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      Accelerated SARS-CoV-2 intrahost evolution leading to distinct genotypes during chronic infection

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          Summary

          The chronic infection hypothesis for novel SARS-CoV-2 variant emergence is increasingly gaining credence following the appearance of Omicron. Here we investigate intrahost evolution and genetic diversity of lineage B.1.517 during a SARS-CoV-2 chronic infection lasting for 471 days (and still ongoing) with consistently recovered infectious virus and high viral loads. During the infection, we found an accelerated virus evolutionary rate translating to 35 nucleotide substitutions per year, approximately two-fold higher than the global SARS-CoV-2 evolutionary rate. This intrahost evolution led to the emergence and persistence of at least three genetically distinct genotypes suggesting the establishment of spatially structured viral populations continually reseeding different genotypes into the nasopharynx. Finally, using unique molecular indexes for accurate intrahost viral sequencing, we tracked the temporal dynamics of genetic diversity to identify advantageous mutations and highlight hallmark changes for chronic infection. Our findings demonstrate that untreated chronic infections accelerate SARS-CoV-2 evolution, ultimately providing opportunity for the emergence of genetically divergent and potentially highly transmissible variants as seen with Delta and Omicron.

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          Most cited references83

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                02 July 2022
                : 2022.06.29.22276868
                Affiliations
                [1 ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
                [2 ]School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, Australia
                [3 ]Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
                [4 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [5 ]Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
                [6 ]Department of Biological and Biomedical Sciences, Yale School of Medicine, New Haven, Connecticut, USA
                [7 ]Yale Center for Genome Analysis, Yale University, New Haven, CT, USA
                [8 ]Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
                [9 ]Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [10 ]Infectious Disease, Yale School of Medicine, New Haven, CT, USA
                [11 ]Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
                Author notes
                [*]

                These authors contributed equally

                [#]

                Senior author

                [@]

                Lead contact

                Author contributions

                CC, MP, RCS, and NDG conceived the study; RCS, DF, WS, and NDG collected the clinical data and/or samples; MIB, AMH, and NDG performed DNA extraction and sequencing library preparation; CC, AMH, MP, KP, CCa, and NDG performed the whole genome sequencing and analysis; SZ and RIS performed Primer ID sequencing; AMH and MAPH performed plaque assay experiments; CC, MP, AMH, and NDG designed the analysis methods and analyzed the data; SCR wrote the IRB protocol; CC, MP, AMH, RCS, and NDG drafted the manuscript; NDG secured funds and supervised the project; All authors reviewed and approved the manuscript.

                [†]

                Yale SARS-CoV-2 Genomic Surveillance Initiative Team Authors

                Kendall Billig, Rebecca Earnest, Joseph R. Fauver, Chaney C. Kalinch, Nicholas Kerantzas, Tobias R. Koch, Bony De Kumar, Marie L. Landry, Isabel M. Ott, David Peaper, Irina R. Tikhonova, and Chantal B.F. Vogels

                Article
                10.1101/2022.06.29.22276868
                9258298
                35794895
                f96fa96f-8e21-4808-b366-3938a2f172c5

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                Article

                sars-cov-2,chronic infection,intrahost evolution,mutation dynamics,covid-19 vaccines,epidemiology,genomic surveillance

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