Exploring High-resolution Magic Angle Spinning (HR-MAS) NMR Spectroscopy for Metabonomic Analysis of Apples

Classical liquid-state high-resolution (HR) NMR spectroscopy has proved a powerful tool in the metabonomic analysis of liquid food samples like fruit juices. In this paper the application of (1)H high-resolution magic angle spinning (HR-MAS) NMR spectroscopy to apple tissue is presented probing its potential for metabonomic studies. The (1)H HR-MAS NMR spectra are discussed in terms of the chemical composition of apple tissue and compared to liquid-state NMR spectra of apple juice. Differences indicate that specific metabolic changes are induced by juice preparation. The feasibility of HR-MAS NMR-based multivariate analysis is demonstrated by a study distinguishing three different apple cultivars by principal component analysis (PCA). Preliminary results are shown from subsequent studies comparing three different cultivation methods by means of PCA and partial least squares discriminant analysis (PLS-DA) of the HR-MAS NMR data. The compounds responsible for discriminating organically grown apples are discussed. Finally, an outlook of our ongoing work is given including a longitudinal study on apples.

Role of proton MR for the study of muscle lipid metabolism

1H-MR spectroscopy (MRS) of intramyocellular lipids (IMCL) became particularly important when it was recognized that IMCL levels are related to insulin sensitivity. While this relation is rather complex and depends on the training status of the subjects, various other influences such as exercise and diet also influence IMCL concentrations. This may open insight into many metabolic interactions; however, it also requires careful planning of studies in order to control all these confounding influences. This review summarizes various historical, methodological, and practical aspects of 1H-MR spectroscopy (MRS) of muscular lipids. That includes a differentiation of bulk magnetic susceptibility effects and residual dipolar coupling that can both be observed in MRS of skeletal muscle, yet affecting different metabolites in a specific way. Fitting of the intra- (IMCL) and extramyocellular (EMCL) signals with complex line shapes and the transformation into absolute concentrations is discussed. Since the determination of IMCL in muscle groups with oblique fiber orientation or in obese subjects is still difficult, potential improvement with high-resolution spectroscopic imaging or at higher field strength is considered. Fat selective imaging is presented as a possible alternative to MRS and the potential of multinuclear MRS is discussed. 1H-MRS of muscle lipids allows non-invasive and repeated studies of muscle metabolism that lead to highly relevant findings in clinics and patho-physiology.

Novel sst2-selective somatostatin agonists. Three-dimensional consensus structure by NMR

The 3D NMR structures of six octapeptide agonist analogues of somatostatin (SRIF) in the free form are described. These analogues, with the basic sequence H-DPhe/Phe2-c[Cys3-Xxx7-DTrp8-Lys9-Thr10-Cys14]-Thr-NH2 (the numbering refers to the position in native SRIF), with Xxx7 being Ala/Aph, exhibit potent and highly selective binding to human SRIF type 2 (sst2) receptors. The backbone of these sst2-selective analogues have the usual type-II' beta-turn reported in the literature for sst2/3/5-subtype-selective analogues. Correlating the biological results and NMR studies led to the identification of the side chains of DPhe2, DTrp8, and Lys9 as the necessary components of the sst2 pharmacophore. This is the first study to show that the aromatic ring at position 7 (Phe7) is not critical for sst2 binding and that it plays an important role in sst3 and sst5 binding. This pharmacophore is, therefore, different from that proposed by others for sst2/3/5 analogues.

Conformational analysis of a potent SSTR3-selective somatostatin analogue by NMR in water solution

The three-dimensional structure of a potent SSTR3-selective analogue of somatostatin, cyclo(3-14)H-Cys(3)-Phe(6)-Tyr(7)-D-Agl(8)(N(beta) Me, 2-naphthoyl)-Lys(9)-Thr(10)-Phe(11)-Cys(14)-OH (des-AA(1, 2, 4, 5, 12, 13)[Tyr(7), D-Agl(8)(N(beta) Me, 2-naphthoyl)]-SRIF) (peptide 1) has been determined by (1)H NMR in water and molecular dynamics (MD) simulations. The peptide exists in two conformational isomers differing mainly by the cis/trans isomerization of the side chain in residue 8. The structure of 1 is compared with the consensus structural motifs of other somatostatin analogues that bind predominantly to SSTR1, SSTR2/SSTR5 and SSTR4 receptors, and to the 3D structure of a non-selective SRIF analogue, cyclo(3-14)H-Cys(3)-Phe(6)-Tyr(7)-D-2Nal(8)-Lys(9)-Thr(10)-Phe(11)-Cys(14)-OH (des-AA(1, 2, 4, 5, 12, 13)[Tyr(7), D-2Nal(8)]-SRIF) (peptide 2). The structural determinant factors that could explain selectivity of peptide 1 for SSTR3 receptors are discussed.

Three-dimensional consensus structure of sst2-selective somatostatin (SRIF) antagonists by NMR

The three-dimensional NMR structures of seven octapeptide analogs of somatostatin (SRIF), based on octreotide, with the basic sequence H-Cpa/Phe2-c[DCys3-Xxx7-DTrp/DAph(Cbm)8-Lys9-Thr10-Cys14]-Yyy-NH2 (the numbering refers to the position in native SRIF), with Xxx7 being Aph(Cbm)/Tyr/Agl(NMe,benzoyl) and Yyy being Nal/DTyr/Thr, are presented here. Most of these analogs exhibit potent and highly selective binding to sst2 receptors, and all of the analogs are antagonists inhibiting receptor signaling. Based on their consensus 3D structure, the pharmacophore of the sst2-selective antagonist has been defined. The pharmacophore involves the side chains of Cpa2, DTrp/DAph(Cbm)8, and Lys9, with the backbone for most of the sst2-selective antagonists comprised a Type-II' beta-turn. Hence, the sst2-selective antagonist pharmacophore is very similar to the sst2-selective agonist pharmacophore previously described.

Improved impurity fingerprinting of heparin by high resolution (1)H NMR spectroscopy

For improving the identification of potential heparin impurities such as oversulfated chondroitin sulfate (OSCS) the standard 2D (1)H-(1)H NMR NOESY was applied. Taking advantage of spin diffusion and adjusting the experimental parameters accordingly additional contaminant-specific signals of the corresponding sugar ring protons can easily be detected. These are usually hidden by the more intense heparin signals. Compared to the current 1D (1)H procedure proposed for screening commercial unfractionated heparin samples and focusing on the contaminants acetyl signals more informative and unique fingerprints may be obtained. Correspondingly measured (1)H fingerprints of a few potential impurities are given and their identification in two contaminated commercial heparin samples is demonstrated. The proposed 2D NOESY method is not intended to replace the current 1D method for detecting and quantifying heparin impurities but may be regarded as a valuable supplement for an improved and more reliable identification of these contaminants.

Reproducibility of cerebral phenylalanine levels in patients with phenylketonuria determined by 1H-MR spectroscopy

The reproducibility of metabolite content determined by MR spectroscopy (MRS) is usually at best a few percent for the prominent singlets. When studying low-concentration metabolites, like phenylalanine (Phe), where tissue content can be <100 micromol/kg, better reproducibility is paramount-particularly in view of using MRS results for potential individual treatment advice. An optimized, targeted spectroscopy method was established at 1.5T and reproducibility was established in 21 patients with phenylketonuria (PKU) where three spectra were recorded in each of three independent sessions, two of which were in immediate succession to minimize physiologic variation. Intersession variation was found to be only 7 micromol/kg Phe for back-to-back repetition of sessions, in close agreement with the variation of 16 micromol/kg observed for single spectra within a session. Analysis of variance proved the individuality of the blood/brain Phe ratio-though this ratio seems to be influenced by physiologic factors that are not stable in time. The excellent reproducibility was achieved through optimization of various factors, including signal-to-noise ratio, repositioning, and prescan calibrations, but also by enforcing as much prior information as possible (e.g., lineshape and phase from reference scans, constant prior-knowledge-locked baseline). While the application of maximum general prior knowledge is a general method to reduce fluctuations, one should remember that it may introduce systematic errors.