Effects of DNA replication on mRNA noise

Peterson, J.R.; Cole, John A.; Fei, Jingyi; Luthey‐Schulten, Zaida

doi:10.1073/pnas.1516246112

Cited by 50 publications

(66 citation statements)

References 42 publications

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“…Building on prior work by us and other authors [18, 20, 21], we have derived expressions for mRNA and protein statistics assuming a simple constitutive model of gene expression that accounts for chromosome replication. We are not the first to consider this effect [19], but to our knowledge we are the first to carefully understand it in the context of other sources of extrinsic noise, and more importantly, critically compare our model with experimental results.…”

Section: Discussionmentioning

confidence: 99%

“…For constitutively expressed genes, the solutions for the messenger mean and variance are known [21], allowing for the simultaneous solution of Equations 2 and 3 (see Equations A12 and A41). Normalizing by cell size and time-averaging over the cell cycle (accounting for the fact that log-phase cells are known to have exponentially distributed ages [23, 24], and grow exponentially during the cell cycle [25]) then yields closed form solutions for the messenger and protein means and variances, E [ m ], Var [ m ], E [ p ], and Var [ p ], that depend on k t , k r , k d , t r , and t D .…”

Section: Modelmentioning

confidence: 99%

“…Building on this work, Peterson et al . [21] showed that the messenger degradation rate, which defines the timescale at which the mean messenger count “relaxes” from its low state before to its high state after gene replication, plays an critical role in accurately describing messenger noise. Earlier analytical models of gene expression either neglect DNA replication entirely, or fail to account for the mRNA relaxation by either tacitly ignoring it or significantly overestimating the messenger degradation rate (which in turn effectively ignores the relaxation).…”

Section: Introductionmentioning

confidence: 99%

“…Assuming cells grow exponentially during the cell cycle, we can write the a cell’s size, s , as:

s false(t false) = s_{0} 2^{t / t_{D}}

Now we can compute the average cell size,

\overset{s}{true}

, as:

\overset{s}{true} = true \int_{0}^{t_{D}} s_{0} 2^{t / t_{D}} \frac{2 \ln (2)}{t_{D}} 2^{- t / t_{D}} d t = 2 \ln false(2 false) s_{0}

Then solving for s 0 such that

\overset{s}{true}

is 1 average cell gives s 0 = 1/(2ln(2)). We can use this to write the size-normalized messenger mean and protein mean simply by dividing

\overset{m}{true} false(t false)

(as computed in [21]) and

\overset{p}{true} false(t false)

by the instantaneous cell size, and we can also write the protein variance by dividing

σ_{p}^{2} false(t false)

by the squared instantaneous cell size:

E {false[m false]}_{norm} = true \int_{0}^{t_{D}} \bar{m} (t) \frac{4 \ln^{2} (2)}{t_{D}} 2^{- 2 t / t_{D}} d t

E {false[p false]}_{norm} = true \int_{0}^{t_{D}} \bar{p} (t) \frac{4 \ln^{2} (2)}{t_{D}} 2^{- 2 t / t_{D}} d t

Var {false[p false]}_{norm} = true \int_{0}^{}

…”

mentioning

confidence: 99%

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Careful accounting of extrinsic noise in protein expression reveals correlations among its sources

Cole

Luthey‐Schulten

2017

Phys. Rev. E

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In order to grow and replicate, living cells must express a diverse array of proteins, but the process by which proteins are made includes a great deal of inherent randomness. Understanding this randomness—whether it arises from the discrete stochastic nature of chemical reactivity (“intrinsic” noise), or from cell-to-cell variability in the concentrations of molecules involved in gene expression or the timings of important cell-cycle events like DNA replication or cell division (“extrinsic” noise)—remains a challenge. In this article we analyze a model of gene expression that accounts for several extrinsic sources of noise, including those associated with chromosomal replication, cell division, and variability in the numbers of RNA polymerase, ribonuclease E, and ribosomes. We then attempt to fit our model to a large proteomics and transcriptomics data set, and find that only through the introduction of a few key correlations among the extrinsic noise sources can we accurately recapitulate the experimental data. These include significant correlations between the rate of mRNA degradation (mediated by ribonuclease E) and the rates of both transcription (RNA polymerase) and translation (ribosomes), and strikingly, an anticorrelation between the transcription and translation rates themselves.

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Section: Discussionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Assuming cells grow exponentially during the cell cycle, we can write the a cell’s size, s , as:

s false(t false) = s_{0} 2^{t / t_{D}}

Now we can compute the average cell size,

\overset{s}{true}

, as:

\overset{s}{true} = true \int_{0}^{t_{D}} s_{0} 2^{t / t_{D}} \frac{2 \ln (2)}{t_{D}} 2^{- t / t_{D}} d t = 2 \ln false(2 false) s_{0}

Then solving for s 0 such that

\overset{s}{true}

is 1 average cell gives s 0 = 1/(2ln(2)). We can use this to write the size-normalized messenger mean and protein mean simply by dividing

\overset{m}{true} false(t false)

(as computed in [21]) and

\overset{p}{true} false(t false)

by the instantaneous cell size, and we can also write the protein variance by dividing

σ_{p}^{2} false(t false)

by the squared instantaneous cell size:

E {false[m false]}_{norm} = true \int_{0}^{t_{D}} \bar{m} (t) \frac{4 \ln^{2} (2)}{t_{D}} 2^{- 2 t / t_{D}} d t

E {false[p false]}_{norm} = true \int_{0}^{t_{D}} \bar{p} (t) \frac{4 \ln^{2} (2)}{t_{D}} 2^{- 2 t / t_{D}} d t

Var {false[p false]}_{norm} = true \int_{0}^{}

…”

mentioning

confidence: 99%

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Careful accounting of extrinsic noise in protein expression reveals correlations among its sources

Cole

Luthey‐Schulten

2017

Phys. Rev. E

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“…whose function depends largely on search time and efficiency of stimulated co-degradation, sequestration or stabilization when in complex with the target mRNA [1]. On longer timescales degradation finely tunes abundances of critical RNAs [2], controls slow shifts in RNA levels during adaptation between different growth states [3], and contributes significantly to the noise in the steady-state distribution observed in populations of cells [4]. These observations and those of many other studies demonstrate that post-transcriptional control of RNA dynamics is critical to understanding the cellular state; however, the factors defining stability over an organism’s entire transcriptome have yet to be fully defined.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

et al. 2016

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BackgroundWhile a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen.ResultsWe use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates.ConclusionsThis study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism’s metabolism than previously thought.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3219-8) contains supplementary material, which is available to authorized users.

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Ribosome biogenesis in replicating cells: Integration of experiment and theory

et al. 2016

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Ribosomes—the primary macromolecular machines responsible for translating the genetic code into proteins—are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume1 to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells’ lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 minute doubling time) E. coli cells, replication was initiated 42 minutes into the cell cycle and completed after an additional 42 minutes. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of non-ribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome.

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Effects of DNA replication on mRNA noise

Cited by 50 publications

References 42 publications

Careful accounting of extrinsic noise in protein expression reveals correlations among its sources

Careful accounting of extrinsic noise in protein expression reveals correlations among its sources

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

Ribosome biogenesis in replicating cells: Integration of experiment and theory

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