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Repulsion from slow-diffusing nutrients improves microbial chemotaxis towards moving sources

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Abstract

Chemotaxis, or the following of chemical concentration gradients, is essential for microbes to locate nutrients. However, microbes often display paradoxical behaviors, such as Escherichia coli being repelled by several amino acids. Here, we explore chemotaxis towards a moving source and demonstrate that when multiple nutrients are released from the source repulsion from certain nutrients actually improves chemotaxis towards the source. Because a moving source leaves most of the nutrient plume behind it, simply following the concentration gradient results in aiming behind the source and potentially failing to intercept it. However, when attraction to a fast-diffusing nutrient and repulsion from a slow-diffusing nutrient are combined, motion in a new direction emerges and the chance of intercepting the source is increased up to six-fold. We demonstrate that this “differential strategy” is robust against numerous variations, including order-of-magnitude increases in the repellent release rate. Finally, we leverage existing data to show that E. coli is attracted to fast-diffusing amino acids and repelled by slow-diffusing ones, suggesting it may utilize a differential strategy and providing an explanation for its repulsion from these amino acids. Our results thus illuminate new possibilities in how microbes can integrate signals from multiple gradients to accomplish challenging chemotactic tasks.

Data availability

The data generated in this study are provided in Supplementary Data 1 – 4 and also available on GitHub (https://github.com/Blox-Blox/Differential-Chemotaxis/tree/704de055009a3470ee612546b0c5cfe27c044b64). Source data are provided with this paper.

Code availability

The simulation and analysis code to reproduce the data and figures of the manuscript are available on GitHub (https://github.com/Blox-Blox/Differential-Chemotaxis/tree/704de055009a3470ee612546b0c5cfe27c044b64).

References

  1. Levin, S. A. & Whitfield, M. Patchiness in marine and terrestrial systems: from individuals to populations [and Discussion]. Philos. Trans. Biol. Sci. 343, 99–103 (1994).

    Google Scholar 

  2. Fudickar, A. M., Jahn, A. E. & Ketterson, E. D. Animal migration: an overview of one of nature’s great spectacles. Annu. Rev. Ecol. Evol. Syst. 52, 479–497 (2021).

    Google Scholar 

  3. Stocker, R. Marine microbes see a sea of gradients. Science 338, 628–633 (2012).

    Google Scholar 

  4. Fenchel, T. Microbial behavior in a heterogeneous world. Science 296, 1068–1071 (2002).

    Google Scholar 

  5. Adler, J. et al. Chemotaxis in bacteria. Annu. Rev. Biochem. 44, 341–356 (1975).

  6. Keegstra, J. M., Carrara, F. & Stocker, R. The ecological roles of bacterial chemotaxis. Nat. Rev. Microbiol. 20, 491–504 (2022).

    Google Scholar 

  7. Bi, S. & Sourjik, V. Stimulus sensing and signal processing in bacterial chemotaxis. Curr. Opin. Microbiol. 45, 22–29 (2018).

    Google Scholar 

  8. Wong-Ng, J., Celani, A. & Vergassola, M. Exploring the function of bacterial chemotaxis. Curr. Opin. Microbiol. 45, 16–21 (2018).

    Google Scholar 

  9. Sourjik, V. & Wingreen, N. S. Responding to chemical gradients: bacterial chemotaxis. Curr. Opin. Cell Biol. 24, 262–268 (2012).

    Google Scholar 

  10. Spiro, P. A., Parkinson, J. S. & Othmer, H. G. A model of excitation and adaptation in bacterial chemotaxis. Proc. Natl. Acad. Sci. USA. 94, 7263–7268 (1997).

    Google Scholar 

  11. Berg, H. C. & Brown, D. A. Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature 239, 500–504 (1972).

    Google Scholar 

  12. Polin, M., Tuval, I., Drescher, K., Gollub, J. P. & Goldstein, R. E. Chlamydomonas Swims with Two “Gears” in a Eukaryotic Version of Run-and-Tumble Locomotion. Science 325, 487–490 (2009).

    Google Scholar 

  13. Adler, J., Hazelbauer, G. L. & Dahl, M. M. Chemotaxis toward sugars in Escherichia coli. J. Bacteriol. 115, 824–847 (1973).

    Google Scholar 

  14. Mesibov, R. & Adler, J. Chemotaxis toward amino acids in Escherichia coli. J. Bacteriol. 112, 315–326 (1972).

    Google Scholar 

  15. Miller, T. R., Hnilicka, K., Dziedzic, A., Desplats, P. & Belas, R. Chemotaxis of Silicibacter sp. Strain TM1040 toward Dinoflagellate Products. Appl. Environ. Microbiol. 70, 4692–4701 (2004).

    Google Scholar 

  16. Tso, W.-W. & Adler, J. Negative chemotaxis in Escherichia coli. J. Bacteriol. 118, 560–576 (1974).

    Google Scholar 

  17. Benov, L. & Fridovich, I. Escherichia coli exhibits negative chemotaxis in gradients of hydrogen peroxide, hypochlorite, and N-chlorotaurine: products of the respiratory burst of phagocytic cells. Proc. Natl. Acad. Sci.USA. 93, 4999–5002 (1996).

    Google Scholar 

  18. Yang, Y. et al. Relation between chemotaxis and consumption of amino acids in bacteria. Mol. Microbiol. 96, 1272–1282 (2015).

    Google Scholar 

  19. Fu, R. & Feng, H. Deciphering bacterial chemorepulsion: the complex response of microbes to environmental stimuli. Microorganisms 12, 1706 (2024).

    Google Scholar 

  20. Oosawa, K. & Imae, Y. Glycerol and ethylene glycol: members of a new class of repellents of Escherichia coli chemotaxis. J. Bacteriol. 154, 104–112 (1983).

    Google Scholar 

  21. Dowdell, A., Paschke, P. I., Thomason, P. A., Tweedy, L. & Insall, R. H. Competition between chemoattractants causes unexpected complexity and can explain negative chemotaxis. Curr. Biol. 33, 1704–1715.e3 (2023).

    Google Scholar 

  22. Oliveira, N. M. et al. Suicidal chemotaxis in bacteria. Nat. Commun. 13, 7608 (2022).

    Google Scholar 

  23. Clerc, E. E. et al. Strong chemotaxis by marine bacteria towards polysaccharides is enhanced by the abundant organosulfur compound DMSP. Nat. Commun. 14, 8080 (2023).

    Google Scholar 

  24. Yang, J. et al. Biphasic chemotaxis of Escherichia coli to the microbiota metabolite indole. Proc. Natl. Acad. Sci. 117, 6114–6120 (2020).

    Google Scholar 

  25. Kalinin, Y., Neumann, S., Sourjik, V. & Wu, M. Responses of Escherichia coli bacteria to two opposing chemoattractant gradients depend on the chemoreceptor ratio. J. Bacteriol. 192, 1796–1800 (2010).

    Google Scholar 

  26. Strauss, I., Frymier, P. D., Hahn, C. M. & Ford, R. M. Analysis of bacterial migration: II. studies with multiple attractant gradients. AIChE J. 41, 402–414 (1995).

    Google Scholar 

  27. Park, J. & Aminzare, Z. A mathematical description of bacterial chemotaxis in response to two stimuli. Bull. Math. Biol. 84, 9 (2021).

    Google Scholar 

  28. Adler, J. & Tso, W.-W. ‘Decision’-making in bacteria: chemotactic response of Escherichia coli to conflicting stimuli. Science 184, 1292–1294 (1974).

    Google Scholar 

  29. Livne, N. & Vaknin, A. Collective responses of bacteria to a local source of conflicting effectors. Sci. Rep. 12, 4928 (2022).

    Google Scholar 

  30. Zhao, X. & Ford, R. M. Escherichia coli chemotaxis to competing stimuli in a microfluidic device with a constant gradient. Biotechnol. Bioeng. 119, 2564–2573 (2022).

    Google Scholar 

  31. Zhao, X. & Ford, R. M. Marine bacteria chemotaxis to crude oil components with opposing effects. Preprint at https://doi.org/10.1101/2022.01.26.477900 (2022).

  32. Middlebrooks, S. A., Zhao, X., Ford, R. M. & Cummings, P. T. A mathematical model for Escherichia coli chemotaxis to competing stimuli. Biotechnol. Bioeng. 118, 4678–4686 (2021).

    Google Scholar 

  33. Englert, D. L., Manson, M. D. & Jayaraman, A. Flow-based microfluidic device for quantifying bacterial chemotaxis in stable, competing gradients. Appl. Environ. Microbiol. 75, 4557–4564 (2009).

    Google Scholar 

  34. Narla, A. V., Cremer, J. & Hwa, T. A traveling-wave solution for bacterial chemotaxis with growth. Proc. Natl. Acad. Sci. 118, e2105138118 (2021).

    Google Scholar 

  35. Cremer, J. et al. Chemotaxis as a navigation strategy to boost range expansion. Nature 575, 658–663 (2019).

    Google Scholar 

  36. Nguyen, T. T. H. et al. Microbes contribute to setting the ocean carbon flux by altering the fate of sinking particulates. Nat. Commun. 13, 1657 (2022).

    Google Scholar 

  37. Stocker, R., Seymour, J. R., Samadani, A., Hunt, D. E. & Polz, M. F. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc. Natl. Acad. Sci. 105, 4209–4214 (2008).

    Google Scholar 

  38. Alldredge, A. L. & Gotschalk, C. In situ settling behavior of marine snow. Limnol. Oceanogr. 33, 339–351 (1988).

    Google Scholar 

  39. Alldredge, A. L. & Gotschalk, C. C. Direct observations of the mass flocculation of diatom blooms: characteristics, settling velocities and formation of diatom aggregates. Deep Sea Res. Part Oceanogr. Res. Pap. 36, 159–171 (1989).

    Google Scholar 

  40. Słomka, J., Alcolombri, U., Secchi, E., Stocker, R. & Fernandez, V. I. Encounter rates between bacteria and small sinking particles. N. J. Phys. 22, 043016 (2020).

    Google Scholar 

  41. Roberts, E. C., Legrand, C., Steinke, M. & Wootton, E. C. Mechanisms underlying chemical interactions between predatory planktonic protists and their prey. J. Plankton Res. 33, 833–841 (2011).

    Google Scholar 

  42. Hartz, A. J., Sherr, B. F. & Sherr, E. B. Using inhibitors to investigate the involvement of cell signaling in predation by marine phagotrophic protists. J. Eukaryot. Microbiol. 55, 18–21 (2008).

    Google Scholar 

  43. Barbara, G. M. & Mitchell, J. G. Bacterial tracking of motile algae. FEMS Microbiol. Ecol. 44, 79–87 (2003).

    Google Scholar 

  44. Walsh, F. & Mitchell, R. Bacterial chemotactic responses in flowing water. Microb. Ecol. 4, 165–168 (1977).

    Google Scholar 

  45. Galilei, G. et al. Dialogo Sopra i Due Massimi Sistemi Del Mondo. (Giovanni Battista Landini, Florence, Grand Duchy of Tuscany, 1632).

  46. Grognot, M. & Taute, K. M. A multiscale 3D chemotaxis assay reveals bacterial navigation mechanisms. Commun. Biol. 4, 1–8 (2021).

    Google Scholar 

  47. Colin, R., Zhang, R. & Wilson, L. G. Fast, high-throughput measurement of collective behaviour in a bacterial population. J. R. Soc. Interface 11, 20140486 (2014).

    Google Scholar 

  48. Stocker, R. & Seymour, J. R. Ecology and physics of bacterial chemotaxis in the Ocean. Microbiol. Mol. Biol. Rev. MMBR 76, 792–812 (2012).

    Google Scholar 

  49. Berg, H. C. E. Coli in Motion. https://doi.org/10.1007/b97370. (Springer, New York, NY, 2004).

  50. Porter, S. L., Wadhams, G. H. & Armitage, J. P. Signal processing in complex chemotaxis pathways. Nat. Rev. Microbiol. 9, 153–165 (2011).

    Google Scholar 

  51. Szurmant, H. & Ordal, G. W. Diversity in chemotaxis mechanisms among the bacteria and archaea. Microbiol. Mol. Biol. Rev. 68, 301–319 (2004).

    Google Scholar 

  52. Wadhams, G. H. & Armitage, J. P. Making sense of it all: bacterial chemotaxis. Nat. Rev. Mol. Cell Biol. 5, 1024–1037 (2004).

    Google Scholar 

  53. Briegel, A. et al. Universal architecture of bacterial chemoreceptor arrays. Proc. Natl. Acad. Sci. 106, 17181–17186 (2009).

    Google Scholar 

  54. Monod, J., Wyman, J. & Changeux, J.-P. On the nature of allosteric transitions: a plausible model. J. Mol. Biol. 12, 88–118 (1965).

    Google Scholar 

  55. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997).

    Google Scholar 

  56. Mello, B. A. & Tu, Y. An allosteric model for heterogeneous receptor complexes: Understanding bacterial chemotaxis responses to multiple stimuli. Proc. Natl. Acad. Sci. 102, 17354–17359 (2005).

    Google Scholar 

  57. Kiørboe, T. & Jackson, G. A. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol. Oceanogr. 46, 1309–1318 (2001).

    Google Scholar 

  58. Mitchell, J. G. et al. Long lag times and high velocities in the motility of natural assemblages of marine bacteria. Appl. Environ. Microbiol. 61, 877–882 (1995).

    Google Scholar 

  59. Mitchell, J. G., Pearson, L., Dillon, S. & Kantalis, K. Natural assemblages of marine bacteria exhibiting high-speed motility and large accelerations. Appl. Environ. Microbiol. 61, 4436–4440 (1995).

    Google Scholar 

  60. Fenchel, T. Motility and chemosensory behaviour of the sulphur bacterium Thiovulum majus. Microbiology 140, 3109–3116 (1994).

    Google Scholar 

  61. Son, K., Menolascina, F. & Stocker, R. Speed-dependent chemotactic precision in marine bacteria. Proc. Natl. Acad. Sci. 113, 8624–8629 (2016).

    Google Scholar 

  62. Johansen, J., Pinhassi, J., Blackburn, N., Zweifel, U. & Hagstrom, Å. Variability in motility characteristics among marine bacteria. Aquat. Microb. Ecol. 28, 229–237 (2002).

    Google Scholar 

  63. Xie, L., Altindal, T., Chattopadhyay, S. & Wu, X.-L. Bacterial flagellum as a propeller and as a rudder for efficient chemotaxis. Proc. Natl. Acad. Sci. 108, 2246–2251 (2011).

    Google Scholar 

  64. Polson, A. On the diffusion constants of the amino-acids. Biochem. J. 31, 1903–1912 (1937).

    Google Scholar 

  65. Gray, D. E. et al. American Institute of Physics Handbook. (McGraw-Hill Book Company, 1972).

  66. Ma, Y., Zhu, C., Ma, P. & Yu, K. T. Studies on the diffusion coefficients of amino acids in aqueous solutions. J. Chem. Eng. Data 50, 1192–1196 (2005).

    Google Scholar 

  67. Miyamoto, S., Ekino, K. & Shimono, K. Estimation of the diffusion coefficients of small molecules by diffusion measurements with agar-gel and theoretical molecular modeling. Chem.-Bio Inform. J. 22, 13–20 (2022).

    Google Scholar 

  68. Miyamoto, S. & Shimono, K. Molecular modeling to estimate the diffusion coefficients of drugs and other small molecules. Molecules 25, 5340 (2020).

    Google Scholar 

  69. Longsworth, L. G. Diffusion measurements, at 25°, of aqueous solutions of amino acids, peptides and sugars. J. Am. Chem. Soc. 75, 5705–5709 (1953).

    Google Scholar 

  70. Fischer, H., Meyer, A., Fischer, K. & Kuzyakov, Y. Carbohydrate and amino acid composition of dissolved organic matter leached from soil. Soil Biol. Biochem. 39, 2926–2935 (2007).

    Google Scholar 

  71. Webb, K. L. & Johannes, R. E. Studies of the release of dissolved free amino acids by marine zooplankton. Limnol. Oceanogr. 12, 376–382 (1967).

    Google Scholar 

  72. Akashi, H. & Gojobori, T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc. Natl. Acad. Sci. 99, 3695–3700 (2002).

    Google Scholar 

  73. Bowen, J. D., Stolzenbach, K. D. & Chisholm, S. W. Simulating bacterial clustering around phytoplankton cells in a turbulent ocean. Limnol. Oceanogr. 38, 36–51 (1993).

    Google Scholar 

  74. Jackson, G. A. Simulation of bacterial attraction and adhesion to falling particles in an aquatic environment. Limnol. Oceanogr. 34, 514–530 (1989).

    Google Scholar 

  75. Mitchell, J. G., Okubo, A. & Fuhrman, J. A. Microzones surrounding phytoplankton form the basis for a stratified marine microbial ecosystem. Nature 316, 58–59 (1985).

    Google Scholar 

  76. Lauga, E. & Powers, T. R. The hydrodynamics of swimming microorganisms. Rep. Prog. Phys. 72, 096601 (2009).

    Google Scholar 

  77. Aranson, I. S. Bacterial active matter. Rep. Prog. Phys. 85, 076601 (2022).

    Google Scholar 

  78. Hu, J., Yang, M., Gompper, G. & Winkler, G. R. Modelling the mechanics and hydrodynamics of swimming E. coli. Soft Matter 11, 7867–7876 (2015).

    Google Scholar 

  79. Lebedev, V. & Laikov, D. A quadrature formula for the sphere of the 131st algebraic order of accuracy. Dokl. Math. 59, 477–481 (1999).

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Acknowledgements

This research was supported by the Sloan Foundation (grant G-2021−16758) and the Schmidt Polymath Award (grant G-21-62100). H.L. acknowledges the Gordon and Betty Moore Foundation for support as a Physics of Living Systems Fellow through Grant No. GBMF4513. We thank the members of the Gore Lab, the MIT Physics of Living Systems community, and the Simons Foundation Principles of Microbial Ecology collaboration for discussion.

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Authors and Affiliations

  1. Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Blox Bloxham, Hyunseok Lee & Jeff Gore

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA

    Blox Bloxham

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  1. Blox Bloxham
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  2. Hyunseok Lee
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  3. Jeff Gore
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Contributions

B.B. conceived the project. B.B., H.L., and J.G. refined the project conceptualization and designed the simulations. B.B. performed the calculations and wrote/performed the simulations in the Main Text. B.B. and H.L. wrote/performed simulations in the Supplementary Information. B.B., H.L., and J.G. analyzed the data and wrote/edited the manuscript. J.G. supervised the project.

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Blox Bloxham or Jeff Gore.

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Bloxham, B., Lee, H. & Gore, J. Repulsion from slow-diffusing nutrients improves microbial chemotaxis towards moving sources.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-71148-x

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