Abstract
Microorganisms present on the surface of tobacco leaves play a significant role in shaping the composition of the tobacco microbial ecosystem, which undergoes continuous changes throughout the curing process. In the present study, a total of four distinct tobacco curing periods were selected for sampling, namely the fresh, yellowing, leaf-drying, and stem-drying stages. The bacterial 16S rRNA gene sequences of the collected samples were subsequently analyzed to identify operational taxonomic units (OTUs). The findings indicated that the complete dataset of leaf microbial samples was clustered, resulting in the identification of 1,783 operational taxonomic units (OTUs). Furthermore, the analysis of diversity revealed a pattern of initially increasing and subsequently decreasing community diversity. Redundancy Analysis (RDA) and weighted gene correlation networks for analysis (WGCNA) were employed in conjunction with environmental factors to assign OTUs to 22 modules for functional analysis. Additionally, a classification model utilizing the random forest algorithm was utilized to identify seven marker microorganisms (Escherichia coli, Faecalibacterium prausnitzii, Faecalibacterium, Escherichia-Shigella, Peptostreptococcaceae, Peptostreptococcales-Tissierellales, and Proteobacteria) that exhibited discriminative characteristics across different time periods. This study aimed to investigate the dynamic changes in the bacterial community throughout the curing process and their impact on the community's function. Additionally, certain bacteria were identified as potential markers for detecting changes in the curing stage. These findings offer a novel opportunity to accurately regulate the curing environment, thereby enhancing the overall quality of tobacco leaf curing.