Integrative analysis of microbiome and metabolome revealed the effect of microbial inoculant on microbial community diversity and function in rhizospheric soil under tobacco monoculture

Abstract

Over-application of chemical fertilizers and continuous cropping obstacles seriously restrict the sustainable development of tobacco production. Localized fertilization of beneficial microbes has potential advantages in achieving higher productivity, but the underlying biological mechanisms of interactions between rhizospheric microorganisms and the related metabolic cycle remain poorly characterized. Here, an integrative analysis of microbiomes with non-targeted metabolomics was performed on 30 soil samples of rhizosphere, root surrounding, and bulk soils from flue-cured tobacco under continuous and non-continuous monocropping systems. The analysis was conducted using UPLC-MS/MS platforms and high-throughput amplicon sequencing targeting the bacterial 16S rRNA gene and fungal ITS gene. The microbial inoculant consisted of Bacillus subtilis, B. velezensis, and B. licheniformis at the ratio of 1:1:1 in effective microbial counts, improved the cured leaf yield and disease resistance of tobacco, and enhanced nicotine and nitrogen contents of tobacco leaves. The bacterial taxa Rhizobium, Pseudomonas, Sphingomonadaceae, and Burkholderiaceae of the phylum Proteobacteria accumulated in high relative abundance and were identified as biomarkers following the application of the microbial inoculant. Under continuous monocropping, metabolomics demonstrated that the application of the microbial inoculant significantly affected the soil metabolite spectrum, and the differential metabolites were significantly enriched to the synthesis and degradation of nicotine (nicotinate and nicotinamide metabolism and biosynthesis of alkaloids derived from nicotinic acid). In addition, microbes were closely related to the accumulation of metabolites through correlation analysis. The interactions between plant roots and rhizospheric microorganisms provide valuable information for understanding how these beneficial microbes affect complex biological processes and the adaption capacity of plants to environments.IMPORTANCEThis study elaborated on how the microbial fertilizer significantly changed overall community structures and metabolite spectrum of rhizospheric microbes, which provide insights into the process of rhizosphere microbial remolding in response to continuous monocropping. we verified the hypothesis that the application of the microbial inoculant in continuous cropping would lead to the selection of distinct microbiota communities by establishing models to correlate biomarkers. Through correlation analysis of the microbiome and metabolome, we proved that rhizospheric microbes were closely related to the accumulation of metabolites, including the synthesis and degradation of nicotine. The interactions between plant roots and rhizospheric microorganisms provide valuable information for understanding how these beneficial microbes affect complex biological processes and the adaption capacity of plants to environments.

Keywords: continuous monocropping; metabolite spectrum; microbial diversity; microbial inoculant; tobacco.

Fig 1

Relative abundances of dominant bacterial (A) and fungal (B) phyla. CCY: Continuous cropping plots with microbial inoculants applied; CCN: continuous cropping plots without microbial inoculants applied; NCY: non-continuous cropping plots with microbial inoculants applied; NCN: non-continuous cropping plots without microbial inoculants applied; RS: rhizosphere soils; SS: root-surrounding soils.

Fig 2

Diversity of microbial communities influenced by microbial inoculants and continuous monocropping. (A, B) Rarefaction curves and Shannon’s index for alpha-diversity measures of OTUs comparing bacteria and fungi, respectively. Error bars correspond to one standard deviation out from the average of biological replicates. (C, D) Unconstrained PCoA (for principal coordinates PCo1 and PCo2) with weighted unifrac distance showing root bacteria and fungi separated in the first two axes (P < 0.001, PERMANOVA).

Fig 3

Graphics of linear discriminant analysis (LDA) effect size (LEfSe) of the biomarker prediction profiles between continuous and non-continuous monocropping samples (A) and between microbial inoculants treatment and CK groups (B). The threshold on the logarithmic LDA score for discriminative features was set to 4.0 at an FDR-adjusted P-value < 0.05. Taxa–taxa interactions at the species level in (C) bacterial and (D) fungal co-occurrence networks using Spearman’s rank correlation analysis set at a permutation value of 200, correlation >0.6, and P-value < 0.05. Circles represent species, and the size of the circle represents abundance. The edges represent the correlation between the two species, and the thickness of the edge represents the strength of the correlation. Orange lines represent a positive correlation and green lines represent a negative correlation.

Fig 4

Metabolomic analysis of soil treated with microbial inoculants. (A) Principal component analysis (PCA) of metabolite profiles among 30 samples. (B) HCA for the metabolites based on their normalized abundances. (C, D) Annotated metabolites in the KEGG database (C) and LIPID MAPS database (D).

Fig 5

Differential metabolomic analysis of tobacco rhizosphere soils between treatment and CK groups in continuous cropping samples. (A) Principal component analysis (PCA) of metabolite profiles. (B) Volcano map of differential metabolites exhibiting upregulated and downregulated expression. Blue and red dots represent downregulated and upregulated metabolites, (P-value < 0.05), respectively; gray dots represent non-differential metabolites. (C) Scatter plot of top 10 KEGG pathways enrichment for differential metabolites. (D) Metabolic pathway map of differential marker metabolites in the synthesis and degradation of nicotine. Metabolites enclosed in a red or blue box indicate those that are significantly upregulated and downregulated, respectively, in inoculant-treated groups, and metabolites enclosed in an orange box indicate those that are upregulated but without significant difference.

Fig 6

Correlation of microorganisms and metabolites between treatment and CK groups in continuous and non-continuous cropping samples. (A, B) Heatmaps in Pearson correlation analysis. The vertical axis represents normalized bacterial OTUs, and the horizontal axis represents the differential metabolites. (C, D) Correlation network analysis. The circles represent bacterial OTUs and colors represent different phyla; rectangles represent different metabolites, and red and blue lines represent positive and negative correlations, respectively.