Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis.
The metabolic phenotype varies widely due to external factors such as diet and gut microbiome composition, among others. Despite these temporal fluctuations, urine metabolite profiling studies have suggested that there are highly individual phenotypes that persist over extended periods of time. This hypothesis was tested by analyzing the exhaled breath of a group of subjects during nine days by mass spectrometry. Consistent with previous metabolomic studies based on urine, we conclude that individual signatures of breath composition exist. The confirmation of the existence of stable and specific breathprints may contribute to strengthen the inclusion of breath as a biofluid of choice in metabolomic studies. In addition, the fact that the method is rapid and totally non-invasive, yet individualized profiles can be tracked, makes it an appealing approach.
Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at:https://artyomovlab.wustl.edu/shiny/gam/.
Berries are associated with health benefits. Little is known about the effect of baseline metabolome on the overall metabolic responses to berry intake.
Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory
- Proceedings of the National Academy of Sciences of the United States of America
- Published 10 months ago
Secondary metabolite diversity is considered an important fitness determinant for plants' biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue-metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.
Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.
- Clinica chimica acta; international journal of clinical chemistry
- Published about 5 years ago
Metabolomics is a powerful technique for the discovery of novel biomarkers and elucidation of biochemical pathways to improve diagnosis, prognosis and therapy. An advantage of this approach is its ability to assess global metabolic profiles to enhance pathologic characterization. Urine is an ideal bio-medium for disease study because it is readily available, easily obtained and less complex than other body fluids. Ease of collection allows for serial sampling to monitor disease and therapeutic response. Because of this potential, this paper will review urine metabolomic analysis, discuss its significance in the post-genomic era and highlight the specific roles of endogenous small molecule metabolites in this emerging field.
Dabigatran etexilate (DABE) is an oral prodrug that is rapidly converted by esterases to dabigatran (DAB), a direct inhibitor of thrombin. To elucidate the esterase-mediated metabolic pathway of DABE, a high-performance liquid chromatography/mass spectrometry based metabolite identification and semi-quantitative estimation approach was developed. To overcome the poor full-scan sensitivity of conventional triple quadrupole mass spectrometry, precursor-product ion pairs were predicted to search for the potential in vitro metabolites. The detected metabolites were confirmed by the product ion scan. A dilution method was introduced to evaluate the matrix effects on tentatively identified metabolites without chemical standards. Quantitative information on detected metabolites was obtained using “metabolite standards” generated from incubation samples that contain a high concentration of metabolite in combination with a correction factor for mass spectrometry response. Two in vitro metabolites of DABE (M1 and M2) were identified, and quantified by the semi-quantitative estimation approach. It is noteworthy that CES1 converts DABE to M1 while CES2 mediates the conversion of DABE to M2. M1 and M2 were further metabolized to DAB by CES2 and CES1, respectively. The approach presented here provides a solution to a bioanalytical need for fast identification and semi-quantitative estimation of CES metabolites in preclinical samples.
Pharmacokinetic study in pigs and in vitro metabolic characterization in pig- and human-liver microsomes reveal marked differences in disposition and metabolism of tiletamine and zolazepam (Telazol)
- Xenobiotica; the fate of foreign compounds in biological systems
- Published about 4 years ago
Abstract 1. An equal-dose combination of tiletamine and zolazepam (Telazol®) is used as a veterinary anesthetic. There also have been reports of human abuse of Telazol®. The pharmacokinetics and metabolic fate of tiletamine and zolazepam and the rationale for their administration as an equal-dose combination are unclear. 2. The single-dose pharmacokinetics of intramuscular tiletamine and zolazepam (3 mg/kg each) in 16 Yorkshire-crossbred pigs were determined. The metabolites of tiletamine and zolazepam in pig plasma and urine were identified by mass spectrometry. The metabolic stability of tiletamine and zolazepam and the kinetics of formation of their metabolites by pig- and human-liver microsomes were determined. 3. Higher concentrations of zolazepam were observed in pig plasma and it was cleared more slowly compared to tiletamine (apparent clearance: 11 versus 134 l/h; half-life: 2.76 versus 1.97 h). Three metabolites of zolazepam and one metabolite of tiletamine were identified in pig urine, plasma and in microsomal incubations. In vitro formation of each of these metabolites in microsomes was biphasic involving a high-affinity/low-capacity and a low-affinity/high-capacity enzyme. The in vitro metabolic stability of tiletamine was considerably lower compared to zolazepam. 4. These results collectively point to major pharmacokinetic and metabolic differences between the two components of this fixed-dose anesthetic combination.
Trametinib, a first-in-class oral MEK inhibitor mass balance study with limited enrollment of two male subjects with advanced cancers
- Xenobiotica; the fate of foreign compounds in biological systems
- Published about 4 years ago
Abstract 1. This study assessed the mass balance, metabolism and disposition of [(14)C]trametinib, a first-in-class mitogen-activated extracellular signal-related kinase (MEK) inhibitor, as an open-label, single solution dose (2 mg, 2.9 MBq [79 µCi]) in two male subjects with advanced cancer. 2. Trametinib absorption was rapid. Excretion was primarily via feces (∼81% of excreted dose); minor route was urinary (∼19% of excreted dose). The primary metabolic elimination route was deacetylation alone or in combination with hydroxylation. Circulating drug-related component profiles (composed of parent with metabolites) were similar to those found in elimination together with N-glucuronide of deacetylation product. Metabolite analysis was only possible from <50% of administered dose; therefore, percent of excreted dose (defined as fraction of percent of administered dose recovery over total dose recovered in excreta) was used to assess the relative importance of excretion and metabolite routes. The long elimination half-life (∼10 days) favoring sustained targeted activity was important in permitting trametinib to be the first MEK inhibitor with clinical activity in late stage clinical studies. 3. This study exemplifies the challenges and adaptability needed to understand the metabolism and disposition of an anticancer agent, like trametinib, with both low exposure and a long elimination half-life.