ArticleFrontiers in microbiology2023
Bacterial dynamic of flue-cured tobacco leaf surface caused by change of environmental conditions.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Complete genome sequence ofMicrobiology resource announcements · 2026Article
- Integrated Multi-Omics Analysis Reveals Microbial Community Restructuring and its Role in Key Carbohydrate Metabolic Pathways During Tobacco Leaf Curing.Microbial ecology · 2025Article
- Bacteria dynamics and its correlation with chemical composition changes in tobacco leaves during flue curing.Applied microbiology and biotechnology · 2025Article
- Influence of tobacco Flower bud extract on microbial community and aroma quality during cigar leaves fermentation.Frontiers in bioengineering and biotechnology · 2025Article
- Regional climate variation structures the phyllosphere microbiome of flue-cured tobacco.Frontiers in plant science · 2025Article
- TCSRNet: a lightweight tobacco leaf curing stage recognition network model.Frontiers in plant science · 2024Article
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Authors and funding
12 authors.
Funding
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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 (
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