Translating New Synthetic Biology Advances for Biosensing Into the Earth and Environmental Sciences

Abstract

The rapid diversification of synthetic biology tools holds promise in making some classically hard-to-solve environmental problems tractable. Here we review longstanding problems in the Earth and environmental sciences that could be addressed using engineered microbes as micron-scale sensors (biosensors). Biosensors can offer new perspectives on open questions, including understanding microbial behaviors in heterogeneous matrices like soils, sediments, and wastewater systems, tracking cryptic element cycling in the Earth system, and establishing the dynamics of microbe-microbe, microbe-plant, and microbe-material interactions. Before these new tools can reach their potential, however, a suite of biological parts and microbial chassis appropriate for environmental conditions must be developed by the synthetic biology community. This includes diversifying sensing modules to obtain information relevant to environmental questions, creating output signals that allow dynamic reporting from hard-to-image environmental materials, and tuning these sensors so that they reliably function long enough to be useful for environmental studies. Finally, ethical questions related to the use of synthetic biosensors in environmental applications are discussed.

Keywords: biogeochemistry; biosensor; cell-free sensors; environmental microbiology; marine; soil; synthetic biology; wastewater.

Figure 1

Synthetic and systems biology provide complementary information. Different -omics methods can obtain top-down systems biology data about the ensemble or organisms and biomolecules present in an environmental sample. In contrast, biosensors created using synthetic biology provide high resolution information about the reactions mediated by individual community members, such as their metabolic activities, perceived concentrations of molecules, time-dependent production or consumption of specific biomolecules, and environmental chemical processing.

Figure 2

Biosensor modules and characteristics. (A) The sensor module (orange) converts environmental information into biochemical information, the processing module (gray) performs computations using biochemical information, and the output module (blue) translates the processed information into a detected signal. (B) A simple one-input, one-output biosensor illustrated using synthetic biology language.

Figure 3

Monitoring cell-cell communication in environmental materials. (A) AHL cell-cell signaling (middle) regulates processes at the cm-scale that contribute to fluxes at a planetary scale, including: (left) the production of enzymes that degrade marine sinking particulate organic carbon, and (right) the transfer of symbiotic plasmids encoding nitrogen-fixing machinery and the production of nitrous oxide. Analytical chemistry methods for monitoring signals can present: (B) high limits of quantification (LOQ) that preclude signal detection, (C) an overestimation of signal levels, since spatial variability and bioavailability are not reflected in bulk extraction data, and (D) limited temporal insight because samples are consumed during analysis. (E) Biosensors can provide a microbe's perspective on the bioavailability of a signal at the micron scale, which varies due to consumption, enzymatic modification, abiotic chemical modification, sorption into the organo-mineral phase, and spatial accessibility. (F) Multiple biosensors are needed to understand the complex signaling that underlies plant-microbe symbiosis whose formation is critical to crop productivity. Biosensors are needed to understand how soil properties, signal chemistry, and amendments affect: (1) microbial growth, (2) microbe-microbe AHL signaling that underlies symbiotic plasmid transfer, and (3) plant-microbe communication mediated by flavonoids and nodulation factors that cause symbiosis formation.

Figure 4

Biosensors can report on horizontal gene transfer (HGT) between cells. (A) Cells use HGT of antibiotic resistance genes (ARG) to improve their fitness. (B) In engineered systems, like wastewater treatment plants, biosensors can report on the effect of operating conditions on HGT rates and the diversity of bacteria that participate in conjugation. (C) HGT biosensors can couple the production of a visual reporter to HGT. With this approach, the donor cells keep the reporter production off, while the receiver cells are unable to repress reporter production. Cells acquiring a conjugative plasmid (cPlasmid) through HGT produce an output that can be quantified using flow cytometry. (D) Biosensors can record a HGT event by writing new sequences into their DNA (red), which can be read out using qPCR or sequencing at later times provided that the DNA remains extractable.

Figure 5

Biosensors can report on spatial and temporal heterogeneity. (A) A denitrifier programmed as a biosensor could be engineered to integrate information about multiple analytes such as the presence of iron required for nitrous oxide production and anoxic conditions. Cells unable to sense both iron AND anoxia remain inactive (yellow), while cells sensing both produce an output (blue). (B) Truth table showing how a biosensor with this AND gate logic only produces the reporter when input A (anoxic conditions) AND input B (iron) are colocalized at the micron scale. (C) Biosensors can monitor diverse intermediates in the sulfur cycle, including those that do not accumulate to high levels because they are rapidly consumed. (D) A comparison of biosensors that report in real-time or using memory. A real-time biosensor (left) only produces the reporter while the environmental input is present because the signal decays. This approach is hard to use when studying transient chemicals that are cryptic. A memory biosensor (right) converts information about transient signals into an output that is stable for long durations such that the biosensor memorizes the information.

Figure 6

Sensing intracellular and extracellular conditions. (A) With intracellular sensors, an environmental analyte (red) must cross the cell membrane into the cell via diffusion or through a transporter to be detected. Analyte binding to a sensor protein in the cytosol increases protein-DNA affinity and leads to the recruitment of RNA polymerase and output transcription. (B) Intracellular analyte binding can also enhance transcription by causing dissociation of protein from the DNA that is blocking output transcription. (C) Intracellular analyte binding to a riboswitch can trigger a conformational change near the ribosomal binding site (RBS) that permits ribosome binding and translation of the output module. (D) Extracellular sensors bind to the analyte using surface-exposed proteins such as the sensor kinase from a TCS. Following analyte binding, the kinase phosphorylates a response regulator within the cell, which binds to DNA and alters output transcription.

Figure 7

Output modules enable reporting from environmental materials. (A) Visual outputs are hard to use in environmental materials. (left) Fluorescent proteins require oxygen to mature, which limits their use in anoxic conditions. (middle) Pigment-producing proteins are hard to image in soils and sediments. (right) Bioluminescence can have a high background in marine samples. (B) The ice nucleation protein module (inp) is compatible with matrix experiments. Following biosensor production, INP levels can be measured from water extracted from a sample. (C) Indicator gas outputs can be monitored in the headspace of environmental materials using gas chromatography without sample disruption. The methyl halide transferase (mht) and ethylene forming enzyme (efe) modules synthesize methyl halides and ethylene, respectively. One gas (green) reports on cell growth, while the second gas (blue) provides information on the analyte detected. The ratio of these gases provides a robust output because it represents the average analyte sensed per cell. (D) Gas vesicle outputs are encoded by an acoustic operon, which yield a unique ultrasound signal upon expression.

Figure 8

Processing modules can increase biosensor sophistication. (A) Two-input AND gates can be created by using modules that encode protein in fragments. The output only occurs when the first AND second fragment are produced. (B) Flexible DNA memory using a toggle switch. In the OFF state, transcriptional repressor A blocks the production of the repressor B and the reporter output. Upon sensing environmental input A, the circuits flip to an ON state where repressor A is momentarily dissociated from promoter P_A, allowing the production of repressor B and the output. Repressor B blocks production of repressor A, stabilizing this ON state. The switch can be flipped back to the OFF state upon sensing of environmental input B. (C) Fixed DNA memory built using recombinases. In the OFF state, no recombinase is made, and the reporter DNA sequence is antiparallel to the regulatory elements required for expression. In the ON state, an environmental input induces recombinase production, which binds a pair of DNA sequences flanking the reporter and inverts the DNA such that the reporter is parallel to the regulatory elements. This flipping of the DNA leads to output production. (D) Fixed DNA memory that uses CRISPR. Input sensing is coupled to the expression of Cas1/2, which incorporates short DNA spacers into a CRISPR array at a rate that is proportional to the input exposure.

Figure 9

Using genetic engineering to tune biosensor performance. (A) Natural sensor modules often report on both target (red) and non-target (gray) analytes. To avoid false positives from the latter, sensors can be engineered to respond to a single input by incorporating mutations. (B) The sensitivity of a biosensor for a given analyte can also be adjusted by mutating the sensor module to adjust the transfer function (dashed line). (C) To improve the dynamic range, the biosensor background in the absence of analyte (OFF state) and the maximum signal (ON state) can also be tuned using mutation.

Figure 10

The benefits of engineering environmental microbes. (A) Biosensors made using model organisms (purple) can present poor fitness under environmental conditions compared with native organisms (yellow). Advances in synthetic biology make possible the programming of native organisms (red) to overcome this limitation. (B) Environmental microbes can be programmed to directly report their behaviors like contributions to the nitrogen cycle. (C) Alternatively, incognito biosensors can be created that spy on a community without interfering with the process of interest.

Figure 11

Strategies for identifying regulatory elements to program environmental microbes. (A) Native promoters and RBS from the organisms being engineered can be used to build the synthetic circuits if their activities are known. (B) Promoter and polymerase pairs that are functionally orthogonal from those in the native organisms can be used to control transcription, such as those encoded by phage. (C) Computational models can design synthetic RBS sequences (sRBS) with a range of translation initiation strengths. (D) Large-scale transcriptomics data can be mined for promoter and RBS sequences that are active at similar levels across a broad range of strains.

Figure 12

Cell-free systems as field-deployable biosensors. (A) To make these biosensors, cells are processed to create a lysate containing the necessary biomolecules for transcription and translation. DNA coding for the biosensor is added to the lysate as well as biochemical fuel (nucleotides and amino acids). The reaction can be lyophilized in tubes or freeze-dried on paper to create stable, portable biosensors. (B) With paper assays (top), qualitative visual outputs (purple) are used as a yes/no reporter of a given environmental conditions, such as the presence of an analyte (red) above a threshold concentration. Control reactions are used to determine if other analytes (yellow) non-specifically activate the sensor. With liquid assays (bottom), a lyophilized reaction is hydrated and analyzed with similar controls. In this approach, the output can be quantified against standards using a fluorimeter or spectrophotometer.