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      De novo emergence of a remdesivir resistance mutation during treatment of persistent SARS-CoV-2 infection in an immunocompromised patient: a case report

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          Abstract

          SARS-CoV-2 remdesivir resistance mutations have been generated in vitro but have not been reported in patients receiving treatment with the antiviral agent. We present a case of an immunocompromised patient with acquired B-cell deficiency who developed an indolent, protracted course of SARS-CoV-2 infection. Remdesivir therapy alleviated symptoms and produced a transient virologic response, but her course was complicated by recrudescence of high-grade viral shedding. Whole genome sequencing identified a mutation, E802D, in the nsp12 RNA-dependent RNA polymerase, which was not present in pre-treatment specimens. In vitro experiments demonstrated that the mutation conferred a ~6-fold increase in remdesivir IC 50 but resulted in a fitness cost in the absence of remdesivir. Sustained clinical and virologic response was achieved after treatment with casirivimab-imdevimab. Although the fitness cost observed in vitro may limit the risk posed by E802D, this case illustrates the importance of monitoring for remdesivir resistance and the potential benefit of combinatorial therapies in immunocompromised patients with SARS-CoV-2 infection.

          Abstract

          Here, the authors identify and validate the emergence of a SARS-CoV-2 resistance mutation to Remdesivir, associated with virological recrudesce in an immunocompromised patient with persistent COVID-19.

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          Most cited references27

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          Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

          Background The ongoing outbreak of the recently emerged novel coronavirus (2019-nCoV) poses a challenge for public health laboratories as virus isolates are unavailable while there is growing evidence that the outbreak is more widespread than initially thought, and international spread through travellers does already occur. Aim We aimed to develop and deploy robust diagnostic methodology for use in public health laboratory settings without having virus material available. Methods Here we present a validated diagnostic workflow for 2019-nCoV, its design relying on close genetic relatedness of 2019-nCoV with SARS coronavirus, making use of synthetic nucleic acid technology. Results The workflow reliably detects 2019-nCoV, and further discriminates 2019-nCoV from SARS-CoV. Through coordination between academic and public laboratories, we confirmed assay exclusivity based on 297 original clinical specimens containing a full spectrum of human respiratory viruses. Control material is made available through European Virus Archive – Global (EVAg), a European Union infrastructure project. Conclusion The present study demonstrates the enormous response capacity achieved through coordination of academic and public laboratories in national and European research networks.
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            PAML 4: phylogenetic analysis by maximum likelihood.

            PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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              A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

              Heng Li (2011)
              Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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                Author and article information

                Contributors
                shiv.gandhi@yale.edu
                albert.ko@yale.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 March 2022
                17 March 2022
                2022
                : 13
                : 1547
                Affiliations
                [1 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Infectious Diseases, Department of Medicine, , Yale University School of Medicine, ; New Haven, CT USA
                [2 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Immunobiology, , Yale University School of Medicine, ; New Haven, CT USA
                [3 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Epidemiology of Microbial Diseases, , Yale School of Public Health, ; New Haven, CT USA
                [4 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Laboratory Medicine, , University of Washington School of Medicine, ; Seattle, WA USA
                [5 ]GRID grid.417307.6, Center for Outcomes Research and Evaluation, , Yale New Haven Hospital, ; New Haven, CT USA
                [6 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Laboratory Medicine, , Yale School of Medicine, ; New Haven, CT USA
                [7 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Molecular, , Cellular and Developmental Biology, Yale University, ; New Haven, CT USA
                [8 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Microbial Pathogenesis, , Yale School of Medicine, ; New Haven, CT USA
                [9 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Chemistry, , Yale University, ; New Haven, CT USA
                [10 ]GRID grid.413575.1, ISNI 0000 0001 2167 1581, Howard Hughes Medical Institute, ; Chevy Chase, MD USA
                [11 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Vaccine and Infectious Disease Division, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                Author information
                http://orcid.org/0000-0002-1850-187X
                http://orcid.org/0000-0002-3552-7684
                http://orcid.org/0000-0003-4607-4470
                http://orcid.org/0000-0002-6925-9948
                http://orcid.org/0000-0001-6118-872X
                http://orcid.org/0000-0002-0450-0868
                http://orcid.org/0000-0002-5239-8511
                http://orcid.org/0000-0003-2495-9403
                http://orcid.org/0000-0002-7824-9856
                http://orcid.org/0000-0002-2048-4028
                http://orcid.org/0000-0001-9023-2339
                Article
                29104
                10.1038/s41467-022-29104-y
                8930970
                35301314
                e9060a93-3d08-44bb-9d8b-ff5e9109c718
                © The Author(s) 2022

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 November 2021
                : 28 February 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: 2T32AI007517-21A1
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2022

                Uncategorized
                sars-cov-2,viral evolution,antiviral agents,viral infection
                Uncategorized
                sars-cov-2, viral evolution, antiviral agents, viral infection

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