Control of Bacillus subtilis Replication Initiation during Physiological Transitions and Perturbations

Differential growth regulates asymmetric size partitioning in Caulobacter crescentus

Cell size regulation has been extensively studied in symmetrically dividing cells, but the mechanisms underlying the control of size asymmetry in asymmetrically dividing bacteria remain elusive. Here, we examine the control of asymmetric division in Caulobacter crescentus, a bacterium that produces daughter cells with distinct fates and morphologies upon division. Through comprehensive analysis of multi-generational growth and shape data, we uncover a tightly regulated cell size partitioning mechanism. We find that errors in division site positioning are promptly corrected early in the division cycle through differential growth. Our analysis reveals a negative feedback between the size of daughter cell compartments and their growth rates, wherein the larger compartment grows slower to achieve a homeostatic size partitioning ratio at division. To explain these observations, we propose a mechanistic model of differential growth, in which equal amounts of growth regulators are partitioned into daughter cell compartments of unequal sizes and maintained over time via size-independent synthesis.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Tight coupling of cell width to nucleoid structure in Escherichia coli

Cell dimensions of rod-shaped bacteria such as Escherichia coli are connected to mass growth and chromosome replication. During their interdivision cycle (τ min), cells enlarge by elongation only, but at faster growth in richer media, they are also wider. Changes in width W upon nutritional shift-up (shortening τ) occur during the division process. The elusive signal directing the mechanism for W determination is likely related to the tightly linked duplications of the nucleoid (DNA) and the sacculus (peptidoglycan), the only two structures (macromolecules) existing in a single copy that are coupled, temporally and spatially. Six known parameters related to the nucleoid structure and replication are reasonable candidates to convey such a signal, all simple functions of the key number of replication positions n(=C/τ), the ratio between the rates of growth (τ-1) and of replication (C-1). The current analysis of available literature-recorded data discovered that, of these, nucleoid complexity NC[=(2n-1)/(n×ln2)] is by far the most likely parameter affecting cell width W. The exceedingly high correlations found between these two seemingly unrelated measures (NC and W) indicate that coupling between them is of major importance to the species' survival. As an exciting corollary, to the best of our knowledge, a new, indirect approach to estimate DNA replication rate is revealed. Potential involvement of DNA topoisomerases in W determination is also proposed and discussed.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

Nutritional control of bacterial DNA replication

All cells must ensure precise regulation of DNA replication initiation in coordination with growth rate and in response to nutrient availability. According to a long-standing model, DNA replication initiation is tightly coupled to cell mass increase in bacteria. Despite controversies regarding this model, recent studies have provided additional support of this idea. The exact molecular mechanisms linking cell growth with DNA replication under different nutrient conditions remain elusive. However, recent studies in Caulobacter crescentus and Escherichia coli have provided insights into the regulation of DNA replication initiation in response to starvation. These mechanisms include the starvation-dependent regulation of DnaA abundance as well as mechanisms involving the small signaling molecule (p)ppGpp. In this review, we discuss these mechanisms in the context of previous findings. We highlight species-dependent similarities and differences and consider the precise growth conditions, in which the different mechanisms are active.

Apparent simplicity and emergent robustness in the control of the Escherichia coli cell cycle

To examine how bacteria achieve robust cell proliferation across diverse conditions, we developed a method that quantifies 77 cell morphological, cell cycle, and growth phenotypes of a fluorescently labeled Escherichia coli strain and >800 gene deletion derivatives under multiple nutrient conditions. This approach revealed extensive phenotypic plasticity and deviating mutant phenotypes were often nutrient dependent. From this broad phenotypic landscape emerged simple and robust unifying rules (laws) that connect DNA replication initiation, nucleoid segregation, FtsZ ring formation, and cell constriction to specific aspects of cell size (volume, length, or added length) at the population level. Furthermore, completion of cell division followed the initiation of cell constriction after a constant time delay across strains and nutrient conditions, identifying cell constriction as a key control point for cell size determination. Our work provides a population-level description of the governing principles by which E. coli integrates cell cycle processes and growth rate with cell size to achieve its robust proliferative capability. A record of this paper's transparent peer review process is included in the supplemental information.

Bacterial Replication Initiation as Precision Control by Protein Counting

Balanced biosynthesis is the hallmark of bacterial cell physiology, where the concentrations of stable proteins remain steady. However, this poses a conceptual challenge to modeling the cell-cycle and cell-size controls in bacteria, as prevailing concentration-based eukaryote models are not directly applicable. In this study, we revisit and significantly extend the initiator-titration model, proposed 30 years ago, and we explain how bacteria precisely and robustly control replication initiation based on the mechanism of protein copy-number sensing. Using a mean-field approach, we first derive an analytical expression of the cell size at initiation based on three biological mechanistic control parameters for an extended initiator-titration model. We also study the stability of our model analytically and show that initiation can become unstable in multifork replication conditions. Using simulations, we further show that the presence of the conversion between active and inactive initiator protein forms significantly represses initiation instability. Importantly, the two-step Poisson process set by the initiator titration step results in significantly improved initiation synchrony with C V ~ 1 / N scaling rather than the standard 1 / N scaling in the Poisson process, where N is the total number of initiators required for initiation. Our results answer two long-standing questions in replication initiation: (i) Why do bacteria produce almost two orders of magnitude more DnaA, the master initiator proteins, than required for initiation? (ii) Why does DnaA exist in active (DnaA-ATP) and inactive (DnaA-ADP) forms if only the active form is competent for initiation? The mechanism presented in this work provides a satisfying general solution to how the cell can achieve precision control without sensing protein concentrations, with broad implications from evolution to the design of synthetic cells.

Replication initiation in bacteria: precision control based on protein counting

Balanced biosynthesis is the hallmark of bacterial cell physiology, where the concentrations of stable proteins remain steady. However, this poses a conceptual challenge to modeling the cell-cycle and cell-size controls in bacteria, as prevailing concentration-based eukaryote models are not directly applicable. In this study, we revisit and significantly extend the initiator-titration model, proposed thirty years ago, and explain how bacteria precisely and robustly control replication initiation based on the mechanism of protein copy-number sensing. Using a mean-field approach, we first derive an analytical expression of the cell size at initiation based on three biological mechanistic control parameters for an extended initiator-titration model. We also study the stability of our model analytically and show that initiation can become unstable in multifork replication conditions. Using simulations, we further show that the presence of the conversion between active and inactive initiator protein forms significantly represses initiation instability. Importantly, the two-step Poisson process set by the initiator titration step results in significantly improved initiation synchrony with C V ~ 1 / N scaling rather than the standard 1 / N scaling in the Poisson process, where N is the total number of initiators required for initiation. Our results answer two long-standing questions in replication initiation: (1) Why do bacteria produce almost two orders of magnitude more DnaA, the master initiator proteins, than required for initiation? (2) Why does DnaA exist in active (DnaA-ATP) and inactive (DnaA-ADP) forms if only the active form is competent for initiation? The mechanism presented in this work provides a satisfying general solution to how the cell can achieve precision control without sensing protein concentrations, with broad implications from evolution to the design of synthetic cells.

Dynamic proteome trade-offs regulate bacterial cell size and growth in fluctuating nutrient environments

Bacteria dynamically regulate cell size and growth to thrive in changing environments. While previous studies have characterized bacterial growth physiology at steady-state, a quantitative understanding of bacterial physiology in time-varying environments is lacking. Here we develop a quantitative theory connecting bacterial growth and division rates to proteome allocation in time-varying nutrient environments. In such environments, cell size and growth are regulated by trade-offs between prioritization of biomass accumulation or division, resulting in decoupling of single-cell growth rate from population growth rate. Specifically, bacteria transiently prioritize biomass accumulation over production of division machinery during nutrient upshifts, while prioritizing division over growth during downshifts. When subjected to pulsatile nutrient concentration, we find that bacteria exhibit a transient memory of previous metabolic states due to the slow dynamics of proteome reallocation. This allows for faster adaptation to previously seen environments and results in division control which is dependent on the time-profile of fluctuations.

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Proliferating bacterial cells exhibit stochastic growth and size dynamics, but the regulation of noise in bacterial growth and morphogenesis remains poorly understood. A quantitative understanding of morphogenetic noise control, and how it changes under different growth conditions, would provide better insights into cell-to-cell variability and intergenerational fluctuations in cell physiology. Using multigenerational growth and width data of single Escherichia coli and Caulobacter crescentus cells, we deduce the equations governing growth and size dynamics of rod-like bacterial cells. Interestingly, we find that both E. coli and C. crescentus cells deviate from exponential growth within the cell cycle. In particular, the exponential growth rate increases during the cell cycle irrespective of nutrient or temperature conditions. We propose a mechanistic model that explains the emergence of super-exponential growth from autocatalytic production of ribosomes coupled to the rate of cell elongation and surface area synthesis. Using this new model and statistical inference on large datasets, we construct the Langevin equations governing cell growth and size dynamics of E. coli cells in different nutrient conditions. The single-cell level model predicts how noise in intragenerational and intergenerational processes regulate variability in cell morphology and generation times, revealing quantitative strategies for cellular resource allocation and morphogenetic noise control in different growth conditions.

Growth rate is modulated by monitoring cell wall precursors in Bacillus subtilis

How bacteria link their growth rate to external nutrient conditions is unknown. To investigate how Bacillus subtilis cells alter the rate at which they expand their cell walls as they grow, we compared single-cell growth rates of cells grown under agar pads with the density of moving MreB filaments under a variety of growth conditions. MreB filament density increases proportionally with growth rate. We show that both MreB filament density and growth rate depend on the abundance of Lipid II and murAA, the first gene in the biosynthetic pathway creating the cell wall precursor Lipid II. Lipid II is sensed by the serine/threonine kinase PrkC, which phosphorylates RodZ and other proteins. We show that phosphorylated RodZ increases MreB filament density, which in turn increases cell growth rate. We also show that increasing the activity of this pathway in nutrient-poor media results in cells that elongate faster than wild-type cells, which means that B. subtilis contains spare 'growth capacity'. We conclude that PrkC functions as a cellular rheostat, enabling fine-tuning of cell growth rates in response to Lipid II in different nutrient conditions.

Cellular resource allocation strategies for cell size and shape control in bacteria

Bacteria are highly adaptive microorganisms that thrive in a wide range of growth conditions via changes in cell morphologies and macromolecular composition. How bacterial morphologies are regulated in diverse environmental conditions is a long-standing question. Regulation of cell size and shape implies control mechanisms that couple the growth and division of bacteria to their cellular environment and macromolecular composition. In the past decade, simple quantitative laws have emerged that connect cell growth to proteomic composition and the nutrient availability. However, the relationships between cell size, shape, and growth physiology remain challenging to disentangle and unifying models are lacking. In this review, we focus on regulatory models of cell size control that reveal the connections between bacterial cell morphology and growth physiology. In particular, we discuss how changes in nutrient conditions and translational perturbations regulate the cell size, growth rate, and proteome composition. Integrating quantitative models with experimental data, we identify the physiological principles of bacterial size regulation, and discuss the optimization strategies of cellular resource allocation for size control.

Effects of antibiotics on bacterial cell morphology and their physiological origins

Characterizing the physiological response of bacterial cells to antibiotic treatment is crucial for the design of antibacterial therapies and for understanding the mechanisms of antibiotic resistance. While the effects of antibiotics are commonly characterized by their minimum inhibitory concentrations or the minimum bactericidal concentrations, the effects of antibiotics on cell morphology and physiology are less well characterized. Recent technological advances in single-cell studies of bacterial physiology have revealed how different antibiotic drugs affect the physiological state of the cell, including growth rate, cell size and shape, and macromolecular composition. Here, we review recent quantitative studies on bacterial physiology that characterize the effects of antibiotics on bacterial cell morphology and physiological parameters. In particular, we present quantitative data on how different antibiotic targets modulate cellular shape metrics including surface area, volume, surface-to-volume ratio, and the aspect ratio. Using recently developed quantitative models, we relate cell shape changes to alterations in the physiological state of the cell, characterized by changes in the rates of cell growth, protein synthesis and proteome composition. Our analysis suggests that antibiotics induce distinct morphological changes depending on their cellular targets, which may have important implications for the regulation of cellular fitness under stress.

Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

Multiple mechanisms for overcoming lethal over-initiation of DNA replication

DNA replication is highly regulated and primarily controlled at the step of initiation. In bacteria, the replication initiator DnaA and the origin of replication oriC are the primary targets of regulation. Perturbations that increase or decrease replication initiation can cause a decrease in cell fitness. We found that multiple mechanisms, including an increase in replication elongation and a decrease in replication initiation, can compensate for lethal over-initiation. We found that in Bacillus subtilis, under conditions of rapid growth, loss of yabA, a negative regulator of replication initiation, caused a synthetic lethal phenotype when combined with the dnaA1 mutation that also causes replication over-initiation. We isolated several classes of suppressors that restored viability to dnaA1 ∆yabA double mutants. Some suppressors (relA, nrdR) stimulated replication elongation. Others (dnaC, cshA) caused a decrease in replication initiation. One class of suppressors decreased replication initiation in the dnaA1 ∆yabA mutant by causing a decrease in the amount of the replicative helicase, DnaC. We found that decreased levels of helicase in otherwise wild-type cells were sufficient to decrease replication initiation during rapid growth, indicating that the replicative helicase is limiting for replication initiation. Our results highlight the multiple mechanisms cells use to regulate DNA replication.

Antibiotic Resistance via Bacterial Cell Shape-Shifting

Bacteria have evolved to develop multiple strategies for antibiotic resistance by effectively reducing intracellular antibiotic concentrations or antibiotic binding affinities, but the role of cell morphology in antibiotic resistance remains poorly understood. By analyzing cell morphological data for different bacterial species under antibiotic stress, we find that bacteria increase or decrease the cell surface-to-volume ratio depending on the antibiotic target. Using quantitative modeling, we show that by reducing the surface-to-volume ratio, bacteria can effectively reduce the intracellular antibiotic concentration by decreasing antibiotic influx. The model further predicts that bacteria can increase the surface-to-volume ratio to induce the dilution of membrane-targeting antibiotics, in agreement with experimental data. Using a whole-cell model for the regulation of cell shape and growth by antibiotics, we predict shape transformations that bacteria can utilize to increase their fitness in the presence of antibiotics. We conclude by discussing additional pathways for antibiotic resistance that may act in synergy with shape-induced resistance.

Length-based separation of Bacillus subtilis bacterial populations by viscoelastic microfluidics

In this study, we demonstrated the label-free continuous separation and enrichment of Bacillus subtilis populations based on length using viscoelastic microfluidics. B. subtilis, a gram-positive, rod-shaped bacterium, has been widely used as a model organism and an industrial workhorse. B. subtilis can be arranged in different morphological forms, such as single rods, chains, and clumps, which reflect differences in cell types, phases of growth, genetic variation, and changing environmental factors. The ability to prepare B. subtilis populations with a uniform length is important for basic biological studies and efficient industrial applications. Here, we systematically investigated how flow rate ratio, poly(ethylene oxide) (PEO) concentration, and channel length affected the length-based separation of B. subtilis cells. The lateral positions of B. subtilis cells with varying morphologies in a straight rectangular microchannel were found to be dependent on cell length under the co-flow of viscoelastic and Newtonian fluids. Finally, we evaluated the ability of the viscoelastic microfluidic device to separate the two groups of B. subtilis cells by length (i.e., 1-5 μm and >5 μm) in terms of extraction purity (EP), extraction yield (EY), and enrichment factor (EF) and confirmed that the device could separate heterogeneous populations of bacteria using elasto-inertial effects.

A High-Content Microscopy Screening Identifies New Genes Involved in Cell Width Control in Bacillus subtilis

How cells control their shape and size is a fundamental question of biology. In most bacteria, cell shape is imposed by the peptidoglycan (PG) polymeric meshwork that surrounds the cell. Thus, bacterial cell morphogenesis results from the coordinated action of the proteins assembling and degrading the PG shell. Remarkably, during steady-state growth, most bacteria maintain a defined shape along generations, suggesting that error-proof mechanisms tightly control the process. In the rod-shaped model for the Gram-positive bacterium Bacillus subtilis, the average cell length varies as a function of the growth rate, but the cell diameter remains constant throughout the cell cycle and across growth conditions. Here, in an attempt to shed light on the cellular circuits controlling bacterial cell width, we developed a screen to identify genetic determinants of cell width in B. subtilis. Using high-content screening (HCS) fluorescence microscopy and semiautomated measurement of single-cell dimensions, we screened a library of ∼4,000 single knockout mutants. We identified 13 mutations significantly altering cell diameter, in genes that belong to several functional groups. In particular, our results indicate that metabolism plays a major role in cell width control in B. subtilis.

IMPORTANCE Bacterial shape is primarily dictated by the external cell wall, a vital structure that, as such, is the target of countless antibiotics. Our understanding of how bacteria synthesize and maintain this structure is therefore a cardinal question for both basic and applied research. Bacteria usually multiply from generation to generation while maintaining their progenies with rigorously identical shapes. This implies that the bacterial cells constantly monitor and maintain a set of parameters to ensure this perpetuation. Here, our study uses a large-scale microscopy approach to identify at the whole-genome level, in a model bacterium, the genes involved in the control of one of the most tightly controlled cellular parameters, the cell width.

Quantitative Examination of Five Stochastic Cell-Cycle and Cell-Size Control Models for Escherichia coli and Bacillus subtilis

We examine five quantitative models of the cell-cycle and cell-size control in Escherichia coli and Bacillus subtilis that have been proposed over the last decade to explain single-cell experimental data generated with high-throughput methods. After presenting the statistical properties of these models, we test their predictions against experimental data. Based on simple calculations of the defining correlations in each model, we first dismiss the stochastic Helmstetter-Cooper model and the Initiation Adder model, and show that both the Replication Double Adder (RDA) and the Independent Double Adder (IDA) model are more consistent with the data than the other models. We then apply a recently proposed statistical analysis method and obtain that the IDA model is the most likely model of the cell cycle. By showing that the RDA model is fundamentally inconsistent with size convergence by the adder principle, we conclude that the IDA model is most consistent with the data and the biology of bacterial cell-cycle and cell-size control. Mechanistically, the Independent Adder Model is equivalent to two biological principles: (i) balanced biosynthesis of the cell-cycle proteins, and (ii) their accumulation to a respective threshold number to trigger initiation and division.

Bacterial cell proliferation: from molecules to cells

Bacterial cell proliferation is highly efficient, both because bacteria grow fast and multiply with a low failure rate. This efficiency is underpinned by the robustness of the cell cycle and its synchronization with cell growth and cytokinesis. Recent advances in bacterial cell biology brought about by single-cell physiology in microfluidic chambers suggest a series of simple phenomenological models at the cellular scale, coupling cell size and growth with the cell cycle. We contrast the apparent simplicity of these mechanisms based on the addition of a constant size between cell cycle events (e.g. two consecutive initiation of DNA replication or cell division) with the complexity of the underlying regulatory networks. Beyond the paradigm of cell cycle checkpoints, the coordination between the DNA and division cycles and cell growth is largely mediated by a wealth of other mechanisms. We propose our perspective on these mechanisms, through the prism of the known crosstalk between DNA replication and segregation, cell division and cell growth or size. We argue that the precise knowledge of these molecular mechanisms is critical to integrate the diverse layers of controls at different time and space scales into synthetic and verifiable models.

Bacterial cell shape control by nutrient-dependent synthesis of cell division inhibitors

By analyzing cell size and shapes of the bacterium Bacillus subtilis under nutrient perturbations, protein depletion, and antibiotic treatments, we find that cell geometry is extremely robust, reflected in a well-conserved scaling relation between surface area (S) and volume (V), S∼V^γ, with γ=0.85. We develop a molecular model supported by single-cell simulations to predict that the surface-to-volume scaling exponent γ is regulated by nutrient-dependent production of metabolic enzymes that act as cell division inhibitors in bacteria. Using theory that is supported by experimental data, we predict the modes of cell shape transformations in different bacterial species and propose a mechanism of cell shape adaptation to different nutrient perturbations. For organisms with high surface-to-volume scaling exponent γ, such as B. subtilis, cell width is not sensitive to growth-rate changes, whereas organisms with low γ, such as Acinetobacter baumannii, cell shape adapts readily to growth-rate changes.

An updated toolkit for exploring bacterial cell wall structure and dynamics

The bacterial cell wall is made primarily from peptidoglycan, a complex biomolecule which forms a bag-like exoskeleton that envelops the cell. As it is unique to bacteria and typically essential for their growth and survival, it represents one of the most successful targets for antibiotics. Although peptidoglycan has been studied intensively for over 50 years, the past decade has seen major steps in our understanding of this molecule because of the advent of new analytical and imaging methods. Here, we outline the most recent developments in tools that have helped to elucidate peptidoglycan structure and dynamics.

Influence of Lactic Acid on Cell Cycle Progressions in Lactobacillus bulgaricus During Batch Culture

Lactic acid has been proved to inhibit the proliferation of lactic acid bacteria in the fermentation process. To shed light on the cell cycle alterations in acidic conditions, the cell division of Lactobacillus bulgaricus sp1.1 in batch culture was analyzed directly by implementing of the intracellular fluorescent tracking assay in different pH adjusted by lactic acid. Cell proliferation and cell division were investigated to be negatively controlled by the decrease of pH, and pH 4.1 was the critical condition of downregulating cell division but retains cell culturability. The cell area and cell length in pH 4.1 were examined by using fluorescent labeling, and they reduced to about 29.18-34.89% and 32.67-40% of cells cultured in the unacidified medium, respectively. The DNA replication initiation was undergoing prompted by the low extent of DNA condensation and higher expression of the dnaA gene in this critical pH. The results indicated that the cell cycle progressions of Lactobacillus bulgaricus sp1.1 in acidic conditions were arrested at intracellular biomass accumulation and cell division stage. These findings provide fundamental insight into cell cycle control of the acidic environment in Lactobacillus bulgaricus sp1.1.

General quantitative relations linking cell growth and the cell cycle in Escherichia coli

Growth laws emerging from studies of cell populations provide essential constraints on the global mechanisms that coordinate cell growth^1-3. The foundation of bacterial cell cycle studies relies on two interconnected dogmas that were proposed more than 50 years ago-the Schaechter-Maaloe-Kjeldgaard growth law that relates cell mass to growth rate¹ and Donachie's hypothesis of a growth-rate-independent initiation mass⁴. These dogmas spurred many efforts to understand their molecular bases and physiological consequences^5-14. Although they are generally accepted in the fast-growth regime, that is, for doubling times below 1 h, extension of these dogmas to the slow-growth regime has not been consistently achieved. Here, through a quantitative physiological study of Escherichia coli cell cycles over an extensive range of growth rates, we report that neither dogma holds in either the slow- or fast-growth regime. In their stead, linear relations between the cell mass and the rate of chromosome replication-segregation were found across the range of growth rates. These relations led us to propose an integral-threshold model in which the cell cycle is controlled by a licensing process, the rate of which is related in a simple way to chromosomal dynamics. These results provide a quantitative basis for predictive understanding of cell growth-cell cycle relationships.