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 < 0.05). LEfSe analysis identified 181 and 240 biomarkers in NB and NF, respectively, with healthy soils enriched in Gemmatimonadetes and Sphingomonadaceae, while diseased soils were dominated by Deltaproteobacteria and Rubrobacteraceae. Functional annotation highlighted the enrichment of sulfur metabolism (ko00920), terpenoid biosynthesis (ko00900), and antibiotic synthesis (ko01130) pathways in NB, whereas NF exhibited upregulated lipopolysaccharide biosynthesis (ko00540) and flagellar assembly (ko02040). A deep learning model (H2O framework, two hidden layers) achieved perfect classification (AUC = 1.0) and identified 10 microbial biomarkers as robust indicators of soil health. These findings elucidate the linkage between rhizosphere microbiome dynamics and tobacco disease resistance, providing a methodological framework for ecological disease control and precision agriculture. The study highlights the potential of microbial biomarkers in guiding sustainable agricultural practices and reducing reliance on chemical pesticides.
References
[1]
Bulgarelli, D., Garrido-Oter, R., Münch, P.C., Weiman, A., Dröge, J., Pan, Y., et al. (2015) Structure and Function of the Bacterial Root Microbiota in Wild and Domesticated Barley. Cell Host & Microbe, 17, 392-403. https://doi.org/10.1016/j.chom.2015.01.011
[2]
Berendsen, R.L., Pieterse, C.M.J. and Bakker, P.A.H.M. (2012) The Rhizosphere Microbiome and Plant Health. Trends in Plant Science, 17, 478-486. https://doi.org/10.1016/j.tplants.2012.04.001
[3]
Savary, S., Ficke, A., Aubertot, J. and Hollier, C. (2012) Crop Losses Due to Diseases and Their Implications for Global Food Production Losses and Food Security. Food Security, 4, 519-537. https://doi.org/10.1007/s12571-012-0200-5
[4]
Handelsman, J. (2004) Metagenomics: Application of Genomics to Uncultured Microorganisms. Microbiology and Molecular Biology Reviews, 68, 669-685. https://doi.org/10.1128/mmbr.68.4.669-685.2004
[5]
Quince, C., Walker, A.W., Simpson, J.T., Loman, N.J. and Segata, N. (2017) Erratum: Corrigendum: Shotgun Metagenomics, from Sampling to Analysis. Nature Biotechnology, 35, 1211. https://doi.org/10.1038/nbt1217-1211b
[6]
Bahram, M., Hildebrand, F., Forslund, S.K., Anderson, J.L., Soudzilovskaia, N.A., Bodegom, P.M., et al. (2018) Structure and Function of the Global Topsoil Microbiome. Nature, 560, 233-237. https://doi.org/10.1038/s41586-018-0386-6
[7]
Mendes, R., Kruijt, M., de Bruijn, I., Dekkers, E., van der Voort, M., Schneider, J.H.M., et al. (2011) Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria. Science, 332, 1097-1100. https://doi.org/10.1126/science.1203980
[8]
Wei, Z., Yang, T., Friman, V., Xu, Y., Shen, Q. and Jousset, A. (2015) Trophic Network Architecture of Root-Associated Bacterial Communities Determines Pathogen Invasion and Plant Health. Nature Communications, 6, Article No. 8413. https://doi.org/10.1038/ncomms9413
[9]
Yan, L., Zhang, W., Duan, W., Zhang, Y., Zheng, W. and Lai, X. (2021) Temporal Bacterial Community Diversity in the Nicotiana Tabacum Rhizosphere over Years of Continuous Monocropping. Frontiers in Microbiology, 12, Article 641643. https://doi.org/10.3389/fmicb.2021.641643
[10]
Liu, C., Zhang, L., Li, H., He, X., Dong, J. and Qiu, B. (2024) Assessing the Biodiversity of Rhizosphere and Endophytic Fungi in Knoxiavalerianoides under Continuous Cropping Conditions. BMC Microbiology, 24, Article No. 195. https://doi.org/10.1186/s12866-024-03357-7
[11]
Knights, D., Costello, E.K. and Knight, R. (2011) Supervised Classification of Human Microbiota. FEMS Microbiology Reviews, 35, 343-359. https://doi.org/10.1111/j.1574-6976.2010.00251.x
[12]
Baranwal, M., Clark, R.L., Thompson, J., Sun, Z., Hero, A.O. and Venturelli, O.S. (2022) Recurrent Neural Networks Enable Design of Multifunctional Synthetic Human Gut Microbiome Dynamics. eLife, 11, e73870. https://doi.org/10.7554/elife.73870
[13]
Han, G., Song, F., Zhang, Z., Ni, W., He, S. and Tian, X. (2010) An Economic and Efficient Method for Further Purification of Crude DNA Extracted from Forest Soils. Journal of Forestry Research, 21, 246-250. https://doi.org/10.1007/s11676-010-0040-0
[14]
Zorrilla, F., Buric, F., Patil, K.R. and Zelezniak, A. (2021) Metagem: Reconstruction of Genome Scale Metabolic Models Directly from Metagenomes. Nucleic Acids Research, 49, e126. https://doi.org/10.1093/nar/gkab815
[15]
Bolger, A.M., Lohse, M. and Usadel, B. (2014) Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics, 30, 2114-2120. https://doi.org/10.1093/bioinformatics/btu170
[16]
Li, D., Wang, H., Chen, N., Jiang, H. and Chen, N. (2024) Metagenomic Analysis of Soil Microbial Communities Associated with Poa alpigenaLindm in Haixin Mountain, Qinghai Lake. Brazilian Journal of Microbiology, 55, 2423-2435. https://doi.org/10.1007/s42770-024-01339-5
[17]
Zverev, A.O., Kichko, A.A., Pinaev, A.G., Provorov, N.A. and Andronov, E.E. (2021) Diversity Indices of Plant Communities and Their Rhizosphere Microbiomes: An Attempt to Find the Connection. Microorganisms, 9, Article 2339. https://doi.org/10.3390/microorganisms9112339
[18]
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., et al. (2011) Metagenomic Biomarker Discovery and Explanation. Genome Biology, 12, R60. https://doi.org/10.1186/gb-2011-12-6-r60
[19]
Bednarek, P., Piślewska-Bednarek, M., Svatoš, A., Schneider, B., Doubský, J., Mansurova, M., et al. (2009) A Glucosinolate Metabolism Pathway in Living Plant Cells Mediates Broad-Spectrum Antifungal Defense. Science, 323, 101-106. https://doi.org/10.1126/science.1163732
[20]
Jones, J.D.G., Staskawicz, B.J. and Dangl, J.L. (2024) The Plant Immune System: From Discovery to Deployment. Cell, 187, 2095-2116. https://doi.org/10.1016/j.cell.2024.03.045
[21]
Pieterse, C.M.J., Van der Does, D., Zamioudis, C., Leon-Reyes, A. and Van Wees, S.C.M. (2012) Hormonal Modulation of Plant Immunity. Annual Review of Cell and Developmental Biology, 28, 489-521. https://doi.org/10.1146/annurev-cellbio-092910-154055
[22]
Gu, S., Wei, Z., Shao, Z., Friman, V., Cao, K., Yang, T., et al. (2020) Competition for Iron Drives Phytopathogen Control by Natural Rhizosphere Microbiomes. Nature Microbiology, 5, 1002-1010. https://doi.org/10.1038/s41564-020-0719-8
[23]
Ruan, Y., Xu, S., Tang, Z., Liu, X., Zhang, Q. and Chen, Z. (2021) Microbial Diversity in Tobacco Rhizosphere Soil at Different Growth Stages. Journal of Biobased Materials and Bioenergy, 15, 606-614. https://doi.org/10.1166/jbmb.2021.2102
[24]
Pérez-García, A., Romero, D. and de Vicente, A. (2011) Plant Protection and Growth Stimulation by Microorganisms: Biotechnological Applications of Bacilli in Agriculture. Current Opinion in Biotechnology, 22, 187-193. https://doi.org/10.1016/j.copbio.2010.12.003
[25]
Bednarek, P. (2012) Sulfur‐Containing Secondary Metabolites from Arabidopsis thaliana and Other Brassicaceae with Function in Plant Immunity. ChemBioChem, 13, 1846-1859. https://doi.org/10.1002/cbic.201200086
