Advances in 13C Metabolic Flux Analysis: Complete-MFA, Co-Culture MFA and Dynamic MFA




Microsoft Word - Met Eng X Abstract (Maciek).docx

Advances in 13C Metabolic Flux Analysis: COMPLETE-MFA, Co-culture MFA and Dynamic

MFA

Maciek R. Antoniewicz

Abstract

Measuring fluxes by 13C metabolic flux analysis (13C-MFA) has become a key activity in metabolic engineering, biotechnology and medicine. In this talk, I will present three major new advances in the field of 13C-MFA that are extending the range of biological systems that can be analyzed with this technique and the types of biological questions that can be addressed. The three major advances that I will discuss are: 1) COMPLETE-MFA (or complementary parallel labeling experiments technique for metabolic flux analysis), which improves the precision and accuracy of flux estimates by about one-order-of-magnitude; 2) co-culture MFA, which allows metabolic fluxes to be measured in multi-cellular systems; and 3) dynamic MFA, which extends flux analysis to dynamically changing biological systems.
1--The COMPLETE-MFA methodology that we have developed is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. Here, I will demonstrate the COMPLETE- MFA approach using all singly labeled glucose tracers, [1-13C], [2-13C], [3-13C], [4-13C], [5-13C], and [6-13C]glucose to determine precise metabolic fluxes for wild-type E. coli, and using all singly labeled xylose tracers, [1-13C], [2-13C], [3-13C], [4-13C], and [5-13C]xylose to determine precise metabolic fluxes for Thermus thermophilus. In these studies, cells were grown in multiple parallel cultures on defined medium with glucose and/or xylose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography- mass spectrometry (GC-MS) and the data from all experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. In all cases, we obtained a statistically acceptable fit with more than 300 redundant measurements. As I will demonstrate, the flux maps that we have determined here are the most precise flux results obtained thus far (by about order-of-magnitude) for any biological system.
2--Microbial communities play an important role in biofuel production, biomedical research, food production, and waste water treatment. Co-culture systems particularly have unique advantages in optimizing product yield as a result of synergistic interactions. To gain insight into these systems we have developed the first methodology for measuring metabolic fluxes in multi- cellular systems. Here, I will demonstrate our novel co-culture 13C-MFA framework that does not require any physical separation of cells or proteins. Specifically, we have developed a new computational approach for modeling isotopic labeling in biological systems that allows fluxes in individual populations to be computationally deconvoluted from the overall co-culture 13C- labeling data. We show that the overall 13C-labeling data has abundant information not only to estimate the fluxes in multiple populations, but also to determine the fraction of each cell population in the co-culture (e.g. to visualize co-culture dynamics). I will demonstrate the co- culture flux analysis methodology using a co-culture system of two E. coli knockout strains, â??zwf (knockout of the first step in the pentose phosphate pathway) and â??pgi (knockout of the first
step in glycolysis pathway), using a yeast/E. coli co-culture, and using a thermophilic co-culture system. The new flux analysis methodology that we have developed for analyzing co-culture systems adds a new dimension to the field of 13C-MFA and provides an enormous resource to the metabolic engineering and biotechnology communities.
3--Finally, we have developed new methods for dynamic 13C-MFA, for biological systems that are not at metabolic steady state. The 13C-DMFA methodology is based on integrating time- series of metabolite measurements and non-stationary 13C-labeling data to quantify flux changes in time. Thus, this allows us to measure for the first time dynamically changing metabolic fluxes, for example, in fed batch fermentations. Three key advantages of our 13C- DMFA method are: 1) time-series of metabolite concentration and labeling data can be applied directly for estimating dynamic fluxes, making data smoothing unnecessary; 2) characteristic metabolic phases during a culture are identified automatically by the algorithm; 3) 13C-labeling data provides insights into transients in fluxes of parallel and cyclic pathways that cannot be observed without labeling.