Project description:Development of high resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2´-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest.
Project description:Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry (HR-MS) in combination with fragment analysis (MS/MS) has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectra pose challenges to downstream analysis, given their complexity and size. Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlopidine. The molecular networking approach also generated networks of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline. The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome. Keywords: Antihypertensive drugs, Drug metabolism, Fragmentation, High-resolution mass spectrometry, Metabolomics, Urine.
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode.
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
Project description:This dataset contains the raw data used for MZmine processing and Feature-based molecular networking of semi-purified fractions obtained from A. timonensis used for bioactive molecular networking to reveal contribution of sulfonolipids to observed biological activity.
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with A1 medium( mzXML file 11-15 blank and 16-20 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with 301 medium( mzXML file 1-5 blank and 6-10 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with SCB medium( mzXML file 41-45 blank and 46-50 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with ISP4 medium( mzXML file 31-35 blank and 36-40 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with ISP2 medium( mzXML file 21-25 blank and 26-30 cultured).