A footprint of plant eco-geographic adaptation on the composition of the barley rhizosphere bacterial microbiota.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 07 2020
31 07 2020
Historique:
received:
19
02
2020
accepted:
15
07
2020
entrez:
2
8
2020
pubmed:
2
8
2020
medline:
15
12
2020
Statut:
epublish
Résumé
The microbiota thriving in the rhizosphere, the thin layer of soil surrounding plant roots, plays a critical role in plant's adaptation to the environment. Domestication and breeding selection have progressively differentiated the microbiota of modern crops from the ones of their wild ancestors. However, the impact of eco-geographical constraints faced by domesticated plants and crop wild relatives on recruitment and maintenance of the rhizosphere microbiota remains to be fully elucidated. Here we performed a comparative 16S rRNA gene survey of the rhizosphere of 4 domesticated and 20 wild barley (Hordeum vulgare) genotypes grown in an agricultural soil under controlled environmental conditions. We demonstrated the enrichment of individual bacteria mirrored the distinct eco-geographical constraints faced by their host plants. Unexpectedly, Elite varieties exerted a stronger genotype effect on the rhizosphere microbiota when compared with wild barley genotypes adapted to desert environments with a preferential enrichment for members of Actinobacteria. Finally, in wild barley genotypes, we discovered a limited, but significant, correlation between microbiota diversity and host genomic diversity. Our results revealed a footprint of the host's adaptation to the environment on the assembly of the bacteria thriving at the root-soil interface. In the tested conditions, this recruitment cue layered atop of the distinct evolutionary trajectories of wild and domesticated plants and, at least in part, is encoded by the barley genome. This knowledge will be critical to design experimental approaches aimed at elucidating the recruitment cues of the barley microbiota across a range of soil types.
Identifiants
pubmed: 32737353
doi: 10.1038/s41598-020-69672-x
pii: 10.1038/s41598-020-69672-x
pmc: PMC7395104
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
12916Références
Alexandratos, N. & Bruinsma, J. World agriculture towards 2030/2050: the 2012 revision. (2012).
Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. U.S.A. 108, 20260–20264. https://doi.org/10.1073/pnas.1116437108 (2011).
doi: 10.1073/pnas.1116437108
pubmed: 22106295
pmcid: 3250154
Porter, J. R. et al. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Food Security and Food Production Systems, 485–533 (2014).
Schlaeppi, K. & Bulgarelli, D. The plant microbiome at work. Mol. Plant–Microbe Interact. 28, 212–217. https://doi.org/10.1094/MPMI-10-14-0334-FI (2015).
doi: 10.1094/MPMI-10-14-0334-FI
pubmed: 25514681
Alegria Terrazas, R. et al. Plant–microbiota interactions as a driver of the mineral turnover in the rhizosphere. Adv. Appl. Microbiol. 95, 1–67. https://doi.org/10.1016/bs.aambs.2016.03.001 (2016).
doi: 10.1016/bs.aambs.2016.03.001
pubmed: 27261781
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).
pubmed: 23373698
Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).
pubmed: 19212403
Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644. https://doi.org/10.1007/s11103-015-0337-7 (2016).
doi: 10.1007/s11103-015-0337-7
pubmed: 26085172
Doebley, J. F., Gaut, B. S. & Smith, B. D. The molecular genetics of crop domestication. Cell 127, 1309–1321 (2006).
pubmed: 17190597
Cordovez, V., Dini-Andreote, F., Carrión, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Ann. Rev. Microbiol. 73, 69–88a (2019).
Escudero-Martinez, C. & Bulgarelli, D. Tracing the evolutionary routes of plant–microbiota interactions. Curr. Opin. Microbiol. 49, 34–40 (2019).
pubmed: 31698159
Newton, A. C. et al. Crops that feed the world 4. Barley: a resilient crop? Strengths and weaknesses in the context of food security. Food Secur. 3, 141. https://doi.org/10.1007/s12571-011-0126-3 (2011).
doi: 10.1007/s12571-011-0126-3
Mascher, M. et al. A chromosome conformation capture ordered sequence of the barley genome. Nature 544, 426. https://doi.org/10.1038/nature22043 (2017).
doi: 10.1038/nature22043
Milner, S. G. et al. Genebank genomics highlights the diversity of a global barley collection. Nat. Genet. 51, 319–326 (2019).
pubmed: 30420647
Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403. https://doi.org/10.1016/j.chom.2015.01.011 (2015).
doi: 10.1016/j.chom.2015.01.011
pubmed: 25732064
pmcid: 4362959
Mwafulirwa, L. et al. Barley genotype influences stabilization of rhizodeposition-derived C and soil organic matter mineralization. Soil Biol. Biochem. 95, 60–69. https://doi.org/10.1016/j.soilbio.2015.12.011 (2016).
doi: 10.1016/j.soilbio.2015.12.011
Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Change 4, 1068–1072 (2014).
Hubner, S. et al. Phenotypic landscapes: phenological patterns in wild and cultivated barley. J. Evol. Biol. 26, 163–174. https://doi.org/10.1111/jeb.12043 (2013).
doi: 10.1111/jeb.12043
pubmed: 23176039
Hubner, S. et al. Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol. Ecol. 18, 1523–1536. https://doi.org/10.1111/j.1365-294X.2009.04106.x (2009).
doi: 10.1111/j.1365-294X.2009.04106.x
pubmed: 19368652
Hübner, S. et al. Phenotypic landscapes: phenological patterns in wild and cultivated barley. J. Evol. Biol. 26, 163–174 (2013).
pubmed: 23176039
Bloom, A. J., Chapin, F. S. III. & Mooney, H. A. Resource limitation in plants-an economic analogy. Annu. Rev. Ecol. Syst. 16, 363–392 (1985).
Comas, L., Becker, S., Cruz, V. M. V., Byrne, P. F. & Dierig, D. A. Root traits contributing to plant productivity under drought. Front. Plant Sci. 4, 442 (2013).
pubmed: 24204374
pmcid: 3817922
Robertson-Albertyn, S. et al. Root hair mutations displace the barley rhizosphere microbiota. Front. Plant Sci. 8, 1094. https://doi.org/10.3389/fpls.2017.01094 (2017).
doi: 10.3389/fpls.2017.01094
pubmed: 28694814
pmcid: 5483447
Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257 (2017).
pubmed: 28585939
pmcid: 5607367
Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).
pubmed: 25974302
Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. U.S.A. 112, E911-920. https://doi.org/10.1073/pnas.1414592112 (2015).
doi: 10.1073/pnas.1414592112
pubmed: 25605935
pmcid: 4345613
Perez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257. https://doi.org/10.1038/ismej.2017.85 (2017).
doi: 10.1038/ismej.2017.85
pubmed: 28585939
pmcid: 5607367
Russell, J. et al. Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat. Genet. 48, 1024 (2016).
pubmed: 27428750
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, 1–16 (2019).
Garrido-Oter, R. et al. Modular traits of the Rhizobiales root microbiota and their evolutionary relationship with symbiotic rhizobia. Cell Host Microbe 24, 155. https://doi.org/10.1016/j.chom.2018.06.006 (2018).
doi: 10.1016/j.chom.2018.06.006
pubmed: 30001518
pmcid: 6053594
Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179 (2018).
pubmed: 30001519
pmcid: 6054916
Paterson, E., Gebbing, T., Abel, C., Sim, A. & Telfer, G. Rhizodeposition shapes rhizosphere microbial community structure in organic soil. New Phytol. 173, 600–610 (2007).
pubmed: 17244055
Neilson, J. W. et al. Significant impacts of increasing aridity on the arid soil microbiome. MSystems 2, e00195-0016 (2017).
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl. Acad. Sci. U.S.A. 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
doi: 10.1073/pnas.1215210110
pubmed: 23236140
pmcid: 3535587
Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143. https://doi.org/10.1186/s40168-018-0519-z (2018).
doi: 10.1186/s40168-018-0519-z
pubmed: 30115122
pmcid: 6097318
Santos-Medellín, C., Edwards, J., Liechty, Z., Nguyen, B. & Sundaresan, V. Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. MBio 8, e00764-00717 (2017).
Naylor, D., DeGraaf, S., Purdom, E. & Coleman-Derr, D. Drought and host selection influence bacterial community dynamics in the grass root microbiome. ISME J. 11, 2691–2704 (2017).
pubmed: 28753209
pmcid: 5702725
Romaniuk, K., Golec, P. & Dziewit, L. Insight into the diversity and possible role of plasmids in the adaptation of psychrotolerant and metalotolerant Arthrobacter spp. to extreme Antarctic environments. Front. Microbiol. 9, 3144 (2018).
pubmed: 30619210
pmcid: 6305408
Hübner, S. et al. Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol. Ecol. 18, 1523–1536 (2009).
pubmed: 19368652
Bouffaud, M. L. et al. Is diversification history of maize influencing selection of soil bacteria by roots?. Mol. Ecol. 21, 195–206. https://doi.org/10.1111/j.1365-294X.2011.05359.x (2012).
doi: 10.1111/j.1365-294X.2011.05359.x
pubmed: 22126532
Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl. Acad. Sci. 110, 6548–6553. https://doi.org/10.1073/pnas.1302837110 (2013).
doi: 10.1073/pnas.1302837110
pubmed: 23576752
Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1800918115 (2018).
doi: 10.1073/pnas.1800918115
pubmed: 29941552
Rolfe, S. A., Griffiths, J. & Ton, J. Crying out for help with root exudates: adaptive mechanisms by which stressed plants assemble health-promoting soil microbiomes. Curr. Opin. Microbiol. 49, 73–82 (2019).
pubmed: 31731229
Cotton, T. A. et al. Metabolic regulation of the maize rhizobiome by benzoxazinoids. ISME J. 13, 1647–1658 (2019).
pubmed: 30796337
pmcid: 6592824
Hu, L. et al. Root exudate metabolites drive plant–soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 1–13 (2018).
Kudjordjie, E. N., Sapkota, R., Steffensen, S. K., Fomsgaard, I. S. & Nicolaisen, M. Maize synthesized benzoxazinoids affect the host associated microbiome. Microbiome 7, 1–17 (2019).
Grün, S., Frey, M. & Gierl, A. Evolution of the indole alkaloid biosynthesis in the genus Hordeum: distribution of gramine and DIBOA and isolation of the benzoxazinoid biosynthesis genes from Hordeum lechleri. Phytochemistry 66, 1264–1272 (2005).
pubmed: 15907959
Larsson, K. A., Zetterlund, I., Delp, G. & Jonsson, L. M. N-Methyltransferase involved in gramine biosynthesis in barley: cloning and characterization. Phytochemistry 67, 2002–2008 (2006).
pubmed: 16930646
Matsuo, H. et al. Gramine increase associated with rapid and transient systemic resistance in barley seedlings induced by mechanical and biological stresses. Plant Cell Physiol. 42, 1103–1111 (2001).
pubmed: 11673626
Voss-Fels, K. P. et al. VERNALIZATION1 modulates root system architecture in wheat and barley. Mol. Plant 11, 226–229 (2018).
pubmed: 29056533
Maurer, A. et al. Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics 16, 290 (2015).
pubmed: 25887319
pmcid: 4426605
Bayer, M. M. et al. Development and evaluation of a barley 50k iSelect SNP array . Front. Plant Sci. 8, 1792. https://doi.org/10.3389/fpls.2017.01792 (2017).
doi: 10.3389/fpls.2017.01792
pubmed: 29089957
pmcid: 5651081
Bdolach, E. et al. Thermal plasticity of the circadian clock is under nuclear and cytoplasmic control in wild barley. Plant Cell Environ. 42, 3105–3120 (2019).
pubmed: 31272129
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).
pubmed: 26633631
Zhang, J. et al. NRT1. 1B associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).
pubmed: 31036930
Thiergart, T. et al. Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nat. Ecol. Evol. 4, 122–131 (2020).
pubmed: 31900452
International Barley Genome Sequencing Consortium et al. A physical, genetic and functional sequence assembly of the barley genome. Nature 491, 711. https://doi.org/10.1038/nature11543 (2012).
doi: 10.1038/nature11543
Druka, A. et al. Genetic dissection of barley morphology and development. Plant Physiol. 155, 617–627. https://doi.org/10.1104/pp.110.166249 (2011).
doi: 10.1104/pp.110.166249
pubmed: 21088227
Kleinhofs, A. et al. A molecular, isozyme and morphological map of the barley (Hordeum vulgare) genome. Theor. Appl. Genet. 86, 705–712 (1993).
pubmed: 24193780
Bulgarelli, D. et al. The CC-NB-LRR-Type Rdg2a resistance gene confers immunity to the seed-borne barley leaf stripe pathogen in the absence of hypersensitive cell death. PLoS ONE 5, e12599. https://doi.org/10.1371/journal.pone.0012599 (2010).
doi: 10.1371/journal.pone.0012599
pubmed: 20844752
pmcid: 2937021
Tottman, D., Makepeace, R. & Broad, H. An explanation of the decimal code for the growth stages of cereals, with illustrations. Ann. Appl. Biol. 93, 221–234 (1979).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. https://doi.org/10.1038/ismej.2012.8 (2012).
doi: 10.1038/ismej.2012.8
pubmed: 22402401
pmcid: 3400413
Caradonia, F. et al. Nitrogen fertilizers shape the composition and predicted functions of the microbiota of field-grown tomato plants. Phytobiomes J. 3, 315–325 (2019).
Pietrangelo, L., Bucci, A., Maiuro, L., Bulgarelli, D. & Naclerio, G. Unraveling the composition of the root-associated bacterial microbiota of Phragmites australis and Typha latifolia. Front. Microbiol. 9, 1650. https://doi.org/10.3389/fmicb.2018.01650 (2018).
doi: 10.3389/fmicb.2018.01650
pubmed: 30116224
pmcid: 6083059
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).
doi: 10.1038/nmeth.f.303
pubmed: 20383131
pmcid: 3156573
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).
doi: 10.1093/bioinformatics/btq461
pubmed: 20709691
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).
doi: 10.1093/nar/gks1219
pubmed: 23193283
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
pubmed: 23071270
Team, R. C. R: A Language and Environment for Statistical Computing. (2013).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS one 8(4), e61217 (2013).
pubmed: 23630581
pmcid: 3632530
Wickham, H. ggplot2: elegant graphics for data analysis (Springer, Berlin, 2016).
Oksanen, J.F. et al. vegan: community ecology package. R package version 2.0–7. 2013 (2014).
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
pubmed: 14734327
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172. https://doi.org/10.1038/ismej.2010.133 (2011).
doi: 10.1038/ismej.2010.133
pubmed: 20827291
Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).
Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).
pubmed: 26028277
Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 1–11 (2019).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
doi: 10.1186/s13059-014-0550-8
pubmed: 25516281
pmcid: 4302049
Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27. https://doi.org/10.1186/s40168-017-0237-y (2017).
doi: 10.1186/s40168-017-0237-y
pubmed: 28253908
pmcid: 5335496
Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).
pubmed: 28645171
pmcid: 5870712
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2006).
pubmed: 17050570
Close, T. J. et al. Development and implementation of high-throughput SNP genotyping in barley. BMC Genomics 10, 582. https://doi.org/10.1186/1471-2164-10-582 (2009).
doi: 10.1186/1471-2164-10-582
pubmed: 19961604
pmcid: 2797026
Peakall, R. O. D. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel, Population genetic software for teaching and research—an update. Bioinformatics 28, 2537 (2012).
pubmed: 22820204
pmcid: 22820204
Peakall, R. & Smouse, P. E. Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).