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Omicron variant is circulating in the presence of a globally acquired immunity unlike the ancestral SARS-CoV-2 isolate. Herein, we investigated the normalized viral load dynamics and viral culture status in 44 fully vaccinated healthcare workers (HCWs) infected with the Omicron BA.1 variant. Viral load dynamics of 38 unvaccinated HCWs infected with the 20A variant during the first pandemic wave was also studied. We then explored the impact of Omicron infection on pre-existing immunity assessing anti-RBD IgG levels, neutralizing antibody titres against 19A, Delta and Omicron isolates, as well as IFN-γ release following cell stimulation with SARS-CoV-2 peptides. We reported that two weeks after diagnosis a greater proportion of HCWs infected with 20A (78.9%, 15/19) than with Omicron BA.1 (44.7%, 17/38; p = 0.02) were still positive by RT-qPCR. We found that Omicron breakthrough infections led to an overall enhancement of vaccine-induced humoral and cellular immunity as soon as a median [interquartile range] of 8 [7–9] days post symptom onset. Among samples with similar high viral loads, non-culturable samples exhibited higher neutralizing antibody titres and anti-RBD IgG levels than culturable samples. Additionally, Omicron infection led to an enhancement of antibodies neutralization capacity against other SARS-CoV-2 isolates. Taken together, the results suggest that Omicron BA.1 vaccine breakthrough infection is associated with a faster viral clearance than that of the ancestral SARS-CoV-2, in addition this new variant leads to a rapid enhancement of the humoral response against multiple SARS-CoV-2 variants, and of the cellular response.

Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed that a multiplex transcriptomic panel prototype termed Immune Profiling Panel (IPP) could capture the dysregulation of immune responses of ICU COVID-19 patients at admission. Nine transcripts were associated with mortality in univariate analysis and this 9-mRNA signature remained significantly associated with mortality in a multivariate analysis that included age, SOFA and Charlson scores. Using a machine learning model with these 9 mRNA, we could predict the 28-day survival status with an Area Under the Receiver Operating Curve (AUROC) of 0.764. Interestingly, adding patients’ age to the model resulted in increased performance to predict the 28-day mortality (AUROC reaching 0.839). This prototype IPP demonstrated that such a tool, upon clinical/analytical validation and clearance by regulatory agencies could be used in clinical routine settings to quickly identify patients with higher risk of death requiring thus early aggressive intensive care.

Summary: Background: In critically ill COVID-19 patients, the initial response to SARS-CoV-2 infection is characterized by major immune dysfunctions. The capacity of these severe patients to mount a robust and persistent SARS-CoV-2 specific T cell response despite the presence of severe immune alterations during the ICU stay is unknown. Methods: Critically ill COVID-19 patients were sampled five times during the ICU stay and 9 and 13 months afterwards. Immune monitoring included counts of lymphocyte subpopulations, HLA-DR expression on monocytes, plasma IL-6 and IL-10 concentrations, anti-SARS-CoV-2 IgG levels and T cell proliferation in response to three SARS-CoV-2 antigens. Findings: Despite the presence of major lymphopenia and decreased monocyte HLA-DR expression during the ICU stay, convalescent critically ill COVID-19 patients consistently generated adaptive and humoral immune responses against SARS-CoV-2 maintained for more than one year after hospital discharge. Patients with long hospital stays presented with stronger anti-SARS-CoV-2 specific T cell response but no difference in anti-SARS-CoV2 IgG levels. Interpretation: Convalescent critically ill COVID-19 patients consistently generated a memory immune response against SARS-CoV-2 maintained for more than one year after hospital discharge. In recovered individuals, the intensity of SARS-CoV-2 specific T cell response was dependent on length of hospital stay. Funding: This observational study was supported by funds from the Hospices Civils de Lyon, Fondation HCL, Claude Bernard Lyon 1 University and Région Auvergne Rhône-Alpes and by partial funding by REACTing (Research and ACTion targeting emerging infectious diseases) INSERM, France and a donation from Fondation AnBer (http://fondationanber.fr/).

Whole-genome sequencing has become an essential tool for real-time genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. The handling of raw next-generation sequencing (NGS) data is a major challenge for sequencing laboratories. We developed an easy-to-use web-based application (EPISEQ SARS-CoV-2) to analyse SARS-CoV-2 NGS data generated on common sequencing platforms using a variety of commercially available reagents. This application performs in one click a quality check, a reference-based genome assembly, and the analysis of the generated consensus sequence as to coverage of the reference genome, mutation screening and variant identification according to the up-to-date Nextstrain clade and Pango lineage. In this study, we validated the EPISEQ SARS-CoV-2 pipeline against a reference pipeline and compared the performance of NGS data generated by different sequencing protocols using EPISEQ SARS-CoV-2. We showed a strong agreement in SARS-CoV-2 clade and lineage identification (>99%) and in spike mutation detection (>99%) between EPISEQ SARS-CoV-2 and the reference pipeline. The comparison of several sequencing approaches using EPISEQ SARS-CoV-2 revealed 100% concordance in clade and lineage classification. It also uncovered reagent-related sequencing issues with a potential impact on SARS-CoV-2 mutation reporting. Altogether, EPISEQ SARS-CoV-2 allows an easy, rapid and reliable analysis of raw NGS data to support the sequencing efforts of laboratories with limited bioinformatics capacity and those willing to accelerate genomic surveillance of SARS-CoV-2.