Design microbiomes for any function or environment based on their component organisms or species (i.e., bottom-up engineering).

Microbiome Engineering

Design microbiomes for any function or environment based on their component organisms or species (i.e., bottom-up engineering).

Current State-of-the-Art

Basic bottom-up, in vitro models (three or four organisms) exist to manipulate rhizosphere microbiomes,¹ photosynthetic organisms,² and co-dependent communities of gut bacteria.³ There has been recent success in engineering microbes found in important but experimentally challenging environments, such as Bacteriodes thetaiotamicron⁴ and Eggerthella lenta,⁵ both found in the gut microbiome. Nascent efforts have been made in oleaginous yeasts, Neurospora, Aspergillus, Neocallimastigomycota, and archaea.⁶ A few studies have used engineered Candida spp. as a live vaccine or for host factor secretion.⁷^,⁸ Standardized protocols, parts, and chassis for diverse organisms will allow for more predictable, fine-tuned control over metabolic outputs and functions in engineered communities.⁹

There are still challenges in predictive computational models for bottom-up microbiome design.¹⁰^,¹¹ A predictive dynamic model of a small anaerobic microbial community was used to infer the design principles behind the microbiome’s stable coexistence.¹² Additional confounding factors have also been identified, such as microbial invaders that do not persist in the microbiome, but can still influence community evolution due to their impact on bioacid availability and pH.¹³

Breakthrough Capabilities & Milestones

Design and engineer functional microbiomes by adding or modifying individual species of microbes.

Generate synthetic microbiomes with two to five characterized species (of different functional guilds) imitating functions of a natural community.

Control community functional dynamics (i.e., abundance of species and/or expression profile) in a synthetic microbiome composed of two to five characterized species (of different functional guilds).

Design a community with a sufficient set of partially redundant guild members so that it is functionally robust against environmental or community variations.

Bottleneck/Challenge: There is relatively little known about how environmental changes or other community members impact growth of even many model organisms.

Potential Solution: Build a collection of well-annotated, diverse traits linked to the guild-definitive trait of a constituent taxa. Individual taxa that express a trait (e.g., nitrate reduction), would be paired with a variety of linked “traits” (e.g., differences in maximal flux, different carbon source requirements, different operational efficiency with temperature). These provide a starting point for designing more complex communities whose members can cover for one another as conditions change.

Predict and engineer interactions between different species within the microbiome.

Establish methods for determining what metabolites are biologically available to a microbiome in any environment (i.e., identify what a microbe can use to grow).

Bottleneck/Challenge: Tracing metabolites to specific microbes is dependent on culture conditions.

Potential Solution: Use single-cell metagenomics or transcriptomics to reduce microbiome complexity without isolating individual species.

Potential Solution: Systematically compare metabolite profiles of increasing microbial diversity to pure cultures to understand higher-order interactions and regulation of metabolites.

From metagenomic data, identify individual microbial species with related or coherent functions.

Bottleneck/Challenge: Lack of data standards and integration across various microbe and microbiome databases (e.g., GutFeeling KnowledgeBase, MGnify).

Potential Solution: Open Access database with ‘hidden Markov models’ based on metagenome/activity data that define guilds.

Bottleneck/Challenge: Many unsequenced or unidentified microbial species are in any given environment, ecosystem, or niche.

Potential Solution: Use untargeted -omics surveys to classify and identify all the microbial species present in various environments.

Bottleneck/Challenge: Genes with disparate sequence and hypothetical proteins may not be flagged as a particular guild leading to the functional mislabeling of microbial species.

Potential Solution: Integrate traditional molecular biology and genetic techniques to the study of synthetic microbial communities to further classify and assign gene function for genes important for interactions and behavior of microbiomes.

Engineer and optimize microbiomes where mutually-required interactions between species and functional guilds are implemented.

Bottleneck/Challenge: The presence of a given metabolite that is intended to trigger population growth of a mitigating community member (e.g., production of a toxic metabolic byproduct that can be neutralized by a complementary organism) may not be detectable as a direct trigger.

Potential Solution: Engineer sensor capabilities into the responsive organism such that it is able to upregulate its activity in response to the metabolite of interest, or in response to another correlated metabolite that is more easily sensed.

Generate predictive models and experimental frameworks to engineer microbiomes with defined species composition and interactions.

Predict and engineer interactions between a microbiome and the environment (e.g., temperature, oxygen concentration, pH, small molecules or drugs, dietary compounds).

Engineer non-model microbiome species to activate a genetic program in response to a single environmental perturbation.

Bottleneck/Challenge: Signal transduction can vary significantly between species, requiring fine-tuning for each new species.¹⁴^,¹⁵,¹⁶^,¹⁷

Potential Solution: Introduce synthetic signaling pathways so inputs/responses can be easily tuned between different guild/microbiome members.

Bottleneck/Challenge: Disentangling environmental responses from effects caused by the loss of a microbiome’s species, guilds, or symbionts.

Potential Solution: High-throughput screening methods to determine the sensor-regulator responses employed across different environmental stimuli in varied community compositions.

Establish and validate experimental frameworks to engineer microbiomes responsive to the desired environmental perturbations.

Engineer a microbiome that responds to multiple environmental perturbations.

Bottleneck/Challenge: Integrating responses for all guilds across multiple environments, including conditions that have null or non-optimal responses.

Potential Solution: Create modeling or machine learning pipelines that identify optimal design strategies depending on how individual guilds behave individually in response to complex environmental cues (e.g., temperature/oxygen/moisture gradients, decreasing pH with growth).

“Routine” prediction and manipulation of any species within a microbiome using environmental perturbations for desired functions

Establish Design-Build-Test-Learn pipelines for bottom-up microbiome engineering – from culturing protocols, to genetic tools, to ‘deployment’ in controlled and natural microbiomes.

Develop standard protocols for microbiome stock maintenance and propagation.

Computationally predict behaviors of individual microbes within the biome and determine minimal nutrient requirements.

Engineer markerless selection methods that allow for stable maintenance of an engineered strain in a microbiome.

Bottleneck/Challenge: We lack the ability to perform rapid, high-throughput genetic manipulation on nonmodel organisms.

Potential Solution: Engineer large collections of Escherichia coli cloning strains with unique DNA methylases, to increase the probability of success when transforming or conjugating DNA into a novel host.

Bottleneck/Challenge: Plasmids can function slightly differently in different host organisms, requiring slight modifications or reengineering.

Potential Solution: Design a broad range of well-defined, minimal, modularized vector systems with different conjugal proteins, markers, and GC-content.

Expand use of and capacity of high-throughput platforms to allow for increased experimental reproducibility and accuracy.

Bottleneck/Challenge: Measurements often need to be performed under specialized conditions to prevent contamination and reduce safety concerns.

Potential Solution: Create dedicated satellite facilities that allow for remote analysis of microbiomes and can train staff on specialized techniques or equipment.

Bottleneck/Challenge: Equipment vendors often use various automation systems that need to be specially programmed to integrate properly.

Potential Solution: Train programming experts to familiarize them with instruments and control softwares from different companies and develop algorithms for full integration from DNA assembly, transformation, outgrowth, and screening.

Bottleneck/Challenge: Many high-throughput systems are engineered with model organisms (primarily Escherichia coli) in mind, and are not well-suited to the needs of non-model organisms (e.g., not built for long incubation time frames, anaerobic conditions, working with non-traditional substrates).

Potential Solution: Engineer automated high-throughput systems that can handle non-model organisms better.

Engineer methods that enable fully autonomous microbiome characterization (e.g., grow in culture, measure metabolism) with limited prior knowledge of a sample.

Bottleneck/Challenge: Many untargeted -omics techniques require either large sample inputs or labor-intensive purification protocols due to limits of detection inherent in the technique.

Potential Solution: Identify a limited set of compounds, genes, or metabolites that are sufficient for characterizing growth to allow for more targeted -omics that can be performed without hands-on interventions.

Rapidly develop tools to control any organism (e.g., identify culture conditions, genetic manipulation, spatial localization), including uncultivated organisms, after initial identification via sequencing.

Bottleneck/Challenge: Every new organism will have unique nutrient/growth requirements and physiology, that make it challenging for existing knowledge to be applied directly.

Potential Solution: Improve tools used to generate metabolic models from metagenomics and metagenome-assembled genomes to enable more rapid cultivation of “uncultivated” organisms.¹⁸

Footnotes

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