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Bakker, B. M.

Publications and source records attributed to Bakker, B. M..

13 recordsLinked to original sources

Glutamine-derived NADH fuels lactate production to maximize ATP yield under oxygen consumption constraints in head and neck cancer

Metabolic reprogramming is one of the hallmarks of cancer. A well-known change is the Warburg effect: glucose is consumed at increased rates and fermented to lactate instead of being respired to carbon dioxide, despite the presence of sufficient oxygen (O2). We show that head and neck squamous cell carcinoma (HNSCC) cells excrete more carbon in the form of lactate than is consumed as glucose, indicating the presence of additional carbon sources for lactate. Amino acid uptake and [U-13C]-glutamine labelling data show that glutamine is highly consumed and oxidized in the tricarboxylic acid (TCA) cycle, but remarkably does not contribute to lactate carbon. To explain these observations, we generated a cell-line specific metabolic model to understand the link between lactate production and glutamine metabolism. In the model, we show how low cellular O2 consumption capacity can cause increased lactate production with glutamine as NADH source. The model suggests that ATP production under limited O2 consumption is augmented by increased glutamine oxidation which yields ATP, FADH2 and NADH. FADH2 can only be respired, despite yielding less ATP than NADH respiration. NADH can be exported from the mitochondrion to be reoxidized by lactate dehydrogenase, permitting more cycles of glutamine oxidation, thereby increasing the total ATP yield. By experimentally inhibiting mitochondrial respiration, we confirmed that non-glucose carbon sources are used for lactate production under decreased O2 consumption capacity. This unexpected feature of O2-constrained ATP production provides a credible mechanism for the increased lactate production via a novel redox cycle in HNSCC cells.

systems biology↗

Ensemble kinetic modelling links residual enzyme activity to clinical symptoms in mitochondrial β-oxidation defects

The mitochondrial fatty acid {beta}-oxidation (mFAO) is an important source of energy when carbohydrate stores are depleted. It is also involved in many diseases, including inherited fatty-acid oxidation deficiencies (mFAODs). Patients with the same genetic variant often present with clinically heterogeneous phenotypes, but the mechanisms contributing to this heterogeneity are poorly understood. To investigate the underlying pathophysiology of different mFAODs, we constructed a computational model of mFAO in human liver, based on experimentally determined enzyme kinetics. A recognised, but seldom addressed challenge in metabolic modelling is the substantial uncertainty about kinetic parameter values. Whereas experimental values of some mFAO parameters are quite reproducible, others vary by up to four orders of magnitude between different reports. To address this, we generated an ensemble of kinetic models, each with the same reaction stoichiometry and rate equations, but different kinetic parameters, sampled from distributions of literature-derived values. We also comprehensively report these values and the arguments based on which they were evaluated. The resulting models were validated against available flux data, yielding a final ensemble of 51 valid models. These models recapitulate recent findings about the accumulation of medium-chain acyl-CoAs and the concomitant depletion of free CoA (CoASH) in medium-chain acyl-CoA dehydrogenase deficiency. We applied the ensemble to a set of known mFAODs, separating them into long-chain (LC-) and short-/medium-chain (S/MC-)mFAODs. The residual activity at which clinical symptoms are known to occur corresponded well with the residual activity in the model at which pathway flux was significantly decreased in LC-mFAODs. Residual activity in S/MC-mFAODs correlated less strongly with pathway flux, but these deficiencies did show a combined flux- and CoASH-reduction effect. This comparison is of importance to researchers and clinicians, as it identifies possible ways in which insights about one mFAOD may be applied to another based on shared biochemical properties. Author SummaryWhen building computer models of metabolic pathways, it is typical to take the "best" experimental data and use that as input into the model. However, especially when working with human cells, ethical and practical constraints often mean that even the "best" experimental data is still subject to substantial uncertainty. We explicitly modelled the uncertainty about the inner workings of fat burning (fatty acid oxidation). The resulting model is known as an "ensemble". The ensemble predicts ranges instead of single outcomes, allowing us to assess the confidence level of our predictions. We assess a set of inherited diseases - enzyme deficiencies - simulating them at different levels of severity with the ensemble. We find that the model does a good job of predicting the severity of the deficiencies at which symptoms will occur. It also allows us to identify a key difference between two subgroups within this group of deficiencies: long-chain and medium-/short-chain, depending on the size of the fats being metabolised. The long-chain variant is predicted to correlate most straightforwardly with the severity of the deficiencies, due to its effect on energy generation. Medium-/short-chain deficiencies, in contrast, have more complex consequences.

systems biology↗

Decoding the Glycan Signature: Unraveling N-Glycosylation Alterations in Glycogen Storage Disease Ia and Ib

Glycogen storage disease (GSD) types Ia and Ib are rare inherited metabolic disorders caused by pathogenic variants in G6PC or SLC37A4, respectively. These defects disrupt glucose homeostasis and may affect protein glycosylation. We systematically profiled sera N-glycomes from retrospectively collected GSD Ia (n=17), GSD Ib (n=8), and control (n=21) samples. Derived traits analyses revealed distinct, subtype-specific, and shared glycomic alterations, including shifts from 2,3-to 2,6-sialylation in both subtypes. GSD Ia showed enhanced branching and reduced fucosylation, whereas GSD Ib displayed elevated fucosylation, bisection, and reduced branching. In GSD Ia patients with hepatocellular adenoma/carcinoma (HCA/HCC), further enrichment of oligomannosidic and 2,3-sialylation was observed. Several individual N-glycans showed strong discriminatory performance, supporting their potential as biomarkers for GSD I subtyping and HCA/HCC surveillance. This study provides the first comprehensive characterization of systemic N-glycans in GSD Ia and Ib, revealing glycomic remodeling as a potential biomarker of disease subtypes and tumor progression. Take-home message (synopsis) of the articleSerum/plasma N-glycomic profiling reveals distinct glycosylation signatures in GSD Ia and GSD Ib, including tumor-associated shifts in GSD Ia, highlighting novel biomarkers for disease subtyping and surveillance of hepatic complications.

biochemistry↗

Proteomics profiling of serum and liver in GSD Ia and Ib patients: insights into complication mechanisms and circulation biomarkers

BackgroundGlycogen Storage Disease (GSD) Types Ia and Ib are rare metabolic diseases caused by gene variants in G6PC1 and SLC37A4, respectively. Although life-threatening fasting hypoglycemia can be controlled by a strict diet, patients often suffer from multiple metabolic abnormalities and severe long-term complications. However, the underlying mechanisms remain incompletely understood, and the lack of effective monitoring biomarkers makes it a challenge to treat patients. Therefore, the aims of this study are to investigate the pathological mechanisms of the disease and disease complications in GSD I and identify potential protein biomarkers. MethodsIn this study, we employed comprehensive untargeted proteomics on stored samples: 26 serum or plasma samples from 18 GSD Ia and 8 GSD Ib patients with 21 matched control sera, complemented by 4 liver samples from 2 GSD Ia patients who received liver transplantation (from the one patient with hepatocellular carcinoma we obtained tissue from both the carcinoma tissue and the adjacent non-carcinoma tissue), and 1 from a GSD Ib patient, compared to 10 donor liver samples. ResultsWe identified a total of 415 proteins in our analyses. Pathway analysis of the differentially regulated proteins revealed distinct changes in serum/plasma of GSD Ia and Ib. The coagulation pathway was the most significantly changed biological process in the GSD Ia patients. Immune response-associated proteins, especially a large number of immunoglobulins, were increased in GSD Ib specifically. Proteins related to liver injury, cholesterol, and amyloidosis were altered in two subtypes, though more pronounced in GSD Ia. Potential biomarkers with significant alterations both in the circulation as well as in the liver tissue were identified specifically for monitoring GSD I subtypes and prognosing liver deterioration, namely GSD Ia (COL163 and PROC), GSD Ib (F11 and CD163), and hepatocellular carcinoma (HCC) in GSD Ia patients (ALDOB and CFHR5). ConclusionsThese findings provide new insights into the differences between the two GSD I subtypes and the pathogenesis of GSD I-related complications, as well as highlighting the potential of protein circulating biomarkers for monitoring complication progression in GSD I and assessing HCC risk in GSD Ia patients. Trial registrationNot applicable. A concise 1 sentence take-home message (synopsis) of the articleThis comprehensive in-depth proteomics study on blood and liver GSD Ia and Ib patient samples provided novel insights into understanding of the disease subtypes as well as the the pathogenesis of GSD I-related complications, such as HCC risk in GSD Ia patients and identified potential biomarkers for the disease subtypes and complications.

systems biology↗

Longitudinal metabolomics data analysis informed bymechanistic models

MotivationMetabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements. ResultsWe arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets are coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. AvailabilityThe code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).

bioinformatics↗

Phenylketonuria: modelling cerebral amino acid and neurotransmitter metabolism

ObjectivePhenylketonuria (PKU) is a metabolic disorder characterised by deficient hepatic phenylalanine hydroxylase activity, leading to elevated phenylalanine levels. Despite adherence to a phenylalanine-restricted diet, many adult PKU patients continue to experience executive function deficits, likely linked to high cerebral phenylalanine concentrations and deficiencies in monoaminergic neurotransmitters. Given the complexity of the interaction between diet and brain neurotransmitter metabolism, we employed computational metabolic modelling alongside experimental data from dietary intervention studies in PKU mice to identify key metabolic drivers underlying these deficits. Our goal was to provide a mechanistic, model-based foundation to support and optimise dietary therapies in PKU. MethodWe developed a computational model simulating large neutral amino acid (LNAA) transport across the blood-brain barrier and the subsequent metabolism of cerebral amino acids and monoaminergic neurotransmitters. The model was validated using direct measurements of brain amino acid concentrations in PKU mice subjected to various dietary regimens. ResultsThe model predicts that cerebral amino acid levels are primarily influenced by their plasma concentrations and, to a lesser extent, by competition among LNAAs for transport mechanisms. Notably, it suggests that cerebral monoaminergic neurotransmitter levels are more significantly affected by elevated phenylalanine levels, likely through non-competitive inhibition of hydroxylase enzymes, than by the availability of precursor amino acids. Consequently, the model indicates that reducing phenylalanine levels, in conjunction with supplementing tyrosine and tryptophan, is more effective in restoring neurotransmitter levels than precursor supplementation alone. ConclusionThis study presents the first comprehensive model integrating LNAA transport and cerebral neurotransmitter metabolism in PKU. The model enhances our understanding of the diseases pathophysiology and highlights the importance of combined therapeutic strategies that target both phenylalanine reduction and precursor amino acid supplementation. Furthermore, it identifies knowledge gaps in LNAA transport mechanisms and offers a framework applicable to other neurological disorders involving diet-gene-neurotransmitter interactions.

systems biology↗

Age and diet modulate the insulin-sensitizing effects of exercise: a tracer-based oral glucose tolerance test

Diet modulates the development of insulin resistance during aging. This includes tissue-specific alterations in insulin signaling and mitochondrial function, which ultimately affect glucose homeostasis. Exercise stimulates glucose clearance, mitochondrial lipid oxidation and enhances insulin sensitivity. It is not well known how exercise interacts with age and diet in the development of insulin resistance. To investigate this, oral glucose tolerance tests (OGTT) with a tracer were conducted in mice ranging from 4 to 21 months of age, fed a low- (LFD) or high-fat diet (HFD), with or without life-long voluntary access to a running wheel (RW). We developed a computational model to derive glucose fluxes, which were commensurate with independent values from steady-state tracer infusions. Both insulin sensitivity indices derived for peripheral tissues and liver (IS-P and IS-L, respectively) were steeply decreased by aging and a HFD. This preceded the age-dependent decline in the mitochondrial capacity to oxidize lipids. In LFD young animals, RW access enhanced the IS-P concomitantly with the muscle {beta}-oxidation capacity. Surprisingly, RW access completely prevented the age-dependent IS-L decrease, but only in LFD animals. This study indicates, therefore, that endurance exercise can improve the age-dependent decline in organ-specific IS mostly in the context of a healthy diet.

physiology↗

Species-specific metabolic reprogramming in human and mouse microglia during inflammatory pathway induction

Metabolic reprogramming is a hallmark of the immune cells in response to inflammatory stimuli. This metabolic process involves a switch from oxidative phosphorylation (OXPHOS) to glycolysis, or alterations in other metabolic pathways. However, most of the experimental findings have been acquired in murine immune cells and little is known about the metabolic reprogramming of human microglia. In this study, we investigated the transcriptomic and metabolic profiles of mouse and iPSC-derived human microglia challenged with the TLR4 agonist LPS. We found that both species displayed a metabolic shift and an overall increased glycolytic gene signature in response to LPS treatment. The metabolic reprogramming was characterized by the upregulation of hexokinases in mouse microglia and phosphofructokinases in human microglia. This study provides the first direct comparison of energy metabolism between mouse and human microglia, highlighting the species-specific pathways involved in immunometabolism and the importance of considering these differences in translational research.

cell biology↗

Analyzing postprandial metabolomics data using multiway models: A simulation study

BackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postpran-dial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using Principal Component Analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased vs. healthy groups. ConclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

systems biology↗

Age-related susceptibility to insulin resistance is due to a combination of CPT1B decline and lipid overload

BACKGROUNDAdvanced age increases the susceptibility to diet-induced insulin resistance (IR). A key driver of this phenomenon is lipid accumulation in the skeletal muscle. It is debated, however, whether this is due to dietary lipid overload or decline of mitochondrial function. To address the interplay of diet and age in the flexibility of muscle lipid and glucose handling, we put young and aged mice on a low- or high-fat diet (HFD). RESULTSAs expected, aged mice were more susceptible to IR when given a HFD than young mice. The HFD induced intramuscular lipid accumulation specifically in aged mice, including C18:0-containing ceramides and diacylglycerols. This was reflected by the mitochondrial {beta}-oxidation capacity, which was upregulated by the HFD in young, but not in old mice. Conspicuously, most {beta}-oxidation proteins were upregulated by the HFD in both groups, but carnitine palmitoyltransferase 1B (CPT1B) declined in aged animals. Computational modelling traced the flux control mostly to CPT1B, suggesting a CPT1B-driven loss of flexibility to the HFD with age. Finally, in old animals glycolytic protein levels were reduced and less flexible to the diet. CONCLUSIONWe conclude that intramuscular lipid accumulation and decreased insulin sensitivity are not due to age-related mitochondrial dysfunction or nutritional overload alone, but rather to their interaction. Moreover, we identify CPT1B as a potential target to counteract age-dependent intramuscular lipid accumulation and thereby IR.

biochemistry↗

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Refsum disease is an inborn error of metabolism that is characterised by a defect in peroxisomal -oxidation of the branched-chain fatty acid phytanic acid. The disorder presents with late-onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanic acid in plasma and tissues of patients. To date, no cure exists for Refsum disease, but phytanic acid levels in patients can be reduced by plasmapheresis and a strict diet.\n\nIn this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. We used transcriptomics (available via GEO database with identifier GSE138379), metabolomics, and proteomics data (available via ProteomeXchange with identifier PXD015518), which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid.\n\nOur model correctly represents the metabolism of phytanic acid and displays fibroblast-specific metabolic functions. Using this model, we investigated the metabolic phenotype of Refsum disease at the genome-scale, and we studied the effect of phytanic acid on cell metabolism. We identified 53 metabolites that were predicted to discriminate between Healthy and Refsum disease patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

systems biology↗

Agent-Based Modeling Predicts HDL-independent Pathway of Removal of Excess Surface Lipids from Very Low Density Lipoprotein

A hallmark of the metabolic syndrome is low HDL-cholesterol coupled with high plasma triglycerides (TG), but it is unclear what drives this close association. Plasma triglycerides and HDL cholesterol are thought to communicate through two distinct mechanisms. Firstly, excess surface lipids from VLDL released during lipolysis are transferred to HDL, thereby contributing to HDL directly but also indirectly through providing substrate for LCAT. Secondly, high plasma TG increases clearance of HDL through core-lipid exchange between VLDL and HDL via CETP and subsequent hydrolysis of the TG in HDL, resulting in smaller HDL and thus increased clearance rates.\n\nTo test our understanding of how high plasma TG induces low HDL-cholesterol, making use of established knowledge, we developed a comprehensive agent-based model of lipoprotein metabolism which was validated using monogenic disorders of lipoprotein metabolism.\n\nBy perturbing plasma TG in the model, we tested whether the current theoretical framework reproduces experimental findings. Interestingly, while increasing plasma TG through simulating decreased lipolysis of VLDL resulted in the expected decrease in HDL cholesterol, perturbing plasma TG through simulating increased VLDL production rates did not result in the expected HDL-TG relation at physiological lipid fluxes. However, model perturbations and experimental findings can be reconciled if we assume a pathway removing excess surface-lipid from VLDL that does not contribute to HDL cholesterol ester production through LCAT. In conclusion, our model simulations suggest that excess surface lipid from VLDL is cleared in part independently from HDL.\n\nAuthor summaryWhile it has long been known that high plasma triglycerides are associated with low HDL cholesterol, the reason for this association has remained unclear. One of the proposed mechanisms is that during catabolism of VLDL, lipoproteins rich in triglyceride, the excess surface of these particles become a source for the production of HDL cholesterol, and that therefore decreased catabolism of VLDL will lead to both higher plasma triglyceride and low HDL cholesterol. Another proposed mechanism is that during increased production of VLDL, there will be increased exchange of core lipids between VLDL and HDL, with subsequent hydrolysis of the triglyceride in HDL, leading to smaller HDL that is cleared more rapidly. To investigate these mechanisms further we developed a computational model based on established knowledge concerning lipoprotein metabolism and validated the model with known findings in monogenetic disorders. Upon perturbing the plasma triglycerides within the model by increasing the VLDL production rate, we unexpectedly found an increase in both triglyceride and HDL cholesterol. However, upon assuming that less excess surface lipid is available to HDL, HDL decreases in response to increased VLDL production. We therefore propose that there must be a pathway removing excess surface lipids that is independent from HDL.\n\nAbbreviations

systems biology↗

Cofactors revisited - predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.

systems biology↗