Abstract
Virus infestation can seriously harm the host plant's growth and development. Turnip yellows virus (TuYV) infestation of host plants can cause symptoms, such as yellowing and curling of leaves and root chlorosis. However, the regulatory mechanisms by which TuYV affects host growth and development are unclear. Hence, it is essential to mine small RNA (sRNA) and explore the regulation of sRNAs on plant hosts for disease control. In this study, we analyzed high-throughput data before and after TuYV infestation in Arabidopsis using combined genetics, statistics, and machine learning to identify 108 specifically expressed and critical functional sRNAs after TuYV infection. First, comparing the expression levels of sRNAs before and after infestation, 508 specific sRNAs were significantly up-regulated in Arabidopsis after infestation. In addition, the results show that AI models, including SVM, RF, XGBoost, and CNN using two-dimensional convolution, have robust classification features at the sequence level, with a prediction accuracy of about 96.8%. A comparison of specific sRNAs with genome sequences revealed that 247 matched precisely with the TuYV genome sequence but not with the Arabidopsis genome, suggesting that TuYV viruses may be their source. The 247 sRNAs predicted target genes and enrichment analysis, which identified 206 Arabidopsis genes involved in nine biological processes and three KEGG pathways associated with plant growth and viral stress tolerance, corresponding to 108 sRNAs. These findings provide a reference for studying sRNA-mediated interactions in pathogen infection and are essential for establishing a vital resource of regulation network for the virus infecting plants and deepening the understanding of TuYV virus infection patterns. However, further validation of these sRNAs is needed to gain a new understanding.