Overflow metabolism in Escherichia coli results from efficient proteome allocation

Geometrically balanced model of cell growth

The proteome balance constraint in metabolic flux balance analysis asserts that the proteome is constructed by ribosomes, which themselves contain many proteins. This leads to a fundamental question of optimal allocation of limited proteome among different pools of enzymes, which include ribosomes themselves. However, recent work points to additional constraints imposed by the cell geometry. In this paper we deduce the proteogeometric constraintπ¯A=πA+θπL/πP, where πA, πP and πL are the proteomic fractions allocated to the cell surface area, protein synthesis and cell membrane phospholipids synthesis and π¯A and θ are constants imposed by geometry of the cell. We illustrate the relevance of this constraint using a reduced model of cell metabolism, illuminating the interplay between cell metabolism and cell geometry.

Biosensor-Assisted Multitarget Gene Fine-Tuning for N-Acetylneuraminic Acid Production in Escherichia coli with Sole Carbon Source Glucose

N-Acetylneuraminic acid (NeuAc) is widely used in the food and medical industries. Microbial fermentation has become one of the most important approaches for NeuAc production. However, current research on NeuAc is confronted with challenges, including high production costs, interference from competitive pathways, and low conversion efficiency, all of which impede its efficient production. In this study, an engineered Escherichia coli capable of utilizing glucose as the sole carbon source for NeuAc production was constructed by optimizing the glucose utilization pathway, competitive pathways, and redox balance of NADH/NAD+. Subsequently, pathway genes were systematically upregulated to identify key target genes for improving NeuAc biosynthesis. The gene cluster glmSA*-glmM-SeglmU was identified as the key engineering target. To achieve multitarget coordinated optimization of this gene cluster in vivo, a highly responsive biosensor for NeuAc was developed, exhibiting a maximum response ratio of 10.62-fold. By the construction of random mutation libraries and integration of the NeuAc-responsive biosensor with high-throughput screening using flow cytometry, the expression levels of three key genes were synergistically optimized. As a result, highly efficient NeuAc-producing strain A39 was successfully obtained. In a 3-L bioreactor, the strain achieved a NeuAc titer of 58.26 g·L-1 with a productivity of 0.83 g·L-1·h-1, representing the highest reported production of NeuAc using glucose as the sole carbon source.

The catabolic nature of fermentative substrates influences proteomic rewiring in Escherichia coli under anoxic growth

BACKGROUND

During anaerobic batch fermentation of substrates by Escherichia coli, there is a decline in cell proliferation rates and a huge demand is placed on cellular proteome to cater to its catabolic and anabolic needs under anoxic growth. Considering cell growth rates as a physiological parameter, previous studies have established a direct relationship between E. coli growth rate and cellular ribosomal content for fast-proliferating cells. In this study, we integrated experimental findings with a systemic coarse-grained proteome allocation model, to characterize the physiological outcomes at slow growth rate during anaerobic fermentative catabolism of different glycolytic and non-glycolytic substrates.

RESULTS

The anaerobic catabolism of substrates favored high ribosomal abundances at lower growth rates. Interestingly, a modification of the previously discussed "growth law", the ratio of active to inactive ribosomal proteome was found to be linearly related to the growth rate for cells proliferating at slow to moderate regime (growth rate < 0.8 h- 1). Also, under nutrient- and oxygen-limiting growth conditions, the proteome proportion allocated for ribosomal activity was reduced, and the resources were channelized towards metabolic activities to overcome the limitations imposed during uptake and metabolizing substrate. The energy-intensive uptake mechanism or lower substrate affinity, expended more catabolic proteome, which reduced its availability to other cellular functions.

CONCLUSIONS

Thus, the nature of catabolic substrates imposed either uptake limitation or metabolic limitation coupled with ribosomal limitation (arising due to anoxic and nutritional stress), which resulted in higher proteome expenditure leading to sub-optimal growth phenotype. This study can form the basis for analyzing E. coli's ability to optimize metabolic efficiency under different environmental conditions- including stress responses. It can be further extended to optimizing the industrial anaerobic conversions for improving productivity and yield.

Engineering an Overflow-Responsive Regulation System for Balancing Cellular Redox and Optimizing Microbial Production

Escherichia coli accumulates acetate as a byproduct in fast growth aerobic conditions when using glucose as carbon source. This phenomenon, known as overflow metabolism, has negative impacts on cell growth and protein expression, also causes carbon loss during biosynthesis in most microbial production scenarios. In this study, we regarded the "waste" metabolite as a useful metabolism indicator, constructed an overflow biosensor to monitor the change of acetate concentration and converted the signal into various regulation outputs. Phloroglucinol is a phenolic compound with several derivatives that exhibit various pharmacological activities. By applying the bifunctional dynamic regulation system on the phloroglucinol production, we released the cellular redox pressure in real-time and reduced the waste of carbon flux on overflow metabolism. Finally, carbon flux was redirected more favorably towards the desired product, resulting in a boosted phloroglucinol titer of 1.30 g/L, increased by 2.04-fold. Overall, this study explored the use of a central byproduct-responsive biosensor system on improving cellular metabolic status, providing a general approach for enhancing bioproduction.

Proteomic insights into survival strategies of Escherichia coli in perchlorate-rich Martian brines

Brines, potentially formed by the deliquescence and freezing point depression of highly hygroscopic salts, such as perchlorates (ClO4-), may allow for the spatial and temporal stability of liquid water on present-day Mars. It is therefore of great interest to explore the microbial habitability of Martian brines, for which our current understanding is, however, still limited. Putative microbes growing in the perchlorate-rich Martian regolith may be harmed due to the induction of various stressors including osmotic, chaotropic, and oxidative stress. We adapted the model organism Escherichia coli to increasing sodium perchlorate concentrations and used a proteomic approach to characterize the adaptive phenotype. Separately, the microbe was adapted to elevated concentrations of sodium chloride and glycerol, which enabled us to distinguish perchlorate-specific adaptation mechanisms from those in response to osmotic, ion and water activity stress. We found that the perchlorate-specific stress response focused on pathways alleviating damage to nucleic acids, presumably caused by increased chaotropic and/or oxidative stress. The significant enrichments that have been found include DNA repair, RNA methylation and de novo inosine monophosphate (IMP) biosynthesis. Our study provides insights into the adaptive mechanisms necessary for microorganisms to survive under perchlorate stress, with implications for understanding the habitability of Martian brines.

Physics of swimming and its fitness cost determine strategies of bacterial investment in flagellar motility

Microorganisms must distribute their limited resources among different physiological functions, including those that do not directly contribute to growth. In this study, we investigate the allocation of resources to flagellar swimming, the most prominent and biosynthetically costly of such cellular functions in bacteria. Although the growth-dependence of flagellar gene expression in peritrichously flagellated Escherichia coli is well known, the underlying physiological limitations and regulatory strategies are not fully understood. By characterizing the dependence of motile behavior on the activity of the flagellar regulon, we demonstrate that, beyond a critical number of filaments, the hydrodynamics of propulsion limits the ability of bacteria to increase their swimming by synthesizing additional flagella. In nutrient-rich conditions, E. coli apparently maximizes its motility until reaching this limit, while avoiding the excessive cost of flagella production. Conversely, during carbon-limited growth motility remains below maximal levels and inversely correlates with the growth rate. The physics of swimming may further explain the selection for bimodal resource allocation in motility at low average expression levels. Notwithstanding strain-specific variation, the expression of flagellar genes in all tested natural isolates of E. coli also falls within the same range defined by the physical limitations on swimming and its biosynthetic cost.

Enhancing recombinant growth factor and serum protein production for cultivated meat manufacturing

The commercial growth factors (GFs) and serum proteins (SPs) contribute to the high cost associated with the serum-free media for cultivated meat production. Producing recombinant GFs and SPs in scale from microbial cell factories can reduce the cost of culture media. Escherichia coli is a frequently employed host in the expression recombinant GFs and SPs. This review explores critical strategies for cost reduction in GFs and SPs production, focusing on yield enhancement, product improvement, purification innovation, and process innovation. Firstly, the review discusses the use of fusion tags to increase the solubility and yield of GFs & SPs, highlighting various studies that have successfully employed these tags for yield enhancement. We then explore how tagging strategies can streamline and economize the purification process, further reducing production costs. Additionally, we address the challenge of low half-life in GFs and SPs and propose potential strategies that can enhance their stability. Furthermore, improvements in the E. coli chassis and cell engineering strategies are also described, with an emphasis on the key areas that can improve yield and identify areas for cost minimization. Finally, we discuss key bioprocessing areas which can facilitate easier scale-up, enhance yield, titer, and productivity, and ultimately lower long-term production costs. It is crucial to recognize that not all suggested approaches can be applied simultaneously, as their relevance varies with different GFs and SPs. However, integrating of multiple strategies is anticipated to yield a cumulative effect, significantly reducing production costs. This collective effort is expected to substantially decrease the price of cultivated meat, contributing to the broader goal of developing sustainable and affordable meat.

Microbial functional guilds respond cohesively to rapidly fluctuating environments

Microbial communities experience environmental fluctuations across timescales from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variations. Understanding how microbial populations respond to these changes is critical for predicting the impact of perturbations, interventions, and climate change on communities. Since communities typically harbor tens to hundreds of distinct taxa, the response of microbial abundances to perturbations is potentially complex. However, while taxonomic diversity is high, in many communities taxa can be grouped into functional guilds of strains with similar metabolic traits. These guilds effectively reduce the complexity of the system by providing a physiologically motivated coarse-graining. Here, using a combination of simulations, theory, and experiments, we show that the response of guilds to nutrient fluctuations depends on the timescale of those fluctuations. Rapid changes in nutrient levels drive cohesive, positively correlated abundance dynamics within guilds. For slower timescales of environmental variation, members within a guild begin to compete due to similar resource preferences, driving negative correlations in abundances between members of the same guild. Our results provide a route to understanding the relationship between functional guilds and community response to changing environments, as well as an experimental approach to discovering functional guilds via designed nutrient perturbations to communities.

Overall biomass yield on multiple nutrient sources

Microorganisms primarily utilize nutrients to generate biomass and replicate. When a single nutrient source is available, the produced biomass typically increases linearly with the initial amount of that nutrient. This linear trend can be accurately predicted by "black box models", which conceptualize growth as a single chemical reaction, treating nutrients as substrates and biomass as a product. However, natural environments usually present multiple nutrient sources, prompting us to extend the black box framework to incorporate catabolism, anabolism, and biosynthesis of biomass precursors. This modification allows for the quantification of co-utilization effects among multiple nutrients on microbial biomass production. The extended model differentiates between different types of nutrients: non-degradable nutrients, which can only serve as a biomass precursor, and degradable nutrients, which can also be used as an energy source. We experimentally demonstrated using Escherichia coli that, in contrast to initial model predictions, different nutrients affect each other's utilization in a mutually dependent manner; i.e., for some combinations, the produced biomass was no longer proportional to the initial amounts of nutrients present. To account for these mutual effects within a black box framework, we phenomenologically introduced an interaction between the metabolic processes involved in utilizing the nutrient sources. This phenomenological model qualitatively captures the experimental observations and, unexpectedly, predicts that the total produced biomass is influenced not only by the combination of nutrient sources but also by their relative initial amounts - a prediction we subsequently validated experimentally. Moreover, the model identifies which metabolic processes - catabolism, anabolism, or precursor biosynthesis-is affected in each specific nutrient combination, offering insights into microbial metabolic coordination.

Hexokinase detachment from mitochondria drives the Warburg effect to support compartmentalized ATP production

Hexokinase (HK) catalyzes the synthesis of glucose-6-phosphate, marking the first committed step of glucose metabolism. Most cancer cells express two homologous isoforms (HK1 and HK2) that can each bind to the outer mitochondrial membrane (OMM). CRISPR screens across hundreds of cancer cell lines indicate that both are dispensable for cell growth in traditional culture media. By contrast, HK2 deletion impairs cell growth in Human Plasma-Like Medium (HPLM). Here, we find that HK2 is required to maintain sufficient cytosolic (OMM-detached) HK activity under conditions that enhance HK1 binding to the OMM. Notably, OMM-detached rather than OMM-docked HK promotes "aerobic glycolysis" (Warburg effect), an enigmatic phenotype displayed by most proliferating cells. We show that several proposed theories for this phenotype cannot explain the HK2 dependence and instead find that HK2 deletion severely impairs glycolytic ATP production with little impact on total ATP yield for cells in HPLM. Our results reveal a basis for conditional HK2 essentiality and suggest that demand for compartmentalized ATP synthesis underlies the Warburg effect.

COBREXA 2: tidy and scalable construction of complex metabolic models

SUMMARY

Constraint-based metabolic models offer a scalable framework to investigate biological systems using optimality principles. Construction and simulation of detailed models that utilize multiple kinds of constraint systems pose a significant coding overhead, complicating implementation of new types of analyses. We present an improved version of the constraint-based metabolic modeling package COBREXA, which utilizes a hierarchical model construction framework that decouples the implemented analysis algorithms into independent, yet re-combinable, building blocks. By removing the need to re-implement modeling components, assembly of complex metabolic models is simplified, which we demonstrate on use-cases of resource-balanced models, and enzyme-constrained flux balance models of interacting bacterial communities. Notably, these models show improved predictive capabilities in both monoculture and community settings. In perspective, the re-usable model-building components in COBREXA 2 provide a sustainable way to handle increasingly complex models in constraint-based modeling.

AVAILABILITY AND IMPLEMENTATION

COBREXA 2 is available from https://github.com/COBREXA/COBREXA.jl, and from Julia package repositories. COBREXA 2 works on all major operating systems and computer architectures. Documentation is available at https://cobrexa.github.io/COBREXA.jl/.

A thermodynamic bottleneck in the TCA cycle contributes to acetate overflow in Staphylococcus aureus

During aerobic growth, S. aureus relies on acetate overflow metabolism, a process where glucose is incompletely oxidized to acetate, for its bioenergetic needs. Acetate is not immediately captured as a carbon source and is excreted as waste by cells. The underlying factors governing acetate overflow in S. aureus have not been identified. Here, we show that acetate overflow is favored due to a thermodynamic bottleneck in the TCA cycle specifically involving the oxidation of succinate to fumarate by succinate dehydrogenase. This bottleneck reduces flux through the TCA cycle, making it more efficient for S. aureus to generate ATP via acetate overflow metabolism. Additionally, the protein allocation cost of maintaining ATP flux through the restricted TCA cycle is greater than that of acetate overflow metabolism. Finally, we show that the TCA cycle bottleneck provides S. aureus the flexibility to redirect carbon toward maintaining redox balance through lactate overflow when oxygen becomes limiting, albeit at the expense of ATP production through acetate overflow. Overall, our findings suggest that overflow metabolism offers S. aureus distinct bioenergetic advantages over a thermodynamically constrained TCA cycle, potentially supporting its commensal-pathogenic lifestyle.

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. Escherichia coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of this metabolic flexibility, we developed a coarse-grained mathematical framework coupling redox chemistry with principles of cellular resource allocation. Our models inherit key qualities from both of their antecedents: i) describing diverse metabolic chemistries and ii) enforcing the simultaneous balancing of atom (e.g., carbon), electron, and energy (adenosine triphosphate) flows, as in redox models, while iii) treating biomass as both the product and catalyst of the growth process, as in resource allocation models. Assembling integrated models of respiration, fermentation, and photosynthesis clarified key microbiological phenomena, including demonstrating that autotrophs grow more slowly than heterotrophs because of constraints imposed by the intracellular production of reduced carbon. Our model further predicted that heterotrophic growth is improved by matching the redox state of biomass to the nutrient environment. Through analysis of [Formula: see text]60,000 genomes and diverse proteomic datasets, we found evidence that proteins indeed accumulate amino acid substitutions promoting redox matching. We therefore propose an unexpected mode of genome evolution where substitutions neutral or even deleterious to the individual biochemical or structural functions of proteins can nonetheless be selected due to a redox-chemical benefit to the population.

Ecological dynamics explain modular denitrification in the ocean

Microorganisms in marine oxygen minimum zones (OMZs) drive globally impactful biogeochemical processes. One such process is multistep denitrification (NO3-→NO2-→NO→N2O→N2), which dominates OMZ bioavailable nitrogen (N) loss and nitrous oxide (N2O) production. Denitrification-derived N loss is typically measured and modeled as a single step, but observations reveal that most denitrifiers in OMZs contain subsets ("modules") of the complete pathway. Here, we identify the ecological mechanisms sustaining diverse denitrifiers, explain the prevalence of certain modules, and examine the implications for N loss. We describe microbial functional types carrying out diverse denitrification modules by their underlying redox chemistry, constraining their traits with thermodynamics and pathway length penalties, in an idealized OMZ ecosystem model. Biomass yields of single-step modules increase along the denitrification pathway when organic matter (OM) limits growth, which explains the viability of populations respiring NO2- and N2O in a NO3--filled ocean. Results predict denitrifier community succession along environmental gradients: Pathway length increases as the limiting substrate shifts from OM to N, suggesting a niche for the short NO3-→NO2- module in free-living, OM-limited communities, and for the complete pathway in organic particle-associated communities, consistent with observations. The model captures and mechanistically explains the observed dominance and higher oxygen tolerance of the NO3-→NO2- module. Results also capture observations that NO3- is the dominant source of N2O. Our framework advances the mechanistic understanding of the relationship between microbial ecology and N loss in the ocean and can be extended to other processes and environments.

An Agent-Based Model of Metabolic Signaling Oscillations in Bacillus subtilis Biofilms

Microbes of nearly every species can form biofilms, communities of cells bound together by a self-produced matrix. It is not understood how variation at the cellular level impacts putatively beneficial, colony-level behaviors, such as cell-to-cell signaling. Here we investigate this problem with an agent-based computational model of metabolically driven electrochemical signaling in Bacillus subtilis biofilms. In this process, glutamate-starved interior cells release potassium, triggering a depolarizing wave that spreads to exterior cells and limits their glutamate uptake. More nutrients diffuse to the interior, temporarily reducing glutamate stress and leading to oscillations. In our model, each cell has a membrane potential coupled to metabolism. As a simulated biofilm grows, collective membrane potential oscillations arise spontaneously as cells deplete nutrients and trigger potassium release, reproducing experimental observations. We further validate our model by comparing spatial signaling patterns and cellular signaling rates with those observed experimentally. By oscillating external glutamate and potassium, we find that biofilms synchronize to external potassium more strongly than to glutamate, providing a potential mechanism for previously observed biofilm synchronization. By tracking cellular glutamate concentrations, we find that oscillations evenly distribute nutrients in space: non-oscillating biofilms have an external layer of well-fed cells surrounding a starved core, whereas oscillating biofilms exhibit a relatively uniform distribution of glutamate. Our work shows the potential of agent-based models to connect cellular properties to collective phenomena and facilitates studies of how inheritance of cellular traits can affect the evolution of group behaviors.

A framework for understanding collective microbiome metabolism

Microbiome metabolism underlies numerous vital ecosystem functions. Individual microbiome members often perform partial catabolism of substrates or do not express all of the metabolic functions required for growth. Microbiome members can complement each other by exchanging metabolic intermediates and cellular building blocks to achieve a collective metabolism. We currently lack a mechanistic framework to explain why microbiome members adopt partial metabolism and how metabolic functions are distributed among them. Here we argue that natural selection for proteome efficiency-that is, performing essential metabolic fluxes at a minimal protein investment-explains partial metabolism of microbiome members, which underpins the collective metabolism of microbiomes. Using the carbon cycle as an example, we discuss motifs of collective metabolism, the conditions under which these motifs increase the proteome efficiency of individuals and the metabolic interactions they result in. In summary, we propose a mechanistic framework for how collective metabolic functions emerge from selection on individuals.

Metabolic footprint and logic through the T cell life cycle

A simple definition of life is a system that can self-replicate (proliferation) and self-sustain (metabolism). At the cellular level, metabolism has evolved to drive proliferation, which requires energy and building blocks to duplicate cellular biomass before division. T lymphocytes (or T cells) are required for adaptive immune responses, protecting us against invading and malignant agents capable of hyper-replication. To gain a competitive advantage over these agents, activated T cells can duplicate their biomass and divide into two daughter cells in as short as 2-6 hours, considered the fastest cell division among all cell types in vertebrates. Thus, the primary task of cellular metabolism has evolved to commit available resources to drive T cell hyperproliferation. Beyond that, the T cell life cycle involves an ordered series of fate-determining events that drive cells to transition between discrete cell states. At the life stages not involved in hyperproliferation, T cells engage metabolic programs that are more flexible to sustain viability and maintenance and sometimes are fine-tuned to support specific cellular activities. Here, we focus on the central carbon metabolism, which is most relevant to cell proliferation. We provide examples of how the changes in the central carbon metabolism may or may not change the fate of T cells and further explore a few conceptual frameworks, such as metabolic flexibility, the Goldilocks Principle, overflow metabolism, and effector-signaling metabolites, in the context of T cell fate transitions.

Metabolism and mRNA translation: a nexus of cancer plasticity

Tumors often face energy deprivation due to mutations, hypoxia, and nutritional deficiencies within the harsh tumor microenvironment (TME), and as an effect of anticancer treatments. This metabolic stress triggers adaptive reprogramming of mRNA translation, which in turn adjusts metabolic plasticity and associated signaling pathways to ensure tumor cell survival. Emerging evidence is beginning to reveal the complex interplay between metabolism and mRNA translation, shedding light on the mechanisms that synchronize ribosome assembly and reconfigure translation programs under metabolic stress. This review explores recent advances in our understanding of the coordination between metabolism and mRNA translation, offering insights that could inform therapeutic strategies targeting both cancer metabolism and translation, with the aim of disrupting cancer cell plasticity and survival.

A respiro-fermentative strategy to survive nanoxia in Acidobacterium capsulatum

Microbial soil habitats are characterized by rapid shifts in substrate and nutrient availabilities, as well as chemical and physical parameters. One such parameter that can vary in soil is oxygen; thus, microbial survival is dependent on adaptation to this substrate. To better understand the metabolic abilities and adaptive strategies to oxygen-deprived environments, we combined genomics with transcriptomics of a model organism, Acidobacterium capsulatum, to explore the effect of decreasing, environmentally relevant oxygen concentrations. The decrease from 10 to 0.1 µM oxygen (3.6 to 0.036 pO2% present atmospheric level, respectively) caused the upregulation of the transcription of genes involved in signal transduction mechanisms, energy production and conversion and secondary metabolites biosynthesis, transport, and catabolism based on clusters of orthologous group categories. Contrary to established observations for aerobic metabolism, key genes in oxidative stress response were significantly upregulated at lower oxygen concentrations, presumably due to an NADH/NAD+ redox ratio imbalance as the cells transitioned into nanoxia. Furthermore, A. capsulatum adapted to nanoxia by inducing a respiro-fermentative metabolism and rerouting fluxes of its central carbon and energy pathways to adapt to high NADH/NAD+ redox ratios. Our results reveal physiological features and metabolic capabilities that allowed A. capsulatum to adapt to oxygen-limited conditions, which could expand into other environmentally relevant soil strains.

Multiscale modelling of bioprocess dynamics and cellular growth

BACKGROUND

Fermentation processes are essential for the production of small molecules, heterologous proteins and other commercially important products. Traditional bioprocess optimisation relies on phenomenological models that focus on macroscale variables like biomass growth and protein yield. However, these models often fail to consider the crucial link between macroscale dynamics and the intracellular activities that drive metabolism and proteins synthesis.

RESULTS

We introduce a multiscale model that not only captures batch bioreactor dynamics but also incorporates a coarse-grained approach to key intracellular processes such as gene expression, ribosome allocation and growth. Our model accurately fits biomass and substrate data across various Escherichia coli strains, effectively predicts acetate dynamics and evaluates the expression of heterologous proteins. By integrating construct-specific parameters like promoter strength and ribosomal binding sites, our model reveals several key interdependencies between gene expression parameters and outputs such as protein yield and acetate secretion.

CONCLUSIONS

This study presents a computational model that, with proper parameterisation, allows for the in silico analysis of genetic constructs. The focus is on combinations of ribosomal binding site (RBS) strength and promoters, assessing their impact on production. In this way, the ability to predict bioreactor dynamics from these genetic constructs can pave the way for more efficient design and optimisation of microbial fermentation processes, enhancing the production of valuable bioproducts.

The Warburg Effect is the result of faster ATP production by glycolysis than respiration

Many prokaryotic and eukaryotic cells metabolize glucose to organism-specific by-products instead of fully oxidizing it to carbon dioxide and water-a phenomenon referred to as the Warburg Effect. The benefit to a cell is not fully understood, given that partial metabolism of glucose yields an order of magnitude less adenosine triphosphate (ATP) per molecule of glucose than complete oxidation. Here, we test a previously formulated hypothesis that the benefit of the Warburg Effect is to increase ATP production rate by switching from high-yielding respiration to faster glycolysis when excess glucose is available and respiration rate becomes limited by proteome occupancy. We show that glycolysis produces ATP faster per gram of pathway protein than respiration in Escherichia coli, Saccharomyces cerevisiae, and mammalian cells. We then develop a simple mathematical model of energy metabolism that uses five experimentally estimated parameters and show that this model can accurately predict absolute rates of glycolysis and respiration in all three organisms under diverse conditions, providing strong support for the validity of the ATP production rate maximization hypothesis. In addition, our measurements show that mammalian respiration produces ATP up to 10-fold slower than respiration in E. coli or S. cerevisiae, suggesting that the ATP production rate per gram of pathway protein is a highly evolvable trait that is heavily optimized in microbes. We also find that E. coli respiration is faster than fermentation, explaining the observation that E. coli, unlike S. cerevisiae or mammalian cells, never switch to pure fermentation in the presence of oxygen.

Acetate production from corn stover hydrolysate using recombinant Escherichia coli BL21 (DE3) with an EP-bifido pathway

BACKGROUND

Acetate is an important chemical feedstock widely applied in the food, chemical and textile industries. It is now mainly produced from petrochemical materials through chemical processes. Conversion of lignocellulose biomass to acetate by biotechnological pathways is both environmentally beneficial and cost-effective. However, acetate production from carbohydrate in lignocellulose hydrolysate via glycolytic pathways involving pyruvate decarboxylation often suffers from the carbon loss and results in low acetate yield.

RESULTS

Escherichia coli BL21 (DE3) was confirmed to have high tolerance to acetate in this work. Thus, it was selected from seven laboratory E. coli strains for acetate production from lignocellulose hydrolysate. The byproduct-producing genes frdA, ldhA, and adhE in E. coli BL21 (DE3) were firstly knocked out to decrease the generation of succinate, lactate, and ethanol. Then, the genes pfkA and edd were also deleted and bifunctional phosphoketolase and fructose-1,6-bisphosphatase were overexpressed to construct an EP-bifido pathway in E. coli BL21 (DE3) to increase the generation of acetate from glucose. The obtained strain E. coli 5K/pFF can produce 22.89 g/L acetate from 37.5 g/L glucose with a yield of 0.61 g/g glucose. Finally, the ptsG gene in E. coli 5K/pFF was also deleted to make the engineered strain E. coli 6K/pFF to simultaneously utilize glucose and xylose in lignocellulosic hydrolysates. E. coli 6K/pFF can produce 20.09 g/L acetate from corn stover hydrolysate with a yield of 0.52 g/g sugar.

CONCLUSION

The results presented here provide a promising alternative for acetate production with low cost substrate. Besides acetate production, other biotechnological processes might also be developed for other acetyl-CoA derivatives production with lignocellulose hydrolysate through further metabolic engineering of E. coli 6K/pFF.

Plasmid-encoded phosphatase RapP enhances cell growth in non-domesticated Bacillus subtilis strains

The trade-off between rapid growth and other important physiological traits (e.g., survival and adaptability) poses a fundamental challenge for microbes to achieve fitness maximization. Studies on Bacillus subtilis biology often use strains derived after a process of lab 'domestication' from an ancestral strain known as Marburg strain. The domestication process led to loss of a large plasmid (pBS32) encoding a phosphatase (RapP) that dephosphorylates the Spo0F protein and thus regulates biofilm formation and sporulation. Here, we show that plasmid pBS32, and more specifically rapP, enhance growth rates by preventing premature expression of the Spo0F-Spo0A-mediated adaptive response during exponential phase. This results in reallocation of proteome resources towards biosynthetic, growth-promoting pathways without compromising long-term fitness during stationary phase. Thus, RapP helps B. subtilis to constrain physiological trade-offs and economize cellular resources for fitness improvement.

Engineering Halomonas bluephagenesis for Synthesis of Polyhydroxybutyrate (PHB) in the Presence of High Nitrogen Containing Media

The trade-offs exist between microbial growth and bioproduct synthesis including intracellular polyester polyhydroxybutyrate (PHB). Under nitrogen limitation, more carbon flux is directed to PHB synthesis while growth is inhibited with diminishing overall carbon utilization, similar to the suboptimal carbon utilization during glycolysis-derived pyruvate decarboxylation. This study reconfigured the central carbon network of Halomonas bluphagenesis to improve PHB yield theoretically and practically. It was found that the downregulation of glutamine synthetase (GS) activity led to a synchronous improvement on PHB accumulation and cell growth under nitrogen non-limitation condition, increasing the PHB yield from glucose (g/g) to 85% of theoretical yield, PHB titer from 7.6 g/L to 12.9 g/L, and from 51 g/L to 65 g/L when grown in shake flasks containing a rich N-source, and grown in a fed-batch cultivation conducted in a 7-L bioreactor also containing a rich N-source, respectively. Results offer better metabolic balance between glucose conversion efficiency and microbial growth for economic PHB production.

A thermodynamic bottleneck in the TCA cycle contributes to acetate overflow in Staphylococcus aureus

During aerobic growth, S. aureus relies on acetate overflow metabolism, a process where glucose is incompletely oxidized to acetate, for its bioenergetic needs. Acetate is not immediately captured as a carbon source and is excreted as waste by cells. The underlying factors governing acetate overflow in S. aureus have not been identified. Here, we show that acetate overflow is favored due to a thermodynamic bottleneck in the TCA cycle, specifically involving the oxidation of succinate to fumarate by succinate dehydrogenase. This bottleneck reduces flux through the TCA cycle, making it more efficient for S. aureus to generate ATP via acetate overflow metabolism. Additionally, the protein allocation cost of maintaining ATP flux through the restricted TCA cycle is greater than that of acetate overflow metabolism. Finally, we show that the TCA cycle bottleneck provides S. aureus the flexibility to redirect carbon towards maintaining redox balance through lactate overflow when oxygen becomes limiting, albeit at the expense of ATP production through acetate overflow. Overall, our findings suggest that overflow metabolism offers S. aureus distinct bioenergetic advantages over a thermodynamically constrained TCA cycle, potentially supporting its commensal-pathogen lifestyle.

Free-energy transduction mechanisms shape the flux space of metabolic networks

The transduction of free energy in metabolic networks represents a thermodynamic mechanism by which the free energy derived from nutrients is converted to drive nonspontaneous, energy-requiring metabolic reactions. This transduction is typically observed in processes that generate energy-rich molecules such as ATP and NAD(P)H, which, in turn, power specific reactions, particularly biosynthetic reactions. This property establishes a pivotal connection between the intricate topology of metabolic networks and their ability to redirect energy for functional purposes. The present study proposes a dedicated framework aimed at exploring the relationship between free-energy dissipation, network topology, and metabolic objectives. The starting point is that, regardless of the network topology, nonequilibrium chemostatting conditions impose stringent thermodynamic constraints on the feasible flux steady states to satisfy energy and entropy balances. An analysis of randomly sampled reaction networks shows that the network topology imposes additional constraints that restrict the accessible flux solution space, depending on key structural features such as the reaction's molecularity, reaction cycles, and conservation laws. Notably, topologies featuring multimolecular reactions that implement free-energy transduction mechanisms tend to extend the accessible flux domains, facilitating the achievement of metabolic objectives such as anabolic flux maximization or flux rerouting capacity. This approach is applied to a coarse-grained model of carbohydrate metabolism, highlighting the structural requirements for optimal biomass yield.

Salmonella Typhimurium screen identifies shifts in mixed-acid fermentation during gut colonization

How enteric pathogens adapt their metabolism to a dynamic gut environment is not yet fully understood. To investigate how Salmonella enterica Typhimurium (S.Tm) colonizes the gut, we conducted an in vivo transposon mutagenesis screen in a gnotobiotic mouse model. Our data implicate mixed-acid fermentation in efficient gut-luminal growth and energy conservation throughout infection. During initial growth, the pathogen utilizes acetate fermentation and fumarate respiration. After the onset of gut inflammation, hexoses appear to become limiting, as indicated by carbohydrate analytics and the increased need for gluconeogenesis. In response, S.Tm adapts by ramping up ethanol fermentation for redox balancing and supplying the TCA cycle with α-ketoglutarate for additional energy. Our findings illustrate how S.Tm flexibly adapts mixed fermentation and its use of the TCA cycle to thrive in the changing gut environment. Similar metabolic wiring in other pathogenic Enterobacteriaceae may suggest a broadly conserved mechanism for gut colonization.

Impact of nutrient excess on physiology and metabolism of Sulfolobus acidocaldarius

Overflow metabolism is a well-known phenomenon that describes the seemingly wasteful and incomplete substrate oxidation by aerobic cells, such as yeasts, bacteria, and mammalian cells, even when conditions allow for total combustion via respiration. This cellular response, triggered by an excess of C-source, has not yet been investigated in archaea. In this study, we conducted chemostat cultivations to compare the metabolic and physiological states of the thermoacidophilic archaeon Sulfolobus acidocaldarius under three conditions, each with gradually increasing nutrient stress. Our results show that S. acidocaldarius has different capacities for the uptake of the two C-sources, monosodium glutamate and glucose. A saturated tricarboxylic acid cycle at elevated nutrient concentrations affects the cell's ability to deplete its intermediates. This includes deploying additional cataplerotic pathways and the secretion of amino acids, notably valine, glycine, and alanine, while glucose is increasingly metabolized via glycogenesis. We did not observe the secretion of common fermentation products, like organic acids. Transcriptomic analysis indicated an upregulation of genes involved in fatty acid metabolism, suggesting the intracellular conservation of energy. Adapting respiratory enzymes under nutrient stress indicated high metabolic flexibility and robust regulatory mechanisms in this archaeon. This study enhances our fundamental understanding of the metabolism of S. acidocaldarius.

Metabolic Plasticity Shapes Microbial Communities across a Temperature Gradient

AbstractA central challenge in community ecology is understanding and predicting the effects of abiotic factors on community assembly. In particular, microbial communities play a central role in the ecosystem, but we do not understand how changing factors like temperature are going to affect community composition or function. In this article, we studied the self-assembly of multiple communities in synthetic environments to understand changes in microbial community composition based on metabolic responses of different functional groups along a temperature gradient. In many microbial communities, different microbial functional groups coexist through the partitioning of carbon sources in an emergent trophic structure (cross-feeding). In this system, respirofermentative bacteria display a preference for the sugars supplied as the only carbon source but secrete secondary carbon sources (organic acids) that are more efficiently consumed by obligate respirators. As a consequence of this trophic structure, the metabolic plasticity of the respirofermenters has downstream consequences for the relative abundance of respirators across temperatures. We found that the effects of different temperatures on microbial composition can largely be described by an increase in fermentation by-products with increasing temperatures from the respirofermentative bacteria. This research highlights the importance of metabolic plasticity and metabolic trade-offs in predicting species interactions and community dynamics across abiotic gradients.

Environmental modulators of algae-bacteria interactions at scale

Interactions between photosynthetic and heterotrophic microbes play a key role in global primary production. Understanding phototroph-heterotroph interactions remains challenging because these microbes reside in chemically complex environments. Here, we leverage a massively parallel droplet microfluidic platform that enables us to interrogate interactions between photosynthetic algae and heterotrophic bacteria in >100,000 communities across ∼525 environmental conditions with varying pH, carbon availability, and phosphorus availability. By developing a statistical framework to dissect interactions in this complex dataset, we reveal that the dependence of algae-bacteria interactions on nutrient availability is strongly modulated by pH and buffering capacity. Furthermore, we show that the chemical identity of the available organic carbon source controls how pH, buffering capacity, and nutrient availability modulate algae-bacteria interactions. Our study reveals the previously underappreciated role of pH in modulating phototroph-heterotroph interactions and provides a framework for thinking about interactions between phototrophs and heterotrophs in more natural contexts.

Mitochondrial ATP generation is more proteome efficient than glycolysis

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

Repeated glucose oscillations in high cell-density cultures influence stress-related functions of Escherichia coli

Engineering microbial cells for the commercial production of biomolecules and biochemicals requires understanding how cells respond to dynamically changing substrate (feast-famine) conditions in industrial-scale bioreactors. Scale-down methods that oscillate substrate are commonly applied to predict the industrial-scale behavior of microbes. We followed a compartment modeling approach to design a scale-down method based on the simulation of an industrial-scale bioreactor. This study uses high cell-density scale-down experiments to investigate Escherichia coli knockout strains of five major glucose-sensitive transcription factors (Cra, Crp, FliA, PrpR, and RpoS) to study their regulatory role during glucose oscillations. RNA-sequencing analysis revealed that the glucose oscillations caused the down-regulation of several stress-related functions in E. coli. An in-depth analysis of strain physiology and transcriptome revealed a distinct phenotype of the strains tested under glucose oscillations. Specifically, the knockout strains of Cra, Crp, and RpoS resulted in a more sensitive transcriptional response than the control strain, while the knockouts of FliA and PrpR responded less severely. These findings imply that the regulation orchestrated by Cra, Crp, and RpoS may be essential for robust E. coli production strains. In contrast, the regulation by FliA and PrpR may be undesirable for temporal oscillations in glucose availability.

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy

A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V). This increase in membrane protein is hypothesized to be essential to competitive Escherichia coli phenotypes. The presented membrane-centric theory uses biophysical properties and metabolic systems analysis to successfully predict the phenotypes of E. coli K-12 strains, MG1655 and NCM3722, which are genetically similar but have SA:V ratios that differ up to 30%, maximum growth rates on glucose media that differ by 40%, and overflow phenotypes that start at growth rates that differ by 80%. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory. Cell geometry and membrane protein crowding are significant biophysical constraints on phenotype and provide a theoretical framework for improved understanding and control of cell biology.

Homeostasis of cytoplasmic crowding by cell wall fluidization and ribosomal counterions

In bacteria, algae, fungi, and plant cells, the wall must expand in concert with cytoplasmic biomass production, otherwise cells would experience toxic molecular crowding1,2or lyse. But how cells achieve expansion of this complex biomaterial in coordination with biosynthesis of macromolecules in the cytoplasm remains unexplained3, although recent works have revealed that these processes are indeed coupled4,5. Here, we report a striking increase of turgor pressure with growth rate in E. coli, suggesting that the speed of cell wall expansion is controlled via turgor. Remarkably, despite this increase in turgor pressure, cellular biomass density remains constant across a wide range of growth rates. By contrast, perturbations of turgor pressure that deviate from this scaling directly alter biomass density. A mathematical model based on cell wall fluidization by cell wall endopeptidases not only explains these apparently confounding observations but makes surprising quantitative predictions that we validated experimentally. The picture that emerges is that turgor pressure is directly controlled via counterions of ribosomal RNA. Elegantly, the coupling between rRNA and turgor pressure simultaneously coordinates cell wall expansion across a wide range of growth rates and exerts homeostatic feedback control on biomass density. This mechanism may regulate cell wall biosynthesis from microbes to plants and has important implications for the mechanism of action of antibiotics6.

The energetic costs of cellular complexity in evolution

The evolutionary history of cells has been marked by drastic increases in complexity. Some hypothesize that such cellular complexification requires a massive energy flux as the origin of new features is hypothetically more energetically costly than their evolutionary maintenance. However, it remains unclear how increases in cellular complexity demand more energy. I propose that the early evolution of new genes with weak functions imposes higher energetic costs by overexpression before their functions are evolutionarily refined. In the long term, the accumulation of new genes deviates resources away from growth and reproduction. Accrued cellular complexity further requires additional infrastructure for its maintenance. Altogether, this suggests that larger and more complex cells are defined by increased survival but lower reproductive capacity.

Including glutamine in a resource allocation model of energy metabolism in cancer and yeast cells

Energy metabolism is crucial for all living cells, especially during fast growth or stress scenarios. Many cancer and activated immune cells (Warburg effect) or yeasts (Crabtree effect) mostly rely on aerobic glucose fermentation leading to lactate or ethanol, respectively, to generate ATP. In recent years, several mathematical models have been proposed to explain the Warburg effect on theoretical grounds. Besides glucose, glutamine is a very important substrate for eukaryotic cells-not only for biosynthesis, but also for energy metabolism. Here, we present a minimal constraint-based stoichiometric model for explaining both the classical Warburg effect and the experimentally observed respirofermentation of glutamine (WarburQ effect). We consider glucose and glutamine respiration as well as the respective fermentation pathways. Our resource allocation model calculates the ATP production rate, taking into account enzyme masses and, therefore, pathway costs. While our calculation predicts glucose fermentation to be a superior energy-generating pathway in human cells, different enzyme characteristics in yeasts reduce this advantage, in some cases to such an extent that glucose respiration is preferred. The latter is observed for the fungal pathogen Candida albicans, which is a known Crabtree-negative yeast. Further, optimization results show that glutamine is a valuable energy source and important substrate under glucose limitation, in addition to its role as a carbon and nitrogen source of biomass in eukaryotic cells. In conclusion, our model provides insights that glutamine is an underestimated fuel for eukaryotic cells during fast growth and infection scenarios and explains well the observed parallel respirofermentation of glucose and glutamine in several cell types.

Robustness of mitochondrial biogenesis and respiration explain aerobic glycolysis

A long-standing observation is that in fast-growing cells, respiration rate declines with increasing growth rate and is compensated by an increase in fermentation, despite respiration being more efficient than fermentation. This apparent preference for fermentation even in the presence of oxygen is known as aerobic glycolysis, and occurs in bacteria, yeast, and cancer cells. Considerable work has focused on understanding the potential benefits that might justify this seemingly wasteful metabolic strategy, but its mechanistic basis remains unclear. Here we show that aerobic glycolysis results from the saturation of mitochondrial respiration and the decoupling of mitochondrial biogenesis from the production of other cellular components. Respiration rate is insensitive to acute perturbations of cellular energetic demands or nutrient supplies, and is explained simply by the amount of mitochondria per cell. Mitochondria accumulate at a nearly constant rate across different growth conditions, resulting in mitochondrial amount being largely determined by cell division time. In contrast, glucose uptake rate is not saturated, and is accurately predicted by the abundances and affinities of glucose transporters. Combining these models of glucose uptake and respiration provides a quantitative, mechanistic explanation for aerobic glycolysis. The robustness of specific respiration rate and mitochondrial biogenesis, paired with the flexibility of other bioenergetic and biosynthetic fluxes, may play a broad role in shaping eukaryotic cell metabolism.

Genome-scale models of metabolism and expression predict the metabolic burden of recombinant protein expression

The production of recombinant proteins in a host using synthetic constructs such as plasmids comes at the cost of detrimental effects such as reduced growth, energetic inefficiencies, and other stress responses, collectively known as metabolic burden. Increasing the number of copies of the foreign gene increases the metabolic load but increases the expression of the foreign protein. Thus, there is a trade-off between biomass and product yield in response to changes in heterologous gene copy number. This work proposes a computational method, rETFL (recombinant Expression and Thermodynamic Flux), for analyzing and predicting the responses of recombinant organisms to the introduction of synthetic constructs. rETFL is an extension to the ETFL formulations designed to reconstruct models of metabolism and expression (ME-models). We have illustrated the capabilities of the method in four studies to (i) capture the growth reduction in plasmid-containing E. coli and recombinant protein production; (ii) explore the trade-off between biomass and product yield as plasmid copy number is varied; (iii) predict the emergence of overflow metabolism in recombinant E. coli in agreement with experimental data; and (iv) investigate the individual pathways and enzymes affected by the presence of the plasmid. We anticipate that rETFL will serve as a comprehensive platform for integrating available omics data for recombinant organisms and making context-specific predictions that can help optimize recombinant expression systems for biopharmaceutical production and gene therapy.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Historic obstacles and emerging opportunities in the field of developmental metabolism - lessons from Heidelberg

The field of developmental metabolism is experiencing a technological revolution that is opening entirely new fields of inquiry. Advances in metabolomics, small-molecule sensors, single-cell RNA sequencing and computational modeling present new opportunities for exploring cell-specific and tissue-specific metabolic networks, interorgan metabolic communication, and gene-by-metabolite interactions in time and space. Together, these advances not only present a means by which developmental biologists can tackle questions that have challenged the field for centuries, but also present young scientists with opportunities to define new areas of inquiry. These emerging frontiers of developmental metabolism were at the center of a highly interactive 2023 EMBO workshop 'Developmental metabolism: flows of energy, matter, and information'. Here, we summarize key discussions from this forum, emphasizing modern developmental biology's challenges and opportunities.

Metabolic cross-feeding between the competent degrader Rhodococcus sp. strain p52 and an incompetent partner during catabolism of dibenzofuran: Understanding the leading and supporting roles

Microbial interactions, particularly metabolic cross-feeding, play important roles in removing recalcitrant environmental pollutants; however, the underlying mechanisms involved in this process remain unclear. Thus, this study aimed to elucidate the mechanism by which metabolic cross-feeding occurs during synergistic dibenzofuran degradation between a highly efficient degrader, Rhodococcus sp. strain p52, and a partner incapable of utilizing dibenzofuran. A bottom-up approach combined with pairwise coculturing was used to examine metabolic cross-feeding between strain p52 and Arthrobacter sp. W06 or Achromobacter sp. D10. Pairwise coculture not only promoted bacterial pair growth but also facilitated dibenzofuran degradation. Specifically, strain p52, acting as a donor, released dibenzofuran metabolic intermediates, including salicylic acid and gentisic acid, for utilization and growth, respectively, by the partner strains W06 and D10. Both salicylic acid and gentisic acid exhibited biotoxicity, and their accumulation inhibited dibenzofuran degradation. The transcriptional activity of the genes responsible for the catabolism of dibenzofuran and its metabolic intermediates was coordinately regulated in strain p52 and its cocultivated partners, thus achieving synergistic dibenzofuran degradation. This study provides insights into microbial metabolic cross-feeding during recalcitrant environmental pollutant removal.

Electricity generation by Pseudomonas putida B6-2 in microbial fuel cells using carboxylates and carbohydrate as substrates

Microbial fuel cells (MFCs) employing Pseudomonas putida B6-2 (ATCC BAA-2545) as an exoelectrogen have been developed to harness energy from various conventional substrates, such as acetate, lactate, glucose, and fructose. Owing to its metabolic versatility, P. putida B6-2 demonstrates adaptable growth rates on diverse, cost-effective carbon sources within MFCs, exhibiting distinct energy production characteristics. Notably, the anode chamber's pH rises with carboxylates' (acetate and lactate) consumption and decreases with carbohydrates' (glucose and fructose) utilization. The MFC utilizing fructose as a substrate achieved the highest power density at 411 mW m-2. Initial analysis revealed that P. putida B6-2 forms biofilms covered with nanowires, contributing to bioelectricity generation. These microbial nanowires are likely key players in direct extracellular electron transport through physical contact. This study established a robust foundation for producing valuable compounds and bioenergy from common substrates in bioelectrochemical systems (BESs) utilizing P. putida as an exoelectrogen.

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.

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Proteome allocation and the evolution of metabolic cross-feeding

In a common instance of metabolic cross-feeding (MCF), an organism incompletely metabolizes nutrients and releases metabolites that are used by another to produce energy or building blocks. Why would the former waste edible food, and why does this preferentially occur at specific locations in a metabolic pathway have challenged evolutionary theory for decades. To address these questions, we combine adaptive dynamics with an explicit model of cell metabolism, including enzyme-driven catalysis of metabolic reactions and the cellular constraints acting on the proteome that may incur a cost to expressing all enzymes along a pathway. After pointing out that cells should in principle prioritize upstream reactions when metabolites are restrained inside the cell, we show that the occurrence of permeability-driven MCF is rare and requires that an intermediate metabolite be extremely diffusive. Indeed, only at very high levels of membrane permeability (consistent with those of acetate and glycerol, for instance) and under distinctive sets of parameters should the population diversify and MCF evolve. These results help understand the origins of simple microbial communities, such as those that readily evolve in short-term evolutionary experiments, and may later be extended to investigate how evolution has progressively built up today's extremely diverse ecosystems.

Serial adaptive laboratory evolution enhances mixed carbon metabolic capacity of Escherichia coli

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

Models and molecular mechanisms for trade-offs in the context of metabolism

Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.