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      Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection

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          ABSTRACT

          Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from transcriptome sequencing (RNA-seq) data sets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hot spots were identified for DVG recombination, and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single-cell RNA-seq analysis indicated the interferon (IFN) stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the next-generation sequencing (NGS) data set from a published cohort study and observed a significantly higher amount and frequency of DVG in symptomatic patients than those in asymptomatic patients. Finally, we observed exceptionally diverse DVG populations in one immunosuppressive patient up to 140 days after the first positive test of COVID-19, suggesting for the first time an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and into how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                Role: Invited Editor
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                19 April 2023
                May-Jun 2023
                19 April 2023
                : 14
                : 3
                : e00250-23
                Affiliations
                [a ] Department of Immunology and Microbiology, University of Rochester Medical Center, Rochester, New York, USA
                [b ] Medical Scientist Training Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
                [c ] Translational Biomedical Sciences PhD Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
                [d ] School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, Oregon, USA
                [e ] Department of Pediatrics and Center for Children’s Health Research, University of Rochester, Rochester, New York, USA
                [f ] Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
                [g ] Department of Biomedical Genetics, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
                [h ] Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York, USA
                University of Maryland School of Medicine
                Johns Hopkins Bloomberg School of Public Health
                Author notes

                Terry Zhou and Nora J. Gilliam contributed equally. Author order was determined by the initiation of the project and drafting of the manuscript.

                Sizhen Li and Simone Spandau contributed equally. Author order was determined in the order of decreasing seniority.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-8560-3438
                https://orcid.org/0000-0002-5320-5793
                Article
                00250-23 mbio.00250-23
                10.1128/mbio.00250-23
                10294654
                37074178
                55c0316b-18c3-4d16-a377-f31d3e26243e
                Copyright © 2023 Zhou et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 1 February 2023
                : 28 March 2023
                Page count
                supplementary-material: 10, Figures: 8, Tables: 0, Equations: 1, References: 95, Pages: 25, Words: 18054
                Funding
                Funded by: University of Rochester Technology Development Fund;
                Award ID: OP346177
                Award Recipient :
                Funded by: University of Rochester HSCCI;
                Award ID: OP211341
                Award Recipient :
                Funded by: Burroughs Wellcome Fund (BWF), FundRef https://doi.org/10.13039/100000861;
                Award ID: ID1014095
                Award Recipient : Award Recipient :
                Funded by: HHS | NIH | National Center for Advancing Translational Sciences (NCATS), FundRef https://doi.org/10.13039/100006108;
                Award ID: TL1-TR002000
                Award Recipient :
                Funded by: HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI), FundRef https://doi.org/10.13039/100000050;
                Award ID: HL122700
                Award Recipient :
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), FundRef https://doi.org/10.13039/100000057;
                Award ID: R35GM145283
                Award Recipient :
                Funded by: UR | School of Medicine and Dentistry, University of Rochester (SMD), FundRef https://doi.org/10.13039/100011125;
                Award ID: OP211968
                Award Recipient :
                Funded by: UR | School of Medicine and Dentistry, University of Rochester (SMD), FundRef https://doi.org/10.13039/100011125;
                Award Recipient :
                Categories
                Research Article
                editors-pick, Editor's Pick
                virology, Virology
                Custom metadata
                May/June 2023

                Life sciences
                defective viral genomes,sars-cov-2,recombination,secondary structure,type i/iii ifn responses,human epithelial cells,rna secondary structure

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