Anti-correlation of LacI Association and Dissociation Rates Observed in Living Cells

Overview

Journal Nat Commun

Date 2025 Jan 17

PMID 39824877

Authors

Vinodh Kandavalli

Spartak Zikrin

Johan Elf

Daniel Jones

Affiliations

Soon will be listed here.

Abstract

The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites. We found that both association and dissociation rates differed significantly between binding sites, and moreover observed a clear anticorrelation between these rates across varying binding site strengths. These results contradict the long-standing hypothesis that TF binding site strength is primarily dictated by the dissociation rate, but may confer the evolutionary advantage that TFs do not get stuck in near-operator sequences while searching.

References

Padovan-Merhar O, Raj A . Using variability in gene expression as a tool for studying gene regulation. Wiley Interdiscip Rev Syst Biol Med. 2013; 5(6):751-9. PMC: 4561544. DOI: 10.1002/wsbm.1243. View

Marklund E, Mao G, Yuan J, Zikrin S, Abdurakhmanov E, Deindl S . Sequence specificity in DNA binding is mainly governed by association. Science. 2022; 375(6579):442-445. DOI: 10.1126/science.abg7427. View

Mitarai N, Semsey S, Sneppen K . Dynamic competition between transcription initiation and repression: Role of nonequilibrium steps in cell-to-cell heterogeneity. Phys Rev E Stat Nonlin Soft Matter Phys. 2015; 92(2):022710. DOI: 10.1103/PhysRevE.92.022710. View

Hammar P, Wallden M, Fange D, Persson F, Baltekin O, Ullman G . Direct measurement of transcription factor dissociation excludes a simple operator occupancy model for gene regulation. Nat Genet. 2014; 46(4):405-8. PMC: 6193529. DOI: 10.1038/ng.2905. View

Elf J, Li G, Xie X . Probing transcription factor dynamics at the single-molecule level in a living cell. Science. 2007; 316(5828):1191-4. PMC: 2853898. DOI: 10.1126/science.1141967. View

Mutalik V, Guimaraes J, Cambray G, Lam C, Christoffersen M, Mai Q . Precise and reliable gene expression via standard transcription and translation initiation elements. Nat Methods. 2013; 10(4):354-60. DOI: 10.1038/nmeth.2404. View

Hamouche L, Poljak L, Carpousis A . Ribosomal RNA degradation induced by the bacterial RNA polymerase inhibitor rifampicin. RNA. 2021; . PMC: 8284325. DOI: 10.1261/rna.078776.121. View

Livak K, Schmittgen T . Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2002; 25(4):402-8. DOI: 10.1006/meth.2001.1262. View

Edelstein A, Tsuchida M, Amodaj N, Pinkard H, Vale R, Stuurman N . Advanced methods of microscope control using μManager software. J Biol Methods. 2015; 1(2). PMC: 4297649. DOI: 10.14440/jbm.2014.36. View

10.

Bernstein J, Khodursky A, Lin P, Lin-Chao S, Cohen S . Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc Natl Acad Sci U S A. 2002; 99(15):9697-702. PMC: 124983. DOI: 10.1073/pnas.112318199. View

11.

Makela J, Kandavalli V, Ribeiro A . Rate-limiting steps in transcription dictate sensitivity to variability in cellular components. Sci Rep. 2017; 7(1):10588. PMC: 5587725. DOI: 10.1038/s41598-017-11257-2. View

12.

Skinner S, Sepulveda L, Xu H, Golding I . Measuring mRNA copy number in individual Escherichia coli cells using single-molecule fluorescent in situ hybridization. Nat Protoc. 2013; 8(6):1100-13. PMC: 4029592. DOI: 10.1038/nprot.2013.066. View

13.

So L, Ghosh A, Zong C, Sepulveda L, Segev R, Golding I . General properties of transcriptional time series in Escherichia coli. Nat Genet. 2011; 43(6):554-60. PMC: 3102781. DOI: 10.1038/ng.821. View

14.

Xie X, Choi P, Li G, Lee N, Lia G . Single-molecule approach to molecular biology in living bacterial cells. Annu Rev Biophys. 2008; 37:417-44. DOI: 10.1146/annurev.biophys.37.092607.174640. View

15.

Baptista I, Ribeiro A . Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli. Biosystems. 2020; 193-194:104154. DOI: 10.1016/j.biosystems.2020.104154. View

16.

Shahrezaei V, Swain P . Analytical distributions for stochastic gene expression. Proc Natl Acad Sci U S A. 2008; 105(45):17256-61. PMC: 2582303. DOI: 10.1073/pnas.0803850105. View

17.

Jones D, Brewster R, Phillips R . Promoter architecture dictates cell-to-cell variability in gene expression. Science. 2014; 346(6216):1533-6. PMC: 4388425. DOI: 10.1126/science.1255301. View

18.

Friedman L, Gelles J . Mechanism of transcription initiation at an activator-dependent promoter defined by single-molecule observation. Cell. 2012; 148(4):679-89. PMC: 3479156. DOI: 10.1016/j.cell.2012.01.018. View

19.

Rydenfelt M, Garcia H, Cox 3rd R, Phillips R . The influence of promoter architectures and regulatory motifs on gene expression in Escherichia coli. PLoS One. 2014; 9(12):e114347. PMC: 4280137. DOI: 10.1371/journal.pone.0114347. View

20.

Sampaio N, Dunlop M . Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control. Curr Opin Microbiol. 2020; 57:87-94. PMC: 7722170. DOI: 10.1016/j.mib.2020.08.002. View