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Transcriptional regulation of living materials via extracellular electron transfer

Abstract

Engineered living materials combine the advantages of biological and synthetic systems by leveraging genetic and metabolic programming to control material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In this system, EET flux from Shewanella oneidensis to a copper catalyst controls hydrogel cross-linking via two distinct chemistries to form living synthetic polymer networks. We first demonstrate that synthetic biology-inspired design rules derived from fluorescence parameterization can be applied toward EET-based regulation of polymer network mechanics. We then program transcriptional Boolean logic gates to govern EET gene expression, which enables design of computational polymer networks that mechanically respond to combinations of molecular inputs. Finally, we control fibroblast morphology using EET as a bridge for programmed material properties. Our results demonstrate how rational genetic circuit design can emulate physiological behavior in engineered living materials.

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Fig. 1: Bacterial sensing and computation actuate material mechanics.
Fig. 2: Transcriptional control of EET proteins enables dynamic cross-linking tunable by inoculation density, inducer concentration and reaction time.
Fig. 3: Transcriptional regulation of mtrC yields predictable control over polymer network mechanics via fluorescent gate parameterization.
Fig. 4: Genetic Boolean logic enables polymer network computation via living cellular actuators.
Fig. 5: Alternative living material chemistries enabled by EET-driven CuAAC cross-linking.
Fig. 6: Fibroblast morphology is dictated by living material mechanics under genetic Boolean regulation.

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Data availability

The data that support the findings of this study are available within the main text and its Supplementary Information file. Experimental data supporting the findings of this study will be available through the Texas Data Repository (https://doi.org/10.18738/T8/B7GAG6). Biological materials are available upon request to B. K. Keitz. Source data are provided with this paper.

Code availability

R code for running statistical analysis will be available through the Texas Data Repository.

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Acknowledgements

Base plasmids for the AND and NAND circuits were generously provided by the Voigt Laboratory via Addgene (grants 49375, 49376 and 49377). This research was financially supported by the Welch Foundation (grant F-1929, B.K.K.), the National Institutes of Health under award number R35GM133640 (B.K.K.), a National Science Foundation (NSF) CAREER award (grant 1944334, B.K.K.), and the Air Force Office of Scientific Research under award number FA9550-20-1-0088 (B.K.K.). A.J.G. and G.P. were supported through NSF Graduate Research Fellowships (program award DGE-1610403). We acknowledge use of shared research facilities supported in part by the Texas Materials Institute, the Center for Dynamics and Control of Materials: an NSF MRSEC (grant DMR-1720595), and the NSF National Nanotechnology Coordinated Infrastructure (grant ECCS-1542159). A.M.R. gratefully acknowledges a Career Award at the Scientific Interface (grant 1015895) from the Burroughs Wellcome Fund. We gratefully acknowledge the use of facilities within the core microscopy lab of the Institute for Cellular and Molecular Biology, University of Texas at Austin. Nuclear magnetic resonance spectra were collected on a Bruker Avance III HD 400 funded by the NSF (award grant CHE 1626211). Flow Cytometry was performed at the Center for Biomedical Research Support Microscopy and Imaging Facility at UT Austin (RRID grant SCR_021756). We gratefully acknowledge C. Moore for his technical advice and expertise. Schematics were created using BioRender.com, graphs were created in Prism GraphPad and Boolean logic statistics were run in R.

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Contributions

A.J.G., G.P., C.M.D. and B.K.K. conceived the project and designed research. A.J.G., G.P. and D.C. performed cross-linking experiments and rheological analysis. A.J.G., G.P., C.M.D., I.E.M.M., A.J.H., T.R.S., R.R., A.E.T. and K.C.S. performed cloning and circuit characterization by growth, fluorescence, quantitative PCR with reverse transcription and iron reduction assays. K.N.H. and G.P. designed and executed fibroblast seeding assays. T.M.F. synthesized alkyne-functionalized PEG and provided reagents. S.M.C. performed statistical analysis. A.M.R. and B.K.K. supervised research. A.J.G., G.P., C.M.D. and B.K.K. wrote the paper with input from all authors.

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Correspondence to Benjamin K. Keitz.

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Extended data

Extended Data Fig. 1 S. oneidensis retains viability after cross-linking and remains metabolically active within hydrogels for at least one week.

a, BacLight live/dead staining of S. oneidensis MR-1 after cross-linking, swelling overnight in 1x PBS, and mechanical characterization by rheology. Cells are predominantly alive (green fluorescence) as opposed to dead (red fluorescence). b–c, Overlaid fluorescence and bright-field microscopy of S. oneidensis MR-1 + sfgfp (left) encased in gels b, one day or c, one week after cross-linking and swelling, and (right) 24 h after inoculating with 1 mM IPTG to induce fluorescence. Images are representative of n = 3 biological replicates.

Extended Data Fig. 2 Dynamic cross-linking couples sensing, computation, and actuation in a synthetic material.

ad, Cross-linking can be transcriptionally regulated using the LacI-PtacsymO regulator-promoter pair controlling mtrA (a) (c) or cymA (b), (d) expression under a-b, stationary phase or c-d, dynamic conditions. Data are fit to an activating gene expression model and right axis is normalized to storage modulus of gels formed using wild-type S. oneidensis harboring an empty vector. Dashed lines represent gel mechanics using corresponding knockout strains harboring an empty vector; if no line is shown, gels did not form. Data shown are mean ± SEM of n = 3 biological replicates. e-f, The difference between the induced and uninduced storage modulus can be controlled via both initial inoculation density and reaction time for LacI-PtacsymO regulator-promoter pair controlling mtrA (e) or cymA (f) (heatmap data represents n = 1).

Source data

Extended Data Fig. 3 Design, Test, Build cycle for TetR Buffer gates allows for rapid prototyping.

a-d, sfgfp response functions for TetR-Buffer gates with varying RBS strength (800 a.u.; 2.9k a.u., 3.4k a.u.; 6.5k a.u. respectively51). e, initial material response function utilizing the same TetR-RBS strength as a, indicating a lack of control below REU c.a. 0.2-0.3. f, material response function utilizing the same TetR-RBS strength as b, predicting a higher dynamic range and greater control. Graphs b, and f, are reprinted from Fig. 3, but are included for clarity. Data shown are mean ± SEM of n = 3 biological replicates. Elements created with Biorender.com.

Source data

Extended Data Fig. 4 Genetic Boolean logic enables concentration-dependent transcriptional responses in S. oneidensis expressing sfgfp.

Relative Expression Units (REU) measured as a function of combinatorial inducer concentration show characteristic transcriptional regulation that follows expected truth tables for multiple genetic Boolean architectures expressing sfgfp (OR, NOR, NAND) or eyfp (AND). S. oneidensis MR-1 harboring each plasmid was grown overnight in aerobically prepared 96-well plates that were then sealed to emulate dynamic cross-linking conditions. Fluorescence was OD600-normalized and referenced to a constitutive fluorescence plasmid to obtain REU. The expected truth tables are shown for maximum and minimum induction conditions (0, ‘OFF’; 1, ‘ON’). Data shown are mean of n = 3 biological replicates.

Source data

Extended Data Fig. 5 Genetic Boolean logic enables synthetic material computation during stationary phase cross-linking.

a, Storage moduli for networks cross-linked using S. oneidensis strains harboring transcriptional Boolean logic circuits controlling mtrC expression under stationary phase conditions. b, The logical architectures span i, OR, ii, NOR, iii, NAND, and iv, AND. In all cases, storage modulus was measured 2 h after inoculation. The expected truth tables are represented below each circuit (0, ‘OFF’; 1, ‘ON’). Each network appropriately responds to combinatorial inputs by increasing/decreasing storage modulus in response to mtrC activation/deactivation. Each plasmid architecture is shown as a cartoon above the corresponding response function, with representations as in Fig. 3. Statistics performed are the results of a general linear hypothesis test (a contrast test) between the ‘OFF’ and ‘ON’ states. Stars reference p value (* p < 0.05, ** p < 0.01, *** p < 0.001), R script is provided in Data Availability, and data shown are mean ± SEM of n = 3 biological replicates.

Source data

Extended Data Fig. 6 Growth kinetics are not affected by induction in S. oneidensis harboring genetic Boolean logic controlling mtrC.

OD600 measured in situ at 30 °C for S. oneidensis harboring each Boolean mtrC construct under varying inducer conditions. An induced empty vector control (pCD8) was also measured as a reference. a, All growth curves shown together and b–e, growth curves of individual circuits under each induction condition. In general, growth was not affected by induction. Data shown are mean ± SEM of n = 3 biological replicates.

Source data

Extended Data Fig. 7 Genetic Boolean logic enables input signal-dependent metal reduction.

a, Raw in situ Fe(III) reduction kinetics measured using ferrozine absorbance and corresponding Monod-type fits49. b, Fitted Fe(III) reduction rate constants for corresponding curves calculated using a Monod-type model, with expected truth tables shown below (0, ‘OFF’; 1, ‘ON’). Gates shown are i. OR ii. NOR iii. AND iv. NAND. Data shown are mean ± SEM of n = 3 biological replicates.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–7 and Tables 1–5.

Reporting Summary

Supplementary Data 1

Flow cytometry raw data and gating image for flow cytometry in Fig. 2d.

Supplementary Data 2

R code for statistical tests for 2 input gates.

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Graham, A.J., Partipilo, G., Dundas, C.M. et al. Transcriptional regulation of living materials via extracellular electron transfer. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01628-y

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