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. 2025 Aug 7;2(9):501–510. doi: 10.1021/cbe.5c00015

Synthetic Whole-Cell Bioelectronic Chemical Sensing with In Situ Genetic Computing

Robert W Bradley †,‡, Estefania Nunez-Bajo §, Firat Guder §,∇, Martin Buck ‡,*, Baojun Wang †,*
PMCID: PMC12478552  PMID: 41031321

Abstract

Biosensors exploit the capabilities of biological systems to acquire a huge variety of chemical or physical information and convert molecular signals into actionable data. Here we took a bottom-up synthetic biology approach to combine the versatility and programmability of whole-cell bacterial biosensors with the sensitivity of electrochemical sensing devices. We built genetic modules to produce different phenazines and wired these to various sensing and information processing modules. A whole-cell bioelectronic sensor with a T7 RNAP-based signal amplifier was first constructed that detected mercury contaminants below the level of WHO safe limit for drinking water. We demonstrated the modularity and programmability of the sensor design by incorporating Boolean logic computation into a dual-input sensor. We subsequently engineered a sensor strain that can produce two phenazine types, giving a two-channel electrochemical output signal based on the detection of differentiated midpoint potentials. Our modular bioelectronic sensor therefore can be readily adapted for different applications and forms the basis for development of low-cost, field-deployable sensing devices.

Keywords: whole-cell biosensor, electrogenetic, bioelectrochemical, phenazine, biological signal amplifier, genetic logic gate

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Introduction

Biosensors have myriad potential applications, but widespread deployment requires improvements in attributes such as sensitivity, robustness and ease of integration with electronic systems. Electronic communication between biological and physical components has proven to be extremely effective for producing compact and sensitive biosensors based on purified enzymes , and could be similarly beneficial in whole-cell systems which can often cope better with complex environmental samples. The concurrent expansion of synthetic biology capabilities and growing understanding of bioelectrochemical phenomena has created opportunities to repurpose the molecular mechanisms underlying microbial electroactivity for useful applications, including sensing. We aimed to demonstrate a whole-cell biosensor with an electronic output that could be easily modified for different sensing requirements. With this goal in mind, we chose a modular, bottom-up approach construction of our biosensor to maximize the control and flexibility we had over elements of the system.

The modularity concept within synthetic biology is the idea that biological components can be described as units with defined properties and activities. , Biosensors are made up of three functional modules that act to transduce or modify the signal: a sensing module; an information processing module; and an output module − (Figure a). Taking a modular approach to the construction of our biosensor allows us to readily generate a diverse set of functions. We also chose a bottom-up approach, using genetic parts from diverse sources and repurposing them in a well-studied, naive chassis organism: Escherichia coli (E. coli). This approach helps to improve the modularity of components by preventing unwanted interactions with other molecules, which would be more likely in a native context.

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Design of synthetic modular phenazine-based whole-cell biosensors. (a) Representation of modularity in biosensor design: the synthetic biosensor (dashed box) is composed of functional modules (yellow) which are each built using modular components (blue). The genetic parts constituting an amplifier module (T7 RNAP expression in response to a sensor-regulator) are shown as an example. (b) Biological production of various phenazines from the core branch point metabolite chorismite is possible via phenazine-1-carboxylic acid. (c) Overview of the sensing device showing an exploded view of engineered cells entrapped in a growth-medium gel matrix on top of a three-electrode screen printed electrode. A silicone gasket forms a well for the gel. Biosensor cells produce phenazine molecules in response to a particular analyte.

Whole-cell biosensors are a popular format based on intact microorganisms: the engineered organism expresses the molecular components of the sensing system, including those for signal processing and reporting. , There are many benefits to be gained from harnessing the sophistication of a living cell, most notably the homeostatic intracellular environment which enables whole-cell sensors to cope with complex or contaminated samples that might inhibit a purified enzyme system. The qualities of self-generation and self-repair also make whole-cell systems facile to produce and give the potential for long-term operation. While constructing a biosensor in the context of a metabolically active organism can be problematic due to the many simultaneous reactions that are occurring, our bottom-up approach should help to insulate the biosensor components.

The output module is the key feature of our biosensor, as we wish to enable direct communication between the organism and electronic components rather than using typical outputs for light-based-detection. Different types of bioelectronic sensors based on electroactive microorganisms have already been demonstrated, the most straightforward being unmodified electroactive bacteria or bacterial communities operating in a microbial fuel cell, as the output current is affected by changes in feedstock quantity and quality, or by the presence of toxins or redox-active molecules. − Electroactive bacteria have also been engineered to modulate their current output in response to specific signals. Essential proteins in the S. oneidensis Mtr system have been placed under the control of arabinose or arsenic-responsive promoters, , and the regulation of phenazine production in Pseudomonas aeruginosa was modified to require the exogenous addition of two quorum sensing signals molecules.

Our approach requires the ability to express all the necessary components for electron transport in our chassis organism, something which is not yet feasible at native levels of activity for direct transfer systems such as the S. oneidensis Mtr system or Geobacter spp. nanowire system. In contrast, the phenazine biosynthesis pathway is a well-defined route for the production of a range of related redox-active shuttle molecules with different properties (Figure b), including their redox midpoint potentials and solubility. − A core phenazine species, phenazine-1-carboxylic acid (PCA), is produced from the central metabolic precursor chorismite by seven enzymes named PhzA to PhzG. , Accessory enzymes can then modify PCA to other phenazine types (Figure b). Our device was designed to employ a three-electrode system as the interface between the bioelectrochemical and electronic components, using low-cost screen-printed electrodes (SPEs) (Figure c). A square-wave voltammetry (SWV) technique would then allow us to determine the concentration of phenazine present at the electrode and identify species with different redox midpoints.

At the input side of the biosensor system we planned to use previously engineered sensing modules that have been constructed via refinement of natural systems, for example to improve sensitivity. , There is significant scope for signal modification before the output module is activated, − and so we also aimed to demonstrate how information processing modules could be used to expand the functionality of the bioelectronic sensor through implementation of in situ signal amplification and Boolean logic. − Further, we aimed to demonstrate the possibility of output signal multiplexing by making use of the different midpoint potentials of the various phenazines.

Experimental Section

Strains and Growth Conditions

E. coli TOP10 was used for all cloning and phenazine expression experiments. Cells were typically cultured in LB-Lennox medium. M9 medium was supplemented with 0.2% casamino acids, 0.4% glycerol, and 1 mM thiamine hydrochloride. Antibiotics (ampicillin at 100 μg mL–1; kanamycin at 30 μg mL–1) were added to the culture medium where appropriate.

Plasmid Construction

All DNA modifying enzymes were obtained from Thermo Scientific. We used an R756 K variant of T7 RNAP with its cognate promoter, and the N- and C-terminal parts were split between residues 179 and 180 as previously described. Plasmids for production of PCA, PCN, 2OHP, and PYO have been submitted to the Addgene plasmid repository.

Cloning the phz Genes

The phzABCDEFG (phzA-G) operon BioBrick was PCR amplified from P. aeruginosa PAO1 genomic DNA and cloned into pUC19. Two rounds of PCR amplification and Gibson assembly were employed to remove EcoR I and then Pst I restriction enzyme sites, replacing nucleotides to produce synonymous mutations, with choices guided by E. coli codon usage. The phzS and phzM open reading frame BioBrick were obtained as synthetic DNA from GeneArt, based on the P. aeruginosa UCBPP-PA14 sequence. To include the wild-type RBSs, sequences including the RBS were amplified from P. aeruginosa PAO1 genomic DNA and spliced with the synthetic DNA, using the unique Sca I site for phzS and Bgl II site for phzM, so that the 5′-UTR and start of the gene use the DNA sequence from PAO1. The phzH open reading frame BioBrick was PCR amplified from P. aeruginosa PAO1 genomic DNA with concomitant mutation of EcoR I and Pst I restriction enzyme sites (replacing nucleotides to produce synonymous mutations, with choices guided by E. coli codon usage), and assembled using Gibson assembly into pSB3K3 linearized by restriction digest at EcoR I and Pst I sites. The phzO open reading frame BioBrick was obtained as synthetic DNA from GeneArt, based on the Pseudomonas chlororaphis 30–84 protein sequence and codon optimized for expression in E. coli.

Balancing phzS and phzM Expression

Initially RBS30-phzM was cloned upstream of RBS30-phzS to form a bicistronic operon. However, when phzMS was coexpressed with phzA-G this led to production of the same red pigmentation observed when phzM was expressed alone. This was confirmed by expression of each gene separately from inducible promoters (phzM expressed from IPTG-inducible Plac, phzS expressed from arabinose-inducible PBAD) with constitutive expression of phzA-G (Supporting Information Figure S4). To ensure higher expression of phzS relative to phzM, the order of the genes in the operon was swapped, and the RBSs sequences were modified. One new operon used the weaker RBS32 (BBa_B0032) for phzM; another used the P. aeruginosa PAO1 wild-type RBS sequences. Both were effective at preventing formation of the red pigment. The wild-type RBSs were used for further experiments. The Salis Lab RBS Calculator (https://salislab.net/software/predict_rbs_calculator) predicts translation rates of 5813 versus 201 for phzS and phzM respectively in the operon with wild-type RBS sequences.

Preparation of Biosensor-Electrode Assemblies

Screen-printed electrodes (DRP-110, Metrohm) were modified with 9 mm bore silicone O-rings (RS Components; nitrile rubber was found to inhibit phenazine production) attached with polyvinyl alcohol (PVA) glue to form a well over the electrodes. For all sensor characterizations, overnight cultures were diluted 100-fold into fresh LB medium and incubated at 37 °C with shaking (200 rpm) for 2–3 h until reaching mid log phase (OD600 = 0.3–0.6). Cells were harvested by centrifugation, washed once in fresh medium, and resuspended in fresh medium containing appropriate antibiotics and inducers to twice the desired final density (typically an OD600 of 5). The cell suspension was warmed to 42 °C for 5 min and immediately diluted 1:1 with melted 1% (w/v) LB-agarose that had been cooled to 50 °C. 80 μL of the cell-gel mixture (OD600 = 5) was immediately pipetted into the wells on top of the electrodes and allowed to solidify. A circular glass microscope coverslip was placed over the well to prevent drying and held in place with Parafilm tape.

Square-Wave Voltammetry

Data was recorded using multiEmStat4 potentiostats (PalmSens) controlled by MultiTrace software. Scans were performed from −0.6 to 0.6 V with a step size of 5 mV, amplitude of 10 mV, and frequency of 10 Hz. Voltammetric baselines were established using control cells (no phz genes) and subtracted in real time during measurement. Final data represent background-corrected signals. Electrode assemblies were incubated at 30 °C as a balance between maintaining high metabolic activity and limiting evaporation from the agar. Where included, ferricyanide was added to a final concentration of 700 μM.

LC–MS of Phenazine Compounds

LB medium from overnight cultures expressing the phzA-G genes for PCA synthesis plus further accessory genes was analyzed by LC–MS after removing cells by centrifugation and filtration. Samples were separated on a Luna 3u C18 100A reverse phase column (100 × 0.5 mm) at a flow rate of 250 μL min–1 with a 15 min 2–80% linear gradient of acetonitrile in 25 mM ammonium acetate (pH 7.0) at 50 °C. A QTRAP 6500 Linear Ion Trap Quadrupole LC/MS/MS Mass Spectrometer was used for targeted EPI.

Results

Production of Phenazines in E. coli

Our first goal was to produce genetic modules containing the phzABCDEFG operon (henceforth phzA-G) and optional accessory PCA-modifying enzymes for phenazine production and bioelectrochemical signal generation in E. coli. The phzA-G operon including native ribosome binding site (RBS) sequences was cloned from P. aeruginosa PAO1 and modified to conform to the BioBrick standard. We also produced BioBricks for the coding sequences of PCA-modifying enzymes; these were synthesized or cloned from P. aeruginosa PAO1 as described in Methods.

To demonstrate PCA production from the phzA-G genes in E. coli, the operon was cloned into the pSB3K3 vector (p15A origin, KanR) downstream of the strong constitutive promoter BBa_J23101. PCA production from pSB3K3–101-phzAG was initially confirmed using UV–vis spectroscopy of filtered media from an overnight culture to identify the characteristic absorbance peaks (observed at 253 and 368 nm) at expected wavelengths (Figure a). Successful production of PCA was verified by LC–MS analysis (see Methods).

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Production of phenazines in E. coli. (a) Absorbance spectrum of filtered media from an overnight culture of E. coli plus pSB3K3–101-phzAG grown in M9 medium plus 80 μM arabinose, blank-corrected with filtered media from cells containing plasmid pSB4A3-PBAD-phzSM (i.e., no core phzA-G genes), average of four technical replicates. (b) Square wave voltammograms of media from overnight cultures of phenazine-producing strains, grown and blank corrected as in (a). The inset legend indicates which combination of genes are being expressed. Shaded areas indicate the standard deviation from three technical replicates. (c) The positions of peaks observed by SWV in (b), relative to the phzAG (phzABCDEFG expressing) sample (PCA). The difference in peak position between sample phzAG (PCA) and samples phzAG + SM (PYO) and phzAG + O(1) (2OHP) is statistically significant (Student’s t-test, p < 0.01); there was no significant difference between sample phzAG and the remaining samples (p > 0.05). Samples phzAG + O(1) and phzAG + O(2) refer to the two peaks observed in the phzAG + O sample. Colored areas indicating the standard deviation of sample means are included to facilitate comparison across samples.

To produce other phenazine molecules, PCA-modifying genes were cloned into the pSB4A3 vector (pSC101 origin, AmpR) downstream of the arabinose-inducible PBAD promoter and the strong RBS30 translation initiation sequence (BBa_B0030). The phzH, phzS and phzO genes are required for production of phenazine-1-carboxamide (PCN), 1-hydroxyphenazine (1OHP) and 2-hydroxyphenazine (2OHP) respectively. , Production of the phenazine pyocyanin (PYO) requires expression of both phzS and phzM, and in preliminary experiments established that it was important to express phzS more strongly than phzM to avoid formation of a red-orange pigment, hypothesized to be aeruginosin, , that was previously observed with phzA-G plus phzM expression (see Methods). The bicistronic synthetic operon phzSM with P. aeruginosa PAO1 wild-type RBS sequences was found to have an appropriate balance of expression. The phzA-G genes were again expressed from pSB3K3 using BBa_J23101. The two plasmids were cotransformed into E. coli and the subsequent dual-plasmid strains were induced with 80 μM arabinose. Filtered media (Supporting Information Figure S1) from overnight cultures was analyzed by absorbance spectroscopy (Supporting Information Figure S2) and LC–MS (Supporting Information Table S1). Both spectroscopy datasets for the PCN, 2OHP and PYO samples are consistent with previous reports, indicating successful production of these compounds, though PCA was also detected by LC–MS in the PCN and 2OHP samples indicating that conversion was not complete. 1OHP was not detected; the obtained spectra were consistent with PCA remaining unconverted in the strain expressing phzA-G plus phzS.

Electrochemical Detection of Phenazines

Sensing Phenazines in Spent Media

The media from induced overnight cultures of phenazine producing strains was analyzed by SWV using commercial screen-printed electrodes (DRP-110 electrodes (Metrohm) carbon working and counter electrodes, plus platinum reference). The phenazines must be excreted in high enough concentrations and interact well with the electrode to enable detection. These measurements would help determine which phenazine expression systems could be taken forward as output modules for the biosensor.

SWV traces are shown in Figure b, with peak positions extracted in Figure c. The results support the findings of the spectroscopic analyses: PCA produced by expression of phzA-G gives a clear current peak on the SWV trace. The peaks produced by strains expressing phzA-G plus either phzS or phzH are not at a significantly different voltage relative to PCA. In the case of phzS, this is consistent with PCA remaining unconverted by this strain; for phzH this can be explained by incomplete conversion and PCN and PCA having similar redox potentials. PYO produced by the expression of phzA-G and phzSM gives a current peak +72 mV from the PCA peak, consistent with previous reports. ,, Media from the strain expressing phzA-G and phzO produces two peaks, only one of which is statistically different from PCA, in agreement with the MS analysis; the more negative peak position of −105 mV is qualitatively consistent with the previously reported midpoint potential of 2OHP.

Taking the spectroscopic and voltametric analyses together, we concluded that (without further optimization of the genetic parts) both PCA and PYO are suitable output signals. Due to its requirement for fewer genetic components, PCA production from phzA-G was primarily used as the biosensor output module.

Configuring the Physical Biosensor

For further experiments we wanted to use a more robust configuration that could stably couple cells with the electrode for many hours and allow for easy analyte addition. Previous studies on enzyme-based biosensors have utilized paper substrate to immobilize catalysts near the electrodes, enabling direct analyte pipetting and field-deployable operation. Building on this foundation, we engineered a whole-cell biosensor compatible with paper-based formats, leveraging microbial cells as self-sustaining catalyst factories. We needed to immobilize our whole-cell sensors within growth medium so 0.5% w/v agarose was used to create a gel pad that sat on the electrode, with the low percentage of agarose chosen as a balance between creating sufficient gel pad robustness and minimizing diffusional barriers (Figure c). LB growth medium was chosen as it enabled higher growth and phenazine production rates compared to M9 minimal medium and had a cleaner background signal in SWV experiments compared to the rich medium Terrific Broth (data not shown).

Commercial screen-printed electrodes were modified through the attachment of a silicone O-ring gasket to form a well for the entrapped cells. Modified SPEs were loaded with 80 μL of the cells-medium-gel mixture, sealed, and incubated at 30 °C. SWV sweeps were performed every 10 min to monitor PCA production by the cells. Optimisation of cell loading was performed using E. coli constitutively expressing PCA from pSB3K3–101-phzAG. As shown in Supporting Information Figure S3, neither the maximum current signal nor the rate at which the signal increases over time improve above an OD600 of 5, so this cell concentration was used for all future experiments.

Connecting the Electrochemical Output to Sensing Modules

As a straightforward proof of our modular biosensor concept we used the arabinose-inducible PBAD promoter to drive expression of phzA-G (Figure a). Arabinose was included in the gel mixture that was cast onto SPE electrodes. Figure b shows the current detected by SWV (maximum peak height within the indicated incubation time) versus arabinose concentration. A positive correlation is evident up to approximately 100 μM arabinose, whereafter expression saturates. Arabinose was detected within 2 h with signal strength increasing with time.

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Synthetic whole-cell bioelectronic sensors for arabinose and mercury. (a,c) Genetic diagrams of the arabinose sugar biosensor construct and the modular amplifying mercury biosensor. The presence of arabinose allows expression of the phzA-G operon from the PBAD promoter, otherwise transcription is repressed (a). The presence of mercury ions induces expression of T7 RNAP from PmerT, which in turn drives expression of the phzA-G operon from PT7 (c). (b,d) Current (maximum SWV peak height) output of the arabinose and mercury biosensors on SPE, measured at three time points. Inducer concentrations were 0 μM, 25 μM, 50 μM, 100 μM, 0.5 mM, 1 mM arabinose and 0 nM, 10 nM, 25 nM, 50 nM, 100 nM, 250 nM HgCl2. The trend lines are drawn between average current output values; error bars indicate the standard deviation of measurements (n = 3). Vertical red dashed line in (d) indicates the WHO guideline limit for mercury in drinking water (33 nM).

To extend the biosensor repertoire we investigated connecting the phenazine output to a mercury sensing module. MerR acts as a repressor-activator at the PmerT promoter, blocking transcription in the absence of Hg2+ but changing conformation to assist polymerase access upon metal ion binding. The MerR-PmerT response to Hg2+ is ultrasensitive and highly specific, which would allow it to cope with samples containing a mixture of metals. Previous studies have shown that a low concentration of the regulator improves the limit of detection and output dynamic range when using MerR/PmerT as a sensing module, so we used the weak J23109 promoter to drive expression of the regulator and maintained the gene on a medium-copy p15A-origin plasmid. However, when connected directly to the electrochemical output (i.e., phzA-G transcribed from PmerT, all components on pSB3K3) the concentration of phenazines was insufficient for detection by the SPE sensor even at saturating concentrations of metal ions (data not shown).

To amplify the signal we used the T7 RNA polymerase (T7 RNAP) which has a very high transcription rate from its cognate promoter PT7. In this modular amplifying system PmerT drives expression of T7 RNAP which in turn transcribes phzA-G from PT7 (Figure c). The T7 promoter and phzA-G operon were cloned onto a high-copy colE1-origin plasmid to further amplify the signal. Figure d shows the maximum current response from SWV experiments with a range of HgCl2 concentrations. Again, a positive correlation was observed at low inducer concentrations until saturation at 100 nM Hg2+. Importantly, PCA could be detected within 3 h from cells induced with 25 nM HgCl2, which is below the WHO guideline limit of 6 μg·L–1 (33 nM for inorganic mercury in drinking water); no signal was observed at 10 nM HgCl2.

Linking an Electrochemical Output to Biological Computation

The implementation of an amplifier within the toxic metal biosensors demonstrates a key strength of biological sensing systems, namely the ability to use signal processing modules to link sensing and output modules. This is made practicable through the separation of the phenazine output functionality onto a distinct plasmid. To extend this functionality we chose to implement a simple AND logic gate based on a split T7 RNAP to integrate two input signals with the phenazine output signal (Figure a). AND logic could be used in applied contexts to increase selectivity for a particular analyte, as shown previously for a zinc sensing system; here we simply demonstrate the proof of concept of logical information processing. N-acyl homoserine lactone (AHL) and arabinose were used to induce expression of the N- and C-terminal parts of T7 RNAP, so that the output would only be generated in the presence of both inducers. The circuit performed as expected, only producing a detectable output when both AHL (100 nM) and arabinose (80 μM) were present (Figure b).

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A whole-cell bioelectronic sensor with in situ AND logic computing. (a) Genetic diagram of the split T7 RNAP AND gate circuit controlling expression of the phzA-G operon: all sensing and T7 RNAP expression parts are located on the same p15A origin plasmid, while the phenazine output is on a separate colE1 origin plasmid. Only in the presence of both arabinose and AHL inducers will both halves of the T7 RNAP be expressed; the split T7 RNAP spontaneously associates and drives phzA-G transcription from its cognate promoter. (b) Current (maximum SWV peak height) output of the AND gate circuit with PCA output. Circles indicate values from individual experiments; column height indicates the mean value for that condition; n.d. means that a current signal was not detected under the same conditions (n.d. = not detected); error bars indicate the standard deviation of measurements (n ≥ 3).

Multiplexing Sensor Output

The various phenazines that we initially produced possess different redox properties that are evident in their square wave voltammograms (Figure b), this has also been verified in microbial fuel cells based on E. coli as the chassis. Thus, we reasoned that we could increase the information content of the electrochemical output from a biosensing strain by engineering it with the capability to produce multiple phenazine types, analogous to expressing fluorescent proteins with different emission wavelengths. Based on their peak current positions, both PCA and 2OHP can be discriminated from PYO (Figure c). The PCA/PYO pair was chosen as the simpler option for further investigation as potential challenges arising from the PYO/2OHP pair sharing PCA as a precursor could be avoided. We used inducible promoters to build a two-input, two-output genetic circuit (Figure a) that produces PCA if AHL (100 nM) is the only input, or PYO if both AHL and arabinose (80 μM) are present (Figure b). The genetic circuit was tested on the biosensor as before, except with the addition of ferricyanide as an internal control to aid quantification of the peak voltages. Different peaks were observed in the “AHL” and “Both” inducer conditions as expected (Figure c), with mean voltages respectively of −476 mV and −397 mV versus ferricyanide. No peaks corresponding to phenazine production were observed for the other two conditions.

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A whole-cell bioelectronic sensor with two output channels. (a) Genetic diagram for the two-output phenazine construct. The AHL-responsive Plux promoter drives expression of the PCA-producing phzA-G operon. Expression of phzSM from the arabinose-responsive PBAD promoter enables conversion of PCA to PYO. All genes were expressed from a single plasmid, pSB3K3–PBAD-phzSM-Plux-phzAG. (b) Logic diagram and truth table for the two-output phenazine construct. “(+) AHL (−) Ara” indicates conditions where only AHL is present, “(+) AHL, (+) Ara” indicates conditions where both AHL and arabinose are present. (c) Square-wave voltammetry traces of the four input conditions. Ferricyanide was included as an internal control, and voltages have been normalized by transposition to the ferricyanide peak, marked with a vertical dashed line at 0 V. Curves indicate the mean current output from multiple experiments (n ≥ 3) and shaded regions indicate the standard deviation. Curves were transposed on the y-axis to facilitate comparison; tick-marks on the y-axis indicate steps of 200 μA. The average voltage of peak maxima in the “AHL” and “AHL + Arabinose” inducer conditions (corresponding to PCA and PYO respectively) are marked with vertical lines within a shaded rectangle around the peak indicating the standard deviation.

Discussion

The potential for further multiplexing is an exciting prospect. Previous studies have shown that multiple phenazine types can be detected from P. aeruginosa growing as a biofilm, including PCA, PYO, PCN, and 5-methylphenazine-1-carboxylic acid (5-MPCA). ,, We produced four different phenazine types, though PCA conversion was incomplete for PCN and 2OHPtuning the relative expression levels of core and accessory enzymes will be required to ensure the accessory enzyme is not overwhelmed by PCA. No 1OHP was detected in our study, but the balance of expression will be particularly important here as the catalytic activity of PhzS alone is over 40 times slower than when in association with PhzM. There are also challenges for multiplexing associated with the fact that some phenazine types are precursors of others, and that the phenazines can diffuse between strains expressing different sets of enzymes, but carefully tuned genetic systems with digital outputs could overcome these issues. Of course, other redox active species could be used in parallel with the phenazine signal, either biosynthesised and exported by the organism (e.g., flavins), or produced by enzymatic modification of an exogenous precursor. Additional possibilities for added complexity exist, including the option of degrading or modifying phenazine species, , enabling communication between cells with a phenazine output to others engineered for redox detection, and matching suitable phenazine types with different chassis organisms that may be better suited to particular environmental contexts. ,

To improve practicality the biosensing organisms could be freeze-dried on the SPE within their media-gel matrix to improve the longevity of the devices and reduce the stringency of storage conditions, being reactivated with the addition of aqueous analyte solution. Cell-free systems have a proven ability to endure freeze-drying and long-term storage, and they can facilitate rapid prototyping of genetic circuits and biosensors, so it would be interesting to explore whether cell-free systems could be used to drive the molecular circuitry of the biosensor. However, the components of the cell-free system would be able to diffuse to and interact with the electrode, potentially leading to inactivation of the electrode or biological components, or unacceptable noise during measurements. A recent report of an electrochemical cell-free sensors used modified electrodes to capture a redox reporter close to the electrode surface via nucleic-acid binding interactions to overcome this issue. For phenazine detection an investigation of different electrode materials may yield greater specificity of detection.

The biosensor designs trialled here all make use of ligand-responsive transcriptional regulatory system that produce a transcriptional signal, and future designs can adopt this template to leverage the wide variety of ligand-responsive regulators available in synthetic biology. Compared with traditional fluorescence and bioluminescence outputs, although using the phenazine biosynthesis pathway as the output has the advantages of multiple outputs and compatibility with electrochemistry, this pathway involves nearly 10 enzyme components (PhzA-G and PhzSM), which may delay the response of the sensor and prolong the detection time; alternative approaches using fewer enzyme components could enable a faster response time. For example, all enzymes of the PhzA-G cascade could be expressed in advance, with one engineered to require ligand-binding for activation , production of PCA would therefore only proceed in the presence of the ligand. This approach could also yield a simplified cell-free system if the relevant enzymes were purified. A complementary approach to speeding up phenazine detection could be through the expression of exporters: A P. aeruginosa efflux pump has been shown to transport 5-MPCA, and this strategy may aid the detection of other reactive or hydrophilic phenazines.

Independently of the choice of biological components, there is the opportunity to improve chemical and physical aspects of the biosensor device in future designs to allow for easy sealing of the biosensor and analyte. The culture medium could be improved through addition of a buffer to stabilize the pH-dependent midpoint potentials of the phenazines, and perhaps to include precursor metabolites such as chorismic acid. On the physical side, is necessary for the sensor to be well-sealed to prevent evaporation, and easy swapping of SPE-gel units is desirable. Integration of temperature control components into the device will enable its use outside of the laboratory, and an inexpensive paper-based substrate could be used to integrate fluidic channels for analyte addition. Disposable sensor arrays could enable testing for multiple replicates of multiple analytes simultaneously.

Conclusions

In this work we demonstrated how a phenazine expression module can be combined with other modular genetic elements to build whole-cell biosensors with a range of capabilities. This study sets the foundation for further improvements, ultimately working toward a practical biosensor for real-world analyte testing – for example, our mercury sensor could be developed to monitor and ensure the safety of drinking water supplies. We detected mercury concentrations below the WHO limit, but other sensing and information processing modules can now be swapped in to attempt to improve the sensitivity further or to adapt the sensor for the detection of other toxic metals such as arsenic. ,, One area for improvement is to reduce the variability of the biosensor output. We were able to produce a semiquantitative response to various concentrations of arabinose and mercury, and further precision ought to be achievable through a more standardized process for casting the biosensor-gel mix on top of the SPEs. However, a quantitative result often may not be required from the biosensor, for example when detecting whether an analyte is simply above a threshold concentration or not. Future biosensor designs could incorporate information-processing modules that create a digital output through the use of ultrasensitive components or motifs such as positive feedback loops, and could be tuned to detect particular concentrations of analyte.

Supplementary Material

be5c00015_si_001.pdf (936.8KB, pdf)
be5c00015_si_002.xlsx (162KB, xlsx)

Acknowledgments

B.W. acknowledges support by the National Key R&D Program of China (2023YFF1204500), ″Pioneer″ and ″Leading Goose″ R&D Program of Zhejiang (2024C03011), National Natural Science Foundation of China (32271475, 32320103001). R.W.B. and M.B. were supported by BBSRC grants (BB/K016288/1, BB/R009171/1). F.G. and E.N.B. were supported by a Bill and Melinda Gates Foundation grant [OPP1212574] and Wellcome Trust grant [207687/Z/17/Z].

Glossary

Abbreviations

PCA

(phenazine-1-carboxylic acid)

PCN

(phenazine-1-carboxamide)

PYO

(pyocyanin)

1OHP

(1-hydroxyphenazine)

2OHP

(2-hydroxyphenazine)

T7 RNAP

(T7 RNA polymerase)

SWV

(square-wave voltammetry)

SPE

(screen-printed electrode).

Plasmids for production of PCA, PCN, 2OHP, and PYO have been deposited within Addgene repository (#165617–20). All source data in the main text supporting the findings of this study are provided in the Supporting Information data file.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/cbe.5c00015.

  • Spent culture medium of E. coli strains expressing various phenazine biosynthesis enzymes; absorbance spectra of phenazines in spent medium; effect of cell density on electrochemical output signal; results of balanced PhzS and PhzM expression; phenazine mass spectrometry data; plasmids used; oligonucleotides used; and DNA sequences for key genetic parts used in this study (PDF)

  • Source data; Figure 2a; Figure 2b,c; Figure 3b; Figure 3d; Figure 4b; Figure 5c (XLSX)

BW, RWB and MB conceived the study. All authors contributed to experimental design. RWB, ENB, and FG conducted experiments and analyzed the data. BW and RWB wrote the manuscript. All authors edited and approved the manuscript.

The authors declare no competing financial interest.

Published as part of Chem & Bio Engineering special issue “Synthetic Biology: Enabling Technologies and Practical Applications”.

References

  1. Amalfitano E., Karlikow M., Norouzi M., Jaenes K., Cicek S., Masum F., Sadat Mousavi P., Guo Y., Tang L., Sydor A., Ma D., Pearson J. D., Trcka D., Pinette M., Ambagala A., Babiuk S., Pickering B., Wrana J., Bremner R., Mazzulli T., Sinton D., Brumell J. H., Green A. A., Pardee K.. A Glucose Meter Interface for Point-of-Care Gene Circuit-Based Diagnostics. Nat. Commun. 2021;12(1):724. doi: 10.1038/s41467-020-20639-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Rocchitta G., Spanu A., Babudieri S., Latte G., Madeddu G., Galleri G., Nuvoli S., Bagella P., Demartis M. I., Fiore V., Manetti R., Serra P. A.. Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids. Sensors. 2016;16(6):780. doi: 10.3390/s16060780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Atkinson J. T., Su L., Zhang X., Bennett G. N., Silberg J. J., Ajo-Franklin C. M.. Real-Time Bioelectronic Sensing of Environmental Contaminants. Nature. 2022;611(7936):548–553. doi: 10.1038/s41586-022-05356-y. [DOI] [PubMed] [Google Scholar]
  4. TerAvest M. A., Ajo-Franklin C. M.. Transforming Exoelectrogens for Biotechnology Using Synthetic Biology. Biotechnol. Bioeng. 2016;113(4):687–697. doi: 10.1002/bit.25723. [DOI] [PubMed] [Google Scholar]
  5. Bradley R. W., Buck M., Wang B.. Tools and Principles for Microbial Gene Circuit Engineering. J. Mol. Biol. 2016;428(5):862–888. doi: 10.1016/j.jmb.2015.10.004. [DOI] [PubMed] [Google Scholar]
  6. Gao Y., Wang L., Wang B.. Customizing Cellular Signal Processing by Synthetic Multi-Level Regulatory Circuits. Nat. Commun. 2023;14(1):8415. doi: 10.1038/s41467-023-44256-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Wang B., Barahona M., Buck M.. A Modular Cell-Based Biosensor Using Engineered Genetic Logic Circuits to Detect and Integrate Multiple Environmental Signals. Biosens. Bioelectron. 2013;40(1):368–376. doi: 10.1016/j.bios.2012.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bereza-Malcolm L. T., Mann G., Franks A. E.. Environmental Sensing of Heavy Metals Through Whole Cell Microbial Biosensors: A Synthetic Biology Approach. ACS Synth. Biol. 2015;4(5):535–546. doi: 10.1021/sb500286r. [DOI] [PubMed] [Google Scholar]
  9. Gui Q., Lawson T., Shan S., Yan L., Liu Y.. The Application of Whole Cell-Based Biosensors for Use in Environmental Analysis and in Medical Diagnostics. Sensors. 2017;17(7):1623. doi: 10.3390/s17071623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. de Lorenzo V.. Beware of Metaphors: Chasses and Orthogonality in Synthetic Biology. Bioeng. Bugs. 2011;2(1):3–7. doi: 10.4161/bbug.2.1.13388. [DOI] [PubMed] [Google Scholar]
  11. Courbet A., Endy D., Renard E., Molina F., Bonnet J.. Detection of Pathological Biomarkers in Human Clinical Samples via Amplifying Genetic Switches and Logic Gates. Sci. Transl. Med. 2015;7(289):289ra83. doi: 10.1126/scitranslmed.aaa3601. [DOI] [PubMed] [Google Scholar]
  12. He W., Yuan S., Zhong W.-H., Siddikee Md. A., Dai C.-C.. Application of Genetically Engineered Microbial Whole-Cell Biosensors for Combined Chemosensing. Appl. Microbiol. Biotechnol. 2016;100(3):1109–1119. doi: 10.1007/s00253-015-7160-6. [DOI] [PubMed] [Google Scholar]
  13. Bereza-Malcolm L., Franks A.. Coupling Anaerobic Bacteria and Microbial Fuel Cells as Whole-Cell Environmental Biosensors. Microbiol. Aust. 2015;36(3):129–132. doi: 10.1071/MA15045. [DOI] [Google Scholar]
  14. Kaur A., Kim J. R., Michie I., Dinsdale R. M., Guwy A. J., Premier G. C.. Microbial Fuel Cell Type Biosensor for Specific Volatile Fatty Acids Using Acclimated Bacterial Communities. Biosens. Bioelectron. 2013;47(15):50–55. doi: 10.1016/j.bios.2013.02.033. [DOI] [PubMed] [Google Scholar]
  15. Si R.-W., Yang Y., Yu Y.-Y., Han S., Zhang C.-L., Sun D.-Z., Zhai D.-D., Liu X., Yong Y.-C.. Wiring Bacterial Electron Flow for Sensitive Whole-Cell Amperometric Detection of Riboflavin. Anal. Chem. 2016;88(22):11222–11228. doi: 10.1021/acs.analchem.6b03538. [DOI] [PubMed] [Google Scholar]
  16. Zhou T., Han H., Liu P., Xiong J., Tian F., Li X.. Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review. Sensors. 2017;17(10):2230. doi: 10.3390/s17102230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Commault A. S., Lear G., Bouvier S., Feiler L., Karacs J., Weld R. J.. Geobacter-Dominated Biofilms Used as Amperometric BOD Sensors. Biochem. Eng. J. 2016;109:88–95. doi: 10.1016/j.bej.2016.01.011. [DOI] [Google Scholar]
  18. Webster D. P., TerAvest M. A., Doud D. F. R., Chakravorty A., Holmes E. C., Radens C. M., Sureka S., Gralnick J. A., Angenent L. T.. An Arsenic-Specific Biosensor with Genetically Engineered Shewanella Oneidensis in a Bioelectrochemical System. Biosens. Bioelectron. 2014;62:320–324. doi: 10.1016/j.bios.2014.07.003. [DOI] [PubMed] [Google Scholar]
  19. Golitsch F., Bücking C., Gescher J.. Proof of Principle for an Engineered Microbial Biosensor Based on Shewanella Oneidensis Outer Membrane Protein Complexes. Biosens. Bioelectron. 2013;47:285–291. doi: 10.1016/j.bios.2013.03.010. [DOI] [PubMed] [Google Scholar]
  20. Li Z., Rosenbaum M. A., Venkataraman A., Tam T. K., Katz E., Angenent L. T.. Bacteria-Based AND Logic Gate: A Decision-Making and Self-Powered Biosensor. Chem. Commun. 2011;47(11):3060–3062. doi: 10.1039/c0cc05037g. [DOI] [PubMed] [Google Scholar]
  21. Jensen H. M., TerAvest M. A., Kokish M. G., Ajo-Franklin C. M.. CymA and Exogenous Flavins Improve Extracellular Electron Transfer and Couple It to Cell Growth in Mtr-Expressing Escherichia Coli. ACS Synth. Biol. 2016;5(7):679–688. doi: 10.1021/acssynbio.5b00279. [DOI] [PubMed] [Google Scholar]
  22. Laursen J. B., Nielsen J.. Phenazine Natural Products: Biosynthesis, Synthetic Analogues, and Biological Activity. Chem. Rev. 2004;104(3):1663–1686. doi: 10.1021/cr020473j. [DOI] [PubMed] [Google Scholar]
  23. Simoska O., Sans M., Eberlin L. S., Shear J. B., Stevenson K. J.. Electrochemical Monitoring of the Impact of Polymicrobial Infections on Pseudomonas Aeruginosa and Growth Dependent Medium. Biosens. Bioelectron. 2019;142:111538. doi: 10.1016/j.bios.2019.111538. [DOI] [PubMed] [Google Scholar]
  24. Simoska O., Cummings D. A. Jr., Gaffney E. M., Langue C., Primo T. G., Weber C. J., Witt C. E., Minteer S. D., Minteer S. D.. Enhancing the Performance of Microbial Fuel Cells via Metabolic Engineering of Escherichia Coli for Phenazine Production. ACS Sustain. Chem. Eng. 2023;11(32):11855–11866. doi: 10.1021/acssuschemeng.3c01593. [DOI] [Google Scholar]
  25. Mavrodi D. V., Ksenzenko V. N., Bonsall R. F., Cook R. J., Boronin A. M., Thomashow L. S.. A Seven-Gene Locus for Synthesis of Phenazine-1-Carboxylic Acid by Pseudomonas Fluorescens2–79. J. Bacteriol. 1998;180(9):2541–2548. doi: 10.1128/JB.180.9.2541-2548.1998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Wang B., Barahona M., Buck M.. Amplification of Small Molecule-Inducible Gene Expression via Tuning of Intracellular Receptor Densities. Nucleic Acids Res. 2015;43(3):1955–1964. doi: 10.1093/nar/gku1388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Wan X., Volpetti F., Petrova E., French C., Maerkl S. J., Wang B.. Cascaded Amplifying Circuits Enable Ultrasensitive Cellular Sensors for Toxic Metals. Nat. Chem. Biol. 2019;15(5):540–548. doi: 10.1038/s41589-019-0244-3. [DOI] [PubMed] [Google Scholar]
  28. Nielsen A. A., Segall-Shapiro T. H., Voigt C. A.. Advances in Genetic Circuit Design: Novel Biochemistries, Deep Part Mining, and Precision Gene Expression. Curr. Opin. Chem. Biol. 2013;17(6):878–892. doi: 10.1016/j.cbpa.2013.10.003. [DOI] [PubMed] [Google Scholar]
  29. Bradley R. W., Wang B.. Designer Cell Signal Processing Circuits for Biotechnology. New Biotechnol. 2015;32(6):635–643. doi: 10.1016/j.nbt.2014.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Wan X., Pinto F., Yu L., Wang B.. Synthetic Protein-Binding DNA Sponge as a Tool to Tune Gene Expression and Mitigate Protein Toxicity. Nat. Commun. 2020;11(1):5961. doi: 10.1038/s41467-020-19552-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Wang B., Barahona M., Buck M.. Engineering Modular and Tunable Genetic Amplifiers for Scaling Transcriptional Signals in Cascaded Gene Networks. Nucleic Acids Res. 2014;42(14):9484–9492. doi: 10.1093/nar/gku593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wang B., Kitney R. I., Joly N., Buck M.. Engineering Modular and Orthogonal Genetic Logic Gates for Robust Digital-like Synthetic Biology. Nat. Commun. 2011;2(1):508. doi: 10.1038/ncomms1516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ho T. Y. H., Shao A., Lu Z., Savilahti H., Menolascina F., Wang L., Dalchau N., Wang B.. A Systematic Approach to Inserting Split Inteins for Boolean Logic Gate Engineering and Basal Activity Reduction. Nat. Commun. 2021;12(1):2200. doi: 10.1038/s41467-021-22404-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pinto F., Thornton E. L., Wang B.. An Expanded Library of Orthogonal Split Inteins Enables Modular Multi-Peptide Assemblies. Nat. Commun. 2020;11(1):1529. doi: 10.1038/s41467-020-15272-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Shis D. L., Bennett M. R.. Library of Synthetic Transcriptional AND Gates Built with Split T7 RNA Polymerase Mutants. Proc. Natl. Acad. Sci. U.S.A. 2013;110(13):5028–5033. doi: 10.1073/pnas.1220157110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Clifford E. R., Bradley R. W., Wey L. T., Lawrence J. M., Chen X., Howe C. J., Zhang J. Z.. Phenazines as Model Low-Midpoint Potential Electron Shuttles for Photosynthetic Bioelectrochemical Systems. Chem. Sci. 2021;12(9):3328–3338. doi: 10.1039/D0SC05655C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mavrodi D. V., Bonsall R. F., Delaney S. M., Soule M. J., Phillips G., Thomashow L. S.. Functional Analysis of Genes for Biosynthesis of Pyocyanin and Phenazine-1-Carboxamide from Pseudomonas Aeruginosa PAO1. J. Bacteriol. 2001;183(21):6454–6465. doi: 10.1128/JB.183.21.6454-6465.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Delaney S. M., Mavrodi D. V., Bonsall R. F., Thomashow L. S.. phzO, a Gene for Biosynthesis of 2-Hydroxylated Phenazine Compounds in Pseudomonas Aureofaciens 30–84. J. Bacteriol. 2001;183(1):318–327. doi: 10.1128/JB.183.1.318-327.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sakhtah H., Koyama L., Zhang Y., Morales D. K., Fields B. L., Price-Whelan A., Hogan D. A., Shepard K., Dietrich L. E. P.. The Pseudomonas Aeruginosa Efflux Pump MexGHI-OpmD Transports a Natural Phenazine That Controls Gene Expression and Biofilm Development. Proc. Natl. Acad. Sci. U.S.A. 2016;113(25):E3538–E3547. doi: 10.1073/pnas.1600424113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Wang Y., Newman D. K.. Redox Reactions of Phenazine Antibiotics with Ferric (Hydr)­Oxides and Molecular Oxygen. Environ. Sci. Technol. 2008;42(7):2380–2386. doi: 10.1021/es702290a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Bosire E. M., Blank L. M., Rosenbaum M. A.. Strain- and Substrate-Dependent Redox Mediator and Electricity Production by Pseudomonas Aeruginosa. Appl. Environ. Microbiol. 2016;82(16):5026–5038. doi: 10.1128/AEM.01342-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Mann S.. Zur Identifizierung und Redoxfunktion der Pigmente von Pseudomonas aureofaciens und P. iodina. Arch. Für Mikrobiol. 1970;71(4):304–318. doi: 10.1007/BF00417128. [DOI] [PubMed] [Google Scholar]
  43. Chandra Sekar N., Mousavi Shaegh S. A., Ng S. H., Ge L., Tan S. N.. A Paper-Based Amperometric Glucose Biosensor Developed with Prussian Blue-Modified Screen-Printed Electrodes. Sens. Actuators, B. 2014;204:414–420. doi: 10.1016/j.snb.2014.07.103. [DOI] [Google Scholar]
  44. World Health Organisation . Guidelines for drinking-water Quality, 4th ed.; WHO, 2017. [Google Scholar]
  45. Bellin D. L., Sakhtah H., Rosenstein J. K., Levine P. M., Thimot J., Emmett K., Dietrich L. E. P., Shepard K. L.. Integrated Circuit-Based Electrochemical Sensor for Spatially Resolved Detection of Redox-Active Metabolites in Biofilms. Nat. Commun. 2014;5(1):3256. doi: 10.1038/ncomms4256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Simoska O., Sans M., Fitzpatrick M. D., Crittenden C. M., Eberlin L. S., Shear J. B., Stevenson K. J.. Real-Time Electrochemical Detection of Pseudomonas Aeruginosa Phenazine Metabolites Using Transparent Carbon Ultramicroelectrode Arrays. ACS Sens. 2019;4(1):170–179. doi: 10.1021/acssensors.8b01152. [DOI] [PubMed] [Google Scholar]
  47. Parsons J. F., Greenhagen B. T., Shi K., Calabrese K., Robinson H., Ladner J. E.. Structural and Functional Analysis of the Pyocyanin Biosynthetic Protein PhzM from Pseudomonas Aeruginosa. Biochemistry. 2007;46(7):1821–1828. doi: 10.1021/bi6024403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ettenauer J., Zuser K., Kellner K., Posnicek T., Brandl M.. 8-Hydroxyquinoline-Glucuronide Sodium Salt Used as Electroactive Substrate for a Sensitive Voltammetric Detection of Escherichia Coli in Water Samples. Procedia Eng. 2016;168:143–146. doi: 10.1016/j.proeng.2016.11.179. [DOI] [Google Scholar]
  49. Costa K. C., Glasser N. R., Conway S. J., Newman D. K.. Pyocyanin Degradation by a Tautomerizing Demethylase Inhibits Pseudomonas Aeruginosa Biofilms. Science. 2017;355(6321):170–173. doi: 10.1126/science.aag3180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Costa, K. C. ; Bergkessel, M. ; Saunders, S. ; Korlach, J. ; Newman, D. K. . Enzymatic Degradation of Phenazines Can Generate Energy and Protect Sensitive Organisms from Toxicity. mBio 2015, 6(6). 10.1128/mbio.01520-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Lawrence J. M., Yin Y., Bombelli P., Scarampi A., Storch M., Wey L. T., Climent-Catala A., Baldwin G. S., O’Hare D., Howe C. J., Zhang J. Z., Ouldridge T. E., Ledesma-Amaro R.. Synthetic Biology and Bioelectrochemical Tools for Electrogenetic System Engineering. Sci. Adv. 2022;8(18):eabm5091. doi: 10.1126/sciadv.abm5091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Schmitz S., Nies S., Wierckx N., Blank L. M., Rosenbaum M. A.. Engineering Mediator-Based Electroactivity in the Obligate Aerobic Bacterium Pseudomonas Putida KT2440. Front. Microbiol. 2015;6:284. doi: 10.3389/fmicb.2015.00284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Simoska O., Gaffney E. M., Lim K., Beaver K., Minteer S. D.. Understanding the Properties of Phenazine Mediators That Promote Extracellular Electron Transfer in Escherichia Coli. J. Electrochem. Soc. 2021;168(2):025503. doi: 10.1149/1945-7111/abe52d. [DOI] [Google Scholar]
  54. Bjerketorp J., Håkansson S., Belkin S., Jansson J. K.. Advances in Preservation Methods: Keeping Biosensor Microorganisms Alive and Active. Curr. Opin. Biotechnol. 2006;17(1):43–49. doi: 10.1016/j.copbio.2005.12.005. [DOI] [PubMed] [Google Scholar]
  55. Pardee K., Green A. A., Ferrante T., Cameron D. E., DaleyKeyser A., Yin P., Collins J. J.. Paper-Based Synthetic Gene Networks. Cell. 2014;159(4):940–954. doi: 10.1016/j.cell.2014.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Moore S. J., MacDonald J. T., Freemont P. S.. Cell-Free Synthetic Biology for in Vitro Prototype Engineering. Biochem. Soc. Trans. 2017;45(3):785–791. doi: 10.1042/BST20170011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Jung J. K., Alam K. K., Verosloff M. S., Capdevila D. A., Desmau M., Clauer P. R., Lee J. W., Nguyen P. Q., Pastén P. A., Matiasek S. J., Gaillard J.-F., Giedroc D. P., Collins J. J., Lucks J. B.. Cell-Free Biosensors for Rapid Detection of Water Contaminants. Nat. Biotechnol. 2020;38(12):1451–1459. doi: 10.1038/s41587-020-0571-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Sadat Mousavi P., Smith S. J., Chen J. B., Karlikow M., Tinafar A., Robinson C., Liu W., Ma D., Green A. A., Kelley S. O., Pardee K. A.. A multiplexed, electrochemical interface for gene-circuit-based sensors. Nat. Chem. 2020;12(1):48–55. doi: 10.1038/s41557-019-0366-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Elkhawaga A. A., Khalifa M. M., El-badawy O., Hassan M. A., El-Said W. A.. Rapid and Highly Sensitive Detection of Pyocyanin Biomarker in Different Pseudomonas Aeruginosa Infections Using Gold Nanoparticles Modified Sensor. PLoS One. 2019;14(7):e0216438. doi: 10.1371/journal.pone.0216438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Cabantous S., Nguyen H. B., Pedelacq J.-D., Koraïchi F., Chaudhary A., Ganguly K., Lockard M. A., Favre G., Terwilliger T. C., Waldo G. S.. A New Protein-Protein Interaction Sensor Based on Tripartite Split-GFP Association. Sci. Rep. 2013;3(1):2854. doi: 10.1038/srep02854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Dagliyan O., Dokholyan N. V., Hahn K. M.. Engineering Proteins for Allosteric Control by Light or Ligands. Nat. Protoc. 2019;14(6):1863–1883. doi: 10.1038/s41596-019-0165-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Hamedi M. M., Ainla A., Güder F., Christodouleas D. C., Fernández-Abedul M. T., Whitesides G. M.. Integrating Electronics and Microfluidics on Paper. Adv. Mater. 2016;28(25):5054–5063. doi: 10.1002/adma.201505823. [DOI] [PubMed] [Google Scholar]
  63. Tahernia M., Mohammadifar M., Hassett D. J., Choi S.. A Fully Disposable 64-Well Papertronic Sensing Array for Screening Electroactive Microorganisms. Nano Energy. 2019;65:104026. doi: 10.1016/j.nanoen.2019.104026. [DOI] [Google Scholar]
  64. Chen S.-Y., Wei W., Yin B.-C., Tong Y., Lu J., Ye B.-C.. Development of a Highly Sensitive Whole-Cell Biosensor for Arsenite Detection through Engineered Promoter Modifications. ACS Synth. Biol. 2019;8(10):2295–2302. doi: 10.1021/acssynbio.9b00093. [DOI] [PubMed] [Google Scholar]
  65. Chen S.-Y., Zhang Y., Li R., Wang B., Ye B.-C.. De Novo Design of the ArsR Regulated Pars Promoter Enables a Highly Sensitive Whole-Cell Biosensor for Arsenic Contamination. Anal. Chem. 2022;94(20):7210–7218. doi: 10.1021/acs.analchem.2c00055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Bradley R. W., Buck M., Wang B.. Recognizing and Engineering Digital-like Logic Gates and Switches in Gene Regulatory Networks. Curr. Opin. Microbiol. 2016;33:74–82. doi: 10.1016/j.mib.2016.07.004. [DOI] [PubMed] [Google Scholar]
  67. Chen S., Gao Y., Fan Y., Pan Y., Zhou N., Wang B.. Trans-splicing denoiser circuits enable highly sensitive cellular sensors for physiological biomarkers. Biosens. Bioelectron. 2025;287:117709. doi: 10.1016/j.bios.2025.117709. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

be5c00015_si_001.pdf (936.8KB, pdf)
be5c00015_si_002.xlsx (162KB, xlsx)

Data Availability Statement

Plasmids for production of PCA, PCN, 2OHP, and PYO have been deposited within Addgene repository (#165617–20). All source data in the main text supporting the findings of this study are provided in the Supporting Information data file.

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