Comprehensive blood atlas of COVID-19 identifies indicators of disease severity

Researchers in the UK have unveiled an integrated multi-omics blood atlas describing the host’s immune response in patients with Coronavirus Disease 2019 (COVID-19) of varying severity and providing a unique reference resource for interpreting data sets from interventional studies.

Via the COBID consortium (COvid-19 Multi-Omics Blood ATlas), David Ahern from Oxford University and colleagues have characterized COVID-19 with varying degrees of severity and identified immune signatures and correlates of the host response.

The team says integrative approaches like those used here are essential to better differentiate COVID-19 patients based on disease severity, underlying pathophysiology, and infectious etiology.

“Our systems-based integrative approach and our blood atlas will influence future drug development, clinical trial design and personalized medical approaches for COVID-19,” the researchers write.

A pre-print version of the research paper is available on the medRxiv * server while the article is being peer-reviewed.

The COVID-19 pathophysiology is very heterogeneous between patients

The pathophysiology induced by an infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) – the causative agent of COVID-19 – includes a complex interplay between lung pathology and reactions of the maladaptive immune system.

Severe COVID-19 is characterized by hypoxia and can lead to acute respiratory distress syndrome, multiple organ failure, and death.

Predisposing factors for serious illness are age, gender, ethnicity, obesity, and comorbidities.

Currently, targeted, precision treatment approaches are limited by an incomplete understanding of clinical heterogeneity in patients and the potentially druggable immune mediators of disease.

Complementary approaches to the composition of individual cells show the variance in certain cell populations according to clinical group and severity (A).  Study design, test modalities and workflow.  The table shows the number of patients tested with the number of samples in brackets in which more than one sample was tested.  * The WHO severity categories show the number of patients at the time of sampling.  ** Single pair convalescent specimen tested for

Complementary approaches to the composition of individual cells show the variance in certain cell populations according to clinical group and severity (A). Study design, test modalities and workflow. The table shows the number of patients tested with the number of samples in brackets in which more than one sample was tested. * The WHO severity categories show the number of patients at the time of sampling. ** Single pair convalescence sample, tested on n = 16 COVID-19 and n = 3 sepsis patients. *** 10 samples tested (8 samples for paired acute-convalescent COVID-19 and 2 healthy ones). (BG) Main cell populations in whole blood for clinical comparison groups tested by single cell mass cytometry (CyTOF) and (B, C) for non-depleted samples (3,893,390 cells) (B) Cell frequencies (C) showing PCA with arrows indicating the driver Variation by cell population and (D) differential frequency analysis of CD66 + depleted whole blood (7,118,158 cells tested) by multimodal identification and annotation of PBMC subpopulations of the patient group (E) based on the curated intersection of CITE-seq data (F, G) UMAP shows the joint visualization of CITE-seq and CyTOF datasets (using CITE-seq data as reference) (F) overlaid CITE-seq (ADT) and mass cytometry (G) cell annotations transferred between datasets and stained by cell type, where matched (94.5% of cells) or mismatched (gray). (HK) CITE-seq composition analysis of smaller subgroups (H, I) PCA for COVID-19 cases in the hospital (H) PC1 vs. PC2 (I) Analysis of the loading of smaller subgroups on PC1. (J) Analysis of the different frequency between clinical groups (K) Analysis of covariates for clinical, demographic and experimental variables for COVID-19 cases in the hospital (using BH-adjusted ANOVA test for significance).

What did the researchers do?

Via the (COMBAT) consortium, Ahern and colleagues have now used a comprehensive multi-omics blood atlas in patients with varying degrees of COVID-19 severity in order to identify cells, mediators and signaling pathways that are characteristic of increasing disease severity.

To characterize the peripheral blood response to COVID-19, researchers analyzed a prospective cohort of adult patients with confirmed SARS-CoV-2 infection who entered clinical services in February and March 2020 – at the start of the UK pandemic and before anyone else Performing approved treatments or vaccinations.

The team recruited a prospective cohort of 116 patients hospitalized at Oxford University Hospitals with COVID-19. Samples were collected from hospital convalescents during acute admission and from 28 days after discharge.

Results from these samples were compared with those from recovery from COVID-19 cases in the community that did not require hospitalization, age-matched healthy volunteers, influenza cases requiring critical care, and all-cause sepsis patients.

What did you find?

The comprehensive multimodal analysis by the team of several well-defined patient cohorts and healthy volunteers identified blood characteristics of COVID-19 severity and specificity affecting particular immune cell populations, components of innate and adaptive immunity, and connectivity with the inflammatory response.

The team found evidence of increased levels of activated CD8 + T cells and natural killer cell populations in cases of COVID-19 and failed clonal expansion in CD8 + T effector and central memory cells with increasing disease severity.

The proteome analysis identified specific plasmacytokine and chemokine levels as biomarkers for severe diseases, with the following characteristics being detected: acute phase inflammation, complement activation, fibrin clots, proteases, serum amyloid, tissue necrosis, receptor-mediated endocytosis and cholesterol transport.

Plasma protein signatures could be used to stratify patients

The study found plasma protein signatures that can be used to divide hospital patients with acute COVID-19 into disease subphenotypes, with cluster membership associated with varying 28-day mortality rates.

“Patient stratification is important given the observed clinical heterogeneity in severe COVID-19. In the past, this variability was a major disruptive factor for clinical studies on targeted immunotherapy for other serious infections, ”the team writes.

The researchers also found that the ratio of integrin alpha 4 (CD49d) to leukosialin (CD43) is specifically increased in COVID-19 patients, including during recovery and convalescence.

It has been shown that decreased CD43 expression leads to neutrophil retention in the bloodstream and increased adhesion to vessel walls, which may be linked to the increased thrombosis observed in COVID-19 patients.

The ratio CD49d: CD43 can be an informative index value for COVID-19-specific neutrophil activity, suggest Ahern and colleagues.

The upregulation of the AP-1 p38 MAPK pathway was a specific feature of COVID-19

The analysis also identified upregulation of the AP-1 p38 MAPK pathway as a specific feature of COVID-19 disease across different immune subgroups.

“Combined with evidence of proliferation and cytokine response in these populations, this supports systemic immune activation and proliferation as a specific characteristic of COVID-19,” the researchers write.

The team says this finding is in line with recent evidence that baricitinib improves recovery in hospital patients. This drug has been shown to act upstream of AP-1, controlling macrophage inflammation and neutrophil recruitment in COVID-19.

What are the effects of the results?

Researchers say the integrated multi-omics blood atlas presented here is a unique reference resource for replication and meta-analysis for interpreting datasets from interventional studies and includes tools for direct visualization (https://mlv.combat.ox.ac. uk) /).

“Integrative approaches, as we have applied them here, are important in order to better differentiate COVID-19 patients according to the severity of the disease, underlying pathophysiology and infectious etiology,” write Ahern and colleagues.

“This will be important as we seek novel therapeutic targets and the possibility of a precision medicine approach to treatment that is timed and targeted to those patients who are most likely to benefit from a particular intervention,” the team concludes.

* Important NOTE

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

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