An Allosteric Signaling Pathway of Human 3-Phosphoglycerate Kinase from Force Distribution Analysis

3-Phosphogycerate kinase (PGK) is a two domain enzyme, which transfers a phosphate group between its two substrates, 1,3-bisphosphoglycerate bound to the N-domain and ADP bound to the C-domain. Indispensable for the phosphoryl transfer reaction is a large conformational change from an inactive open to an active closed conformation via a hinge motion that should bring substrates into close proximity. The allosteric pathway resulting in the active closed conformation has only been partially uncovered. Using Molecular Dynamics simulations combined with Force Distribution Analysis (FDA), we describe an allosteric pathway, which connects the substrate binding sites to the interdomain hinge region. Glu192 of alpha-helix 7 and Gly394 of loop L14 act as hinge points, at which these two secondary structure elements straighten, thereby moving the substrate-binding domains towards each other. The long-range allosteric pathway regulating hPGK catalytic activity, which is partially validated and can be further tested by mutagenesis, highlights the virtue of monitoring internal forces to reveal signal propagation, even if only minor conformational distortions, such as helix bending, initiate the large functional rearrangement of the macromolecule. © 2014 Palmai et al

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Proton transport chains in glucose metabolism: mind the proton

The Embden–Meyerhof–Parnas (EMP) pathway comprises eleven cytosolic enzymes interacting to metabolize glucose to lactic acid [CH3CH(OH)COOH]. Glycolysis is largely considered as the conversion of glucose to pyruvate (CH3COCOO-). We consider glycolysis to be a cellular process and as such, transporters mediating glucose uptake and lactic acid release and enable the flow of metabolites through the cell, must be considered as part of the EMP pathway. In this review, we consider the flow of metabolites to be coupled to a flow of energy that is irreversible and sufficient to form ordered structures. This latter principle is highlighted by discussing that lactate dehydrogenase (LDH) complexes irreversibly reduce pyruvate/H+ to lactate [CH3CH(OH)COO-], or irreversibly catalyze the opposite reaction, oxidation of lactate to pyruvate/H+. However, both LDH complexes are considered to be driven by postulated proton transport chains. Metabolism of glucose to two lactic acids is introduced as a unidirectional, continuously flowing pathway. In an organism, cell membrane-located proton-linked monocarboxylate transporters catalyze the final step of glycolysis, the release of lactic acid. Consequently, both pyruvate and lactate are discussed as intermediate products of glycolysis and substrates of regulated crosscuts of the glycolytic flow

Dimer Pyruvate Kinase M2 Regulates de novo Collagen Synthesis and Crosslinking in Pathological Fibrosis

Fibrosis is a pathologic condition of abnormal accumulation of collagen fibrils. Collagen is synthesized and secreted from myofibroblasts. Collagen is a major extracellular matrix (ECM) protein composed of mainly (Gly-X-Y)n triplets repeats with \u3e30% Gly residue. During fibrosis progression, myofibroblasts must upregulate glycine metabolism to meet the need for collagen synthesis. We report here that pyruvate kinase M2 (PKM2) is upregulated in myofibroblasts. Myofibroblast differentiation promotes dimerization of PKM2. Dimer PKM2 slows the flow rate of glycolysis. Dimer PKM2 channels glycolytic intermediates to de novo glycine synthesis, which facilitates collagen synthesis and secretion in myofibroblasts. Our results show that PKM2 activator that convert PKM2 dimer to tetramer inhibits fibrosis progression in mouse models of liver and lung fibrosis. Furthermore, PKM2 activator alters glycolysis pathway, which consequently affects the reverses fibrosis by reducing glycine production in vivo. Our study uncovers a novel role of PKM2 in tissue/organ, suggesting a possible strategy for treatment of fibrosis diseases. Furthermore, secreted collagen is crosslinked in the extracellular space by the lysyl oxidase (LOX) family proteins. LOX is secreted by the activated myofibroblasts under hypoxic conditions. PKM2 mediates Hif-1α activity in cells under hypoxic conditions. Here, we report that the PKM2- Hif-1α axis induces LOX expression in fibroblasts and cancer cells. PKM2-Hif-1α complex regulates LOX transcription by directly binding to LOX promoter. Here we show that PKM2 activators reduce PKM2-HIF1α association and thereby its nuclear localization. PKM2 activators reduce the production and hence the secretion and the crosslinking capacity of LOX family proteins

Systems biology of energy metabolism in skeletal muscle

The primary function of skeletal muscle tissue is to produce force or cause motion. To perform this task chemical energy stored in nutrients (glucose and fatty acids) has to be converted into an energy currency that can drive muscle contraction (adenosine-tri-phosphate, ATP). This process is known as the energy metabolism of skeletal muscle and consists of a large number of chemical reactions that are organized in metabolic pathways. Unraveling this complex network is important from a fundamental biological perspective, but also essential to understand how a disturbance of muscle bioenergetics can cause metabolic disorders. ?? 31P magnetic resonance spectroscopy (MRS) has emerged as one of the premier methods to study skeletal muscle energy metabolism in vivo. It, however, remains challenging to relate the observed metabolite dynamics to an understanding of the underlying processes at the level of the metabolic pathways. A possible solution for bridging this gap between macroscopic measurements and mechanistic understanding at pathway level is the application of mechanistic computational modeling. This dissertation describes a series of studies in which a mechanistic model of ATP metabolism was developed and applied in the analysis of skeletal muscle bioenergetics. Skeletal muscle cells contain two primary processes that are responsible for the conversion of glucose and fatty acids into ATP. These processes are known as glycolysis and oxidative phosphorylation in mitochondria. The initial mathematical models of these processes were obtained by integration of known enzyme kinetics and thermodynamics. Testing of these models, however, showed that they failed to reproduce many of the in vivo observed metabolite dynamics, as has been described in chapter 1 and 2. These results indicated that the models might be missing essential regulatory mechanisms or that the model parameterization required changes. First, the physiological implications of necessary model adaptations were investigated in a series of studies described in chapters 2 – 5. ?? Numerical analysis of the initial glycolysis model revealed that the experimentally observed slow turnover rate of phosphorylated sugars post exercise could only be explained by rapid deactivation of phosphofructokinase (PFK) and pyruvate kinase (PK) in non-contracting muscle. In particular the deactivation of PFK was crucial for adequate control of pathway flux. Therefore, in a follow-up study, it was tested if the missing regulation at the level of PFK could be explained by calcium – calmodulin mediated activation of this enzyme. To this end, pathway behavior, represented by phosphocreatine (PCr) and pH dynamics, was measured in ischemic skeletal muscle for a wide variety of muscle excitation frequencies (0 – 80 Hz). Next, it was shown that addition of the calcium – calmodulin mediated activation of PFK was necessary to accurately reproduce these data. These results provided important new quantitative support for the hypothesis that this particular mechanism has a key role in the regulation of glycolytic flux in skeletal muscle.?? The initial model of oxidative phosphorylation was first tested against empirically determined mitochondrial input – output relations, i.e., [ADP] – mitochondrial ATP synthesis flux (Jp) and phosphate potential (¿Gp) – Jp. These empirically determined relations were derived from 31P MRS measurements of metabolite dynamics post-exercise. They capture key features of the regulation of oxidative phosphorylation in vivo and are therefore considered relevant for testing the quality of the mathematical model. Numerical model analysis (i.e., parameter sensitivity analysis) was applied to investigate which components significantly influenced predictions of these input – output relations. Based on these results it was concluded that the adenine nucleotide transporter (which facilitates the exchange of ATP and ADP across the inner mitochondrial membrane) has a dominant role in controlling the ADP sensitivity of mitochondria. Furthermore, we identified that Pi feedback control of respiratory chain activity was essential to explain measurements of ¿Gp at low metabolic rates. These insights were used to improve the predictive power of the model, as described in chapters 4 and 5. ?? In the studies described in chapters 2 - 5 the glycolytic and mitochondrial model components were tested for conditions in which only one of the two processes was active (ischemia and post exercise recovery, respectively). It remained therefore unknown if the control mechanisms included in these models could also explain the contribution of mitochondrial versus glycolytic ATP synthesis for conditions in which both processes are active (aerobic exercise). In an attempt to answer this question, dynamics of ATP metabolism were recorded during a full rest – exercise – recovery protocol under aerobic conditions and subsequently used for testing of the integrated mitochondrial + glycolytic model. The results presented in chapter 8 showed that the integrated model could accurately reproduce the observed metabolite and pH dynamics for varying exercise intensities. The main physiological implications of these results were that, substrate feedback control (ADP + Pi) of oxidative phosphorylation combined with substrate feedback control (ADP + AMP + Pi) and control by parallel activation (calcium – calmodulin mediated activation of PFK) of glycolysis, provides a set of key control mechanisms that can explain the regulation of ATP metabolism in skeletal muscle in vivo for a wide range of physiological conditions. By application of several cycles of model development it was possible to improve the models performance to the point it was consistent with 31P MRS measurements of muscle bioenergetics in both healthy humans and animals. As described in chapters 6 and 7, it is was investigated if the model could be applied to analyze the adaptations of muscle physiology that underlie changes in mitochondrial capacity that occur in for instance type 2 diabetes patients or with aging. A decrease of mitochondrial capacity in these subjects can be diagnosed accurately by determining the rate of PCr recovery post exercise. However, the changes in muscle physiology responsible for any observed difference in oxidative capacity cannot be deduced from these measurements. Therefore additional muscle biopsy samples are collected and analyzed for in vitro markers of oxidative capacity. State-of-the-art analyses of these data are typically limited to statistical or intuitive approaches. We investigated if the insight obtained from the combined in vivo + in vitro data sets could be increased by application of our mathematical model. To this end, first, the model was extended from a single uniform cell type model to a three types cell model (type I, IIA, and IIX), capturing the microscopic heterogeneity of muscle tissue. In addition, several key validation tests were conducted, as described in chapter 6. Subsequently, we demonstrated that the model could explain the prolongation of PCr recovery period observed in type 2 diabetes patients by integrating available literature data of in vitro markers of mitochondrial function. Although this result was already very promising, it was also concluded that the approach could be tested more rigorously by obtaining all data (in vivo + in vitro) in a single study. Therefore, the method was further tested in an animal model of decreased mitochondrial function: 8 versus 25 week old Wistar rats. The first main result of this study was that the mathematical model could accurately reproduce the delayed PCr recovery kinetics in 25 week old animals based on in vitro determined changes in muscle physiology. In addition, model predictions provided quantitative insight in the individual contribution of the different factors responsible for the decreased oxidative capacity. This type of information is considered very relevant for the design of (pharmaceutical) therapies aimed at improving mitochondrial function. For example, model predictions of the physiological changes that contribute the most to the decrease in oxidative capacity provide potentially promising targets for therapy design. Based on these considerations it was concluded that application of the mathematical model provides new promising opportunities for future studies of mitochondrial (dys)function in skeletal muscle. ?? In conclusion, through application of a series of iterative cycles of model development combined with multiple new experimental studies it was possible to develop a detailed mechanistic model of ATP metabolism that was consistent with in vivo observations of skeletal muscle bioenergetics for a wide range of physiological conditions. This process provided new insight in the key control mechanisms embedded in the metabolic pathways that have a dominant role in regulating ATP metabolism in skeletal muscle in vivo. In addition, we successfully demonstrated the feasibility and added value of application of the model for integration of in vivo and in vitro measurements of oxidative capacity in future studies of mitochondrial (dys)function in, for example, type 2 diabetes, aging or mitochondrial myopathy

Brain glycogen—new perspectives on its metabolic function and regulation at the subcellular level

Glycogen is a complex glucose polymer found in a variety of tissues, including brain, where it is localized primarily in astrocytes. The small quantity found in brain compared to e.g., liver has led to the understanding that brain glycogen is merely used during hypoglycemia or ischemia. In this review evidence is brought forward highlighting what has been an emerging understanding in brain energy metabolism: that glycogen is more than just a convenient way to store energy for use in emergencies—it is a highly dynamic molecule with versatile implications in brain function, i.e., synaptic activity and memory formation. In line with the great spatiotemporal complexity of the brain and thereof derived focus on the basis for ensuring the availability of the right amount of energy at the right time and place, we here encourage a closer look into the molecular and subcellular mechanisms underlying glycogen metabolism. Based on (1) the compartmentation of the interconnected second messenger pathways controlling glycogen metabolism (calcium and cAMP), (2) alterations in the subcellular location of glycogen-associated enzymes and proteins induced by the metabolic status and (3) a sequential component in the intermolecular mechanisms of glycogen metabolism, we suggest that glycogen metabolism in astrocytes is compartmentalized at the subcellular level. As a consequence, the meaning and importance of conventional terms used to describe glycogen metabolism (e.g., turnover) is challenged. Overall, this review represents an overview of contemporary knowledge about brain glycogen and its metabolism and function. However, it also has a sharp focus on what we do not know, which is perhaps even more important for the future quest of uncovering the roles of glycogen in brain physiology and pathology

Investigation of an energetic coupling between ligand binding and protein folding

The cellular environment presents a protein with many small molecules with which it may interact. Many novel interactions between proteins and non-substrate metabolites are being uncovered through proteome-wide screens. The homodimeric Escherichia coli cofactor-dependant phosphoglycerate mutase (dPGM) was identified as an ATP binding protein in a proteome-wide screen, but dPGM does not use ATP for catalysis. This dissertation elucidates the effect of ATP and other non-substrate metabolites on dPGM. Initial investigations revealed a partially unfolded, monomeric intermediate of dPGM that forms during equilibrium unfolding. ATP binding was found to occur at the active site of dPGM and to be energetically coupled with dimerization; ligand binding events reduce the population of intermediate. An investigation into the structure of the dPGM intermediate revealed a cooperative folding unit that couples the active site and dimer interface of dPGM. By coupling the two binding sites, the cooperative unit is responsible for conveying the allosteric effect observed between dimerization and ligand binding. We found that physiological salts reduce but do not prevent non-substrate metabolite binding at physiological concentrations. Further, anions bind specifically to dPGM and chloride was found to bind to both of the energetically coupled sites on dPGM, the active site and dimer interface. Our findings illustrate how a cooperative link between a ligand binding site and oligomer interface can promote higher order oligomers and reduce intermediate populations. The physiological effect of the cooperative link and ligand binding to dPGM is a large enhancement in the stability of the dimer over the monomer intermediate and, possibly, competitive inhibition

Targeting cancer cell metabolism as a therapeutic strategy

In the past 15 years the field of cancer metabolism has burst providing vast quantities of information regarding the metabolic adaptations found in cancer cells and offering promising hints for the development of therapies that target metabolic features of cancer cells. By making use of the powerful combination of metabolomics and 13C-labelled metabolite tracing we have contributed to the field by identifying a mitochondrial enzymatic cascade crucial for oncogene-induced senescence (OIS), which is a tumour suppressive mechanism important in melanoma, linking in this way OIS to the regulation of metabolism. Furthermore, we have identified the dependency on glutamine metabolism as an important adaptation occurring concomitantly with the acquisition of resistance to vemurafenib (BRAF inhibitor) in melanoma, which opens the possibility to combine therapies targeting glutamine metabolism with BRAF inhibitors, in order to overcome or avoid the onset of resistance in melanoma. Using the same strategy we have discovered an important mechanism of interregulation between glycolysis and amino acid metabolism, identifying the glucose-derived amino acid serine as an activator of the main isoform of pyruvate kinase present in cancer cells, PKM2. In addition, we provide new insights into the mechanism of allosteric regulation of this complex protein and a better understanding of the way it regulates central carbon metabolism. In summary, our results open new possibilities for the development of cancer therapies that manipulate metabolic adaptations found in cancer cells in order to kill them specifically or halt their growth

Is Cancer a Metabolic Disease?

Although cancer has historically been viewed as a disorder of proliferation, recent evidence has suggested that it should also be considered a metabolic disease. Growing tumors rewire their metabolic programs to meet and even exceed the bioenergetic and biosynthetic demands of continuous cell growth. The metabolic profile observed in cancer cells often includes increased consumption of glucose and glutamine, increased glycolysis, changes in the use of metabolic enzyme isoforms, and increased secretion of lactate. Oncogenes and tumor suppressors have been discovered to have roles in cancer-associated changes in metabolism as well. The metabolic profile of tumor cells has been suggested to reflect the rapid proliferative rate. Cancer-associated metabolic changes may also reveal the importance of protection against reactive oxygen species or a role for secreted lactate in the tumor microenvironment. This article reviews recent research in the field of cancer metabolism, raising the following questions: Why do cancer cells shift their metabolism in this way? Are the changes in metabolism in cancer cells a consequence of the changes in proliferation or a driver of cancer progression? Can cancer metabolism be targeted to benefit patients