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Keywords:

  • membrane proteins;
  • subcellular localization;
  • transmembrane

Abstract

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

Application of a computational membrane organization prediction pipeline, MemO, identified putative type II membrane proteins as proteins predicted to encode a single alpha-helical transmembrane domain (TMD) and no signal peptides. MemO was applied to RIKEN's mouse isoform protein set to identify 1436 non-overlapping genomic regions or transcriptional units (TUs), which encode exclusively type II membrane proteins. Proteins with overlapping predicted InterPro and TMDs were reviewed to discard false positive predictions resulting in a dataset comprised of 1831 transcripts in 1408 TUs. This dataset was used to develop a systematic protocol to document subcellular localization of type II membrane proteins. This approach combines mining of published literature to identify subcellular localization data and a high-throughput, polymerase chain reaction (PCR)-based approach to experimentally characterize subcellular localization. These approaches have provided localization data for 244 and 169 proteins. Type II membrane proteins are localized to all major organelle compartments; however, some biases were observed towards the early secretory pathway and punctate structures. Collectively, this study reports the subcellular localization of 26% of the defined dataset. All reported localization data are presented in the LOCATE database (http://www.locate.imb.uq.edu.au).

Organelles serve to isolate biological pathways and cellular functions to specific regions of the cell. Each individual organelle contains a characteristic set of resident proteins that carry out organelle-specific functions. In contrast, numerous proteins transiently move through individual organelles where they are not considered to be residents. These include newly synthesized proteins being transported to their site of function, proteins directly involved in protein trafficking and proteins that can be induced to move from one organelle to another in response to a stimulus (e.g. cell surface receptors). The continuous exchange of lipid and proteins between organelles creates a dynamic environment in which the cell must concentrate and maintain resident proteins within individual organelles.

Recent large-scale sequencing of full-length mRNA transcripts in mouse (1–5) and human (1,2,4,6) has resulted in the identification of numerous hypothetical proteins that have no inferred biological function based on homology to other proteins. The task is to now accurately ascribe biological function to these ‘novel’ proteins. A major determinant of a protein's function is its subcellular localization throughout the various organelles of the cell. Traditionally, cell biology has typically characterized individual proteins to varying degrees of depth. This directed approach allows analysis of the function of each protein and yields an array of experimental data that are subject to experimental variations, such as the cell type analyzed, method of protein detection, and type and position of protein tag. This approach provides vital information about the specifics of individual proteins; however, it fails to address the global understanding of protein sorting as the extent of experimental variation prevents the direct comparison of results (7).

The importance of subcellular localization in determining a novel protein's biological function highlights the need in the genomic era for a rapid, high-throughput assay to determine protein localization. Proteomic approaches have been previously used to attempt to characterize the protein constituents of various organelles [reviewed in (8)]. This methodology has aided in the rapid characterization of the protein complement of numerous organelles but is unable to identify proteins expressed at low levels and is susceptible to various degrees of contamination from other organelles.

Another approach to determining subcellular localizations is the generation of fusion proteins with a detectable protein tag such as green fluorescent protein (GFP). Such high-content assays have already successfully been performed on the yeast models, Saccharomyces cerevisiae (9) and Schizosaccharomyces pombe (10), and in the plant models, Arabidopsis thaliana (11) and Nicotiana benthamiana (12). Within the mammalian context, Simpson et al. (13) have systematically, N- and C-terminally, GFP-tagged full-length human open reading frames (ORFs) using the Gateway® cloning system to report the subcellular localization (see http://www.gfp-cdna.embl.de/) of over 900 novel human proteins. This study did not attempt to identify any features such as the presence of transmembrane domains, signal peptides or targeting motifs that may be affected by the addition of a tag. Collectively, these approaches allow the direct comparison of experimental results and provide a more global view of protein sorting mechanisms used by cells. Additionally, these systematic methods provide the first functional insight to novel proteins by associating them with specific subcellular compartments.

In contrast to Simpson et al. (2000), this study aims to determine the localization of putative type II membrane proteins present in the mouse proteome. Type II membrane proteins are classified as proteins that encode a single alpha-helical transmembrane domain and that lack an endoplasmic reticulum (ER)-targeting signal peptide at their N-terminus. These proteins are predicted to have a distinct topology in the membrane with their N-terminus in the cytoplasm and their C-terminus in the extracellular or lumenal region (14). By focussing on a specific class of membrane proteins, systematic approaches to the engineering of the epitope-tagged reporter constructs can be implemented to minimize the disruption of protein targeting signals by the addition of a protein tag.

Results

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

Within this manuscript a pipeline was developed to identify all putative mouse type II membrane proteins and characterize their subcellular localization (Figure 1). This process was divided into three phases: first, all putative mouse type II membrane proteins within the functional annotation of mouse 3 (FANTOM3) Isoform Protein Set 7 (IPS7) protein set were annotated (5); second, published literature reporting the subcellular localization of type II membrane proteins was identified and third, a high-throughput, polymerase chain reaction (PCR)-based experimental assay was established to characterize the subcellular localization of epitope-tagged proteins in transfected HeLa cells. Finally, all of these data were incorporated into a publicly available database, LOCATE (http://www.locate.imb.uq.edu.au).

image

Figure 1. Overview of approach used to determine the subcellular localization of putative type II membrane proteins.

Defining the complement of mouse type II membrane proteins

Putative type II membrane proteins were identified within the RIKEN IPS7 (5). This protein set was generated exclusively from full-length transcripts that have been directly sequenced and provides the most definitive coverage of the entire mouse proteome. Davis et al. (Melissa J. Davis, Fasheng Zhang, Zheng Yuan and Rohan D. Teasdale, manuscript in preparation) have developed MemO, a membrane organization pipeline, which classifies proteins into different membrane classes based on two predicted features: transmembrane domains (TMDs) and signal peptides. Signal peptides are frequently inaccurately predicted as TMDs. Numerous type II membrane proteins such as the Golgi residents, the glycosyltransferase protein family, encode N-terminal TMDs, termed signal anchors, which occur in the same region as a putative signal peptide. To overcome the problem of conflicts between signal peptides and transmembrane domain predictions at the N-terminus, MemO incorporates an N-terminal feature differentiator, which distinguishes between these two features (15). The incorporation of this utility is imperative to the accurate prediction of type II membrane proteins due to the prevalence of a large number of transmembrane domains near the amino-terminus of type II signal anchor proteins.

The MemO pipeline was applied to the 33 451 full-length protein-coding sequences (CDSs) from the FANTOM3 data (5; Melissa J. Davis, Fasheng Zhang, Zheng Yuan and Rohan D. Teasdale, manuscript in preparation). This represented 19 538 transcriptional units (TUs) that are defined as regions within the genome where all of the exons encoded by a full-length, mature cDNA are derived (3). Putative type II membrane proteins were defined as proteins encoding a single predicted transmembrane domain and lacking a predicted amino-terminal ER-targeting signal peptide. This classification identified 2869 putative type II membrane proteins clustered into 2149 TUs. About 1436 of these TUs contain transcripts that encode only type II membrane proteins, and 1129 of these TUs encode a single transcript. From the remaining 709 TUs, variant proteins are generated which encode individual proteins predicted by MemO to encode soluble proteins or membrane proteins other than Type II membrane proteins. Within this study, we have focused on the 1436 TUs for which all the proteins generated from that TU are predicted to be type II membrane proteins. Within these 1436 TUs, there are 1872 type II membrane proteins that form our starting dataset.

InterPro (http://www.ebi.ac.uk/interpro/) (16) represents a repository of protein families, protein domains and functional sites. Transmembrane and InterPro domain predictions were occasionally observed to overlap. It was observed that in some instances, certain TMDs were inappropriately predicted because they form a hydrophobic portion of an InterPro domain prediction rather than a TMD, which traverses the membrane. Proteins with clear overlap between predicted InterPro and transmembrane domains were subjected to further review. InterPro domains that included transmembrane domains as part of the domain signature were excluded from this analysis. Forty-one transcripts were identified in 30 TUs with weak transmembrane domain predictions that overlapped with strong InterPro domain predictions, and these were removed from the set (Table S1). Removal of these 41 transcripts results in 1831 transcripts from 1408 TUs in the putative type II dataset. The transcripts removed encoded domains such as protein-kinase-like (IPR011009), protein kinase (IPR000719), serine/threonine protein kinase (IPR002290) and tyrosine protein kinase (IPR001245) domains. These putative kinases are likely to be soluble intracellular proteins rather than type II membrane proteins.

Characterization of type II membrane proteins

The putative type II membrane protein dataset was analyzed using InterPro, and 702 InterPro domains were identified in 1154 proteins. InterPro domain predictions were examined to identify domains that are exclusively found in the type II dataset as opposed to other membrane protein classes. This analysis identified 120 predicted InterPro domains unique to the type II dataset (Table S2). Prevalent amongst this list was the peptidase 13, neprilysin domains (IPR008753; IPR000718), which are found in a number of metalloproteases involved in proteolytic cleavage. Also prevalent was the metazoa galactosyltransferase domain (IPR003859), which represents a family of well-characterized type II membrane proteins known to be involved in glycosylation. The galactose-3-sulfotransferase domain (IPR009729) is involved in galactosylceramide sulfotransferase activity and was frequently observed in the set. Finally, the Na+/K+ ATPase, β subunit domain (IPR000402) is a domain characterized in a well-studied type II membrane protein that forms a macromolecular complex, which resides at the plasma membrane. Several domains were also over-represented in the type II dataset. These included the tumour necrosis factor domain (IPR006052), which is a domain found in a well-characterized type II membrane protein. Other well-characterized type II membrane proteins, such as soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) (IPR000727), glycosyltransferases (IPR001173) and C-type lectins (IPR001304), which incorporate a type II anti-freeze domain (IPR002353), are also clearly over-represented in the type II dataset when compared with the entire IPS7 dataset.

The type II dataset was analyzed to identify biological processes and molecular functions which may be potentially over-represented in the set. The 1831 transcripts in the type II dataset were cross-referenced to identify 1095 proteins that can be associated with an mouse genome informatics (MGI) accession number (17). This dataset was subsequently mined for Gene Ontology (GO) (18) annotations using GO stat (http://www.gostat.wehi.edu.au) (19). Several GO annotations are clearly over-represented in the type II dataset. Gene ontologies for sugar binding (GO:0005529), Golgi apparatus (GO:0005794), ER (GO:0005783) and several GO terms associated with transferase activity (GO:0016757, GO:0016758, GO:0016740, GO:0008194, GO:0008378, GO:0008146) were significantly over-represented within this set. This is consistent with knowledge of a large number of expected type II proteins that have modification enzyme activities that are found within the early compartments of the secretory pathway (e.g. glycosyltransferases) (20). Numerous GO terms associated with mitochondria were also significantly over-represented within the type II dataset. This included numerous GOs associated with oxidoreductase activity (GO:0016676, GO:0004129, GO:0015002, GO:0016675, GO:0015399).

Type II membrane proteins include N- and C-terminally anchored membrane proteins (14). Analysing the position of the TMD identified 196 type II proteins that have a TMD predicted to start within the first 10 residues and are thus likely to represent N-terminal anchored proteins. Similarly, 208 type II membrane proteins have a TMD predicted to stop within the last 10 residues of the CDS and thus represent C-terminal anchored proteins. Analysis of the subsets of the N- and C-terminally anchored type II membrane proteins with subcellular localization data available within LOCATE (http://www.locate.imb.uq.edu.au) revealed that more than 50% of the N-terminally anchored proteins were localized to the Golgi while the majority of C-terminally anchored proteins were localized to the plasma membrane (>30%) and mitochondria (>20%). This observation indicates that the position of the transmembrane domain may influence the subcellular localization of Type II membrane proteins.

The putative type II dataset encodes numerous proteins that have previously been characterized as well as a large component of hypothetical, ‘novel’ proteins. 811 of the 1831 proteins in our high confidence set are described by a descriptive, functional gene name. A further 229 transcripts encode proteins that have varying levels of homology to other known proteins. The set is also composed of 791 hypothetical proteins of which 154 have predicted InterPro domains as the only other feature in addition to the proposed TMD and 637 proteins, which represent purely hypothetical proteins that have no annotated information. This highlights the need to assign a biological function to a large proportion of novel proteins within this dataset.

Identifying the subcellular localization of type II membrane proteins

GO data, InterPro domain predictions and the presence of numerous expected protein families reinforce confidence in the type II membrane protein dataset identified using MemO (Melissa J. Davis, Fasheng Zhang, Zheng Yuan and Rohan D. Teasdale, manuscript in preparation). Therefore, we have used this dataset to characterize the subcellular localization of mouse type II membrane proteins. Two approaches were used to determine the subcellular localization of proteins within the type II dataset: a literature mining approach to identify previously published results detailing subcellular localization and a high-throughput, PCR-based experimental approach.

A standardized nomenclature was developed for describing the subcellular localization of both experimentally observed localization patterns and literature-reported localizations. The standardized nomenclature used in this study is outlined in Table S1 and represents a hierarchical approach to classifying protein localization. This hierarchical approach allows localization descriptions appropriate to the specificity of the data. A detailed calling system using GO (21,22) terms was used to describe literature localization data, whereas experimentally determined localizations required alternate descriptions that provide more descriptive terminology. For example, the term cytoplasmic puncta is used to describe punctate labelling in the cytoplasm, which could potentially represent various cellular compartments such as endosomes and lysosomes. Examples of novel proteins exhibiting a variety of localization patterns are shown in Figure 2. Localizations of experimentally characterized proteins were based on comparisons with previously determined localization patterns for different organelles represented by various organelle markers and stains (Figure 3). Numerous proteins are known to cycle through several subcellular compartments rather than having a single resident localization. In such instances, multiple localization descriptions were included for a single protein.

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Figure 2. Subcellular localization of proteins with no previous literature-based localization data to a variety of organellar compartments. N-terminal, myc-epitope-tagged expression constructs were generated using overlapping PCR and expressed in HeLa cells. These constructs were subsequently expressed for 16–24 h prior to fixation with 4% paraformaldehyde, immunodetection and subsequent visualization. Protein accession numbers and their descriptive names are included. Bar represents 10 µm.

image

Figure 3. Validation of experimentally determined localization descriptions. Subconfluent HeLa cells were transiently transfected with N-terminal, myc-epitope-tagged expression constructs generated by overlapping PCR and over-expressed for 16–24 h prior to fixation with 4% paraformaldehyde, immunodetection and subsequent visualization as described in the Materials and Methods. cDNAs of proteins known to reside in the ER, Golgi, plasma membrane and mitochondria were selected and N-terminally myc-tagged and transiently expressed in HeLa cells (A, E, I and M, respectively). Proteins with unknown subcellular localizations (B, F, J and N) were also expressed in HeLa cells where each organelle was labelled with a known marker or stain (C, G, K and O) and the merged images compared (D, H, L and P). Bar represents 10 µm.

Literature mining of localization data

Literature mining was performed using PubMed (http://www.ncbi.nlm.nih.gov/PubMed) to identify primary literature detailing the localization of type II proteins. This search was restricted to experimental data from mammals and was limited to detection of the protein polypeptide rather than its enzymatic activity within intact cells. This search identified the localization of 244 type II proteins previously reported in the literature. The distribution of these proteins across various subcellular compartments is shown in Figure 4A. The literature searches clearly demonstrate that type II membrane proteins are widely distributed across an array of different subcellular compartments. The majority of type II proteins analyzed within the literature appear to be associated with the plasma membrane, Golgi apparatus and ER. Type II membrane proteins were also found to reside in mitochondria, vesicular structures, nucleus and the cytoplasm.

image

Figure 4. Subcellular compartment specific distributions of type II membrane proteins.(A) This chart demonstrates the distribution of type II proteins with previously published localizations across various cellular compartments. The primary localization call from a total of 244 entries was used to generate this chart. (B) This chart demonstrates the distribution of type II proteins across various subcellular structures that have been assayed using a high-throughout, PCR-based experimental assay (Figure 1). Frequently, proteins were described using more than one localization description. The following hierarchy was used to determine a single localization for proteins with multiple localizations: Plasma membrane > mitochondria >Golgi apparatus >punctate structures > nuclear envelope > endoplasmic reticulum >reticular > membrane-associated unknown>nuclear > cytoplasmic. About 169 proteins with subcellular localizations were used to generate this chart.

High-throughput subcellular localization assay

Another approach used to determine subcellular localization utilized a high-throughput, PCR-based approach to generate an N-terminal, myc-epitope-tagged expression construct of the protein of interest. These linear expression constructs were generated for type II membrane proteins for which we had access to the full-length mouse cDNA. Over-expression of these constructs in transiently transfected HeLa cells was detected using immunofluorescence with a monoclonal antibody to the myc-epitope. HeLa cells were selected for use because they possess a suitable morphology to enable efficient classification of localization patterns corresponding to individual organelles and also have a high transfection efficiency when using the linear expression constructs. To minimize the effect of over-expression in the experimental data, we captured images representative of the observed localization patterns for each construct.

Experimentally observed localization patterns were classified into various subcellular compartments by comparisons with endogenous markers and stains for different compartments. Examples of some protein markers demonstrating various localization patterns are illustrated in Figure 3A, E, I and M. These images represent proteins that are N-terminally epitope-tagged and generated using the same overlapping PCR-based protocol used throughout this study. Figure 3A shows ERdj5 (AAN73273), a protein previously demonstrated to localize to the ER (23). The localization pattern represents an intricate, lace-like network that extends throughout the cytoplasm as well as labelling of the nuclear envelope which is typical of ER staining. Figure 3E shows a 28-kDa GOLGI SNAP receptor (SNARE) complex member 1 (F630113H18), a protein that has previously been characterized to localize to the Golgi apparatus (24). This protein exhibits perinuclear staining comprised of short, stack-like structures similar to Golgi staining. Figure 3I shows the macrophage receptor (MARCO) (AAA68638), a protein previously shown to reside at the plasma membrane (25). This plasma membrane protein demonstrates clear labelling at the cell periphery with numerous projections into the extracellular region. Figure 3M shows an amine oxidase (flavin-containing) (EC 1.4.3.4) B homologue (Rattus norvegicus) (6330414K01). This is a homologue of the human equivalent of the protein monoamine oxidase B, which has been demonstrated to localize to mitochondria (26). This protein demonstrates a tubular, reticular network that extends throughout the cytoplasm in addition to some punctate structures that are indicative of mitochondrial labelling. Collectively, these proteins demonstrate that the tagging approach used has successfully enabled the determination of the subcellular localization of type II membrane proteins for each of these independent organelle systems. On the basis of these characteristics and other (data not shown) expression patterns for each organellar compartment, a suitable description was assigned to proteins with no previously known subcellular localization. The hierarchical nomenclature (Table S1) allows for varying degrees of depth describing localizations to prevent inaccurate descriptions of the observed phenotype. For example, proteins that potentially represent endosomal or lysosomal labelling are described as cytoplasmic puncta. Describing such localization patterns is adequate for initial characterization; however, it is clear that in order to establish a protein's organellar location, co-localization studies with specific compartmental markers is essential.

Co-localization studies were performed on a selection of proteins with no previously known subcellular localization information in order to establish the cellular compartments in which they reside (Figure 3). Figure 3B shows a protein similar to the microsomal signal peptidase 23-kDa subunit found in Canis familiaris. It exhibited a clear ER-like staining pattern as is observed for the ER Marker (ERdj5) seen in Figure 3A and was subsequently co-localized with pcCMT (27) (Figure 3C), a protein with a previously described ER localization. As can be observed in the merged image (Figure 3D), there is clear co-localization demonstrating that this novel protein resides in the ER. This is expected for a protein which exhibits homology to a protein that resides in the signal peptidase complex within the ER. Figure 3F – H represent co-localization of RIKEN clone 2610021I17 (Figure 3F) with the known Golgi marker GCC88-GFP (Figure 3G) (28). As can be observed in the merged image (Figure 3H), there is partial co-localization with this Golgi marker, demonstrating that this protein resides within a subcompartment of the Golgi apparatus. Figure 3J–L shows the FXYD ion transport regulator 7 (Figure 3J), co-localized with phalloidin (Figure 3K), a vital stain for actin that highlights the periphery of the cell. As can be observed in the merged image (Figure 3L), expression of the protein can clearly be observed at the periphery of the cell. This provides the first experimental evidence for the localization of this protein at the cell surface. Figure 3N–P shows co-localization data on a protein weakly similar to DC18 (170013C24) (Figure 3N), a protein found in humans, with MitoTracker, a vital stain for mitochondria (Figure 3O). As can be observed in the merged image (Figure 3P), there is clear co-localization, demonstrating a previously uncharacterized mitochondrial localization for this novel protein. Collectively, this data demonstrate the first efforts at localizing these proteins to distinct cellular compartments. This data also reinforces confidence in the assignment of localization descriptions for proteins with no previous localization data based on experimentally observed cellular patterns.

Overall, the experimental approach has characterized the subcellular localization of 169 type II membrane proteins. These proteins demonstrated an array of cellular phenotypes that encapsulate a variety of cellular compartments as is seen in Figure 4B. Proteins within the experimental assay were predominantly localized to punctate structures, ER/nuclear envelope, plasma membrane, Golgi-like and mitochondrial-like structures.

Discussion

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

During the FANTOM3 annotation (5), distinct categories of transcripts encoding type II membrane proteins were specifically targeted for critical review in the systematic annotation process to discard any false transcripts from the dataset. This involved evaluation of each individual transcript and its computational predicted protein-CDS. First, putative protein CDSs less than 150 amino acids in length were reviewed because these potentially represent predicted CDSs in immature transcripts or truncated transcripts that do not overlap with a CDS with stronger supporting evidence. Second, transcripts with greater than 20% of the CDS covered by DNA repeats, as detected by repeat masker (http://www.repeatmasker.org/) (29), were also reviewed as these may represent inaccurate protein open-reading frames or retroviral CDSs, which may not be translated. Third, single exon transcripts with 3′ ends near an adjunct A-rich region in the genome were reviewed as potential oligo dT primed artefacts. Such transcripts may be generated as a result of the oligo dT annealing to regions within an immature pre-mRNA transcript or directly to genomic DNA resulting in the generation of false, truncated cDNAs (5). The predicted CDS within all individual transcripts containing any of the above properties were systematically manually reviewed in the annotation process to exclude CDSs with limited supporting evidence. Typically, in the evaluation of a putative CDS, the presence of peptide sequences predicted to form domains (InterPro) or protein folds was considered sufficient to support a CDS. In contrast, the prediction of signal peptides or transmembrane domains within a CDS was not considered sufficient support for a CDS in isolation. This is because of the low complexity of these protein features and previous observations that numerous translated DNA repeats can be predicted as signal peptides (30). The reviewing process identified and discarded numerous transcripts that represented false transcripts resulting in the higher quality type II membrane protein dataset used in this study.

To identify what proportion of proteins had subcellular localization information MGI was searched to identify proteins with associated GO terms. This identified that less than 15% of the dataset are annotated with a cellular component GO term associating them with an organelle (31). This study reports the development and validation of a methodology to identify and characterize the subcellular localization of 26% of the type II membrane proteins in the dataset by combining computational predictions with experimental validation. A high-throughput, overlapping PCR-based approach has been used to experimentally determine the subcellular localization of 169 type II membrane proteins. These data have been supplemented with previously published experimental evidence mined from the literature describing the localization of 244 type II membrane proteins. Collectively, this represents localization data for 368 TUs within the entire dataset. This effort represents the first directed, high-throughput approach to determining the subcellular localization of a specific class of membrane protein and has resulted in the development of a pipeline approach to identify putative type II membrane proteins and characterize their subcellular localization. Localization data generated by this directed approach can provide insights into the biological function of novel proteins.

The primary concern when designing the tagging approach used in this study was to ensure that the addition of an epitope tag does not disrupt position-dependent sorting signals that are essential for the correct targeting of a protein. Unlike type I membrane proteins, type II membrane proteins do not encode an N-terminal signal peptide. Furthermore, some type II membrane proteins such as tumour necrosis factor-α (TNF-α) are proteolytically processed to yield a C-terminal domain, which is secreted from the cell (32). Therefore, we epitope-tagged the N-terminus (cytoplasmic face) as described by Suzuki et al. (2001). This approach will, however, disrupt the diarginine motif that is required for ER retention located on the N-terminal of type II membrane proteins (33). Analysis of the dataset identified 60 proteins that encoded such a motif and this property was considered within the analytical pipeline. Finally, the nine amino acid myc-epitope was chosen (EQLISEEDL), which allows for rapid characterization of protein localization in fixed samples rather than the 236 amino acid protein, GFP, in order to avoid steric interference.

Proteins exhibiting solely nuclear and/or cytoplasmic localizations were observed in both the experimental (7.10%) and literature (9.84%) datasets. These represent unexpected localizations for type II membrane proteins because they encode a transmembrane alpha helix that traverses the lipid bilayer and should therefore associate with membrane compartments. These nuclear and cytoplasmic localizations may be due to numerous reasons. First, the putative type II dataset will contain a number of false-positive predictions made by MemO. The estimated false-positive prediction error rate is 4.9% for the TMD prediction component of MemO (5, Melissa J. Davis, Fasheng Zhang, Zheng Yuan and Rohan D. Teasdale, manuscript in preparation). Second, the N-terminal tagging system may disrupt integration or translocation of the protein into the membrane. Overall, the proportion of proteins exhibiting nuclear and cytoplasmic localizations provides an estimate of the combined error rate of this study. Alternatively, full-length type II membrane proteins can be proteolytically processed to release a soluble N-terminal polypeptide from a membrane precursor in a process termed regulated intramembrane proteolysis (34), which may result in the generation of a cytoplasmic peptide containing the N-terminus.

A comparison of the observations identified in the literature-based localization data and the experimentally observed localization data identified differences between the distribution of proteins across various cellular compartments. First, a significantly elevated presence of ER-localized proteins (ER-like and nuclear envelope proteins) was observed in the experimental data. This could represent novel ER-localized proteins, misfolded proteins or proteins that are retained within the ER because they represent subunits of larger macromolecular complexes, which are not normally expressed in HeLa cells. Second, an elevated number of proteins that localize to the Golgi apparatus was observed in the literature. This includes a large number of proteins from the glycosyltransferase protein family that have been intensely studied in the literature and therefore were not considered for experimental characterization. Third, a higher proportion of cell surface proteins was also observed within the literature dataset. This may be due to the methodology used to characterize their subcellular localization. Many proteins described in the literature as being localized to the plasma membrane have been analyzed using flow cytometry methods, which do not take into consideration intracellular populations of a protein. Fourth, the experimental dataset displayed an elevated number of proteins found in punctate structures. These punctate structures may represent aggresomes [Reviewed in (35)] consisting of misfolded protein caused by the expression of potentially false CDSs. Alternatively, they may be a direct result of the addition of the myc-epitope tag or be due to the expression of the protein in an inappropriate cell type which can cause the protein to misfold due to the absence of appropriate protein folding and processing machinery. Clearly, the description of punctate structure is the least informative description as the protein could be localized to numerous organelles including, peroxisomes, cytoplasmic vesicles, endosomes, lysosomes, Golgi, subcompartments of the ER or mitochondria. Within the majority of the 80 type II membrane proteins with punctate structures a more definitive description was observed concurrently (24 plasma membrane, 20 reticular, 9 Golgi-like). While the elucidation of the exact nature of the punctate structures will require further co-localization experiments, these proteins have folded sufficiently to be localized to other compartments. Furthermore, 3 of the 27 proteins with only punctate subcellular descriptions have literature data consistent with the observed subcellular localization.

Forty-two proteins with supporting literature evidence for subcellular localization were chosen for experimental characterization using the systematic high-throughput approach used in this pipeline. Thirty-three of these proteins demonstrate agreement between the two localization descriptions. However, nine of these proteins show distinct discrepancies between the two subcellular localizations. Two of these nine discrepancies occurred in proteins that appeared to be trapped in the ER during the experimental assay. Dopamine β-hydroxlyase (AAB24330) is reported in the literature to be localized to chromaffin granules in adrenal medullary cells (36) and neurosecretory vesicles of noradrenergic neurons (37,38). It is reported to exist as both a soluble and a membrane-bound protein. Variation in the cleavage of the signal peptide is reported to result in these different localizations, highlighting the importance of the N-terminus (39). The ER-like localization observed in this study is likely to be due to the N-terminal tagging method. This protein represents a potential false-positive type II prediction made by MemO. The second protein, glycoprotein galactosyltransferase α-1,3 (I420001D20), has been demonstrated to localize to the Golgi apparatus; however, it is believed that the cytoplasmic tail of this protein is responsible for its localization (40). Addition of a myc-epitope or FLAG epitope tag to the N-terminus of α2,6-sialyltransferase and N-acetylglucosaminyltransferase I disrupts Golgi localization resulting in a granular cytoplasmic staining (41), similar to the observation of patchy, reticular ER-like staining observed in this study.

Six of the nine discrepancies between the literature and the experimental datasets were of proteins reported in the literature to reside at the plasma membrane; however, they were localized to punctate structures by the experimental protocol. This can be attributed to a large proportion of literature evidence reporting subcellular localization using flow cytometry as the only method of detection of localization. In these instances, the relative distribution of individual proteins between the plasma membrane and intracellular compartments is not considered. Indeed, three proteins that are discrepant are B-cell Rag-associated protein (D030054H07) (42), natural killer cell receptor (NKR-P1B) (AAK39099) (43) and killer cell leptin-like receptor, subfamily A, member 2 (AAH64711) (44,45) proteins are all demonstrated to localize to the plasma membrane by flow cytometry. The B-cell Rag-associated protein is also reported to have an uncharacterized intracellular localization (42). Furthermore, all three of these proteins have been studied in specialized immune cells and it must be noted that different cell types may express these proteins at the cell surface at different levels, retaining internal stores of the protein in storage compartments. Thus, the detection of cell surface proteins via flow cytometry may contribute to the elevated levels of plasma membrane proteins reported in the literature data when compared with the experimental data generated in this study.

Some proteins form macromolecular complexes and require assembly with other subunits within the ER prior to correct targeting to their appropriate destinations (46,47). If complementary subunits are not present in the cell type in which such proteins are expressed, then proteins are likely to exhibit an ER-like localization (quality control) or a punctate localization, which may represent misfolded protein targeted for degradation. The Na+/K+ ATPase transporting β1 polypeptide (2410046B18) demonstrates some ER-like staining accompanied with punctate cytoplasmic staining. It has been demonstrated in the literature that an ortholog of this protein in Xenopus laevis oocytes requires assembly with an α-subunit at the ER before export to the plasma membrane (46). Similarly, potassium voltage-gated channel Isk-related subfamily gene 3 (4922505J16) also forms a macromolecular complex, the potassium channel (48), and demonstrated a punctate localization experimentally. Alternatively, it is also known that cell surface proteins have dynamic expression at the cell surface. This may result in a variable ratio of the amount of protein at the cell surface and in intracellular stores, depending on factors such as cell type and environmental stimuli. Alternatively, these punctate structures may also represent aggresomes as is the likely case with the OASIS protein (BAA75760), which is reported in the literature to localize to the ER and to translocates to the nucleus upon ER stress yet exhibits a punctate localization in this experiment.

This pilot study has generated descriptions of the subcellular localization of 368 TUs within the computationally defined set of type II membrane proteins. Numerous mouse proteins identified within the FANTOM projects have no previously reported subcellular localization data in the literature. Further analysis of these highlights examples of proteins that are demonstrated to have been correctly folded and targeted to organelles within the experimental dataset. For example, 18 proteins have plasma membrane-like localizations, six proteins have Golgi-like localizations and nine proteins have mitochondrial-like localization patterns. This information represents the first insight into the biological function of many of these novel proteins, which have no inferred function based on predicted InterPro domains or homology to known proteins that can be proposed. Additionally, 37 proteins were localized to ER-like structures, and 36 proteins were localized to punctate, cytoplasmic structures. However, some caution must be exerted when interpreting the localization of these proteins because these may potentially represent misfolded proteins, or proteins that form macromolecular complexes, that are retained in the ER or misfolded proteins that form cytoplasmic aggregates. Further experimental evidence will be needed to validate the subcellular compartments these proteins reside in.

In summary, the collective subcellular localization of approximately 26% of the computationally defined set of type II membrane proteins is reported. These data are incorporated within the LOCATE database (http://www.locate.imb.uq.edu.au) (49) with predicted protein features such as structural and functional domains as well as membrane organization predictions. The LOCATE database also provides links to other major databases [e.g. expression data from the GNF mouse GeneAtlas (50) which, in conjunction with subcellular localization data, can yield insight into biological function. Furthermore, characterization of the subcellular localization of individual proteins enhances our understanding of the protein complement of various organelles and the cellular phenotypes associated with them. Finally, these data also present a training set for the development of computational algorithms to predict subcellular localization.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

Antibodies

Mouse monoclonal antibodies were used to detect the myc-epitope tag (Cell Signalling Technology, Beverly, MA, USA). Secondary antibody conjugates used were goat anti-rabbit IgG-Cy3 (Zymed, San Francisco, CA, USA), goat anti-mouse IgG-Cy3 (Zymed) and goat anti-rabbit IgG-Alexa-488 (Molecular Probes Inc., Eugene, OR, USA).

Fluorescent probes and constructs

MitoTracker Red CMXRos (Molecular Probes Inc.) Texas Red-X Phalloidin (Molecular Probes Inc.) and were used according to the manufacturer's instructions. Plasmids used in this study were Golgi-GCC-GFP (28) and pcCMT (27).

Generation of cDNAs of interest

PCR products from the protein–protein interactors set tagged as described in Suzuki et al. (2001) were used when possible. Clones not available within the protein – protein interactor PCR products set were analyzed by isolating DNA from RIKEN's FANTOM2 cDNA clones [http://www.fantom2.gsc.riken.jp (3)] and from the Mammalian Gene Collection [http://www.mgc.nci.nih.gov (1)]. These were then amplified by PCR with a gene-specific primer at the 5′ end using 5′-GAAGGAGCCGCCACCATGXXXXXXXXXXXX (where X represents the 5′ sequence of the coding region) and a 3′ vector-specific primer encoding 5′-GCGGATAACAATTTCACACAGGAAAC-3′ followed by 18–25 bp of vector-specific sequence (51).

Generation of expression constructs

N-terminally tagged myc-gene of interest expression constructs were generated using a modified overlapping PCR methodology originally reported by Suzuki et al. (2001). Generated expression constructs were comprised of a promoter fragment, the cDNA of interest and a terminator fragment. The promoter fragment encodes a CytoMegalovirus Promoter, a start methionine followed immediately by the myc-epitope and the VP16 sequence encoded by 5′-GAAGGAGCCGCCACCATG-3′. The terminator region encodes a region complementary to the RIKEN P8 primer (51) encoded by 5′-AGCGGATAACAATTTCACACAGGAAA-3′ followed by two SV40 poly-adenylation sequences. Linear expression constructs are generated by fusion of the overlapping regions encoded by the VP16 region and RIKEN P8 sequences encoded by the cDNA of interest at the 5′ and 3′-termini, respectively, to the promoter and terminator fragments. This reaction mix consisted of 50–500 ng of the primary PCR product, 10 fmol of the epitope-promoter fragment, 10 fmol terminator fragment, 0.5 pmols forward external primer, 0.5 pmols reverse external primer, 5 nmol dNTPs, 1.25 × 10−2 Triple Master polymerase (Eppendorf AG, Hamburg, Germany) in 1× Hi-Fi buffer (Eppendorf AG) filled with ddH2O to a final volume of 25 µL. The mix was then subjected to the following reaction conditions: 95 °C for 2 min, 30 cycles at 95 °C for 30 s, 52 °C for 30 s and 72 °C for 2 min before a 4 °C hold. Reaction products were then analyzed by gel electrophoresis to confirm the generation of a complete expression construct as was identified by an increase in size corresponding to the incorporation of promoter and terminator regions.

Cell culture

HeLa cells were cultured in DMEM (Life Technologies Inc., Grand Island, NY, USA) supplemented with 5% heat-inactivated fetal calf serum (Trace Scientific, Victoria, Australia) and 2 mm l-glutamine (Life Technologies Inc.) in 5% CO2 and 95% air at 37 °C.

Transfection and expression

Subconfluent HeLa cells were transiently transfected with PCR expression constructs using Lipofectamine2000 (Life Technologies Inc.) and OptiMEM (Life Technologies Inc.) as per the manufacturer's instructions. 16–24 h post-transfection media cells were treated with fresh growth media containing 1 mm cycloheximide (Sigma-Aldrich, St Louis, MO, USA) for 1 h before immunodetection (52).

Immunofluorescence

HeLa cells growing on glass coverslips were fixed in 4% paraformaldehyde in PBS for 30–90 min prior to permeablization in 0.1% Triton-X-100 for 5 min. The cells were then washed in a blocking solution of 0.8% gelatin (Sigma-Aldrich) and 5% fetal calf serum (Life Technologies Inc.) in PBS three times prior to incubation with the primary antibody for 30–90 min. The cells were subsequently washed three times prior to incubation with the secondary antibody for 30–60 min. The cells were then washed three times with blocking solution prior to three PBS washes and subsequent mounting on slides with MO-WIOL (Calbiochem, Nottingham, UK), which was prepared according to the manufacturer's instructions.

Microscopy

Representative images of the observed localization patterns were captured for each construct and atypical patterns were discarded unless they were representative. Images were captured on an Olympus AX-70 upright fluorescence microscope. All co-localization data were captured on a Zeiss Axiovert 200 M SP LSM 510 META inverted, laser scanning confocal microscope with appropriate band pass filter settings. Data were analyzed using the LSM 510 META (Zeiss, Jenna, Germany) software and images were prepared using Adobe Photoshop 7.0 (Adobe Systems, San Jose, CA, USA).

Acknowledgments

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

This work was supported by funds from the Australian Research Council of Australia and the Australian National Health and Medical Research Council of Australia. This study was supported by a research grant for the RIKEN Genome Exploration Research Project from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government to Yoshihide Hayashizaki, a research grant for the Genome Network Project from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese government to Yoshihide Hayashizaki and a research grant for the Strategic Programs for R & D of RIKEN. This research was also supported by a research grant for Advanced and Innovational Research Program in Life Science to Yoshihide Hayashizaki and a research grant for Advanced and Innovational Research Program in Life Science to Jun Kawai, Rohan D. Teasdale and Sean M. Grimmond are supported by an NHMRC R. Douglas Wright Career Development Award. Confocal microscopy was performed at the Dynamic Imaging Facility for Cancer Biology established with funding from the Australian Cancer Research Foundation. Rajith N. Aturaliya is supported by a Postgraduate Research Scholarship from the IMB, University of Queensland, and Kevin C. Miranda was supported by an NHMRC Biomedical (Dora Lush) Postgraduate Research Scholarship. We acknowledge technical contributions from Cameron Flegg, Milena Gongora and Mark Crowe. We thank colleagues in the Teasdale and Grimmond laboratories for their helpful discussion and technical support. In addition, we thank our colleagues elsewhere for providing reagents. Parts of this work were performed as part of the Renal Regeneration Consortium and was supported by National Institutes of Health (DK63400) as part of the Stem Cell Genome Anatomy Project (http://www.scgap.org/).

Supplementary Material

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

Table S1: Subcellular localization of type II membrane proteins.

Table S2: Frequency of InterPro domains within the type II membrane proteins dataset.

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  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Results
  4. Discussion
  5. Materials and Methods
  6. Acknowledgments
  7. Supplementary Material
  8. References
  9. Supporting Information

Supplementary Table 1

Supplementary Table 2: InterPro Data

FilenameFormatSizeDescription
tra_460_sm_7_5_Table1.pdf235KSupporting info item
tra_460_sm_7_5_Table2.pdf34KSupporting info item

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