Functional Studies Using NMR

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The labelled precursors used were [U- 13 C]glucose a , [3- 13 C]glutamine b , [3- 13 C]pyruvate c and [2- 13 C]acetate d and [ 15 N, 13 C]serine e. Diverse insights into different metabolic pathways are provided by using different labelled precursors as exemplified below:. Using [3- 13 C]pyruvate as a labelled precursor unexpectedly produced [3- 13 C]serine in both GLOR and U cell-lines, although in different amounts Fig.

Structural and functional studies of fungal cellulose-binding domains by NMR spectroscopy

PEP carboxykinase is a gluconeogenesis enzyme that was not expected to be active in either of these hematological cancer cell lines. Using [U- 13 C, 15 N]serine as precursor also led to labelling of creatine Fig. The serine C2 resonance is predominantly a doublet of doublets of doublets because of J C1C2 coupling The C2 resonance of creatine is a doublet of doublets as the glycine-derived moiety gives rise to a J C1C2 coupling Tracer-based metabolism in primary liver samples has great potential for diagnostic purposes in translational medicine, particularly in the context of understanding glucose and lipid homeostasis in fatty liver disease.

Primary human hepatocytes were prepared from liver tissue, as previously described The harvested primary hepatocytes were metabolically active allowing metabolic studies to measure label incorporations. This is demonstrated in Fig. Here we observe label incorporation in alanine, lactic acid, glutamic acid and glutathione. This is in partial agreement with Winnike et al. Feasibility of tracer-based analysis from donor liver samples. Black and blue resonances arise from samples exposed for 4.

Phosphorylcholine was not enriched and is included as a scaling reference but the other metabolites were enriched by [U- 13 C]Glucose. Although NMR has been employed for tracer-based metabolic studies since the s, it is only recent developments in NMR technology that provide the sensitivity required to analyze extracts from mammalian cells. Tracer-based analyses offers considerable opportunities in the context of metabolism in various diseases including cancer.

Observing 13 C directly is very insensitive, even when using 13 C-observe probes, although the recent development of a much more sensitive 13 C-observe micro-cryoprobe, which uses high-temperature superconducting receiver coils, may provide additional advantages by observing 13 C-couplings in the directly observed dimension The use of proton observed 1D spectra is prone to large errors arising from signal overlap and differences between samples.

This is further complicated by the complex line shapes arising from proton-proton and longer-range proton-carbon couplings. One big disadvantage of TOCSY spectra is the long acquisition times, in particular for increased resolution, which are needed to resolve overlapping metabolite signals. Here we propose to base analysis mainly on HSQC spectra and describe a framework to work with reasonable numbers of mammalian cells.

For such analyses it is imperative to use the most sensitive probes, at this stage the best option is the micro-cryoprobe available from Bruker. Here we have used a commercially available micro-cryoprobe at We expect further improvements from such probe developments using high-temperature superconducting receiver coils 34 and by using such probes at much higher field strengths, especially as Tracer-based metabolism has started to make an impact in the analysis of metabolic pathways, some of which can only be probed using isotope-labelled tracers.

This approach is relevant for future drug discovery in the field of metabolism, and also for elucidating the mechanistic differences between healthy and diseased states in cell lines and, as demonstrated here, potentially also in patient-derived tissue. There have already been reports showing the effectiveness of tracer-based metabolism in vivo In particular, Mashimo et al.

This illustrated the power of using multiple labelled precursors which give distinct labelling patterns in target metabolites. Likewise, [1,2- 13 C]glucose and [3- 13 C]glutamine have the potential to determine the balance between glycolysis and glutaminolysis in supplying the TCA cycle. There is great potential for in vivo approaches in metabolically active organs such as the liver and kidney. Such tracer-based studies can be closely linked to DNP enhanced chemical shift MR imaging which can observe the same type of reactions in vivo 40 , 41 , although only within a shorter time frame.

In summary, we present a framework for tracer-based analysis of mammalian cells using an NMR approach that provides robust data for site-specific label incorporations. This type of data can be used to elucidate metabolic mechanisms. With serial sampling, NMR should well complement the well-established MS for metabolic flux analyses. This approach shows that NMR is now suitable for working with limited numbers of mammalian cells. Cell culture: The hepatocellular carcinoma cell line, HuH7, was available in house.

Isotope Labelling: For tracer-based metabolic analysis the cells were transferred to a modified DMEM media lacking the metabolic precursor that was intended to be labelled, and completed with labelled precursors. For the quenching process, the spent medium was aspirated and each flask was washed twice with cold phosphate-buffered saline PBS.

Then, intracellular aqueous metabolites and lipids were extracted using a dual phase extraction procedure adapted from Teng et al. Cells were maintained in an exponential proliferation at density 0.

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Intracellular metabolite extraction: Methanol was pre-chilled on dry ice and MilliQ water and chloroform were pre-chilled on wet ice. The use of glass vials is essential as chloroform will extract small molecules from most plastic materials. The cell pellet was resuspended thoroughly by vortexing for 10 secs and transferred to a glass vial.

Care was taken throughout to avoid carryover of proteins.


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After another wash with PBS, tissue was dissociated using a combined enzymatic digestion buffer 0. These were counted using trypan blue and plated out onto collagen-coated plastic. Key parameters were as follows: spectral width For the 1 H observe dimension, a spectral width of For the 13 C indirect dimension, complex points were acquired with a spectral width of Spectra were acquired with 2 scans and an interscan delay of 1.

Cosine-squared window functions were applied to both dimensions. The free induction decay FID signals were multiplied by a 0. Finally, the spectra were scaled to a constant total spectral area TSA-scaling. Cosine-squared window functions were applied to both dimensions and spectra were phased manually.


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  • To directly derive concentrations from HSQC spectra one needs to consider that J CHx coupling constants vary between different compounds. Nevertheless, the intensity depends on the transfer function. If one wants to derive absolute concentrations from HSQC spectra one has to calibrate for each spin system unless the exact J CHx coupling constant is known.

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    Here we chose not to derive exact concentrations, as this would also require the use of longer recycling times, and relative intensities between labelled and unlabelled, or between different labelled compounds will not be affected by either the transfer function, or T 1 relaxation effects. Moreover, the use of J CC couplings is also independent of these factors. Relative intensities were processed as follows. It is often impossible to prepare differently labelled but otherwise identical samples because the overall amount of sample depends on cell number.

    Therefore, we propose another method to scale HSQC spectra. A common approach to scale one-dimensional spectra is to calculate the total spectral area TSA which reflects the total metabolite concentration. The same approach is not possible for HSQC spectra as intensities depend on concentration and label incorporation. This approach has the advantage that the correction factor uses all the metabolite intensities rather than just one reference metabolite. This method was used to compare intensities between HSQC spectra to obtain label incorporations. Markley, J.

    The future of NMR-based metabolomics. Carrigan, J. ChemPlusChem 81 , — Lane, A. Wiechert, W. Dai, Z. Understanding metabolism with flux analysis: From theory to application. Eakin, R. Carbon nuclear magnetic resonance spectroscopy of living cells and their metabolism of a specifically labeled 13 C substrate. FEBS Lett. Kainosho, M.

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    TROSY in NMR studies of the structure and function of large biological macromolecules.

    In situ analysis of the microbial fermentation process by natural abundance 13 C and 31 P NMR spectroscopy. Ugurbil, K. High-resolution 13 C nuclear magnetic resonance studies of glucose metabolism in Escherichia coli. Szyperski, T. Detecting and dissecting metabolic fluxes using biosynthetic fractional 13 C labeling and two-dimensional NMR spectroscopy. Trends Biotechnol.

    Chikayama, E. Cascante, M. Metabolomics and fluxomics approaches.

    How to use NMR to determine the functional groups

    Essays Biochem. Buescher, J. A roadmap for interpreting 13 C metabolite labeling patterns from cells. Beckonert, O. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Bertini, I. Global metabolomics characterization of bacteria: pre-analytical treatments and profiling.

    Metabolomics 10 , — Bernacchioni, C. NMR metabolomics highlights sphingosine kinase-1 as a new molecular switch in the orchestration of aberrant metabolic phenotype in cancer cells. Wan, Q. Chong, M. Abstract Cellulose has a important environmental role in the preservation of the global carbon cycle and commercial significance as a raw material for industry. Fingerprint nuclear magnetic resonance spectroscopy.

    Trichoderma reesei. Keywords cellulose cellulase cellobiohydrolase endoglucanase cellulose-binding domain fungi NMR spectroscopy Trichoderma reesei biopolymers. Structural and functional studies of fungal cellulose-binding domains by NMR spectroscopy: Dissertation. Mattinen, Maija-Liisa. Mattinen M-L. University of Helsinki. Related Information. Close Figure Viewer.

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