Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity (preprint)

Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized COVID-19 patients. Integrated analysis using k-means reveal four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors are delineated by high and low antibody responses. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the Interferon paradox previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.

Integrated immunovirological profiling validates plasma SARS-CoV-2 RNA as an early predictor of COVID-19 mortality (preprint)

Despite advances in COVID-19 management, it is unclear how to recognize patients who evolve towards death. This would allow for better risk stratification and targeting for early interventions. However, the explosive increase in correlates of COVID-19 severity complicates biomarker prioritisation. To identify early biological predictors of mortality, we performed an immunovirological assessment (SARS-CoV-2 viral RNA, cytokines and tissue injury markers, antibody responses) on plasma samples collected from 144 hospitalised COVID-19 patients 11 days after symptom onset and used to test models predicting mortality within 60 days of symptom onset. In the discovery cohort (n=61, 13 fatalities), high SARS-CoV-2 vRNA, low RBD-specific IgG levels, low SARS-CoV-2-specific antibody-dependent cellular cytotoxicity, and elevated levels of several cytokines and lung injury markers were strongly associated with increased mortality in the entire cohort and the subgroup on mechanical ventilation. Model selection revealed that a three-variable model of vRNA, age and sex was very robust at identifying patients who will succumb to COVID-19 (AUC=0.86, adjusted HR for log-transformed vRNA=3.5; 95% CI: 2.0-6.0). This model remained robust in an independent validation cohort (n=83, AUC=0.85). Quantification of plasma SARS-CoV-2 RNA can help understand the heterogeneity of disease trajectories and identify patients who may benefit from new therapies.

A single BNT162b2 mRNA dose elicits antibodies with Fc-mediated effector functions and boost pre-existing humoral and T cell responses (preprint)

The standard dosing of the Pfizer/BioNTech BNT162b2 mRNA vaccine validated in clinical trials includes two doses administered three weeks apart. While the decision by some public health authorities to space the doses because of limiting supply has raised concerns about vaccine efficacy, data indicate that a single dose is up to 90% effective starting 14 days after its administration. We analyzed humoral and T cells responses three weeks after a single dose of this mRNA vaccine. Despite the proven efficacy of the vaccine at this time point, no neutralizing activity were elicited in SARS-CoV-2 naive individuals. However, we detected strong anti-receptor binding domain (RBD) and Spike antibodies with Fc-mediated effector functions and cellular responses dominated by the CD4+ T cell component. A single dose of this mRNA vaccine to individuals previously infected by SARS-CoV-2 boosted all humoral and T cell responses measured, with strong correlations between T helper and antibody immunity. Neutralizing responses were increased in both potency and breadth, with distinctive capacity to neutralize emerging variant strains. Our results highlight the importance of vaccinating uninfected and previously-infected individuals and shed new light into the potential role of Fc-mediated effector functions and T cell responses in vaccine efficacy. They also provide support to spacing the doses of two-vaccine regimens to vaccinate a larger pool of the population in the context of vaccine scarcity against SARS-CoV-2.

Identification of SARS-CoV-2-specific immune alterations in acutely ill patients (preprint)

Dysregulated immune profiles have been described in symptomatic SARS-CoV-2-infected patients. Whether the reported immune alterations are specific to SARS-CoV-2 infection or also triggered by other acute illnesses remains unclear. We performed flow cytometry analysis on fresh peripheral blood from a consecutive cohort of i) patients hospitalized with acute SARS-CoV-2 infection; ii) patients of comparable age/sex hospitalized for other acute disease (SARS-CoV-2 negative); and iii) healthy controls. Using both data-driven and hypothesis-driven analyses, we found several dysregulations in immune cell subsets (e.g. decreased proportion of T cells) that are similarly associated with acute SARS-CoV-2 infection and non-COVID-19 related acute illnesses. In contrast, we identified specific differences in myeloid and lymphocyte subsets that are associated with SARS-CoV-2 status (e.g. elevated proportion of ICAM-1+ mature/activated neutrophils, ALCAM+ monocytes, and CD38+CD8+ T cells). A subset of SARS-CoV-2-specific immune alterations correlated with disease severity, disease outcome at 30 days and mortality. Our data provides novel understanding of the immune dysregulation that are specifically associated with SARS-CoV-2 infection among acute care hospitalized patients. Our study lays the foundation for the development of specific biomarkers to stratify SARS-CoV-2+ patients at risk of unfavorable outcome and uncover novel candidate molecules to investigate from a therapeutic perspective.

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data (preprint)

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

idCOV: a pipeline for quick clade identification of SARS-CoV-2 isolates (preprint)

idCOV is a phylogenetic pipeline for quickly identifying the clades of SARS-CoV-2 virus isolates from raw sequencing data based on a selected clade-defining marker list. Using a public dataset, we show that idCOV can make equivalent calls as annotated by Nextstrain.org on all three common clade systems using user uploaded FastQ files directly. Web and equivalent command-line interfac-es are available. It can be deployed on any Linux environment, including personal computer, HPC and the cloud. The source code is available at https://github.com/xz-stjude/idcov. A documentation for installation can be found at https://github.com/xz-stjude/idcov/blob/master/README.md.

Major role of IgM in the neutralizing activity of convalescent plasma against SARS-CoV-2 (preprint)

Characterization of the humoral response to SARS-CoV-2, the etiological agent of Covid-19, is essential to help control the infection. In this regard, we and others recently reported that the neutralization activity of plasma from COVID-19 patients decreases rapidly during the first weeks after recovery. However, the specific role of each immunoglobulin isotype in the overall neutralizing capacity is still not well understood. In this study, we selected plasma from a cohort of Covid-19 convalescent patients and selectively depleted immunoglobulin A, M or G before testing the remaining neutralizing capacity of the depleted plasma. We found that depletion of immunoglobulin M was associated with the most substantial loss of virus neutralization, followed by immunoglobulin G. This observation may help design efficient antibody-based COVID-19 therapies and may also explain the increased susceptibility to SARS-CoV-2 of autoimmune patients receiving therapies that impair the production of IgM.

Variant analysis of SARS-CoV-2 genomes in the Middle East (preprint)

Background: Coronavirus (COVID-19) was introduced into society in late 2019 and has now reached over 26 million cases and 850,000 deaths. The Middle East has a death toll of ~50,000 and over 20,000 of these are in Iran, which has over 350,000 confirmed cases. We expect that Iranian cases caused outbreaks in the neighbouring countries and that variant mapping and phylogenetic analysis can be used to prove this. We also aim to analyse the variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to characterise the common genome variants and provide useful data in the global effort to prevent further spread of COVID-19. Methods: The approach uses bioinformatics approaches including multiple sequence alignment, variant calling and annotation and phylogenetic analysis to identify the genomic variants found in the region. The approach uses 122 samples from the 13 countries of the Middle East sourced from the Global Initiative on Sharing All Influenza Data (GISAID). Findings: We identified 2200 distinct genome variants including 129 downstream gene variants, 298 frame shift variants, 789 missense variants, 1 start lost, 13 start gained, 1 stop lost, 249 synonymous variants and 720 upstream gene variants. The most common, high impact variants were 10818delTinsG, 2772delCinsC, 14159delCinsC and 2789delAinsA. Variant alignment and phylogenetic tree generation indicates that samples from Iran likely introduced COVID-19 to the rest of the Middle East. Interpretation: The phylogenetic and variant analysis provides unique insight into mutation types in genomes. Initial introduction of COVID-19 was most likely due to Iranian transmission. Some countries show evidence of novel mutations and unique strains. Increased time in small populations is likely to contribute to more unique genomes. This study provides more in depth analysis of the variants affecting in the region than any other study.