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

Version 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.

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  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

Further reading

    1. Biochemistry and Chemical Biology
    2. Epidemiology and Global Health
    Takashi Sasaki, Yoshinori Nishimoto ... Yasumichi Arai
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    Background: High levels of circulating adiponectin are associated with increased insulin sensitivity, low prevalence of diabetes, and low body mass index (BMI); however, high levels of circulating adiponectin are also associated with increased mortality in the 60-70 age group. In this study, we aimed to clarify factors associated with circulating high-molecular-weight (cHMW) adiponectin levels and their association with mortality in the very old (85-89 years old) and centenarians.

    Methods: The study included 812 (women: 84.4%) for centenarians and 1,498 (women: 51.7%) for the very old. The genomic DNA sequence data were obtained by whole genome sequencing or DNA microarray-imputation methods. LASSO and multivariate regression analyses were used to evaluate cHMW adiponectin characteristics and associated factors. All-cause mortality was analyzed in three quantile groups of cHMW adiponectin levels using Cox regression.

    Results: The cHMW adiponectin levels were increased significantly beyond 100 years of age, were negatively associated with diabetes prevalence, and were associated with SNVs in CDH13 (p = 2.21 × 10-22) and ADIPOQ (p = 5.72 × 10-7). Multivariate regression analysis revealed that genetic variants, BMI, and high-density lipoprotein cholesterol (HDLC) were the main factors associated with cHMW adiponectin levels in the very old, whereas the BMI showed no association in centenarians. The hazard ratios for all-cause mortality in the intermediate and high cHMW adiponectin groups in very old men were significantly higher rather than those for all-cause mortality in the low level cHMW adiponectin group, even after adjustment with BMI. In contrast, the hazard ratios for all-cause mortality were significantly higher for high cHMW adiponectin groups in very old women, but were not significant after adjustment with BMI.

    Conclusions: cHMW adiponectin levels increased with age until centenarians, and the contribution of known major factors associated with cHMW adiponectin levels, including BMI and HDLC, varies with age, suggesting that its physiological significance also varies with age in the oldest old.

    Funding: This study was supported by grants from the Ministry of Health, Welfare, and Labour for the Scientific Research Projects for Longevity; a Grant-in-Aid for Scientific Research (No 21590775, 24590898, 15KT0009, 18H03055, 20K20409, 20K07792, 23H03337) from the Japan Society for the Promotion of Science; Keio University Global Research Institute (KGRI), Kanagawa Institute of Industrial Science and Technology (KISTEC), Japan Science and Technology Agency (JST) Research Complex Program 'Tonomachi Research Complex' Wellbeing Research Campus: Creating new values through technological and social innovation (JP15667051), the Program for an Integrated Database of Clinical and Genomic Information from the Japan Agency for Medical Research and Development (No. 16kk0205009h001, 17jm0210051h0001, 19dk0207045h0001); the medical-welfare-food-agriculture collaborative consortium project from the Japan Ministry of Agriculture, Forestry, and Fisheries; and the Biobank Japan Program from the Ministry of Education, Culture, Sports, and Technology.

    1. Epidemiology and Global Health
    Charumathi Sabanayagam, Feng He ... Ching Yu Cheng
    Research Article Updated

    Background:

    Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).

    Methods:

    We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40–80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004–2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).

    Results:

    ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847–0.856), which was 7.0% relatively higher than by LR 0.795 (0.790–0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.

    Conclusions:

    Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.

    Funding:

    This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.