Kinetics of the cellular intake of a gene expression inducer at high concentrations

Tran, Huy; Oliveira, Samuel M. D.; Goncalves, Nadia; Ribeiro, André S.

doi:10.1039/c5mb00244c

Cited by 15 publications

(28 citation statements)

References 51 publications

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“…2B , Fig. S1 , and Supplementary Information), which protects the target RNA from degradation for the duration of the measurements 37 – 39 . Parameters for the detection of the target RNA were kept the same between lineages to avoid biases in detection.…”

Section: Resultsmentioning

confidence: 99%

“…S1D ). This method relies on the fact that, once tagged with MS2d-GFP, the RNA does not degrade and its fluorescence does not decay for several hours 39 . Waiting times for the first production of RNAs in each lineage were calculated by selecting cells without spots at the beginning of induction (i.e., without leaky expression), and detecting when the first production occurred in each branch of each lineage.…”

Section: Methodsmentioning

confidence: 99%

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Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Mäkelä

Kandavalli

Ribeiro

2017

Sci Rep

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Cell-to-cell variability in cellular components generates cell-to-cell diversity in RNA and protein production dynamics. As these components are inherited, this should also cause lineage-to-lineage variability in these dynamics. We conjectured that these effects on transcription are promoter initiation kinetics dependent. To test this, first we used stochastic models to predict that variability in the numbers of molecules involved in upstream processes, such as the intake of inducers from the environment, acts only as a transient source of variability in RNA production numbers, while variability in the numbers of a molecular species controlling transcription of an active promoter acts as a constant source. Next, from single-cell, single-RNA level time-lapse microscopy of independent lineages of Escherichia coli cells, we demonstrate the existence of lineage-to-lineage variability in gene activation times and mean RNA production rates, and that these variabilities differ between promoters and inducers used. Finally, we provide evidence that this can be explained by differences in the kinetics of the rate-limiting steps in transcription between promoters and induction schemes. We conclude that cell-to-cell and consequent lineage-to-lineage variability in RNA and protein numbers are both promoter sequence-dependent and subject to regulation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Mäkelä

Kandavalli

Ribeiro

2017

Sci Rep

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“…The cells feature a multi-copy reporter gene expressing MS2-GFP and a single-copy target gene, controlled by the promoter of interest, containing 96 MS2-GFP binding sites. Shortly after a target RNA is produced, the binding sites are quickly occupied by the abundant GFPs, allowing the fully tagged RNAs to be visualized using fluorescence microscopy [38]. Once formed, the RNA-96-MS2-GFP complex remains fluorescent for much longer than the cell lifetime [38].…”

Section: Methodsmentioning

confidence: 99%

“…Shortly after a target RNA is produced, the binding sites are quickly occupied by the abundant GFPs, allowing the fully tagged RNAs to be visualized using fluorescence microscopy [38]. Once formed, the RNA-96-MS2-GFP complex remains fluorescent for much longer than the cell lifetime [38]. The constructs used here were engineered previously [7, 11].…”

Section: Methodsmentioning

confidence: 99%

Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements

et al. 2016

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Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.

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“…As a characteristic example, the parameter values used for the inducer circuit shown in Figure 6 were as follows: the IPTG relevant membrane permeability constant [58], μ = 5.0 Â 10 -2 m -1…”

Section: Module One: the Engineered Cellmentioning

confidence: 99%

Modeling information exchange between living and artificial cells

et al. 2017

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Background: The tools of synthetic biology have enabled researchers to explore multiple scientific phenomena by directly engineering signaling pathways within living cells and artificial protocells. Here, we explored the potential for engineered living cells themselves to assemble signaling pathways for non-living protocells. This analysis serves as a preliminary investigation into a potential origin of processes that may be utilized by complex living systems. Specifically, we suggest that if living cells can be engineered to direct the assembly of genetic signaling pathways from genetic biomaterials in their environment, then insight can be gained into how naturally occurring living systems might behave similarly.Methods: To this end, we have modeled and simulated a system consisting of engineered cells that control the assembly of DNA monomers on microparticle scaffolds. These DNA monomers encode genetic circuits, and therefore, these microparticles can then be encapsulated with minimal transcription and translation systems to direct protocell phenotype. The modeled system relies on multiple previously established synthetic systems and then links these together to demonstrate system feasibility.Results: In this specific model, engineered cells are induced to synthesize biotin, which competes with biotinylated, circuit-encoding DNA monomers for an avidinized-microparticle scaffold. We demonstrate that multiple synthetic motifs can be controlled in this way and can be tuned by manipulating parameters such as inducer and DNA concentrations. Conclusions:We expect that this system will provide insight into the origin of living systems as well as serve as a tool for engineering living cells that assemble complex biomaterials in their environment.

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Kinetics of the cellular intake of a gene expression inducer at high concentrations

Cited by 15 publications

References 51 publications

Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements

Modeling information exchange between living and artificial cells

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