Project description:We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.
Project description:Commercial preparations of Ginkgo biloba are very complex mixtures prepared from raw leaf extracts by a series of extraction and prepurification steps. The pharmacological activity is attributed to a number of flavonoid glycosides and unique terpene trilactones (TTLs), with largely uncharacterized pharmacological profiles on targets involved in neurological disorders. It is therefore important to complement existing targeted analytical methods for analysis of Ginkgo biloba preparations with alternative technology platforms for their comprehensive and global characterization. In this work, (1)H NMR-based metabolomics and hyphenation of high-performance liquid chromatography, photo-diode array detection, mass spectrometry, solid-phase extraction, and nuclear magnetic resonance spectroscopy (HPLC-PDA-MS-SPE-NMR) were used for investigation of 16 commercially available preparations of Ginkgo biloba. The standardized extracts originated from Denmark, Italy, Sweden, and United Kingdom, and the results show that (1)H NMR spectra allow simultaneous assessment of the content as well as identity of flavonoid glycosides and TTLs based on a very simple sample-preparation procedure consisting of extraction, evaporation and reconstitution in acetone-d(6). Unexpected or unwanted extract constituents were also easily identified in the (1)H NMR spectra, which contrasts traditional methods that depend on UV absorption or MS ionizability and usually require availability of reference standards. Automated integration of (1)H NMR spectral segments (buckets or bins of 0.02 ppm width) provides relative distribution plots of TTLs based on their H-12 resonances. The present study shows that (1)H NMR-based metabolomics is an attractive method for non-selective and comprehensive analysis of Ginkgo extracts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0195-x) contains supplementary material, which is available to authorized users.
Project description:As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.
Project description:Many species from the Pinaceae family have been recognized as a rich source of lignans, flavonoids, and other polyphenolics. The great common occurrence of conifers in Europe, as well as their use in the wood industry, makes both plant material and industrial waste material easily accessible and inexpensive. This is a promising prognosis for both discovery of new active compounds as well as for finding new applications for wood and its industry waste products. This study aimed to analyze and phytochemically profile 13 wood extracts of the Pinaceae family species, endemic or introduced in Polish flora, using the LC-DAD-ESI-MS/MS method and compare their respective metabolite profiles. Branch wood methanolic extracts were phytochemically profiled. Lignans, stilbenes, flavonoids, diterpenes, procyanidins, and other compounds were detected, with a considerable variety of chemical content among distinct species. Norway spruce (Picea abies (L.) H.Karst.) branch wood was the most abundant source of stilbenes, European larch (Larix decidua Mill.) mostly contained flavonoids, while silver fir (Abies alba Mill.) was rich in lignans. Furthermore, 10 lignans were isolated from the studied material. Our findings confirm that wood industry waste materials, such as conifer branches, can be a potent source of different phytochemicals, with the plant matrix being relatively simple, facilitating future isolation of target compounds.
Project description:Chlorinated paraffins (CPs) are high-volume chemicals used worldwide in various industries as plasticizers, lubricants, and flame retardants. CPs are produced by chlorination of alkane mixtures which leads to complex products of thousands of homologs and congeners. Classic mass spectrometric analyses of CPs allow determining carbon chain lengths and degrees of chlorination while information on the substitution patterns cannot be derived. Therefore, we performed different one- and two-dimensional nuclear magnetic resonance spectroscopy (NMR) experiments, elemental analysis (EA), and gas chromatography coupled with electron capture negative ion mass spectrometry (GC/ECNI-MS) for the analysis of ten technical CP products with 42%, 52%, and 70% chlorine content from four producers. Slight differences in chlorine content but varying chain length compositions were observed for similarly labeled products from different manufacturers. Two-dimensional heteronuclear spectral quantum coherence (HSQC) measurements helped to evaluate ten structural elements in the products and confirmed the presence of geminal chlorine atoms in primary and secondary carbons in products with 70% chlorine. The variation of signal groups increased with increasing chlorine content of the products. Two-dimensional heteronuclear multiple bond coherence (HMBC) analysis of one sample and GC/ECNI-MS measurements indicated the presence of impurities (e.g., C9-CPs, iso-alkanes) in different technical CP products. These methods could in future allow for better distinction of CP mixtures, and an improved trace-back of environmental CPs to the source, based on specific structural features. Additionally, further structural characterization could help in the development of more accurate analysis processes. Graphical Abstract.
Project description:Applications of advanced omics methodologies are increasingly popular in biomedicine. However, large-scale studies aiming at clinical translation are typically siloed to single technologies. Here, we present the first comprehensive large-scale population data combining 209 lipoprotein measures from a quantitative NMR spectroscopy platform and 809 lipid classes and species from a quantitative LC-MS/MS platform. These data with 1018 molecular measures were analyzed in two population cohorts totaling 7830 participants. The association and cluster analyses revealed excellent coherence between the methodologically independent data domains and confirmed their quantitative compatibility and suitability for large-scale studies. The analyses elucidated the detailed molecular characteristics of the heterogeneous circulatory macromolecular lipid transport system and the underlying structural and compositional relationships. Unsupervised neural network analysis─the so-called self-organizing maps (SOMs)─revealed that these deep molecular and metabolic data are inherently related to key physiological and clinical population characteristics. The data-driven population subgroups uncovered marked differences in the population distribution of multiple cardiometabolic risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride species showed similar associations over the entire lipoprotein cascade and indicated systematically increased risk for carotid intima media thickening and other atherosclerosis risk factors, including obesity and inflammation. The metabolic attributes for 27 individual cholesteryl ester species, which formed six distinct clusters, were more intricate with associations both with higher─e.g., CE(16:1)─and lower─e.g., CE(20:4)─cardiometabolic risk. The molecular details provided by these combined data are unprecedented for molecular epidemiology and demonstrate a new potential avenue for population studies.
Project description:A well-known class of antibacterials, 14- and 15-membered macrolides are widely prescribed to treat upper and lower respiratory tract infections. Azithromycin is a 15-membered macrolide antibiotic possessing a broad spectrum of antibacterial potency and favorable pharmacokinetics. Bacterial resistance to marketed antibiotics is growing rapidly and represents one of the major global hazards to human health. Today, there is a high need for discovery of new anti-infective agents to combat resistance. Recently discovered conjugates of azithromycin and thiosemicarbazones, the macrozones, represent one such class that exhibits promising activities against resistant pathogens. In this paper, we employed an approach which combined LC-SPE/cryo NMR, MS/MS and molecular modeling for rapid separation, identification and characterization of bioactive macrozones and their diastereomers. Multitrapping of the chromatographic peaks on SPE cartridges enabled sufficient sample quantities for structure elucidation and biological testing. Furthermore, two-dimensional NOESY NMR data and molecular dynamics simulations revealed stereogenic centers with inversion of chirality. Differences in biological activities among diastereomers were detected. These results should be considered in the process of designing new macrolide compounds with bioactivity. We have shown that this methodology can be used for a fast screening and identification of the macrolide reaction components, including stereoisomers, which can serve as a source of new antibacterials.
Project description:Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA-MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.