Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology

Aerobic glycolysis is a seemingly wasteful mode of ATP production that is seen both in rapidly proliferating mammalian cells and highly active contracting muscles, but whether there is a common origin for its presence in these widely different systems is unknown. To study this issue, here we develop a model of human central metabolism that incorporates a solvent capacity constraint of metabolic enzymes and mitochondria, accounting for their occupied volume densities, while assuming glucose and/or fatty acid utilization. The model demonstrates that activation of aerobic glycolysis is favored above a threshold metabolic rate in both rapidly proliferating cells and heavily contracting muscles, because it provides higher ATP yield per volume density than mitochondrial oxidative phosphorylation. In the case of muscle physiology, the model also predicts that before the lactate switch, fatty acid oxidation increases, reaches a maximum, and then decreases to zero with concomitant increase in glucose utilization, in agreement with the empirical evidence. These results are further corroborated by a larger scale model, including biosynthesis of major cell biomass components. The larger scale model also predicts that in proliferating cells the lactate switch is accompanied by activation of glutaminolysis, another distinctive feature of the Warburg effect. In conclusion, intracellular molecular crowding is a fundamental constraint for cell metabolism in both rapidly proliferating- and non-proliferating cells with high metabolic demand. Addition of this constraint to metabolic flux balance models can explain several observations of mammalian cell metabolism under steady state conditions

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep

State of the art of immunoassay methods for B-type natriuretic peptides: An update

The aim of this review article is to give an update on the state of the art of the immunoassay methods for the measurement of B-type natriuretic peptide (BNP) and its related peptides. Using chromatographic procedures, several studies reported an increasing number of circulating peptides related to BNP in human plasma of patients with heart failure. These peptides may have reduced or even no biological activity. Furthermore, other studies have suggested that, using immunoassays that are considered specific for BNP, the precursor of the peptide hormone, proBNP, constitutes a major portion of the peptide measured in plasma of patients with heart failure. Because BNP immunoassay methods show large (up to 50%) systematic differences in values, the use of identical decision values for all immunoassay methods, as suggested by the most recent international guidelines, seems unreasonable. Since proBNP significantly cross-reacts with all commercial immunoassay methods considered specific for BNP, manufacturers should test and clearly declare the degree of cross-reactivity of glycosylated and non-glycosylated proBNP in their BNP immunoassay methods. Clinicians should take into account that there are large systematic differences between methods when they compare results from different laboratories that use different BNP immunoassays. On the other hand, clinical laboratories should take part in external quality assessment (EQA) programs to evaluate the bias of their method in comparison to other BNP methods. Finally, the authors believe that the development of more specific methods for the active peptide, BNP1–32, should reduce the systematic differences between methods and result in better harmonization of results

Macrophage uptake switches on OCT contrast of superparamagnetic nanoparticles for imaging of atherosclerotic plaques

Angela Ariza de Schellenberger,1,* Wolfram C Poller,2,* Verena Stangl,2 Ulf Landmesser,3 Eyk Schellenberger1 1Department of Radiology, Charité-Universitätsmedizin Berlin, Germany; 2Department of Interventional Cardiology, Charité-Universitätsmedizin Berlin, Germany; 3Department of Cardiology, Charité- Universitätsmedizin Berlin, Berlin Institute of Health (BIH) and Deutsches Zentrum für Herz-Kreislaufforschung (DZHK), Berlin, Germany *These authors contributed equally to this work Background: Optical coherence tomography (OCT) is an intravascular, high-resolution imaging technique that is used to characterize atherosclerotic plaques. However, the identification of macrophages as important markers of inflammation and plaque vulnerability remains difficult. Here, we investigate whether the uptake of very small iron oxide particles (VSOP) in macrophages, that cluster in phagolysosomes and allow high-quality magnetic resonance imaging (MRI) of atherosclerotic plaques, and uptake of ferumoxytol nanoparticles enhance detection of macrophages by OCT.Materials and methods: RAW 264.7 macrophage cells were incubated with VSOP (1 and 2 mM Fe) that have been clinically tested and ferumoxytol (8.9 mM Fe) that is approved for iron deficiency treatment and currently investigated as an MRI contrast agent. The light scattering of control macrophages, nanoparticle-labeled macrophages (2,000,000 in 500 µL) and nanoparticle suspensions was measured in synchronous wavelength scan mode using a fluorescence spectrophotometer. For OCT analyses, pellets of 8,000,000 non-labeled, VSOP-labeled and ferumoxytol-labeled RAW 264.7 macrophages were imaged and analyzed on an OPTIS™ OCT imaging system.Results: Incubation with 1 and 2 mM VSOP resulted in uptake of 7.1±1.5 and 12±1.5 pg Fe per cell, which increased the backscattering of the macrophages in spectrophotometry 2.5- and 3.6-fold, whereas incubation with 8.9 mM Fe ferumoxytol resulted in uptake of 6.6±2 pg Fe per cell, which increased the backscattering 1.5-fold at 700 nm. In contrast, backscattering of non-clustered nanoparticles in suspension was negligible. Accordingly, OCT imaging could visualize significantly increased backscattering and signal attenuation of nanoparticle-labeled macrophages in comparison with controls.Conclusion: We conclude that VSOP and, to a lesser extent, ferumoxytol increase light scattering and attenuation when taken up by macrophages and can serve as a multimodal imaging probe for MRI and OCT to improve macrophage detection in atherosclerotic plaques by OCT in the future. Keywords: intravascular, inflammation, vulnerability, multimodal imaging, optical coherence tomography, magnetic resonance imagin

Virtual Equipment for benchmarking Predictive Maintenance algorithms

This paper presents a comparison of three algorithm types (Bayesian Networks, Random Forest and Linear Regression) for Predictive Maintenance on an implanter system in semiconductor manufacturing. The comparison studies are executed using a Virtual Equipment which serves as a testing environment for prediction algorithms prior to their implementation in a semiconductor manufacturing plant (fab). The Virtual Equipment uses input data that is based on historical fab data collected during multiple filament failure cycles. In an automated study, the input data is altered systematically, e.g. by adding noise, drift or maintenance effects, and used for predictions utilizing the created Predictive Maintenance models. The resulting predictions are compared to the actual time-to-failure and to each other. Multiple analysis methods are applied, resulting in a performance table

Optical characterization of patterned thin films

The present study investigates the use of imaging and mapping ellipsometry to determine the properties of non- ideal and patterned thin fi lm samples. Samples which are candidates for future references and standards were prepared for this purpose. The samples investigated were lithographically patterned SiO 2 and photoresist layers. The thickness andtheopticalconstants of the twomaterials were determined usingspectroscopicellipsometryin the visible spectral range. On a larger lateral scale of several mm lateral resolution, the homogeneity was investigated using a goniospectral rotating compensator ellipsometer. A nulling imaging ellipsometer was used to determine the properties of the sample on a smaller scale of 25 – 150 μ m