Development and application of analytical methods for metabolic cell analysis under patho-physiological conditions

该核心项目的目的是开发和应用新的分析和统计方法，以全面表征肿瘤和免疫细胞代谢组和蛋白质组及其相互作用。核心单元为其他项目提供基于多维核磁共振、联用质谱学(MS)和最先进的统计生物信息学的定量代谢和蛋白质组谱分析和指纹分析方法。在上一个资助期内，我们进行了3800多次分析，以支持KFO的项目。在方法开发方面，建立了用LC-MS准确定量色氨酸代谢产物的分析方法和测定MTAP活性的分析方法。此外，建立了LC-QTOFMS平台，并将其用于不同人类癌细胞与原代细胞代谢差异的比较研究。我们与KFO合作伙伴研究了双氯芬酸对小鼠脑胶质瘤模型的免疫调节作用，以及双氯芬酸对Myc-表达和葡萄糖代谢的影响。对于核磁共振数据的分析，新开发的代谢定量方法可以从复杂的生物基质中准确地定量代谢物。后续数据评估的一个重要方面是使用最佳统计数据分析方法，采用无监督和有监督的技术，其中一些技术已经实施和应用，除其他外，包括亲和传播和随机森林。此外，在最近的两篇综述和三本书的章节中总结了基于核磁共振和基于全面二维气相色谱的代谢组学的最新方法，建立的方法包括糖酵解和TCA循环的中间产物、氨基酸、色氨酸代谢物和蛋氨酸和多胺代谢的中间产物的定量分析，以及利用核磁共振和质谱仪进行代谢指纹图谱的研究。在第二个资助期将开发新的方法，研究癌症脂肪代谢的变化，包括测定花生四烯酸代谢物和分析从葡萄糖和谷氨酰胺转化为脂肪酸和其他代谢物的碳流。为此，将进一步发展现有的核磁共振和质谱学方法，其中包括采用高分辨率的核磁共振谱非均匀采样(NUS)进行碳通量分析。此外，我们还将建立D-和L-2-羟基戊二酸的对映体选择性分离和质谱分析方法。前者在具有异柠檬酸脱氢酶1和2突变的肿瘤中被发现，如胶质瘤和血液系统恶性肿瘤。关于基于核磁共振的指纹数据的分析，我们将集中于对现有的系统评估和新的光谱比对技术的开发。

The aim of the core project is the development and application of novel analytical and statistical methods for the comprehensive characterization of the tumor and immune cell metabolome and proteome and their interactions. The core unit provides the other projects with quantitative metabolic and proteomic profiling and fingerprinting methods based on multidimensional NMR, hyphenated mass spectrometry (MS), and state-of-the-art statistical bioinformatics.In the last funding period we performed more than 3800 analyses to support the projects in the KFO. With respect to method development an analytical method for accurate quantification of tryptophan metabolites by LC-MS and an assay to determine MTAP activity was developed. Furthermore, a LC-QTOFMS platform was established and used in a comparative study on metabolic differences between various human cancer and primary cells. With KFO partners we studied the immune modulation by Diclofenac in a murine glioma model and the effect of Diclofenac on Myc-expression and glucose metabolism. For the analysis of NMR data the newly developed MetaboQuant approach allows accurate metabolite quantification from complex biological matrices. One important aspect in context of subsequent data evaluation is the use of optimal statistical data analysis methods, employing both unsupervised and supervised techniques, several of which were implemented and applied, including among others Affinity Propagation and Random Forests. Furthermore, state-of-the-art methods for NMR and comprehensive two-dimensional gas chromatography based metabolomics were summarized in two recent reviews and three book chapters.Established methods include the quantitative analysis of intermediates of glycolysis and TCA cycle, amino acids, tryptophan metabolites, and intermediates of the methionine and polyamine metabolism as well as metabolic fingerprinting by NMR and MS. Recently established proteomic techniques involve label-free quantification of thousands of proteins in parallel (SWATH-MS). Novel methods to be developed in the second funding period regard the study of alterations in cancer lipid metabolism, including the determination of arachidonic acid metabolites and the analysis of carbon flux from glucose and glutamine into fatty acids and other metabolites. To that end, current NMR and MS methods will be further developed including among others the adaptation of high-resolution non-uniform sampling of NMR spectra (NUS) for carbon flux analysis. Further, we will establish a method for the enantioselective separation and mass spectrometric determination of D- and L-2-hydroxyglutaric acid. The former is found in cancers such as gliomas and hematologic malignancies that harbor mutations in isocitrate dehydrogenase 1 and 2. With respect to the analysis of NMR-based fingerprinting data we will concentrate on the systematic assessment of existing and the development of novel spectra alignment techniques.