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      Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis

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          Abstract

          Background

          Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy.

          Methods

          We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved.

          Findings

          The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets.

          Interpretation

          Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19.

          Funding

          K.R.C is funded by the doi 10.13039/501100001349, National Medical Research Council; (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the doi 10.13039/501100001349, NMRC; Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the doi 10.13039/501100001349, NMRC; under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the doi 10.13039/501100001349, NMRC; under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

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          Most cited references59

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          Integrating single-cell transcriptomic data across different conditions, technologies, and species

          Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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            Clinical and immunologic features in severe and moderate Coronavirus Disease 2019

            Journal of Clinical Investigation
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              Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

              Summary Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity. Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection. Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses. Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response. This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6. We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.
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                Author and article information

                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                The Author(s). Published by Elsevier B.V.
                2352-3964
                16 February 2023
                March 2023
                16 February 2023
                : 89
                : 104472
                Affiliations
                [a ]Program in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore
                [b ]Department of Infectious Diseases, Singapore General Hospital, Singapore
                [c ]Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China
                [d ]Viral Research and Experimental Medicine Centre, SingHealth Duke-NUS Academic Medical Centre, Singapore
                [e ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore
                Author notes
                []Corresponding author.
                Article
                S2352-3964(23)00037-3 104472
                10.1016/j.ebiom.2023.104472
                9934388
                36801619
                d2282bbb-0222-41f7-b66d-c692e9b19402
                © 2023 The Author(s)

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 6 October 2022
                : 22 January 2023
                : 23 January 2023
                Categories
                Articles

                covid-19,mcemp1,hla-dra,transcriptomics,biomarkers,pathogenesis,single cell sequencing,rnaseq,gene expression,systematic review

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