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.

Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response

Abstract

The bacterial SOS response represents a paradigm of gene networks controlled by a master transcriptional regulator. Self-cleavage of the SOS repressor LexA induces a wide range of cell functions that are critical for survival and adaptation when bacteria experience stress conditions1 including DNA repair2, mutagenesis3,4, horizontal gene transfer5,6,7, filamentous growth and the induction of bacterial toxins8,9,10,11,12, toxin–antitoxin systems13, virulence factors6,14 and prophages15,16,17. SOS induction is also implicated in biofilm formation and antibiotic persistence11,18,19,20. Considering the fitness burden of these functions, it is surprising that the expression of LexA-regulated genes is highly variable across cells10,21,22,23 and that cell subpopulations induce the SOS response spontaneously even in the absence of stress exposure9,11,12,16,24,25. Whether this reflects a population survival strategy or a regulatory inaccuracy is unclear, as are the mechanisms underlying SOS heterogeneity. Here, we developed a single-molecule imaging approach based on a HaloTag fusion to directly monitor LexA in live Escherichia coli cells, demonstrating the existence of three main states of LexA: DNA-bound stationary molecules, free LexA and degraded LexA species. These analyses elucidate the mechanisms by which DNA binding and degradation of LexA regulate the SOS response in vivo. We show that self-cleavage of LexA occurs frequently throughout the population during unperturbed growth, rather than being restricted to a subpopulation of cells. This causes substantial cell-to-cell variation in LexA abundances. LexA variability underlies SOS gene-expression heterogeneity and triggers spontaneous SOS pulses, which enhance bacterial survival in anticipation of stress.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Live-cell single-molecule tracking of LexA repressor.
Fig. 2: Dynamics of LexA cleavage and degradation in response to DNA damage.
Fig. 3: Spontaneous LexA degradation in untreated cells is highly heterogeneous.
Fig. 4: Variability in LexA degradation underlies spontaneous SOS induction and expression heterogeneity.

Data availability

The tracking data associated with this article have been deposited at Oxford University Research Archive (ORA): https://ora.ox.ac.uk/objects/uuid:84d35d02-acb0-4d1c-af30-8045afd32542. Source data are provided with this paper.

Code availability

The MATLAB script for LexA diffusion analysis of the tracking data is available at Oxford University Research Archive (ORA): https://ora.ox.ac.uk/objects/uuid:84d35d02-acb0-4d1c-af30-8045afd32542. Other code is available upon request.

References

  1. Baharoglu, Z. & Mazel, D. SOS, the formidable strategy of bacteria against aggressions. FEMS Microbiol. Rev. 38, 1126–1145 (2014).

    CAS  PubMed  Article  Google Scholar 

  2. Courcelle, J., Khodursky, A., Peter, B., Brown, P. O. & Hanawalt, P. C. Comparative gene expression profiles following UV exposure in wild-type and SOS-deficient Escherichia coli. Genetics 158, 41–64 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Schlacher, K. & Goodman, M. F. Lessons from 50 years of SOS DNA-damage-induced mutagenesis. Nat. Rev. Mol. Cell Biol. 8, 587–594 (2007).

    CAS  PubMed  Article  Google Scholar 

  4. Uphoff, S. Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells. Proc. Natl Acad. Sci. USA 115, E6516–E6525 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Beaber, J. W., Hochhut, B. & Waldor, M. K. SOS response promotes horizontal dissemination of antibiotic resistance genes. Nature 427, 72–74 (2004).

    CAS  PubMed  Article  Google Scholar 

  6. Ubeda, C. et al. Antibiotic-induced SOS response promotes horizontal dissemination of pathogenicity island-encoded virulence factors in staphylococci. Mol. Microbiol. 56, 836–844 (2005).

    CAS  PubMed  Article  Google Scholar 

  7. Guerin, É. et al. The SOS response controls integron recombination. Science 324, 1034 (2009).

    CAS  PubMed  Article  Google Scholar 

  8. Kimmitt, P. T., Harwood, C. R. & Barer, M. R. Toxin gene expression by shiga toxin-producing Escherichia coli: the role of antibiotics and the bacterial SOS response. Emerg. Infect. Dis. 6, 458–465 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Livny, J. & Friedman, D. I. Characterizing spontaneous induction of Stx encoding phages using a selectable reporter system. Mol. Microbiol. 51, 1691–1704 (2004).

    CAS  PubMed  Article  Google Scholar 

  10. Mrak, P., Podlesek, Z., van Putten, J. P. M. & Zgur-Bertok, D. Heterogeneity in expression of the Escherichia coli colicin K activity gene cka is controlled by the SOS system and stochastic factors. Mol. Genet. Genomics 277, 391–401 (2007).

    CAS  PubMed  Article  Google Scholar 

  11. Turnbull, L. et al. Explosive cell lysis as a mechanism for the biogenesis of bacterial membrane vesicles and biofilms. Nat. Commun. 7, 11220 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Mavridou, D. A. I., Gonzalez, D., Kim, W., West, S. A. & Foster, K. R. Bacteria use collective behavior to generate diverse combat strategies. Curr. Biol. 28, 345–355.e4 (2018).

    CAS  PubMed  Article  Google Scholar 

  13. Vogel, J., Argaman, L., Wagner, E. G. H. & Altuvia, S. The small RNA IstR inhibits synthesis of an SOS-induced toxic peptide. Curr. Biol. 14, 2271–2276 (2004).

    CAS  PubMed  Article  Google Scholar 

  14. Kelley, W. L. Lex marks the spot: the virulent side of SOS and a closer look at the LexA regulon. Mol. Microbiol. 62, 1228–1238 (2006).

    CAS  PubMed  Article  Google Scholar 

  15. Zong, C., So, L., Sepúlveda, L. A., Skinner, S. O. & Golding, I. Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene. Mol. Syst. Biol. 6, 440 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  16. Helfrich, S. et al. Live cell imaging of SOS and prophage dynamics in isogenic bacterial populations. Mol. Microbiol. 98, 636–650 (2015).

    CAS  PubMed  Article  Google Scholar 

  17. Nanda, A. M., Thormann, K. & Frunzke, J. Impact of spontaneous prophage induction on the fitness of bacterial populations and host–microbe interactions. J. Bacteriol. 197, 410–419 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. Dörr, T., Lewis, K. & Vulić, M. SOS response induces persistence to fluoroquinolones in Escherichia coli. PLoS Genet. 5, e1000760 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. Mok, W. W. K. & Brynildsen, M. P. Timing of DNA damage responses impacts persistence to fluoroquinolones. Proc. Natl Acad. Sci. USA 115, E6301–E6309 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Peyrusson, F. et al. Intracellular Staphylococcus aureus persisters upon antibiotic exposure. Nat. Commun. 11, 2200 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. McCool, J. D. et al. Measurement of SOS expression in individual Escherichia coli K-12 cells using fluorescence microscopy. Mol. Microbiol. 53, 1343–1357 (2004).

    CAS  PubMed  Article  Google Scholar 

  22. Kamenšek, S., Podlesek, Z., Gillor, O. & Zgur-Bertok, D. Genes regulated by the Escherichia coli SOS repressor LexA exhibit heterogeneous expression. BMC Microbiol. 10, 283 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Friedman, N., Vardi, S., Ronen, M., Alon, U. & Stavans, J. Precise temporal modulation in the response of the SOS DNA repair network in individual bacteria. PLoS Biol. 3, 0030238 (2005).

    Article  CAS  Google Scholar 

  24. Manina, G., Griego, A., Singh, L. K., McKinney, J. D. & Dhar, N. Preexisting variation in DNA damage response predicts the fate of single mycobacteria under stress. EMBO J. 38, e101876 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Pennington, J. M. & Rosenberg, S. M. Spontaneous DNA breakage in single living Escherichia coli cells. Nat. Genet. 39, 797–802 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Veening, J.-W., Smits, W. K. & Kuipers, O. P. Bistability, epigenetics, and bet-hedging in bacteria. Annu. Rev. Microbiol. 62, 193–210 (2008).

    CAS  PubMed  Article  Google Scholar 

  27. Balázsi, G., van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. Vincent, M. S. & Uphoff, S. Bacterial phenotypic heterogeneity in DNA repair and mutagenesis. Biochem. Soc. Trans. 48, 451–462 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Lestas, I., Vinnicombe, G. & Paulsson, J. Fundamental limits on the suppression of molecular fluctuations. Nature 467, 174–178 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Da Re, S. et al. The SOS response promotes qnrB quinolone-resistance determinant expression. EMBO Rep. 10, 929–933 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Pribis, J. P. et al. Gamblers: an antibiotic-induced evolvable cell subpopulation differentiated by reactive-oxygen-induced general stress response. Mol. Cell 74, 785–800 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Matic, I. Mutation rate heterogeneity increases odds of survival in unpredictable environments. Mol. Cell 75, 421–425 (2019).

    CAS  PubMed  Article  Google Scholar 

  33. Loś, J. M., Loś, M., Węgrzyn, A. & Węgrzyn, G. Altruism of Shiga toxin-producing Escherichia coli: recent hypothesis versus experimental results. Front. Cell. Infect. Microbiol. 2, 166 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  34. Hilfinger, A. & Paulsson, J. Separating intrinsic from extrinsic fluctuations in dynamic biological systems. Proc. Natl Acad. Sci. USA 108, 12167–12172 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Durfee, T., Hansen, A.-M., Zhi, H., Blattner, F. R. & Jin, D. J. Transcription profiling of the stringent response in Escherichia coli. J. Bacteriol. 190, 1084–1096 (2008).

    CAS  PubMed  Article  Google Scholar 

  36. Layton, J. C. & Foster, P. L. Error-prone DNA polymerase IV is controlled by the stress-response sigma factor, RpoS, in Escherichia coli. Mol. Microbiol. 50, 549–561 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Sassanfar, M. & Roberts, J. W. Nature of the SOS-inducing signal in Escherichia coli. The involvement of DNA replication. J. Mol. Biol. 212, 79–96 (1990).

    CAS  PubMed  Article  Google Scholar 

  38. Wegel, E. et al. Imaging cellular structures in super-resolution with SIM, STED and localisation microscopy: a practical comparison. Sci. Rep. 6, 27290 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Banaz, N., Mäkelä, J. & Uphoff, S. Choosing the right label for single-molecule tracking in live bacteria: side-by-side comparison of photoactivatable fluorescent protein and Halo tag dyes. J. Phys. Appl. Phys. 52, 064002 (2018).

    Article  CAS  Google Scholar 

  40. Moreau, P. L. Effects of overproduction of single-stranded DNA-binding protein on RecA protein-dependent processes in Escherichia coli. J. Mol. Biol. 194, 621–634 (1987).

    CAS  PubMed  Article  Google Scholar 

  41. Schmidt, A. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34, 104–110 (2016).

    CAS  PubMed  Article  Google Scholar 

  42. Thliveris, A. T. & Mount, D. W. Genetic identification of the DNA binding domain of Escherichia coli LexA protein. Proc. Natl Acad. Sci. USA 89, 4500–4504 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Oertel-Buchheit, P., Porte, D., Schnarr, M. & Granger-Schnarr, M. Isolation and characterization of LexA mutant repressors with enhanced DNA binding affinity. J. Mol. Biol. 225, 609–620 (1992).

    CAS  PubMed  Article  Google Scholar 

  44. Stracy, M. et al. Transient non-specific DNA binding dominates the target search of bacterial DNA-binding proteins. Mol. Cell 81, 1499–1514 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Giese, K. C., Michalowski, C. B. & Little, J. W. RecA-dependent cleavage of LexA dimers. J. Mol. Biol. 377, 148 (2008).

    CAS  PubMed  Article  Google Scholar 

  46. Little, J. W. & Gellert, M. The SOS regulatory system: control of its state by the level of RecA protease. J. Mol. Biol. 167, 791–808 (1983).

    CAS  PubMed  Article  Google Scholar 

  47. Neher, S. B., Flynn, J. M., Sauer, R. T. & Baker, T. A. Latent ClpX-recognition signals ensure LexA destruction after DNA damage. Genes Dev. 17, 1084–1089 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Olivares, A. O., Kotamarthi, H. C., Stein, B. J., Sauer, R. T. & Baker, T. A. Effect of directional pulling on mechanical protein degradation by ATP-dependent proteolytic machines. Proc. Natl Acad. Sci. USA 114, E6306–E6313 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Ronen, M., Rosenberg, R., Shraiman, B. I. & Alon, U. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl Acad. Sci. USA 99, 10555–10560 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Mohana-Borges, R. et al. LexA repressor forms stable dimers in solution. The role of specific DNA in tightening protein–protein interactions. J. Biol. Chem. 275, 4708–4712 (2000).

    CAS  PubMed  Article  Google Scholar 

  51. Butala, M. et al. Interconversion between bound and free conformations of LexA orchestrates the bacterial SOS response. Nucleic Acids Res. 39, 6546–6557 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Culyba, M. J., Kubiak, J. M., Mo, C. Y., Goulian, M. & Kohli, R. M. Non-equilibrium repressor binding kinetics link DNA damage dose to transcriptional timing within the SOS gene network. PLoS Genet. 14, e1007405 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. O’Reilly, E. K. & Kreuzer, K. N. Isolation of SOS constitutive mutants of Escherichia coli. J. Bacteriol. 186, 7149–7160 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. Shepley, D. P. & Little, J. W. Mutant LexA proteins with specific defects in autodigestion. Proc. Natl Acad. Sci. USA 93, 11528–11533 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Dri, A. M. & Moreau, P. L. Control of the LexA regulon by pH: evidence for a reversible inactivation of the LexA repressor during the growth cycle of Escherichia coli. Mol. Microbiol. 12, 621–629 (1994).

    CAS  PubMed  Article  Google Scholar 

  56. Schuldiner, S. et al. Induction of SOS functions by alkaline intracellular pH in Escherichia coli. J. Bacteriol. 168, 936–939 (1986).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Shee, C. et al. Engineered proteins detect spontaneous DNA breakage in human and bacterial cells. eLife 2, e01222 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. Cox, M. M. et al. The importance of repairing stalled replication forks. Nature 404, 37–41 (2000).

    CAS  PubMed  Article  Google Scholar 

  59. Michel, B. & Sandler, S. J. Replication restart in bacteria. J. Bacteriol. 199, e00102-17 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  60. Mangiameli, S. M., Merrikh, C. N., Wiggins, P. A. & Merrikh, H. Transcription leads to pervasive replisome instability in bacteria. eLife 6, e19848 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  61. Ghodke, H. et al. Spatial and temporal organization of RecA in the Escherichia coli DNA-damage response. eLife 8, e42761 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  62. Wiktor, J. et al. Live cell imaging reveals that RecA finds homologous DNA by reduced dimensionality search. Preprint at bioRxiv https://doi.org/10.1101/2020.02.13.946996 (2020).

  63. Brent, R. & Ptashne, M. Mechanism of action of the lexA gene product. Proc. Natl Acad. Sci. USA 78, 4204–4208 (1981).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Loewer, A., Batchelor, E., Gaglia, G. & Lahav, G. Basal dynamics of p53 reveals transcriptionally attenuated pulses in cycling cells. Cell 142, 89–100 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Xia, J. et al. Bacteria-to-human protein networks reveal origins of endogenous DNA damage. Cell 176, 127–143 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Fitzgerald, D. M., Hastings, P. J. & Rosenberg, S. M. Stress-induced mutagenesis: implications in cancer and drug resistance. Annu. Rev. Cancer Biol. 1, 119–140 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  67. Marciano, D. C. et al. Negative feedback in genetic circuits confers evolutionary resilience and capacitance. Cell Rep. 7, 1789–1795 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Chia, N., Golding, I. & Goldenfeld, N. λ-prophage induction modeled as a cooperative failure mode of lytic repression. Phys. Rev. E 80, 030901 (2009).

    Article  CAS  Google Scholar 

  69. Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Los, G. V. et al. HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3, 373–382 (2008).

    CAS  PubMed  Article  Google Scholar 

  71. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. Zaslaver, A. et al. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat. Methods 3, 623–628 (2006).

    CAS  PubMed  Article  Google Scholar 

  73. Bryant, J. A., Sellars, L. E., Busby, S. J. W. & Lee, D. J. Chromosome position effects on gene expression in Escherichia coli K-12. Nucleic Acids Res. 42, 11383–11392 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Cohen, S. E. et al. Roles for the transcription elongation factor NusA in both DNA repair and damage tolerance pathways in Escherichia coli. Proc. Natl Acad. Sci. USA 107, 15517–15522 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Ivanković, S. & Đermić, D. DNA end resection controls the balance between homologous and illegitimate recombination in Escherichia coli. PLoS ONE 7, e39030 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. Shurtleff, B. W., Ollivierre, J. N., Tehrani, M., Walker, G. C. & Beuning, P. J. Steric gate variants of UmuC confer UV hypersensitivity on Escherichia coli. J. Bacteriol. 191, 4815–4823 (2009).

    CAS  PubMed  Article  Google Scholar 

  77. Kovačič, L. et al. Structural insight into LexA–RecA* interaction. Nucleic Acids Res. 41, 9901–9910 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. Grimm, J. B. et al. A general method to improve fluorophores for live-cell and single-molecule microscopy. Nat. Methods 12, 244–250 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Martens, K. J. A., Bader, A. N., Baas, S., Rieger, B. & Hohlbein, J. Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): an algorithm for MHz localization rates using standard CPUs. J. Chem. Phys. 148, 123311 (2017).

    Article  CAS  Google Scholar 

  80. Crocker, J. C. & Grier, D. G. Methods of digital video microscopy for colloidal studies. J. Colloid Interface Sci. 179, 298–310 (1996).

    CAS  Article  Google Scholar 

  81. Uphoff, S., Sherratt, D. J. & Kapanidis, A. N. Visualizing protein–DNA interactions in live bacterial cells using photoactivated single-molecule tracking. J. Vis. Exp. (85), e51177 (2014).

    Google Scholar 

  82. Sliusarenko, O., Heinritz, J., Emonet, T. & Jacobs-Wagner, C. High-throughput, subpixel precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics. Mol. Microbiol. 80, 612–627 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Stylianidou, S., Brennan, C., Nissen, S. B., Kuwada, N. J. & Wiggins, P. A. SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells. Mol. Microbiol. 102, 690–700 (2016).

    CAS  PubMed  Article  Google Scholar 

  84. Stracy, M. et al. Single-molecule imaging of UvrA and UvrB recruitment to DNA lesions in living Escherichia coli. Nat. Commun. 7, 12568 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank R. Kohli (University of Pennsylvania) and D. J. Sherratt (University of Oxford) for providing strains, and members of the Uphoff and D. J. Sherratt groups for discussions; and A. Jalin for his contributions during a laboratory internship. Research in the Uphoff laboratory is funded by a Wellcome Trust and Royal Society Sir Henry Dale Fellowship (206159/Z/17/Z to S.U.), a Wellcome-Beit Prize (206159/Z/17/B to S.U.) and a Research Prize Fellowship of the Lister Institute of Preventative Medicine (S.U.). E.C.J. was supported by a Crankstart scholarship. S.U. holds a Hugh Price Fellowship at Jesus College, Oxford. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

S.U. conceived the study. E.C.J. and S.U. generated and characterized cell strains. E.C.J. and S.U. performed single-molecule tracking experiments. S.U. performed microfluidic experiments. S.U. supervised the study and wrote the manuscript with input from E.C.J.

Corresponding author

Correspondence to Stephan Uphoff.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks Harshad Ghodke, Ido Golding and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Assessing the functionality of the LexA-Halo fusion.

a,b,c DNA damage sensitivity assays: 10-fold serial dilutions of LB cultures at OD 0.4 were spotted on LB agarose plates and exposed to indicated UV doses (a), or spotted on LB plates containing ciprofloxacin (b), or methyl methanesulfonate (c). Plates were incubated overnight. Control plates were incubated without DNA damage treatment. The percentages of surviving cells are the number of colonies on treatment plates normalised relative to the number of colonies counted for wild-type (WT) without treatment. Data are mean ±SEM from 6 repeats (UV) or 3 repeats (ciprofloxacin, MMS). * indicates zero survival observed. The LexA-Halo fusion strain shows a moderate increase in UV sensitivity compared to the wild-type AB1157 strain, while the SOS deficient strains with non-cleavable LexAG85D mutant or recA- mutation are hypersensitive to UV damage. LexA-Halo shows the same sensitivity to ciprofloxacin and methyl methanesulfonate as the wild-type. d,e,f, Functionality of LexA-Halo fusion assessed in continuous growth conditions: Single-cell fluorescence dynamics of SOS expression reporter PrecA-GFP during unperturbed growth in mothermachine microfluidic chips for cells expressing wild-type LexA (d, 310 cells), LexA-Halo (e, 501 cells), and non-cleavable LexAG85D (f, 446 cells). Two example cell traces are highlighted per strain. The LexA-Halo fusion strains shows SOS expression pulses similar to the wild-type strain. No pulses are seen in the LexA G85D strain. g, Distributions of PrecA-GFP fluorescence for wild-type LexA, LexA-Halo, and LexAG85D strains show that the LexA-Halo fusion is functional in SOS gene repression. The tail in the distribution for LexA-Halo shows functional SOS induction similar to the wild-type strain. The amplitudes of the SOS expression pulses were slightly reduced compared to the wild-type, matching the reduction in UV tolerance (panel A). h, Distributions of cell survival times during growth in microfluidic chips for wild-type LexA, LexA-Halo, and LexAG85D strains confirms functionality of the LexA-Halo fusion.

Source data

Extended Data Fig. 2 LexA-Halo complementation and characterization of HaloTag labelling.

a, Testing LexA-Halo functionality by complementation of ΔlexA mutant. Representative phase contrast and fluorescence snapshots from 3 independent biological repeats. The PrecA-GFP reporter shows high fluorescence in a ΔlexA strain because the SOS response is unrepressed. The PrecA-GFP reporter becomes repressed to the same level when the ΔlexA allele is complemented with pBAD plasmids expressing LexA (untagged wild-type gene) or the LexA-Halo fusion construct. The ΔlexA strain without PrecA-GFP reporter is included to show the level of cellular autofluorescence. b, Average PrecA-GFP fluorescence per cell ± SEM and individual measurements from 3 repeats. ΔlexA: 1195 cells; ΔlexA PrecA-GFP: 1268 cells; ΔlexA PrecA-GFP pBAD LexA: 1100 cells; ΔlexA PrecA-GFP pBAD LexA-Halo 1241 cells). c, Histograms of PrecA-GFP fluorescence for pBAD LexA (1100 cells) and pBAD-LexA-Halo (1241 cells) complementation. d, Characterisation of LexA-Halo labelling: Average number of observed LexA-Halo tracks per cell after labelling with different concentrations of TMR dye. The labelling efficiency saturates for TMR concentration > 2.5 µM. Arrow indicates concentration used throughout this study. e, D distributions for LexA-Halo do not depend on the concentration of TMR dye used for labelling. f, LexA degradation is unaffected by TMR labelling. D distributions for LexA-Halo 90 min after 50 J/m2 UV treatment for cells labelled with TMR before treatment (top) and for cells labelled with TMR after UV treatment. g, TMR labelling does not affect cell viability or UV sensitivity. Cells expressing LexA-Halo were prepared and labelled with 2.5 µM TMR using the same protocol as for imaging, or mock-labelled in buffer only. 10-fold serial dilutions were spotted on LB agarose plates and exposed to 50 J/m2 UV light before incubating overnight, or incubated without UV exposure.

Source data

Extended Data Fig. 3 Spatial distribution of LexA.

Histograms show the density of LexA-Halo localizations projected onto the long cell axis. Cells were split into 4 classes based on length, < 2.5 µm, 2.5–3.5 µm, 3.5–4.5 µm, 4.5–5.5 µm. In untreated cells, LexA localizations reflect the typical spatial distribution of the nucleoid that splits into two lobes as cells elongate. This spatial pattern is largely absent in cells 180 min after treatment with 50 J/m2 UV when LexA-Halo has been largely converted into free HaloTag. Untreated cells: 4 repeats, 466 cells, 500284 localizations; UV-treated cells: 3 repeats, 164 cells, 103475 localizations.

Source data

Extended Data Fig. 4 Comparison of LexA diffusion in ΔclpX Δlon and wild-type strains without treatment and after exposing cells to a pulse of 50 J/m2 UV.

Combined data from N = 3 repeats per time point. Untreated WT: N = 9 repeats, 2528 cells, 384241 tracks. Untreated ΔclpX Δlon: 458 cells, 33239 tracks. 5 min WT: 280 cells, 23452 tracks. 5 min ΔclpX Δlon: 238 cells, 11249 tracks. 30 min WT: 295 cells, 15408 tracks. 30 min ΔclpX Δlon: 258 cells, 17578 tracks. 90 min WT: 333 cells, 18338 tracks. 90 min ΔclpX Δlon: 232 cells, 17995 tracks. 180 min WT: 307 cells, 18391 tracks. 180 min ΔclpX Δlon: 250 cells, 10858 tracks.

Source data

Extended Data Fig. 5 Quantifying relative abundances of LexA populations.

a, Bound, free, and degraded LexA populations were quantified by fitting a mixture model to D distributions using least squares optimization (3 repeats for each condition), as described in the Materials and Methods. The D distribution of LexA-Halo from fixed cells was used as a model for the bound population (dark blue curve). The mobile part of the D distribution for the non-cleavable LexAK156A mutant was used as a model for the free population (light blue curve). The mobile part of the D distribution for the unconjugated HaloTag was used to model the degraded population (yellow curve). The model distributions were smoothed using moving mean filters. b, Fitted mixture model for LexA-Halo in untreated wild-type cells.

Source data

Extended Data Fig. 6 Dynamics of LexA populations after UV exposure.

a, Relative abundances of degraded (yellow), free (light blue), and DNA-bound (dark blue) LexA populations after exposing cells to a pulse of 50 J/m2 UV. LexA populations were quantified by fitting a mixture model to D distributions at different time points post UV using least squares optimization as shown in Extended Data Fig. 7 (error bars: SEM, 3 repeats, average numbers per time point: 304 cells, 15365 tracks). Lines show moving mean curves. b, Decay of bound LexA population post UV exposure of 5 J/m2 (blue), 20 J/m2 (orange), or 50 J/m2 (grey), 1 repeat per condition. c, Decay rate constants were obtained from exponential fits of the data in panel b. Error bars: ± 95% confidence intervals. The decay of the bound LexA population is slower than that of the free LexA population (Fig. 2e), and the rate does not scale with UV dose. d,e,f Dynamics of LexA populations post 50 J/m2 UV exposure for cells carrying empty pUC19 plasmid, pUC19 with PdinG or PsulA (error bars: SEM, N = 3 biologically independent experiments. Average numbers per time point pUC19 empty: 232 cells, 5669 tracks; PdinG: 273 cells, 13186 tracks; PsulA: 290 cells, 18724 tracks). Bound population (d), free population (e), degraded population (f).

Source data

Extended Data Fig. 7 D distributions for mutant strains and LexA variants.

a, LexA-Halo in wild-type (grey, 9 repeats, 2528 cells, 384241 tracks) and Δdam (blue, 3 repeats, 911 cells, 186197 tracks) strain backgrounds. b, LexA-Halo in wild-type (grey, 9 repeats, 2528 cells, 384241 tracks) and recA- (blue, 3 repeats, 854 cells, 41528 tracks) strain backgrounds. c, Wild-type LexA-Halo (grey, 5 repeats, 1265 cells, 99306 tracks) and non-cleavable LexAK156A mutant (blue, 3 repeats, 368 cells, 24343 tracks), both expressed from pBAD plasmid. d, Wild-type LexA-Halo (grey, 5 repeats, 1265 cells, 99306 tracks) and auto-cleavage deficient LexAV82S mutant (blue, 3 repeats, 1096 cells, 116653 tracks), both expressed from pBAD plasmid. e, Wild-type LexA-Halo (grey, 5 repeats, 1265 cells, 99306 tracks) expressed from pBAD plasmid in wild-type (grey) and recA- (blue, 934 cells, 3 repeats, 1511 cells, 153981 tracks) strain backgrounds.

Source data

Extended Data Fig. 8 Quantification of LexA populations in single cells.

a, Distribution of the number of tracks per cell for LexA-Halo wild-type. b, The abundances of Pfree and Pdegraded are highly correlated with the average diffusion coefficient of LexA-Halo per cell. Each dot represents a single cell from the wild-type LexA-Halo strain. R: Pearsson’s correlation coefficient. c,d, Plots of Pfree versus Pdegraded show that increased level of LexA degradation is correlated with a loss of the pool of free LexA in individual cells from the wild-type and the Δdam strain background. R: Pearsson’s correlation coefficient. e, Evaluation of the statistical fitting error for single cells: Recorded movies were split into two halves, and single-cell quantification of LexA populations was performed independently for each movie half. Pfree from the two movie segments are correlated (R: Pearsson’s correlation coefficient) with a mean absolute difference of 16%, showing that the cell-to-cell variation in LexA abundances is not due to fitting uncertainty. Note that the actual fitting uncertainty is yet lower because the analysis includes the full-length movie data.

Source data

Extended Data Fig. 9 Spontaneous SOS expression pulses are associated with a cell cycle delay.

a, Snapshots of PrecA-GFP expression reporter for untreated cells growing in microfluidic channels. Continuous scale of spontaneous SOS induction levels in wild-type cells. b, No SOS expression pulses are seen in the LexAG85D mutant strain. Identical greyscale as in panel a. c, Histogram of cell cycle durations for wild-type cells growing in microfluidic channels. All cell cycles (grey, 16076 cycles), cell cycles that contain a PrecA-GFP expression pulse (blue, 1013 cycles), cell cycles that contain a PrecA-GFP expression pulse with amplitude > 2000 a.u. (orange, 102 cycles). Percentage of cell cycles with duration > 60 min shown for each category (dashed line). 3 repeats. d, PrecA-GFP pulse amplitude plotted versus the duration of the cell cycle that contained the pulse (3 repeats, 1013 pulses). Pulse amplitude and cell cycle duration are correlated, R: Pearsson’s correlation coefficient. Moving average curve (blue line) shows a minor cell cycle delay for small PrecA-GFP pulses, but a strong delay for pulses with amplitude > 2000 a.u.

Source data

Extended Data Fig. 10 No correlation between LexA variability and expression of a gene that is not part of the SOS regulon.

Imaging PpolA-GFP expression and tracking LexA-Halo diffusion in the same cells shows that there is no correlation between gene expression and the free LexA abundance per cell. Each dot represents a single cell. Black line shows linear fit; R: Pearsson’s correlation coefficient.

Source data

Supplementary information

Supplementary Information

Supplementary Table 1: List of strains. Supplementary Table 2: Summary of LexA populations for different strains.

Reporting Summary

Peer Review File

Source data

Source Data Fig. 1

Source data for Fig 1.

Source Data Fig. 2

Source data for Fig 2.

Source Data Fig. 3

Source data for Fig 3.

Source Data Fig. 4

Source data for Fig 4.

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Source data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Source data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Source data for Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Source data for Extended Data Fig. 9.

Source Data Extended Data Fig. 10

Source data for Extended Data Fig. 10.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jones, E.C., Uphoff, S. Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response. Nat Microbiol 6, 981–990 (2021). https://doi.org/10.1038/s41564-021-00930-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-00930-y

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing