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A Stochastic Single-Molecule Event Triggers Phenotype Switching of a Bacterial Cell
Open accessAbstract
By monitoring fluorescently labeled lactose permease with single-molecule sensitivity, we investigated the molecular mechanism of how an Escherichia coli cell with the lac operon switches from one phenotype to another. At intermediate inducer concentrations, a population of genetically identical cells exhibits two phenotypes: induced cells with highly fluorescent membranes and uninduced cells with a small number of membrane-bound permeases. We found that this basal-level expression results from partial dissociation of the tetrameric lactose repressor from one of its operators on looped DNA. In contrast, infrequent events of complete dissociation of the repressor from DNA result in large bursts of permease expression that trigger induction of the lac operon. Hence, a stochastic single-molecule event determines a cell's phenotype.
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Citations
Cites
Genetic regulatory mechanisms in the synthesis of proteins.
Jacob, F., & Monod, J. (1961) · Journal of Molecular Biology · doi:10.1016/s0022-2836(61)80072-7 · Cited by 5,300
Stochasticity in gene expression: from theories to phenotypes
Kærn, M., Elston, T. C., Blake, W. J., & Collins, J. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1615 · Cited by 2,318
Arkin, A. P., Ross, J., & McAdams, H. H. (1998) · Genetics · doi:10.1093/genetics/149.4.1633 · Cited by 1,492
Stochastic protein expression in individual cells at the single molecule level
Cai, L., Friedman, N., & Xie, X. S. (2006) · Nature · doi:10.1038/nature04599 · Cited by 1,145
Self-citationGene Regulation at the Single-Cell Level
Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S., & Elowitz, M. B. (2005) · Science · doi:10.1126/science.1106914 · Cited by 1,104
Probing Transcription Factor Dynamics at the Single-Molecule Level in a Living Cell
Elf, J., Li, G., & Xie, X. (2007) · Science · doi:10.1126/science.1141967 · Cited by 1,063
Self-citationMultistability in the lactose utilization network of Escherichia coli
Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I., & van Oudenaarden, A. (2004) · Nature · doi:10.1038/nature02298 · Cited by 1,030
Probing Gene Expression in Live Cells, One Protein Molecule at a Time
Yu, J., Xiao, J., Ren, X., Lao, K., & Xie, X. S. (2006) · Science · doi:10.1126/science.1119623 · Cited by 918
Self-citationENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON
Novick, A., & Weiner, M. (1957) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.43.7.553 · Cited by 891
Optimality and evolutionary tuning of the expression level of a protein
Dekel, E., & Alon, U. (2005) · Nature · doi:10.1038/nature03842 · Cited by 766
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Norman, T. M., Lord, N. D., Paulsson, J., & Losick, R. M. (2015) · Annual Review of Microbiology · doi:10.1146/annurev-micro-091213-112852 · Cited by 167
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Capitanio, M., Canepari, M., Maffei, M., Beneventi, D., Mónico, C., Vanzi, F., Bottinelli, R., & Pavone, F. S. (2012) · Nature Methods · doi:10.1038/nmeth.2152 · Cited by 139
Hammar, P., Walldén, M., Fange, D., Persson, F., Baltekin, Ö., Ullman, G., Leroy, P., & Elf, J. (2014) · Nature Genetics · doi:10.1038/ng.2905 · Cited by 126
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Ullman, G., Walldén, M., Marklund, E. G., Mahmutovic, A., Razinkov, I., & Elf, J. (2012) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0025 · Cited by 83
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Greenfeld, M., Pavlichin, D. S., Mabuchi, H., & Herschlag, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030024 · Cited by 82
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Metadata
- Authors
- Choi, P. J., Cai, L., Frieda, K., & Xie, X. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Choi, Paul J.
- Cai, Long
- Frieda, Kirsten
- Xie, X. Sunney
- Venue
- Science
- Published
- 2008-10-17
- Volume
- 322
- Issue
- 5900
- Pages
- 442-446
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC2819113
- OpenAlex
- W1978182054
- Cited by
- 438
- References
- 28
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 13 · 2023: 11 · 2022: 11 · 2021: 14 · 2020: 21 · 2019: 33 · 2018: 28 · 2017: 26 · 2016: 30 · 2015: 27 · 2014: 35 · 2013: 34 · 2012: 37
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (DP1OD000277)
- National Institute of Biomedical Imaging and Bioengineering (R01EB010244)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Diffusion and Search Dynamics
- Keywords
- lac operon, lactose permease, lactose repressor, phenotypic switching, stochastic gene expression, single-molecule fluorescence, Escherichia coli, basal expression, gene expression regulation
- MeSH terms
- Bacterial Proteins, Escherichia coli, Lac Operon, Lactose, Microscopy, Fluorescence, Models, Genetic, Monosaccharide Transport Proteins, Operator Regions, Genetic, Phenotype, Recombinant Fusion Proteins, Repressor Proteins, RNA, Messenger, Stochastic Processes, Gene Expression Regulation, Bacterial, Feedback, Physiological, Symporters, Escherichia coli Proteins, Lac Repressors
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