A direct RNA-protein interaction atlas of the SARS-CoV-2 RNA in infected human cells

  1. Nora Schmidt
  2. Caleb A. Lareau
  3. Hasmik Keshishian
  4. Randy Melanson
  5. Matthias Zimmer
  6. Luisa Kirschner
  7. Jens Ade
  8. Simone Werner
  9. Neva Caliskan
  10. Eric S. Lander
  11. Jörg Vogel
  12. Steven A. Carr
  13. Jochen Bodem
  14. Mathias Munschauer

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

SARS-CoV-2 infections pose a global threat to human health and an unprecedented research challenge. Among the most urgent tasks is obtaining a detailed understanding of the molecular interactions that facilitate viral replication or contribute to host defense mechanisms in infected cells. While SARS-CoV-2 co-opts cellular factors for viral translation and genome replication, a comprehensive map of the host cell proteome in direct contact with viral RNA has not been elucidated. Here, we use RNA antisense purification and mass spectrometry (RAP-MS) to obtain an unbiased and quantitative picture of the human proteome that directly binds the SARS-CoV-2 RNA in infected human cells. We discover known host factors required for coronavirus replication, regulators of RNA metabolism and host defense pathways, along with dozens of potential drug targets among direct SARS-CoV-2 binders. We further integrate the SARS-CoV-2 RNA interactome with proteome dynamics induced by viral infection, linking interactome proteins to the emerging biology of SARS-CoV-2 infections. Validating RAP-MS, we show that CNBP, a regulator of proinflammatory cytokines, directly engages the SARS-CoV-2 RNA. Supporting the functional relevance of identified interactors, we show that the interferon-induced protein RYDEN suppresses SARS-CoV-2 ribosomal frameshifting and demonstrate that inhibition of SARS-CoV-2-bound proteins is sufficient to manipulate viral replication. The SARS-CoV-2 RNA interactome provides an unprecedented molecular perspective on SARS-CoV-2 infections and enables the systematic dissection of host dependency factors and host defense strategies, a crucial prerequisite for designing novel therapeutic strategies.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.15.204404: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    For protein detection, we used the following primary antibodies: Nucleoprotein – Abcam #ab272852, POP1 – Proteintech #12029-1-AP.
    POP1
    suggested: (Proteintech Cat# 12029-1-AP, RRID:AB_2166477)
    We used the following secondary antibodies: IRDye 800CW Goat anti-Rabbit IgG (H + L) (LI-COR)
    anti-Rabbit IgG
    suggested: None
    In the meantime, we coupled 100 μl Protein G Dynabeads (Thermo Fisher Scientific) with 10 μg CNBP antibody (Proteintech, 14717-1-AP) or 10 μg IgG antibody (Cell Signaling Technology, 2729) by incubating antibody-bead suspensions 45 minutes at room temperature.
    CNBP
    suggested: (Proteintech Cat# 14717-1-AP, RRID:AB_2081548)
    Experimental Models: Cell Lines
    SentencesResources
    All subsequent manipulation steps were carried out as described in the eCLIP library preparation protocol80, starting with the reverse transcription of recovered RNA fragments. eCLIP: To facilitate eCLIP experiments in SARS-CoV-2 infected HuH-7 cells, we applied the previously described RAP-MS approach to purify all SARS-CoV-2-bound proteins prior to immunoprecipitating individual proteins from this pool.
    HuH-7
    suggested: None
    Quantification of ribosomal frameshifting: HEK293 cells were transiently transfected with either the control construct or the frameshifting construct of our dual-color EGFP-mCherry translation reporter outlined in Supplementary Figure 3a.
    HEK293
    suggested: CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045)
    Software and Algorithms
    SentencesResources
    Quantification and identification of peptides and proteins (RAP-MS and proteome): MS/MS spectra were searched on the Spectrum Mill MS Proteomics Workbench against a RefSeq-based sequence database containing 41,457 proteins mapped to the human reference genome (hg38) obtained via the UCSC Table Browser (https://genome.ucsc.edu/cgi-bin/hgTables) on June 29, 2018, with the addition of 13 proteins encoded in the human mitochondrial genome, 264 common laboratory contaminant proteins, 553 human non-canonical small open reading frames, 28 SARS-CoV-2 proteins obtained from RefSeq derived from the original Wuhan-Hu-1 China isolate NC_045512.290, and 23 novel unannotated SARS-CoV-2 ORFs whose translation is supported by ribosome profiling31, yielding a total of 42,337 proteins.
    UCSC Table Browser
    suggested: None
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    We added NuPAGE LDS Sample Buffer (Thermo Fisher Scientifc) and incubated samples for 3 min incubation at 95°C.
    Thermo Fisher Scientifc
    suggested: None
    Computational analyses: Gene set and pathway enrichment analysis: We performed a hypergeometric Gene Ontology (GO) enrichment analysis for the expanded SARS-CoV-2 interactome proteins using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (https://david.ncifcrf.gov/tools.jsp) and applying default settings.
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    Genes were ranked based on the product of the log2 fold change and the log10 moderated t-test P value between the SARS-CoV2 treatment and mock treatments. eCLIP and RNA sequencing analysis: Paired-end sequencing reads from (i) eCLIP experiments, or (ii) sequencing of crosslinked RNA fragments following RAP-MS, were trimmed using a custom python script that simultaneously identified the umi-molecular identifier (UMI) associated with each read.
    python
    suggested: (IPython, RRID:SCR_001658)
    Next, we removed PCR duplicates using the UMI-aware deduplication functionality in Picard’s MarkDuplicates.
    Picard’s
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.15.204404 on bioRxiv