Engineer microbiomes robust to evolutionary and environmental pressures.

Microbiome Engineering

Engineer microbiomes robust to evolutionary and environmental pressures.

Current State-of-the-Art

Designing microbiomes that resist natural ecological and evolutionary forces will require a deeper understanding of both the dynamics within a microbiome and how environmental factors impact its growth and function. Lab grown microbiomes are still in their very early stages of development, limiting our understanding of how evolutionary and environmental pressures drive microbiome composition over time.¹ However, some advances in microbiome engineering have been made that will be useful for addressing known challenges.

Balancing the growth rates of microbial species within a microbiome has been challenging,²^,³ but differences have been mitigated by implementing feedback controllers on growth using synthetic genetic circuits.⁴ Predictive methods and computational models that can capture the landscape of fitness across various initial conditions and environmental perturbations are under development.⁵^,⁶ A guild may also need to resist dispersion to maintain its function, which requires the ability to adhere cells into defined morphologies and patterns, as described by this cell-to-cell adhesion toolbox.⁷ Some progress has been made to engineer microbial social interactions,⁸ but additional progress will be needed to engineer interactions between kingdoms, which will be key for resisting evolutionary pressures in microbiomes.

Breakthrough Capabilities & Milestones

Engineer robust and stable microbiomes that are resilient to evolutionary and environmental stress (e.g., environmental variations, dispersion forces, nutrient deprivation).

Engineer high-throughput systems to measure coevolution of species within a guild over time.

Bottleneck/Challenge: Most efforts to observe and quantify coevolution have used simple, two-species communities.⁹

Potential Solution: In coordination with existing microbiome data repositories, establish quantifiable parameters to describe symbiotic relationships and mechanisms of succession between members of a functional guild (i.e., define how guild members interact and compete with each other).

Use a model microbiome to examine responses to well-defined ecological stresses (e.g., temperature change, moisture gradients, UV exposure) in a contained environment.

Design guilds that maintain their function over time and when biologically destabilizing factors are introduced (e.g., mobile genetic elements, plasmids, phages, nutrient deprivation).

Bottleneck/Challenge: Microbes have a tendency to lose genes that are redundant with others in the community.

Potential Solution: Engineer guilds so their defining function can be exchanged between members (e.g., via a plasmid, phage, mobile genetic element) to decrease the overall fitness cost.

Bottleneck/Challenge: Competitive or antagonistic interactions between organisms will introduce strong selective pressures that may disrupt guild function.

Potential Solution: Engineer mutualistic, positive interactions within a guild to help resist selective pressures.

Potential Solution: Engineer succession within a guild, so as one guild member is outcompeted or otherwise impaired, another guild member grows up to fill the functional niche.

Engineer guilds that respond to an environmental stress to preserve function (e.g., buffers environment in response to pH change, antibiotic/antiviral produced in response to predator, ‘nutrient scavenging molecule’ in response to decreases).

Bottleneck/Challenge: A single environmental change can alter many different environmental parameters (e.g., pH changes in response to temperature, nutrient availability may change in response to pH).

Potential Solution: Many existing microbial signal pathways (e.g., two-component systems) respond to multiple environmental signals and could be engineered to compensate for multiple different input signals.

Engineer microbiomes to kill cells that drift away from the guild’s function (e.g., producing a specific metabolite, degrading specific compounds).

Bottleneck/Challenge: It may be difficult to properly tune the response and prevent accidental targeting due to natural fluctuations in activity or metabolism.¹⁰

Potential Solution: Microbiomes will need to be engineered so multiple input signals are received before targeting, similar to signal tuning in T cell receptor development.

Engineer species that help a microbiome resist dispersion forces by producing biofilms or other materials.

Bottleneck/Challenge: Natural, multispecies biofilms are relatively poorly characterized, so significant advances will be needed to engineer biofilm formation.¹¹

Potential Solution: Many of the tools developed to characterize microbiomes will contribute to studying biofilm communities. However, the unique physical properties of biofilms (e.g., chemical resistance, adhesion to surfaces) mean that additional considerations will be needed for all experiments (e.g., high-throughput liquid handling, -omics techniques).

Engineer microbiomes that contain diverse functional guilds (i.e., multiple species per guild) and maintain their function in natural environments.

Bottleneck/Challenge: Engineered microbiomes in natural environments may require stability over significantly longer time periods compared to controlled environments (e.g., weeks in a bioreactor versus months or years in a field).

Potential Solution: Design guilds that can go dormant (e.g., halt growth, form spores) to reduce competition with other guilds, and are responsive to other guilds or the host environment for cues to start growing again.

Engineer guilds that evolve in a manner that is directed by environmental conditions or nutrient availability, so optimal function continues even as changes occur.

Bottleneck/Challenge: Any organism designed to mutate and evolve will have a higher chance of losing engineered function in exchange for increased growth.

Potential Solution: Use burden driven feedback control systems to broadly reduce mutation rates in engineered microbiomes,¹² and further characterize mutation rates and evolutionary drivers to define necessary interventions in different environments (e.g., antibiotic selection may not be needed).

Footnotes

Koskella, B., Hall, L. J., & Metcalf, C. J. E. (2017). The microbiome beyond the horizon of ecological and evolutionary theory. Nature Ecology & Evolution, 1(11), 1606–1615. https://doi.org/10.1038/s41559-017-0340-2
McCardell, R. D., Pandey, A., & Murray, R. M. (2019). Control of density and composition in an engineered two-member bacterial community [Preprint]. Synthetic Biology. https://doi.org/10.1101/632174
Shou, W., Ram, S., & Vilar, J. M. G. (2007). Synthetic cooperation in engineered yeast populations. Proceedings of the National Academy of Sciences, 104(6), 1877–1882. https://doi.org/10.1073/pnas.0610575104
Ziesack, M., Gibson, T., Oliver, J. K. W., Shumaker, A. M., Hsu, B. B., Riglar, D. T., Giessen, T. W., DiBenedetto, N. V., Bry, L., Way, J. C., Silver, P. A., & Gerber, G. K. (2019). Engineered Interspecies Amino Acid Cross-Feeding Increases Population Evenness in a Synthetic Bacterial Consortium. mSystems, 4(4), 15. https://doi.org/10.1128/mSystems.00352-19
Gutiérrez, M., Gregorio-Godoy, P., Pérez del Pulgar, G., Muñoz, L. E., Sáez, S., & Rodríguez-Patón, A. (2017). A New Improved and Extended Version of the Multicell Bacterial Simulator gro. ACS Synthetic Biology, 6(8), 1496–1508. https://doi.org/10.1021/acssynbio.7b00003
Matyjaszkiewicz, A., Fiore, G., Annunziata, F., Grierson, C. S., Savery, N. J., Marucci, L., & di Bernardo, M. (2017). BSim 2.0: An Advanced Agent-Based Cell Simulator. ACS Synthetic Biology, 6(10), 1969–1972. https://doi.org/10.1021/acssynbio.7b00121
Glass, D. S., & Riedel-Kruse, I. H. (2018). A Synthetic Bacterial Cell-Cell Adhesion Toolbox for Programming Multicellular Morphologies and Patterns. Cell, 174(3), 649-658.e16. https://doi.org/10.1016/j.cell.2018.06.041
Kong, W., Meldgin, D. R., Collins, J. J., & Lu, T. (2018). Designing microbial consortia with defined social interactions. Nature Chemical Biology, 14(8), 821–829. https://doi.org/10.1038/s41589-018-0091-7
Hall, A. R., Ashby, B., Bascompte, J., & King, K. C. (2020). Measuring Coevolutionary Dynamics in Species-Rich Communities. Trends in Ecology & Evolution, 35(6), 539–550. https://doi.org/10.1016/j.tree.2020.02.002
Lau, Y. H., Stirling, F., Kuo, J., Karrenbelt, M. A. P., Chan, Y. A., Riesselman, A., Horton, C. A., Schäfer, E., Lips, D., Weinstock, M. T., Gibson, D. G., Way, J. C., & Silver, P. A. (2017). Large-scale recoding of a bacterial genome by iterative recombineering of synthetic DNA. Nucleic Acids Research, 45(11), 6971–6980. https://doi.org/10.1093/nar/gkx415
Røder, H. L., Sørensen, S. J., & Burmølle, M. (2016). Studying Bacterial Multispecies Biofilms: Where to Start? Trends in Microbiology, 24(6), 503–513. https://doi.org/10.1016/j.tim.2016.02.01
Ceroni, F., Boo, A., Furini, S., Gorochowski, T. E., Borkowski, O., Ladak, Y. N., Awan, A. R., Gilbert, C., Stan, G.-B., & Ellis, T. (2018). Burden-driven feedback control of gene expression. Nature Methods, 15(5), 387–393. https://doi.org/10.1038/nmeth.4635

Last updated: October 1, 2020 Back