Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains

  1. Nikhil Faulkner
  2. Kevin W Ng
  3. Mary Y Wu
  4. Ruth Harvey
  5. Marios Margaritis
  6. Stavroula Paraskevopoulou
  7. Catherine Houlihan
  8. Saira Hussain
  9. Maria Greco
  10. William Bolland
  11. Scott Warchal
  12. Judith Heaney
  13. Hannah Rickman
  14. Moria Spyer
  15. Daniel Frampton
  16. Matthew Byott
  17. Tulio de Oliveira
  18. Alex Sigal
  19. Svend Kjaer
  20. Charles Swanton
  21. Sonia Gandhi
  22. Rupert Beale
  23. Steve j Gamblin
  24. John W McCauley
  25. Rodney Stuart Daniels
  26. Michael Howell
  27. David Bauer
  28. Eleni Nastouli
  29. SAFER Investigators
  30. George Kassiotis  Is a corresponding author
  1. The Francis Crick Institute, United Kingdom
  2. University College London, United Kingdom
  3. University College London Hospital, United Kingdom
  4. University of KwaZulu-Natal,SA, South Africa
  5. Africa Health Research Institute, University of KwaZulu-Natal, South Africa
  6. The Francis Crick Insitute, United Kingdom

Abstract

Background: The degree of heterotypic immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains is a major determinant of the spread of emerging variants and the success of vaccination campaigns, but remains incompletely understood.

Methods: We examined the immunogenicity of SARS-CoV-2 variant B.1.1.7 (Alpha) that arose in the United Kingdom and spread globally. We determined titres of spike glycoprotein-binding antibodies and authentic virus neutralising antibodies induced by B.1.1.7 infection to infer homotypic and heterotypic immunity.

Results: Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa variant B.1.351 (Beta) than of the infecting variant. The drop in cross-reactivity was significantly more pronounced following B.1.1.7 than parental strain infection.

Conclusions: The results indicate that heterotypic immunity induced by SARS-CoV-2 variants is asymmetric.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Nikhil Faulkner

    Retroviral Immunology, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Kevin W Ng

    Retroviral Immunology, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1635-6768
  3. Mary Y Wu

    High Throughput Screening STP, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2074-6171
  4. Ruth Harvey

    Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Marios Margaritis

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Stavroula Paraskevopoulou

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Catherine Houlihan

    University College London Hospital, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Saira Hussain

    RNA Virus Replication Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Maria Greco

    RNA Virus Replication Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. William Bolland

    Retroviral Immunology, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  11. Scott Warchal

    High Throughput Screening STP, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Judith Heaney

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  13. Hannah Rickman

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  14. Moria Spyer

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  15. Daniel Frampton

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  16. Matthew Byott

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  17. Tulio de Oliveira

    School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal,SA, Durban, South Africa
    Competing interests
    The authors declare that no competing interests exist.
  18. Alex Sigal

    School of Laboratory Medicine and Medical Sciences, Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8571-2004
  19. Svend Kjaer

    Structural Biology, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9767-8683
  20. Charles Swanton

    Structural Biology, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  21. Sonia Gandhi

    Neurodegradation Biology Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  22. Rupert Beale

    Cell Biology of Infection Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6705-8560
  23. Steve j Gamblin

    Cell Biology of Infection Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  24. John W McCauley

    Worldwide Influenza Centre, The Francis Crick Insitute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4744-6347
  25. Rodney Stuart Daniels

    Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  26. Michael Howell

    High Throughput Screening, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  27. David Bauer

    RNA Virus Replication Laboratory, The Francis Crick Institute, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  28. Eleni Nastouli

    Advanced Pathogen Diagnostics, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  29. SAFER Investigators

  30. George Kassiotis

    Retroviral Immunology, The Francis Crick Institute, London, United Kingdom
    For correspondence
    george.kassiotis@crick.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8457-2633

Funding

Francis Crick Institute

  • Nikhil Faulkner
  • Kevin W Ng
  • Mary Y Wu
  • Ruth Harvey
  • Saira Hussain
  • Maria Greco
  • William Bolland
  • Scott Warchal
  • Svend Kjaer
  • Charles Swanton
  • Sonia Gandhi
  • Rupert Beale
  • Steve j Gamblin
  • John W McCauley
  • Rodney Stuart Daniels
  • Michael Howell
  • David Bauer
  • George Kassiotis

Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg

  • Alex Sigal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Serum or plasma samples were obtained from University College London Hospitals (UCLH) (REC ref: 20/HRA/2505).

Reviewing Editor

  1. Bavesh D Kana, University of the Witwatersrand, South Africa

Publication history

  1. Preprint posted: March 1, 2021 (view preprint)
  2. Received: April 11, 2021
  3. Accepted: July 26, 2021
  4. Accepted Manuscript published: July 29, 2021 (version 1)
  5. Version of Record published: August 9, 2021 (version 2)

Copyright

© 2021, Faulkner et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,591
    Page views
  • 238
    Downloads
  • 23
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Nikhil Faulkner
  2. Kevin W Ng
  3. Mary Y Wu
  4. Ruth Harvey
  5. Marios Margaritis
  6. Stavroula Paraskevopoulou
  7. Catherine Houlihan
  8. Saira Hussain
  9. Maria Greco
  10. William Bolland
  11. Scott Warchal
  12. Judith Heaney
  13. Hannah Rickman
  14. Moria Spyer
  15. Daniel Frampton
  16. Matthew Byott
  17. Tulio de Oliveira
  18. Alex Sigal
  19. Svend Kjaer
  20. Charles Swanton
  21. Sonia Gandhi
  22. Rupert Beale
  23. Steve j Gamblin
  24. John W McCauley
  25. Rodney Stuart Daniels
  26. Michael Howell
  27. David Bauer
  28. Eleni Nastouli
  29. SAFER Investigators
  30. George Kassiotis
(2021)
Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains
eLife 10:e69317.
https://doi.org/10.7554/eLife.69317
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Marta Matuszewska et al.
    Research Article

    Mobile genetic elements (MGEs) are agents of horizontal gene transfer in bacteria, but can also be vertically inherited by daughter cells. Establishing the dynamics that led to contemporary patterns of MGEs in bacterial genomes is central to predicting the emergence and evolution of novel and resistant pathogens. Methicillin-resistant Staphylococcus aureus (MRSA) clonal-complex (CC) 398 is the dominant MRSA in European livestock and a growing cause of human infections. Previous studies have identified three categories of MGEs whose presence or absence distinguishes livestock-associated CC398 from a closely related and less antibiotic-resistant human-associated population. Here, we fully characterise the evolutionary dynamics of these MGEs using a collection of 1180 CC398 genomes, sampled from livestock and humans, over 27 years. We find that the emergence of livestock-associated CC398 coincided with the acquisition of a Tn916 transposon carrying a tetracycline resistance gene, which has been stably inherited for 57 years. This was followed by the acquisition of a type V SCCmec that carries methicillin, tetracycline, and heavy metal resistance genes, which has been maintained for 35 years, with occasional truncations and replacements with type IV SCCmec. In contrast, a class of prophages that carry a human immune evasion gene cluster and that are largely absent from livestock-associated CC398 have been repeatedly gained and lost in both human- and livestock-associated CC398. These contrasting dynamics mean that when livestock-associated MRSA is transmitted to humans, adaptation to the human host outpaces loss of antibiotic resistance. In addition, the stable inheritance of resistance-associated MGEs suggests that the impact of ongoing reductions in antibiotic and zinc oxide use in European farms on livestock-associated MRSA will be slow to be realised.

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Fabrizio Menardo
    Research Article

    Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.