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. 2022 Aug 12;11(9):2909–2916. doi: 10.1021/acssynbio.2c00079

A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

Swetha Sridhar †,*, Caroline M Ajo-Franklin ‡, Caroline A Masiello ‡,§
PMCID: PMC9486965  PMID: 35961652

Abstract

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Microbial biosensors sense and report exposures to stimuli, thereby facilitating our understanding of environmental processes. Successful design and deployment of biosensors hinge on the persistence of the microbial host of the genetic circuit, termed the chassis. However, model chassis organisms may persist poorly in environmental conditions. In contrast, non-model organisms persist better in environmental conditions but are limited by other challenges, such as genetic intractability and part unavailability. Here we identify ecological, metabolic, and genetic constraints for chassis development and propose a conceptual framework for the systematic selection of environmental biosensor chassis. We identify key challenges with using current model chassis and delineate major points of conflict in choosing the most suitable organisms as chassis for environmental biosensing. This framework provides a way forward in the selection of biosensor chassis for environmental synthetic biology.

Keywords: chassis, biosensor, environmental, synthetic biology, marine, soil

Introduction

The development of modular, controllable, and highly tunable genetic circuits in synthetic biology has opened new avenues of research in the environmental sciences.1 The ability to sense and report microbial experiences allows for the design of biosensors—genetic circuits responsive to small molecule signals housed in a microbial host, or chassis. While the bulk of our efforts as a community have been dedicated to the design and optimization of biosensing circuits, far less attention has been devoted to optimizing the choice of organisms to host these circuits.2

Environmental biosensing poses unique restrictions on the choice of a chassis organism. Natural environments are spatially heterogeneous and temporally variable, and these variations severely impact the survival of non-native species.3 Unlike bioproduction facilities or clinical laboratories, natural environments host a robust pre-existing microbiome. Furthermore, most terrestrial and marine environments have low nutrient concentrations with occasional bursts of organic matter that stimulate metabolic activity.4 Deploying biosensors into the environment poses a significant risk due to the potential for uncontrolled proliferation and gene transfer into native species. These additional constraints make the choice of a chassis for environmental biosensing more challenging than for biosensing within bioproduction or clinical settings.

Much of the collective efforts of the synthetic biology community have been invested in optimizing genetic circuits in laboratory-domesticated Escherichia coli, with some tools being expanded into Pseudomonas putida,5,6Bacillus subtilis,7 and Saccharomyces cerevisiae.2,8 In 2016, Adams2 suggested developing universal toolboxes for a select set of chassis—P. putida, B. subtilis, Geobacillus, cyanobacteria, and Deinococcus—to widely deploy for diverse applications in synthetic biology. We believe that advances over the past five years in rapid identification of genetically tractable members of a community,9−11 broad-host range plasmids for non-model organisms,5,12,13 and high-throughput chassis engineering techniques14,15 position the field to take a more ecologically relevant, environment-centric approach for chassis selection.

Here, we advocate for selecting chassis that persist in the environment of interest by considering physical, chemical, and ecological factors that govern chassis persistence. We provide a conceptual framework for the systematic selection of biosensor chassis to host genetic circuits designed for environmental biosensing. We describe factors—genetic, metabolic, and ecological—that must be considered for a chassis organism to be a biosensor in the environment. We discuss how specific chassis organisms meet these factors and outline constraints of current model chassis organisms. Lastly, we describe our vision for developing a range of chassis for synthetic biology.

Environmental Biosensing Poses Unique Design Constraints on Microbial Chassis

Biosensing in the environment poses unique constraints on the choice of a chassis organism. Biosensor persistence in the environment depends on the evolutionary stability of the sensing genetic circuit and the ability of the chassis organism to survive. Additionally, these requirements must be met in a specific ecological and metabolic context. Ecological factors in many environments are still unknown and challenging to characterize. Metabolic factors can also be unknown in most cases and often require rigorous characterization ex situ. Genetic tractability and access to curated genomic databases are not assured for most species from the environment, thereby narrowing down options for chassis organisms. These challenges add significant complexity to choosing a chassis. We posit here that, in environmental biosensing, choosing the right chassis organism is just as important as choosing ideal genetic elements, and a combination of safety, ecological, metabolic, and genetic factors must guide this choice.

Constraint 1: Do No Harm

Biosensing in the environment demands stringent requirements for safety. At minimum, this requirement eliminates the use of known pathogens as chassis. As a first step, we recommend following USDA restrictions on microorganisms that have been identified as plant pathogens and can adversely impact agriculture.16 With the advancement of synthetic biology for non-model organisms, it will be essential to periodically revisit the constraints and impacts of engineered organisms in the environment.

The constraint of safety does not stop at the first step in chassis selection—after determining the ideal chassis for the environment of interest, safety must be revisited.1 In particular, evolutionarily stable strategies for biocontainment must be adopted to ensure the safe deployment of any biosensor chassis in the environment. The NIH provides an escape frequency of 1 in 108 cells as a guideline to determine the success of biocontainment strategies.17 Some successful biocontainment strategies developed include toxin–antitoxin systems,18 auxotrophy,19 inducible killswitches,20−22 phage lytic systems,23 sequence entanglement,24 and xenobiology.25−31 For environmental biosensor chassis, a multipronged approach combining different biocontainment strategies is essential to ensure stringent control on engineered organisms in the environment.32,33

Constraint 2: The Chassis Must Be Ecologically Persistent

To sense in the environment, the chassis organism must persist when exposed to biotic and abiotic stresses in the niche of interest without altering its ecological niche in deleterious ways. Organisms live in complex, multidimensional social hierarchies that regulate their survival and function.34,35 As a consequence of this complexity, robust characterization of the organism’s ecological niche is essential for its adoption as a chassis. For example, validating existing syntrophies of a potential chassis organism in complex communities is essential to establish culture protocols and ensure stable strain maintenance.36−38 We summarize some methods of characterizing the ecological context of an organism below.

Interspecies microbial interactions are highly diverse and can be stimulated by the organisms themselves, other organisms in their surroundings, or their environment.39,40 These interactions are complex and hierarchical, making them challenging to emulate experimentally. However, the development of simulation toolboxes to represent the microbial interactome in silico has supported the prediction of interspecies interactions.40−43 Advances in next-generation sequencing coupled with genome-scale metabolic models and constraint-based reconstruction and analysis present platforms for mining novel microbe–microbe interactions and predicting emergent interspecies interactions.44−46 Insights from these models, while still predictive, can inform the ecological context of a potential chassis organism.

Ultimately, characterizing potential chassis organisms in their native ecological context will require experimental emulation of complex microbiomes and their environmental controls, which is a considerable challenge. Recreating the physical matrix of the organism is challenging due to the spatiotemporal heterogeneity and chemical complexity of natural environments.1,47 Furthermore, recreating the biotic matrix is a considerable challenge as the biotic diversity of the environment of interest is unrepresentable ex situ due to limitations in sampling and culturing.48,49 However, characterizing the ecological persistence of an organism may not be as insurmountable a challenge as replicating its native context. Benchtop incubation studies with a sample of the environment of interest offer a solution to characterizing the ecological persistence of a potential chassis in situ. Such incubation studies could include regular sampling and amplicon sequencing, or nondestructive reporters such as inducible volatiles and gas vesicles that act as indicators for chassis survival.1,50 Such incubation experiments offer the closest analog to the environment of interest without reducing environmental complexity.

Constraint 3: The Chassis Must Be Metabolically Persistent

When choosing a chassis to answer an environmental question, we need to determine if the primary metabolism of the microbe is favorable in the environment of interest. For example, obligate aerobes may not persist in soils or sediments with oxygen gradients. Investigations into the primary metabolism of an organism can be performed using genome-scale metabolic modeling (GEMs). GEMs offer a method to interrogate an organism’s metabolic potential and predict cellular growth on diverse substrates.45,51,52 Additionally, if the microbe is predicted to host multiple functionally similar metabolic pathways, it is imperative to determine the physical conditions under which the microbe chooses one pathway over another. For example, purple nonsulfur bacteria from the marine column can be both autotrophs and heterotrophs and can switch between their metabolisms on the basis of their culture conditions.53,54 Understanding this metabolic compartmentalization is imperative to formulating culture media under different conditions.

Along with a potential chassis’ primary metabolic pathways, we need to know if there are any secondary metabolites produced that may either cross-react or interfere with the measurements of our chosen reporter proteins. For example, the production of a colored compound can interfere with colorimetric assays or the production of a native autoinducer can increase the noise in reporting assays. In addition, characterizing an organism’s behavior under nutrient-deficient conditions and stress resilience could be central to its adoption as a biosensor chassis in natural environments.55

Constraint 4: The Chassis Must Be Genetically Tractable

To use a microbe as a chassis for genetic circuits, we need access to its fully sequenced and well-annotated genome. A well-annotated genome will help determine a microbe’s most central pathways, its antibiotic resistance genes, and its defense mechanisms, such as encoded restriction enzymes.56−64 Using a fully sequenced genome and comparative genomics, we can identify putative functions that the bacterium is associated with and characterize expected phenotypes that can be useful in designing cellular screening assays, such as response to an antibiotic or chemical.

Of equal importance is the need for robust DNA delivery protocols. Robust conjugation and transformation protocols allow the insertion of engineered genetic circuits into a chassis organism. Broad-host range genetic plasmid replication origins are the backbone for circuit engineering in non-model bacteria.65 Additionally, methods for genomic integration of synthetic elements are necessary, especially in cases where reporter copy numbers need to be regulated or plasmid maintenance is less likely. Recent work has exploited diverse genetic tools to enable genomic integration in non-model bacteria—recombinase-based and recombinase-CRISPR-hybrids,66−70 CRISPR-based,71,72 retroelement-based,73,74 transposase-based,75,11,76 CRISPR-transposase hybrids,77 integrative and conjugative elements,10,78 and the novel CRISPR RNA-guided DNA integration.79−82 These tools greatly support the engineering of non-model organisms, which results in novel engineerable chassis for biosensing.

While DNA delivery into non-model bacteria is a broadly discussed field of research,83−85 we have distinct suggestions for plasmids that replicate in Gram-positive and Gram-negative bacteria. After gram verification, plasmid origins can be selected from Jain and Srivastava.65 These broad-host range origins of replication are viable options, especially if these plasmids are stably maintained in an evolutionarily similar organism. Conjugative plasmid transfer has the maximum likelihood of success in most organisms that are culturable in similar conditions as an E. coli donor. Electroporation, heat transformation, protoplast-mediated transformation, transduction, and induced natural competence have varied success rates and are highly organism-dependent.83,85−87

In addition to genetic tractability and engineerability, some aspects of cellular physiology and lifestyle must be characterized to select a chassis. Differentiated lifestyles, biofilm formation, spore formation, secretion systems, and membrane-bound transporters can be important for the choice of an organism as a chassis. Foley and Shuler88 and Kim et al.89 discuss the importance of a robust cell membrane. Characteristics such as surface colonization and hydrophobicity may be essential for biosensing in complex, rapidly fluctuating environments.90

Challenges with Current Chassis Organisms

Escherichia coli has been the preferred chassis for synthetic biology as it offers desirable features that are essential for microbial chassis—aside from the ease of genetic manipulation, it cannot sporulate, making it easy to decontaminate from a surface with simple disinfection/sterilization protocols. Lab strains of E. coli have a doubling time of 20–30 min in aerobic rich media, which allows for rapid and facile engineering in lab time scales. E. coli has a simple cell structure with a fully sequenced chromosome and comprehensive metabolic databases that can better inform choices in circuit and chassis design. All these features make E. coli the standard proof-of-concept chassis organism.

Despite these salient features, E. coli has severe restrictions as a chassis for environmental synthetic biology. Environments are physicochemically complex with gradients in pressure, salinity, and pH that can impact chassis persistence. In such nonideal and uncontrollable conditions, the persistence of lab strains of E. coli is less likely. Furthermore, tools developed in E. coli are not easily portable into other potential chassis.91,92 As Adams2 noted, the adoption of different chassis organisms for biosensor circuits is not as trivial as transferring circuits optimized in E. coli into the potential chassis. Expression and regulation of genetic circuits is a highly host-specific process, so much so that even with synthetic regulatory elements, we observe a difference in expression between different microbes.91

We do not recommend a minimalistic approach to chassis selection for biosensing applications in the environment. To expand the library of chassis organisms, Pseudomonas putida (soil), Bacillus subtilis (soil), and Geobacillus (hot springs) have been considered in the second generation of chassis organisms.2 These organisms are viable chassis candidates due to existing synthetic biology toolkits, demonstrated genetic tractability, and reasonable growth and engineering conditions.93,7,94 While these traits are desirable in a potential chassis organism, each microbe listed above suits a defined niche of applications and is severely restricted outside this niche without extensive and laborious genetic engineering. Thus, we envision additional chassis need to be developed for environmental biosensing.

A Framework for Choosing Environmental Biosensor Chassis

Since each environment poses distinct constraints on the chassis, the ideal place to begin is the environmental problem of interest. Once the environmental context has been established, we can compile a list of native organisms from the relevant ecological niche. Next, we must eliminate pathogenic organisms from this list. Subsequently, using our constraints as a guide, we can shortlist some organisms that are amenable to engineering for biosensing. Lastly, shortlisted organisms can be tested in the laboratory using simulated environmental conditions. We illustrate our framework in Figure 1.

Figure 1.

Figure 1

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The journey from native organism to biosensor chassis. We recommend isolating organisms native to the ecosystems and environments of interest. After eliminating agricultural pathogens, a list of safe organisms will remain that can be narrowed down further based on our framework. This process results in a list of candidate chassis that, after sufficient biocontainment, can be tested in lab simulations of natural environments and evaluated for performance and persistence.

We recommend choosing chassis organisms that are habitat specialists in the environment of interest.89 We make this recommendation considering the trade-off between challenges associated with engineering new tools in specialist organisms compared to challenges in engineering generalist organisms to survive in specific environmental conditions. Habitat specialists are well adapted to existing conditions in an environment. Specialists also fulfill a niche and are effective at competing with organisms in their niche. Since specialists are highly selective of their environment and growth substrates, choosing them may minimize the need for chassis engineering for survival in the environment of interest. For example, a bacterium that is known to colonize mineral-rich surfaces (a habitat specialist) may have specific adaptations to tolerate mineral toxicity and can be used to address questions on the mineral surface but may not have a genetic toolbox. However, we anticipate that a bacterium from bulk soil (a habitat generalist) with a genetic toolbox would have to be engineered further to tolerate high mineral concentrations. This process can be extremely challenging as the mechanisms for mineral tolerance are complex, typically understudied, and require extensive testing after multiple genetic modifications to the generalist organism.

As an example of how to use this framework, we can consider the case of sensing volatile organic compounds in response to drying–wetting cycles in the rhizosphere. Hydrating a dry soil stimulates metabolic activity in the rhizosphere, which releases volatiles that are highly transient and challenging to measure.95,96 These volatiles play central roles in interspecies communication and community structuring, thereby meriting study using nondestructive, unbiased detection methods.96,97 Additionally, volatile concentrations are impacted by the soil matrix, meaning that analytically detected concentrations can be significantly different from bioavailable concentrations.98 Biosensors offer a viable alternative to measuring such transient molecules in situ.1,97 To thrive in the rhizosphere and measure transient signals, we need a chassis organism that is native to the rhizosphere. After eliminating environmental pathogens, we have two distinct ways to choose native organisms as chassis—either we select a host-specific plant growth promoting rhizobacterium (PGPR plu.), such as Acinetobacter baylyi (habitat specialist), or a relatively broad nonsymbiotic rhizosphere colonizer (habitat generalist), such as Azospirillum brasilense. In this case, both these species have sequenced genomes, phenotypic and metabolic characterization, and promising ecological roles that can be harvested for biosensing.99,100Acinetobacter baylyi ADP-1 has been engineered with synthetic biology tools and has been a species of interest due to its nutritional diversity and natural competence.99Azospirillum brasilense has a demonstrated ability to retain plasmids of varying origins and is widely studied.100 Both these organisms are feasible chassis for rhizosphere volatile sensing.

As shortlisting one or a few chassis suitable for the environmental problem of interest may not always be feasible, an alternative approach is to explore non-native organisms that may survive the stringent conditions of the environment of interest. For example, soil and marine sediment environments often experience anaerobic conditions. Since the human intestinal tract is also anaerobic, we could consider obligate anaerobes from this environment, such as Bacteroides thetaiotaomicron, as a potential chassis. This would require additional testing in the laboratory, as we do not know how Bacteroides thetaiotaomicron will affect ecological processes in the niche of interest, but it is worth considering as this organism has well-characterized synthetic genetic elements available for use.101

Significant knowledge gaps must be addressed before considering our recommendations. The journey of an organism from a natural microbial isolate to a widely adopted synthetic biology chassis requires robust bodies of work in genome sequencing and annotation, metabolic profiling and systems biology, and ecological characterization supported by experimental evidence, as discussed extensively by Liu and Deutschbauer,102 Calero and Nikel,103 and Venturelli et al.3 Rigorous, actively curated genomic and metabolic databases, such as BioCyc (specifically EcoCyc and BSubCyc),104,105 KEGG,106 and modelSEED,107 combined with standardized, community-level efforts in engineering multiple bacteria offer a targeted approach to adopting novel bacterial chassis with desirable phenotypes and diverse environmental niches.108 However, these knowledge gaps are challenging to address each time a novel chassis is being considered for adoption. In such cases, we recommend consulting Figure 2, in which we parse our constraints into subconstraints and rank them in a qualitative priority order.

Figure 2.

Figure 2

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Prioritizing chassis constraints. It is challenging to resolve all our constraints in the selection of a chassis organism. In such cases, prioritizing some constraints over others can be a useful way forward. We recommend the above priority axis as a guide to negotiate the various constraints in the choice of a chassis organism.

The rapid development of standardized, functionally characterized, and modular species-specific and broad host range toolkits will facilitate the adoption of novel chassis in environmental biosensing. Efforts in developing species-specific toolkits for Vibrio natriegens, Pseudomonas putida, etc. can be replicated in non-model novel chassis organisms with some modifications and prior characterization.5,109 Broad host range tools help port synthetic biology parts into non-model organisms without known libraries.11,110 Orthogonal transcription/translation tools can be a method to decouple circuit function from host regulatory machinery, especially when there is strong selection against synthetic circuits that compete for native replication resources.111−113 Tools like UBER have been developed with robust broad-host range functionality.114,115 However, more coordinated, community-wide efforts to develop toolkits for genome editing and stable cross-species expression in non-model bacteria will greatly accelerate progress in environmental biosensing.

In summary, we have outlined constraints for the systematic selection of biosensor chassis for environmental synthetic biology. We have articulated a conceptual framework to make this selection based on constraints that must be addressed. We offer some recommendations that can simplify the choice of a chassis organism and describe our vision for a new generation of biosensor chassis. With the systematic, standardized, and rapid development of robust tools for diverse bacteria, we can amplify the impact of synthetic biology tools in the Earth and environmental sciences to address novel questions in the face of a changing planet.

Acknowledgments

We acknowledge funding from the Moore Foundation through Grant # 7524 and the Office of Naval Research (ONR) through Grant # N00014-21-1-2362. The authors also acknowledge valuable conversations with Prashant B. Kalvapalle, Shyam P. Bhakta, Dr. Charilaos Giannitsis, Dr. Joff Silberg, Dr. Lauren Stadler, Dr. Ilenne Del Valle, and Dr. Emily Fulk.

Author Contributions

(CRediT) Swetha Sridhar: Conceptualization, Writing - Original Draft, Writing - Review & Editing, Visualization. Caroline M. Ajo-Franklin: Conceptualization, Writing - Review & Editing, Funding Acquisition. Caroline A. Masiello: Conceptualization, Writing - Review & Editing, Funding Acquisition

The authors declare no competing financial interest.

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