Wolfram Gronwald

Advances in amino acid analysis

AbstractAmino acids are important targets for metabolic profiling. For decades, amino acid analysis has been accomplished by either cation-exchange or reversed-phase liquid chromatography coupled to UV absorbance or fluorescence detection of pre-column or post-column-derivatized amino acids. Recent years have seen great progress in the development of direct-infusion or hyphenated mass spectrometry in the analysis of free amino acids in physiological fluids, because mass spectrometry not only matches optical detection in sensitivity, but also offers superior selectivity. The advent of cryo-probes has also brought NMR spectroscopy within the detection limits required for the analysis of free amino acids. But there is still room for further improvement, including expansion of the analyte spectrum, reduction of sample preparation and analysis time, automation, and synthesis of affordable isotope standards. FigureFully automated gas chromatography-mass spectrometry analysis of amino acids.

Automated GC–MS analysis of free amino acids in biological fluids

A gas chromatography-mass spectrometry (GC-MS) method was developed for the quantitative analysis of free amino acids as their propyl chloroformate derivatives in biological fluids. Derivatization with propyl chloroformate is carried out directly in the biological samples without prior protein precipitation or solid-phase extraction of the amino acids, thereby allowing automation of the entire procedure, including addition of reagents, extraction and injection into the GC-MS. The total analysis time was 30 min and 30 amino acids could be reliably quantified using 19 stable isotope-labeled amino acids as internal standards. Limits of detection (LOD) and lower limits of quantification (LLOQ) were in the range of 0.03-12 microM and 0.3-30 microM, respectively. The method was validated using a certified amino acid standard and reference plasma, and its applicability to different biological fluids was shown. Intra-day precision for the analysis of human urine, blood plasma, and cell culture medium was 2.0-8.8%, 0.9-8.3%, and 2.0-14.3%, respectively, while the inter-day precision for human urine was 1.5-14.1%.

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

Urinary Metabolite Quantification Employing 2D NMR Spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) spectroscopy is a fairly novel method for the quantification of metabolites in biological fluids and tissue extracts. We show in this contribution that, compared to 1D 1H spectra, superior quantification of human urinary metabolites is obtained from 2D 1H-13C heteronuclear single-quantum correlation (HSQC) spectra measured at natural abundance. This was accomplished by the generation of separate calibration curves for the different 2D HSQC signals of each metabolite. Lower limits of detection were in the low to mid micromolar range and were generally the lower the greater the number of methyl groups contained in an analyte. The quantitative 2D NMR data obtained for a selected set of 17 urinary metabolites were compared to those obtained independently by means of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry of amino acids and hippurate as well as enzymatic and colorimetric measurements of creatinine. As a typical application, 2D-NMR was used for the investigation of urine from patients with inborn errors of metabolism.

Nuclear magnetic resonance and mass spectrometry-based milk metabolomics in dairy cows during early and late lactation

Milk production in dairy cows has dramatically increased over the past few decades. The selection for higher milk yield affects the partitioning of available nutrients, with more energy being allocated to milk synthesis and less to physiological processes essential to fertility and fitness. In this study, the abundance of numerous milk metabolites in early and late lactation was systematically investigated, with an emphasis on metabolites related to energy metabolism. The aim of the study was the identification and correlation of milk constituents to the metabolic status of the cows. To investigate the influence of lactation stage on physiological and metabolic variables, 2 breeds of different productivity were selected for investigation by high-resolution nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. We could reliably quantify 44 different milk metabolites. The results show that biomarkers such as acetone and beta-hydroxybutyrate are clearly correlated to the metabolic status of the individual cows during early lactation. Based on these data, the selection of cows that cope well with the metabolic stress of early lactation should become an option.

NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis.

Ketosis is a common metabolic disease in dairy cows. Diagnostic markers for ketosis such as acetone and beta-hydroxybutyric acid (BHBA) are known, but disease prediction remains an unsolved challenge. Milk is a steadily available biofluid and routinely collected on a daily basis. This high availability makes milk superior to blood or urine samples for diagnostic purposes. In this contribution, we show that high milk glycerophosphocholine (GPC) levels and high ratios of GPC to phosphocholine (PC) allow for the reliable selection of healthy and metabolically stable cows for breeding purposes. Throughout lactation, high GPC values are connected with a low ketosis incidence. During the first month of lactation, molar GPC/PC ratios equal or greater than 2.5 indicate a very low risk for developing ketosis. This threshold was validated for different breeds (Holstein-Friesian, Brown Swiss, and Simmental Fleckvieh) and for animals in different lactations, with observed odds ratios between 1.5 and 2.38. In contrast to acetone and BHBA, these measures are independent of the acute disease status. A possible explanation for the predictive effect is that GPC and PC are measures for the ability to break down phospholipids as a fatty acid source to meet the enhanced energy requirements of early lactation.

Automated assignment of NOESY NMR spectra using a knowledge based method (KNOWNOE).

Automated assignment of NOESY spectra is a prerequisite for automated structure determination of biological macromolecules. With the program KNOWNOE we present a novel, knowledge based approach to this problem. KNOWNOE is devised to work directly with the experimental spectra without interference of an expert. Besides making use of routines already implemented in AUREMOL, it contains as a central part a knowledge driven Bayesian algorithm for solving ambiguities in the NOE assignments. These ambiguities mainly arise from chemical shift degeneration which allows multiple assignments of cross peaks. Using a set of 326 protein NMR structures, statistical tables in the form of atom-pairwise volume probability distributions (VPDs) were derived. VPDs for all assignment possibilities relevant to the assignments of interproton NOEs were calculated. With these data for a given cross peak with N possible assignments Ai(i = 1,...,N) the conditional probabilities P(Ai, a|V0) can be calculated that the assignment Aidetermines essentially all (a-times) of the cross peak volume V0. An assignment Akwith a probability P(Ak, a|V0) higher than 0.8 is transiently considered as unambiguously assigned. With a list of unambiguously assigned peaks a set of structures is calculated. These structures are used as input for a next cycle of iteration where a distance threshold Dmaxis dynamically reduced. The program KNOWNOE was tested on NOESY spectra of a medium size protein, the cold shock protein (TmCsp) from Thermotoga maritima. The results show that a high quality structure of this protein can be obtained by automated assignment of NOESY spectra which is at least as good as the structure obtained from manual data evaluation.

Solution Structure of the Ras Binding Domain of the Protein Kinase Byr2 from Schizosaccharomyces pombe

BACKGROUND After activation, small GTPases such as Ras transfer the incoming signal to effectors by specifically interacting with the binding domain of these proteins. Structural details of the binding domain of different effectors determine which pathway is predominantly activated. Byr2 from fission yeast is a functional homolog of Raf, which is the direct downstream target of Ras in mammalians that initiates a protein kinase cascade. The amino acid sequence of Byr2s Ras binding domain is only weakly related to that of Raf, and Byr2s three-dimensional structure is unknown. RESULTS We have solved the 3D structure of the Ras binding domain of Byr2 (Byr2RBD) from Schizosaccharomyces pombe in solution. The structure consists of three alpha helices and a mixed five-stranded beta pleated sheet arranged in the topology betabetaalphabetabetaalphabetaalpha with the first seven canonic secondary structure elements forming a ubiquitin superfold. 15N-(1)H-TROSY-HSQC spectroscopy of the complex of Byr2RBD with Ras*Mg(2+)*GppNHp reveals that the first and second beta strands and the first alpha helix of Byr2 are mainly involved in the protein-protein interaction as observed in other Ras binding domains. Although the putative interaction site of H-Ras from human and Ras1 from S. pombe are identical in sequence, binding to Byr2 leads to small but significant differences in the NMR spectra, indicating a slightly different binding mode. CONCLUSIONS The ubiquitin superfold appears to be the general structural motif for Ras binding domains even in cases with vanishing sequence identity. However, details of the 3D structure and the interacting interface are different, thereby determining the specifity of the recognition of Ras and Ras-related proteins.

RFAC, a program for automated NMR R-factor estimation.

A computer program (RFAC) has been developed, which allows the automated estimation of residual indices (R-factors) for protein NMR structures and gives a reliable measure for the quality of the structures. The R-factor calculation is based on the comparison of experimental and simulated 1H NOESY NMR spectra. The approach comprises an automatic peak picking and a Bayesian analysis of the data, followed by an automated structure based assignment of the NOESY spectra and the calculation of the R-factor. The major difference to previously published R-factor definitions is that we take the non-assigned experimental peaks into account as well. The number and the intensities of the non-assigned signals are an important measure for the quality of an NMR structure. It turns out that for different problems optimally adapted R-factors should be used which are defined in the paper. The program allows to compute a global R-factor, different R-factors for the intra residual NOEs, the inter residual NOEs, sequential NOEs, medium range NOEs and long range NOEs. Furthermore, R-factors can be calculated for various user defined parts of the molecule or it is possible to obtain a residue-by-residue R-factor. Another possibility is to sort the R-factors according to their corresponding distances. The summary of all these different R-factors should allow the user to judge the structure in detail. The new program has been successfully tested on two medium sized proteins, the cold shock protein (TmCsp) from Termotoga maritima and the histidine containing protein (HPr) from Staphylococcus carnosus. A comparison with a previously published R-factor definition shows that our approach is more sensitive to errors in the calculated structure.

CAMRA: Chemical shift based computer aided protein NMR assignments

A suite of programs called CAMRA (Computer Aided Magnetic Resonance Assignment) has been developed for computer assisted residue-specific assignments of proteins. CAMRA consists of three units: ORB, CAPTURE and PROCESS. ORB predicts NMR chemical shifts for unassigned proteins using a chemical shift database of previously assigned homologous proteins supplemented by a statistically derived chemical shift database in which the shifts are categorized according to their residue, atom and secondary structure type. CAPTURE generates a list of valid peaks from NMR spectra by filtering out noise peaks and other artifacts and then separating the derived peak list into distinct spin systems. PROCESS combines the chemical shift predictions from ORB with the spin systems identified by CAPTURE to obtain residue specific assignments. PROCESS ranks the top choices for an assignment along with scores and confidence values. In contrast to other auto-assignment programs, CAMRA does not use any connectivity information but instead is based solely on matching predicted shifts with observed spin systems. As such, CAMRA represents a new and unique approach for the assignment of protein NMR spectra. CAMRA will be particularly useful in conjunction with other assignment methods and under special circumstances, such as the assignment of flexible regions in proteins where sufficient NOE information is generally not available. CAMRA was tested on two medium-sized proteins belonging to the chemokine family. It was found to be effective in predicting the assignment providing a database of previously assigned proteins with at least 30% sequence identity is available. CAMRA is versatile and can be used to include and evaluate heteronuclear and three-dimensional experiments.