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Topical application of carbon dots and mesoporous silica nanoparticle-derived dsRNA-nanocomposites for the control of beet curly top virus and turnip mosaic virus

Zarrabi, S.; Martinez-Campos, E.; Rangel, C.; Delgado-Martin, J.; Arpanaei, A.; Shams-bakhsh, M.; Velasco, L.

2024-12-29 plant biology
10.1101/2024.12.29.628607 bioRxiv
Show abstract

The management of emerging phytoviruses in current agriculture confronts many challenges, including the appearance of new strains or the arrival of new species, requiring a multidisciplinary approach. The usual methods for the control of these pathogens are based on management practices, vector control, seed control, etc., and the introgression of genetic resistance in cultivars. But the development of resistances by classical genetic methods is a costly and time-consuming process. A promising alternative is the control of these pathogens by means of the so-called SIGS (Spray-Induced Gene Silencing) that consists of topical treatments with specific molecules derived from the viruses as double-stranded RNA (dsRNA), triggering the plant defense mechanisms. In this work, we show the control of two viruses, the potyvirus turnip mosaic virus (TuMV) and the curtovirus beet curly top virus (BCTV) by dsRNA nanocomposites with carbon dots or mesoporous silica nanoparticles in Nicotiana benthamiana. In the case of TuMV, the disease was significantly reduced in terms of plant photosynthetic capacity and viral titers, in the dsRNA treated plants. Moreover, when the treatments were carried out with dsRNAs as nanocomposites, the differences were even more noticeable with respect to untreated inoculated plants. In the case of BCTV, a significant delay in symptoms appearance was observed after the treatments with the dsRNA nanocomposites, but not with the naked dsRNAs. Viral titers were reduced either with the naked or the nanoparticle-delivered dsRNAs. The increased efficiency of dsRNA for virus control when supplied with the nanoparticles can be related with the enhanced dsRNA delivery reported in this work.

Published in Scientific Reports (predicted rank #4) · training set

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