In Vitro Identification and In Vivo Confirmation of Inhibitors for Sweet Potato Chlorotic Stunt Virus RNA Silencing Suppressor, a Viral RNase III

High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.

Bioinformatic Analysis Predicts a Novel Genetic Module Related to Triple Gene and Binary Movement Blocks of Plant Viruses: Tetra-Cistron Movement Block

Previous studies have shown that the RNA genomes of some plant viruses encode two related genetic modules required for virus movement over the host body, containing two or three genes and named the binary movement block (BMB) and triple gene block (TGB), respectively. In this paper, we predict a novel putative-related movement gene module, called the tetra-cistron movement block (TCMB), in the virus-like transcriptome assemblies of the moss Dicranum scoparium and the Antarctic flowering plant Colobanthus quitensis. These TCMBs are encoded by smaller RNA components of putative two-component viruses related to plant benyviruses. Similar to the RNA2 of benyviruses, TCMB-containing RNAs have the 5′-terminal coat protein gene and include the RNA helicase gene which is followed by two small overlapping cistrons encoding hydrophobic proteins with a distant sequence similarity to the TGB2 and TGB3 proteins. Unlike TGB, TCMB also includes a fourth 5′-terminal gene preceding the helicase gene and coding for a protein showing a similarity to the double-stranded RNA-binding proteins of the DSRM AtDRB-like superfamily. Additionally, based on phylogenetic analysis, we suggest the involvement of replicative beny-like helicases in the evolution of the BMB and TCMB movement genetic modules.