Kirsten A. M. Ampt

Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis.

BackgroundAnalysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data.ResultsA novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis.ConclusionsFusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis.

Background In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. Methodology The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. Conclusions We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Thermodynamics and NMR studies on Duck, Heron and Human HBV encapsidation signals

Hepatitis B virus (HBV) replication is initiated by binding of its reverse transcriptase (P) to the apical stem-loop (AL) and primer loop (PL) of epsilon, a highly conserved RNA element at the 5′-end of the RNA pregenome. Mutation studies on duck/heron and human in vitro systems have shown similarities but also differences between their P–epsilon interaction. Here, NMR and UV thermodynamic data on AL (and PL) from these three species are presented. The stabilities of the duck and heron ALs were found to be similar, and much lower than that of human. NMR data show that this low stability stems from an 11-nt internal bulge destabilizing the stem of heron AL. In duck, although structured at low temperature, this region also forms a weak point as its imino resonances broaden to disappearance between 30 and 35°C well below the overall AL melting temperature. Surprisingly, the duck- and heron ALs were both found to be capped by a stable well-structured UGUU tetraloop. All avian ALs are expected to adhere to this because of their conserved sequence. Duck PL is stable and structured and, in view of sequence similarities, the same is expected for heron - and human PL.

Determination and identification of estrogenic compounds generated with biosynthetic enzymes using hyphenated screening assays, high resolution mass spectrometry and off-line NMR.

This paper describes the determination and identification of active and inactive estrogenic compounds produced by biosynthetic methods. A hyphenated screening assay towards the human estrogen receptor ligand binding domain (hER)alpha and hERbeta integrating target-ligand interactions and liquid chromatography-high resolution mass spectrometry was used. With this approach, information on both biologic activity and structure identity of compounds produced by bacterial mutants of cytochrome P450s was obtained in parallel. Initial structure identification was achieved by high resolution MS/MS, while for full structure determination, P450 incubations were scaled up and the produced entities were purified using preparative liquid chromatography with automated fraction collection. NMR spectroscopy was performed on all fractions for 3D structure analysis; this included 1D-(1)H, 2D-COSY, 2D-NOESY, and (1)H-(13)C-HSQC experiments. This multidimensional screening approach enabled the detection of low abundant biotransformation products which were not suitable for detection in either one of its single components. In total, the analytical scale biosynthesis produced over 85 compounds from 6 different starting templates. Inter- and intra-day variation of the biochemical signals in the dual receptor affinity detection system was less than 5%. The multi-target screening approach combined with full structure characterization based on high resolution MS(/MS) and NMR spectroscopy demonstrated in this paper can generally be applied to e.g. metabolism studies and compound-library screening.

Active site substitution A82W improves the regioselectivity of steroid hydroxylation by cytochrome P450 BM3 mutants as rationalized by spin relaxation nuclear magnetic resonance studies.

Cytochrome P450 BM3 from Bacillus megaterium is a monooxygenase with great potential for biotechnological applications. In this paper, we present engineered drug-metabolizing P450 BM3 mutants as a novel tool for regioselective hydroxylation of steroids at position 16β. In particular, we show that by replacing alanine at position 82 with a tryptophan in P450 BM3 mutants M01 and M11, the selectivity toward 16β-hydroxylation for both testosterone and norethisterone was strongly increased. The A82W mutation led to a ≤42-fold increase in V(max) for 16β-hydroxylation of these steroids. Moreover, this mutation improves the coupling efficiency of the enzyme, which might be explained by a more efficient exclusion of water from the active site. The substrate affinity for testosterone increased at least 9-fold in M11 with tryptophan at position 82. A change in the orientation of testosterone in the M11 A82W mutant as compared to the orientation in M11 was observed by T(1) paramagnetic relaxation nuclear magnetic resonance. Testosterone is oriented in M11 with both the A- and D-ring protons closest to the heme iron. Substituting alanine at position 82 with tryptophan results in increased A-ring proton-iron distances, consistent with the relative decrease in the level of A-ring hydroxylation at position 2β.

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

AbstractBecause cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl—experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood–brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease. FigureGraphical representation of Hierarchical Models Fusion applied to concatenated plasma and CSF datasets.

NMR and Pattern Recognition Can Distinguish Neuroinflammation and Peripheral Inflammation

Multiple Sclerosis (MScl) is a neurodegenerative disease of the CNS, associated with chronic neuroinflammation. Cerebrospinal fluid (CSF), being in closest interaction with CNS, was used to profile neuroinflammation to discover disease-specific markers. We used the commonly accepted animal model for the neuroinflammatory aspect of MScl: the experimental autoimmune/allergic encephalomyelitis (EAE). A combination of advanced (1)H NMR spectroscopy and pattern recognition methods was used to establish the metabolic profile of CSF of EAE-affected rats (representing neuroinflammation) and of two control groups (healthy and peripherally inflamed) to detect specific markers for early neuroinflammation. We found that the CSF metabolic profile for neuroinflammation is distinct from healthy and peripheral inflammation and characterized by changes in concentrations of metabolites such as creatine, arginine, and lysine. Using these disease-specific markers, we were able to detect early stage neuroinflammation, with high accuracy in a second independent set of animals. This confirms the predictive value of these markers. These findings from the EAE model may help to develop a molecular diagnosis for the early stage MScl in humans.

Application of fluorine NMR for structure identification of steroids

Fluorinated steroids were examined using 1D and 2D homo‐ and heteronuclear 19F NMR, such as 19F1H and 19F13C. The utilization of fluorine NMR accounted for spectral simplification and resulted in a straightforward pathway for the determination of structures including the configuration of these compounds; these steroids present an illustrative example for other types of fluorinated compounds, which are increasingly encountered in drug discovery. The potential of 19F NMR is elaborated on in detail for two compounds containing diastereotopic fluorines with different coupling patterns. The analysis of the coupling patterns and the through‐space interactions resulted in the determination of the structure and configuration. Heteronuclear correlation experiments, i.e. 19F1H HETCOR, 19F13C HMQC and HMBC, and 19F1H HOESY, were applied to determine first the relative stereochemistry and then the molecular configuration at C4 and C5 of a steroidal compound bearing a fused three‐membered ring with two fluorine substituents. These examples proved 19F NMR to be a useful addition to the extensively used 1H and 13C NMR within structure elucidation and configuration determination of small molecules. Copyright

Determination of size and sign of hetero-nuclear coupling constants from 2D 19F-13C correlation spectra.

Fluorinated organic compounds have become increasingly important within the polymer and the pharmaceutical industry as well as for clinical applications. For the structural elucidation of such compounds, NMR experiments with fluorine detection are of great value due to the favorable NMR properties of the fluorine nucleus. For the investigation of three fluorinated compounds, triple resonance 2D HSQC and HMBC experiments were adopted to fluorine detection with carbon and/or proton decoupling to yield F-C, F-C{H}, F-C{C(acq)} and F-C{H,C(acq)} variants. Analysis of E.COSY type cross-peak patterns in the F-C correlation spectra led, apart from the chemical shift assignments, to determination of size and signs of the J(CH), J(CF), and J(HF) coupling constants. In addition, the fully coupled F-C HMQC spectrum of steroid 1 was interpreted in terms of E.COSY type patterns. This example shows how coupling constants due to different nuclei can be determined together with their relative signs from a single spectrum. The analysis of cross-peak patterns, as presented here, not only provides relatively straightforward routes to the determination of size and sign of hetero-nuclear J-couplings in fluorinated compounds, it also provides new and easy ways for the determination of residual dipolar couplings and thus for structure elucidation. The examples and results presented in this study may contribute to a better interpretation and understanding of various F-C correlation experiments and thereby stimulate their utilization.