Electron microscopy for Tomato chlorosis crinicirus (ToCV) particles, histopathological effects and bioinformatics prediction of viral proteins

Background The field trials for detection of ToCV on tomato yield in Egypt should be assess by using protein prediction results for the development of specific ToCV antisera kits. The current study aims to characterize the morphology of ToCV particles by investigating their infection effects on tomato plants histopathology and and predicting viral proteins using bioinformatics. Results ToCV particles exhibit a filamentous, flexible structure measuring approximately 850 nm, as determined using the tissue dip preparation method. Tomato leaves infected with ToCV displayed significant cytopathic effects on tissue and cells. The mesophyll tissue cells had thin cell walls, distorted membranes, and deformed nuclei, chloroplasts, and mitochondria. They also had relatively tiny intercellular spaces. Ultrathin sections revealed that infected tomato leaf cells exhibited deformed cell walls and organelles, including nucleus, chloroplasts, mitochondria, and large vacuoles. The cell wall showed irregular sedimentation, while mitochondria had a degenerated envelope. The nucleus appeared small and damaged. Cytoplasmic alterations included abnormal chloroplasts, mitochondria, and nuclei, as well as overall cellular abnormalities. ToCV infection caused leaf chlorosis, with varying degrees of tissue and cellular alterations. Healthy leaves exhibited a flat lamina and concave petioles. Gene prediction of ToCV-(+) ssRNA genomic segments genome involved identifying sub-sequences of bases that encode proteins and determining the percentage of trees in the involved taxa cluster. Conclusion The first tree (s) specific to the guiding search were recorded automatically using the Neighbor-Joining method via BioNJ algorithms, applying a matrix of pairwise distances calculated using the Maximum Composite Likelihood (MCL) method. The topology was then refined by selecting the structure with the highest log-likelihood score.