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      Combinatorial interactions between viral proteins expand the potential functional landscape of the tomato yellow leaf curl virus proteome

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          Abstract

          Viruses manipulate the cells they infect in order to replicate and spread. Due to strict size restrictions, viral genomes have reduced genetic space; how the action of the limited number of viral proteins results in the cell reprogramming observed during the infection is a long-standing question. Here, we explore the hypothesis that combinatorial interactions may expand the functional landscape of the viral proteome. We show that the proteins encoded by a plant-infecting DNA virus, the geminivirus tomato yellow leaf curl virus (TYLCV), physically associate with one another in an intricate network, as detected by a number of protein-protein interaction techniques. Importantly, our results indicate that intra-viral protein-protein interactions can modify the subcellular localization of the proteins involved. Using one particular pairwise interaction, that between the virus-encoded C2 and CP proteins, as proof-of-concept, we demonstrate that the combination of viral proteins leads to novel transcriptional effects on the host cell. Taken together, our results underscore the importance of studying viral protein function in the context of the infection. We propose a model in which viral proteins might have evolved to extensively interact with other elements within the viral proteome, enlarging the potential functional landscape available to the pathogen.

          Author summary

          Viruses are obligate intracellular parasites that depend on the molecular machinery of their host cell to complete their life cycle. For this purpose, viruses co-opt host processes, modulating or redirecting them. Most viruses have small genomes, and hence limited coding capacity. During the viral invasion, virus-encoded proteins will be produced in large amounts and coexist in the infected cell, which enables physical or functional interactions among viral proteins, potentially expanding the virus-host functional interface by increasing the number of potential targets in the host cell and/or synergistically modulating the cellular environment. Examples of interactions between viral proteins have been recently documented for both animal and plant viruses; however, the hypothesis that viral proteins might have a combinatorial effect, which would lead to the acquisition of novel functions, lacks systematic experimental validation. Here, we use the geminivirus tomato yellow leaf curl virus (TYLCV), a plant-infecting virus with reduced proteome and causing devastating diseases in crops, to test the idea that combinatorial interactions between viral proteins exist and might underlie an expansion of the functional landscape of the viral proteome. Our results indicate that viral proteins prevalently interact with one another in the context of the infection, which can result in the acquisition of novel functions.

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
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                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                PLOS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                18 October 2022
                October 2022
                : 18
                : 10
                : e1010909
                Affiliations
                [1 ] Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
                [2 ] University of the Chinese Academy of Sciences, Beijing, China
                [3 ] Department of Plant Biochemistry, Center for Plant Molecular Biology (ZMBP), Eberhard Karls University, Tübingen, Germany
                [4 ] Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora” (IHSM-UMA-CSIC), Area de Genética, Facultad de Ciencias, Universidad de Málaga, Campus de Teatinos s/n, Málaga, Spain
                The Ohio State University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7348-8842
                Article
                PPATHOGENS-D-22-00669
                10.1371/journal.ppat.1010909
                9633003
                36256684
                610326c9-16b7-48bd-a086-d80c4cfab168
                © 2022 Wang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 April 2022
                : 30 September 2022
                Page count
                Figures: 6, Tables: 0, Pages: 21
                Funding
                Funded by: Chinese Academy of Sciences
                Award ID: XDB27040206
                Funded by: National Foreign Talents project (CN)
                Award ID: G20200113006
                Funded by: Natural Science Foundation of China (NSFC)
                Award ID: 32100249
                Award Recipient :
                Funded by: Natural National Science Foundation of China
                Award ID: 31850410467
                Award Recipient :
                Funded by: President’s International Fellowship Initiative (CN)
                Award ID: 2018PB058, 2020PB0080
                Award Recipient :
                Funded by: Foreign Youth Talent Program (CN)
                Award ID: 20WZ2503900
                Award Recipient :
                Funded by: Shanghai Science and Technology Commission
                Award Recipient :
                Funded by: President’s International Fellowship Initiative (CN)
                Award ID: 2020PB0082
                Award Recipient :
                Funded by: Foreign Youth Talent Program project (CN)
                Award ID: 20WZ2504500
                Award Recipient :
                Funded by: Shanghai Science and Technology Commission
                Award Recipient :
                This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (grant number XDB27040206), the Shanghai Center for Plant Stress Biology, CAS, and the Excellence Strategy of the German Federal and State Governments to RL-D. RL-D was the recipient of a National Foreign Talents project (grant number G20200113006). LW is the recipient of a Young Investigator Grant from the Natural Science Foundation of China (NSFC) (grant number 32100249). LM-P was the recipient of a Young Investigator Grant from NSFC (grant number 31850410467), a President’s International Fellowship Initiative (PIFI) postdoctoral fellowship (2018PB058 and 2020PB0080) from CAS, and a Foreign Youth Talent Program project (grant number 20WZ2503900) from the Shanghai Science and Technology Commission. BGG was the recipient of a President’s International Fellowship Initiative (PIFI) postdoctoral fellowship (2020PB0082), and a Foreign Youth Talent Program project (grant number 20WZ2504500) from the Shanghai Science and Technology Commission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Interactions
                Protein-Protein Interactions
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Interactions
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Leaves
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Cell Nucleus
                Nucleolus
                Biology and Life Sciences
                Microbiology
                Virology
                Virus Effects on Host Gene Expression
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Cloning
                Research and Analysis Methods
                Molecular Biology Techniques
                Cloning
                Biology and Life Sciences
                Microbiology
                Virology
                Viral Replication
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-11-03
                All relevant data are within the manuscript and its Supporting Information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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