Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19
Copyright ©The authors 2022..
Background: The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood.
Methods: A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors.
Results: Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36-21.69; 95% CI 1.51-163.61 and HR 8.39-10.79; 95% CI 1.20-82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality.
Conclusions: By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
ERJ open research - 8(2022), 4 vom: 02. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Šelb, Julij [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 07.12.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1183/23120541.00216-2022 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349873909 |
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| 100 | 1 | |a Šelb, Julij |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19 |
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| 500 | |a Date Revised 07.12.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright ©The authors 2022. | ||
| 520 | |a Background: The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood | ||
| 520 | |a Methods: A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors | ||
| 520 | |a Results: Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36-21.69; 95% CI 1.51-163.61 and HR 8.39-10.79; 95% CI 1.20-82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality | ||
| 520 | |a Conclusions: By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Bitežnik, Barbara |e verfasserin |4 aut | |
| 700 | 1 | |a Bidovec Stojković, Urška |e verfasserin |4 aut | |
| 700 | 1 | |a Rituper, Boštjan |e verfasserin |4 aut | |
| 700 | 1 | |a Osolnik, Katarina |e verfasserin |4 aut | |
| 700 | 1 | |a Kopač, Peter |e verfasserin |4 aut | |
| 700 | 1 | |a Svetina, Petra |e verfasserin |4 aut | |
| 700 | 1 | |a Cerk Porenta, Kristina |e verfasserin |4 aut | |
| 700 | 1 | |a Šifrer, Franc |e verfasserin |4 aut | |
| 700 | 1 | |a Lorber, Petra |e verfasserin |4 aut | |
| 700 | 1 | |a Trinkaus Leiler, Darinka |e verfasserin |4 aut | |
| 700 | 1 | |a Hafner, Tomaž |e verfasserin |4 aut | |
| 700 | 1 | |a Jerič, Tina |e verfasserin |4 aut | |
| 700 | 1 | |a Marčun, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Lalek, Nika |e verfasserin |4 aut | |
| 700 | 1 | |a Frelih, Nina |e verfasserin |4 aut | |
| 700 | 1 | |a Bizjak, Mojca |e verfasserin |4 aut | |
| 700 | 1 | |a Lombar, Rok |e verfasserin |4 aut | |
| 700 | 1 | |a Nikolić, Vesna |e verfasserin |4 aut | |
| 700 | 1 | |a Adamič, Katja |e verfasserin |4 aut | |
| 700 | 1 | |a Mohorčič, Katja |e verfasserin |4 aut | |
| 700 | 1 | |a Grm Zupan, Sanja |e verfasserin |4 aut | |
| 700 | 1 | |a Šarc, Irena |e verfasserin |4 aut | |
| 700 | 1 | |a Debeljak, Jerneja |e verfasserin |4 aut | |
| 700 | 1 | |a Koren, Ana |e verfasserin |4 aut | |
| 700 | 1 | |a Luzar, Ajda Demšar |e verfasserin |4 aut | |
| 700 | 1 | |a Rijavec, Matija |e verfasserin |4 aut | |
| 700 | 1 | |a Kern, Izidor |e verfasserin |4 aut | |
| 700 | 1 | |a Fležar, Matjaž |e verfasserin |4 aut | |
| 700 | 1 | |a Rozman, Aleš |e verfasserin |4 aut | |
| 700 | 1 | |a Korošec, Peter |e verfasserin |4 aut | |
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