A universal trade-off between growth and lag in fluctuating environments

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.

Shaping of microbial phenotypes by trade-offs

Growth rate maximization is an important fitness strategy for microbes. However, the wide distribution of slow-growing oligotrophic microbes in ecosystems suggests that rapid growth is often not favored across ecological environments. In many circumstances, there exist trade-offs between growth and other important traits (e.g., adaptability and survival) due to physiological and proteome constraints. Investments on alternative traits could compromise growth rate and microbes need to adopt bet-hedging strategies to improve fitness in fluctuating environments. Here we review the mechanistic role of trade-offs in controlling bacterial growth and further highlight its ecological implications in driving the emergences of many important ecological phenomena such as co-existence, population heterogeneity and oligotrophic/copiotrophic lifestyles.

Biodiversity is enhanced by sequential resource utilization and environmental fluctuations via emergent temporal niches

How natural communities maintain their remarkable biodiversity and which species survive in complex communities are central questions in ecology. Resource competition models successfully explain many phenomena but typically predict only as many species as resources can coexist. Here, we demonstrate that sequential resource utilization, or diauxie, with periodic growth cycles can support many more species than resources. We explore how communities modify their own environments by sequentially depleting resources to form sequences of temporal niches, or intermediately depleted environments. Biodiversity is enhanced when community-driven or environmental fluctuations modulate the resource depletion order and produce different temporal niches on each growth cycle. Community-driven fluctuations under constant environmental conditions are rare, but exploring them illuminates the temporal niche structure that emerges from sequential resource utilization. With environmental fluctuations, we find most communities have more stably coexisting species than resources with survivors accurately predicted by the same temporal niche structure and each following a distinct optimal strategy. Our results thus present a new niche-based approach to understanding highly diverse fluctuating communities.

Understanding and computational design of genetic circuits of metabolic networks

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Grow now, pay later: When should a bacterium go into debt?

Microbes grow in a wide variety of environments and must balance growth and stress resistance. Despite the prevalence of such trade-offs, understanding of their role in nonsteady environments is limited. In this study, we introduce a mathematical model of "growth debt," where microbes grow rapidly initially, paying later with slower growth or heightened mortality. We first compare our model to a classical chemostat experiment, validating our proposed dynamics and quantifying Escherichia coli's stress resistance dynamics. Extending the chemostat theory to include serial-dilution cultures, we derive phase diagrams for the persistence of "debtor" microbes. We find that debtors cannot coexist with nondebtors if "payment" is increased mortality but can coexist if it lowers enzyme affinity. Surprisingly, weak noise considerably extends the persistence of resistance elements, pertinent for antibiotic resistance management. Our microbial debt theory, broadly applicable across many environments, bridges the gap between chemostat and serial dilution systems.

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

The bacterial lag phase is a key period for resuming growth. Despite its significance, the lag phase remains underexplored, particularly in environmental bacteria. Here, we explore the lag phase of the model marine bacterium Phaeobacter inhibens when it transitions from starvation to growth with a microalgal partner. Utilizing transcriptomics and 13 C-labeled metabolomics, our study reveals that methylated compounds, which are abundantly produced by microalgae, shorten the bacterial lag phase. Our findings underscore the significance of methyl groups as a limiting factor during the lag phase and demonstrate that methyl groups can be harvested from algal compounds and assimilated through the methionine cycle. Furthermore, we show that methylated compounds, characteristic of photosynthetic organisms, induce variable reductions in lag times among bacteria associated with algae and plants. These findings highlight the adjustability of the bacterial lag phase and emphasize the importance of studying bacteria in an environmental context.

One-Sentence Summary

Bacteria use algal compounds as a metabolic shortcut to transition from starvation to growth.

A parallel glycolysis provides a selective advantage through rapid growth acceleration

Glycolysis is a universal metabolic process that breaks down glucose to produce adenosine triphosphate (ATP) and biomass precursors. The Entner-Doudoroff (ED) pathway is a glycolytic pathway that parallels textbook glycolysis but yields half as much ATP. Accordingly, in organisms that possess both glycolytic pathways (for example, Escherichia coli), its raison d'être remains a mystery. In this study, we found that the ED pathway provides a selective advantage during growth acceleration. Upon carbon and nitrogen upshifts, E. coli accelerates growth faster with than without the ED pathway. Concurrent isotope tracing reveals that the ED pathway flux increases faster than that of textbook glycolysis. We attribute the fast response time of the ED pathway to its strong thermodynamic driving force and streamlining of glucose import. Intermittent nutrient supply manifests the evolutionary advantage of the parallel glycolysis; thus, the dynamic nature of an ostensibly redundant pathway's role in promoting rapid adaptation constitutes a metabolic design principle.

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

Rediversification following ecotype isolation reveals hidden adaptive potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about whether communities can regenerate ecological diversity following ecotype removal or extinction and how the rediversified communities would compare to the original ones. Here, we show that simple two-ecotype communities from the E. coli long-term evolution experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different from the original community in ways relevant to the mechanism of ecotype coexistence-for example, in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, although the rediversified community showed unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species where there are even more potential niches, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.

Integrated control of bacterial growth and stress response by (p)ppGpp in Escherichia coli: A seesaw fashion

To thrive in nature, bacteria have to reproduce efficiently under favorable conditions and persist during stress. The global strategy that integrates the growth control and stress response remains to be explored. Here, we find that a moderate induction of (p)ppGpp reduces growth rate but significantly enhances the stress tolerance of E. coli, resulting from a global resource re-allocation from ribosome synthesis to the synthesis of stress-responsive proteins. Strikingly, the activation of stress response by (p)ppGpp is still largely retained in the absence of RpoS. In addition, (p)ppGpp induction could activate the catabolism of alanine and arginine, facilitating the adaption of bacteria to nutrient downshift. Our work demonstrates that the activation of stress response by (p)ppGpp could occur in an RpoS-independent manner and (p)ppGpp enables bacteria to integrate the control of growth and stress response in a seesaw fashion, thus acting as an important global regulator of the bacterial fitness landscape.

A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells

Saccharomyces cerevisiae adjusts its metabolism based on nutrient availability, typically transitioning from glucose fermentation to ethanol respiration as glucose becomes limiting. However, our understanding of the regulation of metabolism is largely based on population averages, whereas nutrient transitions may cause heterogeneous responses. Here we introduce iCRAFT, a method that couples the ATP Förster resonance energy transfer (FRET)-based biosensor yAT1.03 with Antimycin A to differentiate fermentative and respiratory metabolisms in individual yeast cells. Upon Antimycin A addition, respiratory cells experienced a sharp decrease of the normalized FRET ratio, while respiro-fermentative cells showed no response. Next, we tracked changes in metabolism during the diauxic shift of a glucose pre-grown culture. Following glucose exhaustion, the entire cell population experienced a progressive rise in cytosolic ATP produced via respiration, suggesting a gradual increase in respiratory capacity. Overall, iCRAFT is a robust tool to distinguish fermentation from respiration, offering a new single-cell opportunity to study yeast metabolism.

Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model

Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.

Performance and robustness analysis reveals phenotypic trade-offs in yeast

To design strains that can function efficiently in complex industrial settings, it is crucial to consider their robustness, that is, the stability of their performance when faced with perturbations. In the present study, we cultivated 24 Saccharomyces cerevisiae strains under conditions that simulated perturbations encountered during lignocellulosic bioethanol production, and assessed the performance and robustness of multiple phenotypes simultaneously. The observed negative correlations confirmed a trade-off between performance and robustness of ethanol yield, biomass yield, and cell dry weight. Conversely, the specific growth rate performance positively correlated with the robustness, presumably because of evolutionary selection for robust, fast-growing cells. The Ethanol Red strain exhibited both high performance and robustness, making it a good candidate for bioproduction in the tested perturbation space. Our results experimentally map the robustness-performance trade-offs, previously demonstrated mainly by single-phenotype and computational studies.

Searching for Principles of Microbial Ecology Across Levels of Biological Organization

Microbial communities play pivotal roles in ecosystems across different scales, from global elemental cycles to household food fermentations. These complex assemblies comprise hundreds or thousands of microbial species whose abundances vary over time and space. Unraveling the principles that guide their dynamics at different levels of biological organization, from individual species, their interactions, to complex microbial communities, is a major challenge. To what extent are these different levels of organization governed by separate principles, and how can we connect these levels to develop predictive models for the dynamics and function of microbial communities? Here, we will discuss recent advances that point towards principles of microbial communities, rooted in various disciplines from physics, biochemistry, and dynamical systems. By considering the marine carbon cycle as a concrete example, we demonstrate how the integration of levels of biological organization can offer deeper insights into the impact of increasing temperatures, such as those associated with climate change, on ecosystem-scale processes. We argue that by focusing on principles that transcend specific microbiomes, we can pave the way for a comprehensive understanding of microbial community dynamics and the development of predictive models for diverse ecosystems.

Microbial life in slow and stopped lanes

Microbes in nature often lack nutrients and face extreme or widely fluctuating temperatures, unlike microbes in growth-optimized settings in laboratories that much of the literature examines. Slowed or suspended lives are the norm for microbes. Studying them is important for understanding the consequences of climate change and for addressing fundamental questions about life: are there limits to how slowly a cell's life can progress, and how long cells can remain viable without self-replicating? Recent studies began addressing these questions with single-cell-level measurements and mathematical models. Emerging principles that govern slowed or suspended lives of cells - including lives of dormant spores and microbes at extreme temperatures - are re-defining discrete cellular states as continuums and revealing intracellular dynamics at new timescales. Nearly inactive, lifeless-appearing microbes are transforming our understanding of life.

Starvation responses impact interaction dynamics of human gut bacteria Bacteroides thetaiotaomicron and Roseburia intestinalis

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow

IMPORTANCE

Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.

Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs

RNA polymerase (RNAP) is emblematic of complex biological systems that control multiple traits involving trade-offs such as growth versus maintenance. Laboratory evolution has revealed that mutations in RNAP subunits, including RpoB, are frequently selected. However, we lack a systems view of how mutations alter the RNAP molecular functions to promote adaptation. We, therefore, measured the fitness of thousands of mutations within a region of rpoB under multiple conditions and genetic backgrounds, to find that adaptive mutations cluster in two modules. Mutations in one module favor growth over maintenance through a partial loss of an interaction associated with faster elongation. Mutations in the other favor maintenance over growth through a destabilized RNAP-DNA complex. The two molecular handles capture the versatile RNAP-mediated adaptations. Combining both interaction losses simultaneously improved maintenance and growth, challenging the idea that growth-maintenance tradeoff resorts only from limited resources, and revealing how compensatory evolution operates within RNAP.

Fitness cost associated with cell phenotypic switching drives population diversification dynamics and controllability

Isogenic cell populations can cope with stress conditions by switching to alternative phenotypes. Even if it can lead to increased fitness in a natural context, this feature is typically unwanted for a range of applications (e.g., bioproduction, synthetic biology, and biomedicine) where it tends to make cellular response unpredictable. However, little is known about the diversification profiles that can be adopted by a cell population. Here, we characterize the diversification dynamics for various systems (bacteria and yeast) and for different phenotypes (utilization of alternative carbon sources, general stress response and more complex development patterns). Our results suggest that the diversification dynamics and the fitness cost associated with cell switching are coupled. To quantify the contribution of the switching cost on population dynamics, we design a stochastic model that let us reproduce the dynamics observed experimentally and identify three diversification regimes, i.e., constrained (at low switching cost), dispersed (at medium and high switching cost), and bursty (for very high switching cost). Furthermore, we use a cell-machine interface called Segregostat to demonstrate that different levels of control can be applied to these diversification regimes, enabling applications involving more precise cellular responses.

Genome content predicts the carbon catabolic preferences of heterotrophic bacteria

Heterotrophic bacteria-bacteria that utilize organic carbon sources-are taxonomically and functionally diverse across environments. It is challenging to map metabolic interactions and niches within microbial communities due to the large number of metabolites that could serve as potential carbon and energy sources for heterotrophs. Whether their metabolic niches can be understood using general principles, such as a small number of simplified metabolic categories, is unclear. Here we perform high-throughput metabolic profiling of 186 marine heterotrophic bacterial strains cultured in media containing one of 135 carbon substrates to determine growth rates, lag times and yields. We show that, despite high variability at all levels of taxonomy, the catabolic niches of heterotrophic bacteria can be understood in terms of their preference for either glycolytic (sugars) or gluconeogenic (amino and organic acids) carbon sources. This preference is encoded by the total number of genes found in pathways that feed into the two modes of carbon utilization and can be predicted using a simple linear model based on gene counts. This allows for coarse-grained descriptions of microbial communities in terms of prevalent modes of carbon catabolism. The sugar-acid preference is also associated with genomic GC content and thus with the carbon-nitrogen requirements of their encoded proteome. Our work reveals how the evolution of bacterial genomes is structured by fundamental constraints rooted in metabolism.

Conditionally unutilized proteins and their profound effects on growth and adaptation across microbial species

Protein synthesis is an important determinant of microbial growth and response that demands a high amount of metabolic and biosynthetic resources. Despite these costs, microbial species from different taxa and habitats massively synthesize proteins that are not utilized in the conditions they currently experience. Based on resource allocation models, recent studies have begun to reconcile the costs and benefits of these conditionally unutilized proteins (CUPs) in the context of varying environmental conditions. Such massive synthesis of CUPs is crucial to consider in different areas of modern microbiology, from the systematic investigation of cell physiology, via the prediction of evolution in laboratory and natural environments, to the rational design of strains in biotechnology applications.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

A fitness trade-off between growth and survival governed by Spo0A-mediated proteome allocation constraints in Bacillus subtilis

Growth and survival are key determinants of bacterial fitness. However, how resource allocation of bacteria could reconcile these two traits to maximize fitness remains poorly understood. Here, we find that the resource allocation strategy of Bacillus subtilis does not lead to growth maximization on various carbon sources. Survival-related pathways impose strong proteome constraints on B. subtilis. Knockout of a master regulator gene, spo0A, triggers a global resource reallocation from survival-related pathways to biosynthesis pathways, further strongly stimulating the growth of B. subtilis. However, the fitness of spo0A-null strain is severely compromised because of various disadvantageous phenotypes (e.g., abolished sporulation and enhanced cell lysis). In particular, it also exhibits a strong defect in peptide utilization, being unable to efficiently recycle nutrients from the lysed cell debris to maintain long-term viability. Our work uncovers a fitness trade-off between growth and survival that governed by Spo0A-mediated proteome allocation constraints in B. subtilis, further shedding light on the fundamental design principle of bacteria.

Strong environmental memory revealed by experimental evolution in static and fluctuating environments

Evolution in a static environment, such as a laboratory setting with constant and uniform conditions, often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time-average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time-average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. We find that fitness in fluctuating environments indeed often deviates from the expectation based on static components, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments, even if the components of the focal fluctuating environment are excluded from this variance. We employ a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results demonstrate that environmental fluctuations have large impacts on fitness and suggest that variance in static environments can explain these impacts.

Plasticity of growth laws tunes resource allocation strategies in bacteria

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to 'nutrient quality', a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate. We show that it is possible to turn mannose, one of the 'poorest' substrates of E. coli, into one of the 'best' substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary.

Isolation, identification, and screening of biosurfactant-producing and hydrocarbon-degrading bacteria from oil and gas industrial waste

Qatar is one of the biggest oil and gas producers in the world, coupled with it is challenging environmental conditions (high average temperature: >40 °C, low annual rainfall: 46.71 mm, and high annual evaporation rate: 2200 mm) harbors diverse microbial communities that are novel and robust, with the potential to biodegrade hydrocarbons. In this study, we collected hydrocarbon contaminated sludge, wastewater and soil samples from oil and gas industries in Qatar. Twenty-six bacterial strains were isolated in the laboratory from these samples using high saline conditions and crude oil as the sole carbon source. A total of 15 different bacterial genera were identified in our study that have not been widely reported in the literature or studied for their usage in the biodegradation of hydrocarbons. Interestingly, some of the bacteria that were identified belonged to the same genus however, demonstrated variable growth rates and biosurfactant production. This indicates the possibility of niche specialization and specific evolution to acquire competitive traits for better survival. The most potent strain EXS14, identified as Marinobacter sp., showed the highest growth rate in the oil-containing medium as well as the highest biosurfactant production. When this strain was further tested for biodegradation of hydrocarbons, the results showed that it was able to degrade 90 to 100% of low and medium molecular weight hydrocarbons and 60 to 80% of high molecular weight (C35 to C50) hydrocarbons. This study offers many promising leads for future studies of microbial species and their application for the treatment of hydrocarbon contaminated wastewater and soil in the region and in other areas with similar environmental conditions.

Unbalanced response to growth variations reshapes the cell fate decision landscape

The global regulation of cell growth rate on gene expression perturbs the performance of gene networks, which would impose complex variations on the cell-fate decision landscape. Here we use a simple synthetic circuit of mutual repression that allows a bistable landscape to examine how such global regulation would affect the stability of phenotypic landscape and the accompanying dynamics of cell-fate determination. We show that the landscape experiences a growth-rate-induced bifurcation between monostability and bistability. Theoretical and experimental analyses reveal that this bifurcating deformation of landscape arises from the unbalanced response of gene expression to growth variations. The path of growth transition across the bifurcation would reshape cell-fate decisions. These results demonstrate the importance of growth regulation on cell-fate determination processes, regardless of specific molecular signaling or regulation.

Long-term history dependence of growth rates of E. coli after nutrient shifts

According to a widely accepted paradigm of microbiology, steady-state growth rates are determined solely by current growth conditions1-3 and adaptations between growth states are rapid, as recently recapitulated by simple resource allocation models4. However, even in microbes overlapping regulatory networks can yield multi-stability or long-term cellular memory. Species like Listeria monocytogenes5 and Bacillus subtilis "distinguish" distinct histories for the commitment to sporulation6, but it is unclear if these states can persist over many generations. Remarkably, studying carbon co-utilization of Escherichia coli, we found that growth rates on combinations of carbon sources can depend critically on the previous growth condition. Growing in identical conditions, we observed differences in growth rates of up to 25% and we did not observe convergence of growth rates over 15 generations. We observed this phenomenon occurs across combinations of different phosphotransferase (PTS) substrates with various gluconeogenic carbon sources and found it to depend on the transcription factor Mlc.

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth

Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose-lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

A genetic switch controls Pseudomonas aeruginosa surface colonization

Efficient colonization of mucosal surfaces is essential for opportunistic pathogens like Pseudomonas aeruginosa, but how bacteria collectively and individually adapt to optimize adherence, virulence and dispersal is largely unclear. Here we identified a stochastic genetic switch, hecR-hecE, which is expressed bimodally and generates functionally distinct bacterial subpopulations to balance P. aeruginosa growth and dispersal on surfaces. HecE inhibits the phosphodiesterase BifA and stimulates the diguanylate cyclase WspR to increase c-di-GMP second messenger levels and promote surface colonization in a subpopulation of cells; low-level HecE-expressing cells disperse. The fraction of HecE+ cells is tuned by different stress factors and determines the balance between biofilm formation and long-range cell dispersal of surface-grown communities. We also demonstrate that the HecE pathway represents a druggable target to effectively counter P. aeruginosa surface colonization. Exposing such binary states opens up new ways to control mucosal infections by a major human pathogen.

Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.

Coexisting ecotypes in long-term evolution emerged from interacting trade-offs

Evolution of complex communities of coexisting microbes remains poorly understood. The long-term evolution experiment on Escherichia coli (LTEE) revealed the spontaneous emergence of stable coexistence of multiple ecotypes, which persisted for more than 14,000 generations of continuous evolution. Here, using a combination of experiments and computer simulations, we show that the emergence and persistence of this phenomenon can be explained by the combination of two interacting trade-offs, rooted in biochemical constraints: First, faster growth is enabled by higher fermentation and obligate acetate excretion. Second, faster growth results in longer lag times when utilizing acetate after glucose is depleted. This combination creates an ecological niche for a slower-growing ecotype, specialized in switching to acetate. These findings demonstrate that trade-offs can give rise to surprisingly complex communities with evolutionarily stable coexistence of multiple variants in even the simplest environments.

Stress-induced metabolic exchanges between complementary bacterial types underly a dynamic mechanism of inter-species stress resistance

Metabolic cross-feeding plays vital roles in promoting ecological diversity. While some microbes depend on exchanges of essential nutrients for growth, the forces driving the extensive cross-feeding needed to support the coexistence of free-living microbes are poorly understood. Here we characterize bacterial physiology under self-acidification and establish that extensive excretion of key metabolites following growth arrest provides a collaborative, inter-species mechanism of stress resistance. This collaboration occurs not only between species isolated from the same community, but also between unrelated species with complementary (glycolytic vs. gluconeogenic) modes of metabolism. Cultures of such communities progress through distinct phases of growth-dilution cycles, comprising of exponential growth, acidification-triggered growth arrest, collaborative deacidification, and growth recovery, with each phase involving different combinations of physiological states of individual species. Our findings challenge the steady-state view of ecosystems commonly portrayed in ecological models, offering an alternative dynamical view based on growth advantages of complementary species in different phases.

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.

Type-IV pili tune an adhesion-migration trade-off during surface colonization of Pseudomonas aeruginosa

Bacterial pathogenicity relies on both firm surface adhesion and cell dissemination. How twitching bacteria resolve the fundamental contradiction between adhesion and migration is unknown. To address this question, we employ live-cell imaging of type-IV pili (T4P) and therewith construct a comprehensive mathematical model of Pseudomonas aeruginosa migration. The data show that only 10% to 50% of T4P bind to substrates and contribute to migration through random extension and retraction. Individual T4P do not display a measurable sensory response to surfaces, but their number increases on cellular surface contact. Attachment to surfaces is mediated, besides T4P, by passive adhesive forces acting on the cell body. Passive adhesions slow down cell migration and result in local random motion on short time scales, which is followed by directionally persistent, superdiffusive motion on longer time scales. Moreover, passive adhesions strongly enhance surface attachment under shear flow. Δ pilA mutants, which produce no T4P, robustly stick to surfaces under shear flow. In contrast, rapidly migrating Δ pilH cells, which produce an excessive number of T4P, are easily detached by shear. Wild-type cells sacrifice migration speed for robust surface attachment by maintaining a low number of active pili. The different cell strains pertain to disjunct regimes in a generic adhesion-migration trait space. Depending on the nature of the adhesion structures, adhesion and migration are either compatible or a trade-off is required for efficient bacterial surface colonization under different conditions.

Rediversification Following Ecotype Isolation Reveals Hidden Adaptive Potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about if communities can regenerate ecological diversity following species removal or extinction, and how the rediversified communities would compare to the original ones. Here we show that simple two-ecotype communities from the E. coli Long Term Evolution Experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different compared to the original community in ways relevant to the mechanism of ecotype coexistence, for example in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, but with unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.

Shaping bacterial gene expression by physiological and proteome allocation constraints

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

Tradeoff between lag time and growth rate drives the plasmid acquisition cost

Conjugative plasmids drive genetic diversity and evolution in microbial populations. Despite their prevalence, plasmids can impose long-term fitness costs on their hosts, altering population structure, growth dynamics, and evolutionary outcomes. In addition to long-term fitness costs, acquiring a new plasmid introduces an immediate, short-term perturbation to the cell. However, due to the transient nature of this plasmid acquisition cost, a quantitative understanding of its physiological manifestations, overall magnitudes, and population-level implications, remains unclear. To address this, here we track growth of single colonies immediately following plasmid acquisition. We find that plasmid acquisition costs are primarily driven by changes in lag time, rather than growth rate, for nearly 60 conditions covering diverse plasmids, selection environments, and clinical strains/species. Surprisingly, for a costly plasmid, clones exhibiting longer lag times also achieve faster recovery growth rates, suggesting an evolutionary tradeoff. Modeling and experiments demonstrate that this tradeoff leads to counterintuitive ecological dynamics, whereby intermediate-cost plasmids outcompete both their low and high-cost counterparts. These results suggest that, unlike fitness costs, plasmid acquisition dynamics are not uniformly driven by minimizing growth disadvantages. Moreover, a lag/growth tradeoff has clear implications in predicting the ecological outcomes and intervention strategies of bacteria undergoing conjugation.

Optimal dormancy strategies in fluctuating environments given delays in phenotypic switching

Organisms have evolved different mechanisms in response to periods of environmental stress, including dormancy - a reversible state of reduced metabolic activity. Transitions to and from dormancy can be random or induced by changes in environmental conditions. Prior theoretical work has shown that stochastic transitioning between active and dormant states at the individual level can maximize fitness at the population level. However, such theories of 'bet-hedging' strategies typically neglect certain physiological features of transitions to dormancy, including time lags to gain protective benefits. Here, we construct and analyze a dynamic model that couples stochastic changes in environmental state with the population dynamics of organisms that can initiate dormancy after an explicit time delay. Stochastic environments are simulated using a multi-state Markov chain through which the mean and variance of environmental residence time can be adjusted. In the absence of time lags (or in the limit of very short lags), we find that bet-hedging strategy transition probabilities scale inversely with the mean environmental residence times, consistent with prior theory. We also find that increasing delays in dormancy decreases optimal transitioning probabilities, an effect that can be influenced by the correlations of environmental noise. When environmental residence times - either good or bad - are uncorrelated, the maximum population level fitness is obtained given low levels of transitioning between active and dormant states. However when environmental residence times are correlated, optimal dormancy initiation and termination probabilities increase insofar as the mean environmental persistent time is longer than the delay to reach dormancy. We also find that bet hedging is no longer advantageous when delays to enter dormancy exceed the mean environmental residence times. Altogether, these results show how physiological limits to dormancy and environmental dynamics shape the evolutionary benefits and even viability of bet hedging strategies at population scales.

Resuscitation dynamics reveal persister partitioning after antibiotic treatment

Bacteria can survive antibiotics by forming dormant, drug-tolerant persisters. Persisters can resuscitate from dormancy after treatment and prolong infections. Resuscitation is thought to occur stochastically, but its transient, single-cell nature makes it difficult to investigate. We tracked the resuscitation of individual persisters by microscopy after ampicillin treatment and, by characterizing their dynamics, discovered that Escherichia coli and Salmonella enterica persisters resuscitate exponentially rather than stochastically. We demonstrated that the key parameters controlling resuscitation map to the ampicillin concentration during treatment and efflux during resuscitation. Consistently, we observed many persister progeny have structural defects and transcriptional responses indicative of cellular damage, for both β-lactam and quinolone antibiotics. During resuscitation, damaged persisters partition unevenly, generating both healthy daughter cells and defective ones. This persister partitioning phenomenon was observed in S. enterica, Klebsiella pneumoniae, Pseudomonas aeruginosa, and an E. coli urinary tract infection (UTI) isolate. It was also observed in the standard persister assay and after in situ treatment of a clinical UTI sample. This study reveals novel properties of resuscitation and indicates that persister partitioning may be a survival strategy in bacteria that lack genetic resistance.

Remodeling of Carbon Metabolism during Sulfoglycolysis in Escherichia coli

Sulfoquinovose (SQ) is a major metabolite in the global sulfur cycle produced by nearly all photosynthetic organisms. One of the major pathways involved in the catabolism of SQ in bacteria such as Escherichia coli is a variant of the glycolytic Embden-Meyerhof-Parnas (EMP) pathway termed the sulfoglycolytic EMP (sulfo-EMP) pathway, which leads to the consumption of three of the six carbons of SQ and the excretion of 2,3-dihydroxypropanesulfonate (DHPS). Comparative metabolite profiling of aerobically glucose (Glc)-grown and SQ-grown E. coli cells was undertaken to identify the metabolic consequences of the switch from glycolysis to sulfoglycolysis. Sulfoglycolysis was associated with the diversion of triose phosphates (triose-P) to synthesize sugar phosphates (gluconeogenesis) and an unexpected accumulation of trehalose and glycogen storage carbohydrates. Sulfoglycolysis was also associated with global changes in central carbon metabolism, as indicated by the changes in the levels of intermediates in the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway (PPP), polyamine metabolism, pyrimidine metabolism, and many amino acid metabolic pathways. Upon entry into stationary phase and the depletion of SQ, E. coli cells utilize their glycogen, indicating a reversal of metabolic fluxes to allow glycolytic metabolism. IMPORTANCE The sulfosugar sulfoquinovose is estimated to be produced on a scale of 10 billion metric tons per annum, making it a major organosulfur species in the biosulfur cycle. The microbial degradation of sulfoquinovose through sulfoglycolysis allows the utilization of its carbon content and contributes to the biomineralization of its sulfur. However, the metabolic consequences of microbial growth on sulfoquinovose are unclear. We use metabolomics to identify the metabolic adaptations that Escherichia coli undergoes when grown on sulfoquinovose versus glucose. This revealed the increased flux into storage carbohydrates through gluconeogenesis and the reduced flux of carbon into the TCA cycle and downstream metabolism. These changes are relieved upon entry into stationary phase and reversion to glycolytic metabolism. This work provides new insights into the metabolic consequences of microbial growth on an abundant sulfosugar.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.

Differential carbon utilization enables co-existence of recently speciated Campylobacteraceae in the cow rumen epithelial microbiome

The activities of different microbes in the cow rumen have been shown to modulate the host's ability to utilize plant biomass, while the host-rumen interface has received little attention. As datasets collected worldwide have pointed to Campylobacteraceae as particularly abundant members of the rumen epithelial microbiome, we targeted this group in a subset of seven cows with meta- and isolate genome analysis. We show that the dominant Campylobacteraceae lineage has recently speciated into two populations that were structured by genome-wide selective sweeps followed by population-specific gene import and recombination. These processes led to differences in gene expression and enzyme domain composition that correspond to the ability to utilize acetate, the main carbon source for the host, at the cost of inhibition by propionate. This trade-off in competitive ability further manifests itself in differential dynamics of the two populations in vivo. By exploring population-level adaptations that otherwise remain cryptic in culture-independent analyses, our results highlight how recent evolutionary dynamics can shape key functional roles in the rumen microbiome.

Stringent response ensures the timely adaptation of bacterial growth to nutrient downshift

Timely adaptation to nutrient downshift is crucial for bacteria to maintain fitness during feast and famine cycle in the natural niche. However, the molecular mechanism that ensures the timely adaption of bacterial growth to nutrient downshift remains poorly understood. Here, we quantitatively investigated the adaptation of Escherichia coli to various kinds of nutrient downshift. We found that relA deficient strain, which is devoid of stringent response, exhibits a significantly longer growth lag than wild type strain during adapting to both amino acid downshift and carbon downshift. Quantitative proteomics show that increased (p)ppGpp level promotes the growth adaption of bacteria to amino acid downshift via triggering the proteome resource re-allocation from ribosome synthesis to amino acid biosynthesis. Such type of proteome re-allocation is significantly delayed in the relA-deficient strain, which underlies its longer lag than wild type strain during amino acid downshift. During carbon downshift, a lack of stringent response in relA deficient strain leads to disruption of the transcription-translation coordination, thus compromising the transcription processivity and further the timely expression of related catabolic operons for utilizing secondary carbon sources. Our studies shed light on the fundamental strategy of bacteria to maintain fitness under nutrient-fluctuating environments.

Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions

The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.

Coordination of CcpA and CodY Regulators in Staphylococcus aureus USA300 Strains

The complex cross talk between metabolism and gene regulatory networks makes it difficult to untangle individual constituents and study their precise roles and interactions. To address this issue, we modularized the transcriptional regulatory network (TRN) of the Staphylococcus aureus USA300 strain by applying independent component analysis (ICA) to 385 RNA sequencing samples. We then combined the modular TRN model with a metabolic model to study the regulation of carbon and amino acid metabolism. Our analysis showed that regulation of central carbon metabolism by CcpA and amino acid biosynthesis by CodY are closely coordinated. In general, S. aureus increases the expression of CodY-regulated genes in the presence of preferred carbon sources such as glucose. This transcriptional coordination was corroborated by metabolic model simulations that also showed increased amino acid biosynthesis in the presence of glucose. Further, we found that CodY and CcpA cooperatively regulate the expression of ribosome hibernation-promoting factor, thus linking metabolic cues with translation. In line with this hypothesis, expression of CodY-regulated genes is tightly correlated with expression of genes encoding ribosomal proteins. Together, we propose a coarse-grained model where expression of S. aureus genes encoding enzymes that control carbon flux and nitrogen flux through the system is coregulated with expression of translation machinery to modularly control protein synthesis. While this work focuses on three key regulators, the full TRN model we present contains 76 total independently modulated sets of genes, each with the potential to uncover other complex regulatory structures and interactions. IMPORTANCE Staphylococcus aureus is a versatile pathogen with an expanding antibiotic resistance profile. The biology underlying its clinical success emerges from an interplay of many systems such as metabolism and gene regulatory networks. This work brings together models for these two systems to establish fundamental principles governing the regulation of S. aureus central metabolism and protein synthesis. Studies of these fundamental biological principles are often confined to model organisms such as Escherichia coli. However, expanding these models to pathogens can provide a framework from which complex and clinically important phenotypes such as virulence and antibiotic resistance can be better understood. Additionally, the expanded gene regulatory network model presented here can deconvolute the biology underlying other important phenotypes in this pathogen.

Recent functional insights into the magic role of (p)ppGpp in growth control

Rapid growth and survival are two key traits that enable bacterial cells to thrive in their natural habitat. The guanosine tetraphosphate and pentaphosphate [(p)ppGpp], also known as "magic spot", is a key second messenger inside bacterial cells as well as chloroplasts of plants and green algae. (p)ppGpp not only controls various stages of central dogma processes (replication, transcription, ribosome maturation and translation) and central metabolism but also regulates various physiological processes such as pathogenesis, persistence, motility and competence. Under extreme conditions such as nutrient starvation, (p)ppGpp-mediated stringent response is crucial for the survival of bacterial cells. This mini-review highlights some of the very recent progress on the key role of (p)ppGpp in bacterial growth control in light of cellular resource allocation and cell size regulation. We also briefly discuss some recent functional insights into the role of (p)ppGpp in plants and green algae from the angle of growth and development and further discuss several important open directions for future studies.

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.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks

BACKGROUND

Deep-learning-based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac).

RESULTS

We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one's experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions.

CONCLUSIONS

The SyMBac approach will help to adapt and improve the performance of deep-learning-based image segmentation models for accurate processing of high-throughput timelapse image data.