Metabolic modeling of muscle metabolism identifies key reactions linked to insulin resistance phenotypes

An obesogenic FTO allele causes accelerated development, growth and insulin resistance in human skeletal muscle cells

Human GWAS have shown that obesogenic FTO polymorphisms correlate with lean mass, but the mechanisms have remained unclear. It is counterintuitive because lean mass is inversely correlated with obesity and metabolic diseases. Here, we use CRISPR to knock-in FTOrs9939609-A into hESC-derived tissue models, to elucidate potentially hidden roles of FTO during development. We find that among human tissues, FTOrs9939609-A most robustly affect human muscle progenitors' proliferation, differentiation, senescence, thereby accelerating muscle developmental and metabolic aging. An edited FTOrs9939609-A allele over-stimulates insulin/IGF signaling via increased muscle-specific enhancer H3K27ac, FTO expression and m6A demethylation of H19 lncRNA and IGF2 mRNA, with excessive insulin/IGF signaling leading to insulin resistance upon replicative aging or exposure to high fat diet. This FTO-m6A-H19/IGF2 circuit may explain paradoxical GWAS findings linking FTOrs9939609-A to both leanness and obesity. Our results provide a proof-of-principle that CRISPR-hESC-tissue platforms can be harnessed to resolve puzzles in human metabolism.

Association between the triglyceride glucose index and diabetic retinopathy in type 2 diabetes: a meta-analysis

The triglyceride-glucose (TyG) index is an accessible and reliable surrogate indicator of insulin resistance and is strongly associated with diabetes. However, its relationship with diabetic retinopathy (DR) remains controversial. This meta-analysis aimed to assess the relationship between the TyG index and the prevalence of DR. Initial studies were searched from PubMed, Embase, Web of Science, and China National Knowledge Infrastructure (CNKI) electronic databases. The retrieval time range was from the establishment of the database to June 2023. Pooled estimates were derived using a random-effects model and reported as odds ratio (OR) with 95% confidence intervals (CIs). Two researchers independently assessed the methodological quality of the included studies. The Newcastle-Ottawa Quality Scale (NOS) was utilized to assess cohort studies or case-control studies. The Agency for Healthcare Research and Quality (AHRQ) methodology checklist was applied to assess cross-sectional studies. Ten observational studies encompassing 13716 patients with type 2 diabetes were included in the meta-analysis. The results showed that a higher TyG index increased the risk of DR compared with a low TyG index (OR: 2.34, 95% CI: 1.31-4.19, P < 0.05). When the index was analyzed as a continuous variable, consistent results were observed (OR: 1.48, 95% CI: 1.12-1.97, P < 0.005). There was no significant effect on the results of the sensitivity analyses excluding one study at a time (P all < 0.05). A higher TyG index may be associated with an increased prevalence of DR in patients with type 2 diabetes. However, high-quality cohort or case-control studies are needed to further substantiate this evidence.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023432747.

An integrative profiling of metabolome and transcriptome in the plasma and skeletal muscle following an exercise intervention in diet-induced obese mice

Exercise intervention at the early stage of type 2 diabetes mellitus (T2DM) can aid in the maintenance of blood glucose homeostasis and prevent the development of macrovascular and microvascular complications. However, the exercise-regulated pathways that prevent the development of T2DM remain largely unclear. In this study, two forms of exercise intervention, treadmill training and voluntary wheel running, were conducted for high-fat diet (HFD)-induced obese mice. We observed that both forms of exercise intervention alleviated HFD-induced insulin resistance and glucose intolerance. Skeletal muscle is recognized as the primary site for postprandial glucose uptake and for responsive alteration beyond exercise training. Metabolomic profiling of the plasma and skeletal muscle in Chow, HFD, and HFD-exercise groups revealed robust alterations in metabolic pathways by exercise intervention in both cases. Overlapping analysis identified nine metabolites, including beta-alanine, leucine, valine, and tryptophan, which were reversed by exercise treatment in both the plasma and skeletal muscle. Transcriptomic analysis of gene expression profiles in the skeletal muscle revealed several key pathways involved in the beneficial effects of exercise on metabolic homeostasis. In addition, integrative transcriptomic and metabolomic analyses uncovered strong correlations between the concentrations of bioactive metabolites and the expression levels of genes involved in energy metabolism, insulin sensitivity, and immune response in the skeletal muscle. This work established two models of exercise intervention in obese mice and provided mechanistic insights into the beneficial effects of exercise intervention on systemic energy homeostasis.

A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients

Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.

The mitochondrial pyruvate carrier at the crossroads of intermediary metabolism

Pyruvate metabolism, a central nexus of carbon homeostasis, is an evolutionarily conserved process and aberrant pyruvate metabolism is associated with and contributes to numerous human metabolic disorders including diabetes, cancer, and heart disease. As a product of glycolysis, pyruvate is primarily generated in the cytosol before being transported into the mitochondrion for further metabolism. Pyruvate entry into the mitochondrial matrix is a critical step for efficient generation of reducing equivalents and ATP and for the biosynthesis of glucose, fatty acids, and amino acids from pyruvate. However, for many years, the identity of the carrier protein(s) that transported pyruvate into the mitochondrial matrix remained a mystery. In 2012, the molecular-genetic identification of the mitochondrial pyruvate carrier (MPC), a heterodimeric complex composed of protein subunits MPC1 and MPC2, enabled studies that shed light on the many metabolic and physiological processes regulated by pyruvate metabolism. A better understanding of the mechanisms regulating pyruvate transport and the processes affected by pyruvate metabolism may enable novel therapeutics to modulate mitochondrial pyruvate flux to treat a variety of disorders. Herein, we review our current knowledge of the MPC, discuss recent advances in the understanding of mitochondrial pyruvate metabolism in various tissue and cell types, and address some of the outstanding questions relevant to this field.

4-Hydroxybenzoic Acid and β-Sitosterol from Cucurbita ficifolia Act as Insulin Secretagogues, Peroxisome Proliferator-Activated Receptor-Gamma Agonists, and Liver Glycogen Storage Promoters: In Vivo, In Vitro, and In Silico Studies

Insulin secretion and GLUT4 expression are two critical events in glucose regulation. The receptors G-protein-coupled receptor 40 (GPR40) and peroxisome proliferator-activated receptor-gamma (PPARγ) modulate these processes, and they represent potential therapeutic targets for new antidiabetic agent's design. Cucurbita ficifolia fruit is used in traditional medicine for diabetes control. Previous studies demonstrated several effects: a hypoglycemic effect mediated by an insulin secretagogue action, antihyperglycemic effect, and promoting liver glycogen storage. Anti-inflammatory and antioxidant effects were also reported. Moreover, some of its phytochemicals have been described, including d-chiro-inositol. However, to understand these effects integrally, other active principles should be investigated. The aim was to perform a chemical fractionation guided by bioassay to isolate and identify other compounds from C. ficifolia fruit that explain its hypoglycemic action as insulin secretagogue, its antihyperglycemic effect by PPARγ activation, and on liver glycogen storage. Three different preparations of C. ficifolia were tested in vivo. Ethyl acetate fraction derived from aqueous extract showed antihyperglycemic effect in an oral glucose tolerance test and was further fractioned. The insulin secretagogue action was tested in RINm5F cells. For the PPARγ activation, C2C12 myocytes were treated with the fractions, and GLUT4 mRNA expression was measured. Chemical fractionation resulted in the isolation and identification of β-sitosterol and 4-hydroxybenzoic acid (4-HBA), which increased insulin secretion, GLUT4, PPARγ, and adiponectin mRNA expression, in addition to an increase in glycogen storage. 4-HBA exhibited an antihyperglycemic effect, while β-sitosterol showed hypoglycemic effect, confirming the wide antidiabetic related results we found in our in vitro models. An in silico study revealed that 4-HBA and β-sitosterol have potential as dual agonists on PPARγ and GPR40 receptors. Both compounds should be considered in the development of new antidiabetic drug development.

Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models

Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network.

Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism

Background

Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals.

Methods

To understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+ -consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α.

Results

The model simulates the interactions among key regulators such as AKT, mTORC1, AMPK, NAD+ , and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases.

Conclusions

This study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases.

A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models

Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults.

Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling

Background

Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue.

Methods

The muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns.

Results

A machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified.

Conclusions

This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.

CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics

Background

This research aimed at exploring the mechanisms of alterations of metabolites and pathways in T2D from the perspective of metabolomics and transcriptomics, as well as uncovering novel drug candidate for T2D treatment.

Methods

Metabolites in human plasma from 42 T2D patients and 45 non‐diabetic volunteers were detected by liquid chromatography‐mass spectrometer (LC‐MS). Microarray dataset of the transcriptome was obtained from Gene Expression Omnibus (GEO) database. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to conduct pathway enrichment analysis. Connectivity Map (CMap) was employed to select potential drugs for T2D therapy. In vivo assay was performed to verify above findings. The protein expression levels of ME1, ME2 and MDH1 were detected by Western blot to determine the status of NAD/NADH cofactor system.

Results

In our study, differentially expressed metabolites were selected out between healthy samples and T2D samples with selection criteria P value < .05, |Fold Change| > 2, including N‐acetylglutamate and Malate. Genes set enrichment analysis (GSEA) revealed that 34 pathways were significantly enriched in T2D. Based on CMap analysis and animal experiments, Atractyloside was identified as a potential novel drug for T2D treatment via targeting ME1, ME2 and MDH1 and regulating the NAD/NADH cofactor system.

Conclusion

The present research revealed differentially expressed metabolites and genes, as well as significantly altered pathways in T2D via an integration of metabolomics, transcriptomics and CMap analysis. It was also demonstrated that comprehensive analysis based on metabolomics and transcriptomics was an effective approach for identification and verification of metabolic biomarkers and alternated pathways.

Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects

The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions.

Impaired skeletal muscle mitochondrial pyruvate uptake rewires glucose metabolism to drive whole-body leanness

Metabolic cycles are a fundamental element of cellular and organismal function. Among the most critical in higher organisms is the Cori Cycle, the systemic cycling between lactate and glucose. Here, skeletal muscle-specific Mitochondrial Pyruvate Carrier (MPC) deletion in mice diverted pyruvate into circulating lactate. This switch disinhibited muscle fatty acid oxidation and drove Cori Cycling that contributed to increased energy expenditure. Loss of muscle MPC activity led to strikingly decreased adiposity with complete muscle mass and strength retention. Notably, despite decreasing muscle glucose oxidation, muscle MPC disruption increased muscle glucose uptake and whole-body insulin sensitivity. Furthermore, chronic and acute muscle MPC deletion accelerated fat mass loss on a normal diet after high fat diet-induced obesity. Our results illuminate the role of the skeletal muscle MPC as a whole-body carbon flux control point. They highlight the potential utility of modulating muscle pyruvate utilization to ameliorate obesity and type 2 diabetes.

SGLT2 inhibition reprograms systemic metabolism via FGF21-dependent and -independent mechanisms

Pharmacologic inhibition of the renal sodium/glucose cotransporter-2 induces glycosuria and reduces glycemia. Given that SGLT2 inhibitors (SGLT2i) reduce mortality and cardiovascular risk in type 2 diabetes, improved understanding of molecular mechanisms mediating these metabolic effects is required. Treatment of obese but nondiabetic mice with the SGLT2i canagliflozin (CANA) reduces adiposity, improves glucose tolerance despite reduced plasma insulin, increases plasma ketones, and improves plasma lipid profiles. Utilizing an integrated transcriptomic-metabolomics approach, we demonstrate that CANA modulates key nutrient-sensing pathways, with activation of 5' AMP-activated protein kinase (AMPK) and inhibition of mechanistic target of rapamycin (mTOR), independent of insulin or glucagon sensitivity or signaling. Moreover, CANA induces transcriptional reprogramming to activate catabolic pathways, increase fatty acid oxidation, reduce hepatic steatosis and diacylglycerol content, and increase hepatic and plasma levels of FGF21. Given that these phenotypes mirror the effects of FGF21 to promote lipid oxidation, ketogenesis, and reduction in adiposity, we hypothesized that FGF21 is required for CANA action. Using FGF21-null mice, we demonstrate that FGF21 is not required for SGLT2i-mediated induction of lipid oxidation and ketogenesis but is required for reduction in fat mass and activation of lipolysis. Taken together, these data demonstrate that SGLT2 inhibition triggers a fasting-like transcriptional and metabolic paradigm but requires FGF21 for reduction in adiposity.

Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease

The ability to efficiently adapt metabolism by substrate sensing, trafficking, storage, and utilization, dependent on availability and requirement, is known as metabolic flexibility. In this review, we discuss the breadth and depth of metabolic flexibility and its impact on health and disease. Metabolic flexibility is essential to maintain energy homeostasis in times of either caloric excess or caloric restriction, and in times of either low or high energy demand, such as during exercise. The liver, adipose tissue, and muscle govern systemic metabolic flexibility and manage nutrient sensing, uptake, transport, storage, and expenditure by communication via endocrine cues. At a molecular level, metabolic flexibility relies on the configuration of metabolic pathways, which are regulated by key metabolic enzymes and transcription factors, many of which interact closely with the mitochondria. Disrupted metabolic flexibility, or metabolic inflexibility, however, is associated with many pathological conditions including metabolic syndrome, type 2 diabetes mellitus, and cancer. Multiple factors such as dietary composition and feeding frequency, exercise training, and use of pharmacological compounds, influence metabolic flexibility and will be discussed here. Last, we outline important advances in metabolic flexibility research and discuss medical horizons and translational aspects.

Integrating Extracellular Flux Measurements and Genome-Scale Modeling Reveals Differences between Brown and White Adipocytes

White adipocytes are specialized for energy storage, whereas brown adipocytes are specialized for energy expenditure. Explicating this difference can help identify therapeutic targets for obesity. A common tool to assess metabolic differences between such cells is the Seahorse Extracellular Flux (XF) Analyzer, which measures oxygen consumption and media acidification in the presence of different substrates and perturbagens. Here, we integrate the Analyzer's metabolic profile from human white and brown adipocytes with a genome-scale metabolic model to predict flux differences across the metabolic map. Predictions matched experimental data for the metabolite 4-aminobutyrate, the protein ABAT, and the fluxes for glucose, glutamine, and palmitate. We also uncovered a difference in how adipocytes dispose of nitrogenous waste, with brown adipocytes secreting less ammonia and more urea than white adipocytes. Thus, the method and software we developed allow for broader metabolic phenotyping and provide a distinct approach to uncovering metabolic differences.

Animal research for type 2 diabetes mellitus, its limited translation for clinical benefit, and the way forward

Obesity and type 2 diabetes mellitus (T2DM) have reached pandemic proportions worldwide, and considerable research efforts have been dedicated to investigating disease pathology and therapeutic options. The two hallmark features of T2DM, insulin resistance and pancreatic dysfunction, have been studied extensively by using various animal models. Despite the knowledge acquired from such models, particularly mechanistic discoveries that sometimes mimic human T2DM mechanisms or pathways, many details of human T2DM pathogenesis remain unknown, therapeutic options remain limited, and a cure has eluded research. Emerging human data have raised concern regarding inter-species differences at many levels (e.g. in gene regulation, pancreatic cytoarchitecture, glucose transport, and insulin secretion regulation), and the subsequent impact of these differences on the clinical translation of animal research findings. Therefore, it is important to recognise and address the translational gap between basic animal-based research and the clinical advances needed to prevent and treat T2DM. The purpose of this report is to identify some limitations of T2DM animal research, and to propose how greater human relevance and applicability of hypothesis-driven basic T2DM research could be achieved through the use of human-based data acquisition at various biological levels. This report addresses how in vitro, in vivo and in silico technologies could be used to investigate particular aspects of human glucose regulation. We do not propose that T2DM animal research has been without value in the identification of mechanisms, pathways, or potential targets for therapies, nor do we claim that human-based methods can provide all the answers. We recognise that the ultimate goal of T2DM animal research is to identify ways to advance the prevention, recognition and treatment of T2DM in humans, but postulate that this is where the use of animal models falls short, despite decades of effort. The best way to achieve this goal is by prioritising human-centred research.

GSH levels affect weight loss in individuals with metabolic syndrome and obesity following dietary therapy

This study examined the effects of redox status markers on metabolic syndrome (MetS) and obesity before and after dietary intervention and exercise for weight loss. A total of 103 adults suffering from MetS and obesity participated in this study and followed a personalized diet plan for 6 months. Body weight, body fat (BF) percentage (BF%), respiratory quotient (RQ) and the redox status markers, reduced glutathione (GSH), thiobarbituric acid reactive substances (TBARS) and protein carbonyls (CARB), were measured twice in each individual, before and after intervention. Dietary intervention resulted in weight loss, a reduction in BF% and a decrease in RQ. The GSH levels were significantly decreased following intervention, while the levels of TBARS and CARB were not affected. Based on the initial GSH levels, the patients were divided into 2 groups as follows: The high GSH group (GSH, >3.5 µmol/g Hb) and the low GSH group (GSH <3.5 µmol/g Hb). Greater weight and BF loss were observed in patients with high GSH levels. It was observed that patients with MetS and obesity with high GSH values responded better to the dietary therapy, exhibiting more significant changes in weight and BF%. This finding underscores the importance of identifying redox status markers, particularly GSH, in obese patients with MetS. Knowing the levels of GSH may aid in developing a better design of an individualized dietary plan for individuals who wish to lose weight.

Squeezing Flux Out of Fat

Merging transcriptomics or metabolomics data remains insufficient for metabolic flux estimation. Ramirez et al. integrate a genome-scale metabolic model with extracellular flux data to predict and validate metabolic differences between white and brown adipose tissue. This method allows both metabolic phenotyping and the identification of potential therapeutic targets for obesity.

Narrow-leafed lupin (Lupinus angustifoliusL.) seed β-conglutins reverse the induced insulin resistance in pancreatic cells

Narrow-leafed lupin β-conglutin proteins may help to prevent and treat insulin resistance through pleiotropic effects.

Modelling the molecular mechanisms of aging

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.

Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome

Myalgic encephalopathy/chronic fatigue syndrome (ME/CFS) is a debilitating disease of unknown etiology, with hallmark symptoms including postexertional malaise and poor recovery. Metabolic dysfunction is a plausible contributing factor. We hypothesized that changes in serum amino acids may disclose specific defects in energy metabolism in ME/CFS. Analysis in 200 ME/CFS patients and 102 healthy individuals showed a specific reduction of amino acids that fuel oxidative metabolism via the TCA cycle, mainly in female ME/CFS patients. Serum 3-methylhistidine, a marker of endogenous protein catabolism, was significantly increased in male patients. The amino acid pattern suggested functional impairment of pyruvate dehydrogenase (PDH), supported by increased mRNA expression of the inhibitory PDH kinases 1, 2, and 4; sirtuin 4; and PPARδ in peripheral blood mononuclear cells from both sexes. Myoblasts grown in presence of serum from patients with severe ME/CFS showed metabolic adaptations, including increased mitochondrial respiration and excessive lactate secretion. The amino acid changes could not be explained by symptom severity, disease duration, age, BMI, or physical activity level among patients. These findings are in agreement with the clinical disease presentation of ME/CFS, with inadequate ATP generation by oxidative phosphorylation and excessive lactate generation upon exertion.

Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism

OBJECTIVE

Plasma levels of branched-chain amino acids (BCAA) are consistently elevated in obesity and type 2 diabetes (T2D) and can also prospectively predict T2D. However, the role of BCAA in the pathogenesis of insulin resistance and T2D remains unclear.

METHODS

To identify pathways related to insulin resistance, we performed comprehensive gene expression and metabolomics analyses in skeletal muscle from 41 humans with normal glucose tolerance and 11 with T2D across a range of insulin sensitivity (SI, 0.49 to 14.28). We studied both cultured cells and mice heterozygous for the BCAA enzyme methylmalonyl-CoA mutase (Mut) and assessed the effects of altered BCAA flux on lipid and glucose homeostasis.

RESULTS

Our data demonstrate perturbed BCAA metabolism and fatty acid oxidation in muscle from insulin resistant humans. Experimental alterations in BCAA flux in cultured cells similarly modulate fatty acid oxidation. Mut heterozygosity in mice alters muscle lipid metabolism in vivo, resulting in increased muscle triglyceride accumulation, increased plasma glucose, hyperinsulinemia, and increased body weight after high-fat feeding.

CONCLUSIONS

Our data indicate that impaired muscle BCAA catabolism may contribute to the development of insulin resistance by perturbing both amino acid and fatty acid metabolism and suggest that targeting BCAA metabolism may hold promise for prevention or treatment of T2D.

What Have Metabolomics Approaches Taught Us About Type 2 Diabetes?

Type 2 diabetes (T2D) is increasing worldwide, making identification of biomarkers for detection, staging, and effective prevention strategies an especially critical scientific and medical goal. Fortunately, advances in metabolomics techniques, together with improvements in bioinformatics and mathematical modeling approaches, have provided the scientific community with new tools to describe the T2D metabolome. The metabolomics signatures associated with T2D and obesity include increased levels of lactate, glycolytic intermediates, branched-chain and aromatic amino acids, and long-chain fatty acids. Conversely, tricarboxylic acid cycle intermediates, betaine, and other metabolites decrease. Future studies will be required to fully integrate these and other findings into our understanding of diabetes pathophysiology and to identify biomarkers of disease risk, stage, and responsiveness to specific treatments.

Time Course of Molecular and Metabolic Events in the Development of Insulin Resistance in Fructose-Fed Rats

We aimed to determine the time-course of metabolic changes related to the early onset of insulin resistance (IR), trying to evidence breaking points preceding the appearance of the clinical IR phenotype. The model chosen was the fructose (FRU)-fed rat compared to controls fed with starch. We focused on the hepatic metabolism after 0, 5, 12, 30, or 45 days of FRU intake. The hepatic molecular metabolic changes followed indeed a multistep trajectory rather than a continuous progression. After 5 d of FRU feeding, we observed deep modifications in the hepatic metabolism, driven by the induction of lipogenic genes and important glycogen depletion. Thereafter, a steady-state period between days 12 and 30 was observed, characterized by a switch from carbohydrate to lipid utilization at the hepatic level and increased insulin levels aiming at alleviating lipid accumulation and hyperglycemia, respectively. The FRU-fed animals were only clinically IR at day 45 (altered homeostasis model assessment-estimated insulin resistance and muscle glucose transport). Furthermore, the urine metabolome revealed even earlier metabolic trajectory changes that precede the hepatic alterations. We identified several candidate metabolites linked to the tryptophan-nicotinamide metabolism and the installation of fasting hyperglycemia that suggest a role of this metabolic pathway on the development of the IR phenotype in the FRU-fed rats.

Dissecting Germ Cell Metabolism through Network Modeling

Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.

Mathematical modelling of metabolic regulation in aging

The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.