A systemic and molecular study of subcellular localization of SARS-CoV-2 proteins.
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ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC7670843 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
A systemic and molecular study of subcellular localization of SARS-CoV-2 proteins.
Zhang Jing J Cruz-Cosme Ruth R Zhuang Meng-Wei MW Liu Dongxiao D Liu Yuan Y Teng Shaolei S Wang Pei-Hui PH Tang Qiyi Q
Signal transduction and targeted therapy 20201117 1
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Project description:SARS-CoV-2 E and 3a proteins are important for the assembly, budding, and release of viral particles. These two transmembrane proteins have been implicated in forming channels in the membrane that allow the transport of ions to favor viral replication. During an active infection, both proteins generally localize to the endoplasmic reticulum (ER), ER-Golgi intermediate compartment (ERGIC), and the Golgi where viral assembly occurs. The ER and Golgi are critical for the proper packaging and trafficking of cellular proteins along the secretory pathways which determine a protein's final destination inside or outside of the cell. The SARS-CoV-2 virus primarily infects epithelial cells that are highly secretory in nature such as those in the lung and gut. Here we quantified the distribution of SARS-CoV-2 E and 3a proteins along the secretory pathways in a human intestinal epithelial cell line. We used NaturePatternMatch to demonstrate that epitope-tagged E and 3a proteins expressed alone via transient transfection have a similar immunoreactivity pattern as E and 3a proteins expressed by wild-type viral infection. While E and 3a proteins localized with all selected cellular markers to varying degrees, 3a protein displayed a higher correlation coefficient with the Golgi, early/late endosome, lysosome, and plasma membrane when compared to E protein. This work is the first to provide quantification of the subcellular distribution of E and 3a proteins along the multiple components of the secretory pathway and serves as a basis to develop models for examining how E and 3a alter proteostasis within these structures and affect their function.
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Project description:SARS-CoV 3a protein was a unique protein of SARS coronavirus (SARS-CoV), which was identified in SARS-CoV infected cells and SARS patients' specimen. Recent studies revealed that 3a could interact specifically with many SARS-CoV structural proteins, such as M, E and S protein. Expressed 3a protein was reported to localize to Golgi complex in SARS-CoV infected cells. In this study, it was shown that 3a protein was mainly located in Golgi apparatus with different tags at N- or C-terminus. The localization pattern was similar in different transfected cells. With the assay of truncated 3a protein, it was shown that 3a might contain three transmembrane regions, and the second or third region was properly responsible for Golgi localization. By ultra-centrifugation experiment with different extraction buffers, it was confirmed that 3a was an integral membrane protein and embedded in the phospholipid bilayer. Immunofluorescence assay indicated that 3a was co-localized with M protein in Golgi complex in co-transfected cells. These results provide a new insight for further study of the 3a protein on the pathogenesis of SARS-CoV.
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Project description:BackgroundSARS-CoV-2 causes COVID-19 which has a widely diverse disease profile. The mechanisms underlying its pathogenicity remain unclear. We set out to identify the SARS-CoV-2 pathogenic proteins that through host interactions cause the cellular damages underlying COVID-19 symptomatology.MethodsWe examined each of the individual SARS-CoV-2 proteins for their cytotoxicity in HEK 293 T cells and their subcellular localization in COS-7 cells. We also used Mass-Spec Affinity purification to identify the host proteins interacting with SARS-CoV-2 Orf6 protein and tested a drug that could inhibit a specific Orf6 and host protein interaction.ResultsWe found that Orf6, Nsp6 and Orf7a induced the highest toxicity when over-expressed in human 293 T cells. All three proteins showed membrane localization in COS-7 cells. We focused on Orf6, which was most cytotoxic and localized to the endoplasmic reticulum, autophagosome and lysosomal membranes. Proteomics revealed Orf6 interacts with nucleopore proteins (RAE1, XPO1, RANBP2 and nucleoporins). Treatment with Selinexor, an FDA-approved inhibitor for XPO1, attenuated Orf6-induced cellular toxicity in human 293 T cells.ConclusionsOur study revealed Orf6 as a highly pathogenic protein from the SARS-CoV-2 genome, identified its key host interacting proteins, and Selinexor as a drug candidate for directly targeting Orf6 host protein interaction that leads to cytotoxicity.
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Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.
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Project description:BackgroundProteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.ResultsWe systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.ConclusionsPhysical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.
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| S-EPMC7890341 | biostudies-literature
Project description:BackgroundMembrane proteins constitute up to 30% of the human proteome. These proteins have special properties because the transmembrane segments are embedded into lipid bilayer while extramembranous parts are in different environments. Membrane proteins have several functions and are involved in numerous diseases. A large number of prediction methods have been introduced to predict protein subcellular localization as well as the tolerance or pathogenicity of amino acid substitutions.ResultsWe tested the performance of 22 tolerance predictors by collecting information on membrane proteins and variants in them. The analysis indicated that the best tools had similar prediction performance on transmembrane, inside and outside regions of transmembrane proteins and comparable to overall prediction performances for all types of proteins. PON-P2 had the highest performance followed by REVEL, MetaSVM and VEST3. Further, we tested with the high quality dataset also the performance of seven subcellular localization predictors on membrane proteins. We assessed separately the performance for single pass and multi pass membrane proteins. Predictions for multi pass proteins were more reliable than those for single pass proteins.ConclusionsThe predictors for variant effects had better performance than subcellular localization tools. The best tolerance predictors are highly reliable. As there are large differences in the performances of tools, end-users have to be cautious in method selection.
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