Control the temporal dynamics of engineered microbiomes.

Microbiome Engineering

Control the temporal dynamics of engineered microbiomes.

Current State-of-the-Art

Engineering temporal stability will be critical for the advancement of microbiome engineering. However, no framework for predicting or engineering such stability yet exists.¹ The development of a time resolved, high-throughput gut microbiome model system helped generate a predictive dynamic model to examine community evolution over time.² The model was capable of predicting positive and negative interactions within the community, but was not sufficient to predict key species that impacted community assembly. Increasing engineered complexity also has shown to decrease stability over time³^,⁴ so the capacity to decrease a new technology’s microbial cost (e.g., metabolically, genetically) will be critical for its long-term maintenance in a microbiome. Greater temporal control over microbiomes will require a better understanding of the population dynamics and ecological interactions that shape the evolution of a microbiome, such as random mutations and horizontal gene transfer.⁵ Temporal control will also be enabled by advances in molecular clocks that can regulate microbiome functions over time. A synchronized lysis circuit has been engineered in Salmonella typhimurium to induce population lysis at a set cell density, without regard to time.⁶ Emerging tools have extended the time span that engineered genetic clocks can be used to days,⁷^,⁸ but these must continue to be extended to obtain full temporal stability over engineered microbiomes. Some reactive transport models and other simulation tools to predict physical dynamics have been developed.⁹^,¹⁰ However, the state-of-the-art is a far reach from selectively manipulating or eliminating individual constituents of a microbial community, particularly in tailored-spectrum and spatiotemporal means.

Breakthrough Capabilities & Milestones

Determine the physical dynamics of a microbiome over time.

Measure nutrient and water flow through diverse environments (e.g., soil, soil to a plant, in vivo) and community members (e.g., viruses, bacteria, fungi) in high-throughput.

Bottleneck/Challenge: Limited tools to assay environments without specialized equipment/techniques (e.g., isotope analysis) or at different scales.

Potential Solution: Micro and macro-scale controllable environments (e.g., EcoFABs and EcoPods¹¹,¹² for microbiome engineering) to help test different environments at a smaller scale than field tests.

Create high-throughput, multiplexed, and automated systems to quantify physiological parameters of a complex microbiome in a controlled environment.

Bottleneck/Challenge: It is challenging to determine how nutrients are utilized or exchanged between organisms, particularly in high throughput.

Potential Solution: Expand the functionality of stable isotope probing methods (e.g., increase sensitivity, improve sampling processes for anaerobic environments) to enable more accurate metabolite tracing in a complex microbiome.¹³

Measure environmental dispersal and drift of microbes, phages, and chemicals across different environments to help generate predictive ecological models.

Generate methods that enable rapid, high-throughput quantification of physiological parameters in complex, natural microbiomes.

Bottleneck/Challenge: Methods for documenting large spatial volumes primarily rely on microscopy-based techniques, which are poorly suited to measurements in natural environments.

Potential Solution: Design computational models that use measurements at the boundaries of microbiomes, which may be easier to sample, to predict responses (e.g., transcriptional, translational, metabolic) at other locations in the microbiome.

Engineer mechanisms to control the growth and spread of a microbiome over time.

Engineer microbiomes that integrate programmed self-destruction with cell oscillators, so death is initiated based on time rather than cell density.

Use in situ measurements at a single point in time to predict future limiting factors (e.g., space, nutrients, specific trace metals, essential amino acids) for a microbiome.

Bottleneck/Challenge: Many industrial processes are being converted to continuous fermentation (rather than batch fermentation) processes, where genetic drift is a more significant concern than growth-limitation.

Potential Solution: Use genetic data from directed evolution experiments to anticipate mutations, and design genetic tools (e.g., phages, CRISPR) that target and remove cells that acquire those mutations.

Engineer cell-cell circuits that kill a counting strain, plus neighboring species in the microbiome, after a given number of cell divisions is reached.

Engineer a microbiome that contains a ‘universal clock’ species that triggers self-destruction of the microbiome upon reaching a functional endpoint.

Bottleneck/Challenge: Complex microbiomes will contain diverse species that cannot normally be killed using the same mechanism, requiring a synthetic cell to encode multiple genetic pathways to destroy the microbiome.

Potential Solution: Use orthogonal microbe-microbe signaling networks to trigger cell death, so a single signal from the synthetic cell clock can trigger self-destruction of the entire microbiome.

Design microbiomes that can operate at multiple timescales to enable more complex functions (e.g., lay dormant for months to perform functions on second or minute timescales).

Engineer microbiomes that retain function on short to evolutionary (i.e., months to years) time scales.

Model the fitness impacts of microbiome engineering and determine its contribution to escape frequency and competitive advantage.

Design and build genetic components that are more robust to mutation or loss than conventional genetic designs.

Map evolutionary trade offs when multiple pressures are applied to a microbiome (including long-term continuous culturing) and use this information to design resilient organisms or combinatorial controls that prevent evolution away from a desired function.

Bottleneck/Challenge: Designing around evolutionary pressures requires prior knowledge of the pressures and how they influence a microbiome (e.g., coevolutionary design to prevent phage resistance,¹⁴,¹⁵ use multiple antibiotics to prevent antibiotic resistance.¹⁶

Potential Solution: Experimental microbiome models that allow for high-throughput measurements over time, to examine how microbiomes evolve in response to different selective pressures.

Engineer microbiomes that remove or kill community members that lose their engineered function.

Bottleneck/Challenge: Killing or removing groups of cheaters may disrupt spatial structure of microbiome.

Potential Solution: Engineer strains that can recolonize or relocate to spaces where cheaters have been killed or removed.

Bottleneck/Challenge: It may be difficult or impossible to engineer selective forces for some functions.

Potential Solution: Create re-engineering schemes to overcome low levels of function loss without requiring selective pressure (e.g., gene drives).

Engineer an organism that influences future successional dynamics in an environment (e.g., niche construction, niche preemption, and priority effects), so the microbiome influences the surrounding environment past the microbiome’s functional lifespan.

Bottleneck/Challenge: Engineering succession requires in-depth knowledge of not only the engineered microbiome, but also the natural microbiome dynamics that would otherwise grow back.

Potential Solution: Establish best-practices that require measuring and preserving natural microbiomes before engineered microbes are introduced, to always ensure that the original ecology can be reanalyzed.

Footnotes

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