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Yeast in Space

  • Timothy G. HammondEmail author
  • Holly H BirdsallEmail author
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Abstract

Humans and yeast have traveled together through a long history of terrestrial explorations and will continue to do so as mankind explores space. Candida species of yeast accompany us as part of our microbiome and humans will find ways to carry Saccharomyces species of yeast with them on their space travels in order to make bread and beer/wine. In addition to its health and gastronomic roles, yeasts have also proven to be highly useful tools for exploring the biologic impacts of the space environment. An international consortium has produced a library of clones in which each of the ~6,200 known open reading frames of the yeast Saccharomyces cerevisiae has been systematically deleted. For fitness profiling, this library of clones is exposed to a stressor such as galactic cosmic radiation, changes in mass transport, or shear stress and allowed to proliferate for up to 100 generations. By systematically identifying those genes and gene pathways that are required to resistant exposure to spaceflight stimuli, the mediating and buffering gene pathways can be delineated, which can be augmented experimentally to identify countermeasures. At the end of the exposure, the relative abundance of each clone is enumerated by next-generation sequencing. Those clones carrying a deletion of a gene needed for survival in the presence of spaceflight stimuli have reduced growth or fitness. Secondarily, these techniques can be applied to assess drug-induced changes during spaceflight. Broad stimulus libraries, including radiation types and radiomimetics, collected and assessed at 20+ generations are available for comparison.

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Yeast as a Model Organism for Biologic Studies

Yeast is a model organisms for experimentation. Yeasts share a high degree of genetic and biochemical homology with human cells, making it likely that the pathways identified will have clinical relevance (Botstein et al. 1997; Kellis et al. 2003).

Yeasts are particularly well suited for molecular biologic studies (Kumar and Snyder 2001). The entire yeast genome has been sequenced and is available for study (Botstein et al. 1997; Sherlock 2000) at http://genome-www.stanford.edu/Saccharomyces. Yeast can easily be genetically manipulated to allow the identification of cellular response pathways with high fidelity of mechanistic molecular pathways (Smith et al. 2010, 2012; Roemer et al. 2012; Hillenmeyer et al. 2010; Blackman et al. 2012; Ulitsky et al. 2010; Kumar and Snyder 2001; Lee et al. 2014). The yeast genome has extraordinarily few introns and its upstream promoter structures are often relatively simple, compared to metazoan counterparts. This implies that, unlike mammalian systems, once a cluster of yeast genes that changes with the same expression kinetics is identified, bioinformatic analysis of the upstream regions can identify pathways responsible for a specific regulatory response.

Yeasts are a model for aging both in terms of replicative life span defined as the number of daughter cells that can be produced by a mother cell and as chronological life, defined as the length of time that a yeast cell can survive in a nondividing state (Kaeberlein et al. 2007).

In addition to single-cell growth, yeast grows into complex colonies. Yeast cells form giant multicellular colonies with characteristic organized morphologies (Cap et al. 2009, 2012a, b). The metabolic differentiation and regulation within giant yeast colonies parallels that of tumors (Cap et al. 2012a) which makes them a useful model for the study of tumor growth.

Yeast may also provide information on the adaptation of life to outer space. Yeasts are part of a family of fungi spanning a billion years of evolutionary life and adaptation (Scannell et al. 2007), and several yeasts are considered to be extremophiles (Sinha et al. 2017). Numerous family members have a well-characterized history of evolution across billions of years that may offer insights into how organisms adapt to extreme circumstances (Dujon 2010).

Yeast as an Adaptable Organism for Experiments in Spaceflight

Saccharomyces cerevisiae (S. cerevisiae) has been a favored organism for flight microgravity experiments since 1962 (reviewed in Dickson 1991). Several characteristics make this organism particularly suitable for experiments in spaceflight. S. cerevisiae remains fully viable in spaceflight and responds to the microgravity environment with changes in metabolism (Berry and Volz 1979) and phenotype (Walther et al. 1996).

Yeasts have modest culture requirements. They will grow at ambient temperatures and in oxygen/carbon dioxide atmospheres that are also compatible for humans. Yeast can be cultured as free-floating organisms in liquid or as solid colonies on a nutrient source such as agar. Robust generation of carbon dioxide that is so sought after for leavening bread can be enhanced or minimized by manipulation of food sources.

Yeast poses a minimal risk to humans and can be handled with minimal requirements for containment. Candida albicans is a human pathogen, which allows study of a relevant disease-causing organism. Yet virulence is low so that it can be handled without extreme precautions.

Yeasts double in 1–2 h, depending on the media and temperature. This means that multiple generations can be exposed to a stressor such as galactic cosmic radiation over a matter of days compared to much slower replication times for creatures such as worms, fish, or mammals.

Yeast allow for simple experimental design. Yeast can be held in stasis by chilling to 4 ± 2 °C for weeks to months, which facilitates accommodation to launch slips and travel to the space environment. Warming to restart growth and initiate the experiment can activate the yeast. Alternatively, yeast can be transported in a dried state and rehydrated to initiate cell growth. Depending on the needs of the experimental readout, viable yeast can be maintained for weeks to months by re-chilling to 4 ± 2 °C for weeks to months before analysis (Nislow et al. 2015).

Effects of Galactic Cosmic Radiation, Shear Stress, and Changes in Mass Transport on Yeast in Space

Studies of yeast in space have included growth rates, cell function, morphology including changes observed by electron microscopy, and genomic expression (Najrana and Sanchez-Esteban 2016). Space causes yeast to increase their uptake of phosphate (Berry and Volz 1979). Spaceflight also induces a change in yeast redox state (Bradamante et al. 2010; Hammond et al. 2017). Spaceflight places organisms in a microgravity environment with ensuing changes in shear stress and mass transport. The low-shear environment alters yeast phenotype including polarization. When grown in space, yeasts show an increase in the number and distribution of their bud scars, which become more random (Walther et al. 1996; Purevdorj-Gage et al. 2006; Sheehan et al. 2007). The cells also tend to grow in clumps (Purevdorj-Gage et al. 2006). Candida are more prone to form biofilms in space (Searles et al. 2011) and assume a filamentous form (Altenburg et al. 2008).

Transcriptome analysis identifies genes that are upregulated or downregulated by external stressors. This approach has also shown that microgravity changes yeast polarization and cell detachments. Sheehan et al. found that 1,372 yeast genes (36%) were significantly altered by exposure to low-shear modeled microgravity, from which 26% of them were environmental stress response genes (Sheehan et al. 2007). Microgravity alters the expression of genes involved in bud pattern selection (BUD5, RAX1, RAX2, and BUD25), cell separation (DSE1, DSE2, and EGT2), and protein phosphatases (SDP1 and PTP2) that regulate the MAPK signaling pathway and affect cell shape and polarity (Sheehan et al. 2007).

Hammond et al. examined the transcriptional regulation of stress response element (STRE) genes in space using Saccharomyces cerevisiae strains bearing green fluorescent protein-tagged (GFP-tagged) reporters (Coleman et al. 2008). Spaceflight reduced the expression of SSA4 and YIL052c. YIL052c expression was Sfp1-dependent in both terrestrial and spaceflight conditions, whereas SSA4 was Sfp1-dependent in spaceflight but Msn4-dependent in terrestrial cultures. Spaceflight had no effect on a third stress response-dependent gene YST2, which is Sfp1/Rap1 dependent with stress on the ground (Coleman et al. 2008).

Radiation, Changes in Mass Transport, and Shear Stress Effects on Yeast in Space

How Best to Measure the Biologic Effects of Spaceflight Condition: Chronic Galactic Cosmic Ray Exposure, Changes in Mass Transport, and Low-Shear Stress Status?

Because radiation, shear stress, and changes in mass transport tend to change redox status and damage DNA in addition to proteins, the ideal biologic reporter would be cells that have divided in the presence of these stimuli. The model needs to be unbiased and genome-wide. Several options for analysis are available. One classic approach for studying biologic responses is transcriptome analysis, which measures changes in RNA levels to identify genes that were upregulated or downregulated by these stimuli. However, transcriptome analysis has been found to be less than optimal for the study of radiation damage. The cellular response to sheer stress, changes in mass transport, and radiation involves a large number of genes, only a few of which are directly involved in DNA repair and/or gene expression responses (Jelinsky et al. 2000; Gasch et al. 2001; Birrell et al. 2002; Berry et al. 2011). Radiation and other stimuli damage many other cellular macromolecules such as proteins and lipids, which, in turn, induces complex transcriptional responses including in genes involved in protein folding and degradation (Gasch et al. 2000; Jelinsky et al. 2000). Irradiation, in particular, also produces a general stress response that is independent of DNA damage (Gasch et al. 2000). Because transcriptome analysis only identifies which genes have changed, but not how those genes interacted to provide protection against galactic cosmic rays, shear stress, or changes in mass transport, it has poor efficacy as a guide toward the development of radioprotective and other space-based interventions. Fitness profiling or functional genomics is a much more effective approach to identifying the genes and pathways needed for survival during exposure to space-based stimuli. In brief, this technique identifies which genes and which pathways are required to allow an organism to thrive in the presence of a stressor such as shear stress changes in mass transport or galactic cosmic rays. The readout is biologic responses – growth and survival.

Fitness profiling uses a library of yeast, in which one gene has been systematically deleted in each strain of a genome-wide pool, and measures how that impacts the ability of the yeast to survive (i.e., its fitness) in the presence of a stressor. An international consortium has systematically deleted each of the ≈6,200 known open reading frames of the yeast Saccharomyces cerevisiae (Wach et al. 1994; Baudin et al. 1993; Winzeler et al. 1999). The library of clones is exposed to a stressor such as galactic cosmic rays and allowed to proliferate through 20+ generations. At the end of the exposure, the relative abundance of each clone is enumerated. Those clones carrying a deletion of a gene needed for survival in the presence of galactic cosmic rays fail to grow (Shoemaker et al. 1996; Cheung-Ong et al. 2012; Chan et al. 2010; Blackman et al. 2012; Smith et al. 2012; Giaever et al. 2004; Roemer et al. 2012; Pierce et al. 2006, 2007; Hoon et al. 2008; Berry et al. 2011). The enumeration of each clone is facilitated by a unique DNA barcode that was inserted when the clone was engineered. The yeast library contains ~6200 clones, each with its own barcode. By inserting different barcodes, all four yeast deletion series can be studied simultaneously in a single-tube experiment. The four series are (1) heterozygous deletion pool to identify drug targets, (2) decreased abundance to mRNA perturbation (DAmP) pool to further reduce gene product beyond heterozygous deletion pool to increase sensitivity to drug targets, (3) homozygous deletion pool to identify buffering pathways, and (4) an overexpression pool providing oppositely directed confirmatory data. In the presence of a stressor that challenges the pathway in question, growth rates of affected clones are reduced. Each clone has its own barcode, and the entire series can be grown together in a single tube/vessel in the presence of galactic cosmic rays, the DNA extracted, and the numbers of each clone quantified (Smith et al. 2010, 2012; Shoemaker et al. 1996; Cheung-Ong et al. 2012; Chan et al. 2010; Blackman et al. 2012; Giaever et al. 2004; Roemer et al. 2012; Pierce et al. 2006, 2007; Hoon et al. 2008; Berry et al. 2011). The unique barcodes are amplified by PCR and their abundance quantified by next-generation sequencing. The barcode sequence counts reflect the number of genomes, which reflects the abundance of each uniquely marked mutant at the end of each experiment. A ranked list of all genes in the genome is generated for each experiment and then compared using gene set enrichment analysis or GSEA to define those pathways and processes that are affected by growth in the presence of changes in shear stress, mass transport, or cosmic radiation.

In a recent study, summarizing a decade of research, 3250 small molecules, radiation types, and radiomimetics were profiled in a systematic and unbiased manner (Lee et al. 2014). This analysis identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and definable and can be described by a network of 45 major functional genomic signatures (Lee et al. 2014). These results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes and a benchmark to assess the effects of galactic cosmic rays. Notably, we have used this assay to describe the cellular effects and essential buffering pathways required to survive DNA damage and identified those drugs that, when combined with DNA damage, induce a secondary genotoxicity (see (Cheung-Ong et al. 2013) and references therein). This database of targets, target pathways, and cellular responses is an essential benchmark against which new data can be compared. By comparing the results obtained in new experiments to the database of over 10,000 genome-wide profiles, shared cellular responses can be identified using the approach known as “guilt-by association” (Havugimana et al. 2017).

Are Observations in Yeast Applicable to Humans and Human Health?

Yeasts share a high degree of genetic and biochemical homology with human cells, especially in the case of essential genes, and attested to by the Nobel Prize-winning work on the cell cycle, secretion, and autophagy (Zimmermann et al. 2016). Thus, it is likely that the identified pathways will have clinical relevance for human biology (Botstein et al. 1997). Indeed, work from other laboratories (Wong et al. 2016; Hamza et al. 2015) demonstrates that the loss-of-function mutations in the yeast deletion series are conserved reporters for the requirement for a particular human gene in a particular environment. During the life of every cell in our body, the genome is exposed to diverse damage-inducing insults including UV light, environmental carcinogens, and the process of metabolism and catabolism. Furthermore, during each cell division, thousands of errors accumulate during DNA replication. Nearly all of these errors are repaired in healthy cells, but compromises in the DNA repair pathways, by loss-of-function mutations as well as from chronic exposure, can lead to cell death, cancer, and other diseases. Because this genome-wide assay can identify all genes required for survival in these stress conditions, combined with the fact that over 70% of the DNA repair genes are conserved between yeast and human, it allows one to uncover DNA variants that might predispose or protect individuals from such stress.

One major environmental stressor associated with space is radiation, particularly galactic cosmic radiation in deep space. Radiation damage is a major factor limiting space travel and is consequently a high priority topic for space biomedical researchers. Earlier studies on the effects of space radiation on yeast failed to find any change in point mutation rates, DNA replication and/or repair, heritable damage, or colony morphology (Todd 2004; Kiefer and Pross 1999; Fukuda et al. 2000; Takahashi et al. 2001). However, the available endpoint analysis tools limited those studies.

Transcriptome analysis also appears to be less than optimal method for studying the nature of DNA lesions in cells or to the genes involved in their repair. Study of the transcriptional response of Saccharomyces cerevisiae to four DNA-damaging agents, ionizing radiation, UV radiation, and exposure to cisplatin or to hydrogen peroxide, shows that few, if any, of the genes involved in repairing the various potentially lethal DNA lesions produced, including double-strand breaks, pyrimidine dimers, single-strand breaks, base damage, and DNA cross-links, are induced in response to exposure to the agents that produce these lesions (Birrell et al. 2002). Although some genes involved in repairing these lesions are induced by DNA damage, their number is no more than can be accounted for by chance, assuming that gene induction is nonspecific. This finding raises serious questions about any conclusions deduced from gene expression profiling as to the nature of DNA lesions in cells or to the genes involved in their repair.

Several other lines of evidence support the view that gene expression profiling is a poor index of the response to radiation damage. Jelinski et al. exposed yeast to four diverse DNA-damaging agents, alkylating agents, tert-butyl hydroperoxide, and radiation, and found that there was not a common transcriptional response but rather a different transcriptional response to each (Jelinsky et al. 2000). S. cerevisiae exposed to methyl methanesulfonate (MMS) or IR show few changes in the transcription of genes involved in DNA-damage repair. Gasch et al. found only 1 cluster of 9 of approximately 500 induced genes that they considered a specific signature of DNA damage (Gasch et al. 2001).

Two reasons have been proposed for the small proportion of genes known to be involved in protecting against DNA damage that are induced by DNA-damaging agents. The first is that so-called DNA-damaging agents also damage many other cellular macromolecules, which in turn could induce expression of protein chaperone and proteasome genes (as has been observed for MMS treatment) (Jelinsky et al. 2000; Gasch et al. 2000). The second (and somewhat overlapping) explanation is that cytotoxic treatments produce a general stress response that is independent of DNA damage (Gasch et al. 2000). Either of these explanations would be consistent with the small proportion of genes specifically required for protection against DNA damage in the general response to the agents.

Fitness Profiling

In his landmark article “Mining for therapeutic gold,” Francis Collins, the Director of the National Institutes of Health, discussed the myriad difficulties of developing new drugs: failure rates of up to 95%, 13 years from target selection to approval, and a cost of $US1 billion (Collins 2011). A promising alternative that complements the existing approach to drug discovery is to repurpose currently approved drugs (as well as compounds generally recognized as safe or “GRAS”) for new diseases. One of the best examples of this is azidothymidine (AZT), which was developed as a cancer drug, but dropped for lack of efficacy. However, AZT was repurposed as the first antiviral drug effective against HIV and that discovery changed the course of life for millions.

A fundamental element to successful drug repurposing is to define those conditions in which approved drugs may manifest new, unexpected activities (Collins 2011). For example, it is well established that the environment in which a drug acts has profound effects on its efficacy. In the spirit of Dr. Collins goal, we believe NASA, through microgravity studies, can make a valuable and unique contribution to drug repurposing by exploiting the novel environment provided by microgravity. To realize this vision, the repurposing effort must be cost-effective and leverage the expertise of both government and academia. We propose that NASA and academia can partner to use spaceflight to induce yeast to adopt metabolic profiles that effectively model many of the features of tumors only during microgravity culture conditions.

Chemogenomics is the systematic screening of chemicals against individual drug target families to identify novel drugs and/or drug targets (Cheung-Ong et al. 2012; Chan et al. 2010; Blackman et al. 2012; Smith et al. 2012; Giaever et al. 2004; Roemer et al. 2012; Pierce et al. 2006, 2007). We propose that a chemogenomic approach in yeast under spaceflight conditions optimally identifies drug targets, and the buffering pathways, which modulate their function. Drugs kill cancer cells in vitro and in animal models, but the spectrum of metabolic pathways responsible for this effect is not yet defined. Given their known minimal toxicity and safety profile, many drugs are poised to be rapidly moved into clinical trials for anticancer therapy via the FDA 505(b)(2) repurposing pathway once the mechanisms of action are defined.

Yeasts are ideal for the chemogenomic study of drug pathways for several reasons:
  1. 1.

    Yeasts share a high degree of genetic and biochemical homology with human cells, making it likely that the pathways identified will have clinical relevance (Botstein et al. 1997). It has already demonstrated that yeasts are susceptible to drugs in doses that can be generated in aqueous solutions (Lee et al. 2014). In the yeast assays, yeast can divide up to 20 times in the presence of drugs, which is many more divisions than cancer cells would undergo in a typical proliferation inhibition assay. Thus, there is an order of magnitude better opportunity to evaluate the quantitatively modest, but biologically critical, effects of drugs on cell replication activities.

     
  2. 2.

    Yeast can easily be genetically manipulated to allow the identification of cellular response pathways with high fidelity of mechanistic molecular pathways (Smith et al. 2010, 2012; Roemer et al. 2012; Hillenmeyer et al. 2010; Blackman et al. 2012; Ulitsky et al. 2010). The entire yeast genome is cloned, and deletion series can be constructed in a variety of background strains, efficiently and cost-effectively, using fully automatic robotic approaches. The simplicity of handling requirements facilitates adaption of yeast assays to spaceflight conditions.

     
  3. 3.

    Cancers are a heterogeneous mixture of cells with multiple microenvironments within the tumor including altered redox potentials. Yeast grown in the presence of a variety of different stressors can model these microenvironments and provide a homogeneous cellular target for analysis (Acharya et al. 2010). A cell’s metabolic state influences how it responds to a drug. Yeast chemogenomic screens have already uncovered unique profiles of sensitivity for drug stresses in three cancer-relevant contexts (hypoxia, obligate respiration, and normoxia). Yeast chemogenomic experiments flown on the last space shuttle mission, STS-135, show that microgravity provides a cancer-relevant context, which extends and complements current models (Nislow et al. 2015).

     

Fitness profiling, in which the contribution of a functional gene product is profiled, appears to be a superior method for identifying the stress of radiation damage, shear, and changes in mass transport. This genome-wide approach is a more faithful reporter with respect to the requirement for a particular gene in a particular environment and has been applied as a biological reporter to understand the metabolic pathways affecting survival during culture in spaceflight. Fitness profiling with yeast has the added advantage that the stressors of spaceflight can be tested for concordance against an extensive library of more than 3200 physical and pharmacological stressors (Lee et al. 2014).

Yeast chemogenomics make use of the yeast deletion collection: a genome-wide set of strains that provide precise deletion alleles for 96% of the genes in the genome (Cheung-Ong et al. 2012; Chan et al. 2010; Blackman et al. 2012; Smith et al. 2012; Giaever et al. 2004; Roemer et al. 2012; Pierce et al. 2006, 2007). Assembled over a 3-year period by a consortium of 17 laboratories, this collection has been used by hundreds of laboratories to test thousands of different environmental conditions and to define those genes that are required for survival in those conditions (Hoon et al. 2008; Chan et al. 2010; Berry et al. 2011).

Fitness profiling assays are intrinsically simple and easily executed during spaceflight – the deletion series of yeast are grown in the presence of the drug and the relative fitness of each clone is compared (Chan et al. 2010; Smith et al. 2010; Roemer et al. 2012). To discriminate the clones in the series, each clone’s deleted gene has been replaced with a unique DNA barcode. The entire series can be grown in a single tube in the presence of the drug, the DNA extracted, and the numbers of each clone quantified by high-throughput sequencing or hybridization to gene chips (Smith et al. 2010). The “clone-identifying” barcode is unique to each clone, but the insert is flanked by up and down DNA sequences that are identical in every clone so that the extracted DNA for all clones can be amplified together. The ability to quantify growth of a highly complex mixture that has been cultured in a single container makes this easily conducted in spaceflight (Johanson et al. 2007).

The approaches described in Table 1 use a genome-wide screen that can identify unexpected drug targets as well as genes that buffer or block the drug effects. Figure 1 is a schematic of the basic fitness profiling approach for chemogenomics. Table 1 is a comparison of the three major chemogenomic assays: haploinsufficiency profiling (HIP) and decreased abundance by mRNA perturbation (DAmP) that identify drug targets and homozygous profiling (HOP) that identifies drug buffering pathways (Lee et al. 2014).
Table 1

Comparison of different strategies for yeast chemogenomics

Application

HIP

DAmP

HOP

Genome-wide screen

√

 

√

Identification of drug target

√

√

–

Identification of buffering pathways

–

–

√

Fig. 1

Fitness profiling for yeast chemogenomics (1) The yeast deletion series is pooled with each strain included at approximately equal abundance. (2) The pool is grown competitively in the presence or absence of the drug (i.e., metformin). Vulnerable strains expressing diminished quantities of the target grow less well. (3) Genomic DNA is isolated from the pool. (4) Up and down barcodes are PCR amplified in separate reactions. (5) PCR product is hybridized to a barcode microarray to assess relative abundance of each strain by hybridization intensity. The intensity on the microarray serves as a proxy for stain abundance, intensities that are significantly reduced compared with the control identify strains sensitive to the drug. PCR products can also be quantified by high-throughput sequencing. (Reprinted from Smith et al. 2010, with permission from Elsevier)

Haploinsufficiency Profiling (HIP)

Haploinsufficiency Profiling (HIP) to identify drug targets. The HIP assay identifies drug targets (Smith et al. 2010; Roemer et al. 2012). It uses the heterozygous deletion library in which one of the two copies of each of the 6000 genes in the yeast genome has been removed by genetic engineering. The half level of protein produced by the heterozygote is sufficient for growth so long as there is no external stressor targeting that particular protein. However, in the presence of the test drug, the targeted protein is overwhelmed. The clone becomes “haploinsufficient” and grows less well in the presence of the drug compared to the other clones.

Increasing the Sensitivity HIP Assay for Drug Targets: Decreased Abundance by mRNA Perturbation (DAmP)

If using a heterozygote to reduce the gene copy by half is not enough to allow detection of the drug target, yeast can be rendered even more sensitive by disrupting the natural 3′ untranslated region of the gene to generate a hypomorphic allele (Yan et al. 2008). The disruption is produced by inserting the barcode in the correct site for each gene that downregulates that gene’s protein expression to 10% of wild-type levels. This can allow detection of drug-induced haploinsufficiency that is not evident with the heterozygote (Yan et al. 2008). The assay is termed decreased abundance by mRNA perturbation (DAmP) (Smith et al. 2010). By careful selection of unique barcodes for every variant, HIP and DAmP assays can be conducted in the same culture. A DAmP library of 1,402 clones is available consisting of about 958 essential and 444 nonessential “slow-growing alleles” (Yan et al. 2008).

Homozygous Profiling (HOP) to Identify Drug Buffering Pathways

Sometimes, drugs do not have a specific target protein that can be identified by the HIP assay. However, it is still possible to gain insights into the pathways affected by the drug through the use of the HOP assay (Blackman et al. 2012). In HOP, both copies of each gene are deleted and replaced with the unique barcode DNA. For obvious reasons, this is only feasible for genes that are nonessential for yeast survival, as the strains with deletion of essential genes are not viable. The HOP assay identifies genes that buffer the drug target pathway by becoming critical for growth only when the yeasts are grown in the presence of the drug (Smith et al. 2010; Roemer et al. 2012). For example, genes involved in DNA repair are not usually critical for growth but become critical to survival when yeast are exposed to stressors that induce DNA damage. As in the HIP assay, the DNA is extracted and amplified by PCR, and the relative abundance of each clone is measured by high-throughput sequencing. To analyze the data more systematically, the entire growth data set is analyzed by gene set enrichment analysis (GSEA) (Blackman et al. 2012). GSEA provides an algorithmic tool to identify pathways whose components are overrepresented in the drug-sensitive clones. These are compiled into supergroups, based on clustering of genes for known, related biologic functions such as protein complex assembly or RNA processing.

Fitness Profiling of Yeast in Space

Nislow et al. used a molecularly barcoded yeast deletion collection to provide a quantitative assessment of the effects of microgravity on a model organism (Nislow et al. 2015). They developed robust hardware to screen, in parallel, the complete collection of ~4800 homozygous and ~5900 heterozygous (including ~1100 single-copy deletions of essential genes) yeast deletion strains, each carrying unique DNA that act as strain identifiers. They compared strain fitness for the homozygous and heterozygous yeast deletion collections grown in spaceflight and ground, as well as plus and minus hyperosmolar sodium chloride, providing a second additive stressor. The genome-wide sensitivity profiles obtained from these treatments were then queried for their similarity to a compendium of drugs whose effects on the yeast collection have been previously reported. They found that the effects of spaceflight have high concordance with the effects of DNA-damaging agents, specifically 5-fluorouridine, 5-fluorocytosine, and 8-methoxypsoralen. Fitness profiling also showed changes in yeast’s redox state, suggesting mechanisms by which spaceflight may negatively affect cell fitness (Nislow et al. 2015).

Notes

Acknowledgments

National Aeronautics and Space Association grants NNX13AN32G and NNX12AM93G and the Department of Veterans Affairs supported this work. Contents do not represent the view of the Department of Veterans Affairs or the United States of America.

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Durham Veterans Affairs Medical CenterDurhamUSA
  2. 2.Nephrology Division, Department of MedicineDuke University School of MedicineDurhamUSA
  3. 3.Space Policy Institute, Elliot School of International AffairsGeorge Washington UniversityWashingtonUSA
  4. 4.Office of Research and DevelopmentDepartment of Veterans AffairsWashingtonUSA
  5. 5.Departments of Otolaryngology, Immunology, and PsychiatryBaylor College of MedicineHoustonUSA

Section editors and affiliations

  • Luis Zea
    • 1
  1. 1.BioServe Space TechnologiesUniversity of ColoradoBoulderUSA

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