- Host plants and the microbiota living in the rhizosphere are interdependent, mutually reinforced and mutually beneficial but the assembly, functions and microbial interactions of the host-associated microbiota are still unclear.
- Herein, winter wheat was selected as a test plant to explore the assembly process of total bacteria from bulk soil (BS) and rhizosphere soil (RS), and phoD-harbouring bacteria in RS at a long-term (14 years) trial site. We also identified core microbes that are potentially relevant to soil carbon (C), nitrogen (N) and phosphorus (P) cycling genes and soil biogeochemical properties, and investigated the relationships between soil variables, functional genes and wheat productivity.
- The results showed that the microhabitat of the plant, rather than P fertilizer input, was the main factor affecting the microbial diversity, composition and co-occurrence networks. The BS bacterial community was driven by deterministic processes but the RS and phoD bacterial communities were dominated by stochastic processes. The influence of deterministic processes decreased with increasing soil nutrient content. A core microbial community consisted of 10 OTUs in different microhabitats and was significantly related to soil functional genes and properties. In the rhizosphere soil, significant correlations were found between soil variables, soil functional bacteria and genes, and crop productivity.
- Synthesis and applications. This study provides empirical evidence that the assembly process of the microbial community is governed by environmental factors in various soil environments and provides new perspectives on the sustainable improvement of crop productivity.
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CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflict of interest.
DATA AVAILABILITY STATEMENT
All the raw sequencing data were uploaded to the China National GeneBank Sequence Archive with the accession number CNP0000424.
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Appendix S1. Description of experimental design.
Appendix S2. Description of soil physical and chemical determination.
Appendix S3. Details on amplicon sequencing, PCR amplification and pyrosequencing.
Table S1. Spearman correlations between βNTI of BS, RS or phoD bacterial communities and the changes of soil nutrients (based on Euclidean Distance).
Table S2. The topological parameters of co-occurrence networks of bacterial and functional genes.
Table S3. Fit of the neutral model for the BS and RS and phoD bacterial communities in Low-P and High-P fertilizer input treatments. m indicates the estimated migration rate; R2 indicates the fit to the neutral model.
Table S4. The topological parameters of co-occurrence networks of BS, RS and phoD-bacterial communities.
Table S5. The topological parameters of co-occurrence networks of functional genes from BS and RS bacteria communities.
Table S6. The mantel test between the bacteria and functional genes and soil properties in BS.
Table S7. The mantel test between the bacteria and functional genes and soil properties in the RS.
Figure S1. The composition and diversity of bulk soil (BS), rhizosphere soil (RS) and phoD bacterial communities with low-P and high-P fertilizer input treatments. (a) OTU Shannon, (b) richness, and (c) Bray–Curtis dissimilarity between low-P and high-P fertilizer input. *p < 0.05; **p < 0.01; ***p < 0.001 for Kruskal–Wallis tests.
Figure S2. (a) NTI and (b) βMNTD indices of different bacterial communities with low-P and high-P fertilizer input. (c) PCoA plot based on Bray–Curtis distances depicting the distribution patterns of low-P and high-P fertilizer input. The PERMANOVA and ANOSIM tests were used to assess the significant differences between d low-P and high-P fertilizer input. *p < 0.05; **p < 0.01, ***p < 0.001.
Figure S3. Taxonomic composition of (a) BS, (b) RS, and (c) phoD bacterial communities with low-P and high-P fertilizer input at the phylum level.
Figure S4. Enrichment and depletion of OTUs between low-P and high-P fertilizer input in (a) BS, (b) RS, and (c) phoD bacterial communities. The contribution of determinism and stochasticity on (d) BS, (e) RS, and (f) phoD bacterial communities’ assembly with low-P and high-P fertilizer input.
Figure S5. Variation analysis of the abundance of bacteria in different bacterial communities (a–c) and between low-P and high-P fertilizer input in (c) RS and (d) phoD bacterial communities.
Figure S6. Ternary plots depicting the (a) bacterial at phylum level and (b) potential ecological functions significantly enriched in BS, RS and phoD bacterial communities. The size of each circle indicates the relative abundance. Different-colored dots show that function and bacteria are significantly higher in BS, RS, and phoD bacterial communities (p < 0.05), and the numbers in the parentheses denote the numbers of the differentiated functions and bacteria.
Figure S7. Phylogenetic Mantel correlogram showing significant phylogenetic signal across short phylogenetic distances in (a) BS, (b) RS and (c) phoD bacterial communities. Solid and open symbols denote significant and nonsignificant correlations, respectively, relating between-OTU niche differences to between-OTU phylogenetic distances across a given phylogenetic distance.
Figure S8. Random forest analysis showed the associations between βNTI of the (a) BS, (b) RS, and (c) phoD bacterial communities and the changes in environmental variables.
Figure S9. Community potential ecological functions and co-occurrence patterns of BS, RS, and phoD bacterial communities. (a) Metacommunity co-occurrence network of microbial taxa. (b) Differences in degree, (c) betweenness centrality, and (d) closeness centrality between the different bacterial communities. (e-g) Variation analysis of the potential ecological functions of different bacterial communities (BS, RS, and phoD) based on FAPROTAX (top 15). ***p < 0.001 for Kruskal–Wallis tests. The basic topological properties of these networks are shown in Table S2.
Figure S10. Phylogenetic Mantel correlogram showing significant phylogenetic signal across short phylogenetic distances in (a) BS, (b) RS, and (c) phoD bacterial communities with low-P and high-P fertilizer input. Solid and open symbols denote significant and nonsignificant correlations, respectively, relating between-OTU niche differences to between-OTU phylogenetic distances across a given phylogenetic distance.
Figure S11. Metacommunity co-occurrence network of (a) BS, (b) RS, and (c) phoD bacterial communities with low-P and high-P fertilizer input. Each node represents a significantly enriched OTU in low-P and high-P fertilizer input, respectively. The basic topological properties of these networks are shown in Table S4.
Figure S12. Variation analysis of the potential ecological functions of (a) BS, (b) RS, and (c) phoD bacterial communities with low-P and high-P fertilizer input based on FAPROTAX.
Figure S13. Occurrence network analysis showing genes involved in soil C, N, and P cycle under (a) BS and (B) RS bacterial communities. Nodes with pink, blue, and green colors represent soil C, N, and P genes. The colored line shows the correlation of the two nodes, with the red line for the positive relationship and the blue line for the negative relationship. The basic topological properties of these networks are shown in Table S5.
Figure S14. Difference in the relative abundance of soil C, N, and P cycle genes. Different letters in the same row mean a significant difference at p < 0.05 among the BS and RS bacterial communities (Duncan's test).
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- 2016). Taxonomy, physiology, and natural products of actinobacteria. Microbiology and Molecular Biology Reviews, 80, 1–43. https://doi.org/10.1128/MMBR.00019-15
- 2009). Gephi: An open source software for exploring and manipulating networks. ICWSM, 8, 361–362.
- 2017). Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biology, 15, e2001793. https://doi.org/10.1371/journal.pbio.2001793
- 2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336. https://doi.org/10.1038/nmeth.f.303
- 2016). Winter wheat grain yield and summer nitrate leaching: Long-term effects of nitrogen and phosphorus rates on the loess plateau of China. Field Crops Research, 196, 180–190. https://doi.org/10.1016/j.fcr.2016.06.020
- 2020). Global patterns of terrestrial nitrogen and phosphorus limitation. Nature Geoscience, 13, 221–226. https://doi.org/10.1038/s41561-019-0530-4
- 2021). Biodiversity of key-stone phylotypes determines crop production in a 4-decade fertilization experiment. The ISME Journal, 15, 550–561. https://doi.org/10.1038/s41396-020-00796-8
- 2020). Crop production correlates with soil multitrophic communities at the large spatial scale. Soil Biology and Biochemistry, 151, 108047. https://doi.org/10.1016/j.soilbio.2020.108047
- 2018). Two key features influencing community assembly processes at regional scale: Initial state and degree of change in environmental conditions. Molecular Ecology, 27, 5238–5251. https://doi.org/10.1111/mec.14914
- 2015). Soil bacterial phoD gene abundance and expression in response to applied phosphorus and long-term management. Soil Biology and Biochemistry, 88, 137–147. https://doi.org/10.1016/j.soilbio.2015.04.014
- 2020). Climate and soil micro-organisms drive soil phosphorus fractions in coastal dune systems. Functional Ecology, 34, 1690–1701. https://doi.org/10.1111/1365-2435.13606
- 2021). Seed-borne, endospheric and rhizospheric core microbiota as predictors of plant functional traits across rice cultivars are dominated by deterministic processes. New Phytologist, 230, 2047–2060. https://doi.org/10.1111/nph.17297
- 2018). Field study reveals core plant microbiota and relative importance of their drivers. Environmental Microbiology, 20, 124–140. https://doi.org/10.1111/1462-2920.14031
- 2018). Microbial interactions within the plant holobiont. Microbiome, 6, 58. https://doi.org/10.1186/s40168-018-0445-0
- 2017). Effects of over 30-year of different fertilization regimes on fungal community compositions in the black soils of northeast China. Agriculture, Ecosystems & Environment, 248, 113–122. https://doi.org/10.1016/j.agee.2017.07.031
- 2022). Land-use changes alter the arbuscular mycorrhizal fungal community composition and assembly in the ancient tea forest reserve. Agriculture, Ecosystems & Environment, 339, 108142. https://doi.org/10.1016/j.agee.2022.108142
- 2017). Plant cultivars imprint the rhizosphere bacterial community composition and association networks. Soil Biology and Biochemistry, 109, 145–155. https://doi.org/10.1016/j.soilbio.2017.02.010
- 2016). Bacterial communities in oil contaminated soils: Biogeography and co-occurrence patterns. Soil Biology and Biochemistry, 98, 64–73. https://doi.org/10.1016/j.soilbio.2016.04.005
- 2022). Core phylotypes enhance the resistance of soil microbiome to environmental changes to maintain multifunctionality in agricultural ecosystems. Global Change Biology, 28, 6653–6664. https://doi.org/10.1111/gcb.16387
- 2019). Core microbiota in agricultural soils and their potential associations with nutrient cycling. mSystems, 4, e0013-00318. https://doi.org/10.1128/mSystems.00313-18
- 2020). Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China. The ISME Journal, 14, 202–216. https://doi.org/10.1038/s41396-019-0522-9
- 2019). Rhizosphere size and shape: Temporal dynamics and spatial stationarity. Soil Biology and Biochemistry, 135, 343–360. https://doi.org/10.1016/j.soilbio.2019.05.011
- 2018). Diversifying anaerobic respiration strategies to compete in the rhizosphere. Frontiers in Environmental Science, 6, 00193. https://doi.org/10.3389/fenvs.2018.00139
- 2020). Long-term effects of nitrogen and phosphorus fertilization on soil microbial community structure and function under continuous wheat production. Environmental Microbiology, 22, 1066–1088. https://doi.org/10.1111/1462-2920.14824
- 2002). Classification and regression by RandomForest. R News, 2, 18–22.
- 2022). Rhizosphere bacteriome structure and functions. Nature Communications, 13, 836. https://doi.org/10.1038/s41467-022-28448-9
- 2020). Long-term high-P fertilizer input decreased the total bacterial diversity but not phoD-harboring bacteria in wheat rhizosphere soil with available-P deficiency. Soil Biology and Biochemistry, 149, 107918. https://doi.org/10.1016/j.soilbio.2020.107918
- 2023). Community metagenomics reveals the processes of nutrient cycling regulated by microbial functions in soils with P fertilizer input. Plant and Soil. https://doi.org/10.1007/s11104-023-05875-1
- 2023b). Long-term high-P fertilizer input shifts soil P cycle genes and microorganism communities in dryland wheat production systems. Agriculture, Ecosystems & Environment, 342, 108226. https://doi.org/10.1016/j.agee.2022.108226
- 2023). Abundant bacterial subcommunity is structured by a stochastic process in an agricultural system with P fertilizer inputs. Science of the Total Environment, 871, 162178. https://doi.org/10.1016/j.scitotenv.2023.162178
- 2020b). Dynamic microbial assembly processes correspond to soil fertility in sustainable paddy agroecosystems. Functional Ecology, 34, 1244–1256. https://doi.org/10.1111/1365-2435.13550
- 2016). Decoupling function and taxonomy in the global ocean microbiome. Science, 353, 1272–1277. https://doi.org/10.1126/science.aaf4507
- 2022). Core microbiota in the rhizosphere of heavy metal accumulators and its contribution to plant performance. Environmental Science & Technology, 56, 12975–12987. https://doi.org/10.1021/acs.est.1c08832
- 2014). Taxonomical and functional microbial community selection in soybean rhizosphere. The ISME Journal, 8, 1577–1587. https://doi.org/10.1038/ismej.2014.17
- 2007). Stenotrophomonas maltophilia: A new potential biocontrol agent of Ralstonia solanacearum, causal agent of potato brown rot. European Journal of Plant Pathology, 118, 211–225. https://doi.org/10.1007/s10658-007-9136-6
- 2012). Vegan: Community ecology package. R package version 2.0-2.
- 2020). Economically optimal wheat yield, protein and nitrogen use component responses to varying N supply and genotype. Frontiers in Plant Science, 10, 1790. https://doi.org/10.3389/fpls.2019.01790
- 2014). STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics, 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494
- 2013). Going back to the roots: The microbial ecology of the rhizosphere. Nature Reviews Microbiology, 11, 789–799. https://doi.org/10.1038/nrmicro3109
- 2021). Differentiating bacterial community responses to long-term phosphorus fertilization in wheat bulk and rhizosphere soils on the Loess Plateau. Applied Soil Ecology, 166, 104090. https://doi.org/10.1016/j.apsoil.2021.104090
- 2020). Functional compensation dominates the assembly of plant rhizospheric bacterial community. Soil Biology and Biochemistry, 150, 107968. https://doi.org/10.1016/j.soilbio.2020.107968
- 2001). Synergism between Phyllobacterium sp. (N2-fixer) and Bacillus licheniformis (P-solubilizer), both from a semiarid mangrove rhizosphere. FEMS Microbiology Ecology, 35, 181–187. https://doi.org/10.1111/j.1574-6941.2001.tb00802.x
- 2020). Core rhizosphere microbiomes of dryland wheat are influenced by location and land use history. Applied and Environmental Microbiology, 86, e02135-02119. https://doi.org/10.1128/AEM.02135-19
- 2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498–2504. https://doi.org/10.1101/gr.1239303
- 2006). Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environmental Microbiology, 8, 732–740. https://doi.org/10.1111/j.1462-2920.2005.00956.x
- 2013). Quantifying community assembly processes and identifying features that impose them. The ISME Journal, 7, 2069–2079. https://doi.org/10.1038/ismej.2013.93
- 2020). Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nature Ecology & Evolution, 4, 122–131. https://doi.org/10.1038/s41559-019-1063-3
- 2022). Little environmental adaptation and high stability of bacterial communities in rhizosphere rather than bulk soils in rice fields. Applied Soil Ecology, 169, 104183. https://doi.org/10.1016/j.apsoil.2021.104183
- 2018). Core microbiomes for sustainable agroecosystems. Nature Plants, 4, 247–257. https://doi.org/10.1038/s41477-018-0139-4
- 2020). Plant-microbiome interactions: From community assembly to plant health. Nature Reviews Microbiology, 18, 607–621. https://doi.org/10.1038/s41579-020-0412-1
- 2021). Stronger environmental adaptation of rare rather than abundant bacterioplankton in response to dredging in eutrophic Lake Nanhu (Wuhan, China). Water Research, 190, 116751. https://doi.org/10.1016/j.watres.2020.116751
- 2020). Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome, 15, 11. https://doi.org/10.1186/s40793-020-00358-7
- 2021). Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome, 9, 171. https://doi.org/10.1186/s40168-021-01118-6
- 2021). Local community assembly processes shape β-diversity of soil phoD-harbouring communities in the northern hemisphere steppes. Global Ecology and Biogeography, 30, 2273–2285. https://doi.org/10.1111/geb.13385
- 2017). Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. The ISME Journal, 11, 56–66. https://doi.org/10.1038/ismej.2016.108
- 2020). Suspended particles phoD alkaline phosphatase gene diversity in large shallow eutrophic Lake Taihu. Science of the Total Environment, 728, 138615. https://doi.org/10.1016/j.scitotenv.2020.138615
- 2014). Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proceedings of the National Academy of Sciences of the United States of America, 111, E836–E845. https://doi.org/10.1073/pnas.1324044111
- 2017). Stochastic community assembly: Does it matter in microbial ecology? Microbiology and Molecular Biology Reviews, 81, e00002-00017. https://doi.org/10.1128/MMBR.00002-17