Biological timeline for COVID-19 immunogenesis described in new research
Biological timeline for COVID-19 immunogenesis described in new research

Biological timeline for COVID-19 immunogenesis described in new research

On March 21, 2022, the causative agent of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infected over 470 million people worldwide and cost over 6 million lives. Most people infected with SARS-CoV-2 are asymptomatic or show mild flu-like symptoms. In a prospective study of adults with SARS-CoV-2 infection, 91% were asymptomatic or had mild disease as outpatients, while only 9% required hospitalization.

COVID-19 immunopathology is characterized by lymphopenia, lymphocyte dysfunction, congenital immune cell abnormalities, and elevated cytokine production. Early studies of serum cytokine levels in COVID-19 patients showed increased levels of circulating Interleukin 6 (IL-6), leading to the idea of ​​an IL-6-driven cytokine storm and the immunopathology that resulted. Clinical trials of IL-6 neutralizing therapies demonstrate significant clinical benefit with additional benefit in patients receiving adjunctive corticosteroid therapy, suggesting that immune signaling and immune cell activation modulation have clinical significance for disease intensification and resolution.

In a recent study published on bioRxiv* preprint server, a group of researchers from the University of California, San Francisco, looked at intra-patient immunological changes over time to see if there were changes in immune responses associated with successful COVID-19 dissolution. The researchers took longitudinal peripheral blood samples from COVID-19 patients in hospitals, SARS-CoV-2-negative ventilated patients, and healthy people. The authors used mass cytometry and a unique panel of antibodies specific for immune cell phenotyping and detection of phosphorylated cell signaling proteins to explore changes in immune cell signaling states over time.

Examination: A preserved immune pathway for recovery in hospitalized COVID-19 patients. Image credit: NIAID

Longitudinal peripheral blood samples from inpatients both positive and negative for COVID-19

This study collected longitudinal peripheral blood samples (PB) from patients with COVID-19 and patients negative for COVID-19 who were admitted to UCSF Medical Center and Zuckerberg San Francisco General Hospital to examine the proportion of circulating immune cells and cell signaling conditions. which characterizes SARS-COV-2 infections and distinguishes them from other respiratory infections. Patient demographics and clinical factors are associated with PB tests. As controls, healthy PB samples were acquired. To measure the level of 30 protein markers and 14 phosphorylated signaling molecules, all samples were processed, stained and analyzed by mass cytometry.

The final cohort of 230 samples consisted of 205 samples from 81 COVID-19 patients, 14 samples from 7 COVID-19 negative patients and single samples from each of 11 healthy patients who met the quality control standards. Patients with COVID-19 were divided into COVID-19 severity groups according to their WHO score on the day of sampling. The researchers manually gated 38 canonical immune cell groups based on the phenotypic indicators in the antibody panel and examined immune cell population frequencies, protein expression patterns, and immune cell signaling pathways relevant to the progression and resolution of the COVID-19 pathway.

The innate immune arm of COVID-19 infection shows clear compartmental changes in monocyte and neutrophil composition

Because there were significant differences in innate immune compartments between patients with COVID-19, patients with other respiratory diseases, and healthy controls, the researchers examined the composition of neutrophils and monocytes. While the number of neutrophils in COVID-19 patients and healthy people did not differ significantly, the authors discovered that the expression of a number of proteins on neutrophils was different between the two groups.

The expression of CD11c, CD14, CD16, and PD-L1 in COVID-19 patients’ neutrophils was significantly higher, indicating a highly activated and inflammatory neutrophil phenotype. While the frequency of all monocytes was similar between the groups, the composition of monocyte subgroups was significantly different between patients with COVID-19 and other respiratory infections and healthy people. The frequency of intermediate monocytes increased significantly in patients, while the frequency of classical monocytes decreased.

COVID-19 immune phenotype and composition is very different from healthy individuals and has unique properties compared to other serious respiratory infections.  A) Overview of cohorts.  Patients were admitted to the hospital and enrolled in the study at D0.  Peripheral blood samples were collected throughout the stay.  Similar clinical parameters and WHO scores were documented.  205 samples from 81 COVID-19 positive patients were included in the final cohort.  In addition, 14 samples from 7 COVID-19 negative patients with other respiratory diseases and 11 healthy individuals were included in the study.  B) t-SNE plot of all patient samples at D0 (n = 83) using phenotypic markers stained by larger immune cell populations.  Upper right panel: t-SNE plot of healthy specimens (n ​​= 11);  middle right panel: t-SNE plot of COVID-19 negative samples (n = 6);  lower right panel: t-SNE plot of COVID-19 positive samples (n = 66).  C) Immune cell population abundance at D0 in COVID-19 positive (+), COVID-19 negative (-) patients and healthy individuals (H).  P-values ​​obtained by Wilcoxon Rank Sum Test, followed by Benjamini-Hochberg correction with FDR <0.1.  D) Correlation between cell population abundance at D0 and clinical results, e.g.  ventilation duration (vent_duration) and hospital stay (hosp_los) for COVID-19 + patients.  Correlation estimates are obtained by Pearson correlation.  E) Protein expression on neutrophils (F) in COVID-19 positive (COV +), COVID-19 negative (COV-) patients and rapid controls at D0 (Wilcoxon Rank Sum Test, Benjamini-Hochberg correction with FDR < 0,1).  F) Hyppighed af monocytundersæt hos COVID-19 positive (COV+), COVID-19 negative (COV-) patienter og raske kontroller ved D0.  P-værdier opnået ved Wilcoxon Rank Sum Test.

COVID-19 immune phenotype and composition is very different from healthy individuals and has unique properties compared to other serious respiratory infections. A) Overview of cohorts. Patients were admitted to the hospital and enrolled in the study at D0. Peripheral blood samples were collected throughout the stay. Similar clinical parameters and WHO scores were documented. 205 samples from 81 COVID-19 positive patients were included in the final cohort. In addition, 14 samples from 7 COVID-19 negative patients with other respiratory diseases and 11 healthy individuals were included in the study. B) t-SNE plot of all patient samples at D0 (n = 83) using phenotypic markers stained by larger immune cell populations. Upper right panel: t-SNE plot of healthy specimens (n ​​= 11); middle right panel: t-SNE plot of COVID-19 negative samples (n = 6); lower right panel: t-SNE plot of COVID-19 positive samples (n = 66). C) Immune cell population abundance at D0 in COVID-19 positive (+), COVID-19 negative (-) patients and healthy individuals (H). P-values ​​obtained by Wilcoxon Rank Sum Test, followed by Benjamini-Hochberg correction with FDR <0.1. D) Correlation between cell population abundance at D0 and clinical results, e.g. ventilation duration (vent_duration) and hospital stay (hosp_los) for COVID-19 + patients. Correlation estimates are obtained by Pearson correlation. E) Protein expression on neutrophils (F) in COVID-19 positive (COV +), COVID-19 negative (COV-) patients and rapid controls at D0 (Wilcoxon Rank Sum Test, Benjamini-Hochberg correction with FDR <0.1). F) Frequency of monocyte subsets in COVID-19 positive (COV +), COVID-19 negative (COV-) patients and healthy controls at D0. P-values ​​obtained by Wilcoxon Rank Sum Test.

COVID-19 resolution is associated with increased cell signaling at the time of admission

To see if observed changes in cell frequencies were accompanied by defective signaling, the researchers looked at the signal dynamics at late discharge and eventually died individuals. In contrast to patients who resolved COVID-19 in less than 30 days, which showed consistent changes in signaling states from high to low over time, they found no meaningful differences in late discharged and eventually deceased patients. Instead, these individuals had asymmetric signal direction in activated CD8 T cellsno pS6 signaling in cDC1 cells and reduced tpl signaling across monocyte subsets.

Surprisingly, when late discharged patients are within 30 days of discharge, the course of several immune dissolution functions, such as monocytes, neutrophils, and signaling molecules, resembles the recovery course of patients admitted less than 30 days, suggesting that the dissolution phase begins in these patients as well. . These results suggest that late discharge and eventually dead patients have lower immune cell signaling at the time of admission. While some of these cell signaling pathways in these patients became more active over time, others remained unchanged.

Implications

This study presents a basic model for a successful anti-SARS-CoV-2 immune response, as well as an understanding of the key immunological changes that accompany disease recovery after severe COVID-19. This working model of a restorative immune response pathway can be used to contextualize divergent immunological processes in immunosuppressed or immunocompromised patients with poor disease outcomes, long-distance COVID-19 patients, or response to new variants. In addition, this study highlights essential immunological pathways that could be addressed to enable cure of severe disease in COVID-19 patients and possibly other acute respiratory infections by delineating a preserved pathway for effective recovery.

*Important message

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered essential, guide clinical practice / health-related behavior or be treated as established information.

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