Can oxidative potential be a plant risk indicator for heavy metals contaminated soil? Analysis of ryegrass (Lolium perenne L.) metabolome based on machine learning

Abstract

Evaluating the plant risk of soil pollution by plant physiological indices usually requires a long cycle and has significant uncertainty. In this study, oxidative potential (OP) of the in situ heavy metal contaminated soils was measured by the dithiothreitol method. The oxidative stress response of the model plant ryegrass (Lolium perenne L.) induced by heavy metal contaminated soil was evaluated by the biomarkers, including superoxide dismutase and total antioxidant capacity. The comprehensive biomarker response index has a significant exponential correlation with the OP of soil (r ​= ​0.923, p ​< ​0.01) in ryegrass. Metabolomics analysis also showed a significant relationship of the metabolic effect level index of amino acids and sugars with OP. Random forest was selected from four machine learning models to screen the metabolites most relevant to OP, and Shapley additive explanations analysis was used to explain the contribution and the influence direction of the features on the model. Based on the selected 20 metabolites, the metabolic pathways most related to OP in plants, including alkaloid synthesis and amino acids metabolism, were identified. Compared to the plant physiological indices, OP is a more stable and faster indicator for the plant risk assessment of heavy metals contaminated soil.