Distinct clinical and immunological profiles of patients with evidence of SARS-CoV-2 infection in sub-Saharan Africa

  1. Ben Morton
  2. Kayla G. Barnes
  3. Catherine Anscombe
  4. Khuzwayo Jere
  5. Prisca Matambo
  6. Jonathan Mandolo
  7. Raphael Kamng’ona
  8. Comfort Brown
  9. James Nyirenda
  10. Tamara Phiri
  11. Ndaziona P. Banda
  12. Charlotte Van Der Veer
  13. Kwazizira S. Mndolo
  14. Kelvin Mponda
  15. Jamie Rylance
  16. Chimota Phiri
  17. Jane Mallewa
  18. Mulinda Nyirenda
  19. Grace Katha
  20. Paul Kambiya
  21. James Jafali
  22. Henry C. Mwandumba
  23. Stephen B. Gordon
  24. Blantyre COVID-19 Consortium
  25. Clinical
  26. Jacob Phulusa
  27. Mercy Mkandawire
  28. Sylvester Kaimba
  29. Herbert Thole
  30. Sharon Nthala
  31. Edna Nsomba
  32. Lucy Keyala
  33. Peter Mandala
  34. Beatrice Chinoko
  35. Markus Gmeiner
  36. Vella Kaudzu
  37. Samantha Lissauer
  38. Bridget Freyne
  39. Peter MacPherson
  40. Todd D. Swarthout
  41. Pui-Ying Iroh Tam
  42. Laboratory
  43. Simon Sichone
  44. Ajisa Ahmadu
  45. Oscar Kanjewa
  46. Vita Nyasulu
  47. End Chinyama
  48. Allan Zuza
  49. Brigitte Denis
  50. Evance Storey
  51. Nedson Bondera
  52. Danford Matchado
  53. Adams Chande
  54. Arthur Chingota
  55. Chimenya Ntwea
  56. Langford Mkandawire
  57. Chimwemwe Mhango
  58. Agness Lakudzala
  59. Mphatso Chaponda
  60. Percy Mwenechanya
  61. Leonard Mvaya
  62. Dumizulu Tembo
  63. Data and statistics
  64. Marc Y. R. Henrion
  65. James Chirombo
  66. Clemens Masesa
  67. Joel Gondwe
  68. Jennifer Cornick
  69. Kondwani C. Jambo

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Abstract

Although the COVID-19 pandemic has left no country untouched there has been limited research to understand clinical and immunological responses in African populations. Here we characterise patients hospitalised with suspected (PCR-negative/IgG-positive) or confirmed (PCR-positive) COVID-19, and healthy community controls (PCR-negative/IgG-negative). PCR-positive COVID-19 participants were more likely to receive dexamethasone and a beta-lactam antibiotic, and survive to hospital discharge than PCR-negative/IgG-positive and PCR-negative/IgG-negative participants. PCR-negative/IgG-positive participants exhibited a nasal and systemic cytokine signature analogous to PCR-positive COVID-19 participants, predominated by chemokines and neutrophils and distinct from PCR-negative/IgG-negative participants. PCR-negative/IgG-positive participants had increased propensity for Staphylococcus aureus and Streptococcus pneumoniae colonisation. PCR-negative/IgG-positive individuals with high COVID-19 clinical suspicion had inflammatory profiles analogous to PCR-confirmed disease and potentially represent a target population for COVID-19 treatment strategies.

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  1. Version published to 10.1038/s41467-021-23267-w
  2. ScreenIT

    SciScore for 10.1101/2021.02.15.21251753: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Patients over 18 years old were approached for informed written consent if they met inclusion criteria: severe acute respiratory infection (SARI) with suspected or confirmed SARS-CoV-2.
    IRB: The two study protocols were approved by the Malawi National Health Science Research Committee (NHSRC, 20/02/2518 and 19/08/2246) and Liverpool School of Tropical Medicine (study sponsor) Research Ethics Committee (LSTM REC, 20/026 and 19/017).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line AuthenticationContamination: This panel includes the following pathogens: parainfluenza viruses 1, 2, 3 and 4; human coronaviruses NL63, 229E, OC43 and HKU1; human metapneumoviruses A/B; human rhinovirus; human respiratory syncytial viruses A/B; human adenovirus; enterovirus; human parechovirus; human bocavirus; Pneumocystis jirovecii; Mycoplasma pneumoniae; Chlamydophila pneumoniae; Streptococcus pneumoniae; Haemophilus influenzae B; Staphylococcus aureus; Moraxella catarrhalis; Bordetella spp.

    Table 2: Resources

    Antibodies
    SentencesResources
    To measure SARS-CoV-2 antibodies, we used a commercial enzyme linked-immunosorbent assay (ELISA) targeting Spike (S2) and Nucleoprotein (NP) from SARS-CoV-2 (Omega diagnostics, UK; ODL 150/10; Lot #103183).
    Nucleoprotein ( NP
    suggested: (GeneTex Cat# GTX125989, RRID:AB_11168364)
    Flow cytometry analysis: For immunophenotyping, nasal cells were dislodged from curettes by pipetting and stained with an antibody cocktail containing anti-human CD3 APC, anti-human CD14 PE-Cy7, anti-human CD66b PE,
    anti-human CD3
    suggested: (BioLegend Cat# 348805, RRID:AB_2889063)
    anti-human CD14
    suggested: None
    anti-human CD66b
    suggested: None
    Software and Algorithms
    SentencesResources
    Samples were acquired on an LSR FORTESSA flow cytometer (BD Biosciences, UK) and analyzed using Flowjo v10.5.3 (BD Biosciences, USA)
    Flowjo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: Clinical data were analysed using Stata V15.1 (StataCorp, Stata Statistical Software:
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    3.5.1 (R Development Core Team, Vienna, Austria) and GraphPad Prism v9.0.0 (
    R Development Core
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    (GraphPad Software, San Diego, California, USA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: We detected the following sentences addressing limitations in the study:
    Despite the strengths of this study including use of an internationally recognised protocol with standardised data collection tools, well-characterised clinical cohort and paired nasal and systemic immune responses, our study had some limitations. Due to programmatic constraints, it was not feasible to conduct longitudinal sampling among our participants to monitor recovery and response to therapy. In addition, whilst the majority of participants were recruited within the first 72 hours of admission, a proportion of our participants were recruited later in their hospital admission. Lack of critical care facilities precluded universal recruitment and sampling in the most severe cases, and our patient population may therefore not be entirely representative of all participants with SARS-CoV-2 induced SARI. Furthermore, we cannot exclude secondary bacterial infections as potential contributors to increased mortality in the PCR-/IgG+ SARI participants, as we did not have access to blood culture or autopsy results. We have demonstrated that SARS-CoV-2 infection induces a chemokine and neutrophil-dominated profile in the nasal mucosa different from systemic circulation, and distinct from non-COVID-19 SARI. We have identified a subgroup of SARS-CoV-2 PCR-negative IgG positive individuals with clinical and immunological manifestations of COVID-19, who may benefit from standardised COVID-19 clinical management protocols (including use of steroids and beta-lactam antibiotics). Further, ...

    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.
    • Thank you for including a protocol registration statement.

    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. Version published to 10.1101/2021.02.15.21251753 on medRxiv