Longitudinal analyses reveal immunological misfiring in severe COVID-19
Cardiff University review clinical immunology/immunity
First Author: Carolina Lucas
Journal/preprint name: Nature
Paper DOI: doi.org/10.1038/s41586-020-2588-y
Tags: Type 1 (antiviral), Type 2 (anti-helminth), Type 3 (antifungal) immunity, cytokines, immune signature, clinical correlates, dysfunction, growth factors.
Summary
Lucas et al., present novel insight into features of the dysregulated immune response to SARS-CoV-2 infection. It is newly shown that features of immune responses to pathogens other than viruses occur in COVID-19 disease.
Research Highlights
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CD4+ and CD8+ T cells were reduced in association with disease severity, but remaining T cells were activated and T cells in moderate and severe disease produced IFNg. Increased monocytes, eosinophils and low-density neutrophils correlated with disease severity, although neutrophils did not increase over time. Circulating monocytes had reduced HLA-DR expression.
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COVID-19 core immune signalling signature: moderate and severe disease had high circulating IL-1a, IL-1b, IL-12p70, IL-17a and IFNa. Further markers specifically associated with severe disease included thrombopoietin (TPO), IL-33, IL-16, IL-21, IL-23, IFNl, eotaxin, eotaxin 3 and cytokines associated with cytokine release syndrome.
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Longitudinal correlates: persistence of the ‘core signature,’ particularly IFNa, IFNl, and IL-1Ra, 10 or more days from onset of symptoms, occurred in severe patients, whereas they decreased in moderate patients.
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Although viral titre did not correlate with disease severity, it did correlate with levels of certain signalling molecules: IFNa IFNg and TRAIL.
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Patients clustered into 3 groups which had distinct acute immune features, disease severity and outcomes. The clusters were characterised by 4 immune signatures. Signature A included growth factors and wound healing factors. Signature B consisted of a mixture of Types 2 and 3 immunity signalling mediators. Signature C involved a mixed response including type 1, type 2 type 3 immunity. Signature D mainly featured chemokines. Cluster 1 patients experienced moderate disease and limited mortality, which were associated with Signature A. Clusters 2 and 3 patients both experienced more severe disease and increased mortality. Cluster 2 had higher levels of Signature C and D markers (i.e. mixed immune responses and chemokines, respectively). Cluster 3 patients had high levels of type 2 and 3 responses (Signature B) in addition to features of Signatures C and D.
Impact for COVID-19 research:
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This paper presents a novel angle on the variable immunopathology in COVID-19 disease. It suggests that poor control of SARS-CoV-2 infection may occur not only due to dysfunction of the ‘appropriate’ antiviral immune response but also due to the activation of ‘inappropriate’ immune responses to other forms of pathogen, or a mix of these and the antiviral response, reducing the efficacy of the latter.
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The longitudinal clustering analysis provides important insight into immune signatures of COVID-19 disease severity, clinical course and outcome. A biomarker window was described which could inform clinical practice: increased IFNa IFNl, IL-16, IL-2 and certain chemokines within 12 days of symptom onset predicted longer hospital stays and higher mortality.
Methodologies:
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Study Type: Longitudinal, Case- control study: COVID-19 patients (n=113; 253 data points over 7 longitudinal collection points) and healthcare workers (n=108 serving as controls.
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Key Techniques: SARS-oV-2 infection was detected using the US CDC RT–qPCR primer/probe sets for 2019-nCoV_N1, 2019-nCoV_N2, and the human RNase P (RP) as an extraction control. Cytokines and chemokines were measured using the Human Cytokine Array/Chemokine Array 71-403 Plex Panel (HD71). Flow cytometry cell surface and intracellular analysis antibody information is provided: manufacturer, clone and dilution used. Groups comparisons were made by ANOVA (parametric) and Kruskal-Wallis (non-parametric) statistical tests with Dunn’s and Tukey’s post hoc tests, respectively. Clustering was performed using the k-means algorithm, correlations analysis by Spearman’s coefficient and risk ratio by Poisson regression.
Limitations:
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Recruitment of healthcare workers as controls which relied on viral testing may not have exclude those who were negative at the time of testing but who previously contracted SARS-CoV-2, which could also be more likely due to their occupation.