Assessing compensation for loss of vacuolar function

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Assessing compensation for loss of vacuolar function in Saccharomyces cerevisiae

Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by Renmin University of China on 06/06/13 For personal use only.

Pamela A. Marshall, Nicholas Netzel, and Jillian Wisby Guintchev

Abstract: We analyzed how Saccharomyces cerevisiae cells compensate for the lack of a functional vacuole, an acidic membrane-bound degradative and ion storage compartment. We hypothesized that cells lacking a functional vacuole would compensate for the loss of the functions of the vacuole by altering gene expression and (or) metabolic flux. We used gene expression profiling and Biolog phenotype microarray analysis to determine the compensatory mechanisms of cells lacking vacuolar function. In steady state, vps33 and vps41 cells changed the transcriptional profile of some genes, but no complete pathways were upregulated or downregulated. We treated vps41 cells with calcium to tease out cellular compensation for loss of vacuole function under ionic stress; however, changes in gene expression were not utilized to compensate for loss of vacuole function under stress either, as genes whose transcriptional profiles were changed did not function together in any one cellular process. Phenotype microarray analysis indicated that logarithmically growing vps33 or vps41 cells did not seem to compensate for loss of vacuolar function but instead demonstrated additional pleiotropic phenotypes due to the function of the vacuole. Under rich media conditions, yeast utilize the vacuole to regulate stress, ion response, and peptide degradation. However, loss of the vacuole does not lead to observable compensation mechanisms. Key words: Saccharomyces cerevisiae, vacuole, vps mutants, DNA microarray, phenotype microarray. Résumé : Nous avons analysé de quelle façon Saccharomyces cerevisiae compense l’absence de vacuoles fonctionnelles, un compartiment acide de dégradation et d’entreposage du fer, lié à la membrane. Nous avons émis l’hypothèse que les cellules dépourvues de vacuoles fonctionnelles compenseraient la perte de fonction vacuolaire en modifiant l’expression génique et (ou) le flux métabolique. Nous avons utilisé un profil d’expression génique et une analyse des phénotypes sur ses puces Biolog afin de déterminer quels sont les mécanismes compensatoires de la perte de fonction vacuolaire. À l’équilibre, les cellules vps33 et vps41 modifiaient le profil de transcription de certains gènes, mais aucune voie complète n’était régulée à la hausse ou à la baisse. Nous avons traité les cellules vps41 avec du calcium afin de décortiquer les processus utilisés pour compenser la perte de fonction vacuolaire en condition de stress ionique; toutefois, les changements d’expression génique ne sont pas non plus utilisés pour compenser la perte de fonction vacuolaire en condition de stress, car les gènes dont les profils de transcription étaient modifiés ne fonctionnent ensemble dans aucun processus cellulaire. L’analyse de phénotypes sur puce a indiqué que les cellules vps33 et vps41 ne semblent pas compenser la perte de fonction vacuolaire mais adoptent plutôt des phénotypes pléiotropiques supplémentaires, à cause de la fonction de la vacuole. Dans des conditions où le milieu est riche, la levure utilise la vacuole pour réguler le stress, la réponse ionique et la dégradation des peptides. Cependant, la perte de vacuoles n’induit pas de mécanismes de compensation observables. Mots‐clés : Saccharomyces cerevisiae, vacuole, mutants vps, puce d’ADN, puce de phénotypes. [Traduit par la Rédaction]

Introduction The budding yeast Saccharomyces cerevisiae has been used for many years as a model organism for many biological processes, including organelle function and biogenesis. The yeast vacuole, equivalent in its protein sorting pathways and degradative functions to the human lysosome, was an early target of sophisticated yeast genetic manipulation to uncover basic cell biological processes. Many selections and screens were developed over the years in S. cerevisiae in which genes that contribute to vacuolar function, such as proteins that

function in protein sorting to the vacuole, proteins that contribute to vacuolar biogenesis, and vacuolar enzymes, were isolated (Bowers and Stevens 2005). What allowed such a rich collection of vacuolar mutants to be isolated is the fact that mutants with a seemingly total loss of vacuolar function (such as cells lacking all discernable vacuolar structure) are viable under laboratory conditions (Raymond et al. 1992). The yeast vacuole is an acidic membrane-bound organelle with degradative, small molecule storage, and homeostatic functions (Klionsky et al. 1990; Li and Kane 2009). The vacuole is acidic owing to a multisubunit membrane ATPase that

Received 11 July 2011. Revision received 20 October 2011. Accepted 1 November 2011. Published at www.nrcresearchpress.com/cjm on 19 January 2012. P.A. Marshall, N. Netzel, and J.W. Guintchev. Division of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, MC 2352, P.O. Box 37100, Phoenix, AZ 85069, USA. Corresponding author: Pamela A. Marshall (e-mail: [email protected]). Can. J. Microbiol. 58: 132–144 (2012)

doi:10.1139/W11-114

Published by NRC Research Press

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Marshall et al.

pumps protons into the organelle (Klionsky et al. 1990; Li and Kane 2009). The yeast vacuole is first and foremost thought of as a degradative organelle because of the many resident hydrolases it contains. These hydrolases include peptidases, carbohydrate-degrading enzymes, alkaline phosphatase, and trehalase (Klionsky et al. 1990). The vacuole also helps to break down organelles in a process termed autophagy, to recycle the components for further use (Li and Kane 2009). Owing to the acidic and hydrolytic characteristics, many have likened the vacuole to a mammalian lysosome, but the vacuole also serves additional functions. These functions include ion storage and homeostasis (including calcium, zinc, and to a lesser extent iron), basic and neutral amino acid storage, and polyphosphate storage (Klionsky et al. 1990; Li and Kane 2009). The vacuole also serves as a major proton store to help regulate cellular pH (Klionsky et al. 1990). Additionally, this organelle also acts as an ion store to help regulate cellular responses to osmotic shock; when the extracellular medium becomes hyperosmotic, calcium is pumped out of the vacuole and the vacuole fragments (Li and Kane 2009). Calcium seems to activate signal transduction pathways and enzymes to help cells respond long term to hyperosmolarity. The yeast vacuole has one additional function of detoxification (Li and Kane 2009), as the vacuole stores toxic molecules such as heavy metals. Thus, the yeast vacuole has degradative and protein sorting pathways like the mammalian lysosome but functions in additional metabolic pathways (Armstrong 2010). Indeed, research on the yeast vacuole and mutant isolation of vacuolar structure and function has led to a better understanding of human lysosomal biogenesis and protein sorting, as most genes identified in S. cerevisiae that take part in vacuolar protein sorting (VPS), vacuolar biogenesis, or autophagy have a human homologue (Armstrong 2010). However, unlike in yeast, loss-of-function mutants in human genes required for lysosomal function or biogenesis lead to a myriad of disease states, most of which end in early death, depending on the mutation and severity of the loss (Gieselmann 1995). We hypothesized that S. cerevisiae must be able to compensate for loss of vacuolar function and speculated that cells use gene expression or changes in metabolic pathways to do so. We used vps33 and vps41, two null mutants in the VPS pathway, as model strains to test for compensation. Vps33p is a component of the HOPS (homotypic fusion and VPS) and CORVET (class C core vacuole/endosome tethering) complexes and uses ATP for vesicle docking and fusion at the vacuole; vps33 cells secrete vacuolar hydrolases and exhibit no discernable vacuolar structure via electron microscopy (Raymond et al. 1992; Nickerson et al. 2009). vps33 cells also exhibit other pleiotropic phenotypes, such as slow growth, sensitivity to heat, abnormal mitochondrial morphology, sensitivity to ions, and sensitivity to toxins (P.A. Marshall, unpublished results; SGD Project 2011). Vps41p is a member of the HOPS complex and is essential for both the Golgi to endosome protein sorting step and the endosome to VPS step (Nickerson et al. 2009); null vps41 mutants secrete membrane-bound and luminal vacuolar proteases and exhibit highly heterogeneous extremely fragmented vacuole-like organelles, devoid of the electron-dense stain of the wild-type vacuole under electron microscopy examination (Radisky et

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al. 1997; Nakamura et al. 1997). vps41 null cells have a less severe morphological phenotype and less severe metabolic phenotypes than vps33. Cells lacking the VPS41 gene do not exhibit as slow growth nor many of the sensitivities that vps33 cells do; however, vps41 cells are more sensitive than the wild type to some ions and toxins (P.A. Marshall, unpublished results; SGD Project 2011). There are many studies in which researchers have used microarray analysis to assess a transcriptional profile, identify cellular changes in response to extracellular changes, organelle loss, or gene deletions. Transcriptional profiling is often utilized as an analysis of cellular changes or compensation in response to particular perturbations of homeostasis. For example, Yoshimoto et al. (2002) were interested in the genes that are regulated by calcineurin once it is activated by calcium. They identified many genes whose expression is upregulated or downregulated in response to extracellular calcium and showed via microarray analysis of a deletion strain of CRZ1 that this transcription factor induces gene expression directly in response to calcineurin activation in S cerevisiae. Many researchers have used microarray analysis to determine transcriptional responses to changes in nutrients (for example Boer et al. 2003), drugs (Zhang et al. 2002), or solvents (Zhang et al. 2003). Epstein et al. (2001) used strains of Saccharomyces to detect gene expression changes in response to loss of mitochondrial function, in a study analogous to this one. There are many examples of transcriptional profiling by microarray analysis of deletion strains that reveal important insights into yeast biology. Some examples include the following: microarray analysis of cells carrying a deletion of telomerase was used to identify genes upregulated in response to loss of telomerase (Nautiyal et al. 2002), transcriptional profiling of a pde2D strain (Jones et al. 2003) in which the Ras–cAMP pathway is constitutively active demonstrated altered expression leading to may changes including cell wall formation and cell stress response, microarray analysis of deletions in the ergosterol pathway determined responses to drugs (Bammert and Fostel 2000), and microarray analysis was used to determine specific cell wall transcriptional responses in wild-type cells and those lacking cell stress sensors (Bermejo et al. 2010). Thus, transcriptional microarray analysis is useful in examination of cellular responses to a perturbation or gene deletion. We hypothesized that via changes in the transcriptional profile or changes in metabolic flux, yeast cells are better able to compensate for loss of vacuolar function than their human counterparts. We analyzed total loss of function cells (lacking either VPS33 or VPS41 genes) using DNA microarray analysis and Biolog phenotype microarrays to determine the compensatory mechanisms. We had reasoned that yeast would increase gene expression of the proteasome, since loss of vacuolar function would lead to less degradation activity. We had hypothesized that loss of the major site of ion storage would lead to increases in gene expression of the yeast plasma membrane ion, proton, and phosphate transporters to compensate for loss of the major storage site of ions and protons. We had speculated that we would uncover increased expression of transporters on the remaining organelles (such as Pmr1p, a Golgi calcium pump (Antebi and Fink 1992)) to compensate for the loss of the storage function of the vacuole. Published by NRC Research Press

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We hypothesized that by using Biolog analysis, we would also be able to uncover gain-of-function phenotypes in vps33 or vps41, indicating altered metabolic flux to compensate for loss of vacuoles. Altered metabolic flux has been analyzed in many cell types to determine cellular differences in the metabolome, such as S. cerevisiae deletion mutants to try to identify functions of genes (Raamsdonk et al. 2001) and a yeast deletion strain of yap1 (Weiss et al. 2004). Biolog phenotype microarray is a method in which cells or strains are grown in a 96-well plate that has a variety of nutrient sources or toxic molecules (Bochner 2009; Atanasova and Druzhinina 2010). As these cells respire, a tetrazolium dye changes irreversibly from clear to purple via the action of the enzyme succinate dehydrogenase, indicating that the cell can indeed utilize the nutrient source or can respire in the presence of the toxic molecule. The output on the Biolog analysis is measured continuously in an Omnilog reader, which calculates the absorbance of each well. Biolog analysis demonstrates differences in growth, nutrient utilization, or sensitivities to toxins and can be reported as a number that is the difference between the endpoint respiration profiles of the strains or as a curve that demonstrates the change over time as well. Biolog analysis has been performed on yeast strains to determine their metabolic profiles to analyze genetic diversity (Homann et al. 2005). Carbon utilization profiles (Druzhinina et al. 2006), metabolic diversity (Kubicek et al. 2003), and carbohydrate use (Hobbie et al. 2003) were all studied in fungi to analyze differences between species or strains. Biolog use in fungi has been used to complement genomic and proteomic analysis to delve deeper into cellular function (reviewed in Atanasova and Druzhinina 2010). We hypothesized that S. cerevisiae strains lacking vacuole function would have altered metabolic profiles to compensate for the loss of this important organelle, and we used Biolog phenotype microarray analysis to probe these differences. Unexpectedly, we found that yeast do not need to compensate for loss of vacuolar function when grown under laboratory conditions and are able to grow without upregulating the proteasome, plasma membrane phosphate, proton, or ion pumps, or other organellar pumps. We also did not find, via Biolog phenotype microarray plate analysis, gain-of-function phenotypes that would indicate that the cells are changing metabolomics to compensate for loss of vacuolar structure or function. Our hypotheses were in fact, disproven.

Materials and methods Yeast strains and media Strains were obtained from Invitrogen (Carlsbad, California). Strains were BY4742 (MATa his3D1 leu2D0 lys2D0 ura3D0) or deletions in the BY4742 background generated by the Yeast Genome Deletion Project (vps33::kanMX or vps41::kanMX). For RNA extraction, yeast strains were grown at 30 °C in YPD media (1% yeast extract, 2% peptone, 2% dextrose) overnight and then back diluted into fresh YPD media and grown to mid-log phase in YPD media. As well, yeast strains were grown at 30 °C in YPD media overnight and then back diluted into fresh YPD media and grown to mid-log phase and then transferred to YPD media supplemented with 200 mmol/L CaCl2 for 1 h for DNA microarray analysis of vps41 or wild-type cells under calcium induction.

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Yeast cells were grown according to manufacturer’s instructions for Biolog phenotype microarray (PM) plates. Yeast strains were inoculated into Biolog Inoculating Fluid appropriate for the plate used and supplemented with nutrients as required. Cells were incubated in an Omnilog apparatus at 30 °C for Biolog analysis. RNA extraction and DNA microarray analysis RNA was extracted from logarithmically growing cells using a RioPure Yeast kit (Ambion, Austin, Texas), following the manufacturer’s instructions. Microarrays, which are 70mer Illumina oligonucleotide microarrays printed on epoxy slides at Washington University (St. Louis, Missouri), were obtained from the Genome Consortium for Active Teaching (Davidson, North Carolina). The microarrays were prehybridized in 3× SSC + 0.1%SDS with 1 mg/mL salmon sperm DNA at 50 °C for 1 h and then washed with nuclease-free water and dried in a stream of air. A 3DNA Array Detection Array 350 kit (Genisphere, Hatfield, Pennsylvania) was used to prepare cDNA, following the manufacturer’s protocol, using 5 mg of total RNA. cDNA was purified using a Qiagen PCR Clean Up kit (Qiagen, Valencia, California), concentrated to 10 mL using a vacuum concentrator, and hybridized overnight on the array using Genisphere hybridization buffer (vial 7) according to manufacturer’s directions at 35 °C in a humidified chamber. The microarray was then washed sequentially in 2× SSC + 0.2% SDS at 42 °C, 2× SSC at room temperature, and 0.2× SSC at room temperature, and then washed in nuclease-free water. Genisphere hybridization buffer (vial 7) was used according to the manufacturer’s instructions for secondary hybridization at 35 °C. The microarray was then washed sequentially in 2× SSC + 0.2% SDS at 42 °C, 2× SSC at room temperature, and 0.2× SSC at room temperature, and then washed in nuclease-free water (with 1 mmol/L DTT in all solutions to inhibit oxidation) and airdried. Microarrays were scanned on an Agilent Pro scanner (Palo Alto, California) (housed at the Arizona State University Tempe Core DNA Facility) or a GenePix 4100A (Molecular Devices, Sunnyvale, California), and data was analyzed using MAGIC Tool (Heyer et al. 2005). MAGIC Tool is a program that counts pixels in a predetermined area and reports a ratio of expression. Deletion strains were compared with the wild-type strain BY4742 grown under identical conditions. Ratios were normalized to actin = 1 and then log base 2 transformed. An average of two experiments is given in Tables 1–3. In Table 1, the data are from a microarray analysis to determine genes regulated in unison that looked for genes upregulated at least log2 = 0.6 in one strain and then analyzed the gene expression in the other strain for upregulation of at least log2 = 0.3. In Table 2, the data are from a microarray analysis to determine genes regulated in unison that looked for genes downregulated at least log2 = –0.4 in one strain and then analyzed the gene expression in the other strain for downregulation of less than log2 = –0.01. In Table 3, microarray analysis reported true genes (not dubious open reading frames or transposons) that were upregulated higher than log2 = 1.2 in cells grown in calcium and that were downregulated log2 = –1.0 or more, if they were regulated in the opposite manner in the cells grown in YPD. For YPD growth. the cutoff was 0.2 for determination of upregulation and 0 for negative regulation. A two-tailed heteroPublished by NRC Research Press

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Table 1. DNA microarray gene expression analysis of null mutants of vps33 and vps41 to determine genes that were upregulated.

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Gene YLR243w

Name

Function Unknown function, essential gene

vps33 1.2

vps41 3.4

Stress related YLR414c YKL215c YOL132w YMR172c YIL117c YBR126c

PUN1 OXP1 GAS4 HOT1 PRM5 TPS1

2.3 0.7 0.4 0.4 1.1 0.9

0.6 1.1 1.0 0.7 0.4 0.5

YPR075c

OPY2

Unknown function, stress induced 5-Oxoprolinase, involved in glutathione metabolism 1,3-b-Glucanosyltransferase, involved with Gas2p in spore wall assembly Transcription factor for HOG pathway Pheromone-regulated protein; induced during cell integrity signaling Synthase subunit of trehalose-6-phosphate synthase–phosphatase complex, stress induced Signaling protein of HOG pathway

0.9

0.3

ER protein folding Mannosyltransferase of the cis-Golgi apparatus GTP-binding protein of the ARF family; component of COPII; ER to Golgi vesicle formation Polyphosphatidylinositol phosphatase; trans-Golgi network-to-early endosome sorting

0.9 1.2 0.6

1.2 0.4 0.9

0.8

0.5

Functions with Nca2p to regulate mitochondrial expression of Fo-F1 ATP synthase subunits, Atp6p and Atp8p

2.4

0.3

rDNA silencing and mitotic rDNA condensation

1.4

3.6

mRNA capping enzyme, RNA 5′-triphosphatase Protein interacting with Nam7p, putative nonsense-mediated mRNA decay pathway Mitochondrial 3′–5′ RNA exonuclease; involved in 3′-end processing of several RNAs

1.2 0.8

0.3 0.9

0.3

0.9

1.1 1.0 1.3

0.4 0.6 0.7

0.5

1.2

0.6

0.7

Secretory system YFL045c SEC53 YGL038c OCH1 YPL218w SAR1 YOR109w

INP53

Mitochondrial YJL116c NCA3

DNA silencing YKR010c TOF2 RNA processing YMR180c CTL1 YLR363c NMD4 YLR059c

REX2

Miscellaneous YDR380w ARO10 YNL289w PCL1 YKL188c PXA2 YHR088w

RPF1

YBR217w

ATG12

Phenylpyruvate decarboxylase Cyclin ATP-dependent import of long-chain fatty acids into peroxisomes; works with Pxa1p Nucleolar protein involved in the assembly and export of the large ribosomal subunit Conserved ubiquitin-like modifier involved in autophagy and the Cvt pathway

Note: Genes that were upregulated in both strains were identified by a two-tailed heteroscedastic t test, p > 0.05. Gene expression is illustrated as log base 2 of ratio of mutant/wild type gene expression. HOG, high osmolarity glycerol; ER, endoplasmic reticulum; COPII, coatomer complex II; ARF, ADP-ribosylation factor; Cvt, cytoplasm-to-vacuole targeting.

scedastic t test was used to determine if gene regulation was the same between the vps41 and vps33 deletion strains or different for vps41 in YPD or YPD plus calcium media. For all genes described in Tables 1 and 2, data were significant at p > 0.05 and p > 0.001, respectively, and at p < 0.05 for those described in Table 3 (null hypothesis was that the genes were regulated in the same manner). Fig. 1 was generated using Cluster 3.0 (de Hoon et al. 2004) and Java TreeView 1.1.6r2 (Saldanha 2004). Data were analyzed in Cluster 3.0 by hierarchical clustering using Euclidean distance with single linkage analysis.

Biolog phenotype microarray analysis This analysis was performed in house at the Biolog facility in Hayward, California, following the manufacturer’s recommendations, with histidine, leucine, lysine, and uracil supplemented for both strains. Plates were inoculated in the appropriate inoculating fluid (IFY-0 for PM plates 1–8 and IFY-10 for PM plates 9, 10, and 21–30), incubated at 30 °C, and read using an Omnilog scanner (Biolog). The PM plates utilized for analysis were 1–10 and 21–25. Phenotypes were analyzed in duplicate and compared with strain BY4742. Numbers indicate the difference between the average height Published by NRC Research Press

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Table 2. DNA microarray gene expression analysis of null mutants of vps33 and vps41 to determine genes that were downregulated. Gene Name VTC pathway YPL019c VTC3 YER072w VTC1

Function

vps33

vps41

Subunit of the VTC complex Subunit of the VTC complex

–0.4 –0.5

–1.5 –0.8

Small molecule biosynthesis YGL009c LEU1 YNL104c LEU4 YOL058w ARG1 YJR109c CPA2 YDR158w HOM2 YMR108w ILV2 YHR018c ARG4 YDR226w ADK1

Isopropylmalate isomerase a-Isopropylmalate synthase Arginosuccinate synthetase Large subunit of carbamoyl phosphate synthetase Aspartic b semi-aldehyde dehydrogenase Acetolactate synthase Argininosuccinate lyase Adenylate kinase

–0.9 –0.5 –0.7 –0.5 –0.7 –0.4 –0.6 –0.5

–2.9 –0.5 –1.1 –0.5 –0.4 –0.4 –0.4 –0.8

High-affinity inorganic phosphate transporter and low-affinity manganese transporter Repressible acid phosphatase Constitutively expressed acid phosphatase similar to Pho5p

–0.6

–1.7

–0.6 –0.6

–0.2 –0.2

Phosphate metabolism YML123c PHO84 YBR093c YBR092c

PH05 PHO3

Mitochondrial YKL120w YHR208w YOR226c

function OAC1 BAT1 ISU2

YBR054w YKL029c YER073w YEL020w

YRO2 MAE1 ALD5 TIM9

Miscellaneous YGL089c YCR021c YGR072w YJL190c YGL117w YIL169c YPR157w YPL250c YDR033w

MF(ALPHA)2 HPS30 UPF3 RPS22A

MRH1

Mitochondrial inner membrane transporter Mitochondrial branched-chain amino acid aminotransferase Mitchondrial matrix required for synthesis of mitochondrial and cytosolic iron–sulfur proteins Mitochondrial localized unknown function Mitochondrial malic enzyme Mitochondrial aldehyde dehydrogenase Translocase of the inner mitochondrial membrane

–0.7 –0.8 –0.4

–1.6 –1.4 –1.1

–0.3 –0.3 –0.3 –0.6

–1.0 –0.5 –0.4 –1.5

a Factor Protein that negatively regulates the H(+)-ATPase Pma1p Component of the nonsense-mediated mRNA decay pathway Peptide component of small ribosomal subunit Unknown function Unknown function Unknown function Unknown function Unknown function

–1.0 –0.3 –0.5 –0.4 –0.4 –0.5 –0.5 –0.5 –0.7

–0.7 –1.1 –0.2 –0.2 –0.4 –0.2 –0.3 –0.2 –0.3

Note: Genes that were downregulated in both strains were identified by a two-tailed heteroscedastic t test, p > 0.001. Gene expression is illustrated as log base 2 of ratio of mutant/wild type gene expression. VTC, vacuolar transporter chaperone; Pi, inorganic phosphate.

of Omnilog curves of test strain and that of the wild-type BY4742 strain.

Results and discussion Steady-state transcriptional analysis We used DNA microarray analysis to determine what genes were upregulated and downregulated in null strains of vps33 and vps41 (Tables 1 and 2 and Fig. 1) compared with wild-type cells grown in YPD medium in our search for compensatory mechanisms engaged when cells lack vacuolar function. We were interested in studying the genes that were regulated in common between vps33 and vps41 to try to determine steady-state compensation for loss of vacuolar function. The gene with the highest upregulation in both deletion

strains was YLR243W, an essential gene of unknown function. Seven genes that are known to be involved in responses to stress or that are upregulated during stress were increased in both strains. Deletion strains upregulated four genes whose products are involved in secretion. Other genes were also upregulated, as indicated in Table 1. Downregulation (Table 2) was also assessed, and more genes were significantly downregulated than were upregulated. Downregulated genes include enzymes for amino acid biosynthesis, several mitochondrial proteins, and three genes whose products are required for efficient phosphate transport and (or) metabolism. We had hypothesized that we would find increased gene expression of genes coding for proteins that could compensate for loss of vacuolar functions. Surprisingly, we did not Published by NRC Research Press

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Table 3. DNA microarray gene expression analysis of null mutants of vps41 treated with and without calcium.

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Gene YCL042w YER034w YDL245c YLL054c

Name

HXT15

Stress related YFL031w YDR077w YOR010c

HAC1 SED1 TIR2

YFL061w YKR076w YDR536w

DDI1 ECM4 STL1

Function Unknown Unknown Unknown Unknown

Calcium 2.2 1.4 1.3 1.3

Transcription factor for unfolded protein response Stress-induced structural GPI-cell wall glycoprotein in stationary-phase cells Putative cell wall mannoprotein; transcription induced by cold shock and anaerobic conditions Expression 100-fold increased with DNA damage Omega class cytoplasmic glutathione transferase Plasma membrane glycerol proton symporter; highly but briefly transiently induced when cells are osmotically shocked

2.9 1.7 1.4

YPD –0.2 0.2 0.07 0.2

–0.2 –0.2 –0.3

1.3 1.3 1.3

0.2 0.02 0.1

1.4 1.3 1.3 1.3 1.3 –1.2 –1.1

0.02 0.1 –0.4 0.2 0.05 0.2 0.32

Secretory system YMR171c EAR1 YAL002w VPS8 YDL123w SNA1 YHR175w CTR2 YPL163C SVS1 YJR143c PMT4 YML052w SUR7

MVB cargo ubiquitination CORVET component Vacuolar membrane protein unknown function Vacuolar copper transporter Cell wall and vacuolar protein, confers resistance to vanadate Protein O-mannosyltransferase Plasma membrane protein of eisosomes, required for endocytosis

Mitochondrial YDR316w OMS1

Mitochondrial integral membrane protein of unknown function

1.4

–0.2

RNA binding YHR070w YDL160c YDR432w

TRM5 DHH1 NPL3

tRNA(m(1)G37)methyltransferase mRNA binding RNA binding

2.0 1.3 1.3

0.1 –0.04 0.05

DNA binding YPR007c YMR043w YMR164c YER159c YDL048c

REC8 MCM1 MSS11 BUR6 STP4

Meiosis-specific component of sister chromatid cohesion complex Transcription factor for cell type specific and pheromone response Transcription factor for starch utilization and invasive growth Transcription factor Transcription factor

2.0 1.7 1.3 1.3 –1.2

–0.3 –0.2 –0.2 0.03 0.03

Ribosomal YDL166c YBR048w

FAP7 RSP11B

Ribosomal synthesis Small ribosomal subunit

1.4 1.3

–0.3 –0.3

Small molecule YLL062c YEL021w YLR304c YER070w YMR217w

biosynthesis MHT1 URA3 ACO1 RNR1 GUA1

S-Methylmethionine-homocysteine methyltransferase OMP decarboxylase Aconitase Ribonucleotide diphosphate reductase GMP synthase

1.3 1.3 –1.2 –1.0 –1.1

0.1 –0.04 0.3 0.3 0.2

Miscellaneous YLL028w YNL279w YIL160c YDL238c YEL063c YCL064c

TPO1 PRM1 POT1 GUD1 CAN1 CHA1

1.6 1.6 1.4 1.3 1.3 –1.5

0.05 –0.07 –0.2 –0.3 –0.05 0.7

YLR286c

CTS1

Polyamine transporter Plasma membrane protein for membrane fusion during mating Peroxisomal thiolase Guanine deaminase; induced in post-diauxic and stationary cultures Plasma membrane arginine permease L-Serine or L-threonine deaminase, required to use serine and threonine as sole nitrogen source Endochitinase

–1.1

0.2

Note: Genes that were differentially regulated in calcium and standard YPD media were identified by a two-tailed heteroscedastic t test, p < 0.05. Gene expression is illustrated as log base 2 of ratio of mutant/wild type gene expression. GPI, glycosylphosphatidylinositol; MVB, multivesicular body; CORVET, class C core vacuole/endosome tethering; OMP, orotidine-5′-phosphate; GMP, guanine monophosphate. Published by NRC Research Press

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Fig. 1. Cluster analysis and heatmap of genes in vps33 and vps41. Red indicates higher expression in the mutant and green indicates lower expression in the mutant as compared to a wild-type control. Analysis was performed by Cluster (hierarchical clustering by Euclidean distance with single linkage analysis) and viewed with Java TreeView.

find upregulation of expression of genes or pathways that would compensate for loss of vacuolar function, such as proteasome subunits, to compensate for loss of the proteolytic activity of the vacuole. We also did not find increased gene expression of plasma membrane ion transporters, to compensate for the loss of the major ion store in yeast, the vacuole. As well, we did not find gene expression increased in phosphate metabolism genes nor in genes responsible for regulation of cytosolic pH. Microarray analysis demonstrated that gene expression does not seem to be a major method of compensation for loss of the vacuole. Cells lacking a vacuole seem to be under a low level of stress in steady-state, as indicated by their gene expression. Several genes that are involved in stress response were upregulated but not to a major extent, and not all of the components of stress pathways were upregulated. For example, the

mostly highly expressed stress-induced gene (induced upon cell wall damage), PUN1, (Rodríguez-Peña et al. 2005) was induced approximately fourfold in vps33 and less than twofold in vps41. Other stress-induced genes include HOT1 and OPY2, two genes that act in the high osmolarity pathway, a signal transduction pathway activated in response to extracellular hyperosmolarity (Hohmann et al. 2007); however, that pathway encompasses more subunits than were upregulated. It is well characterized that the yeast stress response is modulated through Msn2p and Msn4p and upregulates dozens of genes (Görzer et al. 2003); thus, this gene expression pattern is not consistent with a complete stress response. Several genes directly or indirectly involved in secretion were also upregulated, perhaps as a response to one of the main phenotypes of vps mutants, which is shunting and secreting of vacuolar luminal proteases (Bankaitis et al. 1986; Published by NRC Research Press

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Table 4. Shared phenotypes of null mutants of vps33 and vps41 as determined by Biolog phenotype microarray analysis. Chemical Arg-Lys Lys-Arg Gly-Gly-Leu Val-Lys Gly-His Leu-His Val-His Blasticidin S Hygromycin B Potassium chromate Sodium arsenate Sodium pyrophosphate Citric acid trisodium salt Polymyxin B

Description of phenotype Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Loss of ability to use peptide as a nitrogen Sensitivity to inhibitor of protein synthesis Sensitivity to inhibitor of protein synthesis Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to pH Sensitivity to antibiotic

source source source source source source source

vps33 –95 –90 –87 –74 –120 –130 –99 –266 –497 –348 –433 –511 –484 –456

vps41 –140 –90 –77 –75 –74 –68 –64 –226 –221 –178 –102 –106 –110 –140

Note: Analysis was performed by pair-wise comparison. Average height for each substrate was calculated. Average height is defined as the maximum absorbance level reached by a cell strain respiring on a substrate. A difference value was calculated as follows: average height of mutant strain – average height of wild-type strain. A negative number indicates that the mutant strain was deficient in respiring under this condition.

Rothman and Stevens 1986). Vacuolar proteins are actively sorted away from the bulk of endoplasmic reticulum (ER) and Golgi proteins. Vacuolar proteins, such as carboxypeptidase Y, are sorted via vesicles from the Golgi first to the endosome. The endosome then sorts these proteins into multivesicular bodies that are transported to the vacuole (Hurley 2008). Other vacuolar proteases, such as alkaline phosphatase, are sorted directly from the Golgi to the vacuole via an AP3 coat (Bowers and Stevens 2005). When cells lack the ability to sort vacuolar proteins correctly, these proteins build up in the Golgi and then are shunted via the default pathway of secretion to the cell surface (Bankaitis et al. 1986; Rothman and Stevens 1986). Perhaps these proteins involved in the secretory system are upregulated to help deal with the buildup of vacuolar proteins in the early secretory pathway. However, many more proteins are involved in secretion and in ER and Golgi maintenance and biogenesis, and these proteins are not upregulated in toto, nor do we see upregulation of genes identified previously in secretion stress response analysis (Arvas et al. 2006). Thus the upregulation of a subset of ER and Golgi proteins in vps33 and vps41 strains is probably an anomaly and not a compensatory mechanism. Null mutants of vps33 or vps41 downregulated gene expression of several genes. We had hypothesized that perhaps cells would downregulate vacuolar proteases and enzymes, since they are not needed and instead are secreted outside the cell, but our analysis indicated this was not the case. We had also hypothesized that yeast might downregulate proteins involved in VPS, since they are not being used, but again our analysis did not reveal this. Cells do downregulate VTC1 and VTC3 genes, two components of the vacuolar transporter chaperone (VTC) pathway; these genes code for proteins that influence polyphosphate accumulation in the vacuole (Uttenweiler et al. 2007; Hothorn et al. 2009). Perhaps the cells are downregulating these subunits because they are not needed in vps mutants or because they are abnormally making polyphosphate in vps mutants. However, the null strains did not downregulate additional components of the VTC

pathway, such as Vtc4p, the catalytic subunit of the polyphosphate polymerase (Hothorn et al. 2009). Microarray analysis indicated that vps33 or vps41 null strains downregulate genes involved in other disparate pathways, such as amino acid biosynthesis and mitochondrial structure or function, and phosphate transport or accumulation. vps mutants often have a pleiotropic phenotype of misshapen mitochondria (Wang and Deschenes 2006), so perhaps this downregulation of mitochondrial proteins is related to this phenotype, but again there are many more mitochondrial proteins than were downregulated in our study. We had hypothesized that since the yeast vacuole is the major site for polyphosphate accumulation (Li and Kane 2009), genes required for phosphate transport and metabolism would be upregulated, but in fact three genes in this pathway were downregulated, PHO84, PHO5, and PHO3. Twenty-two genes have been reported to be regulated by the Pho4p pathway (Ogawa et al. 2000); not just the three we found. Thus, we are certain the phosphate pathway is not being downregulated as a whole in these deletion strains. These downregulation phenotypes are difficult to reconcile based upon known functions of the yeast vacuole and pathways hypothesized to be impacted. Thus, downregulation of gene expression does not seem to be a method of compensating for loss of vacuole function. Transcriptional analysis in vps41 cells treated with calcium Since we had hypothesized that changes in transcriptional profile would help yeast compensate for the loss of the vacuole during steady-state growth, and since we did not see any changes when the vps mutants were grown in standard YPD media, we tried to stress vps41 cells to determine if this would reveal a compensation mechanism in the weaker of the two strains. We decided to use calcium as a stressor, since calcium is stored in the yeast vacuole for use in cellular processes and signal transduction (Klionsky et al. 1990; Li and Kane 2009). We grew vps41 cells and wild-type control cells overnight in YPD media and back-diluted them into Published by NRC Research Press

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Table 5. Phenotypes of null mutant of vps33 as determined by Biolog phenotype microarray analysis. Chemical Mechlorethamine hydrochloride Pentamidine isethionate Ferulic acid EGTA BAPTA Compound 48/80 Cisplatin 9-Aminoacridine Isoniazid 3-Amino-1,2,4-triazole Chloroquine Miconazole nitrate Thiourea Poly-L-lysine Domiphen bromide Thioridazine Chlorpromazine Promethazine Amitriptyline L-Tyrosine Ammonia His-Leu His-Ser His-Asp Ile-His His-Met His-Glu His-Tyr His-Val Ala-His Ala-His His-Ala His-Gly Ser-His Tyr-Gly Phe-Ala Phe-Glu Gly-Leu His-Trp Phe-Gly Ala-Lys Ile-Ala Met-His Tyr-Val Tyr-Ala His-His Leu-Ile Succinic acid Fumaric acid Tartaric acid Malic acid 5% Sodium sulfate 4% Sodium sulfate 4% Sodium formate 5% Sodium formate 200 mmol/L sodium phosphate pH 7 3% Urea

Description of phenotype Sensitivity to alkylating agent Sensitivity to antifungal Sensitivity to antioxidant Sensitivity to calcium chelator Sensitivity to calcium chelator Sensitivity to cyclic AMP phosphodiesterase inhibitor Sensitivity to DNA damaging agent Sensitivity to DNA intercalating agent Sensitivity to inhibitor of fatty acid biosynthesis Sensitivity to histidine biosynthesis inhibitor Sensitivity to ion depleter Sensitivity to antifungal Sensitivity to chaotropic agent Sensitivity to detergent Sensitivity to antifungal Sensitivity to efflux pump inhibitor Sensitivity to efflux pump inhibitor Sensitivity to efflux pump inhibitor Sensitivity to membrane transport inhibitor Loss of ability to use L-tyrosine as a nitrogen source Loss of ability to use ammonia as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source Sensitivity to organic acid Sensitivity to organic acid Sensitivity to organic acid Sensitivity to organic acid Sensitivity to hyperosmolarity Sensitivity to hyperosmolarity Sensitivity to hyperosmolarity Sensitivity to hyperosmolarity Sensitivity to hyperosmolarity Sensitivity to hyperosmolarity

vps33 –238 –461 –314 –502 –328 –429 –499 –445 –445 –426 –579 –385 –321 –298 –159 –280 –244 –239 –373 –66 –70 –134 –122 –121 –120 –116 –114 –110 –107 –105 –100 –95 –94 –86 –81 –75 –69 –67 –67 –66 –65 –63 –62 –62 –61 –61 –60 –538 –490 –475 –472 –175 –168 –155 –155 –345 –156

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Table 5 (concluded). Chemical Diamide Methyl viologen Thiophosphate Dithiophosphate Inositol hexaphosphate O-Phosphoryl-ethanolamine pH 4.5 + L-threonine pH 4.5 + L-cysteic acid Kanamycin Apramycin Tobramycin Neomycin Sodium caprylate Thiosulfate Thiophosphate Dithiophosphate Tetrathionate D,L-Lipoamide Glutathione 2-Hydroxyethane sulfonic acid Sodium metasilicate Sodium metaborate Sodium cyanide Sodium orthovanadate Sodium thiosulfate Sodium selenate Sodium dichromate Chromium (III) chloride Aluminum sulfate Cobalt (II) chloride Palladium (II) chloride 2-Deoxy-D-glucose Glycine hydroxamate Glycine hydrochloride

Description of phenotype Sensitivity to drug that depletes glutathione Sensitivity to oxidizing agent Loss of ability to use thiophosphate as a phosphate source Loss of ability to use dithiophosphate as a phosphate source Loss of ability to use inositol hexaphosphate as a phosphate source Loss of ability to use O-phosphoryl-ethanolamine as a phosphate source Sensitivity to pH Sensitivity to pH Sensitivity to antibiotic Sensitivity to antibiotic Sensitivity to antibiotic Sensitivity to antibiotic Sensitivity to ionophore Loss of ability to use thiosulfate as a sulphur source Loss of ability to use thiophosphate as a sulphur source Loss of ability to use dithiophosphate as a sulphur source Loss of ability to use tetrathionate as a sulphur source Loss of ability to use D,L-lipoamide as a sulphur source Loss of ability to use glutathione as a sulphur source Loss of ability to use 2-hydroxyethane sulfonic acid as a sulphur source Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to anion Sensitivity to cation Sensitivity to cation Sensitivity to cation Sensitivity to cation Inability to use 2-deoxy-D-glucose as a carbon source Sensitivity to glycine hydroxamate Sensitivity to glycine hydrochloride

vps33 –405 –487 –90 –64 –86 –62 –152 –152 –532 –462 –392 –276 –287 –125 –69 –64 –61 –90 –72 –64 –513 –377 –341 –333 –423 –394 –180 –442 –437 –417 –305 –267 –285 –392

Note: Analysis was performed by pair-wise comparison. Average height for each substrate was calculated. Average height is defined as the maximum absorbance level reached by a cell strain respiring on a substrate. A difference value was calculated as follows: average height of mutant strain – average height of wild-type strain.

Table 6. Phenotypes of null mutant of vps41 as determined by Biolog phenotype microarray analysis. Chemical L-Phenylalanine

Tween 20 Paromomycin Lys-Tyr Lys-Leu

Description of phenotype Gain of function, ability to use L-phenylalanine as a nitrogen source Gain of function, ability to use L-phenylalanine as a carbon source Gain of function, resistance to antibiotic Loss of ability to use peptide as a nitrogen source Loss of ability to use peptide as a nitrogen source

vps41 63 60 245 –69 –61

Note: Analysis was performed by pair-wise comparison. Average height for each substrate was calculated. Average height is defined as the maximum absorbance level reached by a cell strain respiring on a substrate. A difference value was calculated as follows: average height of mutant strain – average height of wild-type strain.

YPD media containing 200 mmol/L CaCl2 for 1 h, and then extracted total RNA to analyze via DNA microarray analysis. As expected, microarray analysis (Table 3) of vps41 revealed that vps41 cells in calcium change their transcription in several genes as compared with vps41 cells grown in YPD. The genes expected to be upregulated in response to calcium are not seen in this analysis, as the control for this set of experiments is wild-type cells treated with calcium (ratio of vps41 mutant cells/wild-type cells, both grown in calcium medium).

However, our hypothesis that these cells would have whole-scale changes in their expression did not hold, as there are relatively few genes differentially regulated in these cells (Table 3). The most notable gene upregulated in response to calcium was HAC1, a transcription factor for the unfolded protein response (Mori et al. 1996), followed by several unknown genes: YCL042W, YER034W, YDL245C, and YLL054C. We did see six transposons that were more than twofold upregulated (data not shown, see supplementary TaPublished by NRC Research Press

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ble B1). Perhaps this is a stress response as the transposons are trying to mobilize to leave the genome. Again, there were no major pathways upregulated in unison, and instead a smattering of genes of different functions; furthermore, no genes involved in mitochondrial function, phosphate accumulation, or proteasome subunits were upregulated either. Thus the cells do not seem to be compensating via changes in the transcriptional profile for loss of the vacuole, even under calcium stress. Biolog analysis of vacuolar mutants Microarray analysis indicates that modulation of gene expression is not a major method for vps null cells to compensate for loss of vacuolar function in YPD or calcium media. We then hypothesized that perhaps, instead of gene expression, yeast were using changes in the metabolome to compensate for loss of the vacuole in steady-state growth. We used Biolog phenotype microarray analysis to determine metabolic flux differences between wild-type cells and null strains of vps33 and vps41 (Bochner 2009). Phenotypes shared by both strains are presented in Table 4, those displayed by vps33 only in Table 5, and those displayed only by vps41 in Table 6. The entire profile is included in a supplementary document1. Neither strain showed gain-offunction metabolism to compensate for loss of vacuolar function. Instead both strains showed loss of function and chemical sensitivities. Null vps41 cells did demonstrate three gainof-function phenotypes: ability to use Tween 20, ability to use L-phenylalanine, and resistance to paromycin. However, neither strain demonstrated the ability to utilize small molecules more effectively than the wild type strain, as might be expected for cells compensating for lack of a major organelle. Small molecules tested include carbon sources such as sugars, amino acids, and carbonic acids (PM plates 1 and 2A); nitrogen sources such as amino acids, nucleotides, and small peptides (PM plates 3B, 6, 7, and 8); phosphorus and sulphur sources (PM plate 4A); and nutrient supplements such as amino acids, other carbon sources, and vitamins (PM plate 5). Neither null strain demonstrated a greater ability to utilize any of these compounds, as assayed by oxidative growth in the PM plates, except for vps41 and its newfound ability to utilize Tween 20 as a carbon source and L-phenylalanine as a nitrogen source. Null cells generally were not inhibited on PM plate 10, which housed a variety of pHs. There was a marked decrease in the ability of vps33, and to a lesser extent vps41, to utilize small peptides for a nitrogen source, which was expected since the vacuole has been identified previously to be a major site of peptide breakdown (Cueva et al. 1989; Roberts et al. 1989). There were marked differences between vps33 and vps41 on Biolog PM plates, with vps33 demonstrating much more severe loss-of-function phenotypes. The null strain of vps33 was much less able to use small peptides as nitrogen sources than were vps41 or wild-type cells. PM plates 21–25 house a wide variety of chemicals to test for chemical sensitivities, and vps33 demonstrated many more sensitivities than vps41. Chemical sensitivities were seen in both strains, with vps33 having a broader range of sensitivities than vps41. Chemical sensitivities were expected based upon phenotype descrip1Supplementary

tions in the SGD Project (2011). However, our analysis did reveal many more sensitivities for vps33 than has previously been characterized.

Final remarks We had hypothesized that we would uncover gain-offunction or compensatory mechanisms to describe how yeast mutants are viable with null mutations in genes required for vacuolar function and biogenesis as Butow’s group had for mitochondrial mutants (Epstein et al. 2001). However, using DNA microarray and Biolog phenotype microarray analysis we did not uncover any mechanisms by which yeast could counteract a loss of vacuolar function. We did identify that the cells grown in YPD at steady-state seem to be under stress, as they have upregulated genes that are involved in or are upregulated in response to stress. Cells lacking the vacuole do not upregulate ion transporters, proteasome subunits, or phosphate transporters to make up for the loss of the proteolytic or ion storage function of the vacuole. Gene expression of several genes did decrease, but this may be a pleiotropic effect of the slower growth phenotypes of vps33 and vps41 (P.A. Marshall, unpublished data). Even when vps41 cells were treated with calcium to try to uncover differences between wild-type and vps41 cells, it was clear that transcriptional changes do not account for compensation for loss of vacuolar function (Table 3). Unexpectedly, there was no correlation between the DNA microarray data and the phenotype microarray data. The DNA microarray data demonstrated that many genes upregulated or downregulated in both strains; however, these changes seemed to be nearly at random (Tables 1 and 2) and did not correlate with any hypothesized compensatory mechanism, even when the cells were stressed with calcium (Table 3). The phenotype microarray data demonstrated many loss-of function phenotypes (Tables 4, 5, and 6). Much of the phenotype microarray data was expected, as the vacuole is the major site for toxin and peptide breakdown (Klionsky et al. 1990), thus the loss of the peptide breakdown and sensitivities to metals and toxins in the deletions strains was expected. In conclusion, null mutants of vps33 and vps41 were analyzed for gain-of-function phenotypes used to offset the loss of vacuolar function, and we found that yeast grown under laboratory conditions largely do not compensate for loss of vacuolar function, even when grown in calcium, an ion stored in the vacuole. This analysis points to future experiments that can be done to assess cellular changes of vps mutants. Global proteomic analysis, determination of shifts in phosphorylated proteins, and analysis of cellular lipids may give insights into additional cellular changes that occur in cells lacking a vacuole. This type of experiment would be most powerful if one could analyze these data in a time course after temperature shift of a temperature-sensitive vps mutant. More precise metabolomics can be performed (such as using mass spectrometry (as in Castrillo et al. 2003)) to identify shifts in small molecule pools. Analysis of the size, shape, and number of other organelles, such as the mitochondria and peroxisome, may lead to a better understanding of the cellular alterations that occur after loss of vacuolar function.

data are available with the article through the journal Web site at http://nrcresearchpress.com/doi/suppl/10.1139/w11-114. Published by NRC Research Press

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Acknowledgements We wish to thank Dr. Charles Deutch for helpful discussions, GCAT (Genome Consortium for Active Teaching) for supporting our work by training PAM on the use of microarrays and supplying microarrays at costs to undergraduate institutions, and Dr. Malcolm Campbell and Dr. Laurie Heyer and others in GCAT for extensive training in microarray studies and analysis and helpful discussions about using MAGIC Tool. Our thanks are also extended to Eden Tanzosh for early work on the phenotype microarrays. NN and JWG were supported by Salt River Project Scholarships (Division of Mathematical and Natural Sciences, Arizona State University); PAM was supported by an SRCA (Scholarship, Research and Creative Activities) grant (New College of Interdisciplinary Arts and Sciences) and a mini-grant from GCAT. We also thank the reviewers of this manuscript whose insightful comments helped to strengthen our science.

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