Hettling Hannes, Alders David J C, Heringa Jaap, Binsl Thomas W, Groeneveld A B Johan, van Beek Johannes H G M
Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, de Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.
BMC Syst Biol. 2013 Aug 21;7:82. doi: 10.1186/1752-0509-7-82.
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.
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.
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.
心肌细胞的有氧能量代谢对于心脏的收缩功能至关重要。由于能量代谢在心脏组织中分布非常不均匀,尤其是在冠心病期间,因此需要一种方法来量化小组织样本中的代谢通量。在输注用稳定碳同位素标记的底物后采集组织活检样本,这在动物实验中成为可能。然而,提取的组织样本的核磁共振谱中存在明显的噪声水平,这使得代谢通量的计算估计具有挑战性,并且尚未有定义置信区间的好方法。
在此,我们提出一种用于三羧酸(TCA)循环代谢物核磁共振(NMR)测量的计算分析方法。该方法通过对取自猪心脏活体的单个组织活检样本提取物进行测量来验证。在定时输注用于TCA循环的¹³C标记底物后的单个时间点采集的组织样本中,通过核磁共振光谱法测量谷氨酸的同位素富集。用描述TCA循环中碳转化以及与氨基酸碳交换的计算模型分析谷氨酸的核磁共振强度。模型动力学取决于五个通量参数,对这些参数进行优化以拟合核磁共振测量结果。为了确定估计通量的置信区间,我们使用马尔可夫链蒙特卡罗(MCMC)采样的Metropolis - Hastings算法来生成大量可行通量组合的集合,这些组合在测量精度极限内描述数据。为了验证我们的方法,我们在几种实验条件下,例如在冠状动脉狭窄期间,将根据TCA循环通量计算的心肌耗氧量与38颗心脏的活体血气测量结果进行了比较。
尽管核磁共振噪声水平明显,但从核磁共振谱估计的组织样本中的耗氧量与全心的血气氧摄取测量结果相关。MCMC方法在充分考虑量化的测量噪声水平和参数之间的非线性依赖性的情况下,为单个心脏活检中估计的代谢通量提供了置信区间。