Cited by 703Gene Regulatory Network AnalysisBacterial Genetics and BiotechnologyMicrobial Metabolic Engineering and Bioproduction

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Growth Rate-Dependent Global Effects on Gene Expression in Bacteria

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Klumpp, Stefan, Zhang, Zhongge, Hwa, Terence·2009·Cell

DOI 10.1016/j.cell.2009.12.001PMC PMC2818994

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Abstract

Bacterial gene expression depends not only on specific regulatory mechanisms, but also on bacterial growth, because important global parameters such as the abundance of RNA polymerases and ribosomes are all growth-rate dependent. Understanding of these global effects is necessary for a quantitative understanding of gene regulation and for the design of synthetic genetic circuits. We find that the observed growth-rate dependence of constitutive gene expression can be explained by a simple model using the measured growth-rate dependence of the relevant cellular parameters. More complex growth dependencies for genetic circuits involving activators, repressors, and feedback control were analyzed and verified experimentally with synthetic circuits. Additional results suggest a feedback mechanism mediated by general growth-dependent effects that does not require explicit gene regulation if the expressed protein affects cell growth. This mechanism can lead to growth bistability and promote the acquisition of important physiological functions such as antibiotic resistance and tolerance (persistence).

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Experiments in molecular genetics
Miller, J. H. (1972) · Medical Entomology and Zoology · Cited by 27,157
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products.
Datsenko, K., & Wanner, B. (2000) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.120163297 · Cited by 15,902
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Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
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Elowitz, M., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,812
Construction of a genetic toggle switch in Escherichia coli
Gardner, T. S., Cantor, C. R., & Collins, J. J. (2000) · Nature · doi:10.1038/35002131 · Cited by 4,310
Network motifs in the transcriptional regulation network of Escherichia coli
Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. S. (2002) · Nature Genetics · doi:10.1038/ng881 · Cited by 2,970
Bacterial Persistence as a Phenotypic Switch
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., & Leibler, S. (2004) · Science · doi:10.1126/science.1099390 · Cited by 2,952
Culture Medium for Enterobacteria
Neidhardt, F. C., Bloch, P. L., & Smith, D. F. (1974) · Journal of Bacteriology · doi:10.1128/jb.119.3.736-747.1974 · Cited by 2,903
Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements
Lutz, R. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.6.1203 · Cited by 1,694
Molecular mechanisms that confer antibacterial drug resistance
Walsh, C. T. (2000) · Nature · doi:10.1038/35021219 · Cited by 1,579

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Willumsen, B.M. (1975). Expression of constitutive genes in E. coli as function of growth rate (M. Sc. Thesis, University of Copenhagen).

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Authors: Klumpp, S., Zhang, Z., & Hwa, T.
Affiliations: University of California San Diego (US)
Corresponding author: Klumpp, Stefan
ORCID: Klumpp, Stefan
Zhang, Zhongge
Venue: Cell
Publisher: Elsevier BV
Published: 2009-12
Volume: 139
Issue: 7
Pages: 1366-1375
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2009.12.001
PMC: PMC2818994
OpenAlex: W2115606218
Cited by: 703
References: 77
Citations by year: 2026: 47 · 2025: 41 · 2024: 37 · 2023: 40 · 2022: 54 · 2021: 36 · 2020: 44 · 2019: 48 · 2018: 53 · 2017: 36 · 2016: 40 · 2015: 53 · 2014: 47 · 2013: 51 · 2012: 34
FWCI: 10.21
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1355 d)
Locations: Cell (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Max Planck Institute for Plasma Physics (submittedVersion)
Max Planck Digital Library (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion)
Funders: National Institute of General Medical Sciences (R01GM077298, R01-GM77298)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords: bacterial growth rate, constitutive expression, synthetic gene circuits, ribosome abundance, feedback control, antibiotic resistance, antibiotic tolerance, persistence
MeSH terms: Escherichia coli, Homeostasis, Gene Expression Regulation, Bacterial, Feedback, Physiological, Regulatory Elements, Transcriptional, Gene Regulatory Networks

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Growth Rate-Dependent Global Effects on Gene Expression in Bacteria

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