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      A proof-of-concept study on the genomic evolution of Sars-Cov-2 in molnupiravir-treated, paxlovid-treated and drug-naïve patients

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          Abstract

          Little is known about SARS-CoV-2 evolution under Molnupiravir and Paxlovid, the only antivirals approved for COVID-19 treatment. By investigating SARS-CoV-2 variability in 8 Molnupiravir-treated, 7 Paxlovid-treated and 5 drug-naïve individuals at 4 time-points (Days 0-2-5-7), a higher genetic distance is found under Molnupiravir pressure compared to Paxlovid and no-drug pressure (nucleotide-substitutions/site mean±Standard error: 18.7 × 10 −4 ± 2.1 × 10 −4 vs. 3.3 × 10 −4 ± 0.8 × 10 −4 vs. 3.1 × 10 −4 ± 0.8 × 10 −4, P = 0.0003), peaking between Day 2 and 5. Molnupiravir drives the emergence of more G-A and C-T transitions than other mutations ( P = 0.031). SARS-CoV-2 selective evolution under Molnupiravir pressure does not differ from that under Paxlovid or no-drug pressure, except for orf8 (dN > dS, P = 0.001); few amino acid mutations are enriched at specific sites. No RNA-dependent RNA polymerase (RdRp) or main proteases (Mpro) mutations conferring resistance to Molnupiravir or Paxlovid are found. This proof-of-concept study defines the SARS-CoV-2 within-host evolution during antiviral treatment, confirming higher in vivo variability induced by Molnupiravir compared to Paxlovid and drug-naive, albeit not resulting in apparent mutation selection.

          Abstract

          A proof-of-concept study on the genomic evolution of SARS-CoV-2 during antiviral treatment reveals that Molnupiravir induces a higher in vivo intra-host variability compared to Paxlovid and drug-naive.

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          Most cited references43

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          fastp: an ultra-fast all-in-one FASTQ preprocessor

          Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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            ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

            Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Contributors
                carlofederico.perno@opbg.net
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                15 December 2022
                15 December 2022
                2022
                : 5
                : 1376
                Affiliations
                [1 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Multimodal Research Area, Microbiology and Diagnostics of Immunology Unit, , Bambino Gesù Children Hospital IRCCS, ; Rome, Italy
                [2 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Department of Oncology and Hemato-Oncology, , University of Milan, ; Milan, Italy
                [3 ]GRID grid.7548.e, ISNI 0000000121697570, Department of Infectious Diseases, , University of Modena and Reggio Emilia, ; Modena, Italy
                [4 ]Department of Infectious Diseases, AOU Modena, Modena, Italy
                [5 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Academic Department of Pediatrics, , Bambino Gesù Children’s Hospital IRCCS, ; Rome, Italy
                Author information
                http://orcid.org/0000-0002-5207-8147
                http://orcid.org/0000-0002-1254-9995
                http://orcid.org/0000-0002-9120-0424
                http://orcid.org/0000-0002-5724-3914
                http://orcid.org/0000-0002-0825-5388
                Article
                4322
                10.1038/s42003-022-04322-8
                9753865
                36522489
                50d33670-cff2-4a2b-af16-bbbbb8fe533b
                © 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 September 2022
                : 30 November 2022
                Funding
                Funded by: ANIA Foundation supported this work.
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

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

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