Disease-associated Metabolites Research Articles

Gut bacterial phospholipase Ds support disease-associated metabolism by generating choline.

The essential nutrient choline is metabolized by gut bacteria to the disease-associated metabolite trimethylamine (TMA). However, most of the choline obtained via the diet and present in the human body is incorporated into larger metabolites, including the lipid phosphatidylcholine (PC). Here, we report that many choline-utilizing gut microorganisms can hydrolyse PC using a phospholipase D (PLD) enzyme and further convert the released choline to TMA. Genetic and in vitro characterization of the PLD from Escherichia coli MS 200-1 showed this enzyme is essential for bacterial hydrolysis of PC and prefers this substrate. PLDs are also found in gut bacterial isolates that are unable to convert choline to TMA, suggesting that additional members of the gut microbiota may influence access to this substrate. Unexpectedly, this PLD is only distantly related to characterized PLDs from pathogenic bacteria, suggesting a distinct evolutionary history. Together, these results reveal a previously underappreciated role for gut microorganisms in phospholipid metabolism and a potential target for inhibiting TMA production.

Welding fume exposure is associated with inflammation: a global metabolomics profiling study

BackgroundIncreasing evidence suggests that welding fume exposure is associated with systemic inflammation. Although celluar metabolites may be associated with inflammation, there is limited information on metabolomic changes during welding fume exposure. Such changes may play an important role in the occurrence, development, and prevention of metal-associated diseases. We aim to investigate human metabolomics changes pre- and post-welding fume exposure.MethodsThis study included 52 boilermakers totally. We collected plasma samples pre- and post-shift welding fume exposure and prepared samples using the automated MicroLab STAR® system. Metabolite concentrations were measured using ultra performance liquid chromatography - tandem mass spectrometer (UPLC-MS/MS) methods. Two-way analysis of variance was used to test the significance of metabolite changes with false discovery rate correction.ResultsAnalysis detected several metabolic changes after welding fume exposure, mainly involved in the lipid pathway [glucocorticoid class (cortisol, corticosterone, and cortisone), acylcarnitine class, and DiHOME species (9,10-DiHOME and 12,13-DiHOME)], amino acid utilization (isoleucine, proline and phenylalanine), and S-(3-hydroxypropyl) mercapturic acid (3-HPMA). These compounds are all associated with inflammation according to previous studies. Further, additive interaction effects linked smoking and 3-HPMA levels. In the metabolite set enrichment analysis for diseases, the top two disease-associated metabolite pathways were systemic inflammation-related diseases including rheumatoid arthritis and systemic lupus erythematosus.ConclusionsThis global metabolomics study shows evidence that metabolite changes during welding fume exposure are closely associated with systemic inflammation. The altered metabolites detected may be potential health monitoring biomarkers for boilermakers, especially for inflammation-related disease prevention.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

Role of amino acids in rheumatoid arthritis studied by metabolomics.

Rheumatoid arthritis (RA) is a complex, chronic autoimmune disease characterized by various inflammatory symptoms, including joint swelling, joint pain, and both structural and functional joint damage. The most commonly used animal model for studying RA is mice with collagen-induced arthritis (CIA); the wide use of this model is due primarily to many similarities with RA in human patients. Metabolomics is used increasingly in biological studies for diagnosing disease and for predicting and evaluating drug interventions, as a large number of disease-associated metabolites can be analyzed and interpreted from a biological perspective. To profile free amino acids and their biogenic metabolites in CIA mice plasma. Ultra-high-performance liquid chromatography/tandem mass spectrometry coupled with multiple reaction monitoring (MRM) was used for metabolomics study. Profile of 45 amine metabolites, including free amino acids and their biogenic metabolites in plasma was obtained from CIA mice. We found that the plasma levels of 20 amine metabolites were significantly decreased in the CIA group. The results suggest that a disordered amine response is linked to RA-associated muscle wasting and energy expenditure.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.

Revealing disease-associated pathways by network integration of untargeted metabolomics.

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

Screening of diseases associated with abnormal metabolites for evaluation of HPLC in organic aciduria profiling

BackgroundOrganic acid disorders are a heterogeneous group of inborn errors of metabolism, in which organic acids accumulate in the body. They have high prevalence in Egypt because of a high rate of consanguineous marriages. Here we report our experience with the diagnostic evaluation of patients with organic acidemias as well as several other inborn errors of metabolism (IEMs) by liquid chromatography–tandem mass spectrometry (LC/MS–MS), gas-chromatography mass spectrometry (GC/MS) and by isocratic cation exchange “high-performance liquid-chromatography” (HPLC) to evaluate the use of HPLC method for disease-associated metabolite screening. Patients and methodsIn this study, we screened 86 suspected Egyptians patients with organic acid disorders by LC/MS–MS, GC/MS and by HPLC aged from 3days to 12years old. Data obtained from the three methods were statistically analyzed to evaluate the specificity and sensitivity of the HPLC method over the other two methods and to pursue its precision in the diagnosis of organic acid disorders. Moreover, 17 urine samples were collected from patients with several other IEMs to evaluate the efficiency of HPLC in detecting abnormal metabolites in urine samples. ResultsThe screening results showed that diagnostic efficiencies were varied among the three methods, HPLC showing a higher sensitivity of detecting normal urine as well as a highly satisfactory extent for the detection of different metabolic disorders. In addition, some typical urinary HPLC chromatograms of different metabolic disorders were presented to help the investigator who is going to start an organic aciduria screening program by HPLC to be familiar with various patterns. ConclusionThis study has indicated that HPLC is an easy applicable and useful technique for the initial screening of organic acid disorders and many other disease associated metabolites.

P2.025 Amyotrophic lateral sclerosis and Hirayama disease-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy

P2.025 Amyotrophic lateral sclerosis and Hirayama disease-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy