Resource allocation and metabolism: the search for governing principles

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity

In their natural environments, microorganisms mainly operate at suboptimal growth conditions with fluctuations in nutrient abundance. The resulting cellular adaptation is subject to conflicting tasks: growth or survival maximisation. Here, we study this adaptation by systematically measuring the impact of a nitrogen downshift to 24 nitrogen sources on cellular metabolism at the single-cell level. Saccharomyces lineages grown in rich media and exposed to a nitrogen downshift gradually differentiate to form two subpopulations of different cell sizes where one favours growth while the other favours viability with an extended chronological lifespan. This differentiation is asymmetrical with daughter cells representing the new differentiated state with increased viability. We characterise the metabolic response of the subpopulations using RNA sequencing, metabolic biosensors and a transcription factor-tagged GFP library coupled to high-throughput microscopy, imaging more than 800,000 cells. We find that the subpopulation with increased viability is associated with a dormant quiescent state displaying differences in MAPK signalling. Depending on the identity of the nitrogen source present, differentiation into the quiescent state can be actively maintained, attenuated, or aborted. These results establish amino acids as important signalling molecules for the formation of genetically identical subpopulations, involved in chronological lifespan and growth rate determination.

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.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Evaluating the feasibility of medium-chain oleochemical synthesis using microbial chain elongation

Chain elongating bacteria are a unique guild of strictly anaerobic bacteria that have garnered interest for sustainable chemical manufacturing from carbon-rich wet and gaseous waste streams. They produce C6-C8 medium-chain fatty acids, which are valuable platform chemicals that can be used directly, or derivatized to service a wide range of chemical industries. However, the application of chain elongating bacteria for synthesizing products beyond C6-C8 medium-chain fatty acids has not been evaluated. In this study, we assess the feasibility of expanding the product spectrum of chain elongating bacteria to C9-C12 fatty acids, along with the synthesis of C6 fatty alcohols, dicarboxylic acids, diols, and methyl ketones. We propose several metabolic engineering strategies to accomplish these conversions in chain elongating bacteria and utilize constraint-based metabolic modelling to predict pathway stoichiometries, assess thermodynamic feasibility, and estimate ATP and product yields. We also evaluate how producing alternative products impacts the growth rate of chain elongating bacteria via resource allocation modelling, revealing a trade-off between product chain length and class versus cell growth rate. Together, these results highlight the potential for using chain elongating bacteria as a platform for diverse oleochemical biomanufacturing and offer a starting point for guiding future metabolic engineering efforts aimed at expanding their product range.

ONE-SENTENCE SUMMARY

In this work, the authors use constraint-based metabolic modelling and enzyme cost minimization to assess the feasibility of using metabolic engineering to expand the product spectrum of anaerobic chain elongating bacteria.

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.

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.

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.

Modulation of prey size reveals adaptability and robustness in the cell cycle of an intracellular predator

Despite a remarkable diversity of lifestyles, bacterial replication has only been investigated in a few model species. In bacteria that do not rely on canonical binary division for proliferation, the coordination of major cellular processes is still largely mysterious. Moreover, the dynamics of bacterial growth and division remain unexplored within spatially confined niches where nutrients are limited. This includes the life cycle of the model endobiotic predatory bacterium Bdellovibrio bacteriovorus, which grows by filamentation within its prey and produces a variable number of daughter cells. Here, we examined the impact of the micro-compartment in which predators replicate (i.e., the prey bacterium) on their cell-cycle progression at the single-cell level. Using Escherichia coli with genetically encoded size differences, we show that the duration of the predator cell cycle scales with prey size. Consequently, prey size determines predator offspring numbers. We found that individual predators elongate exponentially, with a growth rate determined by the nutritional quality of the prey, irrespective of prey size. However, the size of newborn predator cells is remarkably stable across prey nutritional content and size variations. Tuning the predatory cell cycle by modulating prey dimensions also allowed us to reveal invariable temporal connections between key cellular processes. Altogether, our data imply adaptability and robustness shaping the enclosed cell-cycle progression of B. bacteriovorus, which might contribute to optimal exploitation of the finite resources and space in their prey. This study extends the characterization of cell cycle control strategies and growth patterns beyond canonical models and lifestyles.

Slowest possible replicative life at frigid temperatures for yeast

Determining whether life can progress arbitrarily slowly may reveal fundamental barriers to staying out of thermal equilibrium for living systems. By monitoring budding yeast's slowed-down life at frigid temperatures and with modeling, we establish that Reactive Oxygen Species (ROS) and a global gene-expression speed quantitatively determine yeast's pace of life and impose temperature-dependent speed limits - shortest and longest possible cell-doubling times. Increasing cells' ROS concentration increases their doubling time by elongating the cell-growth (G1-phase) duration that precedes the cell-replication (S-G2-M) phase. Gene-expression speed constrains cells' ROS-reducing rate and sets the shortest possible doubling-time. To replicate, cells require below-threshold concentrations of ROS. Thus, cells with sufficiently abundant ROS remain in G1, become unsustainably large and, consequently, burst. Therefore, at a given temperature, yeast's replicative life cannot progress arbitrarily slowly and cells with the lowest ROS-levels replicate most rapidly. Fundamental barriers may constrain the thermal slowing of other organisms' lives.

Metabolic adaptation to vitamin auxotrophy by leaf-associated bacteria

Auxotrophs are unable to synthesize all the metabolites essential for their metabolism and rely on others to provide them. They have been intensively studied in laboratory-generated and -evolved mutants, but emergent adaptation mechanisms to auxotrophy have not been systematically addressed. Here, we investigated auxotrophies in bacteria isolated from Arabidopsis thaliana leaves and found that up to half of the strains have auxotrophic requirements for biotin, niacin, pantothenate and/or thiamine. We then explored the genetic basis of auxotrophy as well as traits that co-occurred with vitamin auxotrophy. We found that auxotrophic strains generally stored coenzymes with the capacity to grow exponentially for 1-3 doublings without vitamin supplementation; however, the highest observed storage was for biotin, which allowed for 9 doublings in one strain. In co-culture experiments, we demonstrated vitamin supply to auxotrophs, and found that auxotrophic strains maintained higher species richness than prototrophs upon external supplementation with vitamins. Extension of a consumer-resource model predicted that auxotrophs can utilize carbon compounds provided by other organisms, suggesting that auxotrophic strains benefit from metabolic by-products beyond vitamins.

Predicting Metabolic Adaptation Under Dynamic Substrate Conditions Using a Resource-Dependent Kinetic Model: A Case Study Using Saccharomyces cerevisiae

Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

“Omics” Technologies - What Have They Told Us About Uropathogenic Escherichia coli Fitness and Virulence During Urinary Tract Infection?

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infection (UTI), a widespread infectious disease of great impact on human health. This is further emphasized by the rapidly increase in antimicrobial resistance in UPEC, which compromises UTI treatment. UPEC biology is highly complex since uropathogens must adopt extracellular and intracellular lifestyles and adapt to different niches in the host. In this context, the implementation of forefront ‘omics’ technologies has provided substantial insight into the understanding of UPEC pathogenesis, which has opened the doors for new therapeutics and prophylactics discovery programs. Thus, ‘omics’ technologies applied to studies of UPEC during UTI, or in models of UTI, have revealed extensive lists of factors that are important for the ability of UPEC to cause disease. The multitude of large ‘omics’ datasets that have been generated calls for scrutinized analysis of specific factors that may be of interest for further development of novel treatment strategies. In this review, we describe main UPEC determinants involved in UTI as estimated by ‘omics’ studies, and we compare prediction of factors across the different ‘omics’ technologies, with a focus on those that have been confirmed to be relevant under UTI-related conditions. We also discuss current challenges and future perspectives regarding analysis of data to provide an overview and better understanding of UPEC mechanisms involved in pathogenesis which should assist in the selection of target sites for future prophylaxis and treatment.

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

This review is a part of the SI 'Genome-Scale Modeling of Microorganisms in the Real World'. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

BACKGROUND

The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.

RESULTS

In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.

CONCLUSIONS

We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Functional biology in its natural context: A search for emergent simplicity

The immeasurable complexity at every level of biological organization creates a daunting task for understanding biological function. Here, we highlight the risks of stripping it away at the outset and discuss a possible path toward arriving at emergent simplicity of understanding while still embracing the ever-changing complexity of biotic interactions that we see in nature.

Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria

Marine bacterial diversity is immense and believed to be driven in part by trade-offs in metabolic strategies. Here we consider heterotrophs that rely on organic carbon as an energy source and present a molecular-level model of cell metabolism that explains the dichotomy between copiotrophs—which dominate in carbon-rich environments—and oligotrophs—which dominate in carbon-poor environments—as the consequence of trade-offs between nutrient transport systems. While prototypical copiotrophs, like Vibrios, possess numerous phosphotransferase systems (PTS), prototypical oligotrophs, such as SAR11, lack PTS and rely on ATP-binding cassette (ABC) transporters, which use binding proteins. We develop models of both transport systems and use them in proteome allocation problems to predict the optimal nutrient uptake and metabolic strategy as a function of carbon availability. We derive a Michaelis–Menten approximation of ABC transport, analytically demonstrating how the half-saturation concentration is a function of binding protein abundance. We predict that oligotrophs can attain nanomolar half-saturation concentrations using binding proteins with only micromolar dissociation constants and while closely matching transport and metabolic capacities. However, our model predicts that this requires large periplasms and that the slow diffusion of the binding proteins limits uptake. Thus, binding proteins are critical for oligotrophic survival yet severely constrain growth rates. We propose that this trade-off fundamentally shaped the divergent evolution of oligotrophs and copiotrophs.

Marine bacteria utilize carbon as a building block and an energy source and thus exert an important control on the amount of carbon that is sequestered in the ocean versus respired into the atmosphere. They use a spectrum of strategies to consume carbon: while copiotrophic bacteria dominate in nutrient-rich environments, oligotrophic bacteria dominate in nutrient-poor environments and are typically smaller, nonmotile, and slower growing. Yet the paragon oligotroph SAR11 is the planet’s most abundant organism. Despite this, most of our understanding of bacteria derives from research on copiotrophs. Here we use molecular-level models to understand how an oligotroph’s physiology enables it to outperform copiotrophs in nutrient-poor but not in nutrient-rich environments. We contrast copiotrophs’ prevalent method of sugar transport with oligotrophs’ reliance on binding proteins, which trap nutrients in the periplasm. Binding proteins allow cells to attain affinities that are much higher than the transport proteins’ intrinsic affinities. However, our model predicts that attaining such high affinities requires large periplasms with high abundances of the slowly diffusing binding proteins, which precludes high growth rates. By quantifying the benefits and costs of binding proteins, we provide a mechanistic explanation for the divergent evolution of oligotrophs and copiotrophs.

Constrained proteome allocation affects coexistence in models of competitive microbial communities

Microbial communities are ubiquitous and play crucial roles in many natural processes. Despite their importance for the environment, industry and human health, there are still many aspects of microbial community dynamics that we do not understand quantitatively. Recent experiments have shown that the structure and composition of microbial communities are intertwined with the metabolism of the species that inhabit them, suggesting that properties at the intracellular level such as the allocation of cellular proteomic resources must be taken into account when describing microbial communities with a population dynamics approach. In this work, we reconsider one of the theoretical frameworks most commonly used to model population dynamics in competitive ecosystems, MacArthur's consumer-resource model, in light of experimental evidence showing how proteome allocation affects microbial growth. This new framework allows us to describe community dynamics at an intermediate level of complexity between classical consumer-resource models and biochemical models of microbial metabolism, accounting for temporally-varying proteome allocation subject to constraints on growth and protein synthesis in the presence of multiple resources, while preserving analytical insight into the dynamics of the system. We first show with a simple experiment that proteome allocation needs to be accounted for to properly understand the dynamics of even the simplest microbial community, i.e. two bacterial strains competing for one common resource. Then, we study our consumer-proteome-resource model analytically and numerically to determine the conditions that allow multiple species to coexist in systems with arbitrary numbers of species and resources.

Microbial inefficient substrate use through the perspective of resource allocation models

Microorganisms extract energy from substrates following strategies that may seem suboptimal at first glance. Beyond the so-called yield-rate trade-off, resource allocation models, which focus on assigning different functional roles to the limited number of enzymes that a cell can support, offer a framework to interpret the inefficient substrate use by microorganisms. We review here relevant examples of substrate conversions where a significant part of the available energy is not utilised and how resource allocation models offer a mechanistic interpretation thereof, notably for open mixed cultures. Future developments are identified, in particular, the challenge of considering metabolic flexibility towards uncertain environmental changes instead of strict fixed optimality objectives, with the final goal of increasing the prediction capabilities of resource allocation models. Finally, we highlight the relevance of resource allocation to understand and enable a promising biorefinery platform revolving around lactate, which would increase the flexibility of waste-to-chemical biorefinery schemes.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells’ ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Kochanowski et al. quantify cancer cell phenotypes across systematically altered in vitro metabolic environments to search for phenotype-growth relationships, similar to the growth laws found in microbes. Three case studies highlight examples in which such growth relationships are clearly operating (cancer drug survival), weakly present (cell migration), or absent (lactate overflow).

Repetitive Fed-Batch: A Promising Process Mode for Biomanufacturing With E. coli

Recombinant protein production with Escherichia coli is usually carried out in fed-batch mode in industry. As set-up and cleaning of equipment are time- and cost-intensive, it would be economically and environmentally favorable to reduce the number of these procedures. Switching from fed-batch to continuous biomanufacturing with microbials is not yet applied as these cultivations still suffer from time-dependent variations in productivity. Repetitive fed-batch process technology facilitates critical equipment usage, reduces the environmental fingerprint and potentially increases the overall space-time yield. Surprisingly, studies on repetitive fed-batch processes for recombinant protein production can be found for yeasts only. Knowledge on repetitive fed-batch cultivation technology for recombinant protein production in E. coli is not available until now. In this study, a mixed feed approach, enabling repetitive fed-batch technology for recombinant protein production in E. coli, was developed. Effects of the cultivation mode on the space-time yield for a single-cycle fed-batch, a two-cycle repetitive fed-batch, a three-cycle repetitive fed batch and a chemostat cultivation were investigated. For that purpose, we used two different E. coli strains, expressing a model protein in the cytoplasm or in the periplasm, respectively. Our results demonstrate that a repetitive fed-batch for E. coli leads to a higher space-time yield compared to a single-cycle fed-batch and can potentially outperform continuous biomanufacturing. For the first time, we were able to show that repetitive fed-batch technology is highly suitable for recombinant protein production in E. coli using our mixed feeding approach, as it potentially (i) improves product throughput by using critical equipment to its full capacity and (ii) allows implementation of a more economic process by reducing cleaning and set-up times.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

A bacterial size law revealed by a coarse-grained model of cell physiology

Universal observations in Biology are sometimes described as “laws”. In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate. Because they formalize complex emerging principles in biology, growth laws have been instrumental in shaping our understanding of bacterial physiology. Here, we discovered a novel size law that connects cell size to the inverse of the metabolic proteome mass fraction and the active fraction of ribosomes. We used a simple whole-cell coarse-grained model of cell physiology that combines the proteome allocation theory and the structural model of cell division. This integrated model captures all available experimental data connecting the cell proteome composition, ribosome activity, division size and growth rate in response to nutrient quality, antibiotic treatment and increased protein burden. Finally, a stochastic extension of the model explains non-trivial correlations observed in single cell experiments including the adder principle. This work provides a simple and robust theoretical framework for studying the fundamental principles of cell size determination in unicellular organisms.

Bacteria respond to environmental changes by adjusting their molecular composition, cell size and growth rate. This plasticity is thought to result from years of evolution and to be at least in part optimal for bacterial physiology. Over the past decades, quantitative studies of bacterial growth have revealed simple phenomenological relationships, called “growth laws”, which link cell size and cell composition to the growth rate. Simplified mathematical models of cell physiology are useful tools to gain quantitative understanding of the molecular mechanisms that underlie growth laws. For instance, these models helped explaining how optimal allocation of cellular resource to physiological processes and pathways governs the cell molecular composition in response to specific environmental conditions. In this study, we have extended and integrated existing mathematical models and used experimental data from several recent studies to understand the co-regulation of cell composition, cell size and the cellular growth rate. The model predictions uncovered a novel “size law” that links cell size to the levels of metabolic proteins and the fraction of active ribosomes present in the cell. This work provides a useful theoretical tool and a quantitative basis for understanding mechanistically bacterial physiology as a function of external conditions.

Mitochondrial respiration is required to provide amino acids during fermentative proliferation of fission yeast

When glucose is available, many organisms repress mitochondrial respiration in favour of aerobic glycolysis, or fermentation in yeast, that suffices for ATP production. Fission yeast cells, however, rely partially on respiration for rapid proliferation under fermentative conditions. Here, we determined the limiting factors that require respiratory function during fermentation. When inhibiting the electron transport chain, supplementation with arginine was necessary and sufficient to restore rapid proliferation. Accordingly, a systematic screen for mutants growing poorly without arginine identified mutants defective in mitochondrial oxidative metabolism. Genetic or pharmacological inhibition of respiration triggered a drop in intracellular levels of arginine and amino acids derived from the Krebs cycle metabolite alpha‐ketoglutarate: glutamine, lysine and glutamic acid. Conversion of arginine into these amino acids was required for rapid proliferation when blocking the respiratory chain. The respiratory block triggered an immediate gene expression response diagnostic of TOR inhibition, which was muted by arginine supplementation or without the AMPK‐activating kinase Ssp1. The TOR‐controlled proteins featured biased composition of amino acids reflecting their shortage after respiratory inhibition. We conclude that respiration supports rapid proliferation in fermenting fission yeast cells by boosting the supply of Krebs cycle‐derived amino acids.

Amino acids are key substrates to Escherichia coli BW25113 for achieving high specific growth rate

Studying substrate consumption in nutrient-rich conditions is challenging because often the growth medium includes undefined components like yeast extract or peptone. For clear and consistent results, it is necessary to use defined medium, where substrate utilization can be followed. In the present work, Escherichia coli BW25113 batch growth in a medium supplemented with 20 proteinogenic amino acids and glucose was studied. Focus was on the quantitative differences in substrate consumption and proteome composition between minimal and nutrient-rich medium. In the latter, 72% of carbon used for biomass growth came from amino acids and 28% from glucose. Serine was identified as the most consumed substrate with 41% of total carbon consumption. Proteome comparison between nutrient-rich and minimal medium revealed changes in TCA cycle and acetate producing enzymes that together with extracellular metabolite data pointed to serine being consumed mainly for energy generation purposes. Serine removal from the growth medium decreased specific growth rate by 22%. In addition, proteome comparison between media revealed a large shift in amino acid synthesis and translation related proteins. Overall, this work describes in quantitative terms the batch growth carbon uptake profile and proteome allocation of E. coli BW25113 in minimal and nutrient-rich medium.

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from flux balance analyses to self-fabricating metabolism and expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments.

Time-resolved multi-omics analysis reveals the role of nutrient stress-induced resource reallocation for TAG accumulation in oleaginous fungus Mortierella alpina

BACKGROUND

Global resource reallocation is an established critical strategy through which organisms deal with environmental stress. The regulation of intracellular lipid storage or utilization is one of the most important strategies for maintaining energy homeostasis and optimizing growth. Oleaginous microorganisms respond to nitrogen deprivation by inducing lipid hyper accumulation; however, the associations between resource allocation and lipid accumulation are poorly understood.

RESULTS

Here, the time-resolved metabolomics, lipidomics, and proteomics data were generated in response to nutrient availability to examine how metabolic alternations induced by nitrogen deprivation drive the triacylglycerols (TAG) accumulation in M. alpina. The subsequent accumulation of TAG under nitrogen deprivation was a consequence of the reallocation of carbon, nitrogen sources, and lipids, rather than an up-regulation of TAG biosynthesis genes. On one hand, nitrogen deprivation induced the down-regulation of isocitrate dehydrogenase level in TCA cycle and redirected glycolytic flux of carbon from amino acid biosynthesis into fatty acids' synthesis; on the other hand, nitrogen deprivation induced the up-regulation of cell autophagy and ubiquitin-mediated protein proteolysis which resulted in a recycling of preformed protein nitrogen and carbon. Combining with the up-regulation of glutamate decarboxylase and succinic semialdehyde dehydrogenase in GABA shunt, and the phosphoenolpyruvate carboxykinase in the central hub involving pyruvate/phosphoenolpyruvate/oxaloacetate, the products from nitrogen-containing compounds degradation were recycled to be intermediates of TCA cycle and be shunted toward de novo biosynthesis of fatty acids. We found that nitrogen deprivation increased the protein level of phospholipase C/D that contributes to degradation of phosphatidylcholine and phosphatidylethanolamine, and supplied acyl chains for TAG biosynthesis pathway. In addition, ATP from substrate phosphorylation was presumed to be a critical factor regulation of the global resource allocation and fatty acids' synthesis rate.

CONCLUSIONS

The present findings offer a panoramic view of resource allocation by M. alpina in response to nutrient stress and revealed a set of intriguing associations between resource reallocation and TAG accumulation. This system-level insight provides a rich resource with which to explore in-depth functional characterization and gain information about the strategic combination of strain development and process integration to achieve optimal lipid productivity under nutrient stress.

Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis

To which extent gene regulatory programs are optimized by evolution is one of the fundamental biological questions. Swimming motility is one of the costliest bacterial behaviors, but expression of motility genes nevertheless increases when bacteria grow slower on poor carbon sources. Here, we show that competitive fitness benefit provided by the ability of motile bacteria to follow gradients of secondary nutrients shows similar negative dependence on the growth rate as the investment in motility. Thus, bacteria appear to pre-invest into the motile behavior in proportion to the anticipated benefit that can be provided by chemotaxis when nutrient gradients become available in their environment.

Microorganisms possess diverse mechanisms to regulate investment into individual cellular processes according to their environment. How these regulatory strategies reflect the inherent trade-off between the benefit and cost of resource investment remains largely unknown, particularly for many cellular functions that are not immediately related to growth. Here, we investigate regulation of motility and chemotaxis, one of the most complex and costly bacterial behaviors, as a function of bacterial growth rate. We show with experiment and theory that in poor nutritional conditions, Escherichia coli increases its investment in motility in proportion to the reproductive fitness advantage provided by the ability to follow nutrient gradients. Since this growth-rate dependent regulation of motility genes occurs even when nutrient gradients are absent, we hypothesize that it reflects an anticipatory preallocation of cellular resources. Notably, relative fitness benefit of chemotaxis could be observed not only in the presence of imposed gradients of secondary nutrients but also in initially homogeneous bacterial cultures, suggesting that bacteria can generate local gradients of carbon sources and excreted metabolites, and subsequently use chemotaxis to enhance the utilization of these compounds. This interplay between metabolite excretion and their chemotaxis-dependent reutilization is likely to play an important general role in microbial communities.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Genetically diverse uropathogenic Escherichia coli adopt a common transcriptional program in patients with UTIs

Uropathogenic Escherichia coli (UPEC) is the major causative agent of uncomplicated urinary tract infections (UTIs). A common virulence genotype of UPEC strains responsible for UTIs is yet to be defined, due to the large variation of virulence factors observed in UPEC strains. We hypothesized that studying UPEC functional responses in patients might reveal universal UPEC features that enable pathogenesis. Here we identify a transcriptional program shared by genetically diverse UPEC strains isolated from 14 patients during uncomplicated UTIs. Strikingly, this in vivo gene expression program is marked by upregulation of translational machinery, providing a mechanism for the rapid growth within the host. Our analysis indicates that switching to a more specialized catabolism and scavenging lifestyle in the host allows for the increased translational output. Our study identifies a common transcriptional program underlying UTIs and illuminates the molecular underpinnings that likely facilitate the fast growth rate of UPEC in infected patients.

Characterizing the effect of expression of an acetyl-CoA synthetase insensitive to acetylation on co-utilization of glucose and acetate in batch and continuous cultures of E. coli W

BACKGROUND

Due to its high stress tolerance and low acetate secretion, Escherichia coli W is reported to be a good production host for several metabolites and recombinant proteins. However, simultaneous co-utilization of glucose and other substrates such as acetate remains a challenge. The activity of acetyl-CoA-synthetase, one of the key enzymes involved in acetate assimilation is tightly regulated on a transcriptional and post-translational level. The aim of this study was to engineer E. coli W for overexpression of an acetylation insensitive acetyl-CoA-synthetase and to characterize this strain in batch and continuous cultures using glucose, acetate and during co-utilization of both substrates.

RESULTS

Escherichia coli W engineered to overexpress an acetylation-insensitive acetyl-CoA synthetase showed a 2.7-fold increase in acetate uptake in a batch process containing glucose and high concentrations of acetate compared to a control strain, indicating more efficient co-consumption of glucose and acetate. When acetate was used as the carbon source, batch duration could significantly be decreased in the overexpression strain, possibly due to alleviation of acetate toxicity. Chemostat cultivations with different dilution rates using glucose revealed only minor differences between the overexpression and control strain. Accelerostat cultivations using dilution rates between 0.20 and 0.70 h-1 indicated that E. coli W is naturally capable of efficiently co-utilizing glucose and acetate over a broad range of specific growth rates. Expression of acetyl-CoA synthetase resulted in acetate and glucose accumulation at lower dilution rates compared to the control strain. This observation can possibly be attributed to a higher ratio between acs and pta-ackA in the overexpression strain as revealed by gene expression analysis. This would result in enhanced energy dissipation caused by an imbalance in the Pta-AckA-Acs cycle. Furthermore, yjcH and actP, genes co-transcribed with acetyl-CoA synthetase showed significant down-regulation at elevated dilution rates.

CONCLUSIONS

Escherichia coli W expressing an acetylation-insensitive acetyl-CoA synthetase was shown to be a promising candidate for mixed feed processes using glucose and acetate. Comparison between batch and continuous cultures revealed distinct differences in glucose-acetate co-utilization behavior, requiring additional investigations such as multi-omics analysis and further engineering towards even more efficient co-utilization strains of E. coli W.