Collisional fragmentation of central carbon metabolites in LC‐MS/MS increases precision of ¹³C metabolic flux analysis

Abstract

Experimental determination of fluxes by (13)C-tracers relies on detection of (13)C-patterns in metabolites or by-products. In the field of (13)C metabolic flux analysis, the most recent developments point toward recording labeling patterns by liquid chromatography (LC)-mass spectrometry (MS)/MS directly in intermediates in central carbon metabolism (CCM) to increase temporal resolution. Surprisingly, the flux studies published so far with LC-MS measurements were based on intact metabolic intermediates-thus neglected the potential benefits of using positional information to improve flux estimation. For the first time, we exploit collisional fragmentation to obtain more fine-grained (13)C-data on intermediates of CCM and investigate their impact in (13)C metabolic flux analysis. For the case study of Bacillus subtilis grown in mineral medium with (13)C-labeled glucose, we compare the flux estimates obtained by iterative isotopologue balancing of (13)C-data obtained either by LC-MS/MS for solely intact intermediates or LC-MS/MS for intact and fragmented intermediates of CCM. We show that with LC-MS/MS data, fragment information leads to more precise estimates of fluxes in pentose phosphate pathway, glycolysis, and to the tricarboxylic acid cycle. Additionally, we present an efficient analytical strategy to rapidly acquire large sets of (13)C-patterns by tandem MS, and an in-depth analysis of the collisional fragmentation of primary intermediates. In the future, this catalogue will enable comprehensive in silico calculability analyses to identify the most sensitive measurements and direct experimental design.

13C-flux Analysis Reveals NADPH-balancing Transhydrogenation Cycles in Stationary Phase of Nitrogen-starving Bacillus subtilis

“…The vast majority of the current 13 C-flux methods, however, are applicable only to growing cells because the 13 Clabeling patterns are detected in protein-bound amino acids (21)(22)(23). As there is little or no de novo protein biosynthesis in resting cells, we used a recently developed mass spectrometry (MS) method that detects the 13 C-labeling patterns directly in metabolic intermediates (24). An advantage over previous techniques (25)(26)(27)(28)(29) is that we detect the 13 C-labeling pattern not only in intact but also in fragmented carbon backbones of the intermediates that reveal indispensable intramolecular 13 C-label positions for stationary 13 C-flux analysis (19,22,30).…”

Tandem Mass Spectrometry: A New Platform for Fluxomi

“…Pioneering work by Jeffrey et al [2] first demonstrated that the positional labeling information of 13 C-glutamate extracted from heart tissue per fused with various 13 C substrates could be generated using gas chromatography coupled tandem MS (GC-MS/MS) and the GC-MS/MS data could be used to derive metabolic fluxes with similar accuracy and precision to those obtained using NMR and significantly better than those using full-scan GC-MS. Later, Kiefer et al [3] reported the determination of carbon labeling distribution of intracellular metabolites for fluxomics using liquid chromatography tandem MS (LC-MS/MS) and was able to determine the natural abundances of the carbon fractions m and m+1 from six phosphorylated metabolites from glycolysis and pentose phosphate pathway in E. coli with high accuracy. More recently, Ruhl et al [4] exploited the measurement of the 13 C patterns of totally 27 intact and 19 fragmented metabolites from the central metabolic pathways in B. subtilis using LC-MS/MS, and the metabolic fluxes calculated using the LC-MS/MS data showed higher precision than those performed using solely full-scan LC-MS data [4]. Most importantly, these research demonstrated that tandem MS, especially LC-MS/MS that does not require chemical derivation like GC-MS/MS, can provide direct measurements of large sets of isotopic labeling patterns from intracellular intermediates and thus allow resolving fluxes for dynamic systems (non-steady state) and large-scale complex metabolic pathways, which are infeasible with the traditional NMR and MS based fluxomics methods as those rely on indirect measurements of the abundant proteinogenic amino acids and thus require long labeling experimental time for steady state to allow the incorporation of isotopes into proteins and can only derive fluxes for the central metabolic pathways that have the key nodes linked with protein biosynthesis.…”

“…Second, the tandem MS instrumental parameters such as the collision energy for fragmentation, the resolution of mass filter and the number of multiple reaction monitoring (MRM) etc. need to be optimized to capture as many as possible informative daughter mass spectra that are useful for solving the mathematical model and minimize the acquisition of redundant daughter mass spectra that complicate the mathematical model for locating the position of labeled atoms [2,4]. For the computation of fluxes, a mathematical model that describes the relationship between the abundances of isotopomers and the fluxes, expressed as a system of isotopomer mass balance equations, need to be formulated.…”

Interpretation and Integration of 13C-Fluxomics Data

“…and Young et al have achieved good results by using LC-MS or LC-MS/MS to detect the state of 13C labeling [11,12].Based on their work and the increasingly wide use of 13C metabolic flux methods in recent years, more and more metabolic flux data have been accumulated, with fluxomics itself emerging as a new way to describe cell systems. Thus, fluxomics has gradually matured into a new -omics technology that can be mentioned in the same breath with proteomics and metabolomics.…”

“…and Young et al have achieved good results by using LC-MS or LC-MS/MS to detect the state of 13C labeling [11,12].…”