Tandem Mass Spectrometry: A New Platform for Fluxomi

Abstract

Recent advances in various 'omics' technologies enable quantitative monitoring of the biological states of an organism in a highthroughput manner, and thus allows determination of the variations between different biological states on a genomic scale. Transcriptomics measures mRNA transcript levels; proteomics quantifies protein abundance; metabolomics determines concentrations of small cellular metabolites; and fluxomics defines turnover rates of molecules over the metabolic network in a biological system. Among the "omics", fluxomics is the ultimate manifestation of the phenotype and function of an organism as metabolic fluxes underlie all the biological activities including abundance of molecules and cellular signaling, regulation and transport. More importantly, fluxomics provides pathway activity information, which discerns the contribution of each pathway to the overall metabolite concentration and thus helps the understanding of complex metabolism in an organism. This information simply does not exist in concentration-based datasets. However, compared with transcriptomics, proteomics and metablomics, technologies used for fluxomics are less mature and not high-throughput because fluxomics requires isotope labeling experiments and mathematical modeling and both present difficulties. The major barriers are (i) insufficient measurements of isotopic labeling information in the metabolites for mathematical modeling for solving metabolic fluxes; and (ii) lack of appropriate computational algorithms for simulating the isotopic labeling data to find the fluxes with precision and reliability.For the isotope labeling experiment, stable isotope labeled substrates (usually 13 C) are fed into the biological system, the isotope tracers are then distributed over the metabolic pathways and incorporated into the intracellular metabolites via the administration of biochemical reactions. The isotopic pattern and abundance of isotopic isomers (isotopomers) within a metabolite pool can be measured. At present, there are two established methods for measuring isotopomers for fluxomics, i.e., nuclear magnetic resonance (NMR) and mass spectrometry (MS). The NMR technique provides detailed positional isotopic labeling information (the positions of isotope labels in a molecule), which are useful for resolving the mathematical model for fluxes; however, NMR has low sensitivity (in nanomolar detection range for 1 H NMR) and is difficult to quantify isotopomers in low concentration metabolites. The MS, on the other hand, provides sensitive detection of molecules (in femtomolar detection range) and fractional enrichment of mass isotopomers (grouped positional isotopomers); however, it does not distinguish the positional labeling within the mass isotopomer and therefore limits the resolution of the mathematical model. These limitations severely limit the scope of fluxomics, e.g., most of the flux analysis studies have been limited to the central metabolism, and thus there is a clear need for new techniques that can address these issues. ...

A novel platform for automated high-throughput fluxome profiling of metabolic variants

“…Miniaturized cell cultivation systems have been used to perform 13 C-labelling experiments with less effort and cost in labeled substrate (Balcarcel and Clark, 2003;Betts and Baganz, 2006;Ge et al, 2006;Girard et al, 2001;Huber et al, 2009;Isett et al, 2007;Kocincova et al, 2008;Tang et al, 2009). The sensitivity, speed, and robustness of both NMR-based and MS-based isotopic analysis have been improved (Boisseau et al, 2013;Cahoreau et al, 2012;Fan and Lane, 2008;Fischer et al, 2004;Giraudeau et al, 2012;Giraudeau et al, 2011;Massou et al, 2007a;Peng, 2012). Tools have been developed for large scale isotopic data processing (Millard et al, 2012;Poskar et al, 2012) as along with improved algorithms and software for flux calculation and statistical analysis of isotopic data (Antoniewicz et al, 2007;Quek et al, 2009;Raghevendran et al, 2004;Sokol et al, 2012;Weitzel et al, 2012;Zamboni et al, 2005;Zamboni and Sauer, 2004).…”

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

“…Software for optimizing the isotopic composition of the label input exist (Millard et al , 2014b) and miniaturized cell cultivation systems have been developed to perform less labour-intensive, lower cost 13 Clabelling experiments (Betts and Baganz, 2006, Girard et al , 2001, Huber et al , 2009, Isett et al , 2007, Kocincova et al , 2008, Tang et al , 2009). More robust sampling protocols have also been developed (Millard et al , 2014a) and the reliability., speed, and sensitivity of both MS-based and NMR-based isotopic analysis have been improved (Boisseau et al , 2013, Fan and Lane, 2008, Fischer et al , 2004, Giraudeau et al , 2012, Giraudeau et al , 2011, Peng, 2012. Tools have been developed for large scale isotopic data processing , Poskar et al , 2012, with these being complemented byimproved algorithms and software for flux calculation (Antoniewicz et al , 2007, Quek et al , 2009, Weitzel et al , 2012, Zamboni et al , 2005.…”

Recent advances in high-throughput 13C-fluxomics

“…Continuous efforts have enhanced the sensitivity, speed, and robustness of both NMR-based and MS-based isotopic analyses [34][35][36][37][38][39]. To help alleviate the increasing number of samples and thus the amount of data generated by NMR and MS, automated processing tools have been developed for isotopic data extraction [9 ,40] and for post-processing steps, such as correction of MS(/MS) data for naturally occurring isotopes [41] (Figure 1d).…”