Generation of biomes and consortia with desired functions and ecologies.

Engineering Biology

Generation of biomes and consortia with desired functions and ecologies.

We make the distinction between biomes and multicellular organisms through the definition of a biome as containing organisms (including multicellular organisms) with different genomes.

Current State-of-the-Art

While a few microbiome systems are well characterized, such as rhizobium for nitrogen fixation, we are still struggling to understand and how and why consortia of microbes cooperate in nature. Systems with mutual metabolic dependencies (synthetic heterotrophs) have enabled the construction of engineered consortia that are stable in laboratory settings.¹ Our ability to produce synthetic interactions is possible with some ongoing efforts; for example, a small number of synthetic microbial consortia have been created as model systems, consisting of 2-3 different organisms.² Bioremediation and waste-water treatment demonstrate the principles that consortia can be used industrially, while probiotics and fecal microbe transplants demonstrate the principle that the composition of gut flora can be manipulated. Industrial startups in this space are emerging at a rapid pace, but our ability to make targeted changes, such as adding or removing a single organism, in an existing microbiome are very limited. Overall, our ability to understand and manipulate systems with specific functions or to remediate biomes and consortia that cease to function as desired is very limited.

Breakthrough Capabilities & Milestones

Ability to control cell-to-cell communication between different species.

Tightly-controlled promoter-response regulator systems that enable intra- and inter-species cellular communication.

Bottleneck/Challenge: Limited technologies for the exchange of biochemical information within a population of cells.

Potential Solution: Use available cell-cell communication regulators to enable cell-cell communication in broader range of organisms.

Potential Solution: Expand communication systems by using a broader range of natural quorum-sensing/communication modules (such as acyl-homoserine lactones, autoinducer-2, peptide-based signaling, and metabolic signaling).

Synthetic cell-to-cell communication elements and networks that function in a broad range of host organisms.

Bottleneck/Challenge: Current synthetic communication systems have been engineered to function in a limited set of organisms.

Potential Solution: Identify cell-cell communication elements in non-traditional hosts, characterize, and modify for use in synthetic circuits.

Potential Solution: Engineered membranes (specifically, receptors) to transmit information via cell-cell contact.

Potential Solution: Engineered transmission of secreted biomolecules and exosomes.

Signal-response pathways that function in synthetic communities of 5-10 organisms, employing a variety of pathway types and host species.

Bottleneck/Challenge: Cross-talk between communication elements.

Potential Solution: Combine communication modules in a manner that minimizes cross-talk; employ metabolic signaling to coordinate behavior across population as needed.

Bottleneck/Challenge: Feasibility of enabling signaling between all community members.

Potential Solution: Design networks with essential community-level coordination using a limited set of communication modules.

Ability to produce engineered microorganisms that can reliably invade and coexist within a complex community and manipulate the consortium/biome function and behavior.

Bottleneck/Challenge: Ecological understanding of complex microbiomes, rules of coexistence, cooperation, and competition.

Potential Solution: Characterization of broad ranging natural, as well as synthetic, communities during environmental and ecological changes.

Potential Solution: Employ cell-cell communication or metabolic interactions to enable engineered cells to be accepted into/required by the target community.

Ability to characterize, manipulate, and program the three-dimensional (3D) architecture of a biome (i.e., the “ecosystem” of a natural or manipulated biome containing multiple species).

Use of existing technologies (including metagenomics, transcriptomics, proteomics, and mass spectrometry) to better understand the species composition and collective components of microbial communities and consortia.

Non-destructive, 3D visualization of microbial communities from a broad range of environments.

Ability to manipulate the 3D architecture of natural or engineered communities using external inputs (such as molecules, temperature, or pH).

Bottleneck/Challenge: Limited understanding of how communities respond dynamically to environmental changes, especially in non-homogeneous systems.

Potential Solution: Characterization of how natural and engineered communities respond to environmental changes; build spatio-temporal models that incorporate genomic, functional, and environmental outcomes.

Bottleneck/Challenge: Ability to add sensing and actuation capabilities to any cell type in a community setting.

Potential Solution: Targeted gene editing approaches that deliver only to a specific organismal-member of a community.

Programmed communities that self-assemble into a desired 3D architecture.

Bottleneck/Challenge: Tools that enable desired stratification and self-organization (or reorganization) of microbial communities.

Potential Solution: Use strategies from multi-organismal cell development, such as the generation and sensing of gradients and motility.

Potential Solution: Specific binding between cells using extracellularly-displayed proteins to build or lock in specific levels of organization.

Ability to control and/or define the function of an engineered microbial community/biome.

Ability to combine species with specialized functions to enable the production of desired products.

Assembly of consortia to produce desired molecules/products, considering community-level metabolic flux.

Plug-and-play assembly of consortia to produce desired molecules/products from specific starting materials, considering community level metabolic flux and organism-to-organism communication.

For example, developing consortia of different microbial species that are grown/fermented together to create a desired product.

Bottleneck/Challenge: Optimal growth/production conditions of the individual community members are likely to be different.

Potential Solution: Screen for, or predict, conditions that are optimal for the community; tune relative population densities through inoculation ratios and via feedback (cell-to-cell communication).

Potential Solution: Engineer community members to function optimally under target bioreactor/process conditions.

On-demand assembly of consortia that are programmed to respond dynamically, such that they can use different feedstocks, metabolize toxins or toxic byproducts, or produce different products in response to endogenous (system) or exogenous (user) cues.

Targeted modification of an existing microbiome to enable new functions or address dysbiosis – at the host, community, or environment level – through the addition, removal, or reorganization of the community members.

Use of existing technologies (including metagenomics, transcriptomics, proteomics, and mass spectrometry) to characterize functions of microbial communities from a broad range of environments.

Characterize how select microbiomes respond to changes in the environment, including the addition of toxins, the introduction new organisms (pathogens or commensals), and the selective removal of species from the community.

Predictive models of microbiome function and response to a broad range of environmental and ecological changes.

Bottleneck/Challenge: Need to be able to undertake modeling and comparative pathway analysis to determine most robust, prioritized, and resilient systems.

Potential Solution: Controlled laboratory experiments or observational studies where microbiome function is determined as a function of community composition and environment.

Potential Solution: Machine learning approaches to determine whether there are patterns between microbiomes of interest (with respect to both form and function); follow-up with strategies designed to improve mechanistic understanding, as needed.

Ability to modify an existing biome or consortia as desired.

Biome/consortia modifications include: 1) adding functions such as the ability to sense the environment and coordinate responses for defined outcomes (pathogen defense, substrate transformation, and biosensing), and 2) manipulating composition of host-associated communities to address dysbiosis or add new functions.

Bottleneck/Challenge: Need controlled invasion of non-native organism with desired properties or the ability to reintroduce a host-associated organism after engineering.

Potential Solution: In situ gene editing to add function to a community with minimal disruption.

Bottleneck/Challenge: Ability to selectively add and remove community members, including engineered cells, and have them persist in a community for a desired length of time and at a desired population density.

Potential Solution: Kill switches (to remove organisms), auxotrophy/complementation and shared metabolisms (to retain organisms).

Bottleneck/Challenge: Need an integrated understanding of microbial community ecology and function; ability to predict how adding a new member with desired functions will affect community health and stability.

Potential Solution: Focus on developing organisms that can integrate into host consortia and deliver the required functions; recent evidence suggests that function is more important that what organism is carrying it out.

Footnotes

Pacheco, A. R., Moel, M., & Segrè, D. (2019). Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nature Communications, 10(1), 103. View publication.
McCarty, N. S., & Ledesma-Amaro, R. (2019). Synthetic biology tools to engineer microbial communities for biotechnology. Trends in Biotechnology, 37(2), 181–197. View publication.; 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. View publication.