Overflow metabolism in Escherichia coli results from efficient proteome allocation

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.

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.

Understanding and computational design of genetic circuits of metabolic networks

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

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 crowding1,2 or 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.

Recombinant protein expression in proteome-reduced cells under aerobic and oxygen-limited regimes

Industrial cultures are hindered by the physiological complexity of the host and the limited mass transfer capacity of conventional bioreactors. In this study, a minimal cell approach was combined with genetic devices to overcome such issues. A flavin mononucleotide-based fluorescent protein (FbFP) was expressed in a proteome-reduced Escherichia coli (PR). When FbFP was expressed from a constitutive protein generator (CPG), the PR strain produced 47% and 35% more FbFP than its wild type (WT), in aerobic or oxygen-limited regimes, respectively. Metabolic and expression models predicted more efficient biomass formation at higher fluxes to FbFP, in agreement with these results. A microaerobic protein generator (MPG) and a microaerobic transcriptional cascade (MTC) were designed to induce FbFP expression upon oxygen depletion. The FbFP fluorescence using the MTC in the PR strain was 9% higher than that of the WT bearing the CPG under oxygen limitation. To further improve the PR strain, the pyruvate dehydrogenase complex regulator gene was deleted, and the Vitreoscilla hemoglobin was expressed. Compared to oxygen-limited cultures of the WT, the engineered strains increased the FbFP expression more than 50% using the MTC. Therefore, the designed expression systems can be a valuable alternative for industrial cultivations.

Alterations in gene expression and microbiome composition upon calcium-sensing receptor deletion in the mouse esophagus

The calcium-sensing receptor (CaSR), a G protein-coupled receptor, regulates Ca2+ concentration in plasma by regulating parathyroid hormone secretion. In other tissues, it is reported to play roles in cellular differentiation and migration and in secretion and absorption. We reported previously that CaSR can be conditionally deleted in the mouse esophagus. This conditional knockout (KO) (EsoCaSR-/-) model showed a significant reduction in the levels of adherens and tight junction proteins and had a marked buildup of bacteria on the luminal esophageal surface. To further examine the role of CaSR, we used RNA sequencing to determine gene expression profiles in esophageal epithelia of control and EsoCaSR-/-mice RNA Seq data indicated upregulation of gene sets involved in DNA replication and cell cycle in EsoCaSR-/-. This is accompanied by the downregulation of gene sets involved in the innate immune response and protein homeostasis including peptide elongation and protein trafficking. Ingenuity pathway analysis (IPA) demonstrated that these genes are mapped to important biological networks including calcium and Ras homologus A (RhoA) signaling pathways. To further explore the bacterial buildup in EsoCaSR-/- esophageal tissue, 16S sequencing of the mucosal-associated bacterial microbiome was performed. Three bacterial species, g_Rodentibacter, s_Rodentibacter_unclassified, and s_Lactobacillus_hilgardi were significantly increased in EsoCaSR-/-. Furthermore, metagenomic analysis of 16S sequences indicated that pathways related to oxidative phosphorylation and metabolism were downregulated in EsoCaSR-/- tissues. These data demonstrate that CaSR impacts major pathways of cell proliferation, differentiation, cell cycle, and innate immune response in esophageal epithelium. The disruption of these pathways causes inflammation and significant modifications of the microbiome.NEW & NOTEWORTHY Calcium-sensing receptor (CaSR) plays a significant role in maintaining the barrier function of esophageal epithelium. Using RNA sequencing, we show that conditional deletion of CaSR from mouse esophagus causes upregulation of genes involved in DNA replication and cell cycle and downregulation of genes involved in the innate immune response, protein translation, and cellular protein synthesis. Pathway analysis shows disruption of signaling pathways of calcium and actin cytoskeleton. These changes caused inflammation and esophageal dysbiosis.

Active pH regulation facilitates Bacillus subtilis biofilm development in a minimally buffered environment

Biofilms provide individual bacteria with many advantages, yet dense cellular proliferation can also create intrinsic metabolic challenges including excessive acidification. Because such pH stress can be masked in buffered laboratory media-such as MSgg commonly used to study Bacillus subtilis biofilms-it is not always clear how such biofilms cope with minimally buffered natural environments. Here, we report how B. subtilis biofilms overcome this intrinsic metabolic challenge through an active pH regulation mechanism. Specifically, we find that these biofilms can modulate their extracellular pH to the preferred neutrophile range, even when starting from acidic and alkaline initial conditions, while planktonic cells cannot. We associate this behavior with dynamic interplay between acetate and acetoin biosynthesis and show that this mechanism is required to buffer against biofilm acidification. Furthermore, we find that buffering-deficient biofilms exhibit dysregulated biofilm development when grown in minimally buffered conditions. Our findings reveal an active pH regulation mechanism in B. subtilis biofilms that could lead to new targets to control unwanted biofilm growth.IMPORTANCEpH is known to influence microbial growth and community dynamics in multiple bacterial species and environmental contexts. Furthermore, in many bacterial species, rapid cellular proliferation demands the use of overflow metabolism, which can often result in excessive acidification. However, in the case of bacterial communities known as biofilms, these acidification challenges can be masked when buffered laboratory media are employed to stabilize the pH environment for optimal growth. Our study reveals that B. subtilis biofilms use an active pH regulation mechanism to mitigate both growth-associated acidification and external pH challenges. This discovery provides new opportunities for understanding microbial communities and could lead to new methods for controlling biofilm growth outside of buffered laboratory conditions.

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

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.

Optimal control of ribosome population for gene expression under periodic nutrient intake

Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.

A parallel glycolysis provides a selective advantage through rapid growth acceleration

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

Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It's still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.

Eco-evolutionary dynamics: The repeatability of diversification in an experimental microbial community

Understanding the evolution and subsequent maintenance of ecological diversity is a daunting task. Using a historical microbial evolution experiment, a new paper demonstrates the extent to which diversity can re-emerge in reduced communities and the traits through which rediversification occurs.

Dinner date: Neisseria gonorrhoeae central carbon metabolism and pathogenesis

Neisseria gonorrhoeae, the causative agent of the sexually transmitted infection gonorrhea, is a human-adapted pathogen that does not productively infect other organisms. The ongoing relationship between N. gonorrhoeae and the human host is facilitated by the exchange of nutrient resources that allow for N. gonorrhoeae growth in the human genital tract. What N. gonorrhoeae 'eats' and the pathways used to consume these nutrients have been a topic of investigation over the last 50 years. More recent investigations are uncovering the impact of N. gonorrhoeae metabolism on infection and inflammatory responses, the environmental influences driving N. gonorrhoeae metabolism, and the metabolic adaptations enabling antimicrobial resistance. This mini-review is an introduction to the field of N. gonorrhoeae central carbon metabolism in the context of pathogenesis. It summarizes the foundational work used to characterize N. gonorrhoeae central metabolic pathways and the effects of these pathways on disease outcomes, and highlights some of the most recent advances and themes under current investigation. This review ends with a brief description of the current outlook and technologies under development to increase understanding of how the pathogenic potential of N. gonorrhoeae is enabled by metabolic adaptation.

Simultaneous glucose and xylose utilization by an Escherichia coli catabolite repression mutant

As advances are made toward the industrial feasibility of mass-producing biofuels and commodity chemicals with sugar-fermenting microbes, high feedstock costs continue to inhibit commercial application. Hydrolyzed lignocellulosic biomass represents an ideal feedstock for these purposes as it is cheap and prevalent. However, many microbes, including Escherichia coli, struggle to efficiently utilize this mixture of hexose and pentose sugars due to the regulation of the carbon catabolite repression (CCR) system. CCR causes a sequential utilization of sugars, rather than simultaneous utilization, resulting in reduced carbon yield and complex process implications in fed-batch fermentation. A mutant of the gene encoding the cyclic AMP receptor protein, crp*, has been shown to disable CCR and improve the co-utilization of mixed sugar substrates. Here, we present the strain construction and characterization of a site-specific crp* chromosomal mutant in E. coli BL21 star (DE3). The crp* mutant strain demonstrates simultaneous consumption of glucose and xylose, suggesting a deregulated CCR system. The proteomics further showed that glucose was routed to the C5 carbon utilization pathways to support both de novo nucleotide synthesis and energy production in the crp* mutant strain. Metabolite analyses further show that overflow metabolism contributes to the slower growth in the crp* mutant. This highly characterized strain can be particularly beneficial for chemical production by simultaneously utilizing both C5 and C6 substrates from lignocellulosic biomass.IMPORTANCEAs the need for renewable biofuel and biochemical production processes continues to grow, there is an associated need for microbial technology capable of utilizing cheap, widely available, and renewable carbon substrates. This work details the construction and characterization of the first B-lineage Escherichia coli strain with mutated cyclic AMP receptor protein, Crp*, which deregulates the carbon catabolite repression (CCR) system and enables the co-utilization of multiple sugar sources in the growth medium. In this study, we focus our analysis on glucose and xylose utilization as these two sugars are the primary components in lignocellulosic biomass hydrolysate, a promising renewable carbon feedstock for industrial bioprocesses. This strain is valuable to the field as it enables the use of mixed sugar sources in traditional fed-batch based approaches, whereas the wild-type carbon catabolite repression system leads to biphasic growth and possible buildup of non-preferential sugars, reducing process efficiency at scale.

A data-driven approach for timescale decomposition of biochemical reaction networks

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.