Disease-associated Metabolites Research Articles

The consumption of animal products is associated with plasma levels of alpha-aminoadipic acid (2-AAA)

Background and aimsThe cardiometabolic disease-associated metabolite, alpha-aminoadipic acid (2-AAA) is formed from the breakdown of the essential dietary amino acid lysine. However, it was not known whether elevated plasma levels of 2-AAA are related to dietary nutrient intake. We aimed to determine whether diet is a determinant of circulating 2-AAA in healthy individuals, and whether 2-AAA is altered in response to dietary modification. Methods and resultsWe investigated the association between 2-AAA and dietary nutrient intake in a cross-sectional study of healthy individuals (N = 254). We then performed a randomized cross-over dietary intervention trial to investigate the effect of lysine supplementation (1 week) on 2-AAA in healthy individuals (N = 40). We further assessed the effect of a vegetarian diet on 2-AAA in a short-term (4-day) dietary intervention trial in healthy omnivorous women (N = 35). We found that self-reported dietary intake of animal products, including meat, poultry, and seafood, was associated with higher plasma 2-AAA cross-sectionally (P < 0.0001). Supplementary dietary lysine (5g/day) caused no significant increase in plasma 2-AAA; however, plasma 2-AAA was altered by general dietary modification. Further, plasma 2-AAA was significantly reduced by a short-term vegetarian diet (P = 0.003). ConclusionWe identified associations between plasma 2-AAA and consumption of animal products, which were validated in a vegetarian dietary intervention trial, but not in a trial designed to specifically increase the 2-AAA amino acid precursor lysine. Further studies are warranted to investigate whether implementation of a vegetarian diet improves cardiometabolic risk in individuals with elevated 2-AAA.

Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course

Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V.parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V.parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis.

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms

BackgroundLong-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no validated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites associated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning algorithms. MethodsIn total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe subgroups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Machine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk. ResultsA total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely associated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970–0.979 on calibration set (n = 132) and 0.818–0.848 on validation set (n = 33). ConclusionAltered metabolites and disrupted pathways are prevalent in arsenicosis patients; The disrupted metabolism of one carbon and dysfunction of antioxidant defense system may partially be causes of the systematic multi-organ damage and carcinogenesis in arsenicosis patients; Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of arsenicosis.

A microbial transporter of the dietary antioxidant ergothioneine

Low-molecular-weight (LMW) thiols are small-molecule antioxidants required for the maintenance of intracellular redox homeostasis. However, many host-associated microbes, including the gastric pathogen Helicobacter pylori, unexpectedly lack LMW-thiol biosynthetic pathways. Using reactivity-guided metabolomics, we identified the unusual LMW thiol ergothioneine (EGT) in H.pylori. Dietary EGT accumulates to millimolar levels in human tissues and has been broadly implicated in mitigating disease risk. Although certain microorganisms synthesize EGT, we discovered that H.pylori acquires this LMW thiol from the host environment using a highly selective ATP-binding cassette transporter-EgtUV. EgtUV confers a competitive colonization advantage invivo and is widely conserved in gastrointestinal microbes. Furthermore, we found that human fecal bacteria metabolize EGT, which may contribute to production of the disease-associated metabolite trimethylamine N-oxide. Collectively, our findings illustrate a previously unappreciated mechanism of microbial redox regulation in the gut and suggest that inter-kingdom competition for dietary EGT may broadly impact human health.

Dysbiotic human oral microbiota alters systemic metabolism via modulation of gut microbiota in germ-free mice

ABSTRACT Background The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Aim We explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects. Methods The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Gut microbial communities, hepatic gene expression profiles, and serum metabolites were analyzed. Results The gut microbial composition was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of lipid and glucose metabolism-related genes. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with characteristic gut microbial taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. Conclusion The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

Mechanistic Studies of a Skatole-Forming Glycyl Radical Enzyme Suggest Reaction Initiation via Hydrogen Atom Transfer.

Gut microbial decarboxylation of amino acid-derived arylacetates is a chemically challenging enzymatic transformation which generates small molecules that impact host physiology. The glycyl radical enzyme (GRE) indoleacetate decarboxylase from Olsenella uli (Ou IAD) performs the non-oxidative radical decarboxylation of indole-3-acetate (I3A) to yield skatole, a disease-associated metabolite produced in the guts of swine and ruminants. Despite the importance of IAD, our understanding of its mechanism is limited. Here, we characterize the mechanism of Ou IAD, evaluating previously proposed hypotheses of: (1) a Kolbe-type decarboxylation reaction involving an initial 1-e– oxidation of the carboxylate of I3A or (2) a hydrogen atom abstraction from the α-carbon of I3A to generate an initial carbon-centered radical. Site-directed mutagenesis, kinetic isotope effect experiments, analysis of reactions performed in D2O, and computational modeling are consistent with a mechanism involving initial hydrogen atom transfer. This finding expands the types of radical mechanisms employed by GRE decarboxylases and non-oxidative decarboxylases, more broadly. Elucidating the mechanism of IAD decarboxylation enhances our understanding of radical enzymes and may inform downstream efforts to modulate this disease-associated metabolism.

Metabolome Changes With Diet-Induced Remission in Pediatric Crohn’s Disease

The Crohn's disease (CD) exclusion diet (CDED) plus partial enteral nutrition (PEN) and exclusive enteral nutrition (EEN) both induce remission in pediatric CD. CDED+PEN is better tolerated and able to sustain remission. We characterized the changes in fecal metabolites induced by CDED+PEN and EEN and their relationship with remission. A total of 216 fecal metabolites were measured in 80 fecal samples at week (W) 0, W6, and W12, of children with mild to moderate CD in a prospective randomized trial comparing CDED+PEN vs EEN. The metabolites were measured using liquid chromatography coupled to mass spectrometry. Metagenome Kyoto Encyclopedia of Genes and Genomes Orthology analysis was performed to investigate the differential functional gene abundance involved in specific metabolic pathways. Data were analyzed according to clinical outcome of remission (W6_rem), no remission (W6_nr), sustained remission (W12_sr), and nonsustained (W12_nsr) remission. A decrease in kynurenine and succinate synthesis and an increase in N-α-acetyl-arginine characterized CDED+PEN W6_rem, whereas changes in lipid metabolism characterized EEN W6_rem, especially reflected by lower levels in ceramides. In contrast, fecal metabolites in EEN W6_nr were comparable to baseline/W0 samples. CDED+PEN W6_rem children maintained metabolome changes through W12. In contrast, W12_nsr children in the EEN group, who resumed a free diet after week 6, did not. The metabolome of CDED+PEN differed from EEN in the purine, pyrimidine, and sphingolipid pathways. A significant differential abundance in several genes involved in these pathways was detected. CDED+PEN- and EEN-induced remission are associated with significant changes in inflammatory bowel disease-associated metabolites such as kynurenine, ceramides, amino acids, and others. Sustained remission with CDED+PEN, but not EEN, was associated with persistent changes in metabolites. gov, Number NCT01728870.

Выявление потенциальных биомаркеров воспалительных заболеваний кишечника с помощью нецелевой метаболомики сыворотки крови

Objective. To identify candidate biomarkers for inflammatory bowel disease (IBD) using non-targeted serum metabolomics in patients with ulcerative colitis (UC). Patients and methods. This pilot study included 9 male patients with active UC and 11 healthy male controls. Serum metabolomic analysis was carried out by gas chromatography-mass spectrometry (GC-MS). Compounds were identified using the NIST08 mass spectral library. Classification of samples and search for candidate biomarkers were performed using a support vector machine (SVM), projections to latent structures-discriminant analysis (PLS-DA), and a naive Bayes classifier (Naïve Bayes). When choosing a classifier, we were guided by the areas under the ROC curve. Results. Metabolomic analysis revealed 85 compounds, of which 79 were annotated. The normalized serum levels of 14 metabolites (2-hydroxybutyric acid, caprylic acid, erythronic acid, creatinine, β-glycerophosphate, α-glycerophosphate, 2-keto-D-gluconic acid, pentadecanoic acid, trans-palmitoleic acid, palmitic acid, margaric acid, palmitoleic acid, squalene, α-tocopherol) differed significantly between the groups. Serum levels of all these compounds (except α-tocopherol) were elevated in UC patients compared to healthy controls, which was accompanied by an increase in the concentration scatter. The largest area under the ROC curve corresponded to the Naïve Bayes classifier (AUC = 0.931; excellent model quality). Conclusion. The study identified 14 metabolites that can be used as candidate biomarkers for IBD after proper verification, both for diagnosis and for assessment of the efficacy of therapy. The identification of disease-associated metabolites will facilitate the development of novel biotherapeutics. Key words: inflammatory bowel disease, ulcerative colitis, metabolome, biomarkers, gas chromatography-mass spectrometry.

Bioinformatics integrated analysis to investigate candidate biomarkers and associated metabolites in osteosarcoma

BackgroundThis study hoped to explore the potential biomarkers and associated metabolites during osteosarcoma (OS) progression based on bioinformatics integrated analysis.MethodsGene expression profiles of GSE28424, including 19 human OS cell lines (OS group) and 4 human normal long bone tissue samples (control group), were downloaded. The differentially expressed genes (DEGs) in OS vs. control were investigated. The enrichment investigation was performed based on DEGs, followed by protein–protein interaction network analysis. Then, the feature genes associated with OS were explored, followed by survival analysis to reveal prognostic genes. The qRT-PCR assay was performed to test the expression of these genes. Finally, the OS-associated metabolites and disease-metabolic network were further investigated.ResultsTotally, 357 DEGs were revealed between the OS vs. control groups. These DEGs, such as CXCL12, were mainly involved in functions like leukocyte migration. Then, totally, 38 feature genes were explored, of which 8 genes showed significant associations with the survival of patients. High expression of CXCL12, CEBPA, SPARCL1, CAT, TUBA1A, and ALDH1A1 was associated with longer survival time, while high expression of CFLAR and STC2 was associated with poor survival. Finally, a disease-metabolic network was constructed with 25 nodes including two disease-associated metabolites cyclophosphamide and bisphenol A (BPA). BPA showed interactions with multiple prognosis-related genes, such as CXCL12 and STC2.ConclusionWe identified 8 prognosis-related genes in OS. CXCL12 might participate in OS progression via leukocyte migration function. BPA might be an important metabolite interacting with multiple prognosis-related genes.

Molecular basis of C-S bond cleavage in the glycyl radical enzyme isethionate sulfite-lyase

SummaryDesulfonation of isethionate by the bacterial glycyl radical enzyme (GRE) isethionate sulfite-lyase (IslA) generates sulfite, a substrate for respiration that in turn produces the disease-associated metabolite hydrogen sulfide. Here, we present a 2.7 Å resolution X-ray structure of wild-type IslA from Bilophila wadsworthia with isethionate bound. In comparison with other GREs, alternate positioning of the active site β strands allows for distinct residue positions to contribute to substrate binding. These structural differences, combined with sequence variations, create a highly tailored active site for the binding of the negatively charged isethionate substrate. Through the kinetic analysis of 14 IslA variants and computational analyses, we probe the mechanism by which radical chemistry is used for C-S bond cleavage. This work further elucidates the structural basis of chemistry within the GRE superfamily and will inform structure-based inhibitor design of IsIA and thus of microbial hydrogen sulfide production.

Identification of novel cardiovascular disease associated metabolites using untargeted metabolomics.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality around the world. Early diagnosis of CVD could provide the opportunity for sensible management and better clinical outcome along with the prevention of further progression of the disease. Inthe current study, we used an untargeted metabolomic approach to identify possible metabolite(s) that associate well with the CVD and could serve either as therapeutic target or disease-associated metabolite. We identified 26 rationally adjusted unique metabolites that were differentially present in the serum of CVD patients compared with healthy individuals, among them 15 were found to be statistically significant. Out of these metabolites, we identified some novel metabolites like UDP-l-rhamnose and N1-acetylspermidine that have not been reported to be linked with CVD directly. Further, we also found that some metabolites like ethanolamide, solanidine, dimethylarginine, N-acetyl-l-tyrosine, can act as a discriminator of CVD.Metabolites integrating pathway enrichment analysis showed enrichment of various important metabolic pathways like histidine metabolism, methyl histidine metabolism, carnitine synthesis, along with arginine and proline metabolism in CVD patients. Our study provides a great opportunity to understand the pathophysiological role and impact of the identified unique metabolites and can be extrapolated as specific CVD specific metabolites.

Molecular Basis of C–S Bond Cleavage in the Glycyl Radical Enzyme Isethionate Sulfite-Lyase

Desulfonation of isethionate by the bacterial glycyl radical enzyme (GRE) isethionate sulfite-lyase (IslA) generates sulfite, a substrate for sulfite respiration that in turn produces the disease-associated metabolite hydrogen sulfide. Here, we present a 2.7 A resolution X-ray structure of wild-type IslA from Bilophila wadsworthia with isethionate bound. In comparison to other GREs, alternate positioning of the active site β strands allows for distinct residue positions to contribute to substrate binding. These structural differences combined with sequence variations create a highly tailored active site for the binding of the negatively charged isethionate substrate. Through the kinetic analysis of fourteen IslA variants, we probe the mechanism by which radical chemistry is used for C–S bond cleavage. This work further elucidates the structural basis of chemistry within the GRE superfamily and will inform structure-based inhibitor design of IsIA and thus of microbial hydrogen sulfide production.

0346 Metabolic Aging and Sleep Loss: Metabolite Signatures Link Sleep Deprivation and Aging Across Tissues

Abstract Introduction Insufficient sleep is a hallmark of modern society, and sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. The interactions of aging with systemic and local metabolic alterations induced by sleep deprivation are essentially unexplored. In this study, we demonstrate a shared metabolic imprint of SD and aging in plasma, liver, and hippocampus. Methods Young (2 - 4 months) and aged (22 - 24 months) mice were sleep deprived (N = 10/group) for 5 hours followed by collection of blood plasma, liver and hippocampus. The samples were extracted and subjected to UPLC-MS/MS based targeted metabolomics analysis. Results Young animals displayed greater sensitivity to SD induced metabolic changes with &amp;gt;40% more metabolites perturbed in each sample type measured compared to aged animals. Enrichment analysis based on known disease-associated metabolites suggests that plasma change in young animals are of pathological relevance, but not in aged animals. A common hepatic signature of sleep-loss across the two age groups consisted of ketosis and urea cycle perturbation. Approximately 20-30% of measured metabolites exhibit similar changes when the sleep deprivation induced signature is compared with the aging metabolic imprint in a tissue-dependent manner. Central energetics, urea cycle and aromatic amino acid metabolism highlight the common pathways altered by sleep and aging in the periphery. In the hippocampus, choline and acetylcholine pools were depleted, potentially providing insight into the changes in metabolism that accompany analogous defects in memory consolidation. Conclusion These results support the notion that SD makes the ‘young seem old’. The results further connect neurobehavioral observations tying together aging and sleep loss, by implicating molecular mechanisms at the level of metabolism. Support This work was supported by NIH grant R21AG052905 (AMW, AS), P50AG017628 (TA; A.I. Pack, PI) and R01AG062398 (TA, JT). TA was supported by the Brush Family Chair in Biology at Penn and is currently supported by the Roy J. Carver Chair of Neuroscience at Iowa.

1H-NMR based metabolomic profiling of cord blood in gestational hypothyroidism

BackgroundGestational hypothyroidism (GHT) is a common pregnancy-related thyroid disfunction. The adverse outcomes by GHT has been increasingly recognized, leading to more public awareness of the disease. However, comprehensive understanding of the prognosis of GHT has not yet achieved. Metabolomics is a powerful tool in evaluation of disease outcomes, and cord blood represents an excellent candidate for the investigation of gestational outcomes.MethodsIn the present study, we performed 1H-NMR based metabolomics on cord blood of 18 pregnant women with GHT and 18 non hypothyroidism (NHT) control.ResultsThe metabolomic profile of GHT was separated with the NHT control. A total of 8 metabolites with altered abundances were observed, among which Creatinine and O-Phosphocholine were elevated and the others were downregulated in GHT. Spearman rank correlation suggested that the eight differential metabolites were correlated with the GHT related thyroid hormones. Pathway analysis of the differential metabolites indicated that two metabolic pathways were significantly altered in GHT (adjusted P<0.05), including tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis. Enrichment analysis of the differential metabolites against disease-associated metabolite sets suggested that GHT was associated with disease risks of non-insulin dependent diabetes mellitus, isovaleric acidemia, and methylmalonic aciduria.ConclusionsThe results of this study revealed GHT associated metabolic changes in cord blood, providing insights into the metabolic intermediates between GHT and its related disease risks.

Potential Correlation between Dietary Fiber-Suppressed Microbial Conversion of Choline to Trimethylamine and Formation of Methylglyoxal.

Dietary interventions alter the formation of the disease-associated metabolite, trimethylamine (TMA), via intestinal microbial TMA lyase activity. Nevertheless, the mechanisms regulating microbial enzyme production are still unclear. Sequencing of the gut bacteria 16S rDNA demonstrated that dietary intervention changed the composition of the gut microbiota and the functional metagenome involved in the choline utilization pathway. Characterization of the functional profile of the metagenomes and metabonomics analysis revealed that a series of Kyoto Encyclopedia of Genes and Genomes orthologous groups and enzyme groups related to accumulation of methylglyoxal (MG) and glycine were enriched in red meat diet-fed animals, whereas fiber-rich diet suppressed glycine formation via the MG-dependent pathway. Our observations suggest associations between choline-TMA lyase expression and MG formation, which are indicative of a novel role of the gut microbiota in choline metabolism and highlight it as a potential target for inhibiting TMA production.

Prioritizing candidate diseases-related metabolites based on literature and functional similarity

BackgroundAs the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases.ResultsWe construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%.ConclusionIn this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites.

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.