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Chlorine Dioxide Reprograms Rhizosphere Microbial Communities to Enrich Interactions with Tobacco (Nicotiana tabacum)

SHI QI
SHI QI
, 
JILI ZHANG
JILI ZHANG
, 
XINBO LUAN
XINBO LUAN
, 
JUNLIN LI
JUNLIN LI
, 
ZIKANG HE
ZIKANG HE
, 
JUNRU LONG
JUNRU LONG
, 
MENGYUN XU
MENGYUN XU
, 
PING LI
PING LI
, 
ZEPENG CHEN
ZEPENG CHEN
, 
JIANYU WEI
JIANYU WEI
 y 
JIAN YAN
JIAN YAN
   | 24 mar 2023
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Article Category: ORIGINAL PAPER
Publicado en línea: 24 mar 2023
Páginas: 47 - 60
Recibido: 25 nov 2022
Aceptado: 10 feb 2023
DOI: https://doi.org/10.33073/pjm-2023-009
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© 2023 SHI QI et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Summary of experimental design.
Summary of experimental design.

Fig. 2.

ASVs found in a) bacterial and b) fungal microbiomes of different groups. From inside to outside: taxonomic dendrogram showing each group’s bacterial and fungal microbiome. The first color ring identifies microbial phyla within the rhizosphere soil. Other color rings represent the relative abundance of ASVs in different groups.
ASVs found in a) bacterial and b) fungal microbiomes of different groups. From inside to outside: taxonomic dendrogram showing each group’s bacterial and fungal microbiome. The first color ring identifies microbial phyla within the rhizosphere soil. Other color rings represent the relative abundance of ASVs in different groups.

Fig. 3.

Taxonomy of a) bacterial and b) fungal microbiome in different groups. Phyla that accounted for less than 0.6% of total abundance within the four groups were classified as “Others”.
Taxonomy of a) bacterial and b) fungal microbiome in different groups. Phyla that accounted for less than 0.6% of total abundance within the four groups were classified as “Others”.

Fig. 4.

Alteration of predicted metagenomic pathways in a) bacterial and b) fungal microbiome. UP – p < 0.05, log2FC > 1; DOWN – p < 0.05, log2FC < –1; STABLE – p > 0.05 or –1 < log2FC < 1; D/U/A – Degradation/Utilization/Assimilation; GoPMaE – Generation of Precursor Metabolites and Energy. One-sample t-test was used to evaluate the significance of differences between the control and treatment groups.
Alteration of predicted metagenomic pathways in a) bacterial and b) fungal microbiome. UP – p < 0.05, log2FC > 1; DOWN – p < 0.05, log2FC < –1; STABLE – p > 0.05 or –1 < log2FC < 1; D/U/A – Degradation/Utilization/Assimilation; GoPMaE – Generation of Precursor Metabolites and Energy. One-sample t-test was used to evaluate the significance of differences between the control and treatment groups.

Fig. 5.

Relative abundance of bacterial KEGG pathways in levels 1 and 2. CK – H2O treatment; CD – chlorine dioxide treatment. * 0.01 < p ≤ 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001. One-sample t-test was used to evaluate the significance of differences between the control and treatment groups.
Relative abundance of bacterial KEGG pathways in levels 1 and 2. CK – H2O treatment; CD – chlorine dioxide treatment. * 0.01 < p ≤ 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001. One-sample t-test was used to evaluate the significance of differences between the control and treatment groups.

Fig. 6.

KEGG enrichment analysis of bacterial communities. a) Volcano plots of DEGs between the control and the treatment group. The horizontal and vertical lines indicate a significance threshold (p < 0.01, |log2FC| > 1). Red dots represent upregulated DEGs, and blue dots represent downregulated DEGs. b) Bubble diagram of the upregulated and downregulated DEGs in the KEGG database. The size of a bubble represents the number of DEGs. The color of a bubble represents the enrichment value of DEGs. One-sample t-test was used to evaluate the significance of differences between control and treatment groups.
KEGG enrichment analysis of bacterial communities. a) Volcano plots of DEGs between the control and the treatment group. The horizontal and vertical lines indicate a significance threshold (p < 0.01, |log2FC| > 1). Red dots represent upregulated DEGs, and blue dots represent downregulated DEGs. b) Bubble diagram of the upregulated and downregulated DEGs in the KEGG database. The size of a bubble represents the number of DEGs. The color of a bubble represents the enrichment value of DEGs. One-sample t-test was used to evaluate the significance of differences between control and treatment groups.

Fig. 7.

Visualization of the a) bacteria-metabolite and the b) fungi-metabolite network. Network construction is based on Spearman correlation calculation results. Blue dots represent volatile compounds. The other dots represent different family-level microbial taxonomy, respectively. Bacterial dots with different colors represent different bacterial phyla. Fungal dots with different colors represent different fungal phyla. Red lines represent positive correlations, and green lines represent negative correlations (absolute correlation ≥ 0.8, p-value ≤ 0.1).
Visualization of the a) bacteria-metabolite and the b) fungi-metabolite network. Network construction is based on Spearman correlation calculation results. Blue dots represent volatile compounds. The other dots represent different family-level microbial taxonomy, respectively. Bacterial dots with different colors represent different bacterial phyla. Fungal dots with different colors represent different fungal phyla. Red lines represent positive correlations, and green lines represent negative correlations (absolute correlation ≥ 0.8, p-value ≤ 0.1).

Fig. 8.

Relationships among partial network node attributes. a), d), Relationships between degree and within-module connectivity (Zi) in a) bacteria-metabolite and d) fungi-metabolite network; b), e), relationships between degree and among module-connectivity (Pi) in b) bacteria-metabolite and e) fungi-metabolite network; c), f), keystone taxa were speculated based on their topological node features in c) bacteria-metabolite and f) fungi-metabolite network. Blue dots represent metabolite nodes, and red dots represent microbe nodes. A node was identified as a module hub if its Zi ≥ 2.5, as a connector if its Pi ≥ 0.62, and as a network hub if it had Zi ≥ 2.5 and Pi ≥ 0.62. F-tests were performed to evaluate whether models could adequately describe the data.
Relationships among partial network node attributes. a), d), Relationships between degree and within-module connectivity (Zi) in a) bacteria-metabolite and d) fungi-metabolite network; b), e), relationships between degree and among module-connectivity (Pi) in b) bacteria-metabolite and e) fungi-metabolite network; c), f), keystone taxa were speculated based on their topological node features in c) bacteria-metabolite and f) fungi-metabolite network. Blue dots represent metabolite nodes, and red dots represent microbe nodes. A node was identified as a module hub if its Zi ≥ 2.5, as a connector if its Pi ≥ 0.62, and as a network hub if it had Zi ≥ 2.5 and Pi ≥ 0.62. F-tests were performed to evaluate whether models could adequately describe the data.

Fig. 9.

Node attributes analysis. Seven topological node parameters in a) bacteria-metabolite and b) fungi-metabolite network (listed in Table SXII and SXIII) were used for a pharmacy curriculum outcomes assessment (PCoA) analysis. Blue dots represent metabolite nodes, and red dots represent microbe nodes. In order to remove the influence of point overlap on observation, a random offset of 0.05 in the horizontal and vertical directions was added to each point. Analyses of similarity (ANOSIM) were performed on the Bray-Curtis distance matrix to evaluate whether there are differences between the metabolite node group and the microbe node group.
Node attributes analysis. Seven topological node parameters in a) bacteria-metabolite and b) fungi-metabolite network (listed in Table SXII and SXIII) were used for a pharmacy curriculum outcomes assessment (PCoA) analysis. Blue dots represent metabolite nodes, and red dots represent microbe nodes. In order to remove the influence of point overlap on observation, a random offset of 0.05 in the horizontal and vertical directions was added to each point. Analyses of similarity (ANOSIM) were performed on the Bray-Curtis distance matrix to evaluate whether there are differences between the metabolite node group and the microbe node group.

Fig. 10.

Network robustness, volatile metabolite, and tobacco phenotype analysis. a) The natural connectivity of microbe-metabolite networks. Blue dots represent the bacteria-metabolite network, and red dots represent the fungi-metabolite network. b) Abundance of 207 volatile components from 12 plots was used for a pharmacy curriculum outcomes assessment (PCoA) analysis. c) Data on seven phenotypes of 60 tobacco plants were used for a pharmacy curriculum outcomes assessment (PCoA) analysis. The analysis of similarity (ANOSIM) was performed on the Bray-Curtis distance matrix to evaluate whether there are differences between groups.
Network robustness, volatile metabolite, and tobacco phenotype analysis. a) The natural connectivity of microbe-metabolite networks. Blue dots represent the bacteria-metabolite network, and red dots represent the fungi-metabolite network. b) Abundance of 207 volatile components from 12 plots was used for a pharmacy curriculum outcomes assessment (PCoA) analysis. c) Data on seven phenotypes of 60 tobacco plants were used for a pharmacy curriculum outcomes assessment (PCoA) analysis. The analysis of similarity (ANOSIM) was performed on the Bray-Curtis distance matrix to evaluate whether there are differences between groups.
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