Search bioRxiv⌕ Search

Biology subjects

Ebenhoeh, O.

Publications and source records attributed to Ebenhoeh, O..

At least 19 recordsLinked to original sources

Does specific membrane surface area constrain Escherichia coli energy spillage and therefore aerobic and anaerobic growth rates?

The physical dimensions of bacterial cells are finite, tightly regulated, and constrain phenotype based on cell surface area to volume ratios. Additionally, bacterial membranes have limited capacity to host proteins, constraining enzymatic potential per area. We combine these biophysical properties into an aggregate parameter termed specific membrane surface area (sMSA, nm2 gcdw-1) which is readily integrated into systems biology analyses including constraint-based stoichiometric models. sMSA-based analyses have remarkable agency to predict and interpret bacterial phenotypes including maximum growth rate, respiration efficiency, and potential for maintenance energy generation. The presented Escherichia coli research 1) quantitatively recreates the membrane proteome from central metabolism as a function of growth rate, 2) demonstrates that the maximum growth rate of both aerobic and anaerobic E. coli cultures abides by similar cell biology constraints including a Pareto tradeoff between simultaneous synthesis of biomass macromolecules and generation of maintenance energy, 3) quantifies the areal capacity of E. coli membranes to dissipate Gibbs energy during enthalpic and entropic dominated metabolisms, and 4) presents evidence that sMSA theory could permit isogenic populations to host multiple concurrent phenotypes with equivalent fitness metrics.The results support our sMSA theory, based on cell geometry and membrane enzyme capacity, as a substantial driver of metabolism in both oxic and anoxic environments.

systems biology↗

Steady state ratios in bipartite population models emerge through obligate cross-feeding

Cross-feeding, the phenomenon where one organism consumes metabolites secreted by another, is a ubiquitious phenomenon in microbial communities. Obligate, mutually cross-feeding organisms are a special case of mutualism, where each organism produces a resource that the other organism requires for growth. Many obligate, mutually cross-feeding microbial systems have been studied, including naturally occurring as well as synthetically designed communities. Interestingly, in all these experimental systems it has been observed that the communities approach a fixed biomass ratio, independent of the initial biomass inoculation composition. The repeated reports of stable biomass ratios suggest that such ratios are actually a generic property of mutually obligate cross-feeding systems and that, if the system is stable, such a ratio must be approached. As a consequence, it should also be possible to explain and predict this ratio from measurable parameters. Here, we explore mutually obligate cross-feeding systems with mathematical models and show that a stable biomass ratio is a general feature of such systems. Moreover, we show how this ratio can be predicted from key parameters that can be interpreted as the value of the resource to the consuming organism and the cost of producing a resource to the producing organism.

systems biology↗

Multi-omic data fusion reveals the in vivo enzyme kinetics of Vibrio natriegens at the genome-scale

Vibrio natriegens is a halophilic, Gram-negative marine bacterium that is increasingly used in metabolic engineering applications due to its fast growth rate. In sparse minimal medium the organism has a doubling time of 25 minutes, which is about twice as fast as Escherichia coli under similar conditions. Given that its protein density is similarly constrained to that of E. coli, this necessitates that its metabolic enzymes are able to catalyze flux at a higher rate to sustain its metabolism. In this work, we measure the apparent turnover numbers of metabolically active enzymes in V. natriegens under a variety of growth conditions. The apparent turnover numbers of V. natriegens enzymes were measured in vivo by conducting coupled quantitative proteomics and 13C metabolic flux analysis experiments under seven different carbon source conditions in sparse minimal medium. A high quality genome-scale metabolic model was constructed and curated using additional experimental data. This model was extended with enzyme constraints, and subsequently used to find kinetic parameters that minimize the difference between model predictions and experimental observations. This model guided data fusion approach enabled the estimation of 357 apparent turnover numbers for metabolically active enzymes in V. natriegens. Our results reveal that the metabolic enzymes of V. natriegens are in median 14-fold faster than those of E. coli under similar conditions. Moreover, we show that machine learning generated turnover number estimates substantially underestimate the kinetics of V. natriegens. Our turnover number estimates were used to parameterize multiple condition dependent enzyme constrained flux balance analysis models of V. natriegens, which improved their predictive accuracy compared to the machine learning parameterisation. The combined experimental-computational approach employed here sheds light on the mechanism V. natriegens uses to accelerate its growth. This approach can also be extended to other bacteria, increasing the availability of in vivo measured enzyme turnover numbers, and improving the predictive accuracy of enzyme constrained metabolic models of other microbes.

systems biology↗

Integrative Thermodynamics Strategies in Microbial Metabolism

Microbial metabolism is intricately governed by thermodynamic constraints that dictate energetic efficiency, growth dynamics, and metabolic pathway selection. Previous research has primarily examined these principles under carbon-limited conditions, demonstrating how microbes optimize their proteomic resources to balance metabolic efficiency and growth rates. This study extends this thermodynamic framework to explore microbial metabolism under various non-carbon nutrient limitations (e.g., nitrogen, phosphorus, sulfur). By integrating literature data from a range of species it is shown that growth under anabolic nutrient limitations consistently results in more negative Gibbs free energy ({Delta}G) values for the Net Catabolic Reaction (NCR), when normalized per unit of biomass formed, compared to carbon-limited scenarios. The findings suggest three, potentially complementary hypotheses: (1) Proteome Allocation Hypothesis: microbes favor faster enzymes to reduce proteome fraction used for catabolism, thus freeing proteome resources for additional nutrient transporters; (2) Coupled Transport Contribution Hypothesis: The more negative {Delta}G of the NCR may in part stem from the increased reliance on ATP-coupled or energetically driven transport mechanisms for nutrient uptake under limitation; (3) Bioenergetic Efficiency Hypothesis: microbes prefer pathways with more negative {Delta}G to enhance cellular energy status, such as membrane potentials or ATP/ADP ratio, to support nutrient uptake under anabolic limitations. This integrative thermodynamic analysis broadens the understanding of microbial adaptation strategies and provides valuable insights for biotechnological applications in metabolic engineering and fermentation process optimization.

systems biology↗

Rule-Based Deconstruction and Reconstruction of Diterpene Libraries: Categorizing Patterns & Unravelling the Structural Landscape

Terpenoids make up the largest class of specialized metabolites with over 180,000 reported compounds currently across all kingdoms of life. Their synthesis accentuates one of natures most choreographed enzymatic and non-reversible chemistries, leading to an extensive range of structural functionality and diversity. Current terpenoid repositories provide a seemingly endless landscape to systematically survey for information regarding structure, sourcing, and synthesis. Efforts here investigate entries for the 20-carbon diterpenoid variants and deconstruct the complex patterns into simple, categorical groups. This deconstruction approach reduces over 60,000 unique diterpenoid structures to less than 1,000 categorical structures. Furthermore, the majority of diterpene entries (over 75%) can be represented by less than 25 core skeletons. Natural diterpenoid abundance was mapped throughout the tree of life and structural diversity was correlated at an atom-and-bond resolution. Additionally, all identified core structures provide guidelines for predicting how diterpene diversity originates via the mechanisms catalyzed by diterpene synthases. Over 95% of diterpenoid structures rely on cyclization. Here a reconstructive approach is reapplied based on known biochemical rules to model the birth of compound diversity. Reconstruction enabled prediction of highly probable synthesis mechanisms for bioactive taxane-relatives, which were discovered over three decades ago. This computational synthesis validates previously identified reaction products and pathways, as well as enables predicting trajectories for synthesizing real and theoretical compounds. This deconstructive and reconstructive approach applied to the diterpene landscape provides modular, flexible, and an easy-to-use toolset for categorically simplifying otherwise complex or hidden patterns. Significance StatementWe take a deconstructive and reconstructive approach to explore the origins of the diterpene landscape. Introduction of a navigational toolset enables users to survey compound libraries in ways formerly uncharted. Their utility demonstrated here, maps out diterpene cyclization routes, critical intermediate waypoints, and guidance for how to arrive at compounds previously off-the-map. Information acquired from these tools may imply the diterpene landscape is vastly unexplored, with the plateau for discovery potentially still out of sight.

bioinformatics↗

Alternatives to photorespiration: A systems-level analysis reveals mechanisms of enhanced plant productivity

Photorespiration causes a significant decrease in crop yield due to mitochondrial decarboxylation. Alternative pathways (APs) have been designed to relocate the decarboxylating step or even fix additional carbon. To improve the success of transferring those engineered APs from model species to crops we must understand how they will interact with metabolism and how the plant physiology affects their performance. Here we used multiple mathematical modelling techniques to analyse and compare existing AP designs. We show that carbon-fixing APs are the most promising candidates to replace native photorespiration in major crop species. Our results demonstrate the different metabolic routes that APs employ to increase yield and which plant physiology can profit most from them. We anticipate our results to guide the design of new APs and to help improve existing ones. Codehttps://gitlab.com/gain4crops/2024-paper

plant biology↗

Microbial pathway thermodynamics: structural models unveil anabolic and catabolic processes

The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genomescale metabolic model. Interestingly, both approaches are rather successful to characterise and predict the expected product yield. Over the years, especially macrochemical equations have been extensively characterised in terms of their thermodynamic properties. However, a common challenge when characterising microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions.

systems biology↗

dismo: building and analysing discrete spatial ODE models

Summarydismo is a Python package for building and analysing discrete spatial models based on ordinary differential equations. Its primary purpose is to allow arbitrarily complex internal and transport processes to easily be mapped over multiple different regular grids. For this it features one, two and threedimensional layouts, with standard and non-standard (e.g. hexagonal or triangular) grids. Availability and implementationhttps://gitlab.com/qtb-hhu/dismo Contactoliver.ebenhoeh@hhu.de

systems biology↗

A mathematical model of photoinhibition: exploring the impact of quenching processes

Plants are constantly exposed to changing environments, sometimes leading to extreme conditions and stress. For example, sudden exposure to high light leads to excess absorbed light energy, causing reactive oxygen species (ROS) formation. ROS damage the photosynthetic machinery, particularly the D1 protein in photosystem II (PSII), which therefore needs to be continuously repaired and replaced. The effect of the damage inflicted by high light is a prolonged decrease in photosynthetic efficiency. Hence, it is not surprising that photoinhibition has been subject to numerous experimental studies investigating its effects in the context of crop productivity. However, it has become apparent that classical measures of photoinhibition, i.e., changes in the chlorophyll fluorescence parameter Fv/Fm, are not only determined by the loss of PSII core function but also by processes such as energy transfer and quenching. Mathematical models can help dissect the influences on such fluorescence signals and quantify the contributions of various interacting mechanisms. We present a mathematical model with a dynamic description of the photosynthetic electron transport chain (PETC), non-photochemical quenching, and photoinhibition. With our model, we investigate the interconnection between quenching, photoprotection, and fluorescence using simulations and experimental data. We found that different energy-dissipating properties of intact and damaged PSIIs, as well as energy transfer between PSIIs, are critical components that need to be included in the model to ensure a satisfactory fit to the experimental data. We envisage that our model provides a framework for future investigations of photoinhibition dynamics and its importance for plant growth and yield.

plant biology↗

Sea-ice melt determines seasonal phytoplankton dynamics and delimits the habitat of temperate Atlantic taxa as the Arctic Ocean atlantifies

The Arctic Ocean is one of the regions where anthropogenic environmental change is progressing most rapidly and drastically. The impact of rising temperatures and decreasing sea ice on Arctic marine microbial communities is yet not well understood. Microbes form the basis of food webs in the Arctic Ocean, providing energy for larger organisms. Previous studies have shown that Atlantic taxa associated with low light are robust to more polar conditions. In this study, we compared to which extent sea ice melt influences light-associated phytoplankton dynamics and biodiversity over two years at two mooring locations in the Fram Strait. One mooring is deployed in pure Atlantic water, and the second in the intermittently ice-covered Marginal Ice Zone. Time-series analysis of amplicon sequence variants abundance over a two-year period, allowed us to identify communities of co-occurring taxa that exhibit similar patterns throughout the annual cycle. We then examined how alterations in environmental conditions affect the prevalence of species. During high abundance periods of diatoms, polar phytoplankton populations dominated, while temperate taxa were weakly represented. Generally, polar pelagic and ice-associated taxa (such as Fragilariopsis cylindrus or Melosira arctica) were more prevalent in Atlantic conditions whereas temperate taxa (such as Odontella aurita or Proboscia alata) have limited potential to persist in colder ice-impacted waters. In contrast to previous assumptions, we think that sea-ice melt acts as a barrier to the horizontal extent of temperate diatoms by preventing their succession at places strongly influenced by polar conditions such as the melting sea ice.

ecology↗

What controls carbon sequestration in plants under which conditions?

Plants use photosynthesis to harvest sunlight and convert the solar energy into chemical energy, which is then used to reduce atmospheric carbon dioxide into organic molecules. This process forms the basis of all life on Earth, and stands at the beginning of the food chain which feeds the world population. Not surprisingly, many research efforts are currently ongoing aiming at improving plant growth and crop yield, and several of these activities directly target the photosynthetic pathways. Metabolic Control Analysis (MCA) shows that, in general, the control over a metabolic flux, such as carbon fixation, is distributed among several steps and highly dependent on the external conditions. Therefore, the concept of a single rate-limiting step is hardly ever applicable, and as a consequence, any strategy relying on improving a single molecular process in a complex metabolic system is bound to fail to yield the expected results. In photosynthesis, reports on which processes exert the highest control over carbon fixation are contradictory. This refers to both, the photosynthetic light reactions harvesting photons, and the dark reactions of the CalvinBenson-Bassham Cycle (CBB cycle). Here, we employ a recently developed mathematical model, which describes photosynthesis as an interacting supply-demand system, to systematically study how external conditions affect the control over carbon fixation fluxes.

systems biology↗

Approximating carbon fixation - how important is the Calvin-Benson cycle steady-state assumption?

Plants use light energy to produce ATP and redox equivalents for metabolism. Since during the course of a day plants are exposed to constantly fluctuating light, the supply of ATP and redox equivalents is also fluctuating. Further, if the metabolism cannot use all of the supplied energy, the excess absorbed energy can damage the plant in the form of reactive oxygen species. It is thus reasonable to assume that the metabolism downstream of the energy supply is dynamic and as being capable of dampening sudden spikes in supply is advantageous, it is further reasonable to assume that the immediate downstream metabolism is flexible as well. A flexible metabolism exposed to a fluctuating input is unlikely to be in metabolic steady-state, yet a lot of mathematical models for carbon fixation assume one for the Calvin-Benson-Bassham (CBB) cycle. Here we present an analysis of the validity of this assumption by progressively simplifying an existing model of photosynthesis and carbon fixation.

plant biology↗

Growth Mechanics: General principles of optimal cellular resource allocation in balanced growth

The physiology of biological cells evolved under physical and chemical constraints such as mass conservation, nonlinear reaction kinetics, and limits on cell density. For unicellular organisms, the fitness that governs this evolution is mainly determined by the balanced cellular growth rate. We previously introduced Growth Balance Analysis (GBA) as a general framework to model such nonlinear systems, and we presented analytical conditions for optimal balanced growth in the special case that the active reactions are known. Here, we develop Growth Mechanics (GM) as a more general, succinct, and powerful analytical description of the growth optimization of GBA models, which we formulate in terms of a minimal number of dimensionless variables. GM uses Karush-Kuhn-Tucker (KKT) conditions in a Lagrangian formalism. It identifies fundamental principles of optimal resource allocation in GBA models of any size and complexity, including the analytical conditions that determine the set of active reactions at optimal growth. We identify from first principles the economic values of biochemical reactions, expressed as marginal changes in cellular growth rate; these economic values can be related to the costs and benefits of proteome allocation into the reactions catalysts. Our formulation also generalizes the concepts of Metabolic Control Analysis to models of growing cells. GM unifies and extends previous approaches of cellular modeling and analysis, putting forward a program to analyze cellular growth through the stationarity conditions of a Lagrangian function. GM thereby provides a general theoretical toolbox for the study of fundamental mathematical properties of balanced cellular growth.

biophysics↗

Bioenergetics of the secondary metabolites production in photosynthetic glandular trichomes

Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/510054v2_ufig1.gif" ALT="Figure 1"> View larger version (9K): org.highwire.dtl.DTLVardef@2ec6f4org.highwire.dtl.DTLVardef@107d0c1org.highwire.dtl.DTLVardef@1a545corg.highwire.dtl.DTLVardef@98747b_HPS_FORMAT_FIGEXP M_FIG C_FIG Please check the journals author guidelines for whether a graphical abstract, key points, new findings, or other items are required for display in the Table of Contents.

plant biology↗

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

1Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

bioengineering↗

Uncoupling of the Diurnal Growth Program by Artificial Genome Relaxation in Synechocystis sp. PCC 6803

In cyanobacteria DNA supercoiling varies over the diurnal light/dark cycle and is integrated with temporal programs of transcription and replication. We manipulated DNA supercoiling in Synechocystis sp. PCC 6803 by CRISPRi-based knock-down of gyrase subunits and overexpression of topoisomerase I (TopoI), and characterized the phenotypes. Cell division was blocked, most likely due to inhibition of genomic but not plasmid DNA replication. Cell growth continued to 4-5x of the wildtype cell volume, and metabolic flux was redirected towards glycogen in the TopoI overexpression strain. TopoI induction initially lead to down-regulation of GC-rich and up-regulation of AT-rich genes. The response quickly bifurcated and four diurnal co-expression cohorts (dawn, noon, dusk and night) all responded differently, in part with a circadian ({approx} 24 h) pattern. A GC-rich region - 50 bp of transcription start sites is differentially enriched in these four cohorts. We suggest a model where energy- and gyrase-gated transcription of growth genes at the dark/light transition (dawn) generates DNA supercoiling which then facilitates DNA replication and initiates the diurnal transcriptome program.

microbiology↗

COBREXA.jl: constraint-based reconstruction and exascale analysis

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. Availability and implementationhttps://doi.org/10.17881/ZKCR-BT30. Contactchristophe.trefois@uni.lu, wei.gu@uni.lu

systems biology↗

The view of microbes as energy converters illustrates the trade-off between growth rate and yield

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy, but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models provide us with unprecedented detail about metabolic processes inside the cell. However, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black box models and modern approaches that integrate thermodynamics into genome-scale metabolic models. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield. PerspectiveO_LIMicrobial growth is the foundation of many biotechnological applications. The key to optimizing microbial growth lies in thermodynamics, similar to how classical thermodynamics helped optimize steam engines in the 19th century. C_LIO_LIGenome-scale metabolic models have become widely available, and are used to predict microbial growth. These predictions often fail because these models do not distinguish between growth rate and yield. C_LIO_LIClassical black box models present a sound thermodynamic theory, by viewing microbes as energy converters. Incorporating such concepts into genome-scale metabolic models has the promise to advance our fundamental understanding of microbial growth, and thus to improve the predictive power of these models. C_LI

bioengineering↗