Quantification and identification of components in solution mixtures from 1D proton NMR spectra using singular value decomposition

Mapping Local Conformational Landscapes of Proteins in Solution

The ability of proteins to adopt multiple conformational states is essential to their function, and elucidating the details of such diversity under physiological conditions has been a major challenge. Here we present a generalized method for mapping protein population landscapes by NMR spectroscopy. Experimental NOESY spectra are directly compared with a set of expectation spectra back-calculated across an arbitrary conformational space. Signal decomposition of the experimental spectrum then directly yields the relative populations of local conformational microstates. In this way, averaged descriptions of conformation can be eliminated. As the method quantitatively compares experimental and expectation spectra, it inherently delivers an R factor expressing how well structural models explain the input data. We demonstrate that our method extracts sufficient information from a single 3D NOESY experiment to perform initial model building, refinement, and validation, thus offering a complete de novo structure determination protocol.

Magnetic Resonance and Ultrastructural Characterization of PEGylation-associated Vacuolation in Nonclinical Models

Conjugation with polyethylene glycol (PEG) is a strategy for improving the pharmaceutical properties of therapeutic proteins. In nonclinical studies of PEGylated compounds, microscopic tissue vacuolation is often observed, characterized ultrastructurally in this report by lysosomal distension. Although PEGylation-associated vacuolation appears to be of limited toxicologic concern when alternative therapies are limited, the risk-benefit considerations may be impacted by uncertainty about reversibility, lack of methods for monitoring PEG accumulation in vivo without biopsy, and the variability in tissues affected depending on species studied. We demonstrate the use of magnetic resonance spectroscopy (MRS) to measure PEG concentrations at multiple time points in vivo in the kidney with comparison to PEG concentrations ex vivo in body fluids and tissue extracts using nuclear magnetic resonance (NMR) spectroscopy. Furthermore, we demonstrate the use of these techniques to study distribution and elimination of PEG in a dog model of PEGylation-associated vacuolation. This report suggests that MRS could be further investigated as a feasible imaging-based method for monitoring PEG accumulation in a clinical setting in conjunction with NMR quantitation of PEG in plasma and urine.

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.

Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of (1)H NMR spectra of complex mixtures and biofluids

The quantitative interpretation of (1)H NMR spectra of mixtures like the biofluids is a demanding task due to spectral complexity and overlap. Complications may arise also from water suppression, T2-editing, protein interactions, relaxation differences of the species, experimental artifacts and, furthermore, the spectra may contain unknown components and macromolecular background which cannot be easily separated from baseline. In this work, tools and strategies for quantitative Quantum Mechanical Spectral Analysis (qQMSA) of (1)H NMR spectra from complex mixtures were developed and systematically assessed. In the present approach, the signals of well-defined, stoichiometric components are described by a QM model, while the background is described by a multiterm baseline function and the unknown signals using optimizable and adjustable lines, regular multiplets or any spectral structures which can be composed from spectral lines. Any prior knowledge available from the spectrum can also be added to the model. Fitting strategies for weak and strongly overlapping spectral systems were developed and assessed using two basic model systems, the metabolite mixtures without and with macromolecular (serum) background. The analyses show that if the spectra are measured in high-throughput manner, the consistent absolute quantification demands some calibration to compensate the different response factors of the protons and compounds. On the other hand, the results show that also the T2-edited spectra can be measured so that they obey well the QM rules. In general, qQMSA exploits and interprets the spectral information in maximal way taking full advantage from the QM properties of the spectra and, at the same time, offers chemical confidence which means that individual components can be identified with high confidence on the basis of their accurate spectral parameters.

Application of two-dimensional selective-TOCSY HMBC for structure elucidation of impurities in mixture without separation

In the pharmaceutical industry, regulatory expectations driven by patient safety considerations make structure elucidation of impurities at levels greater than 0.1% in the active pharmaceutical ingredient (API) of primary interest. Impurities can be generated from isomers in starting materials, or produced from different process steps toward the final API. Proton peaks belonging to different impurities could be potentially identified in the one-dimensional (1)H NMR spectrum, when evaluated in combination with two-dimensional (2-D) COSY and HSQC data. However, in 2-D HMBC data, correlation responses from different impurities may overlap with those from the major component, causing uncertainty of long-range proton to carbon correlations and quaternary carbon assignments. This observation prompts us to design the 2-D selective-TOCSY HMBC experiment to distinguish responses from different impurities in mixtures to obtain 2-D NMR data for each impurity, thus eliminating the use of a chromatographic isolation step to obtain material for NMR analysis. This methodology is demonstrated for structure elucidation of impurities ranging from 8.2% in the raw material to 0.4% in the API in this study, and would be particularly useful for industrial samples in which the solubility and availability of material are not an issue.

Quantitative 1H NMR. Development and potential of an analytical method: an update

Covering the literature from mid-2004 until the end of 2011, this review continues a previous literature overview on quantitative (1)H NMR (qHNMR) methodology and its applications in the analysis of natural products. Among the foremost advantages of qHNMR is its accurate function with external calibration, the lack of any requirement for identical reference materials, a high precision and accuracy when properly validated, and an ability to quantitate multiple analytes simultaneously. As a result of the inclusion of over 170 new references, this updated review summarizes a wealth of detailed experiential evidence and newly developed methodology that supports qHNMR as a valuable and unbiased analytical tool for natural product and other areas of research.

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data.

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA–protein binding, protein–metabolite interaction, and protein–protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.

It is now possible to score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. The development of analytical approaches that simultaneously integrate different dimensions of data is essential if we are to extract the meaning from large-scale data to elucidate the complexity of living systems. Here, we use a novel Bayesian network reconstruction algorithm that simultaneously integrates DNA variation, RNA levels, metabolite levels, protein–protein interaction data, protein–DNA binding data, and protein–small-molecule interaction data to construct molecular networks in yeast. We demonstrate that these networks can be used to infer causal relationships among genes, enabling the identification of novel genes that modulate cellular regulation. We show that our network predictions either recapitulate known biology or can be prospectively validated, demonstrating a high degree of accuracy in the predicted network.

The structural basis for homotropic and heterotropic cooperativity of midazolam metabolism by human cytochrome P450 3A4

Human cytochrome P450 3A4 (CYP3A4) metabolizes a significant portion of clinically relevant drugs and often exhibits complex steady-state kinetics that can involve homotropic and heterotropic cooperativity between bound ligands. In previous studies, the hydroxylation of the sedative midazolam (MDZ) exhibited homotropic cooperativity via a decrease in the ratio of 1'-OH-MDZ to 4-OH-MDZ at higher drug concentrations. In this study, MDZ exhibited heterotropic cooperativity with the antiepileptic drug carbamazepine (CBZ) with characteristic decreases in the 1'-OH-MDZ to 4-OH-MDZ ratios. To unravel the structural basis of MDZ cooperativity, we probed MDZ and CBZ bound to CYP3A4 using longitudinal T(1) nuclear magnetic resonance (NMR) relaxation and molecular docking with AutoDock 4.2. The distances calculated from longitudinal T(1) NMR relaxation were used during simulated annealing to constrain the molecules to the substrate-free X-ray crystal structure of CYP3A4. These simulations revealed that either two MDZ molecules or an MDZ molecule and a CBZ molecule assume a stacked configuration within the CYP3A4 active site. In either case, the proton at position 4 of the MDZ molecule was closer to the heme than the protons of the 1'-CH(3) group. In contrast, molecular docking of a single molecule of MDZ revealed that the molecule was preferentially oriented with the 1'-CH(3) position closer to the heme than position 4. This study provides the first detailed molecular analysis of heterotropic and homotropic cooperativity of a human cytochrome P450 from an NMR-based model. Cooperativity of ligand binding through direct interaction between stacked molecules may represent a common motif for homotropic and heterotropic cooperativity.

Metabolic profiles show specific mitochondrial toxicities in vitro in myotube cells

Mitochondrial toxicity has been a serious concern, not only in preclinical drug development but also in clinical trials. In mitochondria, there are several distinct metabolic processes including fatty acid β-oxidation, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation (OXPHOS), and each process contains discrete but often intimately linked steps. Interruption in any one of those steps can cause mitochondrial dysfunction. Detection of inhibition to OXPHOS can be complicated in vivo because intermediate endogenous metabolites can be recycled in situ or circulated systemically for metabolism in other organs or tissues. Commonly used assays for evaluating mitochondrial function are often applied to ex vivo or in vitro samples; they include various enzymatic or protein assays, as well as functional assays such as measurement of oxygen consumption rate, membrane potential, or acidification rates. Metabolomics provides quantitative profiles of overall metabolic changes that can aid in the unraveling of explicit biochemical details of mitochondrial inhibition while providing a holistic view and heuristic understanding of cellular bioenergetics. In this paper, we showed the application of quantitative NMR metabolomics to in vitro myotube cells treated with mitochondrial toxicants, rotenone and antimycin A. The close coupling of the TCA cycle to the electron transfer chain (ETC) in OXPHOS enables specific diagnoses of inhibition to ETC complexes by discrete biochemical changes in the TCA cycle.

Integrated pathway analysis of rat urine metabolic profiles and kidney transcriptomic profiles to elucidate the systems toxicology of model nephrotoxicants

In this study, approximately 40 endogenous metabolites were identified and quantified by (1)H NMR in urine samples from male rats dosed with two proximal tubule toxicants, cisplatin and gentamicin. The excreted amount of a majority of those metabolites in urine was found to be dose-dependent and exhibited a strong correlation with histopathology scores of overall proximal tubule damage. MetaCore pathway analysis software (GeneGo Inc.) was employed to identify nephrotoxicant-associated biochemical changes via an integrated quantitative analysis of both urine metabolomic and kidney transcriptomic profiles. Correlation analysis was applied to establish quantitative linkages between pairs of individual metabolite and gene transcript profiles in both cisplatin and gentamicin studies. This analysis revealed that cisplatin and gentamicin treatments were strongly linked to declines in mRNA transcripts for several luminal membrane transporters that handle each of the respective elevated urinary metabolites, such as glucose, amino acids, and monocarboxylic acids. The integrated pathway analysis performed on these studies indicates that cisplatin- or gentamicin-induced renal Fanconi-like syndromes manifested by glucosuria, hyperaminoaciduria, lactic aciduria, and ketonuria might be better explained by the reduction of functional proximal tubule transporters rather than by the perturbation of metabolic pathways inside kidney cells. Furthermore, this analysis suggests that renal transcription factors HNF1alpha, HNF1beta, and HIF-1 might be the central mediators of drug-induced kidney injury and adaptive response pathways.