Control of rRNA synthesis in Escherichia coli at increased rrn gene dosage. Role of guanosine tetraphosphate and ribosome feedback

Baracchini, E; Bremer, H. J.

doi:10.1016/s0021-9258(18)99021-6

Cited by 34 publications

(9 citation statements)

References 29 publications

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“…They observed that the rate of rRNA transcription per operon is reduced to maintain a constant number of rRNA in the cell. In fact, electron micrograph (EM) images showed that fewer RNAP molecules were engaged in transcribing each rrn gene, consistent with previous studies (52). Moreover, RNAP molecules formed bunches separated by gaps along the genes.…”

Section: Data Analysis and Parameter Estimationsupporting

confidence: 90%

“…In a second set of experiments, EM images were used to shed light on a previously reported effect (52), namely that the transcriptional activity of individual rrn genes is inversely proportional to the copy number of these genes in such a way that the net transcriptional output of rrn genes in the cell is kept constant. Surprisingly, the EM showed that when the copy number of ribosomal genes was altered by placing extra rrn genes on plasmids, a very different pattern of polymerase occupancy of the rrn genes emerged.…”

Section: Bursting Accompanies the Downregulation Of The Transcription Of Rrn Operons In The Presence Of Additional Copies Of The Rrn Genementioning

confidence: 99%

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Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

Choubey

Kondev

Sánchez

2018

Biophysical Journal

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Transcription is the dominant point of control of gene expression. Biochemical studies have revealed key molecular components of transcription and their interactions, but the dynamics of transcription initiation in cells is still poorly understood. This state of affairs is being remedied with experiments that observe transcriptional dynamics in single cells using fluorescent reporters. Quantitative information about transcription initiation dynamics can also be extracted from experiments that use electron micrographs of RNA polymerases caught in the act of transcribing a gene (Miller spreads). Inspired by these data, we analyze a general stochastic model of transcription initiation and elongation and compute the distribution of transcription initiation times. We show that different mechanisms of initiation leave distinct signatures in the distribution of initiation times that can be compared to experiments. We analyze published data from micrographs of RNA polymerases transcribing ribosomal RNA genes in Escherichia coli and compare the observed distributions of interpolymerase distances with the predictions from previously hypothesized mechanisms for the regulation of these genes. Our analysis demonstrates the potential of measuring the distribution of time intervals between initiation events as a probe for dissecting mechanisms of transcription initiation in live cells.

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Section: Data Analysis and Parameter Estimationsupporting

confidence: 90%

Section: Bursting Accompanies the Downregulation Of The Transcription Of Rrn Operons In The Presence Of Additional Copies Of The Rrn Genementioning

confidence: 99%

Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

Choubey

Kondev

Sánchez

2018

Biophysical Journal

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“…In an experiment with increased rRNA gene dosage, where ppGpp concentration was shown to be constant, the growth rate of a strain carrying extra rRNA operons on a plasmid indeed decreased by 22% relative to a WT strain carrying a control plasmid expressing nonfunctional rRNA [20], in agreement with the trend predicted by the model. In another experiment with increased rRNA gene dosage, growth rate decreased relative to WT cells containing a control plasmid, and rRNA to total protein ratio was more or less constant (thus appearing to favor the unconstrained CGGR model) although the authors argue that there may be tRNA imbalance in these strains [94]. In addition, the unconstrained CGGR model predicted that ribosome and bulk protein concentration increase with rRNA operon copy number (Figure S5) thus leading to an increase in the macromolecular volume fraction (Figure 3, insert).…”

Section: Effect Of Increased Rrna Operon Copy Number On Growth Ratementioning

confidence: 99%

A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number

Tadmor

Tlusty

2008

PLoS Comput Biol

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We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steady-state growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity.

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“…Yet it is not clear how they perceive their rate of growth, which involves thousands of reactions. Through quantitative studies of E. coli under exponential growth and during growth transitions, here we show that the alarmone ppGpp senses the rate of translational elongation by ribosomes, and together with its roles in controlling ribosome biogenesis and activity [5][6][7][8][9] , closes a key regulatory circuit that enables the cell to perceive the rate of its own growth for a broad class of growth-limiting conditions. This perception provides the molecular basis for the emergence of simple relations among the cellular ribosome content, translational elongation rate, and the growth rate, as manifested by bacterial growth laws 2,10-13 .…”

mentioning

confidence: 91%

Cellular perception of growth rate and the mechanistic origin of bacterial growth law

Balakrishnan

Braniff

et al. 2021

Preprint

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Cells organize many of their activities in accordance to how fast they grow. Yet it is not clear how they perceive their rate of growth, which involves thousands of reactions. Through quantitative studies of E. coli under exponential growth and during growth transitions, here we show that the alarmone ppGpp senses the rate of translational elongation by ribosomes, and together with its roles in controlling ribosome biogenesis and activity, closes a key regulatory circuit that enables the cell to perceive the rate of its own growth for a broad class of growth-limiting conditions. This perception provides the molecular basis for the emergence of simple relations among the cellular ribosome content, translational elongation rate, and the growth rate, as manifested by bacterial growth laws. The findings here provide a rare view of how cells manage to collapse the complex, high-dimensional dynamics of the underlying molecular processes to perceive and regulate emergent cellular behaviors, an example of dimension reduction performed by the cells themselves.

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Control of rRNA synthesis in Escherichia coli at increased rrn gene dosage. Role of guanosine tetraphosphate and ribosome feedback

Cited by 34 publications

References 29 publications

Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number

Cellular perception of growth rate and the mechanistic origin of bacterial growth law

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