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Abstract The mechanisms by which two sister chromosomes separate and partition into daughter cells in bacteria remain poorly understood. A recent theoretical model has proposed that out-of-equilibrium processes associated with mRNA–ribosome (polysome) dynamics play a significant role in this process. Here we investigate the role of ribosomal dynamics on nucleoid segregation and separation inEscherichia coliusing high-throughput fluorescence microscopy in microfluidic devices. We compare our experimental observations with predictions from a reaction-diffusion model that includes the interactions among ribosomal subunits, polysomes, and chromosomal DNA. Our results show that the non-equilibrium behavior of mRNA and ribosomes causes them to aggregate at the midcell and this process contributes to the separation of the two daughter chromosomes. However, this effect is considerably weaker than that predicted by the model. Rather than relying solely on active mRNA–ribosome dynamics, our data suggest that the closing division septum via steric interactions and potentially entropic forces between two DNA strands coupled to cell elongation act as additional mechanisms to ensure faithful partitioning of the nucleoids to two daughter cells. SignificanceThe mitotic spindle separates chromosomes in eukaryotic cells, but bacteria lack this structure. It remains unclear how bacterial chromosomes partition prior to cell division. It has been hypothesized that non-equilibrium dynamics of polysomes, that is mRNA–ribosome complexes, actively drive the separation of bacterial chromosomes. Using quantitative microscopy combined with computational modeling, we show that polysome dynamics significantly contribute to chromosome segregation inEscherichia colibut this process does not constitute the sole mechanism. Our findings suggest the closing division septum via steric interactions and potentially entropic forces between two DNA strands act as additional mechanisms. 

How cells regulate their cell cycles is a central question for cell biology. Models of cell size homeostasis have been proposed for bacteria, archaea, yeast, plant, and mammalian cells. New experiments bring forth high volumes of data suitable for testing existing models of cell size regulation and proposing new mechanisms. In this paper, we use conditional independence tests in conjunction with data of cell size at key cell cycle events (birth, initiation of DNA replication, and constriction) in the model bacterium Escherichia coli to select between the competing cell cycle models. We find that in all growth conditions that we study, the division event is controlled by the onset of constriction at midcell. In slow growth, we corroborate a model where replication-related processes control the onset of constriction at midcell. In faster growth, we find that the onset of constriction is affected by additional cues beyond DNA replication. Finally, we also find evidence for the presence of additional cues triggering initiations of DNA replication apart from the conventional notion where the mother cells solely determine the initiation event in the daughter cells via an adder per origin model. The use of conditional independence tests is a different approach in the context of understanding cell cycle regulation and it can be used in future studies to further explore the causal links between cell events. 

All cells – from bacteria to humans – tightly control their size as they grow and divide. Cells can also change the speed at which they grow, and the pattern of how fast a cell grows with time is called ‘mode of growth’. Mode of growth can be ‘linear’, when cells increase their size at a constant rate, or ‘exponential’, when cells increase their size at a rate proportional to their current size. A cell’s mode of growth influences its inner workings, so identifying how a cell grows can reveal information about how a cell will behave. Scientists can measure the size of cells as they age and identify their mode of growth using single cell imaging techniques. Unfortunately, the statistical methods available to analyze the large amounts of data generated in these experiments can lead to incorrect conclusions. Specifically, Kar et al. found that scientists had been using specific types of plots to analyze growth data that were prone to these errors, and may lead to misinterpreting exponential growth as linear and vice versa. This discrepancy can be resolved by ensuring that the plots used to determine the mode of growth are adequate for this analysis. But how can the adequacy of a plot be tested? One way to do this is to generate synthetic data from a known model, which can have a specific and known mode of growth, and using this data to test the different plots. Kar et al. developed such a ‘generative model’ to produce synthetic data similar to the experimental data, and used these data to determine which plots are best suited to determine growth mode. Once they had validated the best statistical methods for studying mode of growth, Kar et al. applied these methods to growth data from the bacterium Escherichia coli . This showed that these cells have a form of growth called ‘super-exponential growth’. These findings identify a strategy to validate statistical methods used to analyze cell growth data. Furthermore, this strategy – the use of generative models to produce synthetic data to test the accuracy of statistical methods – could be used in other areas of biology to validate statistical approaches.