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Real-time bioelectronic sensing of environmental contaminants

Abstract

Real-time chemical sensing is crucial for applications in environmental and health monitoring1. Biosensors can detect a variety of molecules through genetic circuits that use these chemicals to trigger the synthesis of a coloured protein, thereby producing an optical signal2,3,4. However, the process of protein expression limits the speed of this sensing to approximately half an hour, and optical signals are often difficult to detect in situ5,6,7,8. Here we combine synthetic biology and materials engineering to develop biosensors that produce electrical readouts and have detection times of minutes. We programmed Escherichia coli to produce an electrical current in response to specific chemicals using a modular, eight-component, synthetic electron transport chain. As designed, this strain produced current following exposure to thiosulfate, an anion that causes microbial blooms, within 2 min. This amperometric sensor was then modified to detect an endocrine disruptor. The incorporation of a protein switch into the synthetic pathway and encapsulation of the bacteria with conductive nanomaterials enabled the detection of the endocrine disruptor in urban waterway samples within 3 min. Our results provide design rules to sense various chemicals with mass-transport-limited detection times and a new platform for miniature, low-power bioelectronic sensors that safeguard ecological and human health.

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Fig. 1: An E.coli sensor with a synthetic ET chain.
Fig. 2: Encapsulation of a living electronic sensor enables rapid detection and quantification of thiosulfate.
Fig. 3: Living electronic sensors that express an electrical protein switch enable rapid detection of an endocrine disruptor.
Fig. 4: Living electronic sensors encapsulated with conductive nanoparticles enable rapid detection of pollutants in environmental samples.

Data availability

All data generated or analysed during this study are included in this published article and its Supplementary InformationSource data are provided with this paper.

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Acknowledgements

E.coli EW11 and the genes encoding FNR and SIR were a gift from P. Silver (Harvard University). pSIM19 was a gift from D. Court (NIH-National Cancer Institute). pSS9 (Addgene, plasmid 71655), pSS9-RNA (Addgene, plasmid 71656) and pX2-Cas9 (Addgene, plasmid 8581) were gifts from R. Gill (University of Colorado). We thank S. Li (Rice University) for help with water sampling; X. Chen and C. Masiello (Rice University) with help with TOC measurements; H. Du and D. Fu (Southeast University) with help with TiO2@TiN nanocomposite synthesis; J. Soman (Rice University) for internal reviewing and providing writing suggestions; and M. Baruch (Rice University) for helping to conceptualize the project. Funding was from the Office of Science, Office of Basic Energy Sciences of the US Department of Energy grants DE-SC0014462 (to J.J.S. and G.N.B.); Office of Naval Research grants 0001418IP00037 (to C.M.A.-F.), N00014-17-1-2639 (to J.J.S.) and N00014-20-1-2274 (to C.M.A.-F. and J.J.S.); Cancer Prevention and Research Institute of Texas RR190063 (to C.M.A.-F.); National Science Foundation grant 1843556 (to J.J.S. and G.N.B.); Department of Energy Office of Science Graduate Student Research (SCGSR) Program Fellowship DE‐SC0014664 (to J.T.A.); Loideska Stockbridge Vaughn Fellowship (to J.T.A.); and China Scholarship Council Fellowship CSC-201606090098 (to L.S.). Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under contract number DE-AC02-05CH11231.

Author information

Authors and Affiliations

Authors

Contributions

J.T.A., L.S., C.M.A.-F. and J.J.S. conceptualized the project. J.T.A. performed all molecular biology and genome engineering. J.T.A. and L.S. performed assays to verify the functions of the modules. L.S. and X.Z. developed the cell encapsulation protocol, performed the bioelectrochemical analysis of thiosulfate and 4-HT sensing, and performed water sampling. L.S. synthesized the WO3 and TiO2@TiN nanomaterials. X.Z. and C.M.A.-F. performed diffusion modelling. J.T.A., L.S. and X.Z. analysed and visualized all the data and made the schematic diagrams. J.T.A., L.S., X.Z., J.J.S. and C.M.A.-F. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Jonathan J. Silberg or Caroline M. Ajo-Franklin.

Ethics declarations

Competing interests

J.J.S., J.T.A. and G.N.B. have submitted a patent application (number 16/186,226) covering the use of fragmented proteins as chemical-dependent electron carriers, entitled ‘Regulating electron flow using fragmented proteins’. L.S., X.Z. and C.M.A.-F. declare no competing interests.

Peer review

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Nature thanks Luying Xun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Impact of sulfur source on sulfide evolution from E. coli EW11.

(A) A schematic of sulfur metabolism and regulation in E. coli (yellow) and the redox coupling of the Input module (blue) with this pathway. (B) PbS accumulation and (C) optical density of the I+CO strain containing a vector for expressing Fd after 24 h in M9sa medium containing 2 mM of sulfate, sulfite, or thiosulfate and varying amounts of aTc to control Fd expression. The optical density in sulfate and sulfite containing media were significantly lower than thiosulfate containing media when < 7.81 nM aTc was added (p = 8.47 × 10−5 for 3.91 nM aTc with sulfate, p = 6.98 x 10−8 for 0 nM aTc with sulfate, p = 1.70 × 10−4 for for 3.91 nM aTc with sulfite, p = 7.16 x 10−8) for 0 nM aTc with sulfite. (D) PbS accumulation and (E) optical density after 24 h in M9sa medium containing varying amounts of thiosulfate and varying amounts of aTc to control Fd expression. In media with ≥ 0.25 mM thiosulfate, optical densities were not significantly different in the presence of aTc (p > 0.01 for all concentrations). For panels B-E, symbols and error bars represent the mean and standard deviation, respectively (n = 3 biologically independent samples). P-values were obtained using two-tailed, independent t-tests.

Source data

Extended Data Fig. 2 Effect of Output module expression on cytochrome levels.

(A) Red color intensity values of IC42ACO+ (circle) or IC42ACO (square) cell pellets following aerobic growth in 2xYT medium containing varying amounts of IPTG, which induces expression of CymA-MtrCAB. (B) Blue color intensity values of IC42ACO+ (circle) or IC42ACO (square) in minimal media containing lactate and electrochromic WO3 nanoparticles that change from white to blue when reduced by microbes that present EET. (C) Cell density (OD600) of IC42ACO+ (circle) or IC42ACO (square) grown in M9 minimal medium containing varying amounts of IPTG. Growth of IC42ACO+ was significantly decreased at > 10 µM IPTG (p = 6.55 × 10−3 for 12.5 µM, p = 7.30 × 10−6 for 25 µM, p = 3.87 × 10−4 for 50 µM, p = 4.65 × 10−6 for 100 µM, p = 5.89 × 10−5 for 200 µM). Data represents the mean values with error bars representing one standard deviation (n = 3 biologically independent samples). P-values were obtained using two-tailed, independent t-tests.

Source data

Extended Data Fig. 3 Sulfane sulfur accumulation in SQR expressing cells.

Relative fluorescence of cells treated with the sulfane sulfur probe SSP4. The fluorescence from cells expressing Gs-SQR and Rc-SQR was significantly higher than cells transformed with an empty vector (EV) (p = 4.89 × 10−3 and p = 3.14 × 10−3, respectively). Fluorescence from cells expressing Gs-SQR and Rc-SQR were not significantly different. Error bars represent one standard deviation (n = 3 biologically independent samples) with individual samples shown as white circles and bars heights representing the mean. P-values were obtained using two-tailed, independent t-tests.

Source data

Extended Data Fig. 4 Planktonic cells present a small, noisy current response to thiosulfate.

(A) The chronoamperometric response of planktonic I+C+O+ and IC42AC+O+ cells in a bioelectrochemical reactor. Arrows indicate the addition of thiosulfate to varying concentrations. Data represents the mean values with error bars representing one standard deviation (n = 3 biologically independent samples). The working electrodes were poised at +0.42 VSHE. (B) The signal-to-noise ratio (SNR) was calculated as dividing the average current generated from bacteria by the standard deviation (n = 3) of the current. The average SNR across the 150 to 400 min was calculated to reflect the SNR changes between planktonic (-gel) and encapsulated (+gel) strains, as 140.00 for +gel, I+C+O+, 4.35 for -gel, I+C+O+, 12.27 for +gel, IC42AC+O+ and 2.88 for -gel, IC42AC+O+.

Source data

Extended Data Fig. 5 Linear fit for the thiosulfate sensing with different ranges.

5 min (A) and 30 min (B) sensing time, linear range from 0.1 mM to 20 mM; 5 min (C) and 30 min (D) sensing time, linear range from 0.1 mM to 10 mM.

Source data

Extended Data Fig. 6

Amperometric response and calculated signal intensity of ISC+O+ and IC42AC+O+ upon addition of DMSO (A, B, C) or 4-HT (D, E, F) in each 2-EWE configured BES with working electrodes poised at +0.42 VSHE. Time zero indicates the start of the chronoamperometric measurements.

Source data

Extended Data Fig. 7 Response of I+C+O+ and IC42AC+O+ to thiosulfate in complex urban waterway samples.

Percent increase in the amperometric response of I+C+O+ relative to IC42AC+O+ immediately before and 6.54 min (p = 0.045) after addition of 10 mM thiosulfate in the waterway samples from Brays Bayou (red), Buffalo Bayou (blue), and Galveston Beach (green). Each point represents a single waterway replicate, the center line represents the mean of the response in the three waterway samples with error bars representing one standard deviation.

Source data

Extended Data Fig. 8 Cyclic voltammetry analysis of environmental samples.

(A) Each environmental sample shows multiple pairs of redox peaks, indicating abundant redox active chemicals exist which might interfere with 4-HT sensing. (B) Environmental samples supplemented with 0.2% glucose show no changes to their voltammograms. All CVs were measured at a scan rate of 10 mV/s.

Source data

Extended Data Fig. 9 Addition of TiO2@TiN nanoparticles enables more current collection.

(A) Chronoamperometry and (B) current of I+C+O+ strain encapsulated in an alginate-agarose hydrogel with and without TiO2@TiN nanoparticles upon addition of 1 mM thiosulfate (arrow). The strains encapsulated with nanoparticles respond to thiosulfate more rapidly and with a higher steady-state level. Data represents two biologically independent measurements.

Source data

Extended Data Fig. 10 Simplified 1D geometry for calculation of diffusion timescales for the response of the living bioelectronic sensor.

Schematic of analyte diffusion from bulk solution through the agarose layer to cells embedded in the hydrogel on the electrode surface.

Supplementary information

Supplementary Information

This file contains Supplementary Text detailing the evaluation of sulfur source on assimilation and H2S evolution, Supplementary Tables 1–4 and Supplementary References.

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Atkinson, J.T., Su, L., Zhang, X. et al. Real-time bioelectronic sensing of environmental contaminants. Nature 611, 548–553 (2022). https://doi.org/10.1038/s41586-022-05356-y

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