Project description:Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously. Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.
Project description:Background The study aimed to show the relationship between a large number of circulating metabolites and subsequent cardiovascular disease (CVD) and subclinical markers of CVD in the general population. Methods and Results In 2278 individuals free from CVD in the EpiHealth study (aged 45-75 years, mean age 61 years, 50% women), 790 annotated nonxenobiotic metabolites were measured by mass spectroscopy (Metabolon). The same metabolites were measured in the PIVUS (Prospective Investigation of Vasculature in Uppsala Seniors) study (n=603, all aged 80 years, 50% women), in which cardiac and carotid artery pathologies were evaluated by ultrasound. During a median follow-up of 8.6 years, 107 individuals experienced a CVD (fatal or nonfatal myocardial infarction, stroke, or heart failure) in EpiHealth. Using a false discovery rate of 0.05 for age- and sex-adjusted analyses and P<0.05 for adjustment for traditional CVD risk factors, 37 metabolites were significantly related to incident CVD. These metabolites belonged to multiple biochemical classes, such as amino acids, lipids, and nucleotides. Top findings were dimethylglycine and N-acetylmethionine. A lasso selection of 5 metabolites improved discrimination when added on top of traditional CVD risk factors (+4.0%, P=0.0054). Thirty-five of the 37 metabolites were related to subclinical markers of CVD evaluated in the PIVUS study. The metabolite 1-carboxyethyltyrosine was associated with left atrial diameter as well as inversely related to both ejection fraction and the echogenicity of the carotid artery. Conclusions Several metabolites were discovered to be associated with future CVD, as well as with subclinical markers of CVD. A selection of metabolites improved discrimination when added on top of CVD risk factors.
Project description:Develop a novel de-glyco-assisted methylation site identification (DOMAIN) strategy which enables straightforward, fast, and reproducible analysis of protein methylation in a proteome-wide manner. Combining multidimensional fractionation and multiprotease digestion, our method enabled the identification of 573 methylated forms in 270 proteins, including 311 new methylation forms, in A549 cells. Combining this technique with stable isotope labeling quantitative proteomics and RNA interference, we determined the differential regulation of several putative methylated sites that are related to the protein arginine N-methyltransferase 3 (PRMT3). Collectively, our integrated proteomics workflow for comprehensive mapping of methylation sites enables a better understanding of protein methylation, while providing a rapid and effective approach for global protein methylation analysis in biomedical research.
Project description:When fed with a high-fat safflower oil diet for 3 wk, wild-type mice develop hepatic insulin resistance, whereas mice lacking glycerol-3-phosphate acyltransferase-1 retain insulin sensitivity. We examined early changes in the development of insulin resistance via liver and plasma metabolome analyses that compared wild-type and glycerol-3-phosphate acyltransferase-deficient mice fed with either a low-fat or the safflower oil diet for 3 wk. We reasoned that diet-induced changes in metabolites that occurred only in the wild-type mice would reflect those metabolites that were specifically related to hepatic insulin resistance. Of the identifiable metabolites (from 322 metabolites) in liver, wild-type mice fed with the high-fat diet had increases in urea cycle intermediates, consistent with increased deamination of amino acids used for gluconeogenesis. Also increased were stearoylglycerol, gluconate, glucarate, 2-deoxyuridine, and pantothenate. Decreases were observed in S-adenosylhomocysteine, lactate, the bile acid taurocholate, and 1,5-anhydroglucitol, a previously identified marker of short-term glycemic control. Of the identifiable metabolites (from 258 metabolites) in plasma, wild-type mice fed with the high-fat diet had increases in plasma stearate and two pyrimidine-related metabolites, whereas decreases were found in plasma bradykinin, alpha-ketoglutarate, taurocholate, and the tryptophan metabolite, kynurenine. This study identified metabolites previously not known to be associated with insulin resistance and points to the utility of metabolomics analysis in identifying unrecognized biochemical pathways that may be important in understanding the pathophysiology of diabetes.
Project description:The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
Project description:Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.
Project description:We present the adaptability of Mascot search engine for automated identification of intact glycopeptide mass spectra. The steps involved in adopting Mascot for intact glycopeptide analysis include: i) assigning unique one letter codes for monosaccharides, ii) linearizing glycan sequences and iii) preparing custom glycoprotein databases. Stepped normalized collision energy (NCE) for HCD mostly provided both the peptide and glycan information in a single MS2 spectrum. Using standard glycoproteins, we showed that Mascot can be adopted for automated annotation of both N- and O-linked glycopeptides. In a large scale validation study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in serum samples. This represent a single tool that collectively allows the i) elucidation of N- and O-linked glycopeptide spectra, ii) matching glycopeptides to known protein sequences, and iii) high-throughput, batch wise analysis of large scale glycoproteomics data sets.
Project description:Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes with a pre-trained model representing expert knowledge in the differentiation of true chemical signal from noise. Furthermore, it provides all necessary functions to easily train new models or improve existing ones by transfer learning. Thus, the tool improves peak curation and contributes to the robust and scalable analysis of large-scale experiments. We show how to integrate it into different liquid chromatography-mass spectrometry (LC-MS) analysis workflows, quantify its performance, and compare it to various other approaches. NeatMS software is available as open source on github under permissive MIT license and is also provided as easy-to-install PyPi and Bioconda packages.
Project description:Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.
Project description:Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.