Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes Full text
2022
Jacquiod, Samuel | Spor, Aymé | Wei, Shaodong | Munkager, Victoria | Bru, David | Sørensen, Søren J. | Salon, Christophe | Philippot, Laurent | Blouin, Manuel
Artificial selection of microbiota opens new avenues for improving plants. However, reported results lack consistency. We hypothesised that the success in artificial selection of microbiota depends on the stabilisation of community structure. In a ten‐generation experiment involving 1,800 plants, we selected rhizosphere microbiota of Brachypodium distachyon associated with high or low leaf greenness, a proxy of plant performance. The microbiota structure showed strong fluctuations during an initial transitory phase, with no detectable leaf greenness heritability. After five generations, the microbiota structure stabilised, concomitantly with heritability in leaf greenness. Selection, initially ineffective, did successfully alter the selected property as intended, especially for high selection. We show a remarkable correlation between the variability in plant traits and selected microbiota structures, revealing two distinct sub‐communities associated with high or low leaf greenness, whose abundance was significantly steered by directional selection. Understanding microbiota structure stabilisation will improve the reliability of artificial microbiota selection.
Show more [+] Less [-]Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes Full text
2022
Jacquiod, Samuel | Spor, Aymé | Wei, Shaodong | Munkager, Victoria | Bru, David | Sorensen, Soren J. | Salon, Christophe | Philippot, Laurent, L. | Blouin, Manuel | Agroécologie [Dijon] ; Université de Bourgogne (UB)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Dijon ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Section of Microbiology [Copenhagen] ; Department of Biology [Copenhagen] ; Faculty of Science [Copenhagen] ; University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science [Copenhagen] ; University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH) | IT University of Copenhagen (ITU) | This study was funded by theBourgogne Franche-Comté regionvia the FABER program (grant no2017-9201AAO049S01302).
International audience | Research on artificial selection of microbial community has become popular due to perspectives in improving plant and animal health 1-4 . However, reported results still lack consistency 5-8 . We hypothesized that artificial selection may provide desired outcomes provided that microbial community structure has stabilized along the selection process. In a ten-generation artificial selection experiment involving 1,800 plants, we selected rhizosphere microbiota of Brachypodium distachyon that were associated with high or low levels of leaf greenness, a proxy for plant health 9 . Monitoring of the rhizosphere microbiota dynamics showed strong oscillations in community structure during an initial transitory phase of five generations, with no heritability in the selected property. In the last five generations, the structure of microbial communities displayed signs of stabilization, concomitantly to the appearance of heritability in leaf greenness. Selection pressure, initially ineffective, became successful in changing the greenness index in the intended direction, especially toward high greenness values. We showed a remarkable congruence between plant traits and selected microbial community structures, highlighting two phylogenetically distinct microbial sub-communities correlating with leaf greenness, whose abundance was significantly steered by directional artificial selection. Understanding microbial community structure stabilization can thus help improve the reliability of artificial microbiota selection.
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