TITLE:
Structural Analysis of Tobacco Rhizosphere Soil Microbial Communities Based on Metagenomics and Deep Learning for Association with Disease Resistance
AUTHORS:
Jinming Lu, Haibo Xiang, Rubing Xu, Yanyan Li, Yong Yang
KEYWORDS:
Metagenomics, Rhizosphere Microbiome, Tobacco Disease Resistance, Deep Learning, Community Diversity, Microbial Biomarkers
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.16 No.4,
April
27,
2025
ABSTRACT: The rhizosphere microbiome, often termed the plant’s “second genome”, plays a pivotal role in regulating plant health and disease resistance. This study integrated metagenomic sequencing and deep learning to systematically compare the composition, diversity, and functional metabolism of microbial communities in healthy (NB) and diseased (NF) tobacco rhizosphere soils. Using BGISEQ-500 sequencing, 18 soil samples were analyzed, yielding 8.63 million clean reads and 6.27 million non-redundant genes. Taxonomic profiling revealed Proteobacteria (35%), Actinobacteria (16%), Firmicutes (10%), and Bacteroidetes (7%) as dominant phyla. Significant structural disparities in α-diversity indices (e.g., Shannon and Simpson) and β-diversity (PCoA) were observed between NB and NF groups (ANOVA, p