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: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:GNPS Feature-Based Molecular Networking Workshop - American Gut subset with metadata for plant consumption
See manuscript here: https://msystems.asm.org/content/3/3/e00031-18
Project description:We performed a retrospective study on CSF from 20 DMT-naïve MS patients to investigate the correlation between intrathecal immune proteins and clinical MS phenotype.
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. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
Project description:This SuperSeries is composed of the following subset Series:; GSE11440: Role of Caveolin 1, E-Cadherin, Enolase 2 and PKCa on resistance to methotrexate in human HT29 colon cancer cells; GSE16066: Networking of differentially expressed genes in CaCo2 human colon cancer cells resistant to methotrexate; GSE16070: Networking of differentially expressed genes in human MCF7 breast cancer cells resistant to methotrexate; GSE16080: Networking of differentially expressed genes in human MDA-MB-468 breast cancer cells resistant to methotrexate; GSE16082: Networking of differentially expressed genes in human MIA PaCa2 pancreatic cancer cells resistant to methotrexate; GSE16085: Networking of differentially expressed genes in human K562 erythtoblastic leukemia cells resistant to methotrexate; GSE16089: Networking of differentially expressed genes in human Saos-2 osteosarcoma cells resistant to methotrexate Experiment Overall Design: Refer to individual Series
Project description:Dataset 1 contains bacterial-fungal monocultures and tripartite co-cultures in which Scopulariopsis sp. JB370 are grown with 1) Hafnia alvei JB232 and 2) Pseudomonas psychrophila JB418 or Escherichia coli K12. Dataset 2 contains bacterial-fungal tripartite co-cultures in which Penicillium solitum #12 are grown with 1) Glutamicibacter arilaitensis JB182 or Brevibacterium linens JB5 and 2) Pseudomonas psychrophila JB418 or Escherichia coli K12. All cultures from each dataset were grown on 10% cheese curd agar (CCA) and extracted using acetonitrile. Extracts were analyzed via LC-MS(/MS) and processed with MZmine2 for analysis with MetaboAnalyst 5.0 and GNPS feature based molecular networking.
Project description:Example dataset for Methods in Molecular Biology Chapter - Feature Based Molecular Networking for Metabolite Annotation. This dataset includes the LC-MS/MS raw data (Bruker .d file format and centroided mzXML file format), metadata table used for the ste-by-step instructions, a batch file for MZmine2 data processing, and all resulting files from the MZmine2 and GNPS processing.