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Sepsis is a highly lethal and urgent unmet medical need. It is the result of a complex interplay of several pathways, including inflammation, immune activation, hypoxia, and metabolic reprogramming. Specifically, the regulation and the impact of the latter have become better understood in which the highly catabolic status during sepsis and its similarity with starvation responses appear to be essential in the poor prognosis in sepsis. It seems logical that new interventions based on the recognition of new therapeutic targets in the key metabolic pathways should be developed and may have a good chance to penetrate to the bedside. In this review, we concentrate on the pathological changes in metabolism, observed during sepsis, and the presumed underlying mechanisms, with a focus on the level of the organism and the interplay between different organ systems. Graphical Abstract In this review, Van Wingene, Vandewalle and Libert concentrate on the pathological changes in metabolism observed during sepsis, and the presumed underlying mechanisms.

Depletion of nicotinamide adenine dinucleotide (NAD + ), a central redox cofactor and the substrate of key metabolic enzymes, is the causative factor of a number of inherited and acquired diseases in humans. Primary deficiencies of NAD + homeostasis are the result of impaired biosynthesis, while secondary deficiencies can arise due to other factors affecting NAD + homeostasis, such as increased NAD + consumption or dietary deficiency of its vitamin B3 precursors. NAD + depletion can manifest in a wide variety of pathological phenotypes, ranging from rare inherited defects, characterized by congenital malformations, retinal degeneration, and/or encephalopathy, to more common multifactorial, often age‐related, diseases. Here, we discuss NAD + biochemistry and metabolism and provide an overview of the etiology and pathological consequences of alterations of the NAD + metabolism in humans. Finally, we discuss the state of the art of the potential therapeutic implications of NAD + repletion for boosting health as well as treating rare and common diseases, and the possibilities to achieve this by means of the different NAD + ‐enhancing agents. Graphical Abstract This comprehensive review discusses pathological consequences of NAD + metabolism alterations and the therapeutic potential of NAD + enhancers.

Age‐associated diseases are rising to pandemic proportions, exposing the need for efficient and low‐cost methods to tackle these maladies at symptomatic, behavioral, metabolic, and physiological levels. While nutrition and health are closely intertwined, our limited understanding of how diet precisely influences disease often precludes the medical use of specific dietary interventions. Caloric restriction (CR) has approached clinical application as a powerful, yet simple, dietary modulation that extends both life‐ and healthspan in model organisms and ameliorates various diseases. However, due to psychological and social‐behavioral limitations, CR may be challenging to implement into real life. Thus, CR‐mimicking interventions have been developed, including intermittent fasting, time‐restricted eating, and macronutrient modulation. Nonetheless, possible side effects of CR and alternatives thereof must be carefully considered. We summarize key concepts and differences in these dietary interventions in humans, discuss their molecular effects, and shed light on advantages and disadvantages. Graphical Abstract Can dietary modulations promote better health? The current article comprehensively reviews key concepts of dietary interventions, their clinical application and the beneficial and disadvantageous effects of caloric restriction and fasting on human health.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multi-scale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level provides the foundation needed to support this evolution.

Tumor‐associated macrophages (TAMs) promote tumor growth and metastasis by suppressing tumor immune surveillance. Herein, we provide evidence that the immunosuppressive phenotype of TAMs is controlled by long‐chain fatty acid metabolism, specifically unsaturated fatty acids, here exemplified by oleate. Consequently, en‐route enriched lipid droplets were identified as essential organelles, which represent effective targets for chemical inhibitors to block in vitro polarization of TAMs and tumor growth in vivo . In line, analysis of human tumors revealed that myeloid cells infiltrating colon cancer but not gastric cancer tissue indeed accumulate lipid droplets. Mechanistically, our data indicate that oleate‐induced polarization of myeloid cells depends on the mammalian target of the rapamycin pathway. Thus, our findings reveal an alternative therapeutic strategy by targeting the pro‐tumoral myeloid cells on a metabolic level. Synopsis Tumor‐associated macrophages (TAMs) are the main regulatory cell type in the tumor stroma as well as the microenvironment. This study describes how fatty acids polarize myeloid cells to TAMs and how this polarization is controlled by lipid droplet‐dependent fatty acid metabolism. The fatty acid‐enriched tumor environment itself was sufficient to induce the regulatory phenotype of TAMs, including the up‐regulation of classical markers like CD206, IL‐6, VEGFα, MMP9 or Arg1. The fatty acid‐induced TAM polarization was lipid droplet dependent. mTORC2 activation played a critical role in the generation of the suppressive myeloid cell phenotype. Cell‐specific inhibition of DGAT1 and 2 prevented oleate‐induced polarization into immunosuppressive TAMs in vitro in murine and human cell culture systems as well as in vivo in a murine tumor model. Graphical Abstract Tumor‐associated macrophages (TAMs) are the main regulatory cell type in the tumor stroma as well as the microenvironment. This study describes how fatty acids polarize myeloid cells to TAMs and how this polarization is controlled by lipid droplet‐dependent fatty acid metabolism.

Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering. Synopsis The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage. GECKO is a method that enhances a GEM with enzyme constraints, using both kinetic and omics data. The enzyme‐constrained ecYeast7 model of S. cerevisiae outperforms previous models in simulation capabilities and allows exploring enzyme usage. Directly integrating quantitative proteomic data in ecYeast7 significantly reduces the inherent flux variability of model simulations. Physiological behavior such as maximum specific growth rate, overflow metabolism and gene deletion response can be explained by a limited enzyme pool in cell. Graphical Abstract The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage.

Fitness of bacteria is determined both by how fast cells grow when nutrients are abundant and by how well they survive when conditions worsen. Here, we study how prior growth conditions affect the death rate of Escherichia coli during carbon starvation. We control the growth rate prior to starvation either via the carbon source or via a carbon‐limited chemostat. We find a consistent dependence where death rate depends on the prior growth conditions only via the growth rate, with slower growth leading to exponentially slower death. Breaking down the observed death rate into two factors, maintenance rate and recycling yield, reveals that slower growing cells display a decreased maintenance rate per cell volume during starvation, thereby decreasing their death rate. In contrast, the ability to scavenge nutrients from carcasses of dead cells (recycling yield) remains constant. Our results suggest a physiological trade‐off between rapid proliferation and long survival. We explore the implications of this trade‐off within a mathematical model, which can rationalize the observation that bacteria outside of lab environments are not optimized for fast growth. Synopsis This study shows that Escherichia coli require less maintenance in starvation and survive longer, if they have previously grown slowly. These findings indicate that maintenance rate is a plastic variable that bacteria can adapt to increase their fitness in starvation. Escherichia coli survive longer if they previously grew slower. The decrease in death rate can be traced to a decrease in maintenance rate. The study suggests a trade‐off between growth rate and death rate, that exerts a strong selective pressure on bacteria to grow slow. Graphical Abstract This study shows that Escherichia coli require less maintenance in starvation and survive longer, if they have previously grown slowly. These findings indicate that maintenance rate is a plastic variable that bacteria can adapt to increase their fitness in starvation.

Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans. Synopsis Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism. Sex‐specific whole‐body metabolic reconstructions represent the integrated function of 26 organs and six blood cell types. Stoichiometric reconstructions of metabolism can be constrained with whole‐body physiological and metabolomic data to generate personalized models. Whole‐body metabolic models recapitulate known inter‐organ metabolic cycles and energy use, successfully predict known biomarkers of inherited metabolic diseases, and explore potential host‐microbiome co‐metabolism. Graphical Abstract Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism.

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data‐independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non‐metabolic stresses, and non‐planktonic states. The resulting high‐quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low‐abundant proteins under various metabolic limitations, the non‐specificity of catabolic enzymes upregulated under carbon limitation, the lack of large‐scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain‐dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi‐omics studies of gene regulation and metabolic control in E .  coli . Synopsis Accurate proteomic measurements of absolute protein mass fractions in Escherichia coli allowed the characterization of proteome responses under > 60 diverse growth conditions from a coarse (groups of related proteins) to a fine (individual) protein level. The study presents a mass spectrometric workflow based on data‐independent acquisition proteomics and a novel protein inference algorithm (xTop) optimized for absolute protein quantification. The mass spectrometric data was benchmarked and calibrated with absolute protein mass fractions obtained by ribosome profiling. A plethora of novel biological findings are presented, including lack of large‐scale proteome reallocation under stress compared to nutrient limitations, regulation of outer membrane proteins, and effects important for motility and biofilm formation. Graphical Abstract Accurate proteomic measurements of absolute protein mass fractions in Escherichia coli allowed the characterization of proteome responses under > 60 diverse growth conditions from a coarse (groups of related proteins) to a fine (individual) protein level.