Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Transcriptional regulation of living materials via extracellular electron transfer

Nature Chemical Biology volume 20, pages 1329–1340 (2024)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Martyn, I. & Gartner, Z. J. Expanding the boundaries of synthetic development. Dev. Biol. 474, 62–70 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Luo, N., Wang, S. & You, L. Synthetic pattern formation. Biochemistry 58, 1478–1483 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Sirisaengtaksin, N., Odem, M. A., Bosserman, R. E., Flores, E. M. & Krachler, A. M. The E. coli transcription factor GrlA is regulated by subcellular compartmentalization and activated in response to mechanical stimuli. Proc. Natl Acad. Sci. USA 117, 9519–9528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Goodwin, K. & Nelson, C. M. Mechanics of development. Dev. Cell 56, 240–250 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Humphries, J. et al. Species-independent attraction to biofilms through electrical signaling. Cell 168, 200–209.e12 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Roth, J. G. et al. Advancing models of neural development with biomaterials. Nat. Rev. Neurosci. 22, 593–615 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kholodenko, B. N. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fern, J. & Schulman, R. Modular DNA strand-displacement controllers for directing material expansion. Nat. Commun. 9, 3766 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Rodrigo-Navarro, A., Sankaran, S., Dalby, M. J., del Campo, A. & Salmeron-Sanchez, M. Engineered living biomaterials. Nat. Rev. Mater. 6, 1175–1190 (2021).

    Article  Google Scholar 

  10. Cao, Y. et al. Programmable assembly of pressure sensors using pattern-forming bacteria. Nat. Biotechnol. 35, 1087–1093 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Praveschotinunt, P. et al. Engineered E. coli Nissle 1917 for the delivery of matrix-tethered therapeutic domains to the gut. Nat. Commun. 10, 5580 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Duraj-Thatte, A. M. et al. Water-processable, biodegradable and coatable aquaplastic from engineered biofilms. Nat. Chem. Biol. 17, 732–738 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kang, S.-Y. et al. Engineering Bacillus subtilis for the formation of a durable living biocomposite material. Nat. Commun. 12, 7133 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Smith, R. S. H. et al. Hybrid living materials: digital design and fabrication of 3D multimaterial structures with programmable biohybrid surfaces. Adv. Funct. Mater. 30, 1907401 (2020).

    Article  CAS  Google Scholar 

  15. Gilbert, C. et al. Living materials with programmable functionalities grown from engineered microbial co-cultures. Nat. Mater. 20, 691–700 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Rivera-Tarazona, L. K., Campbell, Z. T. & Ware, T. H. Stimuli-responsive engineered living materials. Soft Matter 17, 785–809 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. González, L. M., Mukhitov, N. & Voigt, C. A. Resilient living materials built by printing bacterial spores. Nat. Chem. Biol. 16, 126–133 (2020).

    Article  PubMed  Google Scholar 

  18. Graham, A. J. & Keitz, B. K. in Engineered Living Materials (ed. Srubar III, W. V.) 27–49 (Springer International, 2023).

  19. Rosales, A. M. & Anseth, K. S. The design of reversible hydrogels to capture extracellular matrix dynamics. Nat. Rev. Mater. 1, 15012 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V. & Pernice, W. H. P. The rise of intelligent matter. Nature 594, 345–355 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Zhang, X. et al. The pathway to intelligence: using stimuli-responsive materials as building blocks for constructing smart and functional systems. Adv. Mater. 31, 1804540 (2019).

    Article  Google Scholar 

  22. English, M. A. et al. Programmable CRISPR-responsive smart materials. Science 365, 780–785 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Zhang, H., Zeng, H., Priimagi, A. & Ikkala, O. Viewpoint: Pavlovian materials—functional biomimetics inspired by classical conditioning. Adv. Mater. 32, 1906619 (2020).

    Article  CAS  Google Scholar 

  24. Ikeda, M. et al. Installing logic-gate responses to a variety of biological substances in supramolecular hydrogel–enzyme hybrids. Nat. Chem. 6, 511–518 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Badeau, B. A., Comerford, M. P., Arakawa, C. K., Shadish, J. A. & DeForest, C. A. Engineered modular biomaterial logic gates for environmentally triggered therapeutic delivery. Nat. Chem. 10, 251–258 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Korevaar, P. A., Kaplan, C. N., Grinthal, A., Rust, R. M. & Aizenberg, J. Non-equilibrium signal integration in hydrogels. Nat. Commun. 11, 386 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Daly, A. C., Prendergast, M. E., Hughes, A. J. & Burdick, J. A. Bioprinting for the biologist. Cell 184, 18–32 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rivera-Tarazona, L. K., Bhat, V. D., Kim, H., Campbell, Z. T. & Ware, T. H. Shape-morphing living composites. Sci. Adv. 6, eaax8582 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shi, L. et al. Extracellular electron transfer mechanisms between microorganisms and minerals. Nat. Rev. Microbiol. 14, 651–662 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Fan, G., Graham, A. J., Kolli, J., Lynd, N. A. & Keitz, B. K. Aerobic radical polymerization mediated by microbial metabolism. Nat. Chem. 12, 638–646 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Graham, A. J. et al. Genetic control of radical cross-linking in a semisynthetic hydrogel. ACS Biomater. Sci. Eng. 6, 1375–1386 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Beliaev, A. S. et al. Gene and protein expression profiles of Shewanella oneidensis during anaerobic growth with different electron acceptors. OMICS J. Integr. Biol. 6, 39–60 (2002).

    Article  CAS  Google Scholar 

  33. Coursolle, D. & Gralnick, J. A. Reconstruction of extracellular respiratory pathways for iron(III) reduction in Shewanella oneidensis strain MR-1. Front. Microbiol. 3, 56 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Brophy, J. A. N. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dundas, C. M., Walker, D. J. F. & Keitz, B. K. Tuning extracellular electron transfer by Shewanella oneidensis using transcriptional logic gates. ACS Synth. Biol. 9, 2301–2315 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Nielsen, A. A. K. et al. Genetic circuit design automation. Science 352, aac7341 (2016).

    Article  PubMed  Google Scholar 

  37. Reis, A. C. & Salis, H. M. An automated model test system for systematic development and improvement of gene expression models. ACS Synth. Biol. 9, 3145–3156 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Rosenfeld, N. & Alon, U. Response delays and the structure of transcription networks. J. Mol. Biol. 329, 645–654 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Xiong, Y. et al. Targeted protein degradation of outer membrane decaheme cytochrome MtrC metal reductase in Shewanella oneidensis MR-1 measured using biarsenical probe CrAsH-EDT2. Biochemistry 50, 9738–9751 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Stanton, B. C. et al. Genomic mining of prokaryotic repressors for orthogonal logic gates. Nat. Chem. Biol. 10, 99–105 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature 469, 212–215 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Partipilo, G., Graham, A. J., Belardi, B. & Keitz, B. K. Extracellular electron transfer enables cellular control of Cu(I)-catalyzed alkyne–azide cycloaddition. ACS Cent. Sci. https://doi.org/10.1021/acscentsci.1c01208 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Gao, H., Chan, N., Oh, J. K. & Matyjaszewski, K. in In-Situ Gelling Polymers (ed. Loh, X. J.) 69–105 (Springer, 2014).

  44. Tibbitt, M. W., Kloxin, A. M., Sawicki, L. & Anseth, K. S. Mechanical properties and degradation of chain and step polymerized photodegradable hydrogels. Macromolecules 46, 2785–2792 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Adzima, B. J. et al. spatial and temporal control of the alkyne–azide cycloaddition by photoinitiated Cu(II) reduction. Nat. Chem. 3, 256–259 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Hillsley, A., Santos, J. E. & Rosales, A. M. A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells. Sci. Rep. 11, 21855 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Tomasek, J. J., Gabbiani, G., Hinz, B., Chaponnier, C. & Brown, R. A. Myofibroblasts and mechano-regulation of connective tissue remodelling. Nat. Rev. Mol. Cell Biol. 3, 349–363 (2002).

    Article  CAS  PubMed  Google Scholar 

  48. Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hay, J. J. et al. Bacteria-based materials for stem cell engineering. Adv. Mater. 30, 1804310 (2018).

    Article  Google Scholar 

  50. Su, L., Fukushima, T. & Ajo-Franklin, C. M. A hybrid cyt C maturation system enhances the bioelectrical performance of engineered Escherichia coli by improving the rate-limiting step. Biosens. Bioelectron. 165, 112312 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Klumpp, S., Zhang, Z. & Hwa, T. Growth rate-dependent global effects on gene expression in bacteria. Cell 139, 1366–1375 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zhang, H. M. et al. Measurements of gene expression at steady state improve the predictability of part assembly. ACS Synth. Biol. 5, 269–273 (2015).

    Article  PubMed  Google Scholar 

  53. Hu, Y., Yang, Y., Katz, E. & Song, H. Programming the quorum sensing-based AND gate in shewanella oneidensis for logic gated-microbial fuel cells. Chem. Commun. Camb. Engl. 51, 4184–4187 (2015).

    Article  CAS  Google Scholar 

  54. Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits 2nd edn (Chapman and Hall, 2019).

  55. Groseclose, T. M., Rondon, R. E., Herde, Z. D., Aldrete, C. A. & Wilson, C. J. Engineered systems of inducible anti-repressors for the next generation of biological programming. Nat. Commun. 11, 4440 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Molinari, S. et al. A de novo matrix for macroscopic living materials from bacteria. Nat. Commun. 13, 5544 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Dai, Z. et al. Living fabrication of functional semi-interpenetrating polymeric materials. Nat. Commun. 12, 3422 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, F. et al. A biocompatible heterogeneous MOF–Cu catalyst for in vivo drug synthesis in targeted subcellular organelles. Angew. Chem. Int. Ed. 58, 6987–6992 (2019).

    Article  CAS  Google Scholar 

  59. Hinz, B., Mastrangelo, D., Iselin, C. E., Chaponnier, C. & Gabbiani, G. Mechanical tension controls granulation tissue contractile activity and myofibroblast differentiation. Am. J. Pathol. 159, 1009–1020 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Witte, K., Rodrigo-Navarro, A. & Salmeron-Sanchez, M. Bacteria-laden microgels as autonomous three-dimensional environments for stem cell engineering. Mater. Today Bio. 2, 100011 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Petaroudi, M., Rodrigo‐Navarro, A., Dobre, O., Dalby, M. J. & Salmeron‐Sanchez, M. Living biomaterials to engineer hematopoietic stem cell niches. Adv. Healthc. Mater. 11, 2200964 (2022).

    Article  CAS  Google Scholar 

  62. Lee, K. Y. & Mooney, D. J. Hydrogels for tissue engineering. Chem. Rev. 101, 1869–1880 (2001).

    Article  CAS  PubMed  Google Scholar 

  63. Jeon, J. W., Cho, I. H., Ha, U. H., Seo, S. K. & Paek, S. H. Chemiluminometric immuno-analysis of innate immune response against repetitive bacterial stimulations for the same mammalian cells. Sci. Rep. 4, 6011 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Schultz, G. S., Davidson, J. M., Kirsner, R. S., Bornstein, P. & Herman, I. M. Dynamic reciprocity in the wound microenvironment. Wound Repair Regen. 19, 134–148 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Freedman, B. R. et al. Enhanced tendon healing by a tough hydrogel with an adhesive side and high drug-loading capacity. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-021-00810-0 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Heveran, C. M. et al. Biomineralization and successive regeneration of engineered living building materials. Matter 2, 481–494 (2020).

    Article  CAS  Google Scholar 

  67. Caro-Astorga, J., Walker, K. T., Herrera, N., Lee, K.-Y. & Ellis, T. Bacterial cellulose spheroids as building blocks for 3D and patterned living materials and for regeneration. Nat. Commun. 12, 5027 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Cubillos-Ruiz, A. et al. An engineered live biotherapeutic for the prevention of antibiotic-induced dysbiosis. Nat. Biomed. Eng. 6, 910–921 (2022).

    Article  CAS  PubMed  Google Scholar 

  69. Karbelkar, A. A., Reynolds, E. E., Ahlmark, R. & Furst, A. L. A microbial electrochemical technology to detect and degrade organophosphate pesticides. ACS Cent. Sci. 7, 1718–1727 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Green, A. A. et al. Complex cellular logic computation using ribocomputing devices. Nature 548, 117–121 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Yeh, Y.-C. et al. Mechanically dynamic PDMS substrates to investigate changing cell environments. Biomaterials 145, 23–32 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Austin J. Graham, Gina Partipilo.

Authors and Affiliations

  1. McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA

    Austin J. Graham, Gina Partipilo, Christopher M. Dundas, Kathleen N. Halwachs, Trevor R. Simmons, Thomas M. FitzSimons, Sarah M. Coleman, Rebecca Rinehart, Darian Chiu, Kenneth C. Sajbel, Adrianne M. Rosales & Benjamin K. Keitz

  2. Interdisciplinary Life Sciences Graduate Program, University of Texas at Austin, Austin, TX, USA

    Ismar E. Miniel Mahfoud & Alexis J. Holwerda

  3. Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA

    Avery E. Tyndall

Authors
  1. Austin J. Graham
    View author publications

    Search author on:PubMed Google Scholar

  2. Gina Partipilo
    View author publications

    Search author on:PubMed Google Scholar

  3. Christopher M. Dundas
    View author publications

    Search author on:PubMed Google Scholar

  4. Ismar E. Miniel Mahfoud
    View author publications

    Search author on:PubMed Google Scholar

  5. Kathleen N. Halwachs
    View author publications

    Search author on:PubMed Google Scholar

  6. Alexis J. Holwerda
    View author publications

    Search author on:PubMed Google Scholar

  7. Trevor R. Simmons
    View author publications

    Search author on:PubMed Google Scholar

  8. Thomas M. FitzSimons
    View author publications

    Search author on:PubMed Google Scholar

  9. Sarah M. Coleman
    View author publications

    Search author on:PubMed Google Scholar

  10. Rebecca Rinehart
    View author publications

    Search author on:PubMed Google Scholar

  11. Darian Chiu
    View author publications

    Search author on:PubMed Google Scholar

  12. Avery E. Tyndall
    View author publications

    Search author on:PubMed Google Scholar

  13. Kenneth C. Sajbel
    View author publications

    Search author on:PubMed Google Scholar

  14. Adrianne M. Rosales
    View author publications

    Search author on:PubMed Google Scholar

  15. Benjamin K. Keitz
    View author publications

    Search author on:PubMed Google Scholar

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.

Corresponding author

Correspondence to Benjamin K. Keitz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

a–d, 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 (download PDF )

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

Reporting Summary (download PDF )

Supplementary Data 1 (download ZIP )

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.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Graham, A.J., Partipilo, G., Dundas, C.M. et al. Transcriptional regulation of living materials via extracellular electron transfer. Nat Chem Biol 20, 1329–1340 (2024). https://doi.org/10.1038/s41589-024-01628-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41589-024-01628-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research