Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Dec 24;139(7):1366-75.
doi: 10.1016/j.cell.2009.12.001.

Growth rate-dependent global effects on gene expression in bacteria

Stefan Klumpp  1 , Zhongge Zhang, Terence Hwa
Affiliations

Growth rate-dependent global effects on gene expression in bacteria

Stefan Klumpp et al. Cell. .

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).

PubMed Disclaimer

Figures

Figure 1
Figure 1. Growth-rate dependence of global cellular parameters affecting gene expression
(A) Transcription rate per gene, (B) gene copy number per cell, (C) mRNA degradation rate, (D) translation rate per mRNA molecule, (E) protein dilution rate due to growth and (F) cell mass, used as a measure for the cell volume V, as functions of the growth rate. All parameters are for constitutively expressed (unregulated) genes. For a description how the data was collected from the literature and for references, see the text and Table S1.
Figure 2
Figure 2. Calculated growth-rate dependence of constitutive gene expression
Expression level of a constitutively expressed gene as characterized by the number of mRNA transcripts of that gene per cell (B), its mRNA concentration (C), its number of protein molecules per cell (D), and the protein concentration (D), calculated from the growth rate dependence of the parameters shown in Fig. 1. All curves are normalized to their value at 1 doubling/hour.
Figure 3
Figure 3. Growth-rate dependence of constitutively expressed genes on the chromosome and plasmid
(A and B) The calculated growth rate dependence of the concentration of a protein encoded by a constitutively expressed gene (red) is compared to experimental data on constitutively expressed genes in various E. coli strains: the orange and purple dots are derived from the activities of tryptophan synthase and ornithine transcarbamylase respectively, from a strain in which their respective regulators were deleted (Willumsen, 1975). The black dots are derived from LacZ expressed from the mutant LacL1 type promoters (Wanner et al., 1977). The green squares are from this work, with LacZ expressed from the synthetic PLTet-O1 promoter in strain EQ37 which contains no TetR. Data in (A) is normalized to total mass as measured by optical density and data in (B) to total protein. All data are plotted relative to their expression levels in cells grown at 1 doubling/hr. (C) Comparison of the calculated protein concentration for genes on the chromosome (shaded grey area, the black line indicates the curve from Fig. 3A), on plasmids pBR322 (red) and R1 (blue).
Figure 4
Figure 4. Growth-rate dependence of simple negative and positive regulation
Concentration of a protein under (A) negative regulation by a constitutively expressed repressor, and (B) positive regulation by a constitutively expressed activator. The two plots are generated, respectively, by Eq. (S9) and Eq. (S11) in the Supporting Text, for non-cooperative (Hill coefficient n=1, squares) and cooperative regulation (n=2, triangles). Black symbols show the concentration of constitutively expressed protein. The parameters used for the plots are r1/K=10 in (A), and a1/K=0.1 and f=100 in (B). (C) Experimental data for the concentrations of LacZ reporter under constitutive expression (strain EQ37: PLtetO1-lacZ, no tetR, black), repression (strain EQ38: Pcon-tetR, PLTet-O1-lacZ, red) and activation (strain EQ40: PLlac-O1-dnxylR, Pu-lacZ, no lacI, green), showing weaker growth-rate dependence under repression and stronger growth-rate dependence under activation as compared to the constitutive case.
Figure 5
Figure 5. Growth-rate dependence of genetic circuits with negative autoregulation
(A) Experimental data for growth-rate dependence of simple repression (EQ38: Pcon-tetR, PLTet-O1-lacZ, red symbols), and autorepression (EQ39: PLTet-O1-tetR, PLTet-O1-lacZ, blue symbols) in presence of the inducer cl-Tc (circles: 50ng/ml; squares: 20ng/ml; triangles: no inducer). For simple repression, induction results in significant growth-rate dependence. Autorepression exhibits growth rate independent LacZ concentrations which is nevertheless tunable by the inducer level. (B) Our model predicts weak growth-rate dependence for a protein E controlled by an autoregulated repressor R. If E and R are driven by the same promoter (solid lines, from Eq. (S14) with r1/Kr=10), weaker growth rate dependence is obtained by increasing cooperativity (larger Hill coefficient n). Independence of growth rate is predicted for E and R driven by different promoters, whose respective Hill coefficients for repression (ne and nr) satisfy ne=nr+1 (dashed line, from Eq. (S17) with r1/Kr=10 and r1/Ke=10).
Figure 6
Figure 6. Effects of growth rate on bistable genetic circuits
The parameter ranges for bistability at different growth rates are plotted for (A) the autoactivator and (B) the toggle switch. The lines describe boundaries of the bistable regime, obtained from linear stability analysis of Eq. (S18) for (A) and Eqs. (S19) and (S20) for (B). The grey areas indicate the parameter range for which bistability is obtained over the full range of growth rates considered here (0.6-2.5 dbl/hr).
Figure 7
Figure 7. Feedback through growth
(A) Growth inhibition due to expression of a toxin is described by a Hill function of the toxin concentration p, characterized by a threshold concentration (pμ) for which the growth rate is reduced to half the maximal growth rate (μ0). (B) Toxin concentration (normalized to its concentration at 1 dbl/hr, p1) and (C) growth rate as functions of the promoter strength (characterized by p1 and normalized to pμ), obtained from Eq. (S22). Growth inhibition results in non-linear increase of the toxin concentration, which is steeper for a gene on a pBR322 plasmid (red) than for a gene on the chromosome (black). For a gene on plasmid R1 (blue) with its strong growth-rate dependence (Fig. 3C), there is a region of bistability with two branches, one with high toxin concentration and slow growth and the other with low toxin concentration and fast growth.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Andrianantoandro E, Basu S, Karig DK, Weiss R. Synthetic biology: new engineering rules for an emerging discipline. Mol Syst Biol. 2006;2:2006.0028. - PMC - PubMed
    1. Atkinson MR, Savageau MA, Myers JT, Ninfa AJ. Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli. Cell. 2003;113:597–607. - PubMed
    1. Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. Bacterial persistence as a phenotypic switch. Science. 2004;305:1622–1625. - PubMed
    1. Bernstein JA, Khodursky AB, Lin PH, Lin-Chao S, Cohen SN. 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;99:9697–9702. - PMC - PubMed
    1. Bintu L, Buchler NE, Garcia HG, Gerland U, Hwa T, Kondev J, Phillips R. Transcriptional regulation by the numbers: models. Curr Opin Genet Dev. 2005;15:116–124. - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources