Engineer the spatial characteristics of a microbiome.

Microbiome Engineering

Engineer the spatial characteristics of a microbiome.

Current State-of-the-Art

Uncontrolled spatial localization of engineered microbiomes, via growth, spread, or other mechanisms, could have detrimental health and environmental consequences, and limit the usefulness of engineered microbiomes. Additional tools are needed to help control the space occupied by engineered microbiomes, in response to external inputs and with autonomous feedback control. CRISPR-Cas circuits can be used to selectively kill bacteria.¹^,²^,³ High-throughput functional genomics in controlled lab environments allows for identification of previously unstudied metabolic pathways.⁴^,⁵

Measuring population and nutrient dynamics in a microbiome has been the subject of extensive research efforts.⁶ Recently, sophisticated culture systems have emerged using 3D printing and microfluidics to systematically environmental conditions.⁷^,⁸^,⁹ These systems can be used to observe nutrient and population dynamics, but tools to accurately mimic natural environments are still emerging. Furthermore, most bacteria remain uncultured,¹⁰^,¹¹ particularly from non-human environmental samples. Some recent measurement successes include MaPS-seq (Metagenomic Plot Sampling by sequencing) which captures the spatial landscape of microbiota from “plots” of the microbiome host,¹² methods for ratiometric gas reporting to detect gene expression and cell counts in soil microbes in a quantitative manner without having to physically alter the soil matrix,¹³ and mass spectrometry methods to measure the chemical makeup of human skin, a complex system that includes interaction between the host, a microbiome, and the surrounding environment.¹⁴ Most methods, however, are destructive and there are limitations on what data can be captured from the same sample. Further, there is limited knowledge of how current model systems replicate variations that occur on short (sub-daily) timescales; and how technical errors/variations impact data produced from models.

Genomically recoded organisms can be engineered with effective growth control strategies.¹⁵^,¹⁶^,¹⁷ Genome reduction approaches to date have largely focused on identifying minimal genomes for model organisms to improve bioproduction (e.g., Escherichia coli MDS42, Bacillus subtilis, Pseudomonas putida). Genome reduction techniques could be applied to shape the environmental niche of microbes and provide evolutionary robustness against complementation for populations deployed for human health, remediation, and plant growth.

Complete spatial and temporal control over individual species will enable more advanced pattern formation within a microbiome. Two-member bacterial populations that maintain the composition of the population (e.g., proportions of two bacterial species) using feedback control via cell-to-cell signaling molecules have been designed.¹⁸^,¹⁹^,²⁰^,²¹ Spatial patterning and Turing patterns have been created by single-species bacterial populations with synthetic circuits inside the individual cells.²²^,²³^,²⁴^,²⁵ Escherichia coli have recently been engineered to form templated two-dimensional patterns using optogenetics, but this functionality has not been demonstrated in any other organism yet.²⁶ More fine-tuned control must be developed for more ambitious applications, such as engineered living materials.²⁷

Breakthrough Capabilities & Milestones

Develop tools to engineer the spatial characteristics of microbiomes.

Generate standardized methods for determining basic physiological parameters (e.g., growth rate, death rate, motility, metabolism) in pure cultures and correlate with measurements of in situ microbiomes (e.g., genome ratio for replication rate).

Bottleneck/Challenge: Cell-to-cell variation within a species could affect physiological parameters for particular cultures.²⁸

Potential Solution: Establish benchmark culturing methods that can be used for both sample collections (e.g., ATCC) and isolated strains.

Bottleneck/Challenge: Some species cannot be grown in monoculture.

Potential Solution: Design well-characterized helper strains that allow for growth in coculture, and enable measurements of physiological parameters in simplified systems.²⁹

Track allele-level genetic changes and transmission of mobile elements (e.g., plasmids, phages) in hosts to measure population dynamics, genome evolution, and genetic exchange within a microbiome.

Constrain environmental niche by removing genes critical for persistence in various environments (e.g., eliminate biofilm formation or carbon utilization functions).

Create large databases of three-dimensional imaged in situ microbiomes (e.g., high-resolution sections of sediment columns characterized for geophysical, chemical and biological properties, tissue organoids with in vivo microbiomes) to help predict properties of novel microbiomes.

Design computational tools that identify gene loci or networks that can be removed without impacting organism growth or viability.

Engineer tools that reduce or prevent horizontal gene transfer in microbiomes to prevent reacquisition of genes that may allow for increased spatial spread.

Bottleneck/Challenge: Lack of genetic tools that enable a precise control of the degree and type of horizontal gene transfer.

Potential Solution: Engineer exclusion systems,³⁰ or recode genomes to eliminate tRNA codons.³¹

Design mechanisms to limit microbiome growth that are orthogonal to nutrient restrictions.

Engineer technologies that can rapidly and robustly reduce microbiome genome sizes in natural environments to constrain spread.

Engineer a microbiome to change size in response to its environment.

Engineer structured microbial communities in an x-y plane across meter-scales in response to synthetic signals (e.g., engineered metabolites, phages).

Bottleneck/Challenge: Cells, viruses, and nutrients diffuse through space in a way that can be difficult to predict and control.

Potential Solution: Design multi-signal inputs for growth, so microbiome has tighter control than a simple on-off switch (i.e., if/and/or logic gates that direct growth).

Potential Solution: Nutritional (or other) auxotrophies that prevent growth in absence of a small molecule (e.g., microbiome fertilizer for fields).

Engineer microbiomes that grow or conform to defined locations (x-y-z space) or can be specifically patterned or distributed in space.

Engineer microbiomes so growth and patterning is determined based on specific environmental cues (e.g., surface texture changes, chemical or nutritional environment).

Engineer microbiomes that grow to fill, or shrink to fit, a functional niche (e.g., nitrogen-fixing microbiomes that continue growing until they reach a patch of soil that has sufficient nitrogen).

Engineer spatially self-organizing communities (i.e., organization is templated rather than shaped by the environment).

Engineer microbiomes that can be used to produce a repeated pattern on a 2D surface.

Bottleneck/Challenge: Escherichia coli that were engineered to be templated onto materials had specific signaling pathways removed to prevent cross-talk. No other microbe’s signaling networks are characterized in a way that would allow for that problem to be identified and addressed a priori.

Potential Solution: Create a catalog of signaling pathways that direct organism motility and adherence to enable more rapid design and engineering of templatable organisms.

Bottleneck/Challenge: Most biological input signals to direct microbial cell localization (e.g., chemicals, secondary metabolites, nutrients) diffuse readily and are not suitable for patterning.

Potential Solution: Engineer plasmids or operons that can be inserted into diverse organisms to make them light-sensitive for optogenetics.

Design 3D structured microbiomes in a controlled environment.

Bottleneck/Challenge: Self-organizing systems that use motility or chemotaxis to direct microbial movement will require more advanced mechanisms for restricting compound diffusion through space.

Potential Solution: Create biodegradable materials that can be 3D printed into chemical-containing matrices that will facilitate growth of complex, patterned microbiomes.

Bottleneck/Challenge: Optogenetic techniques are more precise than chemical-based techniques for controlling a cell’s location, but most solid, microbial growth materials are not optically clear.

Potential Solution: Engineer optically clear, biodegradable materials so optogenetics can be used to determine bacterial adhesion and microbiome structure.

Engineer microbiomes that stop growing once they grow to a fixed size.

Engineer microbiomes that self-localize in complex environments (e.g., soil, human gut) so they can be deployed to hard-to-access locations.

Engineer microbiomes with intrinsic Turing patterning so they form desired structures.

Bottleneck/Challenge: Designing Turing patterns requires robust control over extracellular diffusion and gradient formation.

Potential Solution: Advance engineering of cell-to-cell signalling to enable tighter control over Turing pattern formation.³²^,³³

Engineer microbiomes to create sophisticated three-dimensional structures with defined domains that interact and work together.

Design microbiomes that alter their extracellular environment.

Program the formation of biofilms or other rigid structures that fix the spatial organization of a microbial community.

Generate different types of structured environments that extend beyond natural biofilms.

Build in control of biofilm formation to create programmed, heterogeneous structures.

Create biofilms that function as autonomous organs.

Footnotes

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