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Root microbiota assembly and adaptive differentiation among European Arabidopsis populations

Nature Ecology & Evolution volume 4, pages 122–131 (2020)Cite this article

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Abstract

Factors that drive continental-scale variation in root microbiota and plant adaptation are poorly understood. We monitored root-associated microbial communities in Arabidopsis thaliana and co-occurring grasses at 17 European sites across 3 years. We observed strong geographic structuring of the soil biome, but not of the root microbiota. A few phylogenetically diverse and geographically widespread bacteria consistently colonized plant roots. Among-site and across-year similarity in microbial community composition was stronger for the bacterial root microbiota than for filamentous eukaryotes. In a reciprocal transplant between two A. thaliana populations in Sweden and Italy, we uncoupled soil from location effects and tested their contributions to root microbiota variation and plant adaptation. Community differentiation in plant roots was explained primarily by location for filamentous eukaryotes and by soil origin for bacteria, whereas host genotype effects were marginal. Strong local adaptation between the two A. thaliana populations was observed, with differences in soil properties and microbes of little importance for the observed magnitude of adaptive differentiation. Our results suggest that, across large spatial scales, climate is more important than soil conditions for plant adaptation and variation in root-associated filamentous eukaryotic communities, whereas soil properties are primary drivers of bacterial community differentiation in roots.

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Fig. 1: Microbial community structure in 17 European A. thaliana populations.
Fig. 2: Geographically widespread taxa in the roots of A. thaliana.
Fig. 3: Factors shaping the A. thaliana root microbiota at the continental scale.
Fig. 4: Reciprocal transplant between two A. thaliana populations in Sweden and Italy.

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Data availability

Sequencing reads of samples from the European transect experiment and reciprocal transplant experiment (MiSeq 16S rRNA and ITS reads) have been deposited in the European Nucleotide Archive under accession numbers ERP115101 and ERP115102, respectively.

Code availability

All scripts for computational analysis and the corresponding raw data are available at https://github.com/ththi/European-Root-Suppl.

References

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

    PubMed  PubMed Central  Google Scholar 

  2. Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  4. Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).

    CAS  PubMed  Google Scholar 

  5. Berendsen, R. L. et al. Disease-induced assemblage of a plant-beneficial bacterial consortium. ISME J. 12, 1496–1507 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  7. Haichar, F. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230 (2008).

    CAS  PubMed  Google Scholar 

  8. Stringlis, I. A. et al. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health. Proc. Natl Acad. Sci. USA 115, E5213–E5222 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).

    CAS  PubMed  Google Scholar 

  10. Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Karimi, B. et al. Biogeography of soil bacteria and archaea across France. Sci. Adv. 4, eaat1808 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99, 583–596 (2018).

    PubMed  Google Scholar 

  13. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).

    PubMed  Google Scholar 

  14. Leimu, R. & Fischer, M. A meta-analysis of local adaptation in plants. PLoS ONE 3, e4010 (2008).

    PubMed  PubMed Central  Google Scholar 

  15. Ågren, J. & Schemske, D. W. Reciprocal transplants demonstrate strong adaptive differentiation of the model organism Arabidopsis thaliana in its native range. New Phytol. 194, 1112–1122 (2012).

    PubMed  Google Scholar 

  16. Fournier-Level, A. et al. A map of local adaptation in Arabidopsis thaliana. Science 334, 86–89 (2011).

    CAS  PubMed  Google Scholar 

  17. Wadgymar, S. M. et al. Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation. Methods Ecol. Evol. 8, 738–749 (2017).

    Google Scholar 

  18. Brachi, B. et al. Investigation of the geographical scale of adaptive phenological variation and its underlying genetics in Arabidopsis thaliana. Mol. Ecol. 22, 4222–4240 (2013).

    CAS  PubMed  Google Scholar 

  19. Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).

    PubMed  PubMed Central  Google Scholar 

  20. Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).

    CAS  PubMed  Google Scholar 

  21. Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6, 231 (2018).

    PubMed  PubMed Central  Google Scholar 

  22. Robbins, C. et al. Root-associated bacterial and fungal community profiles of Arabidopsis thaliana are robust across contrasting soil P levels. Phytobiomes J. 2, 24–34 (2018).

    Google Scholar 

  23. Fitzpatrick, C. R., Mustafa, Z. & Viliunas, J. Soil microbes alter plant fitness under competition and drought. J. Evol. Biol. 32, 438–445 (2019).

    PubMed  Google Scholar 

  24. Frachon, L. et al. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nat. Ecol. Evol. 1, 1551–1561 (2017).

    PubMed  Google Scholar 

  25. Busoms, S. et al. Salinity is an agent of divergent selection driving local adaptation of Arabidopsis to coastal habitats. Plant Physiol. 168, 915–929 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/081257v1 (2016).

  27. Glassman, S. I. & Martiny, J. B. H. Broadscale ecological patterns are robust to use of exact sequence variants versus operational taxonomic units. MSphere 3, e00148-18 (2016).

    Google Scholar 

  28. CBOL Plant Working Groupet al. A DNA barcode for land plants. Proc. Natl Acad. Sci. USA 106, 12794–12797 (2009).

    PubMed Central  Google Scholar 

  29. Thiergart, T. et al. Lotus japonicus symbiosis genes impact microbial interactions between symbionts and multikingdom commensal communities. mBio 10, e01833 (2019).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Finkel, O. M. et al. The effects of soil phosphorus content on plant microbiota are driven by the plant phosphate starvation response. PLoS Biol. 17, e3000534 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Chaw, S. M., Chang, C. C., Chen, H. L. & Li, W. H. Dating the monocot–dicot divergence and the origin of core eudicots using whole chloroplast genomes. J. Mol. Evol. 58, 424–441 (2004).

    CAS  PubMed  Google Scholar 

  33. Ågren, J., Oakley, C. G., McKay, J. K., Lovell, J. T. & Schemske, D. W. Genetic mapping of adaptation reveals fitness tradeoffs in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 110, 21077–21082 (2013).

    PubMed Central  Google Scholar 

  34. Schlaeppi, K., Dombrowski, N., Oter, R. G., Ver Loren van Themaat, E. & Schulze-Lefert, P. Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives. Proc. Natl Acad. Sci. USA 111, 585–592 (2014).

    CAS  PubMed  Google Scholar 

  35. Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911–E920 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Yeoh, Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).

    CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  41. Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179.e4 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018).

    CAS  Google Scholar 

  43. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Poupin, M. J., Timmermann, T., Vega, A., Zuñiga, A. & González, B. Effects of the plant growth-promoting bacterium Burkholderia phytofirmans PsJN throughout the life cycle of Arabidopsis thaliana. PLoS ONE 8, e69435 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  46. Anderson, L. C. et al. Bacteria and fungi respond differently to multifactorial climate change in a temperate heathland, traced with 13C-glycine and FACE CO2. PLoS ONE 9, e85070 (2014).

    Google Scholar 

  47. Peay, K. G. et al. Convergence and contrast in the community structure of Bacteria, Fungi and Archaea along a tropical elevation–climate gradient. FEMS Microbiol. Ecol. 93, 5 (2017).

    Google Scholar 

  48. Talbot, J. M. et al. Endemism and functional convergence across the North American soil mycobiome. Proc. Natl Acad. Sci. USA 111, 6341–6346 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Coleman-Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 12151 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Cregger, M. A. et al. The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome. Microbiome 6, 31 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Wright, K. M. et al. Adaptation to heavy-metal contaminated environments proceeds via selection on pre-existing genetic variation. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/029900v2 (2015).

  54. Wright, J. W., Stanton, M. L. & Scherson, R. Local adaptation to serpentine and non-serpentine soils in Collinsia sparsiflora. Evol. Ecol. Res. 8, 1–21 (2006).

    Google Scholar 

  55. Busoms, S. et al. Fluctuating selection on migrant adaptive sodium transporter alleles in coastal Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 115, E12443–E12452 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Postma, F. M. & Ågren, J. Early life stages contribute strongly to local adaptation in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 113, 7590–7595 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Postma, F. M. & Ågren, J. Among-year variation in selection during early life stages and the genetic basis of fitness in Arabidopsis thaliana. Mol. Ecol. 27, 2498–2511 (2018).

    CAS  PubMed  Google Scholar 

  58. Ågren, J., Oakley, C. G., Lundemo, S. & Schemske, D. W. Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: estimates of selection and QTL mapping. Evolution 71, 550–564 (2017).

    PubMed  Google Scholar 

  59. Oakley, C. G., Ågren, J., Atchison, R. A. & Schemske, D. W. QTL mapping of freezing tolerance: links to fitness and adaptive trade-offs. Mol. Ecol. 23, 4304–4315 (2014).

    PubMed  Google Scholar 

  60. Méndez-Vigo, B., Gomaa, N. H., Alonso-Blanco, C. & Picó, F. X. Among- and within-population variation in flowering time of Iberian Arabidopsis thaliana estimated in field and glasshouse conditions. New Phytol. 197, 1332–1343 (2013).

    PubMed  Google Scholar 

  61. Bartoli, C. et al. In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana. ISME J. 12, 2024–2038 (2018).

    PubMed  PubMed Central  Google Scholar 

  62. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  PubMed  Google Scholar 

  64. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Deshpande, V. et al. Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia 108, 1–5 (2016).

    PubMed  Google Scholar 

  67. Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by funds from a European Research Council starting grant (MICRORULES) to S.H., a European Research Council advanced grant (ROOTMICROBIOTA) to P.S.-L. and grants from the Swedish Research Council to J.Å. S.H. and P.S.-L. were also supported by funds from the Max Planck Society, the ‘Priority Programme 2125 DECRyPT’ funded by the Deutsche Forschungsgemeinschaft and the ‘Cluster of Excellence on Plant Sciences’ programme funded by the Deutsche Forschungsgemeinschaft. The laboratory of C.A.-B. was funded by grant BIO2016-75754-P (AEI/FEDER). We thank N. Donnelly for scientific English editing.

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Author notes

  1. These authors contributed equally: Thorsten Thiergart, Paloma Durán.

Authors and Affiliations

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

    Thorsten Thiergart, Paloma Durán, Nathan Vannier, Ruben Garrido-Oter, Paul Schulze-Lefert & Stéphane Hacquard

  2. Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden

    Thomas Ellis & Jon Ågren

  3. Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Ruben Garrido-Oter, Paul Schulze-Lefert & Stéphane Hacquard

  4. Department of Microbial Interactions, IMIT/ZMBP, University of Tübingen, Tübingen, Germany

    Eric Kemen

  5. LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France

    Fabrice Roux

  6. Departamento de Genética Molecular de Plantas, Centro Nacional de Biotecnología (CNB), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain

    Carlos Alonso-Blanco

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Contributions

S.H., P.S.-L. and J.Å. conceived the project. E.K., F.R., C.A.-B., J.Å. and S.H. selected natural A. thaliana populations. P.D. and S.H. collected the samples. P.D. prepared all of the samples and performed the microbial community profiling. P.D., T.T. and N.V. analysed the microbiota data. R.G.-O. provided bioinformatic tools. T.E. and J.Å. prepared the field reciprocal transplant experiment. J.Å., T.E. and P.D. analysed the plant fitness data. S.H. supervised the project. T.T., P.D., J.Å., P.S.-L. and S.H. wrote the paper.

Corresponding authors

Correspondence to Jon Ågren, Paul Schulze-Lefert or Stéphane Hacquard.

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Extended data

Extended Data Fig. 1 Validation of the root fractionation protocol and assessment of primer amplification bias.

a, Protocol to fractionate four microbial niches across a distance gradient from bulk soil to roots’ interior. Roots of A. thaliana grown in their natural environments were briefly washed (1) to separate loosely attached soil particles from the root surface (rhizosphere, RS). After a second washing step, roots were vigorously washed with detergent (three times) to capture microbes that tightly adhere to the root surface (2). The resulting washes were then filtered through a 0.22 µM membrane (rhizoplane, RP). Finally, surface sterilization of root samples by consecutive EtOH and NaClO washes enriched the final root sample in microbial root endophytes (3). b, Validation of the fractionation protocol (depicted in panel a) was performed by printing root washes (left panel) and washed roots (right panel) on 50% Tryptic Soy Agar medium. Sequential detergent washes efficiently release microbes from the root surface and further root surface sterilization prevents the growth of rhizoplane-associated microbes (Wilcoxon rank sum test, P < 0.01). All three detergent steps were combined and filtered to prepare the RP fraction (light green dots, left panel). c, Comparison of bacterial (left panel) and fungal (right panel) classes profiled with the V3V4 and V5V7 regions of the bacterial 16 s rRNA gene, and the ITS1 and ITS2 of the fungal ITS. The correlation between the RA of each microbial class is shown (Pearson correlation: P < 0.001).

Extended Data Fig. 2 Microbial alpha diversity and enrichment signatures in plant roots.

a, Microbial alpha diversity measured across all 17 sites in soil, rhizosphere (RS), rhizoplane (RP), and root samples based on the Shannon index. All samples from a given site were taken into account and the datasets were rarefied to 1,000 reads. Individual data points within each box correspond to samples from the 17 natural sites (Kruskal-Wallis with Dunn’s post hoc test: P < 0.05). b, same as a, but the alpha diversity metric observed OTUs is shown instead of the Shannon index. c, Comparison of taxa RA between soil (dark red) and root (dark green) samples for bacteria (left), fungi (middle), and oomycetes (right). RA measured in soil and root samples across the 17 A. thaliana populations were aggregated at the class (bacteria and fungi) and order (oomycetes) levels. Significant differences are marked with an asterisk (Wilcoxon rank sum test, FDR < 0.05). d, Comparison of taxa RA between Swedish soils (SW1-4, blue) and the other European soils (grey) for bacteria (left), fungi (middle), and oomycetes (right). RA is aggregated at the class (bacteria and fungi) and order (oomycetes) levels and significant differences are marked with an asterisk (Wilcoxon rank sum test: FDR < 0.05).

Extended Data Fig. 3 Between-site variation and shared OTUs in soil and root samples.

a, Between-site distances based on site centroids plotted for soil and root samples for each year and for all years combined together. Distances were calculated using site centroids of Bray-Curtis distances. Statistical significance was tested using a t-test. b, Percentage of shared high-abundant OTUs among soil and root samples. OTUs having RA > 1% were considered. Statistical significance was tested using a t-test. c, Percentage of shared low-abundant OTUs among soil and root samples. OTUs having RA < 1% were considered. Statistical significance was tested using t-test.

Extended Data Fig. 4 Comparison of beta-diversity metrics deriving from OTU and ASV binning approaches.

a, Pearson correlation between R2 values of explanatory factors from PERMANOVA tests with ASV and OTU datasets for bacteria, fungi and oomycetes (n = 10, see Supplementary Table 3). Each dot represents an explanatory factor and Pearson’s r values are indicated. b, Pearson correlation between R2 values of explanatory factors from PERMANOVA tests with ASVs and OTUs datasets for bacteria, fungi and oomycetes (n = 6, see Supplementary Table 5). Each dot represents an explanatory factor and each line represents the correlation between R2 values for a specific compartment. Pearson’s r values for each compartment are indicated. c, Principal coordinate analysis (PCoA) of Bray-Curtis distances using ASVs data. Bacterial, fungal and oomycetal communities from soil, rhizosphere (RS), rhizoplane (RP) and root samples are shown with different colours. Samples from Swedish sites are indicated by open circles. d, Mantel test results of the correlation between Bray-Curtis distance matrices computed based on ASV and OTU datasets.

Extended Data Fig. 5 Geographically widespread taxa at the soil-root interface.

Correlation between OTUs prevalence across sites in soil, rhizosphere (RS), rhizoplane (RP), root and averaged OTUs RA (log2). Bacteria: upper panels. Fungi: middle panels. Oomycetes: lower panels. Blue: geographically restricted OTUs (site prevalence < 20%). Orange: geographically common OTUs (site prevalence 20-80%). Red: geographically widespread OTUs (site prevalence > 80%). For calculating averaged RA, only samples where the actual OTUs are present were considered. The different shapes highlight OTUs detected one year, or across two or three years. RA and prevalence were averaged across the years where one OTU is present. OTUs with RA < 0.1% were excluded from the datasets.

Extended Data Fig. 6 Conservation of geographically widespread OTUs in roots of co-occurring grass species.

a, Diversity of grass species and conservation profiles of geographically widespread OTUs in roots. The Neighbor-Joining tree was constructed based on the plant rbcL locus sequenced from 50 samples collected across 17 sites and consecutive years. Taxonomic assignment at the species level was determined by blast search against the nr database at NCBI. The RA (%) of each of the geographically widespread OTUs is shown for root endopshere samples, together with the cumulative abundance in root samples. b, Bray-Curtis distances constrained by species (n = 17) for bacterial, fungal, and oomycetal communities in root endosphere samples. All distinct plant species identified only once were grouped as others. The percentage of variation explained by plant species is shown for the first and second axis (bacteria: P = 0.014; fungi: P = 0.043; oomycetes: P = 0.35). c, For each microbial group (bacteria, fungi, and oomycetes), Spearman´s rank correlations (P < 0.01) were determined between OTUs prevalence in roots of A. thaliana and OTUs prevalence in roots of neighboring grasses. d, Conservation of geographically widespread OTUs in roots of co-occuring grasses and Lotus japonicus. The RA and proportion of widespread bacterial and fungal OTUs detected in A. thaliana roots are shown for co-occuring grasses (17 sites), as well as for Lotus japonicus grown in the Cologne Agricultural Soil (CAS). All shown OTUs have RA > 0.1%. The total RA of these OTUs in root samples is indicated below the circular diagrams.

Extended Data Fig. 7 Influence of site and harvesting year on microbial community structure in A. thaliana populations.

a, Principal coordinate analysis (PCoA) based on Bray-Curtis distances for soil-, rhizosphere (RS), rhizoplane- (RP), and root-associated microbial communities detected in 17 sites across three successive years in European A. thaliana populations (across all compartments: n = 881 for bacteria, n = 893 for fungi, n = 875 for oomycetes). Microbial communities in each compartment are presented for bacteria, fungi, and oomycetes, and color-coded according to the site. b, Same PCoA plots as in panel a, but data points were color-coded according to the harvesting year (2015, 2016, 2017). OTUs with RA < 0.1% were excluded from the datasets.

Extended Data Fig. 8 Environmental variables and multicollinearity analysis.

a, Heatmap showing variation in 18 environmental variables measured across the 17 European sites. Values for each environmental property were normalized and the scale was adjusted to 1. 0 = lowest value measured value across sites, 1 = highest value measured value across sites). b, Heatmap depicting collinearity between the 18 environmental variables. Significant correlations detected between variables were assessed using Spearman´s rank correlation and variables showing correlation rs > 0.7 and rs < -0.7 (n = 17, P < 0.01) were considered as collinear. Two unique variables (Manganese, Copper) and five groups of collinear variables (group K, group P, group Boron, group NO3, group pH) were defined and used for PERMANOVA analyses.

Extended Data Fig. 9 Reciprocal transplant between two A. thaliana populations in Sweden and Italy.

a, European map showing names and locations of the 17 A. thaliana populations. The IT1 and SW4 sites selected for the reciprocal transplant experiment are highlighted in red and blue, respectively. b, Schematic overview of the reciprocal transplant experiment. Soils and plant genotypes from IT1 and SW4 sites were reciprocally transplanted in the two locations (eight different treatment combinations). The symbols below the schematic view correspond to the symbols used in panels c and d. c, Fitness of Italian and Swedish genotypes (red and blue color, respectively) when reciprocally planted in Italian and Swedish soils (circle and triangle symbols, respectively) and grown at Italian and Swedish locations (filled and open symbols, respectively). Plant survival, fecundity (number of fruits per reproducing plant), and overall fitness (number of fruits per seedling planted). Means based on block means ± SE are given. Note that no Italian plant survived to reproduce at the Swedish site. d, Bray-Curtis distances constrained by genotype for bacterial, fungal, and oomycetal communities in whole root samples (cPCoA, see axis 2). Results are shown for Italian and Swedish genotypes (red and blue color, respectively) planted in Italian and Swedish soils (circle and triangle symbols, respectively) at IT1 site only since no Italian plant survived at the SW4 site. The percentage of variation explained by the two genotypes is plotted along the second axis and refers to the fraction of the total variance of the data that is explained by the constrained factor (that is genotype; bacteria P = 0.001; fungi P = 0.026; oomycetes P = 0.002). Map data in a adapted from Google Maps, 2018.

Extended Data Fig. 10 OTU distribution patterns across root samples in the transplant experiment.

a, Heatmap depicting the RA (log2) of bacterial OTUs in roots of Italian and Swedish genotypes grown in Italian and Swedish soils at IT1 and SW4 locations. OTUs and samples are hierarchical clustered. Enrichment patterns of each OTU was estimated according to the categories described in the lower right side of the figure and highlighted with different colours next to the heatmap. The RA of OTUs falling into one of the six categories is always higher in that category compared to the mean RA measured across all samples. OTUs that are present in all samples (RA > 0.1%) and did not fall in any of the six categories are marked in grey. The heatmap is filtered for OTUs that have at least an average RA of 0.01% across all root samples. Samples have been filtered to contain at least 1,000 reads. Genotype of plants for each sample is indicated below each heatmap. Blue: Swedish genotype. Red: Italian genotype. Note that no Italian plant survived at the Swedish site. GW OTUs: geographically widespread OTUs. b, Heatmap depicting the RA (log2) of fungal OTUs in roots of Italian and Swedish genotypes grown in Italian and Swedish soils at IT1 and SW4 locations. c, Heatmap depicting the RA (log2) of oomycetal OTUs in roots of Italian and Swedish genotypes grown in Italian and Swedish soils at IT1 and SW4 locations. d, Percentage of OTUs falling into one of the six categories are presented as pie charts for each main taxonomic classes. The number of OTUs that belong to each microbial class is given in brackets.

Supplementary information

Reporting Summary

Supplementary Table 1

European sites from which A. thaliana and grasses populations were harvested.

Supplementary Table 2

Primers utilized in this study to profile bacterial, fungal and oomycetal communities in soil and root samples.

Supplementary Table 3

PERMANOVA partitioning of microbial community assemblages based on OTU and ASV distance matrices.

Supplementary Table 4

Description of geographically widespread OTUs detected in A. thaliana root samples.

Supplementary Table 5

PERMANOVA partitioning of microbial community assemblages decomposed by soil, rhizosphere and root compartments, and based on OTU and ASV distance matrices.

Supplementary Table 6

PERMANOVA partitioning of microbial community assemblages decomposed by soil, rhizosphere and root compartments, and based on OTU distance matrices.

Supplementary Table 7

PERMANOVA partitioning of microbial community assemblages decomposed by compartment and by soil, location and genotype in a field reciprocal transplant experiment.

Supplementary Table 8

Survival, number of fruits produced by survivors, and number of fruits per seedling planted in the reciprocal transplant experiment conducted at the sites of the IT1 and SW4 populations.

Supplementary Table 9

Analysis of the effects of soil (Italian versus Swedish) and genotype (Italian versus Swedish) on total fitness (number of fruits per seedling planted) in a field experiment conducted at the site of the Italian genotype in 2016/2017.

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Thiergart, T., Durán, P., Ellis, T. et al. Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nat Ecol Evol 4, 122–131 (2020). https://doi.org/10.1038/s41559-019-1063-3

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