Generation and filtering of gene expression noise by the bacterial cell cycle

Walker, Noreen; Nghe, Philippe; Tans, Sander J.

doi:10.1186/s12915-016-0231-z

Cited by 53 publications

(73 citation statements)

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“…Mycobacteria may use similar mechanisms to adapt the timing of their stress response. Alternatively, this time window may arise from changes to cell state due to cell cycle-related patterns of transcription and translation as in other microbes (23,24). Our findings that mycobacterial drug tolerance has variable components via the timing of birth and cell size at birth open a new conceptual framework of rapid and cyclic changes to drug susceptibility that may be anticipated and quantified.…”

Section: Discussionmentioning

confidence: 83%

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria

Richardson

Bennion

Tan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Mycobacteria grow and divide asymmetrically, creating variability in growth pole age, growth properties, and antibiotic susceptibilities. Here, we investigate the importance of growth pole age and other growth properties in determining the spectrum of responses of Mycobacterium smegmatis to challenge with rifampicin. We used a combination of live-cell microscopy and modeling to prospectively identify subpopulations with altered rifampicin susceptibility. We found two subpopulations that had increased susceptibility. At the initiation of treatment, susceptible cells were either small and at early stages of the cell cycle, or large and in later stages of their cell cycle. In contrast to this temporal window of susceptibility, tolerance was associated with factors inherited at division: long birth length and mature growth poles. Thus, rifampicin response is complex and due to a combination of differences established from both asymmetric division and the timing of treatment relative to cell birth.antibiotic susceptibility | single cell | mathematical modeling | mycobacteria | cell biology

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Section: Discussionmentioning

confidence: 83%

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria

Richardson

Bennion

Tan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…However, future work will consider mechanistic ways of incorporating noise by explicitly modeling the cell cycle, as in [38][39][40][41][42][43]. Moreover, by linking cell size to expression of constitutive genes we hope to uncover strategies for maintaining concentrations of essential proteins [44][45][46][47][48][49], in spite of stochastic and temporal changes in cell size.…”

Section: Discussionmentioning

confidence: 99%

Analysis of noise mechanisms in cell size control

Modi¹,

Vargas-García²,

Ghusinga³

et al. 2016

Preprint

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At the single-cell level, noise features in multiple ways through the inherent stochasticity of biomolecular processes, random partitioning of resources at division, and fluctuations in cellular growth rates. How these diverse noise mechanisms combine to drive variations in cell size within an isoclonal population is not well understood. To address this problem, we systematically investigate the contributions of different noise sources in well-known paradigms of cell-size control, such as the adder (division occurs after adding a fixed size from birth) and the sizer (division occurs upon reaching a size threshold). Analysis reveals that variance in cell size is most sensitive to errors in partitioning of volume among daughter cells, and not surprisingly, this process is well regulated among microbes. Moreover, depending on the dominant noise mechanism, different size control strategies (or a combination of them) provide efficient buffering of intercellular size variations. We further explore mixer models of size control, where a timer phase precedes/follows an adder, as has been proposed in Caulobacter crescentus. While mixing a timer with an adder can sometimes attenuate size variations, it invariably leads to higher-order moments growing unboundedly over time. This results in the cell size following a power-law distribution with an exponent that is inversely dependent on the noise in the timer phase. Consistent with theory, we find evidence of power-law statistics in the tail of C. crescentus cell-size distribution, but there is a huge discrepancy in the power-law exponent as estimated from data and theory. However, the discrepancy is removed after data reveals that the size added by individual newborns from birth to division itself exhibits power-law statistics. Taken together, this study provides key insights into the role of noise mechanisms in size homeostasis, and suggests an inextricable link between timer-based models of size control and heavy-tailed cell size distributions.

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“…In fixed cell volume models, one usually focuses on one single gene, the transcription rate is proportional to the gene copy number, and the translation rate is proportional to the number of mRNAs [30][31][32]. Fixed cell volume models imply that the gene number is the limiting factor in the transcription and the mRNA number is the limiting factor in the translation.…”

Section: Model Of Stochastic Gene Expressionmentioning

confidence: 99%

Homeostasis of protein and mRNA concentrations in growing cells

Lin

Amir

2018

Preprint

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Many experiments show that the concentrations of protein and mRNA fluctuate but on average are constant in growing cells, independent of the genome copy number. However, models of stochastic gene expression often assume constant gene expression rates that are proportional to the gene copy numbers, which are therefore incompatible with experiments. Here, we construct a minimal gene expression model to fill this gap. We show that (1) because the ribosomes translate all proteins, the concentrations of proteins are regulated in an exponentially growing cell volume; (2) the competition between genes for the RNA polymerases makes the gene expression rate independent of the genome number; (3) the fluctuations in ribosome level and cell density can generate a global extrinsic noise in protein concentrations; (4) correlations between mRNA and protein levels can be quantified.Despite the noisy nature of gene expression [1][2][3][4][5][6], various aspects of single cell dynamics, such as volume growth, are effectively deterministic. Recent single-cell measurements show that the growth of cell volumes is often exponential. These include bacteria [7][8][9][10], budding yeast [10][11][12][13][14] and mammalian cells [10,15]. Moreover, the mRNA and protein numbers are on average proportional to the cell volume throughout the cell cycle [16][17][18][19][20]. Therefore, the homeostasis of mRNA concentration and protein concentration is maintained in an exponentially growing cell volume. The exponential growths of mRNA and protein number indicate dynamical transcription and translation rates proportional to the cell volume, and also independent of the genome copy number. However, current gene expression models often assume a fixed cell volume with constant transcription and translation rates [1,5,[21][22][23], which are proportional to the gene copy number. Therefore, fixed cell volume models fail to reveal how cells keep constant mRNA and protein concentrations as the cell volume grows and the genome is replicated.The homeostasis of protein and mRNA concentrations imply that there must be a regulatory mechanism in place to prevent the accumulation of noise over time and to maintain a bounded distribution of concentrations. The goal of this work is to identify such a mechanism by developing a genome-wide coarse-grained model taking into account explicitly cell volume growth. We will consider an idealized cell in which genes are constitutively expressed for simplicity. The ubiquity of homeostasis suggests that the global machineries of gene expression, RNA polymerases (RNAPs) and ribosomes, should play a central role within the model. Indeed, as we will show later, the exponential growth of cell volume, protein and mRNA number originates from the auto-catalytic nature of ribosomes, the limiting factor in the translational process [24][25][26]. The bounded distributions of concentrations are a result of the fact that ribosomes make all proteins. The independence of the mRNA concentration of the genome copy number is a natural resul...

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Generation and filtering of gene expression noise by the bacterial cell cycle

Cited by 53 publications

References 50 publications

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria

Analysis of noise mechanisms in cell size control

Homeostasis of protein and mRNA concentrations in growing cells

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