Menthol, a unique urinary volatile compound, is associated with chronic inflammation in interstitial cystitis.

Chronic inflammation is a potential systemic risk factor for many bladder dysfunctions, including interstitial cystitis (IC). However, the underlying mechanism through which a healthy bladder protects itself from inflammatory triggers remains unknown. In this study, we identified odor compounds in urine obtained from IC patients and healthy controls. Using comprehensive solid-phase microextraction-gas chromatography-time-of-flight-mass spectrometry (SPME-GC-TOF-MS) profiling and bioinformatics, we found that levels of urinary volatile metabolites, such as menthol, were significantly reduced in IC patients, compared to healthy controls. In an attempt to understand the mechanistic meaning of our volatile metabolites data and the role of menthol in the immune system, we performed two independent experiments: (a) cytokine profiling, and (b) DNA microarray. Our findings suggest that lipopolysaccharide (LPS)-stimulated inflammatory events, such as the production and secretion of inflammatory cytokines (e.g., TNF-α, IL-6, and IL-1β) and the activation of NF-κB and associated proteins within a large signaling network (e.g., Akt, TLR1, TNFAIP3, and NF-κB), are suppressed by the presence of menthol. These findings broaden our knowledge on the role of urinary menthol in suppressing inflammatory events and provide potential new strategies for alleviating both the odor and inflammation associated with IC

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry.

Gas chromatography-mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M - CH3]+ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]+ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry

Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M − CH3]+ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]+ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies

Comparing Stable Isotope Enrichment by Gas Chromatography with Time-of-Flight, Quadrupole Time-of-Flight, and Quadrupole Mass Spectrometry.

Stable isotope tracers are applied for in vivo and in vitro studies to reveal the activity of enzymes and intracellular metabolic pathways. Most often, such tracers are used with gas chromatography coupled to mass spectrometry (GC-MS) owing to its ease of operation and reproducible mass spectral databases. Differences in isotope tracer performance of the classic GC-quadrupole MS instrument and newer time-of-flight instruments are not well studied. Here, we used three commercially available instruments for the analysis of identical samples from a stable isotope labeling study that used [U-13C6] d-glucose to investigate the metabolism of the bacterium Rothia mucilaginosa with respect to 29 amino acids and hydroxyl acids involved in primary metabolism. The prokaryote R. mucilaginosa belongs to the family of Micrococcaceae and is present and metabolically active in the airways and sputum of cystic fibrosis patients. Overall, all three GC-MS instruments (low-resolution GC-SQ MS, low-resolution GC-TOF MS, and high-resolution GC-QTOF MS) can be used to perform stable isotope tracing studies for glycolytic intermediates, tricarboxylic acid (TCA) metabolites, and amino acids, yielding similar biological results, with high-resolution GC-QTOF MS offering additional capabilities to identify the chemical structures of unknown compounds that might show significant isotope enrichments in biological studies

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co‐Metabolism in Patients With Alcoholic Hepatitis

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co‐Metabolism in Patients With Alcoholic Hepatitis

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis