Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed

Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis.

ImportanceWhile "omics" studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology.ObjectiveWe sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls.DesignWe employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls.SettingStudy participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States.ParticipantsWe examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry.ResultsMultivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88;Control209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980;Control7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000;Control111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250;Control347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02).Conclusions and relevanceThe metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development

Metabolomic profile of patients with left ventricular assist devices: a pilot study

Background: Metabolomic profiling has important diagnostic and prognostic value in heart failure (HF). We investigated whether left ventricular assist device (LVAD) support has an impact on the metabolomic profile of chronic HF patients and if specific metabolic patterns are associated with the development of adverse events. Methods: We applied untargeted metabolomics to detect and analyze molecules such as amino acids, sugars, fatty acids and other metabolites in plasma samples collected from thirty-three patients implanted with a continuous-flow LVAD. Data were analyzed at baseline, i.e., before implantation of the LVAD, and at long-term follow-up. Results: Our results reveal significant changes in the metabolomic profile after LVAD implant compared to baseline. In detail, we observed a pre-implant reduction in amino acid metabolism (aminoacyl-tRNA biosynthesis) and increased galactose metabolism, which reversed over the course of support [median follow-up 187 days (63–334 days)]. These changes were associated with improved patient functional capacity driven by LVAD therapy, according to NYHA functional classification of HF (NYHA class I-II: pre-implant =0% of the patients; post-implant =97% of the patients; P<0.001). Moreover, patients who developed adverse thromboembolic events (n=4, 13%) showed a pre-operative metabolomic fingerprint mainly associated with alterations of fatty acid biosynthesis and mitochondrial beta-oxidation of short-chain saturated fatty acids. Conclusions: Our data provide preliminary evidence that LVAD therapy is associated with changes in the metabolomic profile of HF and suggest the potential use of metabolomics as a new tool to stratify LVAD patients in regard to the risk of adverse events

LC-MS metabolomics of psoriasis patients reveals disease severity-dependent increases in circulating amino acids that are ameliorated by anti-TNFα treatment

Psoriasis is an immune-mediated highly heterogeneous skin disease in which genetic as well as environmental factors play important roles. In spite of the local manifestations of the disease, psoriasis may progress to affect organs deeper than the skin. These effects are documented by epidemiological studies, but they are not yet mechanistically understood. In order to provide insight into the systemic effects of psoriasis, we performed a nontargeted high-resolution LC-MS metabolomics analysis to measure plasma metabolites from individuals with mild or severe psoriasis as well as healthy controls. Additionally, the effects of the anti-TNFα drug Etanercept on metabolic profiles were investigated in patients with severe psoriasis. Our analyses identified significant psoriasis-associated perturbations in three metabolic pathways: (1) arginine and proline, (2) glycine, serine and threonine, and (3) alanine, aspartate, and glutamate. Etanercept treatment reversed the majority of psoriasis-associated trends in circulating metabolites, shifting the metabolic phenotypes of severe psoriasis toward that of healthy controls. Circulating metabolite levels pre- and post-Etanercept treatment correlated with psoriasis area and severity index (PASI) clinical scoring (R(2) = 0.80; p < 0.0001). Although the responsible mechanism(s) are unclear, these results suggest that psoriasis severity-associated metabolic perturbations may stem from increased demand for collagen synthesis and keratinocyte hyperproliferation or potentially the incidence of cachexia. Data suggest that levels of circulating amino acids are useful for monitoring both the severity of disease as well as therapeutic response to anti-TNFα treatment

Application of Targeted Metabolomic Profiling and Multivariate Data Analysis for the Study of Nutrition and Disease

Organismal metabolomes may represent the lowest and arguably most tractable level of control in complex biological systems. Metabolomics or the characterization and study of organismal small molecule end products from physiological process has great promise for aiding researchers in uncovering the latent properties of complex multiparametric biological states. However to reap its potential benefits select challenges in metabolomic analyses need to be addressed. The work herein describes a subset of the many challenges faced by metabolomic researchers in the context of targeted lipidomic analyses or the quantitative characterization of lipid reporters from physiological processes (i.e. sterol esters, triglycerides, phospholipids, fatty acids, oxylipins and endocannabinoids), Chapter 1. Improvements in analytical methodologies are discussed and a targeted lipidomic platform is developed and validated for the quantitative analysis of sterol ester, triglyceride and phospholipid, lipid class-esterified, fatty acids and oxylipins, Chapter 2. Challenges in the areas of metabolomic data analysis and biological interpretation are addressed through the development of a graphical user interface for multivariate data analysis and visualization, Chapter 3. The quantitative characterization of plasma free fatty acids, oxylipins and endocannabinoids is used to develop a mechanistic biological interpretation for the observed type 2 diabetes-dependent changes in lipids fuel and signaling metabolisms, Chapter 4. The work contained in the following 4 Chapters was carried out to advance the application of targeted lipidomics for the study of nutrition and disease. These goals were achieved through (1) the development and validation of a quantitative lipidomic platform for the analysis of lipid class-esterified fatty acids and oxylipins, which was used to characterize the dietary omega-3 fatty acid-induced changes in the esterified components of the sterol ester, triglyceride and phospholipid pools in the adipose and liver tissues of a hamster animal model; (2) the development of a multivariate data analysis and visualization frame work, imDEV, which links multivariate statistical algorithms with intuitive multidimensional visualizations which aid the biological interpretation of metabolomic experimental results; (3) the application of a targeted metabolomic approach to study the effects of type 2 diabetes on plasma free fatty acids, oxylipins and endocannabinoids, which was used to develop reporters for diabetes-associated changes in fatty acid metabolism and identify markers for nonalcoholic fatty liver disease

kwanjeeraw/grinn v2.8

a Graph database and R package for omic data integratio