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Open AccessOpen Access JUSTC Article 08 June 2023

The effect of receptor interaction on the bacterial chemotactic adaptation rate

  • Hefei National Research Center for Physical Sciences at the Microscale, and Department of Physics, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2023-0058
More Information
  • Author Bio:

    Shujian Ren received her master’s degree from the School of Physics, University of Science and Technology of China, under the supervision of Prof. Junhua Yuan and Prof. Rongjing Zhang

    Chi Zhang is an Associate Research Fellow at Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China (USTC). He received his Ph.D. degree from the School of Physics, USTC, under the supervision of Prof. Junhua Yuan and Prof. Rongjing Zhang. His research mainly focuses on the chemotaxis and motion of bacteria in the biological physics field

    Rongjing Zhang is a Professor at the University of Science and Technology of China. She received her Ph.D. degree from the California Institute of Technology, USA. Her research mainly focuses on biofilm, bacterial motility and behavior, multi scale experimental measurement and characterization, and molecular motor in the biological physics field

  • Corresponding author: E-mail: zhchi@ustc.edu.cn; E-mail: rjzhang@ustc.edu.cn
  • Received Date: 06 April 2023
  • Accepted Date: 18 May 2023
    • Available Online: 08 June 2023
    Abstract

  • Abstract

    Different receptors have evolved in organisms to sense different stimuli in their surroundings. The interaction among the receptors can significantly increase sensory sensitivity and adaptation precision. To study the influence of interaction among different types of chemoreceptors on the adaptation rate in the bacterial chemotaxis signaling network, we systematically compared the adaptation time between the wild-type strain expressing mixed types of receptors and the mutant strain expressing only Tar receptors (namely, the Tar-only strain) under stepwise addition of different concentrations of L-aspartate using FRET (Förster resonance energy transfer) and bead assays. We find that the wild type exhibits faster adaptation than the mutant under the same concentration of saturated stimulus. In contrast, the wild type exhibits slower adaptation than the mutant under unsaturated stimuli that induce the same magnitude of response, and this is independent of the level of receptor expression. The same result is obtained for the network relaxation time by monitoring the steady-state rotational signal of the flagellar motors. By simulating bacterial chemotaxis with different adaptation rates in a stable gradient of chemoattractants, we confirm that the interaction of different types of receptors can effectively promote chemotaxis of Escherichia coli under a stable spatial gradient of attractants while ensuring minimum noise in the cell position distribution.

    Graphical abstract

    The interaction of chemoreceptors in Escherichia coli influences the rate of adaptation and enhances bacterial chemotaxis.

    Abstract

    Different receptors have evolved in organisms to sense different stimuli in their surroundings. The interaction among the receptors can significantly increase sensory sensitivity and adaptation precision. To study the influence of interaction among different types of chemoreceptors on the adaptation rate in the bacterial chemotaxis signaling network, we systematically compared the adaptation time between the wild-type strain expressing mixed types of receptors and the mutant strain expressing only Tar receptors (namely, the Tar-only strain) under stepwise addition of different concentrations of L-aspartate using FRET (Förster resonance energy transfer) and bead assays. We find that the wild type exhibits faster adaptation than the mutant under the same concentration of saturated stimulus. In contrast, the wild type exhibits slower adaptation than the mutant under unsaturated stimuli that induce the same magnitude of response, and this is independent of the level of receptor expression. The same result is obtained for the network relaxation time by monitoring the steady-state rotational signal of the flagellar motors. By simulating bacterial chemotaxis with different adaptation rates in a stable gradient of chemoattractants, we confirm that the interaction of different types of receptors can effectively promote chemotaxis of Escherichia coli under a stable spatial gradient of attractants while ensuring minimum noise in the cell position distribution.

    • Public Summary

    • The wild-type Escherichia coli (E. coli) strain exhibits faster adaptation than the mutant expressing only one type of receptors, when subjected to the same concentration of saturated stimulus.
    • The wild-type strain exhibits slower adaptation than the mutant under unsaturated stimuli that induce the same magnitude of response, and this is independent of the level of receptor expression.
    • The interaction between different types of receptors in E. coli can effectively enhance chemotaxis under a stable spatial gradient of attractants, while simultaneously ensuring minimum noise in the cell position distribution.

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    • References(49)
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    Figure  1.  (a) A typical trace of the step response to 10 μmol/L L-aspartate. The stimulus was added at t = 120 s and removed at t = 600 s. The FRET value was defined as the intensity ratio of the YFP to CFP channel. The definition of the adaptation time is indicated. (b) The adaptation time to 10 μmol/L L-aspartate for the wild-type (W.T.) strain (HCB1288-pVS88) and the Tar-only strain (HCB1414-pLC113-pVS88). The errors denote the standard deviations.

    Figure  2.  (a) The dose‒response measurement to L-aspartate. The blue line is the FRET signal. The red downward arrows denote the time when the stimulus was added, while the black upward arrows indicate the time when the stimulus was removed. The black and red dashed lines represent the pre-stimulus and saturated FRET values, respectively. (b) The dose‒response curve of relative kinase activity (relative to the pre-stimulus value) to the concentration of L-aspartate for both strains. The relative kinase activity was obtained by rescaling the FRET values of 1 (the pre-stimulus value, black dashed line in (a)) to 0.95 (the value after adding a saturated concentration of stimulus, red dashed line in (a)) to the range between 1 and 0. The blue and red lines are the results fitted with the MWC model for the wild-type and mutant strains, respectively. The gray lines indicate the values of relative kinase activity used in the step-response measurements. (c) The relation between adaptation time and relative kinase activity response. The errors denote standard deviations.

    Figure  3.  (a) The dose‒response curves of the Tar-only strain under 1 μmol/L (red dots, wild-type level) and 0.5 μmol/L (green diamonds) inducer (sodium salicylate). The smooth lines are the results fitted with the MWC model. (b) The relation between adaptation time and relative kinase activity response. The errors denote standard deviations.

    Figure  4.  (a) Schematic diagram of the bead assay for the flagellar motor. (b) A typical trace of the rotational speed (blue line) of individual motors. The positive and negative values of speed denote CCW and CW rotation, respectively. (c) The CW bias distribution for the wild-type and Tar-only trains. (d) The correlation times for both strains.

    Figure  5.  (a) Comparison of adaptation times between the wild-type (blue squares) and Tar-only strains (red dots) for all concentrations of L-aspartate. The errors denote standard deviations. (b) The change in mean position over time in a gradient of exponential MeAsp concentration profile L(x) = 91exp(x/5000). The blue dots represent the mean x position. The green squares denote the mean y position. The red line is the linear fit to the blue dots. (c) The effect of adaptation rate on bacterial chemotaxis. Blue and red dots represent the drift velocity in the x direction and the variance of the y positions under different values of kR. The value of kR ranges from 0.005 s−1 to 0.01 s−1 for the wild-type strain, which is marked by the green shaded area.

    [1]
    Armitage J P, Schmitt R. Bacterial chemotaxis: Rhodobacter sphaeroides and sinorhizobium meliloti−Variations on a theme? Microbiology, 1997, 12: 3671–3682. doi: 10.1099/00221287-143-12-3671
    [2]
    Bren A, Eisenbach M. How signals are heard during bacterial chemotaxis: Protein-protein interactions in sensory signal propagation. Journal of Bacteriology, 2000, 182: 6865–6873. doi: 10.1128/JB.182.24.6865-6873.2000
    [3]
    Ud-Din A I M S, Roujeinikova A. Methyl-accepting chemotaxis proteins: A core sensing element in prokaryotes and Archaea. Cellular and Molecular Life Sciences, 2017, 74: 3293–3303. doi: 10.1007/s00018-017-2514-0
    [4]
    Wadhams G H, Martin A C, Armitage J P. Identification and localization of a methyl-accepting chemotaxis protein in Rhodobacter sphaeroides. Molecular Microbiology, 2000, 36: 1222–1233. doi: 10.1046/j.1365-2958.2000.01936.x
    [5]
    Falke J J, Hazelbauer G L. Transmembrane signaling in bacterial chemoreceptors. Trends in Biochemical Sciences, 2001, 26: 257–265. doi: 10.1016/S0968-0004(00)01770-9
    [6]
    Briegel A, Ladinsky M S, Oikonomou C, et al. Structure of bacterial cytoplasmic chemoreceptor arrays and implications for chemotactic signaling. eLife, 2014, 3: 02151. doi: https://doi.org/10.7554/eLife.02151
    [7]
    Bi S, Lai L. Bacterial chemoreceptors and chemoeffectors. Cellular and Molecular Life Sciences, 2015, 72: 691–708. doi: 10.1007/s00018-014-1770-5
    [8]
    Koler M, Peretz E, Aditya C, et al. Long-term positioning and polar preference of chemoreceptor clusters in E. coli. Nature Communications, 2018, 9: 4444. doi: 10.1038/s41467-018-06835-5
    [9]
    Wadhams G H, Armitage J P. Making sense of it all: Bacterial chemotaxis. Nature Reviews Molecular Cell Biology, 2004, 5: 1024–1037. doi: 10.1038/nrm1524
    [10]
    Partridge J D, Nhu N T Q, Dufour Y S, et al. Escherichia coli remodels the chemotaxis pathway for swarming. mBio, 2019, 10 (2): e00316-19. doi: https://doi.org/10.1128/mbio.00316-19
    [11]
    Welch M, Oosawa K, Aizawa S, et al. Phosphorylation-dependent binding of a signal molecule to the flagellar switch of bacteria. Proceedings of the National Academy of Sciences of the United States of America, 1993, 90: 8787–8791. doi: 10.1073/pnas.90.19.8787
    [12]
    Toker A S, MacNab R M. Distinct regions of bacterial flagellar switch protein FliM interact with FliG, FliN and CheY. Journal of Molecular Biology, 1997, 273: 623–634. doi: 10.1006/jmbi.1997.1335
    [13]
    Chang Y, Zhang K, Carroll B L, et al. Molecular mechanism for rotational switching of the bacterial flagellar motor. Nature Structural & Molecular Biology, 2020, 27: 1041–1047. doi: https://doi.org/10.1038/s41594-020-0497-2
    [14]
    Duke T A J, Bray D. Heightened sensitivity of a lattice of membrane receptors. Proceedings of the National Academy of Sciences of the United States of America, 1999, 96: 10104–10108. doi: 10.1073/pnas.96.18.10104
    [15]
    Ames P, Studdert C A, Reiser R H, et al. Collaborative signaling by mixed chemoreceptor teams in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99: 7060–7065. doi: 10.1073/pnas.092071899
    [16]
    Sourjik V, Berg H C. Receptor sensitivity in bacterial chemotaxis. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99: 123–127. doi: 10.1073/pnas.011589998
    [17]
    Lamanna A C, Gestwicki J E, Strong L E, et al. Conserved amplification of chemotactic responses through chemoreceptor interactions. Journal of Bacteriology, 2002, 184: 4981–4987. doi: 10.1128/JB.184.18.4981-4987.2002
    [18]
    Barkai N, Leibler S. Robustness in simple biochemical networks. Nature, 1997, 387: 913–917. doi: 10.1038/43199
    [19]
    Silverman M, Simon M. Chemotaxis in Escherichia coli: Methylation of che gene products. Proceedings of the National Academy of Sciences of the United States of America, 1977, 74: 3317–3321. doi: 10.1073/pnas.74.8.3317
    [20]
    Antommattei F M, Munzner J B, Weis R M. Ligand-specific activation of Escherichia coli chemoreceptor transmethylation. Journal of Bacteriology, 2004, 186: 7556–7563. doi: 10.1128/JB.186.22.7556-7563.2004
    [21]
    Lan G, Schulmeister S, Sourjik V, et al. Adapt locally and act globally: Strategy to maintain high chemoreceptor sensitivity in complex environments. Molecular Systems Biology, 2011, 7: 475. doi: 10.1038/msb.2011.8
    [22]
    Paulick A, Jakovljevic V, Zhang S, et al. Mechanism of bidirectional thermotaxis in Escherichia coli. eLife, 2017, 6: 26607. doi: 10.7554/eLife.26607
    [23]
    Meir Y, Jakovljevic V, Oleksiuk O, et al. Precision and kinetics of adaptation in bacterial chemotaxis. Biophysical Journal, 2010, 99: 2766–2774. doi: 10.1016/j.bpj.2010.08.051
    [24]
    Micali G, Endres R G. Bacterial chemotaxis: Information processing, thermodynamics, and behavior. Current Opinion in Microbiology, 2016, 30: 8–15. doi: 10.1016/j.mib.2015.12.001
    [25]
    Kim C, Jackson M, Lux R, et al. Determinants of chemotactic signal amplification in Escherichia coli. Journal of Molecular Biology, 2001, 307: 119–135. doi: 10.1006/jmbi.2000.4389
    [26]
    Hansen C H, Endres R G, Wingreen N S. Chemotaxis in Escherichia coli: A molecular model for robust precise adaptation. PLoS Computational Biology, 2008, 4: e1. doi: 10.1371/journal.pcbi.0040001
    [27]
    Li M, Hazelbauer G L. Adaptational assistance in clusters of bacterial chemoreceptors. Molecular Microbiology, 2005, 56: 1617–1626. doi: 10.1111/j.1365-2958.2005.04641.x
    [28]
    Endres R G, Wingreen N S. Precise adaptation in bacterial chemotaxis through “assistance neighborhoods”. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103: 13040–13044. doi: 10.1073/pnas.0603101103
    [29]
    Mello B A, Tu Y. Perfect and near-perfect adaptation in a model of bacterial chemotaxis. Biophysical Journal, 2003, 84: 2943–2956. doi: 10.1016/S0006-3495(03)70021-6
    [30]
    Keymer J E, Endres R G, Skoge M, et al. Chemosensing in Escherichia coli: Two regimes of two-state receptors. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103: 1786–1791. doi: 10.1073/pnas.0507438103
    [31]
    Dzinic S H, Shukla M, Mandija I, et al. Variable length tandem repeat polyglutamine sequences in the flexible tether region of the Tsr chemotaxis receptor of Escherichia coli. Microbiology, 2008, 154: 2380–2386. doi: 10.1099/mic.0.2008/016303-0
    [32]
    Mello B A, Tu Y. Quantitative modeling of sensitivity in bacterial chemotaxis: The role of coupling among different chemoreceptor species. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100: 8223–8228. doi: 10.1073/pnas.1330839100
    [33]
    Lan G, Sartori P, Neumann S, et al. The energy-speed-accuracy trade-off in sensory adaptation. Nature Physics, 2012, 8: 422–428. doi: 10.1038/nphys2276
    [34]
    Sourjik V, Vaknin A, Shimizu T S, et al. [17]-in vivo measurement by FRET of pathway activity in bacterial chemotaxis. In: Simon M I, Crane B R, Crane A, editors. Methods in Enzymology: Two-Component Signaling Systems, Part B. Amsterdam: Elsevier, 2007: 365–391.
    [35]
    McEvoy M M, Bren A, Eisenbach M, et al. Identification of the binding interfaces on CheY for two of its targets the phosphatase CheZ and the flagellar switch protein FliM. Journal of Molecular Biology, 1999, 289: 1423–1433. doi: 10.1006/jmbi.1999.2830
    [36]
    Zhang C, He R, Zhang R, et al. Motor adaptive remodeling speeds up bacterial chemotactic adaptation. Biophysical Journal, 2018, 114: 1225–1231. doi: 10.1016/j.bpj.2018.01.018
    [37]
    Liu X, Zhang C, Zhang R, et al. The effect of the second messenger c-di-GMP on bacterial chemotaxis in Escherichia coli. Applied and Environmental Microbiology, 2022, 88: e0037322. doi: 10.1128/aem.00373-22
    [38]
    Tu Y, Shimizu T S, Berg H C. Modeling the chemotactic response of Escherichia coli to time-varying stimuli. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105: 14855–14860. doi: 10.1073/pnas.0807569105
    [39]
    Jiang L, Ouyang Q, Tu Y. Quantitative modeling of Escherichia coli chemotactic motion in environments varying in space and time. PLoS Computational Biology, 2010, 6: e1000735. doi: 10.1371/journal.pcbi.1000735
    [40]
    Monod J, Wyman J, Changeux J P. On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology, 1965, 12: 88–118. doi: 10.1016/S0022-2836(65)80285-6
    [41]
    Vladimirov N, Løvdok L, Lebiedz D, et al. Dependence of bacterial chemotaxis on gradient shape and adaptation rate. PLoS Computational Biology, 2008, 4: e1000242. doi: 10.1371/journal.pcbi.1000242
    [42]
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