Unveiling the spore discharge molecular dynamics driven by osmotic substances in postharvest shiitake mushrooms through cross-omics and establishment of prediction model

From the paper

Abstract

Spore discharge, a critical quality change indicator in postharvest shiitake mushrooms, is challenging to monitor in practice, necessitating predictive modeling. This study established a strong link between osmotic substances and spore discharge intensity through physiological, transcriptomic, and metabolomic analyses. Suitable predictive models for substance content-spore discharge intensity were identified using linear and nonlinear fitting methods. Molecular analysis revealed significant enrichment of metabolic pathways related to mannitol and glycerol metabolism during spore discharge. Combined with the results of physiological analyses determined that mannitol was identified as closely associated with spore discharge intensity. Using data on spore discharge intensity and mannitol content during postharvest storage, a mathematical model was developed using second-order polynomials, Log3P1, Logistic, and Allometric1 models. The Allometric1 model outperformed others with a higher R² (0.93) and closer fit to real data, which is y=3.40355×1055×x−27.39148. Thus, the Allometric1 based mannitol content-spore discharge intensity model is effective for predicting postharvest spore discharge stages in shiitake mushrooms.