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      SARS-CoV-2 Nsp1 cooperates with initiation factors EIF1 and 1A to selectively enhance translation of viral RNA

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          Abstract

          A better mechanistic understanding of virus-host dependencies can help reveal vulnerabilities and identify opportunities for therapeutic intervention. Of particular interest are essential interactions that enable production of viral proteins, as those could target an early step in the virus lifecycle. Here, we use subcellular proteomics, ribosome profiling analyses and reporter assays to detect changes in protein synthesis dynamics during SARS-CoV-2 (CoV2) infection. We identify specific translation factors and molecular chaperones that are used by CoV2 to promote the synthesis and maturation of its own proteins. These can be targeted to inhibit infection, without major toxicity to the host. We also find that CoV2 non-structural protein 1 (Nsp1) cooperates with initiation factors EIF1 and 1A to selectively enhance translation of viral RNA. When EIF1/1A are depleted, more ribosomes initiate translation from a conserved upstream CUG start codon found in all genomic and subgenomic viral RNAs. This results in higher translation of an upstream open reading frame (uORF1) and lower translation of the main ORF, altering the stoichiometry of viral proteins and attenuating infection. Replacing the upstream CUG with AUG strongly inhibits translation of the main ORF independently of Nsp1, EIF1, or EIF1A. Taken together, our work describes multiple dependencies of CoV2 on host biosynthetic networks and proposes a model for dosage control of viral proteins through Nsp1-mediated control of translation start site selection.

          Author summary

          Host-directed antivirals offer a promising therapeutic approach for many viruses, including SARS-CoV-2 (CoV2), but their development requires a deeper understanding of virus-host interactions. Of particular interest are interactions that selectively promote the synthesis of viral proteins, including RNA-binding proteins, translation factors and molecular chaperones. Drugs that inhibit such biosynthetic factors have already entered clinical trials for multiple indications. To identify new cellular targets for intervention, we isolated translating ribosomes from CoV2-infected and control cells and analyzed their interacting partners by mass-spectrometry. We found multiple biosynthetic factors that were specifically enriched on polysomes translating CoV2, including translation initiation factors EIF1 and 1A. These factors control translation start site selection, cooperate with the viral non-structural protein 1 (Nsp1) to selectively enhance translation of viral genomic RNA, and are exploited by CoV2 to regulate the timing and stoichiometry of viral protein synthesis. Targeting EIF1A by siRNA reduces infection with minimal toxicity to the host. Although the nature of interactions between EIF1/1A and Nsp1 remains unclear, this interdependency may provide a new strategy for antiviral therapy.

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing

            SUMMARY The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
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              Andromeda: a peptide search engine integrated into the MaxQuant environment.

              A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. We demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total number of identified peptides.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: Investigation
                Role: Data curationRole: Formal analysisRole: Investigation
                Role: Data curationRole: InvestigationRole: Methodology
                Role: InvestigationRole: MethodologyRole: Resources
                Role: Data curationRole: InvestigationRole: Methodology
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Resources
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                PLOS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                9 February 2024
                February 2024
                : 20
                : 2
                : e1011535
                Affiliations
                [1 ] Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, California, United States of America
                [2 ] Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
                [3 ] Department of Biology and Department of Genetics, Stanford University, Stanford, California, United States of America
                [4 ] Chan Zuckerberg Biohub–San Francisco, Stanford, California, United States of America
                California Institute of Technology, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-5503-9349
                Article
                PPATHOGENS-D-23-01096
                10.1371/journal.ppat.1011535
                10903962
                38335237
                5266550d-4862-4348-aa7b-3fe34f150849
                © 2024 Aviner et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 July 2023
                : 8 January 2024
                Page count
                Figures: 6, Tables: 0, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100015691, Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases;
                Award ID: AI171421, AI137471, AI169460
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100015691, Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases;
                Award ID: AI127447
                Award Recipient :
                This work was supported by NIH grants AI171421, AI137471, AI169460 to R.An. and NIH grants AI127447 to JF. The funders did not play any role in the study design, data collection and analysis, or the decision to publish the study and preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Ribosomes
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Ribosomes
                Biology and Life Sciences
                Genetics
                Gene Expression
                Protein Translation
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Transfection
                Research and Analysis Methods
                Molecular Biology Techniques
                Transfection
                Biology and Life Sciences
                Biochemistry
                Ribosomes
                Polyribosomes
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Ribosomes
                Polyribosomes
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Messenger RNA
                Biology and Life Sciences
                Genetics
                Gene Expression
                Protein Translation
                Translation Initiation
                Biology and life sciences
                Genetics
                Gene expression
                Gene regulation
                Small interfering RNA
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Small interfering RNA
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Guide RNA
                Custom metadata
                vor-update-to-uncorrected-proof
                2024-02-29
                Sequencing data were deposited in SRA database under BioProject number PRJNA932822. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD039981. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA932822 https://www.ebi.ac.uk/pride/archive/projects/PXD039981/.
                COVID-19

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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