Trivalent chromium [Cr (III)] is a frequently detected environmental contaminant that negatively affects plant metabolism, growth, and yield. Plant secondary metabolites are non-nutrient compounds involved in diverse physiological functions in response to abiotic stresses. This study performed biochemical and transcriptomic analyses to clarify the protective role and mechanism of secondary metabolites in rice seedlings under Cr(III) stress. The content of measured secondary metabolites i.e., total soluble phenolics, flavonoids, lignin, and anthocyanin in rice tissues was significantly enhanced under Cr(III) exposure. Additionally, the activities of several enzymes including chalcone synthase, phenylalanine ammonialyase, peroxidase, and anthocyanidin synthase, were positively activated. Transcriptomic analysis revealed a number of differentially expressed genes (DEGs) in Cr(III)-treated rice seedlings and their expression patterns are tissue-specific. Additionally, the DEGs involved in secondary metabolism pathways were evident after KEGG enrichment analysis. Cr(III) exposure resulted in distinct genetic regulation strategies in rice tissues, with different DEGs activating various sub-pathways in the secondary metabolism pathways to cope with Cr(III) stress. Furthermore, the integrated correlation analysis of the secondary metabolites and transcriptome data identified key genes involved in different steps of the sub-pathways of secondary metabolism pathway in rice plants under Cr(III) stress. This study indicates that the accumulation of secondary metabolites in rice plants is a survival and detoxification strategy to reduce the adverse effects of toxic compounds. Future research should explore how key genes in secondary metabolism pathways aid in Cr(III) detoxification and enhance crop stress tolerance.

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.