Data from Differential Subcellular Localization Regulates Oncogenic Signaling by ROS1 Kinase Fusion Proteins
Dana S. NeelDavid V. AllegakoenVictor OlivasManasi K. MayekarGolzar HemmatiNilanjana ChatterjeeCollin M. BlakelyCaroline E. McCoachJulia RotowAnh T. LeNiki KarachaliouRafael RosellJonathan W. RiessRobert NicholsRobert C. DoebeleTrever G. Bivona
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<div>Abstract<p>Chromosomal rearrangements involving receptor tyrosine kinases (RTK) are a clinically relevant oncogenic mechanism in human cancers. These chimeric oncoproteins often contain the C-terminal kinase domain of the RTK joined <i>in cis</i> to various N-terminal, nonkinase fusion partners. The functional role of the N-terminal fusion partner in RTK fusion oncoproteins is poorly understood. Here, we show that distinct N-terminal fusion partners drive differential subcellular localization, which imparts distinct cell signaling and oncogenic properties of different, clinically relevant ROS1 RTK fusion oncoproteins. SDC4-ROS1 and SLC34A2-ROS1 fusion oncoproteins resided on endosomes and activated the MAPK pathway. CD74-ROS1 variants that localized instead to the endoplasmic reticulum (ER) showed compromised activation of MAPK. Forced relocalization of CD74-ROS1 from the ER to endosomes restored MAPK signaling. ROS1 fusion oncoproteins that better activate MAPK formed more aggressive tumors. Thus, differential subcellular localization controlled by the N-terminal fusion partner regulates the oncogenic mechanisms and output of certain RTK fusion oncoproteins.</p>Significance:<p>ROS1 fusion oncoproteins exhibit differential activation of MAPK signaling according to subcellular localization, with ROS1 fusions localized to endosomes, the strongest activators of MAPK signaling.</p></div>ADP ribosylation factor
Small GTPase
Transport protein
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Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization. Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.
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Identifying the subcellular localization of a given protein is an essential part of biological and medical research, since the protein must be localized in the correct organelle to ensure physiological function. Conventional biological experiments for protein subcellular localization have some limitations, such as high cost and low efficiency, thus massive computational methods are proposed to solve these problems. However, some of these methods need to be improved further for protein subcellular localization with class imbalance problem. We propose a new model, generating minority samples for protein subcellular localization (Gm-PLoc), to predict the subcellular localization of multi-label proteins. This model includes three steps: using the position specific scoring matrix to extract distinguishable features of proteins; synthesizing samples of the minority category to balance the distribution of categories based on the revised generative adversarial networks; training a classifier with the rebalanced dataset to predict the subcellular localization of multi-label proteins. One benchmark dataset is selected to evaluate the performance of the presented model, and the experimental results demonstrate that Gm-PLoc performs well for the multi-label protein subcellular localization.
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Objective To investigate the subcellular localization of Mipu1 protein in brain astrocytoma cells and(it's) potential functional role.Methods The full-length open reading frame(ORF) of Mipu1 was fused to the 5' end of green fluorescent protein(GFP) coding sequence.Then the construction was transiently transfected into brain astrocytoma cells.The subcellular localization of Mipu1 was analyzed by fluorescence microscope before and after the cells were stimulated by H_2O_2.Results The Mipu1-GFP fusion construction was successfully transfected into brain astrocytoma cells.Fluorescence microscopy demonstrated that Mipu1 was located in cellular nucleus under normal condition.However there was no change of subcellular localization of Mipu1 when the cells were stimulated by H_2O_2.Conclusion Mipu1 is considered as a nuclear transcription factor based on it's protein structure and subcellular localization.
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Article24 May 2011Open Access Protein localization as a principal feature of the etiology and comorbidity of genetic diseases Solip Park Solip Park School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Jae-Seong Yang Jae-Seong Yang School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Young-Eun Shin Young-Eun Shin Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Juyong Park Juyong Park Physics Department, Kyung Hee University, Seoul, Korea Search for more papers by this author Sung Key Jang Sung Key Jang School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Division of Integrative Bioscience and Biotechnology, Pohang University of Science and Technology, Pohang, Korea Biotechnology Research Center, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Sanguk Kim Corresponding Author Sanguk Kim School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Division of IT Convergence Engineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Solip Park Solip Park School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Jae-Seong Yang Jae-Seong Yang School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Young-Eun Shin Young-Eun Shin Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Juyong Park Juyong Park Physics Department, Kyung Hee University, Seoul, Korea Search for more papers by this author Sung Key Jang Sung Key Jang School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Division of Integrative Bioscience and Biotechnology, Pohang University of Science and Technology, Pohang, Korea Biotechnology Research Center, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Sanguk Kim Corresponding Author Sanguk Kim School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea Division of IT Convergence Engineering, Pohang University of Science and Technology, Pohang, Korea Search for more papers by this author Author Information Solip Park1, Jae-Seong Yang1, Young-Eun Shin2, Juyong Park3, Sung Key Jang1,2,4,5 and Sanguk Kim 1,2,6 1School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea 2Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang, Korea 3Physics Department, Kyung Hee University, Seoul, Korea 4Division of Integrative Bioscience and Biotechnology, Pohang University of Science and Technology, Pohang, Korea 5Biotechnology Research Center, Pohang University of Science and Technology, Pohang, Korea 6Division of IT Convergence Engineering, Pohang University of Science and Technology, Pohang, Korea *Corresponding author. Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang 790-784, Korea. Tel.: +82 54 279 2348; Fax: +82 54 279 2199; E-mail: [email protected] Molecular Systems Biology (2011)7:494https://doi.org/10.1038/msb.2011.29 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Proteins targeting the same subcellular localization tend to participate in mutual protein–protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease-associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations. In particular, certain disease types were more prevalent in specific subcellular localizations. We analyzed the enrichment of disease phenotypes within subcellular localizations, and found that there exists a significant correlation between disease classes and subcellular localizations. Furthermore, we found that two diseases displayed high comorbidity when disease-associated proteins were connected via subcellular localization. We newly explained 7584 disease pairs by using the context of protein subcellular localization, which had not been identified using shared genes or PPIs only. Our result establishes a direct correlation between protein subcellular localization and disease association, and helps to understand the mechanism of human disease progression. Synopsis It was shown that the emergence of phenotypically similar diseases are triggered as a result of molecular connections between disease-causing genes (Oti and Brunner, 2007; Zaghloul and Katsanis, 2010). From a genetics, perspective diseases are associated with certain genes (Goh et al, 2007; Feldman et al, 2008), whereas from a proteomics perspective phenotypically similar diseases are connected via biological modules such as protein–protein interactions (PPIs) or molecular pathways (Lage et al, 2007; Jiang et al, 2008; Wu et al, 2008; Linghu et al, 2009; Suthram et al, 2010). These molecular connections between diseases were observed on the population level as well: diseases connected through molecular connections such as shared genes, PPIs, and metabolic pathways tend to show elevated comorbidity (Rzhetsky et al, 2007; Lee et al, 2008; Zhernakova et al, 2009; Park et al, 2009a, 2009b). While these findings constitute a step toward improving our understanding of the mechanism of disease progression, there are still many more molecule-level connections between disease pairs that need to be explored in order to establish a firmer comorbidity association. Subcellular localization provides spatial information of proteins in the cell; proteins target subcellular localizations to interact with appropriate partners and form functional complexes in signaling pathways and metabolic processes (Au et al, 2007). Abnormal protein localizations are known to lead to the loss of functional effects in diseases (Luheshi et al, 2008; Laurila and Vihinen, 2009). For example, mis-localizations of nuclear/cytoplasmic transport have been detected in many types of carcinoma cells (Kau et al, 2004). A proper identification of protein subcellular localization can hence be useful in discovering disease-associated proteins (Giallourakis et al, 2005; Calvo and Mootha, 2010). With this understanding, we postulate that disease-associated proteins connected by subcellular localizations could also explain the phenotypic similarities between diseases. Furthermore, such connections may also couple to disease progressions that contribute to multiple disease manifestation, that is, comorbidity. Protein subcellular localization has been extensively studied through various methods to determine a variety of protein functions. To the best of our knowledge, the connection between diseases and subcellular localizations are yet to be studied systematically. To resolve this we constructed, for the first time, a human Disease-associated Protein and subcellular Localization (DPL) matrix (top panel in Box 1). Our DPL matrix provides the 'cellular localization map of diseases' that represents the spatial index of diseases in the cell. We found that each disease shows unique characteristics of subcellular localization profile in the DPL matrix. We were interested in determining whether subsets of 1284 human diseases exhibit distinct enrichment profiles across subcellular localizations. We calculated pairwise correlations and performed a hierarchical clustering of the enrichments of the 1284 diseases across 10 different subcellular localizations. Box 1 Schematic overview of the relationship between diseases and subcellular localizations To build disease-associated proteins and subcellular localization matrix, 1284 diseases and 1777 disease-associated proteins were taken from OMIM database (Hamosh et al, 2005). Each disease-associated protein was mapped onto relevant subcellular localizations. Diseases were classified into 22 disease classes by the physiological system affected (Goh et al, 2007). (Middle) Subcellular localization information of the classified disease-associated proteins was attributed to the profile of disease classes. Disease progression was compiled from the hospitalization of 13 million patients from US Medicare database (Park et al, 2009a). Comorbid disease pairs were identified by calculating co-occurring disease pairs in individual patients. Subcellular localization similarity was calculated from the quantitative relationship between comorbid disease pairs and their subcellular localization profiles. Our DPL matrix revealed that 778 diseases (∼62%, P=1.40 × 10−3) are enriched in a single localization and 273 diseases (∼21%, P=3.45 × 10−3) are enriched in dual localizations. In the DPL matrix, certain disease-associated proteins are likely to be found in membrane-bounded organelles such as mitochondria, lysosome, and peroxisome, indicating that the mutations of proteins localized to these compartments are connected to the pathophysiological conditions of those organelles. Meanwhile, certain disease-associated proteins in the DPL matrix are enriched in dual localizations, such as extracellular/plasma membrane or endoplasmic reticulum/Golgi. Although these two pairs of subcellular localizations appear to be distinct compartments at first, they are functionally related compartments in close proximity during protein translocation process in the cell, and thus are likely to share interacting protein partners (Gandhi et al, 2006). Comorbidity represents the co-occurrence of multiple diseases in the same individual (Lee et al, 2008; Hidalgo et al, 2009; Park et al, 2009a). Many comorbid disease pairs have been shown to share common genes in the human disease network. For example, Diabetes and Alzheimer's disease share a risk factor in angiotensin I converting enzyme, and frequently occur together in an individual. In such instances, comorbidity can be partially attributed to the disease connections on the molecular level. To explore the impact of protein subcellular localization on comorbidity, we hypothesized that certain disease pairs could also be connected via subcellular localization by the molecular connections between the disease-associated proteins (bottom panel in Box 1). We found a positive correlation between subcellular localization similarity and relative risk (Figure 3B, Pearson's correlation coefficient between relative risk and subcellular localization similarity=0.81, P=2.96 × 10−5). The subcellular localization similarity represents the correlation of subcellular localization profiles between disease pairs. To our surprise, when we compared the relative risk of disease pairs linked via various molecular connections, we found that disease pairs connected by subcellular localization showed a near three-fold higher comorbidity tendency (with link distances equal to 2 or 3) when compared with random pairs (Figure 3E). We then assessed quantitatively the impact of network distances and subcellular localizations on the comorbidity tendency of disease pairs. We expected the proteins associated with comorbid disease pairs to be located closely in the protein interaction network via fewer links compared with random disease pairs. Indeed, a higher comorbidity tendency was found when two disease-associated proteins were positioned within a shorter distance (gray plots in Figure 3F). Moreover, when subcellular localization information was combined with small network distances, the comorbidity tendency increased dramatically (orange plots in Figure 3F). It suggests that subcellular localization and close network distances, two conceptually distinct molecular connections, contributed synergistically to the comorbidity tendency. Disease progression is not restricted to the mutation of disease-causing genes, but also affected by molecular connections in 'disease modules,' resulting in comorbidity (Fraser, 2006; Lee et al, 2008). In this study, for the first time we applied subcellular localization information to elucidate the molecular connections between comorbid diseases. We believe that, based on our finding, our approach helps to define the boundaries of 'disease modules.' Taken together, integration of diverse molecular connections should improve the molecular level understanding of hitherto unexplained comorbid disease pairs and help us in expanding the scope of our knowledge of the mechanism of human disease progression. Introduction Establishing the interrelationship between the genotype and the phenotype is one of the most challenging yet pertinent problems in biomedical research (Lamb et al, 2006). Molecular and genetic studies of diseases have been devoted to identifying disease-causing mutations through diverse gene-based methods such as recombination mapping and genome-wide association studies (Botstein and Risch, 2003; Broeckel and Schork, 2004). Traditional gene-based approaches have been compiled into a list of disease-associated genes. In addition, the rapid accumulation of functional genomics and proteomics data provides information on the protein–protein interactome, an extensive map of metabolism, and regulatory networks that complement current gene-based approaches (Rual et al, 2005; Stelzl et al, 2005; Duarte et al, 2007; Shlomi et al, 2008). Recently, it was shown that the emergence of phenotypically similar diseases are triggered as a result of molecular connections between disease-causing genes (Oti and Brunner, 2007; Zaghloul and Katsanis, 2010). From a genetics perspective diseases are associated with certain genes (Goh et al, 2007; Feldman et al, 2008), whereas from a proteomics perspective phenotypically similar diseases are connected via biological modules such as protein–protein interactions (PPIs) or molecular pathways (Lage et al, 2007; Jiang et al, 2008; Wu et al, 2008; Linghu et al, 2009; Suthram et al, 2010). These molecular connections between diseases were observed on the population level as well: diseases connected through molecular connections such as shared genes, PPIs, and metabolic pathways tend to show elevated comorbidity (Rzhetsky et al, 2007; Lee et al, 2008; Zhernakova et al, 2009; Park et al, 2009a). While these findings constitute a step toward improving our understanding of the mechanism of disease progression, there are still many more molecule-level connections between disease pairs that need to be explored in order to establish a firmer comorbidity association. Subcellular localization provides spatial information of proteins in the cell; proteins target subcellular localizations to interact with appropriate partners and form functional complexes in signaling pathways and metabolic processes (Au et al, 2007). Mutations in disease-causing genes alter the synthesis of the gene product, or change the targeting process of proper subcellular localizations, which in turn perturb the cellular functions of the proteins. Abnormal protein localizations are known to lead to the loss of functional effects in diseases (Luheshi et al, 2008; Laurila and Vihinen, 2009). For example, mis-localizations of nuclear/cytoplasmic transport have been detected in many types of carcinoma cells (Kau et al, 2004). A proper identification of protein subcellular localization can hence be useful in discovering disease-associated proteins (Giallourakis et al, 2005; Calvo and Mootha, 2010). Also, we have previously demonstrated that proteins associated with the same disease tend to localize in the same subcellular compartments (Park et al, 2009b). With this understanding, we postulate that disease-associated proteins connected by subcellular localizations could also explain the phenotypic similarities between diseases. Furthermore, such connections may also couple to disease progressions that contribute to multiple disease manifestation, that is, comorbidity. In this study, we investigated the interrelationship between diseases and subcellular localizations. Furthermore, we also explored the molecular connections between disease-associated proteins, and applied the subcellular localization similarity of disease pairs to understanding the human disease progression by analyzing comorbid disease pairs (Box 1 and described further in Materials and methods). We constructed, for the first time, a matrix of disease-associated proteins and their subcellular localization which describes the interrelationship between the two. From this matrix, we found that proteins associated with the same disease are likely enriched in particular subcellular localizations in the cell. We also observed that phenotypically similar diseases clustered in the same disease classes are associated with particular subcellular localizations. Furthermore, a positive correlation was found between subcellular localization similarity of disease pairs and comorbidity measures, which explains the molecular connections between comorbid disease pairs connected via subcellular localization. Subcellular localization furthermore enhanced the comorbid tendencies of disease pairs, and uncovered the hitherto-unknown molecular connections between 7584 disease pairs. This constitutes a novel approach to establishing the relationship between protein subcellular localization and the molecular connections of comorbid disease pairs, offering insight into previously unexplained mechanisms of disease progression. Results Systematic construction of the atlas of human disease-associated proteins and their subcellular localizations Protein subcellular localization has been extensively studied through various methods to determine a variety of protein functions. To the best of our knowledge, the connection between diseases and subcellular localizations are yet to be studied systematically. To resolve this, we constructed, for the first time, a human Disease-associated Protein and subcellular Localization (DPL) matrix (top panel in Box 1). For this purpose, we utilized the list of 1284 diseases representing the grouping of phenotypes (MIM record) based on disease names and their 1777 associated proteins available from the Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al, 2005). This approach has been widely used in recent systematic disease analyses of shared molecular characteristics between disease subtypes (Lee et al, 2008; Park et al, 2009a; Li and Patra, 2010). Disease-associated proteins were mapped to their encoded subcellular localizations based on the Swiss Prot annotation scheme and the consensus localization predictions we recently reported (Park et al, 2009b; see Supplementary File 1). We considered 10 different subcellular localizations (cytosol, endoplasmic reticulum (ER), extracellular, Golgi, peroxisome, mitochondria, nucleus, lysosome, plasma membrane, and others) for the localization mapping of disease-associated proteins, although minor localizations were considered simply as 'others' since the number of disease-associated proteins of such locations was too small to analyze (fewer than 10 proteins with confidence). We analyzed the covariance of a disease with a subcellular localization by identifying the number of disease-associated proteins by co-assigning diseases and subcellular localizations. Then, the DPL matrix was built by transforming the covariance into an association score (AS) between a disease and a subcellular localization (see Materials and methods and Supplementary File 2). Diseases have their unique subcellular localization profiles Our DPL matrix provides the 'cellular localization map of diseases' that represents the spatial index of diseases in the cell. We found that each disease shows unique characteristics of subcellular localization profile in the DPL matrix. We were interested in determining whether subsets of 1284 human diseases exhibit distinct enrichment profiles across subcellular localizations. We calculated pairwise correlations and performed a hierarchical clustering of the enrichments of the 1284 diseases across 10 different subcellular localizations (Figure 1). To validate the reliability of ASs, we calculated their Z-values; the Z-value represents the significance of the subcellular localization enrichment of a disease. We observed that the Z-values and subcellular localization-disease association scores are indeed highly correlated (R2=0.97), and we considered an AS ⩾0.05 to be statistically significant (P<0.01; Supplementary Figure 1A). Specifically, diseases that are caused by molecular defects in specific organelles showed significant ASs (AS ⩾0.2, Z-value >10, P<1.00 × 10−10) (Supplementary Figure 1B). For example, Mitochondria Complex I-III deficiency, a well-known mitochondrial disease (Pagliarini et al, 2008; Rotig, 2010), was significantly enriched within the mitochondria (Z-value=10.6, P<1.00 × 10−10) (Supplementary Figure 1B). Also, Adrenoleukodystrophy, a peroxisome biogenesis disorder (Wanders and Waterham, 2005), was significantly enriched within the peroxisome (Z-value=17, P<1.00 × 10−10). Figure 1.Hierarchical clustering demonstrating the intimate relationships between disease-associated proteins and their subcellular localizations. A two-dimensional hierarchical clustering was performed to organize and visualize the matrix of 10 different subcellular localizations and 1284 diseases. Enlarged portions represent clusters of highly enriched diseases in certain subcellular localizations (right panel). Download figure Download PowerPoint Our DPL matrix revealed that 778 diseases (∼62%, P=1.40 × 10−3) are enriched in a single localization and 273 diseases (∼21%, P=3.45 × 10−3) are enriched in dual localizations. In the DPL matrix, certain disease-associated proteins are likely to be found in membrane-bounded organelles such as mitochondria, lysosome, and peroxisome, indicating that the mutations of proteins localized to these compartments are connected to the pathophysiological conditions of those organelles. For example, HMG-CoA synthase deficiency caused by the shortage of mitochondrial 3-hydroxy-3-methlyglutaryl-CoA synthase is enriched in mitochondria, whereas genetic disorders belonging to lysosomal diseases caused by the dysfunction of lysosomal storage enzymes such as GM2-ganglinosidosis and sialidosis are enriched in lysosome (Parenti, 2009). Meanwhile, certain disease-associated proteins in the DPL matrix are enriched in dual localizations, such as extracellular/plasma membrane or ER/Golgi. Although these two pairs of subcellular localizations appear to be distinct compartments at first, they are functionally related compartments in close proximity during protein translocation process in the cell, and thus are likely to share interacting protein partners (Gandhi et al, 2006). Disease-associated proteins localizing in cytosol, interestingly, were not highly enriched when compared with other subcellular localizations. It might be related to the dynamic nature of many cytosolic proteins that are known to shuttle across subcellular compartments and interact with proteins in other localizations. Although calculating the ASs of disease-subcellular localizations turned out to be rigorous (see Materials and methods), we note the existence of potential issues related to the coverage of OMIM database due to the fact that our matrix reflects only the curated disease-gene associations. For instance, diseases with a single associated protein might introduce bias into the enrichment profile in the DPL matrix. To test the validity of the DPL matrix against such bias, we used disease sets having two or more disease-associated proteins and reconstructed the matrix of disease-associated proteins and their subcellular localization (Supplementary Figure 2A). Even without diseases with only one associated proteins, we confirmed that most diseases (∼63%, 307 diseases) were preferentially enriched in particular subcellular compartments when compared with random expectation (Supplementary Figure 2B, P=1.00 × 10−5). Next, we applied the disease-associated protein complex data to test the variations in disease-protein associations (Lage et al, 2007). To reconstruct the DPL matrix in this case, 882 diseases were used along with the disease-associated proteins as the 'seed' from which disease-associated protein complexes were assembled from the physical interactions of disease-associated proteins in the human protein interaction network based on the study of Lage et al (2008). This matrix again confirmed that disease enrichments in particular subcellular localizations are strongly correlated in the DPL matrix based on the OMIM data set (Supplementary Figure 3A). To compare the similarity between subcellular localization profiles, we selected an identical disease set from the matrices based on the OMIM data and on the disease-associated protein complex data, and confirmed that there exists a significant correlation (Supplementary Figure 3B, Pearson's correlation coefficient (PCC)=0.78, P=1.17 × 10−100), indicating the robustness of the properties that the profiles of disease-associated proteins and their subcellular localizations against the variations in disease-protein association data sets. Phenotypically related diseases have similar subcellular localization enrichment profile Subcellular localization enrichments of diseases in the DPL matrix show that certain disease types display strikingly similar enrichment patterns across multiple subcellular localizations. Moreover, we found that in many cases phenotypically similar diseases were enriched in specific subcellular localizations. For instance, many diseases in the metabolic disease class including HMG-CoA synthase-2 deficiency and CPT II deficiency are co-enriched in mitochondria. This suggests that phenotypically similar diseases are clustered on the molecular level, and display similar subcellular localization profiles due to the proteins of same molecular pathway likely being located in the same compartment. We grouped the manually determined classification of 1284 diseases to 22 human disease classes based on the physiological systems affected (Goh et al, 2007), and investigated whether phenotypically similar diseases share similar subcellular localization profiles. Here, we built the Disease Class-associated proteins and their subcellular Localization (DCL) matrix similar to the DPL matrix (middle panel in Box 1). Most disease classes (∼80%) show statistically significant enrichments in particular subcellular localizations (Figure 2A, P=1.00 × 10−5). An interesting example is the class of cancers (Figure 2B, P=1.00 × 10−12)—known to be associated with genes that typically express themselves in a broad range of tissues (Lage et al, 2008)—which appear to be significantly enriched inside the nucleus. This tells us that the molecular connections between cancer-associated proteins in the oncogenic activation of transcription factors localized in the nucleus are key in the progression of cancer (Libermann and Zerbini, 2006). Meanwhile, the immunological disease class is significantly enriched in the extracellular region (Figure 2C, P=1.00 × 10−20) where cell communication and signal transduction take place. Extracellular proteins serve as transducers of extracellular signals into intracellular physiology, having important roles in the modulation of the immune response in disease processes (Lin et al, 2008). Connective tissue diseases are also found to be significantly enriched in the extracellular region (Supplementary Figure 4A, P=1.75 × 10−11); mutations in extracellular matrix proteins are known to cause a wide range of inherited connective tissue diseases (Bateman et al, 2009). Osteoarthritis, a common connective tissue disease, for example, is related to the expression of MATN3 located in the cartilage extracellular matrix that contributes to the development of cartilage (Klatt et al, 2009). In contrast to the disease classes highly enriched in a specific subcellular localization, several other disease class exhibits enrichment within multiple subcellular localizations in the DCL matrix (Supplementary Figure 4B), the developmental disease class being an example. These diseases are known to be related to diverse pathological changes in various cellular processes and signaling pathways (Tomancak et al, 2007; Zhang et al, 2010). Indeed, we found that the proteins associated with developmental diseases are located in diverse subcellular compartments such as the nucleus, t
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Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological functions. In this paper, all motifs in PROSITE were examined and those that are indicative to eukaryotic protein subcellular localizations were picked out. A corresponding motif module was built and combined to our former work: LOCSVMPSI. Prediction results of this combined method were compared to LOCSVMPSI as well as several other existing methods for subcellular localization. The combined method achieved highest overall prediction accuracy among all listed methods and improved the over-all and each-location accuracies of LOCSVMPSI by 3%-8%. Further analysis indicates the combined motif method is very effective in eukaryotic protein subcellular localization prediction.
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Objective:To study the subcellular localization of eIF-5A, and find the clues for its function.Methods:eIF-5A subcellular localization was examined by laser confocal microscope, using the approaches of immunofluorescent staining and GFP-tagging.Results:The localization of eIF-5A was primarily cytoplasmic by indirect immunofluorescent staining. The results of transient transfection showed that GFP-eIF-5A was distributed over whole cell at the beginning of its expression, but was gradually concentrated in cytoplasm. The mutation of lysine 50→alanine affected its distribution in the cytoplasm.Conclusion:eIF-5A might be a nucleocytoplasmic shuttle protein, and its subcellular localization was a dynamic process and hypusine could affect the function of eIF-5A through swaying its subcellular localization.
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MyD88 is a cytoplasmic adaptor protein that is critical for Toll‐like receptor (TLR) signaling. The subcellular localization of MyD88 is characterized as large condensed forms in the cytoplasm. The mechanism and significance of this localization with respect to the signaling function, however, are currently unknown. Here, we demonstrate that MyD88 localization depends on the entire non‐TIR region and that the correct cellular targeting of MyD88 is indispensable for its signaling function. The Toll‐interleukin I receptor‐resistance (TIR) domain does not determine the subcellular localization, but it mediates interaction with specific TLRs. These findings reveal distinct roles for the TIR and non‐TIR regions in the subcellular localization and signaling properties of MyD88.
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Human proteins
Human Protein Atlas
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Objective: To study the subcellular localization of PC-1 in LNCaP prostate cancer cells.Methods: A various of PC-1 segments were amplified and cloned into a eukaryotic expression vector pEGFP-C1,respectively.The sequencing recombinants were transfected into LNCaP cells,observed the distribution of green fluorescence in the cell and validated their expressions by Western blot.Results: The recombinant vectors used for subcellular localization study of PC-1 had been successfully constructed and expressed in LNCaP cell lines.Meanwhile,a sequence that significantly impacts the subcellular localization of PC-1 had been identified.Conclusion: This work provides the basis for further study of subcellular localization and way of functioning of PC-1.
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