Lisa Krämer (Presenter)
Luxembourg Centre for Systems Biomedicine
Bio: I, Lisa Krämer, studied biology and biotechnology at the Saarland University in Germany and started a PhD in the Hiller Lab at the Luxembourg Centre for Systems Biomedicine, LCSB in Luxembourg. The talk today is about stable isotope-assisted metabolomics in the field of nutrition.
Authorship: Lisa Krämer (1), Christian Jäger (1), Doris Jacobs (2), Karsten Hiller (1)
Luxembourg Centre for Systems Biomedicine (1), Unilever R&D Vlaardingen (2)
| Short Abstract Due to their slow glucose release and thus prolonged energy availability, slowly digestible starch containing food products show promising properties in the context of diabetes management. We analysed plasma samples of a nutritional intervention study conducted by Unilever R&D Vlaardingen in the Netherlands to explore the postprandial turnover of starch- and protein-derived metabolites. We compared the metabolic effects of three different flour blends mixed from different amounts of wheat flour, chickpea flour, barley flour and guar gum. 2% of the flour present in the chapati bread was fully 13C-labeled. We applied targeted GC-MS combined with “Mass Isotopomer Distribution (MID)” analysis to reveal the dynamics of starch- and protein hydrolysis. |
Long Abstract
Introduction:
The prevalence of Type 2 Diabetes and cardiovascular diseases in Asia is dramatically increasing [1]. One promising approach to decrease the risk of this condition is the consumption of food products composed of slowly digestible starch where glucose is released more slowly resulting in prolonged energy availability and satiety [2]. In this context, stable isotopic labeling is a valuable tool for the in-depth analysis of the metabolic turnover to evaluate different food products in the postprandial state. In an intervention study, we aimed to investigate if the flour composition of chapati bread varying in the amounts of wheat flour, chickpea flour, barley flour and guar gum as a fiber has an influence on the postprandial metabolism in plasma. For this, 2% of the flour present in the bread was 13C-labeled to follow the dynamics of bread digestion in plasma metabolites. Due to the ultra low 13C enrichment of the glucose-derived compounds in postprandial plasma, we had to optimize the plasma extraction and the GC-MS method. We applied a GC-MS SIM method to enable determination of the isotopic enrichment and the calculation of the mass isotopomer distributions (MID).
Methods:
To determine the dynamics of postprandial metabolism, 12 healthy subjects consumed chapati bread where 2% of the flour was substituted by fully 13C enriched flour. We tested the effect of different compositions of chapati bread varying in the amounts of wheat flour, chickpea flour, barley flour and guar gum on postprandial metabolism. Blood samples were collected at 16 different time points starting 30 minutes before and finishing 360 minutes after bread ingestion. Due to the very low fraction of isotopic enrichment present in the plasma metabolites (< 1%) and the high number of samples (> 2000), optimization of the sample processing and the GC-MS measurement was required. To increase the extraction yield of amino acids, organic acids and sugar derivatives, we tested various commonly used extraction protocols to increase the extraction yield. On the other hand, we optimized the gas chromatographic separation to meet these requirements.
Results:
By increasing the polarity of the solvent, we could increase the average signal of all target metabolites by 33%. At the same time, the relative standard deviation was reduced by 55% pointing to a more robust extraction method. The application of a shorter column (20 m) maintained a baseline separated chromatography of all target metabolites but reduced the overall run time by 40%. In summary, our optimized GC-MS method is less time consuming and the results are of higher quality in terms of MID calculation and relative quantification due to the improved extraction yield.
From the first obtained data, we calculated MIDs using our MetaboliteDetector Software [3]. We did not observe significant effects on in vivo metabolic fluxes caused by the investigated interventions. However, we observed clear differences in the dynamics betweeen starch- and protein hydrolysis. The labeled wheat flour is composed of 13C-labeled starch but also 13C-labeled proteins that are prone to digestion. For the metabolite glutamate, we are able to distinguish the two different labeling profiles within one molecule. Glucose-derived glutamate manifests in M2 isotopologues, while protein-derived glutamate shows M5 isotopologues. This is because glutamate is generated from glucose via glycolysis and TCA cycle where two carbon atoms are conserved while fully labeled five-carbon glutamate is released during protein hydrolysis. Having these information available for many different target metabolites, we obtain a detailed overview about the dynamics of postprandial metabolism and its dynamics.
Conclusion:
By applying an optimized plasma extraction procedure and the newly developed GC-MS method, we facilitated the generation of high quality MID and signal intensity data. We can combine the dynamic labeling data with the metabolic pathway information of the target metabolites to obtain a detailed description of the postprandial dynamics of metabolism.
References & Acknowledgements:
[1] Ramachandran et al. 2012 Trends in prevalence of diabetes in Asian countries. World J Diabetes 3(6): 110-117.
[2] Sands et al. 2009 Consumption of the slow-digesting waxi maize starch leads to blunted plasma glucose and insulin respons but does not influence energy expenditure or appetite in humans. Nutrition Research 229: 383-390.
[3] Hiller et al. 2009 MetaboliteDetector: Comprehensive Analysis Tool for Targeted and Nontargeted GC/MS based Metabolome Analysis. Anal. Chem. 81: 3429-3439
| Description | Y/N | Source |
| Grants | yes | Unilever R&D Vlaardingen |
| Salary | no | |
| Board Member | no | |
| Stock | no | |
| Expenses | no |
IP Royalty: no
| Planning to mention or discuss specific products or technology of the company(ies) listed above: | no |