Yeast Genetics

The meiotic developmental pathway in yeast enables both differentiation of vegetative cells into haploid spores that ensure long-term survival, and recombination of the parental DNA to create genetic diversity. Despite the importance of proper metabolic regulation for the supply of building blocks and energy, little is known about the reprogramming of central metabolic pathways in meiotically differentiating cells during passage through successive developmental stages. Metabolic regulation during meiotic differentiation in budding yeast was analysed by integrating information on genome-wide transcriptional activity, 26 enzymatic activities in the central metabolism, the dynamics of 67 metabolites, and a metabolic flux analysis at mid-stage meiosis. Analyses of mutants arresting sporulation at defined stages demonstrated that metabolic reprogramming is tightly controlled by the progression through the developmental pathway. The correlation between transcript levels and enzymatic acti...

In the last 40 years, several scientific and technological advances in microbiology of the fermentation have greatly contributed to evolution of the ethanol industry in Brazil. These contributions have increased our view and comprehension about fermentations in the first and, more recently, second-generation ethanol. Nowadays, new technologies are available to produce ethanol from sugarcane, corn and other feedstocks, reducing the off-season period. Better control of fermentation conditions can reduce the stress conditions for yeast cells and contamination by bacteria and wild yeasts. There are great research opportunities in production processes of the first-generation ethanol regarding high-value added products, cost reduction and selection of new industrial yeast strains that are more robust and customized for each distillery. New technologies have also focused on the reduction of vinasse volumes by increasing the ethanol concentrations in wine during fermentation. Moreover, conversion of sugarcane biomass into fermentable sugars for second-generation ethanol production is a promising alternative to meet future demands of biofuel production in the country. However, building a bridge between science and industry requires investments in research, development and transfer of new technologies to the industry as well as specialized personnel to deal with new technological challenges.

Industrial processes of ethanol production in Brazil are characterized by large-volume fermenters, short fermentation times, recycling of yeast cells and stressful conditions that can affect yeast cell viability and fermentation yields of distilleries. Moreover, there are several distinct characteristics from one distillery to another, making each one unique.  Industrial fermentation processes are subject to contamination by wild yeasts (Saccharomyces and non-Saccharomyces species) due to difficulties to sterilize large volumes of sugar cane juice, molasses and water. Wild yeast strains may compete with selected yeast strains causing serious operational difficulties destabilizing the process and affecting fermentation yield. Besides contamination by wild yeasts, bacteria compete for fermentable sugar and produce  compounds that are toxic to yeast cells. However, tailored yeast strains are more robust, resistant to stressful conditions and compete better with contaminants than traditional strains do. These tailored strains have shown a higher rate of dominance and persistence in industrial fermentations in comparison with traditional strains and baker yeast. As a result, the fermentation process remains more stable during the ethanol production season. In 2015, 13 distilleries started using tailored yeast strains in their fermentation processes. Here, we present the results from the last eight years of monitoring and use of tailored yeast strains for the Brazilian ethanol industry: a process-driven selection.

In Saccharomyces cerevisiae, the spindle position checkpoint ensures that cells do not exit mitosis until the mitotic spindle moves into the mother/bud neck and thus guarantees that each cell receives one nucleus [1-6]. Mitotic exit is controlled by the small G protein Tem1p. Tem1p and its GTPase activating protein (GAP) Bub2p/Bfa1p are located on the daughter-bound spindle pole body. The GEF Lte1p is located in the bud. This segregation helps keep Tem1p in its inactive GDP state until the spindle enters the neck. However, the checkpoint functions without Lte1p and apparently senses cytoplasmic microtubules in the mother/bud neck [7-9]. To investigate this mechanism, we examined mutants defective for septins, which compose a ring at the neck [10]. We found that the septin mutants sep7Delta and cdc10Delta are defective in the checkpoint. When movement of the spindle into the neck was delayed, mitotic exit occurred, inappropriately leaving both nuclei in the mother. In sep7Delta and cdc10Delta mutants, Lte1p is mislocalized to the mother. In sep7Delta, but not cdc10Delta, mutants, inappropriate mitotic exit depends on Lte1p. These results suggest that septins serve as a diffusion barrier for Lte1p, and that Cdc10p is needed for the septin ring to serve as a scaffold for a putative microtubule sensor.

More than 30 years ago Dan Koshland published an inspirational essay presenting the bacterium as a model neuron (Koshland, Trends Neurosci 6:133–137, 1983). In the article he argued that there are several similarities between neurons and bacterial cells in “how signals are processed within a cell or how this processing machinery can be modified to produce plasticity”. He then explored the bacterial chemosensory system to emphasize its attributes that are analogous to information processing in neurons. In this review, we wish to expand Koshland’s original idea by adding the yeast cell to the list of useful models of a neuron. The fact that yeasts and neurons are specialized versions of the eukaryotic cell sharing all principal components sets the stage for a grand evolutionary tinkering where these components are employed in qualitatively different tasks, but following analogous molecular logic. By way of example, we argue that evolutionarily
conserved key components involved in polarization processes
(from budding or mating in Saccharomyces cervisiae to neurite outgrowth or spinogenesis in neurons) are shared between yeast and neurons. This orthologous conservation of modules makes S. cervisiae an excellent model organism to investigate neurobiological questions. We substantiate this claim by providing examples of yeast models used for studying neurological diseases.

Volatile phenols are aromatic compounds produced by some yeasts of the genus Brettanomyces as defense against the toxicity of hydroxycinnamic acids (p-coumaric acid, ferulic acid and caffeic acid). The origin of these compounds in winemaking involves the sequential action of two enzymes: coumarate decar-boxylase and vinylphenol reductase.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

The Saccharomyces cerevisiae BNI1 gene product (Bni1p) is a member of the formin family of proteins, which participate in cell polarization, cytokinesis, and vertebrate limb formation. During mating pheromone response, bni1 mutants showed defects both in polarized morphogenesis and in reorganization of the underlying actin cytoskeleton. In two-hybrid experiments, Bni1p formed complexes with the activated form of the Rho-related guanosine triphosphatase Cdc42p, with actin, and with two actin-associated proteins, profilin and Bud6p (Aip3p). Both Bni1p and Bud6p (like Cdc42p and actin) localized to the tips of mating projections. Bni1p may function as a Cdc42p target that links the pheromone response pathway to the actin cytoskeleton.

The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1-S transition (START) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle (Chen et al. 2004. Mol Biol Cell 15:3841). The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1-S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains, and compared their observed phenotypes to the model's simulations. The experimental results reported here led to further changes of the mo...

Imperatively Hidden Objects/Factors (IHO/F) refers to relevant items, which we cannot perceive and measure yet. Nevertheless, they have an impact on our predicted outcome. We have created model that allows for the integration and discovery for the still unknown. If researchers would take the time to understand it, they'd design their experimental studies in a much different way because their scientific objective would shift toward feature discovery to enhance feature selection to make even better predictions with supervised machine learning algorithms.

We previously isolated 18 Saccharomyces cerevisiae strains from coconut toddy in Sri Lanka. All the 18 strains (SLY-1 to SLY-18) could grow aerobically up to 18 mmol L−1 vanillin in yeast extract–peptone–dextrose (YPD) medium, and the SLY-10 strain showed the highest vanillin tolerance (up to 21 mmol L−1). Among these 18 strains, even in the presence of 24 mmol L−1 vanillin, five strains (SLY-3, SLY-4, SLY-8, SLY-9 and SLY-10) produced alcohol at 72 h in the range of 11.4–33.1 g L−1 and the SLY-10 strain showed highest alcohol production. The five strains showed the conversion of vanillin to vanillyl alcohol. They also tolerated other strong inhibitors: 4-hydroxybenzoic acid (24 mmol L−1), furfural (30 mmol L−1), 5-hydroxymethyl-2-furaldehyde (5-HMF, 36 mmol L−1) and acetic acid (75 mmol L−1). All these five strains showed no growth and no alcohol production when cultured in the medium with inhibitor mixture of 2.7 mmol L−1 vanillin, 30 mmol L−1 furfural, 30 mmol L−1 5-HMF, 75 mmol L−1 acetic acid and 75 mmol L−1 formic acid but showed significant growth and alcohol production of 16.2–32.7 g L−1 with inhibitor mixture of 30% concentration (0.81 mmol L−1 vanillin, 9 mmol L−1 furfural, 9 mmol L−1 5-HMF, 22.5 mmol L−1 acetic acid and 22.5 mmol L−1 formic acid). The four strains (SLY-3, SLY-4, SLY-8 and SLY-9) were shown to tolerate the inhibitor mixture to a similar extent and better than SLY-10, while SLY-10 was the best to vanillin alone.

Please click here http://singledrug.com/media/Temporal_Analysis_of_Aging_description.pdf to see it.

I plan to add the following:

I found Osh6 at a very high rank of lifespan extending genes.  That is why I want to take all the genes that are part of the same molecular function as Osh6 and run an expression similarity analysis on SPELL (Serial Pattern of Expression Levels Locator) (see spell.yeastgenome.org/). 

I hope to find genes of unknown functions that could be involved in the same molecular function.  If I find this, I'll validate with my time series plots and TFBS similarity analysis.  That way I hope to predict the function of an unknown gene in lipid metabolism.  I will also try the opposite approach, i.e. I'll take genes of unknown function and look for a group of similarly expressed genes, which belong to the same GO term.

I added 19 pages to the second chapter.  I was able to further improve the accuracy of my yeast lifespan predicting machine learning algorithm by adding more features about lipids.  My last draft had an average prediction error of +/-5 replications.  Now it is less than +/-3 replication for the 50th percentile of the replicative lifespan.  To my knowledge nobody has published any research on about using machine learning and feature selection to predict the replicative age.

If you have any more ideas how I could make my dissertation look better, I'd be very thankful for any new feasible ideas and inspirations.  I have mainly been using Python and R for all my analysis.

I found that ribosomal RNA and proteins are most closely follow the cell cycle and that if knocked out the yeast's lifespan can be extended significantly.  I want to use the different cyclical periodically recurring and highly fluctuating gene expression oscillation pattern to discover the functions of genes we have not yet discovered.  I am interested in this because a friend of mine showed me a cancer dataset that looked very similar.  Since cancer is very similar to the yeast in that it keeps constantly dividing and hence its age can be expressed in replication we could use the same guilt-by-association approach to infer the functions or at least the way their expression is controlled by TFs from genes with similar temporal expression patterns of genes with already known functions.

When analyzing the time series plots of the 62 yeast chaperons I discovered that their expression plot trajectories can easily be clustered into 7 expression patterns, which are consisted with the 7 classes of yeast chaperons.  If I had only taken the average centroid of all 62 chaperons I would have been misled because using this approach could not be applied to potentially discover more new currently still unknown chaperons because no unknown chaperon would have time series trajectories, which would highly correlate with the average chaperon expression pattern of all 7 classes since their in-group expression pattern is very homogeneous but between the 7 different chaperon classes the expression pattern is very different.

I intended to elucidate the different ways by which we age by a very similar clustering algorithm, which I have developed based on time series curves over three subsequent yeast cell cycle consisting of 36 mRNA measures each about 25 minutes apart; thus, allowing to measure very steep mRNA expression fluctuation periods by manifold within the only 90 minutes of the cell cycle because even very brief but rapid expression changes that last not much longer than 1/12th of a cell cycle, of which I found surprisingly many.

Ribosomal DNA (rDNA) recombination in budding yeast is regulated by
multiple converging processes, including posttranslational modifications such as
SUMOylation. In this study, we report that the absence of a SUMO E3 ligase, Siz2, results
in increased unequal rDNA exchange. We show that Siz2 is enriched at the replication
fork barrier (RFB) in the rDNA and also controls the homeostasis of Tof2 protein.
siz2 resulted in increased accumulation of total Tof2 in the cell and a
consequent increase in the enrichment of Tof2 at the rDNA. Overproducing Tof2 ectopically
or conditional overexpression of Tof2 also resulted in higher levels of rDNA
recombination, suggesting a direct role for Tof2. Additionally, our chromatin immunoprecipitation
(ChIP) data indicate that the accumulation of Tof2 in a siz2 mutant
resulted in an enhanced association of Fob1, an RFB binding protein at the rDNA at
the RFB. This increased Fob1 association at the RFB may have resulted in the elevated
rDNA recombination. Our study thus demonstrates that the Tof2 levels modulate
recombination at the rDNA.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes.
v

Over the past 15 years, the seismic shifts caused by the convergence of biomolecular, chemical, physical, mathematical and computational sciences alongside cutting-edge developments in information technology and engineering have erupted into a new field of scientific endeavor dubbed Synthetic Biology. Recent rapid advances in high-throughput DNA sequencing and DNA synthesis techniques are enabling the design and construction of new biological parts (genes), devices (gene networks) and modules (biosynthetic pathways), and the redesign of biological systems (cells and organisms) for useful purposes. In 2014, the budding yeast Saccharomyces cerevisiae became the first eukaryotic cell to be equipped with a fully functional synthetic chromosome. This was achieved following the synthesis of the first viral (poliovirus in 2002 and bacteriophage Phi-X174 in 2003) and bacterial (Mycoplasma genitalium in 2008 and Mycoplasma mycoides in 2010) genomes, and less than two decades after revealing the full genome sequence of a laboratory (S288c in 1996) and wine (AWRI1631 in 2008) yeast strain. A large international project – the Synthetic Yeast Genome (Sc2.0) Project – is now underway to synthesize all 16 chromosomes (12 Mb carrying 6,000 genes) of the sequenced S288c laboratory strain by 2018. If successful, S. cerevisiae will become the first eukaryote to cross the horizon of in silico design of complex cells through de novo synthesis, reshuffling and editing of genomes. In the meantime, yeast are being used as cell factories for the semi-synthetic production of high-value compounds, such as the potent antimalarial artemisinin, and food ingredients, such as resveratrol, vanillin, stevia, nootkatone and saffron. As a continuum of previously genetically-engineered industrially-important yeast strains, precision genome engineering is bound to also impact the study and development of wine yeast strains supercharged with synthetic DNA. The first taste of what the future holds is the de novo production of the raspberry ketone aroma compound, 4-[4-hydroxyphenyl]butan-2-one, in a wine yeast strain (AWRI1631), which was recently achieved via metabolic pathway engineering and synthetic enzyme fusion. A peek over the horizon is revealing that the future of ‘Wine Yeast 2.0’ is already here. Therefore, this article seeks to help prepare the wine industry – an industry rich in history and tradition on the one hand, and innovation on the other – for the inevitable intersection of the ancient art practiced by winemakers and inventive science of pioneering ‘synthetic genomicists’. It would be prudent to proactively engage all stakeholders – researchers, industry practitioners, policymakers, regulators, commentators and consumers – in a meaningful dialogue about the potential challenges and opportunities emanating from Synthetic Biology. To capitalize on the new vistas of synthetic yeast genomics, this paper presents wine yeast research in a fresh context, raises important questions and proposes new directions.

Pre-mRNA splicing, the removal of noncoding intron sequences from the pre-mRNA, is a
critical reaction in eukaryotic gene expression. Pre-mRNA splicing is carried out by a remarkable macromolecular machine, the spliceosome, which undergoes dynamic rearrangements of its RNA and protein components to assemble its catalytic center. While significant progress has been made in describing the ‘‘moving parts’’ of this machine, the mechanisms by which spliceosomal proteins mediate the ordered rearrangements within the spliceosome remain elusive. Here we explore recent evidence from proteomics studies revealing extensive post-translational modification
of splicing factors. While the functional significance of most of these modifications remains
to be characterized, we describe recent studies in which the roles of specific post-translational modifications of splicing factors have been characterized.

In Saccharomyces cerevisiae, the spindle position checkpoint ensures that cells do not exit mitosis until the mitotic spindle moves into the mother/bud neck and thus guarantees that each cell receives one nucleus [1-6]. Mitotic exit is controlled by the small G protein Tem1p. Tem1p and its GTPase activating protein (GAP) Bub2p/Bfa1p are located on the daughter-bound spindle pole body. The GEF Lte1p is located in the bud. This segregation helps keep Tem1p in its inactive GDP state until the spindle enters the neck. However, the checkpoint functions without Lte1p and apparently senses cytoplasmic microtubules in the mother/bud neck [7-9]. To investigate this mechanism, we examined mutants defective for septins, which compose a ring at the neck [10]. We found that the septin mutants sep7Delta and cdc10Delta are defective in the checkpoint. When movement of the spindle into the neck was delayed, mitotic exit occurred, inappropriately leaving both nuclei in the mother. In sep7Delta and c...

Both the delivery of secretory vesicles and asymmetric distribution of mRNA to the bud are dependent upon the actin cytoskeleton in yeast. Here we examined whether components of the exocytic apparatus play a role in mRNA transport. By screening secretion mutants in situ and in vivo, we found that all had an altered pattern of ASH1 mRNA localization. These included alleles of CDC42 and RHO3 (cdc42-6 and rho3-V51) thought to regulate specifically the fusion of secretory vesicles but were found to affect strongly the cytoskeleton as well. Most interestingly, mutations in late secretion-related genes not directly involved in actin regulation also showed substantial alterations in ASH1 mRNA distribution. These included mutations in genes encoding components of the exocyst (SEC10 and SEC15), SNARE regulatory proteins (SEC1, SEC4, and SRO7), SNAREs (SEC9 and SSO1/2), and proteins involved in Golgi export (PIK1 and YPT31/32). Importantly, prominent defects in the actin cytoskeleton were observed in all of these strains, thus implicating a known causal relationship between the deregulation of actin and the inhibition of mRNA transport. Our novel observations suggest that vesicular transport regulates the actin cytoskeleton in yeast (and not just vice versa) leading to subsequent defects in mRNA transport and localization.

Heavy metals and metalloids such as cadmium [Cd(II)] and arsenic [As(III)] are widespread environmental toxicants responsible for multiple adverse health effects in humans. However, the molecular mechanisms underlying metal-induced cytotoxicity and carcinogenesis, as well as the detoxification and tolerance pathways, are incompletely understood. Here, we use global fitness profiling by barcode se-quencing to quantitatively survey the Schizosaccharomyces pombe haploid deletome for genes that confer tolerance of cadmium or arsenic. We identified 106 genes required for cadmium resistance and 110 genes required for arsenic resistance, with a highly significant overlap of 36 genes. A subset of these 36 genes account for almost all proteins required for incorporating sulfur into the cysteine-rich glutathione and phyto-chelatin peptides that chelate cadmium and arsenic. A requirement for Mms19 is explained by its role in directing iron–sulfur cluster assembly into sulfite reductase as opposed to promoting DNA repair, as DNA damage response genes were not enriched among those required for cadmium or arsenic tolerance. Ubiqui-none, siroheme, and pyridoxal 59-phosphate biosynthesis were also identified as critical for Cd/As tolerance. Arsenic-specific pathways included prefoldin-mediated assembly of unfolded proteins and protein targeting to the peroxisome, whereas cadmium-specific pathways included plasma membrane and vacuolar transporters , as well as Spt–Ada–Gcn5-acetyltransferase (SAGA) transcriptional coactivator that controls expression of key genes required for cadmium tolerance. Notable differences are apparent with corresponding screens in the budding yeast Saccharomyces cerevisiae, underscoring the utility of analyzing toxic metal defense mechanisms in both organisms.

During mitosis in budding yeast the nucleus first moves to the mother-bud neck and then into the neck. Both movements depend on interactions of cytoplasmic microtubules with the cortex. We investigated the mechanism of these movements in living cells using video analysis of GFP-labeled microtubules in wild-type cells and in EB1 and Arp1 mutants, which are defective in the first and second steps, respectively. We found that nuclear movement to the neck is largely mediated by the capture of microtubule ends at one cortical region at the incipient bud site or bud tip, followed by microtubule depolymerization. Efficient microtubule interactions with the capture site require that microtubules be sufficiently long and dynamic to probe the cortex. In contrast, spindle movement into the neck is mediated by microtubule sliding along the bud cortex, which requires dynein and dynactin. Free microtubules can also slide along the cortex of both bud and mother. Capture/shrinkage of microtubule ends also contributes to nuclear movement into the neck and can serve as a backup mechanism to move the nucleus into the neck when microtubule sliding is impaired. Conversely, microtubule sliding can move the nucleus into the neck even when capture/shrinkage is impaired.