Progressively heterogeneous mismatch of regional oxygen delivery to consumption during graded coronary stenosis in pig left ventricle.

In normal hearts, myocardial perfusion is fairly well matched to regional metabolic demand, although both are distributed heterogeneously. Nonuniform regional metabolic vulnerability during coronary stenosis would help to explain nonuniform necrosis during myocardial infarction. In the present study, we investigated whether metabolism-perfusion correlation diminishes during coronary stenosis, indicating increasing mismatch of regional oxygen supply to demand. Thirty anesthetized male pigs were studied: controls without coronary stenosis (n = 11); group I, left anterior descending (LAD) coronary stenosis leading to coronary perfusion pressure reduction to 70 mmHg (n = 6); group II, stenosis with perfusion pressure of about 35 mmHg (n = 6); and group III, stenosis with perfusion pressure of 45 mmHg combined with adenosine infusion (n = 7). [2-(13)C]- and [1,2-(13)C]acetate infusion was used to calculate regional O2 consumption from glutamate NMR spectra measured for multiple tissue samples of about 100 mg dry mass in the LAD region. Blood flow was measured with microspheres in the same regions. In control hearts without stenosis, regional oxygen extraction did not correlate with basal blood flow. Average myocardial O2 delivery and consumption decreased during coronary stenosis, but vasodilation with adenosine counteracted this. Regional oxygen extraction was on average decreased during stenosis, suggesting adaptation of metabolism to lower oxygen supply after half an hour of ischemia. Whereas regional O2 delivery correlated with O2 consumption in controls, this relation was progressively lost with graded coronary hypotension but partially reestablished by adenosine infusion. Therefore, coronary stenosis leads to heterogeneous metabolic stress indicated by decreasing regional O2 supply to demand matching in myocardium during partial coronary obstruction.

Computational estimation of tricarboxylic acid cycle fluxes using noisy NMR data from cardiac biopsies.

BACKGROUND: The aerobic energy metabolism of cardiac muscle cells is of major importance for the contractile function of the heart. Because energy metabolism is very heterogeneously distributed in heart tissue, especially during coronary disease, a method to quantify metabolic fluxes in small tissue samples is desirable. Taking tissue biopsies after infusion of substrates labeled with stable carbon isotopes makes this possible in animal experiments. However, the appreciable noise level in NMR spectra of extracted tissue samples makes computational estimation of metabolic fluxes challenging and a good method to define confidence regions was not yet available. RESULTS: Here we present a computational analysis method for nuclear magnetic resonance (NMR) measurements of tricarboxylic acid (TCA) cycle metabolites. The method was validated using measurements on extracts of single tissue biopsies taken from porcine heart in vivo. Isotopic enrichment of glutamate was measured by NMR spectroscopy in tissue samples taken at a single time point after the timed infusion of 13C labeled substrates for the TCA cycle. The NMR intensities for glutamate were analyzed with a computational model describing carbon transitions in the TCA cycle and carbon exchange with amino acids. The model dynamics depended on five flux parameters, which were optimized to fit the NMR measurements. To determine confidence regions for the estimated fluxes, we used the Metropolis-Hastings algorithm for Markov chain Monte Carlo (MCMC) sampling to generate extensive ensembles of feasible flux combinations that describe the data within measurement precision limits. To validate our method, we compared myocardial oxygen consumption calculated from the TCA cycle flux with in vivo blood gas measurements for 38 hearts under several experimental conditions, e.g. during coronary artery narrowing. CONCLUSIONS: Despite the appreciable NMR noise level, the oxygen consumption in the tissue samples, estimated from the NMR spectra, correlates with blood-gas oxygen uptake measurements for the whole heart. The MCMC method provides confidence regions for the estimated metabolic fluxes in single cardiac biopsies, taking the quantified measurement noise level and the nonlinear dependencies between parameters fully into account.

Simulating the physiology of athletes during endurance sports events: modelling human energy conversion and metabolism.

The human physiological system is stressed to its limits during endurance sports competition events. We describe a whole body computational model for energy conversion during bicycle racing. About 23 per cent of the metabolic energy is used for muscle work, the rest is converted to heat. We calculated heat transfer by conduction and blood flow inside the body, and heat transfer from the skin by radiation, convection and sweat evaporation, resulting in temperature changes in 25 body compartments. We simulated a mountain time trial to Alpe d'Huez during the Tour de France. To approach the time realized by Lance Armstrong in 2004, very high oxygen uptake must be sustained by the simulated cyclist. Temperature was predicted to reach 39°C in the brain, and 39.7°C in leg muscle. In addition to the macroscopic simulation, we analysed the buffering of bursts of high adenosine triphosphate hydrolysis by creatine kinase during cyclical muscle activity at the biochemical pathway level. To investigate the low oxygen to carbohydrate ratio for the brain, which takes up lactate during exercise, we calculated the flux distribution in cerebral energy metabolism. Computational modelling of the human body, describing heat exchange and energy metabolism, makes simulation of endurance sports events feasible.

Endotoxemia decreases matching of regional blood flow and O2 delivery to O2 uptake in the porcine left ventricle.

Heterogeneity of regional coronary blood flow is caused in part by heterogeneity in O(2) demand in the normal heart. We investigated whether myocardial O(2) supply/demand mismatching is associated with the myocardial depression of sepsis. Regional blood flow (microspheres) and O(2) uptake ([(13)C]acetate infusion and analysis of resultant NMR spectra) were measured in about nine contiguous tissue samples from the left ventricle (LV) in each heart. Endotoxemic pigs (n = 9) showed hypotension at unchanged cardiac output with a fall in LV stroke work and first derivative of LV pressure relative to controls (n = 4). Global coronary blood flow and O(2) delivery were maintained. Lactate accumulated in arterial blood, but net lactate extraction across the coronary bed was unchanged during endotoxemia. When LV O(2) uptake based on blood gas versus NMR data were compared, the correlation was 0.73 (P = 0.007). While stable over time in controls, regional blood flows were strongly redistributed during endotoxin shock, with overall flow heterogeneity unchanged. A stronger redistribution of blood flow with endotoxin was associated with a larger fall in LV function parameters. Moreover, the correlation of regional O(2) delivery to uptake fell from r = 0.73 (P < 0.001) in control to r = 0.18 (P = 0.25, P = 0.009 vs. control) in endotoxemic hearts. The results suggest a redistribution of LV regional coronary blood flow during endotoxin shock in pigs, with regional O(2) delivery mismatched to O(2) demand. Mismatching may underlie, at least in part, the myocardial depression of sepsis.

Electron density fingerprints (EDprints): virtual screening using assembled information of electron density.

We have designed a method to encode properties related to the electron densities of molecules (calculated (1)H and (13)C NMR shifts and atomic partial charges) in molecular fingerprints (EDprints). EDprints was evaluated in terms of their retrospective virtual screening accuracy against the Directory of Useful Decoys (DUD) and compared to the established ligand-based similarity search methods MOLPRINT 2D and FCFP-4. Although there are no significant differences in the overall virtual screening accuracies of the three methods, specific examples highlight interesting differences between the new EDprints fingerprint method and the atom-centered circular fingerprint methods of MOLPRINT 2D and FCFP-4. On one hand, EDprints similarity searches can be biased by the molecular protonation state, especially when reference ligands contain multiple ionizable groups. On the other hand, EDprints models are more robust toward subtle rearrangements of chemical groups and more suitable for screening against reference molecules with fused ring systems than MOLPRINT 2D and FCFP-4. EDprints is furthermore the fastest method under investigation in comparing fingerprints (average 56-233-fold increase in speed), which makes it highly suitable for all-against-all similarity searches and for repetitive virtual screening against large chemical databases of millions of compounds.

CGHnormaliter: a Bioconductor package for normalization of array CGH data with many CNAs.

SUMMARY: CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis. AVAILABILITY AND IMPLEMENTATION: The CGHnormaliter package is implemented in R and under GPL 3.0 license available at Bioconductor: http://www.bioconductor.org CONTACT: heringa@few.vu.nl

Computational quantification of metabolic fluxes from a single isotope snapshot: application to an animal biopsy.

MOTIVATION: Quantitative determination of metabolic fluxes in single tissue biopsies is difficult. We report a novel analysis approach and software package for in vivo flux quantification using stable isotope labeling. RESULTS: We developed a protocol based on brief, timed infusion of (13)C isotope-enriched substrates for the tricarboxylic acid (TCA) cycle followed by quick freezing of tissue biopsies. NMR measurements of tissue extracts were used for flux estimation based on a computational model of carbon transitions between TCA cycle metabolites and related amino acids. To this end, we developed a computational framework in which metabolic systems can be flexibly assembled, simulated and analyzed. Flux parameters were quantified from NMR multiplets by a partial grid search followed by repeated Nelder-Mead optimizations implemented on a computer grid. We implemented a model of the TCA cycle and showed by extensive simulations that the timed infusion protocol reliably quantitates multiple fluxes. Experimental validation of the method was done in vivo on hearts of anesthetized pigs under two different conditions: basal state (n = 7) and cardiac stress caused by infusion of dobutamine (n = 7). About nine tissue samples (40-200 mg dry-weight) were taken per heart. TCA cycle flux was 6.11 +/- 0.28 (SEM) micromol/min x gdw at baseline versus 9.29 +/- 1.03 micromol/min x gdw for dobutamine stress. Oxygen consumption calculated from the TCA cycle flux and from 'gold standard' blood gas-based measurements were close, correlating with r=0.88 (P < 10(-4)). Spatial heterogeneity in metabolic fluxes is detectable amongst the small samples. We propose that our novel isotope snapshot methodology is suitable for flux measurements in biopsies in vivo. AVAILABILITY: Non-profit organizations will, upon request, be granted a non-exclusive license to use the software for internal research and teaching purposes at no charge. A web interface for using the software on our computer grid is available under http://www.ibi.vu.nl/programs/

CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations.

BACKGROUND: Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods. RESULTS: In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers. CONCLUSION: We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at http://www.ibi.vu.nl/programs/cghnormaliterwww.

Robust modelling, measurement and analysis of human and animal metabolic systems.

Modelling human and animal metabolism is impeded by the lack of accurate quantitative parameters and the large number of biochemical reactions. This problem may be tackled by: (i) study of modules of the network independently; (ii) ensemble simulations to explore many plausible parameter combinations; (iii) analysis of 'sloppy' parameter behaviour, revealing interdependent parameter combinations with little influence; (iv) multiscale analysis that combines molecular and whole network data; and (v) measuring metabolic flux (rate of flow) in vivo via stable isotope labelling. For the latter method, carbon transition networks were modelled with systems of ordinary differential equations, but we show that coloured Petri nets provide a more intuitive graphical approach. Analysis of parameter sensitivities shows that only a few parameter combinations have a large effect on predictions. Model analysis of high-energy phosphate transport indicates that membrane permeability, inaccurately known at the organellar level, can be well determined from whole-organ responses. Ensemble simulations that take into account the imprecision of measured molecular parameters contradict the popular hypothesis that high-energy phosphate transport in heart muscle is mostly by phosphocreatine. Combining modular, multiscale, ensemble and sloppy modelling approaches with in vivo flux measurements may prove indispensable for the modelling of the large human metabolic system.