Imaging-Based Live Cell Yeast Screen Identifies Novel Factors

Jan 2, 2009 - The automated construction of double mutants is the basis of synthetic lethality screens for the identification of genetic interactions ...
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Imaging-Based Live Cell Yeast Screen Identifies Novel Factors Involved in Peroxisome Assembly Heimo Wolinski,† Urosˇ Petrovicˇ,‡ Mojca Mattiazzi,‡ Julia Petschnigg,† Bettina Heise,§ Klaus Natter,† and Sepp D. Kohlwein*,† Institute of Molecular Biosciences, University of Graz, Austria, Department of Molecular and Biomedical Sciences, Jozef Stefan Institute, Ljubljana, Slovenia, and Fuzzy Logic Laboratory Linz-Hagenberg, Linz, Austria Received September 15, 2008

We describe an imaging-based method in intact cells to systematically screen yeast mutant libraries for abnormal morphology and distribution of fluorescently labeled subcellular structures. In this study, chromosomally expressed green fluorescent protein (GFP) fused to the peroxisomal targeting sequence 1, consisting of serine-lysine-leucine, was introduced into 4740 viable yeast deletion mutants using a modified synthetic genetic array (SGA) technology. A benchtop robot was used to create ordered highdensity arrays of GFP-expressing yeast mutants on solid media plates. Immobilized live yeast colonies were subjected to high-resolution, multidimensional confocal imaging. A software tool was designed for automated processing and quantitative analysis of acquired multichannel three-dimensional image data. The study resulted in the identification of two novel proteins, as well as of all previously known proteins required for import of proteins bearing peroxisomal targeting signal PTS1, into yeast peroxisomes. The modular method enables reliable microscopic analysis of live yeast mutant libraries in a universally applicable format on standard microscope slides, and provides a step toward fully automated high-resolution imaging of intact yeast cells. Keywords: synthetic genetic array • lipid metabolism • Saccharomyces cerevisiae • confocal microscopy • green fluorescent protein • high-content screen

Introduction In the postgenomic era proteomics and genetic methods have been established to delineate protein function on a genome-wide scale and to reconstruct the network of functional interactions of proteins in eukaryotic cells. Fluorescence microscopy and, in particular, green-fluorescent protein (GFP) technology provide outstanding tools for studying the spatial organization and time-dependent behavior of fluorescently labeled proteins in live specimens. Consequently, in the last years, great efforts have been made in biomedical research to apply imaging techniques for proteomics approaches. Pioneering work has been conducted in yeast, by labeling all proteins with immuno-tags1 or green fluorescent protein.2 In this context, microscope platforms and tools capable of automated sample preparation and automated image acquisition, as well as sophisticated software for processing of acquired screening data in batch mode, have been developed.3-7 Such imagingbased screening platforms allow the analysis of cellular events in a large number of experimental samples and with high throughput. These approaches complement biochemical and genetic methods and have been successfully applied, for * To whom correspondence should be addressed. Sepp D. Kohlwein, Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/ II, A8010 Graz, Austria. Phone: +43 316 380 5487. Fax: +43 316 380 9854. E-mail: [email protected]. † University of Graz. ‡ Jozef Stefan Institute. § Fuzzy Logic Laboratory Linz-Hagenberg.

20 Journal of Proteome Research 2009, 8, 20–27 Published on Web 01/02/2009

example, to characterize morphological phenotypes of cells caused by mutations or by treatment of cells with chemical inhibitors or drugs.8-11 In a recent imaging-based study in insect cells, RNAi technology was used to identify genes involved in lipid droplet biogenesis and morphology.12 The yeast Saccharomyces cerevisiae is widely used as a model system for studying fundamental cellular processes of eukaryotic cells, highlighted by the Nobel Prize in medicine awarded to yeast researchers for their work on the cell cycle (Leland H. Hartwell, Timothy R. Hunt and Paul M. Nurse. Key regulators of the cell cycle. Nobel Prize 2001). Because of its genetic tractability, its ease of genetic manipulation and the wealth of well-established biochemical, genomics and proteomics tools, yeast represents an excellent model for the analysis of protein function. Systematic analyses of yeast deletion mutant collections13 provide an exceptional experimental platform for robotics-based phenotypic screens. The automated construction of double mutants is the basis of synthetic lethality screens for the identification of genetic interactions (synthetic genetic array; SGA).14 However, despite the popularity of this model organism for genetic and biochemical studies and the availability of strain libraries, large-scale, cell-based phenotypic analyses of yeast are rare.2,13,15-18 One challenge for imagingbased yeast screens and the use of commercially available microscopy platforms is the difficult immobilization of the unicellular organism on glass or plastic surfaces. In addition, such “technical” limitations have to be considered in the 10.1021/pr800782n CCC: $40.75

 2009 American Chemical Society

Novel Factors Involved in Peroxisome Assembly context of a potential interference of the preparation method with normal cellular physiology of yeast.19 A further, general limitation of yeast imaging is the small size of the cells of ∼5-8 µm in length. As a consequence, subcellular yeast structures are typically at or below the limit of optical resolution. Thus, conventional fluorescence microscopy applied for phenotypic profiling of subcellular yeast structures usually produces offgrade image data, which in turn may obscure a reliable quantitative analysis. For this reason, high-resolution and three-dimensional imaging as provided by confocal microscope systems is more suitable to sufficiently resolve cellular components of yeast.20 Here, we describe an imaging-based method for reliable high-resolution screens of large collections of fluorescently labeled living yeast strains. The method was applied to screen the yeast deletion mutant collection for abnormal localization of the peroxisomal matrix marker, green-fluorescent protein tagged with the peroxisomal targeting sequence, PTS1 (carboxyl-terminal amino acid sequence serine-lysine-leucine (SKL)).21,22 Proteins involved in peroxisome biogenesis and function are highly conserved from yeast to man.23-26 Currently, 32 genes (plus several isoforms) involved in membrane biogenesis and import of proteins into the peroxisomal matrix, as well as proliferation and inheritance of the organelle, have been identified in various organisms.27 Malfunction of some of these proteins may cause fatal inherited diseases in humans such as Zellweger syndrome, different types of adrenoleukodystrophy, rhizomelic chondrodysplasia punctata or infantile Refsum disease.28-32 Because of the conservation of the principles of peroxisome biogenesis, data obtained from the yeast system have greatly contributed to the elucidation of the molecular basis of human peroxisomal disorders.33,34 Up to now, 17 yeast genes are known to be involved in the import of proteins harboring PTS1 into the peroxisomal matrix of yeast.24,35,36 The imaging-based screen resulted in the identification of two novel open reading frames that were not linked to peroxisome biogenesis before, one of which is a mitochondrial protein that is required for the efficient import of PTS1 proteins into the matrix of yeast peroxisomes. In addition, all genes previously known to be required for correct localization of PTS1-marked proteins were identified, demonstrating the efficacy and reliability of the screen. We show that the approach provides a robust experimental setup and a powerful basis for qualitative and quantitative large-scale analyses of fluorescently labeled yeast structures. The modular procedure is extensible and applicable to yeast drug screens, multiplex imaging and other cell-based proteomics approaches.

Materials and Methods Large-Scale Integration of GFP-PTS1 into Haploid Yeast Deletion Mutants. The sequence for PTS1, coding for the amino acid triplet serine-lysine-leucine was fused to the 3′end of an episomally encoded eGFP. A 1.5 kb piece of the promoter sequence of ADH1 was cloned upstream of the eGFP start codon for the constitutive expression of GFP-PTS1. This construct was linked to the NATr gene for selection of nourseothricin resistance. The whole cassette was chromosomally integrated at the MFA1 locus by directed integrative transformation into the query strain harboring the following markers: MATR can1∆::MFA1pr-HIS3 lyp1∆ his3∆1 leu2∆0 ura3∆0 met15∆0.14 The reporter construct was introduced into the haploid yeast deletion collection, consisting of 4740 mutant

research articles strains (Euroscarf, Germany) following the synthetic genetic array (SGA) protocol,14,37 with minor modifications in the pinning procedure. Two rounds of pinning on haploid selection plates (0.67% yeast nitrogen base without amino acids, 0.2% drop-out mixture, 2% glucose, 50 mg/L canavanine and 50 mg/L S-(2-aminoethyl)-L-cysteine (Sigma, Inc.)) were followed by two rounds of pinning on double selective plates (DM: 0.17% yeast nitrogen base without amino acids and ammonium sulfate, 0.1% monosodium glutamic acid, 0.2% drop-out mixture,14 2% glucose, 200 mg/L Geneticin (Invitrogen, Inc.), 100 mg/L nourseothricin (Werner BioAgents, Inc.), 50 mg/L canavanine and 50 mg/L S-(2-aminoethyl)-L-cysteine (Sigma, Inc.). The colony pinning procedure was performed using a custommade pinning robot (Adept Plus, Postojna, Slovenia) using floating pins with 0.8 mm diameter, and by hand with a 384 solid pin replicator, with pin diameter 1.2 mm and a colony copier (V&P Scientific, San Diego, CA). Double mutants were stored at 4 °C on double selective media plates in a format of 386 yeast colonies per plate. Identified deletion mutants were verified by colony PCR, using deletion-specific primers.13 Preparation of High-Density Yeast Arrays and Cultivation Conditions. High-density arrays of yeast colonies (1536 yeast colonies per plate) were produced using a Singer RoToR benchtop robot (Singer Inc., U.K.). Four plates derived from the SGA process containing 386 yeast colonies each were pinned onto one YPD complete media plate (1% (w/v) Bacto yeast extract (BD, Inc.), 2% (w/v) D(+)-glucose (Roth, Inc., Germany), 2% (w/v) Bacto peptone (BD, Inc.), 2% (w/v) Bacto agar (BD, Inc.)). Prior to microscopic analysis, colonies were incubated for 16-20 h at 30 °C. Preparation of Cell Arrays for High-Resolution Confocal Microscopy. Cell-arrays for high-resolution microscopy were produced as previously described.38 In brief, arrayed yeast colonies grown on YPD plates (1536 colonies/plate) were replicated using the benchtop robot to a freshly prepared 2% solid agar plate, lacking carbon source. The agar-block was cut into 16 segments, each containing 96 (8 × 12) yeast colonies. Each segment was placed on a standard glass slide and aligned. To avoid mixing and air bubbles, thin horizontal and vertical lines were cut into the agar between the colonies prior to covering the cell array with a 25 × 50 mm coverslip. This mounting procedure spread yeast colonies and immobilized cells predominantly as a monolayer between the agar and coverslip and supported wild type-like growth, in the presence of nutrients in the agar. Fluorescence Microscopy. Microscopy was performed using a Leica SP2 AOBS confocal microscope (Leica Microsystems, Mannheim, Germany) with spectral detection. The microscope was equipped with a programmable microscope stage for precise sample positioning (Ma¨rzha¨user, Inc., Wetzlar-Steindorf, Germany) as well as an integrated galvo-stage for axial scanning. For the acquisition of large cell numbers in one image, a 40× oil immersion objective (HCX PL APO, NA: 1.25) was used. GFP fluorescence was excited at 488nm and emission recorded at 500-550 nm. Five optical sections per sample were imaged using the galvo-stage with a scan speed of ∼1.5 frames/ s. Fluorescence and transmission images were acquired simultaneously. For automated stage positioning and image acquisition with predefined scan settings, a customized microscope control software was programmed using the Leica Macro Developer Software. After setting a reference start position, this software addition enabled the user to sequentially or randomly address all 96 “sample fields”. The acquisition of image stacks Journal of Proteome Research • Vol. 8, No. 1, 2009 21

research articles was performed automatically using the adjusted focal plane as the middle section of the z-stack, plus two additional optical sections above and two sections below this plane (x/y/z sampling: 150 × 150 × 500 nm), thus, covering more than 50% of each cell volume. Typically, each image covered 100-200 yeast cells, and the array of 96 yeast colonies was imaged within ∼30 min. Data Processing and Quantification. 3D image data were processed using a custom-made software add-on (“HCS Profiler”) for amira visualization platforms (amira ResolveRT 4.0 including Quantification Plus package; Mercury Computer Systems, Inc.). The tool was programmed using Tool Command Language (Tcl). The software was applied for automated visualization that included maximum-intensity projections of fluorescence data, and single transmission image acquired from the middle section, 3D Gaussian filtering (kernel size: 3 × 3 × 3), segmentation (manual thresholding, see below), and quantification (mean gray level intensity per pixel of segmented data) of each acquired fluorescence data set. Image processing was performed on a standard PC system (Intel Core Duo 1.66 GHz, 3 GB RAM, Nvidia GeForce 8500GT 512 MB graphic card); processing of image data from 96 yeast colonies required about 5 min on this platform. Mean gray level intensity per pixel per z-stack was calculated using HCS-Profiler as follows: in each optical section of a z-stack, for each gray level i, the number of pixels at intensity i and subsequently the mean gray level intensity per pixel per optical section Isec ) ∑iP(i)/∑(i), for i ∈ [Imin, Imax] were computed, whereby Imin was determined by an empirically set threshold value that was determined for a control strain included in each array of colonies, for extraction of structures from the volumetric data sets; Imax ) 255. The mean gray level intensity per pixel per z-stack (Imean) was computed with Imean ) ∑Isec/∑ number of sections. Statistical analyses were performed using SPSS version 15 (SPSS, Inc. Chicago, IL). This method takes into account variability between the colony arrays, and is independent of cell detection, which is difficult to achieve without additional staining due to the highly specific peroxisomal signal and lack of cellular background fluorescence.

Results In this study, we have developed and applied an imagingbased screening method for the identification of factors involved in peroxisome biogenesis in yeast. This strategy is based on (i) labeling of yeast deletion mutants with a chromosomally encoded GFP-reporter construct; (ii) cultivation of labeled cells in a high-density array on solid media plates; (iii) immobilization of cells under conditions to maintain cellular physiology; (iv) high-resolution confocal imaging; and (v) image processing and statistical assessment of localization data. Figure 1 illustrates the workflow of the screening procedure. Details of the working steps of the procedure are described in Materials and Methods. In our setup, imaging of yeast cells was performed semiautomatically, using a customized microscope control software and a 40× oil immersion objective (NA 1.25) on a Leica SP2 confocal microscope. A lower magnification objective with high numerical aperture provides the advantage of high-resolution imaging of a larger field of view with some 100-200 cells, which is preferable for quantitative and statistical analyses of cellular fluorescence intensity distributions. Assessment of the Screening Procedure for Peroxisomal Mutants. The peroxisomal reporter GFP-PTS1 was chromosomally integrated into 4740 haploid yeast deletion mutants 22

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Figure 1. Workflow of the designed procedure for high-resolution live cell screens of fluorescently labeled subcellular yeast structures. See text for details.

by employing the modified synthetic genetic array (SGA) protocol, originally developed for the construction of haploid double mutants.14,37 Expression of the reporter construct under control of the constitutive ADH1 promoter allowed homogeneous staining of peroxisomes that are derepressed after glucose exhaustion in early stationary phase cells. High-density arrays of 1536 yeast colonies on complete media (YPD) plates were generated with the use of a Singer RoToR benchtop replicating robot. The arrayed yeast colonies were cultivated for 16-20 h and “diluted” by two sequential replicating steps to 2% agar plates without nutrients, to obtain small colonies amenable to microscopic inspection. The method ensures the immobilization of yeast cells as a monolayer and without air-bubbles that may interfere with high-resolution imaging, and also prevents cross-contamination of the arrayed yeast colonies. The high quality of the simultaneously recorded fluorescence and transmission images enables easy detection of morphologically compromised and dead yeast cells. Such cells frequently display autofluorescence, which may interfere with the interpretation of the GFP localization data. However, the preparation procedure supports high vitality and therefore avoids autofluorescence phenomena. This setup thus provides a very flexible and versatile solution to the problem of cell immobilization, and since it is based on standard microscope slides, it can be used on upright or inverted microscopes and does not require dedicated (and costly) high-throughput imaging devices. Formation and degradation of yeast peroxisomes are strongly dependent on the nutritional and physiological condition of the cells and may thus obscure imaging-based screens. The abundance of peroxisomes may rapidly decrease under stress conditions that cause their degradation by autophagic processes.39 Therefore, a peroxisomal GFP marker may appear in the vacuolar lumen, which interferes with a reliable interpretation of the fluorescence images. Thus, we evaluated the abundance and distribution of the GFP-PTS1 reporter construct in the wild-type reference strain under different preparation conditions and cultivation times. As shown in Figure 2, about 80% of the cells grown on complete media plates for 16-20 h displayed GFP-labeled peroxisomes. No significant vacuolar GFP signal was observed, demonstrating the lack of pexophagy under the imaging conditions. In addition, no significant stress or nutrient-dependent morphological alterations of cell morphology such as abnormal cell shape, formation of large vacuoles or visibly dead cells were detected in 10 random preparations up to 2 h, which exceeds the time required for the screen of 96 colonies by at least 4-fold (Supplemental

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Figure 2. GFP-PTS1 labeled peroxisomes of a selected PEX wild-type strain (yor202w∆) derived from the SGA process. The cells were cultivated for 20 h on complete media plates to early stationary phase. About 80% of the cells of the imaged population display a GFP-PTS1 signal under these conditions. Peroxisomes of different sizes are detected in individual cells and no significant fluorescence in the vacuole is apparent. The peroxisomal matrix marker GFP-PTS1 is highly fluorescent and, thus, enables high speed confocal imaging with a high signal-to-noise ratio. Images are displayed as maximum-intensity projections of 3D fluorescence data. Single transmission image acquired in the focal plane. Bar ) 10 µm.

Figure 1 in Supporting Information). In contrast, by maintaining cells in 96 or 384 deep-well plates in liquid culture, peroxisomal morphology and signal intensity were significantly affected during this incubation time, presumably due to hypoxic conditions for the cells that rapidly sediment to the bottom of the well (data not shown). It should be noted, however, that cultivation conditions may depend on the organelle or process to be imaged; some organelles, such as vacuoles or mitochondria may be more sensitive to the preparation conditions than peroxisomes and, thus, require optimization of the cultivation conditions during imaging. Statistical Assessment of the Imaging-Based Yeast Screen. Significant differences of the mean fluorescence intensity distribution per cell or per segmented object are indicative of an increased or decreased accumulation of GFPPTS1 in the peroxisomal matrix of a mutant strain. Thus, comparative analysis of GFP fluorescence levels may reveal differences in peroxisome morphology or impaired protein import in the mutant strains, but requires an optimized imaging setup. Instability of the imaging system due to fluctuations of the laser output, for instance, may result in abnormal variations of detected gray level intensities. Therefore, the microscope setup was optimized for control strains that were included in each set of 96 strains, prior to imaging. In a second step, imaged peroxisomes were segmented from all acquired z-stacks from an array of 96 yeast colonies by a calculated threshold value, based on the control strains. The mean gray level intensities per pixel were computed for each strain and normalized to the control strain, and verified for statistical distribution. This analysis shows a near normal distribution of GFP-PTS1 fluorescence intensities of the analyzed mutant strains, demonstrating that this method indeed yields statistically relevant image data of fluorescently labeled yeast peroxisomes (Figure 3). As expected, we found that extreme values of the numerical data reflect significant differences of the size of GFP-PTS1 labeled peroxisomes, as confirmed by visual

inspection of the images of the corresponding mutant strains. It should be noted, however, that this level of analysis is not cell-based due to the lack of appropriate cell registration setups. The peroxisomal labeling is highly specific and does not yield sufficient cellular background staining that allows cell segmentation that was successfully applied to characterize and classify subcellular structures in yeast.6 Such an analysis would permit more refined classification of altered peroxisomal morphology, particle distribution and similar parameters on a cellular basis. Current attempts are, therefore, directed toward implementing additional protocols that permit cell segmentation based on metabolically inert reference labels, for example, red fluorescent protein-tagged reporters, or transmission images. Automated Image Processing and Data Storage. Quantification of fluorescently labeled peroxisomal structures (computation of the mean gray level intensity/pixel value for each selected z-stack) as described above was performed using a custom-made software tool. This software was also applied for automated maximum-intensity projection of acquired z-stacks that were composed of 5 optical sections through the specimens. While a complete 3D data set would require ∼27 optical sections (according to the Nyquist theorem and assuming a cell thickness of 5 µm), which is not feasible for screening purposes, images taken from five planes yielded sufficient information to reliably detect deviations from the wild-type fluorescence distribution. Considerable efforts have been made in recent years to develop software for automated processing of image data derived from large-scale screens.3,4,6,7 Currently available commercial packages only provide support for 2D image data; therefore, as a flexible alternative and independent of cell registration, we have designed a scripting-customized software extension for amira visualization platforms (“HCSProfiler”) for automated processing of 3D data sets in batch mode. This extensible software add-on includes features for automated filtering, visualization, segmentation and quantification of multidimensional, multichannel image data. A demo Journal of Proteome Research • Vol. 8, No. 1, 2009 23

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Figure 3. Quantitative analysis of GFP-PTS1 fluorescence levels in 523 multidimensionally imaged mutant strains. (a) Histogram of computed normalized mean gray level intensity/pixel values computed for each imaged strain. The black curve shows the computed theoretical normal distribution. (b) Larger GFP-PTS1 labeled peroxisomal structures in mutant strain yjr074w∆ showed the highest mean gray level intensity/pixel value of all analyzed mutant strains. (c) Mutant strain ykr048c∆ showed the lowest mean gray level intensity/pixel value of the data set. In this case, peroxisomes appear significantly smaller than in the yjr074w∆ strain. Images were cropped from larger maximum-intensity projections of acquired 3D fluorescence data. Bar ) 5 µm.

movie of the software demonstrating the implemented features is available at http://microscopy.uni-graz.at/hcsprofiler.html. Processed image data are stored in an Oracle-based relational database (YPL+, Leitner et al., manuscript in preparation), which is an extension of the Yeast Protein Localization database (YPL) developed in our laboratory;40,41 image data are available for download at http://microscopy.uni-graz.at. Application of the Screening Method Identifies All Known pex Mutants That Affect PTS1 Protein Localization. The collection of 4740 viable haploid yeast deletion mutants expressing the chromosomally encoded GFP-PTS1 reporter construct under control of the constitutive ADH1 promoter was screened as described, and yielded 14 pex mutants with an exclusively cytosolic localization of the reporter construct. Importantly, even if only a small fraction of cells expressed GFP-PTS1 in a mixture with unlabeled cells, mislocalization of the reporter construct was clearly detectable by visual inspection (supplemental Figure 2a,b in Supporting Information). Three mutants, namely, pex25∆, djp1∆ and pex15∆, showed both cytosolic and peroxisomal localization of the reporter construct (supplemental Figure 2c in Supporting Information), consistent with localization studies described elsewhere.24,35,36 Identification of Novel Mutants with Aberrant GFP-PTS1 Localization. In addition to previously known factors required for peroxisomal localization of proteins harboring the PTS1 signal sequence, the screening revealed several novel genes whose deletion resulted in mislocalization of the GFP marker. The mdh2∆ and afg1∆ mutant strains showed a significant amount of cytosolic GFP, in addition to punctate, presumably peroxisomal structures. MDH2 encodes the cytosolic malate dehydrogenase, which is one of three isozymes (Mdh1 is mitochondrial and Mdh3 is peroxisomal) that catalyze interconversion of malate and oxaloacetate in the glyoxylate cycle, and which is required for gluconeogenesis. AFG1 encodes a mitochondrial, conserved protein of the AAA family that 24

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localizes to the matrix face of the mitochondrial inner membrane and may act as a chaperone in the degradation of misfolded or unassembled cytochrome c oxidase subunits. Furthermore, yjl211c∆ and ygl152c∆ deletion mutants also showed abnormal localization of the GFP reporter. YJL211C and YGL152C are dubious open reading frames and partially overlap with the verified PEX2 and PEX14 genes, respectively. Thus, cytosolic localization of GFP-PTS1 in these mutants is not surprising since Pex2 or Pex14 are missing in the yjl211c∆ and ygl152c∆ deletion mutants, yet their independent identification underscores the efficacy and reliability of the screening method (Figure 4). To confirm a potential impact of the mdh2∆ and afg1∆ deletions on the localization of peroxisomal proteins or on peroxisome assembly, peroxisomal proteins Mdh3-GFP (PTS1 protein), Pot1-GFP (PTS2 protein) and Pex14-GFP (peroxisomal membrane protein) were expressed in the respective deletion mutants and inspected by fluorescence microscopy. As shown in Figure 5, Mdh3-GFP and Pot1-GFP both localized to peroxisomes in the wild-type, in accordance with the literature,42 but remained in the cytosol in the mdh2∆ mutant. The afg1∆ mutant strain also displayed strong cytosolic Mdh3-GFP and Pot1-GFP signals, in addition to punctate peroxisomal structures. Interestingly, the peroxisomal membrane protein Pex14GFP localized to punctate structures in the mdh2∆ and afg1∆ mutants indistinguishable from the wild-type, demonstrating the existence of peroxisomal structures in these mutants. Thus, cytosolic localization of the GFP reporter constructs is not due to the lack of peroxisomes but rather indicates defective targeting or import of both PTS1 and PTS2 harboring peroxisomal proteins in the afg1∆ and mdh2∆ mutants.

Discussion Peroxisome biogenesis is an important cellular process, and numerous human diseases are associated with peroxisomal

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Figure 4. Deletion mutants identified in the screen show abnormal localization of GFP-PTS1 or abnormal peroxisome morphology. Cytosolic distribution of GFP-PTS1 in ygl152c∆ and in yjl211c∆ deletion mutant strains. Both cytosolic and peroxisomal distribution of GFP-PTS1 in mdh2∆ and afg1∆ mutant strains. See text for details. Maximum-intensity projection of 3D fluorescence data. Single transmission image acquired in the focal plane. Images were cropped from original screening data. Bar ) 10 µm.

Figure 5. Subcellular localization of different peroxisomal markers in mdh2∆ and afg1∆ deletion mutants identified in this screen. Three GFP-tagged peroxisomal proteins were expressed from a plasmid under the control of TEF1 promoter in the wild-type (wt), mdh2∆ and afg1∆ mutant strains, and their localization was analyzed by confocal microscopy. Mdh3-GFP (peroxisomal targeting sequence 1) and Pot1-GFP (peroxisomal targeting sequence 2) are peroxisomal matrix proteins; Pex14-GFP is a peroxisomal membrane protein. Cytosolic signal, indicative of mislocalization, was observed with both matrix marker proteins Mdh3-GFP and Pot1-GFP in the mdh2∆ strain. The afg1∆A strain shows strong cytosolic signals of both marker proteins and additional punctate localization indicative of peroxisomes. In both mutant strains and in the wild-type Pex14-GFP is localized to peroxisomes. Bar ) 10 µm.

disorders. Thus, during the past decades, numerous mutant screens have been performed to identify the factors involved in peroxisome biogenesis and function. These screens largely relied on the identification of mutants with specific growth phenotypes resulting from impaired peroxisome function.24,29 These efforts conducted in multiple model systems have yielded a comprehensive list of 32 highly conserved PEX genes (some of which occur in isoforms, such as PEX11 R, β and γ, and a PEX5-like peroxin) required for peroxisome assembly and function;27 17 of the encoded Pex proteins are necessary for correct targeting of proteins bearing a C-terminal targeting sequence 1 (PTS1), to the peroxisome. In an effort to exploit imaging-based high-content screens in yeast, we have focused

in this study on the identification of novel factors involved in PTS1 protein targeting. This screen was based on a GFP-PTS1 “query” construct that was introduced into all viable haploid yeast deletion mutants, and subsequent microscopic analysis of aberrant localization patterns. The screen identified all 17 previously reported yeast genes required for peroxisomal matrix protein import; it also reliably picked up more subtle localization phenotypes, for example, in pex15∆, pex25∆ and djp1∆ mutants that cause only partial cytosolic mislocalization of the peroxisomal protein.24,35,36 Most interestingly, the screen also identified two novel proteins required for efficient peroxisomal targeting, namely, Mdh2 and Afg1, which have escaped previous mutant screens. These proteins are required for both PTS1 Journal of Proteome Research • Vol. 8, No. 1, 2009 25

research articles and PTS2 protein targeting, but do not appear to be involved in peroxisomal membrane biogenesis. Interestingly, the enzymatic activity of Mdh2 as a cytosolic malate dehydrogenase is clearly linked to peroxisome function.36 Mdh2 is involved in the reoxidation of peroxisomal NADH by converting malate that is exported from the peroxisomal matrix to the cytosol, to oxaloacetate, which is subsequently shuttled back into the peroxisome.30 Mdh2-GFP localizes mostly to the cytosol but appears to be also present in peroxisomes (Zlobinskaya and Kohlwein, unpublished). Afg1, the second major candidate protein identified in our screen, is described as a mitochondrial ATPase and member of the AAA protein family. Interestingly, Afg1 shares sequence similarity with Pex1,43 which is a peroxin required for peroxisome biogenesis and import of matrix proteins.44 Afg1 is exclusively localized to mitochondria ( ref 2 and confirmed in this study), providing further evidence for a functional link between mitochondria and peroxisomes, for which, however, the molecular mechanism needs to be determined. In addition to these clear-cut morphological phenotypes observed in mdh2∆ and afg1∆ mutants, a number of mutants displayed statistically significant deviations of fluorescence patterns and intensities (Supplemental Table 1 in Supporting Information). As with all “omics” strategies, additional, more specific tests need to be conducted to verifysor disprovestheir relevance to the process under study, and which is beyond the scope of the present study. Using an already well-characterized experimental systemsperoxisome biogenesisswith great potential and biomedical relevance, we aimed at developing a setup for imaging-based screens in yeast that should comply with the following requirements: (i) highly specific and homogeneous cell labeling; (ii) simple, robust setup for yeast highresolution imaging that can be applied on standard upright or inverse microscopes; (iii) maintenance of physiological conditions for live cell imaging; (iv) standardized imaging conditions of large numbers of cells that permit reliable statistical analyses. Only partially solved is the problem of (yeast) cell detection, which would permit more comprehensive quantitative analysis of particle distributions on a per cell basis.6 Since the GFPPTS1 construct is specifically targeted to peroxisomes (when present), the background signal is not sufficient to delineate the cell boundaries. Cell-based calculations may rely on a high background fluorescence, additional labeling of fixed cells,45,46 addition of a DNA dye, or expression of a red-fluorescent reference construct. A noninvasive method for cell detection that avoids potential physiological interference of such reporters and that solely requires transmission images is currently not available, for high-density yeast arrays. Homogeneous cell labeling was achieved by chromosomal expression of a GFP-tagged query construct, which provides numerous significant advantages for imaging-based screens over strategies that rely on vital dyes: (i) Labeling of organelles and subcellular structures is very specific, dependent on the GFP-tagged query construct and employed targeting sequences. In contrast, specificity of vital dyes is very limited and restricted to a few organelles. (ii) In addition to addressing the morphology of specific cellular structures, this technique can also be expanded to identify mutants that affect dynamic processes, for example, secretion, endocytosis, protein targeting, or organelle inheritance. (iii) Expression of the construct is fairly homogeneous in the cell population due to expression from a chromosomal locus. Expression is either under native or heterologous promoter control, which also provides quantita26

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Wolinski et al. tive information on protein abundance and is a prerequisite for statistically meaningful image analysis. In contrast, since many vital dyes are substrates for pleiotropic drug resistance pumps (Wolinski and Kohlwein, unpublished results), heterogeneous staining patterns in the cell population may rather reflect varying activities of these pumps and thus obscure a quantitative statistical analysis. (iv) The GFP-labeled strain collection can be screened under various nutritional and environmental conditions, and may thus, in addition to morphological information, also provide insight into gene expression levels or protein stability in the mutant strains, based on fluorescence intensities. These major advantages clearly outweigh the disadvantage of the time-requirements for cloning the query construct (1 week) and conducting the SGA protocol to generate the haploid GFP-expressing mutant strains (3 weeks), which, however, can also be multiplexed, thus, reducing the overall time requirements, if multiple screens are to be performed. Significant effort was taken in our approach to immobilize and cultivate cells during microscopy with minimal interference with normal physiology, compared to cells grown in liquid culture. Specifically, organelles reacting to hypoxic conditions, like mitochondria, vacuoles and peroxisomes, are well-maintained using the agar immobilization technique described here. Hypoxic conditions clearly need to be considered in imagingbased live cell screens using microtiter plates. The agar immobilization technique also provides the major advantage that standard microscope slides and upright and inverted setups can be used, which reduces the costs for such imagingbased screens considerably, and should contribute to a wide range of applications in imaging-based yeast screens.

Acknowledgment. This work was supported by grants from the Austrian Federal Ministry for Science and Research, in the framework of the Austrian genomics program, GEN-AU (pilot projects mYeasty and FLUPPY, and project GOLD-Genomics of lipid-associated disorders) to ¨ AD project Sl 14/2007 to S.D.K. and U.P. S.D.K., and O Supporting Information Available: Supporting Information shows time-lapse records of a representative GFP-PTS1 labeled deletion strain and images of known pex mutants identified in this screen, as well as a table of mutants with significant deviations from the wild-type pattern. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Kumar, A.; Cheung, K. H.; Tosches, N.; Masiar, P.; Liu, Y.; Miller, P.; Snyder, M. The TRIPLES database: a community resource for yeast molecular biology. Nucleic Acids Res. 2002, 30 (1), 73–5. (2) Huh, W. K.; Falvo, J. V.; Gerke, L. C.; Carroll, A. S.; Howson, R. W.; Weissman, J. S.; O’Shea, E. K. Global analysis of protein localization in budding yeast. Nature 2003, 425 (6959), 686–91. (3) Baatz, M.; Arini, N.; Schape, A.; Binnig, G.; Linssen, B. Objectoriented image analysis for high content screening: detailed quantification of cells and sub cellular structures with the Cellenger software. Cytometry, Part A 2006, 69 (7), 652–8. (4) Carpenter, A. E.; Jones, T. R.; Lamprecht, M. R.; Clarke, C.; Kang, I. H.; Friman, O.; Guertin, D. A.; Chang, J. H.; Lindquist, R. A.; Moffat, J.; Golland, P.; Sabatini, D. M. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. GenomeBiology 2006, 7 (10), R100. (5) Chen, S. C.; Murphy, R. F. A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images. BMC Bioinf. 2006, 7, 90. (6) Chen, S. C.; Zhao, T.; Gordon, G. J.; Murphy, R. F. Automated image analysis of protein localization in budding yeast. Bioinformatics 2007, 23 (13), i66–71.

research articles

Novel Factors Involved in Peroxisome Assembly (7) Chen, X.; Velliste, M.; Murphy, R. F. Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics. Cytometry, Part A 2006, 69 (7), 631–40. (8) Starkuviene, V.; Pepperkok, R. The potential of high-content highthroughput microscopy in drug discovery. Br. J. Pharmacol. 2007, 152 (1), 62–71. (9) Pepperkok, R.; Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell. Biol. 2006, 7 (9), 690– 6. (10) Rausch, O. High content cellular screening. Curr. Opin. Chem. Biol. 2006, 10 (4), 316–20. (11) Abraham, V. C.; Taylor, D. L.; Haskins, J. R. High content screening applied to large-scale cell biology. Trends Biotechnol. 2004, 22 (1), 15–22. (12) Guo, Y.; Walther, T. C.; Rao, M.; Stuurman, N.; Goshima, G.; Terayama, K.; Wong, J. S.; Vale, R. D.; Walter, P.; Farese, R. V. Functional genomic screen reveals genes involved in lipid-droplet formation and utilization. Nature 2008, 453 (7195), 657–61. (13) Giaever, G.; Chu, A. M.; Ni, L.; Connelly, C.; Riles, L.; Veronneau, S.; Dow, S.; Lucau-Danila, A.; Anderson, K.; Andre, B.; Arkin, A. P.; Astromoff, A.; El-Bakkoury, M.; Bangham, R.; Benito, R.; Brachat, S.; Campanaro, S.; Curtiss, M.; Davis, K.; Deutschbauer, A.; Entian, K. D.; Flaherty, P.; Foury, F.; Garfinkel, D. J.; Gerstein, M.; Gotte, D.; Guldener, U.; Hegemann, J. H.; Hempel, S.; Herman, Z.; Jaramillo, D. F.; Kelly, D. E.; Kelly, S. L.; Kotter, P.; LaBonte, D.; Lamb, D. C.; Lan, N.; Liang, H.; Liao, H.; Liu, L.; Luo, C.; Lussier, M.; Mao, R.; Menard, P.; Ooi, S. L.; Revuelta, J. L.; Roberts, C. J.; Rose, M.; Ross-Macdonald, P.; Scherens, B.; Schimmack, G.; Shafer, B.; Shoemaker, D. D.; Sookhai-Mahadeo, S.; Storms, R. K.; Strathern, J. N.; Valle, G.; Voet, M.; Volckaert, G.; Wang, C. Y.; Ward, T. R.; Wilhelmy, J.; Winzeler, E. A.; Yang, Y.; Yen, G.; Youngman, E.; Yu, K.; Bussey, H.; Boeke, J. D.; Snyder, M.; Philippsen, P.; Davis, R. W.; Johnston, M. Functional profiling of the Saccharomyces cerevisiae genome. Nature 2002, 418 (6896), 387–91. (14) Tong, A. H.; Evangelista, M.; Parsons, A. B.; Xu, H.; Bader, G. D.; Page, N.; Robinson, M.; Raghibizadeh, S.; Hogue, C. W.; Bussey, H.; Andrews, B.; Tyers, M.; Boone, C. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2001, 294 (5550), 2364–8. (15) Narayanaswamy, R.; Niu, W.; Scouras, A. D.; Hart, G. T.; Davies, J.; Ellington, A. D.; Iyer, V. R.; Marcotte, E. M. Systematic profiling of cellular phenotypes with spotted cell microarrays reveals mating-pheromone response genes. GenomeBiology 2006, 7 (1), R6. (16) Howson, R.; Huh, W. K.; Ghaemmaghami, S.; Falvo, J. V.; Bower, K.; Belle, A.; Dephoure, N.; Wykoff, D. D.; Weissman, J. S.; O’Shea, E. K. Construction, verification and experimental use of two epitope-tagged collections of budding yeast strains. Comp. Funct. Genomics 2005, 6 (1-2), 2–16. (17) Natter, K.; Leitner, P.; Faschinger, A.; Wolinski, H.; McCraith, S.; Fields, S.; Kohlwein, S. D. The spatial organization of lipid synthesis in the yeast Saccharomyces cerevisiae derived from large scale green fluorescent protein tagging and high resolution microscopy. Mol. Cell. Proteomics 2005, 4 (5), 662–72. (18) Ohya, Y.; Sese, J.; Yukawa, M.; Sano, F.; Nakatani, Y.; Saito, T. L.; Saka, A.; Fukuda, T.; Ishihara, S.; Oka, S.; Suzuki, G.; Watanabe, M.; Hirata, A.; Ohtani, M.; Sawai, H.; Fraysse, N.; Latge, J. P.; Francois, J. M.; Aebi, M.; Tanaka, S.; Muramatsu, S.; Araki, H.; Sonoike, K.; Nogami, S.; Morishita, S. High-dimensional and largescale phenotyping of yeast mutants. Proc. Natl. Acad. Sci. U.S.A. 2005, 102 (52), 19015–20. (19) Wolinski, H.; Kohlwein, S. D., Microscopic analysis of lipid droplet metabolism and dynamics in yeast. In Methods in Molecular Biology; Vancura, A., Ed.; Membrane Trafficking, Vol. 457; Humana Press: Totowa, NJ, 2008; pp 151-163. (20) Kohlwein, S. D. The beauty of the yeast: live cell microscopy at the limits of optical resolution. Microsc. Res. Tech. 2000, 51 (6), 511–29. (21) Gould, S. J.; Keller, G. A.; Hosken, N.; Wilkinson, J.; Subramani, S. A conserved tripeptide sorts proteins to peroxisomes. J. Cell Biol. 1989, 108 (5), 1657–64. (22) Keller, G. A.; Krisans, S.; Gould, S. J.; Sommer, J. M.; Wang, C. C.; Schliebs, W.; Kunau, W.; Brody, S.; Subramani, S. Evolutionary

(23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35)

(36)

(37) (38)

(39) (40) (41) (42) (43) (44)

(45) (46)

conservation of a microbody targeting signal that targets proteins to peroxisomes, glyoxysomes, and glycosomes. J. Cell Biol. 1991, 114 (5), 893–904. Erdmann, R.; Schliebs, W. Peroxisomal matrix protein import: the transient pore model. Nat. Rev. Mol. Cell Biol. 2005, 6 (9), 738–42. Eckert, J. H.; Erdmann, R. Peroxisome biogenesis. Rev. Physiol. Biochem. Pharmacol. 2003, 147, 75–121. Johnson, T. L.; Olsen, L. J. Building new models for peroxisome biogenesis. Plant Physiol. 2001, 127 (3), 731–9. Sacksteder, K. A.; Gould, S. J. The genetics of peroxisome biogenesis. Annu. Rev. Genet. 2000, 34, 623–652. Schluter, A.; Fourcade, S.; Ripp, R.; Mandel, J. L.; Poch, O.; Pujol, A. The evolutionary origin of peroxisomes: an ER-peroxisome connection. Mol. Biol. Evol. 2006, 23 (4), 838–45. Wanders, R. J. Metabolic and molecular basis of peroxisomal disorders: a review. Am. J. Med. Genet., Part A 2004, 126A (4), 355– 75. Purdue, P. E.; Lazarow, P. B. Peroxisome biogenesis. Annu. Rev. Cell. Dev. Biol. 2001, 17, 701–52. van Roermund, C. W.; Waterham, H. R.; Ijlst, L.; Wanders, R. J. Fatty acid metabolism in Saccharomyces cerevisiae. Cell. Mol. Life Sci. 2003, 60 (9), 1838–51. Fujiki, Y.; Okumoto, K. [Peroxisome biogenesis and human disorders]. Tanpakushitsu Kakusan Koso 2000, 45 (5), 691–9. Gould, S. J.; Valle, D. Peroxisome biogenesis disorders: genetics and cell biology. Trends Genet. 2000, 16 (8), 340–5. van der Klei, I. J.; Veenhuis, M. Yeast and filamentous fungi as model organisms in microbody research. Biochim. Biophys. Acta 2006, 1763 (12), 1364–73. van der Klei, I.; Veenhuis, M. Peroxisomes: flexible and dynamic organelles. Curr. Opin. Cell. Biol. 2002, 14 (4), 500–5. Hettema, E. H.; Ruigrok, C. C.; Koerkamp, M. G.; van den Berg, M.; Tabak, H. F.; Distel, B.; Braakman, I. The cytosolic DnaJ-like protein djp1p is involved specifically in peroxisomal protein import. J. Cell Biol. 1998, 142 (2), 421–34. Smith, J. J.; Marelli, M.; Christmas, R. H.; Vizeacoumar, F. J.; Dilworth, D. J.; Ideker, T.; Galitski, T.; Dimitrov, K.; Rachubinski, R. A.; Aitchison, J. D. Transcriptome profiling to identify genes involved in peroxisome assembly and function. J. Cell Biol. 2002, 158 (2), 259–71. Tong, A. H.; Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 2006, 313, 171–92. Wolinski, H.; Kohlwein, S. D. The fidgety yeast: focus on highresolution live yeast cell microscopy. In Methods in Molecular Biology: Yeast Functional Genomics and Proteomics; Stagljar, I., Ed.; Humana Press: Totowa, NJ, 2008, in press. Veenhuis, M.; Kiel, J. A.; Van Der Klei, I. J. Peroxisome assembly in yeast. Microsc. Res. Tech. 2003, 61 (2), 139–50. Kals, M.; Natter, K.; Thallinger, G. G.; Trajanoski, Z.; Kohlwein, S. D. YPL.db2: the Yeast Protein Localization database, version 2.0. Yeast 2005, 22 (3), 213–8. Habeler, G.; Natter, K.; Thallinger, G. G.; Crawford, M. E.; Kohlwein, S. D.; Trajanoski, Z. YPL.db: the Yeast Protein Localization database. Nucleic Acids Res. 2002, 30 (1), 80–3. Steffan, J. S.; McAlister-Henn, L. Isolation and characterization of the yeast gene encoding the MDH3 isozyme of malate dehydrogenase. J. Biol. Chem. 1992, 267 (34), 24708–15. Lee, Y. J.; Wickner, R. B. AFG1, a new member of the SEC18-NSF, PAS1, CDC48-VCP, TBP family of ATPases. Yeast 1992, 8 (9), 787– 90. Erdmann, R.; Wiebel, F. F.; Flessau, A.; Rytka, J.; Beyer, A.; Frohlich, K. U.; Kunau, W. H. PAS1, a yeast gene required for peroxisome biogenesis, encodes a member of a novel family of putative ATPases. Cell 1991, 64 (3), 499–510. Ohtani, M.; Saka, A.; Sano, F.; Ohya, Y.; Morishita, S. Development of image processing program for yeast cell morphology. J. Bioinform. Comput. Biol. 2004, 1 (4), 695–709. Saito, T. L.; Ohtani, M.; Sawai, H.; Sano, F.; Saka, A.; Watanabe, D.; Yukawa, M.; Ohya, Y.; Morishita, S. SCMD: Saccharomyces cerevisiae Morphological Database. Nucleic Acids Res. 2004, 32 (Database issue), D319–22.

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