Molecular characterization of a tetra segmented ssDNA virus infecting <i>Botrytis cinerea</i> worldwide

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Abstract

Background

Family Genomoviridae was recently established, and only a few mycoviruses have been described and characterized, and almost all of them (Sclerotinia sclerotiorum hypovirulence-associated DNA virus 1, Fusarium graminearum gemyptripvirus 1 and Botrytis cinerea gemydayirivirus 1) induced hypovirulence in their host. Botrytis cinerea ssDNA virus 1 (BcssDV1), a tetrasegmented single-stranded DNA virus infecting the fungus Botrytis cinerea, has been molecularly characterized in this work.

Methods

BcssDV1 was detected in Spanish and Italian B. cinerea field isolates obtained from grapevine. BcssDV1 variants genomes were molecularly characterized via NGS and Sanger sequencing. Nucleotide and amino acid sequences were used for diversity and phylogenetic analysis. Prediction of protein tertiary structures and putative associated functions were performed by AlphaFold2 and DALI.

Results

BcssDV1 is a tetrasegmented single-stranded DNA virus. The mycovirus was composed by four genomic segments of approximately 1.7 Kb each, which are DNA-A, DNA-B, and DNA-C and DNA-D, that coded, respectively, for the rolling-circle replication initiation protein (Rep), capsid protein (CP) and two hypothetical proteins. BcssDV1 was present in several Italian and Spanish regions with high incidence and low variability among the different viral variants. DNA-A and DNA-D were found to be the more conserved genomic segments among variants, while DNA-B and DNA-C segments were shown to be the most variable ones. Tertiary structures of the proteins encoded by each segment suggested specific functions associated with each of them.

Conclusions

This study presented the first complete sequencing and characterization of a tetrasegmented ssDNA mycovirus, its incidence in Spain and Italy, its presence in other countries and its high conservation among regions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-023-02256-z.

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Most cited references 52

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MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

Sudhir Kumar, Glen Stecher, Michael KaWah Li … (2018)

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

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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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SWISS-MODEL: homology modelling of protein structures and complexes

Andrew Waterhouse, Martino Bertoni, Stefan Bienert … (2018)

Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

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Author and article information

Contributors

María A. Ayllón: mariaangeles.ayllon@upm.es

Journal

Journal ID (nlm-ta): Virol J

Journal ID (iso-abbrev): Virol J

Title: Virology Journal

Publisher: BioMed Central (London )

ISSN (Electronic): 1743-422X

Publication date (Electronic): 19 December 2023

Publication date PMC-release: 19 December 2023

Publication date Collection: 2023

Volume: 20

Electronic Location Identifier: 306

Affiliations

[1 ]Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (UPM-INIA/CSIC), ( https://ror.org/04mfzb702) Pozuelo de Alarcón, Madrid, Spain

[2 ]Institute for Sustainable Plant Protection, National Research Council of Italy, ( https://ror.org/008fjbg42) Torino, Italy

[3 ]Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), ( https://ror.org/03n6nwv02) Madrid, Spain

Article

Publisher ID: 2256

DOI: 10.1186/s12985-023-02256-z

PMC ID: 10731770

PubMed ID: 38114992

SO-VID: 25c7cbe2-398c-40b9-b817-dafb79dbda02

License:

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 25 July 2023

Date accepted : 2 December 2023

Funding

Funded by: VIROPLANT, a project that received funding from the European Union’s Horizon 2020 Research and Innovation Program

Award ID: 773567

Custom metadata

ScienceOpen disciplines: Microbiology & Virology

Keywords: hypovirulence,fungi,ssdna viruses,multisegmented genome,replication

Data availability:

ScienceOpen disciplines: Microbiology & Virology

Keywords: hypovirulence, fungi, ssdna viruses, multisegmented genome, replication

Molecular characterization of a tetra segmented ssDNA virus infecting Botrytis cinerea worldwide

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Related collections

Applied use of fungi

Most cited references 52

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

Highly accurate protein structure prediction with AlphaFold

SWISS-MODEL: homology modelling of protein structures and complexes

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