Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins

Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.     

A Systematic Cell‐Based Analysis of Localization of Predicted Drosophila Peroxisomal Proteins

Peroxisomes are membrane‐bound organelles found in almost all eukaryotic cells. They perform specialized biochemical functions that vary with organism, tissue or cell type. Mutations in human genes required for the assembly of peroxisomes result in a spectrum of diseases called the peroxisome biogenesis disorders. A previous sequence‐based comparison of the predicted proteome of Drosophila melanogaster (the fruit fly) to human proteins identified 82 potential homologues of proteins involved in peroxisomal biogenesis, homeostasis or metabolism. However, the subcellular localization of these proteins relative to the peroxisome was not determined. Accordingly, we tested systematically the localization and selected functions of epitope‐tagged proteins in Drosophila Schneider 2 cells to determine the subcellular localization of 82 potential Drosophila peroxisomal protein homologues. Excluding the Pex proteins, 34 proteins localized primarily to the peroxisome, 8 showed dual localization to the peroxisome and other structures, and 26 localized exclusively to organelles other than the peroxisome. Drosophila is a well‐developed laboratory animal often used for discovery of gene pathways, including those linked to human disease. Our work establishes a basic understanding of peroxisome protein localization in Drosophila. This will facilitate use of Drosophila as a genetically tractable, multicellular model system for studying key aspects of human peroxisome disease.      

A Quantitative Proteomics Analysis of Subcellular Proteome Localization and Changes Induced by DNA Damage

A major challenge in cell biology is to identify the subcellular distribution of proteins within cells and to characterize how protein localization changes under different cell growth conditions and in response to stress and other external signals. Protein localization is usually determined either by microscopy or by using cell fractionation combined with protein blotting techniques. Both these approaches are intrinsically low throughput and limited to the analysis of known components. Here we use mass spectrometry-based proteomics to provide an unbiased, quantitative, and high throughput approach for measuring the subcellular distribution of the proteome, termed “spatial proteomics.” The spatial proteomics method analyzes a whole cell extract created by recombining differentially labeled subcellular fractions derived from cells in which proteins have been mass-labeled with heavy isotopes. This was used here to measure the relative distribution between cytoplasm, nucleus, and nucleolus of over 2,000 proteins in HCT116 cells. The data show that, at steady state, the proteome is predominantly partitioned into specific subcellular locations with only a minor subset of proteins equally distributed between two or more compartments. Spatial proteomics also facilitates a proteome-wide comparison of changes in protein localization in response to a wide range of physiological and experimental perturbations, shown here by characterizing dynamic changes in protein localization elicited during the cellular response to DNA damage following treatment of HCT116 cells with etoposide. DNA damage was found to cause dissociation of the proteasome from inhibitory proteins and assembly chaperones in the cytoplasm and relocation to associate with proteasome activators in the nucleus.Many previous studies on organelle proteomics have provided a detailed list of the protein contents of organelles, substructures, or compartments isolated from cells (1–5). Such studies have also used quantitative proteomics in the high throughput assignment of proteins to subcellular compartments using methods such as protein correlation profiling (3, 6), recording the number of ions detected per protein (1, 2), or localization of organelle proteins by isotope tagging (7, 8). However, interpretation of the resulting protein inventory is complicated by the dynamic nature of organelle proteomes and by the fact that many proteins are not exclusive to one compartment but instead partition between separate subcellular locations (9, 10). This is illustrated by our previous studies of the human nucleolar proteome that have identified over 4,000 proteins that can co-purify reproducibly with nucleoli isolated from human cells but many of which are either present in low abundance in nucleoli and/or also have functions in other cellular locations (11). This highlights the importance of not only identifying the presence of a protein in any specific cellular organelle or structure but also measuring its relative abundance in different locations and assessing how this subcellular localization can change between different compartments under different cell growth and physiological conditions.Stable isotope labeling with amino acids in cell culture (SILAC)1 is the use of stable isotopic atoms along with mass spectrometry for quantitative mass spectrometry analysis (12, 13). This method allows quantitative analyses of proteins by comparison of the mass of light and heavier forms of the same peptide from a given protein, arising from the presence of heavier, stable isotopes such as ¹³C, ²H, and ¹⁵N. These stable isotopes are incorporated in proteins by in vivo labeling, i.e. growing the cells in specialized media where specific amino acids, typically arginine and lysine, are replaced with corresponding heavy isotope-substituted forms in which either all carbons or carbons, hydrogens, or nitrogens are isotope-labeled (14). Cleavage at the substituted arginine or lysine by trypsin generates a peptide with a shift in mass relative to the control (i.e. unsubstituted) peptide, and this can easily be resolved by mass spectrometry. The ratio of intensities of the “light” and “heavy” peptide signals identified by mass spectrometry directly correlates with the relative amount of the cognate protein from each sample. This method has been widely used for both relative quantification of protein levels after exposure of cells to drugs and inhibitors and for the identification of specific protein interaction partners (15–18).Here we used a quantitative and high throughput MS-based approach we term “spatial proteomics,” which both measures the relative intracellular localization of proteins and facilitates a comparison of changes in their subcellular localization under different conditions. This approach allows the rapid assignment of the cellular localization of proteins using common fractionation techniques. The major advantage of such a technique over other MS-based localization techniques such as protein correlation profiling or localization of organelle proteins by isotope tagging is that it provides a direct quantitative measurement of what fraction of each protein is localized to each cellular compartment, whereas the other techniques associate proteins showing similar profiles in a density centrifugation gradient while not describing the relative fraction of proteins in all locations. The spatial proteomics approach thus facilitates the comparison of protein localization under different conditions. We applied this spatial proteomics technique to determine the subcellular localization of over 2,000 proteins in HCT116 cells and then compared changes in localization following exposure to the topoisomerase inhibitor etoposide.     

Toolbox: Creating a systematic database of secretory pathway proteins uncovers new cargo for COPI

A third of yeast genes encode for proteins that function in the endomembrane system. However, the precise localization for many of these proteins is still uncertain. Here, we visualized a collection of ~500 N‐terminally, green fluorescent protein (GFP), tagged proteins of the yeast Saccharomyces cerevisiae. By co‐localizing them with 7 known markers of endomembrane compartments we determined the localization for over 200 of them. Using this approach, we create a systematic database of the various secretory compartments and identify several new residents. Focusing in, we now suggest that Lam5 resides in contact sites between the endoplasmic reticulum and the late Golgi. Additionally, analysis of interactions between the COPI coat and co‐localizing proteins from our screen identifies a subset of proteins that are COPI‐cargo. In summary, our approach defines the protein roster within each compartment enabling characterization of the physical and functional organization of the endomembrane system and its components.      

Esub8: A novel tool to predict protein subcellular localizations in eukaryotic organisms

Background

Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed.

Results

Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test.

Conclusions

Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.

     

FGsub: <Emphasis Type="Italic">Fusarium graminearum</Emphasis> protein subcellular localizations predicted from primary structures

Background

The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.

Results

In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.

Conclusions

In this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.

     

Systematic validation of antibody binding and protein subcellular localization using siRNA and confocal microscopy

We have developed a platform for validation of antibody binding and protein subcellular localization data obtained from immunofluorescence using siRNA technology combined with automated confocal microscopy and image analysis. By combining the siRNA technology with automated sample preparation, automated imaging and quantitative image analysis, a high-throughput assay has been set-up to enable confirmation of accurate protein binding and localization in a systematic manner. Here, we describe the analysis and validation of the subcellular location of 65 human proteins, targeted by 75 antibodies and silenced by 130 siRNAs. A large fraction of (80%) the subcellular locations, including locations of several previously uncharacterized proteins, could be confirmed by the significant down-regulation of the antibody signal after the siRNA silencing. A quantitative analysis was set-up using automated image analysis to facilitate studies of targets found in more than one compartment. The results obtained using the platform demonstrate that siRNA silencing in combination with quantitative image analysis of antibody signals in different compartments of the cells is an attractive approach for ensuring accurate protein localization as well as antibody binding using immunofluorescence. With a large fraction of the human proteome still unexplored, we suggest this approach to be of great importance under the continued work of mapping the human proteome on a subcellular level.     

Palmitoylation of stathmin family proteins domain A controls Golgi versus mitochondrial subcellular targeting

Background information. Precise localization of proteins to specialized subcellular domains is fundamental for proper neuronal development and function. The neural microtubule‐regulatory phosphoproteins of the stathmin family are such proteins whose specific functions are controlled by subcellular localization. Whereas stathmin is cytosolic, SCG10, SCLIP and RB3/RB3′/RB3″ are localized to the Golgi and vesicle‐like structures along neurites and at growth cones. We examined the molecular determinants involved in the regulation of this specific subcellular localization in hippocampal neurons in culture. Results. We show that their conserved N‐terminal domain A carrying two palmitoylation sites is dominant over the others for Golgi and vesicle‐like localization. Using palmitoylation‐deficient GFP (green fluorescent protein) fusion mutants, we demonstrate that domains A of stathmin proteins have the particular ability to control protein targeting to either Golgi or mitochondria, depending on their palmitoylation. This regulation involves the co‐operation of two subdomains within domain A, and seems also to be under the control of its SLD (stathmin‐like domain) extension. Conclusions. Our results unravel that, in specific biological conditions, palmitoylation of stathmin proteins might be able to control their targeting to express their functional activities at appropriate subcellular sites. They, more generally, open new perspectives regarding the role of palmitoylation as a signalling mechanism orienting proteins to their functional subcellular compartments.     

Amyloid precursor proteins are constituents of the presynaptic active zone

The amyloid precursor protein (APP) and its mammalian homologs, APLP1, APLP2, have been allocated to an organellar pool residing in the Golgi apparatus and in endosomal compartments, and in its mature form to a cell surface‐localized pool. In the brain, all APPs are restricted to neurons; however, their precise localization at the plasma membrane remained enigmatic. Employing a variety of subcellular fractionation steps, we isolated two synaptic vesicle (SV) pools from rat and mouse brain, a pool consisting of synaptic vesicles only and a pool comprising SV docked to the presynaptic plasma membrane. Immunopurification of these two pools using a monoclonal antibody directed against the 12 membrane span synaptic vesicle protein2 (SV2) demonstrated unambiguously that APP, APLP1 and APLP2 are constituents of the active zone of murine brain but essentially absent from free synaptic vesicles. The specificity of immunodetection was confirmed by analyzing the respective knock‐out animals. The fractionation experiments further revealed that APP is accumulated in the fraction containing docked synaptic vesicles. These data present novel insights into the subsynaptic localization of APPs and are a prerequisite for unraveling the physiological role of all mature APP proteins in synaptic physiology.

     

The Ubiquitous Distribution of Late Embryogenesis Abundant Proteins across Cell Compartments in Arabidopsis Offers Tailored Protection against Abiotic Stress

Late embryogenesis abundant (LEA) proteins are hydrophilic, mostly intrinsically disordered proteins, which play major roles in desiccation tolerance. In Arabidopsis thaliana, 51 genes encoding LEA proteins clustered into nine families have been inventoried. To increase our understanding of the yet enigmatic functions of these gene families, we report the subcellular location of each protein. Experimental data highlight the limits of in silico predictions for analysis of subcellular localization. Thirty-six LEA proteins localized to the cytosol, with most being able to diffuse into the nucleus. Three proteins were exclusively localized in plastids or mitochondria, while two others were found dually targeted to these organelles. Targeting cleavage sites could be determined for five of these proteins. Three proteins were found to be endoplasmic reticulum (ER) residents, two were vacuolar, and two were secreted. A single protein was identified in pexophagosomes. While most LEA protein families have a unique subcellular localization, members of the LEA_4 family are widely distributed (cytosol, mitochondria, plastid, ER, and pexophagosome) but share the presence of the class A α-helix motif. They are thus expected to establish interactions with various cellular membranes under stress conditions. The broad subcellular distribution of LEA proteins highlights the requirement for each cellular compartment to be provided with protective mechanisms to cope with desiccation or cold stress.     

Arabinogalactan Glycosyltransferases Target to a Unique Subcellular Compartment That May Function in Unconventional Secretion in Plants

We report that fluorescently tagged arabinogalactan glycosyltransferases target not only the Golgi apparatus but also uncharacterized smaller compartments when transiently expressed in Nicotiana benthamiana. Approximately 80% of AtGALT31A [Arabidopsis thaliana galactosyltransferase from family 31 (At1g32930)] was found in the small compartments, of which, 45 and 40% of AtGALT29A [Arabidopsis thaliana galactosyltransferase from family 29 (At1g08280)] and AtGlcAT14A [Arabidopsis thaliana glucuronosyltransferase from family 14 (At5g39990)] colocalized with AtGALT31A, respectively; in contrast, N‐glycosylation enzymes rarely colocalized (3–18%), implicating a role of the small compartments in a part of arabinogalactan (O‐glycan) biosynthesis rather than N‐glycan processing. The dual localization of AtGALT31A was also observed for fluorescently tagged AtGALT31A stably expressed in an Arabidopsis atgalt31a mutant background. Further, site‐directed mutagenesis of a phosphorylation site of AtGALT29A (Y144) increased the frequency of the protein being targeted to the AtGALT31A‐localized small compartments, suggesting a role of Y144 in subcellular targeting. The AtGALT31A localized to the small compartments were colocalized with neither SYP61 (syntaxin of plants 61), a marker for trans‐Golgi network (TGN), nor FM4‐64‐stained endosomes. However, 41% colocalized with EXO70E2 (Arabidopsis thaliana exocyst protein Exo70 homolog 2), a marker for exocyst‐positive organelles, and least affected by Brefeldin A and Wortmannin. Taken together, AtGALT31A localized to small compartments that are distinct from the Golgi apparatus, the SYP61‐localized TGN, FM4‐64‐stained endosomes and Wortmannin‐vacuolated prevacuolar compartments, but may be part of an unconventional protein secretory pathway represented by EXO70E2 in plants.