Abstract

Background: HIV infection dysregulates the B cell compartment, affecting memory B cell formation and the antibody response to infection and vaccination. Understanding the B cell response to SARS-CoV-2 in people living with HIV (PLWH) may explain the increased morbidity, reduced vaccine efficacy, reduced clearance, and intra-host evolution of SARS-CoV-2 observed in some HIV-1 coinfections.

Methods: We compared B cell responses to COVID-19 in PLWH and HIV negative (HIV-ve) patients in a cohort recruited in Durban, South Africa, during the first pandemic wave in July 2020 using detailed flow cytometry phenotyping of longitudinal samples with markers of B cell maturation, homing and regulatory features.

Results: This revealed a coordinated B cell response to COVID-19 that differed significantly between HIV-ve and PLWH. Memory B cells in PLWH displayed evidence of reduced germinal center (GC) activity, homing capacity and class-switching responses, with increased PD-L1 expression, and decreased Tfh frequency. This was mirrored by increased extrafollicular (EF) activity, with dynamic changes in activated double negative (DN2) and activated naïve B cells, which correlated with anti-RBD-titres in these individuals. An elevated SARS-CoV-2 specific EF response in PLWH was confirmed using viral spike and RBD bait proteins.

Conclusions: Despite similar disease severity, these trends were highest in participants with uncontrolled HIV, implicating HIV in driving these changes. EF B cell responses are rapid but give rise to lower affinity antibodies, less durable long-term memory, and reduced capacity to adapt to new variants. Further work is needed to determine the long-term effects of HIV on SARS-CoV-2 immunity, particularly as new variants emerge.

Funding: This work was supported by a grant from the Wellcome Trust to the Africa Health Research Institute (Wellcome Trust Strategic Core Award [grant number 201433/Z/16/Z]). Additional funding was received from the South African Department of Science and Innovation through the National Research Foundation (South African Research Chairs Initiative, [grant number 64809]), and the Victor Daitz Foundation.

Data availability

All data generated or analyzed during this study are included in the manuscript and Source data 1.

Article and author information

Author details

  1. Robert Krause

    Africa Health Research Institute, Durban, South Africa
    For correspondence
    robert.krause@ahri.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1558-0397
  2. Jumari Snyman

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  3. Hwa Shi-Hsia

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  4. Daniel Muema

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  5. Farina Karim

    School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9698-016X
  6. Yashica Ganga

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  7. Abigail Ngoepe

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  8. Yenzekile Zungu

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  9. Inbal Gazy

    KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa
    Competing interests
    No competing interests declared.
  10. Mallory Bernstein

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  11. Khadija Khan

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7565-7400
  12. Matilda Mazibuko

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  13. Ntombifuthi Mthabela

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  14. Dirhona Ramjit

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  15. COMMIT-KZN Team

  16. Oliver Limbo

    International AIDS Vaccine Initiative, New York, United States
    Competing interests
    No competing interests declared.
  17. Joseph Jardine

    International AIDS Vaccine Initiative, New York, United States
    Competing interests
    No competing interests declared.
  18. Devin Sok

    International AIDS Vaccine Initiative, New York, United States
    Competing interests
    No competing interests declared.
  19. Ian A Wilson

    Scripps Research Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  20. Willem Hanekom

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  21. Alex Sigal

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    Alex Sigal, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8571-2004
  22. Henrik Kløverpris

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
  23. Thumbi Ndung'u

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2962-3992
  24. Alasdair Leslie

    Africa Health Research Institute, Durban, South Africa
    Competing interests
    No competing interests declared.

Funding

Wellcome Trust (201433/Z/16/Z)

  • Alex Sigal

National Research Foundation (64809)

  • Alex Sigal

Victor Daitz Foundation

  • Alex Sigal

Max Planck Institute for Infection Biology (open access funding)

  • 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: The study protocol was approved by the University of KwaZulu-Natal Biomedical Research Ethics Committee (approval BREC/00001275/2020). Written informed consent was obtained for all enrolled participants.

Copyright

© 2022, Krause 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

  • 919
    views
  • 195
    downloads
  • 11
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Robert Krause
  2. Jumari Snyman
  3. Hwa Shi-Hsia
  4. Daniel Muema
  5. Farina Karim
  6. Yashica Ganga
  7. Abigail Ngoepe
  8. Yenzekile Zungu
  9. Inbal Gazy
  10. Mallory Bernstein
  11. Khadija Khan
  12. Matilda Mazibuko
  13. Ntombifuthi Mthabela
  14. Dirhona Ramjit
  15. COMMIT-KZN Team
  16. Oliver Limbo
  17. Joseph Jardine
  18. Devin Sok
  19. Ian A Wilson
  20. Willem Hanekom
  21. Alex Sigal
  22. Henrik Kløverpris
  23. Thumbi Ndung'u
  24. Alasdair Leslie
(2022)
HIV skews the SARS-CoV-2 B cell response toward an extrafollicular maturation pathway
eLife 11:e79924.
https://doi.org/10.7554/eLife.79924

Share this article

https://doi.org/10.7554/eLife.79924

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Bo Zheng, Bronner P Gonçalves ... Caoyi Xue
    Research Article

    Background:

    In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.

    Methods:

    We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).

    Results:

    275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.

    Conclusions:

    In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.

    Funding:

    This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).

    1. Epidemiology and Global Health
    Marina Padilha, Victor Nahuel Keller ... Gilberto Kac
    Research Article Updated

    Background:

    The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).

    Methods:

    Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.

    Results:

    Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).

    Conclusions:

    Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.

    Funding:

    Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.