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

Mechanistic basis for non-exponential bacterial growth

Bacterial populations typically exhibit exponential growth under resource-rich conditions, yet individual cells often deviate from this pattern. Recent work has shown that the elongation rates of Escherichia coli and Caulobacter crescentus increase throughout the cell cycle (super-exponential growth), while Bacillus subtilis displays a mid-cycle minimum (convex growth), and Mycobacterium tuberculosis grows linearly. Here, we develop a single-cell model linking gene expression, proteome allocation, and mass growth to explain these diverse growth trajectories. By calibrating model parameters with experimental data, we show that DNA-proportional mRNA transcription produces near-exponential growth, whereas deviations from this proportionality yield the observed non-exponential growth patterns. Analysis of gene expression perturbations reveals that ribosome expression primarily controls dry mass growth rate, whereas envelope expression more strongly affects cell elongation rate. Fitting our model to single-cell experimental data reproduces convex, super-exponential, and linear modes of growth, demonstrating how envelope and ribosome expression schedules drive cell-cycle-specific behaviors. These findings provide a mechanistic basis for non-exponential single-cell growth and offer insights into how bacterial cells dynamically regulate elongation rates within each generation.

Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population

Ribosomes are responsible for the synthesis of proteins, the major component of cellular biomass. Classical experiments have established a linear relationship between the fraction of resources invested in ribosomal proteins and the rate of balanced growth of a microbial population. Very little is known, however, about how the investment in ribosomes varies over individual cells in a population. We therefore extended the study of ribosomal resource allocation from populations to single cells, using a combination of time-lapse fluorescence microscopy and statistical inference. We found a large variability of ribosome concentrations and growth rates in conditions of balanced growth of the model bacterium Escherichia coli in a given medium, which cannot be accounted for by the population-level growth law. A large variability in the allocation of resources to ribosomes was also found during the transition of the bacteria from a poor to a rich growth medium. While some cells immediately adapt their ribosome synthesis rate to the new environment, others do so only gradually. Our results thus reveal a range of strategies for investing resources in the molecular machines at the heart of cellular self-replication. This raises the fundamental question whether the observed variability is an intrinsic consequence of the stochastic nature of the underlying biochemical processes or whether it improves the fitness of Escherichia coli in its natural environment.

Genome concentration limits cell growth and modulates proteome composition in Escherichia coli

Defining the cellular factors that drive growth rate and proteome composition is essential for understanding and manipulating cellular systems. In bacteria, ribosome concentration is known to be a constraining factor of cell growth rate, while gene concentration is usually assumed not to be limiting. Here, using single-molecule tracking, quantitative single-cell microscopy, and modeling, we show that genome dilution in Escherichia coli cells arrested for DNA replication limits total RNA polymerase activity within physiological cell sizes across tested nutrient conditions. This rapid-onset limitation on bulk transcription results in sub-linear scaling of total active ribosomes with cell size and sub-exponential growth. Such downstream effects on bulk translation and cell growth are near-immediately detectable in a nutrient-rich medium, but delayed in nutrient-poor conditions, presumably due to cellular buffering activities. RNA sequencing and tandem-mass-tag mass spectrometry experiments further reveal that genome dilution remodels the relative abundance of mRNAs and proteins with cell size at a global level. Altogether, our findings indicate that chromosome concentration is a limiting factor of transcription and a global modulator of the transcriptome and proteome composition in E. coli. Experiments in Caulobacter crescentus and comparison with eukaryotic cell studies identify broadly conserved DNA concentration-dependent scaling principles of gene expression.

Single-cell imaging of the Mycobacterium tuberculosis cell cycle reveals linear and heterogenous growth

Difficulties in antibiotic treatment of Mycobacterium tuberculosis (Mtb) are partly thought to be due to heterogeneity in growth. Although the ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence and drug responses, single-cell growth and cell cycle behaviours of Mtb are poorly characterized. Here we use time-lapse, single-cell imaging of Mtb coupled with mathematical modelling to observe asymmetric growth and heterogeneity in cell size, interdivision time and elongation speed. We find that, contrary to Mycobacterium smegmatis, Mtb initiates cell growth not only from the old pole but also from new poles or both poles. Whereas most organisms grow exponentially at the single-cell level, Mtb has a linear growth mode. Our data show that the growth behaviour of Mtb diverges from that of model bacteria, provide details into how Mtb grows and creates heterogeneity and suggest that growth regulation may also diverge from that in other bacteria.

Investigating the role of membrane lipid composition differences on spray drying survival in Lactobacillus bulgaricus using non-targeted Lipidomics

The cell membrane, consisting of a phospholipid bilayer, is an important defense system of lactic acid bacteria (LAB) against adverse conditions. However, this membrane gets damaged during the process of spray drying of LAB into powder. In this study, two strains of Lactobacillus bulgaricus L9-7 and L4-2-12 with significantly different survival rates of about 22.49% and 0.43% after spray drying were explored at the cell membrane level. A total of 65 significantly different lipid species were screened from the cell membranes of two strains, with cardiolipin (CL) 15:1_22:6_24:0_28:0 being the crucial lipid species affecting membrane resistance. Finally, the KEGG enrichment analysis revealed that glycerophospholipid metabolism was the most predominant pathway, and eleven lipid species were annotated, including CL. Overall, this paper provides valuable insights into enhancing the heat tolerance of LAB.

Tip extension and simultaneous multiple fission in a filamentous bacterium

Organisms display an immense variety of shapes, sizes, and reproductive strategies. At microscopic scales, bacterial cell morphology and growth dynamics are adaptive traits that influence the spatial organization of microbial communities. In one such community-the human dental plaque biofilm-a network of filamentous Corynebacterium matruchotii cells forms the core of bacterial consortia known as hedgehogs, but the processes that generate these structures are unclear. Here, using live-cell time-lapse microscopy and fluorescent D-amino acids to track peptidoglycan biosynthesis, we report an extraordinary example of simultaneous multiple division within the domain Bacteria. We show that C. matruchotii cells elongate at one pole through tip extension, similar to the growth strategy of soil-dwelling Streptomyces bacteria. Filaments elongate rapidly, at rates more than five times greater than other closely related bacterial species. Following elongation, many septa form simultaneously, and each cell divides into 3 to 14 daughter cells, depending on the length of the mother filament. The daughter cells then nucleate outgrowth of new thinner vegetative filaments, generating the classic "whip handle" morphology of this taxon. Our results expand the known diversity of bacterial cell cycles and help explain how this filamentous bacterium can compete for space, access nutrients, and form important interspecies interactions within dental plaque.

Antibacterial activity of lysozyme after association with carboxymethyl konjac glucomannan

The unique high isoelectric point of lysozyme (LYZ) restricts its application in composite antibacterial coating due to the unfavorable liability to electrostatic interaction with other components. In this work, the antibacterial activity of a dispersible LYZ-carboxymethyl konjac glucomannan (CMKGM) polyelectrolyte complex was evaluated. Kinetic analysis revealed that, compared with free LYZ, the complexed enzyme exhibited decreased affinity (Km) but markedly increased Vmax against Micrococcus lysodeikticus, and QCM and dynamic light scattering analysis confirmed that the complex could bind with the substrate but in a much lower ratio. The complexation with CMKGM did not alter the antibacterial spectrum of LYZ, and the complex exerted antibacterial function by delaying the logarithmic growth phase and impairing the cell integrity of Staphylococcus aureus. Since the LYZ-CMKGM complex is dispersible in water and could be assembled easily, it has great potential as an edible coating in food preservation.

Deciphering the Relationship between Cell Growth and Cell Cycle in Individual Escherichia coli Cells by Flow Cytometry

Accurate coordination of chromosome replication and cell division is essential for cellular processes, yet the regulatory mechanisms governing the bacterial cell cycle remain contentious. The lack of quantitative data connecting key cell cycle players at the single-cell level across large samples hinders consensus. Employing high-throughput flow cytometry, we quantitatively correlated the expression levels of key cell cycle proteins (FtsZ, MreB, and DnaA) with DNA content in individual bacteria. Our findings reveal distinct correlations depending on the chromosome number (CN), specifically whether CN ≤2 or ≥4, unveiling a mixed regulatory scenario in populations where CN of 2 or 4 coexist. We observed function-dependent regulations for these key proteins across nonoverlapping division cycles and various nutrient conditions. Notably, a logarithmic relationship between total protein content and replication origin number across nutrient conditions suggests a unified mechanism governing cell cycle progression, confirming the applicability of Schaechter's growth law to cells with CN ≥4. For the first time, we established a proportional relationship between the synthesis rates of key cell cycle proteins and chromosome dynamics in cells with CN ≥4. Drug experiments highlighted CN 2 and 4 as pivotal turning points influencing cellular resource allocation. This high-throughput, single-cell analysis provides interconnected quantitative insights into key molecular events, facilitating a predictive understanding of the relationship between cell growth and cell cycle.

Whole-cell metabolic control analysis

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species.

Nearly maximal information gain due to time integration in central dogma reactions

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth

The physiology of biological cells evolved under physical and chemical constraints, such as mass conservation across the network of biochemical reactions, nonlinear reaction kinetics, and limits on cell density. For unicellular organisms, the fitness that governs this evolution is mainly determined by the balanced cellular growth rate. We previously introduced growth balance analysis (GBA) as a general framework to model and analyze such nonlinear systems, revealing important analytical properties of optimal balanced growth states. It has been shown that at optimality, only a minimal subset of reactions can have nonzero flux. However, no general principles have been established to determine if a specific reaction is active at optimality. Here, we extend the GBA framework to study the optimality of each biochemical reaction, and we identify the mathematical conditions determining whether a reaction is active or not at optimal growth in a given environment. We reformulate the mathematical problem in terms of a minimal number of dimensionless variables and use the Karush-Kuhn-Tucker (KKT) conditions to identify fundamental principles of optimal resource allocation in GBA models of any size and complexity. Our approach helps to identify from first principles the economic values of biochemical reactions, expressed as marginal changes in cellular growth rate; these economic values can be related to the costs and benefits of proteome allocation into the reactions' catalysts. Our formulation also generalizes the concepts of Metabolic Control Analysis to models of growing cells. We show how the extended GBA framework unifies and extends previous approaches of cellular modeling and analysis, putting forward a program to analyze cellular growth through the stationarity conditions of a Lagrangian function. GBA thereby provides a general theoretical toolbox for the study of fundamental mathematical properties of balanced cellular growth.

Microbial models of development: Inspiration for engineering self-assembled synthetic multicellularity

While the field of synthetic developmental biology has traditionally focused on the study of the rich developmental processes seen in metazoan systems, an attractive alternate source of inspiration comes from microbial developmental models. Microbes face unique lifestyle challenges when forming emergent multicellular collectives. As a result, the solutions they employ can inspire the design of novel multicellular systems. In this review, we dissect the strategies employed in multicellular development by two model microbial systems: the cellular slime mold Dictyostelium discoideum and the biofilm-forming bacterium Bacillus subtilis. Both microbes face similar challenges but often have different solutions, both from metazoan systems and from each other, to create emergent multicellularity. These challenges include assembling and sustaining a critical mass of participating individuals to support development, regulating entry into development, and assigning cell fates. The mechanisms these microbial systems exploit to robustly coordinate development under a wide range of conditions offer inspiration for a new toolbox of solutions to the synthetic development community. Additionally, recreating these phenomena synthetically offers a pathway to understanding the key principles underlying how these behaviors are coordinated naturally.

Mycobacterium tuberculosis grows linearly at the single-cell level with larger variability than model organisms

The ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence, and drug response. Yet, we do not understand the growth and cell cycle behaviors of Mycobacterium tuberculosis (Mtb), a slow-growing pathogen, at the single-cell level. Here, we use time-lapse imaging and mathematical modeling to characterize these fundamental properties of Mtb. Whereas most organisms grow exponentially at the single-cell level, we find that Mtb exhibits a unique linear growth mode. Mtb growth characteristics are highly variable from cell-to-cell, notably in their growth speeds, cell cycle timing, and cell sizes. Together, our study demonstrates that growth behavior of Mtb diverges from what we have learned from model bacteria. Instead, Mtb generates a heterogeneous population while growing slowly and linearly. Our study provides a new level of detail into how Mtb grows and creates heterogeneity, and motivates more studies of growth behaviors in bacterial pathogens.

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.

Multiple timescales in bacterial growth homeostasis

In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are "sloppy" with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define "stiff" combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

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.

Distinguishing different modes of growth using single-cell data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

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.

Single-cell growth inference of Corynebacterium glutamicum reveals asymptotically linear growth

Regulation of growth and cell size is crucial for the optimization of bacterial cellular function. So far, single bacterial cells have been found to grow predominantly exponentially, which implies the need for tight regulation to maintain cell size homeostasis. Here, we characterize the growth behavior of the apically growing bacterium Corynebacterium glutamicum using a novel broadly applicable inference method for single-cell growth dynamics. Using this approach, we find that C. glutamicum exhibits asymptotically linear single-cell growth. To explain this growth mode, we model elongation as being rate-limited by the apical growth mechanism. Our model accurately reproduces the inferred cell growth dynamics and is validated with elongation measurements on a transglycosylase deficient ΔrodA mutant. Finally, with simulations we show that the distribution of cell lengths is narrower for linear than exponential growth, suggesting that this asymptotically linear growth mode can act as a substitute for tight division length and division symmetry regulation.

Searching for principles of microbial physiology

Why do evolutionarily distinct microorganisms display similar physiological behaviours? Why are transitions from high-ATP yield to low(er)-ATP yield metabolisms so widespread across species? Why is fast growth generally accompanied with low stress tolerance? Do these regularities occur because most microbial species are subject to the same selective pressures and physicochemical constraints? If so, a broadly-applicable theory might be developed that predicts common microbiological behaviours. Microbial systems biologists have been working out the contours of this theory for the last two decades, guided by experimental data. At its foundations lie basic principles from evolutionary biology, enzyme biochemistry, metabolism, cell composition and steady-state growth. The theory makes predictions about fitness costs and benefits of protein expression, physicochemical constraints on cell growth and characteristics of optimal metabolisms that maximise growth rate. Comparisons of the theory with experimental data indicates that microorganisms often aim for maximisation of growth rate, also in the presence of stresses; they often express optimal metabolisms and metabolic proteins at optimal concentrations. This review explains the current status of the theory for microbiologists; its roots, predictions, experimental evidence and future directions.

Disentangling Intrinsic and Extrinsic Gene Expression Noise in Growing Cells

Gene expression is a stochastic process. Despite the increase of protein numbers in growing cells, the protein concentrations are often found to be confined within small ranges throughout the cell cycle. Generally, the noise in protein concentration can be decomposed into an intrinsic and an extrinsic component, where the former vanishes for high expression levels. Considering the time trajectory of protein concentration as a random walker in the concentration space, an effective restoring force (with a corresponding "spring constant") must exist to prevent the divergence of concentration due to random fluctuations. In this work, we prove that the magnitude of the effective spring constant is directly related to the fraction of intrinsic noise in the total protein concentration noise. We show that one can infer the magnitude of intrinsic, extrinsic, and measurement noises of gene expression solely based on time-resolved data of protein concentration, without any a priori knowledge of the underlying gene expression dynamics. We apply this method to experimental data of single-cell bacterial gene expression. The results allow us to estimate the average copy numbers and the translation burst parameters of the studied proteins.

Non-genetic inheritance restraint of cell-to-cell variation

Heterogeneity in physical and functional characteristics of cells (e.g. size, cycle time, growth rate, protein concentration) proliferates within an isogenic population due to stochasticity in intracellular biochemical processes and in the distribution of resources during divisions. Conversely, it is limited in part by the inheritance of cellular components between consecutive generations. Here we introduce a new experimental method for measuring proliferation of heterogeneity in bacterial cell characteristics, based on measuring how two sister cells become different from each other over time. Our measurements provide the inheritance dynamics of different cellular properties, and the 'inertia' of cells to maintain these properties along time. We find that inheritance dynamics are property specific and can exhibit long-term memory (∼10 generations) that works to restrain variation among cells. Our results can reveal mechanisms of non-genetic inheritance in bacteria and help understand how cells control their properties and heterogeneity within isogenic cell populations.

Strongly oversized fission yeast cells lack any size control and tend to grow linearly rather than bilinearly

During the mitotic cycle, the rod-shaped fission yeast cells grow only at their tips. The newly born cells grow first unipolarly at their old end, but later in the cycle, the 'new end take-off' event occurs, resulting in bipolar growth. Photographs were taken of several steady-state and induction synchronous cultures of different cell cycle mutants of fission yeast, generally larger than wild type. Length measurements of many individual cells were performed from birth to division. For all the measured growth patterns, three different functions (linear, bilinear and exponential) were fitted, and the most adequate one was chosen by using specific statistical criteria, considering the altering parameter numbers. Although the growth patterns were heterogeneous in all the cultures studied, we could find some tendencies. In cultures with sufficiently wide size distribution, cells large enough at birth tend to grow linearly, whereas the other cells generally tend to grow bilinearly. We have found that among bilinearly growing cells, the larger they are at birth, the rate change point during their bilinear pattern occurs earlier in the cycle. This shifting near to the beginning of the cycle might finally cause a linear pattern, if the cells are even larger. In all of the steady-state cultures studied, a size control mechanism operates to maintain homeostasis. By contrast, strongly oversized cells of induction synchronous cultures lack any sizer, and their cycle rather behaves like an adder. We could determine the critical cell size for both the G1 and G2 size controls, where these mechanisms become cryptic. TAKE AWAY: Most individual fission yeast cells in steady-state cultures grow bilinearly. In strongly oversized fission yeast cells, linear growth dominates over bilinear. Above birth length thresholds, both the G1 and G2 size controls become cryptic.

Bacterial Growth: Cell-Cycle Dependent Growth-Rate Homeostasis

In the study of bacterial growth, the prevailing conclusion is that cells grow exponentially at a constant rate throughout the cell cycle. Using a new approach, Nordholt et al. reveal that bacterial growth is biphasic; immediately after division, the cell grows linearly, transitioning to exponential growth towards the end of the cell cycle.

Plasticity of Cyanobacterial Thylakoid Microdomains Under Variable Light Conditions

Photosynthetic light reactions proceed in thylakoid membranes (TMs) due to the activity of pigment-protein complexes. These complexes are heterogeneously organized into granal/stromal thylakoids (in plants) or into recently identified cyanobacterial microdomains (MDs). MDs are characterized by specific ratios of photosystem I (PSI), photosystem II (PSII), and phycobilisomes (PBS) and they are visible as sub-micrometer sized areas with different fluorescence ratios. In this report, the process of long-term plasticity in cyanobacterial thylakoid MDs has been explored under variable growth light conditions using Synechocystis sp. PCC6803 expressing YFP tagged PSI. TM organization into MDs has been observed for all categorized shapes of cells independently of their stage in cell cycle. The heterogeneous PSI, PSII, and PBS thylakoid areas were also identified under two types of growth conditions: at continuous light (CL) and at light-dark (L-D) cycle. The acclimation from CL to L-D cycle changed spatial distribution of photosystems, in particular PSI became more evenly distributed in thylakoids under L-D cycle. The process of the spatial PSI (and partially also PSII) redistribution required 1 week and was accompanied by temporal appearance of PBS decoupling probably caused by the re-organization of photosystems. The overall acclimation we observed was defined as TM plasticity as it resembles higher plants grana/stroma reorganization at variable growth light conditions. In addition, we observed large cell to cell variability in the actual MDs organization. It leads us to suggest that the plasticity, and cell to cell variability in MDs could be a manifestation of phenotypic heterogeneity, a recently broadly discussed phenomenon for prokaryotes.