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Community standards and future opportunities for synthetic communities in plant–microbiota research

Nature Microbiology volume 9, pages 2774–2784 (2024)Cite this article

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Abstract

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. To address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

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Fig. 1: Examples of scientific milestones in plant–microbiota interactions.
Fig. 2: Defining standards for SynCom design and assembly using microbial culture collections.
Fig. 3: Proposed framework for next-generation SynCom experiments and technologies for plant-associated microbes.

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References

  1. Bulgarelli, D. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Turner, T. R., James, E. K. & Poole, P. S. The plant microbiome. Genome Biol. 14, 209 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Geller, A. M. & Levy, A. ‘What I cannot create, I do not understand’: elucidating microbe-microbe interactions to facilitate plant microbiome engineering. Curr. Opin. Microbiol. 72, 102283 (2023).

    Article  PubMed  Google Scholar 

  4. Vorholt, J. A., Vogel, C., Carlström, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).

    Article  PubMed  CAS  Google Scholar 

  5. Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).

    Article  PubMed  CAS  Google Scholar 

  6. Zhang, J. et al. High-throughput cultivation and identification of bacteria from the plant root microbiota. Nat. Protoc. 16, 988–1012 (2021).

    Article  PubMed  CAS  Google Scholar 

  7. Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).

    Article  PubMed  CAS  Google Scholar 

  8. Wippel, K. et al. Host preference and invasiveness of commensal bacteria in the Lotus and Arabidopsis root microbiota. Nat. Microbiol. 6, 1150–1162 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e14 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Robertson-Albertyn, S. et al. Genome-annotated bacterial collection of the barley rhizosphere microbiota. Microbiol. Resour. Announc. 11, e01064-21 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Durán, P. et al. Shared features and reciprocal complementation of the Chlamydomonas and Arabidopsis microbiota. Nat. Commun. 13, 406 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kremer, J. M. et al. Peat-based gnotobiotic plant growth systems for Arabidopsis microbiome research. Nat. Protoc. 16, 2450–2470 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Cole, B. J. et al. Genome-wide identification of bacterial plant colonization genes. PLoS Biol. 15, e2002860 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wheatley, R. M. et al. Lifestyle adaptations of Rhizobium from rhizosphere to symbiosis. Proc. Natl Acad. Sci. USA 117, 23823–23834 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Levy, A. et al. Genomic features of bacterial adaptation to plants. Nat. Genet. 50, 138–150 (2018).

    Article  CAS  Google Scholar 

  16. Schäfer, M. et al. Metabolic interaction models recapitulate leaf microbiota ecology. Science 381, eadf5121 (2023).

    Article  PubMed  Google Scholar 

  17. Sheth, R. U. et al. Spatial metagenomic characterization of microbial biogeography in the gut. Nat. Biotechnol. 37, 877–883 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Lötstedt, B. et al. Spatial host–microbiome sequencing reveals niches in the mouse gut. Nat. Biotech. 42, 1394–1403 (2024).

    Article  Google Scholar 

  19. Johnston, A. E. & Poulton, P. R. The importance of long‐term experiments in agriculture: their management to ensure continued crop production and soil fertility; the Rothamsted experience. Eur. J. Soil Sci. 69, 113–125 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol. 6, 852–864 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).

    Article  PubMed  CAS  Google Scholar 

  23. Beck, A. E., Kleiner, M. & Garrell, A.-K. Elucidating plant-microbe-environment interactions through omics-enabled metabolic modelling using synthetic communities. Front. Plant Sci. 13, 910377 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Garrido-Oter, R. et al. Modular traits of the rhizobiales root microbiota and their evolutionary relationship with symbiotic rhizobia. Cell Host Microbe 24, 155–167.e5 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Mukherjee, S. et al. Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9. Nucleic Acids Res. 51, D957–D963 (2023).

    Article  PubMed  CAS  Google Scholar 

  26. Venkataraman, M. et al. Synthetic biology toolbox for nitrogen-fixing soil microbes. ACS Synth. Biol. 12, 3623–3634 (2023).

    Article  PubMed  CAS  Google Scholar 

  27. Salem, H. & Kaltenpoth, M. The Nagoya Protocol and its implications for microbiology. Nat. Microbiol. 8, 2234–2237 (2023).

    Article  PubMed  CAS  Google Scholar 

  28. Hitch, T. C. A. et al. Broad diversity of human gut bacteria accessible via a traceable strain deposition system. Preprint at https://www.biorxiv.org/content/10.1101/2024.06.20.599854v1 (2024).

  29. Ma, K.-W. et al. Coordination of microbe–host homeostasis by crosstalk with plant innate immunity. Nat. Plants 7, 814–825 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA 114, E2450–E2459 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Mehlferber, E. et al. A cross-systems primer for synthetic microbial communities. Nat. Microbiol. https://doi.org/10.1038/s41564-024-01827-2 (2024).

  32. Mueller, U. G. et al. Artificial selection on microbiomes to breed microbiomes that confer salt tolerance to plants. mSystems 6, e01125-21 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Batstone, R. T., O’Brien, A. M., Harrison, T. L. & Frederickson, M. E. Experimental evolution makes microbes more cooperative with their local host genotype. Science 370, 476–478 (2020).

    Article  PubMed  CAS  Google Scholar 

  34. Li, E. et al. Rapid evolution of bacterial mutualism in the plant rhizosphere. Nat. Commun. 12, 3829 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ordon, J. et al. Chromosomal barcodes for simultaneous tracking of near-isogenic bacterial strains in plant microbiota. Nat. Microbiol. 9, 1117–1129 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Daniel, B. B. J. et al. Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags. Nat. Microbiol. 9, 1103–1116 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sun, X. et al. Metabolic interactions affect the biomass of synthetic bacterial biofilm communities. mSystems 8, e01045-23 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Tkacz, A., Hortala, M. & Poole, P. S. Absolute quantitation of microbiota abundance in environmental samples. Microbiome 6, 110 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Marín, O., González, B. & Poupin, M. J. From microbial dynamics to functionality in the rhizosphere: a systematic review of the opportunities with synthetic microbial communities. Front. Plant Sci. 12, 650609 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Coker, J. et al. A reproducible and tunable synthetic soil microbial community provides new insights into microbial ecology. mSystems 7, e00951-22 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Parnell, J. J., Vintila, S., Tang, C., Wagner, M. R. & Kleiner, M. Evaluation of ready-to-use freezer stocks of a synthetic microbial community for maize root colonization. Microbiol. Spectr. 12, e02401–e02423 (2024).

    Article  PubMed  Google Scholar 

  42. Pacheco, A. R., Pauvert, C., Kishore, D. & Segrè, D. Toward FAIR representations of microbial interactions. mSystems 7, e00659-22 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Liu, S. et al. Opportunities and challenges of using metagenomic data to bring uncultured microbes into cultivation. Microbiome 10, 76 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Cross, K. L. et al. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat. Biotechnol. 37, 1314–1321 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Huang, Y. et al. High-throughput microbial culturomics using automation and machine learning. Nat. Biotechnol. 41, 1424–1433 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Saarenpää, S. et al. Spatial metatranscriptomics resolves host–bacteria–fungi interactomes. Nat. Biotech. 42, 1384–1393 (2024).

    Article  Google Scholar 

  47. Moyne, O. et al. Guild and niche determination enable targeted alteration of the microbiome. Preprint at https://www.biorxiv.org/content/biorxiv/early/2023/05/11/2023.05.11.540389.full.pdf (2023).

  48. Cole, B. et al. Plant single-cell solutions for energy and the environment. Commun. Biol. 4, 962 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Liu, Z. et al. Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation. Nat. Plants 9, 515–524 (2023).

    Article  PubMed  CAS  Google Scholar 

  50. Guimarães, N. M., Azevedo, N. F. & Almeida, C. in Fluorescence In-Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts (eds Azevedo, N. F. & Almeida, C.) 17–33 (Springer, 2021); https://doi.org/10.1007/978-1-0716-1115-9_2

  51. Cao, Z. et al. Spatial profiling of microbial communities by sequential FISH with error-robust encoding. Nat. Commun. 14, 1477 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Ge, X. et al. SRS-FISH: a high-throughput platform linking microbiome metabolism to identity at the single-cell level. Proc. Natl Acad. Sci. USA 119, e2203519119 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Vidal, A. et al. Linking 3D soil structure and plant-microbe-soil carbon transfer in the rhizosphere. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2018.00009 (2018).

  54. Salvato, F., Vintila, S., Finkel, O. M., Dangl, J. L. & Kleiner, M. Evaluation of protein extraction methods for metaproteomic analyses of root-associated microbes. Mol. Plant Microbe Interact. 35, 977–988 (2022).

    Article  PubMed  CAS  Google Scholar 

  55. Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480 (2018).

    Article  PubMed  CAS  Google Scholar 

  56. Finkel, O. M. et al. A single bacterial genus maintains root growth in a complex microbiome. Nature 587, 103–108 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Kleiner, M. et al. Ultra-sensitive isotope probing to quantify activity and substrate assimilation in microbiomes. Microbiome 11, 24 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Nuccio, E. E. et al. HT-SIP: a semi-automated stable isotope probing pipeline identifies cross-kingdom interactions in the hyphosphere of arbuscular mycorrhizal fungi. Microbiome 10, 199 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Berry, D. et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc. Natl Acad. Sci. USA 112, E194–E203 (2015).

    Article  PubMed  CAS  Google Scholar 

  60. Taylor, M. J., Lukowski, J. K. & Anderton, C. R. Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. J. Am. Soc. Mass. Spectrom. 32, 872–894 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Shi, H. et al. Highly multiplexed spatial mapping of microbial communities. Nature 588, 676–681 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Veličković, D., Lin, V. S., Rivas, A., Anderton, C. R. & Moran, J. J. An approach for broad molecular imaging of the root-soil interface via indirect matrix-assisted laser desorption/ionization mass spectrometry. Soil Biol. Biochem. 146, 107804 (2020).

    Article  Google Scholar 

  63. Lohse, M. et al. Direct imaging of plant metabolites in the rhizosphere using laser desorption ionization ultra-high resolution mass spectrometry. Front. Plant Sci. 12, 753812 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Hansen, B. L. et al. Cooperation, competition and specialized metabolism in a simplified root nodule microbiome. mBio 11, e01917-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Ryffel, F. et al. Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. ISME J. 10, 632–643 (2016).

    Article  PubMed  CAS  Google Scholar 

  66. Geier, B. et al. Spatial metabolomics of in situ host–microbe interactions at the micrometre scale. Nat. Microbiol. 5, 498–510 (2020).

    Article  PubMed  CAS  Google Scholar 

  67. Piehowski, P. D. et al. Automated mass spectrometry imaging of over 2,000 proteins from tissue sections at 100-μm spatial resolution. Nat. Commun. 11, 8 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Mellinger, A. L., Muddiman, D. C. & Gamcsik, M. P. Highlighting functional mass spectrometry imaging methods in bioanalysis. J. Proteome Res. 21, 1800–1807 (2022).

    Article  PubMed  CAS  Google Scholar 

  69. Mönchgesang, S. et al. Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data. Sci. Rep. 6, 29033 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Dell’Acqua, M. et al. Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol. 16, 167 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry and community-scale functional networks. Microbiome 10, 33 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Price, M. N. et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557, 503–509 (2018).

    Article  PubMed  Google Scholar 

  75. Luneau, J. S. et al. Genome‐wide identification of fitness determinants in the Xanthomonas campestris bacterial pathogen during early stages of plant infection. N. Phytol. 236, 235–248 (2022).

    Article  CAS  Google Scholar 

  76. Mutalik, V. K. et al. Dual-barcoded shotgun expression library sequencing for high-throughput characterization of functional traits in bacteria. Nat. Commun. 10, 308 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Liu, X. et al. Genome-wide CRISPRi screens reveal the essentialome and determinants for susceptibility to dalbavancin in Staphylococcus aureus. mSystems. 9, e01289–23 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Peters, J. M. et al. A comprehensive, CRISPR-based functional analysis of essential genes in bacteria. Cell 165, 1493–1506 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. 18, 731–743 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Zengler, K. et al. EcoFABs: advancing microbiome science through standardized fabricated ecosystems. Nat. Methods 16, 567–571 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Novak, V. et al. Reproducible growth of Brachypodium distachyon in fabricated ecosystems (EcoFAB 2.0) reveals that nitrogen form and starvation modulate root exudation. Sci. Adv. 10, eadg7888 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Sasse, J. et al. Multilab EcoFAB study shows highly reproducible physiology and depletion of soil metabolites by a model grass. N. Phytol. 222, 1149–1160 (2019).

    Article  CAS  Google Scholar 

  83. Yee, M. O. et al. Specialized plant growth chamber designs to study complex rhizosphere interactions. Front. Microbiol. 12, 625752 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Del Valle, I., Gao, X., Ghezzehei, T. A., Silberg, J. J. & Masiello, C. A. Artificial soils reveal individual factor controls on microbial processes. mSystems 7, e00301–e00322 (2022).

    PubMed  PubMed Central  Google Scholar 

  85. McLaughlin, S., Zhalnina, K., Kosina, S., Northen, T. R. & Sasse, J. The core metabolome and root exudation dynamics of three phylogenetically distinct plant species. Nat. Commun. 14, 1649 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Dar, D., Dar, N., Cai, L. & Newman, D. K. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science 373, eabi4882 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Wei, L. et al. Imaging complex protein metabolism in live organisms by stimulated Raman scattering microscopy with isotope labeling. ACS Chem. Biol. 10, 901–908 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Hornby, D. Suppressive soils. Annu. Rev. Phytopathol. 21, 65–85 (1983).

    Article  Google Scholar 

  89. Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).

    Article  PubMed  CAS  Google Scholar 

  90. Weller, D. M., Raaijmakers, J. M., Gardener, B. B. M. & Thomashow, L. S. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu. Rev. Phytopathol. 40, 309–348 (2002).

    Article  PubMed  CAS  Google Scholar 

  91. Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl Acad. Sci. USA 106, 16428–16433 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Knief, C. et al. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390 (2012).

    Article  PubMed  CAS  Google Scholar 

  93. Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).

    Article  PubMed  CAS  Google Scholar 

  95. Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014).

    Article  PubMed  Google Scholar 

  97. Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Bulgarelli, D., Schlaeppi, K., Spaepen, S., Van Themaat, E. V. L. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).

    Article  PubMed  CAS  Google Scholar 

  99. Pérez-Jaramillo, J. E. et al. Deciphering rhizosphere microbiome assembly of wild and modern common bean (Phaseolus vulgaris) in native and agricultural soils from Colombia. Microbiome 7, 114 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Ji, N., Liang, D., Clark, L. V., Sacks, E. J. & Kent, A. D. Host genetic variation drives the differentiation in the ecological role of the native Miscanthus root-associated microbiome. Microbiome 11, 216 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  101. He, X. et al. Heritable microbiome variation is correlated with source environment in locally adapted maize varieties. Nat. Plants 10, 598–617 (2024).

    Article  PubMed  CAS  Google Scholar 

  102. Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).

    Article  PubMed  CAS  Google Scholar 

  103. Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Liu, X. et al. Phyllosphere microbiome induces host metabolic defence against rice false-smut disease. Nat. Microbiol. 8, 1419–1433 (2023).

    Article  PubMed  CAS  Google Scholar 

  105. Zhou, X. et al. Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease. Nat. Commun. 13, 7890 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580, 653–657 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Oyserman, B. O. et al. Disentangling the genetic basis of rhizosphere microbiome assembly in tomato. Nat. Commun. 13, 3228 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Su, P. et al. Microbiome homeostasis on rice leaves is regulated by a precursor molecule of lignin biosynthesis. Nat. Commun. 15, 23 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Castrillo, G. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Harbort, C. J. et al. Root-secreted coumarins and the microbiota interact to improve iron nutrition in Arabidopsis. Cell Host Microbe 28, 825–837.e6 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. Teixeira, P. J. P. L. et al. Specific modulation of the root immune system by a community of commensal bacteria. Proc. Natl Acad. Sci. USA 118, e2100678118 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Salas-González, I. et al. Coordination between microbiota and root endodermis supports plant mineral nutrient homeostasis. Science 371, eabd0695 (2021).

    Article  PubMed  Google Scholar 

  113. Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. USA 115, E4284–E4293 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Wang, M. et al. Dynamic root microbiome sustains soybean productivity under unbalanced fertilization. Nat. Commun. 15, 1668 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl Acad. Sci. USA 115, E1157–E1165 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We gratefully acknowledge A. Deutchbauer, B. Cole, P. Turnbaugh and R. Ley for helpful comments. The Novo Nordisk Foundation is acknowledged for supporting the Plant-Microbe Interactions Conference that brought many of these authors together as part of a roundtable focused on SynComs. Work on SynComs in the laboratories of Baars, Kovács, Nicolaisen and Kleiner is supported by the Novo Nordisk Foundation INTERACT project under award no. NNF19SA0059360. SynCom work in the Vorholt laboratory is supported by the NCCR Microbiomes (Swiss National Science Foundation (51NF40_180575) and the German Research Foundation (DECRyPT, SPP2125). G.L. acknowledges support from the Growing Health Institute Strategic Programme (BB/X010953/1; BBS/E/RH/230003B). SynCom research in the Garrido-Oter laboratory is funded by the European Union (ERC, PHYCOSPHERES, 101077231), as well as the German Research Foundation under Germany’s Excellence Strategy, EXC-Nummer 2048/1, project no. 390686111 (CEPLAS) and the ‘2125 DECRyPT’ Priority Programme (SPP2125). M.T. and T.R.N. acknowledge support from m-CAFEs Microbial Community Analysis and Functional Evaluation in Soils programme, a Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research DE-AC02-05CH11231. T.R.N. also acknowledges support from the US DOE Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the US DOE operated under contract no. DE-AC02-05CH11231. S.S. acknowledges support from the TATA Transformation prize in Food Security, and the Batch of 1980 Chair Professor position. J.A.F. acknowledges support from Sao Paulo Research Foundation (FAPESP; grant no. 2019/25720-2).

Author information

Authors and Affiliations

  1. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Trent R. Northen & Marta Torres

  2. DOE Joint Genome Institute, Berkeley, CA, USA

    Trent R. Northen

  3. Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA

    Manuel Kleiner

  4. Institute of Biology, Leiden University, Leiden, The Netherlands

    Ákos T. Kovács, Caja Dinesen, Adele Pioppi & Xinming Xu

  5. DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark

    Ákos T. Kovács, Lars Jelsbak, Caja Dinesen & Adele Pioppi

  6. Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark

    Mette Haubjerg Nicolaisen

  7. Intercollegiate Faculty of Biotechnology UG&MUG, University of Gdańsk, Gdańsk, Poland

    Dorota M. Krzyżanowska

  8. Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India

    Shilpi Sharma

  9. Sustainable Soils and Crops, Rothamsted Research, Harpenden, UK

    George Lund

  10. Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA

    Oliver Baars

  11. Terrestrial Ecology Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Nikolaj Lunding Kindtler

  12. Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam, The Netherlands

    Kathrin Wippel

  13. Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil

    Jessica A. Ferrarezi

  14. Center for Agriculture Food Environment, University of Trento, San Michele all’Adige, Trento, Italy

    Malek Marian

  15. Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Tonni Andersen, Paul Schulze-Lefert & Ruben Garrido-Oter

  16. Cluster of Excellence on Plant Sciences (CEPLAS), Düsseldorf, Germany

    Tonni Andersen, Paul Schulze-Lefert & Ruben Garrido-Oter

  17. Department of Plant Molecular Biology, University of Lausanne, Lausanne, Switzerland

    Niko Geldner

  18. Institute of Microbiology, ETH Zurich, Zurich, Switzerland

    Julia A. Vorholt

  19. Earlham Institute, Norwich Research Park, Norwich, UK

    Ruben Garrido-Oter

Authors
  1. Trent R. Northen
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  2. Manuel Kleiner
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  3. Marta Torres
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  4. Ákos T. Kovács
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  5. Mette Haubjerg Nicolaisen
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  6. Dorota M. Krzyżanowska
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  7. Shilpi Sharma
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  8. George Lund
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  9. Lars Jelsbak
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  10. Oliver Baars
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  11. Nikolaj Lunding Kindtler
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  12. Kathrin Wippel
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  13. Caja Dinesen
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  14. Jessica A. Ferrarezi
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  15. Malek Marian
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  16. Adele Pioppi
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  17. Xinming Xu
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  18. Tonni Andersen
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  19. Niko Geldner
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  20. Paul Schulze-Lefert
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  21. Julia A. Vorholt
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  22. Ruben Garrido-Oter
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Contributions

T.R.N. and R.G.-O. developed the idea for this Perspective based on discussions with all authors. T.R.N., R.G.-O., T.A., N.G., P.S.-L. and J.A.V. drafted the manuscript, which was subsequently refined through contributions from M.K., M.T., Á.T.K., M.H.N., D.M.K., S.S., G.L., L.J., O.B., N.L.K., K.W., C.D., J.A.F., M.M. and A.P.

Corresponding authors

Correspondence to Trent R. Northen or Ruben Garrido-Oter.

Ethics declarations

Competing interests

T.R.N. is an inventor on several patents held by the University of California related to devices for studying plant–microbe interactions. The remaining authors declare no competing interests.

Peer review

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Nature Microbiology thanks Jos Raaijmakers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Northen, T.R., Kleiner, M., Torres, M. et al. Community standards and future opportunities for synthetic communities in plant–microbiota research. Nat Microbiol 9, 2774–2784 (2024). https://doi.org/10.1038/s41564-024-01833-4

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