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 |
REPOSITORIES: biostudies-literature
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Project description:Porcine epidemic diarrhea virus (PEDV), a highly pathogenic enteric coronavirus, is regarded as one of the most severe porcine pathogens. To date, there are still no commercial vaccines or drugs that can provide full protection against the epidemic strains. A better understanding of the subcellular location of individual proteins could benefit from studying the protein functions and mechanisms of how the virus regulates key cellular processes, finally leading to the development of antiviral agents. In this study, we characterized the subcellular localization of PEDV proteins using multi-labeled fluorescent immunocytochemistry. As a result, 11 proteins showed cytoplasmic distribution and 10 proteins showed both cytoplasmic and nuclear distribution. Furthermore, we demonstrated that four proteins (Nsp3, Nsp4, Nsp6, and S1) were co-localized in the endoplasmic reticulum (ER), while four proteins (Nsp2, S2, N, and ORF3) were partially observed in the ER, two proteins (E and M) were co-localized in the Golgi apparatus, and two proteins (Nsp2 and E) were partially co-localized with the mitochondria. These viral proteins may perform specific functions at specific cellular locations. Together, these results describe a subcellular localization map of PEDV proteins, which will help to characterize the functions of these proteins in the future.
| S-EPMC9781403 | biostudies-literature
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.
| S-EPMC7114198 | biostudies-literature
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.
| S-EPMC7993076 | biostudies-literature
Project description: Not available
| EMPIAR-10490 | biostudies-other
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.
| S-EPMC2685389 | biostudies-literature
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.
| S-EPMC3314587 | biostudies-literature
Project description:Interferon-induced transmembrane proteins (IFITMs) restrict infections by many viruses, but a subset of IFITMs enhance infections by specific coronaviruses through currently unknown mechanisms. We show that SARS-CoV-2 Spike-pseudotyped virus and genuine SARS-CoV-2 infections are generally restricted by human and mouse IFITM1, IFITM2, and IFITM3, using gain- and loss-of-function approaches. Mechanistically, SARS-CoV-2 restriction occurred independently of IFITM3 S-palmitoylation, indicating a restrictive capacity distinct from reported inhibition of other viruses. In contrast, the IFITM3 amphipathic helix and its amphipathic properties were required for virus restriction. Mutation of residues within the IFITM3 endocytosis-promoting Yxx? motif converted human IFITM3 into an enhancer of SARS-CoV-2 infection, and cell-to-cell fusion assays confirmed the ability of endocytic mutants to enhance Spike-mediated fusion with the plasma membrane. Overexpression of TMPRSS2, which increases plasma membrane fusion versus endosome fusion of SARS-CoV-2, attenuated IFITM3 restriction and converted amphipathic helix mutants into infection enhancers. In sum, we uncover new pro- and anti-viral mechanisms of IFITM3, with clear distinctions drawn between enhancement of viral infection at the plasma membrane and amphipathicity-based mechanisms used for endosomal SARS-CoV-2 restriction.
| S-SCDT-EMBOJ-2020-106501 | biostudies-other
Project description: Not available
| S-EPMC7890341 | biostudies-literature
Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.
| S-EPMC2612013 | biostudies-literature
Project description:Hantavirus assembly and budding are governed by the surface glycoproteins Gn and Gc. In this study, we investigated the glycoproteins of Puumala, the most abundant Hantavirus species in Europe, using fluorescently labeled wild-type constructs and cytoplasmic tail (CT) mutants. We analyzed their intracellular distribution, co-localization and oligomerization, applying comprehensive live, single-cell fluorescence techniques, including confocal microscopy, imaging flow cytometry, anisotropy imaging and Number&Brightness analysis. We demonstrate that Gc is significantly enriched in the Golgi apparatus in absence of other viral components, while Gn is mainly restricted to the endoplasmic reticulum (ER). Importantly, upon co-expression both glycoproteins were found in the Golgi apparatus. Furthermore, we show that an intact CT of Gc is necessary for efficient Golgi localization, while the CT of Gn influences protein stability. Finally, we found that Gn assembles into higher-order homo-oligomers, mainly dimers and tetramers, in the ER while Gc was present as mixture of monomers and dimers within the Golgi apparatus. Our findings suggest that PUUV Gc is the driving factor of the targeting of Gc and Gn to the Golgi region, while Gn possesses a significantly stronger self-association potential.
| S-EPMC6345964 | biostudies-literature