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Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities. ONE SENTENCE SUMMARY: Computer-assisted biosynthetic capacities elucidation accelerates our ability to interpret microbial interactions, allowing us to understand better and establish a balance within ecosystems. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) stands as the second most fatal infectious disease after COVID‐19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics‐based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost‐effectiveness (approximately $3). A panel of 14 m z−1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z−1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964‐0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806‐0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end‐TB strategy. [ABSTRACT FROM AUTHOR]

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant- associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint- based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics- based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network- based analyses have characterized interactions associated with beneficial vs non- beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions. [ABSTRACT FROM AUTHOR]

The mechanistic link between metabolic rate and foraging behaviour is a crucial aspect of several energy‐based ecological theories. Despite its importance to ecology however, it remains unclear whether and how energy requirements and behavioural patterns are mechanistically connected. Here we aimed to assess how modes of behaviour, including cumulative space use, patch selection and time spent in an experimental resource‐patchy environment, are related to a forager's standard metabolic rate (SMR) and its main determinants, i.e. body mass and temperature. We tested the individual behavioural patterns and metabolic rates of a model organism, the amphipod Gammarus insensibilis, across a range of body masses and temperatures. We demonstrated quantitatively that body mass and temperature exert a major influence on foraging decisions and space use behaviour via their effects on metabolic rates and the marginal value of energy. Individual cumulative space use was found to scale allometrically with body mass and exponentially with temperature, with patch giving‐up time falling as body mass and temperature increased. In response to warmer temperatures, the specimens adaptively intensified their foraging effort and explored larger spaces to offset the elevated SMR. Our results showed that SMR explained more variation than body mass and temperature combined, and had greater predictive power for behavioural patterns. Furthermore, foraging decisions regarding patch choice and partitioning were strongly related to mass‐and‐temperature‐adjusted SMR (residual), which is a component of metabolic phenotype. Individuals with higher M–T adjusted SMR initially preferred the most profitable patch and, as time progressed, abandoned the patch earlier and explored more space than the others. These results demonstrate that foraging decisions are intimately associated with variations in standard metabolic rate, whether phenotypic or due to size and temperature combined. This, in turn, sheds light on higher‐order energy‐based ecological processes. [ABSTRACT FROM AUTHOR]

Vitis vinifera, also known as grapevine, is widely cultivated and commercialized, particularly to produce wine. As wine quality is directly linked to fruit quality, studying grapevine metabolism is important to understand the processes underlying grape composition. Genome-scale metabolic models (GSMMs) have been used for the study of plant metabolism and advances have been made, allowing the integration of omics datasets with GSMMs. On the other hand, Machine learning (ML) has been used to analyze and integrate omics data, and while the combination of ML with GSMMs has shown promising results, it is still scarcely used to study plants. Here, the first GSSM of V. vinifera was reconstructed and validated, comprising 7199 genes, 5399 reactions, and 5141 metabolites across 8 compartments. Tissue-specific models for the stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases. The potential of combining ML with GSMMs was explored by using ML to analyze the fluxomics data generated by green and mature grape GSMMs and provide insights regarding the metabolism of grapes at different developmental stages. Therefore, the models developed in this work are useful tools to explore different aspects of grapevine metabolism and understand the factors influencing grape quality. Author summary: Grapevine is one of the most important crops in the world, mainly due to wine production. Understanding grape metabolism is key to improving fruit quality. Towards this aim, we reconstructed a generic genome-scale metabolic model of grapevine to represent grapevine metabolism in silico as well as tissue-specific models by integrating omics data of stem, leaf, and berry. These models were combined into day-night multi-tissue models to explore grapevine metabolism under different conditions. Then, we trained machine learning models with the fluxomics data predicted by the metabolic models to identify the changes in grape metabolism during development. This first effort of integrating omics, metabolic models and machine learning showcases the potential of applying this strategy for more insightful analyses, as additional data becomes available. [ABSTRACT FROM AUTHOR]

Background : Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown. Results: Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5'-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. Conclusions: Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe–nematodes framework for microbiome mediated effects on host animal fitness. [ABSTRACT FROM AUTHOR]

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co‐occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four‐module process for metabolic interaction analysis, namely: (I) Prepare genome‐scale metabolic models; (II) Infer pairwise interactions of genome‐scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome‐assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance‐adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross‐feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome‐assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free. Highlights: The updated integrated Network Analysis Pipeline (iNAP 2.0) is an advanced platform that facilitates the calculation of various metabolic complementarity and competition indices, including the PhyloMint index, SMETANA (species metabolic interaction analysis), and metabolic distance, as well as the construction and analysis of metabolic interaction networks.Innovatively, iNAP 2.0 uses the random matrix theory (RMT) approach to determine the threshold for metabolic interaction networks.PhyloMint PTM in iNAP 2.0 allows users to view potentially transferable metabolites between microbial interactions and integrate them as network nodes to construct a microbe‐metabolite bipartite network alongside the microbial interaction network. [ABSTRACT FROM AUTHOR]

Fungi are diverse organisms with various characteristics and functions. Some play a role in recycling essential elements, such as nitrogen and carbon, while others are utilized in the food and drink production industry. Some others are known to cause diseases in various organisms, including humans. Fungal pathogens cause superficial, subcutaneous, and systemic infections. Consequently, many scientists have focused on studying the factors contributing to the development of human diseases. Therefore, multiple approaches have been assessed to examine the biology of these intriguing organisms. The genome-scale metabolic models (GEMs) have demonstrated many advantages to microbial metabolism studies and the ability to propose novel therapeutic alternatives. Despite significant advancements, much remains to be elucidated regarding the use of this tool for investigating fungal metabolism. This review aims to compile the data provided by the published GEMs of human fungal pathogens. It gives specific examples of the most significant contributions made by these models, examines the advantages and difficulties associated with using such models, and explores the novel approaches suggested to enhance and refine their development. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism. Synopsis: Yeast9 is the latest release of the community-consensus genome-scale model of Saccharomyces cerevisiae metabolism. Among others, Yeast9 has corrected mass & charge balancing; curated gene associations and is annotated with Gibbs free energies. When used to analyse 163 single-cell transcriptomes, Yeast9 reveals a high osmotic adaptation mechanism. Yeast9 is able to elucidate the organism's nitrogen source preference and its metabolic rewiring under nitrogen limitation. When constraint with transcriptomics from 1229 mutants, Yeast9 can predict fluxes that inform machine learning-based gene function prediction. Yeast9 is the latest release of the community-consensus genome-scale model of Saccharomyces cerevisiae metabolism. [ABSTRACT FROM AUTHOR]

Summary: Crassulacean acid metabolism (CAM) leaves are characterized by nocturnal acidification and diurnal deacidification processes related with the timed actions of phosphoenolpyruvate carboxylase and Rubisco, respectively. How CAM leaves manage cytosolic proton homeostasis, particularly when facing massive diurnal proton effluxes from the vacuole, remains unclear.A 12‐phase flux balance analysis (FBA) model was constructed for a mature malic enzyme‐type CAM mesophyll cell in order to predict diel kinetics of intracellular proton fluxes.The charge‐ and proton‐balanced FBA model identified the mitochondrial phosphate carrier (PiC, Pi/H+ symport), which provides Pi to the matrix to sustain ATP biosynthesis, as a major consumer of cytosolic protons during daytime (> 50%). The delivery of Pi to the mitochondrion, co‐transported with protons, is required for oxidative phosphorylation and allows sufficient ATP to be synthesized to meet the high energy demand during CAM Phase III. Additionally, the model predicts that mitochondrial pyruvate originating from decarboxylation of malate is exclusively exported to the cytosol, probably via a pyruvate channel mechanism, to fuel gluconeogenesis. In this biochemical cycle, glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) acts as another important cytosolic proton consumer.Overall, our findings emphasize the importance of mitochondria in CAM and uncover a hitherto unappreciated role in metabolic proton homeostasis. [ABSTRACT FROM AUTHOR]

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology. Author summary: Pathway analysis of constraint-based metabolic models makes it possible to disentangle metabolism into formally defined metabolic pathways. A promising but underexplored application of pathway analysis is to analyze exchanges of metabolites between cells and their environment, which could also help overcome computational challenges and allow scaling to larger systems. Here, we used four different pathway definitions to enumerate combinations of metabolite exchanges that support growth in models of microbial species and a microbial community. We found that metabolite exchanges from different pathway definitions were related to each other through a previously unknown hierarchy, and we show that this hierarchical relationship between pathways holds more generally. Moreover, the fraction of pathways in which each metabolite was exchanged turned out to be remarkably consistent across pathway definitions despite large differences in pathway counts. In summary, our work shows how pathway definitions and their metabolite exchange predictions are related to each other, and it facilitates scalable and interpretable pathway analysis with applications in fields such as metabolic engineering. [ABSTRACT FROM AUTHOR]

Although significant progress has been made in developing preclinical models for metabolic dysfunction‐associated steatotic liver disease (MASLD), few have encapsulated the essential biological and clinical outcome elements reflective of the human condition. We conducted a comprehensive literature review of English‐language original research articles published from 1990 to 2023, sourced from PubMed, Embase, and Web of Science, aiming to collate studies that provided a comparative analysis of physiological, metabolic, and hepatic histological characteristics between MASLD models and control groups. The establishment of a robust metabolic dysfunction‐associated steatotic liver rodent model hinges on various factors, including animal species and strains, sex, induction agents and methodologies, and the duration of induction. Through this review, we aim to guide researchers in selecting suitable induction methods and animal species for constructing preclinical models aligned with their specific research objectives and laboratory conditions. Future studies should strive to develop simple, reliable, and reproducible models, considering the model's sensitivity to factors such as light–dark cycles, housing conditions, and environmental temperature. Additionally, the potential of diverse in vitro models, including 3D models and liver organ technology, warrants further exploration as valuable tools for unraveling the cellular mechanisms underlying fatty liver disease. [ABSTRACT FROM AUTHOR]

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modeling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model iACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii's cellular biomass and identified putative essential genes with no human counterparts, offering new candidates for future antimicrobial development. Finally, we assembled and curated the first collection of metabolic reconstructions for distinct A. baumannii strains and analyzed their growth characteristics. The presented models are in a standardized and well-curated format, enhancing their usability for multi-strain network reconstruction. Author summary: The emergence of multidrug-resistant bacteria, particularly carbapenem-resistant Acinetobacter baumannii, has become a severe global health threat. This pressing issue necessitated the development of new antibiotics, as highlighted by the World Health Organization. To address this need, we aimed to create comprehensive metabolic models to better understand bacterial metabolism and aid in developing novel therapeutic strategies. In this study, we developed a versatile workflow to construct high-quality, simulation-ready genome-scale metabolic models for bacterial pathogens. Applying this workflow, we constructed a metabolic model for A. baumannii and validated its accuracy using experimental data. The model successfully replicated observed metabolic phenotypes and identified essential genes without human counterparts, suggesting potential targets for new antibiotics. Additionally, we assembled and curated the first collection of metabolic reconstructions for distinct A. baumannii strains, analyzing their growth characteristics. These standardized and well-curated models enhance usability, facilitating multi-strain network reconstruction and further research. These findings provide a robust tool for understanding A. baumannii's metabolism, guiding the development of new antimicrobial therapies. [ABSTRACT FROM AUTHOR]

ABSTRACT Isoflavones, secondary metabolites with numerous health benefits, are predominantly found in legume seeds, especially soybean; however, their contents in domesticated soybean seeds are highly variable. Wild soybeans are known for higher seed isoflavone contents than cultivars. Here we used experimental and modelling approaches on wild soybean (W05) and cultivated soybean (C08) to delineate factors influencing isoflavone accumulation. We found imported nutrients were converted into storage compounds, with isoflavone accumulation in W05 seeds being faster than in C08 ones. The isoflavone accumulation during seed development was simulated using context‐specific cotyledon metabolic models of four developmental stages on cultivar C08, and the metabolic burden imposed by increasing biomass was evaluated. Trade‐off analyses between biomass and isoflavone suggest that high biomass requirement in cultivars could limit the reallocation of resources for secondary metabolite production. Isoflavone production in mature seeds was also influenced by biomass compositions. Seeds with higher carbohydrate contents favour isoflavone production, while those with highest protein and oil contents had lowest isoflavone contents. Although seeds could synthesize isoflavones on their own, the predicted fluxes from biosynthesis alone were lower than the empirical levels. Shadow price analyses indicated that isoflavone accumulation depended on both intrinsic biosynthesis and direct contribution from the plant. [ABSTRACT FROM AUTHOR]

In order to further understand the effect of product inhibition on the metabolism of hydrogen production bacteria, and to seek an effective way to increase the hydrogen yield in fermentation, a simplified metabolic model of Ethanoligenens harbinense B49 was constructed to analyse the metabolic flux under acetate and ethanol inhibition separately and to analyse the flux changes of the nodes. Based on the changes in metabolic flux distribution, Glucose 6-phosphate (G6P), Pyruvate (PYR), and Acetyl-CoA (AcCoA) were identified as key nodes of hydrogen production in the metabolic network. Robustness analysis showed that G6P was flexible, while AcCoA and PYR were weakly rigid, indicating that acetate flux could be increased by adding inhibitors or using genetic manipulation. Furthermore, releasing inhibition of acetate could effectively increase hydrogen production. These findings suggested that the addition of acetate separation in ethanol-type fermentation process is expected to improve hydrogen production, which might be a promising way to full-scale produce biohydrogen in industrial applications. Further, for the first time, we report the effect of product inhibition on key nodes in the E. harbinense B49 hydrogen production metabolism network. [ABSTRACT FROM AUTHOR]

In the study of obesity and diabetes, mice are widely used for experimental research, and fasting is a common procedure used to reset metabolism in mouse models. The fasting duration for experimental mice varies greatly in nutritional and metabolic studies, ranging from 2 to 48 h. This study aims to assess the optimal fasting duration for mice fed low- and high-fat diets over a short period of time. C57BL/6J mice were fed a low-fat diet (LFD) or high-fat diet (HFD) and fasted for 4, 6, 8, 10, 12, or 24 h. The effects of different conditions after fasting on the metabolic level of mice were explored, and the data were collected for analysis. Our data indicate that fasting has inconsistent effects on mice fed a low-fat or high-fat diet. To compare the metabolic differences between mice in different dietary levels and thereby secure better scientific data, mice should fast for 6 h in animal experiments. Fasting for 6 h is also recommended when comparing glucose tolerance with insulin tolerance. [ABSTRACT FROM AUTHOR]

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online. Author summary: Simulations of the musculoskeletal system can provide insights into motion mechanics and energetics. To increase confidence in the approach, it is important to establish how predictions are affected by modelling assumptions. In this study, we evaluated how simulations with increasing levels of sophistication and metabolic energy models from the literature agreed with recorded muscle excitations, fiber lengths, and whole-body metabolic rates during walking at different speeds. We found that calibrating muscle passive components: muscle passive forces, and tendon stiffness, estimated better muscle excitations and fiber lengths than using original musculoskeletal model values. The metabolic energy cost of walking was closely estimated with only a subset of metabolic energy models. We estimated that the metabolic energy demands are the highest during three periods of the gait cycle—early stance, pre-swing, and initial swing, at all walking speeds. In all walking speeds, muscles that support plantarflexion accounted for the highest metabolic cost among all the muscle groups. As walking speed increases, the metabolic cost of the stance phase also increases. Our detailed description of metabolic costs at muscle groups and gait phases might help to design assistive devices and rehabilitation treatments. [ABSTRACT FROM AUTHOR]

Automatically finding novel pathways plays an important role in the initial designs of metabolic pathways in synthetic biology and metabolic engineering. Although path-finding methods have been successfully applied in identifying valuable synthetic pathways, few efforts have been made in fusing atom group tracking into building stoichiometry model to search metabolic pathways from arbitrary start compound via Mixed Integer Linear Programming (MILP). We propose a novel method called AFP to find metabolic pathways by incorporating atom group tracking into reaction stoichiometry via MILP. AFP tracks the movements of atom groups in the reaction stoichiometry to construct MILP model to search the pathways containing atom groups exchange in the reactions and adapts the MILP model to provide the options of searching pathways from an arbitrary or given compound to the target compound. Combining atom group tracking with reaction stoichiometry to build MILP model for pathfinding may promote the search of well-designed alternative pathways at the stoichiometric modeling level. The experimental comparisons to the known pathways show that our proposed method AFP is more effective to recover the known pathways than other existing methods and is capable of discovering biochemically feasible pathways producing the metabolites of interest. [Display omitted] • AFP tracks the movement of atom group in stoichiometric model to find pathways. • AFP offers the option of finding pathway from arbitrary compound to given compound. • The results show that AFP is able to uncover biochemically feasible pathway. [ABSTRACT FROM AUTHOR]

Enterococcus faecalis is a versatile lactic acid bacterium with a large variety of implications for humans. While some strains of this species are pathobionts being resistant against most of the common antibiotics, other strains are regarded as biological protectants or even probiotics. Accordingly, E. faecalis strains largely differ in the size and content of their accessory genome. In this study, we describe the genome-scale metabolic network reconstruction of E. faecalis ATCC 19433, a non-resistant human-associated strain. A comparison of the genome-scale metabolic model (GSM) of E. faecalis ATCC 19433 with a previously published GSM of the multi-resistant pathobiontic E. faecalis V583 reveals high similarities in the central metabolic abilities of these two human associated strains. This is reflected, e.g., in the identical amino acid auxotrophies. The ATCC 19433 strain, however, has a 14.1 % smaller genome than V583 and lacks the multiple antibiotic resistance genes and genes involved in capsule formation. Based on the measured metabolic fluxes at different growth rates, the energy demand at zero growth was calculated to be about 40 % lower for the ATCC 19433 strain compared to V583. Furthermore, the ATCC 19433 strain seems less prone to the depletion of amino acids utilizable for energy metabolism. This might hint at a lower overall energy demand of the ATCC 19433 strain as compared to V583. ● A genome-scale metabolic model of E. faecalis ATCC 19433 has been developed. ● The model reproduces experimental flux distributions and amino acid auxotrophies. ● A comparison to a model of E. faecalis strain V583 indicates a lower energy demand of ATCC 19433. [ABSTRACT FROM AUTHOR]

Nitrogen (N) nutrition impacts on primary carbon metabolism and can lead to changes in δ13C of respired CO2. However, uncertainty remains as to whether (1) the effect of N nutrition is observed in all species, (2) N source also impacts on respired CO2 in roots and (3) a metabolic model can be constructed to predict δ13C of respired CO2 under different N sources. Here, we carried out isotopic measurements of respired CO2 and various metabolites using two species (spinach, French bean) grown under different NH4+:NO3− ratios. Both species showed a similar pattern, with a progressive 13C‐depletion in leaf‐respired CO2 as the ammonium proportion increased, while δ13C in root‐respired CO2 showed little change. Supervised multivariate analysis showed that δ13C of respired CO2 was mostly determined by organic acid (malate, citrate) metabolism, in both leaves and roots. We then took advantage of nonstationary, two‐pool modelling that explained 73% of variance in δ13C in respired CO2. It demonstrates the critical role of the balance between the utilisation of respiratory intermediates and the remobilisation of stored organic acids, regardless of anaplerotic bicarbonate fixation by phosphoenolpyruvate carboxylase and the organ considered. [ABSTRACT FROM AUTHOR]

In the ongoing battle against antibiotic-resistant infections, Acinetobacter baumannii has emerged as a critical pathogen in healthcare settings. To understand its response to antibiotic-induced stress, we integrated transcriptomic data from various antibiotics (amikacin sulfate, ciprofloxacin, polymyxin-B, and meropenem) with metabolic modeling techniques. Key metabolic pathways, including arginine and proline metabolism, glycine–serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, and propanoate metabolism, were significantly impacted by all four antibiotics across multiple strains. Specifically, biotin metabolism was consistently down-regulated under polymyxin-B treatment, while fatty acid metabolism was perturbed under amikacin sulfate. Ciprofloxacin induced up-regulation in glycerophospholipid metabolism. Validation with an independent dataset focusing on colistin treatment confirmed alterations in fatty acid degradation, elongation, and arginine metabolism. By harmonizing genetic data with metabolic modeling and a metabolite-centric approach, our findings offer insights into the intricate adaptations of A. baumannii under antibiotic pressure, suggesting more effective strategies to combat antibiotic-resistant infections. [ABSTRACT FROM AUTHOR]

Barrett's esophagus (BE) is a common precancerous lesion that can progress to esophageal adenocarcinoma (EAC). There are significant alterations in the esophageal microbiome in the progression from healthy esophagus to BE to EAC, including an increased abundance of a variety of lactate-producing bacteria and an increase of lactate in the tumor microenvironment, as predicted by metabolic modeling. The role of bacterial lactate in EAC is unknown. Here, we utilize patient-derived organoid (PDO) models of EAC and demonstrate that lactate inhibits the growth and proliferation of EAC PDOs through alterations in the tumor NADH/NAD+ redox state. Further RNA sequencing of EAC PDOs identifies ID1 and RSAD2 as potential regulatory molecules crucial in mediating lactate's ability to suppress glycolysis and proliferation. Gene ontology analysis also identifies the activation of inflammatory and immunological pathways in addition to alterations in the metabolic pathways in EAC PDOs exposed to lactate, suggesting a multi-faceted role for lactate in the pathogenesis of EAC. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we’ve enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular metabolic and physiological states. However, there are still missing reactions in GEMs due to incomplete knowledge. Recent gaps filling methods suggest directly predicting missing responses without relying on phenotypic data. However, they do not differentiate between substrates and products when constructing the prediction models, which affects the predictive performance of the models. In this paper, we propose a hyperedge prediction model that distinguishes substrates and products based on dual-scale fused hypergraph convolution, DSHCNet, for inferring the missing reactions to effectively fill gaps in the GEM. First, we model each hyperedge as a heterogeneous complete graph and then decompose it into three subgraphs at both homogeneous and heterogeneous scales. Then we design two graph convolution-based models to, respectively, extract features of the vertices in two scales, which are then fused via the attention mechanism. Finally, the features of all vertices are further pooled to generate the representative feature of the hyperedge. The strategy of graph decomposition in DSHCNet enables the vertices to engage in message passing independently at both scales, thereby enhancing the capability of information propagation and making the obtained product and substrate features more distinguishable. The experimental results show that the average recovery rate of missing reactions obtained by DSHCNet is at least 11.7% higher than that of the state-of-the-art methods, and that the gap-filled GEMs based on our DSHCNet model achieve the best prediction performance, demonstrating the superiority of our method. [ABSTRACT FROM AUTHOR]

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii. [ABSTRACT FROM AUTHOR]

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years. • Advanced mechanistic metabolic models enhance rational design of cell factories • Machine learning models refine reconstruction of functional metabolic models. • Data-driven AI models provide alternative solutions for strain design in DBTL cycle. • Hybrid AI models with biological insights boost precision in cell factory design. [ABSTRACT FROM AUTHOR]

The reliability and methodology of genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells have advanced. CHO-GEMs have aided in cell line and process development, thus impacting on biomanufacturing efficiency. An integrative model structure can incorporate multiple layers and capture condition-specific cell regulation. Integration of CHO-GEMs with artificial intelligence (AI) and advanced algorithms will enable autonomous bioreactor management for digital biomanufacturing. Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms. [ABSTRACT FROM AUTHOR]

5F-EDMB-PICA is a newly emerged synthetic cannabinoid which has been characterized in relevant literature in recent years. Although phase-I metabolites of 5F-EDMB-PICA have been partly reported, the phase-II metabolism of this synthetic cannabinoid has not been studied yet. In this study, we established a phase-I and phase-II metabolism model in vitro by using pooled human liver microsomes, NADPH regeneration system, and UGT incubation system, with 1 mg/ml 5F-EDMB-PICA added and incubated at 37 °C for 60 min. The metabolites were analyzed by Q Exactive™ Hybrid Quadrupole-Orbitrap™ Mass Spectrometer, via which we discovered and identified 14 phase-I metabolites and 4 phase-II metabolites of 5F-EDMB-PICA, involving pathways such as ester hydrolysis, dehydrogenation, hydrolytic defluorination, hydroxylation, dihydroxylation, glucuronidation, and combinations of the pathways mentioned above. We recommend considering the monohydroxylation metabolites (M9, M10) with higher content and intact ester and 5-fluoropentyl structures as potential biomarkers of 5F-EDMB-PICA. [ABSTRACT FROM AUTHOR]

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented. [ABSTRACT FROM AUTHOR]

Although the concept of endocrine disruptors first appeared almost 30 years ago, the relatively recent involvement of these substances in the etiology of metabolic pathologies (obesity, diabetes, hepatic steatosis, etc.) has given rise to the concept of Metabolic Disrupting Chemicals (MDCs). Organs such as the liver and adipose tissue have been well studied in the context of metabolic disruption by these substances. The intestine, however, has been relatively unexplored despite its close link with these organs. In vivo models are useful for the study of the effects of MDCs in the intestine and, in addition, allow investigations into interactions with the rest of the organism. In the latter respect, the zebrafish is an animal model which is used increasingly for the characterization of endocrine disruptors and its use as a model for assessing effects on the intestine will, no doubt, expand. This review aims to highlight the importance of the intestine in metabolism and present the zebrafish as a relevant alternative model for investigating the effect of pollutants in the intestine by focusing, in particular, on cytochrome P450 3A (CYP3A), one of the major molecular players in endogenous and MDCs metabolism in the gut. [ABSTRACT FROM AUTHOR]

Constraint‐based optimization of microbial strains and model‐based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space‐time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade‐off between growth rate and product yield. Results of a case study with a genome‐scale metabolic model of Escherichia coli indicate higher space‐time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization. [ABSTRACT FROM AUTHOR]

Intravoxel incoherent motion (IVIM) MRI has emerged as a valuable technique for the assessment of tissue characteristics and perfusion. However, there is limited knowledge about the relationship between IVIM‐derived measures and changes at the level of the vascular network. In this study, we investigated the potential use of IVIM MRI as a noninvasive tool for measuring changes in cerebral vascular density. Variations in quantitative immunohistochemical measurements of the vascular density across different regions in the rat brain (cortex, corpus callosum, hippocampus, thalamus, and hypothalamus) were related to the pseudo‐diffusion coefficient D* and the flowing blood fraction f in healthy Wistar rats. We assessed whether region‐wise differences in the vascular density are reflected by variations in the IVIM measurements and found a significant positive relationship with the pseudo‐diffusion coefficient (p < 0.05, β = 0.24). The effect of cerebrovascular alterations, such as blood–brain barrier (BBB) disruption on the perfusion‐related IVIM parameters, is not well understood. Therefore, we investigated the effect of BBB disruption on the IVIM measures in a rat model of metabolic and vascular comorbidities (ZSF1 obese rat) and assessed whether this affects the relationship between the cerebral vascular density and the noninvasive IVIM measurements. We observed increased vascular permeability without detecting any differences in diffusivity, suggesting that BBB leakage is present before changes in the tissue integrity. We observed no significant difference in the relationship between cerebral vascular density and the IVIM measurements in our model of comorbidities compared with healthy normotensive rats. [ABSTRACT FROM AUTHOR]

Abstract Background Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown. Results Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5’-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. Conclusions Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe–nematodes framework for microbiome mediated effects on host animal fitness.

Abstract Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study’s success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities.

Rivers efficiently collect, process, and transport terrestrial‐derived carbon. River ecosystem metabolism is the primary mechanism for processing carbon. Diel cycles of dissolved oxygen (DO) have been used for decades to infer river ecosystem metabolic rates, which are routinely used to predict metabolism of carbon dioxide (CO2) with uncertainties of the assumed stoichiometry ranging by a factor of 4. Dissolved inorganic carbon (DIC) has been less used to directly infer metabolism because it is more difficult to quantify, involves the complexity of inorganic carbon speciation, and as shown in this study, likely requires a two‐station approach. Here, we developed DIC metabolism models using single‐ and two‐station approaches. We compared metabolism estimates based on simultaneous DO and DIC monitoring in the Upper Clark Fork River (USA), which also allowed us to estimate ecosystem‐level photosynthetic and respiratory quotients (PQE and RQE). We observed that metabolism estimates from DIC varied more between single‐ and two‐station approaches than estimates from DO. Due to carbonate buffering, CO2 is slower to equilibrate with the atmosphere compared to DO, likely incorporating a longer distance of upstream heterogeneity. Reach‐averaged PQE ranged from 1.5 to 2.0, while RQE ranged from 0.8 to 1.5. Gross primary production from DO was larger than that from DIC, as was net ecosystem production by 100mmolm−2d−1$$ 100\kern0.5em \mathrm{mmol}\kern0.5em {\mathrm{m}}^{-2}\kern0.5em {\mathrm{d}}^{-1} $$. The river was autotrophic based on DO but heterotrophic based on DIC, complicating our understanding of how metabolism regulated CO2 production. We suggest future studies simultaneously model metabolism from DO and DIC to understand carbon processing in rivers. [ABSTRACT FROM AUTHOR]

AbstractMicrobes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules. [ABSTRACT FROM AUTHOR]

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype. [ABSTRACT FROM AUTHOR]

Because of the advent of genome-editing technology, gene knockout (KO) hamsters have become attractive research models for diverse diseases in humans. This study established a new KO model of diabetes by disrupting the insulin receptor substrate-2 (Irs2) gene in the golden (Syrian) hamster. Homozygous KO animals were born alive but with delayed postnatal growth until adulthood. They showed hyperglycemia, high HbA1c, and impaired glucose tolerance. However, they normally responded to insulin stimulation, unlike Irs2 KO mice, an obese type 2 diabetes (T2D) model. Consistent with this, Irs2 KO hamsters did not increase serum insulin levels upon glucose administration and showed β-cell hypoplasia in their pancreas. Thus, our Irs2 KO hamster provide a unique T2D animal model that is distinct from the obese T2D models. This model may contribute to a better understanding of the pathophysiology of human non-obese T2D with β-cell dysfunction, the most common type of T2D in East Asian countries, including Japan. [ABSTRACT FROM AUTHOR]

Obesity causes significant morbidity and mortality globally. Research in the last three decades has delivered a step-change in our understanding of the fundamental mechanisms that regulate energy homeostasis, building on foundational discoveries in mouse models of metabolic disease. However, not all findings made in rodents have translated to humans, hampering drug discovery in this field. Here, we review how studies in mice and humans have informed our current framework for understanding energy homeostasis, discuss their challenges and limitations, and offer a perspective on how human studies may play an increasingly important role in the discovery of disease mechanisms and identification of therapeutic targets in the future. This Review delves into metabolic disease research performed in mouse models and humans and highlights the challenges in translating research into clinically actionable findings. The discussion includes energy homeostasis, gene-environment interactions, nutritional programming, energy intake and expenditure, and systemic influences on metabolism. [ABSTRACT FROM AUTHOR]

Background: Due to increasing ecological concerns, microbial production of biochemicals from sustainable carbon sources like acetate is rapidly gaining importance. However, to successfully establish large-scale production scenarios, a solid understanding of metabolic driving forces is required to inform bioprocess design. To generate such knowledge, we constructed isopropanol-producing Escherichia coli W strains. Results: Based on strain screening and metabolic considerations, a 2-stage process was designed, incorporating a growth phase followed by a nitrogen-starvation phase. This process design yielded the highest isopropanol titers on acetate to date (13.3 g L−1). Additionally, we performed shotgun and acetylated proteomics, and identified several stress conditions in the bioreactor scenarios, such as acid stress and impaired sulfur uptake. Metabolic modeling allowed for an in-depth characterization of intracellular flux distributions, uncovering cellular demand for ATP and acetyl-CoA as limiting factors for routing carbon toward the isopropanol pathway. Moreover, we asserted the importance of a balance between fluxes of the NADPH-providing isocitrate dehydrogenase (ICDH) and the product pathway. Conclusions: Using the newly gained system-level understanding for isopropanol production from acetate, we assessed possible engineering approaches and propose process designs to maximize production. Collectively, our work contributes to the establishment and optimization of acetate-based bioproduction systems. [ABSTRACT FROM AUTHOR]

Microbial metabolism has been deeply studied over decades and it is considered to be understood to a great extent. Annotated genome sequences of many microbial species have contributed a lot to generating biochemical knowledge on metabolism. However, researchers still discover novel pathways, unforeseen reactions or unexpected metabolites which contradict to the expected canon of biochemical reactions in living organisms. Here, we highlight a few examples of such non‐canonical pathways, how they were found, and what their importance in microbial biotechnology may be. The predictive power of metabolic modelling, well‐founded on biochemical knowledge and genomic information is discussed in the light of both discovery of yet unknown existing metabolic routes and the prediction of others, new to Nature. [ABSTRACT FROM AUTHOR]

Introduction: Stable isotope tracers have been increasingly used in preclinical cancer model systems, including cell culture and mouse xenografts, to probe the altered metabolism of a variety of cancers, such as accelerated glycolysis and glutaminolysis and generation of oncometabolites. Comparatively little has been reported on the fidelity of the different preclinical model systems in recapitulating the aberrant metabolism of tumors. Objectives: We have been developing several different experimental model systems for systems biochemistry analyses of non-small cell lung cancer (NSCLC1) using patient-derived tissues to evaluate appropriate models for metabolic and phenotypic analyses. Methods: To address the issue of fidelity, we have carried out a detailed Stable Isotope-Resolved Metabolomics study of freshly resected tissue slices, mouse patient derived xenografts (PDXs), and cells derived from a single patient using both 13C6-glucose and 13C5,15N2-glutamine tracers. Results: Although we found similar glucose metabolism in the three models, glutamine utilization was markedly higher in the isolated cell culture and in cell culture-derived xenografts compared with the primary cancer tissue or direct tissue xenografts (PDX). Conclusions: This suggests that caution is needed in interpreting cancer biochemistry using patient-derived cancer cells in vitro or in xenografts, even at very early passage, and that direct analysis of patient derived tissue slices provides the optimal model for ex vivo metabolomics. Further research is needed to determine the generality of these observations. [ABSTRACT FROM AUTHOR]

AbstractBoth insufficient and excessive iodine intake can lead to thyroid-related disorders. Although China has made progress in eliminating iodine deficiency over the past few decades, the incidence of thyroid cancer is increasing. Currently, there is a lack of relevant research on the tradeoff between the benefits and risks of salt iodization in China. In this study, we developed a method that combines the total probability algorithm and disease burden to evaluate the appropriate amount of salt iodization. Following the principle of minimizing the comprehensive disease burden and using the metabolic model of human iodine nutrition. Based on the average national iodine level in water, the optimal iodine content in Chinese salt is determined to be 17 mg/kg. However, iodine content in water is not evenly distributed in China. Approximately 3.23% of administrative villages have water iodine concentrations exceeding 80 ug/L, eliminating the need for iodine fortification in salt. Approximately 83.51% of administrative villages need to continue implementing the salt iodization policy, with the optimal iodine content in salt ranging from 15 to 18 mg/kg. In 13.16% of administrative villages, the iodine content in salt is determined based on the local water iodine concentration, ranging from 0 to 15 mg/kg. Our study cracks open a window of insight suggesting that the optimal iodine content for salt is lower than the existing benchmark dictated by the prevailing policy in China. Hence, there is an urgent need to refine and advance the iodine supplementation strategy in salt to pave the way for precision medicine and health-centric iodine supplementation strategies. [ABSTRACT FROM AUTHOR]

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) is a biological process where bacteria digest various types of organic matter under anaerobic conditions. AD has been particularly used for generating biogas from organic wastes, such as food waste. Despite its practical applications, the mechanistic understanding of the AD process remains elusive, especially complex interactions within a microbial community, and between the organic waste and microbial community. One systematic approach to address this challenge is to deploy genome-scale metabolic models (GEMs) of microorganisms involved in AD. GEM is a computational model that describes an entire metabolic network of a cell, and can be simulated under various conditions of interest. In this review, we discuss recent metabolic studies of AD-related microorganisms by using their GEMs across the four major stages of AD. We also suggest future directions in this field that need to be addressed to further optimize the GEMs and the AD process. [ABSTRACT FROM AUTHOR]

Premise: Whole‐genome duplication (WGD, polyploidization) has been identified as a driver of genetic and phenotypic novelty, having pervasive consequences for the evolution of lineages. While polyploids are widespread, especially among plants, the long‐term establishment of polyploids is exceedingly rare. Genome doubling commonly results in increased cell sizes and metabolic expenses, which may be sufficient to modulate polyploid establishment in environments where their diploid ancestors thrive. Methods: We developed a mechanistic simulation model of photosynthetic individuals to test whether changes in size and metabolic efficiency allow autopolyploids to coexist with, or even invade, ancestral diploid populations. Central to the model is metabolic efficiency, which determines how energy obtained from size‐dependent photosynthetic production is allocated to basal metabolism as opposed to somatic and reproductive growth. We expected neopolyploids to establish successfully if they have equal or higher metabolic efficiency as diploids or to adapt their life history to offset metabolic inefficiency. Results: Polyploid invasion was observed across a wide range of metabolic efficiency differences between polyploids and diploids. Polyploids became established in diploid populations even when they had a lower metabolic efficiency, which was facilitated by recurrent formation. Competition for nutrients is a major driver of population dynamics in this model. Perenniality did not qualitatively affect the relative metabolic efficiency from which tetraploids tended to establish. Conclusions: Feedback between size‐dependent metabolism and energy allocation generated size and age differences between plants with different ploidies. We demonstrated that even small changes in metabolic efficiency are sufficient for the establishment of polyploids. [ABSTRACT FROM AUTHOR]

Continuous measurements of dissolved oxygen (DO) are useful for quantifying ecosystem metabolism, which is critical for understanding estuarine biogeochemistry and ecology, but current methods applied to these data may lead to estimates that are physically impossible and poorly constrained errors. Here, we present a new approach for estimating estuarine metabolism: Estuarine BAyesian Single‐station Estimation (EBASE). EBASE applies a Bayesian framework to a simple process‐based model and DO observations, allowing the estimation of critical model parameters, specifically light efficiency and respiration, as informed by a set of prior distributions. EBASE improves upon the stream‐based model from which it was derived by accommodating missing DO data and allowing the user to set the time period over which parameters are estimated. We demonstrate that EBASE can recover known metabolic parameters from a synthetic time series, even in the presence of noise (e.g., due to tidal advection) and when prior distributions are uninformed. Optimization periods of 7 and 30 d are more preferable than 1 d. A comparison with the more‐conventional method of Odum reveals the ability of EBASE to avoid unphysical results (such as negative photosynthesis and respiration) and improves when the DO data are detided. EBASE is available using open‐source software (R) and can be readily applied to multiple years of long‐term monitoring data that are available in many estuaries. Overall, EBASE provides an accessible method to parameterize a simple metabolic model appropriate for estuarine systems and will provide additional understanding of processes that influence ecosystem status and condition. [ABSTRACT FROM AUTHOR]

This study took Nanchang as a research object and established an urban carbon metabolism process model based on carbon flow and an urban carbon metabolism analogy model based on Kleiber's law. The ecological relationship between land types and their changes caused by carbon flow in the process of land use change was studied by applying the ecological network analysis (ENA), and whether the relationship between urban carbon metabolism and the scale of construction land was consistent with the biometabolic law by using the panel data model. The results showed that the net carbon flow in Nanchang continued to show negative values from 2000 to 2020, and the mean value of the mutualism index (M ) was less than 1, both factors indicating a negative impact of land use change. Exploitation and control relationships were the most major ecological relationships, dominated by urban land. The β values of the carbon metabolism models for rural settlements and other construction land were less than 1, implying an analogous relationship between carbon metabolism and biometabolism for both. The study provides a scientific foundation for national and local governments to formulate carbon emission reduction policies and plan low-carbon land use patterns. [ABSTRACT FROM AUTHOR]

Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study's success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner. Methods: We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples. Results: We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920–0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients). Conclusions: We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome. [ABSTRACT FROM AUTHOR]