Common principles and best practices for engineering microbiomes

Abstract

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

Figure 1.

The design-build-test-learn cycle for microbiome engineering. The figure presents key aspects and approaches of each phase of the design-build-test-learn (DBTL) cycle. The cycle starts with a defined engineering objective that determines the design and produces an engineered microbiome that performs the desired function(s).

Figure 2.

Top-down and bottom-up approaches to design microbiomes. The left panel illustrates a bottom-up design workflow starting from pure isolates. Physiological characterization of individual organisms is performed, and metabolic modeling is used to design consortia for desired function (produce light blue compound from dark blue compound). Genetic engineering and synthetic biology strategies are used to optimize system function (identifying gene editing targets that re-route metabolic flux away from toxin (purple) and towards desired product; designing of toxin reporter strain). The right panel illustrates a top-down design starting with an inoculum containing uncultivated microorganisms from the environment. Community characterization of mixed microbiome is performed, and bioprocess modeling (mass balance analysis including kinetics and microbial growth) is used to develop selection strategies to achieve desired function (produce light blue compound from dark blue compound). Reactor engineering design is used to optimize system function. The middle panel shows an integrated top-down bottom-up design. Combinations of uncultivated consortia and defined cultures are selected to achieve desired functions. Community characterization is performed and microbiome modeling that integrates process-based simulation with metabolic modeling is used to develop selection strategies and analyze microbiome metabolic fluxes. The shapes of the microorganisms represent different isolates or communities selected during design.

Figure 3.

Building self-assembled and synthetic microbiomes. (a) This example shows a protocol for assembling synthetic microbiomes from multiple microbiome sources. Complex microbiomes can be taken apart into key functional members using automated microfluidic cell sorting techniques. Isolated or enriched members can then be recombined into synthetic consortia using liquid handling robotics for downstream screening and/or cultivation. (b) Microbiome assembly can also be achieved through environmental selection via bioreactor manipulation or biostimulation (top) or using bioaugmentation with defined cultures (bottom). (c) Another option is microbiome assembly through directed adaptation and/or evolution of the microbiome to acquire or optimize a desired function. (d) In situ microbiome engineering can be used to add new functions to microbiomes residing in the environment.

Figure 4.

Testing microbiome function. (a) Isotopic tracers combined with metaproteome can also be used to measure microbiome metabolic flux by analyzing isotopic labelling patterns of short peptides rather than amino acids (metabolome). (b) Biorthogonal non-canonical amino acid tagging (BONCAT) is a method for rapid profiling of the anabolic processes (growth) in situ using either fluorescent detection or metaproteomics. (c) Metagenomics, metatranscriptomics, metaproteomics, and metabolomics can be integrated to reconstruct and analysis metabolic network expression in microbiomes. (d) An automated microbioreactor platform enables high-throughput analysis of microbiome processes across diverse conditions (for example, with changing environmental or physiological variables). The platform can integrate tools for detailed functional analysis of individual microbiome members to complex communities. HPG: the amino acid homopropargylglycine.

Figure 5.

Learning fundamental principles for microbiome engineering. (a) Model laboratory ecosystems can be used for controlled experiments with simplified microbiomes and environmental properties, representing an in-between of pure lab conditions (such as test tubes or flasks) and complex natural environments (such as soil or the ocean). Continuous cross-examination between laboratory-scale models and natural complex ecosystems will be needed for developing engineering principles and practices that are robust in real systems, while also tractable in the lab. This will require close collaboration between multiple stakeholders, including researchers and end-users (such as hospitals or treatment plants) that have expertise and experience with issues specific to each scale. Key principles that need to be learned to enable systematic microbiome engineering are microbial interaction mechanisms, mechanisms governing functional stability and degeneracy, and frameworks for quantitatively mapping and simulating ecological niches in complex ecosystems.