Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses

Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry.

BackgroundThe left internal thoracic artery (LITA) has been used as the first conduit of choice in coronary artery bypass grafting (CABG) because of excellent long-term patency and outcomes. However, no studies have examined substances other than nitric oxide that could be beneficial for the bypass conduit, native coronary artery or ischemic myocardium. This study was conducted to evaluate differences in metabolic profiles between the LITA and ascending aorta using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS).MethodsTwenty patients who underwent CABG using the LITA were prospectively enrolled. Plasma samples were collected simultaneously from the LITA and ascending aorta. GC-TOF-MS based untargeted metabolomic analyses were performed and a 2-step volcano plot analysis was used to identify distinguishable markers from two plasma metabolome profiles. Semi-quantitative and quantitative analyses were performed using GC-TOF-MS and enzyme-linked immunosorbent assay, respectively, after selecting target metabolites based on the metabolite set enrichment analysis.ResultsInitial volcano plot analysis demonstrated 5 possible markers among 851 peaks detected. The final analysis demonstrated that the L-cysteine peak was significantly higher in the LITA than in the ascending aorta (fold change = 1.86). The concentrations of intermediate metabolites such as L-cysteine, L-methionine and L-cystine in the 'cysteine and methionine metabolism pathway' were significantly higher in the LITA than in the ascending aorta (2.0-, 1.4- and 1.2-fold, respectively). Quantitative analysis showed that the concentration of hydrogen sulfide (H₂S) was significantly higher in the LITA.ConclusionThe plasma metabolome profiles of the LITA and ascending aorta were different, particularly higher plasma concentrations of L-cysteine and H₂S in the LITA

The metabolomics of psoriatic disease.

Metabolomics is an emerging new "omics" field involving the systematic analysis of the metabolites in a biologic system. These metabolites provide a molecular snapshot of cellular activity and are thus important for understanding the functional changes in metabolic pathways that drive disease. Recently, metabolomics has been used to study the local and systemic metabolic changes in psoriasis and its cardiometabolic comorbidities. Such studies have revealed novel insights into disease pathogenesis and suggest new biochemical signatures that may be used as a marker of psoriatic disease. This review will discuss common strategies in metabolomics analysis, current findings in the metabolomics of psoriasis, and emerging trends in psoriatic metabolomics

Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry.

Profiling of body fluids is crucial for monitoring and discovering metabolic markers of health and disease and for providing insights into human physiology. Since human urine and plasma each contain an extreme diversity of metabolites, a single liquid chromatographic system when coupled to mass spectrometry (MS) is not sufficient to achieve reasonable metabolome coverage. Hydrophilic interaction liquid chromatography (HILIC) offers complementary information to reverse-phase liquid chromatography (RPLC) by retaining polar metabolites. With the objective of finding the optimal combined chromatographic solution to profile urine and plasma, we systematically investigated the performance of five HILIC columns with different chemistries operated at three different pH (acidic, neutral, basic) and five C18-silica RPLC columns. The zwitterionic column ZIC-HILIC operated at neutral pH provided optimal performance on a large set of hydrophilic metabolites. The RPLC columns Hypersil GOLD and Zorbax SB aq were proven to be best suited for the metabolic profiling of urine and plasma, respectively. Importantly, the optimized HILIC-MS method showed excellent intrabatch peak area reproducibility (CV < 12%) and good long-term interbatch (40 days) peak area reproducibility (CV < 22%) that were similar to those of RPLC-MS procedures. Finally, combining the optimal HILIC- and RPLC-MS approaches greatly expanded metabolome coverage with 44% and 108% new metabolic features detected compared with RPLC-MS alone for urine and plasma, respectively. The proposed combined LC-MS approaches improve the comprehensiveness of global metabolic profiling of body fluids and thus are valuable for monitoring and discovering metabolic changes associated with health and disease in clinical research studies

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics.

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

<p>Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R.</p> <p>Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites.</p&gt

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

<p>Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R.</p> <p>Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites.</p&gt

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Arteriovenous Blood Metabolomics: A Readout of Intra-Tissue Metabostasis.

The human circulatory system consists of arterial blood that delivers nutrients to tissues, and venous blood that removes the metabolic by-products. Although it is well established that arterial blood generally has higher concentrations of glucose and oxygen relative to venous blood, a comprehensive biochemical characterization of arteriovenous differences has not yet been reported. Here we apply cutting-edge, mass spectrometry-based metabolomic technologies to provide a global characterization of metabolites that vary in concentration between the arterial and venous blood of human patients. Global profiling of paired arterial and venous plasma from 20 healthy individuals, followed up by targeted analysis made it possible to measure subtle (<2 fold), yet highly statistically significant and physiologically important differences in water soluble human plasma metabolome. While we detected changes in lactic acid, alanine, glutamine, and glutamate as expected from skeletal muscle activity, a number of unanticipated metabolites were also determined to be significantly altered including Krebs cycle intermediates, amino acids that have not been previously implicated in transport, and a few oxidized fatty acids. This study provides the most comprehensive assessment of metabolic changes in the blood during circulation to date and suggests that such profiling approach may offer new insights into organ homeostasis and organ specific pathology