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Genome Res. 14:1948-1956, 2004 ©2004 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/04 $5.00 Methods High-Content Screening Microscopy Identifies Novel Proteins With a Putative Role in Secretory Membrane Traffic1 Cell Biology and Biophysics Programme, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany 2 Molecular Genome Analysis, German Cancer Research Centre, 69120 Heidelberg, Germany
Here we describe the establishment of microscope-based functional screening assays in intact cells that allow us to systematically identify new proteins involved in secretory membrane traffic, and proteins that can influence the integrity of the Golgi complex. We were able to identify 20 new proteins that affected either secretory transport, Golgi morphology, or both, when overexpressed in cells. Control experiments with human orthologs to yeast proteins with a role in membrane traffic, or already well characterized mammalian regulators of the secretory pathway, confirmed the specificity and significance of our results. Proteins localized to the Golgi complex or endoplasmic reticulum (ER) showed preferential interference in our assays. Bioinformatic analysis of the new proteins interfering with membrane traffic and/or Golgi integrity revealed broad functional variety, but demonstrated a bias towards proteins with predicted coiled-coil domains and repeat structures. Extending our approach to a much larger set of novel proteins in the future will be an important step toward a more comprehensive understanding of the molecular basis of the secretory pathway. It will also serve as an example for similar microscope-based screens addressing different biological questions.
Automated DNA sequencing of the genomes from various species has now generated an enormous wealth of data that represents the basis for a comprehensive understanding of the molecular organization and function of living cells or organisms. The next essential steps toward this challenging goal will be to use these sequence data to deduce the respective gene coding regions, their regulatory elements, and ultimately the function of the encoded proteins in a cellular context. However, taking into account the 30,000 genes predicted in the human genome (Venter et al. 2001  ) and additionally the plethora of proteins resulting from alternative splicing and posttranslation modifications, it becomes evident that the development and availability of parallel analysis methods is critical to ensure an efficient functional dissection of the human proteome.
In recent years, genetic and biochemical approaches have been applied to identify and characterize single molecular components of the secretory pathway, a process that is central to cell organization. Secretory membrane traffic enables cells to distribute newly synthesized proteins, lipids, and carbohydrates, and thus ensures cell growth, homeostasis, and differentiation. More recently, systematic approaches such as organelle proteomics and yeast two-hybrid screening have attempted to identify new components of the pathway with the goal of reaching a more complete description of the molecular regulation of membrane traffic (Bell et al. 2001
Here we describe the development and application of a screening strategy in intact cells that aims at the targeted identification of new proteins regulating membrane traffic. To this end we have exploited a collection of novel human full-length cDNAs (Wiemann et al. 2001
Application of the Transport Assay We recently described the development and characterization of a fully automated high-content screening (HCS) platform (Liebel et al. 2003 ) which is capable of automatically acquiring and analyzing high-resolution multicolor images of immunostained cells. Using this system we developed a quantitative single-cell-based transport assay (Liebel et al. 2003; see also Methods), which we applied here to reveal interference of ectopically expressed GFP-tagged proteins with membrane traffic in the secretory pathway. This assay is based upon the monitoring of the transport of a well characterized cargo molecule, namely the ts-O45-G temperature-sensitive variant of the vesicular stomatitis viral G-protein VSV-G (Zilberstein et al. 1980). This transmembrane protein has the feature of accumulating in the endoplasmic reticulum (ER) at 39.5°C, but moves vectorially through the secretory pathway to the plasma membrane (PM) at the permissive temperature of 32°C, where an antibody recognizing an external epitope can detect it. This has the advantages that transport in individual cells is highly synchronized, and that background PM staining that may be caused by transport of the marker before and during expression of the GFP-tagged proteins under examination does not occur.
In control experiments, with cells expressing soluble cyano fluorescent protein (CFP) or yellow fluorescent protein (YFP), ts-O45-G transport to the PM occurred with an efficiency similar to that observed in neighboring nontransfected cells (Fig. 1A-C; data not shown). In contrast, when a GTP-restricted dominant negative mutant of the Sar1p GTPase (SAR1p [H79G]; Aridor et al. 1995
We next selected 100 GFP-tagged novel ORFs to be included in the transport assay. This selection was based upon their subcellular localization. Specifically, we chose proteins localizing to membranes of the secretory pathway, cytoskeletal structures, or the cytoplasm (Simpson et al. 2000 ; see also http://gfp-cdna.embl.de/). The assumption was that proteins showing these localizations would be the most likely to affect ts-O45-G transport. To compare the different experiments with different GFP-tagged proteins in a quantitative manner, the relative surface expression of ts-O45-G in transfected cells was normalized in each experiment to the average values obtained for nontransfected cells, giving an immediate indication of the effect of the expressed GFP-tagged proteins on ts-O45-G transport (see Table 1 and Methods for details). However, significant variations in different experiments due to varying average amounts of protein expressed were observed. To account for this problem and to make the data evaluation more robust, the population of transfected cells was divided into three expression classes (low, medium, and high), and the average normalized surface expression of ts-O45-G for each class was determined. Then a linear regression was fitted to the values obtained for these three classes and that of nontransfected cells to account for the changes in surface expression with growing amounts of expressed GFP-tagged protein (see Methods for details). In this way, variations in the average expression of the same protein in different experiments were less critical. Overexpression of soluble YFP and CFP resulted in average slope values of -0.0015 ± 0.0009 and -0.0002 ± 0.0002 (see Fig. 2A), respectively (averaging nine independent experiments each). Only those GFP-tagged proteins showing slopes of the fits that deviated from the values obtained for YFP or CFP more than the respective standard deviation of the population (± 0.0026 and ± 0.0055 for YFP and CFP, respectively) were considered as displaying an effect on ts-O45-G transport (Fig. 3).
Of the 100 ORFs screened, 24 could be classified as inhibitors and one as an accelerator of ts-O45-G transport (Fig. 3, Table 1, Supplemental Table 1). Eleven of these showed a strong transport inhibition that was already apparent by visual inspection of the transfected cells (Figs. 1G-L, 2C; Table 1). Their slopes were typically greater than half of the difference between the slopes obtained for GFP variants and the corresponding GFP-tagged Sar1p [H79G] (slopes for YFP- and CFP-tagged SAR1p [H79G] were -0.0102 ± 0.0012 and -0.0141 ± 0.0034, respectively). The 13 remaining transport inhibitors caused a weaker effect on ts-O45-G transport, which was less obvious to appreciate by visual inspection (Fig. 1D-F), but could be clearly quantified by automated image analysis (Fig. 2B, Supplemental Table 1). Seventy-five of the proteins analyzed were classified as not inhibiting ts-O45-G transport (Supplemental Table 2). Wherever possible, both N- and C-terminal GFP fusions were screened (51 clones). For 38 clones, only the N-terminal fusions and for 11 clones only the C-terminal fusions were tested. Proteins were considered to be effectors regardless of whether one or both tagged versions of the protein were identified as effectors in our assays.
Application of the Golgi Integrity Assay
Similar to the experiments addressing ts-O45-G transport, each GFP-tagged ORF was expressed for 24 h. As a Golgi marker, the matrix protein GM130 (Nakamura et al. 1995
For 15 of the Golgi effectors, the Golgi morphology was only weakly affected, with an increased number of Golgi fragments varying by a factor between 1.2 and 2.0 compared to nontransfected cells. No significant loss of the total Golgi fluorescence occurred in cells transfected with these fusion proteins. In three of the Golgi effectors, the number of scattered GM130-positive Golgi structures was increased by more than a factor of 2.0 compared to nontransfected cells, and hence they were defined as having a strong effect on Golgi morphology (see examples in Fig. 4C,F,I and Table 1). Interestingly, for seven of the effectors the total Golgi-specific fluorescence or the number of Golgi fragments was reduced (Table 1, Supplemental Table 1). This might be due to a possible relocation of Golgi matrix proteins into the ER under conditions of an ER export block, as was recently demonstrated to occur (Puri and Linstedt 2003 ).
Relevance of Subcellular Localization and Targeting Signals
It is well known that the position of the GFP tag has an effect on the subcellular localization of the fusion proteins (Simpson et al. 2001 ), and is therefore also likely to influence their cellular activities. Indeed, fusions with the GFP close to, for example, N-terminal myristoylation or C-terminal prenylation sites interfered with the proper localization of the proteins, and as a result these fusions showed no effect on transport or Golgi complex morphology. In contrast, when in the same proteins the GFP variants were fused to the opposite end, the localization was correct and a corresponding effect in our assays was observed (examples are the MARCKs-like protein AL713653
[GenBank]
and the Rab GTPases AL136727
[GenBank]
and AL136904
[GenBank]
). Similarly, membrane proteins with predicted di-lysine and di-arginine ER-retrieval motifs in their cytoplasmic tails showed only an effect on ts-O45-G transport or Golgi morphology when these motifs were not masked by the GFP.
Analysis of Results According to Functional Predictions
The remaining six proteins known to be involved in membrane traffic (AL713697
[GenBank]
, AL136594
[GenBank]
, AL136715
[GenBank]
, AL390215
[GenBank]
, AL834211
[GenBank]
, AL136559
[GenBank]
) have not been described as being directly relevant to ts-O45-G transport or Golgi morphology. Thus they should be expected to have no effect in our assays, as confirmed by our results. Examples of these proteins are amphiphysin II and synaptotagmin III. The first is involved in the clathrin-mediated endocytosis of synaptic vesicles (Zhang and Zelhof 2002 Other effectors (transport or Golgi integrity assay) could be annotated as being involved in events such as signal transduction (e.g., the protein AL117466 [GenBank] , which is a predicted LIM2 kinase) and protein metabolism (e.g., AL136807 [GenBank] , homologous to RAMP4, which itself is involved in protein glycosylation during translocation). Specificity of our assays could be demonstrated by the finding that none of the proteins predicted to be involved in general cellular or nucleic acid metabolism (e.g., a predicted NADH ubiquinone oxidoreductase, AL080056 [GenBank] , and an ortholog of the glucocorticoid-induced transcription activator AL110191 [GenBank] ) caused any interference with transport or Golgi morphology (Fig. 5B).
Analysis of Results According to Conserved Domains and Motifs With a Role in Membrane Traffic
A number of the effectors revealed here are predicted to contain repeated regions (armadillo, ankyrin, TPR, and WD40). Proteins with such domains are frequently reported to undergo protein-protein interactions with partners relevant to different cellular pathways, including membrane transport (Andrade et al. 2001
We have described the testing and application of two microscopy-based functional assays, which probe constitutive transport through the secretory pathway and Golgi morphology in intact cells. Automated image acquisition and data analyses, carried out on a high-content screening platform (Liebel et al. 2003 ), helped to identify subtle changes in ts-O45-G transport or Golgi morphology that were not apparent by visual inspection of single cells. Furthermore, automation allowed measurements on large populations of cells, ensuring data reliability and quality. The proteins used in our screens were preselected with respect to their subcellular localization to membranes of the secretory pathway, cytoskeletal structures, or the cytoplasm. This preselection should result in an increased likelihood of identifying candidate proteins involved in the regulation of diverse steps in the secretory pathway. Indeed, of 100 proteins screened, 35 showed an effect on either Golgi morphology or ts-O45-G transport, or both (Fig. 3), with 20 being uncharacterized to date. Furthermore, analyzing the subcellular localizations of the effectors in our assays showed that those proteins associated with the ER or Golgi complex affected ts-O45-G transport or Golgi morphology more frequently than those proteins localizing to the PM or the cytoplasm (Fig. 5A). Eleven of the effectors could be classified as strong inhibitors, and we consider them most likely to be directly involved in membrane transport or Golgi morphology. For those classified as weak effectors, further independent experiments [such as involving gene knockdowns by RNA interference (RNAi)] might be necessary to obtain more definite evidence for a direct involvement in membrane transport or Golgi morphology. Several reasons exclude the possibility that transport inhibition and/or disturbance of Golgi morphology were unspecific under conditions of overexpression. First, some effectors in our assays are orthologs of well characterized yeast proteins and, consistent with the data available in yeast, these proteins interfered with transport or Golgi complex morphology. Second, in addition to the proteins predicted to act in membrane traffic, only those having been classified into the functional groups of "signal transduction" or "protein metabolism" perturbed transport or Golgi morphology. In contrast, proteins assigned to act on DNA or RNA, for example, had no effects in our experiments (Fig. 5B). Third, several proteins with a clear role in membrane traffic, but not involved in the transport steps analyzed by our assays (e.g., amphiphysin II and synaptotagmin III) also showed no effect. Finally, many of the proteins that scored as positives contained domains that are characteristic of proteins involved in the secretory pathway (e.g., "KKxx" or "xxRR" motifs in their cytoplasmic tails, see also Fig. 5C). Analysis of the sequence features of the screened proteins also demonstrates the specificity of our assays. For example, the likelihood of inhibiting transport or affecting Golgi morphology did not appear to depend on the presence or number of TM domains. Altogether, this demonstrates that the strategy presented here is able to identify novel proteins with specific roles in secretory membrane traffic and/or Golgi morphology. It will thus be an important tool in future large-scale screens to identify more novel proteins with a role in membrane traffic. The assays and microscopy techniques developed will also be powerful tools in functional screens using cellular knockdowns by RNAi, which are complementary to our overexpression strategy used here. In addition, one limitation of our overexpression approach could be overcome, namely that membrane proteins or those with a signal peptide could be affected when tagged at their N-terminal end, and thus in the absence of information on the C-terminal tagged protein, would either not be identified as effectors in our screens, or would show an effect on ts-O45-G transport due to the GFP tag interfering with their proper translocation into the ER.
About half of the effectors (15) revealed in our screens were positive in both assays. Ten affected only ts-O45-G transport, and 10 interfered with Golgi morphology only (Fig. 3). One possible explanation for these results is that those proteins affecting only ts-O45-G transport act in steps not directly related to Golgi morphology, such as post-TGN (trans-Golgi network) traffic. In contrast, those proteins affecting only Golgi morphology are most likely to be involved in the maintenance of the structure of this organelle. Interestingly, Golgi effectors of this kind frequently carried coiled-coil domains, consistent with the hypothesis that proteins with such domains play a role in Golgi structure (Gillingham and Munro 2003
The Golgi matrix protein GM130 used in our studies as a Golgi marker is known to relocate into punctuate structures throughout the cytoplasm when transport between the ER and Golgi complex is impaired (Seemann et al. 2000
Recent data on the localization of almost the entire yeast genome (Kumar et al. 2002
In summary, our approach described here is able to efficiently identify new proteins with a role in membrane transport and Golgi morphology. In combination with organelle proteomics (Bell et al. 2001
Materials GFP-tagged ORFS were generated and prepared as described (Simpson et al. 2000 ). Recombinant adenoviruses encoding the secretory marker protein ts-O45-G tagged with either CFP or YFP were as described (Keller et al. 2001). The mouse monoclonal anti-ts-O45-G antibody "VG" was a gift from Kai Simons (MPI-CBG).
Transport and Golgi Integrity Assays To measure the integrity of the Golgi complex, HeLa cells were transfected for 24 h with the appropriate plasmids. Thereafter they were fixed in 3% paraformaldehyde, permeabilized with 0.1% Triton X-100, and the Golgi complex was stained with mouse monoclonal anti-GM130 antibody (BD Bioscience) followed by Cy5 conjugated anti-mouse secondary antibodies. Cell nuclei were labeled with Hoechst stain as described above.
Multicolor images of stained cells were acquired using the microscope-based screening platform described (Liebel et al. 2003
Quantification of the Transport Assay To each individual cell were then assigned two parameters: the relative amount of the GFP-fusion protein expressed, and the relative surface expression of ts-O45-G defined as the ratio of ts-O45-G-specific plasma membrane fluorescence ("VG" antibody staining) and the CFP- or YFP-ts-O45-G-specific fluorescence. Measurements for all cells in a single experiment were summarized in a scatter plot with the x-axis relating to the amount of expressed GFP-tagged protein (normalized to the maximum fluorescence obtained in the particular experiment and expressed in percentage; see for example Fig. 2). Cells with a GFP fluorescence <10% were scored as nontransfected cells, and their average relative surface expression was determined. All values obtained for the relative surface expression in one experiment were then normalized to the value obtained for the relative surface expression in nontransfected cells (set as "1"; see Fig. 2). Transfected cells (>10% of GFP fluorescence) were further divided into three expression classes representing low (10%-40%), medium (40%-70%), and high (70%-100%) expressing cells according to their fluorescence relating to the amount of transfected GFP-fusion protein. For each expression class, the average value of the relative surface expression was calculated. This allowed us to deduce from these values directly inhibitory (<1) or stimulatory (>1) effects of the expressed GFP-tagged protein on ts-O45-G transport compared to nontransfected cells. Also, this way of scoring the results allows discrimination of strong perturbing proteins, already showing an inhibition in the "low" expression class compared to weak inhibitors showing an inhibitory effect preferentially in the "high" expression class (lower values in Table 1). The resulting values obtained for the average relative surface expression for the four expression classes were also fitted by the least square method to a linear function of the kind: y = a + bx. The progression of the effect of each GFP-tagged protein on ts-O45-G transport with increasing amounts of GFP-tagged protein expressed is thus characterized as the steepness of the slope (expressed by "b"). Positive "b"-values represent transport acceleration, and negative values represent transport inhibition with respect to nontransfected cells (upper values in Table 1). At least two experiments were performed for each ORF tested, resulting in at least 200 transfected cells scored. For transport effectors, the experiments were repeated three or four times, and the respective standard errors of the means were calculated. If available, both N- and C-terminal GFP fusions of every ORF product were tested and processed separately.
Quantification of the Golgi Integrity Assay
Databases and Analyses
We thank Brigitte Joggerst and Thi Bach Nga Ly for technical assistance, Kai Simons for antibodies, Steffi Bechtel for tagging ORFs to GFP, and Joanne Young for comments on the manuscript. We also thank Olympus, Olympus BioSystems, and Perkin Elmer for continuous support throughout the project. This work has been supported by a grant to R.P. from the Verbundforschung Baden-Wuerttemberg (No. 24-720.431-1-2/2) and grants from the BMBF 01KW0013 (R.P.) and NGFN 01GR1001 (R.P. and S.W.), DHGP 01KW0012 (S.W.).
3 These authors contributed equally to this work. 
4 Corresponding author. [Supplemental material is available online at www.genome.org.] Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2658304.
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ABSTRACT
RESULTS
30 of them being transfected with the respective plasmid. Only cells with normal interphase morphology were taken for analysis. The population of imaged cells was divided into two groups; transfected (>10% of maximum GFP fluorescence obtained, see paragraph above) and nontransfected (<10% of maximum GFP fluorescence obtained) cells, and an average number of Golgi fragments and intensity of each Golgi fragment within each group were calculated as described (Liebel et al. 2003
1/n1+1/n2; see Hassard 1991
S. J. Cell Sci. 111: 1877-1888.
