rate and to a lesser extent. These beneficial effects of water-transfer could not be reproduced by diluting the growth medium and depended on de novo protein synthesis and proteasomes levels. Interestingly, we found that upon water-transfer 27 proteins are downregulated, 4 proteins are upregulated and 81 proteins change their intracellular localization, hinting at an active genetic program enabling the lifespan extension. Furthermore, the aging-related deterioration of the heat shock response (HSR), the unfolded protein response (UPR) and the endoplasmic reticulum-associated protein degradation (ERAD), was largely prevented in water-transferred yeast, as the activities of these proteostatic network pathways remained nearly as robust as in young yeast. The characteristics of young yeast that are actively maintained upon water-transfer indicate that the extended lifespan is the outcome of slowing the rate of the aging process.

Comprehensive survey of condition-specific reproductive isolation reveals genetic incompatibility in yeast

Genetic variation within a species could cause negative epistasis leading to reduced hybrid fitness and post-zygotic reproductive isolation. Recent studies in yeasts revealed chromosomal rearrangements as a major mechanism dampening intraspecific hybrid fertility on rich media. Here, by analysing a large number of Saccharomyces cerevisiae crosses on different culture conditions, we show environment-specific genetic incompatibility segregates readily within yeast and contributes to reproductive isolation. Over 24% (117 out of 481) of cases tested show potential epistasis, among which 6.7% (32 out of 481) are severe, with at least 20% of progeny loss on tested conditions. Based on the segregation patterns, we further characterize a two-locus Dobzhansky–Müller incompatibility case leading to offspring respiratory deficiency caused by nonsense mutation in a nuclear-encoding mitochondrial gene and tRNA suppressor. We provide evidence that this precise configuration could be adaptive in fluctuating environments, highlighting the role of ecological selection in the onset of genetic incompatibility and reproductive isolation in yeast.

Chromosomal rearrangements may hamper intraspecific hybrid fertility. Here the authors show that environment-specific genetic incompatibility segregates readily within intermating populations and leads to intrinsic reproductive isolation within a yeast species.