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Engineering complex communities by directed evolution

Abstract

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

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Fig. 1: Migrant-pool and propagule strategies are limited in their ability to find new, high-functioning microbial communities.
Fig. 2: Directed evolution as an artificial selection strategy for high-performing communities.
Fig. 3: Iteratively combining bottlenecks and migrations to optimize community function selects for high-functioning communities.
Fig. 4: Directed evolution produces communities that are resistant to ecological perturbations.

Data availability

All data generated and analysed in this paper can be found at https://doi.org/10.5281/zenodo.4608427.

Code availability

All simulations were conducted in Python using ecoprospector (https://github.com/Chang-Yu-Chang/ecoprospector). All the data analysis was conducted in R. The complete code used for this paper including the ecoprospector package can be found in the Zenodo repository (https://doi.org/10.5281/zenodo.4608427).

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Acknowledgements

We thank H. Garcia and R. Phillips for inviting most of us, either as instructors or as students, to the Physical Biology of the Cell summer course at the Marine Biology Laboratory (MBL) in Woods Hole, MA, where this project was started and the first version of the ecoprospector package was coded. We also wish to thank B. Von Herzen for his input and discussion while we were at MBL. This work was supported by the National Institutes of Health through grant 1R35 GM133467-01 and by a Packard Foundation Fellowship to A.S. C.-Y.C. was supported by a graduate fellowship Government Scholarship to Study Abroad by the Government of Taiwan. M.R.-G. was supported by a Gaylord Donnelley postdoctoral fellowship through the Yale Institute for Biospheric Studies.

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Contributions

C.-Y.C., J.C.C.V. and A.S. conceived the idea and designed the study. C.-Y.C., J.C.C.V., M. Bassette, J.B., S.G., P.G.L.S., R.W., X.Z. and A.S. contributed to the development of ecoprospector. C.-Y.C. and J.C.C.V. carried out all simulations and made the results figures. C.-Y.C., J.C.C.V. and J.D.-C. made the diagrams. C.-Y.C, J.C.C.V., M. Bender, R.L., M.C.M., S.E., M.R.-G., J.D.-C. and A.S. discussed the results and drafted the paper. M.C.M. compiled the list of previous selection protocols from the literature. C.-Y.C., J.C.C.V. and A.S. wrote the final version of the paper.

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Correspondence to Alvaro Sanchez.

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Peer review information Nature Ecology & Evolution thanks Kevin Esvelt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Non-additive function, costly function, and two empirically motivated functions.

a, Illustration of the different types of community function we have considered. In addition to the additive function used in the main text we have simulated four other community functions: a non-additive pairwise function, a costly function, a function that maximizes the consumption of a target resource, and a function that maximizes resistance to an invader. Panels B-F reproduce the main results reported in Figs. 14. b, Difference in Fmax between the artificial selection line (AS) and no-selection line (NS) for all previously published protocols, corresponding to Fig. 1f. c, Difference in Fmax between parent (before directed evolution) and offspring (after directed evolution) for the 6 types of perturbation considered in Fig. 2, this plot aggregates the results shown in Fig. 2d-i. d, Reproduction of Fig. 3e, to show that iteratively combining migrations and bottlenecks does better than either alone. Q is obtained from each of the three iterative protocols at generation 460 (e) Reproduction of Fig. 4e, where we compare Fmax of the no-selection (NS), directed evolution (DE), and synthetic communities; (F) Mean function (F*) of the DE, NS and Synthetic communities following an ecological perturbation (migration). This corresponds to the y-axis of Fig. 4f.

Extended Data Fig. 2 Alternative ecological scenarios with metabolic cross-feeding.

Besides the rich medium without cross-feeding shown in the main text, we have included two other ecological scenarios: i) rich medium with cross-feeding and ii) simple minimal medium with cross-feeding. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 14.

Extended Data Fig. 3 Functional responses.

The resource import rate depends on its concentration in the environments, which can take a linear (type I), Monod (type II), or Hill (type III) form. A Type-III functional response is used in the simulation presented in the main text. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 14.

Extended Data Fig. 4 Alternative Metacommunity sampling approaches.

We simulate three metacommunity sampling approaches: i) Each community is seeded with 106 cells drawn from a different regional pool, where the species abundances in each regional pool are drawn from a power-law distribution with a=0.01, ii) Each community is seeded with 106 cells drawn from a different regional, where the species abundances in each regional pool are drawn from a log-normal distribution with mean μ=8 and standard deviation σ=8, iii) Each community is seeded with a randomly chosen set of 225 species and they are all set to have the same initial abundance. The simulation in the main text adopts the power-law distribution approach. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 14.

Extended Data Fig. 5 Different distributions of per capita species contribution to additive community function.

Per capita species contribution drawn from i) normal distribution centered around 0 with standard deviation sd=1, ii) normal distribution with mean=11 and sd=1, iii) uniform distribution ranged from min=0 to max=1, iiii) a sparse additive function where 20% of the species contribute to community function.In the main text, per capita species contribution uses normal distribution with mean=0 and sd=1. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 14.

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Chang, CY., Vila, J.C.C., Bender, M. et al. Engineering complex communities by directed evolution. Nat Ecol Evol 5, 1011–1023 (2021). https://doi.org/10.1038/s41559-021-01457-5

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