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      Therapeutic mitigation of measles-like immune amnesia and exacerbated disease after prior respiratory virus infections in ferrets

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          Abstract

          Measles cases have surged pre-COVID-19 and the pandemic has aggravated the problem. Most measles-associated morbidity and mortality arises from destruction of pre-existing immune memory by measles virus (MeV), a paramyxovirus of the morbillivirus genus. Therapeutic measles vaccination lacks efficacy, but little is known about preserving immune memory through antivirals and the effect of respiratory disease history on measles severity. We use a canine distemper virus (CDV)-ferret model as surrogate for measles and employ an orally efficacious paramyxovirus polymerase inhibitor to address these questions. A receptor tropism-intact recombinant CDV with low lethality reveals an 8-day advantage of antiviral treatment versus therapeutic vaccination in maintaining immune memory. Infection of female ferrets with influenza A virus (IAV) A/CA/07/2009 (H1N1) or respiratory syncytial virus (RSV) four weeks pre-CDV causes fatal hemorrhagic pneumonia with lung onslaught by commensal bacteria. RNAseq identifies CDV-induced overexpression of trefoil factor (TFF) peptides in the respiratory tract, which is absent in animals pre-infected with IAV. Severe outcomes of consecutive IAV/CDV infections are mitigated by oral antivirals even when initiated late. These findings validate the morbillivirus immune amnesia hypothesis, define measles treatment paradigms, and identify priming of the TFF axis through prior respiratory infections as risk factor for exacerbated morbillivirus disease.

          Abstract

          Measles virus infection causes immunosuppression and it’s unclear whether this can be prevented by antivirals. Here, using a canine distemper virus ferret model, the authors show that measles-like immune amnesia and lethal exacerbated respiratory disease after consecutive respiratory virus infections can be mitigated by oral antiviral therapy initiated at the onset of primary clinical signs.

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          Most cited references50

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                rplemper@gsu.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 February 2024
                8 February 2024
                2024
                : 15
                : 1189
                Affiliations
                [1 ]Center for Translational Antiviral Research, Georgia State University Institute for Biomedical Sciences, ( https://ror.org/03qt6ba18) Atlanta, GA 30303 USA
                [2 ]Virology Division, Department of Laboratory Medicine, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA 98195 USA
                [3 ]Emory Institute for Drug Development, Emory University, ( https://ror.org/03czfpz43) Atlanta, GA 30322 USA
                [4 ]Department of Viroscience, Erasmus MC, ( https://ror.org/018906e22) Rotterdam, Netherlands
                [5 ]Advanced Translational Imaging Facility, Georgia State University, ( https://ror.org/03qt6ba18) Atlanta, GA 30303 USA
                [6 ]GRID grid.213876.9, ISNI 0000 0004 1936 738X, Department of Pathology, College of Veterinary Medicine, , University of Georgia, ; Athens, GA 30602 USA
                Author information
                http://orcid.org/0000-0003-0620-1674
                http://orcid.org/0000-0003-2507-6944
                http://orcid.org/0000-0001-9334-8150
                http://orcid.org/0000-0002-7022-1410
                http://orcid.org/0000-0001-9379-0416
                http://orcid.org/0000-0001-8491-3824
                http://orcid.org/0000-0003-1962-7578
                http://orcid.org/0000-0003-3539-0718
                http://orcid.org/0000-0003-3599-8969
                http://orcid.org/0000-0002-7443-0527
                http://orcid.org/0000-0003-2034-2107
                Article
                45418
                10.1038/s41467-024-45418-5
                10853234
                38331906
                2fe6180b-262a-4e2d-a992-a3ddc511a61e
                © The Author(s) 2024

                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
                : 3 November 2023
                : 23 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: AI071002
                Award ID: AI171403
                Award ID: AI171403
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                viral pathogenesis,measles virus,influenza virus,antiviral agents
                Uncategorized
                viral pathogenesis, measles virus, influenza virus, antiviral agents

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