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      Establishment of reverse genetics for genotype VII Newcastle disease virus and altering the cell tropism by inserting TMPRSS2 into the viral genome

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          Abstract

          Newcastle disease (ND) is the most important infectious disease in poultry, which is caused by avian orthoavulavirus type 1 (AOAV-1), previously known as Newcastle disease virus (NDV). In this study, an NDV strain SD19 (GenBank accession number OP797800) was isolated, and phylogenetic analysis suggested the virus belongs to the class II genotype VII. After generating wild-type rescued SD19 (rSD19), the attenuating strain (raSD19) was generated by mutating the F protein cleavage site. To explore the potential role of the transmembrane protease, serine S1 member 2 (TMPRSS2), the TMPRSS2 gene was inserted into the region between the P and M genes of raSD19 to generate raSD19-TMPRSS2. Besides, the coding sequence of the enhanced green fluorescent protein (EGFP) gene was inserted in the same region as a control (rSD19-EGFP and raSD19-EGFP). The Western blot, indirect immunofluorescence assay (IFA), and real-time quantitative PCR were employed to determine the replication activity of these constructs. The results reveal that all the rescued viruses can replicate in chicken embryo fibroblast (DF-1) cells; however, the proliferation of raSD19 and raSD19-EGFP needs additional trypsin. We next evaluated the virulence of these constructs, and our results reveal that the SD19, rSD19, and rSD19-EGFP are velogenic; the raSD19 and raSD19-EGFP are lentogenic; and the raSD19-TMPRSS2 are mesogenic. Moreover, due to the enzymatic hydrolysis of serine protease, the raSD19-TMPRSS2 can support itself to proliferate in the DF-1 cells without adding exogenous trypsin. These results may provide a new method for the NDV cell culture and contribute to ND’s vaccine development.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s11262-023-01999-9.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          Kenneth Livak, Thomas D. Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Highly accurate protein structure prediction with AlphaFold

            John Jumper, Richard Evans, Alexander Pritzel … (2021)
            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              MEGA11: Molecular Evolutionary Genetics Analysis Version 11

              Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)
              The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
              Article 0 comments Cited 2715 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Jun Xiao: xxxjjj6288@sina.com
                Weiquan Liu: weiquan8@cau.edu.cn
                Journal
                Journal ID (nlm-ta): Virus Genes
                Journal ID (iso-abbrev): Virus Genes
                Title: Virus Genes
                Publisher: Springer US (New York )
                ISSN (Print): 0920-8569
                ISSN (Electronic): 1572-994X
                Publication date (Electronic): 27 April 2023
                Pages: 1-10
                Affiliations
                [1 ]GRID grid.22935.3f, ISNI 0000 0004 0530 8290, State Key Laboratory of Agrobiotechnology, Department of Biochemistry and Molecular Biology, College of Biological Sciences, , China Agricultural University, ; No. 2 Yuanmingyuan West Road, Beijing, 100193 China
                [2 ]GRID grid.511521.3, School of Medicine, , The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), ; Shenzhen, 518172 Guangdong People’s Republic of China
                [3 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Department of Geriatrics, The Eight Medical Centre, , Chinese PLA General Hospital, ; Beijing, China
                Author notes

                Edited by Juergen Richt.

                Article
                Publisher ID: 1999
                DOI: 10.1007/s11262-023-01999-9
                PMC ID: 10133899
                PubMed ID: 37103648
                SO-VID: fe2253f5-aa1d-489c-a993-b71ca437a643
                Copyright © © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 1 February 2023
                Date accepted : 20 April 2023
                Categories
                Subject: Original Paper

                ScienceOpen disciplines: Microbiology & Virology
                Keywords: ndv,reverse genetics,tmprss2,cell culture
                Data availability:
                ScienceOpen disciplines: Microbiology & Virology
                Keywords: ndv, reverse genetics, tmprss2, cell culture

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