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      Relationship between gene expression patterns from nasopharyngeal swabs and serum biomarkers in patients hospitalized with COVID-19, following treatment with the neutralizing monoclonal antibody bamlanivimab

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          Abstract

          Background

          A thorough understanding of a patient’s inflammatory response to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is crucial to discerning the associated, underlying immunological processes and to the selection and implementation of treatment strategies. Defining peripheral blood biomarkers relevant to SARS-CoV-2 infection is fundamental to detecting and monitoring this systemic disease. This safety-focused study aims to monitor and characterize the immune response to SARS-CoV-2 infection via analysis of peripheral blood and nasopharyngeal swab samples obtained from patients hospitalized with Coronavirus disease 2019 (COVID-19), in the presence or absence of bamlanivimab treatment.

          Methods

          23 patients hospitalized with COVID-19 were randomized to receive a single dose of the neutralizing monoclonal antibody, bamlanivimab (700 mg, 2800 mg or 7000 mg) or placebo, at study initiation (Clinical Trial; NCT04411628). Serum samples and nasopharyngeal swabs were collected at multiple time points over 1 month. A Proximity Extension Array was used to detect inflammatory profiles from protein biomarkers in the serum of hospitalized COVID-19 patients relative to age/sex-matched healthy controls. RNA sequencing was performed on nasopharyngeal swabs. A Luminex serology assay and Elecsys® Anti-SARS-CoV-2 immunoassay were used to detect endogenous antibody formation and to monitor seroconversion in each cohort over time. A mixed model for repeated measures approach was used to analyze changes in serology and serum proteins over time.

          Results

          Levels of IL-6, CXCL10, CXCL11, IFNγ and MCP-3 were > fourfold higher in the serum of patients with COVID-19 versus healthy controls and linked with observations of inflammatory and viral-induced interferon response genes detected in nasopharyngeal swab samples from the same patients. While IgA and IgM titers peaked around 7 days post-dose, IgG titers remained high, even after 28 days. Changes in biomarkers over time were not significantly different between the bamlanivimab and placebo groups.

          Conclusions

          Similarities observed between nasopharyngeal gene expression patterns and peripheral blood biomarker profiles reveal a connection between the circulation and processes in the nasopharyngeal cavity, reinforcing the potential utility of systemic blood biomarker profiling for therapeutic monitoring of patient response. Serological antibody responses in patients correlated closely with reductions in the COVID-19 inflammatory protein biomarker signature. Bamlanivimab did not affect the biomarker dynamics in this hospitalized patient population.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12967-022-03345-3.

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          Most cited references46

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            COVID-19: consider cytokine storm syndromes and immunosuppression

            As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).
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              Clinical and immunologic features in severe and moderate Coronavirus Disease 2019

              Journal of Clinical Investigation
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                Author and article information

                Contributors
                rbenschop@lilly.com
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                18 March 2022
                18 March 2022
                2022
                : 20
                : 134
                Affiliations
                GRID grid.417540.3, ISNI 0000 0000 2220 2544, Eli Lilly and Company, Lilly Corporate Center, ; 893 S Delaware St., Indianapolis, IN 46285 USA
                Author information
                http://orcid.org/0000-0001-6313-5218
                Article
                3345
                10.1186/s12967-022-03345-3
                8931785
                35303909
                9bb43040-f433-433e-aa3a-5cd0ad99c496
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 December 2021
                : 8 March 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Medicine
                biomarkers,covid-19,gene expression,bamlanivimab,luminex,rna-seq,analytes
                Medicine
                biomarkers, covid-19, gene expression, bamlanivimab, luminex, rna-seq, analytes

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