Directed evolution of microbial communities

Sánchez, Álvaro; Vila, Jean C. C.; Chang, Chang‐Yu; Díaz-Colunga, Juan; Estrela, Sylvie; Rebolleda‐Gómez, María

doi:10.32942/osf.io/gsz7j

Cited by 7 publications

(8 citation statements)

References 57 publications

(74 reference statements)

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“…Those differences in community composition that are due to transient ecological dynamics (i.e., to communities not being in equilibrium), stochastic fluctuations in species abundances, rapid and random fluctuations in environmental conditions, or simply experimental error in determining the function of all communities will not contribute to the success of artificial selection (Blouin et al 2015;Penn and Harvey 2004;Chang et al 2020). Quite the contrary: if strong enough, these nonheritable sources of variation can mask the heritable variation, diminishing heritability and therefore the response to selection (Blouin et al 2015;Chang et al 2020;Sanchez et al 2020). Therefore, future attempts to apply artificial selection at the community level should take great care to minimize those sources of variation, for instance, as we have recently discussed at length, ensuring that the environment is as stable as possible, and that communities are in a generationally stable state before selection is applied (Chang et al 2020;Penn and Harvey 2004).…”

Section: Discussionmentioning

confidence: 99%

“…2020; Sanchez et al. 2020). Therefore, future attempts to apply artificial selection at the community level should take great care to minimize those sources of variation, for instance, as we have recently discussed at length, ensuring that the environment is as stable as possible, and that communities are in a generationally stable state before selection is applied (Chang et al.…”

Section: Discussionmentioning

confidence: 99%

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Artificially selecting bacterial communities using propagule strategies†

et al. 2020

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Artificial selection is a promising approach to manipulate microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both used "propagule" selection strategies, whereby the best-performing communities are used as the inocula to form a new generation of communities. Both experiments were contrasted to a random selection control. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used multiple soil communities as the starting inocula, and the function under selection was the communities' cross-feeding potential. In both experiments, the selected communities reached a higher mean function than the control. In the first experiment, this was caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, this response was fueled by the large initial variance in function across communities, and stopped when the top-performing community "fixed" in the metacommunity. Our results are in agreement with basic expectations from breeding theory, pointing to some of the limitations of community-level selection experiments that can inform the design of future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificially selecting bacterial communities using propagule strategies†

et al. 2020

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“…Empirical studies of artificial selection have demonstrated that it is possible to steer microbiota across generations to modify microbial ecosystems properties [4][5][6][7][8] . In some cases, the selected property can be a trait displayed by the host of a microbiota, like with microbial communities associated to plants 5,[10][11][12][13] .…”

Section: Main Textmentioning

confidence: 99%

“…In some cases, the selected property can be a trait displayed by the host of a microbiota, like with microbial communities associated to plants 5,[10][11][12][13] . This opens new avenues for plant breeding via directional artificial selection of rhizosphere microbiota [1][2][3][4] . Still, while previous studies reported significant selection effects 6,7,11 , the selected property may not be perennial and lost during the process 6,9 .…”

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confidence: 99%

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Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes

Jacquiod

Spor

Shi

et al. 2021

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Research on artificial selection of microbial community has become popular due to perspectives in improving plant and animal health1-4. However, reported results still lack consistency5-8. 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 health9. 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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Deciphering the evolution of microbial interactions: in silico studies of two-member microbial communities

Sambamoorthy

Raman

2022

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Microbes thrive in communities, embedded in a complex web of interactions. These interactions, particularly metabolic interactions, play a crucial role in maintaining the community structure and function. As the organisms thrive and evolve, a variety of evolutionary processes alter the interactions among the organisms in the community, although the community function remains intact. In this work, we simulate the evolution of two-member microbial communities in silico to study how evolutionary forces can shape the interactions between organisms. We employ genomescale metabolic models of organisms from the human gut, which exhibit a range of interaction patterns, from mutualism to parasitism. We observe that the evolution of microbial interactions varies depending upon the starting interaction and also on the metabolic capabilities of the organisms in the community. We find that evolutionary constraints play a significant role in shaping the dependencies of organisms in the community. Evolution of microbial communities yields fitness benefits in only a small fraction of the communities, and is also dependent on the interaction type of the wild-type communities. The metabolites cross-fed in the wild-type communities appear in only less than 50% of the evolved communities. A wide range of new metabolites are cross-fed as the communities evolve. Further, the dynamics of microbial interactions are not specific to the interaction of the wild-type community but vary depending on the organisms present in the community. Our approach of evolving microbial communities in silico provides an exciting glimpse of the dynamics of microbial interactions and offers several avenues for future investigations.

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Directed evolution of microbial communities

Cited by 7 publications

References 57 publications

Artificially selecting bacterial communities using propagule strategies†

Artificially selecting bacterial communities using propagule strategies†

Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes

Deciphering the evolution of microbial interactions: in silico studies of two-member microbial communities

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