Epitope-resolved profiling of the SARS-CoV-2 antibody response identifies cross-reactivity with endemic human coronaviruses

  1. Jason T. Ladner
  2. Sierra N. Henson
  3. Annalee S. Boyle
  4. Anna L. Engelbrektson
  5. Zane W. Fink
  6. Fatima Rahee
  7. Jonathan D’ambrozio
  8. Kurt E. Schaecher
  9. Mars Stone
  10. Wenjuan Dong
  11. Sanjeet Dadwal
  12. Jianhua Yu
  13. Michael A. Caligiuri
  14. Piotr Cieplak
  15. Magnar Bjørås
  16. Mona H. Fenstad
  17. Svein A. Nordbø
  18. Denis E. Kainov
  19. Norihito Muranaka
  20. Mark S. Chee
  21. Sergey A. Shiryaev
  22. John A. Altin

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  1. Version published to 10.1016/j.xcrm.2020.100189
  2. ScreenIT

    SciScore for 10.1101/2020.07.27.222943: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationIn order to control for sampling bias within the database, we randomly subsampled overrepresented virus species, including no more than 2000 and 4000 sequences for viruses with RNA and DNA genomes, respectively.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To generate this design, we downloaded all protein sequences available in UniProt, on November 19, 2018, that were linked to 474 viral species-level taxonomy IDs (see Supplemental Materials for details).
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    To perform serological assays, 5uL of a 1:10 dilution of serum/plasma in Superblock T20 (Thermo) was added to 0.1pmol of PepSeq library for a total volume of 10uL and was incubated at 20℃ overnight.
    PepSeq
    suggested: None
    Decision tree analysis was conducted using the DecisionTreeClassifier() method in the Scikit-learn Python module, v0.20.1.
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Structural alignments and image preparation were done with PyMOL (version 2.3.2, Schrodinger, LLC).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

  3. ScreenIT

    SciScore for 10.1101/2020.07.27.222943: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.RandomizationIn order to control for sampling bias within the database, we randomly subsampled overrepresented virus species, including no more than 2000 and 4000 sequences for viruses with RNA and DNA genomes, respectively.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To perform serological assays, 5uL of a 1:10 dilution of serum/plasma in Superblock T20 (Thermo) was added to 0.1pmol of PepSeq library for a total volume of 10uL and was incubated at 20℃ overnight.
    PepSeq
    suggested: None
    Decision tree analysis was conducted using the DecisionTreeClassifier() method in the Scikit-learn Python module, v0.20.1.
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Structural alignments and image preparation were done with PyMOL (version 2.3.2, Schrodinger, LLC).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

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

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. ScreenIT

    SciScore for 10.1101/2020.07.27.222943: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.RandomizationIn order to control for sampling bias within the database, we randomly subsampled overrepresented virus species, including no more than 2000 and 4000 sequences for viruses with RNA and DNA genomes, respectively.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To perform serological assays, 5uL of a 1:10 dilution of serum/plasma in Superblock T20 (Thermo) was added to 0.1pmol of PepSeq library for a total volume of 10uL and was incubated at 20℃ overnight.
    PepSeq
    suggested: None
    Decision tree analysis was conducted using the DecisionTreeClassifier() method in the Scikit-learn Python module, v0.20.1.
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Structural alignments and image preparation were done with PyMOL (version 2.3.2, Schrodinger, LLC).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

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

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. ScreenIT

    SciScore for 10.1101/2020.07.27.222943: (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
    Moreover, for donors reactive to either HR region, the signal strength for Beta1-HR2 was up to ~170-fold (mean ~10-fold) higher than for SARS-CoV-2-HR2, indicating that the anti-HR2 antibodies elicited by SARS-CoV-2 infection generally bind better to Beta1-HR2.
    anti-HR2
    suggested: None
          <div style="margin-bottom:8px">
        <div><b>Beta1-HR2</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Since portions of this region are highly conserved across species (Figure 4B), cross-reactivity with pre-existing anti-CoV antibodies likely accounts for some of its immunodominance in the response to SARS-CoV-2, as discussed further below.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>anti-CoV</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The fact that antibodies against Beta1-HR2 occur in individuals who also have antibodies targeting SARS-CoV-2-HR2, but with, on average, ~10X greater signal strength, is most consistent with a model in which pre-existing B cell clones raised against hCoV-OC43 are recruited into the response to SARS-CoV-2.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SARS-CoV-2</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Under this model, the ~20% of convalescent donors who exhibit detectable reactivity to Beta1-HR2 but not to SARS-CoV-2-HR2 (upper left quadrant of Figure 5D) represent cases where pre-existing antibodies to hCoV-OC43 bind only weakly to SARS-CoV-2 (below the threshold of the PepSeq assay) and have been unable to acquire a high affinity against the new virus.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SARS-CoV-2-HR2</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Therefore, HV included peptides from the complete proteomes of 6/7 human coronaviruses: HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, SARS-CoV, and MERS-CoV, but not SARS-CoV-2 (Figure 1C).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>HCoV-NL63</b></div>
        <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_RW88">CVCL_RW88</a></div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In this study, we describe a customizable platform that enables epitope-resolved profiling of the antibody response (‘PepSeq’), and its application to the study of human CoVs including SARS-CoV-2.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SARS-CoV-2</b></div>
        <div>suggested: (Active Motif Cat# 91345, <a href="https://scicrunch.org/resources/Any/search?q=AB_2847847">AB_2847847</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate this design, we downloaded all protein sequences available in UniProt, on November 19, 2018, that were linked to 474 viral species-level taxonomy IDs (see Supplemental Materials for details).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>UniProt</b></div>
        <div>suggested: (UniProtKB, <a href="https://scicrunch.org/resources/Any/search?q=SCR_004426">SCR_004426</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To perform serological assays, 5uL of a 1:10 dilution of serum/plasma in Superblock T20 (Thermo) was added to 0.1pmol of PepSeq library for a total volume of 10uL and was incubated at 20℃ overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>PepSeq</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Decision tree analysis was conducted using the DecisionTreeClassifier() method in the Scikit-learn Python module, v0.20.1.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Scikit-learn</b></div>
        <div>suggested: (scikit-learn, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002577">SCR_002577</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>Python</b></div>
        <div>suggested: (IPython, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001658">SCR_001658</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Structural alignments and image preparation were done with PyMOL (version 2.3.2, Schrodinger, LLC).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>PyMOL</b></div>
        <div>suggested: (PyMOL, <a href="https://scicrunch.org/resources/Any/search?q=SCR_000305">SCR_000305</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  6. Version published to 10.1101/2020.07.27.222943v1 on bioRxiv