Abstract
The curing process regulates metabolite transformations of leaves and significantly influences the formation of tobacco quality. This study investigated the major physicochemical compositions and metabolic profiles under normal curing (NC), excessive curing (EC), and insufficient curing (IC) treatments. The results indicated that the contents of nicotine, nitrogen, potassium, and chlorine remained stable among treatments, while the sugar content in EC was significantly lower than in IC. LC-MS/MS identified 845 metabolites, with flavonoids as the most abundant class. Comparative analyses identified a series of differentially expressed metabolites (DEMs) among fresh leaf, NC, EC, and IC leaves at the end of 42°C, 54°C, and 68°C, respectively. At the end of 68°C, 256 up-regulated and 241 down-regulated common DEMs across treatments were isolated in comparison to fresh leaf, underscoring the consistency of metabolic changes during curing. Notably, nonivamide varied markedly across treatments, suggesting its potential as a key curing indicator. NC_68°C displayed 11 up-regulated and 17 down-regulated unique DEMs, differing from EC_68°C and IC_68°C, suggesting their potential availability in evaluating tobacco leaf quality. KEGG pathway analysis revealed temporal shifts in metabolic pathways, particularly those involved in secondary metabolite biosynthesis (such as flavonoids, flavones, flavonols) and amino acid metabolism, during the transition from yellowing to color-fixing. Correlation analysis isolated the top 25 DEMs correlated with curing degree and stage, which might play pivotal roles in the curing process and could serve as potential biomarkers for assessing curing degree and stage. Specifically, D-(+)-cellobiose displayed the strongest negative correlation with curing degree, while 5,7-dihydroxychromone exhibited the highest positive correlation coefficient. Furthermore, curcurbitacin IIa showed the highest positive correlation with curing stage, followed by hesperetin and 8-shogaol. Additionally, random forest analysis emphasized morellic acid as a core molecular metabolite across curing degrees, suggesting its potential as a biomarker. Debiased sparse partial correlation (DSPC) network analysis further pinpointed hispidulin as a key metabolite, underscoring its significance in elucidating flavonoid metabolism during the curing process. Collectively, this study enhances the understanding of metabolite transformations underlying tobacco curing processes and provides a valuable reference for optimizing curing strategies to achieve desired outcomes.