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. 2021 Mar 9;5(5):1164-1177.
doi: 10.1182/bloodadvances.2020003568.

A neutrophil activation signature predicts critical illness and mortality in COVID-19

Matthew L Meizlish  1 , Alexander B Pine  2 , Jason D Bishai  3   4 , George Goshua  2 , Emily R Nadelmann  5 , Michael Simonov  6   7 , C-Hong Chang  3 , Hanming Zhang  3 , Marcus Shallow  3 , Parveen Bahel  8 , Kent Owusu  9 , Yu Yamamoto  6 , Tanima Arora  6 , Deepak S Atri  10 , Amisha Patel  2 , Rana Gbyli  2 , Jennifer Kwan  3 , Christine H Won  11 , Charles Dela Cruz  11 , Christina Price  12 , Jonathan Koff  11 , Brett A King  12 , Henry M Rinder  2   8 , F Perry Wilson  6 , John Hwa  3 , Stephanie Halene  2 , William Damsky  7 , David van Dijk  3 , Alfred I Lee  2 , Hyung J Chun  3
Affiliations

A neutrophil activation signature predicts critical illness and mortality in COVID-19

Matthew L Meizlish et al. Blood Adv. .

Abstract

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of more than 3300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, hepatocyte growth factor, interleukin-8, and granulocyte colony-stimulating factor, which were the strongest predictors of critical illness. Evidence of neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, these data suggest a central role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular markers that distinguish patients at risk of future clinical decompensation.

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Conflict of interest statement

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Circulating biomarkers separate COVID-19 patients according to disease severity. (A) Heatmap of proteomic data from the cross-sectional cohort, indicating relative protein levels detected in each subject (columns) for all biomarkers tested (rows). Proteins are categorized by biological function. (B) Visualization of the first 2 principal components (PCs) of a PCA of all biomarker data for each subject.
Figure 2.
Figure 2.
Markers of neutrophil activation accurately identify patients with critical illness. (A) Performance of a random forest (RF) model trained on data from two-thirds of the study subjects in the cross-sectional cohort, predicting ICU status for the remaining one-third of subjects not included in the training set. Perfect classification as depicted is achieved when using data from all biomarkers or from only the top 5 neutrophil markers (highlighted in orange in panel B). (B) Feature importance ranked by proportion of feature contribution to the RF model. Members of the neutrophil activation signature are highlighted in orange. (C) Comparisons of circulating levels of neutrophil markers in controls, non-ICU COVID-19, and ICU COVID-19 patients in the cross-sectional cohort. Asterisks denote statistically significant differences between groups (*P < .05, **P < .01, ***P < .001, ****P < .0001).
Figure 3.
Figure 3.
Circulating neutrophil granule proteins are likely derived from neutrophilic source in COVID-19 patients. (A) Correlations of circulating biomarkers with ANC in the cross-sectional cohort. Neutrophil granule proteins, which show the highest correlation with ANC, are highlighted. R, Spearman’s rank correlation coefficient; P, P value. (B) Violin plots of RETN, LCN2, and MMP-8 mRNA, showing enrichment in a developing neutrophil population, based on reanalysis of single-cell RNAseq data published by Wilk et al of PBMCs from patients with COVID-19.
Figure 4.
Figure 4.
Elevation of the neutrophil activation signature precedes the onset of critical illness. (A) Heatmap indicating relative protein levels detected on day 1 in each subject in the longitudinal cohort (columns) for all biomarkers tested (rows). Proteins are categorized by biological function. (B) Comparisons of circulating levels of neutrophil markers in subjects categorized as controls, non-ICU, ICU-Transfer, and ICU-Admit. Non-ICU indicates patients who remained in a non-ICU unit until discharge; ICU-Transfer indicates patients who were admitted to a non-ICU unit and were transferred to an ICU unit during hospitalization; ICU-Admit indicates patients who were admitted directly to an ICU unit. Asterisks denote statistically significant differences between groups (*P < .05, **P < .01, ***P < .001, ****P < 0.0001). (C) Kaplan-Meier curve depicting the likelihood of ICU admission depending on resistin (RETN) levels on day 1, using the median of the group (non-ICU and ICU-Transfer) as the cutoff value (pg/mL). (D) Kaplan-Meier curve depicting the likelihood of survival depending on resistin levels on day 1, using the median of the group (entire longitudinal cohort) as the cutoff value (pg/mL). ns, not significant.
Figure 4.
Figure 4.
Elevation of the neutrophil activation signature precedes the onset of critical illness. (A) Heatmap indicating relative protein levels detected on day 1 in each subject in the longitudinal cohort (columns) for all biomarkers tested (rows). Proteins are categorized by biological function. (B) Comparisons of circulating levels of neutrophil markers in subjects categorized as controls, non-ICU, ICU-Transfer, and ICU-Admit. Non-ICU indicates patients who remained in a non-ICU unit until discharge; ICU-Transfer indicates patients who were admitted to a non-ICU unit and were transferred to an ICU unit during hospitalization; ICU-Admit indicates patients who were admitted directly to an ICU unit. Asterisks denote statistically significant differences between groups (*P < .05, **P < .01, ***P < .001, ****P < 0.0001). (C) Kaplan-Meier curve depicting the likelihood of ICU admission depending on resistin (RETN) levels on day 1, using the median of the group (non-ICU and ICU-Transfer) as the cutoff value (pg/mL). (D) Kaplan-Meier curve depicting the likelihood of survival depending on resistin levels on day 1, using the median of the group (entire longitudinal cohort) as the cutoff value (pg/mL). ns, not significant.
Figure 5.
Figure 5.
Early elevations in developing and mature neutrophil counts predict increased mortality. Kaplan-Meier curves depicting likelihood of survival based on patients’ first recorded values in the DOM-CovX cohort of absolute immature granulocyte count (A), immature granulocyte percent (B), ANC (C), and absolute monocyte count (D), using the median value as the cutoff.
Figure 6.
Figure 6.
Model of neutrophil development and activation driving pathogenesis of severe COVID-19. We hypothesize that high levels of G-CSF drive emergency granulopoiesis, stimulating rapid neutrophil development and egress of immature neutrophils into the bloodstream. These cells then differentiate into neutrophils and are attracted to the lung, and perhaps other tissues, by the chemokine IL-8 (CXCL8). When activated, these mature neutrophils degranulate, releasing granule proteins that include resistin, lipocalin-2, HGF, and MMP-8. Neutrophil activation causes significant collateral damage that may contribute to severe COVID-19 pathology and clinical decompensation. Created with BioRender.com.
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