Biphasic Cell-Size and Growth-Rate Homeostasis by Single Bacillus subtilis Cells

Nordholt et al. show how growth-rate and protein-concentration homeostasis is achieved in Bacillus subtilis, through systematic rate adjustments during cell-cycle progression. They identify two distinct growth regimes in this bacterium's cell cycle, suggesting a link between growth-rate dynamics and size homeostasis

Effects of growth rate and promoter activity on single-cell protein expression

Protein expression in a single cell depends on its global physiological state. Moreover, genetically-identical cells exhibit variability (noise) in protein expression, arising from the stochastic nature of biochemical processes, cell growth and division. While it is well understood how cellular growth rate influences mean protein expression, little is known about the relationship between growth rate and noise in protein expression. Here we quantify this relationship in Bacillus subtilis by a novel combination of experiments and theory. We measure the effects of promoter activity and growth rate on the expression of a fluorescent protein in single cells. We disentangle the observed protein expression noise into protein-specific and systemic contributions, using theory and variance decomposition. We find that noise in protein expression depends solely on mean expression levels, regardless of whether expression is set by promoter activity or growth rate, and that noise increases linearly with growth rate. Our results can aid studies of (synthetic) gene circuits of single cells and their condition dependence

Taking chances and making mistakes:non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation

Update on transparent testa mutants from Arabidopsis thaliana: characterisation of new alleles from an isogenic collection

Appelhagen I, Thiedig K, Nordholt N, et al. Update on transparent testa mutants from Arabidopsis thaliana: characterisation of new alleles from an isogenic collection. Planta. 2014;240(5):955-970.MAIN CONCLUSION: We present a comprehensive overview on flavonoid-related phenotypes of A. thaliana tt and tds mutants, provide tools for their characterisation, increase the number of available alleles and demonstrate that tds3 is allelic to tt12 and tds5 to aha10. Flavonoid biosynthesis is one of the best-studied secondary metabolite pathways in plants. In the model system Arabidopsis thaliana it leads to the synthesis of three phenolic compound classes: flavonol glycosides, anthocyanins and proanthocyanidins (PAs). PAs appear brown in their oxidised polymeric forms, and most A. thaliana mutants impaired in flavonoid accumulation were identified through screens for lack of this seed coat pigmentation. These mutants are referred to as transparent testa (tt) or tannin-deficient seed (tds). More than 20 mutants of these types have been published, probably representing most of the genes relevant for PA accumulation in A. thaliana. However, data about the genes involved in PA deposition or oxidation are still rather scarce. Also, for some of the known mutants it is unclear if they represent additional loci or if they are allelic to known genes. For the present study, we have performed a systematic phenotypic characterisation of almost all available tt and tds mutants and built a collection of mutants in the genetic background of the accession Columbia to minimise effects arising from ecotype variation. We have identified a novel tt6 allele from a forward genetic screen and demonstrated that tds3 is allelic to tt12 and tds5 to aha10

Metabolism at Evolutionary Optimal States

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization

ლელო N227 (2332

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Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.BN/Marileen Dogterom La