Synergies and antagonisms in virus interactions. (November 2016)

• Record Type: Journal Article
• Authors: Mascia, Tiziana; Gallitelli, Donato
• Is Part Of: Plant science. Volume 252(2016:Nov.)
• Page Start: 176
• Page End: 192
• Publication Date: 2016-11
• Subjects: Plant viruses -- Virome -- Synergism-antagonism -- RNAi -- Transcomplementation -- Endogenous sequences; Botany -- Periodicals; Botanique -- Périodiques; 580
• DOI: 10.1016/j.plantsci.2016.07.015
• Languages: English
• ISSNs: 0168-9452

Abstract

Graphical abstract: Highlights: Metagenomic and NGS analyses reveal census of complex viral populations in plants. Viruses, mutants, virus-like agents and sequences embedded in host genome create complex viromes. Complemented, enhanced or mutually excluded viral traits lead to synergism and antagonism in plants. Similar or different tissue localization influence virus interactions. Mixed infections alter relations with host plant and components of its microbiome. Abstract: Metagenomic surveys and data from next generation sequencing revealed that mixed infections among plant viruses are probably a rule rather than an exception in natural pathosystems. The documented cases may range from synergism to antagonism, which may depend from the spatiotemporal order of arrival of the viruses on the host and upon the host itself. In synergistic interactions, the measurable differences in replication, phenotypic and cytopathological changes, cellular tropism, within host movement, and transmission rate of one of the two viruses or both are increased. Conversely, a decrease in replication, or inhibition of one or more of the above functions by one virus against the other, leads to an antagonistic interaction. Viruses may interact directly and by transcomplementation of defective functions or indirectly, through responses mediated by the host like the defense mechanism based on RNA silencing. Outcomes of these interactions can be applied to the risk assessment of transgenic crops expressing viral proteins, or cross-protected crops for the identification of potential hazards. Prior to experimental evidence, mathematical models may help in forecasting challenges deriving from the great variety of pathways of synergistic and antagonistic interactions. Actually, it seems that such predictions do not receive sufficient credit in the framework of agriculture.