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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.

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.

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.

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.

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.

In mammals, maintenance of energy and nutrient homeostasis during food deprivation is accomplished through an increase in mitochondrial fatty acid oxidation in peripheral tissues. An important component that drives this cellular oxidative process is the transcriptional coactivator PGC‐1α. Here, we show that fasting induced PGC‐1α deacetylation in skeletal muscle and that SIRT1 deacetylation of PGC‐1α is required for activation of mitochondrial fatty acid oxidation genes. Moreover, expression of the acetyltransferase, GCN5, or the SIRT1 inhibitor, nicotinamide, induces PGC‐1α acetylation and decreases expression of PGC‐1α target genes in myotubes. Consistent with a switch from glucose to fatty acid oxidation that occurs in nutrient deprivation states, SIRT1 is required for induction and maintenance of fatty acid oxidation in response to low glucose concentrations. Thus, we have identified SIRT1 as a functional regulator of PGC‐1α that induces a metabolic gene transcription program of mitochondrial fatty acid oxidation. These results have implications for understanding selective nutrient adaptation and how it might impact lifespan or metabolic diseases such as obesity and diabetes.

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.

Prevalence of type 2 diabetes (T2D) and obesity is increasing worldwide. Currently available therapies are not suited for all patients in the heterogeneous obese/T2D population, hence the need for novel treatments. Fibroblast growth factor 21 (FGF21) is considered a promising therapeutic agent for T2D/obesity. Native FGF21 has, however, poor pharmacokinetic properties, making gene therapy an attractive strategy to achieve sustained circulating levels of this protein. Here, adeno‐associated viral vectors (AAV) were used to genetically engineer liver, adipose tissue, or skeletal muscle to secrete FGF21. Treatment of animals under long‐term high‐fat diet feeding or of ob/ob mice resulted in marked reductions in body weight, adipose tissue hypertrophy and inflammation, hepatic steatosis, inflammation and fibrosis, and insulin resistance for > 1 year. This therapeutic effect was achieved in the absence of side effects despite continuously elevated serum FGF21. Furthermore, FGF21 overproduction in healthy animals fed a standard diet prevented the increase in weight and insulin resistance associated with aging. Our study underscores the potential of FGF21 gene therapy to treat obesity, insulin resistance, and T2D. Synopsis This study describes the use of adeno‐associated viral (AAV) vectors to achieve long‐term production of fibroblast growth factor 21 (FGF21) to treat obesity and insulin resistance. AAV‐FGF21 gene transfer to healthy animals also prevented age‐associated weight gain and insulin resistance. A one‐time administration of an AAV vector encoding FGF21 counteract obesity and insulin resistance for more than a year. The approach works in two different animal models of obesity, induced either by diet or genetic mutations. Administration of AAV‐FGF21 to healthy animals promotes healthy aging. AAV‐FGF21 pharmacological effects are demonstrated after genetic engineering of 3 different tissues (liver, adipose tissue and skeletal muscle). FGF21 gene therapy holds great translational potential in the fight against insulin resistance, T2D, obesity and related comorbidities. Graphical Abstract This study describes the use of adeno‐associated viral (AAV) vectors to achieve long‐term production of fibroblast growth factor 21 (FGF21) to treat obesity and insulin resistance. AAV‐FGF21 gene transfer to healthy animals also prevented age‐associated weight gain and insulin resistance.